Manejo de redes con NetworkX_py36.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importar libreria\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se crea el grapho: este es no direccionado\n",
    "G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se agregan nodos al grapho\n",
    "G.add_nodes_from([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# devuelve una lista de nodos\n",
    "G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se agregan los ejes\n",
    "G.add_edge(1,2)\n",
    "G.add_edge(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2), (2, 3)])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# devuelve una lista de tuplas con los nodos conectados por el eje\n",
    "G.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se agrega al nodo 1 una label key con el valor blue (es como agregarle un valor a la key de un diccionario)\n",
    "G.node[1]['label'] = 'blue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeDataView({1: {'label': 'blue'}, 2: {}, 3: {}})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# obtener una lista de nodos con la metadata asociada: devuelve un diccionario donde la key es el nodo. \n",
    "# despues esta la metadata en otro diccionario donde la llave es la etiqueta y despues esta el valor\n",
    "G.nodes(data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': 'blue'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(G.nodes(data=True))[0][1]"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw function\n",
    "nx.draw(G)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agregamos la misma key al resto de los nodos con algun valor\n",
    "G.node[2]['label'] = 'red'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.node[3]['label'] = 'gren'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeDataView({1: {'label': 'blue'}, 2: {'label': 'red'}, 3: {'label': 'gren'}})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes(data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtramos por condicion (usando una list por comprension). Para eso se accede al diccionario de la metadata via \"key\"\n",
    "[n for n,d in G.nodes(data=True) if d['label'] == 'red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se agrega una key:value a los edge. Para acceder se usa una lista con los nodos que une el edge\n",
    "G.edges[1,2]['condicion']=78\n",
    "G.edges[3,2]['condicion']=92"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 3)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ejemplo de como filtrar con listas por comprension atributos de un edge\n",
    "[(u,v) for u,v,d in G.edges(data=True) if d['condicion'] > 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.graph.Graph"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grafo no direccionado\n",
    "type(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.digraph.DiGraph"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grafo direccionado\n",
    "D = nx.DiGraph()\n",
    "type(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.multigraph.MultiGraph"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multi-edge (Directed) graphs \n",
    "M = nx.MultiGraph()\n",
    "type(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "networkx.classes.multidigraph.MultiDiGraph"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MD = nx.MultiDiGraph()\n",
    "type(MD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WEIGHTS\n",
    "# Setear el peso de un edge\n",
    "G.edges[1,2]['weight'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterar sobre la metadata de todos los ejes\n",
    "for u,v,d in G.edges(data=True):\n",
    "    \n",
    "    # Check if node 3 is involved:\n",
    "    if 3 in [u,v]:\n",
    "        \n",
    "        # Cambiarle el valor del peso\n",
    "        d['weight'] = 1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Como encontrar self loops?\n",
    "\n",
    "def find_selfloop_node(G):\n",
    "    '''\n",
    "    Finds all nodes that have self-loops in the graph G\n",
    "    '''\n",
    "    \n",
    "    nodes_in_selfloops = []\n",
    "    \n",
    "    # Iterar sobre todos los ejes de G\n",
    "    for u,v in G.edges():\n",
    "        \n",
    "        # Chequear si el nodo u y el nodo v son iguales:\n",
    "        if u == v:\n",
    "            nodes_in_selfloops.append(u)\n",
    "            \n",
    "    return nodes_in_selfloops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No hay ningun selfloop\n",
    "len(find_selfloop_node(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No devuelve error porque esta ok\n",
    "assert G.number_of_selfloops() == len(find_selfloop_node(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRAFICOS: Arcplot, Matrix plot y Circle plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nxviz as nv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verficiar que este nxviz en el directorio\n",
    "#! conda list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ARCPLOT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Arcplot\n",
    "from nxviz import ArcPlot\n",
    "ap = nv.ArcPlot(G)\n",
    "\n",
    "# Draw m to the screen\n",
    "ap.draw()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary modules\n",
    "from nxviz import ArcPlot\n",
    "\n",
    "# Create the un-customized ArcPlot object: a\n",
    "a = ArcPlot(G)\n",
    "\n",
    "# Draw a to the screen\n",
    "a.draw()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n",
    "\n",
    "# Create the customized ArcPlot object: a2\n",
    "a2 = ArcPlot(G, node_order='label', node_color='label')\n",
    "\n",
    "# Draw a2 to the screen\n",
    "a2.draw()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nxviz.plots import ArcPlot\n",
    "\n",
    "# Iterate over all the nodes in G, including the metadata\n",
    "\n",
    "# Probar mismo Arcplot con el grafo Q que se crea al final de la notebook. Es para ver otro tipo de Arcplot y que no estalle.\n",
    "for n, d in G.nodes(data=True):\n",
    "\n",
    "    # Calculate the degree of each node: G.node[n]['degree']\n",
    "    G.node[n]['degree'] = nx.degree(G,n)\n",
    "\n",
    "# Create the ArcPlot object: a\n",
    "a = ArcPlot(graph=G, node_order='degree')\n",
    "\n",
    "# Draw the ArcPlot to the screen\n",
    "a.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MATRIXPLOT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matrixplot \n",
    "m = nv.MatrixPlot(G)\n",
    "\n",
    "# Draw m to the screen\n",
    "m.draw()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertimos a G en una matriz (1 indica si hay edge entre nodos)\n",
    "A = nx.to_numpy_matrix(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0. , 2. , 0. ],\n",
       "        [2. , 0. , 1.1],\n",
       "        [0. , 1.1, 0. ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertimos a G de nuevo al formtato NetworkX como un grafo dirigido: A_conv\n",
    "A_conv = nx.from_numpy_matrix(A, create_using=nx.DiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x2271aecf710>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check that the `category` metadata field is lost from each node\n",
    "for n, d in A_conv.nodes(data=True):\n",
    "    assert 'category' not in d.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVsAAAFbCAYAAAB7zy3tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAABRpJREFUeJzt27FRQkEYRlGfY0IFZoQ0QGwfWo1DNzRDA4RkVGC4lgAbeB/qOfEX/NGdTXYZYzwB8LOe1z4A4D8QW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxB4mZuffTf7ZTbbz7VPYNLX5bD2CUzbLbcWXrYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQGAZY9w93mw/7h/zEL4uh7VPYNL+eF37BCad3t+WWxsvW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQJiCxAQW4CA2AIExBYgILYAgWWMMTE/z4x5APvjde0TmHR6f137BKbtllsLL1uAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AAGxBQiILUBAbAECYgsQEFuAgNgCBMQWICC2AIFljLH2DQB/npctQEBsAQJiCxAQW4CA2AIExBYgILYAAbEFCIgtQEBsAQLfcecfqYgkYYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ac = nv.MatrixPlot(A_conv)\n",
    "Ac.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nxviz import MatrixPlot\n",
    "\n",
    "# Calculate the largest connected component subgraph: largest_ccs. Python's \n",
    "# built-in sorted() function takes an iterable and returns a sorted list (in ascending order, by default). \n",
    "# Therefore, to access the largest connected component subgraph, the statement is sliced with [-1]\n",
    "largest_ccs = sorted(nx.connected_component_subgraphs(G), key=lambda x: len(x))[-1]\n",
    "\n",
    "# Probar misma matriz con el grafo Q que se crea al final de la notebook. Es para ver otro tipo de matrixplot.\n",
    "\n",
    "# Create the customized MatrixPlot object: h\n",
    "h = MatrixPlot(graph=largest_ccs) # Tambien se pueden incluir otros parametros como node_grouping = 'grouping'\n",
    "\n",
    "# Draw the MatrixPlot to the screen\n",
    "h.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### CIRCOSPLOT "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Circos plot\n",
    "from nxviz import CircosPlot \n",
    "c = CircosPlot(G)\n",
    "\n",
    "# Draw c to the screen\n",
    "c.draw()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = nx.erdos_renyi_graph(n=20,p=0.3)\n",
    "\n",
    "circ = nv.CircosPlot(U)\n",
    "\n",
    "circ.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#U = nx.erdos_renyi_graph(n=20,p=0.3)\n",
    "\n",
    "# Con parametros para estilizar el grafo\n",
    "#circ = nv.CircosPlot(U, node_color='key_a_definir', node_group='key_a_definir')\n",
    "\n",
    "#circ.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nxviz import CircosPlot\n",
    "\n",
    "# Iterate over all the nodes, including the metadata\n",
    "\n",
    "# Probar mismo Arcplot con el grafo Q que se crea al final de la notebook. Es para que no estalle.\n",
    "for n, d in G.nodes(data=True):\n",
    "\n",
    "    # Calculate the degree of each node: G.node[n]['degree']\n",
    "    G.node[n]['degree'] = nx.degree(G,n)\n",
    "\n",
    "# Create the CircosPlot object: c\n",
    "c = CircosPlot(G,node_order='degree')#,node_grouping='grouping',node_color='grouping')\n",
    "\n",
    "# Draw the CircosPlot object to the screen\n",
    "c.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# METRICAS IMPORTANTES\n",
    "\n",
    "## 1) Degree centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2), (2, 3)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Degree centrality: la cantidad de vecinos que tengo sobre la cantidad que podria tener\n",
    "G.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# devuelve los nodos vecinos del especificado en el metodo\n",
    "list(G.neighbors(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.5, 2: 1.0, 3: 0.5}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The key is the node and the value the centrality value\n",
    "nx.degree_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 3}\n"
     ]
    }
   ],
   "source": [
    "# Funcion para obtener nodos segun la cantidad de vecinos\n",
    "def nodes_with_m_nbrs(G, m):\n",
    "    \"\"\"\n",
    "    Returns all nodes in graph G that have m neighbors.\n",
    "    \"\"\"\n",
    "    nodes = set()\n",
    "\n",
    "    # Iterate over all nodes in G\n",
    "    for n in G.nodes():\n",
    "\n",
    "        # Check if the number of neighbors of n matches m\n",
    "        if len(list(G.neighbors(n))) == m:\n",
    "\n",
    "            # Add the node n to the set\n",
    "            nodes.add(n)\n",
    "\n",
    "    # Return the nodes with m neighbors\n",
    "    return nodes\n",
    "\n",
    "# Compute and print all nodes in T that have 6 neighbors\n",
    "six_nbrs = nodes_with_m_nbrs(G, 1)\n",
    "print(six_nbrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 1]\n"
     ]
    }
   ],
   "source": [
    "# Compute the degree of every node: degrees\n",
    "degrees = [len(list(G.neighbors(n))) for n in G.nodes()]\n",
    "\n",
    "# Print the degrees\n",
    "print(degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Devuelve un diccionario donde la key es el nodo y el valor es el degree centrality (Neighbors/possible neighbors). En una red de transporte el nodo mismo es considerado como un self loop que entra en los nodos posibles\n",
    "deg_cent = nx.degree_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Devuelve un histograma con la distribucion del degree centrality\n",
    "plt.hist(list(deg_cent.values()));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n",
       " array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of the degree distribution of the graph\n",
    "plt.hist([len(list(G.neighbors(n))) for n in G.nodes()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WPczC31BI8mzgauDkRCe6BL2fHfwVcLyq/uQ8y8bSqxVx5Z7kHhZ+kr42yRzwHuAKgKr6CHA/cBMwC3yXhauBFavD+b4beBbw4d7V7JmV/AFMHc63KUudb1UdT/LPwGHg+8BHq+qCLxN9qurwZ/s+YHeSIyzcrnhXVa3kT4q8Hng9cCTJQ719vws8F8bbK9+hKkkNauW2jCSpj3GXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAb9Hw2c8pFA4nKZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter plot of the centrality distribution and the degree distribution\n",
    "plt.figure()\n",
    "plt.scatter(x=degrees, y=list(deg_cent.values()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define path_exists()\n",
    "def path_exists(G, node1, node2):\n",
    "    \"\"\"\n",
    "    This function checks whether a path exists between two nodes (node1, node2) in graph G.\n",
    "    \"\"\"\n",
    "    visited_nodes = set()\n",
    "\n",
    "    # Initialize the queue of cells to visit with the first node: queue\n",
    "    queue = [node1]\n",
    "\n",
    "    # Iterate over the nodes in the queue\n",
    "    for node in queue:\n",
    "\n",
    "        # Get neighbors of the node\n",
    "        neighbors = G.neighbors(node)\n",
    "\n",
    "        # Check to see if the destination node is in the set of neighbors\n",
    "        if node2 in neighbors:\n",
    "            print('Path exists between nodes {0} and {1}'.format(node1, node2))\n",
    "            return True\n",
    "            \n",
    "        else:\n",
    "            # Add current node to visited nodes\n",
    "            visited_nodes.add(node)\n",
    "\n",
    "            # Add neighbors of current node that have not yet been visited\n",
    "            queue.extend([n for n in neighbors if n not in visited_nodes])\n",
    "            print('Path doesn\\'t exists between nodes {0} and {1}'.format(node1, node2))\n",
    "            return visited_nodes\n",
    "        \n",
    "        # Check to see if the final element of the queue has been reached\n",
    "        if node == queue[-1]:\n",
    "            print('Path does not exist between nodes {0} and {1}'.format(node1, node2))\n",
    "\n",
    "            # Place the appropriate return statement\n",
    "            return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path doesn't exists between nodes 3 and 1\n",
      "{3}\n"
     ]
    }
   ],
   "source": [
    "print(path_exists(G,3,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "# Devolver el nodo con mayor degree centrality\n",
    "\n",
    "# Compute the degree centralities of G: deg_cent\n",
    "deg_cent = nx.degree_centrality(G)\n",
    "\n",
    "# Compute the maximum degree centrality: max_dc\n",
    "max_dc = max(deg_cent.values())\n",
    "\n",
    "# Find the user(s) that have collaborated the most: prolific_collaborators\n",
    "prolific_collaborators = [n for n, dc in deg_cent.items() if n == max_dc]\n",
    "\n",
    "# Print the most prolific collaborator(s)\n",
    "print(prolific_collaborators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Degree + Betweenes centrality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.0, 2: 1.0, 3: 0.0}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Betweeness centrality\n",
    "# M1 es el numero de nodos en el extremo final y m2 la cantidad de nodos en el puente entre ambos\n",
    "Q = nx.barbell_graph(m1=5, m2=1)\n",
    "\n",
    "# La key es el nodo y el valr es el betweenness centrality\n",
    "nx.betweenness_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the betweenness centrality of G: bet_cen\n",
    "bet_cen = nx.betweenness_centrality(Q)\n",
    "\n",
    "# Compute the degree centrality of G: deg_cen\n",
    "deg_cen = nx.degree_centrality(Q)\n",
    "\n",
    "# Create a scatter plot of betweenness centrality and degree centrality\n",
    "plt.scatter(x=list(bet_cen.values()), y=list(deg_cen.values()))\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{4, 6}\n"
     ]
    }
   ],
   "source": [
    "# Define find_nodes_with_highest_deg_cent()\n",
    "def find_nodes_with_highest_deg_cent(G):\n",
    "\n",
    "    # Compute the degree centrality of G: deg_cent\n",
    "    deg_cent = nx.degree_centrality(G)\n",
    "\n",
    "    # Compute the maximum degree centrality: max_dc\n",
    "    max_dc = max(list(deg_cent.values()))\n",
    "\n",
    "    nodes = set()\n",
    "\n",
    "    # Iterate over the degree centrality dictionary\n",
    "    for k, v in deg_cent.items():\n",
    "\n",
    "        # Check if the current value has the maximum degree centrality\n",
    "        if v == max_dc:\n",
    "\n",
    "            # Add the current node to the set of nodes\n",
    "            nodes.add(k)\n",
    "\n",
    "    return nodes\n",
    "\n",
    "# Find the node(s) that has the highest degree centrality in G: top_dc\n",
    "top_dc = find_nodes_with_highest_deg_cent(Q)\n",
    "print(top_dc)\n",
    "\n",
    "# Write the assertion statement\n",
    "for node in top_dc:\n",
    "    assert nx.degree_centrality(Q)[node] == max(nx.degree_centrality(Q).values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CON ESTA FUNCION SE PODRIA ENCONTAR EL NODO CON MAYOR BETWEENNESS CENTARLITY EN TODA LA RED DE BICIS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define find_node_with_highest_bet_cent()\n",
    "def find_node_with_highest_bet_cent(G):\n",
    "\n",
    "    # Compute betweenness centrality: bet_cent\n",
    "    bet_cent = nx.betweenness_centrality(G)\n",
    "\n",
    "    # Compute maximum betweenness centrality: max_bc\n",
    "    max_bc = max(list(bet_cent.values()))\n",
    "\n",
    "    nodes = set()\n",
    "\n",
    "    # Iterate over the betweenness centrality dictionary\n",
    "    for k, v in bet_cent.items():\n",
    "\n",
    "        # Check if the current value has the maximum betweenness centrality\n",
    "        if v == max_bc:\n",
    "\n",
    "            # Add the current node to the set of nodes\n",
    "            nodes.add(k)\n",
    "\n",
    "    return nodes\n",
    "\n",
    "# Use that function to find the node(s) that has the highest betweenness centrality in the network: top_bc\n",
    "top_bc = find_node_with_highest_bet_cent(Q)\n",
    "\n",
    "# Write an assertion statement that checks that the node(s) is/are correctly identified.\n",
    "for node in top_bc:\n",
    "    assert nx.betweenness_centrality(Q)[node] == max(nx.betweenness_centrality(Q).values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PATH FINDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Triangulos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "0 2\n",
      "0 3\n",
      "0 4\n",
      "0 6\n",
      "0 7\n",
      "0 8\n",
      "0 9\n",
      "0 10\n",
      "0 5\n",
      "1 2\n",
      "1 3\n",
      "1 4\n",
      "1 6\n",
      "1 7\n",
      "1 8\n",
      "1 9\n",
      "1 10\n",
      "1 5\n",
      "2 3\n",
      "2 4\n",
      "2 6\n",
      "2 7\n",
      "2 8\n",
      "2 9\n",
      "2 10\n",
      "2 5\n",
      "3 4\n",
      "3 6\n",
      "3 7\n",
      "3 8\n",
      "3 9\n",
      "3 10\n",
      "3 5\n",
      "4 6\n",
      "4 7\n",
      "4 8\n",
      "4 9\n",
      "4 10\n",
      "4 5\n",
      "6 7\n",
      "6 8\n",
      "6 9\n",
      "6 10\n",
      "6 5\n",
      "7 8\n",
      "7 9\n",
      "7 10\n",
      "7 5\n",
      "8 9\n",
      "8 10\n",
      "8 5\n",
      "9 10\n",
      "9 5\n",
      "10 5\n"
     ]
    }
   ],
   "source": [
    "# Iterar sobre every pair of nodes and not only every edge\n",
    "for n1, n2 in combinations(Q.nodes(),2):\n",
    "    print(n1,n2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define is_in_triangle(): podria servir para encontrar cliques entre las estaciones\n",
    "def is_in_triangle(G, n):\n",
    "    \"\"\"\n",
    "    Checks whether a node `n` in graph `G` is in a triangle relationship or not.\n",
    "\n",
    "    Returns a boolean.\n",
    "    \"\"\"\n",
    "    in_triangle = False\n",
    "\n",
    "    # Iterate over all possible triangle relationship combinations\n",
    "    for n1, n2 in combinations(G.neighbors(n),2):\n",
    "\n",
    "        # Check if an edge exists between n1 and n2\n",
    "        if G.has_edge(n1,n2):\n",
    "            in_triangle = True\n",
    "            break\n",
    "    return in_triangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_in_triangle(Q,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Finding nodes involved in triangles\n",
    "\n",
    "# Write a function that identifies all nodes in a triangle relationship with a given node.\n",
    "def nodes_in_triangle(G, n):\n",
    "    \"\"\"\n",
    "    Returns the nodes in a graph `G` that are involved in a triangle relationship with the node `n`.\n",
    "    \"\"\"\n",
    "    triangle_nodes = set([n])\n",
    "\n",
    "    # Iterate over all possible triangle relationship combinations\n",
    "    for n1, n2 in combinations(G.neighbors(n), 2):\n",
    "\n",
    "        # Check if n1 and n2 have an edge between them\n",
    "        if G.has_edge(n1,n2):\n",
    "\n",
    "            # Add n1 to triangle_nodes\n",
    "            triangle_nodes.add(n1)\n",
    "\n",
    "            # Add n2 to triangle_nodes\n",
    "            triangle_nodes.add(n2)\n",
    "\n",
    "    return triangle_nodes\n",
    "\n",
    "# Write the assertion statement\n",
    "assert len(nodes_in_triangle(Q, 1)) == 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "#Finding open triangles\n",
    "\n",
    "# Define node_in_open_triangle(G,n)\n",
    "def node_in_open_triangle(G, n):\n",
    "    \"\"\"\n",
    "    Checks whether pairs of neighbors of node `n` in graph `G` are in an 'open triangle' relationship with node `n`.\n",
    "    \"\"\"\n",
    "    in_open_triangle = False\n",
    "\n",
    "    # Iterate over all possible triangle relationship combinations\n",
    "    for n1, n2 in combinations(G.neighbors(n),2):\n",
    "\n",
    "        # Check if n1 and n2 do NOT have an edge between them\n",
    "        if not G.has_edge(n1,n2):\n",
    "\n",
    "            in_open_triangle = True\n",
    "\n",
    "            break\n",
    "\n",
    "    return in_open_triangle\n",
    "\n",
    "# Compute the number of open triangles in T\n",
    "num_open_triangles = 0\n",
    "\n",
    "# Iterate over all the nodes in T\n",
    "for n in Q.nodes():\n",
    "\n",
    "    # Check if the current node is in an open triangle\n",
    "    if node_in_open_triangle(Q,n):\n",
    "\n",
    "        # Increment num_open_triangles\n",
    "        num_open_triangles += 1\n",
    "\n",
    "print(num_open_triangles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Cliques y maximal cliques "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Cliques: A group of nodes that are fully connected to one another.\n",
    "* Simplest clique: edge\n",
    "* Simplest complex clique: triangle\n",
    "\n",
    "Maximal cliques: A clique that cannot be extended by adding another nodes in the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4, 0, 1, 2, 3], [4, 5], [6, 5], [6, 7, 8, 9, 10]]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding maximal cliques\n",
    "Z = nx.barbell_graph(m1=5, m2=1)\n",
    "\n",
    "nx.find_cliques(Z)\n",
    "\n",
    "# Lista con los maximal cliques\n",
    "list(nx.find_cliques(Z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object find_cliques at 0x000002271B0EBDB0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.find_cliques(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-e70461a20339>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Check that there are 33 maximal cliques of size 3 in the graph T\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaximal_cliques\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mZ\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m33\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Finding all maximal cliques of size \"n\"\n",
    "\n",
    "# Define maximal_cliques()\n",
    "def maximal_cliques(G, size):\n",
    "    \"\"\"\n",
    "    Finds all maximal cliques in graph `G` that are of size `size`.\n",
    "    \"\"\"\n",
    "    mcs = []\n",
    "    for clique in nx.find_cliques(G):\n",
    "        if len(clique) == size:\n",
    "            mcs.append(clique)\n",
    "    return mcs\n",
    "\n",
    "# Check that there are 33 maximal cliques of size 3 in the graph T\n",
    "assert len(maximal_cliques(Z, 3)) == 33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[4, 0, 1, 2, 3], [6, 7, 8, 9, 10]]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# En el grafo Z hay dos maximal cliques de 5 nodos\n",
    "maximal_cliques(Z, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n es la cantidad de nodos y p la probabilidad que exista un eje entre un par de nodos\n",
    "E = nx.erdos_renyi_graph(n=20, p=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(E)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genera un grafo de manera probabilistica. En el caso anterior, 20 nodos con 37 edges\n",
    "list(E.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos a plotear el nodo 8 y sus vecinos\n",
    "\n",
    "# Definimos los vecinos del nodo 8\n",
    "nodes = list(E.neighbors(8))\n",
    "\n",
    "# se agregan el nodo 8 a la lista con sus vecinos\n",
    "nodes.append(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(3, 8), (3, 12), (8, 12), (8, 14), (8, 17)])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Almacenamos la lista conteniendo el nodo 8 con sus vecinos en un subgrafo\n",
    "E_ocho = E.subgraph(nodes)\n",
    "\n",
    "# Vemos sus ejes: tambien devuelve los ejes que tienen los vecinos de 8 (con otros nodos distintos)\n",
    "E_ocho.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x2271b075c88>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ambos grafos tienen el mismo datatype\n",
    "E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x2271b247b70>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_ocho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploteamos el subgrafo: se le pasa el grafo como objeto\n",
    "nx.draw(E_ocho, with_labels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SUBGRAFO I\n",
    "\n",
    "nodes_of_interest = [12, 8, 7]\n",
    "\n",
    "# Define get_nodes_and_nbrs()\n",
    "\n",
    "def get_nodes_and_nbrs(G, nodes_of_interest):\n",
    "    \"\"\"\n",
    "    Returns a subgraph of the graph `G` with only the `nodes_of_interest` and their neighbors.\n",
    "    \"\"\"\n",
    "    nodes_to_draw = []\n",
    "\n",
    "    # Iterate over the nodes of interest\n",
    "    for n in nodes_of_interest:\n",
    "\n",
    "        # Append the nodes of interest to nodes_to_draw\n",
    "        nodes_to_draw.append(n)\n",
    "\n",
    "        # Iterate over all the neighbors of node n\n",
    "        for nbr in G.neighbors(n):\n",
    "\n",
    "            # Append the neighbors of n to nodes_to_draw\n",
    "            nodes_to_draw.append(nbr)\n",
    "\n",
    "    return G.subgraph(nodes_to_draw)\n",
    "\n",
    "# Extract the subgraph with the nodes of interest: E_draw\n",
    "E_draw = get_nodes_and_nbrs(E, nodes_of_interest)\n",
    "\n",
    "# Draw the subgraph to the screen\n",
    "nx.draw(E_draw)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SUBGRAFO II: extract nodes that have a particular metadata property and their neighbors\n",
    "\n",
    "# Extract the nodes of interest using the metadata with a comprehension list\n",
    "nodes = [n for n, d in G.nodes(data=True) if d['label'] == 'red']\n",
    "\n",
    "# Create the set of nodes: nodeset\n",
    "nodeset = set(nodes)\n",
    "\n",
    "# Iterate over nodes\n",
    "for n in nodes:\n",
    "\n",
    "    # Compute the neighbors of n: nbrs\n",
    "    nbrs = G.neighbors(n)\n",
    "\n",
    "    # Compute the union of nodeset and nbrs: nodeset\n",
    "    nodeset = nodeset.union(nbrs)\n",
    "\n",
    "# Compute the subgraph using nodeset: G_sub\n",
    "G_sub = G.subgraph(nodeset)\n",
    "\n",
    "# Draw T_sub to the screen\n",
    "nx.draw(G_sub)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONNECTED COMPONENTS SUBGRAPH\n",
    "\n",
    "# Se crea el grafo\n",
    "K = nx.erdos_renyi_graph(n=100,p=0.03)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object connected_component_subgraphs at 0x000002271C36BC50>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.connected_component_subgraphs(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<networkx.classes.graph.Graph at 0x2271c447a58>,\n",
       " <networkx.classes.graph.Graph at 0x2271c447a90>,\n",
       " <networkx.classes.graph.Graph at 0x2271c447978>,\n",
       " <networkx.classes.graph.Graph at 0x2271c447a20>,\n",
       " <networkx.classes.graph.Graph at 0x2271c447ac8>]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lo casteamos como lista para ver todos los \"connected_component_subgraphs\": hay cinco subgrafos\n",
    "list(nx.connected_component_subgraphs(K))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# Hay 5 componentes en el subgraph. De esos componentes, a 4 no se podria acceder desde ninguno de los nodos del componente con 96. Es decir, estan aislados. No estan conectados por ningun edge\n",
    "for n in list(nx.connected_component_subgraphs(K)):\n",
    "    print(len(n.nodes()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(K)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object find_cliques at 0x000002271B2330F8>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding a clique\n",
    "Y = nx.erdos_renyi_graph(n=100, p=0.15)\n",
    "\n",
    "nx.find_cliques(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 99, 71],\n",
       " [0, 99, 55],\n",
       " [0, 72, 97],\n",
       " [0, 10],\n",
       " [0, 78],\n",
       " [0, 19],\n",
       " [0, 24, 96, 12],\n",
       " [0, 24, 97],\n",
       " [0, 92],\n",
       " [1, 35, 32, 15],\n",
       " [1, 35, 10],\n",
       " [1, 35, 63],\n",
       " [1, 5, 45],\n",
       " [1, 5, 47],\n",
       " [1, 40, 56],\n",
       " [1, 40, 45],\n",
       " [1, 40, 94, 15],\n",
       " [1, 10, 11],\n",
       " [1, 11, 61],\n",
       " [1, 11, 15],\n",
       " [1, 15, 54],\n",
       " [1, 16, 56],\n",
       " [1, 79, 53],\n",
       " [1, 84, 56],\n",
       " [1, 84, 88],\n",
       " [1, 53, 98],\n",
       " [1, 54, 47, 88, 57],\n",
       " [1, 54, 63],\n",
       " [1, 24, 45],\n",
       " [1, 57, 56],\n",
       " [1, 56, 61],\n",
       " [1, 92, 32],\n",
       " [1, 92, 61, 98],\n",
       " [1, 92, 61, 69],\n",
       " [1, 92, 61, 45],\n",
       " [1, 92, 94, 47],\n",
       " [1, 63, 94],\n",
       " [2, 33],\n",
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       " [2, 99, 80],\n",
       " [2, 99, 25],\n",
       " [2, 99, 34],\n",
       " [2, 99, 75],\n",
       " [2, 7, 25],\n",
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       " [2, 75, 64],\n",
       " [2, 75, 48],\n",
       " [2, 75, 90],\n",
       " [2, 48, 94],\n",
       " [2, 80, 74],\n",
       " [2, 21, 34],\n",
       " [2, 21, 74],\n",
       " [2, 88, 64, 54],\n",
       " [2, 88, 34],\n",
       " [2, 88, 74],\n",
       " [2, 88, 87],\n",
       " [2, 91, 64],\n",
       " [2, 91, 87],\n",
       " [3, 65, 27],\n",
       " [3, 65, 21],\n",
       " [3, 65, 39],\n",
       " [3, 5, 29],\n",
       " [3, 5, 66],\n",
       " [3, 5, 21],\n",
       " [3, 5, 45, 49],\n",
       " [3, 39, 10],\n",
       " [3, 39, 91],\n",
       " [3, 39, 29],\n",
       " [3, 14, 51],\n",
       " [3, 14, 91],\n",
       " [3, 14, 21],\n",
       " [3, 78, 21],\n",
       " [3, 78, 90],\n",
       " [3, 78, 27],\n",
       " [3, 78, 29],\n",
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       " [3, 19, 91],\n",
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       " [3, 58, 10],\n",
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       " [3, 91, 86],\n",
       " [4, 64, 82],\n",
       " [4, 64, 52],\n",
       " [4, 7, 40, 60],\n",
       " [4, 7, 19],\n",
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       " [4, 73, 16],\n",
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       " [6, 99],\n",
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       " [6, 76],\n",
       " [6, 78, 27],\n",
       " [6, 78, 68],\n",
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       " [6, 60, 83],\n",
       " [6, 18, 28],\n",
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       " [6, 26],\n",
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       " [7, 46],\n",
       " [7, 47, 74],\n",
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       " [7, 19, 23],\n",
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       " [7, 51, 74],\n",
       " [7, 53, 74],\n",
       " [7, 25, 58],\n",
       " [7, 25, 23],\n",
       " [7, 60, 56, 40],\n",
       " [7, 60, 56, 58],\n",
       " [7, 60, 56, 84],\n",
       " [7, 60, 74, 23],\n",
       " [8, 97, 72],\n",
       " [8, 97, 56],\n",
       " [8, 34, 88],\n",
       " [8, 34, 17],\n",
       " [8, 34, 93, 72],\n",
       " [8, 34, 93, 16],\n",
       " [8, 98, 17, 13],\n",
       " [8, 98, 42],\n",
       " [8, 42, 16, 56],\n",
       " [8, 42, 16, 93],\n",
       " [8, 11],\n",
       " [8, 75, 83],\n",
       " [8, 13, 56],\n",
       " [8, 13, 19],\n",
       " [8, 45, 17],\n",
       " [8, 45, 30],\n",
       " [8, 45, 55],\n",
       " [8, 83, 72],\n",
       " [8, 87, 72],\n",
       " [8, 87, 88],\n",
       " [8, 55, 88],\n",
       " [8, 55, 93],\n",
       " [9, 68, 11],\n",
       " [9, 38, 53],\n",
       " [9, 41, 94],\n",
       " [9, 10, 58],\n",
       " [9, 10, 11],\n",
       " [9, 10, 87],\n",
       " [9, 45, 58],\n",
       " [9, 15, 11],\n",
       " [9, 15, 94],\n",
       " [9, 84, 56],\n",
       " [9, 56, 58],\n",
       " [12, 96, 20],\n",
       " [12, 96, 61],\n",
       " [12, 67, 68, 61],\n",
       " [12, 67, 60, 84],\n",
       " [12, 37],\n",
       " [12, 80],\n",
       " [12, 81, 68],\n",
       " [12, 20, 84],\n",
       " [12, 24, 60],\n",
       " [12, 58, 60],\n",
       " [13, 32, 17],\n",
       " [13, 98, 21],\n",
       " [13, 98, 61, 17],\n",
       " [13, 98, 53],\n",
       " [13, 98, 95],\n",
       " [13, 36, 21],\n",
       " [13, 36, 95],\n",
       " [13, 73, 56],\n",
       " [13, 73, 18],\n",
       " [13, 73, 19],\n",
       " [13, 47, 59],\n",
       " [13, 86, 58, 60],\n",
       " [13, 56, 58, 60],\n",
       " [13, 56, 21],\n",
       " [13, 56, 61],\n",
       " [13, 59, 17],\n",
       " [13, 59, 95],\n",
       " [14, 64, 91],\n",
       " [14, 84],\n",
       " [14, 69],\n",
       " [17, 33, 32],\n",
       " [17, 34, 32],\n",
       " [17, 34, 51],\n",
       " [17, 70],\n",
       " [17, 45, 51],\n",
       " [17, 45, 61],\n",
       " [17, 45, 23],\n",
       " [17, 51, 98],\n",
       " [17, 85],\n",
       " [17, 23, 59],\n",
       " [17, 89, 32],\n",
       " [17, 89, 98],\n",
       " [18, 64, 82, 75],\n",
       " [18, 64, 70],\n",
       " [18, 34, 50],\n",
       " [18, 34, 51],\n",
       " [18, 35, 73],\n",
       " [18, 35, 82],\n",
       " [18, 35, 29],\n",
       " [18, 35, 22],\n",
       " [18, 70, 31],\n",
       " [18, 42],\n",
       " [18, 75, 50],\n",
       " [18, 45, 51],\n",
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       " [66, 98, 53],\n",
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       " [68, 47, 89],\n",
       " [68, 47, 5],\n",
       " [68, 81, 89],\n",
       " [70, 72],\n",
       " [70, 11],\n",
       " [70, 76],\n",
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       " [73, 56, 16],\n",
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       " [74, 82],\n",
       " [75, 78, 90],\n",
       " [76, 97, 90, 15],\n",
       " [76, 97, 53],\n",
       " [76, 47, 88, 54],\n",
       " [76, 84, 88],\n",
       " [76, 54, 15],\n",
       " [76, 61],\n",
       " [80, 11, 99],\n",
       " [80, 45],\n",
       " [80, 16, 56],\n",
       " [81, 40, 89],\n",
       " [81, 40, 94],\n",
       " [82, 35, 15],\n",
       " [82, 5],\n",
       " [82, 79],\n",
       " [82, 56, 84],\n",
       " [83, 85, 95],\n",
       " [83, 47],\n",
       " [85, 5],\n",
       " [85, 11],\n",
       " [85, 16],\n",
       " [85, 91, 53],\n",
       " [86, 91, 87],\n",
       " [86, 11],\n",
       " [86, 87, 88],\n",
       " [87, 40],\n",
       " [90, 32, 15],\n",
       " [90, 95],\n",
       " [91, 79, 53],\n",
       " [93, 35],\n",
       " [93, 84],\n",
       " [95, 63],\n",
       " [96, 5, 47],\n",
       " [97, 35, 15],\n",
       " [99, 84]]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(nx.find_cliques(Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for clique in nx.find_cliques(Y):\n",
    "    print(len(clique))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "502\n"
     ]
    }
   ],
   "source": [
    "# Maximal cliques\n",
    "cliques = nx.find_cliques(Y)\n",
    "\n",
    "# Count and print the number of maximal cliques in Y\n",
    "print(len(list(cliques)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding a particular maximal clique\n",
    "\n",
    "# Find the author(s) that are part of the largest maximal clique: largest_clique\n",
    "largest_clique = sorted(nx.find_cliques(Y), key=lambda x:len(x))[-1]\n",
    "\n",
    "# Create the subgraph of the largest_clique: Y_lc\n",
    "Y_lc = Y.subgraph(largest_clique)\n",
    "\n",
    "# Create the CircosPlot object: c\n",
    "c = CircosPlot(Y_lc)\n",
    "\n",
    "# Draw the CircosPlot to the screen\n",
    "c.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Caracterizar comunidades\n",
    "\n",
    "# Identify the largest maximal clique: largest_max_clique\n",
    "largest_max_clique = set(sorted(nx.find_cliques(Q), key=lambda x: len(x))[-1])\n",
    "\n",
    "# Create a subgraph from the largest_max_clique: Q_lmc\n",
    "Q_lmc = Q.subgraph(largest_max_clique).copy()\n",
    "\n",
    "# Go out 1 degree of separation\n",
    "for node in list(Q_lmc.nodes()):\n",
    "    Q_lmc.add_nodes_from(Q.neighbors(node))\n",
    "    Q_lmc.add_edges_from(zip([node]*len(list(Q.neighbors(node))), Q.neighbors(node)))\n",
    "\n",
    "# Record each node's degree centrality score\n",
    "for n in Q_lmc.nodes():\n",
    "    Q_lmc.node[n]['degree centrality'] = nx.degree_centrality(Q_lmc)[n]\n",
    "\n",
    "# Create the ArcPlot object: a\n",
    "a = ArcPlot(Q_lmc, node_order='degree centrality')\n",
    "\n",
    "# Draw the ArcPlot to the screen\n",
    "a.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py36",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}