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_fanta.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Callback api\n",
"\n",
"Вконтакт умеет самостоятельно высылать все события происходящие в группе посредством `callback api`\n",
"\n",
"- https://vk.com/dev/callback_api\n",
"- https://vk.com/dev/groups_events\n",
"\n",
"В данном документе собран пример обработки этих данных\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Описание выгрузки\n",
"\n",
"- events_statistics.csv - посуточная статистика, по всем событиям\n",
"- events_moderator.csv - посуточная статистика по действия модератора/группы\n",
"- events_users.cs - посуточная статистика по действия мпользователей\n",
"- moderation_delete_stat.csv - данные по удалению комментариев\n",
"- moderation_delete_meantime.csv - среднее время до удаления\n",
"- moderation_response_stat.csv - данные по ответам на комментарии модератором / группой\n",
"- moderation_response_meantime.csv - среднее время до отевта модератором / группой"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Загрузка базы событий"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# очистка статистки\n",
"!rm fanta/*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"moderator_id = 13503331\n",
"group_id = -24539231\n",
"\n",
"events_txt = {'message_new': 'входящее сообщение', 'message_reply': 'новое исходящее сообщение', 'message_edit': 'редактирование сообщения', 'message_allow': 'подписка на сообщения от сообщества', 'message_deny': 'новый запрет сообщений от сообщества', 'photo_new': 'добавление фотографии', 'photo_comment_new': 'добавление комментария к фотографии', 'photo_comment_edit': 'редактирование комментария к фотографии', 'photo_comment_restore': 'восстановление комментария к фотографии', 'photo_comment_delete': 'удаление комментария к фотографии', 'audio_new': 'добавление аудио', 'video_new': 'добавление видео', 'video_comment_new': 'комментарий к видео', 'video_comment_edit': 'редактирование комментария к видео', 'video_comment_restore': 'восстановление комментария к видео', 'video_comment_delete': 'удаление комментария к видео', 'wall_post_new': 'запись на стене', 'wall_repost': 'репост записи из сообщества', 'wall_reply_new': 'добавление комментария на стене', 'wall_reply_edit': 'редактирование комментария на стене', 'wall_reply_restore': 'восстановление комментария на стене', 'wall_reply_delete': 'удаление комментария на стене', 'board_post_new': 'создание комментария в обсуждении', 'board_post_edit': 'редактирование комментария', 'board_post_restore': 'восстановление комментария', 'board_post_delete': 'удаление комментария в обсуждении', 'market_comment_new': 'новый комментарий к товару', 'market_comment_edit': 'редактирование комментария к товару', 'market_comment_restore': 'восстановление комментария к товару', 'market_comment_delete': 'удаление комментария к товару', 'group_leave': 'удаление участника из сообщества', 'group_join': 'добавление участника или заявки на вступление в сообщество', 'user_block': 'добавление пользователя в чёрный список', 'user_unblock': 'удаление пользователя из чёрного списка', 'poll_vote_new': 'добавление голоса в публичном опросе', 'group_officers_edit': 'редактирование списка руководителей'}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>group_id</th>\n",
" <th>type</th>\n",
" <th>date</th>\n",
" <th>object</th>\n",
" <th>from_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>181741210</td>\n",
" <td>wall_reply_new</td>\n",
" <td>2019-09-28 16:26:46.508471</td>\n",
" <td>{'id': 39, 'from_id': 542380738, 'date': 15696...</td>\n",
" <td>542380738</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>181741210</td>\n",
" <td>wall_reply_new</td>\n",
" <td>2019-09-28 16:31:52.345462</td>\n",
" <td>{'id': 34, 'from_id': 542380738, 'date': 15696...</td>\n",
" <td>542380738</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>24539231</td>\n",
" <td>message_new</td>\n",
" <td>2019-09-28 18:00:01.024108</td>\n",
" <td>{'id': 494346, 'date': 1569693600, 'out': 0, '...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>24539231</td>\n",
" <td>message_new</td>\n",
" <td>2019-09-28 18:01:39.427268</td>\n",
" <td>{'id': 494347, 'date': 1569693699, 'out': 0, '...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>24539231</td>\n",
" <td>wall_reply_new</td>\n",
" <td>2019-09-28 18:02:10.980445</td>\n",
" <td>{'id': 263514, 'from_id': 64237711, 'date': 15...</td>\n",
" <td>64237711</td>\n",
" </tr>\n",
" <tr>\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",
" <td>75590</td>\n",
" <td>75591</td>\n",
" <td>24539231</td>\n",
" <td>group_join</td>\n",
" <td>2019-11-07 14:41:49.294970</td>\n",
" <td>{'user_id': 552953951, 'join_type': 'join'}</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75591</td>\n",
" <td>75592</td>\n",
" <td>24539231</td>\n",
" <td>group_join</td>\n",
" <td>2019-11-07 14:42:17.045110</td>\n",
" <td>{'user_id': 226261179, 'join_type': 'join'}</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75592</td>\n",
" <td>75593</td>\n",
" <td>24539231</td>\n",
" <td>group_leave</td>\n",
" <td>2019-11-07 14:42:22.021598</td>\n",
" <td>{'user_id': 226261179, 'self': 1}</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75593</td>\n",
" <td>75594</td>\n",
" <td>24539231</td>\n",
" <td>message_new</td>\n",
" <td>2019-11-07 14:42:33.620959</td>\n",
" <td>{'id': 527408, 'date': 1573137753, 'out': 0, '...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75594</td>\n",
" <td>75595</td>\n",
" <td>24539231</td>\n",
" <td>group_join</td>\n",
" <td>2019-11-07 14:43:11.935461</td>\n",
" <td>{'user_id': 416842775, 'join_type': 'join'}</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>75595 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" id group_id type date \\\n",
"0 1 181741210 wall_reply_new 2019-09-28 16:26:46.508471 \n",
"1 2 181741210 wall_reply_new 2019-09-28 16:31:52.345462 \n",
"2 3 24539231 message_new 2019-09-28 18:00:01.024108 \n",
"3 4 24539231 message_new 2019-09-28 18:01:39.427268 \n",
"4 5 24539231 wall_reply_new 2019-09-28 18:02:10.980445 \n",
"... ... ... ... ... \n",
"75590 75591 24539231 group_join 2019-11-07 14:41:49.294970 \n",
"75591 75592 24539231 group_join 2019-11-07 14:42:17.045110 \n",
"75592 75593 24539231 group_leave 2019-11-07 14:42:22.021598 \n",
"75593 75594 24539231 message_new 2019-11-07 14:42:33.620959 \n",
"75594 75595 24539231 group_join 2019-11-07 14:43:11.935461 \n",
"\n",
" object from_id \n",
"0 {'id': 39, 'from_id': 542380738, 'date': 15696... 542380738 \n",
"1 {'id': 34, 'from_id': 542380738, 'date': 15696... 542380738 \n",
"2 {'id': 494346, 'date': 1569693600, 'out': 0, '... NaN \n",
"3 {'id': 494347, 'date': 1569693699, 'out': 0, '... NaN \n",
"4 {'id': 263514, 'from_id': 64237711, 'date': 15... 64237711 \n",
"... ... ... \n",
"75590 {'user_id': 552953951, 'join_type': 'join'} NaN \n",
"75591 {'user_id': 226261179, 'join_type': 'join'} NaN \n",
"75592 {'user_id': 226261179, 'self': 1} NaN \n",
"75593 {'id': 527408, 'date': 1573137753, 'out': 0, '... NaN \n",
"75594 {'user_id': 416842775, 'join_type': 'join'} NaN \n",
"\n",
"[75595 rows x 6 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sqlite3\n",
"from pandas.io.json import json_normalize\n",
"import json\n",
"\n",
"# забираем базу\n",
"db = sqlite3.connect('file:/home/v.seregin/lab/fanta/database.sqlite?mode=ro', uri=True)\n",
"df = pd.read_sql_query('select * from event', db)\n",
"df['date'] = pd.to_datetime(df['date'])\n",
"df['object'] = df['object'].apply(json.loads)\n",
"df['from_id'] = df['object'].apply(lambda x: x.get('from_id',np.nan)).astype(pd.Int64Dtype())\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Общая постуточная статистика по событиям"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Общая посуточная статистика пользователи + модераторы</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th>type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td rowspan=\"5\" valign=\"top\">2019-09-28</td>\n",
" <td>group_join</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <td>group_leave</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_allow</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_deny</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_new</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td rowspan=\"5\" valign=\"top\">2019-11-07</td>\n",
" <td>message_new</td>\n",
" <td>156</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_reply</td>\n",
" <td>282</td>\n",
" </tr>\n",
" <tr>\n",
" <td>photo_comment_new</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_delete</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_new</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>496 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Общая посуточная статистика пользователи + модераторы\n",
"date type \n",
"2019-09-28 group_join 28 \n",
" group_leave 21 \n",
" message_allow 4 \n",
" message_deny 1 \n",
" message_new 21 \n",
"... ... \n",
"2019-11-07 message_new 156 \n",
" message_reply 282 \n",
" photo_comment_new 1 \n",
" wall_reply_delete 4 \n",
" wall_reply_new 7 \n",
"\n",
"[496 rows x 1 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stat = df[['type','id']].groupby([df.date.dt.date, 'type']).agg('count')\n",
"df_stat.columns = [\"Общая посуточная статистика пользователи + модераторы\"]\n",
"df_stat.to_csv(\"fanta/events_statistics.csv\")\n",
"df_stat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Постуточная статистика по событиям от модератора"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Ко-во событий от имени модератора / группы</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th>type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>2019-09-29</td>\n",
" <td>wall_reply_new</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-09-30</td>\n",
" <td>wall_reply_new</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-01</td>\n",
" <td>wall_reply_new</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-02</td>\n",
" <td>wall_reply_new</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <td rowspan=\"2\" valign=\"top\">2019-10-03</td>\n",
" <td>message_reply</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_new</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <td rowspan=\"3\" valign=\"top\">2019-10-04</td>\n",
" <td>wall_reply_edit</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_new</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_restore</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-05</td>\n",
" <td>wall_reply_new</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ко-во событий от имени модератора / группы\n",
"date type \n",
"2019-09-29 wall_reply_new 8\n",
"2019-09-30 wall_reply_new 3\n",
"2019-10-01 wall_reply_new 8\n",
"2019-10-02 wall_reply_new 13\n",
"2019-10-03 message_reply 2\n",
" wall_reply_new 9\n",
"2019-10-04 wall_reply_edit 1\n",
" wall_reply_new 2\n",
" wall_reply_restore 1\n",
"2019-10-05 wall_reply_new 2"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stat = df[(df[\"from_id\"]==moderator_id) | (df[\"from_id\"]==group_id)][['type','id']].groupby([df.date.dt.date, 'type']).agg('count')\n",
"df_stat.columns = [\"Ко-во событий от имени модератора / группы\"]\n",
"df_stat.to_csv(\"fanta/events_moderator.csv\")\n",
"df_stat.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Постуточная статистика по событиям от прочих участников"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Ко-во событий</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th>type</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td rowspan=\"8\" valign=\"top\">2019-09-28</td>\n",
" <td>group_join</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <td>group_leave</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_allow</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_deny</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_new</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_reply</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_delete</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_new</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <td rowspan=\"12\" valign=\"top\">2019-09-29</td>\n",
" <td>group_join</td>\n",
" <td>708</td>\n",
" </tr>\n",
" <tr>\n",
" <td>group_leave</td>\n",
" <td>198</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_allow</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_deny</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_edit</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_new</td>\n",
" <td>139</td>\n",
" </tr>\n",
" <tr>\n",
" <td>message_reply</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <td>photo_comment_new</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>user_unblock</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_delete</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_edit</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wall_reply_new</td>\n",
" <td>27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ко-во событий\n",
"date type \n",
"2019-09-28 group_join 28\n",
" group_leave 21\n",
" message_allow 4\n",
" message_deny 1\n",
" message_new 21\n",
" message_reply 4\n",
" wall_reply_delete 4\n",
" wall_reply_new 7\n",
"2019-09-29 group_join 708\n",
" group_leave 198\n",
" message_allow 7\n",
" message_deny 1\n",
" message_edit 1\n",
" message_new 139\n",
" message_reply 16\n",
" photo_comment_new 1\n",
" user_unblock 1\n",
" wall_reply_delete 8\n",
" wall_reply_edit 1\n",
" wall_reply_new 27"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stat = df[(df[\"from_id\"]!=group_id) & (df[\"from_id\"]!=moderator_id)][['type','id']].groupby([df.date.dt.date, 'type']).agg('count')\n",
"df_stat.columns = [\"Ко-во событий\"]\n",
"df_stat.to_csv(\"fanta/events_users.csv\")\n",
"df_stat.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/v.seregin/lab/jupyter/venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/core.py:338: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" fig = self.plt.figure(figsize=self.figsize)\n"
]
}
],
"source": [
"for event in df_stat.index.get_level_values(1).unique().values:\n",
" df_event = df_stat.xs(event, level=1)\n",
" idx = pd.date_range(df_stat.index[0][0], df_stat.index[-1][0])\n",
" df_event = df_event.reindex(idx, fill_value=0)\n",
" df_event.columns = (f\"{events_txt.get(event, event)} ({event})\",)\n",
" df_event.plot(figsize=(20,10)).get_figure().savefig(f\"fanta/{event}.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Обработка событий"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" <td>24539231</td>\n",
" <td>group_join</td>\n",
" <td>2019-11-07 14:42:17.045110</td>\n",
" <td>{'user_id': 226261179, 'join_type': 'join'}</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75592</td>\n",
" <td>75593</td>\n",
" <td>24539231</td>\n",
" <td>group_leave</td>\n",
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"text/plain": [
" id group_id type date \\\n",
"0 1 181741210 wall_reply_new 2019-09-28 16:26:46.508471 \n",
"1 2 181741210 wall_reply_new 2019-09-28 16:31:52.345462 \n",
"2 3 24539231 message_new 2019-09-28 18:00:01.024108 \n",
"3 4 24539231 message_new 2019-09-28 18:01:39.427268 \n",
"4 5 24539231 wall_reply_new 2019-09-28 18:02:10.980445 \n",
"... ... ... ... ... \n",
"75590 75591 24539231 group_join 2019-11-07 14:41:49.294970 \n",
"75591 75592 24539231 group_join 2019-11-07 14:42:17.045110 \n",
"75592 75593 24539231 group_leave 2019-11-07 14:42:22.021598 \n",
"75593 75594 24539231 message_new 2019-11-07 14:42:33.620959 \n",
"75594 75595 24539231 group_join 2019-11-07 14:43:11.935461 \n",
"\n",
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"0 {'id': 39, 'from_id': 542380738, 'date': 15696... 542380738 39 \n",
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"3 {'id': 494347, 'date': 1569693699, 'out': 0, '... NaN NaN \n",
"4 {'id': 263514, 'from_id': 64237711, 'date': 15... 64237711 263514 \n",
"... ... ... ... \n",
"75590 {'user_id': 552953951, 'join_type': 'join'} NaN NaN \n",
"75591 {'user_id': 226261179, 'join_type': 'join'} NaN NaN \n",
"75592 {'user_id': 226261179, 'self': 1} NaN NaN \n",
"75593 {'id': 527408, 'date': 1573137753, 'out': 0, '... NaN NaN \n",
"75594 {'user_id': 416842775, 'join_type': 'join'} NaN NaN \n",
"\n",
" deleter_id \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"... ... \n",
"75590 NaN \n",
"75591 NaN \n",
"75592 NaN \n",
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"\n",
"[75595 rows x 8 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# id ответа\n",
"df['reply_id'] = df[df.type.isin(['wall_reply_new', 'wall_reply_delete'])]['object'].apply(lambda x: x['id']).astype(pd.Int64Dtype())\n",
"df['deleter_id'] = df[df.type == 'wall_reply_delete']['object'].apply(lambda x: x['deleter_id']).astype(pd.Int64Dtype())\n",
"df\n",
"# df['from_id'] = df['object'].apply(lambda x: int(x.get('from_id', 0)))\n",
"# df['user_id'] = df['object'].apply(lambda x: int(x.get('user_id', 0)))\n",
"# df['deleter_id'] = df['object'].apply(lambda x: int(x.get('deleter_id', 0)))\n",
"# df['post_id'] = df['object'].apply(lambda x: int(x.get('post_id', 0)))\n",
"# df['comment_id'] = df['object'].apply(lambda x: x.get('id',0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Расчет времени до удаления коммента"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th>reply_id</th>\n",
" <th>date_x</th>\n",
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" <th>response_time</th>\n",
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" <td>265830</td>\n",
" <td>2019-11-07 10:54:40.131366</td>\n",
" <td>13503331</td>\n",
" <td>2019-11-07 11:17:15.972805</td>\n",
" <td>0.376389</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>445 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" reply_id date_x deleter_id \\\n",
"0 263515 2019-09-28 19:35:40.612116 13503331 \n",
"1 263516 2019-09-28 19:35:52.326412 13503331 \n",
"2 263516 2019-09-28 19:35:52.326412 13503331 \n",
"3 263517 2019-09-28 19:36:00.270410 13503331 \n",
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"444 265830 2019-11-07 10:54:40.131366 13503331 \n",
"\n",
" date_y response_time \n",
"0 2019-09-28 22:24:44.686827 2.817778 \n",
"1 2019-09-28 22:24:43.482553 2.814167 \n",
"2 2019-09-28 22:24:44.604662 2.814444 \n",
"3 2019-09-28 22:24:44.548332 2.812222 \n",
"4 2019-09-29 07:15:33.252939 2.896944 \n",
".. ... ... \n",
"440 2019-11-06 10:22:50.098677 0.092500 \n",
"441 2019-11-06 10:22:50.207598 0.091389 \n",
"442 2019-11-07 09:00:27.109002 12.711944 \n",
"443 2019-11-07 04:40:40.887074 0.003889 \n",
"444 2019-11-07 11:17:15.972805 0.376389 \n",
"\n",
"[445 rows x 5 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_new = df[df.type=='wall_reply_new'][['reply_id','date']]\n",
"df_del = df[df.type=='wall_reply_delete'][['reply_id','deleter_id','date']]\n",
"df_date = pd.merge(df_new, df_del, on='reply_id')\n",
"df_date['response_time'] = (df_date.date_y - df_date.date_x).dt.seconds/60.0/60.0\n",
"df_date.to_csv(\"fanta/moderation_delete_stat.csv\", index=None)\n",
"df_date"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Среднее суточное время до удаления"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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" <td>2019-10-03</td>\n",
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" </tr>\n",
" <tr>\n",
" <td>2019-10-05</td>\n",
" <td>4</td>\n",
" <td>0.329861</td>\n",
" <td>1.104722</td>\n",
" <td>0.001667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-06</td>\n",
" <td>11</td>\n",
" <td>2.345530</td>\n",
" <td>4.278611</td>\n",
" <td>0.001389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-07</td>\n",
" <td>9</td>\n",
" <td>6.458796</td>\n",
" <td>23.520833</td>\n",
" <td>0.003056</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-08</td>\n",
" <td>16</td>\n",
" <td>2.372778</td>\n",
" <td>3.566111</td>\n",
" <td>0.001389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-09</td>\n",
" <td>7</td>\n",
" <td>0.251190</td>\n",
" <td>0.718333</td>\n",
" <td>0.000556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-10</td>\n",
" <td>9</td>\n",
" <td>1.505556</td>\n",
" <td>2.775000</td>\n",
" <td>0.202222</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-11</td>\n",
" <td>6</td>\n",
" <td>9.813565</td>\n",
" <td>13.176111</td>\n",
" <td>1.819722</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-12</td>\n",
" <td>7</td>\n",
" <td>1.625794</td>\n",
" <td>9.906944</td>\n",
" <td>0.000833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-13</td>\n",
" <td>6</td>\n",
" <td>0.611343</td>\n",
" <td>2.751389</td>\n",
" <td>0.003611</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-14</td>\n",
" <td>1</td>\n",
" <td>0.585000</td>\n",
" <td>0.585000</td>\n",
" <td>0.585000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-15</td>\n",
" <td>9</td>\n",
" <td>0.485000</td>\n",
" <td>1.286667</td>\n",
" <td>0.105833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-16</td>\n",
" <td>12</td>\n",
" <td>0.892269</td>\n",
" <td>2.657778</td>\n",
" <td>0.026389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-17</td>\n",
" <td>3</td>\n",
" <td>1.171667</td>\n",
" <td>2.129722</td>\n",
" <td>0.003889</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-18</td>\n",
" <td>7</td>\n",
" <td>2.234722</td>\n",
" <td>11.442500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-19</td>\n",
" <td>17</td>\n",
" <td>3.442925</td>\n",
" <td>6.890000</td>\n",
" <td>0.001111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-20</td>\n",
" <td>11</td>\n",
" <td>2.098788</td>\n",
" <td>9.535278</td>\n",
" <td>0.001667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-21</td>\n",
" <td>7</td>\n",
" <td>4.186587</td>\n",
" <td>23.978889</td>\n",
" <td>0.124444</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-22</td>\n",
" <td>1</td>\n",
" <td>22.062222</td>\n",
" <td>22.062222</td>\n",
" <td>22.062222</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-23</td>\n",
" <td>21</td>\n",
" <td>2.598201</td>\n",
" <td>21.864444</td>\n",
" <td>0.001389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-24</td>\n",
" <td>9</td>\n",
" <td>8.162191</td>\n",
" <td>22.707778</td>\n",
" <td>0.071667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-25</td>\n",
" <td>11</td>\n",
" <td>4.081313</td>\n",
" <td>19.943889</td>\n",
" <td>0.007222</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-26</td>\n",
" <td>70</td>\n",
" <td>2.495238</td>\n",
" <td>19.128056</td>\n",
" <td>0.000833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-27</td>\n",
" <td>17</td>\n",
" <td>0.758758</td>\n",
" <td>3.751389</td>\n",
" <td>0.006944</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-28</td>\n",
" <td>8</td>\n",
" <td>1.146389</td>\n",
" <td>3.415278</td>\n",
" <td>0.030278</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-29</td>\n",
" <td>11</td>\n",
" <td>1.283081</td>\n",
" <td>9.551389</td>\n",
" <td>0.000833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-30</td>\n",
" <td>26</td>\n",
" <td>1.488045</td>\n",
" <td>4.518889</td>\n",
" <td>0.000556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-31</td>\n",
" <td>14</td>\n",
" <td>1.688790</td>\n",
" <td>7.053889</td>\n",
" <td>0.010556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-01</td>\n",
" <td>19</td>\n",
" <td>1.264591</td>\n",
" <td>11.523611</td>\n",
" <td>0.019167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-02</td>\n",
" <td>10</td>\n",
" <td>5.820556</td>\n",
" <td>22.933056</td>\n",
" <td>0.086389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-03</td>\n",
" <td>13</td>\n",
" <td>1.615662</td>\n",
" <td>2.866389</td>\n",
" <td>0.278056</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-04</td>\n",
" <td>9</td>\n",
" <td>1.565494</td>\n",
" <td>4.267778</td>\n",
" <td>0.058056</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-05</td>\n",
" <td>8</td>\n",
" <td>2.084757</td>\n",
" <td>4.098611</td>\n",
" <td>0.011111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-06</td>\n",
" <td>5</td>\n",
" <td>2.598778</td>\n",
" <td>12.711944</td>\n",
" <td>0.004722</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-07</td>\n",
" <td>2</td>\n",
" <td>0.190139</td>\n",
" <td>0.376389</td>\n",
" <td>0.003889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" response_time \n",
" count mean max min\n",
"date_x \n",
"2019-09-28 4 2.814653 2.817778 2.812222\n",
"2019-09-29 8 0.624653 2.896944 0.001944\n",
"2019-09-30 5 3.786722 16.581944 0.003611\n",
"2019-10-01 3 0.021944 0.053611 0.003333\n",
"2019-10-02 8 1.056806 5.731667 0.001389\n",
"2019-10-03 10 0.806972 4.398611 0.001111\n",
"2019-10-04 11 0.610379 4.882500 0.000556\n",
"2019-10-05 4 0.329861 1.104722 0.001667\n",
"2019-10-06 11 2.345530 4.278611 0.001389\n",
"2019-10-07 9 6.458796 23.520833 0.003056\n",
"2019-10-08 16 2.372778 3.566111 0.001389\n",
"2019-10-09 7 0.251190 0.718333 0.000556\n",
"2019-10-10 9 1.505556 2.775000 0.202222\n",
"2019-10-11 6 9.813565 13.176111 1.819722\n",
"2019-10-12 7 1.625794 9.906944 0.000833\n",
"2019-10-13 6 0.611343 2.751389 0.003611\n",
"2019-10-14 1 0.585000 0.585000 0.585000\n",
"2019-10-15 9 0.485000 1.286667 0.105833\n",
"2019-10-16 12 0.892269 2.657778 0.026389\n",
"2019-10-17 3 1.171667 2.129722 0.003889\n",
"2019-10-18 7 2.234722 11.442500 0.000000\n",
"2019-10-19 17 3.442925 6.890000 0.001111\n",
"2019-10-20 11 2.098788 9.535278 0.001667\n",
"2019-10-21 7 4.186587 23.978889 0.124444\n",
"2019-10-22 1 22.062222 22.062222 22.062222\n",
"2019-10-23 21 2.598201 21.864444 0.001389\n",
"2019-10-24 9 8.162191 22.707778 0.071667\n",
"2019-10-25 11 4.081313 19.943889 0.007222\n",
"2019-10-26 70 2.495238 19.128056 0.000833\n",
"2019-10-27 17 0.758758 3.751389 0.006944\n",
"2019-10-28 8 1.146389 3.415278 0.030278\n",
"2019-10-29 11 1.283081 9.551389 0.000833\n",
"2019-10-30 26 1.488045 4.518889 0.000556\n",
"2019-10-31 14 1.688790 7.053889 0.010556\n",
"2019-11-01 19 1.264591 11.523611 0.019167\n",
"2019-11-02 10 5.820556 22.933056 0.086389\n",
"2019-11-03 13 1.615662 2.866389 0.278056\n",
"2019-11-04 9 1.565494 4.267778 0.058056\n",
"2019-11-05 8 2.084757 4.098611 0.011111\n",
"2019-11-06 5 2.598778 12.711944 0.004722\n",
"2019-11-07 2 0.190139 0.376389 0.003889"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean = (df_date[['date_x','response_time']]\n",
" .groupby(df_date.date_x.dt.date)\n",
" .agg(['count','mean', 'max','min']))\n",
"mean.to_csv(\"fanta/moderation_delete_meantime.csv\")\n",
"mean"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7faa73cda4e0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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AQKYUJx31neOHn0J9Q5I06OwveiQAAFKL8AMAAIBMKU07GrrHDz+lw/AzPHwrGAAAeUD4AQAAQKbUZj2Ni8Gx7yv78evfxyEnfgAA+UH4AQAAQKbUo66mpeax76sGW5Kkadhe9EgAAKQW4QcAAACZ4tm+onLj2PfVg/jEz3zQWvRIAACkFuEHAAAAmTEa9lUzY6m6eex7/ea2JCkacuIHAJAfhB8AAABkRtiKX8VuahvHvrdYKmtgyzKjzqLHAgAgtQg/AAAAyIywsyvp81ezH/t+U5czJvwAAPKD8AMAAIDMGHTiEz8lf+tE9/cdX4Vpb5EjAQCQaoQfAAAAZMa4G4efSrB9ovtHrqfSjPADAMgPwg8AAAAyY9qP38hVb+yc6P5xwVeF8AMAyBHCDwAAADIjGhxIkryNk4WfaSlQLQoXORIAAKlG+AEAAEBm2EFLc2vk+c0T3R+VAtUt4QcAkB+EHwAAAGSGM2qpazw5rnui+225Id8OFM3nC54MAIB0IvwAAAAgMwqTjkLjn3yBalOOsQp77cUNBQBAihF+AAAAkBnFaVcD9+Thx6nFPxEL23uLGgkAgFQj/AAAACAzqrOuRoXgxPcXahuSpGF3f1EjAQCQaoQfAAAAZEZt3tW01Djx/WVvU5I06rUWNRIAAKlG+AEAAEBm+DbU/BThp+LHJ37G4cGiRgIAINUIPwAAAMiE+WymQH1F1Y0Tr1FrbEmSZgNO/AAA8oHwAwAAgEzoHT6Q2Zwi/NQb25KkaMBbvQAA+UD4AQAAQCbcexOXW9888Rqe31RkjTQk/AAA8oHwAwAAgEwYdOPwU/JPHn4c11XP1GTGnUWNBQBAqhF+AAAAkAmjThx+yt7WqdbpG08u4QcAkBOEHwAAAGTCJNyXJFUbO6daZ+B4Kkx7ixgJAIDUI/wAAAAgE+b9+BXsXnP7VOuMCr7KM8IPACAfCD8AAADIhOjwgczBxulO/EwKvqpzwg8AIB8IPwAAAMgEM2wptFUVS+VTrTMrBapF4YKmAgAg3Qg/AAAAyAR33FbPeKdeJyoF8mx/ARMBAJB+hB8AAABkQnHS0cD1T72OrTRUM2NNJ+MFTAUAQLoRfgAAAJAJlVlXw0Jw6nWc2oYkqdfeO/VaAACkHeEHAAAAmVCddzUpnj78uNWmJKnfPTj1WgAApB3hBwAAAJlQj0JNS81Tr1P04jWG3f1TrwUAQNoRfgAAAJB6NooU2J6iyunDT9nbkiSNe5z4AQCsP8IPAAAAUm/Q76pk5jLVjVOvVfU3JUmTkPADAFh/hB8AAACkXq+1K+nzBzOfRq0Rn/iZDdqnXgsAgLQj/AAAACD1+u04/BS9zVOv5Te3JUnRsHPqtQAASDvCDwAAAFJv2IsfxFzytk+9VrlS08QWpCEnfgAA64/wAwAAgNSbHIafauP04cc4jrrGkzPhxA8AYP0RfgAAAJB6s8MHMdebpw8/kjRw6ipMugtZCwCANCP8AAAAIPXmg5akz5/Pc1pDx1NxSvgBAKw/wg8AAADSb3igiS2oWvMXsty4EKgy6y1kLQAA0ozwAwAAgNRzR211jSfjLOavr9Oir+o8XMhaAACkGeEHAAAAqVeYdBU6izntI0mzUqC6JfwAANYf4QcAAACpV552NHSDha0XlRvybF82iha2JgAAaUT4AQAAQOpV5l2Nio2FrWcqDZXMXMMBz/kBAKw3wg8AAABSrz7vaVZc3IkfU21KksLOwcLWBAAgjQg/AAAASL3A9jSvNBe2XqG+KUkadPcXtiYAAGn02PBjjPnHxpi7xpi3fuyz/94Y86kx5geH//nl5Y4JAACAvJqMR6qZsewCw0/Ji9caEn4AAGvuKCd+/omkv/6Az/8na+2rh//5F4sdCwAAAIh1W7uSJKe2ubA1K/6WJGkctha2JgAAafTY8GOt/Y4kfvwMAACARPTbcfhxvcWFn6ofrzXrE34AAOvtNM/4+S+NMW8e/hRs42EXGWN+yxjzujHm9d3d3VNsBwAAgDwadvYkSaX64sJPvRGf+CH8AADW3UnDz/8i6VlJr0q6JekfPOxCa+1vW2u/Yq39ys7Ozgm3AwAAQF6NevFzeCrB9sLW9Jtx+LGjzsLWBAAgjU4Ufqy1d6y1c2ttJOkfSvrZxY4FAAAAxKZhHH7qzcWFn0KxpL6tyIzaC1sTAIA0OlH4Mcac/7E//pqktx52LQAAAHAa80H8cyyveWah64amLmfcXeiaAACkTeFxFxhj/qmkn5e0bYy5Kem/k/TzxphXJVlJ1yT950ucEQAAAHk2aCmyRn5jcc/4kaSB46s4JfwAANbbY8OPtfZvPeDjf7SEWQAAAIAvMKOWuqaupusudN2h66lE+AEArLnTvNULAAAAWDp33FFovIWvOy4GqszDha8LAECaEH4AAACQaqVpRwM3WPi6s6KvakT4AQCsN8IPAAAAUq0y62pUWHz4mZcCeZbwAwBYb4QfAAAApFpt3tOk2Fj4urbSlGeHiubzha8NAEBaEH4AAACQap7taV5efPgx1YYcY9Xrtha+NgAAaUH4AQAAQGpF87kC21dUaS58bacar9lv7y18bQAA0oLwAwAAgNTqdQ7kGCtT3Vj42sV6vOagu7/wtQEASAvCDwAAAFIrbN+VJLn1zYWvXfLiNUc9wg8AYH0RfgAAAJBag04cZYre1sLXrvhx+Jn2ecYPAGB9EX4AAACQWqNu/Pydir/48FNvxGvO+u2Frw0AQFoQfgAAAJBa4zA+8VNtbC987XvhZz7gxA8AYH0RfgAAAJBa8/BAklRvLj78eH5Tc2tkR52Frw0AQFoQfgAAAJBa0SAOP/4Swo9xHPVMXQ7hBwCwxgg/AAAASC0zamtgyypXaktZv2/qcieEHwDA+iL8AAAAILXccUdd4y9t/YHjqzjtLW19AACSRvgBAABAahUmHfXd5YWfccFTaUb4AQCsL8IPAAAAUqsy7WjkBktbf1IIVJ0TfgAA64vwAwAAgNSqzrsaF5cXfmalQLWov7T1AQBIGuEHAAAAqeVFPc1KjaWtH5Ub8m24tPUBAEga4QcAAACpZKNIvu1rXmkub49KoKqZaDwaLG0PAACSRPgBAABAKo2GfZXNVKpuLm0Pp7ohSQo7B0vbAwCAJBF+AAAAkErd1l1JklvbWNoebi0+TdTv7C1tDwAAkkT4AQAAQCr123GMKdSXF36KXnyaaNjlxA8AYD0RfgAAAJBKw8NTOCV/a2l7lL04Ko3D1tL2AAAgSYQfAAAApNIkjMNPNdhe2h5VPz7xM+lz4gcAsJ4IPwAAAEil6eEpnHpzZ2l71JtxVJr320vbAwCAJBF+AAAAkErRID6F428sL/x4jfhnZNGQn3oBANYT4QcAAACpZIctTayrWj1Y2h6Val1jW5RGnaXtAQBAkgg/AAAASCVn1FbPeDLOcv/K2jN1OePuUvcAACAphB8AAACkUnHSVuj4S9+n73gqTjjxAwBYT4QfAAAApFJp2tXQXd7PvO4ZOZ6K097S9wEAIAmEHwAAAKRSZdbTqLCC8FPwVZ6HS98HAIAkEH4AAACQSvV5V9NSY+n7TIuBanNO/AAA1hPhBwAAAKnk21Dz8vLDz7zcUN32l74PAABJIPwAAAAgdaaTsTwzlK1sLH2vqBzIs33ZKFr6XgAArBrhBwAAAKnTbe1Kkpza8sOPqTRUNHMN+rzSHQCwfgg/AAAASJ1+Z0+S5NY3l77XvbgUdvaXvhcAAKtG+AEAAEDqDA7DT8nbWvpehVrzvj0BAFgnhB8AAACkzrgXR5hKY3vpexUPTxUNe62l7wUAwKoRfgAAAJA6k96BJKkaLP/ETyWIw88kJPwAANYP4QcAAACpM+/H4cdv7ix9r9ph+JmGB0vfCwCAVSP8AAAAIHXssC1J8pvL/6mXd/hzsvnhngAArBPCDwAAAFLHGbXUVV1uobD0vepB/FYvO+wsfS8AAFaN8AMAAIDUccdt9Yy3kr0KxZJCW5UZceIHALB+CD8AAABIneKko4EbrGy/0HhyJ92V7QcAwKoQfgAAAJA6lVlXI9df2X4Dp64C4QcAsIYIPwAAAEid2rynSamxsv1GBV+lGeEHALB+CD8AAABIHc+Gmq0w/IwLvirzcGX7AQCwKoQfAAAApEo0nyuwPUWVjZXtOSsGqhN+AABriPADAACAVAl7bbnGytRWF37m5UB1S/gBAKwfwg8AAABSJWztSpKcFYYfW2nKN0PNZ7OV7QkAwCoQfgAAAJAq/U4cfor1zZXtaSrx84TCzv7K9gQAYBUIPwAAAEiVUXdPklQOtle2p3t4uijsHKxsTwAAVoHwAwAAgFSZhHF8qTVWF36K9Tj8DHuc+AEArBfCDwAAAFJldhh+vMbOyvYseXH4GXUJPwCA9UL4AQAAQKrYQUuS5G2s7sRPNYifJzTpt1a2JwAAq0D4AQAAQLoMDzS0JVWq9ZVtWQ22JEkzwg8AYM0QfgAAAJAqzrijnvFWuqfXiMNPNGyvdF8AAJaN8AMAAIBUKU466jvBSvesew3NrCM77Kx0XwAAlo3wAwAAgFQpz7oaFPyV7mkcRz1TlzMm/AAA1gvhBwAAAKlSnXU1KTZWvm/f1OVOuivfFwCAZSL8AAAAIFXqUU/T4mp/6iVJQ9dXcUr4AQCsF8IPAAAAUiWwPUWVjZXvO3I9lae9le8LAMAyEX4AAACQGqNBqIqZylabK997WgxUjcKV7wsAwDIRfgAAAJAavfaeJMmpba5871kpUI3wAwBYM4QfAAAApEbY3pUkFepbK987KgXyLeEHALBeCD8AAABIjWEnPvFT9ld/4sdWm6qYqUbD/sr3BgBgWQg/AAAASI1xb1+SVAlWf+LHOXyuUNjZX/neAAAsC+EHAAAAqTEL4+hSa+ysfG+3Foeffudg5XsDALAshB8AAACkxnzQkiT5G6sPP6V6/POyYY8TPwCA9UH4AQAAQGrYYUsz68jzV/8695K/IUka9zjxAwBYH4QfAAAApIYzaqtrPBln9X9NrR0+V2jab618bwAAloXwAwAAgNQojNvqGy+Rve+Fn/mgncj+AAAsA+EHAAAAqVGadjRwg0T29ptx+LGEHwDAGiH8AAAAIDWqs65GxWTCT7lS09CWpBHhBwCwPgg/AAAASI1aFGpabCS2f2jqcibdxPYHAGDRCD8AAABIDc/2NCuv/o1e9wwcT4VJJ7H9AQBYNMIPAAAAUmE2nSjQQLa6kdgMQ9dXcdpLbH8AABaN8AMAAIBU6LX3JUmmmtyJn3HBU2VG+AEArA/CDwAAAFIhbN+VJBXqm4nNMC0GqkZhYvsDALBohB8AAACkwqATn/gpeluJzTAvBarbfmL7AwCwaIQfAAAApMKoF4efcpBc+InKDfm2LxtFic0AAMAiEX4AAACQCtPD8FNvbCc2g6k2VTCR+iFv9gIArAfCDwAAAFJh1j+QJHnNM4nN4Bw+WDo8/NkZAABZR/gBAABAKthhS5LkN5P7qVehHoefAeEHALAmCD8AAABIBTNsqWerKhRLic1Q8uI3io16B4nNAADAIhF+AAAAkArupKOe4yc6Q8WPw8+4x4kfAMB6IPwAAAAgFUqTjgYJh5+qH//MbDZoJzoHAACLQvgBAABAKpSnXQ0LQaIzeM34jWJzwg8AYE0QfgAAAJAKtairaTHh8BNsSJLskPADAFgPhB8AAACkghf1NC03E53BLRTUs1WZcSfROQAAWBTCDwAAABJno0i+7StKOPxIUmg8ueNu0mMAALAQhB8AAAAkrh92VDRzmdpG0qNo4HoqTAk/AID1QPgBAABA4nqtu5Ikt7aZ8CTSyPVVnvWSHgMAgIUg/AAAACBx/faeJKngbSU8iTQp+KoQfgAAa4LwAwAAgMSNunH4KXnJn/iZlQLVojDpMQAAWAjCDwAAABI37h1IkmqN5E/8zMsNebaf9BgAACwE4QcAAACJm/Xj8FNvnkl4EsmWG/LMULPpJOlRAAA4NcIPAAAAEhcN4vATbOwkPIlkqg1JUtg5SHgSAABOj/ADAACAxJlhSyNbVKXmJT2K3MNXyve7+wlPAgDA6RF+AAAAkDhn3FbPJB99JKlYj8PPoEP4AQBkH+EHAAAAiWpt0M4AACAASURBVCtOOuo7ftJjSJJKXhx+Rr1WwpMAAHB6hB8AAAAkrjTtauAGSY8hSaoG8ZvFpn2e8QMAyL7Hhh9jzD82xtw1xrz1Y59tGmP+wBjz4eE/N5Y7JgAAANZZddbVuJiO8FMLNiVJsz4nfgAA2XeUEz//RNJf/3c++7uSvm2tfV7Stw//DAAAAJxIPeppWmomPYYkyW9uS5KiYTvhSQAAOL3Hhh9r7Xck/bvnXH9F0jcP//2bkn51wXMBAAAgR3wbKio3kh5DklSt+ZpaV3bUSXoUAABO7aTP+Dlrrb11+O+3JZ1d0DwAAADImdGwr5oZy1bTceLHOI5CU5cz4sQPACD7Tv1wZ2utlWQf9r0x5reMMa8bY17f3d097XYAAABYM2FrT5Lk1DYTnuRzofHkTnpJjwEAwKmdNPzcMcacl6TDf9592IXW2t+21n7FWvuVnZ2dE24HAACAddXvxOGnUE9P+Bm6nkrTbtJjAABwaicNP78v6TcP//03JX1rMeMAAAAgbwbdOPyU/K2EJ/nc2PVUnnHiBwCQfUd5nfs/lfSapBeNMTeNMX9b0t+X9B8YYz6U9EuHfwYAZFjYbenm33tRb32Xlg9gtcbdfUlSJdhOeJLPTYqBKlGY9BgAAJxa4XEXWGv/1kO++sUFzwIASNDN99/QS/a2Pnn329LP/UrS4wDIkUkYh596Iz2PBZiVAtVDwg8AIPtO/XBnAMB66H36niSp3Pk44UkA5E00OJAk1ZvpOfETlZvybT/pMQAAODXCDwBAkjS7+4EkaWN4I+FJAOSNHbQ0t0Z+sJH0KJ+rBCqbqUZD4g8AINsIPwAASVK5c1WS9MT8M81ns4SnAZAnzrijrvHkuG7So/wFp9qU9Pmr5gEAyCrCDwBA0ucnfcpmqjufXEl4GgB5Uhi3FRo/6THuU6jHp4/6hw+eBgAgqwg/AABF87nOzz/V+4UXJUl7195KeCIAeVKcdjRw0xV+iofhZ0j4AQBkHOEHAKA7N6+oYqY6eOoXJEmDW+8lPBGAPKnOuhoXgqTHuE/Zi8PPOGwlPAkAAKdD+AEAaO/6O5Kk4PmfU1d1mf0PE54IQJ7U5l1NSo2kx7hPrbElSZr2CT8AgGwj/AAANPgsPuFz9vKXdavwlOrhtWQHApArng01T1v4CeLwMx8QfgAA2Ub4AQBI+1cU2qq2zl1Qt35JZ8a80h3AasxnM/l2oKiaole5S/Kb25KkaNhOeBIAAE6H8AMAULV3TbcKT8o4juYbz+mMDtTv8T92ACxf2NmXY6xMysJPqVzRwJZlRt2kRwEA4FQIPwAAbY9vqFO7KEkqnXtBknTrY97sBWD5eq1dSZJb30x4ki8KTV3OmAgOAMg2wg8A5Nxo2Ne5aFfTjWclSRtPvyJJan/yTpJjAciJQXdPklT00hd+Bo6nwqSX9BgAAJwK4QcAcu72tXflGKvimeclSecvv6zIGk3v8mYvAMs36sThp+JvJTzJFw1dT6UZP/UCAGQb4QcAcq51421JUuOpL0mSKtW6bjs7KrY+SnIsADkx6R9IkqqNnYQn+aJJwVdlxokfAEC2EX4AIOdGtz+QJJ1/5st/8dle+Wk1B9cSmghAnswPw493+BatNJkWA1WjMOkxAAA4FcIPAOSc2/pYu9qQF3z+Rp2Bf1nnZ5/KRlGCkwHIg2jQkiQFG+k78TMvBfIs4QcAkG2EHwDIuaB/TXdLT933mdl+XnUz0u6t6wlNBSAvzLClvq2oWConPcoXRJWGPDtQNJ8nPQoAACdG+AGAnDs7vanQu3zfZ/UnXpIk3b36oyRGApAj7ritnvGTHuOBTLUp11j1w07SowAAcGKEHwDIsc7+HW2oK7v57H2f71yOn/fT/+y9JMYCkCPFaVd9N53hx602JUlhezfhSQAAODnCDwDk2K2rb0mSKudevO/zM09c1sCWZfd4pTuA5SpPOxoWgqTHeKBCPQ4/g24r4UkAADg5wg8A5FjvZnyiZ+viy/d9bhxHnxWeUq17NYmxAORIbd7TpJjO8FP0NiVJo95BwpMAAHByhB8AyLHZ7geaWUfnLr70he86tYvaHt9IYCoAeVKPepqWmkmP8UCVw/AzCQk/AIDsIvwAQI6VOx/rlnPugW/TmTSf1bnorsajQQKTAcgDG0UKbE9ROZ3hp9bYliRN+/zUCwCQXYQfAMix5uCG9itPP/C74tkX5BirW1ffWfFUAPJi0O+qZOYy1XSGn3pjS5IUDdsJTwIAwMkRfgAgp6L5XOfnn2kUXH7g942nviRJat14e5VjAciRXntPkuTUNxOe5MH8YEORNbKEHwBAhhF+ACCn7n76sapmIrP93AO/P/9M/Er30e33VzkWgBzpH4afew9RThvHdRWamsyok/QoAACcGOEHAHJq91r8Ey7viS898Hsv2NBdbapw8NEqxwKQI8PuriSp5G0nPMnDhaYud0L4AQBkF+EHAHJqcOtdSdKZy6889Jrd0gUF/WsrmghA3kx6+5KkSrCV8CQPN3Q8Fae9pMcAAODECD8AkFN274r6tqLtcw9+uLMkhf5lnZt9IhtFK5wMQF7MDl+TXm+m98TPyPVVIvwAADKM8AMAOVXrXdOtwpMyzsP/q8BuPaeG+mrt3VrhZADyIhrEr0kPNnYSnuThJkVf1TnhBwCQXYQfAMip7fENdWoXH3lN9fxLkqQ7V99axUgAcsYO25rYgqo1P+lRHmpaDFSLwqTHAADgxAg/AJBD49FA56K7mjSfeeR12xfjN3v1br67irEA5Iw7bqlrvEeePExaVG7Is/2kxwAA4MTS+9+yAICluX31XTnGqnjmhUded+7p5zW2RUW7H6xoMgB5Uhh3FDrpPe0jSbbSUN2MNJ2Mkx4FAIATIfwAQA7t34hf5d546sGvcr/HLRR0yz2vcvfqKsYCkDPlaUdDN93hx6k2JUlh5yDhSQAAOBnCDwDk0PhOfILn3DNffuy1rerT2hzdWPZIAHKoMu9qXGwkPcYjubV74Wcv4UkAADgZwg8A5JB7cEV7aspvbD722lHjGZ2f39JsOlnBZADypD4PNU15+CnUNyRJw246Tvz86Dvf0vX/4RV1WoQoAMDREH4AIIf8/jXdLV040rXuzgsqmbluXX9/yVMByBvf9jSvNJMe45EqXhzIx2E6ws/gh7+ri9FNffjHv5P0KACAjCD8AEAOnZ3eVOhdOtK1weFzgPavv73EiQDkzWQ8Ut2MZNMefoI4/ExSEn62229KkswH/zLhSQAAWUH4AYCc6RzsalNdRZvPHun688/8hCRpdOu9ZY4FIGe6rV1JklN7/E9Ok1QLtiRJs3474Umkfq+tS7OrmlpXL3T/jSbjUdIjAQAygPADADlz++MfSZIqZ1880vWNrbNqKZBzcGWZYwHImX47Dj/u4TN00spvbkuS7DD58HP1h9+Ra6z+bPs/kW+Gev/7/yrpkQAAGUD4AYCc6X4an9zZvPjyke+5XXxKXnhtSRMByKNhd1+SVPK2Ep7k0SrVuibWlR0lH356V74nSXrmN/6ehrakwZvfSngiAEAWEH4AIGdmux9qZh2du/jSke/p1S/pzOSTJU4FIG9GvTj8VILthCd5NOM46hlPzrib9Ciq3fkzXXcuaOeJS3qv/hVd3PuObBQlPRYAIOUIPwCQM+X2R7rtnFWpXDnyPdHmc9pWW932/hInA5An08PwU2+mO/xI0sDUVZgkG35sFOni8G3dCeLnrk2f/Q91Trv6+O0/TXQuAED6EX4AIGcawxvarzx9rHvK5+LnAd3++K1ljAQgh+aD+C1ZXvNMwpM83tD1VJwmG34+ufKmmgqlCz8rSXrmG7+hyBrdff2fJToXACD9CD8AkCPRfK4nZp9qGFw+1n2bT8fPA+refGcZYwHIo0FLkTXyG+l+q5ckjQqByrNeojPcfvu7kqSzL/8VSdL2uQv6sPiitj/9dpJjAQAygPADADly97OrqpqJzNZzx7rv/OWXNbOOpnc/WNJkAPLGjFrqmZoc1016lMeaFn3V5smGH/vJn6qrui688OpffHZw4Rf1/OxD3f30aoKTAQDSjvADADmyd+1tSVL9yaM/2FmSSuWKbjtnVW5/tIyxAOSQO+mqZ/ykxziSWSlQzfYTneFM+01dq3zpvlB2/md/XZJ09Xu/k9RYAIAMIPwAQI70P4tf5X7m0pePfe9+5Wk1B9cXPRKAnCpNOhq4QdJjHElUbsi3/cTeoNVt7+vi/Lr6Z376vs8vvvjTumnOqfLxv0pkLgBANhB+ACBH7P4VDWxZO+cvHvveYfCMzs8/UzSfL2EyAHlTmXU0KmQj/KjSVMnMNBomc+rn+pvfkWOsvOe+ft/nxnF088zP66XBn6vfaycyGwAg/Qg/AJAj1e5VfVZ4SsY5/v/5N9vPq2omunPzyhImA5A3tXlPk2Ij6TGOxKnGc4ad/UT2D6+8psgaXfqpv/qF7/yf/Jsqm6ne/94/T2AyAEAWEH4AIEe2xzfUqR3vVe73eE9+SdLnzwkCgNPwbE/zcjbCT6G+IUkaJBR+6nf/TNfdpx/4BrQX/vIvqaO65u/+XwlMBgDIAsIPAOTEeDTQueiupo1nTnT/2Wfi5wLde04QAJxUNJ/Lt31FlWbSoxxJ6TD8DLurDz/RfK5Lo7e12/zJB35fLJX1YfA1Pdf+E81nsxVPBwDIAsIPAOTE7WvvyTVWhTMvnOj+rTNPqWerMvsfLngyAHnT67bkGitT3Uh6lCMp+1uSpHH/YOV7f/LBDxRoIF346kOvMS/9sjbU1Ydv/OsVTgYAyArCDwDkxMGNdyRJwVNfOtH9xnF0q3hBtd7VRY4FIIfC1q4kya1/8adLaVQN4jmn4eofoHznne9Kks698nMPveb5r/+qJtZV6wffWtVYAIAMIfwAQE6Mb78vSTr3zPFf5X5Pt3ZRO+ObixoJQE4NOnH4KXpbCU9yNPXD8DMftFa/+c0/VVueLjz34J96SVLQ3NL7lZ/SE3f+aHVzAQAyg/ADADnhtD7SnpoKmif/H1rTjWd1Trsa9nsLnAxA3oy6e5Kksp+NEz9+c1uSFA1Xf+LnbOdNXa++/Ni3MQ4u/zVdjG7qkw9/uKLJAABZQfgBgJzww2u6W3zqVGuUzsbPB/rsY97sBeDkxmH8kORasJ3wJEdTLJU1sGWZUWel+3YOdnUx+kSDMz/92Guf/tqvS5I+/f4/W/ZYAICMIfwAQE6cmd5U6F081RrNC69IktqfEH4AnNy8H/9kqr6xk/AkRxeaupzxasPP9R/+f5Ik//lvPPba8xdf1EfuZQXX/2DZYwEAMobwAwA50GntaUsdRZvPnWqdJ56Jw8/kzgeLGAtATkWD+O1Y935ClQV9x1dhutqfufY/fk1za3T5p/7Kka7fPf8LenHytlq7t5Y8GQAgSwg/AJADd66+JUkqnz3Zq9zvqdZ93daOiq0rixgLQE6ZYVsDW1a5Ukt6lCMbuZ7K0+5K9/TuvqFrhUuq+80jXb/1M78q11hd+ZPfXfJkAIAsIfwAQA50b74rSdp8+uVTr7VbvqDG4Pqp1wGQX+64ra7xkx7jWMYFX+V5uLL95rOZLo/e017zp458z7M/+Q3d1abcD//vJU4GAMgawg8A5MDs7oeaW6Pzl08ffgb+ZZ2f3pSNogVMBiCPCpOOBo6X9BjHMi0FqkWrCz833n9DnhnKefqrR77HcV1d3fo5vRR+X6Nhf4nTAQCyhPADADlQbH+kW85ZlcqV0y+2/bw8M9T+7U9OvxaAXCpPuxoWgqTHOJaoFMizqws/d9/5Y0nS+VeO9nyfeyqv/MeqmbE++Df/chljAQAyiPADADnQHN7QfvnCQtaqPfGSpM+fGwQAx1WbdzUuNpIe41iiSlOeHSiaz1eyn3PzT9VSoCefOd5JzRe/9h9pYMsavvXPlzQZACBrCD8AsOZsFOn87FMNg2cWst7OpS9LksLP3lvIegDypx71NCtlK/yYSkOOsep1WyvZ71z3TV2vvSLjHO+v65VqXe97f1mX97/DT3IBAJIIPwCw9u5+dlU1M5bZPt2r3O858+QzGtqS7N6HC1kPQL7YKFJgQ80rR3tTVVo4tXjefmd/6Xu1927rgv1Mw7M/c6L7Z8//DZ3RgT760fcWPBkAIIsIPwCw5navvS1Jqp9/cSHrOa6rW+4TqnY/Xsh6APJlNOyrbKZSdTPpUY6lWI/nHXaXH36uv/lHkqTg+W+c6P5nv/5rmluj3dd/b4FTAQCyivADAGuuf/iTrJ3LX17Ymu3aRW2NbixsPQD50W3dlSQ51Wyd+CnVNyRJw97yw8/go9c0s44u/+TJws/mmSf1Qell7Xz2rxc8GQAgiwg/ALDm7N4VDWxZO+cvLWzNcfNZnYvuaDIeLWxNAPkw6OxJkopetk78VPw4/EzC9tL3Cnb/XNcKl1XzTv4cpM7Tv6Tn5h/p9idXFjgZACCLCD8AsOaqvau6VXhCjusubM3imRdUMJFuXX1nYWsCyIfB4TNySv5WwpMcT60RzzsbLPfhzrPpRJfH72l/49VTrfPEV39dknT9e7+ziLEAABlG+AGANbc1uqF29eJC1wyeil8vfHCD8APgeMaHP5WqBtsJT3I89UY8b7Tk8HP93ddVM2O5F796qnWefuFVfWKeUPXq/7OgyQAAWUX4AYA1NhmPdD66o0lzMa9yv+f8sz8hSRrdfn+h6wJYf7MwDj/15k7CkxyP5zc1t0Yadpa6z96735UkPfHlv3rqtT49++/rpeGfq9c5OPVaAIDsIvwAwBq7de09ucaqeOaFha7rNza1p6bcA54dAeB4okEcIbxmtk78OK6r0NRkxssNP+6n/1Z7aur8xdP/3+3g1b+pkpnrw+99awGTAQCyivADYKG+/3/8A732zf826TFw6OD6W5Kk4KkvLXztO6ULCvrXFr4ugPVmhy1Nrav6KR5cnJS+8eQuOfyc6/1In9S/LOOc/q/pL/zML6glX/P3/sUCJgMAZBXhB8DC2CjS5bf/Z7368W9rNAiTHgeSxnc+kCSdXeCr3O8Jvcs6O7258HUBrDdn3FHXeAsJG6s2cDwVpr2lrb9/56aesrc1PvczC1mvUCzpSuPrer7zPc2mk4WsCQDInuz9Ny6A1Lry5p/ojA5UNRO999r/mfQ4kOQcfKR9NdTYWPxPKuzWc9pQV539OwtfG8D6Ko7bCh0/6TFOZFTwVZ51l7b+jR/+kSSp8cI3Fram+6VfVlOh3v+3/+/C1gQAZAvhB8DC7L3xe5pbo4Eta/I24ScNvPCa7hafWsralXMvSpJuffyjpawPYD2Vph0N3SDpMU5kUvBVnS/vROvo6vc1ta4u/8Tiws8LX/8VTWxBvR/+/sLWBABkC+EHwMLsfPaH+qD0st71v6ZnDr6raD5PeqTcOzO9qV59sa9yv2fr4iuSpO7Nd5eyPoD1VJn1NCpkM/zMSoFq0fLCT2P/z3W1+KwqNW9ha3rBht6r/iU9dfcPZaNoYesCALKD8ANgIW5/ckXPzT9S58Ivyr7wN7Sttq788LtJj5Vr3fa+ttVWtPncUtY/f/FFTayr+e4HS1kfwHqqz7ualrIZfqJSIM/2l7L2dDLW5fH7Oth8deFrD5/5a3rK3taND36w8LUBAOlH+AGwENdf+11J0vmv/oae/8avaWYd7b/xewlPlW+3P47f6FU+/EnWohWKJd1yz6vy/7N339FxX2X+x9/fKdJoRr13yeru3bKd5vTm9EICAcIv1IWwQChLWFhg6WXD0hZCCUvZFEjHKU517NiWe1fvvXdp+vf3x8QJwU1lZu6M9LzO4ZAjfec+nxywR3rm3vsMNwRkfSHE3BStj+GJjFcdY0Z0SxxWzYHTYff72k3HK4jSnJjzyv2+dv7GWwDo2POk39cWQggR+qTxI4Twi6iGrbRpGeQWLyMuKY3qyCWkd76mOta8dvIIVmKO/0e5nzRgySVhsjlg6wsh5haX00GMNoluSVAdZUYMVl/useF+v6/dX7UDgKylF/l97bTsQuqMhSS0ygXPQggxH0njRwgxa+OjQ5RNHqQtddPb43lH8y5ngbeJjsYqxenmL1dvLR5dI31B4Bo/9rgCMj2deNzugNUQQswdo0N9wDsNlHBjjPLtVBof7vP72qaOffSQSFp2od/XBujNupQSZyX93W0BWV8IIUToksaPEGLWqnc+S4TmJmbZdW9/LWeDb1t5y+7HVcWa9yKG6ukypBJpsQashimlhAjNTVdLdcBqCCHmjrGhXgCMtkTFSWbGHO3LPTk66Pe1M8eO0ha95O0PUPwtdc1NGDSd+p1PBGR9IYQQoUsaP0KIWfNUbmEYGyVrL3v7a1kFi2ky5BDdtFVhsvktbqKF/sjcgNaIyfLtJuprOh7QOkKIuWHirZ0yJxso4SYy2rdTyTE64Nd1+zqaydR7cGas8eu6/6hgyXq6SMZc+0LAagghhAhN0vgRQsyKx+2mcOhNamM3YI6IfNf3OtMvptR+lOFB/2+JF2ene71kutuYiF0Q0DppBUsBmOyUI31CiHNzjPreDywxSYqTzExUjK9h5Rzzb+On9ejrAMSXnOfXdf+RZjDQnHwhpeP7sE8EbiS9EEKI0CONHyHErNQeeI1ERtBKrz7lewkrb8CseajdKVNEgq23sxmr5kBLCswo95MSUjIYIhptoD6gdYQQc4NrzHdEyhqfojjJzNjikgFwTwz5dV1HYwVO3cSCpRv9uu4/sy69DqvmoHr33wNaRwghRGiRxo8QYlYGDz2DSzdSfN5Np3yveOUm+omDqucVJJvfepp8o9ytGWUBr9VtyiZ6tDHgdYQQ4c897tspExOmjZ/oeN9OJe+Ef+/4ies/SIO5OKB3sgGUlF/FmB6F/diWgNYRQggRWqTxI4SYlYyu16i2LCU2/tRt+0aTifr48ygZ3YXL6VCQbv4a7/BdtpyyYHHAa43Y8klxtga8jhAi/OlvNUxi4pMVJ5mZSIsVp24C+4jf1nQ67BQ4axlKWuG3Nc8k0mKlJmYdBQPb8Xo8Aa8nhJg5h32CvU/9Qv6sCr+Qxo8QYsbaGyrJ97Yylnf5GZ8xL7qWWCao3vNiEJMJva+WST2C1MzA3vED4E4sJJUBxkb8P+VGCDG3GOyDjGDDaDKpjjIjmsHAiBaNwTnstzUbj+4kUnMRkV/utzXPxltyNSkMUnd4R1DqCSFm5vCWX7P20P0cef2vqqOIOUAaP0KIGWt9a1R7zvpbzvhM6cbrcOhmxo48G6xYAogaaaTTmInBaAx4LUt6KQCdDccCXksIEd6MjiHGtGjVMWZlwmDD5PBf42ew5k0AcpZf7Lc1z6b4vJtx6wb6DzwVlHpCiJkxNb4GgOPEc4qTiLlAGj9CiBmLbn6JJkMuWQULz/iMNTqOKusqcnu3oXu9QUw3vyXaWxmy5gWlVkKu7zjZcOuJoNQTQoQvs2uEcUOM6hizMmmIxuwe9dt65o59dJFCSma+39Y8m7ikNKojF5PW8WpQ6gkhps/jdlM4tg+A/P4d8jO0mDVp/AghZmR4sI9S+1E60zed81l7wZVk6t00Ve0PfDCB02Enw9uFI64gKPUyFizCo2u4emqCUk8IEb4srhHspljVMWbFYYrF4sfGT/bYUdpjlvhtvakYzbucAm8THY1VQa0rhJia+iM7iGOco5GrSKOfhuN7VEcSYU4aP0KIGand+SRmzUPCyhvO+WzBeb6jYN17ZKx7MHQ1V2HSvJhSioNSL9JipcuQSsSQjHQXQpyd1TOCMyJOdYxZcZljiPKM+WWt7rZ60ujHlbHGL+tNVVb5zQC0vHVkWwgRWvoP+ybiRt/wAwB69svRTDE70vgRQsxM9QsMEEvxyk3nfDQlM58aUwkJbS8HPpdgoNl35Co2+8xH8PytLzKX+InmoNUTQoSnaH0Md5g3ftwRsdh0/zR+2o68AUBi2QV+WW+qcoqW0mzIIbppa1DrCiGmJq5jB3XGQhYsWkuNqYTEttdURxJhTho/QohpczkdlIzsoi7+/ClPZunPuoRSdzV9XS0BTifs3b5R7ulBGOV+0mRsARnudhk5KoQ4I6/HQ6w+itcSrzrKrHgj44jRx/1y54areTd23Uz+4uBM9PpHHWkXU2o/yvBgX9BrCyHObGxkkGJnJb1p5wHQn3kxxa5qBnraFScT4UwaP0KIaavZ+zKxjGNaeM2UX5O21retvOFN2VYeaIaBegaJJS4pLWg1teRirJqD3s6moNUUQoSXsdEhjJqOFpWgOsqsaJY4zJqHyYnZ3/OT0H+QxogSIiItfkg2zdorr8eseajdKcewhQgldXtewKx5iFl8BQApq6/HoOnU75TjXmLmpPEjhJi20SPP4NRNlG68bsqvWbBoLZ2kYK6XbeWBFj3WRJc5O6g1bVllAPQ2ykh3IcTpjQ35dpYYbImKk8yOweprXI0ND8xqHfvkOAtcdQwnrfBHrGkrXnUxA8RC1fNK6gshTs9R9RITeiTFqy8FoHDpRnpIxFj3ouJkIpxJ40cIMS2610t2zzYqo1Zii5n6dn3NYKAl5SLKxvcxOe6/aSjiVKnOVkZt+cGtme+bSDPeIRNihBCnNzHcC4A5zBs/xrcaP+PDszsi1XR0JxGah8iCjf6INW1Gk4m6+PMpGd2Fy+lQkkEIcarM/l3UWpcTabECvp+hmxLPo3h0j/xZFTMmjR8hxLS01B4hW+/EXnDFtF9rXXo9UZqT6l1/D0AyATA6PEAyQ3gSgjPK/aSUjDzGdQt6X21Q6wohwsfkcD8AkbHJipPMTkS070MP++jsdvwM1bwJQM6yi2adaabMi64llgmq98hOAiFCQUdTNTl6B5M57/57wVx2FTHaJNV7Zee8mBlp/AghpqWzwndHT/7GW6b92tLyKxnVo3Ael8ZPoHQ1+I5aRaaXBbWuZjDQacrCOtoY1LpCzluK4gAAIABJREFUiPDhHPPtkLHGhXfjxxKTBIBjbHBW60R07qNDSyM5PccfsWakdON12HUzY0eeVZZBCPGOtn1bAMhY9e57NEs2bMahmxk7skVFLDEHSONHCDEtca2vUGcsJC27cNqvjYi0UBO7noLBHTL9KUCG2yoBSMwN3ij3k4as+SQ7ZGqbEOL03GO+HTK2MG/8RMX4jqq5xma+40f3eskZP0ZHzFJ/xZoRa3QcVdbV5PZu88uUMiHE7JiaXqObJHJL3n33ly0mnuqoFWT1bFOUTIQ7afwIIaZssLeTEucJejMvmfEaesnVJDNE7UF54woEV08tXl0jPT/4jR9XQiHp3l7sE2NBry2ECH3eSd8OmZj48G782OJ8O348E0MzXqOrtZYUBvFkrfVXrBlzFl5Jpt5NU9V+1VGEmNc8bjdFY/tpji9HM5z6a/rkgsvI0TtorTuqIJ0Id9L4EUJMWd2bT2DUdJJX3zDjNYrPuxm3bmDg4NN+TCZOihiqp8uQiiXKFvTa5rQSDJpOZ+PxoNcWQoQ+bXKIST0CizVadZRZiYn3NX50+/CM12g/9gYASWXn+yXTbBS8dXS7e4+MdRdCpbpDbxDLOIbi03/AmlN+EwDte2Ssu5i+WTV+NE1r0jTtqKZphzRN2+evUEKI0GSsfYEeEiladt6M14hLTKE6cinpna/5MZk4KW6imb5INfdFxGcvAmCw5YSS+kKI0GawDzKqhXfTB8BkjmBct6DZZ77jx9O0mwk9kvxF6/yYbGaSM/OoMZWQ0Pay6ihCzGsDR1/Eq2sUrtt82u9n5pfSZMgluln+rIrp88eOn4t1XV+h6/oaP6wlhAhRDvsEpWN7aEy64LTbT6djNP9yFnibaW+o9FM6Ab47IzLc7UzG5Cupn1noG+nu6K5WUl8IEdrMzmHGDbGqY/jFqBaNwTEy49cnDh6mMbIEkznCj6lmrj/7Ukrd1fR1yT1tQqgS37GdelMhCSkZZ3ymM20TpfajjA7PbqqgmH/kqJcQYkqqdz+PTbNjWXzNuR8+h5z1NwPQuvvxWa8l3tHX1YJNs0NysZL61ug4uknCNFivpL4QIrRFukeYMMWojuEXk4ZozM6ZHfWyT4yR72pgJHmVn1PNXPpa3xGShjflfVkIFUaHByh2VtKXdvZd9fErNmPWPNTulCsTxPTMtvGjA1s1TduvadpH/RFICBGaJo/9nQk9ktL1p99+Oh1ZBYvf2qr6kh+SiZN63rpbx5pRqixDb2QOsePNyuoLIUJXlHsEp2lu7PiZNEYT4R6d0WsbjuzArHmIKtjg51Qzl79wLR1aKhH1L6qOIsS8VFfxHCbNS+ziK8/6XPGqixnGhqfqhSAlE3PFbBs/5+u6vgq4GvikpmkX/vMDmqZ9VNO0fZqm7evt7Z1lOSGECrrXS17fdqptq/12KWdn+sWU2Y8wPCB/L/jLWEcVACn5S5RlGI/OJ8PdJmOBhRCnsHlHcUXEqY7hFw5zLBbPzCYYDte8CUDuslN+bFZGMxhoSb6IsvF9TI7PrKElhJg5Z80rTOiRFK2++KzPmcwR1MZuoHB4J16PJ0jpxFwwq8aPruvtb/13D/AkcMoNdbquP6jr+hpd19ekpKTMppwQQpGG43tIpxd30VV+WzNh1Q2YNC+1O2WKiL/ofbXYdTOpWQXqMiQVE8s4A70dyjIIIUJTjD6G15KgOoZfuM0xWD0za5BYuvbRpmWQmJrl51SzE73sOiyai6o35QiJEMGW2b+bWusKIi3Wcz9cciWJjFB7cFvgg4k5Y8aNH03TbJqmxZz8Z+AK4Ji/ggkhQkfPvifx6hoLNt7stzVLVm6inzi06uf9tuZ8ZxlppNOYhcFoVJbBmlEGQHejvB0IId5hnxgjSnOiR8WrjuIXnohYbIxP+3W610vuxHE6Y5cFINXslJZfxQhWXCe2qI4ixLzS0VhFjt7BZO5FU3q+eMMNeHSNgUPPBjiZmEtms+MnDdihadphYA+wRdd1OWwoxByU1P4qteZSktP9NybcYDRSn3A+xaO7cTrsflt3PkuytzBozVWaIfmtY2Zj7TKxTQjxjtGhPgAM1kTFSfxDt8QTrU9O+6hFR1M1SQzjzVoboGQzZ46IpDZmPUVDO/C43arjCDFvtO7zNVszVl07pefjktKojlhMaufrAUwl5poZN350XW/QdX35W/9ZrOv6t/0ZTAgRGvo6milx1zCYfanf145YtJlYJqjZI5dJzpbL6SDd240jTt0xL4C0nCIcuhlvb63SHEKI0DI25LvPzWSbG0e9tKg4DJo+7ZHKncdeByB54QUBSDV7eunVbx0heV11FCHmDXPT63SRTG7x1HcCjuRcQqGngZ72xgAmE3OJjHMXQpxVw07faNe0tTf4fe3Sjddh182MHZGtqrPV1VyFWfNgSlEzyv0ko8lEhzETy0iD0hxCiNAyOdIPQER0suIk/mG0+hpY48P903qdp2UP47qF/IVrAhFr1oo33oRLNzJ4UO75ESIY3C4nReP7aUkoRzNM/VfzjLU3AtC484lARRNzjDR+hBBnFdGwlQ4tlfyF/t+WHmWLocq2hty+bTIFapb6W3xHq2KyFypOAoNReSRNykh3IcQ7HCO+o15RcUmKk/iHyeq7q2jirX+vqUoaPEyjpQyjyRSIWLMWl5hCtWUpGV2vqY4ixLxQd3g7sYxjLJ7ezvrc0pV0aKlENr4UoGRirpHGjxDijCbHRykb30dr8oXT+hRiOpyFV5Kp99BUuTcg688X9k7fKPeMBepGuZ/kiC8g3duNy+lQHUUIESJc474jUda4uTHhNSLad1eRfXTqR73GR4fIdzcymrIqULH8YizvcvK9rbTVySX9QgTa4JEX8eoaheumdr/PSZrBQGvSBZSO78c+MRagdGIukcaPEOKMqnf9HYvmwrr0+oDVKDjvFgC69spY99nQBuoZJIa4pDTVUTCllGDWPHQ2yQXPQggf71uNn5iEudH4iYr17VxyjQ9O+TWNR7Zj0rxYCzYEKpZf5Kz3vS+3VcgREiECLb5zO/XmIuKT06f92qgl1xKlOamukAm54tyk8SOEOCPniS2M6lGUll8ZsBrJ6blUm0pJanslYDXmg+ixRrrN/pu6Nhuxbx03G2iRxo8QwkefHMStG4iOmRvj3K2xvh0/rrGpN37GancBkL98aiObVckqWEijIY+YZjlCIkQgjQz1U+ysoi/tvBm9vqT8Kib0SOzHn/NzMjEXSeNHCHFaXo+HgoHt1MSUExFpCWitwezLKHHX0Nch98LMVKqzjVFbnuoYAKQXLAXA3lWlOIkQIlQY7EOMatEBOzYcbLa37iryTg5N+TWW7v00G7JDYmfmuXRlXEKp4xjD/d2qowgxZ9XveR6T5iV28cw+YLVE2ai2rSavb7vclSnOaW68+woh/K7u8A6SGUIvuSrgtdLW3QS8M0FMTM/YyCApDOJOUDvK/aS4xBQGiMXQX6c6ihAiRJicw4xp0apj+E10TDweXUO3D0/ped3rJW/yON2xUx/XrFLS6hsxaV5q35Rj2EIEirP6JSb0SIpXXzLjNVyFV5BOr9yVKc5JGj9CiNPqP/AUbt1A8Xk3B7xWftlqOrRUIupfDHituaizwXcBpyW9VHGSd3Sbc4gea1IdQwgRIiKcw0wYY1XH8BvNYGBUs2GwT23HT1v9URIYhWz/T8gMhKLlF9BHPFqN3B0iRKBkDeymxrpyVjvrF2zwfXjate8Zf8USc5Q0foQQp5Xa+Ro1kYuDsiVdMxhoSb6Ison9TIxN7dNT8Y7hNt9dOvE5ixQnecdodD5prlbVMYQQIcLiHsFunjuNH4BxzYbROTKlZ7uObwcgZdEFgYzkNwajkYbECygdrcDpsKuOI8Sc095QSbbehT13dnd+pWTmU2ssIr7tVT8lE3OVNH6EEKfobK6m0NPISO6lQasZvew6LJqL6p3PBq3mXOHqqcWra2TkL1Qd5W3exCKSGGZ4sE91FCFECLB5R3GZ41TH8KsJQwxm1+iUnvW27mFUjyKvNLRHuf+jiMWbidYmZWKQEAHQtn8LAJmrr5n1Wn2ZmyhxVjLY2znrtcTcJY0fIcQpWnb7zvRnlQf+mNdJpeVXMYIVV6VMJpgu82A9XYYULNbQuT/Dkl4GQFf9EcVJhBChIFofxRM5txo/DlM0ke6pNX5SBg/TGLUIg9EY4FT+U7ZhM5N6BBNH5AMZIfzN3PQ6XaSQUzT7e7+SVl6PUdOp3/WUH5KJuUoaP0KIU0Q1bqVVyySneHnQapojIqmNWU/h4A68Hk/Q6s4FcRPN9EWGxij3kxLzfMfORtpkpLsQ853H7SaWCbyWBNVR/MppjsXiOXfjZ3R4gHxPM+Mp4bPbB8BijabKtoa8vjdkYpAQfuR2OSka309LQrlfJh0WLT/fdydX7VY/pBNzlTR+hBDvMjYySNnkYdrTLg56bb30apIYpubg60GvHa50r5cMdzuT0fmqo7xLRv5CXLoRd2+N6ihCCMUGetoA0KLiFSfxL7c5Fpt37JzPNR3ejkHTsRVuCEIq/3IXXUU6vTQc36M6ihBzRt2hN4hlAmPxZX5Zz2A00hC/keLRClxOh1/WFHOPNH6EEO9Ss/NpIjQ3scuvC3rt4o034dKNDB54Oui1w1V/VyvR2iQkF6uO8i7miEi6DGlEDjWojiKEUKzhiW/i0TXSll+uOopfeSPjiNbHz/ncWP1OvLpG3vLZXeKqwoKNN+PVNXr2PqE6ihBzxuDRF/HqGkXls7/f5yTzwquIZZyafa/4bU0xt0jjRwjxLp7K5xgimpI1wbvY+aS4xBSqLUvJ6Hot6LXDVXfTcQCsGaEzyv2k/qg8EiabVccQQihUf2Qna3qfYF/KzSxYXK46jn9Z4onSnDjsE2d9zNq9nxZjDnEJyUEK5j/J6TnUmMtIaX9ZdRQh5oyEzh3UmYv9Ojm3eMP1OHUjo0f+7rc1xdwijR8hxNs8bjdFwzupi92AyRyhJMNY3uXke1tobziupH64Ge+oAiApb7HiJKeyxxaQ6emQO5uEmKd0rxfns/cxrMVQ9t7vq47jd1qU77Lq0aH+Mz7j9XhYYD9BT9zsL3BVZajwOoo89ex58qeqowgR9oYH+yhyVtGffr5f142OTaDaspyMnm1+XVfMHdL4EUK8rWb/KyQwilZ2tbIMOetvAaB1t2wrnwpvbw0O3Ux6TpHqKKcwJBcRqbnoaq1THUUIocC+Z3/FQtcJ6pd9nrjEFNVx/M5o9d1ZNDFy5sZPa+1hYhlHy1kXrFh+t/a2L3E0ciXLD32T2kPbVccRIqzV73kek+YlbvEVfl97PP8y8rxt8uGpOC1p/Agh3jZ06BmcupHijTcqy5BVsJBGQx4xTS8pyxBOLKNNdBgzQ3JEcHTWQgD6mo4pTiKECLaRoX4WHPw+1aZSVt/wKdVxAsIcnQjA5MjAGZ/pOeFrlKQuDr/7fU4ymkxkf/hhBrV4op/6EEN9XaojCRG2XDUvM65bKFrl/yEqOeW+n99bK2SsuziVNH6EEG/L7H6dassyYuOTlOboyriYUsdRhgd6leYIB4n2FgajclXHOK3UBUsAmOisUpxECBFsJx6+n0R9GMO1PwrJxrQ/REb7xtM7Rs/c+NFb9zCMjZyipcGKFRAJKRmM3vB7kvRBWn9zJx63W3UkIcJS9sAuam0riYi0+H3trILFNBuysTXJnVziVNL4EUIA0Fp3lDxvG+P5/t96Ol2JK2/ApHmpfVOOe52Ny+kgw9OFI65AdZTTSkrNYgQrWr8c9RJiPmk8sZc1XY+xN/l6ildeqDpOwETF+Hb8OCcGz/hM6vBhmiyL5kTzq3jlhRxa9u8sdRxgz0OfVx1HiLDT3nCcLL0bR27gdgB2pl5Eqf0wYyNn/ntJzE/S+BFCANBe4Wuy5G64RXESKF55EX3Eo9U8rzpKSOtqqcGseTCmhNYo95M0g4EuUza2URnpLsR8oXu9TDz1OcY0K6V3/kB1nICyxfumdHnGh077/eHBPvK9rUykrQ5mrIBad8tn2ZOwmQ3tD3Fw659VxxEirLTt2wJA5mr/jXH/ZzHLNhOheajZJdO9xLtJ48cP3C4nh79/BYde+j/VUYSYsZjml2k05JOZr34suMFopCHhfEpGduN02FXHCVkDLb7L+2KzyhQnObNhWz4pjlbVMYQQQbL/+d+x2HmE6sWfJT45XXWcgIqO8x2L9k6e/pP15sO+6ToxRRuDlikYln30QWpNxRS9+Xlaaw+rjiNE2Ihoep1OUsguDNzRz5I1lzKCFXflcwGrIcKTNH784Njrf2P5ZAXmvb9SHUWIGRke6KXUcYyujE2qo7wtYvFmYrRJqiteVB0lZE12VgOQXhC6d0e4EwpJo5+JsWHVUYQQATY2Mkju3u9QZyxkzU2fUR0n4CxRNuy6Geyn//ttvH4XHl0jf/ncOu5mibIR84GHcWtmPA/fxfjo6Xc8CSHe4XY5KRo/QGviejRD4H4FN0dEUhtTTsHQTrweT8DqiPAjjR9/OPC/AJQ5jtDf3aY4jBDTV/vmE5g0L4krb1Ad5W2lGzYzqUcwcfQZ1VFCljZQzxDRIf2pemSabwdZR71M9hJirjv68L+TygDuq3+I0WRSHScoxjQbRsfpGz/RPftpNuYRHZsQ5FSBl55bTPulPyfH00rVg3eje72qIwkR0uoObiNGm8RUfGnAa+nFV5LMEHWHdwS8lggf0viZpe62epZOVHDIugGjplP3xiOqIwkxbVrNC/QRT/HK0Bk3G2WLocq2htzebfID5RlEjzbRbcpWHeOsEnIXATDUdkJxEiFEIDVXH2JNx8Psib+GsjWB/8UmVIwbojE5R075utfjId9eSW/CcgWpgmPJBTewp/BeVo++RsXD31IdR4iQNnj0RTy6RuG6wN3vc1Lhhhvx6Br9B58NeC0RPqTxM0sNLz2IUdNJufXHtGqZ2OrkD5gILy6ng+LR3TTEbwy5qSOuwivJoJfGE3tVRwlJKc5WRmz5qmOcVcaCxXh1DVd3reooQogA0b1eRp74DJOahcI7f6g6TlBNGmIwu0ZP+Xpz9QFitEkMOesUpAqe9Xd9g4O281lT8wDHd8qdIkKcSULnDurMJcQlpQW+VkoGtRELSe54LeC1RPiQxs8seD0e8psf51jkCrIKFtOWeSUL7YcZ6GlXHU2IKave8yKxTGBedK3qKKcoOO8WvLpG914Z6/7PxkeHSGUAT0Kh6ihnZbFG02VIwTxUrzqKECJADm79I0sdB6ksu5ektNDehehvDlM0Fs+pjZ/eE9sBSF8SOjtpA0EzGCj66J/oMGaQvvXj9LQ3qo4kRMgZHuyj2FXFYPr5Qas5mH0xxZ46ejuaglZThDZp/MzC8R1Pk0Ev9mV3AZBafvtbx70eVZxMiKkbO/IsDt1M6cbrVEc5RXJ6DrXmUpLaX1UdJeR0NvgmekWkheYo93/UF5lD3HiT6hhCiACYGBsmc/d/0mDIZ/Ut96mOE3QucyxRnrFTvq617WGQWLILFitIFVwxcYnot/2JKN3OwEN3yjROIf5Jw54tGDWduKVXBq1m+hrfvZ2Nu54MWk0R2qTxMwuuvQ8xSAxLL30fAAVL1tOmZRBVK8e9RHjQvV5ye7dRZV2FNTpOdZzTGsi5lBJ3jXxi8U9O3plz8g6dUDYZs4BMd5vc1STEWYwOD+B2OVXHmLbDD/8H6fThuPIHmMwRquMEnScyDps+fsrX00aO0By1KKDTe0JJ3sLVVJZ/nzJ3JQd/8wnVcYQIKc6aVxjToyhauSloNfMXrqWLFMz1LwWtpght8+PdKAD6u9tYMvom1WnXEmmxAr7trq0ZV7DQfoihvi7FCYU4t+bqA2Tq3dgLrlAd5Ywy1t0MQMObjytOElpc3bV4dY2MBWHwaXJyMTbNTl9Xi+okQoSkmgOvoz+whJofXoJ94tTdI6Gqte4oq9v+xL7Yy1lYHrxPskOJNzKWGH3sXY3t4f5ucr3t2NNWK0wWfKuv+RC7099Hed8T7H36l6rjCBESdK+XnIFd1NpWYo6IDFpdzWCgOek8Ssf3YZ88tTkt5h9p/MxQ7Uu/IULzkLHpo+/6ekr57Zg0LzXbZLqXCH2de3zbPxdsvEVxkjPLK11Fu5aGpeFF1VFCinmonm4tGYs1WnWUc7JmLASgu/Go4iRChJ6qiq1kPn0HDiIocxyj6me34HI6VMc6J93rZeBvn8WJmfw7fqw6jjJaVDwmzcvE+DuTvZoObwMgpvg8VbGUWXPPTzgesYylB75G/ZGdquMIoVx7wwky9R6cecG/78uy+FqsmoOaiheCXluEHmn8zIDu9ZLZ8FeqzIvIW/juT3MKl270/ZIqx71EGEhoe5VaYxGpWQtURzkjzWCgNWUTZRMHmBgbVh0nZMRNNNMXmaM6xpSkvLUraby9SnESIULL8Te3kPvcXQwaEvB++BX2Lv4KKyZ3c/jn78Pr8aiOd1aHX3mE5fa9HCv5BMmZearjKGOIigdgdKjv7a9N1O/ErRtYsDx4F7mGCpM5gvR7HmZEiyHqybsZ7u9WHUkIpdr3bwEgc3Xwh6iUrr+GST2CyWNbgl5bhB5p/MxAZcWL5HrbGVn03lO+pxkMtKRfzsLJg/JmJ0Jaf3cbJc5K+rIuVR3lnKKXX0ek5qJ6pzRUwdd8Tne1MRGTrzrKlKRmLmBCj0Tvk5HuQpx0dNsTFG79ID3GVKI+9iJp2YWU3/4FduV/gjUjL7HnVx8L2Xux7BNjpO78D5oMuay+7d9Ux1HKZPU1fiZH+t/+WkzfQZpMC0L27rxAS0rLZmDzb0n29tH0m9BvYgoRSBHNr9OhpSq56N1ijabatpqc/u0h+34igkcaPzMwvvv3jOpRLLn8A6f9fvK692DWPFTLdC8Rwup3PolB00lZfYPqKOdUuvYKRrDhOiGfWAD097QRo02iJ4X+RC8Ag9FIpymLqJEG1VGECAmHX32E0lc/Qrspm9iPv0hyeu7b31v/ge+wO+0O1vf+ld1/CM2mysFHvkGm3sPYpd8J6p0VoSgiOhGAydFBADxuNwvsVfQnLFcZS7myNZdycPGXWW7fS8UfvqQ6jhBKuJwOiscP0pqwXtlF744Fl5Gp99BSfVBJfRE6pPEzTcMDvSwdeo0TyVee8ZOcouXn06GlEln9TJDTCTF15roX6CaJwqUbVEc5J3NEJDWxGyga2oHH7VYdR7meRt8od2tGqeIkUzdkzSPZIZc7C3HgxT+xcNu/0GzKJ/lfXiQxNetd39cMBtZ99JfsjbuKDS2/puKR7ypKenodjVWsbH6I/TEXs+S861THUS4yxtf4cY4OANBUuQ+bZseYW64yVkhYd+t97I2/mg2tv+Hwq3L3pZh/6g6+TrQ2iblE3e76/A03AdC59yllGURokMbPNFVt/S0WzUXShR854zOawUBL2uUsnDzA8EBvENMJMTX2yXFKx/bSlHRB2Iya1UqvJpERag+8pjqKcmMdvrtykvPCYKLXW5zxhaR7e3DYJ1RHEUKZ/Vt+y7Kdn6bRXEzavVuJS0o77XMGo5GVn/oTB60bKa/6Hvue/XWQk55Z918/ixcD2e+Zvxc6/yNrrK/x4xr37fjpq3wDgIwlwb/INdRoBgNLP/pb6oyFLHjjc7Q3HFcdSYigGjq2FY+uUbgu+Pf7nJSWXUi9sYDY1leVZRChITx+4wsRutdLSs0j1BkLKTrHhX2J624nQvNQI8e9RAiq3v0cVs2BZclm1VGmrPi8m3DpRgYPyk46b28tDt1MWk6R6ihTZk4twajpdDVWqo4ihBJ7n/oFK/Z8npqIRWR9+gVi45PO+rzJHMHCe//G8YhlLN/3ZQ6/+liQkp7Z4df+ysqJnRwu/Bhp2YWq44SE6LhkADyTQwAY2vfSTxyZ+eGzIzOQLNZorO9/GC8ajj+/l8nxUdWRhAiaxM4d1JlLiUtMUZqjJ2MTpc7jcv/sPCeNn2moPbSdAm8T/aV3nvPZ4hUX0kUK5mq5jFaEHvuxvzOhR1K6/hrVUaYsNj6JKssyMrplx0/kSCOdxgyMJpPqKFMWl+0b6d7fckJxEiGCb8/jD7D64FeotCwn/1+fIzo2YUqvs0TZyP3k0zSZFlC67V+orHgxwEnPzGGfIOmNr9KqZbL6PV9RliPU2N7631J/q/GTMXKUFuvisNlNGwyZ+aW0bPop+Z5mjv/6brlkVswLwwO9FLmqGchQP90vYcVmjJpO7a6nVUcRCsm70jQM7fgNE3oki66855zPagYDTWmXsWhiHyND/ed8Xohg0b1e8vu3Ux29FkuUTXWcaRnPv4J8byttdcdUR1Eq0d7CYFTuuR8MIekFSwBwdMlIdzG/VDz6PdYd/TrHotZQ9K9bpj3pKSYukaSPPUOPMZWs5++m/ujuACU9u4OPfItsvZOhTd8mItKiJEMoMpkjGNOj0OzDDPS0k6134khfozpWyFm26RYq8j/GmpGXqXj0e6rjCBFw9Xu2YNR0EpZcqToKxSsuYoBYqFH34YFQTxo/UzQ+OsTi/pc4nnAJMXGJU3pN/NrbiNDcVG+T414idNQf3UUa/biKrlIdZdpyN9wCQFvFE4qTqON2OcnwdGKPK1AdZVpi4hLpJQHTYL3qKEIEze6/fIPyyu9y0LqR0s88g8UaPaN1ElOziLj7aSaJIu7x9wS9+d3VUsvyxt9wwHYBSy+6Oai1w8GYFo3ROULL4W0AxJacpzhRaCr/wHc4ZN3A6qofUVWxVXUcIQLKVfMKo3oUhSvV3/dlNJmoj9tI8cgu3C6n6jhCEWn8TNHxrQ9h0+zEbDz3bp+TSlZuopskTHLcS4SQ3v1P49U1CjfepDrKtGXml9JoyCem+SXVUZTpaq4hQvNgTAmPUe7/qCcih5ixJtUxhAiKXf97P+tr/4sD0Rey5DNPEWmxzmq99NwNI0YOAAAgAElEQVRi7Hc+gREvhr/cRE97o5+SnlvHY58DION2udD5dCYMNkzOYSYbd+HSjRQsU3+0IxQZjEYWfOTPdBtSSXr+o/R1yaRHMTfpXi+5A7uos63CHBGpOg4AxrKriGOcmv1yyfN8JY2fKYqrfJgmQw6la6Y+js9gNNKYehmLxvcyOjwQwHRCTF1y+yvUmMtISstWHWVGujIuptRxbN5eUHfyjpzYzDLFSaZvLGYB6e5W1TGECCjd62XX7+5jQ+Mv2Bd7Gcv+9XG//eCfV7qCvhv+Qpx3hInfXR+UvwePvvE0q8be4FD+/yMjTy4sPp1JUwwR7lFi+w7SZC6Y8c6u+SAuIRnXbX/Cpk/Q87s7cTkdqiMJ4XdtDcfJoBdn/ibVUd5WtOF6XLqR4cNbVEcRigS18ROuW8saj1dQ6q6mq+g9076sL37NbURqLqrf+GuA0gkxdT3tjRR76hjMmXoDM9Qkrb4Rk+al9s0nVUdRYrLTd0dOWsFSxUmmT08sIp4xBns7VUcRIiB0r5fdv/lXNrT+lj3x17Dy049iMkf4tUbxygtpvOK3ZHg66fqf6xgfHfLr+v/I6bAT+/r9tGnprLzjawGrE+6cphhs7iEWOGroT1ihOk7IW7BoLSfWfZtFrmPs/+2nVMcRwu869vuaK1mrQmeISmx8EtWWpWR0v646ilAkqI0fV39TMMv5Tc/rD+LUTZRe/uFpv7Zk9SX0kIixUm5RF+o17nwcgIx14XfM66Si5RfQRzxazfOqoyihDdQzjI34pDTVUaYtKsO3S6m74ajiJEL4n+71UvGrj7Oh849UJN3Imnv/HLDJe0vOu44T5/2EIlcNDT+/CYd9IiB1Djz2HfK8bfRf8I2wGwYQTG5zLLmeNqyaA1N+ueo4YWHNtR9hd+p7WN/zGPv+/qDqOEL4VUTzNjq0NLIKFqmO8i5juZeS722ho1EGbcxHQW38RHnHOfLa34JZctbsE2Ms7H2eo7EXkpCSMe3XG4xGGlIuZdH4HsZGBgOQUIipszRspV1LI690leooM2YwGmlIvIDS0QqcDrvqOEFnG2uiy5QdlqOCk/MXAzDaXqk4iRD+5fV42POLD7G+51F2p97Ouk8+hMFoDGjNlVfcxf4V/8lSxwGO//wOPG63X9fvaW9kWd2vOGTdwPJL7vDr2nONJzIOg6YDkLXkQsVpwsfqD/+ME+YlLNr77zQer1AdRwi/cDkdlIwfoDVxfcj9rJZV7vvgt7Vifu6an++C+v9GJ2Zit389rI58HXv5z8QyTmT5h2a8RuzqW4nUXFRtD6+ml5hbJsaGKZs4QGvKRSH3RjRdEYs3E61NUl0x/3b9pDpaGLHlq44xI+m5pTh1E+7eGtVRhPAbj9vNvp+/n/L+p9iVcRflH/910P6OXXfTvewu/hyrxrax/5d3o3u9flu75ZH7MOIl5dYH/LbmXKVb4gDoJYH0nPC7eF8Vc0Qkqfc8zLhmxfy3DzI82Kc6khCzVnvgNWyanYiS0LtWIadoKa1aJlFNr6iOIhQI7h0/0Znke1vZ/0T4/BARdezPtGnpLNpw7YzXKFt7Ob0kYDghx72EOtU7nyVScxG97DrVUWatbMNmJvUIJo7Mr4l546NDpDKAOyG8RrmfZDSZ6DBmYBkO3jQiIQLJ7XJy8Kd3sG5wC7tyPsz6j/ws6I319e/7D3Zl3c26gWfZ/dt/9cuax9/cwprRVziQ+0GyChb6Zc25THur8dNqWxr2H6wEW3J6Lv3X/IY0bw+Nv7kLr8ejOpIQszJ87EU8ukbBupn/7hhI7akXUjZ5MKD3w4nQFNR3J2tsIscjllNS+TOGB3qDWXpGWmsPs9h5lNb8W2a1ZdtgNNKQfAmLxnbLHzKhjKvyOUawUrruStVRZs1ijabKtoa8vjf8+gl3qOtq9E30ikgN38k6g1F5JNplhK8Ify6ngyP/fStrRl5iV/4n2HDPj5X90r/+ngeoSLqBDR1/ZPef/2NWa7mcDqyvfJlOUlhxx9f9E3COM1oTAHBmrFacJDyVrbucAwu/wIqJXVT88X7VcYSYlaSuHdSay4hLSFYd5bSil15LhOamZtffVUeZ8/q72xjoaVcd421B/wnFsvl7xOljVD761WCXnra2Vx/ErRsovvxjs14retWtWDQXVdsf90MyIabH6/FQMPgmtTHr/TZWWDV30VWk00vD8T2qowTNUKuv8ZOQG1qXBU6HPbaADE9HWB35FeKfOewTHPvJjawa28buos+y4e7vKc2jGQys+cTv2R9zMevrfsLeJ/57xmvt/9sPWOBtpmvj14myxfgx5dwVlZwDQNLiSxQnCV/rbv8S+2Ivp7zp12F3H6gQJw33d1PkqmUw43zVUc6oZO0VjOpRuCrn33UJwaB7vRzf+Rz7f3QDsb9cxsT/XBIyd5IGvfFTuGwj+xKvZVXXY7TWhe5kF6fDTknnsxy1bSA5M2/W65Wtu8I3iejEU35IJ8T01B7cRjJD6CVXqY7iNws23oxX1+jZN38uqHP21AKQsSB8Gz/G1GIiNA9dzXLPjwhP9okxqn5yPSsndlJR9m+sv+vrqiMBvqOUSz/1CEcsa1h1+D84+OL/TnuNvq4WFlf/giOWtay47L0BSDk3Ld64meY7XqN4pVzsPFOawcDijz1EkzGPvG2flqlDIizV73kOg6aTsDR0d9dHRFqojVnLgsE359Wu+UAbGeqn4pHv0vytZSzeeidFY3s5Ensh2XoXB5/5uep4gILGD0DBe76LCzN9T3xRRfkpOfbaIyQxjGHN3X5Zz2gyUZ98CWWjFUyMDftlTSGmauDg077da+fdrDqK3ySn51BjLiO5ff5cUGcerKeL5LD+FD42y3dfSH/LMcVJhJi+yfFRav97M0sn91Gx+GuU3/Fl1ZHeJSLSQtGnnqDWXMbinZ/j2Pbp3S3Y+PDnicRJwi3qjq2FI81gIK8sfKdlhoooWwyRd/0fGjDx5/fisE+ojiTEtLhrX2FUj6Jo5UWqo5yVp+hKUhik/uhO1VHCXv3R3VT89P2YHlhIedX3cBqi2LP8W0R+sYZVn32CKvMiFhz/BfbJcdVR1TR+ktNzOVLwYVZO7OTYjmdURDgn06E/0U0SSy703y/KtpU3Y9UcVG1/wm9rCjEV6Z2vUx25hLjEFNVR/Gow51KK3bX0tM+Py4JjJ5rpjcxRHWNW0guWAjDZWa04iRDTMzYySON/X80i+yH2r/wW5bfdpzrSaVmj48j4xNO0G7NY8PJHqTmwbUqvq6rYytrhF9mfdRc5xcsDnFKI08sqWEzD+T+myFPPwYc+pzqOEFOme73kDOymLno1JnOE6jhnVbDhRry6Ru/+0Pw9PNQ57BPse+ZXVH17A4WPX8ny/uc5lnAptTc8S8m/72XdTfdisUajGQy4N91PKgMcelL9cCtlH+esvP1+OrRUrK9+FY/brSrGaXU2V7Nkcj8NOTdhNJn8tu7C8qsZIBb9uBz3EsHT0VTNAm8To3mXqY7id5nrfI3Zxp1z/+4s3esl3d3GRMwC1VFmJT45nUFi0PrrVEcRYspGhvpp++nVlDiOc3DdD1l746dURzqruKQ0Yj78LMOGOFKeeR/NVQfO+rzH7ca89Yt0k8Ty9/5nkFIKcXorLruTiuSbWd/9MEffmD/HuUV4a6s/Sga9OPM2qY5yTklp2dSaS0nqeE11lLDS0VjFrl/fy8T3Sllz4EvY3EPsLr4Px6ePs+4zD5/2uO+S867jWOQKimseVH7qR1njxxJlo3Pt/RR4m9j/1E9VxTitppcfBCD/so/6dV2jyURt0sUsHN3F5PioX9cW4kxadvkuScwqnzvHvE7KLV1Jm5ZBfPVjc/6c8kBvB7FMoCcWqo4ya93mHKLH5scuLRH+hvu76f75lRS4aji68SesufYjqiNNSXJmHvr7n8SDkahHbqGz+cy77PY9/iMKPY20rfsq1ui4IKYU4vSW/7+f0WTIIePVz4TUVBwhzqRj/3MAZK/ZrDjJ1AxkbaLEXUNfl0xaPRuP283hVx/j8PevIP0P61nX8SeabMs4eskfyfr3Y6x/39eIS0o76xqmy75KEsMcfvwHQUp9ekoPcK+66oNUmhdTeOwnjA4PqIzyNo/bTUHrkxyLWk1Gnv9HJttW3OI77rVDjnuJwBsfHSK/+vfUGQvJKVqqOo7faQYDnUs/Qam7mv1/f1B1nIDqbvTdiWPNKFOcZPZGbPmkOVtVxxDinAZ62un75VXkuRo5ceEvWXnlB1VHmpasgsWM3PoYFhy4/3Aj/d1tpzwz0NPOwsqfcixyBauuCq9/PzF3WazReG/+HTH6OC1/uGfOf7gjwl9kyzbatHSyChaqjjIlqatvAKBhl5xEOZ2BnnZ2/fGrdH97Ecvf+AhZk9VU5N5D30f2s/ILW1h64Q0YjMYprVW29jIOR5WzqPEhRob6A5z8zJQ2fjSDAdM13yOJYY498h8qo7zt2Bt/I41+3CveH5D1y9ZfzSCxeI5N78JFIWbi6F/uJ50+3FeoHTUcSKtv+BQ1phLyDnyPsZFB1XECZqzdN+EkKW+x4iSz50ksIpmhkGn4C3E6fV0tjPzqKrLcrVRd/CArLr1DdaQZKVhSTsfVfyDZ28fgg9ef8kNn3cNfIEq3E3PTA3KhswgpBUvKOVj6GVZM7GLPX3+oOo4QZ+R02CkeP0h74nrVUaasYMl6ekjEVLdVdZSQoXu9VO19mX3/dSvRv1jGhoafMmhOY/+6nxB3fw0b7vkxadkz23lvu+prxDHO8ce/6+fUU6f8Hb545YXsjbuS1R3/FxKjG737/kg/cSy5ODA/4JnMEdQmXsTCkTexT4wFpIYQAI3HK1jT+TB7EjZTVn6F6jgBYzAa0a/6PikMcvSRr6mOEzDe3hqcuon03GLVUWbNku7bTdnVIJO9RGhqrT2M/ddXkOrppu6Kh1i26RbVkWalrPwKajf9D3nuJtp+ecPbP39U73uVdYNb2J9xh0ylEiGp/I77OWxZy/ITP6Spcp/qOEKcVt2B17Bpdswl4XOfpmYw0Jh0AaVje+f9BL3x0SEq/vpjGr69irItt1A6vIODqTfS/J5XWXz/dlZf8yHMEZGzqlG0/HwO2C5kacufGerr8lPy6VHe+AHIu/37uDHS9fgXlObo62hm6fguajKuIyLSErA6UStuwabZqdwhW+tEYHg9HhxPfYZRLZqS9/1YdZyAK11zCXvjr2Z1+19orTuqOk5ARI400mHM8OuF86ok5i4CYLj1hOIkQpzq+M7niP3L1dj0cVo2/x9LzrtOdSS/WHbxrRxe933KHMeo+vmtOOwTGJ7/Ar0ksOS931YdT4jT0gwGsu5+iHHNiv7Xe0JiJLIQ/2z42Iu4dQOF665WHWVaIhdejU2zU7PnRdVRlGiuOkDFL+7B+6Myyo9/E4CKxV/D8Pkqyj/5O/IWrvZrvaTNX8eKncrH1QxRCInGT2rWAg7n3c2qsTc4sfsFZTlqX/o1Js1L9iX+vdT5n5Wtv4YhovEck0kFIjD2PfVTylwnqFvxJeKT01XHCYoFd/wAJxEMPB6aI5ZnK9HeyqAlV3UMv0jPL8Oja7h7alRHEeJd9j71C4pfvIshQwITH9hK2drw+fR2KtZc+xH2Lr6fFRO76PxBOcWeOppXf5no2ATV0YQ4o+T0HNou/BELvE0ceugzquMIcYqk7jepiygjLiFZdZRpKd2wGbtuZvzYc6qjBI3L6WD/cw9x/DsXkPfIxazseYrquPOpuvZxCr5ygPLb7sMWEx+Q2nkLV3Mg7jJWdDym5FLtkGj8AKx4z1fpJgnzS1/B6/EEvb7X4yGn6XGORywlp3h5QGuZIyKpSbiIsuE35ZML4XcDPe2UHv0hxyOWsub6f1EdJ2iS03M5Vvxxlk9WcPjVR1TH8Su3y0mGpwN7XHiPcj8p0mKl05CGeahedRQhAN/PALt++1nWHrqfGstS4u/dFjYXdE5X+e1fZFfex8n3tnAiYimrw2RKmZjfll9yO7tTbmN9z2Mcfu2vquMI8bahvi6KXLUMZpyvOsq0RdliqLauJKd325y/QL2nvZFdv7uPoe+UsnrPZ0hwdbGr4NOMffIIaz73N8rWXhaUe+7Sr/86ZtzUPf7NgNf6ZyHT+ImyxdC66osUe+rY/+z/BL3+iV1byNa7mFxyV1DqWZbfQrQ2SdUOueRZ+Ff9Xz5HlG4n+uafzruLOlfd9m+0GLJI3P6NOXVeubu1lgjNgymlRHUUv+m35BI/2aw6hhDYJ8c5+JNb2ND2e/YkXEvpfVvD7lPb6Vr/we9ycMNPSb37L/PufUKErxX/779pNOSTve0++rpkMqQIDfV7tmDQdBKWXqk6yozY8y8jS++mpfaI6ih+p3u9HHvzWQ788DoSH1xFecvv6Igq5vCFvyHtK5Vs+MB/kpiaFdRM2UVLOJB4Dat6nqSrpTaotUPq3X7VtR/xTec59GPGR4eCWttR8RDD2FhyeWCmef2zhRs3M4wNlxz3En50/M0trB1+gf3Z75+XF3VGRFoYuuCb5OgdHHxM3a35/tbX7LsLJzor/Ee5nzQZW0Cmu13JDk8hThroaafpx5eyevQ1dhd8mrX3/nnWFziGA81gYOWVHyQ5M091FCGmzBJlQ7vtd9j0Cdr/8KE5v0NBhAdP7SuMYKVoxYWqo8xI7vobAejcO3d+Jx0dHqDi0e/R/K1lLHnpLgrGD7Av4046P7iL5V96ieWX3K70zszcm74OQMtT3whq3ZBq/BiMRrxXfpdUBjj6aPC2Pw32drJ05A2qUq7GEmULSk1zRCTV8RdRNrR9Tu1MEOo4HXaiX/kiHVoaK977LdVxlFl28a0ctG5kaf2D9HY0qY7jF5OdvomHqfnhP8r9JC25mCjNSU97g+ooYp5qrjqA/X8uJt9Vx4H1/836D/yn7H4RIsTlL1zD4UVfYLl9LxWPzp0PeER40r1ecgf3UGdbjckcoTrOjGTkldJoyCem5VXVUWatqXIfFT/7IIb/Wkh55XdxGSzsWf4tLF+sZv3HfxkyR7jTc4s5mHojq/q30FYXvAm3IfcTTtnay9gfcwkrWv8YtO1P1S/9lgjNTeqmwF7q/M8il91EjDZJ1c5nglpXzE0HHv4med42ei/4FlG2GNVxlEq99b8w4aH5kc+rjuIXWn8dI9hITMlUHcVvojN9b769TTLSXQTfse1Pk/DItUToDlpu+BurrrpbdSQhxBStu+0LHLJuYGXVAzQcq1AdR8xjrXVHSKcXV/4m1VFmpSv9Ikodxxge7FMdZdreuaz5fPIfvZQVfVs4Eb+Jmuufofjf97HupnuxWKNVxzxF4U1fw4WJzmeDt+sn5Bo/AJm3fg+Atr/9W8Br6V4v6XWPUm0qZcHi8oDX+0cLz7ueEWw4D8+drXVCjfaGSlY0PsiB6AtZfsntquMol1WwkAPZd7Fm5CWqKraqjjNrtrEmukxZc2o3QtqCJQBMdFQpTiLmmz2PP0Dpyx9iwJCM60NbKVm1SXUkIcQ0aAYDuXf/jlHNhuGJe7BPjKmOJOapjv2+aVg5azYrTjI7Ccs3Y9K81O18SnWUKevtaGLX7z7/D5c1d7O74NNMfOooaz/7KCWrLlId8aySM/M4nHEbq4deorlyf1BqhuRvERl5pRzMvos1Iy9Tte+VgNaq3vcK+d5Whhe+N6B1Tici0kJ13AWUDm/H6bAHvb6YG3Svl/7H7sWDkew7f6o6TshYfuc36CER09Z/w+N2q44zKymOVoZt+apj+FVSeg5jehT0BfdiOzF/eT0edv36XtYd/TqVUStI/PTrZOSVqo4lhJiBxNQsOi9+gHxvK4d/f6/qOGKesrRso03LIHNBeN/BWLz6EoaIRq9+QXWUs9K9Xo7vfI4DP7qe+F+vYkPrb+iwFHHogl+T9pVK1n/gP0lIyVAdc8rKbv0aE1jo3/L1oNQLycYPwLI7vk4f8Wgv3B/Qy9tGd/6Ocd3C4ivuDliNszEvu4lYxqnc+ayS+iL8HXzxf1lm38vR0ntJzZob4779wRodR8va+yny1LP/qfBtiE2MDZNGP+74AtVR/EozGOg0Z2MdbVQdRcwDk+OjHPqvG9nQ+Ucqkm5k0X0vEBufpDqWEGIWll50M7vT7qS87wkOvfyw6jhinnE67JRMHKQ9aYPqKLNmNJmoi91A4cjukPywdGxkkIpHv0/zt5azeOudFI7tY3/67bR/YCfL/+1lVlx6h9LLmmcqPjmdo7nvY9XYG9QdfjPg9UK28WOLiadh+X2UuqvY/9xvA1JjZKifxYOvcjzpcmwx8QGpcS4Lz7ueUT0Kx+EnlNQX4W10eIDsim9SZyxkzW1fVB0n5Ky++h5OmJdQfOwBhgd6VceZkc7GSgAi0ubOKPeThq15JDtkJK8IrL6uFlofuIQVY9vZXXwf6z75UNhewimEeLeVH/ov6o0F5O34In0dzarjiHmkdv+rWDUHESWXqo7iF1rpVSQwSu2B11RHeVtz5X4qfv4h+PFCyiu/g1szs3fZN4n4QjXrP/ErsgrCf+jJopu/zDA2xl4I/GCrkG38AKy5/pPUGQvJ3vd9JsdH/b5+5dbfY9UcxJ//Eb+vPVWRFitV8RdQOrQNl9OhLIcIT8f/8iWS9UG81z4gv8ichmYwEHndD4nVR6l8+Muq48zIUOv/b+++46q67z+Ov75sAWUIgiAKKg5cqCiQ3fSXNEmzTBqjSZrZrF/S7Ga26a8jrdkxoxlN2iS/DLNT26QxzTKJAURRcOBWHAxlyF733u/vD4i/DDUOuAcu7+fjwUM459zzfR/gyj2f+x2rAIhM6v1/3L6rPWokcbZS8zNIt9m8Oh/XU8czpL2EwiMfJ+v8u31qriyRvi44JJSAmX8nxLZQ9sLFeNxupyNJN2ptaWLzqjyWvvcsOc/dwtIHzmD9HzPIe+wiVn31vld7q9StWoDL+jFi+slea7M7jTziTFzWj5rlzo5CaW9rpeCD51n1p2MY9trxTN41n+LIo1l76juMuGsJ08663qcWsYmIimF1ysWkN+d2+xQ3PbpPlJ+/P23/dQ/xC2aR8/ofyb7k3i49/8C1r7LRP4XU9KO79LwHK3D8DCIWfUjRV/9i4nFnO5pFeo8NhV8yreIN8mNnkNnDJzBz0oiJR5C38Awydr7F5tVXkpI2zelIB6WtYh0Ag1N6xhKUXSloUCp+JZYdG1cyYkKW03HExxR99hbDP72GZhPCjhlvMdnhv/Ui0j2GjU4nb/ztZK76Pbnz/kjW+b91OpIcpoa6Gko3FFG7dQWuirWE1G4gtnkzgz3lpBhLCuCxhjK/QewOjGNC5b8J/fBddn0YxcbY/yJy+ixGTz2+Wwv9MRWLWB80hrE+Mmw4IiqGVcHjGFz+CVvXLSc4tD8h/cIJDg0nOLhft79pUllawvoPnmDE1jeYQjVlxJKTci2jT/5vpg1K7Na2nTbp7NuoeuAlXP/5PWR0Xw+yHl34AUjLPpmCL49h0pa/s6v0amITkrvkvBsKv2SkeyN5Y25nhMPv/o056gwavryJlsK3QIUfOQBulwv7zxuoMRGMveABp+P0eKNn30vD4x/R9O7N2DGf9ap3/AN2b6KCgcSFRzgdpcvFjz0S12I//N65ktLw13r95IjSc+S9fh9TV/2Zrf5DCb3kLVKTRjodSUS60fSzb2TZlk+Ysm4uGwr/i5GTjnQ6khyAml1llG1YTv32Vdhdawmt20hcyxbiqOLrAe5t1p9S/0R2hqWyPfIUAuLHEDVsIokjxpMYGk4iHfMhLl34Bn6r32HyzncJfu8Nyt+LZUv8iQzMnMXISUd16Wu/ml1ljGjfQN6wK7rsnD1B/bATGLf+IXjl228ou62hhWBaTDCtJoQ2E0ybXwjtfiG4/ENw+4fg9g/FExCCDeiHDQqDwFBMYD/8g8MwwaEEBIURENLxERgSTnBnUWlXSTHNXz3NxLrPyTZuikKmUjr1Hib8aCaDe+G8PYciNDyColGXk7XuAVYu+ifjjzytW9ox1tpuOfHeZGRk2CVLlhz043ZsKib2haMojDqBaTfM65IseY9dxKTK92i9vpiI6NguOefhWPLQ2YysyyXszk0EBgU7HUd6uLzX5pBZ/GeWTHuAjJ86N1SxN8l7/T4yV99DQeYjTDn5EqfjHLB1f5xGm38o4+9Y6HSUbrFi4dsM+/Ra3Pix44Qnu+2PnfQNbpeL/L9eQ1bFPAr7TWfE1a8TPiDK6Vgi4gW7K8tpezybZr9QBt2c61PDQXoz6/Gws3QzOzcW0rhjNaZqHf3rNjK4fStR1O05rskGsyMgidqwFFzRowhOSGNg8ngGJ489qHuj+tpq1nw2j6A17zK2aQlBxs12E8+2hJOIyz6PlLRph10EWvres0zNv5k1p77NmG7soeFtrS1NFC+aj6upFk9bE562JmxbI7Q3Y9qbMa4m/FzN+LtaCHA3E+BpIdDTQlDnRzCthNhWQs3BTV9SRxir404j8YRrSRo5oZuurmdraW6k7t7xVAfGM/qORYf8O2qMWWqtzdjrvt5Q+AHIefoaMktfZuOMfx720Kymhlrc949mTeTRTLvxjcM6V1dZ9uFLTP7qGlYc/zwTjpnhdBzpwSpLSwh+OpMtIWMZf9vHvar3ipPcLhdb/pxBmLuBiF8t6xUvCJsaanE9MIbigSeS+csXnI7TbbZtWIHnldkkunewdOytTJ95m36v5aA11u9m3ZOzmdz0FXmxP2PqFU9q7jORPmbll/NJ+8+F5MecTuYvX3Q6Tp/idrko37qWXZuKaC5dTUDVOiIaN5HQvo1w07znuFrCKA0cRn34cDwxowhNGEfs8AnEDRmJn79/l2aqrapg7Wev0m/dPxjbspwA46HEbwilQ04h4cjzGTY6/ZDOu/iR2YzZvZDQu7bo78xeWI+H1pYmmhvraW1uoHSDQ0YAACAASURBVLW5kbbmetpbGnG1NOJubcDd2oS7tQn/kHDSfjSLUB/s2X6w8l6/n8zVf6Tw2GeZ9KNzDukcPlH4qa+tpv3hSZQHDmXsHV8c1k3B4nceY3rhr1l90mukZZ10yOfpSi1NDbjvHcHKgSeSed3/Oh1HerClD85gfN0X7Pz5p322Kn6oVuf8m7QFs8hJupzsy3r2ELnmxno2zT2FMa0rWH3835hw7FlOR+pW9bXVbHz6PNKbclgcdSrpVz1HUHCI07Gkl9hVuoXa584ixbWJ/DG3kjX7TqcjiYhDcp6+huyylyjIfpwpP/m503F8jqu9jbItxVRuXkFL6UoCq9cT1biJRNc2Qkz7nuN2EUVF8DAa+4+A2NGEDxlP3IiJDByU6MibO1UV29mw8BXCN8xnbOtK/Ixlk18yO4f+lKRjzj/gFaKsx8PO349kR9g4pvzK2YmQxbe0tbZQOWciTX7hjLhrySE9T3yi8AOQ98aDZK76PQVZc5ly0sWHfJ7ie7IJc9eS9OuVPepd5aUPzmB4/RL637VR1WPZqxUL32bCp5eQM/RKsi+9z+k4vdLSB2cwru4Lqi9ZRELyaKfj7FVzYz0bHz2VsS2FLMu4l4zTrnQ6kld43G7y/nYT2TuepzgwjdjLXicmPsnpWNLDbSz6iv5vn0+YbWLDsXOZdPwspyOJiIPaWlsoue9IYt3ltF3+JYMSU5yO1Cu1t7VSumkVVZsLaS0rJqhmHdGNm0l0byfI/P/qWeXEsjMkmaaIkfjHjaV/0jgGj0wnIirGwfT7t6t0CxsXvkzkpn8xpn01AOsDUqlK/inJx1xA/NDUfT62ZE0Bw+b9iLxxd5N5zs3eiix9RP67jzNt+V2HXLj2mcKP2+Vi65+mEmybib51GSH9wg76HCXFSxn22vHkjryBrAt+d8hZusOyBS8wOec6Vv74RcYffYbTcaSHaWlqoPL+qVj8GHTbUoJDQp2O1CuVb9vAgGePYE14Zo98p6alqYENc39KWkshBVP/TMbpVzsdyeuWvvcsaYvvoM70p37Gi4ycdJTTkaSHKvxkHqkLr6PehNNw9itaHU5EANi2vpCBL53AppCxpN36SZcPIfIlrS1NlG5cQfWWFbSVFxNcs47ops0kuksJNO49x5WaOHaFJNMUmUpA3Fgihk4gYeTEXj+PWvnW9Wz5/CUGbnmPVNd6ANYEprF7+KmMPPYCYhKGfev43FfvIWvtfZRelKdFKaTLudrbKP1zOh78SbpzGf4HOcG1zxR+AFZ8/g8mfHIhucOvI+vCPxz043OfvJIp5W/QcM0KonvY0nDNjfXY+0awIuZkn57PQw5NznM3k73tWRUGu0DO87eTveXJHve9bGlqYP3c0xjXsoylk//ItDOvdTqSYzYULiL8nQuJsHWsmv4nTWIu35P76j1MW3M/mwOGE3HZ21226qeI+Ib8t+cyrejuQ75n8DUtTQ3s2FBETckK2suLCdm9noHNm0lwlxFgPEDH6k1lfvHs6pdCS2QqgfFjiRw2gYQRE/rEHCw7Nq1i2+cvM2jrewz3bMFjDcXBE2gYeTqpx51H9KBECu89keiWbST9ttjpuOKjvp48fMnU+w66179PFX4Alt93EiMbl9Nydf5BDQNobWmiac4oNodPYcot8w87R3dY+sAZJDcsI/LXmw66wie+a+u65cS//GOKIo4j46a3nI7T67U0N1J132TaTRCJty/tESvptTQ3sm7uaYxvLmBJ+h+YPuOXTkdyXFXFdnY+O5Ox7avISbiQ6Zc+rP8XBVd7G0ufvorMyrdYFnoEo/97Xp+4IRGRg2M9HpY9dAYT6hex5cx3SZ18jNORulVj/W4qS7dQV1FCc9VW3Lu341dfSkhzOQNbtjHYU46/6bjvc1k/Sv0HU9UvhZaoUQTGj92zRHpIaLjDV9IzlKxdTumil0nY/j7DPNtxWT+KQ9IZ0bKKFbE/JfPavzsdUXyUx+1myz1TCLKtxN9ZdFBTwOyv8NMrX0EPnHEvwS/9iFWv30nMQUyEvPLjV5hKPdunXdx94Q6TGXcmA/M+Y2Xev7WssQAdL1zq3rqeSBNE8nmPOB3HJ4T0C2PXEf9D+qKryX3zfrLO+7WjeTqKPmcwsWUp+ZN+r6JPp4FxQ+h/yyfkPX052aUvUvjgWlKufJUBkQOdjiYOaairYeOTM8lsXkxu3GymXf64ioEislfGz48Rl/6NqkezCJl/JY0jcwjrH+l0rINmPR7q62qoLttMXUUJLVXbcNfuwL+zqDOgbSfRnkoG0MR3J8GoZgA1/jHsDEtlW9SpBA0eS3TyRBKGj2NoSChDHbmi3mHY6HSGjU7Heu5l0+p8KnJeIan0A0JNK6ETdI8m3cfP35+67NtIX3Q1+f98kmlnXd8l5+2VPX4Acv9yBdMqXqfknAUMH595QI9Z+edjiW4rJf7Xa3rsWN+mhlrM/SMpij1VlWQBYMn8p8gouI28tF+TOfNXTsfxGdbjYcV9J5Dcspr2q/MZGDfEkRytLU2smXsGk5oXs3jC/zD97BsdydHT5b1+H1NWzaHUfzB+583TinZ9jPV42Lp2Ge43LmWoeytLx92p/w9F5ICs+up9xi44jyXRpzD9+lecjvMt1uOhrmYXVWVbqN/ZUdSxtTvwayijX3M5A9p3EuOuJMy0fO+xlURSExBLQ9Ag2kLj8QxIJDAykX4xQ4mMH8bAwcmHNB+q7Jv1eKgq3/a9eX9Eupr1eFj/p0wGuGqIur3ogOd29bmhXgC11buwj6azPXgk42779AdX59qxaRWJLx5BzrCryL7k3i7J0F0KHjiNoQ1FRP16o97J7ONqq3fhenQqlQHxpN6R02MLlr1VydrlJLxyPMuiT2b69S97vf2Oos+ZTGrO0+oQB2DVV++T8OGV+ONmy3GPMfG4s52OJN2kpamBTUVfUrtuESHlSxjatIqB1NJg+7HpR0/oZy8iByXnrzeQvePvFGQ+wpSTL3EkQ3tbK2sXL6ChcD7hdeuJaNtJjKeSfqbtW8e5raHKRFETEEtjcBxtYfEwIIGAqCGExQwlIi6ZmMHDCAoOceQ6RMQ7vl7NOW/sHWSee/sBPcbnhnoBRETHkjfmWjLXzGH5x/NIP+G8/R6/9aOnibeG4Sdc4aWEh86mnUnM4s9Znf8f0rJPdjqOOGjNy7eQYevYffo8FX26wbDR6eTGz2R6+TzWL/vcq+P/21pbKH70LNKb88gb9xsVfQ7AuCNOoTThI1penMm4Ty8jd/sKMs+7+wcL/9KzWY+Hiu0b2bFiIe1bcomuKSSlfSNpnavJbDMJbIrIZsOQaQzLPIOJ+1lmV0RkbzIuupe1933FyLy7KB9/NPFJI73SbmP9btYuehd38XuMql3EeBppsYFsCRzJzrBR7Ag7FgYkEBiVRFhsEpHxyQyMS2JQUDCDvJJQRHqq8UefyepFDzGi+EmaG6+hX1j/wzpfr+3xAx2V89I5U/CzHuJuX7bPyrervY3d96Syvd8Y0m9b0GXtd5fG+t34PzCSwtjTybz2b07HoXzbBkr+cQ+e0FhCkzNITMs+qEm15dCsWfIxo/55NovjzyXr6qedjuOz6muraX14MpUBgxl1xyKvFNjaWltYNXcGk5u+Ii/tLjJn3trtbfqSpoZa1jx1AVMaPic/4kQmXPW8urP3Iq0tTWxemcPutYsIKstnSMNKBlENQJMNZnPwKOpiptBveDZDJx7T41bgFJHeacemVUS+cDwlwamMvvWzbutVX1WxnY1fvknQhn8ztmkpwaad3YSzPuIo/NNOZcyRp2tCehE5IKtz/k3aglnkjryRrAv+5weP98mhXl8r/PQNJi38BbmpN5N1/t17PWbZhy8x+atrWH7kkz/YM6inKLj/VJIaVzLwNxsd7emxZsnHxPzrUgbYegLw4Ne5GkAFAykNHU1L7ERCkzMYkpbt2BwpvsjV3kbJnOn0d9cSelMB4QOinI7k0xa/8xjTC39NfvqfmHbmNd3aVntbKysfmcHkpkUH1XVTvs16POS+cAfZJU+xLmAUkZe8zqDEFKdjyV5UlpawbcVntG7OI7JqGSlt6wk27QCUmkGUhk/AnTiN6DFHkZw2vUessicivin/3SeYtvxOcpOvIeviP3XZebdtWMGO3DeJKPmQ0W3F+BlLqRnE1tjj6D/pTEZPP+GgVuYREfla0ZzjSWpZT+BNRT94T+jThR+Aojk/JrmlGM+1BUTGxH9vf+G9J5LQvI6ou9b1mv90l/zrGTKW/Irik19nbOZPnMkw/ykmLP01lX7RuGa+SnRCCltX5VK/OZ+AiiLi6leTZEv3HF9ODGWho2mJnUBY8jSGjMvWO7WHKPfl35O1/kGWZT/K5J9c5HQcn+dxu1n/52wGuioIvnEZ/SOiu6Wd9rZWVsw9mymNX5A7+jayZt/ZLe30Jcs+fIlRi26myfSj6tTnGJPxY6cj9Wmu9ja2rF5M1Zov8d+RT0J9EQl2JwBtNoBNQaPYHT2J4OHZJE04ThNkiohXWY+HgofPZlLdZ2w87S1GZxx/SOfxuN1sKPySqqVvM7jsY5I92wDY4D+CXYk/ZtC0sxk+brqGIovIYVtX8Bmj5p9xQHMV+3zhZ0vxEobMO4GlsTO+NzSqYvtGYv46lcVDLiL78rld3nZ3aairIfDBVJYNOpOsa571atset5u8524ku/QFVgVNJPGKN/ZaUIOOYTJbV+VSv2lxRzGoofg7xaBYysL+vxiUNO4IomIHe+tSeqXybRsY8OwRrA9NZ+KvPtCLBi9ZV7CQUfNPJzf+fLKu+kuXn7+j6PMzpjR+Tu6oXzm+hLwv2bw6n6A3zifWU0Xh5N8x7cxrnY7UZ9TsKqOkaCHNm3IYsKuAlNa1hJpWAHYSzfbw8bQNziBy9FGkjM8+4FUpRES6S21NJU1zs/AYPyJuzDvgXtVtrS2szX2fpqJ/kFL1OYOoxmX9WBs8gfqUnzDsiJ8xeNjobk4vIn3RsvtOZkTTcux1RUREx+7zOJ8v/ADkPX4JU3e9y45ZHzFs7NQ923P+fhvZJU+x48JcEoeP7Za2u8uy+04hsamYmN9s8Npwr8b63ax78ryOYSjRpzP5yr8e9KoBdbur2LYql/rNiwnsLAYNsWV79pcRS1nYGNpiJxKWkkHSuCP2WVjqiwruP5WxDXnUXPwFCSljnI7Tpyx+ZDbpNQsoP/8Tho5K77LzutrbKJr7M6Y0LNzvsFQ5dLsry9n+13MZ37qc3EHnknH5472mh2dvs219ITsWvUbMtg8Y6d4IQLv1Z0vgcKqi0glIzmTIhOOIGzJChWsR6ZHW5H1I6vszKYg8kWk3vr7P4+prq1n35dvYNe8zuu4r+ptmmmwwa8Kn4x51CqOOOpuIgXFeTC4ifdGmlXkMf/NEchIvIfvyR/Z5XJ8o/NTsKsP/iSlsDhnHpNs/Ajp6rlT8YTSVwUOYcMdn3dJud1oy/ykyCm5jzSlvMmb6Cd3eXlnJWppfmMkwdwn5Y35F5rl3dNmL9tqaSratzqFhUz6BO4uIa1izl2LQWFoHTSA8ZRpJadl9shi0/ON5pH9xJTkp15J90T1Ox+lzKsu3EfzkNDb3G8+EWz/skt9/V3sbhY/OZGr9p+Sm3kTW+b/tgqSyN672Npb89Rqydr7OiuApDL1inl6QdwHr8VCytoCynNeI3/4hKZ4tAKwNGEN10o+JGHU0KROPOuzVJkREvCnnuVvI3vZXlkx7gIyfXr5ne2VpCRsXvUG/jR8wprmAIOOmmgFsiDqaoPGnMyb7VEJCwx1MLiJ90dIHz2Rs3Vc0/3fBPufW7ROFH4Dcl39H1vqHKDr2OSb+6GcUffYWEz+7lKXTH2LqKZd1W7vdpb62muCHUimIO5us/36mW9tas/g/xL5/GYG0s+W4x5l43Nnd2h50FoNWfUXD5iUEVhQS17iGIbZ8z/4qIigPGkpD/+HYgaMITRjLoOETGZQ43CeXNm9urGf3/ZNp9etH4u1LNMGpQ76eX2n50U+T/uNZh3WujqLPuUyt/4TckTeQdcHvuiil7M/idx4lffnv2OkXg2vmyySP3evfP9kP6/GwadVidua9RkLphwzzbMdjDWuCxlE3/BRSjp5F3JARTscUETlkrvY2Ntx3LIltmyk54RnqN+YRve0/jHatBWC7iWd73I+JnHwmqVOP77ZVwEREDkTJ2uUMeeU48uNnkXX1U3s9ps8UftpaW9g5Jx23CSDxjgKK5p5NSsMyQm9f12vnFVh+30kMblpH7G/Wd1uxI//dJ5i07G52+sXinjWPYaO7bojLwaqt3tVRDCopwK9yHQMatzC4vYQIGvcc02SDKQ0Ywu6wFNqjRhIcP4aoYeNJGD6u1/6cAXKe+SXZpS+y+qTXSMs6yek4fVZ7Wyulc6bib9uJubXgkJcJd7tcLHv0XDLqPiJ3+HVkXfiHLk4q+7Mm/yNi3ruMfraF9Uc93GtWdHSS9XjYUPgllYvfIKn8Q4bYctzWUBwyicYRP2XEMbOIiR/qdEwRkS5TunkN/Z8/jv6mGYB1AaOoHnIC8ZlnMWz0FA1XFZEeJf+RWUyo+Yi6K/L3upptnyn8wP8v3Z6T9Aumbv07BfEz91kR6w3y//EXpi27gzWnvt3lq9W4XS4WP3c92WUvsTI4naQrXu+RwyKsx0P1rlLKNxbRsGM1dtdaQus2MailhHh27TnObQ1lfvFU9kumZcBw/OLGMGBIGoNHphMRFePgFfywLcVLSJx3IsuifsL0G151Ok6ft+Lzd5jwycXkpFxD9kUHv9xrR9FnFhl1/9GwPQdVbN9I3fPnMqJ9A3kpV5F14Z/0Iv47PG436wo+ZfeSNxlW8RGD2UW79ae4XzotI09l5DHnanVGEfFpxXkLqNu6guTsGerJKCI9WunmNcQ+fwQFsWeQee3fv7e/TxV+rMfD6jnHMa6tEICSWZ8ybMyUbm2zO9XtriLk4dQuL2A11NWw4anZpDflkDfwTKZc+UyvHFrU1FBL6cYV7N66ivaKNQTv3kBUUwmJ7h0EGdee4yqJpGLPsLFUwhLTiE2Z0CMmH/W43aydczTx7VvhmnytetZDLLvvFEY3LqH+8pyDeiHodrkoeGw202o/JCf5arIvntONKeWHtDQ1sPKpi8io+4iC8GMZc9X/Ehoe4XQsR7ldLtYu+Yi6pW8yfNfHDKKaNutPcWgGraNOY/QxM3vkmwAiIiIifV3eYxcxufKfVF6SQ0Lyt1cS7FOFH4CNK3JJefMk1galMfaur7q9ve5WeO+JxDVvIu7udV1SpCjdspbWF2eS5N7K0rTbyDz39i5I2bO42tsoL1lH5ZYVtJQV41e1ngENm0lwbWXAXoaN1YSPwJOQwcAxR5GcNs2rqwEtfudRphf+hsWT/sD0Gdd5rV3Zv9LNaxj4/FGsiDiWjJveOqDHdBR9zmda7QfkDLuK7Evu7eaUciCsx0PeK79j2vq5bPdPpDIsFY9fINYEYP2DwC8A6xeI9Q/EdP6LfxDGPxDjHwj+gZiAIPw6/zX+QfgHBuLnH4QJCCQgIBi/wCD8A4LwDwzCPzAY/4AgAgKDCA4JpX9kjOPzkrna21iT9wGNy95iRNVnxLCbVhvI6rDpuMecxqhjZjIgcqCjGUVERERk/yq2byTyr5kURp3wvZEifa7wA1DwwfNEJY0lZVymV9rrTvnvPs605Xex7vR/MGrKcYd1ruK8BcT9+xcE4Kbk+CeYcMyMrgnZS1iPh6qdO6j45rCx+k0ktGwkht1ARzFoU8hY6mOnEDo8m+RJx3bbu981u8rgiWmUBQ5jzO2fO35zKN+W++xNZG1/juKTX2ds5k/2e6zH7WbJYxcwfff75Ay9guxL7/dSSjlQRZ++Sb9FcwhxN+KPG3/rIoCOj0DrIgD3t3oKdhW3NdSa/tT7RdAYEElLYCTtIdF4QqIxYTEE9I8leEAs/SLjCI+OIzJm8CHPLfVN7W2trMl5j+bCt0mtXkgUdR3LEPfPxI49g9FHn034gKguuEIRERER8ZbcJ69kWvlrlF6wkKTUSXu298nCjy+prd5Fv7mjKRg8i6yr/nLI51n8zmOkL/8tFX5x2NmvMnSUc5M49zTW46Fs63pKV3yGuySXgTWFJLs2E2A8AJT4JVERMRGGTCdu3DEkpU7qkiLN4kdmM7lmATtmfaiVh3qg5sZ6au9Pp9F/AMl35O9zRQ+P282Sx3/O9Jr3yEn6BdmXPejlpNJVrMeD2+3C1d5GW1sr7vY2XO2tuNpbcbe3425vxeXq+NfjasPtasfjasXjasfjasPjase6WvG428HdjqetCZqqMc1VBLZWE9xWQ5hrN/09dUTaOvzN3v8GN9lgdvtF0OAfSXNABG3B0bhDorGhA/EPjyGwfyz9IgYRFh3HgIEJ9I+Ixs/fn9aWJtZ8NZ+2oncZtftzImik0YZQPOBI/MefwZijztKy6yIiIiK9WFXFdvr9ZQrFA45k6s3v7Nm+v8KP1iXsBSKiYynsN4WhFf/BejwHPdzL7XKR/+x1ZJW/zIqQyQy98g0iomO7KW3vZPz8SEge3TlO8koAGut3s6boCxrW59CvYgmjahYSWfMerIBawtgSkkZT3FT6px5JyqRjCOsfeVBtrs79oKN3SOKFZKvo0yP1C+vP6ul3MnXxTeS98wiZ59zyvWM6ij4XdhR9hlxK1iXq6dObGT8/Avw6hmmFhIZ3a1set5va3ZXUVpXRWFNBS+0u2ut24m6sxDRV4d9STXBrDaHtNQxq2ULE7jpCTetez+WyflSbAYTYViaZZuoIZW3EUQSOn8GYo84gowt6EImIiIiI8wbGDSEncRbZpS+waWUew8f/8Cgn9fjpJb6eB2b9Gf8kdfIxB/y4+tpqNj41m/TmXPJizmLKFU/1ykmcewKP2822DSuoWP05bM1jUG0RyZ6tQMdQji0BKVRGpeM/NJOECccxeNiofRbp2lpbKL13GkG2hahbCvQOfA/WMWH8MSS0bcHvl0u/NezP43aT/8TFZFbPJyfxYrIue9jxycLFtzU31lNbVU59dTnNu3fSVrcTV0MltrES/+YqrPEnZPypjD3iNIKCQ5yOKyIiIiLdoLaqAr9HJ7EhbAqTb30f0FAvn1BbVUHoo2NZknA+2Vc+dkCP2bGpmPaXZjLEvZ2l4+4gc+at3Zyy76mt3kVJ4UIaN+UQvnMpw1uKCTMtQMdKYtvCxtM6OIPI1CNJnnjknnk7cl78DdmbHqXwmKeZdPwsJy9BDsCmlXkMe+MnLImdsWfpROvxsPiJS8isepechAvJ+sVcFX1ERERERMQrcv5+G9klT+3pHKLCj48omvNjBrZuI+E3a37wBnN1zr8ZvOBy/PCw7b+eYvxRp3spZd/mdrnYUryEyuIv8NuxmMF1RQyx5QC0WX82B6ZSEz2RSRXvUhyeyZRf/cvhxHKg8h6/hIxd71ByzgJS0qax+C+XkVn5NjmDLyDr8sdU9BEREREREa+pr63G9fBEtoWMZuLtH2uOH1/RknoaiSt+y4YVOYycdOQ+j1v81iOkF/2ecv94zHmvMX7kBC+m7Nv8AwIYMSGLEROy9myrqtjO1sLPaNmcQ0TlctIr3qHNBJI4e66DSeVgjZk9h7rH/kPz/JtZvHA0mZVvkxt/voo+IiIiIiLidf0joskdcSlZG+eyOveD/R6rHj+9SM2uMvo/nkZ+4s/JvuLR7+13u1zk//UasirmURQylWFXvk5EVIwDSWV/2lpbaGluZEDkQKejyEHKe+MBMlf9AYDcuNlkXvkXFX1ERERERMQRzY31NN4/np1BSYy7a5F6/PiCqNjBrAhJJ6nsw++t7lW3u4rNT51LVks+ubHnkHHFXwgIDHIwrexLUHCIJl3tpTJm3EDBls9ojUol69IHVfQRERERERHH9AvrT9GYq8hcM2e/x+mupZdpGnkqQ2wZm1Yt3rNtx6ZV1Dx6LGnNBeSNu5usa55V0UekG/gHBDDlV/8i+xdavUtERERERJyXfub1lLP/kT66c+llUo+dhcv6sTN3HgCrFr1H2IsnEuGpYe2JL5B5zs0OJxQRERERERERbwgOCaX6pCf2e8xhFX6MMScZY9YaYzYYY24/nHPJgYkelMiakIkMKV3A4jcfYtSHP6fWL4qGCxYw/sjTnI4nIiIiIiIiIl6UlnXSfvcfcuHHGOMPPAGcDKQBs40xaYd6PjlwjSNOJcmWMn3l71jdbwpR1y1kyMjxTscSERERERERkR7mcHr8TAc2WGs3WWvbgHnAGV0TS/Yn9bjzqGAguXGzGX/LB1odSkRERERERET26nBW9UoEtn3j6+1A5ncPMsZcAVwBMHTo0MNoTr4WPSgRe/cG4jS5rIiIiIiIiIjsR7dXDqy1z1hrM6y1GbGxsd3dXJ+hFYVERERERERE5IccTvVgB5D0ja+HdG4TEREREREREZEe4HAKP/lAqjEmxRgTBMwC5ndNLBEREREREREROVyHPMePtdZljLkWWAD4A3+z1q7qsmQiIiIiIiIiInJYDmdyZ6y17wPvd1EWERERERERERHpQpohWERERERERETER6nwIyIiIiIiIiLio1T4ERERERERERHxUSr8iIiIiIiIiIj4KBV+RERERERERER8lAo/IiIiIiIiIiI+SoUfEREREREREREfpcKPiIiIiIiIiIiPUuFHRERERERERMRHqfAjIiIiIiIiIuKjVPgREREREREREfFRKvyIiIiIiIiIiPgoFX5ERERERERERHyUCj8iIiIiIiIiIj5KhR8RERERERERER+lwo+IiIiIiIiIiI9S4UdERERERERExEep8CMiIiIiIiIi4qOMtdZ7jRlTD6z1WoMQAdT6cHsxQKUX2/Pm9fn6z07tqT2150x7vnxtak/tqT3n2vPla1N7ak/tfZs378F8/Xup9rrWCcya5gAACctJREFUaGtt/73usdZ67QNY4uX2nvHx9nz2+9kHfnZqT+2pPQfa8+VrU3tqT+05154vX5vaU3tq73vtee0erA98L9Ve17a3z99NXx/q9U8fb8/bvHl9vv6zU3tqT+05054vX5vaU3tqz7n2fPna1J7aU3vO8fXvpdrzEm8P9Vpirc3wWoM+Tt9PERERERER79E9mPRU+/vd9HaPn2e83J6v0/dTRERERETEe3QPJj3VPn83vdrjR0REREREREREvMfX5/jpFsaYEGPMYmNMoTFmlTHmd53bU4wxecaYDcaY14wxQU5nFeluxpiTjDFrO3/vb+/c9oUxZnnnR6kx5l2nc4p0N2PM34wxO40xK/ey72ZjjDXGxDiRTcSbjDFJxphPjTGrO18nXd+5/ZzOrz3GGA2TEJ+3n+dCujEmt/N10hJjzHSns4p4w97uG76x71FjTINT2XydCj+HphU43lo7CUgHTjLGZAH3Ag9ba0cCNcBlDmYU6XbGGH/gCeBkIA2YbYxJs9Yeba1Nt9amAznA207mFPGS54GTvrvRGJMEnAhs9XYgEYe4gJuttWlAFnCNMSYNWAmcBXzuZDgRL9rXc+E+4Hedr5Pu7vxaxKft676hc18GEOVgPJ+nws8hsB2+rkYGdn5Y4Hjgzc7tLwBnOhBPxJumAxustZustW3APOCMr3caYwbQ8bxQjx/xedbaz4Hqvex6GLiVjr8TIj7PWltmrS3o/LweKAYSrbXF1tq1zqYT8Z59PRfo+HswoPOwCKDUmYQiXrXX+4bOgtD9dLxWkm4S4HSA3qrzF3QpMJKOyuVGYLe11tV5yHY6/mMX8WWJwLZvfL0dyPzG12cCH1tr67yaSqSHMMacAeyw1hYaY5yOI+J1xphkYDKQ52wSEWd957lwA7DAGPMAHW/EH+FcMhGv2dd9w7XAfGttmV4rdR/1+DlE1lp3Z/fMIXRUL8c4HEmkJ5oNvOp0CBEnGGNCgTvp6MYv0ucYY8KBt4Ab9AaA9GV7eS5cDdxorU0CbgSeczKfiINCgXOAx5wO4utU+DlM1trdwKdANhBpjPm6F9UQYIdjwUS8YweQ9I2v9/zed05iOx14z4FcIj3BCCAFKDTGbKHj+VFgjIl3NJWIFxhjAum40X3ZWqt53qTP2sdz4SL+f/7DN+h4vSTi6/Z237CRjhE0GzpfK4UaYzY4kM3nqfBzCIwxscaYyM7P+wEn0DFm91PgZ52HXQT8w5mEIl6TD6R2rmgXBMwC5nfu+xnwL2tti2PpRBxkrV1hrR1krU221ibT0aV5irW23OFoIt3KdPTVfw4ottY+5HQeEafs57lQChzb+fnxwHpvZxNxwN7uG9611sZ/47VSU+dCSdLFNMfPoRkMvNA5z48f8Lq19l/GmNXAPGPMH4FlqNum+DhrrcsYcy2wAPAH/matXdW5exYwx7FwIl5mjHkVOA6IMcZsB35rrdXfAemLjgR+Dqwwxizv3HYnEExHd/5Y4D1jzHJr7U8cyijiDft6LlwOzO0cKdACXOFQPhGv+YH7BulmxlotMiIiIiIiIiIi4os01EtERERERERExEep8CMiIiIiIiIi4qNU+BERERERERER8VEq/IiIiIiIiIiI+CgVfkREREREREREfJQKPyIiIiIiIiIiPkqFHxERERERERERH6XCj4iIiIiIiIiIj1LhR0RERERERETER6nwIyIiIiIiIiLio1T4ERERERERERHxUSr8iIiIiIiIiIj4KBV+RERERERERER8lAo/IiIiIiIiIiI+SoUfEREREREREREfpcKPiIiIiIiIiIiPUuGnhzPGDDHG/MMYs94Ys9EYM9cYE7Sf428wxoR6M6OIiIiIiEhvZ4yxxpgHv/H1LcaY/3EwkkiXUOGnBzPGGOBt4F1rbSowCggH7tnPw24AVPgRERERERE5OK3AWcaYGKeDiHQlFX56tuOBFmvt3wGstW7gRuBSY0yYMeYBY8xKY0yRMeaXxpjrgATgU2PMpw7mFhERERER6W1cwDN03HN9izEm2RjzSee918fGmKHGmAhjTIkxxq/zmDBjzDZjTKC3g4vsjwo/Pds4YOk3N1hr64CtwC+AZCDdWjsReNla+yhQCvzIWvsjL2cVERERERHp7Z4AzjfGRHxn+2PAC1/fewGPWmtrgeXAsZ3HnAossNa2ey2tyAFQ4af3Og542lrrArDWVjsbR0REREREpHfrfKP9ReC67+zKBl7p/Px/gaM6P38NOLfz81mdX4v0KCr89Gyrganf3GCMGQAMdSaOiIiIiIiIz3sEuAwIO4Bj5wMnGWOi6bh3+6Q7g4kcChV+eraPgVBjzIUAxhh/4EHgeWABcKUxJqBzX3TnY+qB/t6PKiIiIiIi0vt1jqZ4nY7iz9e+oqNHD8D5wBedxzYA+cBc4F+d87KK9Cgq/PRg1loLzADOMcasB9YBLcCdwLN0zPVTZIwpBM7rfNgzwAea3FlEREREROSQPQh8c3WvXwKXGGOKgJ8D139j32vABWiYl/RQpqO2ICIiIiIiIiIivkY9fkREREREREREfJQKPyIiIiIiIiIiPkqFHxERERERERERH6XCTw9ijEkyxnxqjFltjFlljLm+c3u0MeY/xpj1nf9GdW4fY4zJMca0GmNu+c65rjfGrOw8zw1OXI+IiIiIiIiIOEuFn57FBdxsrU0DsoBrjDFpwO3Ax9baVDqWeL+98/hq4DrggW+exBgzHrgcmA5MAk41xoz0ziWIiIiIiIiISE+hwk8PYq0ts9YWdH5eDxQDicAZwAudh70AnNl5zE5rbT7Q/p1TjQXyrLVN1loXsBA4ywuXICIiIiIiIiI9iAo/PZQxJhmYDOQBcdbass5d5UDcDzx8JXC0MWagMSYUOAVI6qaoIiIiIiIiItJDBTgdQL7PGBMOvAXcYK2tM8bs2WettcYYu7/HW2uLjTH3Ah8CjcBywN2NkUVERERERESkB1KPnx7GGBNIR9HnZWvt252bK4wxgzv3DwZ2/tB5rLXPWWunWmuPAWqAdd2VWURERERERER6JhV+ehDT0bXnOaDYWvvQN3bNBy7q/Pwi4B8HcK5Bnf8OpWN+n1e6Nq2IiIiIiIiI9HTG2v2OGhIvMsYcBXwBrAA8nZvvpGOen9eBoUAJMNNaW22MiQeWAAM6j28A0jqHh30BDKRj4uebrLUfe/ViRERERERERMRxKvyIiIiIiIiIiPgoDfUSEREREREREfFRKvyIiIiIiIiIiPgoFX5ERERERERERHyUCj8iIiIiIiIiIj5KhR8RERERERERER+lwo+IiIiIiIiIiI9S4UdERERERERExEf9H3kE05H7QaDUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Заполняем нулями пробелы\n",
"idx = pd.date_range(mean.index[0], mean.index[-1])\n",
"mean = mean.reindex(idx, fill_value=0)\n",
"mean[(\"response_time\",\"mean\")].plot(figsize=(20,10)).get_figure().savefig(f\"fanta/moderation_delete_meantime.png\")\n",
"mean[(\"response_time\",\"mean\")].plot(figsize=(20,10))\n",
"# df_stat[df_stat.index.get_level_values(1)==\"wall_reply_new\"].plot(figsize=(20,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Расчет времени до ответа на комментарий"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>reply_id</th>\n",
" <th>date_x</th>\n",
" <th>from_id</th>\n",
" <th>date_y</th>\n",
" <th>response_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>263524</td>\n",
" <td>2019-09-29 08:45:14.612536</td>\n",
" <td>-24539231</td>\n",
" <td>2019-09-29 10:25:08</td>\n",
" <td>1.664722</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>263527</td>\n",
" <td>2019-09-29 08:51:49.795747</td>\n",
" <td>-24539231</td>\n",
" <td>2019-09-29 10:26:03</td>\n",
" <td>1.570278</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>263528</td>\n",
" <td>2019-09-29 09:24:53.139219</td>\n",
" <td>-24539231</td>\n",
" <td>2019-09-29 10:23:35</td>\n",
" <td>0.978056</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>263534</td>\n",
" <td>2019-09-29 10:54:59.718837</td>\n",
" <td>-24539231</td>\n",
" <td>2019-09-29 12:02:26</td>\n",
" <td>1.123889</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>263539</td>\n",
" <td>2019-09-29 13:48:31.923808</td>\n",
" <td>-24539231</td>\n",
" <td>2019-09-29 15:24:17</td>\n",
" <td>1.595833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>329</td>\n",
" <td>265814</td>\n",
" <td>2019-11-06 17:13:17.665533</td>\n",
" <td>-24539231</td>\n",
" <td>2019-11-06 19:30:54</td>\n",
" <td>2.293333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>265816</td>\n",
" <td>2019-11-06 17:50:55.760159</td>\n",
" <td>-24539231</td>\n",
" <td>2019-11-06 18:29:43</td>\n",
" <td>0.646389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>331</td>\n",
" <td>265818</td>\n",
" <td>2019-11-06 18:25:38.003400</td>\n",
" <td>-24539231</td>\n",
" <td>2019-11-06 18:29:12</td>\n",
" <td>0.059167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>332</td>\n",
" <td>265825</td>\n",
" <td>2019-11-06 20:36:41.177975</td>\n",
" <td>-24539231</td>\n",
" <td>2019-11-06 20:50:36</td>\n",
" <td>0.231667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>333</td>\n",
" <td>265834</td>\n",
" <td>2019-11-07 13:20:14.999928</td>\n",
" <td>-24539231</td>\n",
" <td>2019-11-07 14:09:07</td>\n",
" <td>0.814444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>334 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" reply_id date_x from_id date_y \\\n",
"0 263524 2019-09-29 08:45:14.612536 -24539231 2019-09-29 10:25:08 \n",
"1 263527 2019-09-29 08:51:49.795747 -24539231 2019-09-29 10:26:03 \n",
"2 263528 2019-09-29 09:24:53.139219 -24539231 2019-09-29 10:23:35 \n",
"3 263534 2019-09-29 10:54:59.718837 -24539231 2019-09-29 12:02:26 \n",
"4 263539 2019-09-29 13:48:31.923808 -24539231 2019-09-29 15:24:17 \n",
".. ... ... ... ... \n",
"329 265814 2019-11-06 17:13:17.665533 -24539231 2019-11-06 19:30:54 \n",
"330 265816 2019-11-06 17:50:55.760159 -24539231 2019-11-06 18:29:43 \n",
"331 265818 2019-11-06 18:25:38.003400 -24539231 2019-11-06 18:29:12 \n",
"332 265825 2019-11-06 20:36:41.177975 -24539231 2019-11-06 20:50:36 \n",
"333 265834 2019-11-07 13:20:14.999928 -24539231 2019-11-07 14:09:07 \n",
"\n",
" response_time \n",
"0 1.664722 \n",
"1 1.570278 \n",
"2 0.978056 \n",
"3 1.123889 \n",
"4 1.595833 \n",
".. ... \n",
"329 2.293333 \n",
"330 0.646389 \n",
"331 0.059167 \n",
"332 0.231667 \n",
"333 0.814444 \n",
"\n",
"[334 rows x 5 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Извлекам отправителя сообщения\n",
"df[\"from_id\"] = df[\"object\"].apply(lambda x: x.get('from_id',0))\n",
"# Фильтруем только сообщения от модератора, забираем дату и id сообщения на которое дан ответ \n",
"df_rep = df[df.from_id == group_id][\"object\"].apply(pd.Series)[[\"from_id\",\"date\",\"reply_to_comment\"]]\n",
"# преаброзуем unixtime в datetime\n",
"df_rep[\"date\"] = pd.to_datetime(df_rep[\"date\"], unit='s')\n",
"# приводим индекс ответа к к reply_id \n",
"df_rep.rename(columns={\"reply_to_comment\":\"reply_id\"}, inplace=True)\n",
"# Ищем сообщения с указанным в ответе reply_id\n",
"df_date = pd.merge(df_new, df_rep, on='reply_id')\n",
"# Расчитываем diff от момента создания до момоента ответа\n",
"df_date['response_time'] = (df_date.date_y - df_date.date_x).dt.seconds/60.0/60.0\n",
"# Сохраняем\n",
"df_date.to_csv(\"fanta/moderation_response_stat.csv\", index=None)\n",
"# выводим\n",
"df_date\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">response_time</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date_x</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>2019-09-29</td>\n",
" <td>9</td>\n",
" <td>2.910309</td>\n",
" <td>17.188611</td>\n",
" <td>0.596111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-09-30</td>\n",
" <td>4</td>\n",
" <td>7.928750</td>\n",
" <td>21.863611</td>\n",
" <td>1.720556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-01</td>\n",
" <td>8</td>\n",
" <td>3.775625</td>\n",
" <td>10.535556</td>\n",
" <td>0.540556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-02</td>\n",
" <td>10</td>\n",
" <td>0.731167</td>\n",
" <td>3.130556</td>\n",
" <td>0.079167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-03</td>\n",
" <td>9</td>\n",
" <td>2.282716</td>\n",
" <td>4.780278</td>\n",
" <td>0.258056</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-04</td>\n",
" <td>4</td>\n",
" <td>1.195833</td>\n",
" <td>2.793333</td>\n",
" <td>0.663333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-05</td>\n",
" <td>3</td>\n",
" <td>4.555556</td>\n",
" <td>10.355833</td>\n",
" <td>1.148333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-06</td>\n",
" <td>3</td>\n",
" <td>0.960185</td>\n",
" <td>1.357500</td>\n",
" <td>0.611667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-07</td>\n",
" <td>12</td>\n",
" <td>2.043449</td>\n",
" <td>11.253889</td>\n",
" <td>0.099444</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-08</td>\n",
" <td>8</td>\n",
" <td>5.706424</td>\n",
" <td>18.895833</td>\n",
" <td>0.137778</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-09</td>\n",
" <td>10</td>\n",
" <td>4.489083</td>\n",
" <td>22.314444</td>\n",
" <td>0.030833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-10</td>\n",
" <td>5</td>\n",
" <td>1.711944</td>\n",
" <td>4.049444</td>\n",
" <td>0.316389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-11</td>\n",
" <td>5</td>\n",
" <td>15.215444</td>\n",
" <td>22.857222</td>\n",
" <td>11.139444</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-12</td>\n",
" <td>14</td>\n",
" <td>1.230159</td>\n",
" <td>2.660000</td>\n",
" <td>0.278889</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-13</td>\n",
" <td>14</td>\n",
" <td>2.123790</td>\n",
" <td>11.101389</td>\n",
" <td>0.361667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-14</td>\n",
" <td>4</td>\n",
" <td>4.590694</td>\n",
" <td>14.281944</td>\n",
" <td>0.051389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-15</td>\n",
" <td>4</td>\n",
" <td>0.550972</td>\n",
" <td>1.141944</td>\n",
" <td>0.197500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-16</td>\n",
" <td>3</td>\n",
" <td>1.246296</td>\n",
" <td>3.079444</td>\n",
" <td>0.286111</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-17</td>\n",
" <td>6</td>\n",
" <td>3.675417</td>\n",
" <td>9.498333</td>\n",
" <td>1.882500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-18</td>\n",
" <td>3</td>\n",
" <td>1.045741</td>\n",
" <td>1.967778</td>\n",
" <td>0.480278</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-19</td>\n",
" <td>4</td>\n",
" <td>4.090694</td>\n",
" <td>5.430833</td>\n",
" <td>1.689167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-20</td>\n",
" <td>3</td>\n",
" <td>5.992685</td>\n",
" <td>12.615278</td>\n",
" <td>1.010278</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-21</td>\n",
" <td>1</td>\n",
" <td>22.217222</td>\n",
" <td>22.217222</td>\n",
" <td>22.217222</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-22</td>\n",
" <td>2</td>\n",
" <td>2.897083</td>\n",
" <td>3.923333</td>\n",
" <td>1.870833</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-23</td>\n",
" <td>4</td>\n",
" <td>9.747222</td>\n",
" <td>14.165833</td>\n",
" <td>0.670278</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-24</td>\n",
" <td>4</td>\n",
" <td>5.723125</td>\n",
" <td>17.348889</td>\n",
" <td>0.565000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-25</td>\n",
" <td>14</td>\n",
" <td>4.470496</td>\n",
" <td>8.549167</td>\n",
" <td>0.311944</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-26</td>\n",
" <td>26</td>\n",
" <td>1.671667</td>\n",
" <td>5.871944</td>\n",
" <td>0.078333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-27</td>\n",
" <td>25</td>\n",
" <td>0.678956</td>\n",
" <td>2.994444</td>\n",
" <td>0.016944</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-28</td>\n",
" <td>20</td>\n",
" <td>2.093875</td>\n",
" <td>8.240278</td>\n",
" <td>0.138611</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-29</td>\n",
" <td>17</td>\n",
" <td>2.917941</td>\n",
" <td>10.130833</td>\n",
" <td>0.085556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-30</td>\n",
" <td>12</td>\n",
" <td>1.352755</td>\n",
" <td>6.969444</td>\n",
" <td>0.147500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-10-31</td>\n",
" <td>18</td>\n",
" <td>0.914306</td>\n",
" <td>4.248056</td>\n",
" <td>0.025000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-01</td>\n",
" <td>5</td>\n",
" <td>1.802500</td>\n",
" <td>2.660278</td>\n",
" <td>0.990556</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-02</td>\n",
" <td>8</td>\n",
" <td>2.955972</td>\n",
" <td>8.075833</td>\n",
" <td>1.562500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-03</td>\n",
" <td>5</td>\n",
" <td>1.096833</td>\n",
" <td>1.841389</td>\n",
" <td>0.359167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-04</td>\n",
" <td>10</td>\n",
" <td>2.193056</td>\n",
" <td>5.026944</td>\n",
" <td>0.135000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-05</td>\n",
" <td>12</td>\n",
" <td>1.447778</td>\n",
" <td>4.158611</td>\n",
" <td>0.016389</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-06</td>\n",
" <td>5</td>\n",
" <td>1.007056</td>\n",
" <td>2.293333</td>\n",
" <td>0.059167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019-11-07</td>\n",
" <td>1</td>\n",
" <td>0.814444</td>\n",
" <td>0.814444</td>\n",
" <td>0.814444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" response_time \n",
" count mean max min\n",
"date_x \n",
"2019-09-29 9 2.910309 17.188611 0.596111\n",
"2019-09-30 4 7.928750 21.863611 1.720556\n",
"2019-10-01 8 3.775625 10.535556 0.540556\n",
"2019-10-02 10 0.731167 3.130556 0.079167\n",
"2019-10-03 9 2.282716 4.780278 0.258056\n",
"2019-10-04 4 1.195833 2.793333 0.663333\n",
"2019-10-05 3 4.555556 10.355833 1.148333\n",
"2019-10-06 3 0.960185 1.357500 0.611667\n",
"2019-10-07 12 2.043449 11.253889 0.099444\n",
"2019-10-08 8 5.706424 18.895833 0.137778\n",
"2019-10-09 10 4.489083 22.314444 0.030833\n",
"2019-10-10 5 1.711944 4.049444 0.316389\n",
"2019-10-11 5 15.215444 22.857222 11.139444\n",
"2019-10-12 14 1.230159 2.660000 0.278889\n",
"2019-10-13 14 2.123790 11.101389 0.361667\n",
"2019-10-14 4 4.590694 14.281944 0.051389\n",
"2019-10-15 4 0.550972 1.141944 0.197500\n",
"2019-10-16 3 1.246296 3.079444 0.286111\n",
"2019-10-17 6 3.675417 9.498333 1.882500\n",
"2019-10-18 3 1.045741 1.967778 0.480278\n",
"2019-10-19 4 4.090694 5.430833 1.689167\n",
"2019-10-20 3 5.992685 12.615278 1.010278\n",
"2019-10-21 1 22.217222 22.217222 22.217222\n",
"2019-10-22 2 2.897083 3.923333 1.870833\n",
"2019-10-23 4 9.747222 14.165833 0.670278\n",
"2019-10-24 4 5.723125 17.348889 0.565000\n",
"2019-10-25 14 4.470496 8.549167 0.311944\n",
"2019-10-26 26 1.671667 5.871944 0.078333\n",
"2019-10-27 25 0.678956 2.994444 0.016944\n",
"2019-10-28 20 2.093875 8.240278 0.138611\n",
"2019-10-29 17 2.917941 10.130833 0.085556\n",
"2019-10-30 12 1.352755 6.969444 0.147500\n",
"2019-10-31 18 0.914306 4.248056 0.025000\n",
"2019-11-01 5 1.802500 2.660278 0.990556\n",
"2019-11-02 8 2.955972 8.075833 1.562500\n",
"2019-11-03 5 1.096833 1.841389 0.359167\n",
"2019-11-04 10 2.193056 5.026944 0.135000\n",
"2019-11-05 12 1.447778 4.158611 0.016389\n",
"2019-11-06 5 1.007056 2.293333 0.059167\n",
"2019-11-07 1 0.814444 0.814444 0.814444"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean = (df_date[['date_x','response_time']]\n",
" .groupby(df_date.date_x.dt.date)\n",
" .agg(['count','mean', 'max','min']))\n",
"mean.to_csv(\"fanta/moderation_response_meantime.csv\")\n",
"mean"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7faa7a95f710>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Заполняем нулями пробелы\n",
"idx = pd.date_range(mean.index[0], mean.index[-1])\n",
"mean = mean.reindex(idx, fill_value=0)\n",
"mean[(\"response_time\",\"mean\")].plot(figsize=(20,10)).get_figure().savefig(f\"fanta/moderation_response_meantime.png\")\n",
"mean[(\"response_time\",\"mean\")].plot(figsize=(20,10))\n",
"# df_stat[df_stat.index.get_level_values(1)==\"wall_reply_new\"].plot(figsize=(20,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Архивация отчета"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"bash: line 3: syntax error near unexpected token `('\n",
"bash: line 3: `zip -9 -y -r -q fanta.(date +%F).zip fanta/'\n"
]
},
{
"ename": "CalledProcessError",
"evalue": "Command 'b'cp fanta.ipynb fanta/\\nrm fanta.zip\\nzip -9 -y -r -q fanta.(date +%F).zip fanta/\\n'' returned non-zero exit status 2.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-9916e9896fc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bash'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cp fanta.ipynb fanta/\\nrm fanta.zip\\nzip -9 -y -r -q fanta.(date +%F).zip fanta/\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/lab/jupyter/venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2357\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2358\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2359\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2360\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/lab/jupyter/venv/lib/python3.6/site-packages/IPython/core/magics/script.py\u001b[0m in \u001b[0;36mnamed_script_magic\u001b[0;34m(line, cell)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscript\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshebang\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;31m# write a basic docstring:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m</home/v.seregin/lab/jupyter/venv/lib/python3.6/site-packages/decorator.py:decorator-gen-110>\u001b[0m in \u001b[0;36mshebang\u001b[0;34m(self, line, cell)\u001b[0m\n",
"\u001b[0;32m~/lab/jupyter/venv/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/lab/jupyter/venv/lib/python3.6/site-packages/IPython/core/magics/script.py\u001b[0m in \u001b[0;36mshebang\u001b[0;34m(self, line, cell)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_error\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_script\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_close\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mCalledProcessError\u001b[0m: Command 'b'cp fanta.ipynb fanta/\\nrm fanta.zip\\nzip -9 -y -r -q fanta.(date +%F).zip fanta/\\n'' returned non-zero exit status 2."
]
}
],
"source": [
"%%bash\n",
"cp fanta.ipynb fanta/\n",
"rm fanta.zip\n",
"zip -9 -y -r -q fanta.(date +%F).zip fanta/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Raw
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Raw
moderation_delete_meantime.csv
response_time response_time response_time response_time
count mean max min
date_x
2019-09-28 4 2.814652777777778 2.8177777777777777 2.812222222222222
2019-09-29 8 0.6246527777777777 2.8969444444444443 0.0019444444444444444
2019-09-30 5 3.7867222222222217 16.581944444444442 0.0036111111111111114
2019-10-01 3 0.021944444444444447 0.053611111111111116 0.0033333333333333335
2019-10-02 8 1.0568055555555553 5.7316666666666665 0.0013888888888888887
2019-10-03 10 0.8069722222222222 4.398611111111111 0.0011111111111111111
2019-10-04 11 0.6103787878787877 4.882499999999999 0.0005555555555555556
2019-10-05 4 0.3298611111111111 1.1047222222222222 0.0016666666666666668
2019-10-06 11 2.345530303030303 4.27861111111111 0.0013888888888888887
2019-10-07 9 6.4587962962962955 23.520833333333332 0.0030555555555555553
2019-10-08 16 2.372777777777778 3.5661111111111112 0.0013888888888888887
2019-10-09 7 0.25119047619047624 0.7183333333333334 0.0005555555555555556
2019-10-10 9 1.5055555555555555 2.775 0.20222222222222222
2019-10-11 6 9.813564814814814 13.176111111111112 1.8197222222222222
2019-10-12 7 1.6257936507936506 9.906944444444443 0.0008333333333333334
2019-10-13 6 0.6113425925925926 2.751388888888889 0.0036111111111111114
2019-10-14 1 0.5850000000000001 0.5850000000000001 0.5850000000000001
2019-10-15 9 0.48499999999999993 1.2866666666666666 0.10583333333333332
2019-10-16 12 0.8922685185185184 2.657777777777778 0.02638888888888889
2019-10-17 3 1.1716666666666666 2.129722222222222 0.0038888888888888888
2019-10-18 7 2.234722222222222 11.442499999999999 0.0
2019-10-19 17 3.442924836601307 6.89 0.0011111111111111111
2019-10-20 11 2.0987878787878786 9.535277777777777 0.0016666666666666668
2019-10-21 7 4.186587301587302 23.97888888888889 0.12444444444444444
2019-10-22 1 22.06222222222222 22.06222222222222 22.06222222222222
2019-10-23 21 2.598201058201058 21.86444444444444 0.0013888888888888887
2019-10-24 9 8.16219135802469 22.70777777777778 0.07166666666666667
2019-10-25 11 4.081313131313131 19.94388888888889 0.007222222222222223
2019-10-26 70 2.495238095238095 19.128055555555555 0.0008333333333333334
2019-10-27 17 0.7587581699346406 3.751388888888889 0.006944444444444445
2019-10-28 8 1.1463888888888891 3.4152777777777774 0.03027777777777778
2019-10-29 11 1.283080808080808 9.551388888888889 0.0008333333333333334
2019-10-30 26 1.488044871794872 4.518888888888889 0.0005555555555555556
2019-10-31 14 1.6887896825396822 7.053888888888889 0.010555555555555556
2019-11-01 19 1.2645906432748537 11.52361111111111 0.019166666666666665
2019-11-02 10 5.820555555555556 22.933055555555555 0.08638888888888889
2019-11-03 13 1.6156623931623932 2.866388888888889 0.27805555555555556
2019-11-04 9 1.5654938271604937 4.267777777777778 0.058055555555555555
2019-11-05 8 2.0847569444444445 4.0986111111111105 0.01111111111111111
2019-11-06 5 2.598777777777778 12.711944444444445 0.004722222222222222
2019-11-07 2 0.19013888888888889 0.3763888888888889 0.0038888888888888888
Raw
moderation_delete_stat.csv
reply_id date_x deleter_id date_y response_time
263515 2019-09-28 19:35:40.612116 13503331 2019-09-28 22:24:44.686827 2.8177777777777777
263516 2019-09-28 19:35:52.326412 13503331 2019-09-28 22:24:43.482553 2.8141666666666665
263516 2019-09-28 19:35:52.326412 13503331 2019-09-28 22:24:44.604662 2.8144444444444447
263517 2019-09-28 19:36:00.270410 13503331 2019-09-28 22:24:44.548332 2.812222222222222
263519 2019-09-29 04:21:43.783550 13503331 2019-09-29 07:15:33.252939 2.8969444444444443
263522 2019-09-29 07:09:44.327677 13503331 2019-09-29 07:15:23.175889 0.0938888888888889
263523 2019-09-29 07:10:12.614645 13503331 2019-09-29 07:15:25.852976 0.08694444444444445
263535 2019-09-29 11:19:03.855755 13503331 2019-09-29 12:02:06.916976 0.7174999999999999
263536 2019-09-29 11:48:45.429934 13503331 2019-09-29 12:02:07.636600 0.2227777777777778
263537 2019-09-29 11:53:13.358689 100 2019-09-29 11:54:29.897056 0.021111111111111112
263540 2019-09-29 13:58:08.779157 248385249 2019-09-29 13:58:16.037276 0.0019444444444444444
263552 2019-09-29 15:33:52.317002 134856310 2019-09-29 16:31:14.790515 0.9561111111111111
263563 2019-09-30 07:46:03.559197 13503331 2019-09-30 08:51:33.354250 1.091388888888889
263564 2019-09-30 08:13:16.613549 13503331 2019-09-30 08:50:57.613500 0.6277777777777778
263564 2019-09-30 08:13:16.613549 13503331 2019-09-30 08:51:01.057827 0.6288888888888889
263574 2019-09-30 13:46:57.016783 283076429 2019-09-30 13:47:10.253798 0.0036111111111111114
263577 2019-09-30 14:41:38.430029 13503331 2019-10-01 07:16:34.375790 16.581944444444442
263594 2019-10-01 08:40:23.141809 100 2019-10-01 08:40:35.479347 0.0033333333333333335
263595 2019-10-01 08:40:42.430211 100 2019-10-01 08:41:15.162923 0.008888888888888889
263605 2019-10-01 11:53:01.865768 13503331 2019-10-01 11:56:15.356713 0.053611111111111116
263625 2019-10-02 01:06:43.624342 13503331 2019-10-02 06:50:38.019380 5.7316666666666665
263627 2019-10-02 05:48:02.780991 13503331 2019-10-02 06:50:34.989108 1.0422222222222222
263631 2019-10-02 06:53:10.282946 212259683 2019-10-02 06:53:15.347729 0.0013888888888888887
263648 2019-10-02 12:23:13.057219 13503331 2019-10-02 13:28:38.040267 1.09
263650 2019-10-02 12:40:41.537785 506819211 2019-10-02 12:44:34.017263 0.06444444444444444
263663 2019-10-02 17:33:34.117231 13503331 2019-10-02 17:52:43.693390 0.31916666666666665
263676 2019-10-02 19:59:38.814424 13503331 2019-10-02 20:05:48.709516 0.10250000000000001
263676 2019-10-02 19:59:38.814424 13503331 2019-10-02 20:05:50.805000 0.10305555555555555
263685 2019-10-03 06:15:13.858298 13503331 2019-10-03 07:06:11.294258 0.8491666666666667
263687 2019-10-03 07:07:50.509279 13503331 2019-10-03 11:31:45.940519 4.398611111111111
263698 2019-10-03 15:51:47.016351 13503331 2019-10-03 16:15:09.208767 0.3894444444444444
263699 2019-10-03 15:51:57.909026 13503331 2019-10-03 16:15:09.184047 0.3863888888888889
263700 2019-10-03 15:52:05.114674 13503331 2019-10-03 16:15:08.392623 0.38416666666666666
263700 2019-10-03 15:52:05.114674 13503331 2019-10-03 16:15:09.363155 0.3844444444444444
263707 2019-10-03 18:06:06.362738 385363977 2019-10-03 18:06:11.135986 0.0011111111111111111
263712 2019-10-03 21:33:30.917221 13503331 2019-10-03 21:52:27.904387 0.31555555555555553
263712 2019-10-03 21:33:30.917221 100 2019-10-03 22:00:21.506504 0.4472222222222222
263712 2019-10-03 21:33:30.917221 100 2019-10-03 22:04:20.725361 0.5136111111111111
263716 2019-10-04 01:30:54.292795 540115662 2019-10-04 01:30:57.729121 0.0008333333333333334
263717 2019-10-04 02:16:14.548572 13503331 2019-10-04 07:09:11.904734 4.882499999999999
263724 2019-10-04 07:59:27.036613 397475628 2019-10-04 08:14:04.337940 0.2436111111111111
263725 2019-10-04 07:59:43.995015 397475628 2019-10-04 08:14:02.656787 0.23833333333333334
263726 2019-10-04 08:01:09.995701 13503331 2019-10-04 08:01:48.908266 0.010555555555555556
263728 2019-10-04 08:24:47.625423 13503331 2019-10-04 09:43:06.255540 1.305
263731 2019-10-04 10:21:13.389166 13503331 2019-10-04 10:22:04.051705 0.01388888888888889
263731 2019-10-04 10:21:13.389166 13503331 2019-10-04 10:22:05.615301 0.014444444444444446
263736 2019-10-04 14:48:43.084413 13503331 2019-10-04 14:48:46.035319 0.0005555555555555556
263737 2019-10-04 14:59:59.521844 506265096 2019-10-04 15:00:12.702416 0.0036111111111111114
263754 2019-10-04 18:16:57.088288 530998090 2019-10-04 18:17:01.014829 0.0008333333333333334
263780 2019-10-05 11:39:38.677686 448286964 2019-10-05 11:39:45.161355 0.0016666666666666668
263784 2019-10-05 12:26:00.259540 255912360 2019-10-05 12:26:53.117894 0.014444444444444446
263801 2019-10-05 16:35:55.522087 13503331 2019-10-05 17:42:12.987126 1.1047222222222222
263809 2019-10-05 19:01:17.346459 13503331 2019-10-05 19:13:12.834823 0.1986111111111111
263815 2019-10-06 06:41:47.142743 13503331 2019-10-06 07:34:47.025216 0.8830555555555556
263820 2019-10-06 08:55:48.120946 13503331 2019-10-06 09:15:36.804347 0.33
263824 2019-10-06 12:53:13.289523 212676462 2019-10-07 17:09:56.516235 4.27861111111111
263832 2019-10-06 16:03:38.924438 13503331 2019-10-06 16:12:27.556636 0.14666666666666667
263833 2019-10-06 17:19:38.942107 13503331 2019-10-06 21:02:12.116898 3.709166666666667
263835 2019-10-06 17:46:49.043233 13503331 2019-10-06 21:05:23.334091 3.3094444444444444
263836 2019-10-06 17:47:16.803535 13503331 2019-10-06 21:05:24.387972 3.3019444444444446
263837 2019-10-06 17:47:23.230587 13503331 2019-10-06 21:05:25.119101 3.300277777777778
263838 2019-10-06 17:47:39.362675 13503331 2019-10-06 21:05:26.914722 3.296388888888889
263839 2019-10-06 17:47:47.234437 13503331 2019-10-06 21:02:26.099835 3.243888888888889
263840 2019-10-06 18:15:25.382589 511233279 2019-10-06 18:15:30.588807 0.0013888888888888887
263841 2019-10-07 05:46:11.142010 425076052 2019-10-07 05:46:22.322603 0.0030555555555555553
263862 2019-10-07 15:01:35.961068 222924685 2019-10-07 15:02:02.190453 0.007222222222222223
263878 2019-10-07 17:56:50.794008 13503331 2019-10-08 17:28:06.117143 23.520833333333332
263879 2019-10-07 17:59:36.033121 1365280 2019-10-08 14:25:45.657307 20.435833333333335
263881 2019-10-07 18:06:05.362582 13503331 2019-10-07 19:23:12.305949 1.285
263882 2019-10-07 18:11:55.136709 13503331 2019-10-07 19:32:48.418679 1.3480555555555556
263885 2019-10-07 18:46:41.953681 13503331 2019-10-07 19:23:06.588359 0.6066666666666667
263886 2019-10-07 18:47:45.049265 13503331 2019-10-07 19:25:09.567630 0.6233333333333333
263892 2019-10-07 20:47:26.458295 13503331 2019-10-08 07:05:23.639114 10.299166666666668
263897 2019-10-08 06:54:14.737869 13503331 2019-10-08 07:05:16.669418 0.1836111111111111
263902 2019-10-08 08:34:04.797059 13503331 2019-10-08 12:08:03.558363 3.5661111111111112
263903 2019-10-08 08:34:51.766013 13503331 2019-10-08 12:08:02.423926 3.5527777777777776
263903 2019-10-08 08:34:51.766013 13503331 2019-10-08 12:08:03.490915 3.5530555555555554
263904 2019-10-08 08:35:02.748513 13503331 2019-10-08 12:07:59.962361 3.5491666666666664
263905 2019-10-08 08:35:33.629937 13503331 2019-10-08 12:07:56.895001 3.539722222222222
263905 2019-10-08 08:35:33.629937 13503331 2019-10-08 12:08:03.468520 3.5413888888888887
263906 2019-10-08 08:36:27.572001 13503331 2019-10-08 12:07:55.652385 3.5244444444444443
263906 2019-10-08 08:36:27.572001 13503331 2019-10-08 12:08:03.357430 3.526388888888889
263907 2019-10-08 08:56:52.469950 13503331 2019-10-08 12:08:12.502912 3.188888888888889
263913 2019-10-08 11:15:02.819827 -186187425 2019-10-08 12:07:39.793961 0.8766666666666667
263914 2019-10-08 11:15:18.121174 -186187425 2019-10-08 12:07:40.434770 0.8727777777777778
263917 2019-10-08 11:38:30.400002 415858120 2019-10-08 11:38:35.688016 0.0013888888888888887
263921 2019-10-08 14:01:25.733142 13503331 2019-10-08 17:25:51.876320 3.4072222222222224
263929 2019-10-08 18:27:50.948639 100 2019-10-08 19:09:41.457420 0.6972222222222223
263932 2019-10-08 19:58:17.847698 13503331 2019-10-08 20:21:18.916741 0.38361111111111107
263943 2019-10-09 09:26:54.256925 13503331 2019-10-09 10:10:00.470298 0.7183333333333334
263945 2019-10-09 09:38:53.873171 13503331 2019-10-09 10:10:01.308498 0.5186111111111111
263946 2019-10-09 10:11:02.696798 298288641 2019-10-09 10:11:05.579386 0.0005555555555555556
263956 2019-10-09 12:50:12.152269 13503331 2019-10-09 13:19:46.324279 0.49277777777777776
263964 2019-10-09 15:18:02.979297 13503331 2019-10-09 15:19:10.466167 0.018611111111111113
263978 2019-10-09 22:32:37.287261 551429635 2019-10-09 22:32:51.210226 0.0036111111111111114
263978 2019-10-09 22:32:37.287261 551429635 2019-10-09 22:32:59.042467 0.005833333333333333
263985 2019-10-10 10:57:51.323286 13503331 2019-10-10 12:22:48.161845 1.4155555555555557
263985 2019-10-10 10:57:51.323286 13503331 2019-10-10 12:22:49.935866 1.416111111111111
263985 2019-10-10 10:57:51.323286 13503331 2019-10-10 12:22:53.976092 1.4172222222222222
263989 2019-10-10 12:37:58.658645 13503331 2019-10-10 12:50:07.014689 0.20222222222222222
263989 2019-10-10 12:37:58.658645 13503331 2019-10-10 12:50:08.659749 0.20277777777777778
263991 2019-10-10 13:33:40.243575 13503331 2019-10-10 16:03:21.298187 2.4947222222222223
263993 2019-10-10 14:27:41.453488 13503331 2019-10-10 16:07:18.135664 1.66
264003 2019-10-10 18:39:52.329224 13503331 2019-10-10 21:26:22.405287 2.775
264004 2019-10-10 19:28:21.979692 13503331 2019-10-10 21:26:21.606900 1.966388888888889
264007 2019-10-11 08:19:44.638343 13503331 2019-10-11 10:08:55.927731 1.8197222222222222
264017 2019-10-11 18:37:02.785132 13503331 2019-10-12 07:47:36.966813 13.176111111111112
264020 2019-10-11 20:21:58.100622 13503331 2019-10-12 07:47:22.733157 11.423333333333334
264021 2019-10-11 20:30:35.887648 13503331 2019-10-12 07:47:14.592579 11.277222222222223
264022 2019-10-11 20:42:22.663227 13503331 2019-10-12 07:47:04.591372 11.078055555555554
264025 2019-10-11 21:40:27.840534 13503331 2019-10-12 07:46:53.038155 10.106944444444444
264029 2019-10-12 05:36:55.643789 555910784 2019-10-12 06:27:26.204758 0.8416666666666667
264040 2019-10-12 08:39:35.814712 13503331 2019-10-12 09:15:02.067906 0.5905555555555555
264054 2019-10-12 11:27:24.682131 272950893 2019-10-12 11:27:32.975396 0.0022222222222222222
264068 2019-10-12 16:49:57.410936 100 2019-10-12 16:52:11.728367 0.037222222222222226
264084 2019-10-12 21:16:59.748576 550783033 2019-10-12 21:17:02.840448 0.0008333333333333334
264085 2019-10-12 21:17:11.419560 550783033 2019-10-12 21:17:16.072956 0.0011111111111111111
264087 2019-10-12 22:27:02.008616 13503331 2019-10-13 08:21:27.869062 9.906944444444443
264106 2019-10-13 11:03:34.867720 159837730 2019-10-13 11:05:25.191242 0.030555555555555555
264108 2019-10-13 11:14:44.524646 159837730 2019-10-13 13:59:49.882968 2.751388888888889
264160 2019-10-13 16:40:30.560016 13503331 2019-10-13 17:18:57.126301 0.6405555555555555
264175 2019-10-13 19:38:43.473565 13503331 2019-10-13 19:38:57.320423 0.0036111111111111114
264175 2019-10-13 19:38:43.473565 100 2019-10-13 19:39:51.417435 0.018611111111111113
264176 2019-10-13 19:42:06.679852 13503331 2019-10-13 19:55:31.520233 0.22333333333333333
264195 2019-10-14 19:35:47.332421 13503331 2019-10-14 20:10:54.018871 0.5850000000000001
264202 2019-10-15 09:35:03.745419 13503331 2019-10-15 10:04:15.767956 0.48666666666666664
264209 2019-10-15 13:42:50.560983 13503331 2019-10-15 15:00:02.714219 1.2866666666666666
264210 2019-10-15 13:43:07.506892 13503331 2019-10-15 15:00:03.156024 1.2819444444444446
264213 2019-10-15 17:40:24.595988 13503331 2019-10-15 17:48:48.342771 0.13972222222222222
264215 2019-10-15 18:39:14.402648 13503331 2019-10-15 19:20:41.787251 0.6908333333333334
264217 2019-10-15 21:36:10.696927 13503331 2019-10-15 21:44:42.053783 0.14194444444444446
264218 2019-10-15 21:37:10.087638 13503331 2019-10-15 21:44:42.005939 0.12527777777777777
264219 2019-10-15 21:38:18.721592 13503331 2019-10-15 21:44:39.958107 0.10583333333333332
264219 2019-10-15 21:38:18.721592 13503331 2019-10-15 21:44:41.683772 0.1061111111111111
264222 2019-10-16 04:48:37.950621 13503331 2019-10-16 07:28:06.368410 2.657777777777778
264227 2019-10-16 11:24:14.629608 13503331 2019-10-16 11:25:49.666702 0.02638888888888889
264228 2019-10-16 11:28:53.649995 13503331 2019-10-16 11:36:49.772322 0.1322222222222222
264228 2019-10-16 11:28:53.649995 13503331 2019-10-16 11:36:51.175877 0.1325
264228 2019-10-16 11:28:53.649995 13503331 2019-10-16 11:36:54.881792 0.13361111111111112
264233 2019-10-16 16:11:52.862084 13503331 2019-10-16 18:03:28.451860 1.859722222222222
264234 2019-10-16 16:39:04.982629 13503331 2019-10-16 18:03:20.671979 1.4041666666666666
264235 2019-10-16 16:48:19.387184 13503331 2019-10-16 18:03:15.028127 1.2486111111111111
264237 2019-10-16 17:16:04.883891 13503331 2019-10-16 18:03:01.782236 0.7822222222222222
264238 2019-10-16 17:16:11.281937 13503331 2019-10-16 18:03:03.776764 0.7811111111111111
264239 2019-10-16 17:16:21.054432 13503331 2019-10-16 18:02:46.790417 0.773611111111111
264240 2019-10-16 17:16:32.895974 13503331 2019-10-16 18:03:04.776756 0.7752777777777777
264266 2019-10-17 18:02:20.252292 443176462 2019-10-17 18:02:34.929964 0.0038888888888888888
264269 2019-10-17 20:19:11.507741 13503331 2019-10-17 22:26:58.862571 2.129722222222222
264270 2019-10-17 21:03:52.565726 13503331 2019-10-17 22:26:46.443493 1.381388888888889
264280 2019-10-18 13:32:45.622809 13503331 2019-10-18 15:51:18.663261 2.309166666666667
264282 2019-10-18 14:57:45.611563 100 2019-10-18 14:57:45.650461 0.0
264282 2019-10-18 14:57:45.611563 100 2019-10-18 14:58:17.106382 0.008611111111111113
264282 2019-10-18 14:57:45.611563 100 2019-10-18 15:54:06.783483 0.9391666666666667
264283 2019-10-18 14:57:53.018570 100 2019-10-18 14:58:17.049955 0.006666666666666667
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264289 2019-10-18 19:28:24.719581 13503331 2019-10-19 06:54:57.746891 11.442499999999999
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265327 2019-10-30 03:26:01.500642 13503331 2019-10-30 07:56:49.294805 4.513055555555556
265328 2019-10-30 03:26:15.177692 13503331 2019-10-30 07:56:45.116791 4.508055555555556
265328 2019-10-30 03:26:15.177692 13503331 2019-10-30 07:56:49.216738 4.509444444444444
265333 2019-10-30 04:30:43.765791 13503331 2019-10-30 07:58:24.912997 3.461388888888889
265356 2019-10-30 08:21:18.550059 13503331 2019-10-30 08:25:10.806276 0.06444444444444444
265375 2019-10-30 12:30:37.330562 100 2019-10-30 12:30:39.705680 0.0005555555555555556
265390 2019-10-30 15:06:26.441251 13503331 2019-10-30 15:33:16.261325 0.4469444444444444
265391 2019-10-30 15:09:45.541058 530190625 2019-10-30 16:32:20.364009 1.376111111111111
265394 2019-10-30 15:27:20.269837 13503331 2019-10-30 15:32:45.719734 0.09027777777777778
265398 2019-10-30 16:06:46.190451 13503331 2019-10-30 16:44:35.885594 0.6302777777777778
265404 2019-10-30 17:46:32.839720 13503331 2019-10-30 19:21:36.232629 1.5841666666666667
265405 2019-10-30 17:46:59.617988 13503331 2019-10-30 19:21:34.106024 1.576111111111111
265405 2019-10-30 17:46:59.617988 13503331 2019-10-30 19:21:36.125760 1.5766666666666667
265407 2019-10-30 18:52:57.060291 526768837 2019-10-30 18:53:17.660344 0.005555555555555555
265409 2019-10-30 19:10:25.551312 13503331 2019-10-30 19:21:49.418054 0.1897222222222222
265410 2019-10-30 19:10:30.794941 13503331 2019-10-30 19:21:47.987194 0.18805555555555556
265415 2019-10-30 21:13:55.713996 100 2019-10-30 21:17:56.581124 0.06666666666666667
265416 2019-10-30 21:14:13.467483 100 2019-10-30 21:17:56.555132 0.06194444444444445
265417 2019-10-30 21:14:21.854259 100 2019-10-30 21:17:56.482607 0.059444444444444446
265418 2019-10-30 21:14:26.317966 100 2019-10-30 21:17:56.488831 0.058333333333333334
265419 2019-10-30 21:14:53.185994 100 2019-10-30 21:17:56.510830 0.05083333333333333
265420 2019-10-30 21:14:59.599062 100 2019-10-30 21:17:56.402786 0.048888888888888885
265426 2019-10-31 00:48:45.692416 13503331 2019-10-31 07:52:00.152694 7.053888888888889
265431 2019-10-31 06:36:53.036569 13503331 2019-10-31 07:51:52.039347 1.2497222222222222
265442 2019-10-31 08:56:52.573927 13503331 2019-10-31 09:18:27.052335 0.35944444444444446
265454 2019-10-31 10:25:39.013849 13503331 2019-10-31 11:11:44.091247 0.7680555555555556
265472 2019-10-31 14:31:37.157822 13503331 2019-10-31 15:01:47.058771 0.5025
265482 2019-10-31 15:10:51.566311 13503331 2019-10-31 18:48:01.233825 3.6191666666666666
265487 2019-10-31 16:16:16.014928 100 2019-10-31 16:29:39.153362 0.22305555555555553
265490 2019-10-31 16:17:56.517824 13503331 2019-10-31 18:51:27.212437 2.558333333333333
265492 2019-10-31 16:19:13.735806 100 2019-10-31 16:19:52.314573 0.010555555555555556
265495 2019-10-31 16:29:46.721760 13503331 2019-10-31 18:51:25.832950 2.3608333333333333
265496 2019-10-31 16:45:33.719200 13503331 2019-10-31 18:50:56.029717 2.089444444444444
265502 2019-10-31 18:42:28.667708 13503331 2019-10-31 18:48:58.989692 0.10833333333333334
265510 2019-10-31 19:08:38.911307 13503331 2019-10-31 21:04:58.329366 1.938611111111111
265513 2019-10-31 20:20:51.638678 13503331 2019-10-31 21:08:56.571324 0.8011111111111112
265525 2019-11-01 10:16:36.583876 100 2019-11-01 10:21:13.051268 0.07666666666666666
265525 2019-11-01 10:16:36.583876 100 2019-11-01 21:48:02.548701 11.52361111111111
265528 2019-11-01 11:51:22.142113 100 2019-11-01 11:58:47.277483 0.12361111111111112
265530 2019-11-01 13:39:06.038814 13503331 2019-11-01 15:37:28.561010 1.9727777777777777
265531 2019-11-01 13:39:36.889847 13503331 2019-11-01 15:37:27.163044 1.9638888888888888
265532 2019-11-01 14:22:23.712151 13503331 2019-11-01 15:37:25.552981 1.2502777777777778
265538 2019-11-01 15:19:45.354835 13503331 2019-11-01 15:37:14.857670 0.2913888888888889
265538 2019-11-01 15:19:45.354835 13503331 2019-11-01 15:37:14.882952 0.2913888888888889
265538 2019-11-01 15:19:45.354835 13503331 2019-11-01 15:37:17.817079 0.29222222222222227
265545 2019-11-01 18:28:19.693706 136776655 2019-11-01 18:31:58.681799 0.06055555555555556
265551 2019-11-01 19:32:27.970330 13503331 2019-11-01 19:33:37.139801 0.019166666666666665
265553 2019-11-01 20:01:14.582538 13503331 2019-11-01 20:26:26.199743 0.4197222222222222
265553 2019-11-01 20:01:14.582538 13503331 2019-11-01 20:26:31.417215 0.4211111111111111
265553 2019-11-01 20:01:14.582538 100 2019-11-01 21:22:42.136635 1.3575000000000002
265554 2019-11-01 20:01:21.462075 13503331 2019-11-01 20:26:31.353229 0.41916666666666663
265554 2019-11-01 20:01:21.462075 100 2019-11-01 21:22:42.122823 1.3555555555555554
265555 2019-11-01 20:01:27.554327 13503331 2019-11-01 20:26:30.184071 0.41722222222222227
265555 2019-11-01 20:01:27.554327 13503331 2019-11-01 20:26:31.198871 0.41750000000000004
265555 2019-11-01 20:01:27.554327 100 2019-11-01 21:22:41.968682 1.353888888888889
265559 2019-11-02 07:41:43.883615 100 2019-11-02 07:47:40.757157 0.09888888888888889
265559 2019-11-02 07:41:43.883615 100 2019-11-02 08:02:32.627190 0.3466666666666667
265563 2019-11-02 09:39:18.001473 13503331 2019-11-02 16:41:53.060259 7.043055555555555
265564 2019-11-02 09:39:37.660375 13503331 2019-11-02 16:41:54.949539 7.038055555555556
265565 2019-11-02 12:15:54.846587 85455146 2019-11-02 12:21:06.755684 0.08638888888888889
265570 2019-11-02 14:16:36.460236 13503331 2019-11-02 16:43:48.636807 2.453333333333333
265588 2019-11-02 19:33:58.979778 335738304 2019-11-05 11:21:57.603115 15.799444444444445
265596 2019-11-02 20:09:01.806569 13503331 2019-11-03 19:05:01.199577 22.933055555555555
265597 2019-11-02 20:10:26.769250 13503331 2019-11-02 21:22:38.412010 1.2030555555555555
265597 2019-11-02 20:10:26.769250 13503331 2019-11-02 21:22:40.484799 1.2036111111111112
265609 2019-11-03 07:43:19.679065 13503331 2019-11-03 08:25:37.858701 0.705
265612 2019-11-03 08:29:16.801928 13503331 2019-11-03 11:21:16.349530 2.866388888888889
265621 2019-11-03 09:39:51.217596 13503331 2019-11-03 11:21:43.929029 1.6977777777777776
265622 2019-11-03 09:41:24.431597 13503331 2019-11-03 11:21:07.411421 1.6616666666666666
265624 2019-11-03 09:47:59.842550 13503331 2019-11-03 11:21:42.402949 1.5616666666666668
265625 2019-11-03 10:49:20.539508 497627202 2019-11-03 11:14:20.776324 0.4166666666666667
265629 2019-11-03 12:10:08.296267 13503331 2019-11-03 13:40:29.915643 1.5058333333333331
265630 2019-11-03 12:10:31.909199 13503331 2019-11-03 13:40:28.552575 1.498888888888889
265633 2019-11-03 12:57:26.714466 100 2019-11-03 13:14:07.722257 0.27805555555555556
265633 2019-11-03 12:57:26.714466 100 2019-11-03 14:49:59.789275 1.8758333333333332
265644 2019-11-03 19:42:51.740871 13503331 2019-11-03 22:16:20.212585 2.557777777777778
265645 2019-11-03 20:04:36.179642 13503331 2019-11-03 22:15:56.406181 2.188888888888889
265645 2019-11-03 20:04:36.179642 13503331 2019-11-03 22:15:57.968577 2.1891666666666665
265647 2019-11-04 03:38:21.506428 13503331 2019-11-04 07:33:00.660368 3.9108333333333336
265650 2019-11-04 04:10:46.274471 13503331 2019-11-04 08:26:50.279448 4.267777777777778
265651 2019-11-04 04:20:02.038492 13503331 2019-11-04 08:27:01.943471 4.116388888888888
265669 2019-11-04 10:33:53.962896 13503331 2019-11-04 10:39:44.644366 0.09722222222222222
265673 2019-11-04 11:10:15.664038 13503331 2019-11-04 11:15:13.046560 0.0825
265674 2019-11-04 11:11:37.807312 13503331 2019-11-04 11:15:13.976689 0.060000000000000005
265675 2019-11-04 11:11:44.612834 13503331 2019-11-04 11:15:14.503971 0.058055555555555555
265684 2019-11-04 18:56:02.770963 13503331 2019-11-04 20:21:57.883348 1.4319444444444445
265690 2019-11-04 22:31:14.165347 13503331 2019-11-04 22:35:07.736240 0.06472222222222222
265696 2019-11-05 04:37:32.194178 13503331 2019-11-05 07:05:02.979764 2.4583333333333335
265700 2019-11-05 08:43:10.131940 13503331 2019-11-05 08:44:08.062669 0.01583333333333333
265712 2019-11-05 09:14:09.144801 217464276 2019-11-05 09:14:49.718013 0.01111111111111111
265726 2019-11-05 09:54:27.205151 13503331 2019-11-05 10:46:43.403434 0.8711111111111111
265750 2019-11-05 13:11:01.178397 13503331 2019-11-05 16:57:00.228218 3.766388888888889
265765 2019-11-05 17:45:29.777765 13503331 2019-11-05 21:51:25.343037 4.0986111111111105
265775 2019-11-05 17:58:16.901348 13503331 2019-11-05 21:51:20.687912 3.8841666666666668
265780 2019-11-05 20:17:19.925603 13503331 2019-11-05 21:51:41.392988 1.5725
265786 2019-11-06 05:26:26.919974 238686030 2019-11-06 05:26:44.481478 0.004722222222222222
265791 2019-11-06 10:17:14.178254 100 2019-11-06 10:22:50.197312 0.09333333333333332
265792 2019-11-06 10:17:16.799365 100 2019-11-06 10:22:50.098677 0.0925
265793 2019-11-06 10:17:20.684167 100 2019-11-06 10:22:50.207598 0.0913888888888889
265824 2019-11-06 20:17:43.343814 100 2019-11-07 09:00:27.109002 12.711944444444445
265829 2019-11-07 04:40:26.048848 100 2019-11-07 04:40:40.887074 0.0038888888888888888
265830 2019-11-07 10:54:40.131366 13503331 2019-11-07 11:17:15.972805 0.3763888888888889
Raw
moderation_response_meantime.csv
response_time response_time response_time response_time
count mean max min
date_x
2019-09-29 9 2.9103086419753086 17.18861111111111 0.5961111111111111
2019-09-30 4 7.928749999999999 21.86361111111111 1.7205555555555556
2019-10-01 8 3.775625 10.535555555555556 0.5405555555555555
2019-10-02 10 0.7311666666666665 3.1305555555555555 0.07916666666666666
2019-10-03 9 2.282716049382716 4.780277777777778 0.25805555555555554
2019-10-04 4 1.1958333333333333 2.7933333333333334 0.6633333333333333
2019-10-05 3 4.555555555555555 10.355833333333333 1.1483333333333334
2019-10-06 3 0.9601851851851851 1.3575000000000002 0.6116666666666667
2019-10-07 12 2.0434490740740743 11.25388888888889 0.09944444444444445
2019-10-08 8 5.706423611111112 18.895833333333332 0.1377777777777778
2019-10-09 10 4.489083333333333 22.314444444444444 0.030833333333333334
2019-10-10 5 1.7119444444444443 4.049444444444444 0.3163888888888889
2019-10-11 5 15.215444444444447 22.857222222222223 11.139444444444445
2019-10-12 14 1.23015873015873 2.6599999999999997 0.2788888888888889
2019-10-13 14 2.1237896825396825 11.10138888888889 0.36166666666666664
2019-10-14 4 4.590694444444444 14.281944444444443 0.051388888888888894
2019-10-15 4 0.5509722222222222 1.1419444444444444 0.19749999999999998
2019-10-16 3 1.2462962962962962 3.079444444444445 0.28611111111111115
2019-10-17 6 3.675416666666667 9.498333333333333 1.8825
2019-10-18 3 1.0457407407407409 1.9677777777777776 0.48027777777777775
2019-10-19 4 4.090694444444444 5.430833333333334 1.6891666666666665
2019-10-20 3 5.992685185185184 12.615277777777777 1.0102777777777778
2019-10-21 1 22.217222222222222 22.217222222222222 22.217222222222222
2019-10-22 2 2.8970833333333332 3.9233333333333333 1.8708333333333333
2019-10-23 4 9.747222222222222 14.165833333333333 0.6702777777777779
2019-10-24 4 5.7231250000000005 17.34888888888889 0.565
2019-10-25 14 4.470496031746032 8.549166666666668 0.3119444444444444
2019-10-26 26 1.6716666666666666 5.871944444444444 0.07833333333333334
2019-10-27 25 0.6789555555555556 2.9944444444444445 0.016944444444444443
2019-10-28 20 2.093875 8.240277777777779 0.1386111111111111
2019-10-29 17 2.9179411764705883 10.130833333333333 0.08555555555555557
2019-10-30 12 1.3527546296296296 6.969444444444445 0.1475
2019-10-31 18 0.9143055555555555 4.248055555555555 0.025
2019-11-01 5 1.8024999999999998 2.660277777777778 0.9905555555555555
2019-11-02 8 2.9559722222222224 8.075833333333334 1.5625
2019-11-03 5 1.0968333333333333 1.841388888888889 0.3591666666666667
2019-11-04 10 2.1930555555555555 5.026944444444444 0.13499999999999998
2019-11-05 12 1.4477777777777778 4.158611111111111 0.016388888888888887
2019-11-06 5 1.0070555555555556 2.2933333333333334 0.059166666666666666
2019-11-07 1 0.8144444444444444 0.8144444444444444 0.8144444444444444
Raw
moderation_response_stat.csv
reply_id date_x from_id date_y response_time
263524 2019-09-29 08:45:14.612536 -24539231 2019-09-29 10:25:08 1.6647222222222224
263527 2019-09-29 08:51:49.795747 -24539231 2019-09-29 10:26:03 1.5702777777777779
263528 2019-09-29 09:24:53.139219 -24539231 2019-09-29 10:23:35 0.9780555555555555
263534 2019-09-29 10:54:59.718837 -24539231 2019-09-29 12:02:26 1.123888888888889
263539 2019-09-29 13:48:31.923808 -24539231 2019-09-29 15:24:17 1.5958333333333334
263541 2019-09-29 14:27:10.223425 -24539231 2019-09-29 15:17:32 0.8391666666666667
263542 2019-09-29 14:44:24.758215 -24539231 2019-09-29 15:22:35 0.6361111111111111
263543 2019-09-29 14:44:54.111065 -24539231 2019-09-29 15:20:41 0.5961111111111111
263547 2019-09-29 15:01:11.158546 -24539231 2019-10-02 08:12:31 17.18861111111111
263562 2019-09-30 07:09:33.546654 -24539231 2019-09-30 08:52:48 1.7205555555555556
263569 2019-09-30 09:17:43.280119 -24539231 2019-09-30 13:37:16 4.325555555555556
263570 2019-09-30 09:49:19.655415 -24539231 2019-09-30 13:37:39 3.8052777777777775
263580 2019-09-30 15:15:10.977824 -24539231 2019-10-01 13:07:00 21.86361111111111
263587 2019-10-01 02:34:20.541131 -24539231 2019-10-01 13:06:29 10.535555555555556
263589 2019-10-01 06:44:48.906169 -24539231 2019-10-01 07:17:15 0.5405555555555555
263592 2019-10-01 07:18:21.401088 -24539231 2019-10-01 11:57:25 4.650833333333334
263599 2019-10-01 11:22:43.355414 -24539231 2019-10-01 11:56:37 0.5647222222222222
263617 2019-10-01 15:49:55.313588 -24539231 2019-10-01 17:45:54 1.9327777777777777
263619 2019-10-01 16:16:42.918265 -24539231 2019-10-01 17:47:45 1.5172222222222222
263620 2019-10-01 16:59:22.520601 -24539231 2019-10-01 17:48:33 0.8194444444444444
263624 2019-10-01 21:18:50.206593 -24539231 2019-10-02 06:57:29 9.643888888888888
263626 2019-10-02 03:45:04.495998 -24539231 2019-10-02 06:52:55 3.1305555555555555
263628 2019-10-02 06:47:24.159314 -24539231 2019-10-02 06:52:10 0.07916666666666666
263639 2019-10-02 10:38:58.619392 -24539231 2019-10-02 11:15:25 0.6072222222222222
263646 2019-10-02 12:10:46.727346 -24539231 2019-10-02 13:29:38 1.3141666666666665
263654 2019-10-02 13:10:08.193712 -24539231 2019-10-02 13:29:09 0.31666666666666665
263662 2019-10-02 17:03:19.274950 -24539231 2019-10-02 17:57:22 0.9005555555555556
263664 2019-10-02 17:38:12.964133 -24539231 2019-10-02 17:56:27 0.30388888888888893
263667 2019-10-02 17:45:06.284491 -24539231 2019-10-02 17:55:32 0.1736111111111111
263674 2019-10-02 19:48:29.875896 -24539231 2019-10-02 20:07:35 0.31805555555555554
263675 2019-10-02 19:58:36.976168 -24539231 2019-10-02 20:08:41 0.16777777777777778
263684 2019-10-03 04:52:17.699014 -24539231 2019-10-03 07:11:24 2.3183333333333334
263689 2019-10-03 07:12:18.913024 -24539231 2019-10-03 10:55:17 3.716111111111111
263691 2019-10-03 11:15:57.907702 -24539231 2019-10-03 11:31:27 0.25805555555555554
263696 2019-10-03 13:19:11.747309 -24539231 2019-10-03 16:17:21 2.9691666666666667
263697 2019-10-03 15:34:18.758102 -24539231 2019-10-03 16:18:54 0.7430555555555556
263702 2019-10-03 15:54:04.596804 -24539231 2019-10-03 16:19:29 0.4233333333333333
263706 2019-10-03 17:08:24.652633 -24539231 2019-10-03 21:55:14 4.780277777777778
263709 2019-10-03 18:54:34.205571 -24539231 2019-10-03 21:54:33 2.9994444444444444
263711 2019-10-03 19:33:52.574177 -24539231 2019-10-03 21:54:05 2.3366666666666664
263735 2019-10-04 14:08:53.124726 -24539231 2019-10-04 14:48:42 0.6633333333333333
263735 2019-10-04 14:08:53.124726 -24539231 2019-10-04 14:48:42 0.6633333333333333
263735 2019-10-04 14:08:53.124726 -24539231 2019-10-04 14:48:42 0.6633333333333333
263767 2019-10-04 19:46:47.171296 -24539231 2019-10-04 22:34:24 2.7933333333333334
263776 2019-10-05 10:33:49.223206 -24539231 2019-10-05 12:43:35 2.1625
263779 2019-10-05 11:35:50.938381 -24539231 2019-10-05 12:44:45 1.1483333333333334
263811 2019-10-05 21:15:22.640035 -24539231 2019-10-06 07:36:44 10.355833333333333
263819 2019-10-06 07:54:45.535410 -24539231 2019-10-06 09:16:13 1.3575000000000002
263825 2019-10-06 12:55:17.379354 -24539231 2019-10-06 13:32:00 0.6116666666666667
263828 2019-10-06 13:59:10.290811 -24539231 2019-10-06 14:53:52 0.9113888888888888
263843 2019-10-07 06:23:26.623038 -24539231 2019-10-07 07:47:33 1.4016666666666666
263846 2019-10-07 08:21:13.879570 -24539231 2019-10-07 09:14:01 0.8797222222222222
263861 2019-10-07 14:59:48.219907 -24539231 2019-10-07 16:02:22 1.0425
263863 2019-10-07 15:02:02.121117 -24539231 2019-10-07 16:01:39 0.9933333333333334
263864 2019-10-07 15:14:05.251411 -24539231 2019-10-07 16:03:45 0.8275
263865 2019-10-07 15:25:17.213047 -24539231 2019-10-07 16:04:22 0.6511111111111112
263872 2019-10-07 16:21:19.654723 -24539231 2019-10-07 16:27:18 0.09944444444444445
263875 2019-10-07 16:37:05.420158 -24539231 2019-10-07 19:36:46 2.9944444444444445
263877 2019-10-07 17:46:46.069077 -24539231 2019-10-07 19:34:13 1.7905555555555557
263880 2019-10-07 18:05:18.703573 -24539231 2019-10-07 19:36:13 1.5150000000000001
263884 2019-10-07 18:32:12.453122 -24539231 2019-10-07 19:36:33 1.0722222222222222
263891 2019-10-07 19:50:56.433578 -24539231 2019-10-08 07:06:11 11.25388888888889
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265397 2019-10-30 16:02:02.233017 -24539231 2019-10-30 16:47:02 0.7497222222222223
265406 2019-10-30 18:21:07.444472 -24539231 2019-10-30 19:22:50 1.0283333333333333
265412 2019-10-30 19:26:04.428731 -24539231 2019-10-30 21:19:59 1.8983333333333334
265436 2019-10-31 06:57:44.903035 -24539231 2019-10-31 07:52:37 0.9144444444444445
265439 2019-10-31 07:11:05.835039 -24539231 2019-10-31 07:53:43 0.7102777777777778
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265450 2019-10-31 10:04:49.542602 -24539231 2019-10-31 10:08:48 0.0661111111111111
265452 2019-10-31 10:19:31.226221 -24539231 2019-10-31 10:27:54 0.13944444444444445
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265463 2019-10-31 12:48:59.565877 -24539231 2019-10-31 13:42:10 0.8861111111111111
265469 2019-10-31 14:27:33.128534 -24539231 2019-10-31 15:06:08 0.6427777777777778
265479 2019-10-31 15:03:09.913939 -24539231 2019-10-31 15:06:21 0.05305555555555555
265484 2019-10-31 15:28:38.737519 -24539231 2019-10-31 18:48:35 3.332222222222222
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265522 2019-11-01 09:53:13.508235 -24539231 2019-11-01 10:52:40 0.9905555555555555
265527 2019-11-01 11:21:58.970232 -24539231 2019-11-01 12:35:58 1.2330555555555556
265541 2019-11-01 16:52:49.170925 -24539231 2019-11-01 19:32:27 2.660277777777778
265541 2019-11-01 16:52:49.170925 -24539231 2019-11-01 19:32:27 2.660277777777778
265544 2019-11-01 18:05:56.312358 -24539231 2019-11-01 19:34:03 1.4683333333333333
265557 2019-11-02 00:04:38.965618 -24539231 2019-11-02 08:09:12 8.075833333333334
265568 2019-11-02 13:48:01.469398 -24539231 2019-11-02 16:43:00 2.9161111111111113
265569 2019-11-02 13:49:28.877250 -24539231 2019-11-02 16:43:46 2.904722222222222
265571 2019-11-02 14:24:03.218021 -24539231 2019-11-02 16:44:25 2.3391666666666664
265574 2019-11-02 15:11:34.143676 -24539231 2019-11-02 16:45:20 1.5625
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265588 2019-11-02 19:33:58.979778 -24539231 2019-11-02 21:30:28 1.9413888888888888
265591 2019-11-02 19:53:56.166615 -24539231 2019-11-02 21:40:40 1.7786111111111111
265623 2019-11-03 09:45:42.421568 -24539231 2019-11-03 11:22:58 1.6208333333333333
265627 2019-11-03 11:52:36.111332 -24539231 2019-11-03 13:43:06 1.841388888888889
265634 2019-11-03 13:27:44.529653 -24539231 2019-11-03 14:18:04 0.8386111111111112
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265641 2019-11-03 18:18:34.616092 -24539231 2019-11-03 19:08:02 0.8241666666666667
265648 2019-11-04 04:06:13.319935 -24539231 2019-11-04 07:33:46 3.4588888888888887
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265659 2019-11-04 08:31:58.219506 -24539231 2019-11-04 10:11:41 1.6616666666666666
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265688 2019-11-04 20:24:40.629306 -24539231 2019-11-04 22:34:02 2.1558333333333333
265689 2019-11-04 21:21:36.009841 -24539231 2019-11-04 22:36:27 1.2472222222222222
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265707 2019-11-05 09:04:02.936372 -24539231 2019-11-05 09:12:06 0.13416666666666668
265708 2019-11-05 09:04:08.103565 -24539231 2019-11-05 09:09:28 0.08861111111111111
265716 2019-11-05 09:17:55.310257 -24539231 2019-11-05 12:21:12 3.0544444444444445
265716 2019-11-05 09:17:55.310257 -24539231 2019-11-05 12:21:12 3.0544444444444445
265718 2019-11-05 09:21:12.254166 -24539231 2019-11-05 09:23:31 0.03833333333333333
265722 2019-11-05 09:33:44.084032 -24539231 2019-11-05 10:47:30 1.2291666666666667
265746 2019-11-05 12:48:47.998109 -24539231 2019-11-05 16:58:19 4.158611111111111
265756 2019-11-05 15:52:17.978548 -24539231 2019-11-05 16:59:06 1.1133333333333333
265761 2019-11-05 17:06:43.140759 -24539231 2019-11-05 17:14:07 0.12305555555555556
265781 2019-11-05 21:06:14.392721 -24539231 2019-11-05 21:52:09 0.765
265798 2019-11-06 11:38:24.836138 -24539231 2019-11-06 13:26:42 1.8047222222222221
265814 2019-11-06 17:13:17.665533 -24539231 2019-11-06 19:30:54 2.2933333333333334
265816 2019-11-06 17:50:55.760159 -24539231 2019-11-06 18:29:43 0.6463888888888889
265818 2019-11-06 18:25:38.003400 -24539231 2019-11-06 18:29:12 0.059166666666666666
265825 2019-11-06 20:36:41.177975 -24539231 2019-11-06 20:50:36 0.23166666666666666
265834 2019-11-07 13:20:14.999928 -24539231 2019-11-07 14:09:07 0.8144444444444444
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