mw-lifecycle-analysis/text_analysis/case2/040425_phab_comments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/0402_https1_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def http_relevant(text):\n",
    "    if pd.isnull(text):\n",
    "        return False\n",
    "    # TODO: expanded dictionary for relevancy\n",
    "    # http, ip, login, auth, SSL, TLS, certificate \n",
    "    \n",
    "    for word in text.split():\n",
    "        if \"://\" not in word.lower() and \"http\" in word.lower():\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22429/86623999.py:36: 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",
      "  mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n"
     ]
    }
   ],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "#cleaning df\n",
    "phab_df['id'] = phab_df.index + 1\n",
    "#may have to build out the reply_to column \n",
    "phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
    "phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
    "\n",
    "phab_df = phab_df.rename(columns={\n",
    "    'AuthorPHID': 'speaker',\n",
    "    'TaskPHID': 'conversation_id',\n",
    "    'WMFaffil':'meta.affil',\n",
    "    'isGerrit': 'meta.gerrit'\n",
    "})\n",
    "\n",
    "# after 12-1-2012 before 12-1-2013\n",
    "phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
    "#filtered_phab_df = phab_df[(phab_df['date_created'] < 1385856000) & (phab_df['date_created'] > 1354320000)]\n",
    "filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379099276)]\n",
    "\n",
    "#removing headless conversations\n",
    "task_phab_df = filtered_phab_df[filtered_phab_df['comment_type']==\"task_description\"]\n",
    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
    "filtered_phab_df = filtered_phab_df[filtered_phab_df['conversation_id'].isin(headed_task_phids)]\n",
    "\n",
    "#TODO: filter out the sourceforge migration \n",
    "# Originally from: http://sourceforge.net in the task task_summary\n",
    "\n",
    "#removing gerrit comments \n",
    "mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
    "\n",
    "#cut down to only the data that is relevant (mentions http)\n",
    "relevant_conversation_ids = task_phab_df[\n",
    "    task_phab_df['comment_text'].apply(http_relevant) |\n",
    "    task_phab_df['task_title'].apply(http_relevant)\n",
    "]['conversation_id'].unique()\n",
    "\n",
    "task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "\n",
    "comment_phab_df = mid_comment_phab_df[mid_comment_phab_df['is_relevant'] == True]\n",
    "#comment_phab_df = mid_comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 96\n",
      "Unique ids: 361\n",
      "Unique speakers: 47\n"
     ]
    }
   ],
   "source": [
    "unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
    "unique_ids = len(comment_phab_df['id'].unique())\n",
    "unique_speakers = len(comment_phab_df['speaker'].unique())\n",
    "\n",
    "print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
    "print(f\"Unique ids: {unique_ids}\")\n",
    "print(f\"Unique speakers: {unique_speakers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d226d781-b002-4842-a3ae-92d4851a5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = str(text)\n",
    "    text = text.replace('*', ' ')\n",
    "    text = text.replace('-', ' ')\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22429/2783900859.py:1: 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",
      "  comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(text):\n",
    "    doc = nlp(text)\n",
    "    dependency_trees = []\n",
    "    \n",
    "    for sentence in doc.sents:\n",
    "        for token in sentence:\n",
    "            token_info = (\n",
    "                token.text, \n",
    "                token.lemma_, \n",
    "                token.dep_, \n",
    "                token.head.text, \n",
    "                list(token.ancestors), \n",
    "                list(token.subtree), \n",
    "                list(token.children)\n",
    "            )\n",
    "            dependency_trees.append(token_info)\n",
    "        \n",
    "    return dependency_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_22429/2805711855.py:1: 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",
      "  comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "370a2767-04f8-4d0b-9b94-9c6a0b408822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2612     Recently (starting maybe 2 days ago), some goo...\n",
       "2989     Although the \"Always use a secure connection w...\n",
       "3080     Originally from: http://sourceforge.net/p/pywi...\n",
       "3084     Originally from: http://sourceforge.net/p/pywi...\n",
       "3096     Originally from: http://sourceforge.net/p/pywi...\n",
       "                               ...                        \n",
       "44209    Originally from: http://sourceforge.net/p/pywi...\n",
       "44217    Originally from: http://sourceforge.net/p/pywi...\n",
       "44265    Originally from: http://sourceforge.net/p/pywi...\n",
       "44277    Originally from: http://sourceforge.net/p/pywi...\n",
       "44316    Originally from: http://sourceforge.net/p/pywi...\n",
       "Name: comment_text, Length: 96, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comment_phab_df[comment_phab_df['comment_type'] == 'task_description']['comment_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5f138688-3d1a-4a27-b16d-d8aa438dafea",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "44",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/envs/jupyter3-notebook/lib/python3.9/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:2606\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:2630\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 44",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcomment_phab_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcomment_text\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m44\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/envs/jupyter3-notebook/lib/python3.9/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m   1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m   1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/envs/jupyter3-notebook/lib/python3.9/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1234\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m   1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
      "File \u001b[0;32m/gscratch/scrubbed/mjilg/envs/jupyter3-notebook/lib/python3.9/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 44"
     ]
    }
   ],
   "source": [
    "comment_phab_df['comment_text'][44]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f61845ce-d91f-4b06-9039-b507905cb972",
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task_title</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>date_created</th>\n",
       "      <th>speaker</th>\n",
       "      <th>meta.affil</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>comment_type</th>\n",
       "      <th>status</th>\n",
       "      <th>meta.gerrit</th>\n",
       "      <th>id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>is_relevant</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>dependency_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>?embedplayer=yes broken for videos with width ...</td>\n",
       "      <td>Ni!\\n\\nI am experiencing an unresponsive black...</td>\n",
       "      <td>1383189120</td>\n",
       "      <td>PHID-USER-wr7prgh3p37xrvbdr6w5</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-lfhsyqihbylzxoeftr7m</td>\n",
       "      <td>task_description</td>\n",
       "      <td>resolved</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-10-31 03:12:00+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Ni!\\n\\nI am experiencing an unresponsive black...</td>\n",
       "      <td>[(Ni, Ni, nsubj, experiencing, [experiencing],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>?embedplayer=yes broken for videos with width ...</td>\n",
       "      <td>**mdale** wrote:\\n\\n@Ryan, I just mean you wil...</td>\n",
       "      <td>1383856310</td>\n",
       "      <td>PHID-USER-ynivjflmc2dcl6w5ut5v</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-lfhsyqihbylzxoeftr7m</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-11-07 20:31:50+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>mdale   wrote:\\n\\n@Ryan, I just mean you wil...</td>\n",
       "      <td>[(  ,   , dep, mdale, [mdale, wrote], [  ], []...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>?embedplayer=yes broken for videos with width ...</td>\n",
       "      <td>Ni!\\n\\n=) Thanks everyone for helping verify a...</td>\n",
       "      <td>1383796532</td>\n",
       "      <td>PHID-USER-wr7prgh3p37xrvbdr6w5</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-lfhsyqihbylzxoeftr7m</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2013-11-07 03:55:32+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Ni!\\n\\n=) Thanks everyone for helping verify a...</td>\n",
       "      <td>[(Ni, Ni, ROOT, Ni, [], [Ni, !, \\n\\n], [!]), (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>?embedplayer=yes broken for videos with width ...</td>\n",
       "      <td>&gt; So putting it back to 200px specifically for...</td>\n",
       "      <td>1383776933</td>\n",
       "      <td>PHID-USER-a5pveeqqwaddgfjiv2fq</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-lfhsyqihbylzxoeftr7m</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013-11-06 22:28:53+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>&gt; So putting it back to 200px specifically for...</td>\n",
       "      <td>[(&gt;, &gt;, dep, seem, [seem], [&gt;], []), (So, so, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>?embedplayer=yes broken for videos with width ...</td>\n",
       "      <td>Many thanks to Brian and Mark for their fine w...</td>\n",
       "      <td>1383775629</td>\n",
       "      <td>PHID-USER-dbudsaorcqut7sg3vvbi</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-lfhsyqihbylzxoeftr7m</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2013-11-06 22:07:09+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Many thanks to Brian and Mark for their fine w...</td>\n",
       "      <td>[(Many, many, amod, thanks, [thanks], [Many], ...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46297</th>\n",
       "      <td>Add Taiwan in Chinese to the monuments database</td>\n",
       "      <td>**romaine.wiki** wrote:\\n\\nhttps://commons.wik...</td>\n",
       "      <td>1377925197</td>\n",
       "      <td>PHID-USER-ynivjflmc2dcl6w5ut5v</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-ze253b4m6dtco37373fc</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>46298</td>\n",
       "      <td>46297.0</td>\n",
       "      <td>2013-08-31 04:59:57+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>romaine.wiki   wrote:\\n\\n</td>\n",
       "      <td>[(  ,   , dep, romaine.wiki, [romaine.wiki, wr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46298</th>\n",
       "      <td>Add Taiwan in Chinese to the monuments database</td>\n",
       "      <td>We're playing with the templates on https://zh...</td>\n",
       "      <td>1377632023</td>\n",
       "      <td>PHID-USER-bdyms27sdtgdvjm7zfz4</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-ze253b4m6dtco37373fc</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>46299</td>\n",
       "      <td>46298.0</td>\n",
       "      <td>2013-08-27 19:33:43+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>We're playing with the templates on    Dennis ...</td>\n",
       "      <td>[(We, we, nsubj, playing, [playing, seems], [W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46299</th>\n",
       "      <td>Add Taiwan in Chinese to the monuments database</td>\n",
       "      <td>The links are all listed on https://commons.wi...</td>\n",
       "      <td>1377427853</td>\n",
       "      <td>PHID-USER-bdyms27sdtgdvjm7zfz4</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-ze253b4m6dtco37373fc</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>46300</td>\n",
       "      <td>46299.0</td>\n",
       "      <td>2013-08-25 10:50:53+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>The links are all listed on  . The Unique Iden...</td>\n",
       "      <td>[(The, the, det, links, [links, listed], [The]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46300</th>\n",
       "      <td>Add Taiwan in Chinese to the monuments database</td>\n",
       "      <td>Looks like some lists are available, but not i...</td>\n",
       "      <td>1376771718</td>\n",
       "      <td>PHID-USER-cw4amt4ewxdze5qcjdca</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-ze253b4m6dtco37373fc</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>46301</td>\n",
       "      <td>46300.0</td>\n",
       "      <td>2013-08-17 20:35:18+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>Looks like some lists are available, but not i...</td>\n",
       "      <td>[(Looks, look, ROOT, Looks, [], [Looks, like, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46301</th>\n",
       "      <td>Add Taiwan in Chinese to the monuments database</td>\n",
       "      <td>We already have a lot of sources in the monume...</td>\n",
       "      <td>1376423842</td>\n",
       "      <td>PHID-USER-cw4amt4ewxdze5qcjdca</td>\n",
       "      <td>False</td>\n",
       "      <td>PHID-TASK-ze253b4m6dtco37373fc</td>\n",
       "      <td>task_subcomment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>46302</td>\n",
       "      <td>46301.0</td>\n",
       "      <td>2013-08-13 19:57:22+00:00</td>\n",
       "      <td>False</td>\n",
       "      <td>We already have a lot of sources in the monume...</td>\n",
       "      <td>[(We, we, nsubj, have, [have], [We], []), (alr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26300 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              task_title  \\\n",
       "0      ?embedplayer=yes broken for videos with width ...   \n",
       "1      ?embedplayer=yes broken for videos with width ...   \n",
       "2      ?embedplayer=yes broken for videos with width ...   \n",
       "3      ?embedplayer=yes broken for videos with width ...   \n",
       "4      ?embedplayer=yes broken for videos with width ...   \n",
       "...                                                  ...   \n",
       "46297    Add Taiwan in Chinese to the monuments database   \n",
       "46298    Add Taiwan in Chinese to the monuments database   \n",
       "46299    Add Taiwan in Chinese to the monuments database   \n",
       "46300    Add Taiwan in Chinese to the monuments database   \n",
       "46301    Add Taiwan in Chinese to the monuments database   \n",
       "\n",
       "                                            comment_text  date_created  \\\n",
       "0      Ni!\\n\\nI am experiencing an unresponsive black...    1383189120   \n",
       "1      **mdale** wrote:\\n\\n@Ryan, I just mean you wil...    1383856310   \n",
       "2      Ni!\\n\\n=) Thanks everyone for helping verify a...    1383796532   \n",
       "3      > So putting it back to 200px specifically for...    1383776933   \n",
       "4      Many thanks to Brian and Mark for their fine w...    1383775629   \n",
       "...                                                  ...           ...   \n",
       "46297  **romaine.wiki** wrote:\\n\\nhttps://commons.wik...    1377925197   \n",
       "46298  We're playing with the templates on https://zh...    1377632023   \n",
       "46299  The links are all listed on https://commons.wi...    1377427853   \n",
       "46300  Looks like some lists are available, but not i...    1376771718   \n",
       "46301  We already have a lot of sources in the monume...    1376423842   \n",
       "\n",
       "                              speaker  meta.affil  \\\n",
       "0      PHID-USER-wr7prgh3p37xrvbdr6w5       False   \n",
       "1      PHID-USER-ynivjflmc2dcl6w5ut5v       False   \n",
       "2      PHID-USER-wr7prgh3p37xrvbdr6w5       False   \n",
       "3      PHID-USER-a5pveeqqwaddgfjiv2fq       False   \n",
       "4      PHID-USER-dbudsaorcqut7sg3vvbi       False   \n",
       "...                               ...         ...   \n",
       "46297  PHID-USER-ynivjflmc2dcl6w5ut5v       False   \n",
       "46298  PHID-USER-bdyms27sdtgdvjm7zfz4       False   \n",
       "46299  PHID-USER-bdyms27sdtgdvjm7zfz4       False   \n",
       "46300  PHID-USER-cw4amt4ewxdze5qcjdca       False   \n",
       "46301  PHID-USER-cw4amt4ewxdze5qcjdca       False   \n",
       "\n",
       "                      conversation_id      comment_type    status  \\\n",
       "0      PHID-TASK-lfhsyqihbylzxoeftr7m  task_description  resolved   \n",
       "1      PHID-TASK-lfhsyqihbylzxoeftr7m   task_subcomment       NaN   \n",
       "2      PHID-TASK-lfhsyqihbylzxoeftr7m   task_subcomment       NaN   \n",
       "3      PHID-TASK-lfhsyqihbylzxoeftr7m   task_subcomment       NaN   \n",
       "4      PHID-TASK-lfhsyqihbylzxoeftr7m   task_subcomment       NaN   \n",
       "...                               ...               ...       ...   \n",
       "46297  PHID-TASK-ze253b4m6dtco37373fc   task_subcomment       NaN   \n",
       "46298  PHID-TASK-ze253b4m6dtco37373fc   task_subcomment       NaN   \n",
       "46299  PHID-TASK-ze253b4m6dtco37373fc   task_subcomment       NaN   \n",
       "46300  PHID-TASK-ze253b4m6dtco37373fc   task_subcomment       NaN   \n",
       "46301  PHID-TASK-ze253b4m6dtco37373fc   task_subcomment       NaN   \n",
       "\n",
       "       meta.gerrit     id  reply_to                 timestamp  is_relevant  \\\n",
       "0            False      1       NaN 2013-10-31 03:12:00+00:00        False   \n",
       "1            False      2       1.0 2013-11-07 20:31:50+00:00        False   \n",
       "2            False      3       2.0 2013-11-07 03:55:32+00:00        False   \n",
       "3            False      4       3.0 2013-11-06 22:28:53+00:00        False   \n",
       "4            False      5       4.0 2013-11-06 22:07:09+00:00        False   \n",
       "...            ...    ...       ...                       ...          ...   \n",
       "46297        False  46298   46297.0 2013-08-31 04:59:57+00:00        False   \n",
       "46298        False  46299   46298.0 2013-08-27 19:33:43+00:00        False   \n",
       "46299        False  46300   46299.0 2013-08-25 10:50:53+00:00        False   \n",
       "46300        False  46301   46300.0 2013-08-17 20:35:18+00:00        False   \n",
       "46301        False  46302   46301.0 2013-08-13 19:57:22+00:00        False   \n",
       "\n",
       "                                          processed_text  \\\n",
       "0      Ni!\\n\\nI am experiencing an unresponsive black...   \n",
       "1        mdale   wrote:\\n\\n@Ryan, I just mean you wil...   \n",
       "2      Ni!\\n\\n=) Thanks everyone for helping verify a...   \n",
       "3      > So putting it back to 200px specifically for...   \n",
       "4      Many thanks to Brian and Mark for their fine w...   \n",
       "...                                                  ...   \n",
       "46297                          romaine.wiki   wrote:\\n\\n   \n",
       "46298  We're playing with the templates on    Dennis ...   \n",
       "46299  The links are all listed on  . The Unique Iden...   \n",
       "46300  Looks like some lists are available, but not i...   \n",
       "46301  We already have a lot of sources in the monume...   \n",
       "\n",
       "                                         dependency_tree  \n",
       "0      [(Ni, Ni, nsubj, experiencing, [experiencing],...  \n",
       "1      [(  ,   , dep, mdale, [mdale, wrote], [  ], []...  \n",
       "2      [(Ni, Ni, ROOT, Ni, [], [Ni, !, \\n\\n], [!]), (...  \n",
       "3      [(>, >, dep, seem, [seem], [>], []), (So, so, ...  \n",
       "4      [(Many, many, amod, thanks, [thanks], [Many], ...  \n",
       "...                                                  ...  \n",
       "46297  [(  ,   , dep, romaine.wiki, [romaine.wiki, wr...  \n",
       "46298  [(We, we, nsubj, playing, [playing, seems], [W...  \n",
       "46299  [(The, the, det, links, [links, listed], [The]...  \n",
       "46300  [(Looks, look, ROOT, Looks, [], [Looks, like, ...  \n",
       "46301  [(We, we, nsubj, have, [have], [We], []), (alr...  \n",
       "\n",
       "[26300 rows x 15 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get VAD scores\n",
    "import numpy as np\n",
    "#https://saifmohammad.com/WebPages/nrc-vad.html\n",
    "column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
    "vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
    "vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
    "\n",
    "def vad_scoring(dependency_tree):\n",
    "    valence = []\n",
    "    arousal = []\n",
    "    dominance = []\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            valence.append(vad_dict[lemma]['Valence'])\n",
    "            arousal.append(vad_dict[lemma]['Arousal'])\n",
    "            dominance.append(vad_dict[lemma]['Domination'])\n",
    "\n",
    "    # Compute average scores across the comment\n",
    "    avg_valence = np.mean(valence) if valence else 0\n",
    "    avg_arousal = np.mean(arousal) if arousal else 0\n",
    "    avg_dominance = np.mean(dominance) if dominance else 0\n",
    "\n",
    "    return [avg_valence, avg_arousal, avg_dominance]\n",
    "\n",
    "def dominance_prevail(dependency_tree):\n",
    "    dominant_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Domination'] >= 0.75:\n",
    "                dominant_words += 1\n",
    "            if vad_dict[lemma]['Domination'] <= 0.25:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "\n",
    "def arousal_prevail(dependency_tree):\n",
    "    arousal_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Arousal'] >= 0.75:\n",
    "                arousal_words += 1\n",
    "            if vad_dict[lemma]['Arousal'] <= 0.25:\n",
    "                arousal_words += 1\n",
    "    return arousal_words\n",
    "\n",
    "def valence_prevail(dependency_tree):\n",
    "    valence_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Valence'] >= 0.75:\n",
    "                valence_words += 1\n",
    "            if vad_dict[lemma]['Valence'] <= 0.25:\n",
    "                valence_words += 1\n",
    "    return valence_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11370/2858732056.py:2: 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",
      "  comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
      "/tmp/ipykernel_11370/2858732056.py:3: 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",
      "  comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
      "/tmp/ipykernel_11370/2858732056.py:4: 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",
      "  comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
      "/tmp/ipykernel_11370/2858732056.py:5: 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",
      "  comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)\n"
     ]
    }
   ],
   "source": [
    "#establishing per-comment VAD scores \n",
    "comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
    "comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
    "comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
    "comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11370/335308388.py:1: 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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_11370/335308388.py:1: 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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_11370/335308388.py:1: 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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
    "comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "\n",
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "pattern = r'\\b(http|https)\\b'\n",
    "\n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['comment_text']\n",
    "    timestamp = row['timestamp']\n",
    "    comment_id = row['id']\n",
    "    conversation_id = row['conversation_id']\n",
    "    WMFaffil = row['meta.affil']\n",
    "    \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''        \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            resolved_dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''\n",
    "#resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations)            \n",
    "dependency_relations_df = pd.DataFrame(dependency_relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82498686-14f4-40c8-9e33-27b31f115b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now analysis/plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11370/1702682277.py:7: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n"
     ]
    },
    {
     "data": {
      "image/png": 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HHzB69Gji4+Pxer2kpaVF70OuS9j++eef6dOnT43t/fr1iz6/t27duh30mHWxc+dO/v73v5ORkYHb7SYtLS167L3rvuOOO/jmm2/o1KkTw4YNY8aMGbUGrI0bN3LOOecwceJE7rvvvhrf3zPOOIOjjz6aiy66iIyMDM4880yeffbZBgXvvQMqEA3eVe2o6r2rrW3s+56vW7eOgoIC0tPTa1w3xcXFNSbIuuqqqxg0aBCffvopN910U7Xwvm7dOiDyh5F9j/Wvf/2L8vJyCgoK2LZtG7t3765TffsT62t4f/Z9r6H2a3Z/evXqxejRo2t8dO/evUF11cf8+fNZunQpK1as4LvvvmPDhg01/qjRu3dvnnzySbZv385XX33FrbfeSlxcHBdffDHLli1rslpFROpD92yLiMRI9+7dOeecc3jkkUe49tprazwfDoexWCy8+eabNZbHgUivaZVjjjmGYDDIRx99xMqVK6O/kB977LGsXLmSH374gW3bttX5F/X4+PhqS07tT211ARiGEf383//+N5MmTeK0007jqquuIj09HZvNxuzZs6P3Wh4Kp9NZY/buQ7V+/XpOOOEE+vbty5w5c+jUqRMOh4M33niDuXPnNspSYrHo1QY4/fTT+fDDD7nqqqsYPHgwCQkJhMNhTjzxxGp1n3766Rx77LG89NJLLFmyhDvvvJPbb7+dF198MXp/PUBWVhZZWVm88cYb/O9//4ve07533e+//z4rVqzg9ddf56233uKZZ57h+OOPZ8mSJfttEwdSl3ZUV+FwmPT0dJ566qlan9/7/mWI9J5Xheqvv/66xrEA7rzzzhpzC1RJSEigvLy83nXuK9bX8P7E8r02y7Bhw2q0y/2x2WwMHDiQgQMHMnz4cEaNGsVTTz1Vp59vIiJNTWFbRCSGrr/+ev79739z++2313iuR48eGIZBt27d6N279wGPM2zYMBwOBytXrmTlypXRGYmPO+44Hn300eiMy4c6OVpDPP/883Tv3p0XX3yxWi9pbcO64ZfexL39+OOPNWYYr6sePXrw9ttvs3Pnzv32br/66quUl5fzyiuvVOv5q23G9H17eqt06dKFtWvX1tj+ww8/RJ+PtV27drF8+XJmzpzJjTfeGN1e23sIkSB92WWXcdlll5GXl8cRRxzBLbfcUi1su1wuXnvtNY4//nhOPPFE3nvvPfr371/tOFarlRNOOIETTjiBOXPmcOutt3LdddexYsWKRgkxVe9dba9r3/e8R48eLFu2jKOPPvqgf9AIh8NMmjSJpKQkrrjiCm699Vb+8Ic/MGHChOixAJKSkg74utLS0nC73XWqb39idQ3vr30K0YC+v1t3RETMpmHkIiIx1KNHD8455xwefvhhcnJyqj03YcIEbDYbM2fOrNHrZBhGtaWdXC4XRx55JP/5z3/YvHlztV6x3bt3M2/ePHr06EFWVlbjv6h9VPWk7f0aPvnkk+h9qPt6+eWX8fv90ceffvopn3zySbVAWB8TJ07EMAxmzpxZ47mqmmqrsaCggAULFtT4mvj4+GpLNFUZP348n376abXXVVJSwiOPPELXrl1r3FscC7XVDZFZ0/cWCoVqDIVPT0+nffv2tfbKJicn8/bbb0eXB9t7BMLOnTtr7F/V6xuLHt7aZGVlMXjwYBYtWlTtdSxdurTGvfCnn346oVCIWbNm1ThOZWVlte/dnDlz+PDDD3nkkUeYNWsWv/nNb7j00kujs20PGTKEHj16cNddd1FcXFzjeFXL4NlsNsaNG8fLL7/M5s2bo89///33vP3223V6jbG6huPj4wFqbaNtxcqVKwkGgzW2V837UNeh/SIiTU092yIiMXbdddfx5JNPsnbt2mo9iD169OCf//wn06dPZ9OmTZx22mkkJiayceNGXnrpJS6++GKmTZsW3f/YY4/ltttuIzk5mYEDBwKRQNWnTx/Wrl3LpEmTmvqlAXDKKafw4osv8vvf/56TTz6ZjRs38tBDD3HYYYfVGmB69uzJMcccw6WXXkp5eTn33HMPqampXH311Yd0/lGjRnHuuecyb9481q1bFx1evXLlSkaNGsXkyZMZO3YsDoeDU089lUsuuYTi4mIeffRR0tPTa/SCDRkyhAcffJB//vOf9OzZk/T0dI4//niuvfZa/vOf/3DSSScxZcoUUlJSWLRoERs3buSFF16I2ZD3vSUlJXHcccdxxx13EAwG6dChA0uWLImuN1ylqKiIjh078oc//IFBgwaRkJDAsmXL+Oyzz7j77rtrPXa7du2i62mPHj2aVatW0aFDB26++Wbef/99Tj75ZLp06UJeXh4PPPAAHTt2rDY5XKzNnj2bk08+mWOOOYYLLriAnTt3ct9999G/f/9q7WjEiBFccsklzJ49mzVr1jB27Fjsdjvr1q3jueee49577+UPf/gD33//PTfccAOTJk3i1FNPBWDhwoUMHjyYyy67jGeffRar1cq//vUvTjrpJPr378/5559Phw4d8Pv9rFixgqSkJF599VUAZs6cyVtvvcWxxx7LZZddRmVlZbS+r776qk6vMRbX8JAhQ4DIz5UzzzwTu93OqaeeGg3hbcHtt9/O6tWrmTBhAocffjgQmSzwiSeeICUlhSuuuMLcAkVE9seMKdBFRFqDvZf+2lfVUlZ7L/1V5YUXXjCOOeYYIz4+3oiPjzf69u1rXH755cbatWur7ff6668bgHHSSSdV237RRRcZgPHYY4/Vqc59lyCr7377Lr0VDoeNW2+91ejSpYvhdDqNX/3qV8Zrr71WY7+qpYXuvPNO4+677zY6depkOJ1O49hjjzW+/PLLGueIj4+vta59j2sYkeXH7rzzTqNv376Gw+Ew0tLSjJNOOslYvXp1dJ9XXnnFOPzwww2Xy2V07drVuP32243HH3+8xjJKOTk5xsknn2wkJiYaQLVlitavX2/84Q9/MLxer+FyuYxhw4YZr732WrVaqpb+eu655/bzzu5fbUt/bd261fj9739veL1eIzk52fjjH/9oBAKBaktClZeXG1dddZUxaNAgIzEx0YiPjzcGDRpkPPDAA9WOX9v39KeffjKysrKMfv36Gdu2bTOWL19u/O53vzPat29vOBwOo3379sZZZ51l/Pjjjwetf39Lf91555019t27/iovvPCC0a9fP8PpdBqHHXaY8eKLL9b6/TYMw3jkkUeMIUOGGG6320hMTDQGDhxoXH311UYgEDAqKyuNI4880ujYsWO1pcQMwzDuvfdeAzCeeeaZ6LYvvvjCmDBhgpGammo4nU6jS5cuxumnn24sX7682te+9957xpAhQwyHw2F0797deOihh4ybbrrpoEt/VTmUa7i292nWrFlGhw4dDKvVWq39ArUu/9alSxfjvPPOO2BtB/peGYYRfZ1NsfRXbT9D9/bBBx8Yl19+uTFgwAAjOTnZsNvtRufOnY1JkyYZ69evP2gNIiJmsRhGC5pBQ0RERERERKQF0D3bIiIiIiIiIjGmsC0iIiIiIiISYwrbIiIiIiIiIjGmsC0iIiIiIiISYwrbIiIiIiIiIjGmsC0iIiIiIiISY3FmF9DYwuEwgUCAxMRELBaL2eWIiIiIiIhIM2cYBkVFRbRv3x6r9dD6qFt92A4EAnTq1MnsMkRERERERKSF2bJlCx07djykr231YTsxMRGIvElJSUlNdt5gMMiSJUsYO3Ysdru9yc4rUh9qp9JSqe1KS6W2Ky2B2qm0VLFsu4WFhXTq1CmaJw9Fqw/bVUPHk5KSmjxsezwekpKS9ENKmi21U2mp1HalpVLblZZA7VRaqsZouw25FVkTpImIiIiIiIjEmMK2iIiIiIiISIwpbIuIiIiIiIjEWKu/Z7uuQqEQwWAwZscLBoPExcVRVlZGKBSK2XFFYulQ26ndbsdmszViZSIiIiIiLVubD9uGYZCTk0N+fn7Mj5uZmcmWLVu0vrc0Ww1pp16vl8zMTLVvEREREZFatPmwXRW009PT8Xg8MQsO4XCY4uJiEhISDnkRdJHGdijt1DAMSktLycvLAyArK6sxSxQRERERaZHadNgOhULRoJ2amhrTY4fDYSoqKnC5XArb0mwdajt1u90A5OXlkZ6eriHlIiIiIiL7aNMpsOoebY/HY3IlIi1P1XUTy7kORERERERaizYdtqvonlOR+tN1IyIiIiKyfwrbIiIiIiIiIjGmsC11YrFYePnll80uQ0REREREpEVQ2G5hLBbLAT9mzJix36/dtGkTFouFNWvWNFm9IiIiIiIibVGbno08VkpKSrDb7eTn5+P1egkGg9HZmmMtOzs7+vkzzzzDjTfeyNq1a6PbEhISGuW8IiIiIiIiUnfq2W6gsrIy7rjjDjIyMqIfd9xxBxUVFY2y5FdmZmb0Izk5GYvFEn2cnp7OnDlz6NixI06nk8GDB/PWW29Fv7Zbt24A/OpXv8JisTBy5EgAPvvsM8aMGUO7du1ITk5mxIgRfP755zGvXUREREREpK1Qz/ZeDMOgtLS0zvuHQiHuvvtubr755ui2/Pz86OO///3vlJSU1Cl0ezyeBs/ufO+993L33Xfz8MMP86tf/YrHH3+c3/72t3z77bf06tWLTz/9lGHDhrFs2TL69++Pw+EAoKioiPPOO4/77rsPwzC4++67GT9+POvWrSMxMbFBNYmIiIiIiLRFCtt7KS0trfMw7Hbt2rFp0ybmzZtX6/Pz5s3j6quvpmvXrmzfvv2gxysuLiY+Pr5e9e7rrrvu4pprruHMM88E4Pbbb2fFihXcc889zJ8/n7S0NABSU1PJzMyMft3xxx9f7TiPPPIIXq+X9957j1NOOaVBNYmIiIhI81PbbZAN/V1URKrTMPJDlJmZSV5eHvn5+bU+n5+fz7Zt26qF2sZUWFhIIBDg6KOPrrb96KOP5vvvvz/g1+bm5vKXv/yFXr16kZycTFJSEsXFxWzevLkxSxYRERERE+zvNsiysjKzSxNpVdSzvRePx0NxcXGd97fb7Xi93loDt9frJSsri48++qhOw8M9Hk99So2p8847jx07dnDvvffSpUsXnE4nw4cPp6KiwrSaRERERCT2SkpKuOOOO/Z7G+TVV18dvdVQRBpGPdt7sVgsxMfH1/kjGAwyZcqUWo81ZcoUysrK8Hg8dTpWQ+/XTkpKon379nzwwQfVtn/wwQccdthhANEfnKFQqMY+U6ZMYfz48fTv3x+n01mnoe8iIiIi0rLY7fYD3gZpt9ubuCKR1ks92w0QHx/P9OnTgcgPp6p7XqZMmcL06dObvGf4qquu4qabbqJHjx4MHjyYBQsWsGbNGp566ikA0tPTcbvdvPXWW3Ts2BGXy0VycjK9evXiySefZOjQoRQWFnLVVVc12tJlIiIiImKe/Pz86KjMdu3akZmZSU5ODtu3byc/P5+CggK8Xq+pNYq0FurZbiCXy8XVV19Nbm4ueXl55ObmRoffhMPhJq1lypQpTJ06lX/84x8MHDiQt956i1deeYVevXoBEBcXx7x583j44Ydp3749v/vd7wB47LHH2LVrF0cccQTnnnsuU6ZMIT09vUlrFxEREZHG5/V6GTZsGK+98Rabt2zl7RWr2LxlK6++/ibDhg0jOTnZ7BJFWg2LYRiG2UU0psLCQpKTkykoKCApKanac2VlZWzcuJFu3brhcrliet5wOExhYSFJSUmNst62SCw0pJ025vUjcjDBYJA33niD8ePHa8ijtChqu2K20tJSwlh564dClq0roLQijMdhZXSvJE7sm4yVMHa7Xe1UWqRY/ow9UI6sK6VAEREREZE2wmZ3sWRdMa98u4vSisgozNKKMK98m8+SdcXY7PoDukisKGyLiIiIiLQRNquFJT/sqvW5JT/swmZt2KS9IvILhW0RERERkTaiNBiK9mjXeK4iTGkwVOtzIlJ/CtsiIiIiIm2Ex27D46g9AngcVjx2WxNXJNJ6KWyLiIiIiLQRobDB2L6+Wp8b29dHKNyq504WaVJaZ1tEREREpI1w2q2c0j+FcDjMsh9/mY18bF8fpw5IwW6zEtRQcpGYUNgWEREREWlDAls3k2aUc+/EXhSWhUhwgMVixW7ToFeRWNIVJSIiIiLShmzatInr//UaU1/cwNwVWznp4hsp2Lnd7LJEWh2FbRERERGRNiQQCODxZVBUHmJrfgXlNg+BQMDsskRaHYVtaXKPPPIInTp1wmq1cs8999S6bcaMGQwePDj6NZMmTeK0006LPh45ciRXXHFFk9Zd5d1338VisZCfn9/k5960aRMWi4U1a9Y0+blFRESkdfD7/bi9adHHbm86fr/fxIpEWieF7RbmoYceIjExkcrKyui24uJi7HY7I0eOrLZvVShcv349AF27dsVisbB48eIax+3fvz8Wi4WFCxdGt1Xtv/dHx44dG1R/YWEhkydP5pprrsHv93PxxRfXum3atGksX758v8d58cUXmTVrVp3Pa2ZAPlT7/oFBREREJBb8gQAeX3r0sceXoZ5tkUagsB0DoYpywpWVBIuLCFdWEqoob7RzjRo1iuLiYv73v/9Ft61cuZLMzEw++eQTysrKottXrFhB586d6dGjR3Rbp06dWLBgQbVjfvzxx+Tk5BAfH1/jfDfffDPZ2dnRjy+++KJB9W/evJlgMMjJJ59MVlYWHo+n1m0JCQmkpqbu9zgpKSkkJiY2qBb5RUVFhdkliIiISBPJzttJnNMTfaywLdI4FLYbKBwMEnh3KatnXcvqm69l9axrCby7FEIhrNbYv719+vQhKyuLd999N7rt3Xff5Xe/+x3dunXj448/rrZ91KhR1b7+7LPP5r333mPLli3RbY8//jhnn302cXE1J6dPTEwkMzMz+pGWllZjn71dc8019O7dG4/HQ/fu3bnhhhsIBoMALFy4kIEDBwLQvXv3aE/6vts2bdpUYxj5vvYdRv7kk08ydOjQaL1/+tOfyMvLAyJDr6veB5/Ph8ViYdKkSQCEw2Fmz55Nt27dcLvdDBo0iOeff77aud544w169+6N2+1m1KhRbNq06YDvAcCcOXMYOHAg8fHxdOrUicsuu4zi4uLo87W9vnvuuYeuXbtGn1+0aBH//e9/o6MK9v6eb9iwgVGjRuHxeBg0aBAfffRRtWO98MIL9O/fH6fTSdeuXbn77rurPd+1a1dmzZrFeeedR+fOnbnkkkuoqKhg8uTJZGVl4XK56NKlC7Nnzz7oaxUREZGWJa+gFAALYQBcSSlsDeSYWZJIq6SwvRfDMAhVlNf5o7JsN/4Vb+Nf9iah3bsBCO3ejX/Zm/hXvI3DaqnzsQzDqHOdo0aNYsWKFdHHK1asYOTIkYwYMSK6fffu3XzyySc1wnZGRgbjxo1j0aJFAJSWlvLMM89wwQUXNPTtAyLhfOHChXz33Xfce++9PProo8ydOxeAM844g2XLlgHw6aefkp2dzR//+Mca2zp16lTv8waDQWbNmsWXX37Jyy+/zKZNm6KBulOnTrzwwgsArF27luzsbO69914AZs+ezRNPPMFDDz3Et99+y5VXXsk555zDe++9B8CWLVuYMGECp556KmvWrOGiiy7i2muvPWg9VquVefPm8e2337Jo0SLeeecdrr766jq/nmnTpnH66adz4oknRkcV/OY3v4k+f9111zFt2jTWrFlD7969Oeuss6K3FqxevZrTTz+dM888k6+//poZM2Zwww03VLtFAOCuu+5i0KBBvPfee1x//fXMmzePV155hWeffZa1a9fy1FNPRcO/iIiItB4FZZGQ7bWHsRiRz3Pziw/0JSJyCLTO9l7CwQo+u35qnfaNi0/gV9NvJueDd2t9PueDd2k/cgxfzL6RypKD//A68p9zsDmcdTr3qFGjuOKKK6isrGT37t188cUXjBgxgmAwyEMPPQTARx99RHl5eY2wDXDBBRfwj3/8g+uuu47nn3+eHj167LcX+ZprruH666+PPr711luZMmXKfmvbe9+uXbsybdo0Fi9ezNVXX43b7Y4ODU9LSyMzMxOg1m31tfcfC7p37868efM48sgjKS4uJiEhgZSUFADS09Pxer0AlJeXc+utt7Js2TKGDx8e/dpVq1bx8MMPM2LECB588EF69OgR7Rnu06cPX3/9NbfffvsB69m7171r167885//5K9//SsPPPBAnV5PQkICbreb8vLyWt+TadOmcfLJJwMwc+ZM+vfvz08//UTfvn2ZM2cOJ5xwAjfccAMAvXv35rvvvuPOO++M/gEC4Pjjj2fq1KkUFhaSlJTE5s2b6dWrF8cccwwWi4UuXbrUqVYRERFpOQzDoCQU6W9LibdTEaqgJGxlZ0nQ5MpEWh9Te7Zrm4DLYrFw+eWXA1BWVsbll19OamoqCQkJTJw4kdzcXDNLjrInJhEsLor2aO8rtHs3lcXF2BOTYn7ukSNHUlJSwmeffcbKlSvp3bs3aWlpjBgxInrf9rvvvkv37t3p3Llzja8/+eSTKS4u5v333+fxxx8/YK/2VVddxZo1a6Iff/7znwH461//SkJCQvSjyjPPPMPRRx9NZmYmCQkJXH/99WzevDnm78G+Vq9ezamnnkrnzp1JTExkxIgRAAc8908//URpaSljxoyp9lqeeOKJ6KRy33//Pb/+9a+rfV1VMD+QZcuWccIJJ9ChQwcSExM599xz2bFjB6WlpQ14lb84/PDDo59nZWUBRIfNf//99xx99NHV9j/66KNZt24doVAoum3o0KHV9pk0aRJr1qyhT58+TJkyhSVLlsSkVhEREWk+du7ciSMh0gmR4Y3H67IBUKTpW0RiztSe7c8++6zaL//ffPMNY8aM4Y9//CMAV155Ja+//jrPPfccycnJTJ48mQkTJvDBBx80Sj1Wu4Mj/zmnzvtbrDZsbnetgdvmdmNPSuawy6bW6d5tq91R5/P27NmTjh07smLFCnbt2hUNlu3bt6dTp058+OGHrFixguOPP77Wr4+Li+Pcc8/lpptu4pNPPuGll17a77natWtHz549a2y/+eabmTZtWrVtH330EWeffTYzZ85k3LhxJCcns3jx4hr3C8daSUkJ48aNY9y4cTz11FOkpaWxefNmxo0bd8CJv6ruoX799dfp0KFDteeczrqNMqjNpk2bOOWUU7j00ku55ZZbSElJYdWqVVx44YVUVFTg8XiwWq01bh2oure9Lux2e/Rzi8UCRO4/r499J8Q74ogj2LhxI2+++SbLli3j9NNPZ/To0TXuYRcREZGWKxAI4PZlAJCa4KC03IW/tIJKm5uKigocjrr/TioiB2Zq2N53sq3bbruNHj16MGLECAoKCnjsscd4+umno6FxwYIF9OvXj48//pijjjoq5vVYLJY6D+WGyCzkmUePxL/szRrPZR49kmBFOXaXu1EmShs1ahTvvvsuu3bt4qqrropuP+6443jzzTf59NNPufTSS/f79RdccAF33XUXZ5xxBj6fr97nT09PJz09vdq2Dz/8kC5dunDddddFt/3888/1PnZ9/fDDD+zYsYPbbrster/33rO1A9H/OPb+485hhx2G0+lk8+bN0T9Y7Ktfv3688sor1bbtPQldbVavXk04HObuu++Ofu+fffbZavukpaWRk5ODYRjRsLzv2tkOh6NavXXVr1+/Gn+Q+uCDD+jduzc2m+2AX5uUlMQZZ5zBGWecwR/+8AdOPPFEdu7cGR2GLyIiIi1bIBDA44v8Du7zxBEMxUN2BW5fOjk5ObWOihSRQ9NsJkirqKjg3//+NxdccAEWi4XVq1cTDAYZPXp0dJ++ffvSuXPnGjMvm8XmcNJh1Dg6jD4Jm9sd2eZ202H0SXQYNY7yyvoHpboaNWoUq1atYs2aNdWC4ogRI3j44YepqKio9X7tKv369WP79u01lgFriF69erF582YWL17M+vXrmTdv3gF7zWOlc+fOOBwO7rvvPjZs2MArr7xSYw3uLl26YLFYeO2119i2bRvFxcUkJiYybdo0rrzyShYtWsT69ev5/PPPue+++6ITyP31r39l3bp1XHXVVaxdu5ann366xkRj++rZsyfBYDBaz5NPPhm9l77KyJEj2bZtG3fccQfr169n/vz5vPlm9T/adO3ala+++oq1a9eyffv2Ovd8/+Mf/2D58uXMmjWLH3/8kUWLFnH//ffXGImwrzlz5vCf//yHH374gR9//JHnnnuOzMzM6D3uIiIi0vL5/X7c3kiHic8dh88T6Xvz+DLw+/1mlibS6jSbCdJefvll8vPzoxM45eTk4HA4avyin5GRQU7O/pcmKC8vp7z8l3WuCwsLgcgQ3X3DSjAYxDAMwuFwvYfgRtlstB8xhg7Hn0iobDc2lxsjFMKwWgmHw9Hjx9qIESPYvXs3ffv2JS0tLXqOY489lqKiIvr06UNGRkaNc+9dT1WP9t777Pte1Kf+U045hSuuuILJkydTXl7O+PHjuf7665k5c2b0GHv/e6BtVUOs9368by1Vj1NTU3n88cejM2ofccQR3HHHHZx22mnRY2ZlZTFjxgyuvfZazj//fM4991wWLFjAzJkzadeuHbNnz2bDhg14vV5+9atfMX36dMLhMB07duS5557jH//4B/fddx/Dhg3jn//8JxdddNF+283AgQO5++67uf3225k+fTrHHnsst9xyC5MmTYp+TZ8+fbj//vu57bbbmDVrFhMmTOAf//gHjz76aPSYF154IStWrGDo0KEUFxezfPny6OzgB3r/Bg8ezOLFi5kxYwazZs0iKyuLmTNn8uc//7nG+1f1PhuGQXx8PHfccQfr1q3DZrNx5JFH8tprr9VoI3u3FcMwCAaDB+0xF4m1qp/n9bn9QqQ5UNsVs23ZsgWPL9JRk+CA3Y7ICDuPL4PNmzczdOhQtVNpsWLZdmNxDItRnzWnGtG4ceNwOBy8+uqrADz99NOcf/751YIzwLBhwxg1atR+Z4OeMWMGM2fOrLH96aefxuPxVNsWFxdHZmYmnTp1avD9KVWTu+0dYERas4qKCrZs2UJOTk502TERERFp3h566GFSz7gTqy2O8Qk/URK2815pFwpzfyZt7XOccsopZpco0iyUlpbypz/9iYKCApKSDm3S62bRs/3zzz+zbNkyXnzxxei2zMxMKioqyM/Pr9a7nZube8DloaZPn87Uqb8s31VYWEinTp0YO3ZsjTeprKyMLVu2kJCQgMvlit0LItJTWFRURGJiYvSeXJHmpiHttKysDLfbzXHHHRfz60fkYILBIEuXLmXMmDHVJgwUae7UdsVsjz25GKstDgyD08aPYWdpJe+9tgWPN51kr5fx48ernUqLFcu2WzVCuiGaRdhesGAB6enp0XWDAYYMGYLdbmf58uVMnDgRgLVr17J58+YDLr3kdDprnUnabrfXeMNDoRAWiwWr1RrzScyqht1WHV+kOWpIO7VarVgsllqvLZGmovYnLZXarphlV2klXsBlDeFyOmhni8SBOKebvB0F1dql2qm0VLFou7Fo+6aH7XA4zIIFCzjvvPOIi/ulnOTkZC688EKmTp1KSkoKSUlJ/O1vf2P48OGNMhO5iIiIiEhrV1Ae+UN7kjMyos0RZ8VOJUHiyCusuZytiBw608P2smXL2Lx5MxdccEGN5+bOnYvVamXixImUl5czbtw4HnjgAROqFBERERFp2SorKyk3Ir11qQm/jARNsBvsCkZ6vUUkdkwP22PHjt3vhGIul4v58+czf/78Jq5KRERERKR1ycvLw7Vn2a9M7y8TB/s8dnYVQGlItz6KxJKuKBERERGRNiAQCODxpQHgi//lftSM5EjwtriSKC4uNqU2kdZIYVtEREREpA3w+/249/Rs+9y/DHBNT3YD4PZlEAgETKlNpDVS2BYRERERaQMiPdsZQGToeBWfJxK8PQrbIjGlsC0iIiIi0gb4/X48vkjPttdji27/JWynK2yLxJDCtsTEI488QqdOnbBardxzzz21bpsxYwaDBw+Ofs2kSZM47bTToo9HjhzJFVdc0aR1V3n33XexWCzk5+c3+bk3bdqExWJhzZo1TX5uERERaTv82Tm4klKB6sPIqz53+9Lx+/2m1CbSGilst0CTJk3CYrFw2223Vdv+8ssvY7FYmryewsJCJk+ezDXXXIPf7+fiiy+uddu0adNYvnz5fo/z4osvMmvWrDqf18yAfKj2/QODiIiISFPJzS8BwGKESXDW7Nl2J7XDH8gxpTaR1khhOwbKg2EqQwaFZZVUhgzKg+FGP6fL5eL2229n165djX6ug9m8eTPBYJCTTz6ZrKwsPB5PrdsSEhJITU3d73FSUlJITExswspbt4qKCrNLEBERkWZkR3HkdwOPLVytgybRZcNihLFYrQR2FJhVnkiro7DdQMFQmNe/28nk539i8nPrmfz8T7z+3U4qDbBaG+/tHT16NJmZmcyePfuA+73wwgv0798fp9NJ165dufvuu6s937VrV2699VYuuOACEhMT6dy5M4888ki1fa655hp69+6Nx+Ohe/fu3HDDDQSDQQAWLlzIwIEDAejevTsWi6XWbZs2baoxjHxf+w4jf/LJJxk6dCiJiYlkZmbypz/9iby8PCAy9HrUqFEA+Hw+LBYLkyZNAiAcDjN79my6deuG2+1m0KBBPP/889XO9cYbb9C7d2/cbjejRo1i06ZNB3wfAebMmcPAgQOJj4+nU6dOXHbZZdWWx6jt9d1zzz107do1+vyiRYv473//i8ViwWKx8O6770b33bBhA6NGjcLj8TBo0CA++uijaseqy/dy1qxZ/PnPfyYpKYmLL76YiooKJk+eTFZWFi6Xiy5duhy0zYiIiEjrtCdr43Xbqm23Wiy4rJUA7Cgub+qyRFothe29GEakV7quH7srQrz6zU5e/moHpRWR3uzSijAvf7WD177ZiSXOSXll3Y5lGEa9arXZbNx6663cd999bN26tdZ9Vq9ezemnn86ZZ57J119/zYwZM7jhhhtYuHBhtf3uvvtuhg4dyhdffMFll13GpZdeytq1a6PPJyYmsnDhQr777jvuvfdeHn30UebOnQvAGWecwbJlywD49NNPyc7O5o9//GONbZ06darX6wMIBoPMmjWLL7/8kpdffplNmzZFA3WnTp144YUXAFi7di3Z2dnce++9AMyePZsnnniChx56iG+//ZYrr7ySc845h/feew+ALVu2MGHCBE499VTWrFnDRRddxLXXXnvQeqxWK/PmzePbb79l0aJFvPPOO1x99dV1fj3Tpk3j9NNP58QTTyQ7O5vs7Gx+85vfRJ+/7rrrmDZtGmvWrKF3796cddZZVFZG/uOr6/fyrrvuYtCgQXzxxRfccMMNzJs3j1deeYVnn32WtWvX8tRTT0XDv4iIiLQdZWVlhO3xAKQluWo8n+SI9HQXltfvd1IR2b+4g+/SdlRUGvxl8bo67ZvotDFnQneW/FD7MO4lP+zi5P4pTH1xA0XloYMe79Eze+G01+9+69///vcMHjyYm266iccee6zG83PmzOGEE07ghhtuAKB3795899133HnnndHQCjB+/Hguu+wyINKLPXfuXFasWEGfPn0AuP7666P7du3alWnTprF48WKuvvpq3G53dGh4WloamZmZALVuq68LLrgg+nn37t2ZN28eRx55JMXFxSQkJJCSkgJAeno6Xq8XgPLycm699VaWLVvG8OHDo1+7atUqHn74YUaMGMGDDz5Ijx49oj3Dffr04euvv+b2228/YD1797p37dqVf/7zn/z1r3/lgQceqNPrSUhIwO12U15eXut7Mm3aNE4++WQAZs6cSf/+/fnpp5/o27dvnb+Xxx9/PP/4xz+ijzdv3kyvXr045phjsFgsdOnSpU61ioiISOsSCARw+9KAX9bV3ltqgpPc8jC7DXu9O4FEpHbq2T5EyW4bhWWV0R7tfZVWhCksC5G8zzCdWLv99ttZtGgR33//fY3nvv/+e44++uhq244++mjWrVtHKPTLHwAOP/zw6OcWi4XMzMzocG2AZ555hqOPPprMzEwSEhK4/vrr2bx5cyO8mupWr17NqaeeSufOnUlMTGTEiBEABzz3Tz/9RGlpKWPGjCEhISH68cQTT7B+/Xog8r78+te/rvZ1VcH8QJYtW8YJJ5xAhw4dSExM5Nxzz2XHjh2UlpY24FX+Yu/vQ1ZWFkD0+1DX7+XQoUOr7TNp0iTWrFlDnz59mDJlCkuWLIlJrSIiItKyVF9ju2Z/W1ZKpNfbkZjCzp07m7Q2kdZKPdt7ccRZePTMXnXe32a14HFYaw3cHocVr9vGDeM6YbUc/G8ajrhDm0X8uOOOY9y4cUyfPr1aD2d92O32ao8tFgvhcOQ1ffTRR5x99tnMnDmTcePGkZyczOLFi2vcLxxrJSUljBs3jnHjxvHUU0+RlpbG5s2bGTdu3AEn/qq6h/r111+nQ4cO1Z5zOp2HXM+mTZs45ZRTuPTSS7nllltISUlh1apVXHjhhVRUVODxeLBarTX+Elx1b3td7P19qJq0pOr7UFfx8fHVHh9xxBFs3LiRN998k2XLlnH66aczevToGvewi4iISOsWCARweyNrbPvc9hrPt0t0AkW4vVprWyRWFLb3YrFY6jWUuzwYZmxfHy9/taPGc2P7+qioqMTtjGvUidIAbrvtNgYPHhwd9l2lX79+fPDBB9W2ffDBB/Tu3RubrW497h9++CFdunThuuuui277+eefG170Qfzwww/s2LGD2267LXq/9//+979q+zgcDoBqPbuHHXYYTqeTzZs3R3vC99WvXz9eeeWVats+/vjjA9azevVqwuEwd999d/T7+eyzz1bbJy0tjZycHAzDiIblfdfOdjgc1eqtq4Z8L5OSkjjjjDM444wz+MMf/sCJJ57Izp07o8PwRUREpPXz+/14fJHfFb219Gx796y17fFlKGyLxIiGkTeA027l1AEpnHZ4Kh5H5K30OKycdngqpw5IIRQsa5I6Bg4cyNlnn828efOqbf/HP/7B8uXLmTVrFj/++COLFi3i/vvvZ9q0aXU+dq9evdi8eTOLFy9m/fr1zJs3j5deeinWL6GGzp0743A4uO+++9iwYQOvvPJKjTW4u3TpgsVi4bXXXmPbtm0UFxeTmJjItGnTuPLKK1m0aBHr16/n888/57777mPRokUA/PWvf2XdunVcddVVrF27lqeffrrGRGP76tmzJ8FgMFrPk08+yUMPPVRtn5EjR7Jt2zbuuOMO1q9fz/z583nzzTer7dO1a1e++uor1q5dy/bt2+vc832o38s5c+bwn//8hx9++IEff/yR5557jszMzOg97iIiItI2RO7Z3jOM3F0zbPs8kd5ujy+D7OzsJq1NpLVS2G4gu83KyYelcP8fenL/H3tw/x96cvJhKdgs9R8C3BA333xzjfMdccQRPPvssyxevJgBAwZw4403cvPNN9druPlvf/tbrrzySiZPnszgwYP58MMPo5N0Naa0tDQWLlzIc889x2GHHcZtt93GXXfdVW2fDh06MHPmTK699loyMjKYPHkyALNmzeKGG25g9uzZ9OvXjxNPPJHXX3+dbt26AZEg/8ILL/Dyyy8zaNAgHnroIW699dYD1jNo0CDmzJnD7bffzoABA3jqqadqLKHVr18/HnjgAebPn8+gQYP49NNPa4Thv/zlL/Tp04ehQ4eSlpZWo7d6fw71e5mYmMgdd9zB0KFDOfLII9m0aRNvvPFGo4+2EBERkeYlkLsdhzsBqL1nu+o+bvVsi8SOxWjl0w0WFhaSnJxMQUEBSUlJ1Z4rKytj48aNdOvWDZer5hIIDREOhyksLCQpKUnBRpqthrTTxrx+RA4mGAzyxhtvMH78+BrzTog0Z2q7YpbRvz2d9n+chY0QC849rMbzZcEwF+9Zlcf10X2cPG6M2qm0OLH8GXugHFlXSoEiIiIiIq1c/u7InDEJcbX3s7nsVmxGJQC5+bFZaUWkrVPYFhERERFpxQzDoKQy8mt/bct+VfHYIoG8KpiLSMMobIuIiIiItGKFhYVYPZFhsBlez37387ojK5wUBw9tSVoRqU5hW0RERESkFQsEAnj2zETeLnH/86ykJ7kBqLA6D2mpUhGpTmGbyNAaEakfXTciIiItQyAQwO1NB2pf9qtKpi8yW7nbm05BQUGT1CbSmrXpsF01Q11pqSaBEKmvqutGs5SKiIg0b36/H48vErZrW/arSmpC1Vrb6ezYsaNJahNpzfZ/tbUBNpsNr9dLXl4eAB6PB4slNveohMNhKioqKCsr09Jf0mwdSjs1DIPS0lLy8vLwer3YbLZGrlJEREQaIjKM/EjgwBOkVfV6e3wZ7Ny5pilKE2nV2nTYBsjMzASIBu5YMQyD3bt343a7YxbgRWKtIe3U6/VGrx8RERFpvvyBAO7BBx9GXhXE3d50dv6ws0lqE2nN2nzYtlgsZGVlkZ6eTjAYjNlxg8Eg77//Pscdd5yG2Uqzdajt1G63q0dbRESkhQjk7STe7gDAe4Cw7Y2G7TR27tzVJLWJtGZtPmxXsdlsMQ0PNpuNyspKXC6XwrY0W2qnIiIird/24nLiAYelkjjb/keyJbviwDCw2uLI3x27TiiRtko3E4uIiIiItGIFuyPLeCU6DnzLmM1qwUEkZBdXKiaINJSuIhERERGRViocDrM7HBnMmhrvOOj+iY7I0p5lYd0uJtJQCtsiIiIiIq3U9u3bcSa3AyDD6zno/imeyG1llXEH31dEDkxhW0RERESklfL7/bi9kZnIUxMO3rOd4Y0HwOZOpqysrFFrE2ntFLZFRERERFqpyBrbkbB9oJnIq2T6Ij3abl8GgUCgUWsTae0UtkVEREREWqlI2M4AfllH+0B8e4aRe3zpZGdnN2ptIq2dwraIiIiISCu19zByXx16tqv28fjS8fv9jVqbSGunsC0iIiIi0kr5A9m4klMB8NapZzuyj9uXoZ5tkQZS2BYRERERaaVydhVhtdrAMEhyHXw5r6qw7YxPJpCT19jlibRqCtsiIiIiIq3UjqJyANy2EFaL5aD7u+1WrOFKAAI7ixu1NpHWTmFbRERERKSVKqqI/JvsrNuv/RaLBZc1ErZ3lgQbqyyRNkFhW0RERESkFQoGg1RYnQCkJTnr/HVJjkgPeFGF0Sh1ibQVCtsiIiIiIq1QdnY2nj0zkacne+r8dakJDgDKDDuGocAtcqgUtkVEREREWqFAIIA7usa2vc5fl5WSAEBcvJfCwsJGqU2kLVDYFhERERFphQKBAB7fnjW267DsV5V2iS4gsvxXIBBolNpE2gKFbRERERGRVsjv9+PeM4zc56572PZ5IkuEeXwZ+P3+RqlNpC1Q2BYRERERaYX27tn21qNn27snmHvUsy3SIArbIiIiIiKtkD8nD2eCF6jfMPKqXnC3Lw2/wrbIIVPYFhERERFphXJ3FQNgJYTHXvdf+5NdkWHktjgHW3N3NEptIm2BwraIiIiISCu0q7QSgHibgcViqfPXxdksUFECQF7B7kapTaQtUNgWEREREWmFiisjAdu7Z8Kz+rCHygDYuSewi0j9KWyLiIiIiLQyJSUl4Iisl52e5Kn317ssQQCKgzEtS6RNMT1s+/1+zjnnHFJTU3G73QwcOJD//e9/0ecNw+DGG28kKysLt9vN6NGjWbdunYkVi4iIiIg0b4FAALcvA4D0ZFe9vz7BbgBQYXESDodjWptIW2Fq2N61axdHH300drudN998k++++467774bn88X3eeOO+5g3rx5PPTQQ3zyySfEx8czbtw4ysrKTKxcRERERKT58vv9vyz7VY81tqskuyIxwZXcju3bt8e0NpG2ov5XXgzdfvvtdOrUiQULFkS3devWLfq5YRjcc889XH/99fzud78D4IknniAjI4OXX36ZM888s8lrFhERERFp7gKBAG5vJGz7DiFse2xhCEbW2vb7/aSnp8e6RJFWz9Sw/corrzBu3Dj++Mc/8t5779GhQwcuu+wy/vKXvwCwceNGcnJyGD16dPRrkpOT+fWvf81HH31Ua9guLy+nvLw8+riwsBCAYDBIMNh0N51UnaspzylSX2qn0lKp7UpLpbYrTWXLli14fIMBSHTUr80Fg0Hc1sjEaG5fBps3b2bAgAGNUaZITMXyZ2wsjmFq2N6wYQMPPvggU6dO5f/+7//47LPPmDJlCg6Hg/POO4+cnBwAMjIyqn1dRkZG9Ll9zZ49m5kzZ9bYvmTJEjye+k8O0VBLly5t8nOK1JfaqbRUarvSUqntSmP74IMPSPjtWADWfLqKn6z1Cw5uixMAjzedJUv+HfP6RBpTLH7GlpaWNvgYpobtcDjM0KFDufXWWwH41a9+xTfffMNDDz3Eeeedd0jHnD59OlOnTo0+LiwspFOnTowdO5akpKSY1F0XwWCQpUuXMmbMGOx2e5OdV6Q+1E6lpVLblZZKbVeayr8XP0ecM9LRdNqJJ+CIq/tUTcFgkNfeXg6AKykFX2o7xo8f3yh1isRSLH/GVo2QbghTw3ZWVhaHHXZYtW39+vXjhRdeACAzMxOA3NxcsrKyovvk5uYyePDgWo/pdDpxOp01ttvtdlP+UzPrvCL1oXYqLZXarrRUarvS2HYUV9ARsFNJvLvm78YH47CEsYRDGFYbObtK1F6lRYnFz9hYtHlTZyM/+uijWbt2bbVtP/74I126dAEik6VlZmayfPny6POFhYV88sknDB8+vElrFRERERFpKXbtjtxznXCIecFiAScVAGwvKj/I3iJSG1N7tq+88kp+85vfcOutt3L66afz6aef8sgjj/DII48AYLFYuOKKK/jnP/9Jr1696NatGzfccAPt27fntNNOM7N0EREREZFmyTAMSkORPrWU+EPvnUuwG5SFIH9PcBeR+jE1bB955JG89NJLTJ8+nZtvvplu3bpxzz33cPbZZ0f3ufrqqykpKeHiiy8mPz+fY445hrfeeguXy2Vi5SIiIiIizdOuXbtwJKQCkOGNP+Tj+OLtbC+E0pAtVqWJtCmmhm2AU045hVNOOWW/z1ssFm6++WZuvvnmJqxKRERERKRl8vv9uH2RdbFTExyHfJwMr4d1hUFC9niCwaDu2xapJ1Pv2RYRERERkdgKBAJ49oRtn+fQ+9bapyQC4PFlkJ2dHZPaRNoShW0RERERkVYkEAjg9u4J2+5DD9vePV/r8aUTCARiUptIW6KwLSIiIiLSivj9/mjPtrcBPds+d+RebbcvHb/fH5PaRNoShW0RERERkVbEHwjgTk4DGtazXTUE3ePLwK+ebZF6U9gWEREREWlFcnYUYo2zg2GQ3KBh5JGe7TiHC3/O9liVJ9JmKGyLiIiIiLQi24rKAHBZQ9islkM+jt1mxRauACB7Z3FMahNpSxS2RURERERakcJyA4Ak56EH7SpuayUAO0oqGnwskbZGYVtEREREpJUIhUKUG5H1sFMTnA0+XrIzEhcKlbVF6k1hW0RERESklcjNzcXljUyOluH1NPh47RIjgb3MOPR7v0XaKoVtEREREZFWIhAIRJf9Som3N/h47VMTAbC6kyku1n3bIvWhsC0iIiIi0koEAgHc3kjYbsiyX1UyvPFAZPmv7OzsBh9PpC1R2BYRERERaSX8fn+0Z9vraXjYrrbWtt/f4OOJtCUK2yIiIiIirURkGHkG8EtQbohfwnY6gUCgwccTaUsUtkVEREREWgl/dg6upFQgNsPIq47hSkrFH9AwcpH6UNgWEREREWklcnZGJjGzGGESnLYGHy/RZYNwCIvVypa8XQ0+nkhborAtIiIiItJK7CwNAuCxhbFYLA0+ntViwWFEFtnOK9jd4OOJtCUK2yIiIiIirURRuQGA1x27X/Pj48IA7NpdGbNjirQFCtsiIiIiIq1AWVkZIbsHgLQkd8yOWzWreXGw4T3lIm2JwraIiIiISCuQnZ0dXfYrPTl2YTsjORLgKyxODMOI2XFFWjuFbRERERGRVsDv9+P2RsJ2LJb9qtKhXRIAzqRUdu3SJGkidaWwLSIiIiLSCkTW2I6EbW8Mlv2qkpboAsDjy8Dv98fsuCKtncK2iIiIiEgrEAnbGQD4PPaYHbfqnm23N51AIBCz44q0dgrbIiIiIiKtQLVh5DHs2a4aku7xZShsi9SDwraIiIiISCsQyN2Gw5MI/NIbHQspe4K73R3P5kBuzI4r0topbIuIiIiItAJ5BbsBsBHCbY/dr/lOuxVLqAKA7J1FMTuuSGunsC0iIiIi0grsKg0CkBAX++W5XJbIsbcVlsX82CKtlcK2iIiIiEgLZxgGJZU2ILbLflVJdET+LSgLx/zYIq2VwraIiIiISAtXVFSE1RNZDzvD64n58VPjI2m7NGyL+bFFWiuFbRERERGRFs7v9+PZMxN5uz3rYsdSpi8egFCch8rKypgfX6Q1UtgWEREREWnhAoEA7j1rbHtjuOxXlQ7tIr3mbm8aeXl5MT++SGuksC0iIiIi0sIFAgE8vj1rbDfCPdtVw8i11rZI3Slsi4iIiIi0cH6/H/eeYeS+RujZrgrwbl8Gfr8/5scXaY0UtkVEREREWjh/IBC9Z9vbCD3bPo8dAHdyO/zq2RapE4VtEREREZEWLnvbLmwOJwBed+xnDE9y2cAIY7XFsSV3V8yPL9IaKWyLiIiIiLRw2wrLAHBQid0W+1/xbVYLcaFyAHJ2Fcf8+CKtkcK2iIiIiEgLV1AeBiDRaWm0c7htIQB2lFQ02jlEWhOFbRERERGRFiwcDrM7HBk6nhpvb7TzeF2R6FCkrC1SJwrbIiIiIiIt2Pbt23EmpgKQ4Y1vtPO0S4zcE15mNF6gF2lNFLZFRERERFqwQCCA25cBQGqCo9HO0z41CQCLK4mysrJGO49Ia6GwLSIiIiLSgvn9fjy+Pct+NcIa21XapyQA4PFlkJ2d3WjnEWktFLZFRERERFqwQCCAe88a275GDNu+PfeDe3zp+P3+RjuPSGuhsC0iIiIi0oIFAgE8e4aR+zyNGLb3BHm3L51AINBo5xFpLRS2RURERERasK3+AK7kyARp3sYM23uO7YxPZmsgp9HOI9JaKGyLiIiIiLRgOTuLsFptYBgkOW2Ndh633QqhIABbthU02nlEWguFbRERERGRFmxnSWTha7cthNVqabTzWCwWnEY5AHmFuxvtPCKthcK2iIiIiEgLVlBuAJDsbPxf7ePtkXPll1Y2+rlEWjqFbRERERGRFioYDBK0RNbWbpfobPTzVU2SVlzZeD3oIq2FqWF7xowZWCyWah99+/aNPl9WVsbll19OamoqCQkJTJw4kdzcXBMrFhERERFpPnJycqIzkWd4PY1+vqpzBK0uDMNo9POJtGSm92z379+f7Ozs6MeqVauiz1155ZW8+uqrPPfcc7z33nsEAgEmTJhgYrUiIiIiIs2H3+/H7duzxrbH3ujn69AuCQB7QgpFRUWNfj6Rlqzx1gaoawFxcWRmZtbYXlBQwGOPPcbTTz/N8ccfD8CCBQvo168fH3/8MUcddVRTlyoiIiIi0qw01RrbVTK88UABbm86fr+fpKSkRj+nSEtlethet24d7du3x+VyMXz4cGbPnk3nzp1ZvXo1wWCQ0aNHR/ft27cvnTt35qOPPtpv2C4vL6e8vDz6uLCwENhzP0sw2LgvZi9V52rKc4rUl9qptFRqu9JSqe1KrG3ZsgW3tzsAiY7YtK0DtdPEyO3heHwZbN68mZ49ezb4fCKxEsufsbE4hqlh+9e//jULFy6kT58+ZGdnM3PmTI499li++eYbcnJycDgceL3eal+TkZFBTk7Ofo85e/ZsZs6cWWP7kiVL8Hga/z6WfS1durTJzylSX2qn0lKp7UpLpbYrsbJy5Uo8J0Q6ob7+7EN+tlXE7Ni1tdPScBzQE7cvjTfefIyysrKYnU8kVmLxM7a0tLTBxzA1bJ900knRzw8//HB+/etf06VLF5599lncbvchHXP69OlMnTo1+riwsJBOnToxduzYJh3mEgwGWbp0KWPGjMFub/z7Z0QOhdqptFRqu9JSqe1KrD3/4stYErwAnDpuFPEOW4OPeaB2Whk2ePPZjdjiHKRkdGT8+PENPp9IrMTyZ2zVCOmGMH0Y+d68Xi+9e/fmp59+YsyYMVRUVJCfn1+tdzs3N7fWe7yrOJ1OnM6ayx7Y7XZT/lMz67wi9aF2Ki2V2q60VGq7EivbCneTDlgJkexxYrHEbkmu2tqpHbBW7iYc5yY3v1TtWJqlWPyMjUXbNn028r0VFxezfv16srKyGDJkCHa7neXLl0efX7t2LZs3b2b48OEmVikiIiIi0jzsLIncVxpvM2IatA/EZYmcc3tx+UH2FGnbTO3ZnjZtGqeeeipdunQhEAhw0003YbPZOOuss0hOTubCCy9k6tSppKSkkJSUxN/+9jeGDx+umchFRERERIDiykjA9robPny8rpIcFkpDUFAWarJzirREpobtrVu3ctZZZ7Fjxw7S0tI45phj+Pjjj0lLSwNg7ty5WK1WJk6cSHl5OePGjeOBBx4ws2QRERERkWahpKQEHAkApCcf2nxHhyI1wUFOAewON6s7UkWaHVOvkMWLFx/weZfLxfz585k/f34TVSQiIiIi0jIEAgHcvnQA0pKaLmxn+hL4tiBIpc1NOBzGam1Wd6aKNBu6MkREREREWqBAIIDHlwGAz9N0fWid0pIBcHvT2L59e5OdV6SlUdgWEREREWmBAoEAbm+kZ9vnbrqw3S7RBYDbl0EgEGiy84q0NArbIiIiIiItkN/vx7NnGLm3CXu2q3rRPb4M/H5/k51XpKVR2BYRERERaYH8gUA0bDflMPKqYO9K9LE1kN1k5xVpaRS2RURERERaoOy8HcQ5PQB4m3AYeYLDCuFKALbk5TfZeUVaGoVtEREREZEWKK9gNwB2KnHGNd2v9RaLhbhQGQA5u0qa7LwiLU29r8otW7awdevW6ONPP/2UK664gkceeSSmhYmIiIiIyP7ll4UASLA3/bk9tsi5d5YEm/7kIi1EvcP2n/70J1asWAFATk4OY8aM4dNPP+W6667j5ptvjnmBIiIiIiJSnWEYlFRaAEiJb/q07XVFYkRRhdHk5xZpKeodtr/55huGDRsGwLPPPsuAAQP48MMPeeqpp1i4cGGs6xMRERERkX3s2rULe0IKAOleT5OfPy0psvxXGY4mP7dIS1HvsB0MBnE6nQAsW7aM3/72twD07duX7GzNRigiIiIi0tgCgQAeXwYA7RKcTX7+DqlJkU+ciQSDGkouUpt6h+3+/fvz0EMPsXLlSpYuXcqJJ54IRC741NTUmBcoIiIiIiLV+f1+3N6mX/arSod2yQB4fOnk5OQ0+flFWoJ6h+3bb7+dhx9+mJEjR3LWWWcxaNAgAF555ZXo8HIREREREWk8gb3W2G7KZb+qVN0n7val4/f7m/z8Ii1Bva/MkSNHsn37dgoLC/H5fNHtF198MR5P098vIiIiIiLS1kTC9m8Ac3q2q87p8WXg969v8vOLtASHdGXu2rWLTZs2YbFY6Nq1K6mpqXTt2jXGpYmIiIiISG22+v24j0wDwGdCz7Z3T9iOc7jYkpPX5OcXaQnqNYz822+/5bjjjiMjI4Nf//rXDBs2jPT0dI4//njWrl3bWDWKiIiIiMhecnYUYo2zg2GQZELYdtisWIK7Adi6raDJzy/SEtT5yszJyWHEiBGkpaUxZ84c+vbti2EYfPfddzz66KMce+yxfPPNN6SnpzdmvSIiIiIibd72onISAZc1RJzVYkoNDioox01ewW5Tzi/S3NU5bM+dO5cuXbrwwQcf4HK5ottPPPFELr30Uo455hjmzp3L7NmzG6VQERERERGJKCgLAZDoNCdoAyTYDcqB/D21iEh1dR5GvnTpUq655ppqQbuK2+3mqquu4u23345pcSIiIiIiUl0oFKIcB2DOGttVUjyRGclLKuu9wJFIm1DnK2PDhg0cccQR+31+6NChbNiwISZFiYiIiIhI7fLy8nB5I5OjZXjNWw2o6txBa83OOBGpR9guKioiKSlpv88nJiZSXFwck6JERERERKR2fr8fjzcyT1LVetdm6JiWDIDN46WkpMS0OkSaq3pNXVhUVFTrMHKAwsJCDMOISVEiIiIiIlK7QCCA2xcJ214TZiKvkuVLAArx+DIIBAL06tXLtFpEmqM6X52GYdC7d+8DPm+xmDdBg4iIiIhIWxAIBPD4MgDwecwL2749veoeX7rCtkgt6nx1rlixojHrEBERERGROvD7/bgzBgLgM7FnO2XPuV1JqWz1rzGtDpHmqs5X54gRIxqzDhERERERqQN/dg7u3u0Ac3u2E1w2jHAIi9XGzzk7TKtDpLmq89VZWFhYp/0ONImaiIiIiIg0TO6uYlIBixEmwWkzrQ6rxYKtcjdhRwLZOzVRssi+6hy2vV7vAe/JrrpnOxTSovYiIiIiIo1lR3EFqYDHFjZ9ziS3JUgJsL2o3NQ6RJoj3bMtIiIiItKCFAcjAdvrrvMqvo0myWmhJAyF5VqVSGRfumdbRERERKSFKC8vpzLODUC7xNqX5G1KqQlOsgtht2HecHaR5kr3bIuIiIiItBCBQACPN7LGdnqyx+RqICslgW8KQ1TaPFoKWGQfumdbRERERKSFCAQCuH2RsJ0Sb95M5FU6Z/hg03Zcye3YtWsXKSkpZpck0mwc0j3bhmEwfvx4/vWvf9GhQ4dGKUxERERERKrz+/149oRtr4lrbFdJT4oMaXf7MggEAgrbIns55Hu2bTYbRx11FN27d495USIiIiIiUlMgEMDt7QWArxmEbe+edb493nT8fj8DBgwwuSKR5sP8KQxFRERERKROAoEAHl8GAD6P3eRqIGVP4Le749kcyDW5GpHmRWFbRERERKSF8Ofk4fAkAr/0KpvJabdCZRkAW/LyzS1GpJlpUNjWbIMiIiIiIk0nr2A3ADZCuO3No9/MHoqE7Zz8EpMrEWle6vznsAkTJlR7XFZWxl//+lfi4+OrbX/xxRdjU5mIiIiIiFSza3clWUBCnGF2KVGeuDAFwK6SoNmliDQrdQ7bycnJ1R6fc845MS9GRERERERqZxgGxcHIyFJfMxhCXsXrslEQhKKgRr2K7K3OV+mCBQsasw4RERERETmAoqIibO5IB1h6ssfkan6RluTi5x1QjsPsUkSalXr9SWzTpk0sXbqUiooKRo4cSf/+/RurLhERERER2UtkJvLIGttpSS6Tq/lFx3bJ/G9HGTgTCIVC2Gw2s0sSaRbqHLZXrFjBKaecwu7dkUkZ4uLiePzxxzWcXERERESkCfj9ftx7wra3GayxXaVTuhfW5uD2ppGXl0dWVpbZJYk0C3WewvCGG25gzJgx+P1+duzYwV/+8heuvvrqxqxNRERERET2qL7GdvMJ26kJkeHjbl8Gfr/f5GpEmo86h+1vvvmGW2+9laysLHw+H3feeSd5eXns2LGjMesTEREREREiYdvtjfRs+5pRz7bPYwfAndwOvz9gcjUizUedw3ZhYSHt2rWLPvZ4PLjdbgoKChqlMBERERER+cVWvx/PnrDtbUY928kuG4YRxmqL4+ec7WaXI9Js1Osqffvtt6stARYOh1m+fDnffPNNdNtvf/vb2FUnIiIiIiIAZG/bhcfhBMDrbj6TkFmtFqzBUgxHAlu3qSNOpEq9wvZ5551XY9sll1wS/dxisRAKhRpelYiIiIiIVLO9qJzOgINK7LY6D1BtEk6jgjJgW2GZ2aWINBt1DtvhcLgx6xARERERkQPI311JZyDRYTG7lBoS7AZlQH6ZOt5EqtT7T2KFhYX7fe6nn35qUDEiIiIiIlJTOBxmtxHpJ0tNsJtcTU0p8ZGaSiqbz/B2EbPVO2yffPLJlJXVHB6ydu1aRo4ceciF3HbbbVgsFq644orotrKyMi6//HJSU1NJSEhg4sSJ5ObmHvI5RERERERaoh07duBMTAUgwxtvcjU1VdVUaXOZXIlI81HvsJ2QkMCECROorKyMbvv+++8ZOXIkEydOPKQiPvvsMx5++GEOP/zwatuvvPJKXn31VZ577jnee+89AoEAEyZMOKRziIiIiIi0VH6/H7cvMhN5SrzD5Gpq6pTuBcDqTqa8vNzcYkSaiXqH7RdffJGCggLOPvtsDMPgm2++YeTIkZx11lnce++99S6guLiYs88+m0cffRSfzxfdXlBQwGOPPcacOXM4/vjjGTJkCAsWLODDDz/k448/rvd5RERERERaqkAggMeXAYCvGS37VaVjuyQAPL50AgGttS0ChxC23W43r7/+OmvXruX000/nhBNO4M9//jNz5sw5pAIuv/xyTj75ZEaPHl1t++rVqwkGg9W29+3bl86dO/PRRx8d0rlERERERFqiQCCAe88a2z538wvbvj33bHt8GQrbInvU6Urdd1I0q9XKM888w5gxY5g4cSI33HBDdJ+kpKQ6n3zx4sV8/vnnfPbZZzWey8nJweFw4PV6q23PyMggJydnv8csLy+vNnSlqq5gMEgwGKxzbQ1Vda6mPKdIfamdSkultistldquHKrNmzfjSe0NQIKjcdvQobTThD2pwhGfxIaf1zBsmNq4NL1Y/oyNxTHqFLa9Xi8WS80lBgzD4KGHHuLhhx/GMIx6rbO9ZcsW/v73v7N06VJcrthNpDB79mxmzpxZY/uSJUvweDwxO09dLV26tMnPKVJfaqfSUqntSkultiv19cmnn5F21pkAfP7hu3xnbfwlturTTg0DwsGuWO0uVnz4GUnxmihNzBOLn7GlpaUNPkadwvaKFSsafKJ9rV69mry8PI444ojotlAoxPvvv8/999/P22+/TUVFBfn5+dV6t3Nzc8nMzNzvcadPn87UqVOjjwsLC+nUqRNjx46tV697QwWDQZYuXcqYMWOw25vf8gwioHYqLZfarrRUartyqB59YjFWqw2MMKeNH4vV2nhrbR9qO3154eeE7S7iUzMZP358o9Unsj+x/Bl7oCWv66pOYXvEiBENPtG+TjjhBL7++utq284//3z69u3LNddcQ6dOnbDb7Sxfvjw6y/natWvZvHkzw4cP3+9xnU4nTqezxna73W7Kf2pmnVekPtROpaVS25WWSm1X6mtnaZAUwG0L43Q2zWzk9W2nbmslJcCukqDat5gqFj9jY9GG6z27wltvvUVCQgLHHHMMAPPnz+fRRx/lsMMOY/78+dVmFD+QxMREBgwYUG1bfHw8qamp0e0XXnghU6dOJSUlhaSkJP72t78xfPhwjjrqqPqWLSIiIiLSYhVVGAAkO+s9v3GTSXZaKTGgsMLsSkSah3pfrVdddVW0S/3rr79m6tSpjB8/no0bN1Ybvh0Lc+fO5ZRTTmHixIkcd9xxZGZm8uKLL8b0HCIiIiIizVkwGKTCEhm52S6x5gjO5iI1IdLjvjvc/GZLFzFDva+EjRs3cthhhwHwwgsvcOqpp3Lrrbfy+eefN/jejHfffbfaY5fLxfz585k/f36DjisiIiIi0lLl5OTg9qYBkJ7c9BP+1lX7lES+LjIIxXmikyeLtGX17tl2OBzRmdmWLVvG2LFjAUhJSYnJTeQiIiIiIvKLQCCAx5cBQEp8870XunNm5HZSR1IqRUVFJlcjYr5692wfc8wxTJ06laOPPppPP/2UZ555BoAff/yRjh07xrxAEREREZG2zO/34/amA+DzNN8h2pm+BGAnHl86gUCgSVcCEmmO6t2zff/99xMXF8fzzz/Pgw8+SIcOHQB48803OfHEE2NeoIiIiIhIWxYIBPDsCdted/MN21V/CHB709i61W9yNSLmq/fV2rlzZ1577bUa2+fOnRuTgkRERERE5BeBQABPpyOB5t2z7XXHYRhhbHEOfs7OM7scEdM16GotKyujoqL63P4aLiIiIiIiEjtbs3Nw9vMC4GvGPdtxVguWilJwJrAlr8DsckRMV+9h5CUlJUyePJn09HTi4+Px+XzVPkREREREJHby8iOTE1sJ43E033W2AezhcgBy80tMrkTEfPW+Wq+++mreeecdHnzwQZxOJ//617+YOXMm7du354knnmiMGkVERERE2qydpUEA4m3hZr+cVnxcGICdpZUmVyJivnqPQ3n11Vd54oknGDlyJOeffz7HHnssPXv2pEuXLjz11FOcffbZjVGniIiIiEibVLznrk2v22ZuIXXgddvIr4SSyub9RwGRplDvnu2dO3fSvXt3IHJ/9s6dO4HIkmDvv/9+bKsTEREREWnDSkpKwJkIQFqy2+RqDi49KVJjOQ6TKxExX73Ddvfu3dm4cSMAffv25dlnnwUiPd5erzemxYmIiIiItGXZ2dm4fZFlv6qCbHPWIS0ZAMOZSDgcNrkaEXPVO2yff/75fPnllwBce+21zJ8/H5fLxZVXXslVV10V8wJFRERERNoqv98fXWO7OS/7VaVLZgoAruR27Nixw+RqRMxV7yv2yiuvjH4+evRofvjhB1avXk3Pnj05/PDDY1qciIiIiEhbFggEcPsygMg61s1dWqILAI8vA7/fT1pamskViZin3j3bTzzxBOXl5dHHXbp0YcKECfTt21ezkYuIiIiIxFAgEMDjazk921U1uhJ9bPFnm1yNiLkOaRh5QUHNReqLioo4//zzY1KUiIiIiIjAVr8ftzfSO+xrAT3b8Q4rRiiyVNmmbA0jl7at3mHbMIxa1/fbunUrycnJMSlKREREREQgO28Hdlc8AN4W0LNtsViwVpQA4N9es4NOpC2p8xX7q1/9CovFgsVi4YQTTiAu7pcvDYVCbNy4kRNPPLFRihQRERERaYu2Fe6mA2CnEmdcvfvJTOGkgjJgW1GZ2aWImKrOYfu0004DYM2aNYwbN46EhITocw6Hg65duzJx4sSYFygiIiIi0lbtKq2kA5BgN7uSukt0QBlQUKalv6Rtq3PYvummmwDo2rUrZ5xxBi6Xq9GKEhERERFp6wzDoDRkAyAlvuWkbV+8nW1lRGsXaavqPRblvPPOU9AWEREREWlk+fn5xCX4AEhP9phcTd1leSMjYINWZQZp21rGjR8iIiIiIm2M3+/H440s+9Uu0WlyNXXXKd0LgNWTTDAYNLcYERMpbIuIiIiINEOBQAC3LwNoGWtsV+mcEemN93jTycnJMbkaEfMobIuIiIiINEN+vx+PL9Kz7W0Ba2xXqbq/3OPLwO8PmFyNiHkOOWxXVFSwdu1aKisrY1mPiIiIiIiwp2fbmwaArwWF7ar1wG0OJ5v86tmWtqveYbu0tJQLL7wQj8dD//792bx5MwB/+9vfuO2222JeoIiIiIhIW+QPBKL3bLekYeQOmxWjvASAzbm7TK5GxDz1DtvTp0/nyy+/5N133602K/no0aN55plnYlqciIiIiEhblb09H2ucHQyDpBbUsw1gC+0GIGdXscmViJin3lftyy+/zDPPPMNRRx2FxWKJbu/fvz/r16+PaXEiIiIiIm3V9qJyEgGnNUSc1XLQ/ZsTt7WSEmB7cYXZpYiYpt4929u2bSM9Pb3G9pKSkmrhW0REREREDl1huQFAkrPl/Y6d7IrEjKI9r0GkLap32B46dCivv/569HFVwP7Xv/7F8OHDY1eZiIiIiEgbFQqFKDMis3qnJjhMrqb+0hIit5uWYTe5EhHz1HsY+a233spJJ53Ed999R2VlJffeey/fffcdH374Ie+9915j1CgiIiIi0qbk5eXhSm4HQIY33uRq6i8rNYEviyEU5zG7FBHT1Ltn+5hjjmHNmjVUVlYycOBAlixZQnp6Oh999BFDhgxpjBpFRERERNqUQCCAx5cBQGp8y+sd7pKZCoA9MYWSkhKTqxExxyFNa9ijRw8effTRWNciIiIiIiKA3+/H7YvMk+RtYTORA3RITQR24fGmk52dTc+ePc0uSaTJ1fvKrVpXe386d+58yMWIiIiIiEj1nu2WtMZ2lRRPpDfelZTKlq1+hW1pk+p95Xbt2vWAs46HQqEGFSQiIiIi0tYFAgHc6QMB8LXAnu0Elw0jVInFFsfGwDZGmV2QiAnqfeV+8cUX1R4Hg0G++OIL5syZwy233BKzwkRERERE2qqtgWzcvSITpHlbYM+21WKBimJwe9m6rcDsckRMUe8rd9CgQTW2DR06lPbt23PnnXcyYcKEmBQmIiIiItJW5e4qJhWwGGESnDazyzkkjnA5QSA3v9TsUkRMUe/ZyPenT58+fPbZZ7E6nIiIiIhIm7WzJAiAxxaO9BK3QPFxYQB27a40uRIRc9S7Z7uwsLDaY8MwyM7OZsaMGfTq1StmhYmIiIiItFWF5QYAye6Y9Y01OZ8njvwQlFS23Ncg0hD1Dtter7fGBGmGYdCpUycWL14cs8JERERERNqi8vJyQnYPAGmJLpOrOXRpSW427oJyi8PsUkRMUe+wvWLFimqPrVYraWlp9OzZk7i4ljd5g4iIiIhIc5KdnY3HG1ljOz3ZY3I1h65TWjKf7gqBIwHDMA64opFIa1TvdDxixIjGqENERERERAC/34/bFwnbLXGN7Spds9rBj7k4k9PIz8/H5/OZXZJIk6r31fvKK6/Ued/f/va39T28iIiIiEibFggE8LSCsJ3hjfTKe3zpbN3qV9iWNqfeV+9pp52GxWLBMIxq2/fdZrFYCIVCDa9QRERERKQNCQQCuL09AfC5W27Yrqrd7ornZ/9mBg4cYHJFIk2r3lMDLlmyhMGDB/Pmm2+Sn59Pfn4+b775JkcccQRvv/024XCYcDisoC0iIiIicgj8fn/0nm1vC+7ZdtqthCsia2z/nLPT5GpEml69r94rrriChx56iGOOOSa6bdy4cXg8Hi6++GK+//77mBYoIiIiItKWBHK34RiYBIDPYze5moaxBkvB4cG/vfDgO4u0MvXu2V6/fj1er7fG9uTkZDZt2hSDkkRERERE2q68gkhvsI0QbnvLXqPaRQUA24vKTK5EpOnV++o98sgjmTp1Krm5udFtubm5XHXVVQwbNiymxYmIiIiItDU7S4IAJMQZB9mz+Ut0RJb7KigPm1yJSNOrd9h+/PHHyc7OpnPnzvTs2ZOePXvSuXNn/H4/jz32WGPUKCIiIiLSZpRURn5Fb8kzkVdJiY8Mgy8N2UyuRKTp1Tts9+zZk6+++opXX32VKVOmMGXKFF577TW+/vprevbsWa9jPfjggxx++OEkJSWRlJTE8OHDefPNN6PPl5WVcfnll5OamkpCQgITJ06s1qMuIiIiItKaFBYWYnUnA5Ce7DG5mobL8iUAUGlzm1yJSNM7pD+XWSwWxo4dy9ixYxt08o4dO3LbbbfRq1cvDMNg0aJF/O53v+OLL76gf//+XHnllbz++us899xzJCcnM3nyZCZMmMAHH3zQoPOKiIiIiDRHgUAA9541ttslOk2upuE6ZXghtwyrO5lQKITNph5uaTvqFLbnzZvHxRdfjMvlYt68eQfcd8qUKXU++amnnlrt8S233MKDDz7Ixx9/TMeOHXnsscd4+umnOf744wFYsGAB/fr14+OPP+aoo46q83lERERERFqCQCCAZ0/YbukzkQN0zUyFr/y4venk5eWRlZVldkkiTaZOYXvu3LmcffbZuFwu5s6du9/9LBZLvcL23kKhEM899xwlJSUMHz6c1atXEwwGGT16dHSfvn370rlzZz766COFbRERERFpdfz+SDCF1nHPdrtEFwAubzu2+gMK29Km1OkK3rhxY62fx8LXX3/N8OHDKSsrIyEhgZdeeonDDjuMNWvW4HA4aiwzlpGRQU5Ozn6PV15eTnl5efRxYWFkTb9gMEgwGIxp7QdSda6mPKdIfamdSkultistldquHMyWLVvweI8EIMFuTluJZTv12AyMcAir1caPm/wMHnR4g48psj+xbLuxOIbpfy7r06cPa9asoaCggOeff57zzjuP995775CPN3v2bGbOnFlj+5IlS/B4mn6SiaVLlzb5OUXqS+1UWiq1XWmp1HZlfz766COSTjsFgDWfrGSd1bw/zMSqnVaWpmNPSOG9Tz4nwd7ylzOT5i8Wbbe0tLTBx6h32A6FQixcuJDly5eTl5dHOFx9zbx33nmnXsdzOBzRWcyHDBnCZ599xr333ssZZ5xBRUUF+fn51Xq3c3NzyczM3O/xpk+fztSpU6OPCwsL6dSpE2PHjiUpKaletTVEMBhk6dKljBkzBru95d9vI62T2qm0VGq70lKp7crBLHr6WWyOyMRop500GrvN0uQ1xLqdvvToh4SB+JRMxo8f3/ACRfYjlm23aoR0Q9Q7bP/9739n4cKFnHzyyQwYMACLJbY/AMLhMOXl5QwZMgS73c7y5cuZOHEiAGvXrmXz5s0MHz58v1/vdDpxOmvO3Gi32035T82s84rUh9qptFRqu9JSqe3K/uwsCZIAOKjE43KYWkus2qnbFqIE2FUaVLuXJhGLthuLtlrvsL148WKeffbZmPxVavr06Zx00kl07tyZoqIinn76ad59913efvttkpOTufDCC5k6dSopKSkkJSXxt7/9jeHDh2tyNBERERFplQrKQgAkmpuzY8rrslICFFWYXYlI06p32N572HdD5eXl8ec//5ns7GySk5M5/PDDefvttxkzZgwQmQXdarUyceJEysvLGTduHA888EBMzi0iIiIi0pyEw2FKQ5F1qFMSWk/abpfowl8C5bSe1yRSF/UO2//4xz+49957uf/++xs8hPyxxx474PMul4v58+czf/78Bp1HRERERKS527FjB86kVAAykuNNriZ22qcm8mUJhO2t5zWJ1EW9w/aqVatYsWIFb775Jv37968xlv3FF1+MWXEiIiIiIm1FIBDA7YussZ3ainq2u2alwuYC4uK9lJeX1zq/kkhrVO+w7fV6+f3vf98YtYiIiIiItFl+vx+PNxK2fR7TV+iNmc7pXqAAjy+D7OxsunbtanJFIk2j3lfxggULGqMOEREREZE2LdKznQGAz916wnaKJzIS1hGfxKYtfoVtaTOsZhcgIiIiIiKRsF3Vs+1tRT3bLruVcLAMgI2B7SZXI9J06nwV+3y+WidES05Opnfv3kybNi06i7iIiIiIiNTPVn8AV5d2QOvq2bZYLFBeDHYX/u2FZpcj0mTqfBXfc889tW7Pz89n9erVnHLKKTz//POceuqpsapNRERERKTNyNlZhNdqA8MgyWUzu5yYchjlVAK5BaVmlyLSZOocts8777wDPj948GBmz56tsC0iIiIicgh2FJfjBdzWEFZrw5bYbW4S4gzygV2llWaXItJkYnbP9imnnMIPP/wQq8OJiIiIiLQpheVhAJJcrW9aparZ1UtCre+1iexPzFp7eXk5DkfrWQ9QRERERKSpBINBKqwuANISW9861OnJbgAqaH2vTWR/Yha2H3vsMQYPHhyrw4mIiIiItBm5ubm4vWkApCd7TK4m9jqleSOfuBJMrUOkKdX5nu2pU6fWur2goIDPP/+cH3/8kffffz9mhYmIiIiItBV+vz+67FdKvN3kamKvW/t2sG4bzqR2FBUVkZiYaHZJIo2uzmH7iy++qHV7UlISY8aM4cUXX6Rbt24xK0xEREREpK0IBAK4fRnAL/c3tyZZqYnANjzedLZu9dOvX1+zSxJpdHW+klesWNGYdYiIiIiItFmRnu3OAHhb0RrbVbzuOAwjjDXOzoat2Qrb0iZoOkAREREREZNFerYjw8h9rTBsx1kthMuKAfg5Z6fJ1Yg0DYVtERERERGT+XNycSX6gNY5jBzAFiwBILCj0ORKRJqGwraIiIiIiMlyd0WCqNUI43G0zl/RXZZKALYXlZtciUjTaJ1XsoiIiIhIC7KzJAiAJy6MxWIxuZrGkeiI/FtQHja3EJEmorAtIiIiImKy4spIwPa5bSZX0nhSEyJpuzTcOofJi+xLYVtERERExESlpaUYjngA0pLdJlfTeLJ8CQCEbK33NYrsTWFbRERERMREgUAAz541ttMSXSZX03g6ZUQmgLO6kwmHNZRcWj+FbREREREREwUCATzeyLJfKfF2k6tpPN3bpwHg8qaxY8cOk6sRaXwK2yIiIiIiJvL7/bj3hG1vK1xju0r6niHyrkQfm7cGTK5GpPEpbIuIiIiImCgQCOBJiQwjb61rbAPEO6yEKysAWL811+RqRBqfwraIiIiIiIn8gQBub2SIta8V92xbLBaMskIAtuTlm1uMSBNQ2BYRERERMVEgdzt2V2Q2cm8r7tkGiAuVAZCTX2JyJSKNT2FbRERERMRE2wp3A2CnEmdc6/713GMNAbCjuMLkSkQaX+u+mkVEREREmrn83ZEAmtB6JyKP8roj8aM4aDG5EpHGp7AtIiIiImISwzAorowET18rXvarSlpiZEbychwmVyLS+BS2RURERERMkp+fjz0hBYCMZI/J1TS+9u0SAQjbW/9rFVHYFhERERExSSAQwLNnje12iU6Tq2l83bLaARCX4CMYDJpcjUjjUtgWERERETGJ3+/HvSdse1vxsl9VumalAuDxZpCTk2NyNSKNS2FbRERERMQkgUAAT0oGAL5WvuwX/HJfus3hZMOWbJOrEWlcCtsiIiIiIiYJBAK4vWkA+NpAz7bDZiW0uwiAjYHtJlcj0rgUtkVERERETLLV74+GbW8b6NkGoKIYAP/2QpMLEWlcCtsiIiIiIibJ2VGALc4BhkFyG+jZBnAa5QDkFZSaXIlI41LYFhERERExybbCMgCc1hBxVovJ1TSNhD3LieeXhcwtRKSRKWyLiIiIiJiksDwMQJKjbQRt+GUiuJJKRRFp3dTCRURERERMEAqFKDMcAKQmOkyupulkeD0AVFhb/7ri0rYpbIuIiIiImCAvLw9XcjsAMpLjTa6m6XRK8wJgcSaZW4hII1PYFhERERExQWTZr3QAUvasP90WdOsQmX3dkZRKaakmSZPWS2FbRERERMQEgUAAT0oG8Mt9zG1Bp7RkANzJ7diy1W9yNSKNR2FbRERERMQE/r3W2G5LYTvRFUc4FARg/dZck6sRaTwK2yIiIiIiJggEAnj2DCP3tpE1tgGsFgvh0gIAfs7ZaXI1Io1HYVtERERExAT+7BxcSalA2+rZBrBV7gYgsLPY5EpEGo/CtoiIiIiICXJ2FmGxWrEYYRKcNrPLaVIuS2QY+faiMpMrEWk8CtsiIiIiIibYWRIJnG5bGKvFYnI1TSvJGXm9heWGyZWINB6FbRERERERExRVRIKm19X2fiVPTXAAsDvctobPS9vS9q5sERERERGTlZeXU2lzA5CW5DK5mqaXlZIIQCjOY3IlIo3H1LA9e/ZsjjzySBITE0lPT+e0005j7dq11fYpKyvj8ssvJzU1lYSEBCZOnEhurpYIEBEREZGWKzs7G48vssZ2enLbC5ydM3wAWN1JGIaGkkvrZGrYfu+997j88sv5+OOPWbp0KcFgkLFjx1JSUhLd58orr+TVV1/lueee47333iMQCDBhwgQTqxYRERERaZhAIIDbF1n2q63NRA7Qo0Pktbu8aezatcvkakQah6lX9ltvvVXt8cKFC0lPT2f16tUcd9xxFBQU8Nhjj/H0009z/PHHA7BgwQL69evHxx9/zFFHHWVG2SIiIiIiDeL3+/F404C2GbazfAkA2F3xbNqaTUpKiskVicRes7qyCwoii9tXXWyrV68mGAwyevTo6D59+/alc+fOfPTRR7WG7fLycsrLy6OPCwsLAQgGgwSDwcYsv5qqczXlOUXqS+1UWiq1XWmp1HalypYtW3D7+gCQ6LA0qzbRFO3UClSWlRDnimftpgAD+/VutHNJ2xHLthuLYzSbsB0Oh7niiis4+uijGTBgAAA5OTk4HA68Xm+1fTMyMsjJyan1OLNnz2bmzJk1ti9ZsgSPp+nvh1m6dGmTn1OkvtROpaVS25WWSm1XVq1ahWfssQB8+ekHbLRVmFxRTY3dTitLEohzxfP+p1/gMUob9VzStsSi7ZaWNrxNNpuwffnll/PNN9+watWqBh1n+vTpTJ06Nfq4sLCQTp06MXbsWJKSkhpaZp0Fg0GWLl3KmDFjsNvtTXZekfpQO5WWSm1XWiq1XanyzPMvEhcf+d30dycej9vefBYJaqp2+sL85QAkpGQyfvz4RjuPtB2xbLtVI6QbolmE7cmTJ/Paa6/x/vvv07Fjx+j2zMxMKioqyM/Pr9a7nZubS2ZmZq3HcjqdOJ3OGtvtdrsp/6mZdV6R+lA7lZZKbVdaKrVd2V5URiZgI0SSp+bvrs1BY7fT+LgQJcCu3SFdDxJTsWi7sWiTpv4JzTAMJk+ezEsvvcQ777xDt27dqj0/ZMgQ7HY7y5cvj25bu3YtmzdvZvjw4U1droiIiIhITOwqrQQgPq7tLnvldUf6/YqDFpMrEWkcpvZsX3755Tz99NP897//JTExMXofdnJyMm63m+TkZC688EKmTp1KSkoKSUlJ/O1vf2P48OGaiVxEREREWqyqgNkWZyKvkpbkwl8G5RaH2aWINApTr+4HH3wQgJEjR1bbvmDBAiZNmgTA3LlzsVqtTJw4kfLycsaNG8cDDzzQxJWKiIiIiMRGUVERVncyAOnJbpOrMU+H1CTW+MGwx5tdikijMDVsG8bBh824XC7mz5/P/Pnzm6AiEREREZHGFQgEcPvSAUhLdJlcjXm6tW8H/lLiEnyEQiFsNpvZJYnEVPOZ9lBEREREpA3w+/14vGkAeNvwMPJu7SPvgSu5HTm5eSZXIxJ7CtsiIiIiIk0o0rOdAUCKp+3Owp2a4CQcCmG12vhpc7bZ5YjEnMK2iIiIiEgTivRsR4aRe91td+i01WohtLsAgE3Z202uRiT2FLZFRERERJqQf697tn3utjuMHMBSUQyAf0eRyZWIxJ7CtoiIiIhIE8retos4R2RitLZ8zzaA06gAIK9gt8mViMSewraIiIiISBPaVhgJlg4qsdva9q/jCXtuWc8vC5lbiEgjaNtXt4iIiIhIEyvYHQmWCQ6TC2kGUuIjPfullYol0vqoVYuIiIiINBHDMCgNRwJmarzSdoY3HoCgte2uNy6tl8K2iIiIiEgT2b59O47EFOCXoNmWdUr3AmBxJZpbiEgjUNgWEREREWkigUAAT8qeNbY1jpweHdIAcCS1o7y83ORqRGJLYVtEREREpIkEAoHoGtspbXwmcoCumakAODyJbNoaMLkakdhS2BYRERERaSJ+vx/3nrDtbeNrbAN4nHFUlpcCsH5LrsnViMSWwraIiIiISBMJBAJ4fJFh5G19je0q4d2FAGzO3WVyJSKxpbAtIiIiItJE/IFsXN52AKSoZxsAW2WkZzuws8jkSkRiS2FbRERERKSJZO8owGq1gWGQ5LKZXU6z4LZUArCjuMLkSkRiS2FbRERERKSJVAVKtzWE1WoxuZrmIdkZeR8Ky8MmVyISWwrbIiIiIiJNpKjCACDJpaBdJTXRCcBuw25yJSKxpbAtIiIiItIEgsEg5UTW1m6X6DK5muajfUoiAOE4j8mViMSWwraIiIiISBPIzc3F7Yss+5WRrGBZpXOGDwCrO9nkSkRiS2FbRERERKQJBAIBPHvW2E6J15DpKj07RZZCcyW3o6Cw0ORqRGJHYVtEREREpAn4/X7ce8K2V8t+RbVvl4wRDmONs/PT5myzyxGJGYVtEREREZEmEAgE8Pgivbg+j8J2lTirhcrSAgA2bM0zuRqR2FHYFhERERFpAoFAIHrPtk8929UY5UUAbN2Wb24hIjGksC0iIiIi0gS2BnJwJUYmA/OqZ7saR7gMgNyCUpMrEYkdhW0RERERkSaQtydIWo0w8Q79Gr43jy0MwM6SoMmViMSOrnIRERERkSawo7gCAE9cGIvFYnI1zUvVsPrioOKJtB5qzSIiIiIiTaB4T6et120zt5BmKC3ZBUCFxWFyJSKxo7AtIiIiItLISktLMZwJAKQnuU2upvnpkJoMQNgRb3IlIrGjsC0iIiIi0sgCgQCePWtspyW5TK6m+enWvh0A9oQUDMMwuRqR2FDYFhERERFpZIFAAPeesO3z2E2upvnp2Smy/rgzwUt23naTqxGJDYVtEREREZFGFggE8PgigdKnZb9q8CW4CAXLAVj3c7bJ1YjEhsK2iIiIiEgj8/v9uH1VPdsK2/uyWCxUluwCYFPODpOrEYkNhW0RERERkUa29z3bXrfCdm2sFSUABHYUmlyJSGwobIuIiIiINLJA3g7s7shM2z6F7Vo5iaxDnldQZnIlIrGhsC0iIiIi0sjy8ksBiCOE065fwWuTuGeJ7YKykLmFiMSIrnQRERERkUaWvydAJti1rNX+pOyZpb00ZDO5EpHYUNgWEREREWlEhmFQUhn5tTtFy37tV4YvMsw+aNM65NI6KGyLiIiIiDSi/Px84uK9AGR4PeYW04x1TvcBYHElmVyJSGwobIuIiIiINKK919hul+g0uZrmq0eHNACciakEg0GTqxFpOIVtEREREZFGFAgEcGvZr4Pq3jHyHtkcTjZuzTG5GpGGU9gWEREREWlEfr8ft29P2PYobO+P0x5HsKQAgJ+25JpcjUjDKWyLiEiTKS0PUhkKU7A78m9puYYJikjrFxlGHgnbKerZPqDw7kjY/jl3l8mV7J/+L5O60tUuIiJNoqIyzJvf5bP0x3xKK8J4HFbG9PZy6sBUHHH626+ItF7+QAD3kMj9yOrZPjBb5W4AcnYVm1xJ7fR/mdSHrnYREWl0peVB3vwun/9+s/OXbRXh6OOTDvPicWo5HBFpnbK35ZMQ5wDDIFk92wfktlYSBHYUV5hdSg36v0zqS39+ERGRRueIs7H0x/xan1v6Yz6OOFvTFiQi0oS2F5cD4LSGiLNaTK6meUt2ReJJYYVhciU16f8yqS+FbRERaXQlFSFKK8K1PldaEaakItTEFYmINJ2CssjPvySHgvbBtEuILI1WZjS/HuLicv1fJvWjsC0iIo0u3mHD46j9vxyPw0q8Q70BItI6hUIhyozI0PHUBIfJ1TR/7VMTAQjHeUyu5Bevvb+aKQ+8ifsg/5d59H+Z7MPUsP3+++9z6qmn0r59eywWCy+//HK15w3D4MYbbyQrKwu3283o0aNZt26dOcWKiMgh+3l7CWP6+Gp9bkxvLxWV6g0QkdZp27ZtuJIjk6NleONNrqb565KZAoDN4zW1jmCwkvufXcrpd7zKsz8nkJ/YnW+zSxjTu/a6xvTx8VWghCn3Pk9+YfOc3E2anqlhu6SkhEGDBjF//vxan7/jjjuYN28eDz30EJ988gnx8fGMGzeOsrKyJq5UREQO1dT7XuDRj7czpq+X3w1IifYKeBxWfjcwlZP6p2hCGRFptQKBAG7vnmW/4vWz7mB6dsoEwJmUQmFxSZOfP3dHPv/30Muc/cAqPi3vjCurN6HKIAS+oqIgl1MHptb8v2xACuP6+Xju8+3kpwzk4kWfc98zSwmHax9yLm2HqdMhnnTSSZx00km1PmcYBvfccw/XX389v/vd7wB44oknyMjI4OWXX+bMM89sylJFROQQXPvgS2z3DoDCCha98REXnnzU/7d33+FRlFsAh39bk03vDUINEEpCl6YISEdEERSlKBYsCIKKXhuIolewAleKIioqRcBGF2nSRDpIL6Gm97bZOvePSBQJfZPdTc77PDya3ezOSfbMZM7M952PPvHBFJhtGPQa9p0vYPzyBJp4pvJA97bODlcIIRzu/PnzJWtsy7JfVxcVGoDdmohaq+PY6SSaN4wpl+3uPpzA9GU7yPOthc67Pp7eYM7PJsR0lmf6tCa2Zv+S7+3RIKDkb5m3XoPZasNTpyKq6Bgnsw14BoSz3QwPvLeE4Z3r0L55g3L5GYTrcdk9PiEhgeTkZDp37lzymL+/P61atWLr1q2XLbZNJhMmk6nk69zcXAAsFgsWS/ktOH9hW+W5TSGul+SpKEvjZy8l0af4BCMgYx9PDrgbAMVuw0sLdquFyfNWooqK59R5aHLybMkdjauR3BXuSnK38jl79iyGwOJjm59e5RafvbPz1JyfiWdAOMfOJBNft3qZbcdut/PThp38uCsRdWQD1GHx6ABj+lka+hQw/L7bCfBrBlz8u9Cp//5bptht6NTFc/Of6tuRB3ILGD/nF7L8Y/GIiuXT/Va+XvcdYwd2ICKk9OlUwnEcmbuOeA+Voigu0VdfpVLxww8/cPfddwOwZcsW2rVrR2JiIpGRkSXfd99996FSqViwYEGp7/PGG28wfvz4Sx6fO3cuXl6u02hBCCEqsmV7zlNU6w4ALEfXc1+zcNSlLHeTbzTz/Tk/vMNrkHd6H4Pqa9DK0ilCiApk7ty52G8dgSEglDu8EwjQmK7+okruq4PgUzUWv1O/0iW+qsPf32yxsv5QCkm6aHyq1C15PO/UXmL0mbSpE45Gc3OzbU+lZLMhSYdPreYAmHIzCcjYTbdGkTf93qJ8FBYW8uCDD5KTk4Ofn98NvYfL3tm+US+//DLPPfdcyde5ublER0fTtWvXG/4l3QiLxcLq1avp0qULOp3MzxGuSfJUlIX3vlmJsUZHVIBnyl4+e+1h1OrLn1hE7jrErP1GfKvHs+HsPj4Y3vuq25DcFe5Kcrfy+fHnJVj9ggG4s0sH/Dxd/4Kis/N04ZElAHgHhdOzZ0+Hve/51Aymfr+Js4TjWaMRPoDNYkKbdphB7evSYcA9DtsWwNPAnGWbWJlgxxBSFaPfHcw7cYIHm4dw521NHbotUcyRuXthhPTNcNliOyKieLhNSkrKRXe2U1JSaNKkyWVf5+HhgYeHxyWP63Q6pxwsnLVdIa6H5On1KTRZ0Gs1F83VkgZfxT6au4qD6tqo1Wp0yfuY8Xy/KxbaAJ1axbP96HIOUJuswEbM++V3hvS67Zq2J7nrOmS/uD6Su5VHWk4hQWo1KsVOoI8HapX7rLXtrDz11toxAtlGu0O2//u+o3y2ai/GwDpoA+LwBEx5mURazzPinlupHR1/09u4nEfv7sgDRhPjv1jGGV0NDBG1+eE8/PjRj4x94DZqX+P0KXF9HJG7jsg9ly22a9asSUREBGvWrCkprnNzc9m2bRtPPfWUc4MTQlRaZqudFQezWX00m0KzHS+9mi51A+gdF4xeW7mHhX2y8Fd2mqui1mhQJ+1j5vN9r1poX/DS4J48PGkh9sh4lp3W0urUeerVqFLGEQtHkf1CiMvLyDMRBBg0drcqtJ0p0EuLESiw3vjvy263M3/VVpbsS0EbUR9VRDxawJh6imYhVkY8fAc+Xm0cFvOVeBk8mPh0X46cOs/bC7ZAVDxKZDyvLT9HTeV3Xh/aE4OHrMFeETm12M7Pz+f48eMlXyckJLBnzx6CgoKoVq0ao0aNYsKECdSpU4eaNWvy+uuvExUVVTKvWwghylOhycKKg9n89Gfm34+Z7SVf92gQUGnv5H32wzq25Eeg0WohcR+zXuiLVnN9QyUnP92Txz7djCG0Oq9/t5OvR4Xhoa+cv093IvuFEFeW91ePJX9PufB0rcL8DCTawKy6dLTq1eQXFvG/hWvYma7FEFYDXVQIAObEg/SOD+WBgV2u+UKwo9WrUYU5L/Xn+7V/MG9XJobwmpyjPkM+2cDd9Q0M7HGrU+ISZcepe/2OHTto2rQpTZsWz1l47rnnaNq0KWPHjgXgxRdfZMSIEQwbNoyWLVuSn5/PypUr8fT0dGbYQohKSq/VsPpodqnPrT6ajb6SNvb6aulG1mcFo9HqsCXu57PRd193oQ3g7+PN6E7VsRQV4BlZj+en/VQG0QpHu9p+odNqyMjJK9+ghHARJpMJq8YAQKifnL9eqyoh/gAoHj7X/JqE8ymMmrKYR7/YxUF1DIawGlhNRnTJ+3gqHua/dA8De9zqtEL7n/p2uoW5z3YmxnIUc34WhtBqrEoPZeDExew7esrZ4QkHcuqd7Q4dOnClZugqlYo333yTN998sxyjEkKISx07k0xQcDCFZnupzxea7RSYbfgbnP9HvDzNX7WFVSm+aPUeWBP/5PNRd93U3ei2Teqx9cBK9tprkhscx+yfN/DIXbc7MGJxs+x2O5v3HGHJ1kNoAyIZ3af5FfeLbKONj35L5/jpfVCYgTcmwnw01Ajzp1GtKJrG1sTHS4oQUTElJSVhCCheYzvcX1bFuVa1qoRCihWdTxB2u/2KBfL6HQf4cu1BLMF10QQ2wgMoykmjGqk826890RFNyi3u66HTaRn7SG/Op2Qw7uu1FIU2RBPViHc3ZhO2chHjH+mBv4+3s8MUN8ll52wLIYSz2e12fly/g8Xbz+JfvREf9QvDS68utbDw0qvx1leuO9vfr/2Dn88Y0HoaMCceZPbIOx0y5+z5gd155L2FWCPi+eW8J62On6FhTDUHRCxuVF6BkUVr/mDz0VRydaF4BkWCX0N8dRr8PLVX3C/8PDXkGG14+oeAfwg2IAlIMsLWA2DffxJTdirqomx8NRYi/fTUigigcZ1o4mKqodPJqYpwX6mpqUTVqgdAgJfk8rWqWy0C3wNJ+Af4k5aVS3hwwEXP2+12vly6kVWHsvCIqg+R8WgAY8pJWkXA8MfvcJs50FXCg5n1Qn/WbNvPzA3H8YysR6Y+jsdn76BDpIUn7+3kEnfjxY2RvV6IMiKded2X0WRm+uK1bD1vxxBRG02UP/kWOJ6ST5e6ARfNTb2gS71AjiTn4aexEB0R4oSoy9fS33ay8LgGncELU+JhZj3THS/D9c+tu5zJT9/JI9M3YAivyfjv9/HVsxFuc+JUURw8eZZF6/dwKM2CKrgWWo8ICI/AE7BZzFjTTlA9UEWhscrl94u6AdhsNl65PZBdh09x6Ewq5zILyCwCk8YHrV8oOoMPhqBIIBIzcBo4nQPrdliwbT2EOScFrTmXAJ2NyABP6lYJpmm96tSpFnnNJ6ByPL5x8ru7cQUFBcTHx9MyJYCT2QreGpuzQ3IbOr2eD/vWIrfISoBBS6HJgpeHjpz8AiYvWMv+XAOGkGg8oiKw223Ykw/Rt3kV+g7s5raF6R2t4ujYsiGT569ma4YXnoER/F4EG977mRFdY7m1aayzQxQ3QIptIcqAdOZ1T+dTMvh40QZO20PxDKiJIaJ4/U1d+hEe6lifhlX8qBNefPfu359tl9gA3ll1lpNnznJ/Aw/uvaOVk3+asvPL1r18c8iO3suPoqQjfPrUHfh6Gxy6DV9vA2O61eajrbl4RtbhuU9+Yvpz/R26DXExq83Gso27+XXPKZKtPhjCa4KuLrqo4ueLctLxLkykVe0g7ut8C0H+cSWv7R1XfKHlcsc8r/BgqoQH8+8V1O12OyfOJrPryGmOnc/gfJaRbIsGq84XfUA4Gp0HhpBoAAqA48DxVFiemo+laA/WnFT0tnyCPKBqsBex0WG0qF+TKuHBJduQ4/GNk9/djSsqKmLSpElMmTKF2178moCqdVj50yLa1nxEeg9dxeXyrkfDICYsTyBFXxdDCFiM+fjmnuTJXs1pVv9eZ4ftEGq1mtEPduOhnDxen72SbP9YPKLqM2OPha/XLuTNh7tccpdfuDYptoVwMOnM635K1t8MiEHr1/CK62/qtWp6NAigT3zwRXd69h5N4ETCOQzBUfxw1srmqYuZ+GSfCjcEdt32P5m9pwi9TwDG5GNMH9aBAL9rb2BzPW6Jq0Obg7+ww+xDQWg8n36/jmF9O5bJtiqr5PQsFvy6nZ2nczD5RuPhGwjBcRgAxW6nKPUkVT2L6N68Nl1at7nsHaPL7RdXK8jUajV1qkdRp3rUJc9ZLFb+PHGWvcfOciIpi+RcM3k2HTZPfzwCwtF5eqPzrAlAzl//DpyFxWfTMecfx5afzluDbuNAskmOxzdA/pbduIKCAiZNmlTSc8grMByAmVM/wGBM4cUXX8TbW+biluaKeadSMaBNdSb+sIvaukye7d+BiJDmToy27AT5+/LJ6P5sP3CcD5fuQxfVEGNYPCPnH6SpbzYvDOx2Q41IRfmrWGeBQrgA3VU68/aJDy71OVG+Lrv+ZtppmgZZGPFQJ3y9S19/88IJ5oVmaFqNmjZxdZhWJYznPluNOiqO9IBGDPpwGRMGtCy1kHBHW/YcYcb2XDx8gzGmnGDq0HaEBPiV6TZH3t+Vx95fiDk8nrWpPrQ6corG9WqU6TYrMrvdzpa9R1my9RAJ+Rr0obVQa6tDFHhQfKdInXWKuEgD/Ts1o3Z0r2t+79L2i5uh02lpGluTprE1L3mu0Ghi1+EEDiQkcjI5h9R8KwV4gFcQnv6h6H0C8A0OpmaoDx//llLq+8vx+Mqu1mVefneXp9PpmDJlCgBavQG9d/Fx0piZwpQpU3j11VedGZ5Lu2LeHcliar/afP1k20qzLGTLhjHMaxjD5z+tZ2WCHUNwFQ4SzAMfrmRomyh63trU2SGKq5BiW4ibZLfbWbf9AMu3H0UfGMVzV+nMWxk7VruSQqOJKQvXsDNNjSGs5kXrb94ZF8qDAzvf8Hyv0CB/vhrTl3fnrOBPaxU8omJ5bdlpekQf5+He7R35Y5S77QeO8/GmdDz9QzCmJvDx4FZEhASWy7Y/fro3Q6etxRBemwk/H+CrZyLRyRDWa5ZfWMTitdvZdDiZbF0IhqAo8G2Ap2/x88b0swTZs+jQsAp3d2yBwcP17xR5GTy4tWnpcxgzc/LYeSiB7EILBSbbFY/H+SYbfp647RzPsnIqMQ1f/wD5W3aDsrOzyc7OBsAQEAqApagAS1EB2UWQk5NDaGioEyN0XQXmK++zhWYb/obKUWj/06N9OvCA0cS42cs4p6+BISKGeSfsLNyykLED21OzSrizQxSXIcW2EDcgOzef737dzu8n0sn3jMAzIAz8G11TZ16vStax2lWcSkxl8uKNJKoj8fCrjSEMrGYjnpnHeKxLPG0H3+OQ7ajVal55uBcbdx1iyvrTGEKrszYb/vhwIR8M7+OWTb72HjnFe2sT8QwIx5h2hkkDml40J7as+Xh58nKvWCb9loEhIoZR037mk5F3l9v23dGRU+dZuG43B1LNqIJqovUMg/AwDPzd3CwmAO6+tREtG3Z2drgOFeTvS5fWxVM/rDb7FY/HBr2GJz77jdqGQkb2v8OhTf7c0fJNu5m3+QQ+1Rrxcf9gWX3hBthsNnx9fQkICCA7O5uqtepRNUDP2VPnAAgICMDf39/JUboub71G8u4yvAwevDe8L4cTzvH2d7+jiorDFhnPy0vPEMNWXh/aq9Lc8XcnUmwLcY32HT3F4g37OJJpQxNSC40+CiKi8KS4aLOnJxAarKGwqOoVO1bvTyxg9fqNvDS4Z/n/EJXQbzsP8sWaA5iD66IJKL/1N29rVp/4utUYPX051oh48kPjGTJlNa/0blDqsFhXdeD4GSasPIUhKBJjxjneubehU66gN6tfi/Z/nuT3Ij+KwuKZ8f06qlWyHkNX6gpttdlYsWkPq/ckkGT5q7mZtk5JczNTbjqGgkRuqRnIfb1vISQg7gpbqjjMVtsVO6UfSCrA5BXJQWDo7J1UsScx8t7bqBEVVv7BOonFYuWzH9ezPsGIZ2RdVFFxFFjh2FVWXzicmMOSubN4aczzMjLgL4mJiQwaNIhnn32W8ePHU7tOPTp16kS+RcHXoxr3115BwoljWCwW9Hr3u/BaHq62z5qttpueouLuYmtW5euX+rHo199ZsDcbQ1hNzuDHoP+t496GPgzo1lZWEXAhUmwLcRkms4Ulv+1k7f6zpCn+GEKrg2c99Bc682an4FuUQps6ofTv3BJ/nyYlr+0dV/xH9N+dNLvVD2TCyjMkUpshExfy4RPdynzOa2Vkt9v5aulGVjp5/U1/H29mj+nP1AWr2ZIdiCEihkkbMmi79zgj7u9S5tu/WcdOJ/LG0mMYgqtizExi/F11nTr//On+ndnzwUKKwuLZkh2Eoj7otFjK2+W68/ZsFMTEBZs4lKvHwzcIgv7Z3CyBKh6FdG8eQ+fWrStlMx0vDx2944pHYZTWUTsrJ5fQnD85SwSe/iGkE8yrK5LQZ2zgkc6NuK1ZfSf/BGUnLTOHjxeu42hRAIagaDwjwW61oEo9zAPtatOoih91r7L6wu7cSDb0uZevZs0gPLxyD2NdsWIFQ4YMIT09ncDAQOYtWMiS/RmM/vHUP353zXmia1fp5H4FV9tn5Xf3t36dW9Pnditvf7Wcw9YIDKHV2VOkp7fJxkpZRcBlqBRFUZwdRFnKzc3F39+fnJwc/PzKr6ixWCwsX76cnj17otPJlSR3cTY5nQW/bmfP+QKs/tVLmpoA2O02zCknqOZloVerutzevMEVr+aXdlVRp1YxZvpPpPvXR63WYMw4x+OtQujapnF5/HiXqGh5mpNfwJTv1rIv2xNDaDWAi9ff7HSL0+7A7D6cwDtLDmKIiAFAm7yPj57qib+Pa3akTTifwosL9mEIrUZRdgqvdq3mEo3JCo0mHvrfrxgiYsg/f5TPn+pAoL+vs8MqU6V1572gT1wwNYI9mLw+saS5WaMIT/p3bEZMtUgnROuarnaXp9Bo4pPFa/kjGQzhtUoeNyceonuDIIb0utVhxw5nH3f3HjnFtKXbyfWthc5QvJKAuSCHYONphvdpTYNa0Rd9f2m/u8RcC2+vSMCm0pJ9/jj7vnyFWf/7gM6dK9aUhGthNpt55ZVX+OCDDwBo0qQJa9f/xrpTJn7cl3HJ998dH0yvBkF46Fy76HF2nsqd2etzJimN8d+sZ/iA7iRkmPhp/6W516dRUKVYRcCRueuIOlKK7TLi7IOUuDZ2u50NOw+ybNtRzhTq0IfXRq3+++6PuSAXbc5pmlTx5v7OLYmOCHHIdr9b/TuLjljx9A/FZjFTy3qS8Y/1LvdCsKLk6bHTiUz5YQtpHlXR+xQ37bqw/uawns1o0aC2kyMsZjSZef6Tn8gPLZ5Pakw9xciO1Vzu7tnZ5HSe+3YnhrAaFOWkMaZjBC0bxjg7rBL7jp7inbWp6L390SXv5fMx9zk7pDJltdl5ZtGJy85hnHJvbZZt3EW31nGVfs7xzbLb7Sxa8wc/7jqPJqIBqr+Oyca0M8QHFDHyvk43fYHMWcfdH9ZuZ9H2M6jCY1Frigc2GtPP0dC3gFH3dbruJfzOZpl4d1UCeRYVhdmprPvgSZ54sA9vvvkmWm3lGDh58uRJBgwYwPbt2wEYOXIkkyZNQqPV88yi45fdZ//XLwatRlXe4V6XinJ+UNlYbHZGXOHvxf/61a7ww/BdrdiuHEdDIf4hJ7+Ahb9uZ+uxNPI8w/EMCAf/hnj+1a/EmHaaUFUOneKi6d2+OR76lg6P4b4urWndKI2X5mxEF9WQ07pYBr73I5Meau+wgr4yWP37Pr7ecARbaCya4Dj0QFFWMrV0mYxywfU3DR56pj3Xny+X/MaKszoMYTWYvsvIxn3L+M+QHi4x7zE5PYvnvtmOIbwWprwMRt0a6lKFNkB83Rp02HecLUZ/LBGNmTz/F54d0NXZYZWZ/Kt01DZabNzTsUU5R1UxqdVq7uvSmvu6wI6DJ5i5fBf5/rUxhFbjGPDE1/sJNZ1j5D1t3WJJP5PZwrRFa9ly3oohIgZNVKPix5MO06WuPw8/2OGGpxdEB3rwVu/aTPr1LImE0fWVr/ly6kg2bLidefPmUa1aNUf+KC7nu+++4/HHHyc3N5fAwEC++OIL+vTpA0BukfXKHbUtNvw0cgouHK/wKt3c8002Arycf65RmcieLiqF/cdOs3jDXg5n2FCH1ESrj4SISDwBm7kIW/pJ6gVp6Ns+nsb1yuekvVpkKN+OuZvXPv2Z0x4x6KIa8vzCg9zfwIN772hVLjG4I6vNxuc/bWDN8Xw8I+tB1F/zsZOPcVs1PU882dHlu3E+3Ls97U4n8tr87XhExXKYGIa89z0fPd6F0CDndalNy8xhxBdbMETEYM7P4qlb/GnbpJ7T4rmSR/vczvo3v0Bf93Z+zw2mzf5j3BJXx9lhOdyabX9ya/MG0p3XCVo0qE2LBrVJTs/io+/Wk2ALxjMgnByfQN5Yk4EmbTODb69X0vnclSSmZfLxd+s5ZQ/FM6AGhojiLvTa9MMM6VCfOwb3cch2grx1jO1RncnrEzmUAp2en8nWz1+ncePGzJ49m3vuccwqD66ksLCQUaNG8dlnnwHQrl075s6dW3JxocBsw1OrvvKqJDrZZ0XZuFo3d4NewxPTfqVpuIrH7r7xi23i2kmxLdza5eb0WCxWlmzcxZq9p0m1+2EIqwEe/2xulopPUTJtYkLof0dLAvycM2darVbzzpN3s3zTbr7YkYYhOIofzlrZPHUxE5/sg05XOXfR0j5Xi8XM+3N/5XChL4bgKsXNfGxWSDnE/a1r0nvwnc4O+7rUqR7Fty/cyUvTfyTFJxZ1VBxPz9nFoy0D6d6uSbnHk52bz/BZGzBE1sNckMujTb3o0KJhucdxPe5tHMx3J49iiKzLe7+cYHatqvh6G5wdlkNYbTZenfkz5w11CIwqkO68ThQREsjEp+/BZLYw4/t1bDprKe69EBXP18fgsw0/0bmOH4/0ae/0E9c/9h/j01V7KPSPQevXEE/AlJdFuOUcI+9pR0y1/g7fppdew5g7qjJraxJbEvJoN+y/7Fk8mb59+zJ8+HDef/99PD0rxtIBBw4c4P777+fAgQOoVCpeffVVxo0bVzJs/kSakU82JjGoZShd6gWWOm+2a2wgNrvi8sPIhXu6Wjf3P5MKMPpGs6UQ1kz9jVivXJ69r6M06y1DlfNMXlQIl+vO271BEG/8dJgUkz+ExGPgQnOzk0R7meh1Sz06tGjnEkN2L+h5a1NaNshh9GerUUfFkR7QiEEfLmPCgJZuMVTRkS73uXatH4glPB5DrhlzYS6BBad4uvctxNXp5+yQb5hWo+GDZ+5l8ZptLDhoxhAcxddHLfy2/0cmDLur3HI0r8DIE9PX4BkVi8WYz+CGGpe8W/dvep2W1/s04p21KRjCavLstKXMHuP4YqK8JZxP4eVvtqCPaoAa+H7dLsY+eBsg3XmdyUOv49kBXXkW+Gn9Dr7bdgpVeH08o2LZVACrp66nvnc+o+7rRFA5Nu2z2+0s+OV3ft6bjDaiPqrweLQUzzNvEmhi5EN34Ovdukxj0GpUDGsXSZCXjqUHMmly77N4B0Uybfp4Nm3axIIFC6hXzzVHyVwLRVGYNWsWzz77LEajkYiICL755hvuuOMOAOyKwoqDWSzanYZNgdVHshnVoQoqFfxyOKtkn+0aG0jvRkHo5OKYKCNX6+Z+LiUdn7R9ZHrXwBAcxWmieGbBkX+cU1V38k9Q8UiDtDIijSXK1rV0531v+WG02adpXMWb++9oQbXIUCdEen3sdjvvzlnBn9YqaD0MmPIy6RFt4eHe7ctke66Wp1f7XGsH61m8Yl2FvAp7NjmdF7/6DV1U8d1kS+IBJg65rczzNr+wiMemrkAf1QBrUSH9a1u5p5Pj+xQ42j9z95sVW1ibFYZKraaxOoHnB3Z3dng3bO6Kzfx0Ejz8QrCZi6jLaV4feidqtVq687qgfUdPMX3pdrK9a6LzKi6wzQW5BBWeYvhdrWgYc+m8ZUcddwuNJqYuWsOOFHXxuup/MScepGejEAb1cM5F5V+PZPH19lQUBVIPbmHNR8/goVUxbdo0hgwZUu7x3KycnByeeOIJFixYAEC3bt2YM2cOYWHFa7HnGq18uiWZfYkFANxS3ZdHWofjpddgstjRqFUUWmx46TTY7IrLdyG/wNXOD8T1udrfi8ycPD7+bi2HCopHC8K/Rgve7lo9b66HqzVIk2K7jMhBqmxdrdvi1H61sdtsLj9393I27jrElPVni9f2BnzS9vHB8D4OXxva1fL0al2XK3oXTbvdzrhZSziprYVGp6coJ41762q4v2ubMtme0WTmkY+XootqiNVspE+0scy25Wj/zt1nPl5EbnAclqICRrT0dtm55pdjMlsYM/0nsgIaolKrMaaf5al2EXS6pZGzQxPXID07l8nfreNwoR+G4OLRSHarBVIPM6BNTe5s//eJ680ed08lpjJl8UbOqyPw8CtuqGk1G/HMPMYjneO4tWmsY36om7DzbB7TNyZhtimY007x0/iBFOVmMHjwYKZNm4aPz/V1PneW7du3M2DAAE6ePIlWq+Wdd97h+eefL7mIcTCpgOmbk8gx2tBrVAxqGcbtMf6oVO4/RNzVzg9E2bikD85fSvrg3OP6fXD+zdWK7Yp71ioqHKvNxqffr+PVb7eQbbxKp0+z+xbaALc1q8+sYe3QJu8DID80niFTVrP7cIKTIysbKRnZzPx5C1lX+VwLzLZyjqx8qdVq3hrWh4fq2zFmnMPTP5QlSf6MmrIIi8Xq0G2ZzBYe+/hndFENsVlMdI/Ic5tCuzQfPN2HoqQj6Dy9+WjtabJz850d0jU7cPwMgz9eSXZQHCq1GlXSPmY+0koKbTcSEuDHW8P6MHf4bbQxnKMo6QhqrQ51VBzfnfbhvklLmbpg9U3txxt3HWLopIW8uiKJ9IBGePiFUJSTTmjOn7zbswqzx/R3iUIboHm0L//pEo2vhwZ9aA0e/GgV/pE1+frrr2nWrBm7d+92dohXZLfb+eCDD2jbti0nT56kRo0abNy4kTFjxqBWq7HZFRbtSWfir+fIMdqo4q/njZ7V6VAnoEIU2qLy0Go0PNG3E9+9eBeDYoogcR82qwVDRB12mKszeMYW/jP9B5LTs5wdqtuSYtvJCgoKMJvNpKamYjabKSgocHZILiczJ4+xn/3EA1PXs6kgimxdGH6eWrz0padvRenO6+/jzewx/WmpP4OlMA9DRAyTNmQwdcFqZ4fmMLsOnWTY+wsZtfgE+4oqx+d6Lbq2acz0h1tC4j7Uag2ZgXEM/Gg5hxPOOeT9LRYrj3/0I5qoOGxWCx2DMhnS6zaHvLezeOh1vHVfc0x5mRhCqzNqxgpnh3RNZn6/lrd+TcYzsi6WogLiVSf56sX+5TrnVziOTqflqX538N2LdzGwtrHkxNUzsg7bzdUYOH0zr322hJyCooteV2iyYLXZyTEW/7fQZAGKi74vl/zGgIk/8tkBNbbIeDR6T4wpJ4lTneTLR1vwwTP3uuQ0qZhQA693r0aYrw6Lzof+7y2jQduuHDt2jNatWzN16lRccXBlWloavXv35oUXXsBqtdKvXz92795N69bF894zCiz8d/VZft6fgQJ0iPHnjZ7VqRoga9wL99a1TWPmvNSfNzoF4Z+xH3N+Fp6BEST6NGD09yd5/P2F7Dh4wtlhuh0ptp2oqKiISZMmER4eXvJv0qRJFBUVXf3FlcCB42d4+sOFPD3vMKc8YzEEV8VSmIf93G4Ki8x0qRtQ6usudOetKEbc34UXbw/GmHwcnZcv283VeOS9heTku+eFGbvdzqJff+fBiYv58I8iisLj0Rl8SD2XQHJWQaX5XK8mJMCPOS/1pyEnsBYV4hlZjzdWnuPzn9bf1PtabTYe/+gHiIrHbrXQzieZx+7u4JCYna1ejSr0rmFDsduxR8bz7pzlzg7psvILixj2/kI2F1RB7+2PMSWB0a19eWFQD2eHJhykW9smzHmpP2M7BuKXsR9zfjaegREk+TZkRX4MT0/+gSOnEkuaQj6z6AQjFp3kmUUnWHEwG6PZxkvzdrI2Oxx9VH0Uux1r4p/cGZHFgue6MWZQD7wMrl3gRfjpGdutGrWCPTHZ1bR+egp9n3wZs9nMyJEj6du3L5mZl/bocJZ169bRuHFjli9fjqenJzNmzOC7774jICAAKB4e/9rSUxxNNWLQqXn6tkgeaROBhzQpFBVInepRTB3Vj5mD46hjOYox7Qw6gw+m8Hg+3m7iwYmL+W7179jtpY9EFBeTOdtl5GrzBQoKCpg0aRJvvvnmJc+NHTuWF198EW9v7/II1eUs/W0n87cmQFgsam3x786YkXjJ8gRmq50l+zMqTXdeo8nM85/8RH5ocZdoY+opRnasxm3N6t/we5bnnCyjycwnC9ewLRkM4bVKHjclHqJ7/UAeuvM21Gp1pftcr8XWvUf48NcEDGHFTZAMqfv46Om7rvtE22638/gHi7FExGO322ihP8uoB7qVRchl7kq5++zkRWQFxWE1GXmyqZ72zRs4KcrS/b7vKB/8crKkqZVn6j4+fKo3Pl4VY3kkUbrs3HymLFzH/lwDhpBoAEZ1iCIhveiKzT7fX3kMn5wTPNGzGS0a1C7vsB3CZLHzycZE9pwvQAVEFx7kvZEDMJvNREdHM2/ePNq1a+e0+KxWK2+99RZvvfUWiqJQv359FixYQFxcHFDcJ2b+zjRWH8kGoFawJ0/fFkmYr2P7qLgSmbMtLrDb7Xy1dCMrD2XhEfX3Oacx5SS3RMAz/e9weE+hm+Fqc7al2C4jV/ugzWYz4eHhZGdnX/JcQEAAKSkp6PWuk7hlzWKxMuP7dWw8bcIzsk7J40VJR+hU25tH77q91DWnK2N33i+X/MaKszo8fIOwmow00p7nP0N63FDX2fL4Y3o2OZ3Ji37jDGF4+hcPdbSZi9BnHGXoHQ1LLYQq4+d6NXkFRkZPX4o5/K+LLSkneKlnvWs++bbb7Tz10WKMYfEodjtx6lO8ONh976JeKXctFiuDPlyGR1QsxrQzzHy0tcsMy37/mxXsNIaj8/TGXJBDp7AChvXt6OywRDkyFhUxdvKX5PnWYvKwzjy7+PJNIafcW5vs3DzCgvydEKlj2ewKc/5IYd2xHACaB5v537P3cvz4MTQaDePHj+c///kPmnJeq/zcuXMMHDiQ3377DYBHH32UyZMnl9zwSM4188nGRE5nmgDo0SCQ/k1CK/w62VJsi9L8tvMgX6w5gDm4Lhp98QXiopx0oklmVL/biY4IcXKErldsV87bRC4gOzu71EL7wnPp6emcOHHCJeczOVJKRjYvT/+BgdM3s91cDc/IOtisFkjcx8DaRr578S6evPeOUgttKF5PUKtR428o/m9lKMge7t2eCb2qY0o8jNbDwGFNDEPe+560zBxnh3aRrXuP8Oh7C3lp6TlS/Rvi6R+KKTeD4Ow/mdA9gi9e7H/ZO46V8XO9Gl9vA7Ne6E8bwznMBbkYwmvzwaZsPp636qqvtdvtjJhcXGgD1LOfcOtC+2p0Oi1vP3ALptwMDKHVGDVjpdOHu2Xm5PHQpIXsU2qh8/SmKOkor3eOkEK7EtJqNNwaG8nUxztiNNuu2BTSaLFViEIbQKNW8XCrcPo1KT4Z35mhZ8SnvzJw0BBsNhuvvfYa3bp1IykpqdxiWrJkCY0bN+a3337D19eXuXPnMmvWrJJCe/PJHMYuO8XpTBO+Hhqe71SFB5qHVfhCW4jLad+8AV+82J8J3SMIzv4TU24Gnv4hpPk34qVl53jkvYVs2XPE2WG6FCm2nSQgIKBkDlBISAiNGjUiJCSk5Dl/f39at25NrVq1GDFiBCtXrqxQc7l3H05g2AfFjbHO+zTAMzACc342fhn7GdsxkDkv9adb2ybODtNl1akexTfP9SIk+0/sNivqqDienrOLlZv3ODUuu93Otys2MWDiD0zfB5aIeLQeBoypCTRQTvD50GZ8NOJealYJd2qc7uypfnfwSqdQipKOoTP4sMtag6GTFl6x+/Zz//uBvJDiQrtG0WFeG3pneYXrNDHVIrknBux2G0TF89+vnDd/e822/TwxextKZPGogoDM/Xw9qnupazCLysXHQ1OpmkKqVCruigtmWNsINCrYcc5Ig8Fv8ekXc/Dy8mLNmjU0adKEVauufhHxZphMJkaPHs1dd91FZmYmzZs3Z9euXTzwwAMAFFnsfLo5iZmbkymyKtQPN/DWndVpXMU9liwToqzVrBLORyPu5fOhzWignMCYmoBWb8AaEc+M/TBg4g98s3yT0y90uwIptp3EYrHw5ptvsnT5Ss6cPceqdZs4c/YcS5atYPz48ezatYvc3FxOnTrF//73P3r06EFwcDB9+vTh008/5fz5887+Ea6b3W5n0ZptPDhxMR9sM1IUVtwYy5h+lhjzUWYObsT/RvWjXo0qzg7VLeh0Wj4ccS/3ROdTlJ2KITiKr49qeWXGj+V+cMsvLOK/Xy3j/o9Wsyo9FH1UAxS7HUviAXqEZrBgdFf+M6SnzEl1kPi6Nfh6VDf8Mvaj2O3YIuN5fNZWdh0+dUlX45MpuXhWbwZAlfyDvPl4HydHX34GdGtLWO4hAA7ao1mzbX+5bt9ut/PGrJ+ZfUDBEBJNUU46PcOzmPJsP7demlA4jtlqq5RNIW+t7c/znariqVNzOMXIyaBbWbdlJ/Hx8aSmptK9e3deeuklLBaLw7d97Ngx2rZty8cffwzA6NGj2bx5MzExMQCcySxi3PLTbDqZi0oFfRsH81LnaIK8ZJ8V4t98vDz5z5CeLBjdlR6hGVgSD6DY7eijGvBLRij3f7Sad75cRl6BseQ1l1t9oaKSOdtl5FrmC1ytEVRBQQFr1qxh6dKlLFu2jMTExIte36RJE+6880569epFy5Yty32e07UymsxMW7SW35OUSxpjdasfyJBet6J10djdxdnkdF786jd0UQ0BsCQeYOKQ2666HMzNzmtJOJ/C5MUbSdJG4eEbDIDVZMSQdYzHuzWmdXzd6/9hxHX5ZvkmliSoqVk1kle7RfPLoayLjyn1AukSG8CXy7Yy8l73Xt7rn641d602G4PeX4I+qj7GjHNMG9KC0HIYlpuYlskLs9ehjSpeK9uceJD/DmorozrEJblbmZtCnsks4oO158kyWgk0aHmmXQgfT3iFadOmAdCqVSvmzZtHzZo1HbK9uXPn8sQTT5Cfn09wcDBffvkld95ZPNJHURTWHM1m3o40LHaFQC8tT90aSWy4l0O27W5kzra4Ub/vO8pnq/ZiDKyD1sMAgCkvk1ahZobfcxtLy/h452pztqXYLiNX+6BNFjvLDmby476MS567Oz6YXg2C8ND9nXSKorB3796Swnvbtm0XzecOCQmhZ8+e9OrVi65du5YMUXem8ykZfLRoA2fsoXgGhAFgs5jQpR/h4U4N6NCioZMjrFjsdjvjZi3hpLYWGp2eopw0+tXTcl+X1pd9zY0ekNZt/5Ov1h3CElIPja64I3ZRdirV1WmM6nc7VcKDb/rnEdfu+JkkTBpvDqeZ+Wn/pceUPo2C6NEgoELNfb+e3D2VmMqL3x/F0z8Ue+J+5ozpe0MNBa/Vj+u2M3d/IZ6BEdisFqKLjjFh2F1yUVEApeduZW4KmV5g4YM15zifY8agUzPy9iiObF3Fo48+SnZ2Nv7+/syaNYt+/frd8DYKCgoYOXIks2fPBqB9+/Z8++23VK1atfh5k43Pf09mx5ni6ThNqnjzeNsIfD1L7xdTGUixLW7W+ZQMPl60gdN/1QFXXH3BgecprlZsV+xLpi5Mo1bxy+GsUp/75XAWajUYLX8PBVapVDRp0oTXXnuNrVu3kpKSwldffcV9992Hn58f6enpzJkzh/vvv5/Q0FA6duzI+++/z+HDh8u9ydqFxlhjlpwl1a8hngFhmPIyCMr6kwndwvnyxf5SaJcBtVrNW8P68FB9O8aMc3j6h/Jzoh+jpizGYrHe9PtbbTY+/2k990/8kS8O67BHxqPReWBMPkETTQJfDWvFe8P7SqHtBDHVIqkX4cPqI6UfU1YfzUavrbyFXo2oMO6vryvpb/DWF0vLZDtWm40xn3zPolMGPAMjMGYm8UBtE+8+dY8U2uKKKnNTyBBvHa91q0ZsuAGjxc77a88R3uQOdu/eTevWrcnJyaF///489dRTGI3Gq7/hv+zbt48WLVowe/ZsVCoV48aNY+3atSWF9rFUI68tO8WOM/lo1DCwRRijO1ap1IW2EI5QJTyY94b35athrbjNL4WGkd6sPppd6vdW5PMUOZI4SaHlyh1Is402Plp3jjyTjQhfPRF+eiL8dET46gn30xMWFMyQIUMYMmQIFouFzZs3s2zZMpYuXcrhw4dZv34969evZ8yYMdSqVYtevXpx5513cvvtt+PhcX1r814Lu93OvFVbWLIvDX1UA4iIR0vxWtDNQ6w88/AdMl+3nHRt05hm9XN5buYq1FHxZAY2YuBHy3mzfzNia1a97vfLzs1n8sK1/JnrhSEkGo+oSOx2G0ryIe5tGc3dA7uX6V1CcW0KrtLVuMBsw99QeT+ne+9oxbZPvifZryFHVTVYtWWPQ5swHj+TxKvztuER1RA1YEvcz9RH7yAiJNBh2xCiovL20DDmjqp8ujmZbafzmLk5mf5NQtiwYQPjxo3j3XffZcaMGWzevJkFCxZQv379q76noijMnDmTUaNGYTKZiIqK4ttvv6VDhw4A2BWFZQcyWbwnHbsCYb46ht8WRc1gOVcRwpEMHnoe79OeHKOlUp6nSLHtJF664g6kl1tb089TQ47RRp7JRo7RyJHUi6/mqoBgb11xAe6nJyK8MYNGteCFsW+Tm3KGFcuXs2zZMtavX8/JkyeZOnUqU6dOxdvbmy5dutCrVy969uxJVFTUTf0c+YVFTF24hl3pWgxhNdBHFc8RNice5K74MAYM7CKFmBOEBPgx56X+vDtnOftMkXhG1uONlefoEnWcR/t0uOh7IyIiSn2PwwnnmPrjVjI8q6H3qYchBCzGfPzyTvJkrxY0jb23HH4Sca289Vc+plS0rsY34p0n+zDo/Z/RRzVg1h+JNK6b5ZBi+MK8eY+o+ljNRuqrzvJqGQ9VF6Ki0WnUPHVbJEHeWlYczGLhnnQyCi1MePsdOnbsyODBg9m/fz8tWrRg6tSpDB06FJWq9CW4srOzeeyxx1i8eDEAPXv25MsvvyQ0tPgcJcdoZebmJP5MKgSgTQ1fHm4VjkGOk0KUmcp6niJztsuII+Zs2xSF5DwLKblmki/8y7OQnGu+aIj5v2nUEOZTfCc8yFNFxtljHPjjN9YuXcSpIxd3423WrFnJXe8WLVpccnJ4uXlkxY2xNpGkjbykMdZjXeNp07jejfzaRBnYuvcIH/6agCGsuMGMIXUfHw/vg6JSlfrZrty8h283HcceFotGW5y7RZlJxHhk82z/DoQHBzjxpxGXU2iysOJgdpnPhXIVNzon62xyOs8vPIhnQDi2xD/5esw9N1wUm8wWnp/2EzmBDVGp1RjTzjC8fZRMkxFXJHNhr+6XQ1l8uyMVBWha1Zunb4siKz2VwYMH8+uvvwLw4IMPMnPmTFQqFTqdjuzsbAICAkhPT2fw4MGsXbsWnU7Hu+++y6hRo0r28z8TC5i5OYmcIht6jYrBt4TTvrbfZQv3ykryVDhaeZ2nuNqcbbmz7SQeOjW9GwUBxXO0L3Tk6xobSO9GQeg0xX8UagVrqPWvIU2KopBXZCMpz0xKruWvItxcXJTnWbDYFJJyzSTlmv96RSQ0vp/bGt9PR5WC2pRN+umjnD60m+zkU0yft4SJH00lwEtPjx49uPPOO+nRowc6DwMrDmZf0jGwe4Mgpm/LITOwER5AUU4a1VSpPHtve6IjmpTfL1FckzaN6zE7phqjpy/FHB5PYEwLbCo1qw5mXfLZdmsQxPp0f1RRcWiAoqSjtK/hyRNPd0Snk8OFK/Py0NE7rvjCV2XsanytoiNCeLCRgUWnLGiiGjFu1hLeGnb9y6HtO3qKCT/+iWdkHCpAk7SPz57sQYCfrMMrxM3qWj+QQG8tMzYlsftcAe+uPsvojlVYtWoVEydO5PXXX2fXrl0UFRUxdepUpkyZUlJsP/PMM8yfP5+BAwfy9ttv07JlSwCsdoXv96Sz7EAmChAdoOfp9lFU8Xf81DohxKUq63mK3NkuI9d6VcVksaNRqyi02PDSabDZlYu6kF8vu6KQWWglOfev4jvXQnJe8V3xtHwL9it82qb8bHKTT5GXcprPX3+MP1NtpV99igumRrAH7y7YSOtINU/364TBQ3/DMYvyM23hr7Rp3YqTGabSu1b/9dl+NHcVD7StRa/bmjkhSnEzKktX45u9cv3KjB85510fm9XCgzHm68r1aQt/5bcMf/TefliM+dzik87oB7tddwyicpI7htfuaGohH607T4HZTrivjhc6VSXcT8+WLVvIy8tj06ZNTJgw4ZLXvf766zz//PP4+xcv85eeb2HapkSOpxUB0KluAA82D62wJ/eOIHkqykpZn6e42p1tKbbLiCsepKx2hbT8fwxLz/urGM81k1n4d7dqXw8NH/atxbOLT1x2XsXUfrXRqJA5iW7IYrMzYtGVP9sLIyuEcFU3e4y12+0Meu8HtFGNKMpM4uMH44kKDbria/IKjCUjRACMKSd4qWc9WjSofUM/g6icXPH8wJUl5ph4f8150gss+HpoeK5jFWqHGjCZTERERJCdnX3JawICAkhJSUGv17P9TB6fb00uvoumU/NImwhuqe5b/j+Im5E8Fe7K1YptGRdaiWjVKiL99ET6XXoX2mS1k/LXsPQCk41805U7Gxeabfgb5ODrjgqv0rW6sIJ2gxTin9RqNe8/0pFRc/fhGRTJ87PX8+2Yuy97AbGk98FfhbZX2j5mDL8LL4MMQRWiLEX5ezC2RzU+XHuOU5km/rv6LE/fFkVVj8JSC20obpCWnpnN6tMKa/5aaqh2iCdP3xZFqI+cuwghyo+cUQsAPLRqqgV60rK6Lx3qBuDvWdwxsDQVuWNgZXChG2Rp5LMVlUlUaBAPNfPDZjGji2rIqzN/KvX7Jn69nKl/FGIIq4k5P4v2vknMeK6/FNpClJMAg5ZXulYjPsobs01h8obz7ElXExAQAEBISAiNGjUiJCQEgKp1G/O/PwpKCu1eDYN4tVs1KbSFEOVO7myLUpmtNrrUDSh1znaXugGYrTa0MtTYLclnK8Tfet3WjN8P/YQlOJ7+HTpjsRWP7vDWazCarby/eCsJ1EbrCUVJR254vXohxM3x1KkZ1bEKX21LYcPxHL7ZmcHzk7+jaaidTh07kJVfRKCPJ1t2H2B1ii/ncsz4emh44tZI4qO8nR2+EKKSkmJblKqydgysDOSzFeJi4x/rjdGisPJQFp9uSb5on3i+XzveXnkK05m9vDe6j3TlF8KJtGoVj7QOJ9hby+8JeYzs1pHVh7IY/dOZv/fbetV4pXsAC3al0b9JKAFess8KIZxHjkDisvRaNT0aBNAnPviijoFSjLk/+WyF+FuRxcbKf639WWi2l3w9ukMkEQExzgpPCPEPKpWKu+NDaF3Dj9WHS9lv/1ppY3DLcDxvYnUXIYRwBDkKiSvy8tCh1ajxNxT/tyIuIVRZeXnoUOw2Du/bhWKvmMtDCXEt9FoNq/+a2/lvq49mE+JrKN+AhBBXFeKtY/WRrFKfW30kC61aVc4RCSHEpaTYFqKSS05OdnYIQjhVwVU69BeYbeUckRDiagotV1lZwyL7rRDC+aTYFkIIUalJh34h3I+X7sr7rZdO9lshhPNJse1kNrMJu9WKJT8Pu9WKzWxydkhCCFGpXOjQX5oLHfqFEK7FZlfoGhtY6nNdYwOx2ZVyjuhSrn6O5+rxCVERSIM0J7JbLCSuX03y5vXYjEY0BgMR7TpQpWM31DqZPyuEEOVBOvQL4X48dGrubBgEisIvR/7eb7vWC6B3wyB0Tt5vXf0cz9XjE6KikGLbSWxmE4nrV3P+1xV/P2Y0lnwd2f4OVGo5wRNly2axgK34arZaKX3umxCuyNG5q0FF91i/izr0m8wWNHYrNrPz75CJikOOu46h2O1k7NzGrYGh9O5bjwKjGW+DnsxjR0jfdoDQ5q2cdh6l2O0k/bbGZc/xrhZfVIcuoJJzUCEcQYptJ1GpNSRvXl/qc8mb1xPVoQu7/zsWa0F++QYmKp1wYM/va50dhhDXrSxyV+vtg87XD0terhx/RZmR4+7N0Xr70PTlNzn3y1JsRuMl+63GYCCsZRunnEddiM1Vz/GuJb4qnbqDIhcZhXAEKbadxFZkxGY0lv6c0Yg1Px+dr5+c7AkhRDmyFuTLcVcIF6fz9cOSn1dyHvXv/daZ51H/ju3fnH2Ody3x2YqM4OFZzpEJUTG5RbH9ySef8N5775GcnEzjxo2ZOnUqt9xyi7PDuikaTwMag6HUg53GYEDn50+jZ15wQmSiMrFYLKxatYpu3bqhkzlawo1I7gp3JbnrGCq1xmXPo1w5Nrh6fBpPAza5sy2EQ7h8sb1gwQKee+45ZsyYQatWrfj444/p1q0bR44cISwszNnh3TDFbiOiXYeL5stcENGuA4rdhkbv4YTIRGViV6lBo0Wj90AjJ33CjUjuCncluesYNrPJZc+jXDk2uLb4ZM62EI7h8nvShx9+yOOPP87QoUNp0KABM2bMwMvLi9mzZzs7tJui0XtQpWM3qnTugcZgKH7MYKBK5x5U6dhNCm0hhBBCiMtw5fMoV47NHeIToiJx6TvbZrOZnTt38vLLL5c8plar6dy5M1u3bnViZI6h1umI6tCFKp26YysyovE0oNhtsuSCEEIIIcRVuPJ5lCvHBq4fnxAVhUsX2+np6dhsNsLDwy96PDw8nMOHD5f6GpPJhMlkKvk6NzcXKJ4jZbFYyi7Yf7mwratuU6Uu7vjo4Vk8P0alxl6OcYrK7ZrzVAgXI7kr3JXkroO58nmUK8cGV4xP8lS4K0fmriPew6WL7Rvx3//+l/Hjx1/y+C+//IKXl1e5x7N69epy36YQ10vyVLgryV3hriR3hTuQPBXuyhG5W1hYeNPv4dLFdkhICBqNhpSUlIseT0lJISIiotTXvPzyyzz33HMlX+fm5hIdHU3Xrl3x8/Mr03j/yWKxsHr1arp06SLdRoXLkjwV7kpyV7gryV3hDiRPhbtyZO5eGCF9M1y62Nbr9TRv3pw1a9Zw9913A2C321mzZg3PPPNMqa/x8PDAw+PSxg46nc4pBwtnbVeI6yF5KtyV5K5wV5K7wh1Ingp35YjcdUTuu3SxDfDcc8/x0EMP0aJFC2655RY+/vhjCgoKGDp0qLNDE0IIIYQQQgghSuXyxfb9999PWloaY8eOJTk5mSZNmrBy5cpLmqYJIYQQQgghhBCuwuWLbYBnnnnmssPGhRBCCCGEEEIIV6N2dgBCCCGEEEIIIURFI8W2EEIIIYQQQgjhYFJsCyGEEEIIIYQQDibFthBCCCGEEEII4WBSbAshhBBCCCGEEA4mxbYQQgghhBBCCOFgUmwLIYQQQgghhBAOJsW2EEIIIYQQQgjhYFpnB1DWFEUBIDc3t1y3a7FYKCwsJDc3F51OV67bFuJaSZ4KdyW5K9yV5K5wB5Knwl05Mncv1I8X6skbUeGL7by8PACio6OdHIkQQgghhBBCCHeSl5eHv7//Db1WpdxMqe4G7HY7iYmJ+Pr6olKpym27ubm5REdHc/bsWfz8/Mptu0JcD8lT4a4kd4W7ktwV7kDyVLgrR+auoijk5eURFRWFWn1js68r/J1ttVpN1apVnbZ9Pz8/OUgJlyd5KtyV5K5wV5K7wh1Ingp35ajcvdE72hdIgzQhhBBCCCGEEMLBpNgWQgghhBBCCCEcTIrtMuLh4cG4cePw8PBwdihCXJbkqXBXkrvCXUnuCncgeSrclavlboVvkCaEEEIIIYQQQpQ3ubMthBBCCCGEEEI4mBTbQgghhBBCCCGEg0mxLYQQQgghhBBCOJgU20IIIYQQQgghhINJsS2EuCEmk8nZIQghhBBCCOGypNh2QSdPnmTz5s3ODkOIyzpy5Ahjx47FarU6OxQhrovZbKawsNDZYQghhBCiEtA6OwBxsX379tG9e3d69epF3bp1CQ0NdXZIQlxk3759tGrVCpPJRMeOHenevbuzQxLimhw6dIgJEyZw/PhxmjVrxtNPP01cXJyzwxLiqo4fP84PP/xAeno6jRo1okePHoSEhDg7LCEucvToUb744gtSU1Np2rQp3bt3JyYmxtlhCXFVaWlp6HQ6AgICHP7ecmfbhSQkJNCtWzcGDRrEzJkzpdAWLmfv3r20bt2aRx55hP79+zN37lyMRiOKojg7NCGu6MCBA9x66614eHjQq1cvfvzxR7744gtnhyXEVf3555+0bt2adevWcfz4cR577DHuv/9+lixZ4uzQhChx8OBBbrnlFnbv3k1WVhavvPIKw4cPZ/bs2c4OTYgrOnjwINHR0Tz55JPk5eU5/P1Vipwlu4w5c+bw888/s2jRIqxWKx999BFHjx6lSpUqdOjQgQ4dOjg7RFGJ7dq1iw4dOjBy5EgmTJjAhx9+yFtvvcWuXbuoWbMmiqKgUqmcHaYQl8jLy+Oee+6hWbNmTJo0CYCZM2eydetW/ve//+Hj4+PkCIUoXXZ2Nr169aJDhw68/fbbQHHx3aRJE5o0acKzzz7L4MGDnRylqOzMZjOPPPII3t7ezJw5E/h7utnZs2cZOHAgw4cPd3KUQlwqOTmZvn374unpyd69e+natSuffvopvr6+DtuG3Nl2Ibt27SppOtW5c2d+/PFH8vLymDdvHq+//jqffvqpkyMUlVV2dja33norw4YNY8KECQAMHz6cWrVq8dZbb0mhLVxebm4usbGxJV/v37+f3bt306RJE+67776SE0QhXInFYsFoNNK1a1fsdjuFhYXExsbStm1bbDYbX3/9NQcPHnR2mKKS0+v1JCcno1YXlxWKolCvXj0mTpxI3bp1+e6771i2bJmToxTiYoqisHv3bmrWrMmkSZNYunQpK1euZNiwYQ69wy3FtguJj4/Hw8OD+fPno9Pp+P7775k/fz7r1q2jZs2aLFq0iMzMTGeHKSqhgIAAtmzZwvvvvw8UH6C0Wi1du3Zlx44dZGRklDwuhKspLCwkKyuLLVu2sHz5csaNG8cXX3zBY489xvjx49Hr9cydO5c9e/Y4O1QhLpKXl8ehQ4dITU1FrVbj5eXF+fPnMZvNjBkzhu3bt7N48WJnhykqMUVRsFgsVK1alczMTEwmE4qiYLfbqVGjBq+99homk4m5c+c6O1QhLqJSqWjevDmPPfYYLVq0oE2bNhcV3Lm5uSXfe1Pnt4pwGVu2bFE8PDyU5s2bK/369bvouYMHDyoqlUr59ddfnRSdEIpit9sv+m9iYqLi7e2tvPPOO84MS4irWr58uRITE6PcfffdSnh4uDJ37tyS506cOKF4eXkpn3/+uRMjFKJ0o0ePVjw8PJRx48YpU6ZMUfz9/ZUnnnhCURRFee+995R27dopBQUFJcdlIZxhzZo1ilqtVj755BNFUYrPE6xW60XPHTx40JkhCnFFNptNURRF2bx5sxIQEKAMGDBAyc3NVcxmszJ9+vQbrsGkG7mTnD17lkOHDpGWlkaXLl3w9fWlTZs2fPDBB4wcORJFUUhISKBmzZoAhIWF0bp16zLpkidEaf6dowEBAej1emw2GxqNBpvNRmRkJMOGDWPZsmUMGjSI6OhoZ4ctxEW527lzZ/z8/OjRowebN2/Gw8ODzp07U716dQDsdjshISE0bdoUf39/J0cuKrt/H3eDgoJ488038fPzY86cOYSHh/Pcc88xduxYgJLRbl5eXs4MW1QyhYWF6HQ6dDodiqKgKAqdOnXiv//9LyNGjMBgMDB06FA0Gg0A/v7+1KtXT/JUON0/c/ffLkyDaNu2LcuXL6dnz5488cQTGAwGvvnmmxufsuOwywHimu3du1cJDw9XmjVrpuj1eqVhw4bKCy+8oGRmZiqKoihvvfWWolKplEcffVTZtGmTkpqaqrz22mtKzZo1lcTERCdHLyqD0nJ0zJgxSlZWlqIoSsnVakVRlBUrVii+vr7KkiVLnBStEH8rLXeff/55JSMjQ1GU4tEYtWrVUr744gtFURTFbDYr48aNU6Kjo5XTp087MXJR2f07d+vXr6+89NJLJcfdtLS0kv+/YNiwYcpjjz2mmM1mubMtysWBAweUTp06KUuWLFEsFouiKH+PdissLFTGjh2rqFQqZdy4ccqePXuUrKws5T//+Y9Sp04dJTU11Zmhi0qutNy9kg0bNigqlUoJCgpSdu7cecPblWK7nGVnZyvNmjUrOfkzGo3Kyy+/rLRt21a5++67Swru2bNnK5GRkUpERITSoEEDpVq1asquXbucHL2oDK6Uo3369CkpWv5ZcHft2lW5/fbbFZvNJid8wmmulrtpaWmKoijK22+/rahUKqVVq1ZKhw4dlCpVqsjxVTjV5XK3TZs2yl133aWkp6crivJ3UXPs2DHlxRdfVPz8/JQ///zTmaGLSuTUqVNKbGysotFolBo1aii//PJLqUXL7NmzlfDwcKVKlSpK/fr1laioKDnGCqe61ty9wGQyKU8++aTi6+urHDhw4Ka2LQ3SyllWVhbZ2dn07t2boKAgPD09eeONN3jsscdISkpixIgR5ObmMnToUDZv3szixYv55JNP2Lp1K02bNnV2+KISuFKOpqamMnLkSPLy8tBoNNjtdgCeeOIJPv/8c9RqtXQlF05ztdx99tlnycvL45VXXmHhwoU0bdqUHj16sH79ejm+Cqe6XO4+/vjjFx13VSoVmZmZfPXVV6xdu5b169fTsGFDZ4cvKgGr1coPP/xAbGwsJ0+eJDY2liFDhrBu3TqsVutF3zt06FC2bNnCt99+y4cffsi2bdvkGCuc5npy94I9e/awceNG1qxZQ4MGDW5q+1JslzMfHx+8vLzYv38/UNzdTq/X89BDDzFkyBAOHTrEjz/+CEDNmjVp27YtHTp0ICoqyolRi8rkSjk6aNCgi3JU+as7Y9++faldu7azQhYCuL7cvffee5k+fTovvvgiMTExToxaiCvn7uDBgzl8+HBJ7gYFBfHUU0+xbNkyKWBEudFoNLRs2ZLBgwdTrVo1VqxYQePGjXnooYdYt24dFosFoGQOd61atbj99tvp3r07VatWdXL0ojK71tz9p7p16/Lbb7/RsmXLm96+SlFkrZ7yZLFYeOCBB0hKSmLu3LklTXou6NatGzqdjqVLlzopQlHZSY4Kd3UtuavVamW9V+Fy5Lgr3IHdbi9pInVB9+7d2bt3L3PmzKFTp05oNBqWLl1Kx44d8fb2dlKkQlzsenK3Q4cO+Pj4OGzbcme7HCmKgk6nY9q0aZw4cYKRI0eSmpp60dptvXv3Jj09naKiIidGKioryVHhrq41dzMyMiR3hUuR465wF/8sVi4Mv125ciVNmjThoYce4pdffuGJJ55g9OjRF61RLISzXU/u5uXlOXbbDn03cUUqlQqz2UxYWBgrV65k27ZtDBo0iB07dmCz2YDiOQLBwcGXXH0RojxIjgp3Jbkr3JXkrnAX/7wApNVqS4bfrlixgiZNmtCrVy++/fZb5s+fT2RkpLPCFOISzsxdGUZehhRFuahZ1IX1iTMyMjCbzRiNRnr06IGPjw9Wq5VatWqxZs0aNm3aRHx8vBMjF5WF5KhwV5K7wl1J7gp3cLk8zcrKIj09nTp16gDFdwm1Wi2jR49mzpw5bNy48aYbSglxM1wtd+USaRkwm80AmEwmoPhDt1qtaDQaTp06RXx8PGvWrKFWrVps376dUaNG0aVLF1q2bMn27dvlj6koc5Kjwl1J7gp3Jbkr3MGV8vT06dM0atSInTt3lny/Vqtl1qxZTJ48mV9++UUKbeE0rpq7cmfbwQ4fPszEiRNJTEwkIiKCMWPG0KhRIwDOnTtHXFwc/fv3Z+bMmSiKIkPCRLmTHBXuSnJXuCvJXeEOridP/3nn8Pjx42i1WmrUqOGkyEVl58q5K0dzB9q/fz9t27bFw8OD2rVrk5KSwocffojFYsFms/HHH3/wyCOPMGPGDFQqlfwxFeVOclS4K8ld4a4kd4U7uN48/aeYmBgptIXTuHruyp1tB0lISKBLly4MGDCACRMmADB+/HjOnDnD559/TlFREZ6eniXzBoQob5Kjwl1J7gp3Jbkr3IHkqXBX7pC7cvnUQbZv3067du0YOXJkyWO5ubns2LGDW265hS5durBq1So0Gg1yfUM4g+SocFeSu8JdSe4KdyB5KtyVO+Su3Nl2kOzsbFJSUqhXrx4A7733HmPHjuXdd9/F09OTvXv38vnnn7Njxw7i4uKcHK2ojCRHhbuS3BXuSnJXuAPJU+Gu3CF3tU7ZagVyYVhCQEAAAQEBQHE3vGPHjvHzzz/TpUsXoHiYw08//cSePXvkQCXKleSocFeSu8JdSe4KdyB5KtyVO+WuFNs3KDs7m4CAADQazUXzABRFQa/XM2PGDNRqNXa7HbVajUqlIjIykujoaCdHLioLyVHhriR3hbuS3BXuQPJUuCt3zF2Zs30DDh06RLNmzRg7diwAGo0Gu90OUNLl7sJ/L3QV/eyzz7BarcTGxjohYlHZSI4KdyW5K9yV5K5wB5Knwl25a+7Kne3rdPbsWR588EG0Wi0//PADWq2WsWPHXnQVBf7+sI8cOcLMmTP58ssvWbduHREREc4MX1QCkqPCXUnuCncluSvcgeSpcFfunLtSbF8HRVGYN28eUVFRjBo1is2bNzNv3jyAkg/8n0MaDhw4wDfffMOmTZtYv3498fHxzgxfVAKSo8JdSe4KdyW5K9yB5KlwV+6eu9KN/DolJyezatUqHnroIVJTU5k2bRoLFixgwIABjBs3DuCiKywHDhwgNDSUsLAwZ4YtKhHJUeGuJHeFu5LcFe5A8lS4K3fOXSm2b1JSUhIzZ8685AP//vvv6du3r5OjE0JyVLgvyV3hriR3hTuQPBXuyp1yV4aRX0VSUhJnz54lKyuLzp07lwxRsNvtJR3uhg0bBsD8+fNRFIWcnBwmT57MuXPniIqKcmb4ohKQHBXuSnJXuCvJXeEOJE+Fu6pQuauIy9q7d69SvXp1pW7duoq/v78SGxurzJ07V8nIyFAURVFsNptit9sVRVGUxMREZezYsYpKpVICAwOVHTt2ODN0UUlIjgp3Jbkr3JXkrnAHkqfCXVW03JWlvy4jLS2N+++/n4EDB7JixQoOHjxI48aNeeutt5gyZQppaWkl8wIAIiMjSUhIwNfXl02bNtG8eXMnRi8qA8lR4a4kd4W7ktwV7kDyVLirCpm7zq72XdWBAweUGjVqXHKF5KWXXlLi4uKUSZMmKQUFBSWPz5o1SwkICFB27dpV3qGKSkpyVLgryV3hriR3hTuQPBXuqiLmrtzZvgyLxYLVaqWwsBAAo9EIwLvvvkvHjh2ZPn06x48fL/n+O++8k127dtG0aVOnxCsqH8lR4a4kd4W7ktwV7kDyVLiripi70o38Cm655RZ8fHxYu3YtACaTCQ8PDwBatmxJTEwM8+bNu2htNyHKk+SocFeSu8JdSe4KdyB5KtxVRctdubP9l4KCAvLy8sjNzS15bObMmRw4cIAHH3wQAA8PD6xWKwDt27enoKAAwC0+aOH+JEeFu5LcFe5Kcle4A8lT4a4qQ+5KsQ0cPHiQvn37cvvtt1O/fn2+/fZbAOrXr8/kyZNZvXo1/fv3x2KxlEzKT01NxdvbG6vVigwOEGVNclS4K8ld4a4kd4U7kDwV7qqy5G6lX2f74MGDtG/fniFDhtCiRQt27tzJ0KFDadCgAU2bNuWuu+7C29ubp59+mvj4eGJjY9Hr9Sxbtozff/8drbbS/wpFGZMcFe5Kcle4K8ld4Q4kT4W7qky5W6nnbGdmZvLAAw8QGxvL5MmTSx7v2LEjcXFxTJkypeSxvLw8JkyYQGZmJp6enjz11FM0aNDAGWGLSkRyVLgryV3hriR3hTuQPBXuqrLlrvtcFigDFouF7Oxs+vXrB4DdbketVlOzZk0yMzMBUBQFRVHw9fVl4sSJF32fEGVNclS4K8ld4a4kd4U7kDwV7qqy5a77RexA4eHhfPPNN9x2220A2Gw2AKpUqVLyYapUKtRq9UUT91UqVfkHKyolyVHhriR3hbuS3BXuQPJUuKvKlruVutgGqFOnDlB8tUSn0wHFV1NSU1NLvue///0vs2bNKumE564ftnBPkqPCXUnuCncluSvcgeSpcFeVKXcr9TDyf1Kr1SiKUvJBXriyMnbsWCZMmMDu3bvdajK+qHgkR4W7ktwV7kpyV7gDyVPhripD7lb6O9v/dKFXnFarJTo6mvfff59JkyaxY8cOGjdu7OTohJAcFe5Lcle4K8ld4Q4kT4W7qui5696XChzswtUUnU7HZ599hp+fH5s2baJZs2ZOjkyIYpKjwl1J7gp3Jbkr3IHkqXBXFT135c52Kbp16wbAli1baNGihZOjEeJSkqPCXUnuCncluSvcgeSpcFcVNXcr9TrbV1JQUIC3t7ezwxDisiRHhbuS3BXuSnJXuAPJU+GuKmLuSrEthBBCCCGEEEI4mAwjF0IIIYQQQgghHEyKbSGEEEIIIYQQwsGk2BZCCCGEEEIIIRxMim0hhBBCCCGEEMLBpNgWQgghhBBCCCEcTIptIYQQQgghhBDCwaTYFkIIIVzAww8/zN133+3sMIQQQgjhIFpnByCEEEJUdCqV6orPjxs3jsmTJ6MoSjlFVLqHH36Y7OxsfvzxR6fGIYQQQlQEUmwLIYQQZSwpKank/xcsWMDYsWM5cuRIyWM+Pj74+Pg4IzQhhBBClBEZRi6EEEKUsYiIiJJ//v7+qFSqix7z8fG5ZBh5hw4dGDFiBKNGjSIwMJDw8HA+++wzCgoKGDp0KL6+vsTExLBixYqLtvXnn3/So0cPfHx8CA8PZ/DgwaSnp5c8v2jRIuLi4jAYDAQHB9O5c2cKCgp44403+Oqrr/jpp59QqVSoVCrWr18PwEsvvUTdunXx8vKiVq1avP7661gslpL3fOONN2jSpAmzZ8+mWrVq+Pj48PTTT2Oz2Zg0aRIRERGEhYXx9ttvXxSrSqVi+vTp9OjRA4PBQK1atVi0aJHjPwAhhBDCCaTYFkIIIVzUV199RUhICH/88QcjRozgqaeeon///rRt25Zdu3bRtWtXBg8eTGFhIQDZ2dl06tSJpk2bsmPHDlauXElKSgr33XcfUHyH/YEHHuCRRx7h0KFDrF+/nr59+6IoCi+88AL33Xcf3bt3JykpiaSkJNq2bQuAr68vX375JQcPHmTy5Ml89tlnfPTRRxfFeuLECVasWMHKlSuZN28en3/+Ob169eLcuXNs2LCBiRMn8tprr7Ft27aLXvf6669z7733snfvXgYOHMiAAQM4dOhQOfx2hRBCiLKlUpw9QUwIIYSoRL788ktGjRpFdnb2RY//e750hw4dsNlsbNy4EQCbzYa/vz99+/Zlzpw5ACQnJxMZGcnWrVtp3bo1EyZMYOPGjaxatarkfc+dO0d0dDRHjhwhPz+f5s2bc+rUKapXr35JbNc6Z/v9999n/vz57NixAyi+s/3ee++RnJyMr68vAN27d+fIkSOcOHECtbr42n5sbCwPP/ww//nPf4DiO9tPPvkk06dPL3nv1q1b06xZM6ZNm3aNv1EhhBDCNcmcbSGEEMJFxcfHl/y/RqMhODiYuLi4ksfCw8MBSE1NBWDv3r2sW7eu1PnfJ06coGvXrtxxxx3ExcXRrVs3unbtSr9+/QgMDLxiHAsWLGDKlCmcOHGC/Px8rFYrfn5+F31PjRo1SgrtC7FpNJqSQvvCYxdivaBNmzaXfL1nz54rxiOEEEK4AxlGLoQQQrgonU530dcqleqixy50Obfb7QDk5+fTu3dv9uzZc9G/Y8eO0b59ezQaDatXr2bFihU0aNCAqVOnUq9ePRISEi4bw9atWxk4cCA9e/Zk6dKl7N69m1dffRWz2XxdsV547EKsQgghREUnxbYQQghRQTRr1owDBw5Qo0YNYmJiLvrn7e0NFBe87dq1Y/z48ezevRu9Xs8PP/wAgF6vx2azXfSeW7ZsoXr16rz66qu0aNGCOnXqcPr0aYfF/Pvvv1/ydf369R32/kIIIYSzSLEthBBCVBDDhw8nMzOTBx54gO3bt3PixAlWrVrF0KFDsdlsbNu2jXfeeYcdO3Zw5swZvv/+e9LS0kqK2xo1arBv3z6OHDlCeno6FouFOnXqcObMGebPn8+JEyeYMmVKSXHuCAsXLmT27NkcPXqUcePG8ccff/DMM8847P2FEEIIZ5FiWwghhKggoqKi2Lx5Mzabja5duxIXF8eoUaMICAhArVbj5+fHb7/9Rs+ePalbty6vvfYaH3zwAT169ADg8ccfp169erRo0YLQ0FA2b97MXXfdxejRo3nmmWdo0qQJW7Zs4fXXX3dYzOPHj2f+/PnEx8czZ84c5s2bR4MGDRz2/kIIIYSzSDdyIYQQQjiFSqXihx9+uGh9cSGEEKKikDvbQgghhBBCCCGEg0mxLYQQQgghhBBCOJissy2EEEIIp5CZbEIIISoyubMthBBCCCGEEEI4mBTbQgghhBBCCCGEg0mxLYQQQgghhBBCOJgU20IIIYQQQgghhINJsS2EEEIIIYQQQjiYFNtCCCGEEEIIIYSDSbEthBBCCCGEEEI4mBTbQgghhBBCCCGEg0mxLYQQQgghhBBCONj/AVEVHteVaa5VAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "#task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
    "task_phab_df = task_phab_df[task_phab_df['is_relevant'] == True]\n",
    "task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
    "#task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
    "\n",
    "task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "wmf_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] == True)]\n",
    "wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "other_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] != True)]\n",
    "other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "unaff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] != True)]\n",
    "unaff_new_tasks = unaff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "aff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] == True)]\n",
    "aff_new_tasks = aff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total', marker='o')\n",
    "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "#sns.lineplot(x=aff_new_tasks.index, y=aff_new_tasks.values, color='#c7756a',linestyle=\"dotted\", label=\"WMF-affiliated new authors\", marker='x')\n",
    "#sns.lineplot(x=unaff_new_tasks.index, y=unaff_new_tasks.values, color='#5da2d8', linestyle=\"dotted\", label=\"Nonaffiliated new authors\", marker='x')\n",
    "\n",
    "plt.title('New Phabricator Tasks Indexed with HTTPS')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Unique taskPHIDs')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#plt.savefig('031825_new_tasks_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11370/3743952880.py:4: 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",
      "  unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
      "/tmp/ipykernel_11370/3743952880.py:17: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_11370/3743952880.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
      "/tmp/ipykernel_11370/3743952880.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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UqZPq16+vzp07S5JKly6tgIAADRgwQB06dFCTJk0eekDulStX6ujRo+ratauCgoK0f/9+TZ06Vfv379eWLVvk4uKiZ599Vn/88YfmzJmjTz75xP6ZFX/b7KhRo/Tuu++qbdu26tGjhy5cuKDPPvtMNWvW1O7du+23BX399dfq1auXqlatqv79++vo0aNq0aKFsmfPrvz58z/Ufmzfvl2bNm1S+/bt9dhjj+nYsWOaPHmyateurQMHDtivu2HDhmnMmDH28zkqKko7duzQrl27VL9+/STXndI5J0k3btzQqlWrVLNmTRUoUCDVtU+bNk23bt3Siy++aB/raP/+/apWrZry5cunN998U97e3po3b55atWqlH3/8Uc8884yk1J1PxYsX14gRI/Tee+/pxRdfVI0aNSRJVatWTbQeY4xatGih1atXq3v37ipbtqxWrFihwYMH66+//tInn3zi0H7Dhg1asGCBevfuLV9fX3366ad67rnndOLECeXIkSPF/R81apRcXFz0xhtv6Pz58xo/frzq1aun8PBweXl5qVOnThoxYoS+//57h1tWb9++rfnz5+u5556Tp6dnoutO63uS3vue0rWTlN69eytXrlx67733dP36dYd5bdu2VUhIiMaMGaMtW7bo008/1T///JMgAE9JSrX16NFDM2bMUOvWrfXaa69p69atGjNmjA4ePKiFCxc6rOvw4cPq0KGDevXqpZ49e+rxxx9PUy3xbty4oVq1aumvv/5Sr169VKBAAW3atElDhgzRmTNn7A97iPfdd9/p6tWr6tWrl1xcXPR///d/evbZZ3X06NEHuk1ZerjrEUAm47xOWgD+zUqWLGmefvrpBNP3799vJJkpU6bYp0VERJjy5csbSfafQoUKmUOHDqVqW97e3qZbt24Jpv/yyy9Gklm+fHmSNaZHd/ORI0caSWbVqlX2aT/88IORZNatW5egfZs2bUxQUFCi60rtrRk2m81+rHLkyGE+/fTTVNWa1O17rVq1Mh4eHubPP/+0Tzt9+rTx9fU1NWvWTHad8bcNnDhxIsG8J5980jz11FPGGGNiY2NNQECAqVu3rkObixcvGm9vbyPJ7NixI1X7MWfOHIfzpWLFiuZ///tfgn19kPMiOREREcbT09N06tQpXbaVmnOwV69e9v10dXU1rVu3NpcuXUqx1vhtz5w50xhjzJkzZ4wks3btWnP16lXj5uZmfvnlF2PM3VsVJZlRo0YZY4xZv369kWRmz57tsM7ly5c7TL969aoJCAgwPXv2dGh39uxZ4+/v7zD93lupNmzYYPz8/EzTpk3NrVu3HJa9/xawFStWGElm2bJlDu1Kly6d4rF7kNv3goODE1y758+fNzabzbz22mv2abdu3UpwK29kZKSx2WxmxIgRDtMlJbh9L6napk2blqCm+49J/LL3fk4kdptN/HVy774kdZvPsWPHjJubm/0ciLd3716TJUsW+/Tbt2+bwMBAU7ZsWYfbYaZOnWokPfTte4ntx+bNm40k8+2339qnlSlTxjRt2jTZ9af2nLvfnj17jCTz6quvpmo/4t8PPz8/c/78eYd5devWNU888YTDNuPi4kzVqlVNkSJF7NNSez4l929EWFiYCQ4Otr9etGiRkWTef/99h3atW7c2Li4u5siRI/ZpkoyHh4fDtPjj8NlnnyW7//G37+XLl8/htrp58+YZSWbChAn2aVWqVDGVK1d2WH7BggVGklm9enWS20jre5IR+57cLXLBwcEO/7bGX8fVq1c3d+7ccWgbf/teixYtHKb37t3b4RbqxK7ze2u+97pJqrbw8HAjyfTo0cNh+qBBg4wk8/vvvzvsw4P+23j/v2UjR4403t7e5o8//nBo9+abbxo3Nzf77wvx+5gjRw6Hf9cWL15sJJmff/7ZPi2p24PvP+/T43oEkLlw+x6AB3Lz5s1EB+qN/yvozZs37dN8fX1VsmRJ9enTRwsWLNCkSZN0584dtWrVKlWPTE7LttLbunXrNHz4cLVt21ZPP/20Q02SkqzrYWtatmyZli5dqo8//lgFChRI8BfYtIiNjdWvv/6qVq1aqVChQvbpefLk0fPPP68NGzYoKioqyeVTu6+urq7q1auXVq1apSFDhigiIkI7d+5U27Ztdfv2bYd1paROnTpauXKlfvjhB7300ktyd3dPcAzS+7y4ceOG2rRpIy8vL33wwQcZuq179e/fXytXrtSMGTPUuHFjxcbG2o9XcqpWrSpXV1dt2LBBkuy9m5588kn5+PiodOnS9lv44v8bP8j5Dz/8IH9/f9WvX18XL160/1SoUEE+Pj5avXq1pLu9cy5fvqwOHTo4tHNzc1PlypXt7e61evVqNWzYUHXr1tWCBQuSHdBbkurVq6e8efNq9uzZ9mn79u3T//73P73wwgupOIJpV6JECXsvFOluj4PHH39cR48etU+z2Wz2XmWxsbH6+++/5ePjo8cffzzVT3hMT/f2+rl165YuXryop556SpJSVc+CBQsUFxentm3bOryXQUFBKlKkiP293LFjh86fP6+XXnrJ4TbcLl26yN/fP133IyYmRn///bdCQ0MVEBDgsB8BAQHav3+/IiIiUlxnWs+5+M+7xG4RS85zzz3n0HPm0qVL+v3339W2bVtdvXrVfkz//vtvNWzYUBEREfrrr78kZcz5tHTpUrm5ualfv34O01977TUZYxweuCHdvdbie89Kd3v0+fn5OZz3yencubPDMWvdurXy5MmjpUuXOrTZunWr/vzzT/u02bNnK3/+/KpVq1aS607re2L1vielZ8+eDj0879WnTx+H16+88oq99vQSv66BAwc6TH/ttdckSb/88ovD9IIFC6phw4YPvd0ffvhBNWrUULZs2Rw+T+rVq6fY2FitW7fOoX27du2ULVs2++v4z9+HOf4Pcz0CyFy4fQ/AA/Hy8kp0zJ748RLiv3jcuXNH9erVsz+1LV69evVUsmRJjR07Vh9++KFu376tS5cuOawrV65ccnNzS/W2Uiu5bd3r0KFDeuaZZ1SqVCl99dVXDvPit5lUXWmt6X516tSRJDVu3FgtW7ZUqVKl5OPj43BLRGpduHBBN27cSLSbfvHixRUXF6eTJ0/ab1u6X1r2dcSIEbp48aL+7//+zx7sNGjQQN27d9eUKVPstzBduHDB4dHePj4+Drc35c6d2z4OWevWrTV69GjVr19fERER9tv/UntepLQt6e6XxPbt2+vAgQNatmxZgqccpvc5eK9ixYqpWLFiku5+oWvQoIGaN2+urVu3JvvI7YCAAJUsWdIheCpXrpy9lqpVqzrM8/DwsN9eGxERoStXrigwMDDRdZ8/f97eTpJDIHuv+2/fvXXrlpo2baoKFSpo3rx5DmOQJcXV1VUdO3bU5MmTdePGDWXNmlWzZ8+Wp6en2rRpk+LyqXH/cUzs9qBs2bI5jKcVFxenCRMmaNKkSYqMjHQ4h1Jzq1N6u3TpkoYPH665c+fa3594V65cSXH5iIgIGWOSfCpl/C00x48fl6QE7dzd3R1C7Qd18+ZNjRkzRtOmTdNff/3lMP7PvfsxYsQItWzZUkWLFlWpUqXUqFEjderUSaVLl3ZY34Occ/Hn7b3jxqXGvU+zlO7eBmqM0bvvvqt333030WXOnz+vfPnyZcj5dPz4ceXNmzdBkFO8eHH7/Hul5rxPzv3nhIuLi0JDQx3GA2vXrp369++v2bNn67333tOVK1e0ZMkSDRgwINnPs7S+J1bve1LuPyfudf/xKly4sFxdXZMdTy6tjh8/LldXV4db2yUpKChIAQEBCY5DcvWmRUREhP73v/8leXvj/Z9R9x//+IDqYY7/w1yPADIXQikADyRPnjyJ/sXpzJkzkmT/Ur9u3Trt27dP48aNc2hXpEgRFS9e3P6ledOmTfYgJl5kZKRCQkKUJ08e+3qT21ZqJbeteCdPnlSDBg3k7++vpUuXJvjFN0+ePA413F9XWmtKTuHChVWuXDnNnj37gUKph3Xvvt4/nsyZM2fsQYd0d0ytr776SqNGjdIff/yh3Llzq2jRonr++ecdfnF+8sknHX5ZHjp0qH1Q18S0bt1ab7/9thYvXqxevXrZ60rNeZGabfXs2VNLlizR7NmzEw1g0vscTE7r1q3Vq1cv/fHHHymO91G9enVNmTJFly9f1saNGx3Gnqlataq++eYbxcTEaMOGDapQoYK9Z1dcXJwCAwMdeifdK/6LRvyAtTNnzkx0LLD7A4D4geIXL16s5cuXq1mzZqna586dO2vs2LFatGiROnTooO+++07NmjVLsWdOSj3Vbty44dAuXlI9G+4NSEaPHq13331X3bp108iRI5U9e3a5urqqf//+DgP5WqVt27batGmTBg8erLJly8rHx0dxcXFq1KhRquqJi4uTi4uLli1bluj+P+yYV6n1yiuvaNq0aerfv7+qVKkif39/ubi4qH379g77UbNmTf35559avHixfv31V3311Vf65JNPNGXKFPXo0cPe7kHOudDQUGXJkkV79+5NU+33h8/x9Q4aNCjJ3ifxn3mZ4XxKzXn/sLJly6ZmzZrZQ6n58+crOjo6xV6PD/qepFZG7Xta/iBxfyiXVEh3b2D5oOtOysP+wSxeXFyc6tevr9dffz3R+UWLFnV4nZrjH//QiPsldTwe5noEkLkQSgF4IGXLltXq1asVFRXl0Fti69at9vmSdO7cOUmJ/1IRExOjO3fuSLr7hLb7n6oU/yW4bNmyWr9+veLi4hwGmt66dauyZs2a4JeflCS3LenuIOMNGjRQdHS0Vq1aZQ9l7lWqVCllyZJFO3bsUNu2be3Tb9++rfDwcIdp6eHmzZspPk1OSvwX01y5cilr1qw6fPhwgnmHDh2Sq6trsoMXx7+XO3bscAigTp8+rVOnTiU6WP29PZ1iY2O1Zs0aVa5c2f7Fd/bs2Q5BQko9MOLb3tuTIrXnRUrbGjx4sKZNm6bx48erQ4cOSR6D9DwHk5PYvialevXqmjx5sn777Tft3r3b/oQm6W4odfPmTf3yyy86evSonnvuOfu8woUL67ffflO1atWS/ZISf7tLYGCg6tWrl2I98U/Oatmypdq0aaNly5Yl+jSl+5UqVcoevD722GM6ceKEQ8/KpCR3bkt3B/XNmjVrgqcspsb8+fNVp04dff311w7TL1++/EDrexj//POPVq1apeHDh+u9996zT0/s1rakvpwWLlxYxhgVLFgw2fM1/qmBERERDgFtTEyMIiMjVaZMmQfdDUl3j2tYWJg+/vhj+7Rbt24l+sTJ7Nmzq2vXruratauuXbummjVratiwYQ6h1IOcc1mzZtXTTz+t33//XSdPnnzgwdvjP0vc3d1TvD5Sez6lNlyQ7r5Xv/32m65everwh5NDhw7Z56en+883Y4yOHDmSoPda586d1bJlS23fvl2zZ89WuXLlkuyJGy+t70lG7Htajn1qREREOPTmOXLkiOLi4ux/AIvvLXT/uX9/76bkagsODlZcXJwiIiLsvcSku797Xb58Od3PgXiFCxfWtWvXUvXvQmply5Yt0dv5EjseiUnL9Qggc2FMKQAPpHXr1oqNjdXUqVPt06KjozVt2jRVrlzZ/gtl/JefuXPnOiy/a9cuHT58WOXKlZN095eRevXqOfzE925o3bq1zp07pwULFtiXv3jxon744Qc1b948xfFD7pfctq5fv64mTZror7/+0tKlS5O81cXf31/16tXTrFmzHG43mDlzpq5du/ZAtx3duXMn0a7s27Zt0969exM8/S4x3t7eCX7BdXNzU4MGDbR48WKH2wbOnTun7777TtWrV09wG9a9SpYsqWLFimnq1KkO4eLkyZPl4uKi1q1bJ1vTRx99pDNnztjHuJCkatWqORz/+F8mL168mOhfSuNvn7z3GKT2vEhqW5I0duxYffTRR3rrrbf06quvJrkP6X0OSglvb5DufvH/9ttv5eXlpRIlSqS4jvgxosaNG6eYmBiHnlLxvQz/7//+z6GtdLfXTWxsrEaOHJlgnXfu3LGfQw0bNpSfn59Gjx6tmJiYBG0Te/S7h4eHFixYoCeffFLNmzfXtm3bUtwPSerUqZN+/fVXjR8/Xjly5FDjxo1TXCb+3P7555914sQJh3knTpzQzz//rAYNGiT5V/qU1n3/ufjDDz84ZUyS+Prvr+f+J1xJdz8DpIRfdJ999lm5ublp+PDhCdZjjNHff/8t6e41litXLk2ZMsVhbLPp06cnGhylVWLH9bPPPkvwh4v4euL5+PgoNDQ00XD+Qc65oUOHyhijTp066dq1awnm79y5UzNmzEh2HYGBgapdu7a++OKLRHtS3nt9pPZ8Sur9S0yTJk0UGxurzz//3GH6J598IhcXl1RdQ2nx7bffOvx7N3/+fJ05cybBdho3bqycOXPqww8/1Nq1a1M9Nlxa3pOM2Pe0HPvUmDhxosPr+KA9vjY/Pz/lzJkzwfhLkyZNSnVtTZo0kZTwsyC+d3rTpk0frPgUtG3bVps3b9aKFSsSzLt8+bL9D45pUbhwYR06dMjhutmzZ4+9R31K0nI9Ashc6CkF4IFUrlxZbdq00ZAhQ3T+/HmFhoZqxowZOnbsmMNfgitUqKD69etrxowZioqKUoMGDXTmzBl99tln8vLyUv/+/VPcVuvWrfXUU0+pa9euOnDggHLmzKlJkyYpNjZWw4cPd2i7bt06+y94Fy5c0PXr1/X+++9Luns7SM2aNZPdVseOHbVt2zZ169ZNBw8e1MGDB+3zfHx81KpVK/vrUaNGqWrVqqpVq5ZefPFFnTp1Sh9//LEaNGigRo0aOaz3888/1+XLl3X69GlJ0s8//6xTp05Juns7i7+/v65du6b8+fOrXbt2KlmypLy9vbV3715NmzZN/v7+SY6RcK8KFSrot99+07hx45Q3b14VLFhQlStX1vvvv6+VK1eqevXq6t27t7JkyaIvvvhC0dHR9tAiOWPHjlWLFi3UoEEDtW/fXvv27dPnn3+uHj16OPx1dtasWfrxxx9Vs2ZN+fj46LffftO8efPUo0cPh546SZk1a5amTJliH5T96tWrWrFihVauXKnmzZs79NxIy3mRmIULF+r111+330o6a9Ysh/n169d3GNcqvc/BXr16KSoqSjVr1lS+fPl09uxZzZ49W4cOHdLHH3+cqtupChQooPz582vz5s0KCQlJcBth1apV9eOPP8rFxUXVqlWzT69Vq5Z69eqlMWPGKDw8XA0aNJC7u7siIiL0ww8/aMKECWrdurX8/Pw0efJkderUSeXLl1f79u2VK1cunThxQr/88ouqVauW4EuhdPe2iiVLlujpp59W48aNtXbtWpUqVSrZfXn++ef1+uuva+HChXr55ZdT/Zjw0aNH66mnnlL58uX14osvKiQkRMeOHdPUqVPl4uKi0aNHp2o992vWrJlGjBihrl27qmrVqtq7d69mz56dLuMqpZWfn59q1qyp//u//1NMTIzy5cunX3/9VZGRkQnaVqhQQZL09ttvq3379nJ3d1fz5s1VuHBhvf/++xoyZIiOHTumVq1aydfXV5GRkVq4cKFefPFFDRo0SO7u7nr//ffVq1cvPf3002rXrp0iIyM1bdq0dNn3Zs2aaebMmfL391eJEiW0efNm/fbbbwnGVSpRooRq166tChUqKHv27NqxY4fmz5+f5C3MaT3nqlatqokTJ6p3794qVqyYOnXqpCJFiujq1atas2aNfvrpJ/t1m5yJEyeqevXqeuKJJ9SzZ08VKlRI586d0+bNm3Xq1Cnt2bPHvt+pOZ8KFy6sgIAATZkyRb6+vvL29lblypUTHQuoefPmqlOnjt5++20dO3ZMZcqU0a+//qrFixerf//+DgN7p4fs2bOrevXq6tq1q86dO6fx48crNDRUPXv2dGjn7u6u9u3b6/PPP5ebm1uSPVDvl5b3JCP2PalrJz4QSqvIyEi1aNFCjRo10ubNmzVr1iw9//zzDr0Ne/TooQ8++EA9evRQxYoVtW7dOv3xxx+prq1MmTIKCwvT1KlTdfnyZdWqVUvbtm3TjBkz1KpVqwRDFaSXwYMH66efflKzZs3UpUsXVahQQdevX9fevXs1f/58HTt2LM09Srt166Zx48apYcOG6t69u86fP68pU6aoZMmSyT6M5V6pvR4BZDJWPuoPwKPl5s2bZtCgQSYoKMjYbDbz5JNPJvqo4Rs3bpgRI0aYEiVKGC8vL+Pv72+aNWtmdu/eneptXbp0yXTv3t3kyJHDZM2a1dSqVcts3749Qbv4RzEn9nPv45WTEv/I5MR+7n0kcbz169ebqlWrGk9PT5MrVy7Tp08fh0dmp2a98Y94jo6ONq+++qopXbq08fPzM+7u7iY4ONh079490UdUJ+bQoUOmZs2axsvLy0hyeIT1rl27TMOGDY2Pj4/JmjWrqVOnjtm0aVOq1muMMQsXLjRly5Y1NpvNPPbYY+add94xt2/fdmizdetWU7NmTZMtWzbj6elpypQpY6ZMmWLi4uJStY3t27ebNm3amAIFChibzWa8vb1N+fLlzbhx40xMTEyC9qk9LxKT3LmiRB5fnt7n4Jw5c0y9evVM7ty5TZYsWUy2bNlMvXr1zOLFi1NVf7wOHToYSeb5559PMG/cuHFGkilevHiiy06dOtVUqFDBeHl5GV9fX/PEE0+Y119/3Zw+fdqh3erVq03Dhg2Nv7+/8fT0NIULFzZdunQxO3bssLcJCwsz3t7eDstdvHjRlChRwgQFBZmIiAhjTNKP/TbGmCZNmhhJaTovjTHm4MGDpl27diYwMNBkyZLFBAYGmvbt25uDBw8maBscHGyaNm2aYPr9dd26dcu89tprJk+ePMbLy8tUq1bNbN68OdH6JZk+ffo4TIt/bPnYsWMdpsc/Sv7ea/r+dSb2qPhTp06ZZ555xgQEBBh/f3/Tpk0bc/r06UQ/20aOHGny5ctnXF1dE2zrxx9/NNWrVzfe3t7G29vbFCtWzPTp08ccPnzYYR2TJk0yBQsWNDabzVSsWNGsW7cu2fcuMRcuXEhQ3z///GO6du1qcubMaXx8fEzDhg3NoUOHTHBwsMPn1fvvv28qVapkAgICjJeXlylWrJgZNWqUw2dOas+55OzcudM8//zzJm/evMbd3d1ky5bN1K1b18yYMcPExsYaY5J+L+P9+eefpnPnziYoKMi4u7ubfPnymWbNmpn58+fb26TlfFq8eLEpUaKEyZIli8N5EBYWluDfoatXr5oBAwbY6y9SpIgZO3Zsgs/cxM5RY0yC456Y1atXG0lmzpw5ZsiQISYwMNB4eXmZpk2bmuPHjye6zLZt24wk06BBg2TXnZjUvCcZte9JXTv3t42/jpP7N+DAgQOmdevWxtfX12TLls307dvX3Lx506HtjRs3TPfu3Y2/v7/x9fU1bdu2NefPn0/TdR0TE2OGDx9uChYsaNzd3U3+/PnNkCFDzK1btxLsb2KffalRsmTJBOfp1atXzZAhQ0xoaKjx8PAwOXPmNFWrVjUfffSR/TpN7tpJbB9nzZplChUqZDw8PEzZsmXNihUrEpz36XE9AshcXIxJx9ENAQAAHtAzzzyjvXv36siRI84uBcBD2LNnj8qWLatvv/1WnTp1cnY5AIBMjDGlAACA0505c0a//PILX2CBR8CXX34pHx8fPfvss84uBQCQyTGmFAAAcJrIyEht3LhRX331ldzd3dWrVy9nlwTgAf388886cOCApk6dqr59+z7weEwAgP8OQikAAOA0a9euVdeuXVWgQAHNmDFDQUFBzi4JwAN65ZVXdO7cOTVp0iRVD5wAAIAxpQAAAAAAAGA5xpQCAAAAAACA5QilAAAAAAAAYLl/9ZhScXFxOn36tHx9feXi4uLscgAAAAAAAP7zjDG6evWq8ubNK1fXpPtD/atDqdOnTyt//vzOLgMAAAAAAAD3OXnypB577LEk5/+rQylfX19Jd3fSz8/PydUAAAAAAAAgKipK+fPnt+c2SflXh1Lxt+z5+fkRSgEAAAAAAGQiKQ21xEDnAAAAAAAAsByhFAAAAAAAACxHKAUAAAAAAADL/avHlEqt2NhYxcTEOLsMAHikeHh4JPt4VwAAAABIziMdShljdPbsWV2+fNnZpQDAI8fV1VUFCxaUh4eHs0sBAAAA8C/0SIdS8YFUYGCgsmbNmuKo7wCA1ImLi9Pp06d15swZFShQgM9XAAAAAGn2yIZSsbGx9kAqR44czi4HAB45uXLl0unTp3Xnzh25u7s7uxwAAAAA/zKP7GAg8WNIZc2a1cmVAMCjKf62vdjYWCdXAgAAAODf6JENpeJxSwkAZAw+XwEAAAA8jEc+lAIAAAAAAEDmQyiVyQwbNkxly5a1v+7SpYtatWrltHpSKyQkROPHj0/39d5/PB5VLi4uWrRokbPLSJPp06crICAg3dZXu3Zt9e/fP93WBwAAAADI3P7TodTZs2f1yiuvqFChQrLZbMqfP7+aN2+uVatWpet20vJle9CgQem+fenfGXpkds46phcuXJCHh4euX7+umJgYeXt768SJE8ku828JNzPCiRMn1LRpU2XNmlWBgYEaPHiw7ty549Bm9uzZKlOmjLJmzao8efKoW7du+vvvv1Nc98SJExUSEiJPT09VrlxZ27Ztc5g/depU1a5dW35+fnJxcdHly5dTXOeePXvUoUMH5c+fX15eXipevLgmTJiQoN2aNWtUvnx52Ww2hYaGavr06Q7z161bp+bNmytv3rxJnqvDhg1TsWLF5O3trWzZsqlevXraunVrijUCAAAAQHr4z4ZSx44dU4UKFfT7779r7Nix2rt3r5YvX646deqoT58+ltdjjNGdO3fk4+OTqZ8WGD+A/L/V7du3nV3CQ9u8ebPKlCkjb29v7dq1S9mzZ1eBAgWcXVamFBsbq6ZNm+r27dvatGmTZsyYoenTp+u9996zt9m4caM6d+6s7t27a//+/frhhx+0bds29ezZM9l1f//99xo4cKCGDh2qXbt2qUyZMmrYsKHOnz9vb3Pjxg01atRIb731Vqpr3rlzpwIDAzVr1izt379fb7/9toYMGaLPP//c3iYyMlJNmzZVnTp1FB4erv79+6tHjx5asWKFvc3169dVpkwZTZw4McltFS1aVJ9//rn27t2rDRs2KCQkRA0aNNCFCxdSXS8AAAAAPDDzL3blyhUjyVy5ciXBvJs3b5oDBw6YmzdvJrps48aNTb58+cy1a9cSzPvnn3/s/3/8+HHTokUL4+3tbXx9fU2bNm3M2bNn7fOHDh1qypQpY7799lsTHBxs/Pz8TLt27UxUVJQxxpiwsDAjyeEnMjLSrF692kgyS5cuNeXLlzfu7u5m9erV9vXFCwsLMy1btjTDhg0zOXPmNL6+vqZXr14mOjra3iY4ONh88sknDvtQpkwZM3ToUPv8e7cfHBxsbzdp0iRTqFAh4+7ubooWLWq+/fZbh/VIMpMmTTLNmzc3WbNmta/zfsHBwWbUqFGma9euxsfHx+TPn9988cUXDm1ef/11U6RIEePl5WUKFixo3nnnHXP79m2HNmPGjDGBgYHGx8fHdOvWzbzxxhsOxyMx+/btM02bNjW+vr7Gx8fHVK9e3Rw5csTh+L3//vsmT548JiQkxBhjzIkTJ0ybNm2Mv7+/yZYtm2nRooWJjIy0r3Pbtm2mXr16JkeOHMbPz8/UrFnT7Ny502F/kzqmixYtMuXKlTM2m80ULFjQDBs2zMTExNjn//HHH6ZGjRrGZrOZ4sWLm19//dVIMgsXLkx2P+O98cYb5tVXXzXGGPPRRx+Zdu3aJdt+6NChCc7B1atXG2NSfk/Cw8NN7dq1jY+Pj/H19TXly5c327dvN8YYM23aNOPv729ve/78eVOhQgXTqlUrc+vWLXPp0iXz/PPPm5w5cxpPT08TGhpqvvnmmyTrrFWrlunTp4/p06eP8fPzMzly5DDvvPOOiYuLM8YYM3z4cFOyZMkEy5UpU8a88847ia5z6dKlxtXV1eGanTx5svHz87NfQ2PHjjWFChVyWO7TTz81+fLlS+aoGlOpUiXTp08f++vY2FiTN29eM2bMmARt46/3ez9b0qJ3796mTp069tevv/56gmPRrl0707Bhw0SXT+35Ff+Z+ttvv6WqrpQ+ZwEAAAD8NyWX19zrP9lT6tKlS1q+fLn69Okjb2/vBPPjx8mJi4tTy5YtdenSJa1du1YrV67U0aNH1a5dO4f2f/75pxYtWqQlS5ZoyZIlWrt2rT744ANJ0oQJE1SlShX17NlTZ86c0ZkzZ5Q/f377sm+++aY++OADHTx4UKVLl0603lWrVungwYNas2aN5syZowULFmj48OGp3t/t27dLkqZNm6YzZ87YXy9cuFCvvvqqXnvtNe3bt0+9evVS165dtXr1aoflhw0bpmeeeUZ79+5Vt27dktzOxx9/rIoVK2r37t3q3bu3Xn75ZR0+fNg+39fXV9OnT9eBAwc0YcIEffnll/rkk0/s8+fNm6dhw4Zp9OjR2rFjh/LkyaNJkyYlu29//fWXatasKZvNpt9//107d+5Ut27dHG7PWrVqlQ4fPqyVK1dqyZIliomJUcOGDeXr66v169dr48aN8vHxUaNGjew9qa5evaqwsDBt2LBBW7ZsUZEiRdSkSRNdvXo12WO6fv16de7cWa+++qoOHDigL774QtOnT9eoUaMk3T2nnn32WXl4eGjr1q2aMmWK3njjjWT3Ubp7C1pAQIACAgI0btw4ffHFFwoICNBbb72lRYsWKSAgQL1790502UGDBqlt27Zq1KiR/RysWrVqqt6Tjh076rHHHtP27du1c+dOvfnmm3J3d0+wjZMnT6pGjRoqVaqU5s+fL5vNpnfffVcHDhzQsmXLdPDgQU2ePFk5c+ZMdj9nzJihLFmyaNu2bZowYYLGjRunr776SpLUrVs3HTx40H6sJWn37t363//+p65duya6vs2bN+uJJ55Q7ty57dMaNmyoqKgo7d+/X5JUpUoVnTx5UkuXLpUxRufOndP8+fPVpEmTJOu8ffu2du7cqXr16tmnubq6ql69etq8eXOy+/ggrly5ouzZszvs173blu7u18Ns+/bt25o6dar8/f1VpkwZ+/TatWurS5cuD7xeAAAAAEiSNRlZxnjQnlJbt241ksyCBQuSXf+vv/5q3NzczIkTJ+zT9u/fbySZbdu2GWPu9kLJmjWrvWeUMcYMHjzYVK5c2f66Vq1a9p4t8eJ7TixatMhhemI9pbJnz26uX79unzZ58mTj4+NjYmNjjTEp95QyJvGeElWrVjU9e/Z0mNamTRvTpEkTh+X69+9vUhIcHGxeeOEF++u4uDgTGBhoJk+enOQyY8eONRUqVLC/rlKliundu7dDm8qVKyfbU2rIkCGmYMGCCXpcxQsLCzO5c+d26Fk2c+ZM8/jjj9t74BhjTHR0tPHy8jIrVqxIdD2xsbHG19fX/Pzzz/ZpiR3TunXrmtGjRztMmzlzpsmTJ48xxpgVK1aYLFmymL/++ss+f9myZSn2ZImJiTGRkZFmz549xt3d3ezZs8ccOXLE+Pj4mLVr15rIyEhz4cKFJJeP7zGWkvvfE19fXzN9+vRE28b3lDp06JDJnz+/6devn8Mxbd68uenatWuK24xXq1YtU7x4cYd1vPHGG6Z48eL2140bNzYvv/yy/fUrr7xiateuneQ6e/bsaRo0aOAw7fr16/ZeivHmzZtnfHx8TJYsWYwk07x58yTPKWOM+euvv4wks2nTJofpgwcPNpUqVUrQ/mF6Sm3cuNFkyZLF4dwsUqRIgvPsl19+MZLMjRs3EqwjufPr559/Nt7e3sbFxcXkzZvX/tkWr1OnTubNN99MdFl6SgEAAABIDD2lkmGMSVW7gwcPKn/+/A49m0qUKKGAgAAdPHjQPi0kJES+vr7213ny5HEYVyY5FStWTLFN/ADM8apUqaJr167p5MmTqdpGUg4ePKhq1ao5TKtWrZrDvqW2RkkOPb1cXFwUFBTkcBy+//57VatWTUFBQfLx8dE777zjMED3wYMHVblyZYd1VqlSJdlthoeHq0aNGon23on3xBNPyMPDw/56z549OnLkiHx9feXj4yMfHx9lz55dt27d0p9//ilJOnfunHr27KkiRYrI399ffn5+unbtWooDiu/Zs0cjRoywr9fHx8feS+7GjRv2cypv3ryp3kdJypIli0JCQnTo0CE9+eSTKl26tM6ePavcuXOrZs2aCgkJSbEXUmJSek8GDhyoHj16qF69evrggw/sxyfezZs3VaNGDT377LOaMGGCXFxc7PNefvllzZ07V2XLltXrr7+uTZs2pVjPU0895bCOKlWqKCIiQrGxsZKknj17as6cObp165Zu376t7777Ltnee6lx4MABvfrqq3rvvfe0c+dOLV++XMeOHdNLL70k6W7vt3vfz9mzZz/U9u7VuHFj+3pLliyZYP6+ffvUsmVLDR06VA0aNEi37d4rflyqTZs2qVGjRmrbtq3Ddfvtt99qzJgxGbJtAAAAAP9tWZxdgDMUKVJELi4uOnToULqs7/5AxMXFRXFxcalaNrHbB9PK1dU1QdCWngOSp7bG5I7D5s2b1bFjRw0fPlwNGzaUv7+/5s6dq48//vihavPy8kqxzf31X7t2TRUqVEg0XMiVK5ckKSwsTH///bcmTJig4OBg2Ww2ValSJcWB0q9du6bhw4fr2WefTTDP09MzxVqTUrJkSR0/flwxMTGKi4uTj4+P7ty5Yx8cPzg42H47Wmql5j0ZNmyYnn/+ef3yyy9atmyZhg4dqrlz5+qZZ56RJNlsNtWrV09LlizR4MGDlS9fPvuyjRs31vHjx7V06VKtXLlSdevWVZ8+ffTRRx898HFo3ry5bDabFi5cKA8PD8XExKh169ZJtg8KCkrwRLxz587Z50nSmDFjVK1aNQ0ePFjS3XDV29tbNWrU0Pvvv6+KFSsqPDzcvnzu3Llls9nk5uZ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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task_phab_df = phab_df[phab_df['comment_type'] == \"task_description\"]\n",
    "unaff_tasks_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
    "# Rank speaker's task values within each group\n",
    "unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "\n",
    "# Filter dates 08-01-2013 to 09-30-2013\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1380499200) & (unaff_tasks_phab_df['date_created'] > 1375315200)]\n",
    "# Bin the speakers based on the number of tasks they created\n",
    "bins = [0, 6, 26, 51, float('inf')]\n",
    "labels = ['0-5', '6-25', '26-50', '51+']\n",
    "min_speakers_task = unaff_tasks_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
    "min_speakers_task = min_speakers_task.rename(columns={'speakers_task': 'min_speakers_task'})\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
    "unaff_tasks_phab_df['task_bins'] = pd.cut(unaff_tasks_phab_df['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Calculate the weekly breakdown of binned speakers_task values\n",
    "unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
    "\n",
    "speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
    "\n",
    "# Reshape the DataFrame for use with Seaborn\n",
    "weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
    "speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(data=weekly_breakdown, x='week', y='count', hue='task_bins', palette='colorblind')\n",
    "#sns.barplot(data=speaker_breakdown, x='week', y='speakers', hue='task_bins', palette='colorblind')\n",
    "plt.title(\"08-01-2013 to 09-30-2013 Weekly Unaffiliated Task Creation by Contirbutor Tenure\")\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Tasks')\n",
    "plt.legend(title=\"Contributor had created # tasks by 08-01-2013:\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('031625_weekly_tasks_by_history.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11370/2708736932.py:27: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
      "/tmp/ipykernel_11370/2708736932.py:28: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
      "/tmp/ipykernel_11370/2708736932.py:35: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
      "/tmp/ipykernel_11370/2708736932.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comments for each date group:\n",
      "date_group\n",
      "Before announcement                      182\n",
      "After announcement, before deployment    101\n",
      "After deployment                         694\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group:\n",
      "date_group\n",
      "Before announcement                      56\n",
      "After announcement, before deployment    41\n",
      "After deployment                         95\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         181\n",
      "                                                      True            1\n",
      "After announcement, before deployment  False          False         101\n",
      "                                                      True            0\n",
      "After deployment                       False          False         679\n",
      "                                                      True           15\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         55\n",
      "                                                      True           1\n",
      "After announcement, before deployment  False          False         41\n",
      "                                                      True           0\n",
      "After deployment                       False          False         92\n",
      "                                                      True          10\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         961\n",
      "               True           16\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         114\n",
      "               True           10\n",
      "Name: speaker, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nplot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'new_commenter\\', scatter=False, legend=False, palette=palette)\\nplot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\\nplot1.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\\nplot1.add_legend(title=\"Comment publication timestamp:\")\\nfig1 = plot1.fig\\n# Plot for arousal_wc\\nplot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\\nplot2.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot2.add_legend(title=\"Comment publication timestamp:\")\\n#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\\n\\nplot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\\nplot3.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot3.add_legend(title=\"Comment publication timestamp:\")\\n'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [\n",
    "    pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-28 00:00:01+00:00'),\n",
    "    pd.Timestamp('2100-08-28 00:00:01+00:00')\n",
    "]\n",
    "labels = ['Before announcement', 'After announcement, before deployment', 'After deployment']\n",
    "\n",
    "#creating variables of interest\n",
    "affective_comment_phab_df = comment_phab_df\n",
    "affective_comment_phab_df['date_group'] = pd.cut(affective_comment_phab_df['timestamp'], bins=bins, labels=labels, right=False)\n",
    "affective_comment_phab_df['speakers_comment'] = affective_comment_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "#all comments prior to june 1 2013\n",
    "subset_comment_phab_df = affective_comment_phab_df[affective_comment_phab_df['date_created'] <= 1370044800]\n",
    "#getting counts \n",
    "comment_counts = subset_comment_phab_df.groupby('speaker')['speakers_comment'].max().reset_index()\n",
    "comment_counts = comment_counts.rename(columns={'speakers_comment': 'pre_june_2013_comments'})\n",
    "#merge back \n",
    "affective_comment_phab_df = affective_comment_phab_df.merge(comment_counts, on='speaker', how='left')\n",
    "affective_comment_phab_df['pre_june_2013_comments'] = affective_comment_phab_df['pre_june_2013_comments'].fillna(0)\n",
    "\n",
    "affective_comment_phab_df['new_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] <= 10\n",
    "affective_comment_phab_df['est_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] > 50\n",
    "\n",
    "palette = ['#31449c', '#4a7c85', '#c5db68']\n",
    "\n",
    "comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
    "speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
    "\n",
    "print(\"Number of comments for each date group:\")\n",
    "print(comment_counts)\n",
    "print(\"\\nNumber of speakers for each date group:\")\n",
    "print(speaker_counts)\n",
    "\n",
    "comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each date group and engaged commenter subgroup:\")\n",
    "print(comment_counts_engaged)\n",
    "print(\"\\nNumber of speakers for each date group and engaged commenter subgroup:\")\n",
    "print(speaker_counts_engaged)\n",
    "\n",
    "comment_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(comment_counts_wmf)\n",
    "print(\"\\nNumber of speakers for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(speaker_counts_wmf)\n",
    "\n",
    "#comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
    "#fig, axes = plt.subplots(2, 1, figsize=(10, 12), sharex=True)\n",
    "affective_comment_phab_df['polarized_wc'] = affective_comment_phab_df['dominant_wc'] + affective_comment_phab_df['valence_wc'] + affective_comment_phab_df['arousal_wc'] \n",
    "plot1 = sns.lmplot(data=affective_comment_phab_df, x=\"date_created\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", row='est_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Established Author: {row_name}\", col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "'''\n",
    "plot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row='new_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "# Plot for arousal_wc\n",
    "plot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\n",
    "plot2.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")\n",
    "#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\n",
    "\n",
    "plot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\n",
    "plot3.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot3.add_legend(title=\"Comment publication timestamp:\")\n",
    "'''\n",
    "# Show plots\n",
    "#fig1.savefig('031725_engaged_commenter_D_scoring_fig.png')\n",
    "#plot2.fig.savefig('031725_engaged_commenter_A_scoring_fig.png')\n",
    "#plot3.fig.savefig('031725_engaged_commenter_V_scoring_fig.png')\n",
    "#plt.savefig('031625_engaged_commenter_VAD_scoring_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
    "plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/1559616732.py:4: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'resolved_dependency_relations_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(WMF|Foundation)\\b'\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(bots)\\b'\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filtered_dependencies \u001b[38;5;241m=\u001b[39m dependency_relations_df[dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[0;32m----> 5\u001b[0m resolved_filtered_dependencies \u001b[38;5;241m=\u001b[39m \u001b[43mresolved_dependency_relations_df\u001b[49m[resolved_dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      8\u001b[0m gs \u001b[38;5;241m=\u001b[39m GridSpec(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, height_ratios\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'resolved_dependency_relations_df' is not defined"
     ]
    }
   ],
   "source": [
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots)\\b'\n",
    "filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
    "\n",
    "# Main plot: Token depth by timestamp\n",
    "'''\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
    "ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Dependency Type')\n",
    "ax0.legend().set_visible(False)\n",
    "'''\n",
    "# Calculate the median depth over time\n",
    "filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax0.set_title('Median Depth of \"VE\" in Phabricator Sentence Dependency Trees')\n",
    "ax0.set_ylabel('Median Depth')\n",
    "ax0.set_xlabel('')\n",
    "\n",
    "# Calculate the median depth over time\n",
    "resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
    "resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
    "resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax1 = plt.subplot(gs[1])\n",
    "sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax1.set_title('Median Depth of \"VE\" in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
    "ax1.set_ylabel('Median Depth')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "\n",
    "#plt.savefig('031625_VE_depth_fig.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}