conviction/conviction_cadCAD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "from cadCAD.configuration import Configuration\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from scipy.stats import expon, gamma\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses the differential games framework developed by BlockScience. It is currently in private beta, and building towards a full open source release.\n",
    "\n",
    "**Description:**\n",
    "\n",
    "cadCAD is a Python library that assists in the processes of designing, testing and validating complex systems through simulation. At its core, cadCAD is a differential games engine that supports parameter sweeping and Monte Carlo analyses and can be easily integrated with other scientific computing Python modules and data science workflows.\n",
    "\n",
    "To learn more about cadCAD, follow our [tutorial series](https://github.com/BlockScience/cadCAD-Tutorials/tree/master/01%20Tutorials)\n",
    "\n",
    "**Installing cadCAD:**\n",
    "\n",
    "cadCAD is in private beta. Tokens are issued to participants. Replace `<TOKEN>` in the installation URL below\n",
    "```bash\n",
    "pip3 install cadCAD --extra-index-url https://<TOKEN>@repo.fury.io/blockscience/\n",
    "```\n",
    "\n",
    "If you'd like to participate in the beta program, contact cadcad [at] block [dot] science.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper functions\n",
    "def get_nodes_by_type(g, node_type_selection):\n",
    "    return [node for node in g.nodes if g.nodes[node]['type']== node_type_selection ]\n",
    "\n",
    "def get_edges_by_type(g, edge_type_selection):\n",
    "    return [edge for edge in g.edges if g.edges[edge]['type']== edge_type_selection ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125020.46534074425\n"
     ]
    }
   ],
   "source": [
    "#generate an initial set of 'n' participants\n",
    "network = nx.DiGraph()\n",
    "n = 100\n",
    "for i in range(n):\n",
    "    network.add_node(i)\n",
    "    network.nodes[i]['type']=\"participant\"\n",
    "    \n",
    "    h_rv = expon.rvs(loc=0.0, scale=1000)\n",
    "    network.nodes[i]['holdings'] = h_rv\n",
    "    \n",
    "    s_rv = np.random.rand() \n",
    "    network.nodes[i]['sentiment'] = s_rv\n",
    "\n",
    "participants = get_nodes_by_type(network, 'participant')\n",
    "initial_supply = np.sum([ network.nodes[i]['holdings'] for i in participants])\n",
    "print(initial_supply)\n",
    "\n",
    "initial_funds = initial_supply\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5493976373731984\n"
     ]
    }
   ],
   "source": [
    "#generate initial proposals\n",
    "m = 7\n",
    "for j in range(m):\n",
    "    ind = n+j\n",
    "    network.add_node(ind)\n",
    "    network.nodes[ind]['type']=\"proposal\"\n",
    "    network.nodes[ind]['conviction']=0\n",
    "    network.nodes[ind]['status']='candidate'\n",
    "    network.nodes[ind]['age']=0\n",
    "    \n",
    "    r_rv = gamma.rvs(3,loc=0.001, scale=10000)\n",
    "    network.node[ind]['funds_requested'] = r_rv\n",
    "    \n",
    "    for i in range(n):\n",
    "        network.add_edge(i, ind)\n",
    "        \n",
    "        rv = np.random.rand()\n",
    "        a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "        network.edges[(i, ind)]['affinity'] = a_rv\n",
    "        network.edges[(i,ind)]['tokens'] = 0\n",
    "        network.edges[(i, ind)]['conviction'] = 0\n",
    "\n",
    "proposals = get_nodes_by_type(network, 'proposal')\n",
    "total_requested = np.sum([ network.nodes[i]['funds_requested'] for i in proposals])\n",
    "print(total_requested/initial_funds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'proposal',\n",
       " 'conviction': 0,\n",
       " 'status': 'candidate',\n",
       " 'age': 0,\n",
       " 'funds_requested': 27581.372211726168}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.nodes[get_nodes_by_type(network, 'proposal')[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'participant',\n",
       " 'holdings': 1368.7265371328936,\n",
       " 'sentiment': 0.8242453852153833}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.nodes[get_nodes_by_type(network, 'participant')[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Participants Token Holdings')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n",
    "plt.title('Histogram of Participants Token Holdings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Proposals Funds Requested')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zde4SW02IuCQipkXEtJ6enoFVamZmA9JSwEsaRQr370bED+o0WQFMroxPAlZue3lmZjZYrbyLRsC3gSURcUGDZnOBE/O7aQ4B1kfEqgZtzczsZdBKH/xhwAnAvZIW5mmfBaYARMQc4EbgaGAp8BzwkfaXamZmA9E04CPiF9TvY6+2CeCMdhVlZmbbzp9kNTMrlAPezKxQDngzs0I54M3MCuWANzMrlAPezKxQDngzs0I54M3MCuWANzMrlAPezKxQDngzs0I54M3MCuWANzMrlAPezKxQDngzs0I54M3MCuWANzMrlAPezKxQDngzs0I54M3MCuWANzMrlAPezKxQDngzs0I54M3MCuWANzMrVNOAl3SppNWS7mswf7qk9ZIW5tu57S/TzMwGamQLbS4DZgNX9NPmjog4pi0VmZlZWzQ9g4+I24GnX4ZazMysjdrVB3+opEWSbpK0f6NGkmZJmi9p/po1a9q0ajMzq6cdAX83sGdEvAX4Z+BHjRpGxCURMS0ipvX09LRh1WZm1sg2B3xEbIiIjXn4RmCUpPHbXJmZmW2TbQ54SXtIUh4+KC9z7bYu18zMtk3Td9FIugqYDoyXtAL4PDAKICLmAMcBp0naBPwemBkRMWQVm5lZS5oGfEQc32T+bNLbKM3MbBjxJ1nNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK5QD3sysUA54M7NCOeDNzArlgDczK1TTgJd0qaTVku5rMF+SLpK0VNI9kqa2v0wzMxuoVs7gLwOO7Gf+UcC++TYL+Ma2l2VmZtuqacBHxO3A0/00mQFcEcmvgXGSJrSrQDMzG5yRbVjGRGB5ZXxFnraqtqGkWaSzfKZMmTLoFfaefcOg77utln35vR1bt5m1zyshR9pxkVV1pkW9hhFxSURMi4hpPT09bVi1mZk10o6AXwFMroxPAla2YblmZrYN2hHwc4ET87tpDgHWR8RW3TNmZvbyatoHL+kqYDowXtIK4PPAKICImAPcCBwNLAWeAz4yVMWamVnrmgZ8RBzfZH4AZ7StIjMzawt/ktXMrFAOeDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyuUA97MrFAtBbykIyU9KGmppLPrzD9Z0hpJC/Pto+0v1czMBmJkswaSRgBfB44AVgB3SZobEffXNL0mIs4cghrNzGwQWjmDPwhYGhGPRMSfgKuBGUNblpmZbatWAn4isLwyviJPq/UBSfdIuk7S5HoLkjRL0nxJ89esWTOIcs3MrFWtBLzqTIua8R8DvRFxAPD/gMvrLSgiLomIaRExraenZ2CVmpnZgLQS8CuA6hn5JGBltUFErI2IP+bRbwJva095ZmY2WK0E/F3AvpL2kjQamAnMrTaQNKEyeiywpH0lmpnZYDR9F01EbJJ0JnAzMAK4NCIWS/oCMD8i5gJ/J+lYYBPwNHDyENZsZmYtaBrwABFxI3BjzbRzK8PnAOe0tzQzM9sW/iSrmVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVywJuZFcoBb2ZWKAe8mVmhHPBmZoVqKeAlHSnpQUlLJZ1dZ/72kq7J8+dJ6m13oWZmNjBNA17SCODrwFHAfsDxkvaraXYq8LuI2Af4J+Ar7S7UzMwGppUz+IOApRHxSET8CbgamFHTZgZweR6+Dni3JLWvTDMzG6iRLbSZCCyvjK8ADm7UJiI2SVoPvAp4qtpI0ixgVh7dKOnBwRTdIeOBp9R9r03GU/M4dAt9pXtrp3v3e7fWDV1Ue50cGUjte7a6nlYCvt6ZeAyiDRFxCXBJC+scdiTNj4hpna5joLq1bnDtndCtdYNrr6eVLpoVwOTK+CRgZaM2kkYCuwJPt6NAMzMbnFYC/i5gX0l7SRoNzATm1rSZC5yUh48Dbo2Irc7gzczs5dO0iyb3qZ8J3AyMAC6NiMWSvgDMj4i5wLeBKyUtJZ25zxzKojukK7uW6N66wbV3QrfWDa59K/KJtplZmfxJVjOzQjngzcwK9YoKeEmXSlot6b7KtN0l3SLpofx3tzxdki7KX79wj6SplfuclNs/JOmkyvS3Sbo33+eidn3YS9JkST+TtETSYkkf76Lad5B0p6RFufbz8/S98tdaPJS/5mJ0nt7way8knZOnPyjpzyvT+/0qjTZswwhJv5F0fTfVLmlZfkwXSpqfp3XDMTNO0nWSHsjH/KFdUvcb8r7uu22QdFZHa4+IV8wNeCcwFbivMu2rwNl5+GzgK3n4aOAm0nv8DwHm5em7A4/kv7vl4d3yvDuBQ/N9bgKOalPdE4CpeXhn4Lekr43ohtoFjM3Do4B5uaZrgZl5+hzgtDx8OjAnD88ErsnD+wGLgO2BvYCHSRf9R+ThvYHRuc1+bT5u/hvwPeD6PN4VtQPLgPE107rhmLkc+GgeHg2M64a6a7ZhBPAE6UNJHau9rRvVDTegly0D/kFgQh6eADyYhy8Gjq9tBxwPXFyZfnGeNgF4oDJ9i3Zt3ob/CxzRbbUDOwF3kz4J/RQwMk8/FLg5D98MHJqHR+Z2As4Bzqks6+Z8v5fum6dv0a4NNU8Cfgq8C7g+19IttS9j64Af1scMsAvwKPkNIN1Sd53t+DPg3zpd+yuqi6aB10TEKoD899V5er2vaJjYZPqKOtPbKr/sP5B0JtwVtecujoXAauAW0lnruojYVGd9W3ztBdD3tRcD3aZ2uRD4NPBiHn9VF9UewE8kLVD6mhAY/sfM3sAa4F9zt9i3JI3pgrprzQSuysMdq90B31ijr18Y6PT2FSSNBb4PnBURG/pr2qCWjtQeES9ExFtJZ8MHAW/qZ33DpnZJxwCrI2JBdXI/6xs2tWeHRcRU0jfBniHpnf20HS61jyR1o34jIg4EniV1azQyXOreXFC6JnMs8H+aNW1QS9tqd8DDk5ImAOS/q/P0Rl/R0N/0SXWmt4WkUaRw/25E/KCbau8TEeuA20j9jeOUvtaidn2NvvZioNvUDocBx0paRvoW1XeRzui7oXYiYmX+uxr4IenJdbgfMyuAFRExL49fRwr84V531VHA3RHxZB7vXO3t7nsa7je27oP/n2x5AeSrefi9bHkB5M48fXdSH+Fu+fYosHued1du23cB5Og21SzgCuDCmundUHsPMC4P7wjcARxDOrupXqg8PQ+fwZYXKq/Nw/uz5YXKR0gXskbm4b3YfKFy/yE4bqaz+SLrsK8dGAPsXBn+JXBklxwzdwBvyMPn5ZqHfd2V+q8GPjIc/k/b+k8w3G+kPrFVwPOkZ8NTSX2kPwUeyn/7dqRIP3TyMHAvMK2ynFOApflWfSCnAffl+8ym5kLRNtR9OOml2D3Awnw7uktqPwD4Ta79PuDcPH1v0jsClpICc/s8fYc8vjTP37uyrL/P9T1I5d0DeV/8Ns/7+yE6dqazOeCHfe25xkX5trhv2V1yzLwVmJ+PmR+RQm7Y152XvROwFti1Mq1jtfurCszMCuU+eDOzQjngzcwK5YA3MyuUA97MrFAOeDOzQjngzcwK5YA3MyvU/weVGLjxRsIpOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([ network.nodes[i]['funds_requested'] for i in proposals])\n",
    "plt.title('Histogram of Proposals Funds Requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Affinities between Participants and Proposals')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist([ network.edges[e]['affinity'] for e in network.edges])\n",
    "plt.title('Histogram of Affinities between Participants and Proposals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Affinities between Participants and Proposals weighted by holdings')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist([ network.edges[e]['affinity'] for e in network.edges], weights = [network.nodes[e[0]]['holdings']for e in network.edges],alpha = 1)\n",
    "plt.title('Histogram of Affinities between Participants and Proposals weighted by holdings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 200 #iterations of graph update in our simulation\n",
    "#param for conviction accumilation\n",
    "alpha = .5 #later we should set this to be param so we can sweep it\n",
    "\n",
    "#sentiment of the outside world which drives grants\n",
    "initial_sentiment = .8\n",
    "\n",
    "#sentiment decay rate\n",
    "mu =.001 #later we should set this to be param so we can sweep it\n",
    "\n",
    "#maximum share of funds a proposal can take\n",
    "beta = .2 #later we should set this to be param so we can sweep it\n",
    "# tuning param for the trigger function\n",
    "rho = .001\n",
    "\n",
    "#minimum periods passed before a proposal can pass\n",
    "tmin = 7\n",
    "\n",
    "#how close to a participant's highest affinity proposal that\n",
    "#another proposal has to be for them to be happy about its advancement\n",
    "sensitivity = .75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# Settings of general simulation parameters, unrelated to the system itself\n",
    "# `T` is a range with the number of discrete units of time the simulation will run for;\n",
    "# `N` is the number of times the simulation will be run (Monte Carlo runs)\n",
    "# We'll cover the `M` key in a future article. For now, let's leave it empty\n",
    "simulation_parameters = {\n",
    "    'T': range(T),\n",
    "    'N': 1,\n",
    "    'M': {}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_conditions = {'network':network,\n",
    "                      'supply': initial_supply,\n",
    "                      'funds':initial_funds,\n",
    "                      'sentiment': initial_sentiment}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions for partial state update block 1\n",
    "\n",
    "def gen_new_participant(network, new_participant_holdings):\n",
    "    \n",
    "    i = len([node for node in network.nodes])\n",
    "    \n",
    "    network.add_node(i)\n",
    "    network.nodes[i]['type']=\"participant\"\n",
    "    \n",
    "    s_rv = np.random.rand() \n",
    "    network.nodes[i]['sentiment'] = s_rv\n",
    "    network.nodes[i]['holdings']=new_participant_holdings\n",
    "    \n",
    "    for j in get_nodes_by_type(network, 'proposal'):\n",
    "        network.add_edge(i, j)\n",
    "        \n",
    "        rv = np.random.rand()\n",
    "        a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "        network.edges[(i, j)]['affinity'] = a_rv\n",
    "        network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']\n",
    "        network.edges[(i, j)]['conviction'] = 0\n",
    "    \n",
    "    return network\n",
    "    \n",
    "\n",
    "def gen_new_proposal(network):\n",
    "    j = len([node for node in network.nodes])\n",
    "    network.add_node(j)\n",
    "    network.nodes[j]['type']=\"proposal\"\n",
    "    \n",
    "    network.nodes[j]['conviction']=0\n",
    "    network.nodes[j]['status']='candidate'\n",
    "    network.nodes[j]['age']=0\n",
    "    \n",
    "    r_rv = gamma.rvs(3,loc=0.001, scale=10000)\n",
    "    network.node[j]['funds_requested'] = r_rv\n",
    "    \n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    proposing_participant = np.random.choice(participants)\n",
    "    \n",
    "    for i in participants:\n",
    "        network.add_edge(i, j)\n",
    "        if i==proposing_participant:\n",
    "            network.edges[(i, j)]['affinity']=1\n",
    "        else:\n",
    "            rv = np.random.rand()\n",
    "            a_rv = 1-4*(1-rv)*rv #polarized distribution\n",
    "            network.edges[(i, j)]['affinity'] = a_rv\n",
    "            \n",
    "        network.edges[(i, j)]['conviction'] = 0\n",
    "        network.edges[(i,j)]['tokens'] = 0\n",
    "    return network\n",
    "        \n",
    "        \n",
    "\n",
    "def driving_process(params, step, sL, s):\n",
    "    \n",
    "    #placeholder plumbing for random processes\n",
    "    arrival_rate = 10/s['sentiment']\n",
    "    rv1 = np.random.rand()\n",
    "    new_participant = bool(rv1<1/arrival_rate)\n",
    "    if new_participant:\n",
    "        h_rv = expon.rvs(loc=0.0, scale=1000)\n",
    "        new_participant_holdings = h_rv\n",
    "    else:\n",
    "        new_participant_holdings = 0\n",
    "    \n",
    "    network = s['network']\n",
    "    affinities = [network.edges[e]['affinity'] for e in network.edges ]\n",
    "    median_affinity = np.median(affinities)\n",
    "    \n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate' ]\n",
    "    \n",
    "    funds = s['funds']\n",
    "    total_funds_requested = np.sum(fund_requests)\n",
    "    \n",
    "    proposal_rate = 10/median_affinity * total_funds_requested/funds\n",
    "    rv2 = np.random.rand()\n",
    "    new_proposal = bool(rv2<1/proposal_rate)\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    funds = s['funds']\n",
    "    scale_factor = 1+4000*sentiment**2\n",
    "    \n",
    "    #this shouldn't happen but expon is throwing domain errors\n",
    "    if scale_factor > 1: \n",
    "        funds_arrival = expon.rvs(loc = 0, scale = scale_factor )\n",
    "    else:\n",
    "        funds_arrival = 0\n",
    "    \n",
    "    return({'new_participant':new_participant,\n",
    "            'new_participant_holdings':new_participant_holdings,\n",
    "            'new_proposal':new_proposal, \n",
    "            'funds_arrival':funds_arrival})\n",
    "\n",
    "def update_network(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    #placeholder plumbing for new proposals and new participants\n",
    "    new_participant = _input['new_participant'] #T/F\n",
    "    new_proposal = _input['new_proposal'] #T/F\n",
    "    # IF THEN logic to create new nodes // left out for now since always FALSE\n",
    "    if new_participant:\n",
    "        new_participant_holdings = _input['new_participant_holdings']\n",
    "        network = gen_new_participant(network, new_participant_holdings)\n",
    "    \n",
    "    if new_proposal:\n",
    "        network= gen_new_proposal(network)\n",
    "    \n",
    "    #update age of the existing proposals\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    for j in proposals:\n",
    "         network.nodes[j]['age']=  network.nodes[j]['age']+1\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def increment_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']\n",
    "    funds_arrival = _input['funds_arrival']\n",
    "\n",
    "    #increment funds\n",
    "    funds = funds + funds_arrival\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def increment_supply(params, step, sL, s, _input):\n",
    "    \n",
    "    supply = s['supply']\n",
    "    supply_arrival = _input['new_participant_holdings']\n",
    "\n",
    "    #increment funds\n",
    "    supply = supply + supply_arrival\n",
    "    \n",
    "    key = 'supply'\n",
    "    value = supply\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#partial state update block 2\n",
    "def check_progress(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    \n",
    "    completed = []\n",
    "    for j in proposals:\n",
    "        if network.nodes[j]['status'] == 'active':\n",
    "            grant_size = network.nodes[j]['funds_requested']\n",
    "            likelihood = 1.0/(5+np.log(grant_size))\n",
    "            if np.random.rand() < likelihood:\n",
    "                completed.append(j)\n",
    "    \n",
    "    return({'completed':completed})\n",
    "\n",
    "def complete_proposal(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    completed = _input['completed']\n",
    "    for j in completed:\n",
    "        network.nodes[j]['status']='completed'\n",
    "        for i in participants:\n",
    "            force = network.edges[(i,j)]['affinity']\n",
    "            sentiment = network.node[i]['sentiment']\n",
    "            network.node[i]['sentiment'] = get_sentimental(sentiment, force, decay=False)\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_sentiment_on_completion(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    completed = _input['completed']\n",
    "    \n",
    "    grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])\n",
    "    \n",
    "    grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    \n",
    "    force = grants_completed/grants_outstanding\n",
    "    if (force >=0) and (force <=1):\n",
    "        sentiment = get_sentimental(sentiment, force, True)\n",
    "    else:\n",
    "        sentiment = get_sentimental(sentiment, 0, True)\n",
    "    \n",
    "    \n",
    "    key = 'sentiment'\n",
    "    value = sentiment\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def get_sentimental(sentiment, force, decay=True):\n",
    "    sentiment = sentiment*(1-int(decay)*mu) + force\n",
    "    \n",
    "    if sentiment > 1:\n",
    "        sentiment = 1\n",
    "        \n",
    "    return sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trigger_threshold(requested, funds, supply):\n",
    "    \n",
    "    share = requested/funds\n",
    "    if share < beta:\n",
    "        return rho*supply/(beta-share)**2\n",
    "    else: \n",
    "        return np.inf\n",
    "        \n",
    "    \n",
    "\n",
    "#partial state update block 3\n",
    "def trigger_function(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    funds = s['funds']\n",
    "    supply = s['supply']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    \n",
    "    accepted = []\n",
    "    for j in proposals:\n",
    "        if network.nodes[j]['status'] == 'candidate':\n",
    "            requested = network.nodes[j]['funds_requested']\n",
    "            age = network.nodes[j]['age']\n",
    "            if age > tmin:\n",
    "                threshold = trigger_threshold(requested, funds, supply)\n",
    "                conviction = network.nodes[j]['conviction']\n",
    "                if conviction >threshold:\n",
    "                    accepted.append(j)\n",
    "                    \n",
    "    return({'accepted':accepted})\n",
    "\n",
    "def decrement_funds(params, step, sL, s, _input):\n",
    "    \n",
    "    funds = s['funds']\n",
    "    network = s['network']\n",
    "    accepted = _input['accepted']\n",
    "\n",
    "    #decrement funds\n",
    "    for j in accepted:\n",
    "        funds = funds - network.nodes[j]['funds_requested']\n",
    "    \n",
    "    key = 'funds'\n",
    "    value = funds\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_proposals(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    accepted = _input['accepted']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    #bookkeeping conviction and participant sentiment\n",
    "    for j in accepted:\n",
    "        network.nodes[j]['status']='active'\n",
    "        network.nodes[j]['conviction']=np.nan\n",
    "        #change status to active\n",
    "        for i in participants:\n",
    "        \n",
    "            edge = (i,j)\n",
    "            #reset tokens assigned to other candidates\n",
    "            network.edges[(i,j)]['tokens']=0\n",
    "            network.edges[(i,j)]['conviction'] = np.nan\n",
    "            \n",
    "            #update participants sentiments (positive or negative) \n",
    "            affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]\n",
    "            if len(affinities)>1:\n",
    "                max_affinity = np.max(affinities)\n",
    "                force = network.edges[(i,j)]['affinity']-sensitivity*max_affinity\n",
    "            else:\n",
    "                force = 0\n",
    "            \n",
    "            #based on what their affinities to the accepted proposals\n",
    "            network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)\n",
    "            \n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_sentiment_on_release(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    accepted = _input['accepted']\n",
    "    \n",
    "    proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])\n",
    "    \n",
    "    proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])\n",
    "    \n",
    "    sentiment = s['sentiment']\n",
    "    force = proposals_accepted/proposals_outstanding\n",
    "    if (force >=0) and (force <=1):\n",
    "        sentiment = get_sentimental(sentiment, force, False)\n",
    "    else:\n",
    "        sentiment = get_sentimental(sentiment, 0, False)\n",
    "    \n",
    "    key = 'sentiment'\n",
    "    value = sentiment\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def participants_decisions(params, step, sL, s):\n",
    "    \n",
    "    network = s['network']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']\n",
    "    \n",
    "    gain = .01\n",
    "    delta_holdings={}\n",
    "    proposals_supported ={}\n",
    "    for i in participants:\n",
    "        force = network.nodes[i]['sentiment']-sensitivity\n",
    "        delta_holdings[i] = network.nodes[i]['holdings']*gain*force\n",
    "        \n",
    "        support = []\n",
    "        for j in candidates:\n",
    "            affinity = network.edges[(i, j)]['affinity']\n",
    "            cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])\n",
    "            if affinity > cutoff:\n",
    "                support.append(j)\n",
    "        \n",
    "        proposals_supported[i] = support\n",
    "    \n",
    "    return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})\n",
    "\n",
    "def update_tokens(params, step, sL, s, _input):\n",
    "    \n",
    "    network = s['network']\n",
    "    delta_holdings = _input['delta_holdings']\n",
    "    proposals = get_nodes_by_type(network, 'proposal')\n",
    "    proposals_supported = _input['proposals_supported']\n",
    "    participants = get_nodes_by_type(network, 'participant')\n",
    "    \n",
    "    for i in participants:\n",
    "        network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]\n",
    "        supported = proposals_supported[i]\n",
    "        total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])\n",
    "        for j in proposals:\n",
    "            if j in supported:\n",
    "                normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity\n",
    "                network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']\n",
    "            else:\n",
    "                network.edges[(i, j)]['tokens'] = 0\n",
    "            \n",
    "            prior_conviction = network.edges[(i, j)]['conviction']\n",
    "            current_tokens = network.edges[(i, j)]['tokens']\n",
    "            network.edges[(i, j)]['conviction'] =current_tokens+alpha*prior_conviction\n",
    "    \n",
    "    for j in proposals:\n",
    "        network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])\n",
    "    \n",
    "    key = 'network'\n",
    "    value = network\n",
    "    \n",
    "    return (key, value)\n",
    "\n",
    "def update_supply(params, step, sL, s, _input):\n",
    "    \n",
    "    supply = s['supply']\n",
    "    delta_holdings = _input['delta_holdings']\n",
    "    delta_supply = np.sum([v for v in delta_holdings.values()])\n",
    "    \n",
    "    supply = supply + delta_supply\n",
    "    \n",
    "    key = 'supply'\n",
    "    value = supply\n",
    "    \n",
    "    return (key, value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The Partial State Update Blocks\n",
    "partial_state_update_blocks = [\n",
    "    { \n",
    "        'policies': { \n",
    "            #new proposals or new participants\n",
    "            'random': driving_process\n",
    "        },\n",
    "        'variables': {\n",
    "            'network': update_network,\n",
    "            'funds':increment_funds,\n",
    "            'supply':increment_supply\n",
    "        }\n",
    "    },\n",
    "    {\n",
    "      'policies': {\n",
    "          'completion': check_progress #see if any of the funded proposals completes\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'sentiment': update_sentiment_on_completion, #note completing decays sentiment, completing bumps it\n",
    "            'network': complete_proposal #book-keeping\n",
    "        }\n",
    "    },\n",
    "        {\n",
    "      'policies': {\n",
    "          'release': trigger_function #check each proposal to see if it passes\n",
    "        },\n",
    "        'variables': { # The following state variables will be updated simultaneously\n",
    "            'funds': decrement_funds, #funds expended\n",
    "            'sentiment': update_sentiment_on_release, #releasing funds can bump sentiment\n",
    "            'network': update_proposals #reset convictions, and participants sentiments\n",
    "                                        #update based on affinities\n",
    "        }\n",
    "    },\n",
    "    { \n",
    "        'policies': { \n",
    "            'participants_act': participants_decisions, #high sentiment, high affinity =>buy\n",
    "                                                        #low sentiment, low affinities => burn\n",
    "                                                        #assign tokens to top affinities\n",
    "        },\n",
    "        'variables': {\n",
    "            'supply': update_supply,\n",
    "            'network': update_tokens #update everyones holdings \n",
    "                                    #and their conviction for each proposal\n",
    "        }\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The configurations above are then packaged into a `Configuration` object\n",
    "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n",
    "                       partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
    "                       sim_config=simulation_parameters #dict containing simulation parameters\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "single_proc: [<cadCAD.configuration.Configuration object at 0x1a1f48e1d0>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:47: RuntimeWarning: invalid value encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:47: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:93: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:80: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "exec_mode = ExecutionMode()\n",
    "exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "raw_result, tensor = executor.main() # The `main()` method returns a tuple; its first elements contains the raw results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(raw_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>funds</th>\n",
       "      <th>network</th>\n",
       "      <th>run</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>substep</th>\n",
       "      <th>supply</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>275205.632845</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>4</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>1</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>2</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>3</td>\n",
       "      <td>77794.439364</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>279051.211671</td>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998001</td>\n",
       "      <td>4</td>\n",
       "      <td>77173.221287</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             funds                                            network  run  \\\n",
       "796  275205.632845  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "797  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "798  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "799  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "800  279051.211671  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...    1   \n",
       "\n",
       "     sentiment  substep        supply  timestep  \n",
       "796   0.999000        4  77794.439364       199  \n",
       "797   0.999000        1  77794.439364       200  \n",
       "798   0.998001        2  77794.439364       200  \n",
       "799   0.998001        3  77794.439364       200  \n",
       "800   0.998001        4  77173.221287       200  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a227be2e8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.supply.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a356e1c18>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.sentiment.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35398198>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(x='timestep', y='funds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad(vec, length,fill=True):\n",
    "    \n",
    "    if fill:\n",
    "        padded = np.zeros(length,)\n",
    "    else:\n",
    "        padded = np.empty(length,)\n",
    "        padded[:] = np.nan\n",
    "        \n",
    "    for i in range(len(vec)):\n",
    "        padded[i]= vec[i]\n",
    "        \n",
    "    return padded\n",
    "\n",
    "def make2D(key, data, fill=False):\n",
    "    maxL = data[key].apply(len).max()\n",
    "    newkey = 'padded_'+key\n",
    "    data[newkey] = data[key].apply(lambda x: pad(x,maxL,fill))\n",
    "    reshaped = np.array([a for a in data[newkey].values])\n",
    "    \n",
    "    return reshaped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['conviction'] = df.network.apply(lambda g: [g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') ])\n",
    "df['candidate_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))\n",
    "df['candidate_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))\n",
    "df['active_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))\n",
    "df['active_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))\n",
    "df['completed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))\n",
    "df['completed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal')]))\n",
    "df['share_of_funds_requested'] = df['funds_requested']/df.funds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a35a82438>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.plot(x='timestep',y=['candidate_count','active_count','completed_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a36188da0>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='timestep',y=['candidate_funds','active_funds','completed_funds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'conviction')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(df.timestep,make2D('conviction', df))\n",
    "plt.title('conviction by proposal')\n",
    "plt.xlabel('time $t$')\n",
    "plt.ylabel('conviction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'share_of_funds_requested')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(df.timestep,make2D('share_of_funds_requested', df))\n",
    "plt.title('share_of_funds_requested by proposal')\n",
    "plt.xlabel('time $t$')\n",
    "plt.ylabel('share_of_funds_requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'share_of_funds_requested')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(make2D('share_of_funds_requested', df), make2D('conviction', df),alpha=.5)\n",
    "plt.ylabel('conviction')\n",
    "plt.xlabel('share_of_funds_requested')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_net= rdf.network.values[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "completed\n",
      "active\n",
      "active\n",
      "active\n",
      "active\n",
      "candidate\n"
     ]
    }
   ],
   "source": [
    "last_props=get_nodes_by_type(last_net, 'proposal')\n",
    "\n",
    "for j in last_props:\n",
    "    print(last_net.nodes[j]['status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_participants = get_nodes_by_type(last_net, 'participant')\n",
    "\n",
    "pos = {}\n",
    "last_n = len(last_participants)\n",
    "for ind in range(last_n):\n",
    "    i = last_participants[ind] \n",
    "    pos[i] = np.array([0, ind-last_n/2])\n",
    "\n",
    "last_m = len(last_props) \n",
    "for ind in range(last_m):\n",
    "    j = last_props[ind] \n",
    "    pos[j] = np.array([1, last_n/last_m *ind-last_n/2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Qwi0ZLQAYMLx48f1/PPPS+vr1Rqqun7NCLRktABgwmc/+1lpY0N64YVqDZ0/T2UoU8hoAcCEZ555hspQSSKjBQA7qAyVIDJaALCDylAJIqMFADuoDJUgMloAsCPBylBZnk/6ZaVEnDhRy9VpVyTdXb03AHCoffh979PPOp1q87Rzc9KlS2aKVpDRktECgBm/2tykMlRymKMFADM6nQ6VoZJDRgsAJrz55pvDHxKrDMUcLXO0AGBGfvWqtLjIHG1SyGgBwA4qQyWIOVoAsIPKUAkiowUAO6gMlSAyWgCwg8pQCSKjBQAzrt97L5WhksNVxwBgxqnZWV3KMq46TgoZLQCYcfHqVWlpqVojy8tmgqxEoGWOFgCMuHDhgo7WNUdrCEPH99wj/c//VG7ml5I+VL03AHBoZVmmwZUrFKxITtVJ91u2a2kFAA6vPM8pWJGk2dlamqn49WoAgETBChSb8B86AQB8FKxI0HY9g7715MUAcMhRsCJBVYcobqlnphcADrnTp5MrWEGgJdACgBnf2Nio3kieS2fOVG+nJgRaClYAgBkf/pM/kVZWhlcOTyLLpNVVM1/tkQi0FKwAACO+8pWv6KGHHpLW1oZZ6STyfLi+IQRaMloAMOE3v/nN8If19WoNVV2/ZlSG4p8KAIAZ+dWrVIZKDhktANhBZagEMUcLAHZQGSpBZLQAYAeVoRJERgsAdlAZKkFktABgB5WhEkRGCwBmXPj936/eCJWhjCGjBQATjh07puW1NWlpqVpDy8tmvtojEWjJaAHAiFdeeWW/u9AIClacPCldvly5mUuSFqv3BgAONQpWpIiMFgDsoGBFgpijBQA7KFiRIDJaALCDghUJIqMFADsoWJEgMloAsIOCFQkiowUAM7YfeaR6IxSsMIaMFgDMmD11SlpZGV45PIksk1ZXzXy1RyLQktECgBGbm5vDH9bWhlnpJPJ8uL4hBFoyWgAwYX5+fvjD+nq1hqquXzPzgTbPc4XFq2otZkVGCwB2bGxIL7xQrY3z56Vr1+rpTw3MB9osy5RNOlZ/S2lgJqMFADuoDGXDJIE3FmzzPFdORgsAdiRYGaq93x24E4oCc5ZlZLQAYAmVoRJERgsAdlAZKkFktABgB5WhEkRGCwB21FHRicpQxpDRAoAJL774onTiBJWhkkNGCwAmfO5znxv+QGWoxJDRAoAJP/nJT4Y/UBkqMWS0AGAHlaESREYLAHZQGSpBZLQAYEeClaEItGS0AGAHlaESREYLAHZQGSpBZLQAYAeVoRJERgsAJjzwwANUhkoSGS0AmPCFL3xhWBlqaalaQ8vLVIYyhYwWAEx45JFH1Ov19rsbtSPQktECgBntt96SXnqpWiMXLlCwwhQyWgCwg4IVCSKjBQA7KFiRIDJaALCDghUJIqMFADsoWJEgMloAsCPBghVZnk/633UTceJELVenXZF0d/XeAMChll+9Ki0uVpunnZuTLl0y811aMloyWgAw4a233homPysrwyuHJ5Fl0uqqmSArEWiZowUAI+666y71+31pbW1YRnESeT5c3xACLRktAJjx+uuvS+vr1Rqpun7NmKNljhYAzGCONkVktABgB5WhEsQcLQDYQWWoBJHRAoAdVIZKEBktANhBZagEkdECgB1UhkoQVx0DgAlvvvmm7ur1uOo4OWS0AGDCBz7wgWHys7RUraHlZTNBViLQMkcLAEZsbm7qqaee2u9u1I6h45MnpcuXKzdzSdJi9d4AwKH27s9/rvfedx9Dx0khowUAM977ve9Vb4SCFcYwRwsAdlCwIkFktABgBwUrEkRGCwB2ULAiQWS0AGBHggUrCLRktABgwpe+9CXpzJnqDeV5Pe3UhEBLRgsAJvzyl78cFqxYWRleOTyJLJNWV818tUci0JLRAoAR586d09e//nVpbW2YlU4iz4frG0KgJaMFADPOnz8vra9Xa6Tq+jWjMhSVoQDAjPzqVf6pQHLIaAHAjjoqOlEZyhjmaAHADipDJYiMFgDsoDJUgshoAcAOKkMliIwWAOygMlSCyGgBwITnnnuOylBJIqMFABM+85nP6DfdLpWhkkNGCwBm3HPPPVSGSg4ZLQCYce7cOSpDJYfKUABgBpWhUkRGCwB2UBkqQczRAoAdVIZKEBktANhBZagEkdECgB1UhkoQGS0AmPCLX/wiycpQXHXMVccAYMbgyhVlH/wgVx0nhYwWAMyYPXVKWlqq1sjyspkgKxFomaMFAEO6NSU/lrT3uwP7jowWAMwYFayo4sIF6do1M1ktGS0ZLQDYQcGKBJHRAoAdFKxIEBktANhBwYoEkdECgB0UrEgQGS0AmHD27FkKViTpxInh1WkVXZF0d/XeAMChNTc3p81f/Yp/k5ccMloAMGFra0t/+tBD0srK8MrhSWSZtLpqJshKBFrmaAHAkBdffFFaW5MmHWzN8+H6hhBoyWgBwIxHHnlEWl+v1kjV9WvGHC1ztABgxqgyFHO0CSGjBQA7qAyVIOZoAcAOKkMliIwWAOygMlSCyGgBwIQjR45QGSpJZLQAYMKHPvShJCtDEWjJaAHAhIsXL+pH995bvaE8l86cqd5OTQi0ZLQAYMb//dSnpKWlao0sL5v5ao9EoCWjBQA0ikBLRgsAZrz9059KL71UrZELF2opRFQXAi0ZLQCY8X+ef756IxSsMKbq1W23bNbSCgAcchSsSFBNgXa7llYA4JCjYEWCZmdraabi+RcAQKJgBYpN+C+KAQC3PPHEE0kWrODf5P3e70k/+1nlZl6X9AfVewMAh9q7P/+53nvfffybvKRUnXS/pZ6ZXgA43N734Q9LKyvDK4cnkWXS6qqZICsRaAm0AGDN2tqwjOIk8ny4viEEWgpWAIAZR48eldbXqzVSdf2aMUd74kQtFUSuSLq7em8A4FDLr16VFheZo00KGS0A2FFHRScqQxlDCUYAsIPKUAkiowUAO6gMlSAyWgAw4ctf/jKVoZJERgsAJnzrW99STmWoBHHVMQCY8dH3v1+vXb/OVcdJIaMFADN+8sYbVIZKDnO0AGALlaESQ0YLAGacO3eOylDJYY4WAMygMlSKyGgBwA4qQyWIOVoAsIPKUAkiowUAE44ePUplqCSR0QKACe+88w6VoZJERpuEqakpZVmmbNLv3gEw4f/99KdUhkoOVx0DgBnHJW3MzXHVcVLIaKOmpqbUbrfVarX2uyvmZVmmqamp224HIcN2fbTeTxwe1yRpaalaI8vLZoKsJLX3uwP7jjnaqMFgoMFgsN/dKDU1NTxP9INEnud3vN95nivP81E/rA4SFQVVv/9W+w4cZARaMtodpqamRllsr9czfeAdDAa3BY4sy9RqtUbBY6/9d23Fgs5ubTW9r2JZ5ySvGVuHIAsrBleuSB/8YLVGLlwYTgkayWoZOiaj3WEwGKjb7arb7e448FodWszzXP1+/7bbYDCYOAi5rNgP1nUFIX+YudVqjW5FQ81uebdMbGh6nPemaJnY6427HZY+E2X9sdZXlMu+850aGrFVsIKMlox2LHUGGnfv2txL2/764QG03+9PnMGO26fwdV3A858LA3QsaIcZpPvZb79sqDf8fZyMNBZUJ9lf/vb6wpMbt2/8feT665Z1w/xF2+vvu3A7wn1dhEz9gEmwYAWBlox2hyzLND09rZmZGU1PT6vVaqnX62l7e1vdble9Xq9S++ME1/Cg7D/uZ4B+Zucv5+aXB4PBjuw29pqxYOe/nh8swsBRFADDABsLvrHXcsuGASfcH/6yRX2P7dOytv35ZXdz+07Sjn0+7npu3TzPd6zn9qFr13+/Yn2dmpqK7rtwf/n9wAGWYMEKAi0Z7Q55nqvT6ajTaX6Ldhv6DINgnufq9XoTBfswQIa38GImPzgPBoPRa/oH/7JgWHTz2/C30d0PBoNREJIUHSKOBXjXX//EIhyWLrsSOpaFu9fyh+OLgm0sk3XthEE0tg1+O3sJlmUnbsw7HzzT09NJFqwg0B7yjNZlsNPT05qdnVW73Va73Vae59re3h5lsuGc7ThigU2KH9TL5oNj2V+4XFlmGj7uB7zwsXEuePID0W7CwBxmxmFmLmmUwfmBx39tvw/+CYEfZCeZxy3qt6TbAnlZ0I/dFz3vt+G/nr980QhAWVYde23Y1+129aqk01W/R0vBCmNOnpQuX67czCVJi9V7Y0Y4dBfOtYXzbLtlIv4BuSibc9mXn4m5wN9ut3cEjvCA7PfFDRn3+331er3ohVKSosEozILKstPdssyyAFH02rtlyf7P4TbHhnnL2vfbLtrW8L0d5wSjbGg6fL/C/VM2NO3W838v60PsZ/81w89T+Nl2c/7h/vRPbPr9vrIsU7vd3vH5caMgWTac7nBtu3YljR53bfnbH+6bw5Sdp1iwgoz2kGW0sQNLUabpH3DK2gvbDdtyv0vxLDcMcGHgCIce/SzQHayKLrzxi26EwckFXf+A6V/hG7vSN8w8nfAgGA49hwExnEMump8MA2MsKBbtm7C9sI0wo4/dYu9l2WehKFP37/3Pjb+/XHDyT4pardaOky33fvrvjdu/blrBjcB0u131+/3o+uHruusP3Ou3223Nzs7u2JduX/gnbC7Ihp+7MOiG2+/ee/dYu92Ofg78/X9YXJPU/7M/U+tf/kWaZLuzTFpdNRNkJQLtgZqjjR0wwwAWHhz9dR33B+0OYm7YeHZ2VnNzc5qfn9fc3JxmZmZGF0VNTU2NMsRut6vt7W1tbW2N5nO73e7o3h2w3EErDGBlWZvfd7c9bv1xg7/fbiwI+9lyWVAtyzRdv8J+u/1bFGBjAddlTu4+XD72HvrBMnYy4G9PuI3u5CR2UVn4GfMzsXDEwL3XsffffWamp6dHnzH3eQsDcLhN/mv4n6Vut6sbN26Mfnft+fetVksLCws7Al/YXvgZ9UdR5ubmduyL8DM3TpB13N+MP4Lifw79frnny74Dfpiy2tZDD0n//M+TrZzn0tpavR2qiEB7ADLa3S4+8f8oYwEhXM4/03bzsNevX99Tn8JMNsxmXEYQC2Thz7FMLHYBTxic/WHEMED5B8Verxd9zF1NHR48w++37pbhhn102xQ7+PpBuSi79rM6PyhkWTYKXGGG5gea2L7297G/f/39GetjbBg57G+4b10m6fq+tbU12iY/Q/UDb+w7xe75sG+xKQI/iIZTBX5AdO27k8rYexSONoTfzXaf7zDb9f/e/L8H/6TJ3/9hRu4Py5eNDqXu05/+tLS+Xq2R9XXpscfq6VANmKM1MEcbZkaSSg/mjn/A8Q9k7us5Lht12ap7zL/Nzs7u+DqPC47ujNsfinPZS7/fV6fT2ZEd+MuE9+Gt2+2OhtOKgkYYOMKDcnjgLMvo3D4uyqb9g2VR8Ah/LyqSMckysQAfm68u+mpT0edkt6w8zFxjh4JYtu5+j83jhtm4n6W7E57YiY//uL8/yk7IirYvPNGMnSiEJwyur2H/Y8PeYXCN7auikxR/vViQLhqROkzyq1elxUXmaJPSYEbrH7zCA0M4BOoCixtiix1QfeEfcXiG3+l0dPPmzdGBLpY1lGWbfmYWO7D7B3O3fS5Qu21392WZU9FcZmzodXt7u3SZ3drZ7TX8ZZyibS67Dx9rt9uamZkpXCbcl/4+Cx8L92Usc47t77JRkJjwuXBdv10/8/KHPsPPaXiLfQZi73vRzy4wh38HsROAcL8UBenwoqbY53acfeL/7KZp/M+WC+yh2PtadgKUpDoqOmW3KkM99VT1tmpAoK36fw9v2Sx4PBY0w8wltsxut3GWLyvth3Kxg2h4X/ZcXcvcidfYyzJ1bpd0e6CepJ1x+uEHOP9391gY/GPL+MuGj/mBOfx9amqqtI9FJzWHLsA6VIZK0JEjtTRz1+/+rv7xb/5mx6X8ALBfykYT/AAfjihMcvNH1cKpgaK59G9+85u39Xl9fV36u7+rZwdQGcqQP/xD6fXXKzdz9P779fDDD9fQIQBInwu0WZbpRz/6ke6///7hE//wD/W8gKHKUPz3nj/+Y2mq4m6YmpI+/vF6+gMAh8DLL7+sV155RYPB4LdBVpJOn64+pUdlKGM2NqRTp6QqtX1nZqRf/9rMFW4AcGBtbCR31TEZ7Yny3ErJAAABmklEQVQT0oMPVmvjE58w84YCwIF24oS0sjK8cngSmb3KUGS0kvTjH0sPPCDdvLn3dRcWpB/+UPrYx2rvFgAcSokdk8lopeH86tmzwzdoLxYWhusZekMB4MBL7JjMVcfOE08M7598cvgdrLJEP8uGk+1nz/52PQBAfRI6JjN0HPrP/5Seflo6f3745m16pSjm54dv9uqq9LWvmTtrAoDkJHBMJtAWuXZtWMLr4sXhF5+PHRteLn7mjKlJdgA4FA7wMZlACwBAg7gYCgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBoEIEWAIAGEWgBAGgQgRYAgAYRaAEAaBCBFgCABhFoAQBo0P8CeVJKfGzxaXgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(last_net, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['node_count']=df.network.apply(lambda g:len(g.nodes))\n",
    "df['net_growth']= df.node_count.diff()\n",
    "nets = df[df.net_growth>0].network.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(G, pos, ax, **kwds)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m         \u001b[0mdraw_networkx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    127\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_off\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw_networkx\u001b[0;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0mnode_collection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw_networkx_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 278\u001b[0;31m     \u001b[0medge_collection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw_networkx_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    279\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwith_labels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0mdraw_networkx_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py\u001b[0m in \u001b[0;36mdraw_networkx_edges\u001b[0;34m(G, pos, edgelist, width, edge_color, style, alpha, arrowstyle, arrowsize, edge_cmap, edge_vmin, edge_vmax, ax, arrows, label, node_size, nodelist, node_shape, **kwds)\u001b[0m\n\u001b[1;32m    688\u001b[0m             \u001b[0;31m# individually to the axes instance.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m             \u001b[0marrow_collection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 690\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrow\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    691\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m     \u001b[0;31m# update view\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_patch\u001b[0;34m(self, p)\u001b[0m\n\u001b[1;32m   1968\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1969\u001b[0m             \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1970\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\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   1971\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1972\u001b[0m         \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_remove_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_patch_limits\u001b[0;34m(self, patch)\u001b[0m\n\u001b[1;32m   1986\u001b[0m                 ((not patch.get_width()) and (not patch.get_height()))):\n\u001b[1;32m   1987\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1988\u001b[0;31m         \u001b[0mvertices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1989\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1990\u001b[0m             \u001b[0mxys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_patch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/patches.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4231\u001b[0m             \u001b[0m_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconcatenate_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4233\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_path\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   4234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4235\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_path_in_displaycoord\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mtransform_path\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1520\u001b[0m         \u001b[0mthat\u001b[0m \u001b[0mbegan\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mline\u001b[0m \u001b[0msegments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1521\u001b[0m         \"\"\"\n\u001b[0;32m-> 1522\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path_non_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\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   1523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1524\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mtransform_path_affine\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1532\u001b[0m         \u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransform_path_non_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1533\u001b[0m         \"\"\"\n\u001b[0;32m-> 1534\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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   1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1536\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform_path_non_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36mtransform_path_affine\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m   1763\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1764\u001b[0m         return Path(self.transform_affine(path.vertices),\n\u001b[0;32m-> 1765\u001b[0;31m                     path.codes, path._interpolation_steps)\n\u001b[0m\u001b[1;32m   1766\u001b[0m     \u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_path_affine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vertices, codes, _interpolation_steps, closed, readonly)\u001b[0m\n\u001b[1;32m    135\u001b[0m                 raise ValueError(\"'codes' must be a 1D list or array with the \"\n\u001b[1;32m    136\u001b[0m                                  \"same length of 'vertices'\")\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMOVETO\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    138\u001b[0m                 raise ValueError(\"The first element of 'code' must be equal \"\n\u001b[1;32m    139\u001b[0m                                  \"to 'MOVETO' ({})\".format(self.MOVETO))\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(nets)-1):\n",
    "    nx.draw(nets[i],pos=pos, alpha=.5, node_color='b')\n",
    "    nx.draw(nets[i+1],pos=pos,  alpha=.5, node_color='g')\n",
    "    plt.title(\"Update: $G_{\"+str(i)+\"}$ to $G_{\"+str(i+1)+\"}$\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}