simulateVaryType.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import copy\n",
    "import pickle\n",
    "from networkFunctions import *\n",
    "from plotFunctions import *\n",
    "from populationGraph import populationGraph\n",
    "from adversarialAgent import adversarialAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Network parameters\n",
    "msgUtility = 10\n",
    "costFactor = 0.1\n",
    "numNodes = 100\n",
    "numMsg = 10000\n",
    "myseed = 67\n",
    "maxEpochs = 10000\n",
    "msgCost = costFactor*msgUtility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict = {\"msgUtility\" : 10,\n",
    "\"costFactor\" : 0.1,\n",
    "\"numNodes\" : 100,\n",
    "\"numMsg\" : 1000,\n",
    "\"myseed\" : 67,}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Utilitarian', 'Deontic', 'Virtue', 'Transcendence']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrDict = {\"Utilitarian\":utilitarianAgent, \"Deontic\":deonticAgent, \"Virtue\":virtuousAgent, \"Transcendence\":transcendenceAgent}\n",
    "[val for val in attrDict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'type' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-89b77233332f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprevStates\u001b[0m \u001b[0;34m=\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----> 2\u001b[0;31m commonAttrs = {\"burnoutThreshold\":2*(dict[\"numMsg\"]*dict[\"costFactor\"]*dict[\"msgUtility\"])/dict[\"numNodes\"],\n\u001b[0m\u001b[1;32m      3\u001b[0m \"burnoutDrop\":2,\"gamma\":0.5, \"maxVirtueUtility\" : 5*(dict[\"numMsg\"]/dict[\"numNodes\"]), \"forwardProb\" : 1}\n\u001b[1;32m      4\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"type\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtranscendenceAgent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"advRatio\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'type' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "prevStates = []\n",
    "commonAttrs = {\"burnoutThreshold\":2*(dict[\"numMsg\"]*dict[\"costFactor\"]*dict[\"msgUtility\"])/dict[\"numNodes\"],\n",
    "\"burnoutDrop\":2,\"gamma\":0.5, \"maxVirtueUtility\" : 5*(dict[\"numMsg\"]/dict[\"numNodes\"]), \"forwardProb\" : 1}\n",
    "dict[\"type\"] = transcendenceAgent\n",
    "dict[\"advRatio\"] = 0\n",
    "attr = \"type\"\n",
    "attrDict = {\"Utilitarian\":utilitarianAgent, \"Deontic\":deonticAgent, \"Virtue\":virtuousAgent, \"Transcendence\":transcendenceAgent}\n",
    "# attrRange = np.arange(0,1,0.1)\n",
    "var = (dict[\"numMsg\"]*dict[\"costFactor\"]*dict[\"msgUtility\"])/dict[\"numNodes\"]\n",
    "dict[attr] = list(attrDict.keys())\n",
    "outcomeNetwork = {}\n",
    "\n",
    "for val in attrDict:\n",
    "    if attr == \"advRatio\":\n",
    "        network = populationGraph(dict[\"numNodes\"],[1-val, val],[dict[\"type\"], adversarialAgent], commonAttrs)\n",
    "    elif attr == \"type\":\n",
    "        network = populationGraph(dict[\"numNodes\"],[0.25,0.25,0.25,0.25],[deonticAgent,utilitarianAgent,virtuousAgent,transcendenceAgent], commonAttrs)\n",
    "    else:\n",
    "        commonAttrs[attr] = val\n",
    "        network = populationGraph(dict[\"numNodes\"],[1],[dict[\"type\"]],commonAttrs)\n",
    "    e = 0\n",
    "    min_run = 10\n",
    "    # network.plotGraph()\n",
    "    while e<maxEpochs:\n",
    "        e+=1\n",
    "        G = network.getGraph()\n",
    "        msgList = network.createMsgs(dict[\"numMsg\"], myseed=32)  #Message list creation\n",
    "        prevStates = [copy.deepcopy(G.nodes[i]['agent']) for i in range(dict[\"numNodes\"])]\n",
    "        nf, nd = network.transmitMsgs()\n",
    "        for i in range(dict[\"numNodes\"]) : G.nodes[i]['agent'].epochUpdate()\n",
    "        curStable = [G.nodes[i]['agent'].isStable(prevStates[i]) for i in range(dict[\"numNodes\"])]\n",
    "        if np.count_nonzero(curStable) == dict[\"numNodes\"] and e > min_run:\n",
    "            break\n",
    "    print(e)\n",
    "\n",
    "    #Test run\n",
    "    # print(network.getNetworkProp('utility'))\n",
    "    network.reset()\n",
    "    G = network.getGraph()\n",
    "    # print(network.getNetworkProp('utility'))\n",
    "    msgList = network.createMsgs(dict[\"numMsg\"])  #Message list creation\n",
    "    nf, nd = network.transmitMsgs()\n",
    "    # print(network.getNetworkProp('utility'))\n",
    "    outcomeNetwork[val] = copy.deepcopy(network)\n",
    "\n",
    "\n",
    "dict[\"outcomeNetwork\"] = outcomeNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'msgUtility': 10,\n",
       " 'costFactor': 0.1,\n",
       " 'numNodes': 100,\n",
       " 'numMsg': 1000,\n",
       " 'myseed': 67,\n",
       " 'type': ['Utilitarian', 'Deontic', 'Virtue', 'Transcendence'],\n",
       " 'advRatio': 0,\n",
       " 'outcomeNetwork': {'Utilitarian': <populationGraph.populationGraph at 0x7f93b8d44910>,\n",
       "  'Deontic': <populationGraph.populationGraph at 0x7f93b8cfd370>,\n",
       "  'Virtue': <populationGraph.populationGraph at 0x7f93b8d497f0>,\n",
       "  'Transcendence': <populationGraph.populationGraph at 0x7f93b8d88bb0>}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(dict, open('pickle_files/vary_type.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outcome network  {'Utilitarian': <populationGraph.populationGraph object at 0x7f93b8d44910>, 'Deontic': <populationGraph.populationGraph object at 0x7f93b8cfd370>, 'Virtue': <populationGraph.populationGraph object at 0x7f93b8d497f0>, 'Transcendence': <populationGraph.populationGraph object at 0x7f93b8d88bb0>}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[9320, 9040, 8780, 9280]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(dict[\"outcomeNetwork\"],'utility',\"type\",plotType='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outcome network  {0.0: <populationGraph.populationGraph object at 0x7fae03e3ee50>, 0.1: <populationGraph.populationGraph object at 0x7fae0487eb50>, 0.2: <populationGraph.populationGraph object at 0x7fae03e52250>, 0.30000000000000004: <populationGraph.populationGraph object at 0x7fae03ee5220>, 0.4: <populationGraph.populationGraph object at 0x7fae048c0340>, 0.5: <populationGraph.populationGraph object at 0x7fae04887ca0>, 0.6000000000000001: <populationGraph.populationGraph object at 0x7fae04b159a0>, 0.7000000000000001: <populationGraph.populationGraph object at 0x7fae04b15310>, 0.8: <populationGraph.populationGraph object at 0x7fae048c0f40>, 0.9: <populationGraph.populationGraph object at 0x7fae03e79580>}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[8740, 6460, 4840, 3320, 1000, -1160, -2220, -3760, -5380, -7180]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(dict[\"outcomeNetwork\"],\"utility\",\"advRatio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19.37, 18.23, 17.42, 16.66, 15.5, 14.42, 13.89, 13.12, 12.31, 11.41] [87.4, 64.6, 48.4, 33.2, 10.0, -11.6, -22.2, -37.6, -53.8, -71.8] [0.32, 0.28, 0.24, 0.23, 0.19, 0.16, 0.13, 0.09, 0.05, 0.04]\n"
     ]
    }
   ],
   "source": [
    "y_cost = [np.mean(i.getNetworkProp(\"cost\")) for i in outcomeNetwork.values()]\n",
    "y_utility = [np.mean(i.getNetworkProp(\"utility\")) for i in outcomeNetwork.values()]\n",
    "y_burnouts = [np.mean(i.getNetworkProp(\"burnout\")) for i in outcomeNetwork.values()]\n",
    "print(y_cost, y_utility, y_burnouts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_binsize = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "y2_vp = [1.46, 11.06, 16.9, 18.08, 18.16, 18.16, 18.16, 18.16, 18.16, 18.16]\n",
    "y4_vp = [2.92, 13.36, 22.84, 31.48, 35.48, 37.44, 37.96, 38.2, 38.2, 38.2]\n",
    "y6_vp = [4.38, 14.82, 25.32, 34.68, 43.32, 50.52, 53.46, 55.44, 56.82, 57.06]\n",
    "y8_vp = [5.84, 18.64, 28.56, 35.76, 46.24, 56.32, 63.04, 67.68, 71.28, 73.92]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y8_cost = [15.32, 16.09, 16.7, 17.12, 17.75, 18.36, 18.76, 19.04, 19.25, 19.41] \n",
    "y6_cost = [15.32, 16.16, 17.0, 17.75, 18.45, 19.02, 19.25, 19.41, 19.52, 19.54] \n",
    "y4_cost = [15.32, 16.59, 17.72, 18.76, 19.23, 19.47, 19.53, 19.56, 19.56, 19.56] \n",
    "y2_cost = [15.31, 17.63, 19.03, 19.32, 19.34, 19.34, 19.34, 19.34, 19.34, 19.34] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y8_utility = [6.4, 21.8, 34.0, 42.4, 55.0, 67.2, 75.2, 80.8, 85.0, 88.2]\n",
    "y6_utility = [6.4, 23.2, 40.0, 55.0, 69.0, 80.4, 85.0, 88.2, 90.4, 90.8]\n",
    "y4_utility = [6.4, 31.8, 54.4, 75.2, 84.6, 89.4, 90.6, 91.2, 91.2, 91.2]\n",
    "y2_utility = [6.2, 52.6, 80.6, 86.4, 86.8, 86.8, 86.8, 86.8, 86.8, 86.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y8_burnouts = [0.09, 0.15, 0.17, 0.23, 0.28, 0.32, 0.36, 0.38, 0.41, 0.42]\n",
    "y6_burnouts = [0.09, 0.15, 0.22, 0.28, 0.32, 0.38, 0.41, 0.42, 0.43, 0.43]\n",
    "y4_burnouts = [0.09, 0.16, 0.27, 0.35, 0.41, 0.42, 0.43, 0.43, 0.43, 0.43]\n",
    "y2_burnouts = [0.11, 0.27, 0.39, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Virtue Points')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line1, =plt.plot(x2_binsize,y2_vp)\n",
    "line2, =plt.plot(x2_binsize,y4_vp)\n",
    "line3, =plt.plot(x2_binsize,y6_vp)\n",
    "line4, =plt.plot(x2_binsize,y8_vp)\n",
    "plt.legend([line1,line2,line3,line4],[2,4,6,8],title=\"virtue change\")\n",
    "# plt.axhline(y=18.16, color=\"red\")\n",
    "plt.xlabel(\"Bin Size\")\n",
    "plt.ylabel(\"Average Virtue Points\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Cost')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_binsize = list(outcomeNetwork.keys())\n",
    "line1, =plt.plot(x_binsize,y2_cost)\n",
    "line2, =plt.plot(x_binsize,y4_cost)\n",
    "line3, =plt.plot(x_binsize,y6_cost)\n",
    "line4, =plt.plot(x_binsize,y8_cost)\n",
    "plt.legend([line1,line2,line3,line4],[2,4,6,8],title=\"virtue change\")\n",
    "# plt.axhline(y=18.16, color=\"red\")\n",
    "plt.xlabel(\"Bin Size\")\n",
    "plt.ylabel(\"Average Cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Utility')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_binsize = list(outcomeNetwork.keys())\n",
    "line1, =plt.plot(x_binsize,y2_utility)\n",
    "line2, =plt.plot(x_binsize,y4_utility)\n",
    "line3, =plt.plot(x_binsize,y6_utility)\n",
    "line4, =plt.plot(x_binsize,y8_utility)\n",
    "plt.legend([line1,line2,line3,line4],[2,4,6,8],title=\"virtue change\")\n",
    "# plt.axhline(y=18.16, color=\"red\")\n",
    "plt.xlabel(\"Bin Size\")\n",
    "plt.ylabel(\"Average Utility\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Burnouts')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_binsize = list(outcomeNetwork.keys())\n",
    "line1, =plt.plot(x_binsize,y2_burnouts)\n",
    "line2, =plt.plot(x_binsize,y4_burnouts)\n",
    "line3, =plt.plot(x_binsize,y6_burnouts)\n",
    "line4, =plt.plot(x_binsize,y8_burnouts)\n",
    "plt.legend([line1,line2,line3,line4],[2,4,6,8],title=\"virtue change\")\n",
    "# plt.axhline(y=18.16, color=\"red\")\n",
    "plt.xlabel(\"Bin Size\")\n",
    "plt.ylabel(\"Average Burnouts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-ca488f9af0f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0myvals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomeNetwork\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetResilience\u001b[0m\u001b[0;34m(\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      5\u001b[0m \u001b[0mxvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Adv 0.0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Adv 0.2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Adv 0.4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Adv 0.6\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Adv 0.8\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myvals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'g'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\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      7\u001b[0m \u001b[0;31m# for i in range(len(xvals)):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#     plt.text(i,yvals[i]/2,yvals[i],ha = 'center')\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/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2405\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2406\u001b[0m         data=None, **kwargs):\n\u001b[0;32m-> 2407\u001b[0;31m     return gca().bar(\n\u001b[0m\u001b[1;32m   2408\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2409\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1564\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\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[0;32m-> 1565\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2338\u001b[0m                 \u001b[0myerr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_dx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2340\u001b[0;31m         x, height, width, y, linewidth = np.broadcast_arrays(\n\u001b[0m\u001b[1;32m   2341\u001b[0m             \u001b[0;31m# Make args iterable too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2342\u001b[0m             np.atleast_1d(x), height, width, y, linewidth)\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubok\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\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/python3.8/site-packages/numpy/lib/stride_tricks.py\u001b[0m in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0;31m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;31m# consistently\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m32\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    192\u001b[0m     \u001b[0;31m# unfortunately, it cannot handle 32 or more arguments directly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mpos\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\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;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yvals = [outcomeNetwork[\"Utilitarian\"].getResilience(), outcomeNetwork[\"Deontology\"].getResilience(), outcomeNetwork[\"Virtue\"].getResilience(), outcomeNetwork[\"Transcendence\"].getResilience()]\n",
    "yvals = []\n",
    "for i in outcomeNetwork.keys():\n",
    "    yvals.append(outcomeNetwork[i].getResilience())\n",
    "xvals = [\"Adv 0.0\", \"Adv 0.2\", \"Adv 0.4\", \"Adv 0.6\", \"Adv 0.8\"]\n",
    "plt.bar(xvals, yvals,color=['b','g','r','y'], align='center', alpha=0.5, ecolor='black', capsize=10)\n",
    "# for i in range(len(xvals)):\n",
    "#     plt.text(i,yvals[i]/2,yvals[i],ha = 'center')\n",
    "plt.ylabel(\"Resilience\")\n",
    "plt.xlabel(\"Type of Agent\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0.0, 0.2, 0.4, 0.6000000000000001, 0.8])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomeNetwork.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[69.6, 30.8, 11.0, -18.4, -56.8]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janvichhabra/Documents/acad/Comparative-Ethics/plotFunctions.py:92: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_xticks(range(len(outcomeNetwork)),list(outcomeNetwork.keys()))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "utility_list = plotNetworkVaryParams(outcomeNetwork, 'utility', attr, plotType=\"bar\", metric=\"mean\")\n",
    "print(utility_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18.48, 16.54, 15.55, 14.08, 12.16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "costList = plotNetworkVaryParams(outcomeNetwork, 'cost', attr, plotType=\"bar\", metric=\"mean\")\n",
    "print(costList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38, 23, 19, 9, 5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "burnoutList = plotNetworkVaryParams(outcomeNetwork, 'burnout', attr, plotType=\"bar\", metric=\"sum\")\n",
    "print(burnoutList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8.48, 6.54, 5.55, 4.08, 2.16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forwardsList = plotNetworkVaryParams(outcomeNetwork, 'forwards', attr, plotType=\"bar\", metric=\"mean\")\n",
    "print(forwardsList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.52, 3.46, 4.45, 5.92, 7.84]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropsList = plotNetworkVaryParams(outcomeNetwork, 'drops', attr, plotType=\"bar\", metric=\"mean\")\n",
    "print(dropsList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.52, 3.46, 4.45, 5.92, 7.84]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropsList = plotNetworkVaryParams(outcomeNetwork, 'drops', attr, plotType=\"bar\", metric=\"mean\")\n",
    "print(dropsList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotAgentWiseVaryParams(outcomeNetwork, \"utility\", attr, plotType=\"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'populationGraph' object has no attribute 'nodes'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-106659beb0a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplotAgentWiseVaryParams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomeNetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"cost\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"stem\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Documents/acad/Comparative-Ethics/plotFunctions.py\u001b[0m in \u001b[0;36mplotAgentWiseVaryParams\u001b[0;34m(outcomeNetwork, prop, attr, plotType)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplotAgentWiseVaryParams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomeNetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"line\"\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     53\u001b[0m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\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---> 54\u001b[0;31m     \u001b[0mnumNodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutcomeNetwork\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\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     55\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumNodes\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     56\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Agent id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'populationGraph' object has no attribute 'nodes'"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAgentWiseVaryParams(outcomeNetwork, \"cost\", attr, plotType=\"stem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janvichhabra/Documents/acad/Comparative-Ethics/plotFunctions.py:36: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  ax.stem(range(numNodes), propertyValues, color,markerfmt=color+'o', basefmt=\" \", label=label)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAgentWiseVaryParams(outcomeNetwork, \"burnout\", attr, plotType=\"stem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/janvichhabra/Documents/acad/Comparative-Ethics/plotFunctions.py:92: MatplotlibDeprecationWarning: Passing the minor parameter of set_xticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.\n",
      "  ax.set_xticks(range(len(outcomeNetwork)),list(outcomeNetwork.keys()))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[9200, 7160, 8460, 9200]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(outcomeNetwork, 'utility', attr, plotType=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1960.0, 1858.0, 1923.0, 1960.0]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(outcomeNetwork, 'cost', attr, plotType=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 32, 36, 40]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(outcomeNetwork, 'burnout', attr, plotType=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[960, 858, 923, 960]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(outcomeNetwork, 'forwards', attr, plotType=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 142, 77, 40]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotNetworkVaryParams(outcomeNetwork, 'drops', attr, plotType=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "20.0\n"
     ]
    }
   ],
   "source": [
    "print(G.nodes[0]['agent'].burnoutCount)\n",
    "print(G.nodes[0]['agent'].burnoutThreshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAgentwiseProp(G, \"cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not applicable property\n"
     ]
    }
   ],
   "source": [
    "plotAgentwiseProp(G, \"virtuePoints\", plotMean=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAgentwiseProp(G, \"burnout\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotAgentwiseProp(G, \"utility\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3",
   "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": "3.8.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0796865d59219048d2439a5fb954342bfceb6ce1fcebec34352eb55e8445235a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}