Fig_2-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import model\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate points for fig 2b inset graph\n",
    "\n",
    "from model import *\n",
    "network = 'random'\n",
    "n_agents = 60\n",
    "deg = 3\n",
    "n_beliefs = 25\n",
    "n_concepts = 25\n",
    "pathlength = 2\n",
    "threshold = 1.1\n",
    "clustering = 'average'\n",
    "randomization = 'shuffle'\n",
    "\n",
    "adopt_func = fast_adopt\n",
    "susceptible_func = fast_susceptible\n",
    "\n",
    "args = (network, n_agents, deg, n_beliefs, n_concepts, pathlength, threshold, clustering, randomization)\n",
    "g, beliefs = setup(*args[:5])\n",
    "\n",
    "g1 = copy.deepcopy(g)\n",
    "\n",
    "# interdependent\n",
    "inter_sus = pd.Series(measure_susceptibility(g1, beliefs, susceptible_func)) / n_agents * 100\n",
    "seed = pd.Series(measure_diffusion(g1, beliefs)) / n_agents * 100\n",
    "simulate_simultaneous(g1, beliefs, n_steps=10, adopt=adopt_func)\n",
    "inter_diff = pd.Series(measure_diffusion(g1, beliefs)) / n_agents * 100\n",
    "inter_final_suscep = np.mean(list(measure_susceptibility(g1, beliefs, susceptible_func).values())) / n_agents * 100\n",
    "\n",
    "\n",
    "\n",
    "# independent (match)\n",
    "for step, g2, s2 in simulate_independent_match_susceptibility(copy.deepcopy(g),\n",
    "                                                              sus_target=inter_final_suscep / 100,\n",
    "                                                              n_steps=10,\n",
    "                                                              n_concepts=n_concepts,\n",
    "                                                              beliefs=beliefs):\n",
    "\n",
    "    pass\n",
    "indep_suscep = pd.Series(s2) / n_agents * 100\n",
    "indep_diff = pd.Series(measure_diffusion(g2, beliefs)) / n_agents * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate network for fig 2C,D inset graphs\n",
    "\n",
    "#fig = plt.figure(figsize=(1.2,1.5))\n",
    "\n",
    "inter = {(0,1):10, (1,2):7, (2,3):4, (3,4):2, (4,5):1, \n",
    "         (0,2):6, (3,5):1, (2,6):5, (1,6):7, (5,2):1,(5,7):0.5, (3,6):3}\n",
    "G = nx.from_edgelist(inter)\n",
    "nx.set_edge_attributes(G, inter, name='weight')\n",
    "\n",
    "#pos = nx.spring_layout(G, weight=None)\n",
    "pos = {\n",
    "    0: [ 0.05337489, -1.        ],\n",
    "    1: [-0.38712707, -0.80563193],\n",
    "    2: [-0.03981886, -0.30618229],\n",
    "    3: [-0.21645782,  0.26372263],\n",
    "    4: [-0.09271407,  0.85974724],\n",
    "    5: [0.31097159, 0.43462805],\n",
    "    6: [-0.55557835, -0.27382355],\n",
    "    7: [0.92734968, 0.82753985]\n",
    "}\n",
    "def rotate(x, rotation=0):\n",
    "    length = sqrt(x[0]**2 + x[1]**2)\n",
    "    angle = np.arctan2(x[0], x[1])\n",
    "    new_angle = angle+rotation\n",
    "    return [length*cos(new_angle), length*sin(new_angle)]\n",
    "\n",
    "pos = {k: rotate(v, -.3) for k, v in pos.items()}\n",
    "\n",
    "edges = G.edges()\n",
    "weights = [G[u][v]['weight']/2 for u,v in edges]\n",
    "\n",
    "nx.draw_networkx\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=weights, node_size=0, with_labels=False)\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=inter, font_size=5, alpha=1, font_color='r')\n",
    "\n",
    "indep = {(0,1):12, (1,2):2, (2,3):5, (3,4):10, (4,5):2, \n",
    "         (0,2):1, (3,5):2, (2,6):14, (1,6):2, (2,5):4, (5,7):16, (3,6):1}\n",
    "G2 = nx.from_edgelist(indep)\n",
    "nx.set_edge_attributes(G2, indep, name='weight')\n",
    "\n",
    "dx = 0\n",
    "dy = 1.5 \n",
    "pos2 = {k:[v[0]+dx, v[1]+dy] for k,v in pos.items()}\n",
    "plt.box('off')\n",
    "\n",
    "weights2 = [G2[u][v]['weight']/4 for u,v in G2.edges()]\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=weights2, node_size=0, with_labels=False)\n",
    "nx.draw_networkx_edge_labels(G2, pos2, edge_labels=indep, font_size=5, alpha=1);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168000, 26)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load precomputed simulation results\n",
    "files = glob.glob('data/main_article_df_*.pickle') \n",
    "dfs = itertools.chain.from_iterable([pd.read_pickle(f) for f in files])\n",
    "df = pd.concat(dfs)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>inter best connected belief corr</th>\n",
       "      <td>-0.011339</td>\n",
       "      <td>0.135734</td>\n",
       "      <td>0.307291</td>\n",
       "      <td>0.414336</td>\n",
       "      <td>0.477915</td>\n",
       "      <td>0.510033</td>\n",
       "      <td>0.523733</td>\n",
       "      <td>0.528838</td>\n",
       "      <td>0.530661</td>\n",
       "      <td>0.531278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter susceptibles</th>\n",
       "      <td>21.701948</td>\n",
       "      <td>36.913447</td>\n",
       "      <td>54.727019</td>\n",
       "      <td>63.739998</td>\n",
       "      <td>66.460983</td>\n",
       "      <td>67.204750</td>\n",
       "      <td>67.416306</td>\n",
       "      <td>67.480479</td>\n",
       "      <td>67.500921</td>\n",
       "      <td>67.508353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter adopters</th>\n",
       "      <td>8.378954</td>\n",
       "      <td>14.682261</td>\n",
       "      <td>28.502624</td>\n",
       "      <td>44.954324</td>\n",
       "      <td>54.023134</td>\n",
       "      <td>57.296884</td>\n",
       "      <td>58.356907</td>\n",
       "      <td>58.708683</td>\n",
       "      <td>58.829501</td>\n",
       "      <td>58.872095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter net initial suscep correlation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.275353</td>\n",
       "      <td>0.218654</td>\n",
       "      <td>0.199642</td>\n",
       "      <td>0.204812</td>\n",
       "      <td>0.214609</td>\n",
       "      <td>0.220708</td>\n",
       "      <td>0.223650</td>\n",
       "      <td>0.224829</td>\n",
       "      <td>0.225272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter top decile semantic clustering</th>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.133476</td>\n",
       "      <td>0.231561</td>\n",
       "      <td>0.345554</td>\n",
       "      <td>0.434677</td>\n",
       "      <td>0.468194</td>\n",
       "      <td>0.478957</td>\n",
       "      <td>0.482346</td>\n",
       "      <td>0.481464</td>\n",
       "      <td>0.482319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter top decile semantic modularity</th>\n",
       "      <td>0.474643</td>\n",
       "      <td>0.422304</td>\n",
       "      <td>0.333921</td>\n",
       "      <td>0.263131</td>\n",
       "      <td>0.222890</td>\n",
       "      <td>0.208592</td>\n",
       "      <td>0.204709</td>\n",
       "      <td>0.204148</td>\n",
       "      <td>0.204043</td>\n",
       "      <td>0.203891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter correlation similarity 95%</th>\n",
       "      <td>0.088647</td>\n",
       "      <td>0.162159</td>\n",
       "      <td>0.258018</td>\n",
       "      <td>0.312406</td>\n",
       "      <td>0.331758</td>\n",
       "      <td>0.337646</td>\n",
       "      <td>0.339111</td>\n",
       "      <td>0.339421</td>\n",
       "      <td>0.339528</td>\n",
       "      <td>0.339557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter correlation similarity 5%</th>\n",
       "      <td>-0.091452</td>\n",
       "      <td>-0.089178</td>\n",
       "      <td>-0.094963</td>\n",
       "      <td>-0.115019</td>\n",
       "      <td>-0.143832</td>\n",
       "      <td>-0.161009</td>\n",
       "      <td>-0.168513</td>\n",
       "      <td>-0.171489</td>\n",
       "      <td>-0.172598</td>\n",
       "      <td>-0.172997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter pca1 explained variance</th>\n",
       "      <td>0.774695</td>\n",
       "      <td>2.141502</td>\n",
       "      <td>7.488508</td>\n",
       "      <td>10.245113</td>\n",
       "      <td>9.137874</td>\n",
       "      <td>8.419366</td>\n",
       "      <td>8.214540</td>\n",
       "      <td>8.163262</td>\n",
       "      <td>8.150246</td>\n",
       "      <td>8.145925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter pca2 explained variance</th>\n",
       "      <td>0.738585</td>\n",
       "      <td>1.794558</td>\n",
       "      <td>3.885944</td>\n",
       "      <td>4.992357</td>\n",
       "      <td>5.355010</td>\n",
       "      <td>5.504863</td>\n",
       "      <td>5.568652</td>\n",
       "      <td>5.593726</td>\n",
       "      <td>5.603430</td>\n",
       "      <td>5.606967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter pca total variance</th>\n",
       "      <td>22.916458</td>\n",
       "      <td>36.678487</td>\n",
       "      <td>57.942017</td>\n",
       "      <td>69.418309</td>\n",
       "      <td>70.119715</td>\n",
       "      <td>69.624057</td>\n",
       "      <td>69.449157</td>\n",
       "      <td>69.405982</td>\n",
       "      <td>69.394848</td>\n",
       "      <td>69.391114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter kurtosis of PC1</th>\n",
       "      <td>-0.463364</td>\n",
       "      <td>2.725304</td>\n",
       "      <td>-0.479526</td>\n",
       "      <td>-0.839525</td>\n",
       "      <td>-0.541882</td>\n",
       "      <td>-0.507589</td>\n",
       "      <td>-0.521254</td>\n",
       "      <td>-0.526260</td>\n",
       "      <td>-0.527939</td>\n",
       "      <td>-0.528496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match susceptibles</th>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "      <td>67.513051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match best connected belief corr</th>\n",
       "      <td>-0.011339</td>\n",
       "      <td>-0.010057</td>\n",
       "      <td>-0.008305</td>\n",
       "      <td>-0.007265</td>\n",
       "      <td>-0.007728</td>\n",
       "      <td>-0.007903</td>\n",
       "      <td>-0.008015</td>\n",
       "      <td>-0.007983</td>\n",
       "      <td>-0.007970</td>\n",
       "      <td>-0.007967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match top decile semantic clustering</th>\n",
       "      <td>0.072710</td>\n",
       "      <td>0.073566</td>\n",
       "      <td>0.073259</td>\n",
       "      <td>0.073380</td>\n",
       "      <td>0.073390</td>\n",
       "      <td>0.073337</td>\n",
       "      <td>0.073280</td>\n",
       "      <td>0.073354</td>\n",
       "      <td>0.073328</td>\n",
       "      <td>0.073609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match top decile semantic modularity</th>\n",
       "      <td>0.474622</td>\n",
       "      <td>0.475505</td>\n",
       "      <td>0.474754</td>\n",
       "      <td>0.474063</td>\n",
       "      <td>0.474467</td>\n",
       "      <td>0.474203</td>\n",
       "      <td>0.474033</td>\n",
       "      <td>0.474191</td>\n",
       "      <td>0.474163</td>\n",
       "      <td>0.473985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match adopters</th>\n",
       "      <td>8.378954</td>\n",
       "      <td>33.622873</td>\n",
       "      <td>51.390500</td>\n",
       "      <td>58.240822</td>\n",
       "      <td>60.367354</td>\n",
       "      <td>60.998991</td>\n",
       "      <td>61.189726</td>\n",
       "      <td>61.248235</td>\n",
       "      <td>61.266272</td>\n",
       "      <td>61.271745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match net initial suscep correlation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.105838</td>\n",
       "      <td>0.386640</td>\n",
       "      <td>0.577205</td>\n",
       "      <td>0.660971</td>\n",
       "      <td>0.689612</td>\n",
       "      <td>0.698681</td>\n",
       "      <td>0.701462</td>\n",
       "      <td>0.702292</td>\n",
       "      <td>0.702530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match correlation similarity 95%</th>\n",
       "      <td>0.088647</td>\n",
       "      <td>0.294193</td>\n",
       "      <td>0.251867</td>\n",
       "      <td>0.198841</td>\n",
       "      <td>0.178089</td>\n",
       "      <td>0.171486</td>\n",
       "      <td>0.169404</td>\n",
       "      <td>0.168757</td>\n",
       "      <td>0.168565</td>\n",
       "      <td>0.168502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match correlation similarity 5%</th>\n",
       "      <td>-0.091452</td>\n",
       "      <td>-0.079058</td>\n",
       "      <td>-0.060639</td>\n",
       "      <td>-0.063015</td>\n",
       "      <td>-0.067490</td>\n",
       "      <td>-0.069665</td>\n",
       "      <td>-0.070504</td>\n",
       "      <td>-0.070781</td>\n",
       "      <td>-0.070862</td>\n",
       "      <td>-0.070890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match pca1 explained variance</th>\n",
       "      <td>0.774695</td>\n",
       "      <td>7.221613</td>\n",
       "      <td>6.385390</td>\n",
       "      <td>4.933587</td>\n",
       "      <td>4.496453</td>\n",
       "      <td>4.392345</td>\n",
       "      <td>4.366356</td>\n",
       "      <td>4.359636</td>\n",
       "      <td>4.357721</td>\n",
       "      <td>4.357194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match pca2 explained variance</th>\n",
       "      <td>0.738585</td>\n",
       "      <td>4.415844</td>\n",
       "      <td>4.265647</td>\n",
       "      <td>3.684026</td>\n",
       "      <td>3.497054</td>\n",
       "      <td>3.450599</td>\n",
       "      <td>3.438440</td>\n",
       "      <td>3.434935</td>\n",
       "      <td>3.433885</td>\n",
       "      <td>3.433517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match pca total variance</th>\n",
       "      <td>22.916458</td>\n",
       "      <td>62.900216</td>\n",
       "      <td>69.737611</td>\n",
       "      <td>68.763111</td>\n",
       "      <td>68.186103</td>\n",
       "      <td>68.016077</td>\n",
       "      <td>67.970119</td>\n",
       "      <td>67.957120</td>\n",
       "      <td>67.953027</td>\n",
       "      <td>67.951601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match kurtosis of PC1</th>\n",
       "      <td>-0.463364</td>\n",
       "      <td>-1.040924</td>\n",
       "      <td>-0.419158</td>\n",
       "      <td>0.612557</td>\n",
       "      <td>1.094917</td>\n",
       "      <td>1.291388</td>\n",
       "      <td>1.355545</td>\n",
       "      <td>1.375510</td>\n",
       "      <td>1.381703</td>\n",
       "      <td>1.383410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF diffusion</th>\n",
       "      <td>8.378954</td>\n",
       "      <td>11.823752</td>\n",
       "      <td>12.361845</td>\n",
       "      <td>12.437100</td>\n",
       "      <td>12.446961</td>\n",
       "      <td>12.448174</td>\n",
       "      <td>12.448314</td>\n",
       "      <td>12.448326</td>\n",
       "      <td>12.448326</td>\n",
       "      <td>12.448326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF susceptibles</th>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF susceptibility</th>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "      <td>21.701948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter percent explained variance</th>\n",
       "      <td>3.380518</td>\n",
       "      <td>5.838577</td>\n",
       "      <td>12.924142</td>\n",
       "      <td>14.758517</td>\n",
       "      <td>13.031819</td>\n",
       "      <td>12.092611</td>\n",
       "      <td>11.828134</td>\n",
       "      <td>11.761611</td>\n",
       "      <td>11.744742</td>\n",
       "      <td>11.739147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match percent explained variance</th>\n",
       "      <td>3.380518</td>\n",
       "      <td>11.481062</td>\n",
       "      <td>9.156308</td>\n",
       "      <td>7.174759</td>\n",
       "      <td>6.594383</td>\n",
       "      <td>6.457804</td>\n",
       "      <td>6.423935</td>\n",
       "      <td>6.415274</td>\n",
       "      <td>6.412844</td>\n",
       "      <td>6.412202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inter eccentricity</th>\n",
       "      <td>1.048890</td>\n",
       "      <td>1.193331</td>\n",
       "      <td>1.927076</td>\n",
       "      <td>2.052160</td>\n",
       "      <td>1.706416</td>\n",
       "      <td>1.529441</td>\n",
       "      <td>1.475140</td>\n",
       "      <td>1.459360</td>\n",
       "      <td>1.454510</td>\n",
       "      <td>1.452822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match eccentricity</th>\n",
       "      <td>1.048890</td>\n",
       "      <td>1.635387</td>\n",
       "      <td>1.496934</td>\n",
       "      <td>1.339184</td>\n",
       "      <td>1.285783</td>\n",
       "      <td>1.272922</td>\n",
       "      <td>1.269865</td>\n",
       "      <td>1.269205</td>\n",
       "      <td>1.269035</td>\n",
       "      <td>1.269018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              0          1          2  \\\n",
       "inter best connected belief corr      -0.011339   0.135734   0.307291   \n",
       "inter susceptibles                    21.701948  36.913447  54.727019   \n",
       "inter adopters                         8.378954  14.682261  28.502624   \n",
       "inter net initial suscep correlation        NaN   0.275353   0.218654   \n",
       "inter top decile semantic clustering   0.072581   0.133476   0.231561   \n",
       "inter top decile semantic modularity   0.474643   0.422304   0.333921   \n",
       "inter correlation similarity 95%       0.088647   0.162159   0.258018   \n",
       "inter correlation similarity 5%       -0.091452  -0.089178  -0.094963   \n",
       "inter pca1 explained variance          0.774695   2.141502   7.488508   \n",
       "inter pca2 explained variance          0.738585   1.794558   3.885944   \n",
       "inter pca total variance              22.916458  36.678487  57.942017   \n",
       "inter kurtosis of PC1                 -0.463364   2.725304  -0.479526   \n",
       "match susceptibles                    67.513051  67.513051  67.513051   \n",
       "match best connected belief corr      -0.011339  -0.010057  -0.008305   \n",
       "match top decile semantic clustering   0.072710   0.073566   0.073259   \n",
       "match top decile semantic modularity   0.474622   0.475505   0.474754   \n",
       "match adopters                         8.378954  33.622873  51.390500   \n",
       "match net initial suscep correlation        NaN   0.105838   0.386640   \n",
       "match correlation similarity 95%       0.088647   0.294193   0.251867   \n",
       "match correlation similarity 5%       -0.091452  -0.079058  -0.060639   \n",
       "match pca1 explained variance          0.774695   7.221613   6.385390   \n",
       "match pca2 explained variance          0.738585   4.415844   4.265647   \n",
       "match pca total variance              22.916458  62.900216  69.737611   \n",
       "match kurtosis of PC1                 -0.463364  -1.040924  -0.419158   \n",
       "NF diffusion                           8.378954  11.823752  12.361845   \n",
       "NF susceptibles                       21.701948  21.701948  21.701948   \n",
       "NF susceptibility                     21.701948  21.701948  21.701948   \n",
       "inter percent explained variance       3.380518   5.838577  12.924142   \n",
       "match percent explained variance       3.380518  11.481062   9.156308   \n",
       "inter eccentricity                     1.048890   1.193331   1.927076   \n",
       "match eccentricity                     1.048890   1.635387   1.496934   \n",
       "\n",
       "                                              3          4          5  \\\n",
       "inter best connected belief corr       0.414336   0.477915   0.510033   \n",
       "inter susceptibles                    63.739998  66.460983  67.204750   \n",
       "inter adopters                        44.954324  54.023134  57.296884   \n",
       "inter net initial suscep correlation   0.199642   0.204812   0.214609   \n",
       "inter top decile semantic clustering   0.345554   0.434677   0.468194   \n",
       "inter top decile semantic modularity   0.263131   0.222890   0.208592   \n",
       "inter correlation similarity 95%       0.312406   0.331758   0.337646   \n",
       "inter correlation similarity 5%       -0.115019  -0.143832  -0.161009   \n",
       "inter pca1 explained variance         10.245113   9.137874   8.419366   \n",
       "inter pca2 explained variance          4.992357   5.355010   5.504863   \n",
       "inter pca total variance              69.418309  70.119715  69.624057   \n",
       "inter kurtosis of PC1                 -0.839525  -0.541882  -0.507589   \n",
       "match susceptibles                    67.513051  67.513051  67.513051   \n",
       "match best connected belief corr      -0.007265  -0.007728  -0.007903   \n",
       "match top decile semantic clustering   0.073380   0.073390   0.073337   \n",
       "match top decile semantic modularity   0.474063   0.474467   0.474203   \n",
       "match adopters                        58.240822  60.367354  60.998991   \n",
       "match net initial suscep correlation   0.577205   0.660971   0.689612   \n",
       "match correlation similarity 95%       0.198841   0.178089   0.171486   \n",
       "match correlation similarity 5%       -0.063015  -0.067490  -0.069665   \n",
       "match pca1 explained variance          4.933587   4.496453   4.392345   \n",
       "match pca2 explained variance          3.684026   3.497054   3.450599   \n",
       "match pca total variance              68.763111  68.186103  68.016077   \n",
       "match kurtosis of PC1                  0.612557   1.094917   1.291388   \n",
       "NF diffusion                          12.437100  12.446961  12.448174   \n",
       "NF susceptibles                       21.701948  21.701948  21.701948   \n",
       "NF susceptibility                     21.701948  21.701948  21.701948   \n",
       "inter percent explained variance      14.758517  13.031819  12.092611   \n",
       "match percent explained variance       7.174759   6.594383   6.457804   \n",
       "inter eccentricity                     2.052160   1.706416   1.529441   \n",
       "match eccentricity                     1.339184   1.285783   1.272922   \n",
       "\n",
       "                                              6          7          8  \\\n",
       "inter best connected belief corr       0.523733   0.528838   0.530661   \n",
       "inter susceptibles                    67.416306  67.480479  67.500921   \n",
       "inter adopters                        58.356907  58.708683  58.829501   \n",
       "inter net initial suscep correlation   0.220708   0.223650   0.224829   \n",
       "inter top decile semantic clustering   0.478957   0.482346   0.481464   \n",
       "inter top decile semantic modularity   0.204709   0.204148   0.204043   \n",
       "inter correlation similarity 95%       0.339111   0.339421   0.339528   \n",
       "inter correlation similarity 5%       -0.168513  -0.171489  -0.172598   \n",
       "inter pca1 explained variance          8.214540   8.163262   8.150246   \n",
       "inter pca2 explained variance          5.568652   5.593726   5.603430   \n",
       "inter pca total variance              69.449157  69.405982  69.394848   \n",
       "inter kurtosis of PC1                 -0.521254  -0.526260  -0.527939   \n",
       "match susceptibles                    67.513051  67.513051  67.513051   \n",
       "match best connected belief corr      -0.008015  -0.007983  -0.007970   \n",
       "match top decile semantic clustering   0.073280   0.073354   0.073328   \n",
       "match top decile semantic modularity   0.474033   0.474191   0.474163   \n",
       "match adopters                        61.189726  61.248235  61.266272   \n",
       "match net initial suscep correlation   0.698681   0.701462   0.702292   \n",
       "match correlation similarity 95%       0.169404   0.168757   0.168565   \n",
       "match correlation similarity 5%       -0.070504  -0.070781  -0.070862   \n",
       "match pca1 explained variance          4.366356   4.359636   4.357721   \n",
       "match pca2 explained variance          3.438440   3.434935   3.433885   \n",
       "match pca total variance              67.970119  67.957120  67.953027   \n",
       "match kurtosis of PC1                  1.355545   1.375510   1.381703   \n",
       "NF diffusion                          12.448314  12.448326  12.448326   \n",
       "NF susceptibles                       21.701948  21.701948  21.701948   \n",
       "NF susceptibility                     21.701948  21.701948  21.701948   \n",
       "inter percent explained variance      11.828134  11.761611  11.744742   \n",
       "match percent explained variance       6.423935   6.415274   6.412844   \n",
       "inter eccentricity                     1.475140   1.459360   1.454510   \n",
       "match eccentricity                     1.269865   1.269205   1.269035   \n",
       "\n",
       "                                              9  \n",
       "inter best connected belief corr       0.531278  \n",
       "inter susceptibles                    67.508353  \n",
       "inter adopters                        58.872095  \n",
       "inter net initial suscep correlation   0.225272  \n",
       "inter top decile semantic clustering   0.482319  \n",
       "inter top decile semantic modularity   0.203891  \n",
       "inter correlation similarity 95%       0.339557  \n",
       "inter correlation similarity 5%       -0.172997  \n",
       "inter pca1 explained variance          8.145925  \n",
       "inter pca2 explained variance          5.606967  \n",
       "inter pca total variance              69.391114  \n",
       "inter kurtosis of PC1                 -0.528496  \n",
       "match susceptibles                    67.513051  \n",
       "match best connected belief corr      -0.007967  \n",
       "match top decile semantic clustering   0.073609  \n",
       "match top decile semantic modularity   0.473985  \n",
       "match adopters                        61.271745  \n",
       "match net initial suscep correlation   0.702530  \n",
       "match correlation similarity 95%       0.168502  \n",
       "match correlation similarity 5%       -0.070890  \n",
       "match pca1 explained variance          4.357194  \n",
       "match pca2 explained variance          3.433517  \n",
       "match pca total variance              67.951601  \n",
       "match kurtosis of PC1                  1.383410  \n",
       "NF diffusion                          12.448326  \n",
       "NF susceptibles                       21.701948  \n",
       "NF susceptibility                     21.701948  \n",
       "inter percent explained variance      11.739147  \n",
       "match percent explained variance       6.412202  \n",
       "inter eccentricity                     1.452822  \n",
       "match eccentricity                     1.269018  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = df.groupby(level=0).aggregate('mean')\n",
    "# have to cast as type 'float64', because for some reason some of the columns are type 'object'.\n",
    "means['NF susceptibility'] = means['inter susceptibles'].loc[0]\n",
    "means['inter percent explained variance'] = means['inter pca1 explained variance']/means['inter pca total variance'] * 100\n",
    "means['match percent explained variance'] = means['match pca1 explained variance']/means['match pca total variance'] * 100\n",
    "means['inter eccentricity'] = means['inter pca1 explained variance']/means['inter pca2 explained variance']\n",
    "means['match eccentricity'] = means['match pca1 explained variance']/means['match pca2 explained variance']\n",
    "means = means.loc[:9]\n",
    "means.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 100,\n",
    "    \"figure.figsize\": (8, 10),\n",
    "    \"figure.facecolor\": \"white\", #\"lightyellow\",\n",
    "    \n",
    "    \"savefig.dpi\": 150,\n",
    "    \n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": \"Helvetica\",\n",
    "    \"font.size\": 8,\n",
    "    \n",
    "    \"lines.linewidth\": 2,\n",
    "    \"lines.marker\": \"o\",\n",
    "    \"lines.markersize\": 5,\n",
    "    \n",
    "    \"axes.spines.left\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.edgecolor\": \"grey\",\n",
    "    \"axes.labelsize\": 8,\n",
    "    \"axes.labelcolor\": \"grey\",\n",
    "    \"axes.facecolor\": \"#00000000\",\n",
    "    \n",
    "    \"xtick.color\": \"grey\",\n",
    "    \"xtick.labelsize\": 8,\n",
    "    \"xtick.major.size\": 2,\n",
    "    \n",
    "    \"ytick.major.size\": 0,\n",
    "    \"ytick.color\": \"grey\",\n",
    "    \"ytick.labelsize\": 8,\n",
    "    \n",
    "    \"text.color\": \"grey\",\n",
    "    \"text.usetex\": False\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 650x650 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6.5, 6.5), facecolor='lightyellow')\n",
    "# sociological science:\n",
    "# 8 pt font in tables\n",
    "# 10 pt font in text\n",
    "# 1 inch margins\n",
    "\n",
    "x1, x2 = 0, .58\n",
    "y1, y2 = .91, .43\n",
    "textdx, textdy = .03, .04\n",
    "\n",
    "ytick_fontdict={'fontsize':7}\n",
    "\n",
    "####### upper left ##########\n",
    "ax1 = plt.subplot(2, 2, 1)\n",
    "\n",
    "means['NF diffusion'].plot(ax=ax1, color='k', markevery=9)\n",
    "means['NF susceptibility'].plot(ax=ax1, color='k', linestyle='--', markevery=9)\n",
    "means['inter adopters'].plot(ax=ax1, color='r', markevery=9)\n",
    "means['inter susceptibles'].plot(ax=ax1, color='r', linestyle='--', markevery=9)\n",
    "\n",
    "ax1.set_xlim(-.2, 9.2)\n",
    "ylim = (0, 72)\n",
    "ax1.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax1.set_xlabel('Time step')\n",
    "ax1.set_xticks(range(0,10,1))\n",
    "ax1.set_xticklabels(range(0,10,1))\n",
    "\n",
    "\n",
    "# left\n",
    "ax1.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "               means['NF diffusion'].iloc[0]])\n",
    "ax1.set_yticklabels(\n",
    "           [('%.0f%%' % means['NF susceptibility'].iloc[0]).rjust(7), \n",
    "            ('%.0f%%' % means['NF diffusion'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "# lefter left\n",
    "ax1L = ax1.twinx()\n",
    "ax1L.set_ylim(*ylim)\n",
    "ax1L.yaxis.set_label_position('left')\n",
    "ax1L.yaxis.set_ticks_position('left')\n",
    "ax1L.tick_params(axis='y', labelcolor=\"k\")\n",
    "ax1L.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "                 means['NF diffusion'].iloc[0]])\n",
    "ax1L.set_yticklabels(['Susceptible       ', \n",
    "                        'Adopters      '])\n",
    "\n",
    "# right\n",
    "ax1R = ax1.twinx()\n",
    "ax1R.set_ylim(*ylim)\n",
    "ax1R.yaxis.set_label_position(\"right\")\n",
    "ax1R.set_yticks([means['NF susceptibility'].iloc[-1], \n",
    "                means['NF diffusion'].iloc[-1],\n",
    "                means['inter susceptibles'].iloc[-1], \n",
    "                means['inter adopters'].iloc[-1]\n",
    "               ])\n",
    "ax1R.set_yticklabels([('%.0f%%' % means['NF susceptibility'].iloc[-1]).ljust(8), \n",
    "                      '%.0f%%' % means['NF diffusion'].iloc[-1],\n",
    "                      '%.0f%%' % means['inter susceptibles'].iloc[-1], \n",
    "                      '%.0f%%' % means['inter adopters'].iloc[-1]\n",
    "                     ], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y1-0.03, s='Average percentage of the population', rotation=90, va='top')\n",
    "plt.figtext(x=x1, y=y1, s='A. Susceptibility and adoption coevolve', va='top', color='k', size=10)\n",
    "plt.figtext(x=x1+textdx, y=y1-textdy, s='Interdependent beliefs spread\\nfurther by creating susceptibility\\nto one another.', va='top', size=6)\n",
    "plt.figtext(x=.47, y=.705, va='top', size=6, ha='right',\n",
    "            s='In contrast, susceptibility is \\nfixed for independent beliefs,\\nand so adoption is limited.')\n",
    "\n",
    "\n",
    "######### Upper Right ########\n",
    "ax2 = plt.subplot(2, 2, 2)\n",
    "means['match net initial suscep correlation'].plot(ax=ax2, color='k', markevery=(1,8))\n",
    "means['inter net initial suscep correlation'].plot(ax=ax2, color='r', markevery=(1,8))\n",
    "\n",
    "ax2.plot([1,1], \n",
    "         [means['match net initial suscep correlation'].loc[1], \n",
    "          means['inter net initial suscep correlation'].loc[1]], \n",
    "         color='white', markersize=2.5, zorder=20, linewidth=0)\n",
    "\n",
    "ax2.set_xlim(-.2, 9.2)\n",
    "ylim = (0, .72)\n",
    "ax2.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax2.set_xlabel('Time step')\n",
    "ax2.set_xticks(range(0,10,1))\n",
    "ax2.set_xticklabels(range(0,10,1))\n",
    "\n",
    "# left\n",
    "ax2.set_yticks([means['inter net initial suscep correlation'].iloc[1], \n",
    "                means['match net initial suscep correlation'].iloc[1]])\n",
    "ax2.set_yticklabels(\n",
    "    [('%.02f' % means['inter net initial suscep correlation'].iloc[1]).rjust(8), \n",
    "     ('%.02f' % means['match net initial suscep correlation'].iloc[1]).rjust(8)], fontdict=ytick_fontdict\n",
    ")\n",
    "\n",
    "# right\n",
    "ax2R = ax2.twinx()\n",
    "ax2R.set_ylim(*ylim)\n",
    "ax2R.yaxis.set_label_position(\"right\")\n",
    "\n",
    "ax2R.set_yticks([means['inter net initial suscep correlation'].iloc[-1], \n",
    "                means['match net initial suscep correlation'].iloc[-1]])\n",
    "ax2R.set_yticklabels([('%.02f' % means['inter net initial suscep correlation'].iloc[-1]).ljust(8), \n",
    "                     ('%.02f' % means['match net initial suscep correlation'].iloc[-1]).ljust(8)], \n",
    "                     fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y1-0.03, s='Adoption correlation with initial susceptibility', rotation=90, va='top')\n",
    "plt.figtext(x=x2, y=y1, s='B. Adoption becomes unpredictable', va='top', color='k', size=10)\n",
    "plt.figtext(x=x2+textdx, y=y1-textdy, s='The eventual popularity of\\nan independent belief is\\neasy to predict from \\nhow many people\\nare susceptible\\nto begin with.', va='top', size=6)\n",
    "plt.figtext(x=.95, y=.63, s='In contrast, adoption of \\ninterdependent beliefs is only weakly \\ninfluenced by initial susceptibility.', \n",
    "            va='top', size=6, ha='right')\n",
    "\n",
    "#### inset graph\n",
    "ax2b = fig.add_axes([.82,.7, .12, .12])\n",
    "ax2b.plot(inter_sus[seed.index], inter_diff[seed.index], 'r.', markersize=1, alpha=.4)\n",
    "ax2b.plot(indep_suscep[seed.index], indep_diff[seed.index], 'k.', markersize=1, alpha=.4)\n",
    "plt.ylim(0, max(max(indep_diff), max(inter_diff)))\n",
    "plt.xlim(0, max(max(inter_sus), max(indep_suscep)))\n",
    "ax2b.spines['left'].set_visible(True)\n",
    "ax2b.spines['left'].set_color('lightgrey')\n",
    "ax2b.spines['bottom'].set_color('lightgrey')\n",
    "ax2b.set_xticks([])\n",
    "ax2b.set_yticks([])\n",
    "ax2b.set_xlabel('Initially Susceptible', fontsize=6)\n",
    "ax2b.set_ylabel('Final Adopters', fontsize=6, va='center')\n",
    "\n",
    "\n",
    "\n",
    "# ######## Lower Left ############\n",
    "ax3 = plt.subplot(2, 2, 3)\n",
    "ax3.plot(means.index, means['match best connected belief corr'], color='k', markevery=(0,9))\n",
    "ax3.plot(means.index, means['inter best connected belief corr'], color='r', markevery=(0,9))\n",
    "\n",
    "ax3.set_xlim(-.2, 9.2)\n",
    "ylim = (0, .57)\n",
    "ax3.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax3.set_xlabel('Time step')\n",
    "ax3.set_xticks(range(0,10,1))\n",
    "ax3.set_xticklabels(range(0,10,1))\n",
    "\n",
    "\n",
    "# left\n",
    "ax3.set_yticks([means['inter best connected belief corr'].iloc[0], \n",
    "               means['match best connected belief corr'].iloc[0]])\n",
    "ax3.set_yticklabels(\n",
    "           [('%.02f' % means['inter best connected belief corr'].iloc[0]).rjust(7), \n",
    "            ('%.02f' % means['match best connected belief corr'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax3R = ax3.twinx()\n",
    "ax3R.set_ylim(*ylim)\n",
    "ax3R.yaxis.set_label_position(\"right\")\n",
    "ax3R.set_yticks([means['inter best connected belief corr'].iloc[-1], \n",
    "                means['match best connected belief corr'].iloc[-1]])\n",
    "ax3R.set_yticklabels([('%.02f' % means['inter best connected belief corr'].iloc[-1]).ljust(8), \n",
    "                      '%.02f' % means['match best connected belief corr'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y2-.03, s='Correlation with leading adjacent belief', rotation=90, va='top')\n",
    "plt.figtext(x=x1, y=y2, s='C. Adjacent beliefs have correlated adoption', va='top', color='k', size=10)\n",
    "plt.figtext(x=x1+textdx, y=y2-textdy, va='top', size=6,\n",
    "            s=\"The popularity of an interdependent belief\\nbecomes correlated with the popularity\\nof the most successful belief\\nit interacts with.\")\n",
    "plt.figtext(x=.47, y=.115, va='top', size=6, ha='right',\n",
    "            s='In contrast, independent beliefs are \\nuncorrelated with adjacent beliefs.')\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax3b = fig.add_axes([.315,.14, .15, .16])\n",
    "inter = {(0,1):16, (1,2):13, (2,3):3, (3,4):8, (4,5):1, \n",
    "         (0,2):11, (3,5):5, (2,6):14, (1,6):10, (5,2):1,(5,7):1, (3,6):3}\n",
    "G = nx.from_edgelist(inter)\n",
    "nx.set_edge_attributes(G, inter, name='weight')\n",
    "\n",
    "pos = {\n",
    "    0: [ 0.05337489, -1.        ],\n",
    "    1: [-0.38712707, -0.80563193],\n",
    "    2: [-0.03981886, -0.30618229],\n",
    "    3: [-0.21645782,  0.26372263],\n",
    "    4: [-0.09271407,  0.85974724],\n",
    "    5: [0.31097159, 0.43462805],\n",
    "    6: [-0.55557835, -0.27382355],\n",
    "    7: [0.42734968, 0.96753985]\n",
    "}\n",
    "def rotate(x, rotation=0):\n",
    "    length = sqrt(x[0]**2 + x[1]**2)\n",
    "    angle = np.arctan2(x[0], x[1])\n",
    "    new_angle = angle+rotation\n",
    "    return [length*cos(new_angle), length*sin(new_angle)]\n",
    "pos = {k: rotate(v, -.3) for k, v in pos.items()}\n",
    "\n",
    "edges = G.edges()\n",
    "weights = [G[u][v]['weight']/4 for u,v in edges]\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=weights, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=inter, font_size=5, alpha=1, font_color='r', ax=ax3b)\n",
    "\n",
    "indep = {(0,1):12, (1,2):1, (2,3):5, (3,4):8, (4,5):10, \n",
    "         (0,2):1, (3,5):2, (2,6):14, (1,6):3, (2,5):3, (5,7):16, (3,6):13}\n",
    "G2 = nx.from_edgelist(indep)\n",
    "nx.set_edge_attributes(G2, indep, name='weight')\n",
    "\n",
    "dx = 0\n",
    "dy = -.7\n",
    "pos2 = {k:[v[0]+dx, v[1]+dy] for k,v in pos.items()}\n",
    "\n",
    "weights2 = [G2[u][v]['weight']/4 for u,v in G2.edges()]\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=weights2, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G2, pos2, edge_labels=indep, font_size=5, alpha=.5, ax=ax3b);\n",
    "\n",
    "plt.box('on')\n",
    "ax3b.spines['left'].set_visible(True)\n",
    "ax3b.spines['left'].set_color('lightgrey')\n",
    "ax3b.spines['bottom'].set_color('lightgrey')\n",
    "ax3b.spines['right'].set_visible(True)\n",
    "ax3b.spines['right'].set_color('lightgrey')\n",
    "ax3b.spines['top'].set_visible(True)\n",
    "ax3b.spines['top'].set_color('lightgrey')\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.25, 0.45)\n",
    "\n",
    "plt.figtext(x=.315, y=.32, s='Sample Adopter Counts', va='top', size=6)\n",
    "\n",
    "###### Lower Right #########\n",
    "ax4 = plt.subplot(2, 2, 4)\n",
    "ax4.plot(means.index, means['match top decile semantic clustering'], color='k', markevery=(0,9))\n",
    "ax4.plot(means.index, means['inter top decile semantic clustering'], color='r', markevery=(0,9))\n",
    "\n",
    "\n",
    "\n",
    "ax4.set_xlim(-.2, 9.2)\n",
    "ylim = (0, .57)\n",
    "ax4.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax4.set_xlabel('Time step')\n",
    "ax4.set_xticks(range(0,10,1))\n",
    "ax4.set_xticklabels(range(0,10,1))\n",
    "\n",
    "# left\n",
    "ax4.set_yticks([means['inter top decile semantic clustering'].iloc[0], \n",
    "                means['match top decile semantic clustering'].iloc[0]])\n",
    "ax4.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[0]), \n",
    "                     ('%.02f' % means['match top decile semantic clustering'].iloc[0])], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax4R = ax4.twinx()\n",
    "ax4R.set_ylim(*ylim)\n",
    "ax4R.yaxis.set_label_position(\"right\")\n",
    "ax4R.set_yticks([means['inter top decile semantic clustering'].iloc[-1], \n",
    "                means['match top decile semantic clustering'].iloc[-1]])\n",
    "ax4R.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[-1]).ljust(8), \n",
    "                     '%.02f' % means['match top decile semantic clustering'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y2-.03, s='Clustering coefficient of leading 10% beliefs', rotation=90, va='top')\n",
    "plt.figtext(x=x2, y=y2, s='D. Popular beliefs cluster together', va='top', color='k', size=10)\n",
    "plt.figtext(x=x2+textdx, y=y2-textdy, s='The most popular interdependent\\nbeliefs are tightly clustered,\\nand mutually supportive\\nof one another.', va='top', size=6)\n",
    "plt.figtext(x=.95, y=.155, s='In contrast, popular independent \\nbeliefs only interact by chance.', \n",
    "            va='top', ha='right', size=6)\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax4b = fig.add_axes([.815, .18, .13, .1])\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=[2 if v>2 else 0 for v in weights], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=[2 if v>2 else 0 for v in weights2], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "plt.box('on')\n",
    "ax4b.spines['left'].set_visible(True)\n",
    "ax4b.spines['left'].set_color('lightgrey')\n",
    "ax4b.spines['bottom'].set_color('lightgrey')\n",
    "ax4b.spines['right'].set_visible(True)\n",
    "ax4b.spines['right'].set_color('lightgrey')\n",
    "ax4b.spines['top'].set_visible(True)\n",
    "ax4b.spines['top'].set_color('lightgrey')\n",
    "plt.figtext(x=.815, y=.3, s='Most Popular Beliefs', va='top', size=6)\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.3, .5)\n",
    "\n",
    "\n",
    "\n",
    "#### Figure Title text ###\n",
    "plt.suptitle('The Effect of Reciprocal Facilitation on Emergent Belief Structures', \n",
    "             x=0, y=1, ha='left', va='top', fontsize=13, color='k')\n",
    "plt.figtext(x=0, y=.96, s='Independent diffusion',  ha='left', va='top', fontsize=11, color='k')\n",
    "plt.figtext(x=.265, y=.96, s='|',  ha='left', va='top', fontsize=11, color='lightgrey')\n",
    "plt.figtext(x=.28, y=.96, s='Interdependent diffusion', va='top', ha='left', fontsize=11, color='r')\n",
    "\n",
    "plt.subplots_adjust(left=.17, right=.95, top=.87, bottom=.07, wspace=.6, hspace=.5)\n",
    "\n",
    "plt.savefig('Fig2_emergent_belief_structures', transparent=False, dpi=150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:73: MatplotlibDeprecationWarning: Passing the fontdict parameter of _set_ticklabels() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 343x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 100,\n",
    "    \"figure.figsize\": (8, 10),\n",
    "    \"figure.facecolor\": \"white\", #\"lightyellow\",\n",
    "    \n",
    "    \"savefig.dpi\": 150,\n",
    "    \n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": \"Helvetica\",\n",
    "    \"font.size\": 8,\n",
    "    \n",
    "    \"lines.linewidth\": 1.5,\n",
    "    \"lines.marker\": \"o\",\n",
    "    \"lines.markersize\": 4,\n",
    "    \n",
    "    \"axes.spines.left\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.edgecolor\": \"grey\",\n",
    "    \"axes.labelsize\": 8,\n",
    "    \"axes.labelcolor\": \"grey\",\n",
    "    \"axes.facecolor\": \"#00000000\",\n",
    "    \n",
    "    \"xtick.color\": \"grey\",\n",
    "    \"xtick.labelsize\": 8,\n",
    "    \"xtick.major.size\": 2,\n",
    "    \n",
    "    \"ytick.major.size\": 0,\n",
    "    \"ytick.color\": \"grey\",\n",
    "    \"ytick.labelsize\": 8,\n",
    "    \n",
    "    \"text.color\": \"grey\",\n",
    "    \"text.usetex\": False\n",
    "})\n",
    "\n",
    "figsize=(3.43, 6)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "\n",
    "left = .23\n",
    "right = .63\n",
    "top = .93\n",
    "bottom = .05\n",
    "\n",
    "ylabel_size=6\n",
    "#ytick_size=6\n",
    "xtick_size=6\n",
    "xlabel_size=6\n",
    "subtitle_size=8\n",
    "ytick_fontdict={'fontsize':6}\n",
    "\n",
    "inset_x = .24\n",
    "inset_y = .9*inset_x*figsize[0]/figsize[1]\n",
    "inset_dy = .04  # vertical distance btw y1 title location and top of inset\n",
    "x1, x2, x3 = .005, .005, .98-inset_x\n",
    "y1, y2, y3, y4 = .96, .71, .465, .22\n",
    "textdx, textdy = .07, .025 # distance from the xlabels for comments\n",
    "\n",
    "####### AAAA ##########\n",
    "ax1 = plt.subplot(4, 1, 1)\n",
    "\n",
    "means['NF diffusion'].plot(ax=ax1, color='k', markevery=9)\n",
    "means['NF susceptibility'].plot(ax=ax1, color='k', linestyle='--', markevery=9)\n",
    "means['inter adopters'].plot(ax=ax1, color='r', markevery=9)\n",
    "means['inter susceptibles'].plot(ax=ax1, color='r', linestyle='--', markevery=9)\n",
    "\n",
    "ax1.set_xlim(-.2, 9.3)\n",
    "ylim = (0, 72)\n",
    "ax1.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax1.set_xlabel('Time step', fontsize=xlabel_size)\n",
    "ax1.set_xticks(range(0,10,1))\n",
    "ax1.set_xticklabels(range(0,10,1), {\"fontsize\":xtick_size})\n",
    "\n",
    "\n",
    "# left\n",
    "ax1.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "               means['NF diffusion'].iloc[0]])\n",
    "ax1.set_yticklabels(\n",
    "           [('%.0f%%' % means['NF susceptibility'].iloc[0]).rjust(7), \n",
    "            ('%.0f%%' % means['NF diffusion'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "# lefter left\n",
    "ax1L = ax1.twinx()\n",
    "ax1L.set_ylim(*ylim)\n",
    "ax1L.yaxis.set_label_position('left')\n",
    "ax1L.yaxis.set_ticks_position('left')\n",
    "ax1L.tick_params(axis='y', labelcolor=\"k\")\n",
    "ax1L.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "                 means['NF diffusion'].iloc[0]])\n",
    "ax1L.set_yticklabels(['Susceptible        ', \n",
    "                        'Adopters       '], fontdict=ytick_fontdict)\n",
    "\n",
    "# right\n",
    "ax1R = ax1.twinx()\n",
    "ax1R.set_ylim(*ylim)\n",
    "ax1R.yaxis.set_label_position(\"right\")\n",
    "ax1R.set_yticks([means['NF susceptibility'].iloc[-1], \n",
    "                means['NF diffusion'].iloc[-1],\n",
    "                means['inter susceptibles'].iloc[-1], \n",
    "                means['inter adopters'].iloc[-1]\n",
    "               ])\n",
    "ax1R.set_yticklabels([('%.0f%%' % means['NF susceptibility'].iloc[-1]).ljust(8), \n",
    "                      '%.0f%%' % means['NF diffusion'].iloc[-1],\n",
    "                      '%.0f%%' % means['inter susceptibles'].iloc[-1], \n",
    "                      '%.0f%%' % means['inter adopters'].iloc[-1]\n",
    "                     ], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y1-0.03, s='Average % of the population', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x1, y=y1, s='A. Susceptibility and adoption coevolve', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x3-.03, y=y1-textdy+.01, va='top', size=6,\n",
    "            s='Interdependent beliefs\\n'\\\n",
    "              'spread further by creating\\n'\\\n",
    "              'susceptibility to one\\n'\\\n",
    "              'another.')\n",
    "plt.figtext(x=x3-.03, y=y1-inset_y, va='top', size=6, ha='left',\n",
    "            s='In contrast, susceptibility\\n'\\\n",
    "              'is fixed for independent\\n'\\\n",
    "              'beliefs, and so adoption\\n'\\\n",
    "              'is limited.')\n",
    "\n",
    "\n",
    "######### BBBB ########\n",
    "ax2 = plt.subplot(4, 1, 2)\n",
    "means['match net initial suscep correlation'].plot(ax=ax2, color='k', markevery=(1,8))\n",
    "means['inter net initial suscep correlation'].plot(ax=ax2, color='r', markevery=(1,8))\n",
    "\n",
    "\n",
    "ax2.plot([1,1], \n",
    "         [means['match net initial suscep correlation'].loc[1], \n",
    "          means['inter net initial suscep correlation'].loc[1]], \n",
    "         color='white', markersize=2, zorder=20, linewidth=0)  # white center of first point in b\n",
    "\n",
    "ax2.set_xlim(-.2, 9.3)\n",
    "ylim = (0, .75)\n",
    "ax2.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax2.set_xlabel('Time step', fontsize=xlabel_size)\n",
    "ax2.set_xticks(range(0,10,1))\n",
    "ax2.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "# left\n",
    "ax2.set_yticks([means['inter net initial suscep correlation'].iloc[1], \n",
    "                means['match net initial suscep correlation'].iloc[1]])\n",
    "ax2.set_yticklabels(\n",
    "    [('%.02f' % means['inter net initial suscep correlation'].iloc[1]).rjust(8), \n",
    "     ('%.02f' % means['match net initial suscep correlation'].iloc[1]).rjust(8)], fontdict=ytick_fontdict\n",
    ")\n",
    "\n",
    "# right\n",
    "ax2R = ax2.twinx()\n",
    "ax2R.set_ylim(*ylim)\n",
    "ax2R.yaxis.set_label_position(\"right\")\n",
    "\n",
    "ax2R.set_yticks([means['inter net initial suscep correlation'].iloc[-1], \n",
    "                means['match net initial suscep correlation'].iloc[-1]])\n",
    "ax2R.set_yticklabels([('%.02f' % means['inter net initial suscep correlation'].iloc[-1]).ljust(8), \n",
    "                     ('%.02f' % means['match net initial suscep correlation'].iloc[-1]).ljust(8)], \n",
    "                     fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y2-0.03, s='Adoption correlation with\\ninitial susceptibility', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x2, y=y2, s='B. Adoption becomes unpredictable', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x2+textdx, y=y2-textdy, va='top', size=6,\n",
    "            s='The popularity of an indepen-\\n'\\\n",
    "              'dent belief is easy to\\n'\\\n",
    "              'predict from how many\\n'\\\n",
    "              'people are susceptible\\n'\\\n",
    "              'to begin with.')\n",
    "plt.figtext(x=right, y=y2-textdy-.03, va='top', size=6, ha='right',\n",
    "            s='In contrast, adoption\\n'\\\n",
    "              'of interdependent beliefs\\n'\\\n",
    "              'is only weakly influenced\\n'\\\n",
    "              'by initial susceptibility.')\n",
    "\n",
    "\n",
    "#### inset graph\n",
    "ax2b = fig.add_axes([x3,y2-inset_dy-inset_y, inset_x, inset_y])\n",
    "ax2b.plot(inter_sus[seed.index], inter_diff[seed.index], 'r.', markersize=1, alpha=.4)\n",
    "ax2b.plot(indep_suscep[seed.index], indep_diff[seed.index], 'k.', markersize=1, alpha=.4)\n",
    "plt.ylim(0, max(max(indep_diff), max(inter_diff)))\n",
    "plt.xlim(0, max(max(inter_sus), max(indep_suscep)))\n",
    "ax2b.spines['left'].set_visible(True)\n",
    "ax2b.spines['left'].set_color('lightgrey')\n",
    "ax2b.spines['bottom'].set_color('lightgrey')\n",
    "ax2b.set_xticks([])\n",
    "ax2b.set_yticks([])\n",
    "ax2b.set_xlabel('Initially Susceptible', fontsize=6)\n",
    "ax2b.set_ylabel('Final Adopters', fontsize=6, va='center')\n",
    "\n",
    "\n",
    "\n",
    "# ######## CCCC ############\n",
    "ax3 = plt.subplot(4, 1, 3)\n",
    "ax3.plot(means.index, means['match best connected belief corr'], color='k', markevery=(0,9))\n",
    "ax3.plot(means.index, means['inter best connected belief corr'], color='r', markevery=(0,9))\n",
    "\n",
    "ax3.set_xlim(-.2, 9.3)\n",
    "ylim = (0, .59)\n",
    "ax3.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax3.set_xlabel('Time step', fontsize=xlabel_size)\n",
    "ax3.set_xticks(range(0,10,1))\n",
    "ax3.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "\n",
    "# left\n",
    "ax3.set_yticks([means['inter best connected belief corr'].iloc[0], \n",
    "               means['match best connected belief corr'].iloc[0]])\n",
    "ax3.set_yticklabels(\n",
    "           [('%.02f' % means['inter best connected belief corr'].iloc[0]).rjust(7), \n",
    "            ('%.02f' % means['match best connected belief corr'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax3R = ax3.twinx()\n",
    "ax3R.set_ylim(*ylim)\n",
    "ax3R.yaxis.set_label_position(\"right\")\n",
    "ax3R.set_yticks([means['inter best connected belief corr'].iloc[-1], \n",
    "                means['match best connected belief corr'].iloc[-1]])\n",
    "ax3R.set_yticklabels([('%.02f' % means['inter best connected belief corr'].iloc[-1]).ljust(8), \n",
    "                      '%.02f' % means['match best connected belief corr'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y3-.03, s='Correlation with most\\npopular adjacent belief', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x1, y=y3, s='C. Adjacent beliefs have correlated adoption', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x1+textdx, y=y3-textdy, va='top', size=6,\n",
    "            s='The popularity of an interdep-\\n'\\\n",
    "              'endent belief becomes\\n'\\\n",
    "              'correlated with the\\n'\\\n",
    "              'popularity of the \\n'\\\n",
    "              'most successful\\n'\\\n",
    "              'belief it interacts\\n'\\\n",
    "              'with.')\n",
    "plt.figtext(x=right, y=y3-textdy-.09, va='top', size=6, ha='right',\n",
    "            s='In contrast, independent\\n'\\\n",
    "              'beliefs are uncorrelated\\n'\\\n",
    "                'with adjacent beliefs.')\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax3b = fig.add_axes([x3, y3-inset_dy-inset_y-.005, inset_x, inset_y])\n",
    "inter = {(0,1):16, (1,2):13, (2,3):3, (3,4):8, (4,5):1, \n",
    "         (0,2):11, (3,5):5, (2,6):14, (1,6):10, (5,2):1,(5,7):1, (3,6):3}\n",
    "G = nx.from_edgelist(inter)\n",
    "nx.set_edge_attributes(G, inter, name='weight')\n",
    "\n",
    "pos = {\n",
    "    0: [ 0.05337489, -1.        ],\n",
    "    1: [-0.38712707, -0.80563193],\n",
    "    2: [-0.03981886, -0.30618229],\n",
    "    3: [-0.21645782,  0.26372263],\n",
    "    4: [-0.09271407,  0.85974724],\n",
    "    5: [0.31097159, 0.43462805],\n",
    "    6: [-0.55557835, -0.27382355],\n",
    "    7: [0.42734968, 0.96753985]\n",
    "}\n",
    "def rotate(x, rotation=0):\n",
    "    length = sqrt(x[0]**2 + x[1]**2)\n",
    "    angle = np.arctan2(x[0], x[1])\n",
    "    new_angle = angle+rotation\n",
    "    return [length*cos(new_angle), length*sin(new_angle)]\n",
    "pos = {k: rotate(v, -.3) for k, v in pos.items()}\n",
    "\n",
    "edges = G.edges()\n",
    "weights = [G[u][v]['weight']/4 for u,v in edges]\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=weights, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=inter, font_size=5, alpha=1, font_color='r', ax=ax3b)\n",
    "\n",
    "indep = {(0,1):12, (1,2):1, (2,3):5, (3,4):8, (4,5):10, \n",
    "         (0,2):1, (3,5):2, (2,6):14, (1,6):3, (2,5):3, (5,7):16, (3,6):13}\n",
    "G2 = nx.from_edgelist(indep)\n",
    "nx.set_edge_attributes(G2, indep, name='weight')\n",
    "\n",
    "dx = 0\n",
    "dy = -.7\n",
    "pos2 = {k:[v[0]+dx, v[1]+dy] for k,v in pos.items()}\n",
    "\n",
    "weights2 = [G2[u][v]['weight']/4 for u,v in G2.edges()]\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=weights2, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G2, pos2, edge_labels=indep, font_size=5, alpha=.5, ax=ax3b);\n",
    "\n",
    "plt.box('on')\n",
    "ax3b.spines['left'].set_visible(True)\n",
    "ax3b.spines['left'].set_color('lightgrey')\n",
    "ax3b.spines['bottom'].set_color('lightgrey')\n",
    "ax3b.spines['right'].set_visible(True)\n",
    "ax3b.spines['right'].set_color('lightgrey')\n",
    "ax3b.spines['top'].set_visible(True)\n",
    "ax3b.spines['top'].set_color('lightgrey')\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.25, 0.45)\n",
    "\n",
    "plt.figtext(x=x3-.005, y=y3-inset_dy, s='Sample Adopter Counts', va='bottom', size=6)\n",
    "\n",
    "###### DDDD #########\n",
    "ax4 = plt.subplot(4, 1, 4)\n",
    "ax4.plot(means.index, means['match top decile semantic clustering'], color='k', markevery=(0,9))\n",
    "ax4.plot(means.index, means['inter top decile semantic clustering'], color='r', markevery=(0,9))\n",
    "\n",
    "\n",
    "ax4.set_xlim(-.2, 9.3)\n",
    "ylim = (0, .53)\n",
    "ax4.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax4.set_xlabel('Time step', fontsize=xlabel_size)\n",
    "ax4.set_xticks(range(0,10,1))\n",
    "ax4.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "# left\n",
    "ax4.set_yticks([means['inter top decile semantic clustering'].iloc[0], \n",
    "                means['match top decile semantic clustering'].iloc[0]])\n",
    "ax4.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[0]), \n",
    "                     ('%.02f' % means['match top decile semantic clustering'].iloc[0])], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax4R = ax4.twinx()\n",
    "ax4R.set_ylim(*ylim)\n",
    "ax4R.yaxis.set_label_position(\"right\")\n",
    "ax4R.set_yticks([means['inter top decile semantic clustering'].iloc[-1], \n",
    "                means['match top decile semantic clustering'].iloc[-1]])\n",
    "ax4R.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[-1]).ljust(8), \n",
    "                     '%.02f' % means['match top decile semantic clustering'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y4-.03, s='Clustering coefficient\\nof leading 10% beliefs', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x2, y=y4, s='D. Popular beliefs cluster together', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x2+textdx, y=y4-textdy, va='top', size=6,\n",
    "            s='The most popular interdepen-\\n'\\\n",
    "              'dent beliefs are tightly\\n'\\\n",
    "              'clustered, and mutually\\n'\\\n",
    "              'supportive of one\\n'\\\n",
    "              'another.')\n",
    "plt.figtext(x=right, y=y4-textdy-.07, va='top', ha='right', size=6,\n",
    "            s='In contrast,\\n'\\\n",
    "               'popular independent beliefs\\n'\\\n",
    "           'beliefs only interact by chance.')\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax4b = fig.add_axes([x3, y4-inset_dy-inset_y-.005, inset_x, inset_y])\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=[2 if v>2 else 0 for v in weights], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=[2 if v>2 else 0 for v in weights2], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "plt.box('on')\n",
    "ax4b.spines['left'].set_visible(True)\n",
    "ax4b.spines['left'].set_color('lightgrey')\n",
    "ax4b.spines['bottom'].set_color('lightgrey')\n",
    "ax4b.spines['right'].set_visible(True)\n",
    "ax4b.spines['right'].set_color('lightgrey')\n",
    "ax4b.spines['top'].set_visible(True)\n",
    "ax4b.spines['top'].set_color('lightgrey')\n",
    "plt.figtext(x=x3, y=y4-inset_dy, s='Most Popular Beliefs', va='bottom', size=6)\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.3, .5)\n",
    "\n",
    "\n",
    "\n",
    "#### Figure Title text ###\n",
    "# plt.suptitle('The Effect of Reciprocal Facilitation on Emergent Belief Structures', \n",
    "#              x=0, y=1, ha='left', va='top', fontsize=13, color='k')\n",
    "plt.figtext(x=.51, y=1, s='Independent diffusion',  ha='left', va='top', fontsize=11, color='k')\n",
    "plt.figtext(x=.49, y=1, s='|',  ha='left', va='top', fontsize=11, color='lightgrey')\n",
    "plt.figtext(x=0, y=1, s='Interdependent diffusion', va='top', ha='left', fontsize=11, color='r')\n",
    "\n",
    "plt.subplots_adjust(left=left, right=right, top=top, bottom=bottom, wspace=.6, hspace=.5)\n",
    "\n",
    "plt.savefig('Fig2_emergent_belief_structures.png', transparent=False, dpi=150)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:73: MatplotlibDeprecationWarning: Passing the fontdict parameter of _set_ticklabels() positionally is deprecated since Matplotlib 3.3; the parameter will become keyword-only two minor releases later.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 343x500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 100,\n",
    "    \"figure.facecolor\": \"white\", #\"lightyellow\",\n",
    "    \n",
    "    \"savefig.dpi\": 150,\n",
    "    \n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": \"Helvetica\",\n",
    "    \"font.size\": 8,\n",
    "    \n",
    "    \"lines.linewidth\": 1.5,\n",
    "    \"lines.marker\": \"o\",\n",
    "    \"lines.markersize\": 4,\n",
    "    \n",
    "    \"axes.spines.left\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.edgecolor\": \"grey\",\n",
    "    \"axes.labelsize\": 8,\n",
    "    \"axes.labelcolor\": \"grey\",\n",
    "    \"axes.facecolor\": \"#00000000\",\n",
    "    \n",
    "    \"xtick.color\": \"grey\",\n",
    "    \"xtick.labelsize\": 8,\n",
    "    \"xtick.major.size\": 2,\n",
    "    \n",
    "    \"ytick.major.size\": 0,\n",
    "    \"ytick.color\": \"grey\",\n",
    "    \"ytick.labelsize\": 8,\n",
    "    \n",
    "    \"text.color\": \"grey\",\n",
    "    \"text.usetex\": False\n",
    "})\n",
    "\n",
    "figsize=(3.43, 5)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "\n",
    "left = .23\n",
    "right = .63\n",
    "top = .95\n",
    "bottom = .05\n",
    "\n",
    "ylabel_size=6\n",
    "#ytick_size=6\n",
    "xtick_size=6\n",
    "xlabel_size=6\n",
    "xlabel_pad=1\n",
    "subtitle_size=8\n",
    "ytick_fontdict={'fontsize':6}\n",
    "\n",
    "inset_x = .24\n",
    "inset_y = .9*inset_x*figsize[0]/figsize[1]\n",
    "inset_dy = .03  # vertical distance btw y1 title location and top of inset\n",
    "x1, x2, x3 = .005, .005, .98-inset_x\n",
    "y1, y2, y3, y4 = .97, .72, .475, .23  # heights of subtitles\n",
    "textdx, textdy = .07, .025 # distance from the xlabels for comments\n",
    "\n",
    "####### AAAA ##########\n",
    "ax1 = plt.subplot(4, 1, 1)\n",
    "\n",
    "means['NF diffusion'].plot(ax=ax1, color='k', markevery=9)\n",
    "means['NF susceptibility'].plot(ax=ax1, color='k', linestyle='--', markevery=9)\n",
    "means['inter adopters'].plot(ax=ax1, color='r', markevery=9)\n",
    "means['inter susceptibles'].plot(ax=ax1, color='r', linestyle='--', markevery=9)\n",
    "\n",
    "ax1.set_xlim(-.3, 9.3)\n",
    "ylim = (0, 73)\n",
    "ax1.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax1.set_xlabel('Time step', fontsize=xlabel_size, labelpad=xlabel_pad)\n",
    "ax1.set_xticks(range(0,10,1))\n",
    "ax1.set_xticklabels(range(0,10,1), {\"fontsize\":xtick_size})\n",
    "\n",
    "\n",
    "# left\n",
    "ax1.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "               means['NF diffusion'].iloc[0]])\n",
    "ax1.set_yticklabels(\n",
    "           [('%.0f%%' % means['NF susceptibility'].iloc[0]).rjust(7), \n",
    "            ('%.0f%%' % means['NF diffusion'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "# lefter left\n",
    "ax1L = ax1.twinx()\n",
    "ax1L.set_ylim(*ylim)\n",
    "ax1L.yaxis.set_label_position('left')\n",
    "ax1L.yaxis.set_ticks_position('left')\n",
    "ax1L.tick_params(axis='y', labelcolor=\"k\")\n",
    "ax1L.set_yticks([means['NF susceptibility'].iloc[0], \n",
    "                 means['NF diffusion'].iloc[0]])\n",
    "ax1L.set_yticklabels(['Susceptible        ', \n",
    "                        'Adopters       '], fontdict=ytick_fontdict)\n",
    "\n",
    "# right\n",
    "ax1R = ax1.twinx()\n",
    "ax1R.set_ylim(*ylim)\n",
    "ax1R.yaxis.set_label_position(\"right\")\n",
    "ax1R.set_yticks([means['NF susceptibility'].iloc[-1], \n",
    "                means['NF diffusion'].iloc[-1],\n",
    "                means['inter susceptibles'].iloc[-1], \n",
    "                means['inter adopters'].iloc[-1]\n",
    "               ])\n",
    "ax1R.set_yticklabels([('%.0f%%' % means['NF susceptibility'].iloc[-1]).ljust(8), \n",
    "                      '%.0f%%' % means['NF diffusion'].iloc[-1],\n",
    "                      '%.0f%%' % means['inter susceptibles'].iloc[-1], \n",
    "                      '%.0f%%' % means['inter adopters'].iloc[-1]\n",
    "                     ], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y1-0.03, s='Average % of population', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x1, y=y1, s='A. Susceptibility and adoption coevolve', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x3-.03, y=y1-textdy+.01, va='top', size=6,\n",
    "            s='Interdependent beliefs\\n'\\\n",
    "              'spread further by creating\\n'\\\n",
    "              'susceptibility to one\\n'\\\n",
    "              'another.')\n",
    "plt.figtext(x=x3-.03, y=y1-inset_y+.03, va='top', size=6, ha='left',\n",
    "            s='In contrast, susceptibility\\n'\\\n",
    "              'is fixed for independent\\n'\\\n",
    "              'beliefs, and so adoption\\n'\\\n",
    "              'is limited.')\n",
    "\n",
    "\n",
    "######### BBBB ########\n",
    "ax2 = plt.subplot(4, 1, 2)\n",
    "means['match net initial suscep correlation'].plot(ax=ax2, color='k', markevery=(1,8))\n",
    "means['inter net initial suscep correlation'].plot(ax=ax2, color='r', markevery=(1,8))\n",
    "\n",
    "\n",
    "ax2.plot([1,1], \n",
    "         [means['match net initial suscep correlation'].loc[1], \n",
    "          means['inter net initial suscep correlation'].loc[1]], \n",
    "         color='white', markersize=2, zorder=20, linewidth=0)  # white center of first point in b\n",
    "\n",
    "ax2.set_xlim(-.3, 9.3)\n",
    "ylim = (0, .77)\n",
    "ax2.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax2.set_xlabel('Time step', fontsize=xlabel_size, labelpad=xlabel_pad)\n",
    "ax2.set_xticks(range(0,10,1))\n",
    "ax2.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "# left\n",
    "ax2.set_yticks([means['inter net initial suscep correlation'].iloc[1], \n",
    "                means['match net initial suscep correlation'].iloc[1]])\n",
    "ax2.set_yticklabels(\n",
    "    [('%.02f' % means['inter net initial suscep correlation'].iloc[1]).rjust(8), \n",
    "     ('%.02f' % means['match net initial suscep correlation'].iloc[1]).rjust(8)], fontdict=ytick_fontdict\n",
    ")\n",
    "\n",
    "# right\n",
    "ax2R = ax2.twinx()\n",
    "ax2R.set_ylim(*ylim)\n",
    "ax2R.yaxis.set_label_position(\"right\")\n",
    "\n",
    "ax2R.set_yticks([means['inter net initial suscep correlation'].iloc[-1], \n",
    "                means['match net initial suscep correlation'].iloc[-1]])\n",
    "ax2R.set_yticklabels([('%.02f' % means['inter net initial suscep correlation'].iloc[-1]).ljust(8), \n",
    "                     ('%.02f' % means['match net initial suscep correlation'].iloc[-1]).ljust(8)], \n",
    "                     fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y2-0.03, s='Adoption correlation with\\ninitial susceptibility', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x2, y=y2, s='B. Adoption becomes unpredictable from initial susceptibility', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x2+textdx, y=y2-textdy, va='top', size=6,\n",
    "            s='The popularity of an indepen-\\n'\\\n",
    "              'dent belief is easy to\\n'\\\n",
    "              'predict from how many\\n'\\\n",
    "              'people are susceptible\\n'\\\n",
    "              'to begin with.')\n",
    "plt.figtext(x=right, y=y2-textdy-.03, va='top', size=6, ha='right',\n",
    "            s='In contrast, adoption\\n'\\\n",
    "              'of interdependent beliefs\\n'\\\n",
    "              'is only weakly influenced\\n'\\\n",
    "              'by initial susceptibility.')\n",
    "\n",
    "\n",
    "#### inset graph\n",
    "ax2b = fig.add_axes([x3,y2-inset_dy-inset_y, inset_x, inset_y])\n",
    "ax2b.plot(inter_sus[seed.index], inter_diff[seed.index], 'r.', markersize=1, alpha=.4)\n",
    "ax2b.plot(indep_suscep[seed.index], indep_diff[seed.index], 'k.', markersize=1, alpha=.4)\n",
    "plt.ylim(0, max(max(indep_diff), max(inter_diff)))\n",
    "plt.xlim(0, max(max(inter_sus), max(indep_suscep)))\n",
    "ax2b.spines['left'].set_visible(True)\n",
    "ax2b.spines['left'].set_color('lightgrey')\n",
    "ax2b.spines['bottom'].set_color('lightgrey')\n",
    "ax2b.set_xticks([])\n",
    "ax2b.set_yticks([])\n",
    "ax2b.set_xlabel('Initially Susceptible', fontsize=6)\n",
    "ax2b.set_ylabel('Final Adopters', fontsize=6, va='center')\n",
    "\n",
    "\n",
    "\n",
    "# ######## CCCC ############\n",
    "ax3 = plt.subplot(4, 1, 3)\n",
    "ax3.plot(means.index, means['match best connected belief corr'], color='k', markevery=(0,9))\n",
    "ax3.plot(means.index, means['inter best connected belief corr'], color='r', markevery=(0,9))\n",
    "\n",
    "ax3.set_xlim(-.3, 9.3)\n",
    "ylim = (0, .6)\n",
    "ax3.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax3.set_xlabel('Time step', fontsize=xlabel_size, labelpad=xlabel_pad)\n",
    "ax3.set_xticks(range(0,10,1))\n",
    "ax3.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "\n",
    "# left\n",
    "ax3.set_yticks([means['inter best connected belief corr'].iloc[0], \n",
    "               means['match best connected belief corr'].iloc[0]])\n",
    "ax3.set_yticklabels(\n",
    "           [('%.02f' % means['inter best connected belief corr'].iloc[0]).rjust(7), \n",
    "            ('%.02f' % means['match best connected belief corr'].iloc[0]).rjust(8)], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax3R = ax3.twinx()\n",
    "ax3R.set_ylim(*ylim)\n",
    "ax3R.yaxis.set_label_position(\"right\")\n",
    "ax3R.set_yticks([means['inter best connected belief corr'].iloc[-1], \n",
    "                means['match best connected belief corr'].iloc[-1]])\n",
    "ax3R.set_yticklabels([('%.02f' % means['inter best connected belief corr'].iloc[-1]).ljust(8), \n",
    "                      '%.02f' % means['match best connected belief corr'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y3-.03, s='Correlation with most\\npopular adjacent belief', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x1, y=y3, s='C. Adjacent beliefs have correlated adoption', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x1+textdx, y=y3-textdy, va='top', size=6,\n",
    "            s='The popularity of an interdep-\\n'\\\n",
    "              'endent belief becomes\\n'\\\n",
    "              'correlated with the\\n'\\\n",
    "              'popularity of the \\n'\\\n",
    "              'most successful\\n'\\\n",
    "              'belief it interacts\\n'\\\n",
    "              'with.')\n",
    "plt.figtext(x=right, y=y3-textdy-.09, va='top', size=6, ha='right',\n",
    "            s='In contrast, independent\\n'\\\n",
    "              'beliefs are uncorrelated\\n'\\\n",
    "                'with adjacent beliefs.')\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax3b = fig.add_axes([x3, y3-inset_dy-inset_y-.005, inset_x, inset_y])\n",
    "inter = {(0,1):16, (1,2):13, (2,3):3, (3,4):8, (4,5):1, \n",
    "         (0,2):11, (3,5):5, (2,6):14, (1,6):10, (5,2):1,(5,7):1, (3,6):3}\n",
    "G = nx.from_edgelist(inter)\n",
    "nx.set_edge_attributes(G, inter, name='weight')\n",
    "\n",
    "pos = {\n",
    "    0: [ 0.05337489, -1.        ],\n",
    "    1: [-0.38712707, -0.80563193],\n",
    "    2: [-0.03981886, -0.30618229],\n",
    "    3: [-0.21645782,  0.26372263],\n",
    "    4: [-0.09271407,  0.85974724],\n",
    "    5: [0.31097159, 0.43462805],\n",
    "    6: [-0.55557835, -0.27382355],\n",
    "    7: [0.42734968, 0.96753985]\n",
    "}\n",
    "def rotate(x, rotation=0):\n",
    "    length = sqrt(x[0]**2 + x[1]**2)\n",
    "    angle = np.arctan2(x[0], x[1])\n",
    "    new_angle = angle+rotation\n",
    "    return [length*cos(new_angle), length*sin(new_angle)]\n",
    "pos = {k: rotate(v, -.3) for k, v in pos.items()}\n",
    "\n",
    "edges = G.edges()\n",
    "weights = [G[u][v]['weight']/4 for u,v in edges]\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=weights, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels=inter, font_size=5, alpha=1, font_color='r', ax=ax3b)\n",
    "\n",
    "indep = {(0,1):12, (1,2):1, (2,3):5, (3,4):8, (4,5):10, \n",
    "         (0,2):1, (3,5):2, (2,6):14, (1,6):3, (2,5):3, (5,7):16, (3,6):13}\n",
    "G2 = nx.from_edgelist(indep)\n",
    "nx.set_edge_attributes(G2, indep, name='weight')\n",
    "\n",
    "dx = 0\n",
    "dy = -.7\n",
    "pos2 = {k:[v[0]+dx, v[1]+dy] for k,v in pos.items()}\n",
    "\n",
    "weights2 = [G2[u][v]['weight']/4 for u,v in G2.edges()]\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=weights2, node_size=0, with_labels=False, ax=ax3b)\n",
    "nx.draw_networkx_edge_labels(G2, pos2, edge_labels=indep, font_size=5, alpha=.5, ax=ax3b);\n",
    "\n",
    "plt.box('on')\n",
    "ax3b.spines['left'].set_visible(True)\n",
    "ax3b.spines['left'].set_color('lightgrey')\n",
    "ax3b.spines['bottom'].set_color('lightgrey')\n",
    "ax3b.spines['right'].set_visible(True)\n",
    "ax3b.spines['right'].set_color('lightgrey')\n",
    "ax3b.spines['top'].set_visible(True)\n",
    "ax3b.spines['top'].set_color('lightgrey')\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.25, 0.45)\n",
    "\n",
    "plt.figtext(x=x3-.005, y=y3-inset_dy, s='Sample Adopter Counts', va='bottom', size=6)\n",
    "\n",
    "###### DDDD #########\n",
    "ax4 = plt.subplot(4, 1, 4)\n",
    "ax4.plot(means.index, means['match top decile semantic clustering'], color='k', markevery=(0,9))\n",
    "ax4.plot(means.index, means['inter top decile semantic clustering'], color='r', markevery=(0,9))\n",
    "\n",
    "\n",
    "ax4.set_xlim(-.3, 9.3)\n",
    "ylim = (0, .53)\n",
    "ax4.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax4.set_xlabel('Time step', fontsize=xlabel_size, labelpad=xlabel_pad)\n",
    "ax4.set_xticks(range(0,10,1))\n",
    "ax4.set_xticklabels(range(0,10,1), fontsize=xtick_size)\n",
    "\n",
    "# left\n",
    "ax4.set_yticks([means['inter top decile semantic clustering'].iloc[0], \n",
    "                means['match top decile semantic clustering'].iloc[0]])\n",
    "ax4.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[0]), \n",
    "                     ('%.02f' % means['match top decile semantic clustering'].iloc[0])], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax4R = ax4.twinx()\n",
    "ax4R.set_ylim(*ylim)\n",
    "ax4R.yaxis.set_label_position(\"right\")\n",
    "ax4R.set_yticks([means['inter top decile semantic clustering'].iloc[-1], \n",
    "                means['match top decile semantic clustering'].iloc[-1]])\n",
    "ax4R.set_yticklabels([('%.02f' % means['inter top decile semantic clustering'].iloc[-1]).ljust(8), \n",
    "                     '%.02f' % means['match top decile semantic clustering'].iloc[-1]], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y4-.03, s='Clustering coefficient\\nof leading 10% beliefs', rotation=90, va='top', fontsize=ylabel_size)\n",
    "plt.figtext(x=x2, y=y4, s='D. Popular beliefs cluster together', va='top', color='k', size=subtitle_size)\n",
    "plt.figtext(x=x2+textdx, y=y4-textdy, va='top', size=6,\n",
    "            s='The most popular interdepen-\\n'\\\n",
    "              'dent beliefs are tightly\\n'\\\n",
    "              'clustered, and mutually\\n'\\\n",
    "              'supportive of one\\n'\\\n",
    "              'another.')\n",
    "plt.figtext(x=right, y=y4-textdy-.07, va='top', ha='right', size=6,\n",
    "            s='In contrast,\\n'\\\n",
    "               'popular independent beliefs\\n'\\\n",
    "           'beliefs only interact by chance.')\n",
    "\n",
    "\n",
    "#### Inset ####\n",
    "ax4b = fig.add_axes([x3, y4-inset_dy-inset_y-.005, inset_x, inset_y])\n",
    "\n",
    "nx.draw_networkx(G, pos, edgelist=edges, edge_color='r', width=[2 if v>2 else 0 for v in weights], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "\n",
    "nx.draw_networkx(G2, pos2, edgelist=edges, edge_color='k', width=[2 if v>2 else 0 for v in weights2], \n",
    "                 node_size=0, with_labels=False, ax=ax4b)\n",
    "plt.box('on')\n",
    "ax4b.spines['left'].set_visible(True)\n",
    "ax4b.spines['left'].set_color('lightgrey')\n",
    "ax4b.spines['bottom'].set_color('lightgrey')\n",
    "ax4b.spines['right'].set_visible(True)\n",
    "ax4b.spines['right'].set_color('lightgrey')\n",
    "ax4b.spines['top'].set_visible(True)\n",
    "ax4b.spines['top'].set_color('lightgrey')\n",
    "plt.figtext(x=x3, y=y4-inset_dy, s='Most Popular Beliefs', va='bottom', size=6)\n",
    "plt.xlim(-1.2, 1.2)\n",
    "plt.ylim(-1.3, .5)\n",
    "\n",
    "\n",
    "\n",
    "#### Figure Title text ###\n",
    "# plt.suptitle('The Effect of Reciprocal Facilitation on Emergent Belief Structures', \n",
    "#              x=0, y=1, ha='left', va='top', fontsize=13, color='k')\n",
    "plt.figtext(x=.48, y=1, s='Independent diffusion',  ha='left', va='top', fontsize=10, color='k')\n",
    "plt.figtext(x=.455, y=1, s='|',  ha='left', va='top', fontsize=10, color='lightgrey')\n",
    "plt.figtext(x=0, y=1, s='Interdependent diffusion', va='top', ha='left', fontsize=10, color='r')\n",
    "\n",
    "plt.subplots_adjust(left=left, right=right, top=top, bottom=bottom, wspace=.6, hspace=.5)\n",
    "\n",
    "plt.savefig('Fig2_emergent_belief_structures.png', transparent=False, dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 650x350 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize=(6.5, 3.5)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "# sociological science:\n",
    "# 8 pt font in tables\n",
    "# 10 pt font in text\n",
    "# 1 inch margins\n",
    "\n",
    "x1, x2 = 0, .58\n",
    "y1 = .84\n",
    "textdx, textdy = .03, .08\n",
    "\n",
    "ytick_fontdict={'fontsize':7}\n",
    "\n",
    "####### left ##########\n",
    "ax1 = plt.subplot(1, 2, 1)\n",
    "\n",
    "means['match correlation similarity 95%'].plot(ax=ax1, color='k', markevery=9)\n",
    "means['match correlation similarity 5%'].plot(ax=ax1, linestyle='-', color='k', markevery=9)\n",
    "means['inter correlation similarity 95%'].plot(ax=ax1, color='r', markevery=9)\n",
    "means['inter correlation similarity 5%'].plot(ax=ax1, linestyle='-', color='r', markevery=9)\n",
    "\n",
    "plt.hlines(0, 0, 9, linewidth=1, color='grey')\n",
    "\n",
    "ax1.set_xlim(-.2, 9.2)\n",
    "ylim = (-.26, .36)\n",
    "ax1.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax1.set_xlabel('Time step')\n",
    "ax1.set_xticks(range(0,10,1))\n",
    "ax1.set_xticklabels(range(0,10,1))\n",
    "\n",
    "ax1.set_yticks([means['inter correlation similarity 95%'].iloc[0],\n",
    "                0,\n",
    "                means['inter correlation similarity 5%'].iloc[0]])\n",
    "ax1.set_yticklabels([('%.02f' % means['inter correlation similarity 95%'].iloc[0]),\n",
    "                     0,\n",
    "                     ('%.02f' % means['inter correlation similarity 5%'].iloc[0])]\n",
    "                    , fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "\n",
    "# lefter left\n",
    "ax1L = ax1.twinx()\n",
    "ax1L.set_ylim(*ylim)\n",
    "ax1L.yaxis.set_label_position('left')\n",
    "ax1L.yaxis.set_ticks_position('left')\n",
    "ax1L.tick_params(axis='y', labelcolor=\"k\")\n",
    "ax1L.set_yticks([means['inter correlation similarity 95%'].iloc[0],\n",
    "                means['inter correlation similarity 5%'].iloc[0]])\n",
    "# ax1L.set_yticklabels(['$95^{th}$ percentile         ', \n",
    "#                      '$5^{th}$ percentile          '], fontdict=ytick_fontdict)\n",
    "ax1L.set_yticklabels(['Within-Camp         ', \n",
    "                     'Across-Camp          '], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax1R = ax1.twinx()\n",
    "ax1R.set_ylim(*ylim)\n",
    "ax1R.yaxis.set_label_position(\"right\")\n",
    "ax1R.set_yticks([means['inter correlation similarity 95%'].iloc[-1], \n",
    "                means['match correlation similarity 95%'].iloc[-1],\n",
    "                0,\n",
    "                means['match correlation similarity 5%'].iloc[-1],\n",
    "                means['inter correlation similarity 5%'].iloc[-1],\n",
    "               ])\n",
    "ax1R.set_yticklabels([('%.02f' % means['inter correlation similarity 95%'].iloc[-1]), \n",
    "                     ('%.02f' % means['match correlation similarity 95%'].iloc[-1]),\n",
    "                     0,\n",
    "                     ('%.02f' % means['match correlation similarity 5%'].iloc[-1]), \n",
    "                     ('%.02f' % means['inter correlation similarity 5%'].iloc[-1]), \n",
    "                    ], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y1, va='top', color='k', size=10,\n",
    "            s='A: \"Camps\" become more distinct')\n",
    "plt.figtext(x=x1, y=y1-.06, rotation=90, va='top',\n",
    "            s='Pairwise correlation between individuals')\n",
    "\n",
    "plt.figtext(x=.48, y=.74, va='top', ha='right', size=6,\n",
    "            s='With interdependent \\ndiffusion, the most similar \\nindividuals become even \\nmore alike.')\n",
    "plt.figtext(x=.48, y=.555, va='top', ha='right', size=6, \n",
    "            s='In contrast, independent beliefs \\ninitially increase similarity within \\ncamps, but the effect fades as \\nbeliefs spread more broadly.')\n",
    "plt.figtext(x=.48, y=.19, va='top', ha='right', size=6, \n",
    "            s='Interdependence also amplifies \\ndifferences across camps.')\n",
    "\n",
    "plt.figtext(x=x1+textdx+.005, y=.47, va='top', ha='left', size=6, \n",
    "            s=\"($95^{th}$ percentile)\")\n",
    "plt.figtext(x=x1+textdx+.005, y=.275, va='top', ha='left', size=6, \n",
    "            s=\"($5^{th}$ percentile)\")\n",
    "\n",
    "\n",
    "######### Right #########\n",
    "ax2 = plt.subplot(1, 2, 2)\n",
    "\n",
    "means['match percent explained variance'].plot(ax=ax2, color='k', markevery=9)\n",
    "means['inter percent explained variance'].plot(ax=ax2, color='r', markevery=9)\n",
    "\n",
    "ax2.set_xlim(-.2, 9.2)\n",
    "ylim = (0, 18)\n",
    "ax2.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax2.set_xlabel('Time step')\n",
    "ax2.set_xticks(range(0,10,1))\n",
    "ax2.set_xticklabels(range(0,10,1))\n",
    "\n",
    "# left\n",
    "ax2.set_yticks([#2.5,\n",
    "                means['inter percent explained variance'].iloc[0], \n",
    "                means['match percent explained variance'].iloc[0]])\n",
    "ax2.set_yticklabels([#'2.5%',\n",
    "                     ('%.0f%%' % means['inter percent explained variance'].iloc[0]), \n",
    "                     ('%.0f%%' % means['match percent explained variance'].iloc[0])], fontdict=ytick_fontdict)\n",
    "\n",
    "# right\n",
    "ax2R = ax2.twinx()\n",
    "ax2R.set_ylim(*ylim)\n",
    "ax2R.yaxis.set_label_position(\"right\")\n",
    "\n",
    "ax2R.set_yticks([#2.5, \n",
    "                 means['inter percent explained variance'].iloc[-1], \n",
    "                 means['match percent explained variance'].iloc[-1]])\n",
    "ax2R.set_yticklabels([#'2.5%',\n",
    "                      ('%.0f%%' % means['inter percent explained variance'].iloc[-1]), \n",
    "                     ('%.0f%%' % means['match percent explained variance'].iloc[-1])], \n",
    "                     fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x2, y=y1, va='top', color='k', size=10, \n",
    "            s='B: Beliefs align along an axis')\n",
    "plt.figtext(x=x2-.003, y=y1-.06, rotation=90, va='top', \n",
    "            s='Percent of variation falling along a single axis')\n",
    "\n",
    "plt.figtext(x=x2+textdx, y=y1-textdy+.015, va='top', ha='left', size=6, \n",
    "            s='When beliefs interact, individuals align\\nalong a left-right axis and beliefs\\nbecome associated with each other.')\n",
    "plt.figtext(x=.95, y=.325, va='top', ha='right', size=6, \n",
    "            s='In contrast, independent \\nbeliefs are more evenly \\nmixed in the population,\\nand a left-right axis \\nis less obvious.')\n",
    "\n",
    "\n",
    "###### Inset ####\n",
    "n_pts = 600\n",
    "\n",
    "insize = .6\n",
    "ax3a = fig.add_axes([x2+.105, .175, \n",
    "                     insize/figsize[0], insize/figsize[1]])\n",
    "\n",
    "\n",
    "dmeans = [0, 0]  \n",
    "dstds = [.3, .25]\n",
    "dcorr = .001         # correlation\n",
    "dcovs = [[dstds[0]**2          , dstds[0]*dstds[1]*dcorr], \n",
    "        [dstds[0]*dstds[1]*dcorr,           dstds[1]**2]] \n",
    "\n",
    "xs, ys = np.random.multivariate_normal(dmeans, dcovs, n_pts).T\n",
    "ax3a.plot(xs, ys, '.', markersize=2, color='k', alpha=.1)\n",
    "\n",
    "ax3a.set_xticks([])\n",
    "ax3a.set_yticks([])\n",
    "ax3a.spines['bottom'].set_visible(False)\n",
    "plt.vlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "plt.hlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "ax3a.set_xlim(-1,1)\n",
    "ax3a.set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "ax3b = fig.add_axes([.95-insize/figsize[0]+.005, .625, \n",
    "                     insize/figsize[0], insize/figsize[1]])\n",
    "\n",
    "dstds = [.4, .15]\n",
    "dcorr = .001         # correlation\n",
    "dcovs = [[dstds[0]**2          , dstds[0]*dstds[1]*dcorr], \n",
    "        [dstds[0]*dstds[1]*dcorr,           dstds[1]**2]] \n",
    "\n",
    "xs, ys = np.random.multivariate_normal(dmeans, dcovs, n_pts).T\n",
    "ax3b.plot(xs, ys, '.', markersize=2, color='r', alpha=.1)\n",
    "\n",
    "\n",
    "ax3b.set_xticks([])\n",
    "ax3b.set_yticks([])\n",
    "ax3b.spines['bottom'].set_visible(False)\n",
    "plt.vlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "plt.hlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "ax3b.set_xlim(-1,1)\n",
    "ax3b.set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "######### Figure Text ##########\n",
    "\n",
    "\n",
    "plt.suptitle('The Effect of Agreement Cascades on Emergent Polarization', \n",
    "             x=0, y=1, ha='left', va='top', fontsize=13, color='k')\n",
    "plt.figtext(x=0, y=.935, s='Independent diffusion',  ha='left', va='top', fontsize=11, color='k')\n",
    "plt.figtext(x=.265, y=.935, s='|',  ha='left', va='top', fontsize=11, color='lightgrey')\n",
    "plt.figtext(x=.28, y=.935, s='Interdependent diffusion', va='top', ha='left', fontsize=11, color='r')\n",
    "\n",
    "plt.subplots_adjust(left=.185, right=.95, top=.79, bottom=.12, wspace=.6)\n",
    "\n",
    "plt.savefig('Fig3_emergent_polarization', transparent=False, dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 343x280 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 100,\n",
    "    \"figure.facecolor\": \"white\", #\"lightyellow\",\n",
    "    \n",
    "    \"savefig.dpi\": 150,\n",
    "    \n",
    "    \"font.family\": \"sans-serif\",\n",
    "    \"font.sans-serif\": \"Helvetica\",\n",
    "    \"font.size\": 8,\n",
    "    \n",
    "    \"lines.linewidth\": 1.5,\n",
    "    \"lines.marker\": \"o\",\n",
    "    \"lines.markersize\": 4,\n",
    "    \n",
    "    \"axes.spines.left\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.edgecolor\": \"grey\",\n",
    "    \"axes.labelsize\": 6,\n",
    "    \"axes.labelcolor\": \"grey\",\n",
    "    \"axes.facecolor\": \"#00000000\",\n",
    "    \n",
    "    \"xtick.color\": \"grey\",\n",
    "    \"xtick.labelsize\": 6,\n",
    "    \"xtick.major.size\": 1.5,\n",
    "    'xtick.major.pad': 1,\n",
    "    \n",
    "    \"ytick.major.size\": 0,\n",
    "    \"ytick.color\": \"grey\",\n",
    "    \"ytick.labelsize\": 5,\n",
    "    'ytick.major.pad': 2,\n",
    "    \n",
    "    \"text.color\": \"grey\",\n",
    "    \"text.usetex\": False\n",
    "})\n",
    "\n",
    "left = .3\n",
    "right = .63\n",
    "top = .89\n",
    "bottom = .08\n",
    "\n",
    "figsize=(3.43, 2.8)\n",
    "fig = plt.figure(figsize=figsize)\n",
    "\n",
    "x1, x2 = 0.005, .58\n",
    "y1, y2 = .94, .45\n",
    "textdx, textdy = .07, .05\n",
    "\n",
    "ytick_fontdict={'fontsize':6}\n",
    "\n",
    "####### AAAA ##########\n",
    "ax1 = plt.subplot(2, 1, 1)\n",
    "\n",
    "means['match correlation similarity 95%'].plot(ax=ax1, color='k', markevery=9)\n",
    "means['match correlation similarity 5%'].plot(ax=ax1, linestyle='-', color='k', markevery=9)\n",
    "means['inter correlation similarity 95%'].plot(ax=ax1, color='r', markevery=9)\n",
    "means['inter correlation similarity 5%'].plot(ax=ax1, linestyle='-', color='r', markevery=9)\n",
    "\n",
    "plt.hlines(0, 0, 9, linewidth=1, color='grey')\n",
    "\n",
    "ax1.set_xlim(-.3, 9.3)\n",
    "ylim = (-.26, .36)\n",
    "ax1.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax1.set_xlabel('Time step', fontsize=6, labelpad=1)\n",
    "ax1.set_xticks(range(0,10,1))\n",
    "ax1.set_xticklabels(range(0,10,1))\n",
    "\n",
    "\n",
    "ax1.set_yticks([means['inter correlation similarity 95%'].iloc[0],\n",
    "                0,\n",
    "                means['inter correlation similarity 5%'].iloc[0]])\n",
    "ax1.set_yticklabels([('%.02f' % means['inter correlation similarity 95%'].iloc[0]),\n",
    "                     0,\n",
    "                     ('%.02f' % means['inter correlation similarity 5%'].iloc[0])]\n",
    "                    , fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "\n",
    "# lefter left\n",
    "ax1L = ax1.twinx()\n",
    "ax1L.set_ylim(*ylim)\n",
    "ax1L.yaxis.set_label_position('left')\n",
    "ax1L.yaxis.set_ticks_position('left')\n",
    "ax1L.tick_params(axis='y', labelcolor=\"k\")\n",
    "ax1L.set_yticks([means['inter correlation similarity 95%'].iloc[0],\n",
    "                means['inter correlation similarity 5%'].iloc[0]])\n",
    "# ax1L.set_yticklabels(['$95^{th}$ percentile         ', \n",
    "#                      '$5^{th}$ percentile          '], fontdict=ytick_fontdict)\n",
    "ax1L.set_yticklabels(['Within-Camp         ', \n",
    "                      'Across-Camp         '], fontdict=ytick_fontdict)\n",
    "\n",
    "\n",
    "# right\n",
    "ax1R = ax1.twinx()\n",
    "ax1R.set_ylim(*ylim)\n",
    "ax1R.yaxis.set_label_position(\"right\")\n",
    "ax1R.set_yticks([means['inter correlation similarity 95%'].iloc[-1], \n",
    "                means['match correlation similarity 95%'].iloc[-1],\n",
    "                0,\n",
    "                means['match correlation similarity 5%'].iloc[-1],\n",
    "                means['inter correlation similarity 5%'].iloc[-1],\n",
    "               ])\n",
    "ax1R.set_yticklabels([('%.02f' % means['inter correlation similarity 95%'].iloc[-1]), \n",
    "                     ('%.02f' % means['match correlation similarity 95%'].iloc[-1]),\n",
    "                     0,\n",
    "                     ('%.02f' % means['match correlation similarity 5%'].iloc[-1]), \n",
    "                     ('%.02f' % means['inter correlation similarity 5%'].iloc[-1]), \n",
    "                    ], fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y1, va='top', color='k', fontsize=8,\n",
    "            s='A: \"Camps\" become more distinct')\n",
    "plt.figtext(x=x1, y=y1-.04, rotation=90, va='top', fontsize=6,\n",
    "            s='Pairwise correlation between\\nrandomly chosen individuals')\n",
    "\n",
    "plt.figtext(x=right+textdx, y=y1-.04, va='top', ha='left', size=6,\n",
    "            s='Interdependent diffusion\\nleads the most similar\\nindividuals to become\\neven more alike.')\n",
    "# plt.figtext(x=right+textdx, y=y1-.15, va='top', ha='left', size=6, \n",
    "#             s='In contrast, independen\\nbeliefs initially increase\\nsimilarity within camps,\\nbut the effect fades as  beliefs spread more broadly.')\n",
    "plt.figtext(x=right+textdx, y=y1-.265, va='top', ha='left', size=6, \n",
    "            s='Interdependence\\namplifies differences\\nacross camps.')\n",
    "\n",
    "plt.figtext(x=left-.065, y=.73, va='top', ha='right', size=6, \n",
    "            s=\"($95^{th}$ percentile)\")\n",
    "plt.figtext(x=left-.065, y=.635, va='top', ha='right', size=6, \n",
    "            s=\"($5^{th}$ percentile)\")\n",
    "\n",
    "\n",
    "######### BBBB #########\n",
    "ax2 = plt.subplot(2, 1, 2)\n",
    "\n",
    "means['match percent explained variance'].plot(ax=ax2, color='k', markevery=9)\n",
    "means['inter percent explained variance'].plot(ax=ax2, color='r', markevery=9)\n",
    "\n",
    "ax2.set_xlim(-.3, 9.3)\n",
    "ylim = (0, 15)\n",
    "ax2.set_ylim(*ylim)\n",
    "\n",
    "# bottom\n",
    "ax2.set_xlabel('Time step', fontsize=6, labelpad=1)\n",
    "ax2.set_xticks(range(0,10,1))\n",
    "ax2.set_xticklabels(range(0,10,1))\n",
    "\n",
    "# left\n",
    "ax2.set_yticks([#2.5,\n",
    "                means['inter percent explained variance'].iloc[0], \n",
    "                means['match percent explained variance'].iloc[0]])\n",
    "ax2.set_yticklabels([#'2.5%',\n",
    "                     ('%.0f%%' % means['inter percent explained variance'].iloc[0]), \n",
    "                     ('%.0f%%' % means['match percent explained variance'].iloc[0])], fontdict=ytick_fontdict)\n",
    "\n",
    "# right\n",
    "ax2R = ax2.twinx()\n",
    "ax2R.set_ylim(*ylim)\n",
    "ax2R.yaxis.set_label_position(\"right\")\n",
    "\n",
    "ax2R.set_yticks([#2.5, \n",
    "                 means['inter percent explained variance'].iloc[-1], \n",
    "                 means['match percent explained variance'].iloc[-1]])\n",
    "ax2R.set_yticklabels([#'2.5%',\n",
    "                      ('%.0f%%' % means['inter percent explained variance'].iloc[-1]), \n",
    "                     ('%.0f%%' % means['match percent explained variance'].iloc[-1])], \n",
    "                     fontdict=ytick_fontdict)\n",
    "\n",
    "plt.figtext(x=x1, y=y2, va='top', color='k', fontsize=8, \n",
    "            s='B: Beliefs align along an axis')\n",
    "plt.figtext(x=x1, y=y2-.06, rotation=90, va='top', \n",
    "            s='Percent of variation falling\\nalong a single axis', fontsize=6)\n",
    "\n",
    "plt.figtext(x=x1+textdx, y=y2-textdy, va='top', ha='left', fontsize=6, \n",
    "            s='When beliefs interact, indiv-\\niduals align along a left-\\nright axis and beliefs\\nbecome associated\\nwith each other.')\n",
    "plt.figtext(x=1-insize/figsize[0]-.025, y=.19, va='top', ha='right', fontsize=6, \n",
    "            s='In contrast, independent beliefs\\nare more evenly mixed in the population,\\nand a left-right axis is less obvious.')\n",
    "\n",
    "\n",
    "###### Inset ####\n",
    "n_pts = 600\n",
    "\n",
    "insize = .6\n",
    "ax3a = fig.add_axes([1-insize/figsize[0]-.005, .07, \n",
    "                     insize/figsize[0], insize/figsize[1]])\n",
    "\n",
    "\n",
    "dmeans = [0, 0]  \n",
    "dstds = [.3, .25]\n",
    "dcorr = .001         # correlation\n",
    "dcovs = [[dstds[0]**2          , dstds[0]*dstds[1]*dcorr], \n",
    "        [dstds[0]*dstds[1]*dcorr,           dstds[1]**2]] \n",
    "\n",
    "xs, ys = np.random.multivariate_normal(dmeans, dcovs, n_pts).T\n",
    "ax3a.plot(xs, ys, '.', markersize=2, color='k', alpha=.1)\n",
    "\n",
    "ax3a.set_xticks([])\n",
    "ax3a.set_yticks([])\n",
    "ax3a.spines['bottom'].set_visible(False)\n",
    "plt.vlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "plt.hlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "ax3a.set_xlim(-1,1)\n",
    "ax3a.set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "ax3b = fig.add_axes([right+.07, .23, \n",
    "                     insize/figsize[0], insize/figsize[1]])\n",
    "\n",
    "dstds = [.4, .15]\n",
    "dcorr = .001         # correlation\n",
    "dcovs = [[dstds[0]**2          , dstds[0]*dstds[1]*dcorr], \n",
    "        [dstds[0]*dstds[1]*dcorr,           dstds[1]**2]] \n",
    "\n",
    "xs, ys = np.random.multivariate_normal(dmeans, dcovs, n_pts).T\n",
    "ax3b.plot(xs, ys, '.', markersize=2, color='r', alpha=.1)\n",
    "\n",
    "\n",
    "ax3b.set_xticks([])\n",
    "ax3b.set_yticks([])\n",
    "ax3b.spines['bottom'].set_visible(False)\n",
    "plt.vlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "plt.hlines(0, -1, 1, colors='lightgrey', linewidth=1)\n",
    "ax3b.set_xlim(-1,1)\n",
    "ax3b.set_ylim(-1,1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "######### Figure Text ##########\n",
    "\n",
    "\n",
    "# plt.suptitle('The Effect of Agreement Cascades on Emergent Polarization', \n",
    "#              x=0, y=1, ha='left', va='top', fontsize=13, color='k')\n",
    "plt.figtext(x=.48, y=.995, s='Independent diffusion',  ha='left', va='top', fontsize=10, color='k')\n",
    "plt.figtext(x=.455, y=.995, s='|',  ha='left', va='top', fontsize=10, color='lightgrey')\n",
    "plt.figtext(x=0, y=.995, s='Interdependent diffusion', va='top', ha='left', fontsize=10, color='r')\n",
    "\n",
    "plt.subplots_adjust(left=left, right=right, top=top, bottom=bottom, hspace=.5)\n",
    "\n",
    "plt.savefig('Fig3_emergent_polarization.png', transparent=False, dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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