testing_sharing_parameters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "from lib.graph_local_classes import GraphStructure, GraphParams, InnerGraphSimulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "from lib.graph_enumerator import generate_graphs\n",
    "from lib.node_semantics import Node_Name_Rule, Edge_Semantics_Rule\n",
    "from lib import config, result_config\n",
    "from lib.likelihood_calculations_shared_params import Inference\n",
    "from lib.utils import filename_utility\n",
    "from lib.misc import cond_to_data\n",
    "import time\n",
    "from joblib import Parallel, delayed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "graph_iter = generate_graphs(**config.generator_dictionary)\n",
    "graphs = list(graph_iter)\n",
    "for graph in graphs:\n",
    "    Node_Name_Rule.graph_semantics_apply(graph,config.node_semantics)\n",
    "    Edge_Semantics_Rule.graph_semantics_apply(graph,config.edge_semantics)\n",
    "\n",
    "inference_obj = Inference()\n",
    "\n",
    "options = config.options\n",
    "options['data_sets']=cond_to_data(config.conds[1,:])\n",
    "options['stigma_sample_size']=2000\n",
    "options['param_sample_size']=2\n",
    "\n",
    "scale_free_bounds = (10**(-.1),10**(.1))\n",
    "# scale_free_bounds = (10**(-.25),10**(.25))\n",
    "# scale_free_bounds = (10**-1,10**1)\n",
    "# scale_free_bounds = (10**-3,10**3)\n",
    "\n",
    "options['scale_free_bounds']=scale_free_bounds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-2)]: Done   3 out of   2 | elapsed:  2.0min remaining:  -40.4s\n",
      "[Parallel(n_jobs=-2)]: Done   2 out of   2 | elapsed:  2.0min finished\n"
     ]
    }
   ],
   "source": [
    "result_graphs, result_posterior, result_loglik, result_dict, result_params = inference_obj.p_graph_given_d(graphs,config.options)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "edges_q = result_config.edges_of_interest.copy()\n",
    "\n",
    "posterior = result_posterior\n",
    "for i,g in enumerate(graphs):\n",
    "    for edge in edges_q:\n",
    "        if edge in g.edges():\n",
    "            edges_q[edge]+=posterior[i]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63168612198522534"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(result_posterior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('A_★', 'B_★'): 0.99999999999997113,\n",
       " ('A_★', 'C_★'): 0.031644545567001262,\n",
       " ('A_★', 'D_★'): 0.042244825208054317,\n",
       " ('B_★', 'C_★'): 0.33666899750642104,\n",
       " ('B_★', 'D_★'): 0.99999492264721102,\n",
       " ('C_★', 'B_★'): 0.031648623135006729,\n",
       " ('C_★', 'D_★'): 0.64228132345026334,\n",
       " ('D_★', 'B_★'): 0.63169153478740225,\n",
       " ('D_★', 'C_★'): 0.67392817765079072}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "small_val = .05\n",
    "\n",
    "cond1 = [0,small_val,small_val,small_val]\n",
    "cond2 = [0,1,3,2]\n",
    "cond3 = [0,3,2,1]\n",
    "cond4 = [0,1,2,2]\n",
    "\n",
    "conds = np.array([cond1,cond2,cond3,cond4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_loglik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "graph_iter = generate_graphs(**config.generator_dictionary)\n",
    "graphs = list(graph_iter)\n",
    "for graph in graphs:\n",
    "    Node_Name_Rule.graph_semantics_apply(graph,config.node_semantics)\n",
    "    Edge_Semantics_Rule.graph_semantics_apply(graph,config.edge_semantics)\n",
    "\n",
    "max_graph = graphs[0]\n",
    "num_graphs = len(graphs)\n",
    "# num_params = config.options[\"param_sample_size\"]\n",
    "num_params = 2\n",
    "loglikelihood_by_param = np.zeros(shape = (num_params,num_graphs))\n",
    "print(loglikelihood_by_param.__class__)\n",
    "max_graph_params = GraphParams.from_networkx(max_graph) # fix this when you can\n",
    "# max_graph_params.sample()\n",
    "# blah = inference_obj.subgraph_loglik(graphs[1],max_graph_params,options=config.options)\n",
    "param_list = [None]*num_params\n",
    "for i in range(num_params):\n",
    "    param_list[i] = max_graph_params.sample()\n",
    "# blah = Parallel(n_jobs = -1, backend = \"multiprocessing\")( for i,x in enumerate(range(1,10)))\n",
    "    loglikelihood_by_param = np.array(Parallel(n_jobs=-1, backend=\"multiprocessing\")(\n",
    "            delayed(inference_obj.subgraph_loglik)(graph, max_graph_params,\n",
    "                options=options) for graph in graphs))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(num_params):\n",
    "    param_list[i] = max_graph_params.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def helper_subgraph_loglik(graphs,max_graph_params,options=None):\n",
    "    return np.array([inference_obj.subgraph_loglik(graph,max_graph_params,options=options) for graph in graphs])\n",
    "loglikelihood_by_param = Parallel(n_jobs=-1, backend=\"multiprocessing\")(\n",
    "            delayed(helper_subgraph_loglik)(graphs, max_graph_params.from_dict(params),\n",
    "                options=options) for params in param_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# do not go past here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "       -646.24475154, -395.57123248, -467.13907017, -414.4977221 ,\n",
       "                -inf,          -inf, -546.98280741,          -inf,\n",
       "                -inf, -626.98544282, -364.51456156, -468.09630605,\n",
       "       -598.19915483, -494.59804994,          -inf,          -inf,\n",
       "                -inf,          -inf,          -inf, -573.65555843,\n",
       "                -inf,          -inf, -587.90938738, -612.72891905,\n",
       "       -522.5440872 , -432.19593926, -526.86485641, -401.94782587,\n",
       "                -inf, -537.47458983, -692.78254909, -518.11908038,\n",
       "                -inf,          -inf,          -inf,          -inf,\n",
       "                -inf,          -inf,          -inf,          -inf,\n",
       "       -584.49152201,          -inf, -404.36707215,          -inf,\n",
       "                -inf, -358.92777983, -378.31278744, -418.48711856,\n",
       "       -698.07290352,          -inf, -329.34388624, -546.82013066,\n",
       "                -inf, -521.03366418, -711.34505291, -568.49813086,\n",
       "       -581.3109137 , -455.70072565, -293.5537258 , -408.73187266,\n",
       "       -381.19712555, -784.02127521, -460.99111388, -523.10772892,\n",
       "                -inf,          -inf, -526.92940998,          -inf,\n",
       "       -389.91513166, -452.46368042, -660.59383275,          -inf,\n",
       "       -472.10490268,          -inf, -620.25502416, -354.5984223 ,\n",
       "                -inf,          -inf, -522.64765302, -548.84789921,\n",
       "       -719.0622669 ,          -inf,          -inf,          -inf,\n",
       "                -inf, -355.62663012, -297.32830217, -654.54189272,\n",
       "       -324.7031465 , -585.68949314,          -inf, -441.15493663])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loglikelihood_by_param[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-65c029e8f48d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mloglikelihood_by_param\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "loglikelihood_by_param.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(loglikelihood_by_param.__class__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 1.4142135623730951,\n",
       " 1.7320508075688772,\n",
       " 2.0,\n",
       " 2.23606797749979,\n",
       " 2.449489742783178,\n",
       " 2.6457513110645907,\n",
       " 2.8284271247461903,\n",
       " 3.0]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Parallel(n_jobs = -1, backend = \"multiprocessing\")(delayed(sqrt)(x) for i,x in enumerate(range(1,10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_gen = [delayed(sqrt)(x,idx = i) for i,x in enumerate(range(1,10))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<function math.sqrt>, (1,), {'idx': 0}),\n",
       " (<function math.sqrt>, (2,), {'idx': 1}),\n",
       " (<function math.sqrt>, (3,), {'idx': 2}),\n",
       " (<function math.sqrt>, (4,), {'idx': 3}),\n",
       " (<function math.sqrt>, (5,), {'idx': 4}),\n",
       " (<function math.sqrt>, (6,), {'idx': 5}),\n",
       " (<function math.sqrt>, (7,), {'idx': 6}),\n",
       " (<function math.sqrt>, (8,), {'idx': 7}),\n",
       " (<function math.sqrt>, (9,), {'idx': 8})]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-128-c4807b9ff71c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "for i,x in enumerate(range(1,5)):\n",
    "    print(i,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'r': array([  7.28887461,  57.01761847,   8.54070358,  12.90071227,\n",
      "        16.59509896,   1.7601836 ,  49.47634301,   1.81576445,\n",
      "         0.50490592,  20.34303725,  14.63032422,   1.08552227,\n",
      "        31.78241691,  36.33860303,  14.41687462]), 'lambda0': [18.62627296023096], 'psi_shape': 1.0, 'r_shape': 1.0, 'scale_free_bounds': (0.01, 100), 'names': [('A_int', 'A_obs'), ('A_int', 'A_★'), ('A_★', 'A_obs'), ('A_★', 'B_★'), ('A_★', 'C_★'), ('A_★', 'D_★'), ('B_★', 'B_obs'), ('B_★', 'C_★'), ('B_★', 'D_★'), ('C_★', 'B_★'), ('C_★', 'C_obs'), ('C_★', 'D_★'), ('D_★', 'B_★'), ('D_★', 'C_★'), ('D_★', 'D_obs')], 'mu': array([  2.86044561e+00,   4.79573854e-01,   7.07358855e-01,\n",
      "         3.49711220e-01,   2.17602414e-01,   5.25812291e-01,\n",
      "         2.77978135e-01,   5.33475236e-01,   5.75700583e+01,\n",
      "         1.62187470e-01,   6.05699152e-01,   5.03472105e+01,\n",
      "         8.27638020e-02,   1.30686555e-02,   4.70509167e-01]), 'n': 15, 'p': 0.8, 'psi': array([ 20.84942935,  27.34415903,   6.04134231,   4.51152382,\n",
      "         3.61113359,   0.92552617,  13.75334154,   0.96866537,\n",
      "        29.06746319,   3.29938574,   8.86157497,  54.6530184 ,\n",
      "         2.63043366,   0.47489669,   6.78327167])}\n",
      "{'r': array([  9.52855003,   2.39395283,  38.72398587,  12.44261209,\n",
      "        45.17064661,  15.07717917,   4.96468024,  30.99938484,\n",
      "         7.69403603,  18.55259721,  20.24654865,  47.44620752,\n",
      "         7.29568879,   0.68312537,  32.23225   ]), 'lambda0': [18.62627296023096], 'psi_shape': 1.0, 'r_shape': 1.0, 'scale_free_bounds': (0.01, 100), 'names': [('A_int', 'A_obs'), ('A_int', 'A_★'), ('A_★', 'A_obs'), ('A_★', 'B_★'), ('A_★', 'C_★'), ('A_★', 'D_★'), ('B_★', 'B_obs'), ('B_★', 'C_★'), ('B_★', 'D_★'), ('C_★', 'B_★'), ('C_★', 'C_obs'), ('C_★', 'D_★'), ('D_★', 'B_★'), ('D_★', 'C_★'), ('D_★', 'D_obs')], 'mu': None, 'n': 15, 'p': 0.8, 'psi': array([ 14.11968968,  19.90083698,  54.49189038,   2.42892767,\n",
      "        39.70689803,   2.14981838,   2.2262068 ,   9.86407775,\n",
      "        13.43058103,  21.15311455,  10.04837569,   7.3793286 ,\n",
      "         2.46029101,   4.67208242,  40.45149414])}\n",
      "{'r': array([  7.28887461,  57.01761847,   8.54070358,  12.90071227,\n",
      "        16.59509896,   1.7601836 ,  49.47634301,   1.81576445,\n",
      "         0.50490592,  20.34303725,  14.63032422,   1.08552227,\n",
      "        31.78241691,  36.33860303,  14.41687462]), 'lambda0': [18.62627296023096], 'psi_shape': 1.0, 'r_shape': 1.0, 'scale_free_bounds': (0.01, 100), 'names': [('A_int', 'A_obs'), ('A_int', 'A_★'), ('A_★', 'A_obs'), ('A_★', 'B_★'), ('A_★', 'C_★'), ('A_★', 'D_★'), ('B_★', 'B_obs'), ('B_★', 'C_★'), ('B_★', 'D_★'), ('C_★', 'B_★'), ('C_★', 'C_obs'), ('C_★', 'D_★'), ('D_★', 'B_★'), ('D_★', 'C_★'), ('D_★', 'D_obs')], 'mu': None, 'n': 15, 'p': 0.8, 'psi': array([ 20.84942935,  27.34415903,   6.04134231,   4.51152382,\n",
      "         3.61113359,   0.92552617,  13.75334154,   0.96866537,\n",
      "        29.06746319,   3.29938574,   8.86157497,  54.6530184 ,\n",
      "         2.63043366,   0.47489669,   6.78327167])}\n"
     ]
    }
   ],
   "source": [
    "print(max_graph_params.to_dict())\n",
    "print(max_graph_params.from_dict(param_list[0]).to_dict())\n",
    "print(max_graph_params.from_dict(param_list[1]).to_dict())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ContObj:\n",
    "    \n",
    "    def __iter__(self):\n",
    "        return iter(['hi', 'banana','red'])\n",
    "    \n",
    "    def __getitem__(self, value):\n",
    "        \n",
    "        return np.ones(shape=[value]*value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cont_obj = ContObj()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.,  1.]]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_obj[4][:, :,3, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'ConfObj' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-847281381b83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcont_obj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'ConfObj' object is not iterable"
     ]
    }
   ],
   "source": [
    "for x in iter(cont_obj):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<networkx.classes.digraph.DiGraph at 0x1128d3dd8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3eb8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3ef0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3f28>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3f60>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3f98>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3fd0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6048>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6080>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e60b8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e60f0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6128>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e61d0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6208>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6240>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6278>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e62b0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e62e8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6320>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6358>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e6390>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128e63c8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112878f98>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128745c0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874550>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874390>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874f28>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874710>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874400>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874470>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128746d8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874e80>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874cc0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874c18>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128742e8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874908>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874da0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874860>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874198>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874a20>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128749e8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128749b0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874b00>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874fd0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874b38>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874cf8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874b70>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874be0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874a90>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874c50>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874d30>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874f98>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874438>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874e10>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874e48>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874f60>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874208>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874ba8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128746a0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128740f0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874c88>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874240>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874358>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874d68>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874588>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874ef0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128744e0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874eb8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128741d0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x112874048>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d34a8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3400>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3048>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3080>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d3208>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d31d0>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d30b8>,\n",
       " <networkx.classes.digraph.DiGraph at 0x1128d30f0>,\n",
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     "execution_count": 8,
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