Analysis.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "e3865c49",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: multiprocess in /opt/homebrew/anaconda3/envs/pystan_source/lib/python3.11/site-packages (0.70.14)\n",
      "Requirement already satisfied: dill>=0.3.6 in /opt/homebrew/anaconda3/envs/pystan_source/lib/python3.11/site-packages (from multiprocess) (0.3.6)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import arviz as az \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import stan\n",
    "from os.path import exists\n",
    "import pickle\n",
    "import os\n",
    "import json\n",
    "from multiprocessing import Pool\n",
    "!pip install multiprocess\n",
    "import pickle \n",
    "%matplotlib inline\n",
    "import glob\n",
    "import itertools\n",
    "import nest_asyncio\n",
    "import time\n",
    "import string\n",
    "nest_asyncio.apply()\n",
    "\n",
    "from src.model import *\n",
    "from src.util import *\n",
    "from src.plotting import *\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8892a41d-410d-464d-8955-c998207aa417",
   "metadata": {},
   "source": [
    "<h1> Setup</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2217eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Set names of your dependent variable, \n",
    "##Binary indicator of pre-post,\n",
    "##Window on either side of the discontinuity (must be symmetric), \n",
    "## and User ID column\n",
    "outcome = 'non_covid'\n",
    "indicator = 'SU_banned'\n",
    "window = 30\n",
    "\n",
    "user_column = 'user_id'\n",
    "\n",
    "#Make some output directories\n",
    "make_dir = lambda fname: os.makedirs(fname) if not os.path.exists(fname) else None\n",
    "make_dir('output')\n",
    "make_dir('output/posteriors/')\n",
    "make_dir('output/figures')\n",
    "make_dir('output/figures/main')\n",
    "make_dir('output/figures/evaluation')\n",
    "\n",
    "\n",
    "#Load model code\n",
    "with open('./src/model.stan', 'r') as file:\n",
    "    model_code = file.read() \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a0af98",
   "metadata": {},
   "source": [
    "<h1>Data Processing</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "711d22d8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>users</th>\n",
       "      <th>group</th>\n",
       "      <th>suspended_user</th>\n",
       "      <th>outcome</th>\n",
       "      <th>posterior_location</th>\n",
       "      <th>completed</th>\n",
       "      <th>raw_data_file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1038842391241867265</td>\n",
       "      <td>median</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>non_covid_tweets</td>\n",
       "      <td>./output/posteriors/non_covid_tweets_AlexBeren...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/AlexBerenson_1038842391241...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1058364568177647616</td>\n",
       "      <td>median</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>non_covid_tweets</td>\n",
       "      <td>./output/posteriors/non_covid_tweets_AlexBeren...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/AlexBerenson_1058364568177...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1117424294370402307</td>\n",
       "      <td>median</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>non_covid_tweets</td>\n",
       "      <td>./output/posteriors/non_covid_tweets_AlexBeren...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/AlexBerenson_1117424294370...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1128822301737275397</td>\n",
       "      <td>median</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>non_covid_tweets</td>\n",
       "      <td>./output/posteriors/non_covid_tweets_AlexBeren...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/AlexBerenson_1128822301737...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1133731222709055488</td>\n",
       "      <td>median</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>non_covid_tweets</td>\n",
       "      <td>./output/posteriors/non_covid_tweets_AlexBeren...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/AlexBerenson_1133731222709...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>888135405446365184</td>\n",
       "      <td>top</td>\n",
       "      <td>RWMaloneMD</td>\n",
       "      <td>covid_tweets</td>\n",
       "      <td>./output/posteriors/covid_tweets_RWMaloneMD_88...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/RWMaloneMD_888135405446365...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>948162149066657792</td>\n",
       "      <td>top</td>\n",
       "      <td>RWMaloneMD</td>\n",
       "      <td>covid_tweets</td>\n",
       "      <td>./output/posteriors/covid_tweets_RWMaloneMD_94...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/RWMaloneMD_948162149066657...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>949407894394556417</td>\n",
       "      <td>top</td>\n",
       "      <td>RWMaloneMD</td>\n",
       "      <td>covid_tweets</td>\n",
       "      <td>./output/posteriors/covid_tweets_RWMaloneMD_94...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/RWMaloneMD_949407894394556...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>952539957096210434</td>\n",
       "      <td>top</td>\n",
       "      <td>RWMaloneMD</td>\n",
       "      <td>covid_tweets</td>\n",
       "      <td>./output/posteriors/covid_tweets_RWMaloneMD_95...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/RWMaloneMD_952539957096210...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>964873883391332357</td>\n",
       "      <td>top</td>\n",
       "      <td>RWMaloneMD</td>\n",
       "      <td>covid_tweets</td>\n",
       "      <td>./output/posteriors/covid_tweets_RWMaloneMD_96...</td>\n",
       "      <td>False</td>\n",
       "      <td>./output/split_data/RWMaloneMD_964873883391332...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  users   group suspended_user           outcome  \\\n",
       "0   1038842391241867265  median   AlexBerenson  non_covid_tweets   \n",
       "1   1058364568177647616  median   AlexBerenson  non_covid_tweets   \n",
       "2   1117424294370402307  median   AlexBerenson  non_covid_tweets   \n",
       "3   1128822301737275397  median   AlexBerenson  non_covid_tweets   \n",
       "4   1133731222709055488  median   AlexBerenson  non_covid_tweets   \n",
       "..                  ...     ...            ...               ...   \n",
       "45   888135405446365184     top     RWMaloneMD      covid_tweets   \n",
       "46   948162149066657792     top     RWMaloneMD      covid_tweets   \n",
       "47   949407894394556417     top     RWMaloneMD      covid_tweets   \n",
       "48   952539957096210434     top     RWMaloneMD      covid_tweets   \n",
       "49   964873883391332357     top     RWMaloneMD      covid_tweets   \n",
       "\n",
       "                                   posterior_location  completed  \\\n",
       "0   ./output/posteriors/non_covid_tweets_AlexBeren...      False   \n",
       "1   ./output/posteriors/non_covid_tweets_AlexBeren...      False   \n",
       "2   ./output/posteriors/non_covid_tweets_AlexBeren...      False   \n",
       "3   ./output/posteriors/non_covid_tweets_AlexBeren...      False   \n",
       "4   ./output/posteriors/non_covid_tweets_AlexBeren...      False   \n",
       "..                                                ...        ...   \n",
       "45  ./output/posteriors/covid_tweets_RWMaloneMD_88...      False   \n",
       "46  ./output/posteriors/covid_tweets_RWMaloneMD_94...      False   \n",
       "47  ./output/posteriors/covid_tweets_RWMaloneMD_94...      False   \n",
       "48  ./output/posteriors/covid_tweets_RWMaloneMD_95...      False   \n",
       "49  ./output/posteriors/covid_tweets_RWMaloneMD_96...      False   \n",
       "\n",
       "                                        raw_data_file  \n",
       "0   ./output/split_data/AlexBerenson_1038842391241...  \n",
       "1   ./output/split_data/AlexBerenson_1058364568177...  \n",
       "2   ./output/split_data/AlexBerenson_1117424294370...  \n",
       "3   ./output/split_data/AlexBerenson_1128822301737...  \n",
       "4   ./output/split_data/AlexBerenson_1133731222709...  \n",
       "..                                                ...  \n",
       "45  ./output/split_data/RWMaloneMD_888135405446365...  \n",
       "46  ./output/split_data/RWMaloneMD_948162149066657...  \n",
       "47  ./output/split_data/RWMaloneMD_949407894394556...  \n",
       "48  ./output/split_data/RWMaloneMD_952539957096210...  \n",
       "49  ./output/split_data/RWMaloneMD_964873883391332...  \n",
       "\n",
       "[1200 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Load data\n",
    "all_timeseries = pd.read_csv('./dat/all_reliable_amplifier_activity_counts_10_20_22.csv')\n",
    "super_user_names = all_timeseries['suspended_user'].unique()\n",
    "\n",
    "\n",
    "#Get a list of all the follower for a given removed user\n",
    "get_users = lambda x: all_timeseries[(all_timeseries['group']==x[0]) & (all_timeseries['suspended_user']==x[1])]['user_id'].unique()\n",
    "\n",
    "#A function to make a dataframe for for followers relevant to each condition. \n",
    "def make_condition_df(condition):\n",
    "    users = get_users(condition)\n",
    "    df = pd.DataFrame({'users':users})\n",
    "    df['group'] = condition[0]\n",
    "    df['suspended_user'] = condition[1]\n",
    "    \n",
    "    df1 = df.copy()\n",
    "    df2 = df.copy()\n",
    "    df1['outcome'] = 'non_covid_tweets'\n",
    "    df2['outcome'] = 'covid_tweets'\n",
    "    \n",
    "    df = pd.concat([df1,df2])\n",
    "    df['posterior_location'] = './output/posteriors/'+df['outcome'] + '_' + df['suspended_user'] + '_' + df['users'].astype(str) + '.json'\n",
    "    df['completed'] = False\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "#Create a list of all unique conditions\n",
    "all_conditions = list(itertools.product(['median','top'],super_user_names))\n",
    "\n",
    "#Create a dataframe for each condition, use this to construct a df tracking files to process \n",
    "processing_df = pd.concat([make_condition_df(condition) for condition in all_conditions])\n",
    "processing_df.to_csv('./output/processing_df.csv',index=False)\n",
    "\n",
    "\n",
    "#Create a directory to store data for each follower \n",
    "make_dir('./output/split_data/')\n",
    "\n",
    "#This function splits the data by follower and saves it to a unique csv \n",
    "split_row = lambda row: all_timeseries[(all_timeseries['group']==row['group']) & \n",
    "               (all_timeseries['suspended_user']==row['suspended_user']) & \n",
    "               (all_timeseries['user_id']==row['users'])].to_csv('./output/split_data/'+row['suspended_user']+'_'+str(row['users'])+'.csv',index=False)\n",
    "\n",
    "#Apply splitting to the entire dataset  \n",
    "processing_df.apply(split_row,axis=1)\n",
    "\n",
    "#Add the location of the split data file to the processing df\n",
    "processing_df['raw_data_file'] = './output/split_data/'+processing_df['suspended_user']+'_'+processing_df['users'].astype(str)+'.csv'\n",
    "\n",
    "processing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2240f903",
   "metadata": {},
   "source": [
    "<h2>Run the model</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aba0205d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "processing_df['completed'] = processing_df['posterior_location'].apply(lambda x: os.path.exists(x))\n",
    "print(processing_df['completed'].mean() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64a0662",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logger = logging.getLogger('pystan')\n",
    "logger.addHandler(logging.NullHandler())\n",
    "\n",
    "with open('./src/model.stan', 'r') as file:\n",
    "    model_code = file.read() \n",
    "\n",
    "\n",
    "\n",
    "#open model code\n",
    "with open('./src/model.stan', 'r') as file:\n",
    "    model_code = file.read() \n",
    "\n",
    "\n",
    "for row in processing_df.iterrows():\n",
    "    completed = process_row(row[1], model_code,  window=30)\n",
    "    if completed:\n",
    "        processing_df.loc[row[0],'completed'] = True\n",
    "        processing_df.to_csv('./output/processing_df.csv',index=False)\n",
    "print(sum(processing_df['completed']))\n",
    "print(glob.glob('./output/posteriors/*').__len__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "800b9c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sum(processing_df['completed']))\n",
    "print(glob.glob('./output/posteriors/*').__len__())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec0bda6a",
   "metadata": {},
   "source": [
    "<h1>Figure 1</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a574b80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posteriors = glob.glob('./output/posteriors/*')\n",
    "\n",
    "def plot_posterior_predictive( posteriors, id,axs, window=30):\n",
    "    idata = az.from_json(posteriors[id])\n",
    "\n",
    "    plt.scatter(np.arange(window*2), idata.observed_data.y,color=np.array([47,47,56])/255,zorder=2,s=10)\n",
    "\n",
    "\n",
    "    #We can extract it (it's in log scale, so we take an exp)\n",
    "    #It is a matrix of 4000 x window*2, containing samples from the posterior predictive distribution\n",
    "    exp_post = np.exp(idata.posterior_predictive.exp_hat)\n",
    "\n",
    "        \n",
    "    #Our idata object has actual data, let's plot that\n",
    "    #Stored as seen below\n",
    "    plt.scatter(np.arange(window*2), idata.observed_data.y,color=np.array([47,47,56])/255,zorder=2,s=10)\n",
    "\n",
    "    #And plot various credible intervals, perhaps with shading.\n",
    "    for q in (1,3,6,11, 25,50,75):\n",
    "        cis = np.nanpercentile(np.array(exp_post).reshape(4000,window*2), q=[0+q/2, 100-q/2],axis=0)\n",
    "        plt.fill_between(np.arange(window*2), cis[0], cis[1], alpha = q/100,color= orange)\n",
    "\n",
    "    #exp_hat contains the mean function\n",
    "    ci = np.nanpercentile(np.exp(np.array(idata.posterior_predictive.mu_hat)).reshape(4000,window*2),\n",
    "                    axis=0,\n",
    "                    q=[5.5,50, 94.5])\n",
    "\n",
    "    #Plot the 94\\% credible interval as being shaded\n",
    "    plt.fill_between(np.arange(window*2), ci[0], ci[2], alpha=.35, color='grey')\n",
    "    \n",
    "    #Plot the median as a line\n",
    "    plt.plot(np.arange(window*2), ci[1], color=np.array([47,47,56])/255)\n",
    "    \n",
    "    \n",
    "    #exp_hat contains the mean function\n",
    "    ci = np.nanpercentile(np.exp(np.array(idata.posterior_predictive.mu_hat_without_ban)).reshape(4000,window*2),\n",
    "                    axis=0,\n",
    "                    q=[5.5,50, 94.5])\n",
    "\n",
    "    #Plot the 94\\% credible interval as being shaded\n",
    "    # plt.fill_between(np.arange(window*2)[29:], ci[0][29:], ci[2][29:], alpha=.35, color='powderblue')\n",
    "    \n",
    "    #Plot the median as a line\n",
    "    plt.plot(np.arange(window*2)[29:], ci[1][29:], color=np.array([47,47,56])/255, ls=':')\n",
    "    \n",
    "\n",
    "    #Clean things up\n",
    "    plt.ylabel('Posts per day')\n",
    "    plt.xlabel('Day')\n",
    "    plt.ylim(0,np.max(idata.observed_data.y)*1.5)\n",
    "    plt.xlim(0,window*2-1)\n",
    "    plt.plot([window-1,window-1],\n",
    "                [0, 4000],\n",
    "                color=np.array([47,47,56])/255,\n",
    "                ls='--')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig('./output/figures/fig1.png',dpi=300)\n",
    "\n",
    "plot_posterior_predictive(posteriors, 100, plt.gca(), window=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b32062-3c69-46c3-be60-42e278a0d15f",
   "metadata": {},
   "source": [
    "<h1> Plotting Posteriors for all users</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10bee0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot in parallel, so this doesn't take forever. \n",
    "make_dir('./output/figures/posteriors/')\n",
    "import multiprocess as mp\n",
    "if __name__ ==  '__main__': \n",
    "    pool = mp.Pool()\n",
    "    results =  pool.map(plot_posterior_and_change, glob.glob('./output/posteriors/*'))\n",
    "    pool.close()\n",
    "    pool.join()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c56096f",
   "metadata": {},
   "source": [
    "<h1>Aggregate across fits</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc96de0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       ".xr-header > ul {\n",
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       "\n",
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       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
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       "\n",
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       ".xr-array-wrap {\n",
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       "  padding: 0 5px !important;\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       "\n",
       ".xr-index-preview {\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;timestep&#x27; (timestep: 60)&gt;\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59])\n",
       "Coordinates:\n",
       "  * timestep  (timestep) int64 0 1 2 3 4 5 6 7 8 ... 51 52 53 54 55 56 57 58 59</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'timestep'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>timestep</span>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b888952e-5807-46ce-8952-cf7dbffe9908' class='xr-array-in' type='checkbox' checked><label for='section-b888952e-5807-46ce-8952-cf7dbffe9908' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 1 2 3 4 5 6 7 8 9 10 11 12 ... 48 49 50 51 52 53 54 55 56 57 58 59</span></div><div class='xr-array-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59])</pre></div></div></li><li class='xr-section-item'><input id='section-1e8a382c-402e-40da-aa7b-17f8d6f9add1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e8a382c-402e-40da-aa7b-17f8d6f9add1' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>timestep</span></div><div class='xr-var-dims'>(timestep)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 54 55 56 57 58 59</div><input id='attrs-4c40d3b4-26fd-49ff-a48a-50a09b9ddd68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c40d3b4-26fd-49ff-a48a-50a09b9ddd68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab80eed1-2329-414c-a3f9-894093efb306' class='xr-var-data-in' type='checkbox'><label for='data-ab80eed1-2329-414c-a3f9-894093efb306' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e314528d-2f03-4f0e-b832-8c43bf08f65d' class='xr-section-summary-in' type='checkbox'  ><label for='section-e314528d-2f03-4f0e-b832-8c43bf08f65d' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>timestep</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-65559713-5c65-403f-b805-7c758e4b14ef' class='xr-index-data-in' type='checkbox'/><label for='index-65559713-5c65-403f-b805-7c758e4b14ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;timestep&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ebc721f-ca1a-457f-820a-7aa283ccf209' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ebc721f-ca1a-457f-820a-7aa283ccf209' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'timestep' (timestep: 60)>\n",
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59])\n",
       "Coordinates:\n",
       "  * timestep  (timestep) int64 0 1 2 3 4 5 6 7 8 ... 51 52 53 54 55 56 57 58 59"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata = az.from_json(processing_df['posterior_location'].iloc[0])\n",
    "idata.observed_data['timestep']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f644a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gather median change by group, outcome, and suspended user\n",
    "from src.util import get_median_change\n",
    "import multiprocess as mp\n",
    "grouped =  processing_df.groupby(['group', 'outcome', 'suspended_user'])\n",
    "if __name__ ==  '__main__': \n",
    "    pool = mp.Pool()\n",
    "    results =  pool.map(get_median_change, grouped) \n",
    "    pool.close()\n",
    "    pool.join()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37ac90ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gather median change by group, outcome, and suspended user\n",
    "group_flattened = grouped.count().reset_index()\n",
    "group_flattened['results'] = results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834c314b",
   "metadata": {},
   "source": [
    "<h1>Figure 2</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "004309f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from src.plotting import plot_change_by_group\n",
    "fig, axs = plt.subplots(1,1, figsize=(6,14/5))\n",
    "\n",
    "grouped = group_flattened.groupby(['group','outcome'])\n",
    "\n",
    "\n",
    "groups = []\n",
    "plt.sca(axs)\n",
    "plot_posterior_predictive(posteriors, 100, plt.gca(), window=30)\n",
    "plt.savefig('./output/figures/fig1.png', dpi=300)\n",
    "plt.savefig('./output/figures/fig1.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74e8f3b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from src.plotting import plot_change_by_group\n",
    "fig, axs = plt.subplots(4,1, figsize=(6,14 - 14/5))\n",
    "\n",
    "grouped = group_flattened.groupby(['group','outcome'])\n",
    "\n",
    "\n",
    "groups = []\n",
    "# plt.sca(axs[0])\n",
    "# plot_posterior_predictive(posteriors, 100, plt.gca(), window=30)\n",
    "\n",
    "\n",
    "plt.sca(axs[0])\n",
    "group = grouped.get_group(('top','non_covid_tweets'))\n",
    "plt.title('Highly engaged followers: non-Covid posts')\n",
    "groups.append(plot_change_by_group(group, ax=plt.gca(), color=grey))\n",
    "plt.xlabel('Followers (thousands)')\n",
    "\n",
    "\n",
    "plt.sca(axs[1])\n",
    "plt.title('Moderately followers: non-Covid posts')\n",
    "group = grouped.get_group(('median','non_covid_tweets'))\n",
    "groups.append(plot_change_by_group(group, ax=plt.gca(), color=grey))\n",
    "plt.xlabel('Followers (thousands)')\n",
    "\n",
    "\n",
    "plt.sca(axs[2])\n",
    "plt.title('Highly engaged followers: Covid posts')\n",
    "group = grouped.get_group(('top','covid_tweets'))\n",
    "groups.append(plot_change_by_group(group, ax=plt.gca(), color=orange))\n",
    "plt.xlabel('Followers (thousands)')\n",
    "\n",
    "plt.sca(axs[3])\n",
    "plt.title('Moderately engaged followers: Covid posts')\n",
    "group = grouped.get_group(('median','covid_tweets'))\n",
    "plt.xlabel('Followers (thousands)')\n",
    "\n",
    "groups.append(plot_change_by_group(group, ax=plt.gca(), color=orange,xlab=True))\n",
    "for n, ax in enumerate(axs):\n",
    "    ax.text(-0.075, 1.1, string.ascii_uppercase[n], transform=ax.transAxes, \n",
    "            size=20, weight='bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./output/figures/fig2.png',dpi=300)\n",
    "plt.savefig('./output/figures/fig2.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12e8bba",
   "metadata": {},
   "source": [
    "<h2>Summarize chages in activity</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a1b5981",
   "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></th>\n",
       "      <th></th>\n",
       "      <th>followers (thousands)</th>\n",
       "      <th>3%</th>\n",
       "      <th>11%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>89%</th>\n",
       "      <th>97%</th>\n",
       "      <th>follower_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcome</th>\n",
       "      <th>group</th>\n",
       "      <th>suspended_user</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">covid_tweets</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">median</th>\n",
       "      <th>AlexBerenson</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-17.088852</td>\n",
       "      <td>-9.263685</td>\n",
       "      <td>-2.511253</td>\n",
       "      <td>6.075516</td>\n",
       "      <td>15.482943</td>\n",
       "      <td>23.640741</td>\n",
       "      <td>34.748684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-25.099138</td>\n",
       "      <td>-22.056565</td>\n",
       "      <td>-19.284760</td>\n",
       "      <td>-15.797225</td>\n",
       "      <td>-12.101666</td>\n",
       "      <td>-8.707929</td>\n",
       "      <td>-4.697310</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusyDrT</th>\n",
       "      <td>100.0</td>\n",
       "      <td>23.322601</td>\n",
       "      <td>33.810610</td>\n",
       "      <td>44.071284</td>\n",
       "      <td>57.432373</td>\n",
       "      <td>71.340935</td>\n",
       "      <td>83.184566</td>\n",
       "      <td>97.824520</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IslamRizza</th>\n",
       "      <td>55.0</td>\n",
       "      <td>-53.198153</td>\n",
       "      <td>-45.186099</td>\n",
       "      <td>-37.883879</td>\n",
       "      <td>-27.870434</td>\n",
       "      <td>-16.702404</td>\n",
       "      <td>-6.552552</td>\n",
       "      <td>8.004917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RWMaloneMD</th>\n",
       "      <td>520.0</td>\n",
       "      <td>-14.290747</td>\n",
       "      <td>-7.995425</td>\n",
       "      <td>-2.276897</td>\n",
       "      <td>5.371165</td>\n",
       "      <td>13.198735</td>\n",
       "      <td>19.900203</td>\n",
       "      <td>28.983638</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naomirwolf</th>\n",
       "      <td>143.0</td>\n",
       "      <td>-72.295739</td>\n",
       "      <td>-69.961525</td>\n",
       "      <td>-67.963676</td>\n",
       "      <td>-65.388799</td>\n",
       "      <td>-62.432312</td>\n",
       "      <td>-59.628090</td>\n",
       "      <td>-56.004366</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pastorlocke</th>\n",
       "      <td>113.0</td>\n",
       "      <td>-78.848160</td>\n",
       "      <td>-77.020730</td>\n",
       "      <td>-75.173900</td>\n",
       "      <td>-72.943615</td>\n",
       "      <td>-70.554707</td>\n",
       "      <td>-68.544592</td>\n",
       "      <td>-65.570092</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">top</th>\n",
       "      <th>AlexBerenson</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-42.674915</td>\n",
       "      <td>-39.102194</td>\n",
       "      <td>-35.820968</td>\n",
       "      <td>-31.338289</td>\n",
       "      <td>-26.766250</td>\n",
       "      <td>-23.133781</td>\n",
       "      <td>-18.462282</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-24.061922</td>\n",
       "      <td>-20.565000</td>\n",
       "      <td>-17.507650</td>\n",
       "      <td>-13.246353</td>\n",
       "      <td>-9.048331</td>\n",
       "      <td>-5.500866</td>\n",
       "      <td>-0.892173</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusyDrT</th>\n",
       "      <td>100.0</td>\n",
       "      <td>67.144139</td>\n",
       "      <td>82.726657</td>\n",
       "      <td>97.872102</td>\n",
       "      <td>116.779625</td>\n",
       "      <td>138.688358</td>\n",
       "      <td>158.009959</td>\n",
       "      <td>183.049973</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IslamRizza</th>\n",
       "      <td>55.0</td>\n",
       "      <td>-39.183673</td>\n",
       "      <td>-29.982378</td>\n",
       "      <td>-21.762598</td>\n",
       "      <td>-10.748241</td>\n",
       "      <td>1.714770</td>\n",
       "      <td>12.383249</td>\n",
       "      <td>27.128056</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RWMaloneMD</th>\n",
       "      <td>520.0</td>\n",
       "      <td>-42.401067</td>\n",
       "      <td>-38.800200</td>\n",
       "      <td>-35.621236</td>\n",
       "      <td>-31.662902</td>\n",
       "      <td>-27.244486</td>\n",
       "      <td>-23.590633</td>\n",
       "      <td>-19.033915</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naomirwolf</th>\n",
       "      <td>143.0</td>\n",
       "      <td>-65.001919</td>\n",
       "      <td>-62.847404</td>\n",
       "      <td>-60.961962</td>\n",
       "      <td>-58.508423</td>\n",
       "      <td>-55.839693</td>\n",
       "      <td>-53.484777</td>\n",
       "      <td>-50.685044</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pastorlocke</th>\n",
       "      <td>113.0</td>\n",
       "      <td>-74.178581</td>\n",
       "      <td>-72.329437</td>\n",
       "      <td>-70.378944</td>\n",
       "      <td>-68.018450</td>\n",
       "      <td>-65.275104</td>\n",
       "      <td>-63.216185</td>\n",
       "      <td>-60.468154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">non_covid_tweets</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">median</th>\n",
       "      <th>AlexBerenson</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-55.495881</td>\n",
       "      <td>-51.860081</td>\n",
       "      <td>-48.513296</td>\n",
       "      <td>-44.243627</td>\n",
       "      <td>-39.452718</td>\n",
       "      <td>-35.753071</td>\n",
       "      <td>-30.503974</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-10.885252</td>\n",
       "      <td>-7.803886</td>\n",
       "      <td>-5.071245</td>\n",
       "      <td>-1.740661</td>\n",
       "      <td>1.655508</td>\n",
       "      <td>4.753129</td>\n",
       "      <td>8.268483</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusyDrT</th>\n",
       "      <td>100.0</td>\n",
       "      <td>-37.026954</td>\n",
       "      <td>-32.441926</td>\n",
       "      <td>-28.602031</td>\n",
       "      <td>-23.145164</td>\n",
       "      <td>-17.038561</td>\n",
       "      <td>-11.786919</td>\n",
       "      <td>-4.517980</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IslamRizza</th>\n",
       "      <td>55.0</td>\n",
       "      <td>3.068211</td>\n",
       "      <td>14.352707</td>\n",
       "      <td>23.967193</td>\n",
       "      <td>36.301468</td>\n",
       "      <td>49.685580</td>\n",
       "      <td>61.064329</td>\n",
       "      <td>74.866844</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RWMaloneMD</th>\n",
       "      <td>520.0</td>\n",
       "      <td>31.557033</td>\n",
       "      <td>39.632559</td>\n",
       "      <td>46.792506</td>\n",
       "      <td>56.945128</td>\n",
       "      <td>67.776131</td>\n",
       "      <td>76.772601</td>\n",
       "      <td>87.499046</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naomirwolf</th>\n",
       "      <td>143.0</td>\n",
       "      <td>-30.530278</td>\n",
       "      <td>-25.288453</td>\n",
       "      <td>-20.396434</td>\n",
       "      <td>-13.731110</td>\n",
       "      <td>-6.752706</td>\n",
       "      <td>-0.553845</td>\n",
       "      <td>7.643541</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pastorlocke</th>\n",
       "      <td>113.0</td>\n",
       "      <td>-39.990421</td>\n",
       "      <td>-35.442058</td>\n",
       "      <td>-31.301084</td>\n",
       "      <td>-25.532000</td>\n",
       "      <td>-19.545237</td>\n",
       "      <td>-14.076616</td>\n",
       "      <td>-7.373067</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">top</th>\n",
       "      <th>AlexBerenson</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-76.879643</td>\n",
       "      <td>-75.521067</td>\n",
       "      <td>-74.088920</td>\n",
       "      <td>-72.320717</td>\n",
       "      <td>-70.432746</td>\n",
       "      <td>-68.707291</td>\n",
       "      <td>-66.765353</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average</th>\n",
       "      <td>344.0</td>\n",
       "      <td>-19.418982</td>\n",
       "      <td>-17.395278</td>\n",
       "      <td>-15.428289</td>\n",
       "      <td>-13.010012</td>\n",
       "      <td>-10.494201</td>\n",
       "      <td>-8.434514</td>\n",
       "      <td>-5.832720</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BusyDrT</th>\n",
       "      <td>100.0</td>\n",
       "      <td>-39.000121</td>\n",
       "      <td>-34.668400</td>\n",
       "      <td>-30.700110</td>\n",
       "      <td>-25.238544</td>\n",
       "      <td>-19.440586</td>\n",
       "      <td>-13.915236</td>\n",
       "      <td>-6.754417</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IslamRizza</th>\n",
       "      <td>55.0</td>\n",
       "      <td>-6.171175</td>\n",
       "      <td>0.901312</td>\n",
       "      <td>7.194916</td>\n",
       "      <td>15.165656</td>\n",
       "      <td>23.854803</td>\n",
       "      <td>30.641488</td>\n",
       "      <td>39.607327</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RWMaloneMD</th>\n",
       "      <td>520.0</td>\n",
       "      <td>10.909254</td>\n",
       "      <td>17.758370</td>\n",
       "      <td>23.825728</td>\n",
       "      <td>31.492845</td>\n",
       "      <td>39.595507</td>\n",
       "      <td>47.051909</td>\n",
       "      <td>55.476854</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naomirwolf</th>\n",
       "      <td>143.0</td>\n",
       "      <td>-21.173372</td>\n",
       "      <td>-16.935277</td>\n",
       "      <td>-13.078843</td>\n",
       "      <td>-8.327255</td>\n",
       "      <td>-3.163660</td>\n",
       "      <td>1.290833</td>\n",
       "      <td>6.729920</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pastorlocke</th>\n",
       "      <td>113.0</td>\n",
       "      <td>-33.375348</td>\n",
       "      <td>-29.152527</td>\n",
       "      <td>-25.691630</td>\n",
       "      <td>-20.707939</td>\n",
       "      <td>-15.408461</td>\n",
       "      <td>-10.882383</td>\n",
       "      <td>-5.619798</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        followers (thousands)         3%  \\\n",
       "outcome          group  suspended_user                                     \n",
       "covid_tweets     median AlexBerenson                    344.0 -17.088852   \n",
       "                        Average                         344.0 -25.099138   \n",
       "                        BusyDrT                         100.0  23.322601   \n",
       "                        IslamRizza                       55.0 -53.198153   \n",
       "                        RWMaloneMD                      520.0 -14.290747   \n",
       "                        naomirwolf                      143.0 -72.295739   \n",
       "                        pastorlocke                     113.0 -78.848160   \n",
       "                 top    AlexBerenson                    344.0 -42.674915   \n",
       "                        Average                         344.0 -24.061922   \n",
       "                        BusyDrT                         100.0  67.144139   \n",
       "                        IslamRizza                       55.0 -39.183673   \n",
       "                        RWMaloneMD                      520.0 -42.401067   \n",
       "                        naomirwolf                      143.0 -65.001919   \n",
       "                        pastorlocke                     113.0 -74.178581   \n",
       "non_covid_tweets median AlexBerenson                    344.0 -55.495881   \n",
       "                        Average                         344.0 -10.885252   \n",
       "                        BusyDrT                         100.0 -37.026954   \n",
       "                        IslamRizza                       55.0   3.068211   \n",
       "                        RWMaloneMD                      520.0  31.557033   \n",
       "                        naomirwolf                      143.0 -30.530278   \n",
       "                        pastorlocke                     113.0 -39.990421   \n",
       "                 top    AlexBerenson                    344.0 -76.879643   \n",
       "                        Average                         344.0 -19.418982   \n",
       "                        BusyDrT                         100.0 -39.000121   \n",
       "                        IslamRizza                       55.0  -6.171175   \n",
       "                        RWMaloneMD                      520.0  10.909254   \n",
       "                        naomirwolf                      143.0 -21.173372   \n",
       "                        pastorlocke                     113.0 -33.375348   \n",
       "\n",
       "                                              11%        25%         50%  \\\n",
       "outcome          group  suspended_user                                     \n",
       "covid_tweets     median AlexBerenson    -9.263685  -2.511253    6.075516   \n",
       "                        Average        -22.056565 -19.284760  -15.797225   \n",
       "                        BusyDrT         33.810610  44.071284   57.432373   \n",
       "                        IslamRizza     -45.186099 -37.883879  -27.870434   \n",
       "                        RWMaloneMD      -7.995425  -2.276897    5.371165   \n",
       "                        naomirwolf     -69.961525 -67.963676  -65.388799   \n",
       "                        pastorlocke    -77.020730 -75.173900  -72.943615   \n",
       "                 top    AlexBerenson   -39.102194 -35.820968  -31.338289   \n",
       "                        Average        -20.565000 -17.507650  -13.246353   \n",
       "                        BusyDrT         82.726657  97.872102  116.779625   \n",
       "                        IslamRizza     -29.982378 -21.762598  -10.748241   \n",
       "                        RWMaloneMD     -38.800200 -35.621236  -31.662902   \n",
       "                        naomirwolf     -62.847404 -60.961962  -58.508423   \n",
       "                        pastorlocke    -72.329437 -70.378944  -68.018450   \n",
       "non_covid_tweets median AlexBerenson   -51.860081 -48.513296  -44.243627   \n",
       "                        Average         -7.803886  -5.071245   -1.740661   \n",
       "                        BusyDrT        -32.441926 -28.602031  -23.145164   \n",
       "                        IslamRizza      14.352707  23.967193   36.301468   \n",
       "                        RWMaloneMD      39.632559  46.792506   56.945128   \n",
       "                        naomirwolf     -25.288453 -20.396434  -13.731110   \n",
       "                        pastorlocke    -35.442058 -31.301084  -25.532000   \n",
       "                 top    AlexBerenson   -75.521067 -74.088920  -72.320717   \n",
       "                        Average        -17.395278 -15.428289  -13.010012   \n",
       "                        BusyDrT        -34.668400 -30.700110  -25.238544   \n",
       "                        IslamRizza       0.901312   7.194916   15.165656   \n",
       "                        RWMaloneMD      17.758370  23.825728   31.492845   \n",
       "                        naomirwolf     -16.935277 -13.078843   -8.327255   \n",
       "                        pastorlocke    -29.152527 -25.691630  -20.707939   \n",
       "\n",
       "                                               75%         89%         97%  \\\n",
       "outcome          group  suspended_user                                       \n",
       "covid_tweets     median AlexBerenson     15.482943   23.640741   34.748684   \n",
       "                        Average         -12.101666   -8.707929   -4.697310   \n",
       "                        BusyDrT          71.340935   83.184566   97.824520   \n",
       "                        IslamRizza      -16.702404   -6.552552    8.004917   \n",
       "                        RWMaloneMD       13.198735   19.900203   28.983638   \n",
       "                        naomirwolf      -62.432312  -59.628090  -56.004366   \n",
       "                        pastorlocke     -70.554707  -68.544592  -65.570092   \n",
       "                 top    AlexBerenson    -26.766250  -23.133781  -18.462282   \n",
       "                        Average          -9.048331   -5.500866   -0.892173   \n",
       "                        BusyDrT         138.688358  158.009959  183.049973   \n",
       "                        IslamRizza        1.714770   12.383249   27.128056   \n",
       "                        RWMaloneMD      -27.244486  -23.590633  -19.033915   \n",
       "                        naomirwolf      -55.839693  -53.484777  -50.685044   \n",
       "                        pastorlocke     -65.275104  -63.216185  -60.468154   \n",
       "non_covid_tweets median AlexBerenson    -39.452718  -35.753071  -30.503974   \n",
       "                        Average           1.655508    4.753129    8.268483   \n",
       "                        BusyDrT         -17.038561  -11.786919   -4.517980   \n",
       "                        IslamRizza       49.685580   61.064329   74.866844   \n",
       "                        RWMaloneMD       67.776131   76.772601   87.499046   \n",
       "                        naomirwolf       -6.752706   -0.553845    7.643541   \n",
       "                        pastorlocke     -19.545237  -14.076616   -7.373067   \n",
       "                 top    AlexBerenson    -70.432746  -68.707291  -66.765353   \n",
       "                        Average         -10.494201   -8.434514   -5.832720   \n",
       "                        BusyDrT         -19.440586  -13.915236   -6.754417   \n",
       "                        IslamRizza       23.854803   30.641488   39.607327   \n",
       "                        RWMaloneMD       39.595507   47.051909   55.476854   \n",
       "                        naomirwolf       -3.163660    1.290833    6.729920   \n",
       "                        pastorlocke     -15.408461  -10.882383   -5.619798   \n",
       "\n",
       "                                        follower_counts  \n",
       "outcome          group  suspended_user                   \n",
       "covid_tweets     median AlexBerenson                NaN  \n",
       "                        Average                     0.0  \n",
       "                        BusyDrT                     NaN  \n",
       "                        IslamRizza                  NaN  \n",
       "                        RWMaloneMD                  NaN  \n",
       "                        naomirwolf                  NaN  \n",
       "                        pastorlocke                 NaN  \n",
       "                 top    AlexBerenson                NaN  \n",
       "                        Average                     0.0  \n",
       "                        BusyDrT                     NaN  \n",
       "                        IslamRizza                  NaN  \n",
       "                        RWMaloneMD                  NaN  \n",
       "                        naomirwolf                  NaN  \n",
       "                        pastorlocke                 NaN  \n",
       "non_covid_tweets median AlexBerenson                NaN  \n",
       "                        Average                     0.0  \n",
       "                        BusyDrT                     NaN  \n",
       "                        IslamRizza                  NaN  \n",
       "                        RWMaloneMD                  NaN  \n",
       "                        naomirwolf                  NaN  \n",
       "                        pastorlocke                 NaN  \n",
       "                 top    AlexBerenson                NaN  \n",
       "                        Average                     0.0  \n",
       "                        BusyDrT                     NaN  \n",
       "                        IslamRizza                  NaN  \n",
       "                        RWMaloneMD                  NaN  \n",
       "                        naomirwolf                  NaN  \n",
       "                        pastorlocke                 NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changes = pd.concat(groups).drop(['users','posterior_location','completed','raw_data_file','results'],axis=1\n",
    "                       ).rename({'followers':'followers (thousands)'},axis=1\n",
    "                       )\n",
    "changes.to_csv('./output/changes.csv',index=False)\n",
    "changes = changes.groupby(['outcome','group','suspended_user']).mean()\n",
    "changes.to_csv('./output/changes_grouped.csv',index=False)\n",
    "changes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f72d2d",
   "metadata": {},
   "source": [
    "<h1>Diversity Analysis</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e569388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>user_id</th>\n",
       "      <th>before_suspension</th>\n",
       "      <th>after_suspension</th>\n",
       "      <th>covid_before_suspension</th>\n",
       "      <th>covid_after_suspension</th>\n",
       "      <th>non_covid_before_suspension</th>\n",
       "      <th>non_covid_after_suspension</th>\n",
       "      <th>suspended_user</th>\n",
       "      <th>measurement</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1037541129019842560</td>\n",
       "      <td>0.677775</td>\n",
       "      <td>0.660092</td>\n",
       "      <td>0.618318</td>\n",
       "      <td>0.660960</td>\n",
       "      <td>0.655966</td>\n",
       "      <td>0.588174</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>gini</td>\n",
       "      <td>top</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1067266550</td>\n",
       "      <td>0.705150</td>\n",
       "      <td>0.637978</td>\n",
       "      <td>0.606616</td>\n",
       "      <td>0.518125</td>\n",
       "      <td>0.688048</td>\n",
       "      <td>0.624193</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>gini</td>\n",
       "      <td>top</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1089143694681391105</td>\n",
       "      <td>0.701304</td>\n",
       "      <td>0.666755</td>\n",
       "      <td>0.633372</td>\n",
       "      <td>0.518174</td>\n",
       "      <td>0.671774</td>\n",
       "      <td>0.653655</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>gini</td>\n",
       "      <td>top</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1106989053054869505</td>\n",
       "      <td>0.743774</td>\n",
       "      <td>0.703384</td>\n",
       "      <td>0.663620</td>\n",
       "      <td>0.614547</td>\n",
       "      <td>0.734829</td>\n",
       "      <td>0.692828</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>gini</td>\n",
       "      <td>top</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1110361520</td>\n",
       "      <td>0.679684</td>\n",
       "      <td>0.674712</td>\n",
       "      <td>0.597801</td>\n",
       "      <td>0.660954</td>\n",
       "      <td>0.661175</td>\n",
       "      <td>0.622337</td>\n",
       "      <td>AlexBerenson</td>\n",
       "      <td>gini</td>\n",
       "      <td>top</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0              user_id  before_suspension  after_suspension  \\\n",
       "0           0  1037541129019842560           0.677775          0.660092   \n",
       "1           1           1067266550           0.705150          0.637978   \n",
       "2           2  1089143694681391105           0.701304          0.666755   \n",
       "3           3  1106989053054869505           0.743774          0.703384   \n",
       "4           4           1110361520           0.679684          0.674712   \n",
       "\n",
       "   covid_before_suspension  covid_after_suspension  \\\n",
       "0                 0.618318                0.660960   \n",
       "1                 0.606616                0.518125   \n",
       "2                 0.633372                0.518174   \n",
       "3                 0.663620                0.614547   \n",
       "4                 0.597801                0.660954   \n",
       "\n",
       "   non_covid_before_suspension  non_covid_after_suspension suspended_user  \\\n",
       "0                     0.655966                    0.588174   AlexBerenson   \n",
       "1                     0.688048                    0.624193   AlexBerenson   \n",
       "2                     0.671774                    0.653655   AlexBerenson   \n",
       "3                     0.734829                    0.692828   AlexBerenson   \n",
       "4                     0.661175                    0.622337   AlexBerenson   \n",
       "\n",
       "  measurement group  \n",
       "0        gini   top  \n",
       "1        gini   top  \n",
       "2        gini   top  \n",
       "3        gini   top  \n",
       "4        gini   top  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diversity = pd.read_csv('dat/all_reliable_amplifier_inequality_measurements.csv')\n",
    "df_diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31e6e578",
   "metadata": {},
   "outputs": [],
   "source": [
    "shannon = df_diversity[df_diversity['measurement']=='shannon']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a053b24",
   "metadata": {},
   "source": [
    "<h2>Shannon Diversity</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c9b083c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/ipykernel_46040/1766112243.py:96: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shannon_top['id'] = np.arange(shannon_top.shape[0])\n",
      "/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/ipykernel_46040/1766112243.py:100: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  shannon_median['id'] = np.arange(shannon_median.shape[0])\n",
      "\n",
      "Building: found in cache, done.Messages from stanc:\n",
      "Warning in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_i8fxrket/model_foxieeo2.stan', line 7, column 4: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
      "Warning in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_i8fxrket/model_foxieeo2.stan', line 8, column 4: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
      "    stanc\n",
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      "Sampling: 100% (16000/16000)\n",
      "Sampling: 100% (16000/16000), done.\n",
      "Messages received during sampling:\n",
      "  Gradient evaluation took 0.000174 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.74 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.000151 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.51 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.000143 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.43 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.000142 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.42 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Building: found in cache, done.Messages from stanc:\n",
      "Warning in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_i8fxrket/model_foxieeo2.stan', line 7, column 4: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
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      "Warning in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_i8fxrket/model_foxieeo2.stan', line 8, column 4: Declaration\n",
      "    of arrays by placing brackets after a variable name is deprecated and\n",
      "    will be removed in Stan 2.32.0. Instead use the array keyword before the\n",
      "    type. This can be changed automatically using the auto-format flag to\n",
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      "Messages received during sampling:\n",
      "  Gradient evaluation took 0.000196 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.96 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.000146 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.46 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.000141 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.41 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Gradient evaluation took 0.00015 seconds\n",
      "  1000 transitions using 10 leapfrog steps per transition would take 1.5 seconds.\n",
      "  Adjust your expectations accordingly!\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Inverse scale parameter is 0, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n",
      "  Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:\n",
      "  Exception: gamma_lpdf: Shape parameter is inf, but must be positive finite! (in '/var/folders/bj/y3syvs594cz3lmwxk8fwswzr0000gq/T/httpstan_br4e6wa0/model_foxieeo2.stan', line 42, column 8 to column 69)\n",
      "  If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,\n",
      "  but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.\n"
     ]
    }
   ],
   "source": [
    "shannon_code = '''\n",
    "\n",
    "data {\n",
    "    int<lower=0> N;\n",
    "    int<lower=0> N_SU;\n",
    "    vector<lower=0>[N] y; \n",
    "    int<lower=0> superuser[N];\n",
    "    int<lower=0, upper=1> after[N]; \n",
    "} \n",
    "\n",
    "parameters {\n",
    "    real alpha;\n",
    "    real<lower=0> after_sigma_group;\n",
    "    real<lower=0> before_sigma_group;\n",
    "    vector<lower=0>[N_SU] kappa;\n",
    "    vector[N_SU] before_SU_tilde;\n",
    "    vector[N_SU] after_SU_tilde;\n",
    "\n",
    "}\n",
    "\n",
    "transformed parameters {\n",
    "    vector[N_SU] before_su = before_sigma_group * before_SU_tilde;\n",
    "    vector[N_SU] after_su = after_sigma_group * after_SU_tilde;\n",
    "    \n",
    "}\n",
    "\n",
    "\n",
    "model { \n",
    "    vector[N] mu;\n",
    "\n",
    "    alpha ~ normal(logit(.57), .5); //Emprical prior for grand mean\n",
    "    kappa ~ exponential(1);\n",
    "    before_sigma_group ~ exponential(1);\n",
    "    after_sigma_group ~ exponential(1);\n",
    "\n",
    "\n",
    "    before_SU_tilde ~ std_normal(); // non-centered parameterization\n",
    "    after_SU_tilde ~ std_normal(); // non-centered parameterization\n",
    "\n",
    "    for (n in 1:N){\n",
    "        mu[n] = exp(alpha + (1-after[n])*before_su[superuser[n]] + after[n] * after_su[superuser[n]]);\n",
    "        y[n] ~ gamma(kappa[superuser[n]], mu[n]/kappa[superuser[n]]);\n",
    "\n",
    "    }\n",
    "\n",
    "} \n",
    "\n",
    "\n",
    "generated quantities {\n",
    "\n",
    "    vector[N_SU] change;\n",
    "    vector[N_SU] before_hat;\n",
    "    vector[N_SU] after_hat;\n",
    "    vector[N] y_pred;\n",
    "    vector[N] mu_pred;\n",
    "\n",
    "    for(n in 1:N) {\n",
    "        mu_pred[n] = exp(alpha + (1-after[n])*before_su[superuser[n]] + after[n] * after_su[superuser[n]]);\n",
    "        y_pred[n] = gamma_rng(kappa[superuser[n]], mu_pred[n]/kappa[superuser[n]]); \n",
    "    }\n",
    "\n",
    "\n",
    "    for (s in 1:N_SU){\n",
    "        before_hat[s] = inv_logit(alpha + before_su[s]);\n",
    "        after_hat[s] = inv_logit(alpha + after_su[s]);\n",
    "        change[s] =  after_hat[s] - before_hat[s]; \n",
    "    }\n",
    "} \n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "def melt_shannon(shannon):\n",
    "    melted = pd.melt(shannon, id_vars=['id','suspended_user'],value_vars=['before_suspension', 'after_suspension'])\n",
    "    melted.head()\n",
    "    melted['variable'].unique()\n",
    "    melted = melted.dropna()\n",
    "    melted = melted[melted['value'] > 0]\n",
    "    melted['value'].max()\n",
    "    \n",
    "    shannon_data = {'N':melted.shape[0], \n",
    "              'after':np.array(melted['variable']=='after_suspension').astype('int'),\n",
    "              'y':melted['value'].values,\n",
    "             'superuser':pd.Categorical(melted['suspended_user']).codes.astype('int')+1,\n",
    "             'userid':pd.Categorical(melted['id']).codes.astype('int')+1,\n",
    "             'F':melted['id'].unique().size,\n",
    "             'N_SU':melted['suspended_user'].unique().size,\n",
    "\n",
    "              'ga':2,\n",
    "               'gb':2}\n",
    "    \n",
    "    return melted, shannon_data\n",
    "\n",
    "shannon_top = shannon[shannon['group']=='top']\n",
    "shannon_top['id'] = np.arange(shannon_top.shape[0])\n",
    "shannon_melted_top, shannon_data_top = melt_shannon(shannon_top) \n",
    "\n",
    "shannon_median = shannon[shannon['group']=='median']\n",
    "shannon_median['id'] = np.arange(shannon_median.shape[0])\n",
    "shannon_melted_median, shannon_data_median = melt_shannon(shannon_median) \n",
    "\n",
    "\n",
    "\n",
    "shannon_posterior_median = stan.build(shannon_code, data=shannon_data_median)\n",
    "shannon_fit_median = shannon_posterior_median.sample(num_chains=4, num_samples=3000)\n",
    "\n",
    "shannon_posterior_top = stan.build(shannon_code, data=shannon_data_top)\n",
    "shannon_fit_top = shannon_posterior_top.sample(num_chains=4, num_samples=3000)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e61aa497",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
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       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>3.079</td>\n",
       "      <td>0.116</td>\n",
       "      <td>2.854</td>\n",
       "      <td>3.284</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>2420.0</td>\n",
       "      <td>3778.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>after_sigma_group</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1492.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>before_sigma_group</th>\n",
       "      <td>0.047</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1499.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kappa[0]</th>\n",
       "      <td>10.332</td>\n",
       "      <td>0.669</td>\n",
       "      <td>9.140</td>\n",
       "      <td>11.619</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.012</td>\n",
       "      <td>1804.0</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kappa[1]</th>\n",
       "      <td>10.058</td>\n",
       "      <td>0.613</td>\n",
       "      <td>8.950</td>\n",
       "      <td>11.220</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.009</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>2673.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[593]</th>\n",
       "      <td>22.050</td>\n",
       "      <td>2.747</td>\n",
       "      <td>16.898</td>\n",
       "      <td>27.114</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[594]</th>\n",
       "      <td>22.050</td>\n",
       "      <td>2.747</td>\n",
       "      <td>16.898</td>\n",
       "      <td>27.114</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[595]</th>\n",
       "      <td>22.050</td>\n",
       "      <td>2.747</td>\n",
       "      <td>16.898</td>\n",
       "      <td>27.114</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>mu_pred[596]</th>\n",
       "      <td>22.050</td>\n",
       "      <td>2.747</td>\n",
       "      <td>16.898</td>\n",
       "      <td>27.114</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[597]</th>\n",
       "      <td>22.050</td>\n",
       "      <td>2.747</td>\n",
       "      <td>16.898</td>\n",
       "      <td>27.114</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.049</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1247 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "alpha                3.079  0.116   2.854    3.284      0.002    0.002   \n",
       "after_sigma_group    0.045  0.061   0.000    0.132      0.003    0.002   \n",
       "before_sigma_group   0.047  0.061   0.000    0.138      0.002    0.002   \n",
       "kappa[0]            10.332  0.669   9.140   11.619      0.017    0.012   \n",
       "kappa[1]            10.058  0.613   8.950   11.220      0.013    0.009   \n",
       "...                    ...    ...     ...      ...        ...      ...   \n",
       "mu_pred[593]        22.050  2.747  16.898   27.114      0.067    0.049   \n",
       "mu_pred[594]        22.050  2.747  16.898   27.114      0.067    0.049   \n",
       "mu_pred[595]        22.050  2.747  16.898   27.114      0.067    0.049   \n",
       "mu_pred[596]        22.050  2.747  16.898   27.114      0.067    0.049   \n",
       "mu_pred[597]        22.050  2.747  16.898   27.114      0.067    0.049   \n",
       "\n",
       "                    ess_bulk  ess_tail  r_hat  \n",
       "alpha                 2420.0    3778.0    1.0  \n",
       "after_sigma_group     1492.0     714.0    1.0  \n",
       "before_sigma_group    1499.0     691.0    1.0  \n",
       "kappa[0]              1804.0    1543.0    1.0  \n",
       "kappa[1]              2224.0    2673.0    1.0  \n",
       "...                      ...       ...    ...  \n",
       "mu_pred[593]          1845.0    1504.0    1.0  \n",
       "mu_pred[594]          1845.0    1504.0    1.0  \n",
       "mu_pred[595]          1845.0    1504.0    1.0  \n",
       "mu_pred[596]          1845.0    1504.0    1.0  \n",
       "mu_pred[597]          1845.0    1504.0    1.0  \n",
       "\n",
       "[1247 rows x 9 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(shannon_fit_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0486e5c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
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       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>1.902</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.973</td>\n",
       "      <td>2.813</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2265.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
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       "      <th>after_sigma_group</th>\n",
       "      <td>1.554</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.631</td>\n",
       "      <td>2.693</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
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       "      <td>4894.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>before_sigma_group</th>\n",
       "      <td>1.565</td>\n",
       "      <td>0.611</td>\n",
       "      <td>0.584</td>\n",
       "      <td>2.674</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>2632.0</td>\n",
       "      <td>4672.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>kappa[0]</th>\n",
       "      <td>4.994</td>\n",
       "      <td>0.664</td>\n",
       "      <td>3.800</td>\n",
       "      <td>6.246</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>11970.0</td>\n",
       "      <td>7803.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kappa[1]</th>\n",
       "      <td>13.340</td>\n",
       "      <td>1.887</td>\n",
       "      <td>9.864</td>\n",
       "      <td>16.963</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.013</td>\n",
       "      <td>11139.0</td>\n",
       "      <td>7885.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[592]</th>\n",
       "      <td>26.481</td>\n",
       "      <td>7.474</td>\n",
       "      <td>13.600</td>\n",
       "      <td>40.611</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.052</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[593]</th>\n",
       "      <td>26.481</td>\n",
       "      <td>7.474</td>\n",
       "      <td>13.600</td>\n",
       "      <td>40.611</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.052</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[594]</th>\n",
       "      <td>26.481</td>\n",
       "      <td>7.474</td>\n",
       "      <td>13.600</td>\n",
       "      <td>40.611</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.052</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[595]</th>\n",
       "      <td>26.481</td>\n",
       "      <td>7.474</td>\n",
       "      <td>13.600</td>\n",
       "      <td>40.611</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.052</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu_pred[596]</th>\n",
       "      <td>26.481</td>\n",
       "      <td>7.474</td>\n",
       "      <td>13.600</td>\n",
       "      <td>40.611</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.052</td>\n",
       "      <td>10007.0</td>\n",
       "      <td>7534.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1245 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "alpha                1.902  0.503   0.973    2.813      0.010    0.008   \n",
       "after_sigma_group    1.554  0.596   0.631    2.693      0.011    0.008   \n",
       "before_sigma_group   1.565  0.611   0.584    2.674      0.011    0.008   \n",
       "kappa[0]             4.994  0.664   3.800    6.246      0.006    0.004   \n",
       "kappa[1]            13.340  1.887   9.864   16.963      0.018    0.013   \n",
       "...                    ...    ...     ...      ...        ...      ...   \n",
       "mu_pred[592]        26.481  7.474  13.600   40.611      0.073    0.052   \n",
       "mu_pred[593]        26.481  7.474  13.600   40.611      0.073    0.052   \n",
       "mu_pred[594]        26.481  7.474  13.600   40.611      0.073    0.052   \n",
       "mu_pred[595]        26.481  7.474  13.600   40.611      0.073    0.052   \n",
       "mu_pred[596]        26.481  7.474  13.600   40.611      0.073    0.052   \n",
       "\n",
       "                    ess_bulk  ess_tail  r_hat  \n",
       "alpha                 2265.0    4605.0    1.0  \n",
       "after_sigma_group     2711.0    4894.0    1.0  \n",
       "before_sigma_group    2632.0    4672.0    1.0  \n",
       "kappa[0]             11970.0    7803.0    1.0  \n",
       "kappa[1]             11139.0    7885.0    1.0  \n",
       "...                      ...       ...    ...  \n",
       "mu_pred[592]         10007.0    7534.0    1.0  \n",
       "mu_pred[593]         10007.0    7534.0    1.0  \n",
       "mu_pred[594]         10007.0    7534.0    1.0  \n",
       "mu_pred[595]         10007.0    7534.0    1.0  \n",
       "mu_pred[596]         10007.0    7534.0    1.0  \n",
       "\n",
       "[1245 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(shannon_fit_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3fb4827c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def adjust(x, window=30):\n",
    "    return 100*x / np.median(x[:window])\n",
    "\n",
    "\n",
    "\n",
    "def plot_shannon(melted, fit,axs, ylim=.2):\n",
    "    plt.sca(axs)\n",
    "    codes = pd.Categorical(melted['suspended_user']).codes\n",
    "    convert_dict = json.load(open('./dat/user_followers_count.json','rb'))\n",
    "    for code in np.unique(codes):\n",
    "        account = melted[codes==code].iloc[0]['suspended_user']\n",
    "        xloc = convert_dict[account]/1000\n",
    "        mu = np.mean(fit['change'][code])\n",
    "        plt.scatter(xloc, mu,color='k',zorder=1)#np.array([249,214,73])/255)\n",
    "\n",
    "        for ps in [3, 6, 11, 25,50,]:\n",
    "            ci = np.nanpercentile(fit['change'][code], q=[0+ps/2, 100-ps/2])\n",
    "            plt.plot([xloc, xloc], [ci[0], ci[1]],\n",
    "                     solid_capstyle='projecting',\n",
    "                     lw=10,alpha=ps/50*.5,color=red,zorder=2)\n",
    "    #plt.plot([0,600], [mu, mu], color='k',zorder=3)\n",
    "    #plt.fill_between([0, 600], [ci[0], ci[0]], [ci[1], ci[1]], color=red, alpha=.25,zorder=1)\n",
    "    plt.ylim(-ylim, ylim)\n",
    "    plt.plot([0, 600],[0,0], ls='--',color='k') \n",
    "    plt.xlim(0,550)\n",
    "    plt.ylabel('Change in \\nShannon Diversity (bits)')\n",
    "    plt.xlabel('Followers (thousands)')\n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b01d49f",
   "metadata": {},
   "source": [
    "<h1>Figure 3</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8342f6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,1, figsize=(6,11/2))\n",
    "\n",
    "\n",
    "plot_shannon(shannon_melted_top, shannon_fit_top,axs[0],ylim=.05)\n",
    "plt.title('Shannon diversity: highly engaged')\n",
    "plot_shannon(shannon_melted_median, shannon_fit_median,axs[1],ylim=.05)\n",
    "plt.title('Shannon diversity: moderately engaged')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "for n, ax in enumerate(axs):\n",
    "    ax.text(-0.075, 1.1, string.ascii_uppercase[n], transform=ax.transAxes, \n",
    "            size=20, weight='bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./output/figures/fig3.png',dpi=300)\n",
    "plt.savefig('./output/figures/fig3.pdf',dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pystan_source",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "9fb9e528dd9429ad7e27dd089f7aedbfc1e65580b51b541a36394fd0a2cf6a69"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}