ace_dagwithnotear_algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.utils import data_load\n",
    "import pandas as pd\n",
    "from src.s3_utils import pandas_from_csv_s3\n",
    "import re"
   ]
  },
  {
   "source": [
    "# Data processing: Join PHQ9, GAD7 and ACE datasets together by record_id and redcap_event_name"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_load(data_keys={'phq9', 'generalized_anxiety_disorder_scale_gad7', 'ace', 'surveys', 'study_ids', 'check_in_adherence_log'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcomes = pd.merge(data['phq9'], data['generalized_anxiety_disorder_scale_gad7'],  how='outer', left_on=['record_id','redcap_event_name'], right_on = ['record_id','redcap_event_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_df = pd.merge(data['ace'].drop(columns=['redcap_event_name']).dropna(), outcomes, how='left', on='record_id')"
   ]
  },
  {
   "source": [
    "# Convert redcap_event_name to date for PHQ9, GAD7 and ACE datasets"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read study ids\n",
    "id_df = data['study_ids'][['record_id', 'evidation_id']]\n",
    "id_df.rename(columns={'evidation_id': 'user_id'}, inplace=True)\n",
    "\n",
    "# add ids to survey\n",
    "overall_df = overall_df.merge(id_df, on=['record_id'])\n",
    "overall_df.user_id = overall_df.user_id.fillna(-1).astype(int)\n",
    "\n",
    "# standarize naming convention for easier processing later on\n",
    "overall_df.redcap_event_name = overall_df.redcap_event_name.replace('postnatal_checkin_arm_1','postnatal_ci_1_arm_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read check-in dates\n",
    "ci_df = data['check_in_adherence_log']\n",
    "cols = ['record_id'] + [col for col in ci_df.columns if '_date' in col]\n",
    "ci_df = ci_df[cols]\n",
    "\n",
    "# standarize naming convention for easier processing later on\n",
    "ci_df = ci_df.rename(columns={'checkin_postnatal_date': 'checkin_postnatal_date_1'})\n",
    "\n",
    "# add dates to survey, need to map it using the check_in_adherence_log\n",
    "def conver_checkin_string(x):\n",
    "    x = x.split('_arm')[0] #delete all characters after the word 'arm'\n",
    "    num = int(re.search(r'\\d+', x).group())\n",
    "    if 'postnatal' in x:\n",
    "        return f'checkin_postnatal_date_{num}'\n",
    "    else:\n",
    "        return f'checkin_{num}_date'\n",
    "\n",
    "# map checkin_postnature_date_{num} OR checkin_{num}_date to the actual date\n",
    "def map_date(x):\n",
    "    checkin_string_col = x['checkin_string']\n",
    "    return x[checkin_string_col]\n",
    "\n",
    "overall_df = overall_df.merge(ci_df, on=['record_id'])\n",
    "overall_df['checkin_string'] = overall_df.redcap_event_name.apply(conver_checkin_string)\n",
    "overall_df['date'] = overall_df.apply(map_date, axis=1)\n",
    "overall_df = overall_df[overall_df.columns.drop(list(overall_df.filter(regex='checkin_')))]\n",
    "overall_df['date'] = pd.to_datetime(overall_df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "          date     redcap_event_name\n203 2021-04-30            ci_1_arm_1\n204 2021-05-11            ci_2_arm_1\n205 2021-05-25            ci_3_arm_1\n206 2021-06-08            ci_4_arm_1\n207 2021-06-22            ci_5_arm_1\n208 2021-08-02            ci_8_arm_1\n209 2021-08-17            ci_9_arm_1\n210 2021-09-07           ci_10_arm_1\n211 2021-10-01  postnatal_ci_1_arm_1\n212 2021-10-26  postnatal_ci_2_arm_1\n213 2021-11-18  postnatal_ci_3_arm_1",
      "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>date</th>\n      <th>redcap_event_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>203</th>\n      <td>2021-04-30</td>\n      <td>ci_1_arm_1</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>2021-05-11</td>\n      <td>ci_2_arm_1</td>\n    </tr>\n    <tr>\n      <th>205</th>\n      <td>2021-05-25</td>\n      <td>ci_3_arm_1</td>\n    </tr>\n    <tr>\n      <th>206</th>\n      <td>2021-06-08</td>\n      <td>ci_4_arm_1</td>\n    </tr>\n    <tr>\n      <th>207</th>\n      <td>2021-06-22</td>\n      <td>ci_5_arm_1</td>\n    </tr>\n    <tr>\n      <th>208</th>\n      <td>2021-08-02</td>\n      <td>ci_8_arm_1</td>\n    </tr>\n    <tr>\n      <th>209</th>\n      <td>2021-08-17</td>\n      <td>ci_9_arm_1</td>\n    </tr>\n    <tr>\n      <th>210</th>\n      <td>2021-09-07</td>\n      <td>ci_10_arm_1</td>\n    </tr>\n    <tr>\n      <th>211</th>\n      <td>2021-10-01</td>\n      <td>postnatal_ci_1_arm_1</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>2021-10-26</td>\n      <td>postnatal_ci_2_arm_1</td>\n    </tr>\n    <tr>\n      <th>213</th>\n      <td>2021-11-18</td>\n      <td>postnatal_ci_3_arm_1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.set_option('display.max_columns', 10000)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('max_colwidth', None)\n",
    "overall_df.loc[overall_df['record_id'] == 28][['date', 'redcap_event_name']] #check if dates are correctly processed"
   ]
  },
  {
   "source": [
    "# Add Global survey data - PROMIS quality of life"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_dict0 = {\n",
    "    0: 4,\n",
    "    1: 3,\n",
    "    2: 3,\n",
    "    4: 2,\n",
    "    5: 2,\n",
    "    6: 2,\n",
    "    7: 1,\n",
    "    8: 1,\n",
    "    9: 1,\n",
    "    10: 0\n",
    "}\n",
    "answer_dict1 = {\n",
    "    'Excellent': 4,\n",
    "    'Very good': 3,\n",
    "    'Good': 2,\n",
    "    'Fair': 1,\n",
    "    'Poor': 0\n",
    "}\n",
    "answer_dict2 = {\n",
    "    'Completely': 4,\n",
    "    'Mostly': 3,\n",
    "    'Moderately': 2,\n",
    "    'A little': 1,\n",
    "    'Not at all': 0\n",
    "}\n",
    "answer_dict3 = {\n",
    "    'Never': 4,\n",
    "    'Rarely': 3,\n",
    "    'Sometimes': 2,\n",
    "    'Often': 2,\n",
    "    'Always': 0\n",
    "}\n",
    "answer_dict4 = {\n",
    "    'None': 4,\n",
    "    'Mild': 3,\n",
    "    'Moderate': 2,\n",
    "    'Severe': 1,\n",
    "    'Very severe': 0\n",
    "}\n",
    "# question 119 and 114 are not used in Global Physical Health and Global Mental Health. Refer to the BUMP data dictionary excel sheet\n",
    "drop_question_ids = [114, 119]\n",
    "promis_survey = data['surveys'].loc[data['surveys']['title']=='Global survey']\n",
    "promis_survey.drop(promis_survey[promis_survey['question_id'].isin(drop_question_ids)].index, inplace=True)\n",
    "for qid in promis_survey['question_id'].unique():\n",
    "    question = promis_survey.loc[promis_survey['question_id'] == qid]\n",
    "    if qid == 123:\n",
    "        question['answer_text'] = question['answer_text'].astype(int)\n",
    "        question.replace({\"answer_text\": answer_dict0}, inplace=True)\n",
    "    elif qid in {115, 116, 117, 118}:\n",
    "        question.replace({\"answer_text\": answer_dict1}, inplace=True)\n",
    "    elif qid == 120:\n",
    "        question.replace({\"answer_text\": answer_dict2}, inplace=True)\n",
    "    elif qid == 121:\n",
    "        question.replace({\"answer_text\": answer_dict3}, inplace=True)\n",
    "    elif qid == 122:\n",
    "        question.replace({\"answer_text\": answer_dict4}, inplace=True)\n",
    "    promis_survey.loc[promis_survey['question_id'] == qid] = question\n",
    "promis_survey['date'] = pd.to_datetime(promis_survey['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "        id  user_id  survey_id          title           event_date                 from                   to                  created_at                  updated_at  question_id                                                        question_text answer_text       date\n0  1598695     2160         11  Global survey  2022-06-15 00:00:00  2022-06-15 05:00:00  2022-06-16 04:00:00  2022-06-15 04:29:34.002629  2022-06-15 14:54:00.989426          123  In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?           4 2022-06-15\n1  1598693     2038         11  Global survey  2022-06-15 00:00:00  2022-06-15 05:00:00  2022-06-16 04:00:00  2022-06-15 04:29:32.154151  2022-06-15 16:53:41.712904          123  In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?           4 2022-06-15\n4  1594549      190         11  Global survey  2022-06-15 00:00:00  2022-06-14 05:00:00  2022-06-15 04:00:00   2022-06-14 05:28:01.57594  2022-06-15 01:58:49.913336          123  In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?           4 2022-06-15\n5  1589873     1032         11  Global survey  2022-06-13 00:00:00  2022-06-13 06:00:00  2022-06-14 05:00:00  2022-06-13 06:30:20.323898  2022-06-13 16:58:08.632105          123  In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?           4 2022-06-13\n6  1604557     2166         11  Global survey  2022-06-17 00:00:00  2022-06-16 05:00:00  2022-06-17 04:00:00  2022-06-16 04:26:11.000158  2022-06-17 01:01:11.174852          123  In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?           4 2022-06-17",
      "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>id</th>\n      <th>user_id</th>\n      <th>survey_id</th>\n      <th>title</th>\n      <th>event_date</th>\n      <th>from</th>\n      <th>to</th>\n      <th>created_at</th>\n      <th>updated_at</th>\n      <th>question_id</th>\n      <th>question_text</th>\n      <th>answer_text</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1598695</td>\n      <td>2160</td>\n      <td>11</td>\n      <td>Global survey</td>\n      <td>2022-06-15 00:00:00</td>\n      <td>2022-06-15 05:00:00</td>\n      <td>2022-06-16 04:00:00</td>\n      <td>2022-06-15 04:29:34.002629</td>\n      <td>2022-06-15 14:54:00.989426</td>\n      <td>123</td>\n      <td>In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?</td>\n      <td>4</td>\n      <td>2022-06-15</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1598693</td>\n      <td>2038</td>\n      <td>11</td>\n      <td>Global survey</td>\n      <td>2022-06-15 00:00:00</td>\n      <td>2022-06-15 05:00:00</td>\n      <td>2022-06-16 04:00:00</td>\n      <td>2022-06-15 04:29:32.154151</td>\n      <td>2022-06-15 16:53:41.712904</td>\n      <td>123</td>\n      <td>In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?</td>\n      <td>4</td>\n      <td>2022-06-15</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1594549</td>\n      <td>190</td>\n      <td>11</td>\n      <td>Global survey</td>\n      <td>2022-06-15 00:00:00</td>\n      <td>2022-06-14 05:00:00</td>\n      <td>2022-06-15 04:00:00</td>\n      <td>2022-06-14 05:28:01.57594</td>\n      <td>2022-06-15 01:58:49.913336</td>\n      <td>123</td>\n      <td>In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?</td>\n      <td>4</td>\n      <td>2022-06-15</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1589873</td>\n      <td>1032</td>\n      <td>11</td>\n      <td>Global survey</td>\n      <td>2022-06-13 00:00:00</td>\n      <td>2022-06-13 06:00:00</td>\n      <td>2022-06-14 05:00:00</td>\n      <td>2022-06-13 06:30:20.323898</td>\n      <td>2022-06-13 16:58:08.632105</td>\n      <td>123</td>\n      <td>In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?</td>\n      <td>4</td>\n      <td>2022-06-13</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1604557</td>\n      <td>2166</td>\n      <td>11</td>\n      <td>Global survey</td>\n      <td>2022-06-17 00:00:00</td>\n      <td>2022-06-16 05:00:00</td>\n      <td>2022-06-17 04:00:00</td>\n      <td>2022-06-16 04:26:11.000158</td>\n      <td>2022-06-17 01:01:11.174852</td>\n      <td>123</td>\n      <td>In the past 7 days:\\r\\n\\r\\nHow would you rate your pain on average?</td>\n      <td>4</td>\n      <td>2022-06-17</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "promis_survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "promis_mental_lst = [f'promis_global10_mental_{x}' for x in range(1,5)]\n",
    "promis_physical_lst = [f'promis_global10_physical_{x}' for x in range(1,5)]\n",
    "mental_health_question_ids = [115, 117, 118, 121]\n",
    "physical_health_question_ids = [116, 120, 122, 123]\n",
    "promis_survey_processed = pd.DataFrame(columns=['user_id', 'date'] + promis_mental_lst + promis_physical_lst)\n",
    "\n",
    "for uid in promis_survey['user_id'].unique():\n",
    "    each_user_survey = promis_survey.loc[promis_survey['user_id'] == uid]\n",
    "    for date in each_user_survey['date'].unique():\n",
    "        # answer_text = each_user_survey.loc[each_user_survey['date'] == date].sort_values(by='question_id')['answer_text'].to_numpy()\n",
    "        each_date_survey = each_user_survey.loc[each_user_survey['date'] == date]\n",
    "        mental_health_answers = each_date_survey[each_date_survey['question_id'].isin(mental_health_question_ids)].sort_values(by='question_id')['answer_text']\n",
    "        physical_health_answers = each_date_survey[each_date_survey['question_id'].isin(physical_health_question_ids)].sort_values(by='question_id')['answer_text']\n",
    "        if len(mental_health_answers) == 4 and len(physical_health_answers) == 4:\n",
    "            promis_survey_processed = promis_survey_processed.append(pd.DataFrame([[uid, date] + mental_health_answers.tolist() + physical_health_answers.tolist()], columns=['user_id', 'date'] + promis_mental_lst + promis_physical_lst), ignore_index = True)"
   ]
  },
  {
   "source": [
    "# Process PHQ9, GAD and PROMIS data by taking the average over time for each individual"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # more balanced set up\n",
    "# ace_levels = {\n",
    "#     (0, 1) : 0,\n",
    "#     (2, 10): 1\n",
    "# }\n",
    "# phq9_levels = {\n",
    "#     (0, 4) : 0,\n",
    "#     (5, 9): 1,\n",
    "#     (10, 14): 1,\n",
    "#     (15, 19): 1,\n",
    "#     (20, 27): 1\n",
    "# }\n",
    "# gad_levels = {\n",
    "#     (0, 4) : 0,\n",
    "#     (5, 9): 1,\n",
    "#     (10, 14): 1,\n",
    "#     (15, 21): 1\n",
    "# }\n",
    "# promis_levels = {\n",
    "#     \"Always\" : 1,\n",
    "#     \"Often\": 1,\n",
    "#     \"Sometimes\": 1,\n",
    "#     \"Rarely\": 0,\n",
    "#     \"Never\": 0\n",
    "# }\n",
    "\n",
    "# from literature\n",
    "ace_levels = {\n",
    "    (0, 4) : 0,\n",
    "    (5, 10): 1\n",
    "}\n",
    "phq9_levels = {\n",
    "    (0, 4) : 0,\n",
    "    (5, 9): 1,\n",
    "    (10, 14): 2,\n",
    "    (15, 19): 3,\n",
    "    (20, 27): 4\n",
    "}\n",
    "gad_levels = {\n",
    "    (0, 4) : 0,\n",
    "    (5, 9): 1,\n",
    "    (10, 14): 2,\n",
    "    (15, 21): 3\n",
    "}\n",
    "promis_mental_levels = {\n",
    "    (0, 11) : 0,\n",
    "    (12, 16): 1,\n",
    "}\n",
    "promis_physical_levels = {\n",
    "    (0, 10) : 0,\n",
    "    (11, 16): 1,\n",
    "}\n",
    "def map_levels(x, map_dict):\n",
    "    for key in map_dict:\n",
    "        if isinstance(x, str):\n",
    "            if x == key:\n",
    "                return map_dict[key]\n",
    "        else:\n",
    "            if x >= key[0] and x <= key[1]:\n",
    "                return map_dict[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_overall_df = pd.DataFrame(columns=['user_id', 'ace_sum', 'phq9_sum', 'gad_sum', 'promis_mental_mean', 'promis_physical_mean'])\n",
    "ace_lst = [f'ace_{x}' for x in range(1,11)]\n",
    "phq9_lst = [f'phq9_{x}' for x in range(1,11)]\n",
    "gad_lst = [f'gad_{x}' for x in range(1,9)]\n",
    "for uid in overall_df['user_id'].unique():\n",
    "    each_df = overall_df.loc[overall_df['user_id']==uid]\n",
    "    ace_sum = each_df[ace_lst].sum(axis=1)\n",
    "    ace_sum_mean = ace_sum.apply(map_levels, map_dict=ace_levels).mean()\n",
    "\n",
    "    phq9_sum = each_df[phq9_lst].sum(axis=1)\n",
    "    phq9_sum_mean = phq9_sum.apply(map_levels, map_dict=phq9_levels).mean()\n",
    "\n",
    "    gad_sum = each_df[gad_lst].sum(axis=1)\n",
    "    gad_sum_mean = gad_sum.apply(map_levels, map_dict=gad_levels).mean()\n",
    "\n",
    "    promis_mental_sum = promis_survey_processed.loc[promis_survey_processed['user_id']==uid][promis_mental_lst].sum(axis=1)\n",
    "    promis_mental_mean = promis_mental_sum.apply(map_levels, map_dict=promis_mental_levels).mean()\n",
    "\n",
    "    promis_physical_sum = promis_survey_processed.loc[promis_survey_processed['user_id']==uid][promis_physical_lst].sum(axis=1)\n",
    "    promis_physical_mean = promis_physical_sum.apply(map_levels, map_dict=promis_physical_levels).mean()\n",
    "    \n",
    "    processed_overall_df = processed_overall_df.append({'user_id': uid, 'ace_sum': ace_sum_mean, 'promis_physical_mean': promis_physical_mean, 'promis_mental_mean': promis_mental_mean, 'phq9_sum': phq9_sum_mean, 'gad_sum': gad_sum_mean}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_overall_df = processed_overall_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# processed_overall_df.to_csv('processed_causal_ace_4nodes_ref_levels_reverse.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# processed_overall_df[['phq9_sum', 'gad_sum', 'promis_mean']] = processed_overall_df[['phq9_sum', 'gad_sum', 'promis_mean']] * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Matplotlib created a temporary config/cache directory at /tmp/matplotlib-939dl5j6 because the default path (/home/ubuntu/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 14
    },
    {
     "output_type": "display_data",
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   ],
   "source": [
    "processed_overall_df['ace_sum'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 15
    },
    {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "processed_overall_df['phq9_sum'].round().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
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     }
    }
   ],
   "source": [
    "processed_overall_df['gad_sum'].round().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
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    }
   ],
   "source": [
    "processed_overall_df['promis_mental_mean'].round().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "processed_overall_df['promis_physical_mean'].round().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   user_id  ace_sum  phq9_sum   gad_sum  promis_mental_mean  promis_physical_mean\n0     28.0      1.0  1.000000  1.357143                0.00              0.777778\n1     29.0      0.0  1.384615  1.615385                0.00              0.571429\n2     30.0      0.0  0.083333  0.333333                0.00              1.000000\n3     31.0      0.0  1.333333  2.333333                0.00              0.000000\n5     37.0      0.0  0.916667  0.500000                0.25              0.750000",
      "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>user_id</th>\n      <th>ace_sum</th>\n      <th>phq9_sum</th>\n      <th>gad_sum</th>\n      <th>promis_mental_mean</th>\n      <th>promis_physical_mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>28.0</td>\n      <td>1.0</td>\n      <td>1.000000</td>\n      <td>1.357143</td>\n      <td>0.00</td>\n      <td>0.777778</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>29.0</td>\n      <td>0.0</td>\n      <td>1.384615</td>\n      <td>1.615385</td>\n      <td>0.00</td>\n      <td>0.571429</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>30.0</td>\n      <td>0.0</td>\n      <td>0.083333</td>\n      <td>0.333333</td>\n      <td>0.00</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>31.0</td>\n      <td>0.0</td>\n      <td>1.333333</td>\n      <td>2.333333</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>37.0</td>\n      <td>0.0</td>\n      <td>0.916667</td>\n      <td>0.500000</td>\n      <td>0.25</td>\n      <td>0.750000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "processed_overall_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx\n",
    "import notears.notears as notears\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = processed_overall_df[['ace_sum', 'phq9_sum', 'gad_sum', 'promis_mental_mean', 'promis_physical_mean']].to_numpy().tolist()\n",
    "output_dict = notears.run(notears.notears_standard, data, notears.loss.least_squares_loss, notears.loss.least_squares_loss_grad, e=1e-8, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Acyclicity loss: 4.401245590202052e-09\nLeast squares loss: 0.5650673414085177\n"
    }
   ],
   "source": [
    "print('Acyclicity loss: {}'.format(output_dict['h']))\n",
    "print('Least squares loss: {}'.format(output_dict['loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fccb3aea358>"
     },
     "metadata": {},
     "execution_count": 23
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.matshow(output_dict['W'])\n",
    "plt.title(\"Learned adjacency matrix\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "acyclic_W = notears.utils.threshold_output(output_dict['W'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fccb2c82a90>"
     },
     "metadata": {},
     "execution_count": 25
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 288x288 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.matshow(acyclic_W)\n",
    "plt.title(\"Learned adjacency matrix (thresholded)\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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YBpEP8jhRpSJ++OEHuLu7o0mTJt8+WEI0NDTQvn17hIeHw87OrsbOS8ondcHn2tkIWy89LfkGj4dP988j7aIvwERQbKCDhrbfoW6r7iUOLSgoxLuI08VeU1NTg4WFBaKiohAYGIjo6Gjk5ubC1NQUZmZmxf7o6+tTIBKZ8N+JKsOHD5eriSriCg0Nxe3bt3Ho0KEaP7eDgwMuXrxIwSdFpC74tNTqoE9r7RLP8fHrNoDe2PXfboCJYNlUHSZ9eyI4OBgFBQUAvvzmpaOjA29v76JbDmlpaYiOji76c+rUKURHR6OgoKAoBL/2Ds3MzKCjo0M/TIhUoIkq4issLMTcuXOxefNmTm432tvbV/sMUlIxUvkAe1krt4hDESJkBqyEm1NfvHr1CoGBgSgoKMDKlSsRHByMyMhIODg4wMXFBQMHDoSamlqJNt69e1csEL/+4fF4JXqHZmZmdCuJ1Ah5WVFF0nbt2gV/f39cvnyZk19chUIhtLW1ERUVBQMDgxo/PylJKoMPqNpanTaNlbF48WIEBQWhadOmqFevHoKDgwEAqampOHXqFPz8/HDz5k3Y2NjAxcUFTk5OaNCgQZltM8aQkpJSIgyjoqKgrKxcrGf49Q9NpiFVVdbWP/b29rTMnRjS0tJgYmKCy5cvo127dpzVMXz4cDg6OsLNzY2zGsj/k9rgA6q+O8OdO3fg7u6OvLw8+Pj4oGfPnsU+9/HjR5w5cwb+/v4IDQ1Fz5494erqiiFDhoi9ogNjDImJiaX2EOvVq1dqD1FDQ6MS3w0iT2rT1j9cmjlzJhQVFbF9+3ZO69i3bx9CQ0Nx7NgxTusgX0h18AHAo/h0+F55jtAnqeDhy8PpX33dj69vG23MtG5Z6sLUjDH88ccf+PHHH2FlZYUNGzagcePGJY779OkTzp07B39/fwQFBaFr165wcXHB0KFDoaenV+G6GWOIj48v1jOMjo5GbGwsGjRoUCIMTU1Noa6uXuHzkNqjtIkqU6ZMoYkqlfTgwQM4ODggLi4ODRs25LSW169fo0uXLkhJSaGtw6SA1AffV2lZefCLjEdc0idk5hZAXUUJAv36cO0k3g7s2dnZ2LhxI3bu3IlZs2ZhwYIFqFevXqnHfv78GRcuXIC/vz8CAwPRrl07uLi4YNiwYTA2Nq7SdYhEIrx586ZE7/DrYrn/nVRjYmJS6jgkqT2io6Oxf//+ookqkydPhqurK01UqQLGGPr06YOxY8di+vTpXJcDABAIBDh69Cg6d+7MdSlyT2aCT1LevHmDhQsXIjw8HOvXr8eYMWPK/W06Ly8PwcHB8Pf3x+nTp9GqVSu4uLjAxcUFzZs3l1hdIpEIr169KuoZfv3z5MkT6OrqlughmpiY0A9GGZaRkYE///wT+/fvp4kq1eDPP//E+vXrcffuXal5aN/d3R16enpYtGgR16XIPbkLvq/Cw8Ph4eEBZWVl+Pj4oGvXrt/8TEFBAa5cuQI/Pz+cPHkShoaGRSEoEAiqpU6hUIiXL1+W6CE+ffoUBgYGJSbVCAQCWpJNSn2dqHLgwAGcOnWKJqpUk+zsbJiYmODo0aPo1asX1+UUOXfuHDZt2oTQ0FCuS5F7cht8wJde1qFDh7BkyRLY2dlh3bp1Yk83FgqFCA8Ph7+/P/z9/aGhoQEXFxe4urqiXbt21T4mU1hYiOfPn5cIxBcvXsDY2LhED7FNmzaoU+fbt4SJ5NFElZq1bNkyPH/+XOomkmRnZ0NPTw9JSUk0fMExuQ6+rzIzM7F27Vrs27cP8+fPh6enZ4V6TSKRCLdu3SoKQUVFxaKeYJcuXWp0YkJBQQGePXtW4pGLV69eoWnTpiUCsVWrVrT9UjWgiSrc+Oeff9ClSxc8fPgQRkZGXJdTgo2NDTw9PeHo6Mh1KXKNgu9fXrx4gR9++AH379/Hxo0b4eLiUuEfUowxREZGFoVgXl4ehg0bBhcXF1haWnI2oysvLw9Pnz4t0UN88+YNmjdvXmog0u23iqOJKtwaNmwYOnfujCVLlnBdSqm8vb0RHx+PHTt2cF2KXKPgK0VISAg8PDzQqFEjbNu2DR06dKhUO4wxREdHw8/PD/7+/vjw4QOGDh0KFxcX9OrVSyqCJTc3F0+ePCkxqSYhIQGtWrUqEYgtWrSQmskC0oImqkiHS5cuYdq0aYiJiZHace779+9j1KhRePLkCdelyDUKvjIUFhbi119/xfLlyzFkyBCsXr0aOjo6VWrzyZMnRT3Bt2/fYsiQIXB1dUXfvn2l7nbj58+fERcXV6KHmJycjNatWxcLw7Zt26JZs2ZS+3zS+6w8+N2LR1xyJjJzC6GuogiBnjqGdxbvUZjS/Heiiq2tLaZMmUITVThSUFCADh06YM2aNXB2dua6nDKJRCLo6+vj1q1baNq0KdflyC0Kvm/4+PEjVq1ahcOHD2PRokWYM2eORELqn3/+wd9//w0/Pz88ffoUjo6OcHFxgb29vdT+tgoAWVlZiI2NLRGI79+/h0AgKNFDbNKkCWeB+PBtOnZdeY6wp6kAUGyrq6+LH1i30cbMPi1hbqwhVps0UUU6bd++HWfPnsXFixelfgx13Lhx6N27Ny1czSEKPjHFxcVh/vz5ePbsGTZv3gxHR0eJ/QOLj49HQEAA/P398eDBAwwYMAAuLi4YMGBAmQ/ZS5tPnz4hJiam2Co10dHRSE9Ph4mJSYlAbNy4cbX+gKrqcnf/RhNVpFtqaipMTU0RFhYGU1NTrsv5psOHD+PkyZPw9/fnuhS5RcFXQefPn4enpyeMjY2xdetWmJmZSbT9lJSUon8Ut27dQr9+/eDi4gJHR0eZXNIsPT29KBD//ScrK6vUvRAlsTlwVRY4/3f4lTZRxcXFRWZ+GZEX06ZNQ7169bB161auSxFLUlISTE1NkZqaSrfFOULBVwkFBQXw9fXF6tWrMWrUKKxcubJadmL48OEDTp8+DT8/P1y9ehV9+vSBi4sLBg8eLPM7P3z48KHUhb3z8vKqtDlwWVtascICpAX5IvfVA4hys6CooYeGfdyg2qJL0TGqSnzsH9MWUVcDaaKKjIiMjMTAgQMRFxcnU4u/m5ubY/fu3bCysuK6FLlEwVcF79+/x/Lly3HixAksW7YMM2bMqLbf4DIyMnDu3Dn4+fnh8uXL6N69O1xcXODs7AxdXd1qOScX3r9/X+rWTyKRqNSdLv67OfC0w3dLbGIMAKL8XGTe8odau37gN9BGzou7eH96Iwwm74SixpfvH48H1E17BsH7azRRRQYwxtCrVy+4ubnhu+++47qcClmwYAHq1q2LFStWcF2KXKLgk4DHjx9j3rx5SEpKwtatW2Fvb1+t58vOzsb58+fh7++P8+fPo0OHDkWLaBsaGlbrubnAGCtzc2AFBYWiEGwqaIdf3zVBvlC8/0sn7p+NBj1Go56gR9FrdRQVcGOhTaVne5Kac+zYMWzZsgW3bt2SuUdsLl26hKVLl+LmzZtclyKXKPgkhDGG06dPY/78+TAxMcHmzZvRunXraj9vbm4ugoKC4O/vjzNnzkAgEBStGlPbp0szxpCcnFwUgude5uGVmhnyxRjaE2Z/RLzvZBhM3g4lzf/fcUNFUQHz7Fpjeu8W1Vg5qaqsrCwIBAL89ddfMnm7MDc3F9ra2njz5g3nWybJI+l88EoG8Xg8DBkyBNHR0ejVqxesrKzg5eWFjIyMaj2viooKBg8ejEOHDiE5ORnLli1DXFwcunbtii5dumDdunV4+vRptdbAFR6PB319ffTr1w/u7u4wtbITK/SYsBDvT2+CWjvbYqEHfNnvMS7pUzVVTCRl7dq16Nu3r0yGHvDl323Pnj0REhLCdSlyiYJPwurUqYMFCxYgKioK6enpaNOmDfbu3QuhUPjtD1eRsrIy+vfvj3379iEpKQkbNmxAfHw8rK2t0a5dO6xYsQJRUVGorZ38zNzCbx7DmAjvz24G+IpoZDejjHYKJF0akaAXL15g79698Pb25rqUKnFwcMDFixe5LkMuUfBVEz09Pfz6668IDAzE4cOH0blzZ4SFhdXY+RUVFWFjY4Ndu3YhPj4ee/bsQUZGBgYOHAiBQIDFixfj3r17tSoE1VXKn4jCGENa4HYIs9OhPXQxePzSj1dXUaqO8oiEeHp6wsvLS+ydVKSVvb09goKCatW/QVlBwVfNOnXqhKtXr2Lx4sVwc3ODq6sr/vnnnxqtQUFBAT169MDWrVvx+vVrHDlyBCKRCCNHjkTz5s3h5eWFmzdvQiQS/7k3aSTQU0cdxbL/L/3h4i4UpL2FjusyKCiVPnlFRVEBAv361VUiqaKLFy8iOjoa8+bN47qUKjMxMYFQKKy1QxHSjIKvBvB4PIwYMQKxsbEwNzdHly5dsGTJEmRlZXFSS9euXbF+/Xo8e/YMJ0+eRN26dTF16lQ0btwYc+fORVhYWI3cmpU0185lb0NTmPEOWQ8uID/lJeJ3jMebza54s9kVWdHFNwVlAFw7Sd92NuTL87MeHh7YunVrrdhbksfjFfX6SM2iWZ0ciI+Px6JFixASEoK1a9di/PjxUrHAc2xsbNEi2omJiUU7SVhbW0NJSTZu/5X1HJ84eAA0sl4jZu886OjoQEtLC5qamrC1tcWiRYskXiupmK1btyIoKAiBgYG1Zqm4v/76C7///jvOnj3LdSlyhYKPQxEREXB3dwdjDD4+PrC0tOS6pCIvXrwoCsEXL17AyckJLi4usLOzk+rftstauUUcrCAPU5p+wiqPKUU9Xj6fDzc3N+zfv1/SpZIKSElJQdu2bXHt2jUIBAKuy5GYtLQ0NGvWDKmpqVL976q24b6bIccsLCxw8+ZNzJkzB8OHD8fYsWMRHx/PdVkAgBYtWmDBggW4desW7t+/jw4dOsDb2xt6enoYO3Ys/v77b3z+/JnrMkswN9bAkoECqCpV7P/aqkoKcDOvj10/L8CQIUOgqqoK4Ms2Mra2ttVRKqmAr2PktSn0AEBTUxMCgQA3btzguhS5QsHHMQUFBYwfPx5xcXFo1qwZzM3NsWrVKqkKFWNjY7i7u+PatWuIiYlBz549sWvXLujr62P48OE4fvw4Pn2Snmffxlk0xZKBJlBV4uNbd8R4vC9rdC4ZaIJV4+1w7do1PH78GKqqquDz+fj++++xcuVKDBo0CM+ePauZCyDF3LlzB4GBgVi6dCnXpVQLBwcHGuerYXSrU8q8evWqqKfl7e2NkSNHSu14xvv373Hq1Cn4+/sjPDwcffv2hYuLC5ycnKRiNYpH8enwvfIcoU9SwcOXh9O/+rofX9822php3RLtjTSK3ktLS4ODgwOSkpIQGxsLFRUV+Pj4wNvbG9999x2WLFkCNTW1Gr8eeSQSidCjRw9MmzYNkyZN4rqcanHt2jW4u7sjMjKS61LkBgWflLp69Src3d1Rr149+Pj4oHPnzlyXVK709HScOXMG/v7+CAkJgZWVFVxdXTFkyBDON2lNy8qDX2Q84pI+ITO3AOoqShDo14drp7J3YM/Pz8f06dPx6NEjnD59GoaGhkhMTMSCBQsQFhaGDRs2YNSoUVL7S0ltcfjwYezYsQMRERFSMQGsOhQUFEBbWxtPnz6Fjo4O1+XIB0akVmFhIdu3bx/T09NjkyZNYklJSVyXJJbMzEx2/PhxNnz4cKaurs769u3Ldu7cyRISErgurUJEIhFbt24dMzY2ZpGRkUWvX7t2jXXo0IH17t2bPXz4kMMKa7fMzExmYGDAIiIiuC6l2g0ZMoQdPXqU6zLkBgWfDEhPT2deXl5MU1OTrV+/nuXm5nJdktg+f/7MAgIC2Lhx45iGhgbr0aMH27JlC3v16hXXpYntxIkTTEtLi506darotcLCQubr68u0tbXZ7Nmz2YcPHzissHZasGABc3Nz47qMGuHr68smTJjAdRlyg4JPhjx9+pQNHjyYNW/enAUEBDCRSMR1SRWSm5vLzp07xyZPnsw0NTVZly5d2Pr169mzZ8+4Lu2bbt26xfT19dnmzZuLfd/fv3/PZsyYwXR1ddnevXtZYWEhh1XWHk+fPmWamposMTGR61JqxPPnz5menp7M/ZuWVTTGJ4OCg4Ph4eEBPT09bNu2De3ateO6pAorKChAWFgY/P39ERAQAF1dXbi6usLFxQWmpqZcl1eq169fw8nJCVZWVtixY0exh/rv37+P2bNnIy8vDzt37oSFhQWHlco+R0dH9OnTBz/88APXpdSYli1b4u+//0b79u25LqX24zp5SeUUFBSwnTt3Mm1tbfb999+z1NRUrkuqtMLCQhYWFsbmzp3LjIyMmEAgYEuWLGH379+Xut+AMzIy2IABA5idnR37+PFjsfdEIhH7/fffmYGBAXNzc5OZMVlpc+7cOda6dWuWl5fHdSk16vvvv2cbN27kugy5UDunSckBRUVFzJo1C3FxcVBUVISJiQm2bduGggLZ21KHz+ejd+/e8PHxwevXr/Hbb78hLy8Pw4YNQ8uWLYse72BScHNCXV0dp0+fhkAggJWVVbEFx3k8HsaPH4/Y2Fhoa2ujbdu22LJli0z+nXAlPz+/aD1OZWVlrsupUbRNUQ3iOnmJZERHRzN7e3vWpk0bFhgYyHU5EiESiVhkZCRbsmQJa9OmDTMyMmLu7u7s6tWrUjGWtmPHDqanp8euX79e6vuxsbHM3t6emZiYsODg4BquTjZt2LCBDRo0iOsyOJGRkcHU1NRYdnY216XUehR8tYhIJGJnzpxhrVq1YgMGDGCxsbFclyQxIpGIRUVFsZUrV7L27dszPT099v3337NLly6xgoICzuo6d+4c09LSYseOHSv1fZFIxAICAljTpk3ZsGHDZGo2a01LTExkmpqa7OnTp1yXwplevXqx8+fPc11GrUe3OmsRHo8HR0dHREVFoV+/fujZsyc8PDzw8eNHrkurMh6PBzMzMyxbtgwPHz7E1atX0bhxY/z444/Q19fHlClTcP78eeTn59doXQMHDkRISAgWLVqElStXlrgdy+Px4OzsjJiYGJibm6NTp05YuXIlcnJyarROWbBo0SJMmTIFrVq14roUztA2RTWE6+Ql1SclJYVNmzaN6ejoMF9fX057RtXpn3/+YZs3b2ZWVlasYcOGbNy4cSwgIIB9/vy5xmpISkpi3bp1Y2PHjmU5OTllHvfq1Svm4uLCmjZtKpOPpFSXiIgIZmBgwDIzM7kuhVO3bt1ipqamXJdR69HjDHLg4cOH8PDwwPv377Ft27ZavdtAYmIiAgIC4Ofnh/v378PBwQEuLi4YOHBgta+vmZOTAzc3t6Iayluq7dKlS5g7dy6MjY3h4+NT63YdqAiRSAQLCwvMnj0bEyZM4LocTgmFQujo6ODhw4cwMqINkasN18lLaoZIJGJ+fn6sWbNmzNnZmT1//pzrkqpdSkoK27t3L3NwcGDq6urM2dmZHT58mKWnp1fbOYVCIVu8eDFr3rw5i4mJKffY/Px8tmXLFqalpcW8vLxYRkZGtdUlzQ4ePMgsLCyYUCjkuhSpMGLECLZ//36uy6jVKPjkTE5ODlu7di1r1KgRW7Bggdz8sP3w4QP77bffmJOTE6tfvz4bMGAA279/P3v//n21nO/gwYNMR0dHrNmcSUlJbOLEiczAwID9/vvvcnX7Mz09nenr67Pbt29zXYrU2L9/Pxs5ciTXZdRqFHxyKiEhgbm5uTF9fX22f/9+ufptOyMjgx07doy5uLgwdXV1Zmtry3x9fSX+wPmVK1eKljITx82bN1mXLl2YlZUVu3fvnkRrkVbz589nkydP5roMqfL27VumqakpFY/s1FYUfHLu9u3bzNLSknXq1Ildu3aN63JqXHZ2NvP392ejR49mGhoarFevXmzbtm3szZs3Emn/6dOnrFWrVmz+/Pli/SATCoVs3759TEdHh02fPr3aeqTSIDY2lmlpabHk5GSuS5E6pqam1AuuRvQ4g5zr2rUrrl+/jvnz52P06NEYNWoU3rx5w3VZNaZu3boYNmwYjh07hqSkJCxYsAAPHjxAhw4dYGFhgY0bN+Lly5eVbr9Vq1aIiIjA3bt34eLiguzs7HKPV1BQwNSpUxEXFwclJSWYmJhg9+7dEAqFla5BGjHGMG/ePCxatAi6urpclyN16LGGasZ18hLpkZWVxZYvX84aNWrEli5dyrKysrguiTP5+fns4sWLRY+DdOzYka1evbrSiwLk5eWxiRMnso4dO7L4+HixP/fw4UPWu3dv1qFDh1rVIz9z5gwTCARytx6nuM6fP8969erFdRm1FgUfKeH169ds1KhRzMjIiB05ckSuJluUprCwkIWGhrLZs2czAwMDZmpqypYtW8YePnxYoe/N141tjYyMim1sK87n/vjjD2ZkZMTGjh0rcxv6/ldubi5r0aIFu3DhAtelSK3s7GympqYmN5PPahoFHylTeHg469y5M7OwsGC3bt3iuhypIBQK2Y0bN5inpydr0qQJa9myJVu4cCG7c+eO2CFY2sa24vj06RNbtGgR09TUZN7e3jLbW1q3bh0bPHgw12VIvX79+rGTJ09yXUatRMFHyiUUCtmBAweYvr4+mzBhgsz3NiRJJBKxu3fvskWLFrFWrVqxJk2asHnz5rHw8PBvzpK9desWMzAwYJs2bapwj/rZs2ds0KBBrHXr1jK3rmNCQgLT1NSUi+dIq2rDhg1s5syZXJdRK1HwEbFkZmayH3/8kTVq1IitXr26RpcDkwUikYg9evSILV++nLVt25YZGBiwWbNmsZCQkDKXinv9+jVr164dmzZtGsvPz6/wOc+cOcNatGjBBg8ezF68eFHVS6gR48aNY4sWLeK6DJnw4MED1qJFC67LqJUo+EiFvHjxgg0dOpQ1bdqUnThxQu7H/8oSFxfH1qxZwzp16sS0tbXZd999xy5cuFAi4DIyMtjAgQNZv379SmxsK46cnBy2Zs0a1qhRI/bTTz9J9ZY2169fZ4aGhuzTp09clyITRCIR09XVpd5xNaDgI5USEhLC2rdvz3r37s3u37/PdTlS7eXLl2zjxo3MwsKCNWrUiE2YMIGdPn26aDHrgoICNnfuXGZiYlLpntubN2/YyJEjWePGjdlff/0ldb+QFBYWss6dO7MjR45wXYpMGT9+PPP19eW6jFqHgo9UWmFhIduzZw/T0dFh3333HUtJSeG6JKn39u1btn37dtanTx/WoEEDNmrUKObn58eysrKKNrYNDw+vdPuhoaGsXbt2zMbGhkVFRUmw8qrZt28fs7KykrpAlnZHjhxhzs7OXJdR61DwkSr7+PEjmzdvHtPU1GQbN26U2dmGNS05OZnt2bOH2dnZMXV1dTZs2DD2ww8/MC0tLXb06NFKt1tQUMC2b9/OtLS0mIeHR7Uuyi2Ojx8/Ml1dXblZhk2SUlJSWIMGDSo1BkzKRiu3kCrT0NDAli1bcP36dYSGhsLMzAxnzpwpsSkrKU5XVxfTp09HUFAQXr58CUdHR8TExODz58+YOnUqnJ2dkZaWVuF2FRUVMWfOHMTExCArKwsCgQAHDx6ESCSqhqv4tpUrV2Lw4MHo1KkTJ+eXZTo6OmjevDlu3brFdSm1Cu3HRyTuwoULmDdvHoyNjbF161aYmZlxXZJMycjIwJEjR7B06VJ8+vQJvXv3xsiRI+Hs7AwdHZ0Kt3fnzh3MmTMHALBjxw507dpV0iWXKSYmBn369EFMTEy5+xOSsv34449QVlbGqlWruC6l1qAeH5G4/v3749GjR3B0dIS1tTVmz55dqZ6LvGrQoAFmzZqFhIQEODk5ISEhAefPn0fr1q1hbW2NHTt2ICEhQez2unbtihs3bmDGjBkYPHgwpk6dinfv3lXjFXzBGIOHhwd++uknCr0qcHBwwMWLF7kuo1ah4CPVQklJCXPnzkVsbCwYYzAxMcGOHTtQUFDAdWkyQ1VVFX5+fnB1dcWjR48QGhoKT09P3L17F+3atYOVlRU2b96MV69efbMtBQUFTJw4EXFxcVBXV4eZmRm2b9+OwsLCaqv/9OnTSEhIwMyZM6vtHPLAysoKsbGx+PDhA9el1B6cjjASufH48WNma2vLTE1N2cWLF7kuR+b89ttvTFtbu2hj27y8PHb+/Hk2depUpq2tzTp37szWrl3Lnjx5IlZ70dHRzNbWlrVt25aFhoZKvN6cnBzWrFkzsTbiJd82aNAg9ueff3JdRq1BwUdqjEgkYidPnmQtWrRgjo6OYv+QJl983dj2l19+KfZ6QUEBu3z5Mps5cybT19dn7dq1YytWrGCPHz8u9/EBkUjETpw4wRo3bsxGjBghsT0IGWNs9erVbOjQoRJrT975+PiwKVOmcF1GrUGTW0iNy8vLw/bt2+Ht7Y2JEydi6dKlaNCgAddlyYRnz57B0dERTk5O8Pb2Bp/PL/a+SCTCzZs34e/vD39/f6ioqMDV1RUuLi7o2LEjeDxeiTY/f/6M9evXY9euXfD09MT8+fOhoqLyzVreZ+XB71484pIzkZlbCHUVRQj01NHDgA9ri864e/cumjVrJrFrl2dPnjxBv3798ObNm1L/DknFUPARziQnJ+Onn37C2bNnsWrVKkyZMqXED3JS0ocPH+Di4gJ1dXUcPXoUampqpR7HGMPdu3eLQlAoFGLYsGFwdXVFt27doKBQfIj/5cuX8PT0RFRUFLZt2wZHR8dS2334Nh27rjxH2NNUAEBe4f8/JqGiqIBCoRCNlbKwdWp/mBtrSOai5RxjDE2bNsWFCxdgYmLCdTkyj4KPcC4yMhLu7u749OkTfHx80KdPH65Lknr5+fmYMWMG7t+/jzNnzsDIyKjc4xljePToUVEIZmRkYNiwYXBxcUHPnj2L/cJx8eJFzJ07Fy1btsS2bdvQqlWroveORLzCmsA45BYKUd5PDh4PUFHkY8lAAcZZNK3q5RIA3333HczMzODh4cF1KTKPZnUSznXq1AlXr17F4sWL4ebmBldXV/zzzz9clyXVlJWVsX//fowcORKWlpaIjIws93gejwdzc3OsWrUK0dHRCA4Oho6ODjw8PGBoaIgZM2YgODgYBQUFcHBwwOPHj2FtbQ1LS0ssWrQIWVlZ/wu9WOQUlB96AMAYkFMgxJrAWByJeCW5C5djDg4OCAoK4rqMWoF6fESq5OTkYNOmTdi2bRtmzJiBRYsWlXkrj3zh7++PGTNm4Ndff8WQIUMq/PkXL14U9QRfvHiBwYMHw8XFBf369UNaWhoWLlyIRi07IkhoipwCYbHPCnM+IS3QB7mv7kNBVR0N+7ihnpl1sWNUlfj4c5oF2htpVOEqycePH9GkSRO8e/dOrDFYUjbq8RGpoqqqiqVLl+Lhw4d48+YN2rRpg0OHDnG23JYscHFxQWBgIGbOnInNmzdXeKm4Fi1aYMGCBbh16xbu378Pc3NzeHt7Q09PDz/88AMmTZqEnOa9kVsoLPHZD0G7weMrwWjOEWg5eSEtyBf5qa+LHZNbKITvledVukYCNGzYEGZmZrh+/TrXpcg86vERqRYREQF3d3cwxuDj4wNLS0uuS5Jab968gaOjIywsLLBr1y4oKSlVqb3k5GQEBATgzbuP+LuwY7FJLAAgys/F222jYDB1F5QaGQIA3p/ZDH59TTS0nljs2DqKCrix0AaaanWqVJO8W758OXJzc+Ht7c11KTKNenxEqllYWODmzZuYM2cOhg8fjrFjxyI+Pp7rsqRS48aNcf36dSQmJmLAgAFIT0+vUnt6enr4/vvv0cR6ZKnvF35IAE+BXxR6AKCk0wwF/+nxAQAPgF8k/b1VFS1fJhkUfETqKSgoYPz48YiLi0OzZs2KJml8/vyZ69KkTv369XHq1Cm0bdsWlpaWePnyZZXbjEvOLNHbAwBRQQ54dVSLvaZQpy5E+Tkljs0tFCEu6VOVa5F33bp1w+vXr5GcnMx1KTKNgo/IDDU1NaxevRr37t1DVFQUTExMcPz4cdr+6D/4fD62bduG2bNno0ePHlUeE8rMLX09TwUlVbC84iHH8j5DQVm11OMzc2md1qpSVFSEjY0NgoODuS5FplHwEZnTtGlT/PXXXzh8+DC8vb3Rq1cv3Lt3j+uypM6sWbNw8OBBDB06FEePHq10O+oqiqW+rtjIEEwkRMGH/98pIv/dP1DSblJGO1UbcyRf2Nvb02MNVUTBR2RW7969cffuXUycOBGOjo6YPHky3QL6j/79+yMkJARLlizBihUrKtU7Fuipo45iyR8VCsoqqNvGEunXjkKUn4vc+Bh8fn4L9cz6ljhWRVEBAv36lboGUtzX4KOZzpVHwUdkGp/Px9SpUxEXFwdNTU20bdsW3t7eyMvL47o0qdG2bVtERETgwoULGDt2LHJzcyv0edfOZa8K08h+JlhhPuJ3jMX70xuhaT8TyqX0+BgA107lry5DxNOsWTM0aNAAjx494roUmUXBR2qFBg0aYOPGjbh58yZu3LgBU1NTBAQE0Pjf/+jp6SE0NBRCoRC2trZITU0V+7NaanXQp7U2Slsbma9aHzouP6HxfH8YzTxY4uF14MvyZX3baNOjDBJkb29PszurgIKP1CqtWrXCqVOnsGfPHixduhS2trb0m/H/qKqq4o8//oCNjQ26d++OmJgYsT87y7olVBQrt4C4iiIfM61bVuqzpHS0fFnVUPCRWsnOzg4PHjwoWnrr+++/r1Avp7ZSUFDAzz//jBUrVsDa2lrs2YHmxhpYMlAAVaUK/sgQ5sPTphktVyZh1tbWuH37NrKzs7kuRSZR8JFaS1FREbNmzUJcXByUlZVhamqKbdu2oaCAptVPmDABfn5+GD9+PH755RexPjPOoimWDDSBqhK/1Nue/8bjfVmj0yzvCQ79NAVZWVkSqJp8Vb9+fXTu3BlhYWFclyKTKPhIrdeoUSP4+PggLCwMFy5cQLt27RAYGMh1WZzr3bs3wsPDsWXLFsyfPx9CYcm1OP9rnEVT/DnNAg6muqijqACV/8z2VFFUQB1FBTiY6uLPaRY4s3UBTExMMGjQIOqdSBiN81UerdVJ5ApjDIGBgfD09ETz5s2xZcsWud/Y88OHD3B1dUX9+vXL3dj2v9Ky8uAXGY+4pE/IzC2AuooSBPr14drJqNhEFpFIhMmTJyM+Ph5nzpyBqmrpD7iTirl37x7GjRuH2NhYrkuRORR8RC7l5+dj586dWLduHcaOHYvly5ejYcOGXJfFmfz8fHz//feIjIwUa2PbihIKhZgwYQLS0tJw8uRJ2lZHAkQiEXR1dXHv3j00btyY63JkCt3qJHJJWVkZnp6eiImJQW5uLgQCAXx9fVFYWPryXLWdsrIyfv31V4waNQoWFhYSXwmHz+fj0KFDUFdXh6urK/Lz8yXavjxSUFCAnZ0dze6sBAo+Ite0tbWxZ88eBAUF4cSJE+jYsSMuX77MdVmc4PF4WLhwIbZv347+/fvj5MmTEm1fUVERR48ehZKSEkaOHEmTjCSAli+rHLrVScj/MMYQEBAALy8vtG/fHps2bULLlvL5/Nndu3fh7OyMefPmwdPTE7xvTeOsgPz8fLi4uEBVVRXHjh2DomLpa4GSb0tMTETbtm2RmpoKPr9yz1nKI+rxEfI/PB4Pw4YNQ0xMDLp37w4LCwssXLgQmZmZXJdW47p06YKbN2/i999/x/Tp0yXaO1NWVoafnx8+ffqECRMmiDWblJTOwMAAhoaGuHv3LtelyBQKPkL+Q0VFBYsWLcLjx4/x7t07tGnTBvv375e7H9DGxsYIDw8v2tj248ePEmu7Tp06+Pvvv5GamorJkyfL3fdWkmhz2oqj4COkDPr6+jh48CBOnz6NAwcOoFu3bggPD+e6rBr1741trays8OLFC4m1raqqilOnTuHNmzeYPn067TZQSTTOV3E0xkeIGBhjOH78OBYuXAgrKyts2LBB7qaQ+/r64ueff4afnx969OghsXazsrLQv39/tGvXDr6+vhIdT5QHOTk50NHRQXx8PBo0aMB1OTKBenyEiIHH42H06NGIi4uDQCBAx44dsWzZMrlajWTmzJkS2dj2v9TU1BAYGIgHDx7A3d2ddtSoIFVVVVhZWSEkJITrUmQGBR8hFVC3bl2sWLECDx48wPPnzyEQCHD06FG5+WH9dWPbn376CcuXL5fYdaurq+P8+fO4efMmvLy85Ob7KSm0fFnF0K1OQqrg+vXrcHd3h5KSEnx8fNCtWzeuS6oRKSkpGDJkCJo3b44DBw5IbCWWDx8+wNbWFv3798fatWvptqeYoqKi4OTkhJcvX9L3TAzU4yOkCnr06IHbt29j2rRpcHZ2hpubGxITE7kuq9rp6uoiNDQUIpEINjY2ePfunUTabdSoEYKDg3Hu3DmsWLFCIm3KAzMzM+Tn50t08lFtRsFHSBUpKChg0qRJePLkCQwMDNCuXTusWbMGOTk5XJdWrb4+gG5rawsLC4sKbWxbHi0tLVy6dAknTpzA6tWrJdJmbcfj8eh2ZwVQ8BEiIfXr18e6detw584d3Lt3D6ampvDz86vV41X/3dhWUtPqdXR0cPnyZRw+fBgbNmyQSJu1HT3WID4a4yOkmoSGhsLDwwMaGhrw8fFBhw4duC6pWl27dg3Dhw/HihUrMGPGDIm0mZCQgD59+mDWrFmYN2+eRNqsrd6/f48WLVogNTUVysrKXJcj1ajHR0g16du3LyIjIzFmzBg4ODhg2rRpEhsLk0a9evVCeHg4tm3bBk9PT4msxmJoaIiQkBDs2LEDu3btkkCVtZeWlhZatWqFiIgIrkuRehR8hFQjPp+P6dOn48mTJ1BTU4OpqSk2bdpUa7fladmyJW7evIkHDx5g6NChyMrKqnKbjRs3RkhICDZs2IC9e/dKoMrai5YvEw8FHyE1QENDA1u2bMH169cRGhoKMzMznDlzplaO/zVs2BAXLlyAjo4OevXqhfj4+Cq32bRpU4SEhODnn3/GgQMHJFBl7UTjfOKhMT5COHDhwgXMmzcPxsbG2Lp1K8zMzLguSeIYY9i4cSO2b9+OU6dOoXPnzlVu88mTJ7CxscH69esxfvx4CVRZu+Tn50NbWxsvXryAlpYW1+VILerxEcKB/v3749GjR3B0dIS1tTVmz56NtLQ0rsuSKB6PhwULFkh0Y9s2bdogODgYCxcuxPHjx6teZC2jrKyMPn364NKlS1yXItUo+AjhiJKSEubOnYvY2FgwxmBiYoIdO3bUup3Jhw0bhvPnz2P27NnYtGlTlW/vmpqa4uLFi/Dw8IC/v7+Eqqw9aJzv2+hWJyFSIioqCh4eHkhMTMTWrVvh4ODAdUkS9fbtWzg5OaFr167w9fWFkpJSldp78OABHBwcsHfvXgwZMkRCVcq+Z8+ewdraGvHx8bR8WRmox0eIlGjbti2Cg4Oxbt06zJo1C05OTnj69CnXZUmMsbExrl27hqSkJPTv37/KG9t26NABgYGBmDZtGgIDAyVUpexr2bIl6tSpg+joaK5LkVoUfIRIER6PhyFDhiA6Ohq9e/eGlZUV5s+fj/T0dK5Lk4ivG9u2b98elpaWVV5bsnPnzjh9+jQmTpxIsxn/5+vyZfT9KBsFHyFSqE6dOvjhhx8QHR2NjIwMCAQC7N27VyIPhXONz+dj69atcHd3R48ePaq8q3337t0REBCAcePG0Z50/+Pg4EDBVw4a4yNEBkRGRsLDwwOZmZnYtm0brK2tuS5JIi5evIjx48djy5YtGDduXJXaCgsLg6urK/z9/dG7d28JVSib0tPTYWxsjHfv3kFVVZXrcqQO9fgIkQGdOnVCWFgYFi9ejIkTJ8LV1RX//PMP12VVmYODA0JDQ7F06VIsW7asSjM++/Tpg+PHj8PV1RU3btyQYJWyR0NDA+3bt69yb7q2ouAjREbweDyMGDECsbGx6NChA7p06YLFixfj06dPXJdWJWZmZoiIiEBwcDDGjBmD3NzcSrdla2uLw4cPw9nZGbdv35ZglbKHHmsoGwUfITJGVVUVP/30Ex49eoS3b99CIBDg0KFDEIlEXJdWabq6uggJCQFjrMob2zo4OODAgQNwcnJCZGSkBKuULTTBpWw0xkeIjIuIiICHhwdEIhF8fHxgaWnJdUmVJhKJsGLFChw+fBhnz56t0lJuJ0+exIwZM3Dx4kWYm5tLsErZIBQKoa2tjaioKBgYGHBdjlShHh8hMs7CwgI3btzA3LlzMXz4cIwdO1YiC0NzQUFBAatWrcKqVavQt2/fKvVYnJ2dsXPnTvTv3x9RUVESrFI28Pl82NraIjg4mOtSpA4FHyG1gIKCAsaNG4e4uDg0b94c5ubmWLVqFT5//sx1aZUyfvx4+Pv7Y8KECdi9e3el23F1dcXmzZthb2+PuLg4CVYoG2icr3R0q5OQWujVq1dYsGABbt26BW9vb4wcOVIml696/vw5HB0dMWDAAGzatAl8Pr9S7fz+++9YvHgxQkND0apVKwlXKb3evHmDzp07IyUlBQoK1M/5ioKPkFrs6tWr8PDwQN26deHj4yORrYFq2sePH+Hq6oq6devijz/+gJqaWqXa2b9/P1auXInQ0FC0aNFCwlVKLxMTExw5ckQm/+6rC/0KQEgt1rt3b9y5cweTJk2Co6MjJk+ejOTkZK7LqpCvG9vq6uqiZ8+eePv2baXamTJlChYvXgxbW1u8evVKskVKMZrdWRIFHyG1HJ/Px5QpU/DkyRNoaWmhbdu28Pb2Rl5eHteliU1JSQn79u3D2LFjYWlpiXv37lWqnRkzZmD+/PmwsbGpdIDKGhrnK4ludRIiZ549ewYvLy9ERUVh06ZNcHZ2lqnxv4CAAEybNg179+7F0KFDK9XGli1bsHv3boSFhdX6qf7Z2dnQ09NDYmIi6tevz3U5UoF6fITImVatWuHUqVPYs2cPli5dCltbWzx69IjrssQ2dOhQXLhwAXPmzMHGjRsrtcyZp6cnpkyZAhsbG5m79VtR9erVQ7du3RAWFsZ1KVKDgo8QOWVnZ4cHDx7AxcUF/fr1w/fff4/U1FSuyxJL586dERERgaNHj+K7775Dfn5+hdv48ccfMXbsWNja2srMdVeWvb093e78Fwo+QuSYoqIiZs2ahbi4OCgrK8PU1BTbtm1DQUEB16V9k5GREcLDw5GSklLpjW2XLl2KYcOGoV+/fkhLS6uGKqUDbVNUHAUfIQSNGjWCj48PwsLCcOHCBbRr104mdjVXU1PDyZMn0aFDB1haWuL58+cVbmPVqlXo378/7OzsqrwrvLRq37490tPT5Wo2a3ko+AghRUxNTXH+/Hls3rwZ8+bNw4ABAxAbG8t1WeXi8/nYsmULPDw80LNnT1y7dq1Cn+fxeFi/fj2sra3h4OCAjIyMaqqUOwoKCvRYw79Q8BFCiuHxeBg0aBAeP34MOzs79O7dGx4eHlLfG5oxYwZ+//13uLi44PDhwxX6LI/Hw+bNm9G9e3cMGDBA5rd6Kg2N8/0/Cj5CSKmUlZXh6emJmJgY5ObmQiAQwNfXF4WFhVyXViZ7e3uEhoZi2bJlWLp0aYW2auLxeNi+fTvat2+PgQMHIjs7uxorrXl2dnYICQmR6r+/mkLBRwgpl7a2Nvbs2YOgoCCcOHECHTt2xOXLl7kuq0xmZma4desWLl++jDFjxiAnJ0fsz/J4PPj6+qJVq1ZwcnKS2UW+S6Onp4cmTZrI/Qa9AAUfIURM5ubmCAkJwcqVK/Hdd9/B2dm5UpNJaoKOjg5CQkLA4/FgY2ODlJQUsT+roKCAffv2wcjICM7OzlXaEV7a0DjfFxR8hBCx8Xg8DBs2DDExMejevTssLCywcOFCZGZmcl1aCSoqKjh27Bjs7e1hYWFRoT35+Hw+Dh48CE1NTQwbNkymlncrDy1f9gUFHyGkwlRUVLBo0SI8fvwY7969Q5s2bbB//34IhUKuSyuGx+Nh5cqV+Pnnn2FjY1OhH/p8Ph+///476tati+HDh1fqIXlp06NHD0RHR0v9RKXqRsFHCKk0fX19HDx4EKdPn8aBAwfQrVs3hIeHc11WCePGjcPff/8NNze3Cm1sq6SkhGPHjoHH42H06NEy8WB/eVRUVNCzZ0+pHqOtCRR8hJAq69q1K8LDw+Hl5YUxY8Zg1KhRePPmDddlFdOzZ09cv34d27dvh4eHh9i9U2VlZfz111/Izc3F+PHjZX5WJI3zUfARQiTka68oLi4OAoEAHTt2xLJly6TqsYAWLVrgxo0bePz4MYYMGSL283p16tSBv78/Pn78iIkTJ0rdLd2K+Lp8mTxvzEPBRwiRqLp162LFihV48OABnj9/DoFAgKNHj0rND9qvG9vq6+tXaGNbFRUVnDx5EklJSZg6dWqFnhGUJgKBAEKhEE+fPuW6FM5Q8BFCqoWxsTGOHTuG48ePY+vWrbCyspKaZ8iUlJSwd+9ejB8/HpaWlrh7965Yn1NVVcXp06fx4sULzJgxQybDj8fjyf2i1RR8hJBq1aNHD9y+fRvTp0/H0KFD4ebmhsTERK7LAo/Hg5eXF3bu3IkBAwbg77//Futz9erVw7lz5xAdHY05c+ZITU+2IuR9+TIKPkJItVNQUMDEiRMRFxcHQ0NDtG/fHmvWrKnQqirVxdnZGRcvXsTcuXPh7e0tVpDVr18fgYGBuHv3LubNmydz4devXz9cvXq11jyfWFEUfISQGlO/fn2sXbsWt2/fRmRkJExNTeHn58d5cHTq1AkRERE4fvw4pk6dKtYzew0aNMCFCxdw7do1LFy4kPNrqIhGjRrBxMQEN27c4LoUTlDwEUJqXPPmzeHv748DBw7g559/hrW1NR48eMBpTUZGRrh27RpSU1PF3ti2YcOGCAoKQlBQEH766SeZCj95Huej4COEcKZv376IjIzEmDFj0L9/f0ybNg3v3r3jrB41NTUEBASgY8eOsLCwEGstUk1NTQQHB+PUqVP4+eefa6BKyZDncT4KPkIIp/h8PqZPn464uDioqanB1NQUmzZt4myJMD6fj82bN8PT0xM9e/bE1atXv/kZbW1tXL58GX/88QfWrVtXA1VWXffu3fHy5UtOf9HgCgUfIUQqaGhoYMuWLbh+/TpCQ0NhZmaGM2fOcHb7cPr06Th8+DBcXV3x+++/f/N4XV1dhISE4ODBg9i0aVMNVFg1SkpK6Nu3L4KDg7kupcbxmCzdlCaEyI0LFy5g3rx5MDY2xtatW2FmZsZJHTExMXB0dMSYMWOwatUqKCiU31+Ij49Hnz594O7ujrlz59ZQlZWze/duRERE4NChQ1yXUqOox0cIkUr9+/fHo0eP4OjoCGtra8yePRtpaWk1XoepqSkiIiIQEhKC0aNHf/MRDCMjI4SEhGDr1q0VWhCbC/K6fBkFHyFEaikpKWHu3LmIjY0FYwwmJibYsWNHje+S8HVjWz6fj759+35zY9smTZrg8uXLWLduHX799dcaqrLimjdvjnr16uHx48dcl1KjKPgIIVJPS0sLu3btQkhICE6dOgVzc/Man5GooqKCo0ePon///ujevfs3N7Zt3rw5Ll++jBUrVkj1rUR5fKyBgo8QIjPatm2L4OBgrFu3DrNmzYKTk1ONLrbM4/GwYsUKrFmzBjY2Nrhw4UK5x7dq1QqXLl3C4sWLcfTo0RqqsmLk8bEGCj5CiEzh8XgYMmQIoqOj0bt3b1hZWWH+/PlIT0+vsRrGjh2LgIAATJo0Cbt27Sr3WIFAgKCgIHh5eeHEiRM1VKH4+vbti4iICHz+/JnrUmoMBR8hRCbVqVMHP/zwA6Kjo5GRkQGBQIC9e/fW2F55PXr0wPXr17Fz5064u7uXe14zMzNcuHABc+bMQUBAQI3UJy51dXV07NhRrOcVawsKPkKITNPV1cWvv/6KwMBAHDlyBJ07d8aVK1dq5NzNmzfHzZs3ER0d/c2Nbc3NzREYGIgZM2bgzJkzNVKfuORtnI+CjxBSK3Tq1AlhYWFYvHgxJk6cCFdXV/zzzz/Vfl4NDQ2cP38ehoaG6NmzJ968eVNujWfPnsWUKVO+OT5Yk+RtnI+CjxBSa/B4PIwYMQKxsbHo0KEDunbtiiVLliArK6taz6ukpIQ9e/bAzc0NlpaWuHPnTpnHdu3aFadOncKECRNw6dKlaq1LXJ06dUJKSgri4+O5LqVGUPARQmodVVVV/PTTT3j48CHevHmDNm3a4NChQ9W6YzqPx4Onpyd8fX0xcOBA+Pv7l3mspaUl/P39MXr06Bq7LVsePp+Pfv36yc3tTgo+QkitZWhoiMOHD8Pf3x+7d++GhYUFbt68Wa3nHDJkCC5evAgPDw+sX7++zFVRevXqhb/++gsjRoxAeHh4tdYkDnka56O1OgkhckEkEuHYsWP48ccf0adPH3h7e8PIyKjazpeQkAAnJyd06NABe/bsgbKycqnHBQcHY+zYsTh16hQsLS2rrZ5viY+PR4cOHZCSkgI+n89ZHTWBenyEELmgoKCAcePGIS4uDs2bN4e5uTlWrVpVbc+vGRoa4urVq0hLS4O9vT0+fPhQ6nF2dnY4dOgQhgwZgrt371ZLLeIwMjKCnp4eIiMjOauhplDwEULkipqaGn7++Wfcu3cPUVFRMDExwfHjx6tloWY1NTX8/fff6NKlCywsLPDs2bNSjxswYAD279+PQYMG4f79+xKvQ1zyMruTgo8QIpeaNm2Kv/76C4cPH8aGDRvQq1cv3Lt3T+Ln4fP52LRpE7y8vNCzZ0+EhYWVepyTkxN2796NAQMG4NGjRxKvQxzyMs5HY3yEELknFArx22+/4aeffsKAAQOwdu1a6OnpSfw8X8fzNm7cCDc3t1KP+fPPP+Hh4YHLly/D1NRU4jWU5/Pnz9DV1UVCQgLU1dVr9Nw1iXp8hBC5x+fzMWXKFDx58gRaWlpo27YtvL29kZeXJ9Hz2NnZISwsDKtWrcKSJUtKfbxi5MiR2LhxI+zs7PDkyROJnv9b6tatCwsLC4SGhtboeWsaBR8hhPyPuro6NmzYgIiICNy8eROmpqYICAiQ6PifiYkJIiIicOXKFYwcObLUjW3HjRuHNWvWoF+/fnj+/LnEzi0OebjdSbc6CSGkDJcuXYKHhwd0dHSwbds2tG/fXmJt5+bmYsqUKXj+/DlOnTpV6q3Vffv2YfXq1bhy5QqaNWsmsXOX59GjRxg2bFiNB25Noh4fIYSUoV+/fnjw4AFcXV1hZ2eH77//HqmpqRJpW0VFBUeOHMHAgQNhYWFR6i7o3333HRYuXAgbG5ty1wCVpHbt2iE7OxsvXryokfNxgYKPEELKoaioiJkzZyI2NhbKysowNTXFtm3bUFBQUOW2eTweli9fjrVr18LGxgbnz58vcczMmTPh7u4OGxsbJCQkVPmc4tRkb29fq293UvARQogYGjVqBB8fH4SFheHChQto164dAgMDJdL2mDFjcPLkSUyePBk7d+4s8b6HhwemT58OGxsbJCUlSeSc5ant43w0xkcIIRXEGENgYCA8PT3RvHlzbNmyBSYmJlVu9+XLl3B0dES/fv2wZcsWKCoqFnt/7dq1OHz4MK5cuQJdXd0qn68s7969Q+vWrZGamgolJaVqOw9XqMdHCCEVxOPxMGjQIDx+/Bh2dnbo3bs3PDw88PHjxyq127x5c9y4cQOxsbEYPHgwMjMzi72/ePFijBw5Ev369cP79++rdK7y6OjooEWLFrh161a1nYNLFHyEEFJJysrK8PT0RExMDHJzcyEQCODr64vCwsJKt6mhoYHAwEAYGxuXurHt8uXL4eTkhH79+pW5/qck1Oblyyj4CCGkirS1tbFnzx4EBQXhxIkT6NixIy5fvlzp9r5ubDtx4kRYWlri9u3bRe/xeDysWbMGdnZ2sLe3R3p6ugSuoKTaPM5HY3yEECJBjDEEBATAy8sL5ubm2LRpE1q0aFHp9k6fPo0pU6Zg9+7dcHV1LXaeefPmISIiAkFBQRJfYiw/Px/a2tp4+fIlNDU1Jdo216jHRwghEsTj8TBs2DDExMSgW7du6N69OxYuXFhivE5cgwcPxsWLFzFv3jysW7euaBUZHo+HrVu3olOnThgwYACysrIkeRlQVlZG7969q9RzlVYUfIQQUg1UVFSwaNEiPH78GO/evYNAIMCBAwdKXZ/zWzp16oSIiAicOHECkyZNQn5+PoAv4bdz506Ymppi0KBByM7Olug11Nbn+ehWJyGE1IA7d+7Aw8MDubm58PHxQc+ePSvcRnZ2NsaOHYv09HT4+/sX3YIUiUSYPHky3r59i7Nnz0JVVVUiNT99+hS2trZ48+YNeDyeRNqUBtTjI4SQGtC1a1eEh4fDy8sLY8aMwahRoyq8DFm9evXg7++Prl27wtLSEk+fPgXwZXf5/fv3Q09PD87OzsjNzZVIza1atQKfz0dcXJxE2pMWFHyEEFJDeDweRo8ejbi4OAgEAnTs2BHLli2r0C1KPp+PjRs3wsvLC7169cKVK1eKXj906BAaNGgAV1dXiWypxOPx4ODgUOsea6DgI4SQGla3bl2sWLECDx48wPPnzyEQCHD06NEKbX80bdo0HD16FCNGjMDBgwcBfFlX9OjRo1BWVsbIkSMlsp5obRznozE+Qgjh2PXr1+Hu7g4lJSX4+PigW7duYn82NjYWjo6OGDlyJFavXg0FBQXk5+fDxcUFKioq+OOPP0osfVYRHz9+RJMmTfDu3TuoqKhUuh1pQj0+QgjhWI8ePXD79m1Mnz4dQ4cOhZubGxITE8X67NeNba9evYqRI0fi8+fPUFZWhp+fH7KysjBhwgQIhcJK19awYUO0bdsW169fr3Qb0oaCjxBCpICCggImTpyIuLg4GBoaon379lizZk2pO7T/l7a2Ni5dugRlZWVYW1sjKSkJderUwd9//43U1FRMmjSpSuFX25Yvo+AjhBApUr9+faxduxa3b99GZGQkTE1N4efn983xv68b2zo6OsLCwgKPHj2CqqoqTp06hbdv32L69OmVeoYQqH3Ll9EYHyGESLHQ0FB4eHhAQ0MDPj4+6NChwzc/88cff2Du3Lk4dOgQBg4ciKysLAwYMABt27aFr69vhZ/JKywshI6ODmJiYqCnp1fJK5Ee1OMjhBAp1rdvX0RGRmLMmDHo378/pk2bhnfv3pX7mdGjR+PUqVOYMmUKduzYATU1NQQGBuLBgwdwd3ev0OxR4MtsURsbGwQHB1flUqQGBR8hhEg5Pp+P6dOnIy4uDvXr14epqSk2bdpUtHRZaaysrHDjxg3s3r0bs2fPhqqqKs6fP4+bN2/Cy8urwuFXm8b56FYnIYTImCdPnmD+/Pl48uQJtmzZAkdHxzJvX6anp2PEiBHg8/n4888/UVhYCFtbWzg4OGDdunVi3/Z89eoVunfvjqSkJCgoyHafSbarJ4QQOdSmTRucPXsWO3fuxMKFC+Hg4IDo6OhSj9XQ0MC5c+fQpEkT9OjRA58+fUJwcDACAwOxYsUKsc/ZtGlTaGho4NGjRxK6Cu5Q8BFCiIxycHDAw4cP4ejoiL59+2L27NlIS0srcZySkhJ2796NyZMnw9LSEi9evMClS5fg5+eH1atXV+h8teF2JwUfIYTIMCUlJcydOxexsbEAvjzQvmPHjhLLlfF4PMybNw979uyBo6Mjrly5gsuXL+PIkSPw9vYW61y1ZfkyGuMjhJBaJCoqCvPmzUNiYiK2bt0Ke3v7Esfcv38fQ4YMwYwZMzBhwgRYW1tj1qxZmDdvXrltZ2VlQV9fH8nJyahXr151XUK1o+AjhJBahjGGM2fOwNPTEyYmJti8eTNat25d7JjExEQ4OTmhXbt2WLJkCRwcHODp6YnZs2eX23bfvn3h5uaGgoIC9OrVCwKBoDovpVrQrU5CCKlleDweBg8ejOjoaPTu3RtWVlbw8vJCRkZG0TEGBga4evUqMjIyMHXqVPj7+2Pjxo345ZdfSm0zOTkZ06dPx7179zB16lTMnDkT4eHhNXVJEkXBRwghtVSdOnXwww8/IDo6Gunp6WjTpg327t1btG7n141tu3fvjhEjRuDXX3/FmjVrcODAgRJtZWZm4rfffsOnT58gFAqhqqqKTp061fQlSQTd6iSEEDkRGRkJDw8PZGZmwsfHB3369Cl679dff8WSJUuwadMm/Pjjj1i/fj2MjY0xe/Zs3Lx5E/Xr10dQUBCcnZ2Rk5MDRUVFZGdnQ1lZmcMrqhwKPkIIkSOMMZw4cQILFixAly5dsHHjRjRr1gwAcPnyZYwePRru7u5Yv349srKyoKysjMOHD2PEiBEAgMOHD8PNzQ1aWlrfXDpNWlHwEUKIHMrJycHmzZuxbds2TJ8+HYsWLYKamhri4uLQo0cPfPjwoejYQYMG4ezZs0X/PW3aNLx9+xbnz5/novQqo+AjhBA5lpCQgB9//BEhISFYu3YtunbtCjMzs2LHKCoq4tOnT8V2YH+flQe/e/GIS85EZm4h1FUUIdBTx/DORtBUq1PTl1EhFHyEEEIQEREBDw8PREdHIysrq8T7u3fvxowZM/DwbTp2XXmOsKepAIC8wv/f409FUQEMgHUbbczs0xLmxho1VH3FUPARQggBAMTExKBDhw5Fq76oqqqiTp06qFevHu7cuYPLr/OwJjAOuYVClJccPB6gosjHkoECjLNoWjPFVwA9zkAIIQQAsGHDBgCAuro6lJWVkZOTA0tLS8THx/8v9GKRU1B+6AEAY0BOgRBrAmNxJOJV9RdeQdTjI4QQAgD48OEDnj9/joyMDGRkZODFixfQ0NCAxcCRGLUvAjkFwlI/V/AhAYn7Z6OeoAe0nLyKvaeqxMef0yzQ3kijBq5APIpcF0AIIUQ6NGrUCN26dSvx+rTDd5FbWHroAcCHoD2oo9+q1PdyC4XwvfIce8Z1kVidVUW3OgkhhJTpfVYewp6mlnl7MzsmDAoq9aDSxLzU9xkDQp+kIi0rrxqrrBgKPkIIIWXyuxdf5nuivM9Iv3YUDW2mltsGD4BfZNnt1DQKPkIIIWWKS84s9sjCv6VfPQw1c3soqmuV20ZuoQhxSZ+qo7xKoeAjhBBSpszcwlJfz095idzXD6HedYiY7RR8+6AaQpNbCCGElEldpfSYyH3zGIUZKYj3nQQAYPm5ABMh6b079Cf5lNKOUrXWWREUfIQQQsok0FNHHcXkErc71To4oJ5J76L/zrz9NwozUtDIYVaJNlQUFSDQr1/ttYqLbnUSQggpk2tno1JfV1BSAV+tYdEfnpIKeIrK4NdtUOJYBsC1U+ntcIF6fIQQQsqkpVYHfVprIzg2pdwVWzR6jS31dR4P6NtGW6oWrqYeHyGEkHLNsm4JFUV+pT6rosjHTOuWEq6oaij4CCGElMvcWANLBgqgqlSxyFBVUsCSgQKpWq4MoFudhBBCxPB1l4XasDsDLVJNCCFEbI/i0+F75TlCn6SChy8Pp3/1dT++vm20MdO6pdT19L6i4COEEFJhaVl58IuMR1zSJ2TmFkBdRQkC/fpw7UQ7sBNCCCFShSa3EEIIkSsUfIQQQuQKBR8hhBC5QsFHCCFErlDwEUIIkSsUfIQQQuQKBR8hhBC5QsFHCCFErlDwEUIIkSsUfIQQQuQKBR8hhBC5QsFHCCFErlDwEUIIkSsUfIQQQuQKBR8hhBC5QsFHCCFErlDwEUIIkSsUfIQQQuQKBR8hhBC5QsFHCCFErlDwEUIIkSv/ByS8EVn1DabCAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "G = networkx.DiGraph(acyclic_W)\n",
    "networkx.draw(G, with_labels=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "weighted_G = networkx.DiGraph((output_dict['W'] * acyclic_W).round(1))\n",
    "layout = networkx.spring_layout(weighted_G)\n",
    "networkx.draw(weighted_G, layout, node_size=1000, with_labels=True, font_weight='bold', font_size=15)\n",
    "labels = networkx.get_edge_attributes(weighted_G,'weight')\n",
    "networkx.draw_networkx_edge_labels(weighted_G,pos=layout,edge_labels=labels)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "# Individual similarity analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ace_lst = [f'ace_{x}' for x in range(1,11)]\n",
    "phq9_lst = [f'phq9_{x}' for x in range(1,11)]\n",
    "gad_lst = [f'gad_{x}' for x in range(1,9)]\n",
    "processed_individual_questions_df = pd.DataFrame(columns=['user_id', 'ace_sum'] + phq9_lst + gad_lst + promis_mental_lst + promis_physical_lst)\n",
    "\n",
    "for uid in overall_df['user_id'].unique():\n",
    "    each_df = overall_df.loc[overall_df['user_id']==uid]\n",
    "    ace_sum = each_df[ace_lst].sum(axis=1)\n",
    "    ace_sum_mean = ace_sum.apply(map_levels, map_dict=ace_levels).mean()\n",
    "\n",
    "    phq9_sum = each_df[phq9_lst].mean(axis=0)\n",
    "\n",
    "    gad_sum = each_df[gad_lst].mean(axis=0)\n",
    "\n",
    "    promis_mental_sum = promis_survey_processed.loc[promis_survey_processed['user_id']==uid][promis_mental_lst].mean(axis=0)\n",
    "\n",
    "    promis_physical_sum = promis_survey_processed.loc[promis_survey_processed['user_id']==uid][promis_physical_lst].mean(axis=0)\n",
    "    \n",
    "    processed_individual_questions_df = processed_individual_questions_df.append(pd.DataFrame([[uid, ace_sum_mean] + phq9_sum.tolist() + gad_sum.tolist() + promis_mental_sum.tolist() + promis_physical_sum.tolist()], columns=['user_id', 'ace_sum'] + phq9_lst + gad_lst + promis_mental_lst + promis_physical_lst), ignore_index = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_individual_questions_df = processed_individual_questions_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "28"
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "len(processed_individual_questions_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = processed_individual_questions_df[phq9_lst + gad_lst + promis_mental_lst + promis_physical_lst].to_numpy().tolist()\n",
    "output_dict = notears.run(notears.notears_standard, data, notears.loss.least_squares_loss, notears.loss.least_squares_loss_grad, e=1e-8, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Acyclicity loss: 4.407617382184981e-09\nLeast squares loss: 3.6308403147128234\n"
    }
   ],
   "source": [
    "print('Acyclicity loss: {}'.format(output_dict['h']))\n",
    "print('Least squares loss: {}'.format(output_dict['loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fccb0407a90>"
     },
     "metadata": {},
     "execution_count": 57
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 288x288 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.matshow(output_dict['W'])\n",
    "plt.title(\"Learned adjacency matrix\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "acyclic_W = notears.utils.threshold_output(output_dict['W'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.colorbar.Colorbar at 0x7fccaa8b5320>"
     },
     "metadata": {},
     "execution_count": 59
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 288x288 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.matshow(acyclic_W)\n",
    "plt.title(\"Learned adjacency matrix (thresholded)\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python361064bitbumpenvvenv908c53ecbdb647f396dedf9f95ba2333",
   "display_name": "Python 3.6.10 64-bit ('bump_env': venv)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}