{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of Operation Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import openLA as la\n",
    "import OpenLA as la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "course_info, event_stream = la.start_analysis(files_dir=\"dataset_sample\", course_id=\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>userid</th>\n",
       "      <th>contentsid</th>\n",
       "      <th>operationname</th>\n",
       "      <th>pageno</th>\n",
       "      <th>marker</th>\n",
       "      <th>memo_length</th>\n",
       "      <th>devicecode</th>\n",
       "      <th>eventtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>PREV</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>tablet</td>\n",
       "      <td>2018-04-09 10:57:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>PREV</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>tablet</td>\n",
       "      <td>2018-04-09 11:00:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>PREV</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>tablet</td>\n",
       "      <td>2018-04-09 11:03:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>PREV</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>tablet</td>\n",
       "      <td>2018-04-10 10:14:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>PREV</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>tablet</td>\n",
       "      <td>2018-04-10 10:27:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263279</th>\n",
       "      <td>A_U99</td>\n",
       "      <td>C8</td>\n",
       "      <td>NEXT</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>pc</td>\n",
       "      <td>2018-06-05 16:16:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263280</th>\n",
       "      <td>A_U99</td>\n",
       "      <td>C8</td>\n",
       "      <td>ADD MARKER</td>\n",
       "      <td>4</td>\n",
       "      <td>difficult</td>\n",
       "      <td>0</td>\n",
       "      <td>pc</td>\n",
       "      <td>2018-06-05 16:18:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263281</th>\n",
       "      <td>A_U99</td>\n",
       "      <td>C8</td>\n",
       "      <td>NEXT</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>pc</td>\n",
       "      <td>2018-06-05 16:19:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263282</th>\n",
       "      <td>A_U99</td>\n",
       "      <td>C8</td>\n",
       "      <td>NEXT</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>pc</td>\n",
       "      <td>2018-06-05 16:20:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263283</th>\n",
       "      <td>A_U99</td>\n",
       "      <td>C8</td>\n",
       "      <td>PREV</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>pc</td>\n",
       "      <td>2018-06-05 16:21:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263284 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       userid contentsid operationname  pageno     marker  memo_length  \\\n",
       "0        A_U1         C1          PREV      10        NaN            0   \n",
       "1        A_U1         C1          PREV       9        NaN            0   \n",
       "2        A_U1         C1          PREV       8        NaN            0   \n",
       "3        A_U1         C1          PREV      30        NaN            0   \n",
       "4        A_U1         C1          PREV      29        NaN            0   \n",
       "...       ...        ...           ...     ...        ...          ...   \n",
       "263279  A_U99         C8          NEXT       3        NaN            0   \n",
       "263280  A_U99         C8    ADD MARKER       4  difficult            0   \n",
       "263281  A_U99         C8          NEXT       4        NaN            0   \n",
       "263282  A_U99         C8          NEXT       5        NaN            0   \n",
       "263283  A_U99         C8          PREV       6        NaN            0   \n",
       "\n",
       "       devicecode            eventtime  \n",
       "0          tablet  2018-04-09 10:57:15  \n",
       "1          tablet  2018-04-09 11:00:59  \n",
       "2          tablet  2018-04-09 11:03:31  \n",
       "3          tablet  2018-04-10 10:14:12  \n",
       "4          tablet  2018-04-10 10:27:24  \n",
       "...           ...                  ...  \n",
       "263279         pc  2018-06-05 16:16:18  \n",
       "263280         pc  2018-06-05 16:18:34  \n",
       "263281         pc  2018-06-05 16:19:24  \n",
       "263282         pc  2018-06-05 16:20:45  \n",
       "263283         pc  2018-06-05 16:21:03  \n",
       "\n",
       "[263284 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_stream.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After conversion: the operation count in each contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "operation_count = la.convert_into_operation_count(event_stream=event_stream,\n",
    "                                                  user_id=course_info.user_id()[:2],\n",
    "                                                  contents_id=course_info.contents_id()[:2],\n",
    "                                                  operation_name=[\"OPEN\", \"CLOSE\", \"ADD MARKER\", \"ADD MEMO\", \"ADD BOOKMARK\"],\n",
    "                                                  separate_marker_type=False)"
   ]
  },
  {
   "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>userid</th>\n",
       "      <th>contentsid</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>ADD MARKER</th>\n",
       "      <th>ADD MEMO</th>\n",
       "      <th>ADD BOOKMARK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C1</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A_U1</td>\n",
       "      <td>C2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A_U10</td>\n",
       "      <td>C1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A_U10</td>\n",
       "      <td>C2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userid contentsid  OPEN  CLOSE  ADD MARKER  ADD MEMO  ADD BOOKMARK\n",
       "0   A_U1         C1     9    8.0         2.0       0.0           3.0\n",
       "1   A_U1         C2     2    0.0         0.0       0.0           2.0\n",
       "2  A_U10         C1     2    2.0         8.0       1.0           1.0\n",
       "3  A_U10         C2     1    1.0         0.0       0.0           0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operation_count.df # related to users \"A_U1\" and \"A_U10\", and contents \"C1\" and \"C2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the data to CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "operation_count.to_csv(save_file=\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of users in the data\n",
    "operation_count.num_users()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A_U1', 'A_U10']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# User ids in the data\n",
    "operation_count.user_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C1', 'C2']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contents ids in the data\n",
    "operation_count.contents_id()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OPEN', 'CLOSE', 'ADD MARKER', 'ADD MEMO', 'ADD BOOKMARK']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Operation names in the data\n",
    "operation_count.operation_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of selected operation logs\n",
    "operation_count.operation_count(operation_name=\"ADD MARKER\", \n",
    "                                user_id=\"A_U1\", \n",
    "                                contents_id=\"C1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ADD MARKER': 2.0, 'ADD BOOKMARK': 3.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operation_count.operation_count(operation_name=[\"ADD MARKER\", \"ADD BOOKMARK\"], \n",
    "                                user_id=\"A_U1\", \n",
    "                                contents_id=\"C1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize information\n",
    "The average operation count of all users in all contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168c961b860>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eVXd/M8kD059b17oOAAAAAACPNjcG75nG86rqEddMT/GeneShJJ9Y60aq6owkT85SEP76WtcBAAAAAODRZsXg7r4jyU1JdiS5dNnpq7L0JO+7unvf/oNVdWZVnXngxKo6rapOWb5+VT01yTumP3+/u789+xMAAAAAALCqLQvMfXmSW5JcX1XnJrktyXOTnJOl10O8dtn826axDjj2/CRvq6qbk9yR5L4k25O8MEvvI741yb9d8DMAAAAAALCK2TG4u++oql1JXpfk/CwF3K8luT7JVd1934xlPpXk3Un+SZJnZ+mH5x5I8hdJ/iDJ73b33y/0CQAAAAAAWNUiTwanu7+S5OKZc+sgx/4iyS8sck8AAAAAANZv7g/IAQAAAABwDBODAQAAAAAGIAYDAAAAAAxADAYAAAAAGIAYDAAAAAAwADEYAAAAAGAAYjAAAAAAwADEYAAAAACAAYjBAAAAAAADEIMBAAAAAAYgBgMAAAAADEAMBgAAAAAYgBgMAAAAADAAMRgAAAAAYABiMAAAAADAAMRgAAAAAIABiMEAAAAAAAMQgwEAAAAABiAGAwAAAAAMQAwGAAAAABiAGAwAAAAAMAAxGAAAAABgAGIwAAAAAMAAxGAAAAAAgAGIwQAAAAAAA9iy2RsAAABgdTuueN9mbwGOOXuvuWCztwBwVPFkMAAAAADAAMRgAAAAAIABiMEAAAAAAAMQgwEAAAAABiAGAwAAAAAMQAwGAAAAABiAGAwAAAAAMAAxGAAAAABgAGIwAAAAAMAAxGAAAAAAgAGIwQAAAAAAAxCDAQAAAAAGIAYDAAAAAAxADAYAAAAAGIAYDAAAAAAwADEYAAAAAGAAYjAAAAAAwADEYAAAAACAAYjBAAAAAAADEIMBAAAAAAYgBgMAAAAADEAMBgAAAAAYgBgMAAAAADAAMRgAAAAAYABiMAAAAADAAMRgAAAAAIABiMEAAAAAAANYKAZX1alV9faq+mpVfauq9lbVdVX15AXXecp03d5pna9O65662PYBAAAAAJhjy9yJVbUzyS1JTkry3iSfT/KcJK9Icn5Vnd3d985Y58RpndOTfDjJ7yc5M8nFSS6oqrO6+0uLfhAAAAAAAI+unggAACAASURBVFa2yJPBb8lSCL6suy/s7iu6+wVJrk1yRpKrZ67zm1kKwdd297nTOhdmKSqfNN0HAAAAAIANNCsGV9VpSc5LsjfJm5edvjLJviQXVdXWVdbZmuSiaf6Vy06/aVr/n0/3AwAAAABgg8x9MvgF03hTdz984InufiDJx5M8McnzVlnnrCTfk+Tj03UHrvNwkpumP8+ZuS8AAAAAAGaYG4PPmMbbVzj/xWk8/QitAwAAAADAAubG4OOn8f4Vzu8/fsIRWgcAAAAAgAVs2aB1ahr7cK5TVZckuWT688Gq+sI67wcb6alJvr7Zm+DR6rc2ewcMwnfAUcp3AEeI74CjlO8AjhDfAUcp3wEcIb4DjlIDfwc8faUTc2Pw/id2j1/h/HHL5h2Wdbr7hiQ3rHIP2BRVdWt379rsfQCbw3cAjM13AIzNdwCMzXcAx5K5r4nY/wTuSu/yfcY0rvQu4I1eBwAAAACABcyNwXum8byqesQ1VfWkJGcneSjJJ1ZZ5xPTvLOn6w5c53FJzlt2PwAAAAAANsCsGNzddyS5KcmOJJcuO31Vkq1J3tXd+/YfrKozq+rMZes8mOTGaf7uZev8yrT+n3T3l2Z/Ajh6eIUJjM13AIzNdwCMzXcAjM13AMeM6p73m29VtTPJLUlOSvLeJLcleW6Sc7L0Wod/1t33HjC/k6S7a9k6J07rnJ7kw0k+meQHk/zLJHdP69yxrk8FAAAAAMAjzI7BSVJV35/kdUnOT3Jikq8l+a9Jruru+5bNPWgMns49JcmVSS5M8o+S3JvkA0n+XXf/9Zo+CQAAAAAAK1ooBgMAAAAAK6uqH+zu2zZ7H3Awc39ADgAAmKGqtlTV8t/ZAACOQVX17xec/4+T/Nlh2g6smyeDAQBgA1RVJfn5LL0O7end/V2bvCVgg1XV1iQ/l+TsJN+XpLP0+sSPJ/nDA39UHXhsqKqHk1za3b8zY+72JB9Lcor/D+BotWWzNwAAAEe7qnpykl9J8k+T/EOSjyZ5a3d/czr/L5L8xyRnTJe8ZzP2CRw+VfWSJNcmeUqS5b+N84tJXl9Vr+zu3zvimwMOpy8nub6q/qa7/8tKk6rqlCR7kpya5OojtTlYlCeDYYaq+tdrue7/tXfnUZaV5b3Hv79uxAkVAdFGhlbGXLgGhICC2ijNZBLRgCgKMshgVCQBJY5IHKJRc50SJREEvAGVixjHgN0S2oBXENREQXBABgUZRByRBvq5f7yHa6+yhnOKOmdXVX8/a51VXXu/e63fH31Onf3s933eqvroTGeR1I0k1wLvq6oPrHZsb2Dvqjp+nPFvBt5UVT54lea4JBsAlwGb8fsCUAErgD2BU4Ajeuc+T9sU+VsdRJU0JEmOBd5He59/HVgG3Nj7fWNgKbAz7bPhuKr6x46iSpphvbYPlwCPpH33/8o4Yx5L+16wFfDeqjphtCml/lkMlvrQWxbSz5slq40ri0DS/NH7HDi5qt6y2rE304o+f7AEbLJzkuaWJO8Bjgf+CziL9vf+EGBbWkFoL+BSWgHosq5yShqOJFsBVwK/AF5cVRdMMG5P4GzgUcB2VfW90aWUNExJdgQuoq0OWlJV317t3Pq9c9vSVg29vIuMUr8sVEn9eQv9FYMXAAcBWww3jiRJGqF9geuBXapqJUCSfwSups0M/gStQOQsC2l+Oo72EGi/qrpkokFVtSzJfrQ2Mq+itZaRNA9U1RVJnkdbAfTvSXatqhuSPAr4Eq0QfIaFYM0FFoOlPlTVyVONSbKU1itwC+B3tGVkkiRp7ltMu8Fbef+BqroryeeBl9FawlgIluavPYCLJisE36+qvprkP2htIyTNI1W1PMlhtFVCFyTZF/g4sAPwSVrvcGnWsxgsPUBJtqUVgffpHfrfwBuq6sfdpZIkSTPoocAt4xy/tffz2hFmkTR6GwOfGWD8FcBThpRFUoeq6hO9/sDvpa0QWhv4LHCwD4Y1VyzoOoA0VyV5bJJ/Ab5FWz76H8COVXWohWBJktYc3vxJ817x+80j+zHIWElzTFW9H3gnrRB8PvD8qrqv21RS/5wZLA0oyUOBE4ETgHWAq4ATq+qLnQaTJEnDtH2Sl4w9BpDkEMYp/lTVx0YRTNLQ/RjYaYDxOwI3DimLpBFLMlGht4C9gd8l430NcEN5zU5xIoPUn7RP9yNom8ktoi0XfTNwalWt6jKbpOFLsoq2EuBbqx3eHvhj4MxxLtkBeFJVLRxBPElD1Hv/T/SlOROd8/0vzQ9J/gk4BlgyVd/gJLvSNpA7papeMYp8koar9z1gYFXlanzNShaDpT4k2YfWF3hb4C7gH4B3VdVvOg0maWSm+SWwLAZJc1+S06dzXVUdPtNZJI1eki1pqwF/DryoqpZPMG4P2mZS6wLbVdX3RpdSkqT+WAyW+rDajKAfAG8Fburnuqq6cJi5JI1OkkOnc11VjTdrWJIkzSFJXgW8j3ZPcBmwnNYKooBNgaXAzrTVAsdX1fs6iipJ0qQsBkt9GDMjsO83jTMCJUmSpPmh1zf8fbSZv2PvCQLcCZxQVdNaTSBp/kiyXVV9p+sc0ngsBkt9cHmoJEnqV5JHAutW1Q1dZ5E0s5KsAxwAPI22j0iAm4GLgXOr6lcdxpPUsSSb0/YZOrCqHtR1Hmk8FoMlSZoBSf4YeCbtpvA/q+ryjiNJmiG9XcRPrqq3rnbsBcALquovxhn/ZuAkVwhJkjR/JHk68CfAPcDFVfXN1c49Dvhb4DDgQcBNVbVxFzmlqazVdQBJkuaCJM8AjgQ+VFVfG3PuZOBNY469v6qOH11CSUOU3mt12wD7dZBFkiSNUJK1gE8Bfzbm+Lur6rVJDgY+BKwD3AK8Ezhl5EGlPlkMlobA5aHSvPR84EDg2NUP9mYInATcR9tB/Fe9scclWV5VXxx1UEmSNHOSbDqd67wXkOaNVwB/DvwGWEF7QLwEeE2S3wAn0+4BXgt8sKru6iin1BeLwVIfBl0eCvw1rTjk8lBp/ngqcGlV/WLM8WNom8i8qqo+DJDkg8B/AYcDFoMlSZrbrmOATaR7Cu+3pfnihbRi7w5VdS1Akq2By2mF4P8Cnl1VP+0soTQA/zhJ/XF5qKSNGL+w+yzaLIGP3H+gqq5OcgGtp5gkSZr77gW+0fspac3yR8B59xeCAarqmiTnAQcDL7MQrLnEYrAkSf1Zn9YD7P/rbRTxOOBLVTX25vD7wJ4jyiZJkobn17ReoJsApwOnVdV1nSaSNEqPAMZr+3J97+e3RphFesAWdB1AkqQ54i7gsWOOPbn385v8obtx9pAkSfPBIuBo4MfAG4AfJDk/yf69jaUkzW+h7Q8y1n0AVbVytHGkB8Y/XJIk9edqYN8ka602C/hPaT0BvzrO+E2Am0cVTtLQHZZk99V+XwyQ5MJxxi4eQR5JI1JVvwFOBU5N8iTgKODFtBVAtyc5Azi1qr7fXUpJQ7buOJtJrguQZBP+sK2km0hq1rIYLElSf84F3gV8NskpwFbAS4FfAMvGGb8b8N3RxZM0ZIsZv8i7+wTjB91sStIcUFX/DRyb5DXAgbTC8GuAVydZARxjUVial47rvcZz3TjH3ERSs5b/MSVJ6s8HgYOAfYC9e8cCvLqqfrf6wCS70IpGHxxlQElD88yuA0iaXXp/+z8GfCzJU4BzgCW0jaYsBkvzyw34kFfziMVgqX8uD5XWYFV1d5JnAH8NPBX4GfDJqvrCOMO3Bz4DfG6EESUNSVWtGPSaJAuHkUXS7JHkabSZwfsDD6NtJuWycGmeqarFXWeQZlKqfLghTSXJqmlcVlXljaAkSXNckp2r6rIBxi8APlFVBw4xlqQOJFkfOBQ4Etiatlns54B/AZaVN9iSpFnOmcFSf1weKknSmusLSXYdoA/ombSZgpLmiSRLabOA9wPWBn4AvA44o6pu7TKbpNkjySOAg4Ejq2rHrvNI43FmsCRJA0rycOAA2iZxG9F6iN0MXAKc29t1XNI8keRe2tLvXavqp1OM/Qhtc8lvVdWTR5FP0nAl+SGtDdzdwKeBj1TVRV1mkjS79HqHH0XbWPLhuFJYs5jFYGlAvaVhVVV3dJ1F0uglORh4L7AebQO51RVwB/BXVXXWqLNJGo4kRwH/DPw38Iyq+uUE4z4AvBK4ClhSVT8bXUpJw9JrGXcPsJy2Z0A/qqoOHV4qSV1L8ijgEFoReDvavcFvgP9De2j0fzuMJ03IYrDUpyT7Af8APKF36IfAq6vqs92lkjRKSY4F3kf7ovd1YBlwY+/3jYGlwM60ovBxVfWPHUWVNMOSvBF4C7AC2LuqVo45//fAa4Dv0wrGt4w+paRhcP8QSatLshtwNG2l4EP4/QSR84EDq+rXXWWT+mExWOpDb8nHxcCCMafuA55eVV8bfSpJo5RkK+BK4BfAi6vqggnG7QmcDTwK2K6qvje6lJKGKck/AX8JnAc8//6NopKcDJwEXEcrBP+4q4ySZl6SJdO5rqpWzHQWSd1I8mja5pFHAdvQCsC3AGcBZ9BWD51aVUd3lVHqlxvISf05nlYIfgvwT7QP/lcCb+ydc7dwaf47jvbe36+qLploUFUt660k+E/gVbTPCknzwyuBDYG/oH0feHmS19IKwT8B9rAQLM0/FnWlNVuSf6X97X8IsJLWO/wM4N+r6r7emM7ySYNyZrDUhyQ3AtdW1ZIxx1cAm1fVxt0kkzQqSa4GflxVS/scvxzYuKq2GW4ySaOUZG3gAuAZwJdp7WFupfUIvqbLbJIkaeb1WsWsAt4D/H1V/XyCMc4M1pwwdsm7pPFtCIzXCuJS4DEjziKpGxsDVwww/oreNZLmkV6v4OcA/0UrBP8MWGohWJq/krwkyZPGHFs7ySMnGL8kyUmjSSdpBH5Jq5+dAJyd5AVJHtxxJmnabBMh9edBtF1Bx/oNvo+kNUXx+80h+uFaMWmeSPLRcQ7/tPfzh8Dx4ywPrap66VCDSRqVM4CTaT1B7/c6WouY8TaJ27137i1DziVpNBYBL6RtGrc3sBfwiySfAE6vqq93GU4alEUsSZL682NgpwHG7wjcOKQskkbrsEnO7dx7jVWAxWBJkua4qroLOB04Pcm2wDHAi4GXAcf02skNOnFE6ozFYKl/hyXZfcyxxQBJLhxnfFXVHsMOJWlkLqR92dttsg3kAJLsSpsVdMoogkkausO7DiBJkrpXVVcCr0ryGuAFwFHAbr3ThyV5HHAq8Pn7N5eTZhuLwVL/Fvde49l9nGPuzijNL++jLQ37dJIXVdXy8QYl2QP4OHAf8P4R5pM0JFV1ZtcZJEnS7FFVdwMfAz6WZBvabOGDgT8Fng3cAmzUXUJpYhaDpf48s+sAkrpVVd9PcgKtKHxBksuA5bRWEAVsSttMamfaErHjq+p7XeWVJEmSNHxVdTXw10n+BjiANoHkGd2mkiZmMVjqQ1Wt6DqDpO5V1QeS3EkrCO/CH/YJDXAncEJVnT7qfJJmhyR7AW+tql26ziJJkkajqlYCZwNnJ9mi6zzSRCwGS9OQZAmtL9BGtBmBNwOXWDSW5r+q+liS82hP/Z9G2104tM+Bi4Fzq+pXHUaUNERJ1gPurapfjnPuqcDf4WwgaT5aN8mmq/8OkGQT/nDTqHVHlkrSrFRVP+g6gzSRVNnWVOpXrwj8YWDr+w/1ft7/RroaeLlFYUlJHgKsPV7BSNLck2R/4F38fv+AbwPHVNWlSTYEPgQ8j/bd4FvASVX1+S6ySppZSVYxjf1AqmrhEOJIGrEkJ03jsqqqt854GGkGWAyW+tS7Cfw4bUb9TcBFtF6hATambSK3EXAv8MKqOq+ToJJmhSSnA4dUlatwpDkuydNpf/fHzv67k/b3/3PAJsCVwJv9DiDNL0muY3rF4CfMfBpJo7baA6Gx3wMmUz4Q0mzlDarUhyQbAWfSCr3HAqdW1X1jxiwAXkrrJfqxJF+rqptGHlbSbDLIF0ZJs9df0d7PrwNO6x17GfAW4EJgHeCVwClVtaqThJKGpqoWd51BUufupT38vQDwb73mNGcGS31I8i7g1cD+VfXpKcY+FzgPeHdV/c0o8kmafXozg1/ijABp7kvyE+CqqtpzzPEv02YGH11Vp413rSRJmtt63+ufDzyUtjr4NOD0qvpxp8GkaVrQdQBpjtgHuHSqQjBAVf0bcCmw79BTSZKkUXgMcMU4xy/v/fzUCLNIkqQRqqrDaZtGvxL4GfC3wI+SfC7Jc3qrhKU5wzYRUn82A04dYPxXgaOGlEWSJI3WWsBvxzn+W4CqunO0cSSNUpJNp3NdVd0w01kkdaOqfkXbTP7DSXak3e8fBDwb+GmSjwIfraofdRhT6ovFYKk/DwJWDjD+HsCl4ZIkSdLcdx2DbyBXeL8tzUtVdQVwRZLjaQXhI4E3AK9Nsk9VfbnTgNIU/OMk9edm4H8OMH5b4KdDyiJJkkbvsCS7jzm2GCDJheOMr6raY9ihJI3MvcA3ej8liar6bZJP0iaCPb73eni3qaSpWQyW+vMV4EVJtqmqqycbmOSPgL2Bs0aSTNJIJLmv6wySOrW49xrP7uMcc5dmaf74NbAOsAlwOnBaVV3XaSJJnUqyC61VxIG0AvBNwNuAi7vMJfUjVX5PlabS6wn0deBa4DlVddUE4/4I+BzwBGCXqrp8vHGS5p4kq6ZxWVWVLWOkOS7JkulcV1UrZjqLpNFL8nB+vxR8Z2AVsBz4CPCZqnK2sLQGSPJo4BDaZ8G2tM+CL9I+C75YVdO5X5BGzmKw1Kckfw+8htY7+Dzgy8CNtJk/mwJLgecBawP/UFWv6SiqJEmSpCFI8iTabMAXA48CbgfOAE6tqu93GE3SkPQeCh8F/AXwEFof8dNoG8bd3GE0aVosBksDSHIS8EZai5Wxb54A9wF/B5xcvrkkSVojJVkXOLSq3t91FknDkeQhtOXhRwG70e4NVgDHWBSW5pfeCsF7gM/TZgEvo492UM4U1mxlMVgaUJLNgCNoX/oW0YrAN9N6A51RVT/qMJ6kIUnyYOA/gV8B+1TVPROMWxv4d1rvsKdPNE7S/JPk6cDRwP7Ag20TI60ZkjwFOIe2edTzquqzHUeSNIN6xeBBi2dVVe7TpVnJ/5jSgKrqeuDNXeeQNHIvBnYE/nyyAm9VrUzyblr/sBfTlo5KmqeSrAccRpsduBXtIfGvgP/dYSxJI5DkabT3/v7Aw4DrgRs6DSVpGG7AjWE1jzgzWJKkPiT5PLB1VW3Z5/hrgB9U1Z8ON5mkLiR5Jq0IdP9+AaFtNPt3wCeq6rcdxpM0JEnWBw6lbSC1NXAvbQPpfwGW2SpOkjTbOTNYkqT+7ECb7duvrwDPHlIWSR1I8hjaLOAjgS1oBeBbgH8FTgAurKqPdhZQ0tAkWUp7ALQf7QHQD4DX0drE3dplNkmzT5I/qqrvdp1DGo/FYEmS+rMBrejTr1uA9YeURdII9YpARwPPoRWBVgKfprWB+fequi/JCd0llDRMSX4ILAbuBj4FfKSqLuoyk6TRSfK2qnrjAOO3AJbT+ohLs47FYEmS+nMXsM4A49cBfjekLJJG60u0XoHfpBWAz66qOzpNJGmUngDcA/wHcB9wRJIjprimqurQoSeTNAqvT/KTqvrwVAOTbApcCDxu+LGk6bEYLElSf24E/mSA8TvhJjLSfFLAz4Dbgd90nEXS6D0I2HeA8UXrLSxp7vsR8IEkt1TVeRMNSvJ42kOjjYG3jyqcNCiLwZIk9eci4OVJdqqqyycbmGRHYFfgg6MIJmnoDqH1Ct0TWAr8MsnHgTOr6tJOk0kahWd2HUBSp/YGLgHOSrJ3VX1l7IAkjwW+TFtJ8N6qetOIM0p9i5udSpI0tSRbA1fSZgg/e6INIZJsQ9tobhNgu6q6ZnQpJQ1Tkq1ovYNfQusjXsDVwJnAO4FTq+ro7hJKkqRh6E32uIjWMmZJVX17tXPr985tC5xSVS/vIqPUL4vBkiT1KclJwMm0zaPOpfUD+zGtILQxsAewP/Bg4KSqels3SSUNU5IH0d7rRwNLVjt1DfAm4DNVdW8X2SRJ0nD0NpT9PK1l1K5VdUOSR9HuCXYAzqiqqfqJS52zGCxJ0gCSvB54M6134Ng/oqHNFji5qt4x6mySRi/J5rSi8KHAhrTPhduBj1XVa7rMJkmSZlaSFwJnAd+j9RH/OLAL8EngRWWRTXOAxWBJkgaUZDPgCGA3YBGtCHwTcDFwelVd32E8SR1IshbwXFpheA+AqlrYaShJkjTjkhwHvJe2WnBt4LPA/lV1X6fBpD5ZDJYkSZJmUJInAC+tqjd2nUWSJM28JH8HvBY4H9ivqu7pOJLUN4vBkiRJkiRJ0jiSTGfGb1XVWjMeRpoB/seUJEmSJpFk0+lcV1U3zHQWSZI0chnRNdJIODNYkiRJmkSSVfzhhpFTcUaQJEmSZh2/oEqSJElTuxf4Ru+npDVcbzPZx9AeFN3mSgBJ0lxhMViSJEma3K+BdYBNgNOB06rquk4TSRq5JBsArwcOAjYcc+4W4CzgHVV1RwfxJEnqi20iJEmSpEkkeTit+HMksDOwClgOfAT4TFU5W1ia55JsCSyjPRQKbZXAz3r/Xo820aqA64GlVXVtR1ElDVGSTYAjgN2AjWjv+5uBi4EzXCWgucBisCRJktSnJE8CjgJeDDwKuB04Azi1qr7fYTRJQ5JkAfA1YCfgIuBtwMVVtbJ3/sHA04E3AEuAr1XVrt2klTQsSd4AnER7+DN2g7iiPSR6S1W9fdTZpEFYDJYkSZIGlOQhwIG0wvButJvAFcAxFoWl+SXJPsAXgXOAg2qCm+gkAT4J7A/sU1XLRpdS0jAleQdwIq3g+2+0lQI30orCGwN7As+lFYrfVVWv6yiqNCWLwZIkSdIDkOQptCLR44HnVdVnO44kaQYl+QhwMLBpVd02xdgNgRuAM6vqmFHkkzRcSXYCLgN+BOxXVd+ZYNy2wGeBxcAuVXX5yEJKA1jQdQBJkiRpLkrytCRn0voHb0wrANkrUJp/ngxcMlUhGKCqbqX1Dn3y0FNJGpVXACuBP5uoEAxQVVcCf0abPfzyEWWTBmYxWJIkSepTkvWTHJ/kKlpbiBcC5wP7AE+sqm91GlDSMGwCXDnA+CuBzYaURdLoPQP4UlV9d6qBvTEXALsPO5Q0XRaDJUmSpCkkWZrkk8BPgPfQegK+Dtikqg6oqi9N1EdU0pz3SODOAcbfCTxiSFkkjd4iBn8g9LghZZEesLW6DiBJkiTNZkl+SOv/dzfwKeAjVXVRl5kkjdTawH0DjF/Vu0bS/HAPg72n1+5dI81KFoMlSZKkyT2BdlP3H7SC0BFJjpjimqqqQ4eeTNKoOPNfWnP9CNhtgPG7AdcNJ4r0wMXVbJIkSdLEkqyaxmVVVQtnPIykket9Bgx84+xngDQ/JHkXcAJwQFV9eoqxzwXOA95dVX8zinzSoCwGS5IkSZNIsmQ611XVipnOImn0fCAkrdmSbAR8DwhwHPDRqlo1ZkyAI4D39w5tVVU3jTSo1CeLwZIkSZIkSdIEkhwAfBxYANwEXATcSFs1sCmwO7BR7/cXVdU5nQSV+mAxWJIkSZIkSZpEkmcBHwa27B26v6CW3s8fAC+vquWjziYNwmKwJEmSJEmSNIVeO4glwNOARbRC8M3AxcCKse0jpNnIYrAkSZIkSX1Isj1wELAz8BjazMDbgEuBs6vq2x3GkyRpShaDJUmSJEmaRJKFwAeBo2kzATNmSPVeHwKOK2+0pXmtN0N4fdr7/g7f85pL1uo6gCRJkiRJs9x7gJcBK4FzaJtH/YRWFN4IeBZwAPAK4HfAiZ2klDRUSQ6iPRR6CrB27/DKJP8X+Jeq+kRn4aQ+OTNYkiRJkqQJJNkCuBq4Edinqq6ZYNw2wPnA44Gtq+ra0aWUNExJHgV8EtiTP1wZcL8ClgEHVtUvR5VNGtSCrgNIkiRJkjSLHUIr/hw2USEYoKquBg4FFgIHjyibpNE4B9gL+BFtBcBWwEN7r616x67tjTmno4xSX5wZLEmSJA0oyWastnlUVd3QcSRJQ5JkGbCoqrbrc/x3gJuqaq/hJpM0CkkOoBV4zwcOqKrfTjDuIcCngH1os4M/NbqUUv+cGSxJkiT1IckGSf5Xkptps38uBS4DfpTkpiTvTrJetyklDcE2tPd7vy7tXSNpfngJ8HPgRRMVggGq6ne0VQF30lYJSLOSxWBJkiRpCkm2BC4HjgMeC9wH3Arc1vv344DjgcuTPLGrnJKGYl3a+71ftwCPHlIWSaO3E/CFqrpzqoFV9XPgC71rpFnJYrAkSZI0iSQLgLOATYEVwFJgnapaVFWPAx5B6xH4FWAx8K8dRZU0HA8H7hpg/N3Aw4aURdLorQcM0g7qht410qxkMViSJEma3F60GT7nAHtU1YVVtfL+k1V1d1UtB54FnAvskmTPbqJKkqQZ9itg/QHGrwf8ekhZpAdsra4DSJIkSbPc/rSZfsfWJLsvV1UleSXwHOAAYNmI8kkavucmWdzn2B2GmEPS6F0F7J1kQVWtmmxgkoW0DeSuGkkyaRosBkuSJEmTezJwSVXdNtXAqro1ycW9ayTNH9v3Xv2a8MGRpDnn34B/AP4WeNMUY98MbAZ8YNihpOmyGCxJkiRNbhPg4gHGXwkcNKQskkbv8K4DSOrUKcCxwOuTPAF4a1Vds/qAJFsDbwReBFwP/PPIU0p9shgsSZIkTe6RwJQ7iK/mTtqmcpLmgao6s+sMkrpTVXcl+XNa+6cXAQcluQm4kbYKYFNgIyDALcBzqmqQTSelkXIDOUmSJGlyawP3DTB+Ve8aSWugJAuS7Nd1Dkkzp6qupPUD/yiwEng88BTgqb1/rwROB55cVd/pKqfUD2cGS5IkSVOz/6ekSSXZDDiS1lZiEbCw20SSZlJV3QIc2dssdifa+zzAzcDXq+p3XeaT+pVJNkSWJEmSL3kOigAACa1JREFU1nhJVjGNYnBVWQiS5rkkC4H9gKOBpbTVtwUsr6q9u8wmSdJ4nBksSZIkTS0DjnfGhTSPJXkibRbwYcBje4dvp20adVpVXd9RNEkdSPIIYEFV/aLrLNJU7BksSZIkTaKqFkzj5axgaZ5JslaS5ydZBnwPeC2wHnAe7YHRZ6rqJAvB0vyT5GFJXpLkhCRLVju+T5Lv0jaPvSPJN5Ls2l1SaWrODJYkSZIkaQJJtgSOAg4FNqAVfr8BnAGcXVV39NrJSJqHkjwa+Cqw1WrH3gF8FvgM8KDVhm8PfCnJDlX1/ZEGlfpkMViSJEmSpIldQ2v9civwXuD0qrqy20iSRuhEYGvgCmAFsDtwArAD8BPaw6JLgUf3jr8KeDVwTAdZpSlZDJYkSZL6lGR74CBgZ+AxtALRbbSbwLOr6tsdxpM0PAV8ETjXQrC0xvlz4IfAU6vq3iRrAd8F9gGeU1Vf7o37NfBXSXYD9ugmqjQ1ewZLkiRJU0iyMMmHgMtps32WAP8D2JY2Q+hE4JtJPpBk0M3mJM1ubwKuBw4HLklyVZITkyzqOJek0XgCcH5V3QvQ+3l+79xXxxn/VeDxI8omDcxisCRJkjS19wAvA+4FzgKOBPYFnt3798d7514B/H1HGSUNQVW9vao2p73nPw1sDrwTuCHJF5Ic2GlAScP2UOD2Mcd+BlBVPx9n/B3A2sMOJU2XbSIkSZKkSSTZAjiWNjNwn6q6ZpxhH03yNtpMob9OckpVXTvKnJKGq6ouAC5IsiFwBL9/KLQPrY3E9kl2rKorOowpaThqit+lOcOZwZIkSdLkDgECHDZBIRiAqroaOBRYCBw8omySRqyqbq2qd1bVFsCewLnAPcBOwGVJvpnkFZ2GlCRpAqnyYYYkSZI0kSTLgEVVtV2f478D3FRVew03maTZIskGwGHAS4GtgaqqhZ2GkjQjkqwCruu97rcY2AxYMc4li4HN/AzQbGUxWJIkSZpEkhuBL1XVS/scfxqwZ1VtOtxkkmajJLsDR1aVKwSkeaBXDB6UD4Q0a9kzWJIkSZrcusCtA4y/BXj0kLJImuWq6iLgoo5jSJo5z+w6gDSTLAZLkiRJk3s4cNcA4+8GHjakLJIkaYSqarxWENKc5QZykiRJkiRJkrQGcGawJEmSNLXnJlnc59gdhphDkiRJmjY3kJMkSZIm4cYxkiRJmi+cGSxJkiRN7vCuA0iSJEkzwZnBkiRJkiRJkrQGcAM5SZIkaQYlWZBkv65zSJIkSWPZJkKSJEmaAUk2A46ktZVYBNgzWJIkSbOKxWBJkiRpmpIsBPYDjgaW0lbeFbC8y1ySJGl4eg+AH0P7m39bVd3QcSSpb/YMliRJkgaU5Im0WcCHAY/tHb4d+GfgtKq6vqNokiRpCJJsALweOAjYcMzpW4CzgHdU1R2jziYNwmKwJEmS1IckawHPo80CfiZtFvBK4HPA/sCpVXV0dwklSdIwJNkSWAZsAgS4F/hZ79/r0VbeF3A9sLSqru0oqjQlN5CTJEmSJpFkyyTvAn4CfALYA/gW8Cpgo6p6fpf5JEnS8CRZQJv1uymwgtYWap2qWlRVjwMeAewFfAVYDPxrR1GlvtgzWJIkSZrcNbTZPrcC7wVOr6oru40kSZJGZC9gJ+Ac4KAas8S+qu4Glif5MvBJYP8ke1bVstFHlabmzGBJkiRpagV8ETjXQrAkSWuU/YG7gWPHFoJX1zv3SuAe4IARZZMGZjFYkiRJmtybaD0ADwcuSXJVkhOTLOo4lyRJGr4nA5dU1W1TDayqW4GLe9dIs5LFYEmSJGkSVfX2qtoc2Bf4NLA58E7ghiRfSHJgpwElSdIwbQIMsiroSmCzIWWRHjCLwZIkSVIfquqCqjqAdlP4etps4X2Bj9PaSGyfZMcOI0qSpJn3SODOAcbfSdtUTpqVLAZLkiRJA6iqW6vqnVW1BbAncC6tP+BOwGVJvpnkFZ2GlCRJM2Vt4L4Bxq/qXSPNShaDJUmSpGmqqi9X1QuAjYETge8Bfwx8oNNgkiRpJk24cZw012SSjRAlSZIkDSjJ7sCRVXVw11kkSdIDk2QV0ygGV9XCIcSRHjCLwZIkSZIkSdI4esXgQZXFYM1WFoMlSZIkSZIkaQ1gz2BJkiRJkiRJWgNYDJYkSZIkSZKkNcBaXQeQJEmSJEmSZrsk2wMHATsDj6FtLHcbcClwdlV9u8N4Ul/sGSxJkiRJkiRNIMlC4IPA0UB6r9VV7/Uh4Liy2KZZzJnBkiRJkiRJ0sTeA7wMWAmcA1wE/IRWFN4IeBZwAPAK4HfAiZ2klPrgzGBJkiRJkiRpHEm2AK4GbgT2qaprJhi3DXA+8Hhg66q6dnQppf65gZwkSZIkSZI0vkNoM4APm6gQDFBVVwOHAguBg0eUTRqYM4MlSZIkSZKkcSRZBiyqqu36HP8d4Kaq2mu4yaTpcWawJEmSJEmSNL5tgEsHGH9p7xppVrIYLEmSJEmSJI1vXeDWAcbfAjx6SFmkB8xisCRJkiRJkjS+hwN3DTD+buBhQ8oiPWAWgyVJkiRJkiRpDbBW1wEkSZIkSZKkWey5SRb3OXaHIeaQHrBUVdcZJEmSJEmSpFknyappXFZVtXDGw0gzwJnBkiRJkiRJ0vgO7zqANJOcGSxJkiRJkiRJawA3kJMkSZIkSZJmQJIFSfbrOoc0EdtESJIkSZIkSQ9Aks2AI2ltJRYB9gzWrGQxWJIkSZIkSRpQkoXAfsDRwFLaCvwClneZS5qMxWBJkiRJkiSpT0meSJsFfBjw2N7h24F/Bk6rqus7iiZNyQ3kJEmSJEmSpEkkWQt4Hm0W8DNps4BXAp8D9gdOraqju0so9ceZwZIkSZIkSdI4kmwJHAUcCmwABPgGcAZwdlXdkWRVdwmlwVgMliRJkiRJksZ3Da0P8K3Ae4HTq+rKbiNJ07eg6wCSJEmSJEnSLFbAF4FzLQRrrrMYLEmSJEmSJI3vTcD1wOHAJUmuSnJikkUd55KmxWKwJEmSJEmSNI6qentVbQ7sC3wa2Bx4J3BDki8kObDTgNKAUlVdZ5AkSZIkSZJmvSQbAkcARwJPpLWQALgC+MuquqKrbFI/LAZLkiRJkiRJA0qyB3A0sB+wNq0w/N/AqVX1T11mkyZiMViSJEmSJEmapiQbAIcBLwW2BqqqFnYaSpqAxWBJkiRJkiRpBiTZHTiyqg7uOos0HovBkiRJkiRJkrQGWNB1AEmSJEmSJEnS8FkMliRJkiRJkqQ1gMVgSZIkSZIkSVoDWAyWJEmSJEmSpDWAxWBJkiRJkiRJWgP8P5D4F2PyVNB2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "la.visualize_operation_count_bar(operation_count,\n",
    "                                 user_id=None,\n",
    "                                 contents_id=None,\n",
    "                                 operation_name=None,\n",
    "                                 calculate_type=\"average\",\n",
    "                                 xlabel=None,\n",
    "                                 ylabel=None,\n",
    "                                 ax=None,\n",
    "                                 figsize=(20, 10),\n",
    "                                 fontsize=20,\n",
    "                                 save_file=None)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}