2021q3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine, text, MetaData\n",
    "from sqlalchemy.exc import IntegrityError\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('postgresql:///piwheels')\n",
    "conn = engine.connect()\n",
    "meta = MetaData()\n",
    "meta.bind = conn\n",
    "meta.reflect(views=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = '2021-07-01'\n",
    "start_time = '2021-07-01 00:00:00'\n",
    "end_date = '2021-09-30'\n",
    "end_time = '2021-10-01 00:00:00'\n",
    "one_year_ago = '2020-10-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>downloads_in_period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7047854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   downloads_in_period\n",
       "0              7047854"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_in_period\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "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>downloads_to_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55661834</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   downloads_to_date\n",
       "0           55661834"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS downloads_to_date\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date < '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "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>project_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>875834</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_page_hits\n",
       "0             875834"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_page_hits\n",
    "    FROM project_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>project_json_downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1094432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   project_json_downloads\n",
       "0                 1094432"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS project_json_downloads\n",
    "    FROM project_json_downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>web_page_hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>397834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   web_page_hits\n",
       "0         397834"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS web_page_hits\n",
    "    FROM web_page_hits\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>m</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>1850781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>1240661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>1552508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1960137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>3424032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>3878585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2956219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>2752983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>2762739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>2325759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>2523731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>2198364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            m        c\n",
       "0  2020-10-01  1850781\n",
       "1  2020-11-01  1240661\n",
       "2  2020-12-01  1552508\n",
       "3  2021-01-01  1960137\n",
       "4  2021-02-01  3424032\n",
       "5  2021-03-01  3878585\n",
       "6  2021-04-01  2956219\n",
       "7  2021-05-01  2752983\n",
       "8  2021-06-01  2762739\n",
       "9  2021-07-01  2325759\n",
       "10 2021-08-01  2523731\n",
       "11 2021-09-01  2198364"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_month = pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) m, COUNT(*) AS c\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{one_year_ago}' AND '{end_date}'\n",
    "    GROUP BY m\n",
    "    ORDER BY m\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
    "\n",
    "months = [m.month_name()[:3] for m in downloads_by_month['m']]\n",
    "downloads = [c for c in downloads_by_month['c']]\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(12), downloads, color=colors)\n",
    "plt.title('Downloads by month')\n",
    "plt.xticks(range(12), months)\n",
    "\n",
    "plt.savefig('downloads-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>d</th>\n",
       "      <th>downloads</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>112953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>90084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-07-03</td>\n",
       "      <td>68950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>73506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>89507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>59820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>82292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2021-09-28</td>\n",
       "      <td>76716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2021-09-29</td>\n",
       "      <td>81520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>90958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             d  downloads\n",
       "0   2021-07-01     112953\n",
       "1   2021-07-02      90084\n",
       "2   2021-07-03      68950\n",
       "3   2021-07-04      73506\n",
       "4   2021-07-05      89507\n",
       "..         ...        ...\n",
       "87  2021-09-26      59820\n",
       "88  2021-09-27      82292\n",
       "89  2021-09-28      76716\n",
       "90  2021-09-29      81520\n",
       "91  2021-09-30      90958\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "downloads_by_day = pd.read_sql(f\"\"\"\n",
    "    SELECT accessed_at::date AS d, COUNT(*) AS downloads\n",
    "    FROM downloads\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY d\n",
    "    ORDER BY d\n",
    "\"\"\", conn)\n",
    "\n",
    "downloads_by_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = downloads_by_day.plot.bar(title='Downloads by day')\n",
    "p.figure.savefig('downloads-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48256952"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_sql(f\"\"\"\n",
    "    SELECT COUNT(*) AS searches_in_period\n",
    "    FROM searches\n",
    "    WHERE accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "\"\"\", conn)\n",
    "\n",
    "searches = df.values[0][0]\n",
    "searches"
   ]
  },
  {
   "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",
       "    }\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>month</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>19092849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>17141762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>12022341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  searches\n",
       "0 2021-07-01  19092849\n",
       "1 2021-08-01  17141762\n",
       "2 2021-09-01  12022341"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', accessed_at) AS month, COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>day</th>\n",
       "      <th>searches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>663189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>573578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-07-03</td>\n",
       "      <td>520430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-07-04</td>\n",
       "      <td>516349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-07-05</td>\n",
       "      <td>616019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2021-09-26</td>\n",
       "      <td>322228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2021-09-27</td>\n",
       "      <td>420188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2021-09-28</td>\n",
       "      <td>399966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2021-09-29</td>\n",
       "      <td>410543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>443022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           day  searches\n",
       "0   2021-07-01    663189\n",
       "1   2021-07-02    573578\n",
       "2   2021-07-03    520430\n",
       "3   2021-07-04    516349\n",
       "4   2021-07-05    616019\n",
       "..         ...       ...\n",
       "87  2021-09-26    322228\n",
       "88  2021-09-27    420188\n",
       "89  2021-09-28    399966\n",
       "90  2021-09-29    410543\n",
       "91  2021-09-30    443022\n",
       "\n",
       "[92 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searches_by_day = pd.read_sql(f\"\"\"\n",
    "    SELECT accessed_at::date AS day, COUNT(*) AS searches\n",
    "    FROM searches\n",
    "    WHERE accessed_at::date BETWEEN '{start_date}' AND '{end_date}'\n",
    "    GROUP BY day\n",
    "    ORDER BY day\n",
    "\"\"\", conn)\n",
    "\n",
    "searches_by_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = searches_by_day.plot.bar(title='Searches by day')\n",
    "p.figure.savefig('searches-by-day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>total_time_saved_in_quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12048 days 00:50:30.453081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  total_time_saved_in_quarter\n",
       "0  12048 days 00:50:30.453081"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    SELECT JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1                           \n",
    "            WHEN 'linux_armv6l' THEN 6                                                       \n",
    "            ELSE 0           \n",
    "        END *\n",
    "        CASE                        \n",
    "            WHEN b.duration > INTERVAL '1 day' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'               \n",
    "        END                  \n",
    "        )) AS total_time_saved_in_quarter                        \n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "    AND d.accessed_at::date between '{start_date}' and '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>month</th>\n",
       "      <th>time_saved_in_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>8074 days 17:26:14.875176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>6374 days 06:58:39.848346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>3878 days 06:59:28.147684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>3727 days 17:44:07.111225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>5639 days 08:25:11.612328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>6756 days 17:05:34.494183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>5148 days 08:09:03.090226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-05-01</td>\n",
       "      <td>4542 days 20:14:38.898707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>4240 days 19:47:36.009394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>3731 days 01:39:11.882878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>4142 days 21:19:43.043882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-09-01</td>\n",
       "      <td>4169 days 01:51:35.526321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        month       time_saved_in_month\n",
       "0  2020-10-01 8074 days 17:26:14.875176\n",
       "1  2020-11-01 6374 days 06:58:39.848346\n",
       "2  2020-12-01 3878 days 06:59:28.147684\n",
       "3  2021-01-01 3727 days 17:44:07.111225\n",
       "4  2021-02-01 5639 days 08:25:11.612328\n",
       "5  2021-03-01 6756 days 17:05:34.494183\n",
       "6  2021-04-01 5148 days 08:09:03.090226\n",
       "7  2021-05-01 4542 days 20:14:38.898707\n",
       "8  2021-06-01 4240 days 19:47:36.009394\n",
       "9  2021-07-01 3731 days 01:39:11.882878\n",
       "10 2021-08-01 4142 days 21:19:43.043882\n",
       "11 2021-09-01 4169 days 01:51:35.526321"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_saved_by_month = pd.read_sql(f\"\"\"\n",
    "    SELECT date_trunc('month', d.accessed_at) as month, JUSTIFY_INTERVAL(SUM(\n",
    "        CASE f.platform_tag\n",
    "            WHEN 'linux_armv7l' THEN 1\n",
    "            WHEN 'linux_armv6l' THEN 6\n",
    "            ELSE 0\n",
    "        END *\n",
    "        CASE\n",
    "            WHEN b.duration > INTERVAL '1 day' THEN INTERVAL '0'\n",
    "            WHEN b.duration > INTERVAL '6.7 seconds' THEN b.duration - INTERVAL '6.7 seconds'\n",
    "            ELSE INTERVAL '0'\n",
    "        END\n",
    "            )) AS time_saved_in_month\n",
    "    FROM downloads d\n",
    "    JOIN files f ON d.filename = f.filename\n",
    "    JOIN builds b ON b.build_id = f.build_id\n",
    "    WHERE f.abi_tag <> 'none'\n",
    "    AND d.accessed_at::date between '{one_year_ago}' and '{end_date}'\n",
    "    GROUP BY month\n",
    "    ORDER BY month\n",
    "\"\"\", conn)\n",
    "\n",
    "time_saved_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = [m.month_name()[:3] for m in time_saved_by_month['month']]\n",
    "days_saved = [ts.days for ts in time_saved_by_month['time_saved_in_month']]\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(12), days_saved, color=colors)\n",
    "plt.title('Time saved by month')\n",
    "plt.xticks(range(12), months)\n",
    "\n",
    "plt.savefig('time-saved-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>requests</td>\n",
       "      <td>116496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>six</td>\n",
       "      <td>102660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>numpy</td>\n",
       "      <td>100423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pip</td>\n",
       "      <td>98708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>idna</td>\n",
       "      <td>94551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>importlib-metadata</td>\n",
       "      <td>93747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>91259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>certifi</td>\n",
       "      <td>87703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>typing-extensions</td>\n",
       "      <td>85543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pillow</td>\n",
       "      <td>83796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              package       c\n",
       "0            requests  116496\n",
       "1                 six  102660\n",
       "2               numpy  100423\n",
       "3                 pip   98708\n",
       "4                idna   94551\n",
       "5  importlib-metadata   93747\n",
       "6             urllib3   91259\n",
       "7             certifi   87703\n",
       "8   typing-extensions   85543\n",
       "9              pillow   83796"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_downloads = pd.read_sql(f\"\"\"\n",
    "    select b.package, count(*) c\n",
    "    from downloads d\n",
    "    join files f on f.filename = d.filename\n",
    "    join builds b on b.build_id = f.build_id\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by b.package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (116,496)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (102,660)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (100,423)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (98,708)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/idna'>idna</a></strong> (94,551)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/importlib-metadata'>importlib-metadata</a></strong> (93,747)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/urllib3'>urllib3</a></strong> (91,259)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/certifi'>certifi</a></strong> (87,703)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/typing-extensions'>typing-extensions</a></strong> (85,543)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pillow'>pillow</a></strong> (83,796)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for i, row in top_10_downloads.iterrows():\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{row['package']}'>{row['package']}</a></strong> ({row['c']:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pip</td>\n",
       "      <td>7423597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>wheel</td>\n",
       "      <td>4427701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>3375412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pyrogram</td>\n",
       "      <td>1762252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>future</td>\n",
       "      <td>742655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>youtube-dl</td>\n",
       "      <td>664821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pyngrok</td>\n",
       "      <td>645709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>speedtest-cli</td>\n",
       "      <td>399889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>six</td>\n",
       "      <td>379356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>requests</td>\n",
       "      <td>377486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         package        c\n",
       "0            pip  7423597\n",
       "1          wheel  4427701\n",
       "2     setuptools  3375412\n",
       "3       pyrogram  1762252\n",
       "4         future   742655\n",
       "5     youtube-dl   664821\n",
       "6        pyngrok   645709\n",
       "7  speedtest-cli   399889\n",
       "8            six   379356\n",
       "9       requests   377486"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_searches = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pip'>pip</a></strong> (7,423,597)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/wheel'>wheel</a></strong> (4,427,701)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/setuptools'>setuptools</a></strong> (3,375,412)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyrogram'>pyrogram</a></strong> (1,762,252)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/future'>future</a></strong> (742,655)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/youtube-dl'>youtube-dl</a></strong> (664,821)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pyngrok'>pyngrok</a></strong> (645,709)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/speedtest-cli'>speedtest-cli</a></strong> (399,889)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/six'>six</a></strong> (379,356)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests'>requests</a></strong> (377,486)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_searches.values:\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lilurl</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>opencv-python</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>scipy</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>scikit-image</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tensorflow</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>imagecodecs</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pandas</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 package     c\n",
       "0                 lilurl  2401\n",
       "1                  numpy  1354\n",
       "2          opencv-python  1102\n",
       "3  opencv-contrib-python  1044\n",
       "4                  scipy  1006\n",
       "5           scikit-image   653\n",
       "6             tensorflow   559\n",
       "7            imagecodecs   523\n",
       "8           cryptography   474\n",
       "9                 pandas   437"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_project_pages = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from project_page_hits\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_project_pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/lilurl'>lilurl</a></strong> (2,401)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (1,354)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-python'>opencv-python</a></strong> (1,102)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (1,044)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scipy'>scipy</a></strong> (1,006)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/scikit-image'>scikit-image</a></strong> (653)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/tensorflow'>tensorflow</a></strong> (559)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/imagecodecs'>imagecodecs</a></strong> (523)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/cryptography'>cryptography</a></strong> (474)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/pandas'>pandas</a></strong> (437)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_project_pages.values:\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>package</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>awscrt</td>\n",
       "      <td>20679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>numpy</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>opencv-contrib-python</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>v2ray-runtime-windows</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>requests-toolkit</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>github-http-api</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bevy-events</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>newrelic-plugins-agent</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>v2ray-runtime</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>protobuf</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  package      c\n",
       "0                  awscrt  20679\n",
       "1                   numpy    458\n",
       "2   opencv-contrib-python    107\n",
       "3   v2ray-runtime-windows    104\n",
       "4        requests-toolkit    103\n",
       "5         github-http-api     96\n",
       "6             bevy-events     95\n",
       "7  newrelic-plugins-agent     95\n",
       "8           v2ray-runtime     92\n",
       "9                protobuf     91"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_project_json = pd.read_sql(f\"\"\"\n",
    "    select package, count(*) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by package\n",
    "    order by c desc\n",
    "    limit 10;\n",
    "\"\"\", conn)\n",
    "\n",
    "top_10_project_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<ol>\n",
      "<li><strong><a href='https://www.piwheels.org/project/awscrt'>awscrt</a></strong> (20,679)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/numpy'>numpy</a></strong> (458)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/opencv-contrib-python'>opencv-contrib-python</a></strong> (107)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/v2ray-runtime-windows'>v2ray-runtime-windows</a></strong> (104)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/requests-toolkit'>requests-toolkit</a></strong> (103)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/github-http-api'>github-http-api</a></strong> (96)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/bevy-events'>bevy-events</a></strong> (95)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/newrelic-plugins-agent'>newrelic-plugins-agent</a></strong> (95)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/v2ray-runtime'>v2ray-runtime</a></strong> (92)</li>\n",
      "<li><strong><a href='https://www.piwheels.org/project/protobuf'>protobuf</a></strong> (91)</li>\n",
      "</ol>\n"
     ]
    }
   ],
   "source": [
    "print(\"<ol>\")\n",
    "for pkg, c in top_10_project_json.values:\n",
    "    print(f\"<li><strong><a href='https://www.piwheels.org/project/{pkg}'>{pkg}</a></strong> ({c:,})</li>\")\n",
    "print(\"</ol>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>320273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        c\n",
       "0  320273"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select count(distinct(package)) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>user_agent</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>python-requests</td>\n",
       "      <td>859378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mozilla</td>\n",
       "      <td>212817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python-urllib</td>\n",
       "      <td>21131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ccbot</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>clark-crawler2</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>node-fetch</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tinybottestua</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>java</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ahc</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>default</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[object promise]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>curl</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>wordpress</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>wget</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>inetdex bot</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>apache-httpclient</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tinytestbot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>com.google.drive.shareextension</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>dalvik</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>opera</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>grammarly</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         user_agent       c\n",
       "0                   python-requests  859378\n",
       "1                           mozilla  212817\n",
       "2                     python-urllib   21131\n",
       "3                             ccbot     982\n",
       "4                    clark-crawler2      55\n",
       "5                        node-fetch      25\n",
       "6                     tinybottestua      14\n",
       "7                              java       7\n",
       "8                               ahc       3\n",
       "9                           default       3\n",
       "10                 [object promise]       3\n",
       "11                             curl       2\n",
       "12                        wordpress       2\n",
       "13                             wget       2\n",
       "14                      inetdex bot       2\n",
       "15                apache-httpclient       1\n",
       "16                      tinytestbot       1\n",
       "17  com.google.drive.shareextension       1\n",
       "18                           dalvik       1\n",
       "19                            opera       1\n",
       "20                        grammarly       1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(f\"\"\"\n",
    "    select user_agent, count(*) c\n",
    "    from project_json_downloads\n",
    "    where accessed_at::date between '{start_date}' and '{end_date}'\n",
    "    group by user_agent\n",
    "    order by c desc\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>os_name</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linux</td>\n",
       "      <td>48221974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Windows</td>\n",
       "      <td>12494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FreeBSD</td>\n",
       "      <td>12372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Darwin</td>\n",
       "      <td>9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Open Surface RT - Linux</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CYGWIN_NT-10.0-18363</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CYGWIN_NT-10.0-19042</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   os_name         c\n",
       "0                    Linux  48221974\n",
       "1                  Windows     12494\n",
       "2                  FreeBSD     12372\n",
       "3                   Darwin      9978\n",
       "4  Open Surface RT - Linux       112\n",
       "5     CYGWIN_NT-10.0-18363        17\n",
       "6     CYGWIN_NT-10.0-19042         4\n",
       "7                     None         1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os_usage = pd.read_sql(f\"\"\"\n",
    "    select os_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by os_name\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "os_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.93% linux\n"
     ]
    }
   ],
   "source": [
    "linux_usage_pc = 100 * (os_usage.values[0][1] / searches)\n",
    "print(f'{linux_usage_pc:2.2f}% linux')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>distro_name</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Raspbian GNU/Linux</td>\n",
       "      <td>42562444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debian GNU/Linux</td>\n",
       "      <td>5240197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ubuntu</td>\n",
       "      <td>305544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OSMC</td>\n",
       "      <td>28180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CentOS Linux</td>\n",
       "      <td>26462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          distro_name         c\n",
       "0  Raspbian GNU/Linux  42562444\n",
       "1    Debian GNU/Linux   5240197\n",
       "2              Ubuntu    305544\n",
       "3                OSMC     28180\n",
       "4        CentOS Linux     26462"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_usage = pd.read_sql(f\"\"\"\n",
    "    select distro_name, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    group by distro_name\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47802641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c\n",
       "0  47802641"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debian_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "debian_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.06% debian\n"
     ]
    }
   ],
   "source": [
    "debian_usage_pc = 100 * (debian_usage.c[0] / searches)\n",
    "print(f'{debian_usage_pc:2.2f}% debian')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.63% ubuntu\n"
     ]
    }
   ],
   "source": [
    "ubuntu_usage = pd.read_sql(f\"\"\"\n",
    "    select count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name = 'Ubuntu'\n",
    "    order by c desc\n",
    "    limit 5\n",
    "\"\"\", conn)\n",
    "\n",
    "ubuntu_usage_pc = 100 * (ubuntu_usage.c[0] / searches)\n",
    "print(f'{ubuntu_usage_pc:2.2f}% ubuntu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Debian / Raspberry Pi OS': 47802641, 'Others': 454311}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debian = debian_usage.values[0][0]\n",
    "others = searches - debian\n",
    "\n",
    "distro_usage = {\n",
    "    'Debian / Raspberry Pi OS': debian,\n",
    "    'Others': others,\n",
    "}\n",
    "\n",
    "distro_usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JB8hZ/jnPvkMup63fBJHPvQxVpaqio1ApsSyJbmLPykT6m5OQf2Cn6Cjkx6QgPcKHPovgrg+KjkKlwLIkuoHlaCquvPYf2C9fEB2FAoSuaUtEjnuVJ2f3cixLomtyf1qFq+/P4CQe8jhlbBVET54JdfVaoqPQbbAsKeDJVguuvj8Dxp9WiY5CAUwKMiDqP68hKLmV6ChUDJYlBTTb5QtIn/4fWI6kio5CBCgUCBvyDEL7DBCdhG7CsqSAlXdgF9LfnARH1lXRUYiKMHTqiYjREyGp1aKj0DUsSwpI2cs/R9bn83m9SfJa2vpNEDVpBif+eAmWJQUU2W5DxntTYVr/vegoRE5x4o/3YFlSwJAt+bjy+gvI2/mr6ChEpSbpr038SeLEH5FYlhQQHKZcXHllLE+CTr5JoUD4sDEIeaCf6CQBi2VJfs+emYHLLz8N6/G/RUchKpfQx0Yg7JFhomMEJJYl+TXblYu4PGkkbGdPiY5C5BKh/YYjbMCTomMEHJYl+S3b5Qu4PHEEbOfPiI5C5FKhfR9H2MBRomMEFJYl+SXbpfO49MII2C+eFR2FyC1CHhyI8MefER0jYPCCauR3bBfO4tLzT7Aoya/lfLMImZ/NFh0jYLAsya/Yzp/BpReehP3SedFRiNwu55tFyPryE9ExAgLLkvyGPSsTl196ipfXooCSveQD5KxcKjqG32NZkl+QrRZcmTqWk3koIGV+8i5y164UHcOvsSzJ58myjIyZU2D566DoKETCXJ33Ooyb1oqO4bdYluTzshbNh2nLz6JjEInlcCDj3VeQzy+NbsGyJJ+W+8u3yPl6gegYRN7BZsWV6f+BPf2y6CR+h2VJPivvwC5cnTtddAwir+LIuIIr08ZDtlpER/ErLEvySdZTJ3Dltf8ANpvoKERex3LkEDLmvi46hl9hWZLPsWdm4PKU5yAbc0RHIfJapnXfIWf1l6Jj+A2WJfkUR34errw6lmfnISqFzE9nIe/ALtEx/ALLknxKxqxXYPn7T9ExiHyD3Y70N16A7QK/XJYXy5J8Ru5Pq2De8ovoGEQ+xZGdhStTx8GRZxYdxaexLMkn2M6fQebHM0XHIPJJ1rRjyJg5RXQMn8ayJK8n2+1If/slyGaT6ChEPsu8bT2yeUxymbEsyetlL/sUlsN/iI5B5POyln4Iyz9HRMfwSSxL8mr5h/9E9lefio5B5B9sNmTMegWynccn3ymWJXkth9mEjHdeAux20VGI/Ib1+N/I+d/nomP4HJYlea3Mj96B7dxp0TGI/E7Wl5/Aeuof0TF8CsuSvJJpxyYYf14tOgaRf7JZkfHuK5C51abUWJbkdewZV3B19jTRMYj8muXIIeSsWio6hs9gWZLXyXjvVTiyM0XHIPJ72Us+hPVMmugYPoFlSV7FtOVn5O3eLjoGUUCQLfnImPUqZIdDdBSvx7IkryFb8pG5YI7oGEQBxfLXQeR++5XoGF6PZUleI2fVF7BfOi86BlHAyVo0H7bzZ0TH8GosS/IK9qvpyP56oegYRAFJzs/D1fdniI7h1ViW5BWyFs2HbDaKjkEUsPL2bEfeH3tEx/BaLEsSznL8bxjXfSc6BlHAy1o4T3QEr8WyJOEyP3kX4Gw8IuEshw/C/Ntm0TG8EsuShDLv2IT8g7tFxyCia7IWzeehJMVgWZIwss2GzM9mi45BRDewnjwO06YfRcfwOixLEib3u69gO3dKdAwiuknWko8g23gZrxuxLEkIe3Ymsr7kdSqJvJH94lnkrl0hOoZXYVmSEDkrl0I25oiOQUS3kf3Vp3Dk5YmO4TVYluRxDpMRud8vFx2DiErguJqO3NVfiI7hNViW5HG5a1dyVEnkA7K/WQxHTrboGF6BZUkeJdtsyF39pegYRFQKsjEHOSuXiI7hFViW5FGmjT/CfuWi6BhEVEq5P66AbMkXHUM4liV5jCzLyF6xWHQMIroDjuxMGDetFR1DOJYleUz+vt9hO/WP6BhEdIdyv1smOoJwLEvymBx+4Ih8kvWfI8j/c5/oGEKxLMkjbOfPIG/3NtExiKiMcr79SnQEoViW5BE53/+PVxYh8mHmHZtgT78sOoYwLEtyO0deHow/fys6BhGVh8MO4y+B+zlmWZLbmTav5UkIiPxA7i/fQpZl0TGEYFmS25k2/yQ6AhG5gP3CWeQf2CU6hhAsS3Ire9ZV5P+xV3QMInIR40+rREcQgmVJbmXevgFw2EXHICIXMe3YBHtOlugYHseyJLcy/bpedAQiciWrBeat60Sn8DiWJblNwSbYPaJjEJGLmX//VXQEj2NZktuYt2/kJlgiP5R/YFfAXRiaZUluY/o18DbVEAUC2ZKP/P2/i47hUSxLcgtugiXyb+adgbUplmVJbsFNsET+zbxra0CdoIBlSW5hCsDZckSBxJFxBdZjf4mO4TEsS3I5e1Ym8g9yEyyRvwukWbEsS3I582+buAmWKACYd24RHcFjWJbkcvl/8vR2RIHAevxv2K5cFB3DI1iW5HKWvw6KjkBEHpIXILNiWZbkUvar6bCdPyM6BhF5SKDst2RZkkvlc1RJFFDyD+6GbLeJjuF2LEtyKUvqAdERiMiDZEs+rCf/ER3D7ViW5FIcWRIFHksAHG/JsiSXka0WWI4fFh2DiDzMGgCfe5YluYzlaCpgtYiOQUQeZjn2t+gIbseyJJfhJliiwGQ9cQSy3b9PRMKyJJfh8ZVEgUnOz4PtTJroGG7FsiSX4ciSKHD5+yQfliW5hPXcaTgyM0THICJBLMf9e78ly5JcwnryuOgIRCSQ9Zh/z4hlWZJL2C+eEx2BiASy/PO3X18MmmVJLmFjWRIFNNlsgu3sSdEx3IZlSS5hu3BWdAQiEszqx/stWZbkEtwMS0S2i+dFR3AbliW5hO2S/35IiKh07FeviI7gNixLKjd7ViZks0l0DCISzJ7BsiS6LdtF7q8kIo4siUpk9+P9FERUeg6OLIlujyNLIgK4GZaoRDzGkoiAghOqO0y5omO4BcuSyo2bYYnoOn8dXbIsqdxsly+IjkBEXoJlSXQbstE/N7sQ0Z0L2LJUKpVITExEQkICGjVqhHfeeQcOh6PEx2zatAk9evQo9nfdunVDZmZmmcLezGq1okmTJrfcX6NGDTRo0AANGzZEmzZtcPKka89X2LZtW+zevdul63QmLS0NQUFBSExMRHx8PEaMGAGHw4Fvv/0Wb7zxRrGPWbVqFRo2bIh69eqhQYMGWLVqVeHvfvvtNzRr1gyJiYmoV68epkyZUuZsjjweY0lEBRx+eviIytkCQUFB2L9/PwDg0qVLePTRR5GdnY1XXnmlTE/4ww8/lOlxxdm6dSvuu+++Yn+3ceNGVKhQAZMnT8a0adPw8ccfu+x5y8Nms0GlUt32dklq1aqF/fv3w2azoX379li1ahX69OmDnj173rLsgQMHMH78ePzyyy+oWbMmTpw4gU6dOiEuLg4NGzbEoEGD8PXXX6NRo0aw2+34+++yn9NRzjOX+bFE5F/s6f5Zlne0GTYmJgYfffQR5s6dC1mWYbfbMWHCBCQlJaFhw4b48MMPC5fNzs5G9+7dUbdu3cJREFAw6rtypeDN7NWrF5o2bYqEhAR89NFHhY8NDg7Giy++iEaNGqF58+a4ePFisXnWrl2Lrl27lpi5RYsWOHu24NCGtLQ03H///WjSpAmaNGmC7du3AwDOnz+P1q1bIzExEfXr18evv/5amGPMmDFISEhAhw4dcPny5cL1Ll68uHD5nTt3AgCMRiMef/xxJCcno3Hjxli9ejUAYOHChejZsyfat2+PDh063HJ74MCBRUZ9/fv3L3xscVQqFVq2bIljx45h4cKFeOqpp25Z5u2338akSZNQs2ZNAEDNmjUxceJEvPXWWwAKvvhUqlQJQMHWg/j4+BLfx9uRrRbAbi/TY4nI/9izr4qO4BZ3vM8yLi4Odrsdly5dwqeffoqwsDDs2rULu3btwscff4wTJ04AAHbu3Ik5c+YgNTUVx48fx4oVK25Z12effYY9e/Zg9+7dmD17NtLT0wEUlE7z5s1x4MABtG7d+rajwo0bN6Jt27Yl5l27di169eoFoKDsf/nlF+zduxfLli3DM888AwD44osvkJKSgv379+PAgQNITEwszHHvvffi0KFDaNOmTZHRtMlkwv79+zF//nw8/vjjAIDXXnsN7du3x86dO7Fx40ZMmDABRqMRALB3714sX74cmzdvvuX20KFDsXDhQgBAVlYWtm/fju7du9/2NZlMJqxfvx4NGjS47TKHDh1C06ZNi9x3/bUAwJgxY1C3bl307t0bH374IfLy8kp8H2/HwVElEd3IZhOdwC3KNcHn559/xqJFi5CYmIhmzZohPT0dR48eBQAkJycjLi4OSqUS/fr1w9atW295/OzZswtHj6dPny58rEajKdzn2bRpU6Slpd3y2LNnzyIyMhJ6vb7YbO3atUOVKlXw448/ol+/fgAK9nEOHz4cDRo0wEMPPYTU1FQAQFJSEhYsWIApU6bgjz/+QEhISMGbo1Cgb9++AIABAwYUeQ3X19m6dWtkZ2cjMzMTP//8M9544w0kJiaibdu2yMvLw6lTpwAAnTp1QmRkZOHjb7zdpk0bHD16FJcvX8aXX36JBx98sNhNs8ePH0diYiLuu+8+dO/e3emouiQvv/wydu/ejc6dO+OLL75Aly5dyrQeboIlohvJfrqlqXQ7y27wzz//QKlUIiYmBrIsY86cOUhJSSmyzKZNmyBJUpH7br69adMmrFu3Djt27IBery8sFwBQq9WFyyuVStiK+aaydu3aW573Rhs3bkR4eDj69++PyZMnY+bMmXj33XcRGxuLAwcOwOFwQKfTASgovC1btuD777/H4MGDMXbsWAwcOPCWdd74Gop7fbIs45tvvkHdunWL/O7333+HwWAoct/NtwcOHIglS5bgq6++woIFC4p9Tdf3WZZGfHw89uzZg0aNGhXet2fPHiQkJBRZ38iRIzF8+HBER0cjPT0dUVFRpVr/dbLVekfLE5Gfs3NkicuXL2PEiBF46qmnIEkSUlJS8P7778N67R/MI0eOFG523LlzJ06cOAGHw4Fly5ahVatWRdaVlZWFiIgI6PV6HD58GL/99tsdBS/N/kqVSoVZs2Zh0aJFyMjIQFZWFipVqgSFQoHFixfDfu0b0MmTJxEbG4vhw4dj2LBh2Lt3LwDA4XBg+fLlAAo21d74GpYtWwagYJJRWFgYwsLCkJKSgjlz5kCWZQDAvn37Sv16Bg8ejFmzZgFAmfcf3mj8+PF4/fXXC0flaWlpmD59OsaNGwcA+P777wtzHj16FEqlEuHh4Xf+RH76LZKIyiZgR5ZmsxmJiYmwWq1QqVR47LHHMHbsWADAsGHDkJaWhiZNmkCWZURHRxdOVElKSsJTTz2FY8eOoV27dujdu3eR9Xbp0gUffPAB6tWrh7p166J58+alDm2323Hs2DHcc889TpetVKkS+vXrh3nz5mHUqFF48MEHsWjRInTp0qVwdLdp0ya89dZbUKvVCA4OxqJFiwAUjP527tyJadOmISYmprAgAUCn06Fx48awWq347LPPAAAvvfQSnnvuOTRs2BAOhwM1a9bEmjVrSvWaYmNjUa9evcL9q+WVmJiIN998E//6179gtVqhVqsxY8aMwv2xixcvxpgxY6DX66FSqbB06VIolco7fyKHf34wiKiM/LQsJfn68MKHbN26FUuWLMEHH3zg1ucJDg5Gbq5nDrg3mUxo0KAB9u7di7CwMI88pytY/jmCi08/KjoGeQG7QoW80CiYgyNhNoTBrA+DVaWDz/0DQ+USWbUymg0bKjqGy93xPktv0KpVq1s26/qydevWYejQoRgzZoxPFSUAwMkJKihwKB02GDIvwpD5/4d6WdU6mEOjkBccCZMhHOagMJh1wTDpgmFU62FS62FSamFUaGCCGkYokeuQYLRLMFpl5FrtsNpZt74kOTQCzcr42DNnzmD06NFITU2Fw+FAjx498NZbbyE1NRXnzp1Dt27dAABTpkxBcHAwxo8f77rgTvhkWXqKp0aVHTt2dPlZhjxF9tOd+eQaamse1OlnEZpe9su4WbUGmEMiYQqOgNkQAbM+FCZtCMxaA4waPUyqIJiUOhgVGhihgklWwigrkGsDcq0O5FodsDtYuJ6iuGnyY2nJsow+ffpg5MiRWL16Nex2O5544gm8+OKLSEhIwO7duwvLsrzsdvsd73ZiWVK5SEr+FSL3Uucboc43IvTK6TKvwxIUAlNIJPIMETAbwmEKCi340RhgVuthVOtgUmgLC9coK2F0KGC0FxSu0WIHB7ilo1KUrSw3bNgAnU6HIUOGACg4EuLdd99F9erVoVarIcsytm7diokTJwIAUlNT0bZtW5w6dQrPPfdc4XHzS5YswezZs2GxWNCsWTPMnz8fSqUSwcHBePLJJ7Fu3TrMmzcPa9aswbfffguVSoXOnTvj7bffLvl1lelVEV2jMASLjkDklMacA405B0DZt+DkGcILNicHR8CsD4M5KBRGXTDM2uCCzckqHYxKLUzSjYUrIdcGGK0OGK12BMIAV6Us2+H7xZ1IJTQ0FDVq1MCQIUNw5MgRzJ07F0DBZtjDhw9j48aNyMnJQd26dTFy5EgcO3YMy5Ytw7Zt26BWqzFq1CgsXboUAwcOhNFoRLNmzfDOO+8gPT0dQ4cOxeHDhyFJUqnOV86ypHJRBIeKjkDkETpjJnTGTIQXf/ZNp2RJQp4hHOaQKOQZwgv24epCYNKFwKgxwKzRF2xOVmphktQwQoVcWVmw/9YuI9cqw2Sxe/2EKa3KMxez6t69O7RaLbRaLWJiYnDx4kWsX78ee/bsQVJSEoCCozliYmIAFIxUH3zwQQBAWFgYdDodhg4dih49etz2wh83YllSuUjBIYBCwYk+RE5Isoyg3KsIyi37uVMdCiXygsMLZhwHR8KsD4NJF1JQuhp9waQp1bVNytcK1yQrkGuXkGuTYbQ6YLK697OqK2NZxsfHFx7Xfl12djZOnTpV7BnNtFpt4Z+vn7xGlmUMGjQIr7/++q25dLrC/ZQqlQo7d+7E+vXrsXz5csydOxcbNmwoMR/LkspFkiQo9MFw5GaLjkLk9xQOO/TZ6dBnp5d5HXaFCvkhkTCFRMKsD4fZEAaTLhQmXTDM12YoG1U6GBVamCTVtU3KioIRrk1GjsWBPNvtC1evKVutdOjQAS+88AIWLVqEgQMHwm63Y9y4cRg8eDBiY2Px+++/l2odDzzwAMaMGYOYmBhkZGQgJycH1atXL7Jcbm4uTCYTunXrhvvuuw9xcXFO182ypHJThISyLIl8hNJhgz7rEvRZl8q8DrtSDXNoFMzXZyhfnzClDUEdWQOgzh2vU5IkrFy5EqNGjcLUqVPhcDjQrVs3TJ8+HUajsfC829cn+BQnPj4e06ZNQ+fOneFwOKBWqzFv3rxbyjInJwcPPPAA8vLyIMsyZs6c6TyfL56UgLzLhecGwno0VXQMIvIC4U+OR0jPR0THcDnP7Iklv6YIDhEdgYi8hCI0XHQEt2BZUrkpQjgjlogKKFmWRMXj4SNEdB1HlkS3wbIkousUoT52futSYllSubEsieg6jiyJboP7LIkIABThkVDogkTHcAuWJZWbKray6AhE5AXUVWuIjuA2LEsqN3U152e/ICL/p7qrhugIbsOypHJTRkT57U59Iio9jiyJnFDfxdElUaBT3VVTdAS3YVmSS6iqsyyJAp2am2GJSqb242+UROScpNVBGV1RdAy3YVmSS6ir1xIdgYgEUlWtDkmSRMdwG5YluQRHlkSBzZ8n9wAsS3IRZWQFKEI4I5YoUPnz5B6AZUkuxOMtiQIXR5ZEpaSq5t/fLIno9vz5hAQAy5JciCNLosAkabRQV6kuOoZbsSzJZTRxdUVHICIBNPUaQlKrRcdwK5YluYymbn1IfnrFASK6PV3De0VHcDuWJbmMpFZDW7+x6BhE5GHaxGTREdyOZUkupUtsJjoCEXmQpDdAUydedAy3Y1mSS2kbsyyJAok2oTEkpVJ0DLdjWZJLaWrUhiIiSnQMIvIQXaMk0RE8gmVJLqcLgP0XRFRAy7IkKhvutyQKDIrQcKhr1hEdwyNYluRyOu63JAoI2gZN/fpKIzdiWZLLKaOioeLZfIj8ni4xMDbBAixLchPutyTyf4GyvxJgWZKbcFMskX9TRlf0+/PB3ohlSW6hbdAUUPn3uSKJApm+TYroCB7FsiS3UATpEZR0n+gYROQmhg7dRUfwKJYluY2+XVfREYjIDdS16wXcJflYluQ2Qcn3QzIEi45BRC5maN9NdASPY1mS20hqDfT3dRAdg4hcSamEvm0X0Sk8jmVJbqVvy02xRP5E17QllGERomN4HMuS3ErbsCmU0bGiYxCRiwTiJliAZUluJkkSDB17io5BRC4gGUIQ1Ky16BhCsCzJ7QydHwAU/KtG5Ov0rTpC0mhFxxCC/4KR26liKkLXuLnoGERUToG6CRZgWZKHGFJ6iY5AROWgrFgFmoRE0TGEYVmSRwQ1aw1FRJToGERURoYOPQLmclzFYVmSR0gqFYI7PyA6BhGVgaTVIrj7Q6JjCMWyJI8JfqAfJK1OdAwiukOGTj2hDAsXHUMoliV5jDIsAoZuD4qOQUR3QqFESO8BolMIx7Ikjwrt8xgkbWBOPSfyRUGt2kNVsYroGMKxLMmjlJEVYEjpLToGEZVS6IODREfwCixL8rjQfw8C1BrRMYjICV2T5tDUvkd0DK/AsiSPU0ZFw9CJp8Aj8nahjz4hOoLXYFmSEKEPDwZUatExiOg2dE2aQ1uvoegYXoNlSUKooivC0KGH6BhEdBscVRbFsiRhQvsOAZRK0TGI6CYcVd6KZUnCqGIrB/SJmYm8FUeVt2JZklAhDz8OKDi6JPIWQfd34qiyGCxLEkpd+S4E/+th0TGICIAUZEDE8LGiY3glliUJF/bYSCgrxIqOQRTwwvoPhzIqWnQMr8SyJOEUQXqEP8Fvs0QiqWvURnDPR0TH8FosS/IK+vs6QJfUSnQMosAkSYgY9QIkpUp0Eq/FsiSvETHyP7yEF5EA+vbdoU1IFB3Dq7EsyWuoYisj9JFhomMQBRRFcCjCH39GdAyvx7IkrxLSZwBU1eNExyAKGGEDR0EZHik6htdjWZJXkVQqRI6eBEiS6ChEfk9dJx6Grn1Ex/AJLEvyOtqERF6VhMjdFApEjHoekoI1UBp8l8grhT3+DBSh4aJjEPktQ5c+0N6dIDqGz2BZkldShoQhfPgY0TGI/JLqrpoIH/qs6Bg+hWVJXsvQvjv07bqKjkHkVyStDhUmvgGFLkh0FJ/CsiSvFvHUJKiqcXYskauEj5gAdfVaomP4HJYleTWFLggVJr4Jid+CicpN374bgjs/IDqGT2JZktdTV6uJiKdfFB2DyKepqtZAxOiJomP4LJYl+QRD2y4wdHtQdAwinyRptYjifspyYVmSz4h4YhzUteuJjkHkc8KfnABNjdqiY/g0liX5DEmtQYWJb0AyhIiOQuQz9O26Ijill+gYPo9lST5FVbEKosZMFh2DyCeoqtZAxFOTRMfwCyxL8jlBLdoipPcA0TGIvBr3U7oWy5J8UtiQp6Dh9feIiqdQImrCa9xP6UIsS/JJklKFCv99G6qq1UVHIfI6EU9NRFCLtqJj+BWWJfksZWg4oqfOhSKygugoRF4jbNBoTuhxA5Yl+TRVTCVEvzoHkiFYdBQi4YIf6IfQh4eIjuGXWJbk8zQ166DCf98G1BrRUYiE0bftivDhY0XH8FssS/ILuob3Imr8q4BCKToKkcfp7m2JyDGTIUmS6Ch+i2VJfkPfqiMin30J4D8YFEA09zRA1MQZkFQq0VH8GsuS/IqhYw9EjHxedAwij1BVi0OFKbOg0OlER/F7LEvyO8Hd/43wYc+JjkHkVsroWERPnQNlSJjoKAGBZUl+KaT3AIQOGCE6BpFbKCvEInraPKgqxIqOEjAkWZZl0SGI3CV7+efIWjgX4F9z8hOqytUQ/dp8qGIqio4SUFiW5PeMG39ExqxXAZtVdBSiclHH3Y3oqXOhDI8UHSXgsCwpIOQd2IUr08ZDNhlFRyEqE018I0RPeQ8KnoBDCJYlBQzLiaO4MvlZ2NMviY5CdEd097ZE1MQZnPUqEMuSAort8gVcnvwMbCf/ER2FqFQMKb0RMfp5SEoeRykSy5ICjiM3B1emjUP+H3tFRyG6PUlC2MBRPNerl2BZUkCSrRakz5wM85ZfREchupVKjcgxk2Fo20V0ErqGZUkBS5ZlZH46C7krl4qOQlRIERKGqBdnQNegqegodAOWJQW8nO+WIfOTWTy0hITTxDdC1ITXeAylF2JZEgGwHP0L6TNehO3cKdFRKBBJEkIeGoywAU9yIo+XYlkSXeMwm3D1/RkwrV8jOgoFEEV4FKLGvwJd4+aio1AJWJZENzFuWour817nCQzI7bSJyYga9yqUkRVERyEnWJZExbCdP4P0GS/CcuSQ6CjkjxRKhPV/AiEPD4Gk4PUsfAHLkug2ZJsNWYvfR843i3gidnIZZYVYRP3nNWgTEkVHoTvAsiRyIm//TqS/8zIcGVdERyEfp0u+H5Fjp/AalD6IZUlUCvasTGS8OwV5u7aKjkI+SDKEIHzQaAR3/7foKFRGLEuiO2Da8jMyP3sP9ssXRUchXyBJMHT8F8KGPA1lWIToNFQOLEuiO+TIy0PO/xYgZ8USyJZ80XHIS6lr3YOIUc9De08D0VHIBViWRGVku3AWmZ+8C/OOTaKjkBdRhIQhbOBIGLr04UxXP8KyJCqnvL2/4eqHb8N2Jk10FBJJoYCh0wMIGzwaytBw0WnIxViWRC4g22zI/e4rZH3xMU9mEIA0d8cjfOTz0N6dIDoKuQnLksiF7FfTkblwbsEp8/jR8nuK0HCEDRwFQ0ovbnL1cyxLIjfI//tPZC/9CHl7touOQm6grBCLkD4DYEjpDYVOJzoOeQDLksiNLEf/Qvayz2D+bRNHmn5AVbU6Qv49CIZ23SCpeHWQQMKyJPIA68njyP56AUxbfgEcdtFx6A6pa92D0IcHI6hle25uDVAsSyIPsp0/g5zVX8K47jvIZpPoOOSEtkFThD48BLomvHxWoGNZEgngyM1B7k8rkfvdMp4NyNtIEnTJ9yP04SE8oQAVYlkSCSTbbTBv24CcNV/DknqA+zUFUoSGQ9+2C4K79Ia6ei3RccjLsCyJvITt0nmYNv8E0+afYD1xVHScwKBUQte0JQwd/4Wg5PshqdWiE5GXYlkSeSHrqX9g2rQWxs0/wX7hrOg4fkddux70bVJgaNcVyogo0XHIB7Asibxc/t9/Fow4t/wMx9V00XF8lrpGbejv74Sg1p2hrnyX6DjkY1iWRD5CdjiQf3A3TJt/gnnHJjhyskRH8m4qFTR14qFr0hz6Vp2grlZTdCLyYSxLIh8kyzKsaceQ/+de5P+xF/mH9sORGeCjTqUSmjrx0DZoCl3De6GJbwSFLkh0KvITLEsiP2E9k1ZQnH/uQ/6hvf5/SIpCCU3tutA2uBfahk2hjU+EQm8QnYr8FMuSyE/ZLp67Vp57kZ+6H7bzZ3367EGKyApQV6kOTe160Da8F9r6iVDog0XHogDBsiQKELLNBtvFs7CdPQ3b+dOwnTsF27nTsJ47DfulC15RpJIuCKoq1aCuUh2qqtWhqlK94M9VqnHUSEKxLImo+CK9fBGyKReO3Fw4TLlw5OZANhsBh6P0K1YqoQgyQArSQ6E3QAoyXPtvwW1FcChUlaoWFqOqQoz7XiRRObAsiajUZFmGnGeGbLFAtuYDVitkq+XajxWSSl20GHn5KvITLEsiIiIneK0ZIiIiJ1iWRERETrAsiYiInGBZEhEROcGyJCIicoJlSURE5ATLkoiIyAmWJRERkRMsSyIiIidYlkRERE6wLImIiJxgWRIRETnBsiQiInKCZUlEROQEy5KIiMgJliUREZETLEsiIiInWJZEREROsCyJiIicYFkSERE5wbIkIiJygmVJRETkBMuSiIjICZYlERGREyxLIiIiJ1iWRERETrAsiYiInGBZEhEROcGyJCIicoJlSURE5ATLkoiIyAmWJRERkRMsSyIiIidYlkRERE6wLImIiJxgWRIRETnBsiQiInKCZUlEROTE/wEBBPd9raivoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(distro_usage.values(), labels=distro_usage.keys())\n",
    "plt.title('Linux distro usage')\n",
    "\n",
    "plt.savefig('distro-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>distro_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>43096039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>3055783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  distro_version         c\n",
       "0             10  43096039\n",
       "1              9   3055783\n",
       "2              8       447"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distro_versions = pd.read_sql(f\"\"\"\n",
    "    select distro_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and distro_version in ('8', '9', '10')\n",
    "    group by distro_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "distro_versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 93.4%\n",
      "9 6.6%\n",
      "8 0.0%\n"
     ]
    }
   ],
   "source": [
    "total = sum(distro_versions.c)\n",
    "\n",
    "for v in distro_versions.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "version_names = {'10': 'Buster', '9': 'Stretch', '8': 'Jessie'}\n",
    "plt.pie(distro_versions.c[:2], labels=[version_names[v] for v in distro_versions.distro_version][:2])\n",
    "plt.title('Raspbian/Debian version usage')\n",
    "\n",
    "plt.savefig('debian-usage.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>arch</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>armv7l</td>\n",
       "      <td>34280447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aarch64</td>\n",
       "      <td>9220922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x86_64</td>\n",
       "      <td>3025941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>armv6l</td>\n",
       "      <td>1646874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i686</td>\n",
       "      <td>33443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>armv8l</td>\n",
       "      <td>21254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AMD64</td>\n",
       "      <td>12494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>amd64</td>\n",
       "      <td>12372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arm64</td>\n",
       "      <td>2699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ppc64</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>arm</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mips</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>armv8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ppc64le</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       arch         c\n",
       "0    armv7l  34280447\n",
       "1   aarch64   9220922\n",
       "2    x86_64   3025941\n",
       "3    armv6l   1646874\n",
       "4      i686     33443\n",
       "5    armv8l     21254\n",
       "6     AMD64     12494\n",
       "7     amd64     12372\n",
       "8     arm64      2699\n",
       "9     ppc64       317\n",
       "10      arm       144\n",
       "11     mips        31\n",
       "12    armv8         8\n",
       "13  ppc64le         5\n",
       "14     None         1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(arch.c[:4], labels=arch.arch[:4])\n",
    "plt.title('Architecture usage')\n",
    "\n",
    "plt.savefig('arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "armv7l 76.0%\n",
      "aarch64 20.3%\n",
      "armv6l 3.6%\n"
     ]
    }
   ],
   "source": [
    "arm_arch = pd.read_sql(f\"\"\"\n",
    "    select arch, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and arch in ('armv6l', 'armv7l', 'aarch64')\n",
    "    group by arch\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "total = sum(arm_arch.c)\n",
    "\n",
    "for v in arm_arch.values:\n",
    "    print(v[0], f'{(100 * v[1] / total):.1f}%') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(arm_arch.c, labels=arm_arch.arch)\n",
    "plt.title('PiOS/Debian Arm architecture usage')\n",
    "\n",
    "plt.savefig('debian-arch.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>pyver</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.7</td>\n",
       "      <td>31147542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.7</td>\n",
       "      <td>5243826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.8</td>\n",
       "      <td>4595643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9</td>\n",
       "      <td>2154373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>1656419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.6</td>\n",
       "      <td>195502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pyver         c\n",
       "0   3.7  31147542\n",
       "1   2.7   5243826\n",
       "2   3.8   4595643\n",
       "3   3.9   2154373\n",
       "4   3.5   1656419\n",
       "5   3.6    195502"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "py_vers = pd.read_sql(f\"\"\"\n",
    "    select substring(py_version from 1 for 3) pyver, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and os_name = 'Linux'\n",
    "    and distro_name in ('Raspbian GNU/Linux', 'Debian GNU/Linux')\n",
    "    and arch in ('armv6l', 'armv7l', 'aarch64')\n",
    "    and py_name = 'CPython'\n",
    "    group by pyver\n",
    "    order by c desc\n",
    "    limit 6\n",
    "\"\"\", conn)\n",
    "\n",
    "py_vers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j8ODBsFgssNvtmDRpEubOndtsmWeeeQbr168HABiNRpSXl0Ov13NI2zJHdeWVLdFVsJ48wjuOVxMCdQgYeBsCho6GNrcnnQvrI6hQ24gxhoaGBuh0OthsNgwcOBDvvPMO+vbt2+Ly7777Lvbt24ePPvrIzUmvZz60B/X//RKm7Rvcew4oAQAoozshYMgo6EbfBVV0J95xiAvRif1tJAgCdDodAMBms8Fms7W61bF06dLrtmDdSTSbYFz/I+q//zft0nPmKC+F4auPYfjPYvj3H4qgCVOh7dqddyziArSF2g4OhwMFBQUoKirCE088gXnz5rW43Llz59C3b1+UlJRAqVS6NaPt0gXUf/8VGtZ8D9ZgcOu6SdtpMnMRNGEq/AfcCkFJ2zXeggq1A/R6Pe688068++67yM3Nve7xefPmoaSkBO+++67bMlmOH0bdFx/CvHsLQP+lsqGMioFu7BToRt0FhS6IdxziJCrUDvrLX/6CgIAA/P73v7/usR49euDvf/87+vfv7/Ic1lNHUfuvfzYWKZEtwc8fujGTEDRpGpQhobzjkA6i6+naqKKiomnE3mQyYfXq1cjKyrpuuePHj6Ompgb9+vVzaR5r0XFUzH0Gl59+gMrUCzCzCYZlS1D68DjoP/07REMd70ikA+jgTRuVlpZi2rRpcDgcEEURU6ZMwdixYzFr1iwUFhZi3LhxAIAvvvgCd999t8tOk7GePoG6zz9oHLEnXoeZjDB89THqf/g3gu66D0ET7oPCz493LNJGtMsvE/byMugXvQ3TlrV0jNSHKMIjETL1EQSOGE+DVzJAherhmM2Kuv8shuHfH4NZLLzjEE5UCckIe+w5+PVs+bxn4hmoUD2YacdG6Be+BXtpCe8oxEP4Dx6OsEd+B2V4JO8opAVUqB7IdukC9B/Mh3nXZt5RiAcSAgIRcv+voRs7heZp9TBUqB5EtJhR98UiGJZ/BtisvOMQD6dO74rwJ1+Apks27yjkCipUD2E5uh/VC+bCfukC7yhEThQK6EZPRMi0J6AI1PFO4/OoUDljVgv0i/+B+m+X+uRs+EQaysgYhP9uLvy6F/KO4tOoUDmynjqGqjf/DHtJMe8oxBsoFAi6636E3P9rCCo6xYoHKlQOmCjC8J9PUfvZPwG7nXcc4mXUXbIR8dzLUMcn8Y7ic6hQ3cxeXobq+bNgObyXdxTixQQ/f4Q++jvoRk7gHcWnUKG6kXnfdlS9/iLEulreUYiP8B9wK8KeehHKoBDeUXwCFaqb1P3nU9R++g9ApBnziXspo2IQ+ef50HS+fjIfIi0qVBcTzSZUv/0XmDat5h2F+DBB64fwZ2YjYNBw3lG8GhWqC9lLS1D50u9gO3eadxRCAADBdz+M4Ptm0k0DXYQK1UVMu7eg+o0/Q6yneS2JZ/HvNwThv/sLFP4BvKN4HSpUF6j7ejFqP3mPTtQnHkudko7IWW9BFRPHO4pXoUKVEGMM+g8XoP6bz3lHIeSmFMGhiHjxdfjl9uQdxWtQoUqEOeyoXjAXxvU/8Y5CSJsJGi0iXngN/r0H8Y7iFahQJSCazah69Q8w797KOwoh7adUIvzZuQgcMop3EtmjQnWSw1CLyjlPw3r8EO8ohHScQoGwx/8I3ei7eCeRNSpUJ9grL6Piz0/Bfv4M7yiESCJk+m8QPPEB3jFkiwq1g+ylJSh/YSYcFWW8oxAiqeBfTUfIA4/zjiFLVKgdYK+8jPI/PALH5Uu8oxDiEro7foWwmc/xjiE7dEOadnLUVKHi//2aypR4tfr/fgn9ond4x5AdKtR2cBhqUfGnJ2C/eJ53FEJczrBsCeq++ph3DFmhQm0j0ViPij8/BVtxEe8ohLhN7ad/R/1PX/OOIRtUqG0gms2omPM0bKeO8o5CiNvV/GMejDRbWptQod4Es9lQ+fLvYD2yn3cUQvgQRVS9+WeY9mzjncTjUaHeRPW7L8OybwfvGITwZbej6q/PwXL0AO8kHo0KtRV1//4ExrU/8I5BiEdgFjMq5z4D28VzvKN4LCrUGzBt34Daxf/gHYMQjyLW16Hypd9DNDbwjuKRqFBbYD17ClVv/pnmMyWkBfYLZ1H15p9B1wRdjwr1Fxz6alTOfQbMZOQdhRCPZd6xEXX/+ifvGB6HCvUazGZF5cu/p+vzCWmDui8XwbhlHe8YHoUK9RrV770K67GDvGMQIg+MoXrBHFjpYpcmVKhXNKz5HsY1/+UdgxBZYSYjKl/6HRyGWt5RPAIVKgDbxfOoef913jEIkSVH2UVUz5/NO4ZH8PlCZXY7ql5/kQahCHGCeddm1P/wH94xuPP5Qq399O+wFR3jHYMQ2dMvetvnT/r36UI1790Ow/J/8Y5BiFdgFjOq3vgzmMPOOwo3PluoDn01qubPBujkZEIkYzt1FHWffcA7Bjc+W6jVC+ZA1FfxjkGI16n796c+O4mKTxZqw5rvYd69lXcMQryT6EDV/Fk+eb2/zxWqo1YP/aIFvGMQ4tUcZReh/9D3fs98rlD1i96GWEcnIRPiag2rvoXl8D7eMdzKpwrVfHA3jGu/5x2DEN/AGKr//iqY3XdG/X2mUJnNipq/v8o7BiE+xX7+DAzLlvCO4TY+U6h1X30Me4lvn3RMCA91X3wIe3kp7xhu4ROFaispRt2/P+UdgxCfxCwW6Be+xTuGW/hEoda8/wZgs/KOQYjPMm1dD/P+nbxjuJzXF6p53w66aykhHqDmn296/WWpXl2ojDHoP3mPdwxCCBoHqBpWfMM7hkt5daGaNq+hmaQI8SB1X34EZrXwjuEyXluozGFH7eL/4x2DEHINR1W5V8+b6rWF2rDyG9gvnecdgxDyC3X/+RSi2cQ7hkt4ZaGKZjNql37IOwYhpAWivhr1333BO4ZLeGWh1n/7OcTqSt4xCCE3YPh6CcSGet4xJOd1hSoaG2BYRrPwE+LJxPo6GJZ/xjuG5LyuUOtXLIdYX8c7BiHkJgzffg5HnZ53DEl5VaEyux3133zOOwYhpA2YscHrRvy9qlCN63+Co6qcdwxCSBvV//gfr5rez6sKtW6570wTRog3EKsrYdy8hncMyXhNoZr3bYf93BneMQgh7eRNp1B5TaEavlnKOwIhpAOsJw7DcuIw7xiS8IpCtV08B/MeuospIXLlLVupXlGo9T/8B2CMdwxCSAcZN6+BwwsuxpF9oTKHHcYNK3nHIIQ4w25H/Y9f807hNNkXqnn3Voj6at4xCCFOalj1DZgo8o7hFNkXagPdFpoQr+CoqoDl0B7eMZwi60J1GGph2rGJdwxCiESM63/iHcEpsi5U44ZVgN3GOwYhRCLGrevAZHxDTXkX6rofeEcghEiINdTDtHMz7xgdJttCtZUUw+olJwMTQv7H+LN8d/tlW6jGdT/yjkAIcQHTri2ynXxatoVq2r6BdwRCiCvYrDBuWcs7RYfIslDt5aWwnTvNOwYhxEVMm1bzjtAhsixUOR+0JoTcnPnQXohmM+8Y7SbLQjXvonNPCfFqNissh3bzTtFusitU0WyG5aC8r6YghNycee923hHaTXaFajmwE8xq4R2DEOJi5r3beEdoN9kVqmkXHT8lxBfYS87BXl7KO0a7yK5Qzbu28I5ACHETuU0cL6tCtZUUw1F5mXcMQoibmPfI6ziqrArVeuwQ7wiEEDcyH9gJ5pDPbaZlVaiW4wd5RyCEuBEzNsB25hTvGG0mq0K1HqctVEJ8jfXUEd4R2kw2hSoa62E7f4Z3DEKIm1lPHuUdoc1kU6jWE0cAmd9vhhDSftZTVKiSs9DuPiE+yXb+rGyu65dNoVppQIoQ3yQ6YDt9nHeKNpFRodLs/IT4KutJeQxMyaJQHVUVEOvreMcghHAil+OosihUW8k53hEIIRxZTx3jHaFNZFGo9ovFvCMQQjiyX74oiyumZFGotIVKiI9zOOAoL+Od4qZkUaj2i1SohPg6e2kJ7wg3JYtCpS1UQggVqgSYzQqHzCaZJYRIjwpVAvZLF+iSU0II7GVUqE6zX7rAOwIhxAPQFqoEHPoq3hEIIR6AClUCjtoa3hEIIR6AWcxw6Kt5x2iVxxeqWKvnHYEQ4iHEulreEVrl+YXq4X+RCCHu4+lzenh8oTpoC5UQcoVYb+AdoVUeX6hiHR1DJYQ0Eutpl98pnn4QmhDiPrSF6gTGGMQ6Pe8YhBAPIRpoC7XDmNkEOBy8YxBCPARtoTrD7vnzHxJC3Ie2UJ0ghwllCSHuIxobeEdolUcXKu3uE0Ka8fCJkjy6UBkVKiHkWk4UqtlsRu/evZGXl4ecnBzMnj27xeW++uorZGdnIycnB1OnTm3XOlQdTucOtMtPCLkW63iharVarFu3DjqdDjabDQMHDsTo0aPRt2/fpmVOnTqFV199FVu2bEFYWBjKy8vbtQ6PLlQm0hYqaWTM7YmwaAGiwOAA4GBX3oNBZAx2ARAhwiEwAALntMRVAmJiENXBrxUEATqdDgBgs9lgs9kgCM1/VhYuXIgnnngCYWFhAIDo6Oh2rcOjC5WOoRIAMOnCsLLnb9HJfhYRfqeQEW1DtLoSKuvlFpd3QIBDoYJDUMIhKOBQKOGA4srHAhxQQBQUcAhofBxofBMEONjVfzcWtQONb3bG4IAIB7v65oCDiRDhgF20X/nYAYdoh4PZ4RBtTe9FRntaUkkMj0AXJ77e4XCgoKAARUVFeOKJJ9CnT59mj588eRIAMGDAADgcDsyZMwejRo1q8/N7dKHSMVQCADv7TsEPZdWYLHRGg7YTjpXuxoWSasSGp6FPVhA6R1oRKlyCwtZ4So0SIpSilUNSBQANIGgA5f8+yxgaS124UvIKBRy48l5ofBMhXPm30PgHAcL/ih6NBd9Y9ldLXmzcSmfiNUXvuPK+pYK3w8Fsje9FGxg8e3DnRgQnh32USiX2798PvV6PO++8E4cPH0Zubm7T43a7HadOncLPP/+MkpISDB48GIcOHUJoaGibnt+jC1VQePSYGXGTtUGZqK6xQZuohrHUHyoMQm5WOs6c34pvt149xhWAjMQY9Ozsj+QQI3TiRQgOI9fcVwkCoGIOqNiVDQS3bieoGt+u/ipdKXrx6ha7Qvm/Lfmm978oeeCaor9a8NcedhEbS/66rXgRDlwt9ivv2bUFf2VL/krZt4VCUN58oTYIDQ3F0KFDsWLFimaFmpCQgD59+kCtViM1NRUZGRk4deoUevXq1abn9exC9Q/kHYFwVpaaj8M1jbvMB631SIcGANBQ0wnx4eOBTodRdOYQGGM4ecGAkxcar6RRKkKRn56CbslqxAfWwt9WAtCudxMFRCiYCLWjbUUm5ZqbtuIFNBU9Y4AoqBq32hXKxi144ZqteDS+9wtM7fCaKyoqoFarERoaCpPJhNWrV+P5559vtsyECROwdOlSPPTQQ6isrMTJkyeRlpbW5nV4dKEqAqhQfd2GbmOBK1Ngbrhcje5hCTAaGovRblMBtnxkp6egtGorqqsrm77OITLsOVmDPY2HxOCvjUXvrFB0jRMQo62C2loKgLn51ZAbEQRACTuUItDa0QhB1/FCLS0txbRp0+BwOCCKIqZMmYKxY8di1qxZKCwsxLhx4zBy5EisWrUK2dnZUCqVeOONNxAREdH218EY89ifKuZwoGRcn5svSLySQ6HCr0e9jkrj/7YspyfFQ3Hp+n1mQSHCL/g0Tp7eBXsbLlmOCNaid1YwMqLtCFdehtJG9y6TAyF2MJQZD/OOcUMevYUqKJUQtH5gFjPvKISDI73HNStTAFhTVYWRQih+uRnARAVM+i5IT0pAvWUXSi4Wt/rcVXUW/LSzAj8BADRIjk1HYXogUsPNCMElCHbPnoTDZyk0vBO0yqMLFWg8jkqF6pvWx/UBfjEd7vkGMwLjNai/3PIovqXBH2oMbhq0MhrbNjB1rqwB58oarxMXBB2yU+LQI9UPicEGBNgvQhAtTr0WIhGlP+8ErfL4QlUEBECkW0n7nPqwWGytVaCl45ynmAmd0Ppob0NNHOLDx4N1Ooyi04fatW7GgCNn63DkbOPBW7UqEgUZochNVKKTfw20tksAo1P6eBA0wbwjtMrjC5VG+n3Ttt6TYbO0fHh/VVklHg2Mg8XUeqnZbWrA1gO5GSm4VLkV1dUd+8Nss4vYfrQa2482fhwUEI/eWSHIimWI0lTc8AID4gJqKlSn0Ei/b1rtlwZYWj6lxyYyOMIUwE0K9aqG2jCEaUcjJvM0ThbthMPJC0YMRhvW7q3EWgCAArHhaeiVqUN6pA1hilIobHqnnp+0ggrVOYrgUN4RiJudz+qPotrWz4/cWFuD/tC1+TlFUQGzvgsykuJRZ9mFi5fOORuzSVm1Cf/dZrrykT/SE6JR0NkfKaGedYGBN6BdficpI9s3OQGRvw1ZIwB968scra3HiJgw1Fe178R0szEAGtyC3KyLOH1uG0wm6cuuqMSAopLGswQUQgjy0pORl6xGvK6OLjBwljqId4JWyaBQY3hHIG5kU/thnTkIbbk+86LahpAOrqehJh4JkePB1IdQdOZwB5/l5kQG7Dulx75TjR/7a2NRmBmK7HgBsXSBQTsJVKjOUkVRofqSA33uQq25bcc4fyqrwP3qGNhsHZvow25VA9aeyMlIxaWKraipcf3ZJCaLA5sOVmHTwcaPw4OT0SszCJkxDkSoyqG0Vrb+BL5MrYMgePb8Hh5fqLSF6lvWRfcAqtu2xVZvd0AVp4LtknMzSxlrwxDmNxqxGUU4eXqX04NW7VFdZ8HKXRasBACokRidjl5dApAWYaELDH7Jr6MzobqPxxeqKiaOdwTiJvqYFOyqad/u725jHXLg5/S6maiAuTYDGcnxqDXvwqVL551+zo64UN6AC+XXXGCQHI/8VA2SQhoQYC/x6QsMhA4WqtlsxuDBg2GxWGC32zFp0iTMnTu32TKffPIJnnvuOcTHxwMAnnzyScyYMaPd6/L4QlWGR9Llpz5iS+FEONp5U8ttlXr0jkhEw03OCmgrc0MgNOzKoFXxNpjMppt/kYswBhwprsWR4saP1apI9OwSitxEBeICa6G1XvStCwz8OjZA3ZZbnwDAr371K7z33ntORfT4QgUAZWwc7OfO8I5BXGy1IgFA+4uxJtABjYS3axcEAQ01CUiMHg+H6hBOnzki3ZM7wWYXseNYNXYca/w4KCAevTNDkNmJIVpTCZW1jG9AFxP8O1aobbn1iVQ8+wjvFarYBN4RiIud7n4rzhs6tpW5orwSCoX0vyA2iwZiQwFyM8YiNDRc8ud3lsFow9p9lfjHj1WY842Av21Lxfbq7qhSdoWoDuMdT3KCf6cOf63D4UB+fj6io6MxfPjw6259AgBff/01unfvjkmTJuHChQsdWo8sClWd3Jl3BOJiP6ff2uGvLTdb4R+tljBNcw214Qj3H4OumX2gVEozY7wrlNeY8f32Ciz4rx6zvvXDx/u74GB9HurUXcCUAbzjOS+g44V69dYnJSUl2LlzJw4fbn6q3B133IHi4mIcPHgQw4cPx7Rp0zq0Hlns8mvSMnlHIC5k9Q/CeoMfWp1Z+CaO2huQDNeVKhMVMOszkZGcgFrTTlwq7dgWjDudvlSP05fqAVy9wCAF3ZNVSNDVwc92EUIbbzviEdRBENRtvzLuRm5065NrJ5GeMWMG/vCHP3To+eWxhdqZCtWb7e47CcYOnkt61drLVfAPdP3Wo7khEBrHEORk3Qo/P+fPLnCXxgsMavDpmgr89RsL/ro6Bisu5OKC2A1WTTw8/dbbQkDHD/tVVFRAr9cDQNOtT7KyspotU1pa2vTv7777Dl27du3QumSxharqlAAhIBDM2M4hYCILa0NzgRrnRqtFBphDBcANPyKCIMBYk4Ck6AmwKw/gzNljrl+pxMxWBzYfqsLmKzMbhgUlNQ5wxdo98wKDINfe+uRvf/sbvvvuO6hUKoSHh+OTTz7p0Lo8+hYo1yr/wyOwHNnHOwaRWGVCVzyW/pAkF192DgrAUJP7J88IDKlCSflW6PU1bl+3qyREBaBXhg6dIzzjDgaKro9DEeX5t0OSxRYq0LjbT4XqfTb1GA8m0e/qaYMRd3SKRH2Fc1dOtVdDbQQiAsYgNvoUThbthijK85731yqpMKKk4urEMTp0TY5DzzQ/JIbUI9B+EYLo3vPCnbk5nzvJplBpYMr7MEHAajEGHTn39EaKFWZEchgaEB1KWGqzkJWaiJqGHSgtK3F7Blc6dq4Ox8413sFApQxHzy5h6JakRKdAPfxcfYGBKrDD56C6m2wKlQamvM/xnrejrEHakeYVZZWY7hcLq4XPVqKpPhBaNhS5WSUoKt4Gs9n7rvCzOxh2Hq/GzuONHwf6xzXeorvpAoPLkHIGLUGXItlzuZp8CjUpDYJGC2b13WuZvc365AHX3YTPWWaHCEQqgYv8drsbr7RKRFJUDGzKAzhbLL9Bq/ZoMNmxfl8l1u8DAAHRYSnonRmE9Cg7whWlUNicPLbsxICUu8mmUAWVCpqu3WE5sIt3FCIBY3AENtWp4cy5pzeyta4WheB/IrvNqgHQC7mZabhQtgW1tXrekdyi8QKDq1vmfkiL64LC9ACkhJkQJF6E0M4JG4Qg+VzYI5tCBQC//N5UqF5iZ5/JsDh57umN7K+pw5DoENRXe8aJ6w36CEQG3I5OMSdxsmiPVwxatceZS/U403SBQTC6dU5Cfoqm8QID+0UIYmuDiAKEEPkc7pNVoWrzevGOQCSyJjAD0LvuViCXtXZ40u0dRVEJS21XZKUmorphB8rKLvKOxIXIgANFehwoavxYq45GYWYIchKUiPWrhsZ6Cc32WnRJENSe9D/ZOtmchwoAzOHAxXuGgTXU845CnFCa1gNPJN3j0nWEadSYooiEw+6JP94MAWEXUHR2GywWGhO4VohOjT5ZIciMFRGpKocqqgDKzq79WZGSLC49vUpQKuHXrYB3DOKkDd3GunwdNVYbtFGuu7bfOQKMNUlIjrkTqSny2Z11h9p6G1btrsS731dj9jcqnLb34B2pXWRVqACgze/NOwJxgkOpxlqre6aW22/x7NuH2CwawNQHuZljEBzc0dsNei+VSoWU1DTeMdpFdoXql0eFKmeHe49Hlck9t1HeWF6DgCDPHyZo0EciSnc7MjMKoVDI7lfSZVJSUqDRaHjHaBfZ/e+pk1KhjPD8m3WRlq3r5N4/iIYgTzyGej3RoYK1NhtZaRMQQ/dRAwBkZGTwjtBusitUAPArHMA7AukAQ0QctundO03cqspKuOhuFy5hMugQgGHIyboFWq2WdxyuqFDdJGDgbbwjkA7Y1msS7KJ7txgvGi0IjJbXbmPjoFUyUmInICVZfqUihcjISMTGxvKO0W6yLFRtXiEUwaG8Y5B2WqPlcwnhScbvzqXOsJq1EMx9kZs52ucGrfLy8nhH6BBZFqqgVMG//1DeMUg7nMsagCKJbvXcXqvKKuHn77n3grqZBn0UonVjkdmlwGcGrfLz83lH6BDZ/u8EDBrOOwJphw1ZI7it2y4y2MNk+6MOAHA4lLDW5SArbTyiozt+szo56NSpE6Ki5DnwLNufMm23Aig88Na+5Ho2jT/WmZy/wZozNtR6x2z6JkMQAoVhyMkaLLtTitpKrlungIwLVVAqabdfJvb3nYg6iwsnIG6DY7X10EV6SwEpYKxJQWqnCUhJ7sI7jKQEQZDt8VNAxoUK0G6/XKyLzOcdAQBQonLvrVFczWr2g2Duh9zM0QgKcv+9tFwhKSkJoaGhvGN0mKwLVZvbk07y93D6mDTsqvGM6ep+KiuHWiPrH/kWNeijEBN8BzK79IQgp5NuWyDnrVNA5oUqKBQIHDGBdwzSis2Fd8HNp57eUINdhDLS8y9F7QiHXQlrXS66dh6P6Gj5nb8JAAqFAt26deMdwymyLlQACBx1J6CU7ykx3m61Ip53hGZ2Get4R3ApkyEYgcJtyMkaJLtBq8zMTAQFBfGO4RTZF6oqMhr+fW/hHYO04FTebbhg8IxZ86/aUalHYKinTusnFQWMNalI7XQnkpPSeYdps379+vGO4DTZFyoA6G6fzDsCacHPnYfwjtCi6gC+Zxy4i9WshcLSH7mZozx+yy8yMhJdusj/jAWvKFS/vF5QJaTwjkGuYQkIxgaDH+8YLVpRXgmFQt6DN+3RoI9GTPAdyOjSw2MHrfr06eOx2drDKwoVAHS3T+IdgVxjd5/JMLroJnzOqjBb4R/t7bv9zTnsKtjquiG783hERcXwjtOMWq1GYWEh7xiS8JpCDRw2FoKfP+8Y5Iq1odm8I7TqiL19tzL2FkZDMIKUw5GdNQhqtWf8USksLIS/f/t/d81mM3r37o28vDzk5ORg9uzZ1y1z7tw5DBs2DN27d8eQIUNQUlIiReQb8ppCVQTqEDBkFO8YBEBFYlccqPHs45TrLlfBP9A7T6G6GcYUMNWkonP8nUhK5HvPe0EQMHDgwA59rVarxbp163DgwAHs378fK1aswPbt25st8/vf/x4PPPAADh48iFmzZuGFF16QIvYNeU2hAkDwpGl0CpUH2Jg/AR5y6ukNiQww+daMeNexmPygtA5AbuZI6HR85lrIzs5GREREh75WEISm3DabDTab7brjsEePHsWtt94KABg6dCi+/fZb5wLfhFcVqqpTAgKGjOYdw6eJCiXWiPK4em1ddZWsZvN3lQZ9DGJDxyEjPd/tA0ODBg1y6usdDgfy8/MRHR2N4cOHo0+fPs0ez8vLw7JlywAAy5cvh8FgQFVVlVPrbI1XFSoABN/9MKCgrVRejheMweUG99yEz1ln6k0IjJLXye+u4rCpYDN0R3b6OERGRrtlnampqUhJSXHqOZRKJfbv34+SkhLs3LkThw8fbvb4m2++iQ0bNqBHjx7YsGED4uPjoXThXqzXFao6LhEBt/Cbe9PXrU/szztCu5wRzLwjeBRjXQiCVSOQnTnA5YNWo0ZJN+YRGhqKoUOHYsWKFc0+HxcXh2XLlmHfvn3461//2rSsq3hdoQJXt1K98qV5NGNwJDbXesbIcVutLKuAxo9+Vq7FmAImfecrg1ZpLllHVlYWkpOTnXqOiooK6PV6AIDJZMLq1auRlZXVbJnKykqIYuPpe6+++iqmT5/u1Dpvxit/ktQJKQgYTFup7rajz2RYHJ557umNWBwMiKBDRC1pHLQaiNysEQgMlG7QShAEjBw50unnKS0txdChQ9G9e3f06tULw4cPx9ixYzFr1ix89913AICff/4ZmZmZyMjIwOXLl/Hiiy86vd7WCIwxTx+Q7RDbhWKUPT4FEOX1Cy5nL054C8f08jh+eq28sCD0MgTyjuHRlGobBL+jKDp9EM5WRl5eHu655x6JknkWr9xCBQB1Im2lutOl9AJZlikAHKgxQBcur0MV7uawqWE35DUOWjkxB7FCocDw4d47MbzXFioAhEx7EoJWyzuGT9iQczvvCE4p08rzj4G7GetCEKweiezMAVCp2n9hRGFhISIjI12QzDN4daGqomMRNGka7xhez6FUY601lHcMp/xUVgGVik5KbYurg1bpCXciMSG1zV+nUqkwbNgwFybjz6sLFQCCJj4AZZQ8ZzCXi0O9J6DaJO8tvFqbHZoo2u1vD4vJHyrbIORmjUBAwM2PQffv3x8hId59eZrXF6pC64fQh5/mHcOrrevUi3cESeyzGHhHkKWGmljEh49Hl87db3ilVVBQUNMloN7M6wsVAAIG3QZttwLeMbySISIO2/Xesau8qbwGAcG+OWGKs+w2Fez1+cjuMg4RLQxa3X777fDz88z5caXkE4UKAKEzn6NLUl1gS6/JsHvKXfgkUKfzntfCg7E2BCGakcjO7N80aJWeno78/Hy+wdzEZwpVk5IO3ei7eMfwOms0KbwjSGplRSUEn/mtcA0mKmDSp6NLYuM9rcaPH887ktv41I9OyP2/hiLce0/ZcLfi7EE4U+dZN+FzVqnJQhOmSMRs9EevHuMQFSWP2cek4FOFqggKRvhTrr30zJdsyPTOE7SPiybeEbxCWLgWPXr7TpkCPlaoAODfexACh9/BO4bs2TT+WG/0zss115RVwi+Ajrc7QxCAW0Z0glLpHQOWbeVzhQoAoY/+js5NddK+vhNRZ/Hs25x0lJ0x2MJ8qwiklpMXjphOAbxjuJ1PFqoiQIfwp2eBpmvvuLWR+bwjuNTPNTW8I8hWcKgGvQe4Z5JqT+OThQoAfvm9oRtDt57uiOpO6dhT492zeJ2oa4Aukgan2kupFDD89nioNb5ZLb75qq8Imf4bqDol8I4hO1t63gkvOvX0hi6orLwjyE7fwTGIjPbd27n7dKEq/PwR/uwcOuG/nVYr4nhHcIufysp9dkurI1LTg5CbH847Blc+/9Oizc5HyP0zeceQjVP5I1Bi8K5zT2/EaBehjKRLUdsiKFiNW4b7xh/a1vh8oQJA0OQH4dfbudvZ+or1abfwjuBWOxpqeUfweAoFcNvtCdD60Z4eFSoa73ET8excKGPoL2xrLAHB2GjwrQm7d1XVQhdG0/q1ps/AGETH+u5x02tRoV6hCApG5P97nWb4b8WuvlNgtHn36H5LKv2983xbKSSn6dC9IIJ3DI9BhXoNTXoWwp6kS1NvZG1INu8IXKy4XOFzV/y0hS5IjSEj43nH8ChUqL8QeOsY6MZ75x0ZnVGenIsDNfKelb+jKi02+NFs/s1otAqMnpAIPzpu2gwVagtCH/4ttHm9ecfwKJvyxvGOwNUhWz3vCB5DoQBGjE1EeKT3TxjdXlSoLRCUKkT+6XWoUzN4R/EIokKJ1XbfnvZw/eVq+OvoFCoAuGV4HOKTvHNiHGdRod6AIkCHqL/8jUb+ARwvuB3lRt/c3b+KATAG807BX6/+UcjIDuUdw2NRobZCGR6JqJfehSI4lHcUrtYl9ucdwSOsqar06fl0snJD0bOPb81v2l5UqDehjk9G5Oy3IWh983iRMSQam2tp4AEAzjWYfXY2/8QUHQYN68Q7hsejQm0DbVYuIv74GqD0vWLZ3mcSrA4fmAmljU7D92bzj4z2w/DbE6BQ+PDmeRtRobaRf++BCH/qT7xjuN2agHTeETzKqsuV0Pr5zq9NULAao8Yn0iQxbUTfpXYIHH4HQh95lncMt7mUXojjet8ejPoli4NBjPCNPZWQUA3GTUlBoI7OwW0rKtR2CpowFaG/ft4nZvvfkDuGdwSPtLlOzzuCy4WGN5apLojKtD2oUDsgaOxkhD31YuMZzl7KrtZgjSWUdwyPdKjGAF2E9xZNeKQW4yanICCQzrttL+9tBBfTjZyA8GfmeO3k1Id634kaE+3u30ipxjvnhI2M9sMdk1PgH0Bl2hFUqE4IvHUMIv7wV68c/V8XU8A7gkdbUVYJlcq7DvtEx/pj7KRkuj7fCVSoTgoYdBsiX5gHqLxnF7AuMhHb9d5VFlKrtdmh9qIJU2Lj/HH7xCRotVSmzqBClYB/vyGInPUWBH/vuL55a6+JcPjCXfictNds4B1BEnGJARhzVzI0GipTZ1GhSsS/oB+i31wEZVQs7yhOW61O5h1BFrZU1CAwRN7HGtOzQjBmQhLUaqoCKdB3UUKalHTELPgUmsxc3lE6rDhnEM7WeeeAiyvoA+W7JV/YLwrDRsdDqaIakAp9JyWmDItA9Gv/hP/g4byjdMjPGfLMzcuqigrZnT2nVAoYNiYeBX1pohOpyexHQR4EjRYRf3gFwXfP4B2lXWzaQKxv8I7jwO5SarIiQEYTpgQEqnDH5GSkZ4bwjuKVqFBdRBAEhNw/E+G/+wuglscv3N6+E2Gw0g3p2uuYaOQdoU2iO/njrqmpiOkUwDuK16JCdbHAW8cget4HUMZ4/s3M1kV05x1BltaWVcEvwLNHyLNyQzFuMl2X72pUqG6gzcxF7LufwX/gbbyj3FB1p3TsqfG9W0RLwc4YrKGeed6uUilg0K2xuGV4HN251Q2oUN1EEahD5AuvIezJ/wdBq+Ud5zqbC+4EnXracetqqnlHuE5ktB8m3puG7Lxw3lF8BhWqm+lG34Xotz6FKimNd5QmTBCwGnTvLGcUGYwI8pDBKUEA8ntFYMLdqQiL8Lw/3t6MCpWDxvNVFyNwxHjeUQAAp/JH4GI9nXvqrGKFhXcEBAWrccekZPQZGOPULr7ZbEbv3r2Rl5eHnJwczJ49+7plzp8/j6FDh6JHjx7o3r07fvzxR2eiewWBMUY7ehwZN61GzftvQNTz22X8512vYGW1vK/48QT+KgUe1MTCZuVzLDqjawgGDI2FRoLr8RljaGhogE6ng81mw8CBA/HOO++gb9++Tcs8+uij6NGjB37961/j6NGjGDNmDIqLi51et5zRFipnAYOGI/b9fyPgtju4rN8cGIqNBs/YVZU7k12EItL9o/1aPyWG356AoaPiJSlToPG0P51OBwCw2Wyw2WwQfjGpuiAIqKurAwDU1tYiLo4OG9EWqgcxH9iFmvdegf3SBbetc/OwR/CWo4vb1ufteoYHo2ed+87zTErVYfBtnVxyOpTD4UBBQQGKiorwxBNPYN68ec0eLy0txYgRI1BTU4OGhgasWbMGBQW+Pe0jbaF6EL+8Xoh5bymCJj/otjlW1wR3dct6fMXe6jrowlx/rmdImAajJyRi9IQkl51bqlQqsX//fpSUlGDnzp04fPhws8eXLl2KBx98ECUlJfjxxx9x//33QxR9+9Q7KlQPo9D6IfTBJxHz9hJoMrJduq7y5G44WEOz8kutwt9131O1RoE+A6Mx+f7OSEoNctl6rhUaGoqhQ4dixYoVzT6/aNEiTJkyBQDQr18/mM1mVFZWuiWTp6JC9VCatAxEz/8EYb/5E5QRrpnEYkPeOJc8r6/7qazSJSfRd+kagl9N64z8XpEuP0m/oqICer0eAGAymbB69WpkZWU1WyYpKQlr164FABw7dgxmsxlRUb494QoN7XowQaGAbuQEBNwyCvXffIa6rxeDGRskeW6HQok19ggAtIUqtWqrDdpENYylVkmeLyrGD/2HxCI2zn3HZktLSzFt2jQ4HA6IoogpU6Zg7NixmDVrFgoLCzFu3DjMnz8fjzzyCBYsWABBEPDJJ59cN3Dla2hQSkYctTWo+3wh6lcsA+zOFeHh3hMwK6C/RMnILw2JCUd6lXNnT/gHKNGrfzSyckN9vqjkggpVhmyXLqD207/DtHlNh5/j3btew/pqOuLjKgKAp8ISYDS0/w9foE6F7gUR6NotjGbSlxna5ZchdVwiIl94DZYTh1H35Ucw79wEtOPvojEkGltqlQDob6mrMAD1QQyKdtx2KihYjfxekcjMDqFZ9GWKtlC9gO1CMQzLlqBh/U+A7ebH7daNeALvWem+Ua6WFOiHkdbQm/6tCw3XoEevSKRnhUChoF17OaNC9SKO6koYvvsC9T9+DdZw402jF8a/hRO1NBjlDk/HJ6H+cst/5CKitOjRKxJpGcF0jNRLUKF6IdHYgPoVy1H/7VI4Ki83e6wkow9+EzeRUzLfMzYuCrHlzS/SSEgORG5+OJLT3HMeKXEfKlQvxux2mHZsQMPKb2Hetx0QRXw2YTa+1tN9o9xFrRDwaGAcFAKQmROKrrlhCA6luRO8FRWqj7CXl6Fh9bf4jS0PRyvkcQ8kuVMKAvqmhuPh/CRkp4TSjPk+gArVxzDGsP9iLX48Uoa1J8phsNCxVKl1idLh9pxYjMqORUQgbY36EipUH2a1i9h0uhIrjpZhx7kamGx0x9OO6hKlQ7/UcIzIikFmDB0b9VVUqAQAYHOIOHCxFtvOVmHb2WoUVdTTWaqtCNQo0SclHP1TI9AvNQLRQXSrEUKFSm6gst6CHcXV2FZcjZ3nqlFjpFukdInSoX9aBPqnhqN7fAhUCjr5njRHhUpuijGG45cN2F5cjW1nq3G4tBY2h/f/2MQGa5HbKQT9UsPRLzUCUTraCiWto0Il7WZ3iDhbZcSpinoUVdTj1JW3qgZpZldyN6VCQGp4ADKig5AZo0NGdBAyonUI9nP9RNHEu1ChEslUN1ibyrWooh4ny+tRXN3gUVuzOq0KnSMDkXmlNDOidegcqYOGrp0nEqBCJS5lF0VU1ltRbbSixmhFVcOVfzfYUGW0orrhf4/pTTaIHfhpVAoCwgLUiNRpERGoQWSgBhGBGkQEav/3b13jv/3U7r+JHvEdVKjEYzhEhlqTDVaHCIfIIDIGB2MQRUBkDApBgEopQHnlvUqhgFopQKdVQUHXwhMPQIVKCCESoQNHhBAiESpUQgiRCBUqIYRIhAqVEEIkQoVKCCESoUIlhBCJUKESQohEqFCJrFy4cAFDhw5FdnY2cnJy8M4771y3zBtvvIH8/Hzk5+cjNzcXSqUS1dXVHNISX0Mn9hNZKS0tRWlpKXr27AmDwYCCggJ88803yM7ObnH5//73v1iwYAHWrVvn5qTEF9EWKpGVTp06oWfPngCAoKAgdO3aFRcvXrzh8kuXLsU999zjrnjEx9EWKpGt4uJiDB48GIcPH0ZwcPB1jxuNRiQkJKCoqAjh4eEcEhJfQ1uoRJbq6+sxceJEvP322y2WKdC4uz9gwAAqU+I2VKhEdmw2GyZOnIh7770Xd9111w2X++KLL2h3n7gV7fITWWGMYdq0aQgPD8fbb799w+Vqa2uRmpqKCxcuIDAw0H0BiU9T8Q5ASHts2bIFS5YsQbdu3ZCfnw8AeOWVV3D+/HkAwMyZMwEAy5cvx4gRI6hMiVvRFiohhEiEjqESQohEqFAJIUQiVKiEECIRKlRCCJEIFSohhEiECpUQQiRChUoIIRKhQiWEEIlQoRJCiESoUAkhRCJUqIQQIhEqVEIIkQgVKiGESIQKlRBCJEKFSgghEqFCJYQQiVChEkKIRKhQCSFEIlSohBAiESpUQgiRCBUqIYRIhAqVEEIk8v8BCagiZ6axwdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(py_vers.c, labels=py_vers.pyver)\n",
    "plt.title('Python version usage')\n",
    "\n",
    "plt.savefig('py-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7 69.23%\n",
      "2.7 11.65%\n",
      "3.8 10.21%\n",
      "3.9 4.79%\n",
      "3.5 3.68%\n",
      "3.6 0.43%\n"
     ]
    }
   ],
   "source": [
    "total = sum(row[1] for row in py_vers.values)\n",
    "\n",
    "for ver, c in py_vers.values:\n",
    "    print(ver, f'{100 * (c / total):.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>installer_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.1</td>\n",
       "      <td>11070109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.2.4</td>\n",
       "      <td>9608094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.1.3</td>\n",
       "      <td>8857941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.1.2</td>\n",
       "      <td>3928579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.2.3</td>\n",
       "      <td>1806022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.2.1</td>\n",
       "      <td>1765630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21.2.2</td>\n",
       "      <td>1726675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0.1</td>\n",
       "      <td>1484950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19.0.1</td>\n",
       "      <td>1384180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.3.4</td>\n",
       "      <td>1166210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  installer_version         c\n",
       "0              18.1  11070109\n",
       "1            21.2.4   9608094\n",
       "2            21.1.3   8857941\n",
       "3            21.1.2   3928579\n",
       "4            21.2.3   1806022\n",
       "5            21.2.1   1765630\n",
       "6            21.2.2   1726675\n",
       "7             9.0.1   1484950\n",
       "8            19.0.1   1384180\n",
       "9            20.3.4   1166210"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_versions = pd.read_sql(f\"\"\"\n",
    "    select installer_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and installer_name = 'pip'\n",
    "    and installer_version is not null\n",
    "    group by installer_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "pip_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in pip_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.01 else '' for v in pip_versions.values]\n",
    "\n",
    "plt.pie(pip_versions.c, labels=labels)\n",
    "plt.title('pip version usage')\n",
    "\n",
    "plt.savefig('pip-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>setuptools_version</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.8.0</td>\n",
       "      <td>20245839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.4.0</td>\n",
       "      <td>8555846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.2.0</td>\n",
       "      <td>3215939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.1.0</td>\n",
       "      <td>2159879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.0.0</td>\n",
       "      <td>1667188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>40.7.1</td>\n",
       "      <td>1379115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.2.1</td>\n",
       "      <td>1192779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.2.0</td>\n",
       "      <td>723296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>56.0.0</td>\n",
       "      <td>684684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>51.1.2</td>\n",
       "      <td>671373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  setuptools_version         c\n",
       "0             40.8.0  20245839\n",
       "1             57.4.0   8555846\n",
       "2             41.2.0   3215939\n",
       "3             57.1.0   2159879\n",
       "4             57.0.0   1667188\n",
       "5             40.7.1   1379115\n",
       "6             49.2.1   1192779\n",
       "7             57.2.0    723296\n",
       "8             56.0.0    684684\n",
       "9             51.1.2    671373"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setuptools_versions = pd.read_sql(f\"\"\"\n",
    "    select setuptools_version, count(*) c\n",
    "    from searches\n",
    "    where accessed_at >= '{start_time}' AND accessed_at < '{end_time}'\n",
    "    and installer_name = 'pip'\n",
    "    and setuptools_version is not null\n",
    "    group by setuptools_version\n",
    "    order by c desc\n",
    "\"\"\", conn)\n",
    "\n",
    "setuptools_versions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tb7N7927S09O57777Op3zz3/+k0gkwkcffcS7777Lvffey759+zodl8i1pOFDJl5J6gelpaU8++yzXHfddQAIIXjllVe46KKLAFi1ahVPPfVUp/MURSEYDBKPxwmFQlitVjweT4djEr2WNHzIxCtJ/eBb3/oWt912G6ra8idVW1uLz+dD13UAioqKKCsr63TeRRddhMvlIj8/n+LiYm666SYyMjI6HJPotaThQyZeSTpKzzzzDDk5OcybNy/pczdu3IimaZSXl7N3715+85vfsGfPngGIUhpK9FQHIEnD3Ztvvsnq1at57rnnCIfDNDU1ceONN9LQ0EA8HkfXdUpLSyksLOx07j/+8Q+WLVuGxWIhJyeHhQsXsnnzZiZMmNB2TGZmZkLXkoYP2eKVpKP085//nNLSUvbt28ejjz7K6aefzsMPP8xpp53GE088AcADDzzA+eef3+nc4uJiXnnlFQCCwSBvv/02U6dO7XCMoigJXUsaPmTilaQB8stf/pLbb7+dkpISamtr+eIXvwjA6tWrueWWWwD4+te/TiAQYMaMGRx33HFcc801zJo1C4BzzjmH8vLyHq8lDU+KEEKkOghJkqTRRLZ4JUmSBplMvJIkSYNMJl5JkqRBJhOvJEnSIJOJV5IkaZDJBRTSkGSGwxi1hzADfkRzALM5iBkMIJqDmId/FsFAy22hIGYkjKJbUCzWw/9ZwGJFsX72c9t9NjtaVg5aVi5adi5aRjaKKtsg0uCRiVdKGTMSJl5RSrzsAPGKg8TLDxIvP0C8vBSjrhoGa6ajpqFlZKNl56Jn57Uk5Jw89KxctJx8LGPGoVisgxOLNCrIebzSgBNCED+4l8gnHxDd9cnhRFuKUVs1eMn1aGgalqJxWCZOwTphMpYJU7BOnIrqTkt1ZNIwJROv1O9ENEJk51ain3zQkmy3f4Tpb0x1WP1LUdALx2Kdegy2qTOxTjkGy9iJKJqW6sikYUAmXumoGY31RLa+35Jot31AdPd2iMdSHdagUxwu7HOOw37cyTiOOxktIyvVIUlDlEy8Up/ED5XT/OYrhN58heiOj4ZHl8FgUhQsE6fiOO5k7MctxDp5BoqipDoqaYiQiVdKWOzgPkJvvkzzhleIfboj1eEMK6ovE/v8k3AcfzL2uSegOl2pDklKIZl4pR5FP91BaMMrNG94lfgBWaC7X+g69lnH4VryORwnntYy9U0aVWTilTqJ1xwiuOZJml9fS7yiNNXhjGiqx4fztLNxL70Ay9iJqQ5HGiQy8Uptwh9sJvDMY4TefgNMI9XhjDrWqcfgOut8nKcuRbU7Uh2ONIBk4h3lzFAzza88h//Zx4nvl10JQ4HicOFctATXWRdgmzoz1eFIA0Am3lEqVrafwH8eJ/jyM4jmYKrDkbphGVdC2uevxHnqMjlHeASRiXeUCW1ch3/1o0Te3yingA0jesEY0i65Btfp56BocqX/cCcT7ygR2riOxofuJfbp9lSHIh0FLa8QzyXX4DrjPBRdJuDhSibeES60eQNND99LdOfWVIci9SMtJx/PxatwLTlfTkcbhmTiHaEin7xPw9/+QPSTD1IdijSAtKxc0i66CveyFbKC2jAiE+8IEyvdR+P9dxJ667VUhyINIi0zG+8138R12tmpDkVKgEy8I4RRV0PjP/5E8IWnwZBzcEcr2zHHkv7Vm+VijCFOJt5hTghB4NknaHzgTjktTGqhaaQtvwzP5V9GdThTHY3UBZl4h7HYwX3U/eEnRLe+n+pQpCFIy8zBd923cC46K9WhSEeQiXcYEvE4Tf+8n6bH/gqxaKrDkYY425zjSf/qd7EUjUt1KNJhMvEOM5EdH1N/x0+I7dud6lCk4US3kLbicjyXXodqt6c6mlFPJt5hwgyHaPz73QT+8xiYZqrDkYYpvaCYzJt/hrVkaqpDGdVk4h0Gwlvepu7On2EcKk91KNJIoFvwrvo6aSsul7tipIhMvEOYiMdpuO93BFY/mupQpBHIPu8kMr5zK5ovI9WhjDoy8Q5R8Zoqan/+P0S3f5jqUKQRTPVlkvlfP8J+7AmpDmVUkYl3CAq/v5Ha276P2Vif6lCk0UBRSFtxBd5VX5eFdwaJTLxDiBCCpsfuo+nhP8kBNGnQWSZNJ/O7P8VSMCbVoYx4MvEOEaa/idrf3EJ40/pUhyKNYorDScaN/4fzlCWpDmVEk4l3CIju3k7Nz27GOFSW6lAkCRQF71Vfw3PJNamOZMSSiTfFAmufov6Pt8kVaNKQ41p6Aelf/x+548UAkIk3hRruvxP/P+9PdRiS1C37sSeQ+b1foDrdqQ5lRJGJNwWEYVB/188Jrn0q1aFIUq8s40rIuvV36Nl5qQ5lxJCJd5CJWJTaX36f0FuvpjoUSUqYlplN1g9/i3WiXGrcH2TiHURmc4CaH/8XkY/eTXUokpQ0xeEk87s/w3H8yakOZdiTiXeQGA11VN/yDWKf7kh1KJLUd6pG+vX/jfvci1IdybAmE+8giB8qp/oHNxAvP5DqUCSpX/i+chNpyy9NdRjDlprqAEa66L7dHLrpWpl0pRGl4d5fE1jzr1SHMWzJxDuAoru3U/XdL2HW1aQ6FEnqd/V3/YLAi6tTHcawJBPvAImV7af6lm8ggv5UhyJJA0MI6u/4CcFX16Q6kmFHJt4BEK+povoHN8jqYtLIZ5rU3X4rzeteSnUkw4pMvP3M8DdSfcsNGFUVqQ5FkgaHaVD7q+8Teuu1VEcybMjE24/McIiaW79FfP+eVIciSYPLMKj5xfcIyep6CZGJt5+IeJzan36X6PaPUh2KJKVGPEbtz24m/N47qY5kyJOJtx8I06TuN7cQ3vJWqkORpJQS0Qg1P/0u0X27Ux3KkCYTbz9ouPdXNL/xQqrDkKQhQYSC1Pzo2xgNdakOZciSifcoNT76FwLP/DPVYUjSkGJUVVDz/25CyDrTXZKJ9yiENq2n6aF7Ux2GJA1J0e0fUve7H6c6jCFJJt4+ih8qp+7Xt4AsdSFJ3Wp+7XmaHr8/1WEMOTLx9oGIRan52c2YgaZUhyJJQ17jg3cT3vJ2qsMYUmTi7YP6P/6K2O5tqQ5DkoYH06T2tu8Tr5SbubaSiTdJwRf/Q3Dtv1MdhiQNK6a/kZqf/DdmOJzqUIYEmXiTEN2zk/o//iLVYUjSsBTbu5P6O3+a6jCGBJl4E2QGA9T+7LuISCTVoUjSsNX86hqa172Y6jBSTibeBAghqP3NLcQrSlMdiiQNe/V3/wJjlNeolok3AYGn/kH4nTdSHYYkjQhmUyN1fxjdXQ4y8fYiXlFK44N3pzoMSRpRwhvXEXjh6VSHkTIy8fai7g8/lf26kjQAGv50O/FRWrdaJt4eBNY+ReSDTakOQ5JGJBEKUnf7rYzGjc5l4u2GUVdDw32/T3UYkjSiRT56l8DqR1MdxqCTibcb9X/8pdyoUpIGQeMDdxIr3ZfqMAaVTLxdaH7zFUIbXk11GJI0KohIhLrf/BBhGKkOZdDIxHsEM+Cn/p7bUh2GJI0q0Z1bCb7wVKrDGDQy8R6h4b7fYY7yyd2SlAqND92L2RxMdRiDQibedsIfbCY4iucWSlIqmQ11ND3+t1SHMShk4j1MCEHDX25PdRiSNKoFnn5kVMztlYn3sNAbLxDbszPVYUjSqCaiERr+dkeqwxhwMvECwojT+NA9qQ5DkiQg9MaLRLZ9mOowBpRMvEDwhaeJlx9MdRiSJB3W8JffpjqEATXqE68ZCdP0yF9SHYYkSe1Et39E8+trUx3GgBn1iTfwn8cwaqtTHYYkSUdouP9ORHRkFqga1YnXDPjx//OBVIchSVIXjKoK/CO0jsOoTrxN//q73KJdkoYw/1P/QMSiqQ6j343axGvU1YzKqkiSNJyY9bUEX3ku1WH0u1GbeJse+ysiHEp1GJIk9cL/5EMjrmbvqEy8RlMDwRfl0mBJGg7ipfsIb1yX6jD61ahMvME1T8rtfCRpGPH/68FUh9CvRl3iFfE4gWf/meowJElKQmTre0R2fJzqMPrNqEu8zetelPN2JWkYGkmt3lGXeGvXrk51CJIk9UHorVeJV5SmOox+MaoS70fljVyZt5K/X3ArleNnpzocSZKSYZr4//1wqqPoF4oYafM0enDrc5/w7NZKABRgfobK2RVvM+udf6Oao2e/J0karhSbjfwHnkNL86Y6lKMyahJvYyjGufe8SSRudrqvwG3hXOMAi976B66GQymITpKkRPm+ejNp512c6jCOyqhJvA9vOsDvXtvd4zE2XeWMtDDLtj5D0c63BykySZKSYZ02i9xf/zXVYRyVUZN4L/nrO+ytTXwjvVnpOufWbuHYNx9DM2IDGJkkScnKv+9p9LzCVIfRZ6Mi8e6uDnDZ/Rv7dG62U+dctZLFbz+Cp0YWS5ekocBz5fV4L70u1WH02ahIvH9c9yl/fXv/UV3Doimc6o2zbOdaJnz8Wv8EJklSn+hF48i/94lUh9FnoyLxXvjntzjY0H8Fcab6dM5t/Jjj33wES1QW2pGkVMj9/UNYS6amOow+0VMdwEDbVtnUr0kXYHtDnO1MJf30n7LMWsMZmx4no6LngTtJkvpX8LU1wzbxjvgW7x2v7ebBTQcG9DE0BRamC5btfY2p760Z0MeSJKmFmpFFwQPPoajDbx3YiE+8y+/dQEVTeNAeb4LHwrmhHZz05j+wNcvdLSRpIGX/5C7scxekOoykjejE+1F5I9c+/G5KHjvNqnGWo5El7z9Jzv6RU1VJkoYS55mfI/PbP0x1GEkb0Yn39ld28ci7qZ0CpgDHZyicU/YmMzc+jTJyX25JGnSKK43CR15C0bRUh5KUEZ14z//TBsobB6+boTdFaRbOi+3l5A0P42yqTXU4kjQi5Nx+P7YpM1MdRlKGX690gkrrm4dU0gUo9ce4J1zElxZ8j/tX/JjyknmpDkmShr3IB5tSHULSRmzi3XigPtUhdCsUM1ldb+eGopX8ZMVv2LLwUgx1xM/sk6QBEX5/+CXeEfvXvmn/0E287W2pN9hiOZa8sxdwDqUs3vAP3PUVqQ5LkoaN6LYPENEIitWW6lASNiL7eIUQLL17PfXNw6+4jU1TWeyJsGz7GsZuW5/qcCRpWMj+6d3Y5xyf6jASNiK7GnZXB4dl0gWIGCZr6y18O3c5/7fidt5evIqYxZ7qsCRpSAsPs37eEZl4Nx2oS3UI/WJrfZzbmMHXzvwZT37uf2jIHZfqkCRpSIq8/06qQ0jKiOxq+Pa/PmD9npE3XUtXFU7xGSz79GUmffBiqsORpKFDVSl85GVUd1qqI0nIiGvxxk2T90obUh3GgIibglfrVG5OX8LN5/+WdWd8iajDleqwJCn1TJPwR6lZpdoXIy7x7jgUIBgd+RtX7mqM8VtjEl9e9GMePf8H1BQNzypNktRfhlN3w4ibTrajyp/qEAZVU8Tg8YiHJyZdy4nHw9kH3mD65mdSHZYkDbrozq2pDiFhIy7x7qwKpDqElDAFvFkHb7oXMXb5GXwuvJuT3nwYe7Ah1aFJ0qCIHdiLEAJFUVIdSq9G3ODaFx9+lw/LG1MdxpDgsmqc5fRz1vtPkrvvw1SHI0kDLv8vT6HnF6U6jF6NqBavEILd1aOzxduVYNTg31EnT427gvnHqpxT8TbHvPNvVHPk94FLo1Ns326ZeAdbaUOI5phMKkcSwKY6k0224yk4ZyHnGQdYtOFhnI1VqQ5NkvpVdP9uHCcuTnUYvRpRiXe09u8mozwQ40/k88Bx3+VMT5ilH/+Hop3DZzRYknoS2/9pqkNIyMhKvLKbIWGRuMmzdVaeLfg8s2es5Jyadzl2w+NoxvBcai1JALF9MvEOup2jbCpZf/mgPs4H2myyl87jXLWSxW8/gqcmtTt3SFJfxMv2I2IxFIsl1aH0aETNahjsjS1HKoumcKo3ztk71zL+49dSHY4kJSX3zkewjp+U6jB6NGJWrhmmoMofSXUYI0LMELxUp/FfWefw/QtuZ8Np1xKzOlIdliQlZDj0846YrobqQARj5DTeh4xtDXG2MZX003/K2dYaztj4GOmVQ/+NLY1esX27Ux1Cr0ZM4pWt3YFVH47zj7CPx6Z9hYUnCc7e+ypT3ns+1WFJUidG1dDfwWXEJN5Dftm3OxgMAW/UKbzhPZ0Jy5dyXmgHJ775D2zNTakOTZIAMBqG/rZfIybxyhbv4NvTFOMOJvC3k3/IUkcjS977F9kHhk+hEmlkMhuGfi3uEZN4D8nEmzL+qMETUTf/mrCKBfMVzi59k5mbnkaRfe5SChiNssU7aKpkV0PKCeDtOsHbzpMYc96pnBvby8kbHsbZNPRbINLIYTY1IkwTRR26k7aGbmRJki3eoeWgP8Y94SK+vOB73L/ix5SXzEt1SNJoYRqY/qFdoXDEJF7Zxzs0NcdMVtfbuaFoJT9Z8Ru2LLwUQx0xX7SkIcpsGNob3o6Yv4DGsKwxMNRtqTfYYjmWvLMXcC6lnLrhYdz1lakOSxqBjIZ6LGNTHUX3RkTiFUIQjZupDkNKUGUwxn3k8tCxN3GaN8LST55j7PY3Ux2WNIKYjbLFO+AicRM5fj78RAyT5+ssPJ93PjOnfZ5z695n3oZH0WPRVIcmDXOG7GoYeGFZ/HzY+7g+zsfKTDLP/AXn6FWc9s4j+Kr2pzosaZgyh/iUspGReGU3w4hRG4rzIBk8MvMGFvkMln76EpM+eCnVYUnDjBkc2rW5R0bilS3eESduCl6pU3kl/Swmn38u5wY+YcGGf2ANBVMdmjQMCGNo54SRkXhli3dE29kYYyeT8Cz6Mcvs9Zz57hNklW5PdVjSUDbEN3QdGYlXtnhHhaaIweMRD09MupYTj4ezD7zB9M3PpDosaSiSLd6BJxPv6GIKeLMO3nQvYtzyMzgvvJuT3nwYe7Ah1aFJQ4SQLd6BFzflZLLRal9TjDsZy98W/h9LnX7O3/MsptEyHc3ARKOlhgSKiUAHAapiAgoKKqYwUVEQqgIIVEAxFYSmERcmmgChaESJoaOimCaKMNB1jagAVYCJggIg4miKStQERWldFCpQFAWBiqoYmEJBYKIKE03ViIoYuqoghEocE80QKKqKaYIi4mioxFUdQ7TEpmISU0ATCqqiIkwToYKiCEwBCIGuWQibLQuKNGGAoiBQ0BUFTDAUE10omLTMgVdUFUVViaGgmCZCUVFNE1URqCaYio4pTCyKSkzEQG153fSWh0NRFeKAouoIU6AIA1VVMQ2BooAiQKFlyqemKhi0vD7CNFEFGAgUQEWgqGAoCkIAZstzRgFFUdqeX8vTUBBCIBAtBxyhIKtgAN5t/WdEJF6LNmJWPkt9FImZ5HuKaF60iLiiEo7p6BYDixLHiKsYho6hCjRVQRVxEBpxE1AEqqKC2pIM1cN/9C27mSggBEJRMIWKqigcTlctCVpt+cCPI1BQEEJBQUERaktCECYIMFoyB6gqmAJNMUFRMM2WsQlF4bPcIWhN4y1Z7XBS4nAiNxUFFQ4nHRUwW5Lb4esYCmitCQqIAcrhf8cVFdOMg6ISpSWRtdwjEO3yl9aSnw9/mLQ8R5PDGRQQigChIlpORW09V7QkSENpd1tbo0hBU1vibvlJHD6h9bU7/HPLRQ5/WAFCOXxs6/9peWwEnwWgIMyWFCwUgTChNiOHwj6/mwbeiEi8Nl0m3tHMpilcnz8GI/gR6RPq2XlgEuNz9xOodHMwkoUzI4w3rZZc4sQiGo1RD6rQsehRTFpaaBGhI0wrMQSqaqBqLS1T/XCyVE2DiKJiCh2dlqRjCoEJWFQDVWn5ozcVE0EcXahoioaiKmAYLS1JTOK6jkBraQXqGnA4aQgDU1FRFANdUdAUQDGJ0xKHONwyVxWl5bpxs+UaQgHTIC4EBgqKqRAXCqaqAyqmqoGqH3580RKnaaCoLS1/FYGimGhKawYFRTXaWpoCEwUN/XCCbEn7LZ8UihAtuVgIFNHSOlaEwDQFBmCYLXm35aNKIS5UTKXl3yYaqtpyLaGocDi5qqLlGuLwhx6Ilv8fzrNKa7IWRssHjtLSUkYTaErLMSoCi21op7ahHV2CZOIdvVy6ypdyiggeChMyd9KoNTLL9PJq+XRmF2xkrhIhti3EO+oYIq58fEoVxd4KVHctVjs4RQZqXYxw0EYglk6zsNMYi2B1mpgugdUaI92iYY9GsYSDRBQrTaadcNyBaTowDAtBw8AQMXQ9jsUGhtVA1WPY1CguTcWqgh6PokcjYBjEsBBRLEQMHUPYiJsWTOHANFWEqWIaEBcCVYmjKgaKaqDrAnSBoQmEZqBYDVQ1jq4IbKqKRQGroqApCpowUYwQmhlHM00UQ2CaEFN0DBQi6MQNDRMNU+iYwkrcVCGuYgoVREvCFEJBmC2tSk0xUVSBiolQTFTVxMRo+VBQW7o7TFWACqqigaagqiqaUNFV0ABNmKimgW6aYIZb/i8MEGbLt4zD3RAGLd8pTEXBRMVU1MMtfrWlBawoCDSE0DDNw+e0xouJYYI1Zkvl27JXIyLxWmXiHZW8Fp1rMwvwV0VxpdegNMXwmgbKGBfZ7+/kA3Mhc7M30Dw3yNkN2UTee5YXrGN5LT6H3HoLaqSMTGcFxb4g1sJaFH03duEmy8jCG3LhrA0TrBQcEA4qFQ8hkUXIoqPYIMMRwW1tQLUE0PQ4Lt2CS+hYI1EIRPEHNJpMJ43CSdy0Exc24liIGiYxVSGmglBMnDYDjx7FZW1GVyMIPYLQwqiKiVVVsWkWbKoFqyFQozHUuAmROGFDI2hYaMZK2NAICCtCWFGwILAQFxqGUDAMg7gwiSMwFAVDUdBUE91iYtEEdi2OTTOwqVE01UBRYqDGEa3/KTFMJY6mgkZLa9uqWtAVFYuqo6GhKgqqYULchLhoaerGTYiYGIYgYmrE0IgLnbiwEBEqcWwIUwe0w98ANEzR8m9By2VavlEIDFNgHG71G6ZACIGpmmiagqK19OBoKmhaS7eRYhEImzXVb88ejYjEa9O1VIcgDbJsu5XLPbn4a1oG0pqCu8jzWPHGQmw03uI0z3TuCDWiVpzA+Oa3qSp4j+ZTcrm0Oo2mTX9ng72I1/MWMNZcQH2liVlWRix+kHHpjRTl1IJzP/vdBofMKNa4m2J1LDlGFva6ANTUUl8VZa/qpFw4iNsziNvSiaoClAhZliBZjmbs1iBuSyWG1ozDasWtObHhQAkL8EdQglGMIPhNJ03CRq2w0iycxLFiqnZM3YWhahhKS+tSFzEsIoLbEsFlieKyRLFboljUJoQew1AjmFocTRXYdBtWxYpVtWDBghLXIGogIgZEDJRIDDNiEhVWIsJGWFgJC52QcB5OjBomOiZqS8sSHVOzYqoqBmAqYAoT8/DXflXEsaoGNjWOXY9jVVt+tmpxrJY4uhZHUeJoahhVjWMhjqnGEYoJWksrWqgt3SmaqmNRdFRFoyW16yjoKFhQhYaK1tIsjpsQN1DiJiJuoMYFqiFQIyY205HS92dvRkjilS3e0aTQaeMiRw6BupaRe6sjzM79e5g6L5O0mJ9d0e0szV/GJa8/xx8nXIpZexzeprc5seQAa7J2cdI5Z3L2wRAnvXM/27R0npm0lCYyWJA2noN1Ecr8lfib9+FQajh2vIWxviBRPqLcZqc8N0ytr4E8tZhxWiZzzGxsjVHMuh3Eaqupbw6xT3WxR/MR9uSCfSqG5kQjilU0YxMBMm1BvA4Tuy+KoYcwrX50Pc5YzY6VNCzChR7TwB/FGoyhByOYwQhN2KnFSh1WqoSFIFaENR0sLtAdoNoQAnRi6ETRzCiKGUExIzgtEZzWGA5LBIcusLkVUAyw+BF6E5pu4lDj2DCxqDpWxYaGHRUburChmjpE4ijROErYQIu2/KdG40TjChFhpRkrzWg0oxFAo15oRLACDkyhgWZF6LaW/ysWUCwIpWWGhECgEUcVcVQMFBFHEXGEGUPHwKbFsOkGFq0lmVu0OPrhf+tKHIseR+jxw90eJmkeg/TUvk17pIjWYcZhrCkc44w/rEt1GNIgmJjm5Fw9g2Z/vO02u3cn23a+zcWn5DA77QP+rkRZZr2EzAMR1u46yMfjz6E4XE88/CYr51SxyWohGIYzjaUYZQdp2LCWyohg7fxLeSns45TMdCbiIFzfjM1dTSC0n9qa/cyblMaMAkGO9RDNIkS51UO5iFEWrkTEDKbY51KkTiAzno49YBBvqiPWVEustpIGfzP79TQOegqpScsHewaq7saq6DgIoxsBRDyA2xLA6wjhtQVxWsNgjxO3xIlpUeKKwCrSsJCG1XRiM+3YQqAHo2iBMIo/iBkM0iA06hQbtYqNOsVKneYkaE0DixNVd6KoNhTVil3RsAkTjSiaiKAYUYQZRhgRhAjjsMRwWqM4LFHsehS7HsNhiWOxCIRmYuompmoSV+PElThx4qjY0RQ7Kg5004qODU1YWv6LCbSoiR4xUKMGaiSOEomihqOY0TjNik6zohNAJ4hGUNEJotMsVILoxBUL6LaW/zQrqFYUVQcsoOigqGhCQcdk2mQfJ83NT90btRcjIvGGYwan/O71VIchDbDpXjenCy/h5vaT401qmlfj9zdxzZJsJlo+5GmbC2vUyef8S6jZ8Ay/Gr+cyWnjsAUrqK19nS+dEuaQGuOl4D4WO5ZT0pBH6NA+Gtc9h7/Jz+unrGK1OhYLKmdmZZHmVwgHwzg8dQTD+ykr/5T8DAsLJrsYnx7EbZQSUlTK7V7KMSkLV1HbXEa+dRwltlkUKsWkR9OwNpsYoQCxQD3R+ipiVWU0NAU46MqhLHsipe48wlYfup6GU7PhVVSsohk1HsCM+xGiCZ8jjNfejNcexuswwRonpseIqFECSpy4acGGB5twYTMdpOHBHrOgByKowRBKoBkz6McINBGIC2pVG3WKnTrNQb3dS73Vg193oGhOVM2GqliwqxZcqoZDUbGYJoqIoIoIGGFMI4IggmGGMc0Idj2K0xrDbolh12K4rAYuOzgtJrpmYGoGhmpiaCZxYkQVg5YrgCIsaNjRDreyLVjRhBWrsGLHjm6q6GETJRpHjcZRIzGUSBQiUUQkgoiGCRuCoGrBe9IiMo8/IVVv1V6NiMQrhGDBr1+VNXlHsLkZHk6KphEJd1yR5PJV8/GONQDccG4GeWIrr7ty+LBuC9+0/ACzrpbdT/2d7xx/E1ekFyBipezd/zI3nGVDM8t4SVcJh0Kcb/0Czvo4wcpPaXr9WaIN9WxeuJLVGceyrSHOwux0jnWkEauOYZpxHGl1hGIHKC3bjWnEmD/Fx6xilTxbDZZoBWHNToUtnXJFUBapobr5IMIUTHDMZIJ1BnmiAG/Egd4cQxgxYs1NxBpriNZWEKs4SFNDE2X5UyjLKaHUncch3YOmufFY7GRpFlwCdLMZEQ9gmn5ihh+rEsDrCONzRMh2Czz2GFjiRLQYISWGXxH4zTiqsGMXbmzCRRpePHhwGHb0YAwlGIRAECMUwAg0YQQaCYUj1Kl26jQ79TYfDa4M6m1e6nUnDbS0njXVik214NV10lQNh6JhNRVU0wAzAmYEYYbhcJo1zQhxI0QsHu7QmnbZTNLsKml2cFhNHHocXY1jqCZx1SCGQUwxiCjQerUwBkJo6MKOBRuasDKu+HTy8uYO/hs1QSMi8QKcddc66pvl9j8j0UnZ6cwOOohFOxdDMm0b2H9gNwD/fb4Hb2wH77sLWVf7Dhd5vsaYWje1W15mvZrDH90LuNyZi6KXsn3XS3xlaTp54mPecxfyVv27nOhaxvzm2SihMP6D2/G/9gxmoImdc5bwzIQlbKgHp6Zxdl42hXErgZooimri9NQRjh+krHw3oVCIvAwHJ0xzMykrgscsRTGCRFUrlbZ0ylWV8lg9lcH9GGYMq+JgsmMOY/Up5Jo5uMNW1FAUhEk8GiLmryNWf4jooTJiZfsIROJUjJtNWU4JB935HNQ8hLHh0+3k2+ykKzrWuEAxgwjDjyBAzPATjTSRZguR7oiS61XJcJt4rBFMNUpEjRFQBX4F/MIgEI9gEy7swkWa4sOnZOAWaThNO3rYgEAzIhjADAcxmltaz4a/gUjAT4Nqo86SRkNaNg3uTOrtPup1F3WKnQZTR6BjU3V8FgvpFgtuVcOpqFhNFc0EYgYcbj0rSgTU6OFEHSZuhInFQ0SjIaxaDIcew2UTeF0aaQ4Ft03BZTVxWAzSJi3HlTNtkN+piRsxiffyBzays2po1+CUknd6bgaTG23E453fplZ7hF0H/9m2Auz7F9hwRPexz5nHf+o3M95+DBf4zyYebabi/l/xh8/9mBrhZWHYjc1dxtYdL3PZqVlMd35EpT2TFyLlxKMRLnBfQ3atFWHE8O/5AP8bzyPCIaqLZ7Dm2EtYG3QRiplM9bpY5EvHWi8INxsoionT00DEOEhZxW6am4OoCsydlM7c8ToFjjqs0TJAEFc0DtkzKVd1ymINVDYfIGaEAUjTMphiP5ZibSKZRgbOZhU12tKoMI0o0WAjscYaYjUVRMsPECvbT9jqomLcLMryJnHQnc8BzUtFTMej2RjrtJNjseIWGnrUxIwHgCBCCRA3/DSHG1DiAdJdMfJ8OjkeSHfGcethhBKiWYnjV3X8ioqfOE1GhHA8gkO48Sg+0tUcvIoPt3DjMGxYokCwGTMUxAgfbj0HmzD8jRhN9cQb62kUOg1p2TR686hPy6LBkU69xU2d6qBOWKiLKQhTxWexkGWz4LNY8Gg6TkXFJlQ0A4gJjKiBMMMoahRVj4ISRRBmydIF5OVnDs6btA9GTOL99r8+YP2e2lSHIfWjswuyKa7VMYyu36J23y627Xir7edbLzDRo4eos/p42P8xAN+w/AC9OUr91g1UfrCFGxf8NydlpJNfpeFKL+Xj7a9w1vxsTsnbTgx41eZmZ8OHTHMu4Iz4EizBKEY8TNO2TQQ2vATxOM3eHF5deCWrKaS6OY6uKJyem8k03UmoKoZpCsDE6W0kZpZSXrmbQMAPQKbHxonTPUzOiZJOOUq8CQADlWp7JuWahbK4n/LmA0Tjn9UezrEUM8k2myJ1HBkxD7ZmEyXe0u0ihEk8HCQaqCNWe4jYoVKipfswG2oJu3xUjjuG0pzJlHoKOKB52B/VEYZCidtFod1GumrBHlcQYQPDCKDpzW1JORRuIBBswG2JUphlJc+nk+U28dhiOLUgmAECKgQ0O02Kgh8TvxmhKRbAMAzS8JGl55OuZuPBi8t04jCsWGIKNIcwI80YkWaMUEvrOR5oxPA3YDTUYTTUEhQajRmFNKTn0+jJod6RTr3VQ73moE5YqTVUasMmmlDId9rIslpJt+h8Y/kU0tOG7lzeEZN4f7Z2O//+sDzVYUj9ZEVhLlnVCqLbUsuC2tBqmpoa2275f8v9KPEABhp/jBxEYPJ5z/UU13ow4lEq/v4rNs27gJ/pc7hubBGUxXGm72fr9tc5dnI6K6YeRIn7+cRVyOsN72Oacc7zrGJCQw6KYRKPNtP4wXqaN70BQhC3WNm48Aus9s5mZ2NLizTbbmVpThZZIY1AfawtVqenkThlVBza3RazosDM8V7mTbBS5G7EHittWclFyyrZGnsm5bqNMiNIefNBQrGmdk9fYZx9GhOtx5BPId6oE0tzHMX87AUz4lFiwXqiDTXEqsuJlR8gVr4fDIOI09PSZZE76XBC9rI/qlMTijPR7WKcy0GubiUNDT0KsWAMoTSjW9sn5Ub8gQZiET9jsu0UZFrJ9SikOw3SLGHsSgBhNBHQbAQ0G02qjh+TJjOCPx7EH2vEYupk60VkaXn41CzS8OAyHNgMC5aogHAUMxbGiIa6bD0bDbUYdTVENJ3GrOKWJO3J5bybvollCM/vHzGJ988b9vKnN/emOgypH1w6Jp+0SkFP70yXr5aPdzzb9rOqwI/PruRwLTLuJ4w/UkOxbSqfDywHoHH3FprW/ovfX/hL3qhTuLGwmMChKM70vWzdvo5x+W6uWdCIFq2izupjrdFITfNB8qzjWa5fhquxZQpbtLmJxndfIfzhprbH3z73bJ4Zfzpv1Ym2Qd5jMzyc6PYiag2ikZaEKITA5fFjqGVUVu2moeGzvcHSnBZOnO5lWp5BplaBGuu4b1idLZ1y3UmZGaK8uZRAtOOGjhZslDjnME6fQq6ZS1rEhtYc7VC7SwiDWDhArLGWaF0lscpSYgf3YgZaknrUkUbF2FmU5U+mNK2AA7qP/VGdimCMDKuFSR4XRTY7GZqOI64iwoJwIIpuDbckZTXYoaXc2NhAlsdCcY6D/AydLDd47TFcWjNW0YiI+QlYXfg1O35VpwmBX8Twx5tpijbQHG0kQ88lx1JEppaHV0knjTSchgNbXEOLtsxyEEaUeDSMEWnGNGO4rriu1/dZKo2YxPv0h+X8ZK3clWC4W1VciKW891qqwv42+/bvbPs522fjxpP2tf38lNXJQX/L/TdYf4AlGMU041Q8/FsaLU5uPO4mhAFXe/IJNsZw+HbxyY63SE+z8vUzwB7dj4HOemcGH9ZvAeCUtPM5NjCzrb814q+l4e21RHdubXvcyvGzWTPn87zgdxI5vDOKXVM5Ky+LCcJOsDra4QPFkRZAaGUcqt5NXV3HrrLJY9I4rsTOWG8AR+wgiug4eNxo9VBucVMmopSHymkMV3V6nVyql8mOYxmrlZBlZOIKaaiRzoPQ8ViYWKCeWEM10apyYuX7iVeWwuEWdNThomLsbMrzJlPqKeSA7mV/zEJ5IIaKwoQ0J+NdDnItVjzo6FGIBw3CoRhWRwSLLdTWUg5HWlrKjY316CqMyXVSlGVrGfRzGXgsEexKAN2oh3iYgMWN3+Js6WcG/CJGU7wZf6yJpmgtmlDJsY4hWy8kQ8sh3V7ImNnn9fYWSqkRk3jf2lvLN5/4INVhSEfhS2OLEGXxXo+z2KLsLn28bVANYNpYD5fP2NH286vObD6ufw+AFZ4vM67WB4D/4Cc0rH6YjYuu4BfqLKZ63ZwW8xCLmth9O9i24x1sFo1vnO3CF2+53h5nPi/7txOO+3FpPlY4ryWrTm9rSYbqy2lc9xyxg5994wqk5/PKwitZbeRRF/rsOY112TkjMxO3X+mwCATA4QoiLGVU13xKTW11x/tsGgum+ZhZCNmWSrRoTafXJaC7Kbd5KBMxysOHqAt13fWWaSlgsm0ORep4MuM+7M0CJdb5dTdNg1ioqWURSE0l0cqDxEr3Ipo/63uO2VxUjjuGsvwphxOyry0hGwLSrRYme1wU2e1kajoOQ0UJC0L+OIZhYHNED7eUA8RNP+HwZ0nZMAwyPTaKcx0UZFjITgOfI45La8YmmlDjjQjToNnixK+78GsW/IDNPZbZU67v8rkPFSMm8e6uDnDZ/RtTHYbUBwpwffEYYuWJTQd0+D7lkx1vdrht4cwMzi7+rOX5nruA9bUt74cC2yRWBlYALQNRFf+8C6O6kt9f+Eter1M4Kz+LsTU6QoDN+wnbd25GUeBLS7MoVj8CwG9x86ISp8zfMnVthvNETo+fgR6Mtl23ufoAja89g1Fd0RZHzOrgnVMu5ynXDPY0ffb8FOCUnHRm21rmBh85a8PubEaxVlBT9ylV1ZWdXoPx+W6On+xkfHoQV/wgihnpdExId1BuS6ccg7JwNTXNpbQUsuysyDaZSdZZ5FOEL+bGGuzYX9xKCIERDREN1hOrqyJWXUa0bD9GVQXtm/Ixm4tDY2dSlj+Fg95CDurp7I9ZKDuckFUFxrudTHA5yLPa8KBhjSnEgsbhBTKipaVsDcHhpBwKNx7uvqgnHo+jawpjclwtrWWfRqbLxGOLYMuajnvSii6f51AxYhKvXDY8PKkKfH1MMaHyaELHCyGoj/yHxsaGDrefuyCbEzM/bPt5jzOfZ+s/64P9uu0HWAMtjxGs3EPdv+6jKauIG+d/m8awwTXFhWiHuzgsno/YuaultXzRKTnM8XwMomX3iE3uPDbVvYvAREPnPM/VjG/IQjFa+3ANAmW7aHrtGczGjn20W4/7HP8Zcyob6zomNK9FZ1leFvlRK4Hazq+DzRFCs1VQ27CXykNlne636irHTfVxzBiVPFs1erRzogaIqDYq7OmUK1AWqaWq+QCm6LpbR0NnomM24y3TyDPz8ETtaM0xlG7ShWnEiYUaWwbyDi8CiZXuQ0TCHY6LWR0cGnsMZfmTKfOO4YDFx/64ldJAHONw0XSPrjHJ66bYbidTt+AyW1rJYX/88AeUwGo/3H2hBjDMjt0XZ599NgsXLuwyzqFixCRegNPueINApPevqtLQYFEVvlYwhmBlYkkXwOWt5eOdz3a6/bLFOcxwftbVVGtL5x9NH7X9fL7ni0yobZnXKYRJ5er7iJfu451FV/JL9RgAbiwqJlgZRQiBJe0Ddn3aksgXz8nijKJdKGZLEilz5PJC8962wa0C60Q+p6/E2fjZe8804vj3fYT/jTUdvpoDlJfM49ljVvByk43oEVPlpnvdLPKmo9ebhEOdk6LVHkF3VFDfuJeKylK6+vMtyHJywhQXE7PChxdwNHf5WsYUC5X2DMpVtWUucXA/htn978KuuJnsmMs4fTLZZhaukI4W7v5bihAm8UhzyxLpukPEqsqIle7DqO3cFx2z2KkeO5PSgqmUeYs4YElnf9zKwXYJWaGlq2ai20me1Y5P0bDGFeJBk1Cw9bUXLLugiLHjvd3GNRSMqMR77cOb+ai8qfcDpZRz6CpfyS0icCjxpNty4jvs3bej081fWprFWO2zRBtXdP4Y3k/rLIc863guDV7c1i/bXFdG7SN3A/DbC29jXd3houq+QgINMUCgurbw6Z6W7ouZE7xcckwFaqxlKlhIc/CKxcKexk/aHvPUtAuZE5jWNvgGYBhRmna8S2D9CxDr+Fybssbw0omX80wsh4ZwxwaDriqcmZvJVNVJc3WULr71Y7FFsTgrafDvpaLiYIc+71aaqnDsJB+zx2mHF3CUt70mRzLQqbRnUKHplMUbqQgeIGaEujy2Vbqey2T7nLbiQM5muuwv7vA4RoxYsKFliXR1ObGKA8TKDnR6fQDiFitVxcdQXjCVg94iDloz2hJy+70WXbrKpDQ3Y512rj9nElm+oV0IfUQl3p+s3cbTH1b0fqCUUh5d44vZhfirk0u6FluUT8v+idHF1t03npdOtvlJh9v+JkIEop/NFPi6/QdY/Z895qG1DxHdvY2mrDF8c963aIoYTExzslT4iIZNwATHZvbua5ktU5jt5EsLgx2+yn/oKmR9wxaMw5tLpmnpXOC8lsw6rcM0rng0RNPWtwi+/RocsQNuzOZiwylX8JRzKvubOrcgcx1WlmZnkd6sEWzouoVpsUWxOqtoDOylrHx/l0kYIMtr54RpaYcXcJShxP1dHgdgolJlz6BCt1IWD1Ae3E8kHuz2+FYF1gmU2GYfLg7kxtpstHXFdEcIs2Wam7+OWF37RSB1XR4ft1ipGjOT8oIplPmKOWBNZ3/cxqGwycvfWISmdt4AcyhJKPGOGzeOtLQ0NE1D13U2b97MypUr2bGjpeXR0NCAz+fj/fff7/J8wzCYP38+hYWFPPPMM53uj0QiXHXVVbz77rtkZmby2GOPMW7cuKSfzD82H+S3r+5K+jxp8GTaLFzpzWurpZsMZ/oetm5f3+V9N5/vIi22u8Nt/7Y6KPV/9n44z3MNk2qz234O+6up/vvvAHh78SpuYwYAi3MzmFRnbdlBVzExbRvbpq6lOS184ywdZ3RP23WqbZmsjVVRH/rsQ3+m8yROi52O3tzxwyUW9tP43uuEtrzFkYSi8NHxK1hduJAtdV33vR6X6eV4lwezxuiydgWAboljc1fRFNxLWfm+Lj+ooGUBx6yJPo4db6HI1YAtVta2gKMrLYs6MqjQHYcXdZTSHGvo9vhWKhrjHTOZYJneoThQd/3F7RmxSMtAXkM1seoKouX7iVcchG6ekzZxCgV3PNzrdVMt4cS7efNmsrKyurz/v/7rv/B6vdxyyy1d3n/77bezefNmmpqauky8d999Nx9++CH33HMPjz76KP/+97957LHHknwq8M6+Om745/tJnycNjnyHjZWunMNf5ZMjhKAx9iz19V23gP5vhY4tcrDDba84s9l6eEoZQLZlDJc3X9ahJVr9+pOEP34XgNsvvI31hy9/ZXEBtvLDuwCrJnH9LQ4c/BQAXVP4xjleMo1tbdeJKRZed3jZVv9+2206Vs7zXM24+oxOMwQiwXoa33mRyLaup0AenHICz85YzisNlg5fqVs5dZWludmMNW0EevjmoFni2A/XFC4t20M83n03gNdl5YTpHqblGWSo5agJJNV6q49yi5MyM0x5qAx/JLFl+y3FgeYyVp/coThQIu1UIUxiIT/Rw7WOY4dKiR3cgxnw41y8jMz//klCMaTSUSdeIQTFxcW88sorTJo0qdP9paWlrFq1iu9///vcfvvtXSbepUuXcuutt3LiiScSj8fJy8ujuroaRUnu60JdMMrSu7tuEUmpNdZl53xbFs1NfRv8dHrr2Lqz83un1Y/Oj6LFOv7Rv+sqYENdxymGX7P/AFu77oZocwOH7v81CEFjdjE3HnsjTZGW1tQ3xxTTXNFyrKKaRLX1lJbuazu3tf5veztdhbza+CHRdn2jRbZJnKddgqOx8wdOuPEQDevXENvX9Te1htxxvLjgcp6NZLTFdaSJaU5OS0/H0aQQCnT/+mqagd1Tc7im8B6i0Z67eqYWe5hfYmOsx48jdhBE77+7Jksa5dY0yswo5eEKGsKHej2nlUfLZLL9WIq1CWQZGTjaFQdKRDwaggnj0GfNSficVEko8Y4fP5709HQUReErX/kKX/7yl9vue+ONN/jOd77D5s2buzz3oosu4nvf+x5+v59f//rXXSbemTNn8vzzz1NUVATAxIkTeeedd7ptYffknD+upzqQ5ICNNKCmel0sIb3dyHPyFOcm9uzd1u39/++8+rZZB60+deXzXN2mDred41nFlNrcDrfVbHye0KaWqYgbFl/Nr5kOHB4AzChs6xZRVYOQso7y8gNt537uxGwWZHzSISk1WrysFUEOBfd1eJzFaRcxOzAZNdrxdRBCEKo9SOMbzxKvKO3y+UWcHt48+UqespVQ6u86GSnAqbkZzLK6iVTHMLqo6NZK1QwcnlqaIwcoK/+USKTzPOD2HDaNE6enM73AJNtyqMsFHF0J6C4qbF7KRJzy8CFqQ90P7nUl11LMJPscCpWxnYoDdem4eZCdfN4YbAntubZ+/XoKCwupqqpiyZIlTJ06lUWLFgHwyCOPcNlll3V53jPPPENOTg7z5s3jtdde67egezIlJ43qgKxSNlTMTk/j5LiHUKjvSVe3xthzYGe399utWqekC+CJd04m74ReZLJyZYf+Re/sUwht2QCGwUmv3c9JF/6KDXWCUNzkyeZqPufIJBIyME0Np34K+XmvUVHZMp/2P29VUzNjCudM2Ns2bcsba+TzqLydMY8tdVtoTTSv+Z9gi5bJBRnXkFGntn2tVhQFZ1YxjhVfIVjxKU2vP4NR1zGx2ZqbOP2Fu1isanxwwudZnbuAD+o7JiABvHaojteoI91qYVl+FrkRvcv+dNPQCNbnADkUZRyL3VNHOHaAsvLdhMOdX8tQxOCV92p45T0ACxMKJnHcJCfj0wO44qVdLuAAcMeDTIoHafkurBByllBh81GmmJRHqqluLkV0XwmJQ7EDHIodaHeLwri06Uy0zOy6OJDX0+21hpKkZzXceuutuN1ubrrpJuLxOIWFhbz77rttrdX2vve97/Hggw+i6zrhcJimpiYuvPBCHnrooQ7H9VdXA8Af1+/hr2/tS/o8qf8tyPIxL+RsKxDTV61FbLpTlO3k+uM+7XR7TLFwT7hz4aSvOr6P/YjZA3UfvE5w/QsANOSM5Vtzv9n21f7k7HSmN9rapnRpljj+6KscqvpsMG1KsYfL51ajxjr2Qe935vFSYBfNscYOt89yncKp0VM7Db5By1LdwIFP8L/+LGag+1kH+6afwrNTz+G1Br1trmtXjklPY2GaD63eJNLF3OD2FMXE4akn2lZTuOs5wO3ZLBrHT/MxswhyrdXo0cS7F6KqlQp7BmUKlEfrOBQ8gJlAl0Z7rcWBJjnmMvHYi5M6N1V6TbzBYBDTNElLSyMYDLJkyRJuueUWli1bxvPPP8/Pf/5zXn+99/3OXnvttW67Gu666y4++uijtsG1J598kscff7xPT+j1XdXc9NRHvR8oDahFOenM8DuIxY4u6QI0xp6jrq77r7ZzSnxcNLnrboj7RIDmaEOH25Z5rmBabUGH24xYhIr7f4mItrTc3jztWn4jprbdf9mYfFwVn/2p6JY4DZGXqa7+LMnkpNv56uIolkjH1WVB3cmLqsJBf8f5xzpWlnuuobg+vcvluaYRo2n3+wTWPd9pBVh7dfklvHD8ZTwX8hGIdp9YLarCWblZTFIdLcV6ev3VmDi9De1qCie20UBh9uEFHJkh0sxSlF7mArcXU/TDBeJbF3UcIN5Na/pIU3JP45xZP0j4sVKp18S7Z88eVqxoWfccj8f5whe+wPe//30Arr76ak444QSuv/6zghTl5eVcd911PPfccx2uc2TiveWWW5g/fz7Lly8nHA5z5ZVX8t5775GRkcGjjz7KhAkT+vSEGkMxlty5Tu6/lkJn5Wcxvt7SYx9jopyeBrbuWt3jMafNzeKM/K4/bP9lsVEe6NgaTtdzWRW+utN0poYdG/G/9HTbz7+68Fe8VffZMTcUFxNut7TZYotSG3yZ2nYFbRw2jRvPtuOOdpzaJgS86y7gnfp3Oy3THWObwrnqRTi6mMMLLR8KjdveIbjhpW6nUQGEXem8ccqVPK2PpyLQ86BUgdPGWVlZeIMKwcZEWpgCp7eJuDhIeeVu/P7EFirpmsKxk33MLtYocNRiiVaQTB+vgcYhRwblqoXyeBMVwf0dBi7bWzzl68wtvjDha6fSiFpA0UpuA5Q6ywtzyKtWu1xp1ReqczOf7v2kx2MuWJjNfO+HXd73kjOrwxSvVtc7v99ploEZj1L+0O2IYMvX+4bc8dw4+wb8h1uRNk3ha1lj8Nd2TL7V/hc6THNTFfjaOS0bbx6pwpHNC6FSmiLVne473XMJs5pKul35FYsEafpgPc2b19FTsWJD1XnvpEt4Ons+W+t7T6onZvmY50jDqDES+oYihMDp9WNSSkXV7k51M3qS5bNz0rQ0JmVH8VGKEk/u79QUCtWODMo1G+VGkPLgAcKHF4F8YcEfyfVMTup6qTIiE+9vX93FPzYf7P1AqV9dXJSH71CPOSEpuiXO3srHe5x7CnDVGdlMtnWdeDe7CnirrnPVuiWey5hZO6bT7U17P6LxuUfbfl5/+nXcbn72x1zgtHGhntVhi3mrPUxlwwudEtDlp+cwzf4hR7bwIqqNV21OdjV0bqV79SxWOK7G127w7UjR5gYaN73SNv+4J58ecxrPTFrG+nqFbnZQauPSVZbl5TDm8EaeiXKk+RFqGZXVn1Jfn/jAtqLA7Ik+5o63UORuwBYphW6qp3VHCKi1Z1Bh8zB79i2oytDddaK9EZl4139aw7ef7PoPURoY7Rcc9Bdn+j62bn+j1+O+ek4mhXzc5X27nPk8X7+p0+0eLZNrI9d16m4wTYPKx+7oMKvglxf+mnfaVRQ7PtPL3ICzw15wNkeY8rq1HbYiAlg6P5uT87ajdFF8ZqurgDcaPuiyD3O281ROjZ2C1sXgW6twUzWNb60lurv7aXataoqm8fz8S3g+mEZzAq3akjQnp6VnYG8UhIK9F6ZvZXcHQC+nqubTDl0wifC6LZw03cfU3BjpanlbXYxEKOnHoB1zU1KPl0ojMvEGo3HO+MO6Hkd6pf5z7dgi1AQKmCerKb4moT/e7yz3khHveveRKnsmjzV2vTrsK87/7VBRrFWgbCf1Tz3Q9nN93kRunPW1DgNXFxXl4Tui+qLdGeJg9fNtG1u2mj8lnfOnHOjya3WtLZ218XpqmzvP37VgY7nnWsbUe7scfGvVXFdG47rniLdb3NHtsZ5MXl94FavVMRxKYF61qsDpuZnM0F2Eq2PdbjzaFbsriGKpoLru0w6DkImaPtbD/BI7Y9IaccRKe1zAoY6/BHXMuUk/RqqMyMQLcM1Dm/m4QlYqG2jXjx1DvCz5JcC9cXoa2brr6d4PBP73AkeH2gntRVUr94a6vu+MtJXMqhvb6XYhTCqfvId45WezE9ad8SV+a3Rcmfm14jFEjyjebncF2V/5PM1HlIKcUOBm1fENaNHOHyRxRWe9I4OPDm8xdKSxtmmco17YaQrckTE3H9pH4+vPYNT0nuQMzcLmhZfydMZctjck9qGZabOwLCeL7LDebiPPxNicLTWFa+r3cOhQ8pvSuuw6J0z3Mb3AJEur7LRKUZt7K0ra+KSvmyojNvHe9can3P/O/lSHMWKpCnxtTMdR/n69vmsLn+7puvvgSD+8QMES7f6P+S9mU8cdeg9zaT6+FL2+y9Zkc/V+ah//U4fbfnHhrzsUMddVhW/kjulUZc3uDrCvfA2hUMfR90yPja+dYWKLHKArn7ryebnpk24rgJ2ZtpKZ/ok9ll0UwiRQup2m157FbGro9rj2ds05i/9MOJMN9ZDol8Q56R5OSvNCnXG4klvirI4wur2SuoY9VB4q67KmcG8mFrTswDHOF8BJA/qC3/Rp3n+qjNjEKwvmDBxdVfha4Zi2Ogb9TbPE2H/oCWKxxFpVPz6/ucf+wCcsVioCXbd6v+z6Hq6Gzn2YQggOPXc/sX2fTQuryy/hxmO+SrBdl0OO3cpKe06nGgmOND97Std0WgVms2h88xwn3ljXK/GaLB5eINxtvOl6LufbryK9vuckY5px/Hs+bCnEHup9EQRA1dhjWHPsRaz1uwjHE0umNq1lbvBEHJ028kyE1R7B4jhEXdMeKioO9ikJz507h5UrL036vFRSUx3AQJld6MWqjdinlzJ2TeWG/IFLugA2d0XCSRdA7aVGrFdP6/a+XWrXxWkURcG78OwOt2VU7OY6s+MiiKpwlHVKE7reMRGG/GlMGLMMq9Xa4fZIzODXq/2UipldPq4n1sSFsTjHZRyH0sWfZ338EPcHfsXrvk0YDmsXV2ihqjrekmPJv+q/cS8+F3RLt8e2ytn/Eav+/UP+vPlXXOuqIsvZe0WBiCH4T3k1vys/wPPWeowCDacnoUoEAETDNoL1xdiMxUwas5KZU0+jqHAcqpr43+7kyVMSPnaoGLEtXoD/+veHvLE7sWIeUu/cusZ12YU9liHsDwFzbcKDMV63hf9e1PVX91YbXQW808WUMgCn6uHLsa91O3hV9crjnUo3/vzCX7PpiH3Tzi/MIftQ52Th9DSwc9+aLj9ILlmUwyz3R3Q3harUmcsLwT0Eo/Vd3m9V7Jyfdi2FdWm91raNR5tp/Pgtmt95jUQnWccsdjadfBlPeWaxu4vKaj05OTudufY0YjUx4rHkU4xuiR2uKbyvx5rCqqrygx/8AKfTmfRjpNKITrxrPqnklmd7nnwvJSbdauHq9PwOiwcGgiOtiU92P5Xw8SVFaVw9q/sCOgA7XQWs7SbxAlzn+h/SGrpORpFgHVX3/6bDbXUFk/jmjK90mpb1lbFjMLoYaHR569m+Z02X85FPm5vF6YU7uy0yE9IdvKTp7GvqfsrYOPsMzmFFh3KX3YmF/DRseY3w+2/3emx72+ady3/GLuadOpHUqlCPrrE0L5uiuBV/EnOD29MsMezuavzN+ykr39vhdZwwYUKHaonDxYhOvMFonKV3rSeSYH+V1LVch5XL3LlJj2T3he5+j12fJl5r4/hp6Swf3/OHa6U9i382vt/t/YvSVjCvrnMt6VY1b/6H0BGJ6rUzv8Id8YkdblMV+GZ+MYGqzgnG5atl2+7nu2y5zZro46KZ3c9bFQI+cBeyoX4LhujmdyAUlngvZUbT+F73PAOIBOpofPsFIjuSq2tSOX4Oz835PC82OYj0sp3PkaZ63Szy+rA2iA4LUJKh6Qb2tGqC4ZbC7kuXLuXkk0/u07VSaUQnXoCb/v0hr8vuhj4rdtlZYc8mmORXzb7Q9DgHqp/otUB3e0vnZ3NKTs+LZcKanT837+72frvi5nrjhm73BYuF/VT+7bZOX9F/euFvePeILXoyrBaucOd2WfDd5avmk11ru9wPrSjbyXVH7Od2pCp7FmsjPRcXT9fzWGG/Cm/XvROdhBoqaVy/htj+7l+frvgzC3jlxCtZbeRSn2TJT11ROD03k2m6k1BVDLOP8+1VzWDlqol4vMOrmwFG8OBaqzOn5KQ6hGGrJM3JBZasQUm6APa0iqSSLoDH2fsUIrsRxt7DAFtYBPD3UMbVYk/DuWBxp9uvf+denJaOf0J10RivmI1YLJ3/tIIN2UyfvKTLgaPS6mZ+84KVkLX7uag54RpWKi6m+mZ3e0x9vJK/Bm5jnW9Lj4NvrRy+PHLPvZqMi7+MnlvY6/Gt0mrLOf+ZX3LPy9/n29ouxnt6H7xrFReCFypr+H3pAf6t1hIuUHH7Ej+/VVaOe1gmXRgFiXdRSRY2fcQ/zX4305fGMiWd5h62kulvVXW9L309UpotsdaS15bZ4/07lJ67K7wzTwRLx0SWWbaTa+lcB/jjBj/707v+sArW5zJ98hldzjn1N8e47Zkotdq0buOwmlGWhGpZknE8Fs3R7XGbgy9xj/EbyjKDiF7mtyqKgitnLHkXXk/6BatQfT2/Vu1ZoiFOefnP/Gb1t/lx8E2Oy0jub606HOWhA+X8ruYg73lD2AosWG2JXaNk8vAoet6VEZ+RnFadE8cn/kaSYF6ml1PjaX3uh+sLh9tPVVX3X7O747QmFqPP0vMf6cbAiwit+wIrutWJe+GSTref9tI9HJvR+bznyquhsOtpVcH6fGZMOa3L5BuLm/z2Pw3sic3qMd6pwXJWOieS7Sru9pioCPF401087X6OSFrvrV9FVXEXTib/shvxnnMJitPd6zntzdz0NN978ibuLH2cZRkxLFpyCxrerW3krgMH+Wu4kupcE3eOle4+MxQFJk7xJnX9oWTEJ16AJbK7IWEnZ6dzfMhFJMnVSEcrRtcLBnrj0BPrBvGqth7vj4oQTZ6ek7hnynwUe8evtooQXL/xz526HADu21+KO7frhBesL2LGlFO7fay/vljNpobZ0EO1rfRoAxfHFeZkzOsx7r3hj7g79FM+ySxH6L3PsVVVDc/42eRfdRNpZyxHsfb82h2pYPdmvvzk9/nTh3/g8rR6vPbkKoaFDZOny6r4XfkBXrA2YBbqONM6xl1Y7MLpSny+8FAzKhLvKROzcFiGR7m4VDozL5OZfjux6OAmXU0z2H8w+W4GAJuS2O4E3gQ2Dt9G5/q57Wm6lbRTz+50e1bpdq5VOm8xJIC/VZXj8nbdfxmsL2bG1EXdPt7TG6p4bv9UhNZ9P6aGwSnBQ3wu/TgcPbXqFcHapod40Ho/jemJtUQ1zYJv6gLyrv4urpOXQA/fCLrirT7A5//zc+59/Ra+YdlDcVry/bj7g2H+ur+UP9SXsisjijPfiq4rTJnuS/paQ8moSLwOq8bSabm9HziKnVuQzYQ6K/F+2DUiWXZP8oNqrXS63xKnPa/R+/U3B15C6D0nl7QJs1A9vk63n/bi3cxN73xuYyzO87E6LNau/9Sa68cxferCbh9vw8d1PPzBGExLeo9xjWuu4FJrPkVp3U+LA6iNlfPXwC9Zn/4Bhr337gcA3WInY/Zi8q75Lo75J9Pt9/9uWENBTnvxHn77zHf4YfidLl+n3gjg9ao67jh4gGcsdYwt6X6wdDgYFYkX4OK5iY/YjjafL8qjoFrr87Seo1Vd33VJx0RoZs/LhVv5ellWDBAjQoOn58FEVdXxnNq5/KAiBNdv/gv2LgZydzQF2eWJdJuvQvUTmTb5hG4fc/uBJu5el07MWtDtMdCyo+8FkRAnZByPovT8p70psJZ7zdspzwz1OvjWymJzk7XgbHJX3YRtxrEJndOeIgSz3/4X//fv/+J3lU+xJMNAV5MvbHPCxCwsw3zAfHhHn4TJOWnMLhy+nfED5bIx+aRX9t+uEclyuAN9KhMIoKkKSgIJFcARD2HVXb0et030XkDfPWYqWlbnb1DZB7dxjbavy3Neqqwllt99Sy/cOJmpk4/r9v7KuhC/fl4jYJ3Y7THQ0hg9LljOhd45pPUykyMimnms6Q/8J+15ogkMvrWyunzkLP482VfeiHXi1N5P6ELx9g189cnv8aetf+RSTyMeW+Kt4AtnD/9G1KhJvCBbvUe6uriww865qRBX+jaoBpDptZHMxom+XhIRwLuBVxGWngdtFEXtstULcOYLdzG7m6/S9x8ow5XXfYKLNE5jyqTuW5LBcJxfrQ5xSJ3RY3wABaEqLtXSmejt/dhPQx9wV+inbM+s6LWrpT27J4fcZVeSeelX0Qu6n13RE9+hPVyy+qfcu+5HfN26n0J3z/3Ax+R7mJST3GyLoWhUJd7TJ+eQ4Uz8k30k+/LYIvTywZsu1hVV7fugGkCWJ7nfpVfv/RtPnCj1ab33B7tyJ6AXjet0uyIEX93yty67HAD+UlmKO7375BJpnMGkku4XSBim4A/P1LEjMht6GTC0G2HOCTeyOGMBmtrLa6UI1jQ9yIOWv9PkSy4tODOLyLvgy6R//lq0rL7NILI1N3HGC3fx++du4gexd5mV3vWH30Vzi/p0/aFmVCVei6Zyweye+8lGOgX42tgxmAOwVU+yHN5KIpHEZiV0JSOJ1VIAPs2e0HFbzfd7PUZRFLwnd57hAJCz/2Ou1ruumBaMm6wO1WCzd/2npygKsaZZlEzoumxkqwdfruLNmmMQSu+vwTHBMi5xTyXD0ft7vzZexn3BX7Ah/UPMBAffWuN2500k/+Ib8J73BdS0vnXrqabBsW8+xq3//g6/qX6W0zNMWqcDZzitLJk6MqaGjqrEC3Dh7AK0YVSpvj/pisINxcVEB2Crnr6oaej7oBqAz5Xc2zeRKWUAW4K9dzdAS0uvuz7OM1+4q9tW255AiK2uMN2NfymKghGcy8Tx03t8/DUbq1n96WSE3vtX76xIHZcIKzPS5/R6LMA7gee51/wdlZnhhAffABRVwzN2BvmXfwfPWRei2LtfXdeb8Vtf54Yn/4d7dv6Fi7wBrpybj2WE1NgeGc8iCblpdhaVZKU6jEFn0xS+XjiG0ABt1ZMshytIZbs9zfrCk1gDto3XTOwDx8SgNi3B+cEnLevydtU0+NqWv3W7XP21Q3WE8nr681MQoWMZN67nIt+bttfxt835GNbe39MWEef05hqWZRyPtYe5wa3CIsAjTXfwrPtFou7kunVUTcc7aV5LIfZTz06oEHt3Mst2cvnzv+SyGSNnBeqoS7wAl80fk+oQBpVTV/lq7hiClUMj6QIYWucFB8ly25MbGPRGE5sBAfCx+W5Cx9k92dhndj0glrP/I1bpB7s996ED5TgKuk9oQqio4eMYW1zSYwx7ygP84dU0IrbE3teTguVc6hxHniuxzSF3hbdwd/hn7Mg8lNTgG4BmsZE+82Tyr/5uS6GhJHaWaM95xnloXl+fzh2KRmXinVvkY35xzxPSRwqvRecrWUVd1ohNFVUzOHAUg2qtnJbkBgddRnOPhWXaez/wBqY1sVaa97gzu11UcNYLd3JMN10OAH8uK8Wd0f3jCKGix05gTFHPSbKmMcyvnhU0WiYnFnO0iQvjBvMyjqO3QToAoZg81/QAD1sfwp/k4BuAbnOSOX8JuVf/N/bZxyd3sqrhufDKpB9zKBuViRfgKwuHz1bQfZVls3BNen6fK/8PFIenilA41PuBvbCpyT8vry2xbiahmNS4E9sk0ur04pjX9eoz1TT42nv3Y+umbzJsmDwZrMbu6L4laZoqVvMkCgs6b0Xf4VrR1v3cjkkobg2Tk4IVXOA7FqfFl9A51bGD/CX4C95K/xjTlnz3gdXhIfvk88m56ttYJ/c8gNjKuegs9PyRMZuh1ahNvHOKfBw/duS2egucNi5PyyNQNzQG0tqrazy6QbVWNiWx5cLt+SyJj7Z/ZG5O+FjvnEXd1jLI3fchq6yl3Z57IBhmi70ZtYdVXKah4eBk8ntJQELAPWtq+Cg4m0T/vMeEDnGZJYexnsQXQ7wdeI4/8XsOZUaSGnxrZUvLInfJZWR94QYsYyZ0f6Cq4ll5bdLXH+pGbeIF+MrCHn7hw9gEt4OLbNkEG4Ze0rW7gpRXdN/vmQxNJN9q9iY4pQzgw8D6hFt1FpsL14lndHv/WWv/wMweuhw2VNfT2Es5EcPQcKmLyM3tfVrYY69X8WrlDEQvVdlaOY1mPhcOsDBjAaqSWNWvkBngH02/Z03aS8SSHHxr5UjPJ/dz15Bx8ZfQcvI733/yGViKR96301GdeGcVejlhXEaqw+hXU71uztUyaPanfp5uV0x9X79dSzUCSZ/jTeYtrwiq3YkPyHmnL+i2hKJqGnzt/b932+UA8NjBCuw9DLYBGHEdj+VUsrN7L/r08pYantg+AVNPrGC4osCxwTI+75mJ1574fNkdoXe5K/wzdmZW9VjTuPvHVXHljCP/81/Dd/6VqL7Df5Oqhvey4beRZSJGdeKFkdXXOyfdwxmml1AwtSvSuqOqZr8MqgE4bFq3O/P2xGsm94H0QfydhI/VLHbcpyzt9v68ve9zpa3nKXT3lh4kLavn5BuPWfDZTycrM7vXmD7Y3cCf384hbk28Ol9euIZLFTeTfT0XY29PKCbPNt3PI7Z/EPD1rQSroqqkFU0l/7Jv4Tn7YtznfH5EtnZBJl5mFnhZOGH4zw88McvHiRE3kdDQTLoADu8hQqHEBqx6k+1LchLvYb5Yco+/tfktTHvig0ieSXNRXN2XLFz6/B+Y7uv+q3zMFDzaWInD1XPyikctZDjPICOj9/fuwaogv33RTsg6rtdjW1nNKEtDdZyRsQA9we4KgEOx/fw5+HM2pn/Sp8E3aCnE7i05lvSrv9Gn84eDUZ94Ab68cHyCa5qGpsW5GcwOOga9gHmy6v07++1amUkuF27ligWSSiQAVS5/wseqmhXPqed0e79mxvn6hw9i7WFbnIpQlLf0AFovW+fEolay087E5+t9kLgxGOW2Z2LU6clVE5seLGOlaxJZzuTmvr8ZeIa/cCdVmdEkyhi1M3YMOPq+6m2ok4kXmJ7n4ZwZeakOo0+W5mcxpcFGPJbaKmO9sTtDlJXt77frpbv7tu2LooDX3vtX9Pbei72V1PHusTNQ07tviebveY8rbT3vL7e5tpGa7N5/p9GwjVzfEjye3mdrxOImt69uZJ+ReBcCQEa0gUsMjVnpydXgDZqNPNz0O9Z6XyfmSmLwzWKBkp7LXw53MvEe9s1TS3DbhtceThcU5lBcq2MYQzvpAgjL0a9Ua8/r7PtbN5kpZQDbQ5sS3q0BDn9VPvW8Ho9Z9vzvmdZDlwPAv0orsRT23rKPhuwUZJxFWlpiuzL8ZW017zb2vJ/bkTTinNpcxbnpx2HXk9v9YVvzO9wd/Tm7M2sQidRamDSxJfmOYDLxHpbhsnL9ycOnI3/lmHyyq1TE0O5dAEBRTQ6U9s+gWivPUXwL9Sa4eq29Q66GpI53FZSg53Vf/1kz43z944d77HIA+POBUtzZvSf9SMhBUdZSXK7EatX++80q1h6YhkjytZjQXMGl9kIK03pexnwkE4P/NP2VR+2PEexp8M3thuKRv6RfJt52LppTxKTsoV9k+ariAtIqRMp2jUiW01NNc3Pi07IS4UpwW/euJDWl7LAt0Q1JHa8oKp5Tui6W3qpg97tcbj/U4zFxIXi4vhJnAl0r4WYnY3OX4nD0XgAHYN1Htfzjw2LMBFettUqLBbggEuH4jONQknwtK6N7+VPw52zO2NH1kuzpU/tcz2E4GfnPMAmaqnDzmZOH9EDbdcWFWMuHQTO3nYbAjn6/pkPv+zxln5l80t4V3oLhSG6RgDO7GMvYnluG56z5HVN76XKoDkd5Q2lC13t/Z4aCLsYXLsNuT2zWx7b9Tdy9LqPX/dyOpCqCBcEKVvjm4rYmPxd+nf9p/qLeRXVG/LPBt/w8yBr+M4wSIRPvEWYX+YbsQNtXi8dAineNSJbNEaKsvP8G1dqu24c6Da088b5Naatw1iV1fE/F0ltpZpwbPv4Hll66HN6vb6I8M7HffcjvZuKYZdhsic3eqKwL8eu1GsFe9nPrSmHoEJfqmYz39lw7uCtBo4GH/LfzgucNoh47TOvb/m3DkUy8XRhqA22qAjcUjyFWPvSWAPdGse5HDECfiIW+F9lJi/rR1OQHbzZH1yV9jsOXh21qz7MICnZv5nJ7Va/XWl1WhVqY2PuyuclDSfFSrNbEWunBUJzbVoeoUpNPoA4jxHnhJhZlLOjT6/pJ6G22jW0Ae3LT/IYzmXi7MJQG2nRV4RuFxYSHY9JVTA6UfTIg19bMvi/EUBTw2JKbUgawN/wR8T7s2eddsKTXY859/ndM8fWetP5yoBR3bmIxNDf5mDRuKZYEZwgYpuCOZ+rZGe19P7euzA6WcbF7Oun2zjUXepLvncGsouVJP95wJhNvNy6eW8S8Mb6UxuDQVW7IH1oFzJPh9NUQDCZfTyERfanT0F6yU8palTtqkj7H5s7APueEHo/RjBg3bP0Heg8VygBMAQ9UV+D0JNjybUxnyviz0PXEv8H9/aUqNtTOggSL5bSXHallJXamJbjFkKZaOWvGTSijbDsumXi7oSoKt54zPWVdDh5d4/rsIgKHhmfSBWgM9t9KtfZ8bguIo+vr9umJjfwfaXPk9b493rGLex2tL9y1icud1b1eqz4a4yWjAYs1sT/fYGMmUyeehZZEAZvn3qli9adTEJor4XNaWUSMM5trOCvjeKy9TFc7aeLVZLj6tjX8cCYTbw/yPHa+e2ZiFf37U4bVwrWZBfirh2/StTnClJb276KJVn2t09Cel74Vctkf2ZbcKqzDLI40nMef2utx5635LZO9vXcNfNIYYK838e6nYEMW00qWoCYxVWvj9joeeK8Qw9q3mQZTguWsdEwg1zWuy/sLfDOZN/biPl17uJOJtxdnT89j6bTEKzsdrXyHlau8eQRqh1+fbnuK7cCADKpB8tu6d8XbhyllrcoSGAjr8jGPOanXTR81I8YN2x7rtcsB4PmKakSCg20AwYYcpk8+I6mv9btL/fzhNQ/RBPdzO5Iv1sjn4ybHZsynfb+xRbOzdMbNKN1ttTzCjc5nnaSbz5xMbtrAj7iOddm5xJFDoH54J10wKS3fOmBX9zn71lptz2v0fVbEpsirfTpPtzpxn9z7QFvRzre5zJlYX/Jf95fiyku8BR6sz2fGlOSSb01DmF89J/BbJiV8TnsaJguDlSxPn4fD0lIbePGUG/A5k5s7PJLIxJuANLuFW8+ZPqALK6Z4XCy3ZBFsGpoFzJPh8tXi9yde0StZXufR/ybSIv6Ed1o4UmlkV593XPBMOQ7F3nv/8ufW/paSBLocBPC3yjJcCcyIaBWsL2DGlMUJHw8Qihj8anWAchLbJ60rY5srucyaz8LizzOzsOf5zSOdTLwJml+czhcGaFv4WelpLBE+QoHhn3QB/KFdA3p9t+3ouzBUReBJcOPLrhzspbpYdzTdStqpvScdPRblG9sfQ0ugy6EpbvBctA6rLfE/52D9GGZMXZTw8dAyo+Lu52r5uLlv080AXLqT4yZe06dzRxKZeJPw9UUTmZqbXGWm3hyX6eXkWBrhIVzAPBlWe5iDpXsG9DGOpk5De16rr8/nvhN+uc/npk2Yherp/bHH7Hiby1y1CV1zV1OQnWmR7naZ71Jz/ThmTD058RMOe/S1Kl47NBOhJtnqV3S0aV9H0Udund1EycSbBIum8qsLjiHD2T8l607JSWd+s4toeHjVXuiJZj+IaQ7s87Hr/dMH7tOTnyrVqjK6l2gfuxtUVcezqPti6e0tf/72hLocAF6qrCWan1z/d3P9BKZN6XmOcZeP9W4NT+4oQSRRIlKd+AWUtKGxMCnVZOJNUp7Hzi/PP6bXtfW9WZKXyfQmO7HYyEm6ICitGJiVau3ZlOT3WuuKl6Obo33AVt7nc93F09Cyet9QUo9F+fqOJxLqcgB44EAZzvzkPhDCDZOZNvn4pM4BeG9XPX/ZmIth7f15KLknoxZ0vwvzaCMTbx/MKfLx32f0fX7v5wqyGV9nxYgPk7qOCXL5amlqahzwx9GPok5De76jXITxTuilvm1rw+Gykb0US281dvubrHQnXqDnLxWluNOT+1YWbpzKlEnzkjoHYH9lkNtfshO2ju3+IPc41Emrkr72SCYTbx+tmF3IxXO7L3TdnYuL8sir1jDNkZV0AQLh3YPyOJrRP7V9vfGjS+BVsQNE0/re7eTKnYBe2EPCauf8Nb9lQoLzl5vjJk+FqrHZk+t2iDbNYPKkuUmdA9AYiHHbM3Hqu9rPzeJFm3EjSrL9wSOcTLxH4TunT2J+sS/h468oLsBbybApYJ4Miy3KgYMDn3g1VUEx+men4rSoHzWJ7W+6st/a83btPVEUBe8pifX1WmJhvrHrXwl3OewLhPnI2Uyy6xOijTOZNPGY5E4ConGT36xuZL/R7lzFgjb9Gyi25Ov1jnQy8R4FXVX5+fJjKPD2voT12rGF2IdZAfNk6I4DAz6oBpDltUGfv+B3pGGSZju6wttvN794VNE4M4uwTkysDu3Ybeu5xF2f8LXfqKqnOTe5sQhFUYgH5jBxwoykzmv157U1bGlq2c9NnXIdirdviy5GOpl4j5LPYeE3K2bhtHTfcvrK2DGoZSNjulhXhBCUVQ78oBpAlq9/VxB6rb1vjd6T2ngZkaNcwuw9aVnCx16w5nbGJ/F4Dx+swFGQ7Nd8BTM4l/Hj+laY/Mn1VWx3rkLNSX62xGghE28/KMl2c9sFnWc6KMDXi8dglA33JcA9c/vqaGxsGJTHykjr32pxvj5U3zrSXsvR7bBh92Rjm5HY1umWWJhvfPokyUyq+XNZKe7MZD8cVJTwfMaNTb7FumDBAmbO670g0GgmE28/WTAugx+fO4PWLjhdUfjGmGIiw7CAebIC0cEZVIOj29a9y+v1cdlwe+80v4g4ynqyvuPOINHVD+O2ruPitMRnj4QNkyf8VdiTrHEhhIoaXUDxmMS3BJo2bRrnn39+Uo8zGsnE24/OnJLDzUumYNdUbigYQ3PF8C3rmCiLLcrBQRhUa+V19G/FDG8/jHTWxw8RPsqWuNXlwzFvYcLHr3j+dsYl0eVQ2hzhXWsQNcHBuVbCVLEYJ1BUOK7XYydMmMDll1+eVOnJ0Uq+Qv3swtmF/Pa0GcO6gHkydMdBDGPw+q9dtv4dwDvaKWWt9uhHX3vYO+cUSLBYuSUa4ht7nk6qy+GtmgYac5P/oDENDZs4iYL87muVFBUVsWrVqqR2uhjNZOIdAPPnZnPsgr4XYBkuhBCUH9o2qI/ptPRvISFvNIDSD38GbzevPeruBovNjevE0xM+fvzHr/F5T1NSj/HYwUpsBckPBpqGjlNdRF5e57nr+fn5XHvttQnvaizJxDtgjjsph1nzRvb8RbevgYaG5LY8P1p2rX/7zDXiuPthnmmTUUvIc/R1gj3TT0CxJp7APv/8bxmb5KyKP5WWkpaV/IIGI67h1heRk/PZZpa5ubl88YtfxOns21ZKo5VMvAPoxEV5zDnu6OaJDmXBQRxUa2Ul3O/X9Fr75wPyU+3To76GbrHjPmVpwsdbIkG+sXc1yXTdxkzBPxorcbiS/6AwYha81sVkZeWQl5fHl7/8Zdxud9LXGe1k4h1gC07O5fiFvRcRGW50a4wDBwdmM8seH1f0z6q19nx6/ySOd5pfQPTDwJJn0lwUV+JVvyZ89Cqf9yRXeP5QKMoGPYCmJ989Eo9ZKCk+hy996Uu4XEc/HW80kol3EMw9PouFi/NSHUa/srpKB3VQrZXST3Ua2vOp/TMg5DfqafYc/Z+UqlnxJFAsvb2Lnr+d4iTrRrxb20hVZvKDlbn5Ds4+f5JMukdBJt5BMnNuBqeeVZBUoeqhrKJqcFaqteewaShm/5SEbM/bjwWLdqv9s/uGe+xMVF/i3VSWSJBv7P9PUl0OAP8uO4RemHjCLip2ce7nx2KzHX1/9mgmE+8gmjrDxxlnF6IeZS3fVHN666mrS2xnhP6U3c/LhVt5jf7rN34n2D/dDaqq4Vl8blLnTPzwFS70BJJ+rL8cKCUtp/fBtpKpXpZdUIzFItPG0ZKv4CCbOMXLuRcWJ12ybygJx49+EKkvsjwDU1rQG/XT1z3EjhQ0Gwl6+uda7vxJ6LnJlR696IXfMCbJLoe4EDxYW4Gzh0Ugs+dlcvqyArRh3mgYKmTiTYGCIhcrLh2HN3341SjVLXH2Hxj8QTWA9H6u09BKF3HcR1ksp72d6o5+uY6iqglvEdTKGgpyw4Hnku5yqInEeI1G9CMG2xQFTj49jxMW5Sa1JbzUM5l4U8SbbuOCS8eTXzS85j9a3aXE46nZDbm/lwt3uHY/1ox9J/ACQuufPy1n9lgsYxOvlQAw6YMXucCb/CDkh/V+yjI++91arCpLl49hxuyRPR89FWTiTSG7XePcC8cyZYYv1aEkrLJ6cFeqtZc2gJvT9teUMoCwCODvp82oFUXBuzC5GQ4Al6y9naI+7I7xn/JqlEIdj9fCikvHM3ZC/+6qLbWQiTfFNE1h8VkFnHBKDkO9tojT00BtbXXKHt9lHbhC695+3ppmh9p/H1CO9HxsU5LbFcIa8vONg2v61HP9sdLMii+MJz1TLgEeKEP8T330mD0/i/M+Pxana+gWGYkYe1L6+A594Lo4+qNKWXsbAy8gEix4kwjvCWclfc6k91/gAl9yC05WLRjL7y6ajd0+dN+HI4FMvENIfpGLi66YQGHx0JuYrlli7D/YP4NGfWVT+38ObytvvH+ryUVFiCZP/7XQbe4M7HMWJH3eyrW/odDde5eDx67zqwuO4YZFE1HlINqAk4l3iHE4dc69sJh5J2QNqcUWNnc5sVhqi7pbBqBOQytfNLkqX4nYzsf9ej3vsYtJtj/KGvLzjbLne+xymJnv4aGrjmPxpOyjik9KnEy8Q5CiKMw/MYdzVhTjSHLXgIFyqCZ1g2qtNLP/lwu3sogYTquvX6+5KfASQu+/35/V4cF5/KKkz5v83lqWp3euO6wqLV0Lf77sWPK9AzhyKXUiE+8QVjTWzcVXTmTcxNSOLDvSmqipqUppDIoC6gDUaWjPZ+3fSnIxIjSk9W89C+8xJ4Oe/GyFlWtvp6Bdl0OO28adF8/hhkUT0ftp6puUOPmKD3EOp87S5WNYfFYBVltqfl1RkZqVau353FYQA1uUx2vp/w+4bXzYr9fTrQ7cC5ckfZ69uZEbyl9AAc6dkcej1xzPcWPl/NxUkYl3mJgyw8fFV05kzLjBHXjT9Dj7D2wf1MfsykDVaWjPp/T/SsJ3A68gLP07Q8Az9TgUe/ILb2bs38zfPzeOW8+ZTpr96Lakl46OTLzDiDvNwjkrxnLqknxsg9T6tadVpHxQDSCjD4sBkuUdgGvGiVKf1r8zJjTdStqiZUmd4zrrfPLufpypUyf0ayxS38jEOwxNnZnOyqtLBmXFW1Vt6gfVAHx92C0hWV5jYKarbTU/6Pdrpk2Yjerx9XqcXjSO7J/9kYwb/w/VLVehDRUy8Q5TDqfO4rMKuODScWTl2AfmMdL8VFVXDsi1k+UZwDoNrbzR5EsqJmJLsP+7G1RN77GAjmKz4131dfLufAT77OP69bGloycT7zCXm+/kwi+MZ+Fpef0++BYTqV2p1l6avX9XlnXFZkZwWDz9fl0Tg9q0/m9Nu4unoWV13lbKuegs8v70LzyXXINikX25Q5FMvCOAoijMnJPBpVeXMH12er/UfNA0g/0Hh0Y3A4DTOjjbDHltA7M56VZzS79fU1FUPKee1/azdfIMcn75ZzJv/hl6Vm6/P57Uf2Ti7YZhGMydO5fzzmt5Y995552UlJSgKAo1NTVdnvP+++9z4oknMmPGDGbNmsVjjz3W5XGRSISVK1dSUlLCggUL2LdvX7/E7HDqnHJ6PitXlVAy9ehabnZPBdFo/w4KHQ1HP2/r3h2f3v8tXoD3Aq9jWvu/9enKnYD92BPJ/J9fkPvbB7DNnNvvjyH1P5l4u/H73/+eadOmtf28cOFCXnrpJcaOHdvtOU6nk7///e9s3bqV559/nm9961s0NDR0Ou6+++4jPT2d3bt38+1vf5ubb765X2P3+KyccXYRF10xgeLxfSt3WF2f+ilk7VmVgavT0J5XHZhpa0IxqXV3Xj12VOx2lFkzyf7R73Gecmb/XlsaUDLxdqG0tJRnn32W6667ru22uXPnMm7cuB7Pmzx5MpMmTQKgoKCAnJwcqqs7l1F8+umnWbVqFQAXXXQRL7/8MqKfq2MBZGbbOfuCYpZfMo6CJAquO9ICHDpU3u/xHA0L/Zy0uuEbwGt/aG7qnwvZbDB9Giw+BcYUJV2/QUo9+Rvrwre+9S1uu+021KN4Q2/cuJFoNMrEiZ13DygrK2PMmDEA6LqO1+ultnbgNo/ML3TyuYvHseKy8YwvSeu1+E6coTOo1mqglwu38hgD173yYWA9pu0ouhscDpg+tSXhjiuWCXcYk0U3j/DMM8+Qk5PDvHnzeO211/p0jYqKCq688koeeOCBo0re/S0nz8FZnxtDY32ED96tZecnjRhGx5a2qg6tQTUAXVNQjOTqyvaVLzYwU8oAUATVriC5kSRXyPl8MH4s5OUypErWSX02dLLCEPHmm2+yevVqxo0bx6WXXsorr7zCFVdckfD5TU1NnHvuufz0pz/lhBNO6PKYwsJCDh48CEA8HqexsZHMzIEZTe+KN93GojML+MIXJzH3+Czsjs8WJzi8lUQig9Ofmqgsrx0Y+OlkAHYjjK0ftwE60ofGO4kdqCiQnwcnLoCTFrT8WybdEUMm3iP8/Oc/p7S0lH379vHoo49y+umn89BDDyV0bjQaZcWKFVx11VVcdNFF3R63fPlyHnjgAQCeeOIJTj/99JTs4Op06Ry/MIcrvjSZM84uJL/QSW3D0BpUA8j0Du5cVJ8ta8Cu/XHzW5g91Umw22DSRDjtVJg7G9J9AxaLlDoy8SbojjvuoKioiNLSUmbNmtU28LZ58+a2fz/++OO88cYb3H///cyZM4c5c+bw/vvvA3DLLbewevVqAL74xS9SW1tLSUkJt99+O7/4xS9S8pxaaZpCyVQvyy8Zxxcuv5RTTz2VtLShs7x0MOo0tOcdgEUU7VW5/B1vUBTIzmpJtIsXwaSSlgQsjViKGIjhdGnYM02THTt28O6777Jjx46UFsr53InZLEjv3/KKPXnHlc/Gun6agdCFac4FLGs8taU1W5Df0o1g7f/KaNLQJQfXpC6pqsq0adOYNm0asViMnTt3snXrVrZv305z8+AMdLXyDEwpim55+7Q3byIU8rxTycqZjZh7Cooz+dKO0sggE6/UK4vFwowZM5gxYwaGYbBv3z62bt3K1q1baWxsHPDHd9sG90uZ1+y/1r2iqBT6jmFSziJKchbitst9zSTZ1SAdpdLSUj755BM+/fRTSktLMYz+r6nw7c/5yDQGb4pbSHfwl+CuPp+f7hzDmIw5FKXPoThjLg7rQFT6lYYz2eKVjkpRURFFRUVAy6yO/fv3s2fPHvbu3UtZWVm/9A3b1CgMTo0cABzxEFbdRTSeyKINhQxXMQW+GYxJn0NR+mzc9oGbFSGNDDLxSv3GarUyadKktmXThmFQUVHB/v37OXDgABUVFdTW1ibdKh6sOg3t+WyZVHWReG16GnneqeR7p1Pgm06eZyo2y8DN+5VGJtnVIA0qwzCoq6ujqqqKqqoqqqur2/7f3cKNH58fQY3VDWKUCm94iqk3QmS4ikl3jSHDNZYM5xjZmpX6hUy80pDR2NhITU0NTU1N+P3+tv9fNKMcJVYPMT/EgiD62n2hgiUNLGkoVs/hf3tQDv8fewaKPQfsWSiqLCAuDRyZeKVhRwgTzBiY8Zb/i9hnPyNAUUHRWv5TLaDaQLOgqHKurDQ0yMQrSZI0yOSSYUmSpEEmE68kSdIgk4lXkiRpkMnEOwKNGzeOY445hjlz5jB//nwAVq5c2VYxbdy4ccyZM6fLc6+99lpycnKYOXNmt9cXQvDNb36TkpISZs2axZYt/b+DriSNZHIBxQj16quvkpX12ZzT9jse/9d//Rdeb9fLWK+++mpuuOEGrrrqqm6vvWbNGnbt2sWuXbt45513+OpXv8o77yRY4FuSJNniHW2EEDz++ONcdtllXd6/aNEiMjIyerzG008/zVVXXYWiKJxwwgk0NDRQUVExEOFK0ogkE+8IpCgKZ511FvPmzeNPf/pTh/vWrVtHbm5u27Levmi/WSe01GsoKyvr8/UkabSRiXcEWr9+PVu2bGHNmjXcddddvPHGG233PfLII922dkeyo+n3fv7555kyZQolJSXd7hYSiURYuXIlJSUlLFiwgH379g3QM5FGAtnHOwIVFhYCkJOTw4oVK9i4cSOLFi0iHo/z5JNP8u677x719Vs364SW0pCtjzmU9aXf2zAMvv71r/Piiy9SVFTEcccdx/Lly5k+fXqH4+677z7S09PZvXs3jz76KDfffHOH60tSe7LFO8IEg0H8fn/bv1944YW2GQovvfQSU6dObSvj2FfLly/n73//O0II3n77bbxeL/n5+Ucde6r01O+9ceNGSkpKmDBhAlarlUsvvZSnn36603FPP/00q1atAuCiiy7i5ZdfRi4KlbojE+8Ic+jQIU4++WRmz57N8ccfz7nnnsuyZcsAePTRRzsll/Lycs4555y2ny+77DJOPPFEduzYQVFREffddx8A99xzD/fccw8A55xzDhMmTKCkpIQvfelL3H333YP07Pqur/3eifZntz9O13W8Xi+1tbX9/CykkUJ2NYwwEyZM4IMPPujyvvvvv7/TbQUFBTz33HNtPz/yyCNdnnv99de3/VtRFO66666jC3SQrV+/nsLCQqqqqliyZAlTp05l0aJFwOjt95ZSR7Z4pVGhq35voK3fe+XKld2el0h/dvvj4vE4jY2NZGZm9vfTkEYImXilEe9o+r2PO+44du3axd69e4lGozz66KMsX76803HLly/ngQceAOCJJ57g9NNPR1EGardiabiTiVca8Y6m31vXde68806WLl3KtGnTuOSSS5gxYwYAt9xyC6tXrwbgi1/8IrW1tZSUlHD77bd3O+1MkkDW45UkSRp0ssUrSZI0yGTilSRJGmQy8UqSJA0ymXglSZIGmUy8kiRJg0wmXkmSpEEmE68kSdIgk4lXkiRpkMnEK0mSNMhk4pUkSRpkMvFKkiQNMpl4JUmSBplMvJIkSYNMJl5JkqRBJhOvJEnSIJOJV5IkaZDJxCtJkjTIZOKVJEkaZDLxSpIkDTKZeCVJkgaZTLySJEmDTCZeSZKkQfb/AWFseXPIEsz7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = sum(v[1] for v in setuptools_versions.values)\n",
    "labels = [v[0] if v[1] / total > 0.03 else '' for v in setuptools_versions.values]\n",
    "\n",
    "plt.pie(setuptools_versions.c, labels=labels)\n",
    "plt.title('setuptools version usage')\n",
    "\n",
    "plt.savefig('setuptools-vers.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "from glob import glob\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "ONE_TB = 1e12\n",
    "months = ('202007', '202008', '202009', '202010', '202011', '202012', '202101', '202102', '202103', '202104', '202105', '202106', '202107', '202108', '202109')\n",
    "\n",
    "bandwidth = {}\n",
    "bandwidth_totals = defaultdict(int)\n",
    "for month in months:\n",
    "    bandwidth[month] = defaultdict(int)\n",
    "    files = glob('/home/ben/piwheels-logs/' + month + '/*-sizes.csv')\n",
    "    for file in files:\n",
    "        with open(file) as f:\n",
    "            r = csv.reader(f)\n",
    "            for log_type, count in r:\n",
    "                bandwidth[month][log_type] += int(count)\n",
    "                bandwidth_totals[month] += int(count) / ONE_TB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202007 3.92 TB\n",
      "202008 6.38 TB\n",
      "202009 6.27 TB\n",
      "202010 6.58 TB\n",
      "202011 4.43 TB\n",
      "202012 3.32 TB\n",
      "202101 3.79 TB\n",
      "202102 5.87 TB\n",
      "202103 7.07 TB\n",
      "202104 5.35 TB\n",
      "202105 4.82 TB\n",
      "202106 4.72 TB\n",
      "202107 4.02 TB\n",
      "202108 4.29 TB\n",
      "202109 3.91 TB\n"
     ]
    }
   ],
   "source": [
    "for month, data in bandwidth_totals.items():\n",
    "    print(month, f'{data:.2f} TB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pip downloads': 9366969296154,\n",
       " 'Misc': 2446768732550,\n",
       " 'Searches': 387471006722,\n",
       " '': 16139500833}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1, m2, m3 = months[-3:]\n",
    "\n",
    "bandwidth_in_period = {\n",
    "    'pip downloads': bandwidth[m1]['LOGDOWNLOAD'] + bandwidth[m2]['LOGDOWNLOAD'] + bandwidth[m3]['LOGDOWNLOAD'],\n",
    "    'Misc': bandwidth[m1]['OTHER'] + bandwidth[m2]['OTHER'] + bandwidth[m3]['OTHER'],\n",
    "    'Searches': bandwidth[m1]['LOGSEARCH'] + bandwidth[m2]['LOGSEARCH'] + bandwidth[m3]['LOGSEARCH'],\n",
    "    '': (\n",
    "        bandwidth[m1]['LOGPROJECT'] + bandwidth[m2]['LOGPROJECT'] + bandwidth[m3]['LOGPROJECT'] +\n",
    "        bandwidth[m1]['LOGJSON'] + bandwidth[m2]['LOGJSON'] + bandwidth[m3]['LOGJSON'] +\n",
    "        bandwidth[m1]['LOGPAGE'] + bandwidth[m2]['LOGPAGE'] + bandwidth[m3]['LOGPAGE']\n",
    "    ),\n",
    "}\n",
    "\n",
    "bandwidth_in_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(bandwidth_in_period.values(), labels=bandwidth_in_period.keys())\n",
    "plt.title('Bandwidth breakdown')\n",
    "\n",
    "plt.savefig('bandwidth.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pip downloads 76.67%\n",
      "Misc 20.03%\n",
      "Searches 3.17%\n",
      " 0.13%\n"
     ]
    }
   ],
   "source": [
    "for k, v in bandwidth_in_period.items():\n",
    "    pc = 100 * (v / sum(bandwidth_in_period.values()))\n",
    "    print(k, f'{pc:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAAAwcPs7QH3Fx8c10pIatpyTJ/MaKQcAAAgkjCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAgdubmYqLi/Xyyy/rxIkTcrlcevjhh3XjjTc6nQ0AAMBvvCpJWVlZSkxM1JQpU1RRUaELFy44nQsAAMCvPJ5uKykp0aFDhzR8+HBJktvtVmRkpOPBAAAA/MllWZZ1pRmOHTumJUuWqF27dvriiy+UkJCgCRMmKDw8vMZ82dnZys7OliSlp6ervLzcudSSwsJCHV2+ty5ccHY7q7ndblVUVPhkXQ1FVmf4Kis/W4GLrM4gqzOaUtbQUPP7nseS9Nlnn+l3v/udnn32WXXp0kVZWVlq1qyZxo4de8UV5uXl1T+tF+Lj4xxdvrdOnnR2O6tFR0ersLDQJ+tqKLI6w1dZ+dkKXGR1Blmd0ZSyxsWZ3/c8nm5r3bq1WrdurS5dukiS+vXrp6NHjzZuOgAAgADjsSRdc801at26tT0ylJubq3bt2jkeDAAAwJ+8+nTbxIkTtWDBAlVUVCgmJkaPPPKI07kAAAD8yquS1KFDB6WnpzudBQAAIGB4VZIANEzjXQzdsOX46mJoALga8LUkAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMHD7OwAA1Fd8fFwjLalhyzl5Mq+RcgAIJIwkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGXn132+TJkxUeHq6goCAFBwcrPT3d6VwAAAB+5fUX3Kalpally5ZOZgEAAAgYnG4DAAAwcFmWZXmaafLkyWrevLkk6bbbblNKSspl82RnZys7O1uSlJ6ervLy8kaOWlNYWKijy/fWhQvObmc1t9utiooKn6yroch6uaZ0vJK17ngfuBxZnUFWZ4SGmt9LvCpJZ86cUVRUlM6ePas//OEPeuCBB9StW7crviYvL69+Sb0UHx/n6PK9dfKks9tZLTo6WoWFhT5ZV0OR9XJN6Xgla93xPnA5sjqDrM6IizO/l3h1ui0qKkqS1KpVK91666369NNPGy8ZAABAAPJYksrKylRaWmr//759+9S+fXvHgwEAAPiTx0+3nT17VnPnzpUkVVZWauDAgUpMTHQ6FwAAgF95LElt27ZVZmamL7IAAAAEDG4BAAAAYEBJAgAAMPD6jtsAgPprvNsVNGw5vrpdAXA1YCQJAADAgJIEAABgQEkCAAAw4JokAEANXD8FXMJIEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAw4AtufYAviwQAoOlhJAkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAz4dBsAAD4QFx/fOMtp4OvzTp5slBz/ChhJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAw8LokVVVVafr06UpPT3cyDwAAQEDwuiStW7dO8Y10S3UAAIBA59V3t50+fVq7d+/WnXfeqf/7v/9zOhMAAF7h+9DgJK9K0quvvqrx48ertLS01nmys7OVnZ0tSUpPT1d0dHTjJAxwTWk7fZXV7XY3mf3SlLI2hqa0rWR1BlmdQdbLXQ3vrx5L0q5du9SqVSslJCTowIEDtc6XkpKilJQU+3FhYWHjJKxVQ3t/4/BuO5tS1oaLjo722boayndZm9IxQNa6IqszvMkaGEmvvqyNoSn9LoiLM//reCxJH3/8sXbu3KmPPvpI5eXlKi0t1YIFC/Too482ekgAAIBA4bEkjRs3TuPGjZMkHThwQGvXrqUgAQCAq55X1yQBAIB/HVwQf0mdSlL37t3VvXt3p7IAAAAEDO64DQAAYEBJAgAAMKAkAQAAGHDhNpqs+PjGuutIw5Zz8mReI+UAAAQSRpIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAgdvfARBY4uPjGmlJDVvOyZN5jZQDAID6YSQJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGDg8WtJysvLlZaWpoqKClVWVqpfv35KTU31RTYAAAC/8ViSQkJClJaWpvDwcFVUVOjpp59WYmKibrzxRl/kAwAA8AuPp9tcLpfCw8MlSZWVlaqsrJTL5XI8GAAAgD+5LMuyPM1UVVWlJ554QqdOndLIkSM1fvz4y+bJzs5Wdna2JCk9PV3l5eWNn/Y7wsJCHV2+ty5c8LydZK07sjqDrM4gqzO8yRoaFuaDJJ6VX7jgcR6y1p03WRtDaKj5mPd4uk2SgoKClJmZqeLiYs2dO1fHjx9X+/bta8yTkpKilJQU+3FhYWED4nojzuHle8e77SRrXZHVGWR1Blmd4U3WwEhKVqc43yUuiYszb3GdPt0WGRmp7t27a8+ePY2RCQAAIGB5LElFRUUqLi6WdOmTbvv27VN8fLzjwQAAAPzJ4+m2b775RosWLVJVVZUsy1JycrL69Onji2wAAAB+47Ek3XDDDZozZ44vsgAAAAQM7rgNAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADBwe5qhsLBQixYt0rfffiuXy6WUlBSNHj3aF9kAAAD8xmNJCg4O1r333quEhASVlpZqxowZ6tGjh9q1a+eLfAAAAH7h8XTbtddeq4SEBElSs2bNFB8frzNnzjgeDAAAwJ/qdE1SQUGBjh49qs6dOzuVBwAAICC4LMuyvJmxrKxMaWlpuvPOO5WUlHTZ89nZ2crOzpYkpaenq7y8vHGTfk9YWKijy/fWhQuet5OsdUdWZ5DVGWR1hjdZQ8PCfJDEs/ILFzzOQ9a68yZrYwgNNR/zHq9JkqSKigrNmzdPgwYNMhYkSUpJSVFKSor9uLCwsB4x6yLO4eV7x7vtJGtdkdUZZHUGWZ3hTdbASEpWpzjfJS6JizNvscfTbZZl6eWXX1Z8fLzuuOOORg8GAAAQiDyOJH388cfavHmz2rdvr2nTpkmS7rnnHvXu3dvxcAAAAP7isSTddNNNWr16tS+yAAAABAzuuA0AAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGLg9zbB48WLt3r1brVq10rx583yRCQAAwO88jiQNHTpUM2fO9EUWAACAgOGxJHXr1k3Nmzf3RRYAAICAwTVJAAAABh6vSfJWdna2srOzJUnp6emKjo5urEUHtKa0nWR1BlmdQVZnkNUZZHWGv7M2WklKSUlRSkqK/biwsLCxFl2LOIeX7x3vtpOsdUVWZ5DVGWR1hjdZAyMpWZ3ifJe4JC7OvMWcbgMAADDwOJL0wgsv6ODBgzp37pz+8z//U6mpqRo+fLgvsgEAAPiNx5L029/+1gcxAAAAAgun2wAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABgQEkCAAAwoCQBAAAYUJIAAAAMKEkAAAAGlCQAAAADShIAAIABJQkAAMCAkgQAAGBASQIAADCgJAEAABhQkgAAAAwoSQAAAAaUJAAAAANKEgAAgAElCQAAwICSBAAAYEBJAgAAMKAkAQAAGFCSAAAADChJAAAABpQkAAAAA7c3M+3Zs0dZWVmqqqrSiBEjNGbMGIdjAQAA+JfHkaSqqiotW7ZMM2fO1Pz585WTk6Mvv/zSF9kAAAD8xmNJ+vTTTxUbG6u2bdvK7Xarf//+2rFjhy+yAQAA+I3HknTmzBm1bt3afty6dWudOXPG0VAAAAD+5tU1Sd7Izs5Wdna2JCk9PV1xcXGNtWgjy3J08XXgeTvJWh9kdQZZnUFWZ3jxeyRAwnr1G4+sdeZsk/DM40hSVFSUTp8+bT8+ffq0oqKiLpsvJSVF6enpSk9Pb9yEDpoxY4a/I3iNrM4gqzPI6gyyOoOszmhKWWvjsSR16tRJ+fn5KigoUEVFhd5//33dcsstvsgGAADgNx5PtwUHB2vixIl67rnnVFVVpWHDhun666/3RTYAAAC/8eqapN69e6t3795OZ/G5lJQUf0fwGlmdQVZnkNUZZHUGWZ3RlLLWxmVZAXJ1FgAAQADha0kAAAAMrvqSdPr0ac2ZM0ePPvqofv3rXysrK0sVFRW1zv/mm2/qwoULPkwopaamauXKlfbjN954Q6tXr/ZpBm/9/Oc/17Rp0/T4449r2rRpWrt2raqqqvwdy6N7773X3xE8qt631f8VFBTUOu+sWbP02Wef+TBdTampqVqwYIH9uLKyUpMmTQroT7d++OGHSk1N1cmTJ/0dxagp7lOpafxsfZenvP7+2Qr04/S7Xn/9dT3++OOaOnWqpk2bpiNHjvg7UqNrtPskBSLLsjR37lz96Ec/0vTp01VVVaUlS5Zo1apVtf6grFu3ToMGDVJYWJjPcoaEhGj79u0aM2aMWrZs6bP11kdoaKgyMzMlSWfPntWCBQtUWlqq1NRUPydr+r67bwNdWFiYTpw4ofLycoWGhmrfvn3GW4NcSWVlpYKDgx1KeLmcnBzddNNNysnJqdPxWlVVpaAg5/+ebIx9iqavvsepr33yySfatWuXMjIyFBISoqKioisOQDRVV3VJ2r9/v0JDQzVs2DBJUlBQkO6//3796le/0t13363Vq1dr7969crlcGjFihCzL0pkzZ/T73/9eLVu2VFpamk9yBgUFKSUlRW+++abuueeeGs8VFBTopZde0rlz59SyZUs98sgjioiI0NSpU/WnP/1JQUFBKisr02OPPaaFCxfK7fbdP2mrVq304IMP6sknn9Tdd98ty7L05z//WQcPHtTFixc1cuRI3XbbbZKkf/zjH9qyZYuCgoKUmJioX/ziFz7LWa2srExz5sxRcXGxKioqNHbsWN16660qKCjQ7Nmz1bVrV33yySeKiorS9OnTFRoa6vOM3/f5559rxYoVKisrs//9r732WknS5s2b9fLLL6uqqkoPP/ywOnfu7NNsvXr10u7du9WvXz/l5ORowIABOnz4sKRLX2eUlZWlixcvKjQ0VI888oji4uK0ceNGbd++XWVlZaqqqtLvf/97n2QtKyvT4cOHlZaWpoyMDKWmpurAgQNavXq1wsPDderUKXXv3l2//OUvFRQUpHvvvVe33XabcnNzNWnSJN10000+yVmffZqWlqYHHnhAHTp0kCQ99dRTmjRpkv3YFw4cOKC1a9fa98VZtmyZOnXqpKFDh2ry5MkaMmSIdu3apYqKCj3++OOKj4/3Wba65vWn2o7T2rLu3r1bK1euVFhYmLp27aqCggKf3Zvom2++UYsWLRQSEiJJ9h/4tb1nzZo1SzfccIMOHjzot/es+riqT7edOHFCHTt2rDEtIiJC0dHRWr9+vb7++mvNmTNHc+fO1aBBgzR69GhFRUUpLS3NZwWp2siRI7V161aVlJTUmL58+XINGTJEc+fO1cCBA7V8+XJFRESoQ4cOOnjwoCRp9+7d6tmzp08LUrW2bduqqqpKZ8+e1fr16xUREaHZs2dr9uzZeu+991RQUKCPPvpIO3fu1PPPP6/MzEz95Cc/8XlO6dKI3dSpU5WRkaG0tDStXLlS1Z9byM/P16hRo/THP/5RERER2rZtm8/zlZeX26faMjMzVVFRoeXLl2vKlCnKyMjQsGHDtGrVKnv+CxcuKDMzU5MmTdJLL73k87wDBgxQTk6OysvL9cUXX6hLly72c3FxcXrmmWc0Z84cpaam6i9/+Yv93NGjRzVlyhSfFSRJ2rFjhxITExUXF6cWLVro888/l3SpeEycOFHz58/XV199pQ8//FDSpX3buXNnZWZm+qwgSfXbp8OGDdPGjRslSXl5ebp48aJPC5I3WrRooYyMDP3oRz/S2rVr/R0nYNV2nJqUl5dr6dKlmjlzpjIyMlRUVOTDpFLPnj11+vRp/eY3v9Err7yigwcPBvx7Vn1c1SNJV3LgwAGNHDnSHu5v3ry5X/NERERo8ODBWrduXY0RjCNHjmjq1KmSpMGDB+vPf/6zJKl///56//33dfPNNysnJ0cjR470S+7v2rt3r44fP24XjJKSEuXn5ys3N1dDhw61T2H6a19blqVVq1bp0KFDcrlcOnPmjM6ePStJiomJsX+xJCQk6Ouvv/Z5vu+fbjt+/LhOnDihZ599VtKl0z7Vo0iSNHDgQElSt27dVFJSouLiYkVGRvos7w033KCvv/5aOTk56tWrV43nSkpKtGjRIp06dUrSpVNr1Xr06OHzYyAnJ0ejR4+WdOlnZ+vWrerTp486d+6stm3bSpI9atOvXz8FBQWpX79+Ps0o1W+fJicna82aNRo/frw2bNjg99EQk6SkJEmXfraqiyguV9txapKXl6eYmBjFxMRIuvR+UP3VYL4QHh6ujIwMHTp0SAcOHND8+fP1s5/9LKDfs+rjqi5J7dq10/bt22tMKykpUWFhodq0aeOnVLW7/fbb9cQTT3j1JnfLLbdo1apVOn/+vD7//HPdfPPNzgc0+OqrrxQUFKRWrVrJsiw98MADSkxMrDHP3r17/ZLt+7Zu3aqioiKlp6fL7XZr8uTJKi8vlyR7yFi6dPqzerq/tWvXTs8995xX87pcLofTXO6WW27Ra6+9plmzZuncuXP29L/97W/q3r27fQH6d0eNfHm9nySdP39e+/fv1/Hjx+VyuewPGlzp3m8hISE+uQ7JpK77NCwsTD169NDOnTv1wQcf+OVC7+DgYH33bjIXL16s8Xz1KHdQUFCNwuwvnvL6Q23H6a233hpwWasFBQWpe/fu6t69u9q3b6933nkn4N+z6uqqPt32wx/+UBcuXNCmTZskXWq1K1eu1NChQ9WzZ0+9++679g/s+fPnJV1qx2VlZX7J27x5cyUnJ2v9+vX2tBtvvFHvv/++pEu/5KuH/sPDw9WpUydlZWWpT58+fnlDLyoq0tKlSzVq1Ci5XC4lJibqn//8p33xXl5ensrKytSjRw9t3LjR/tRg9b72tZKSErVq1Uput1v79+/3y2hRXcTFxamoqEiffPKJJKmiokInTpywn68+Lg4fPqyIiAhFRET4POOwYcN01113qX379jWml5SU2BcdV58K8pdt27Zp8ODBWrx4sRYtWqSXXnpJMTExOnz4sD799FMVFBSoqqpKH3zwgU9PrdWmPvt0xIgRysrKUqdOnfwyUhsdHa0vv/xSFy9eVHFxsXJzc32eoS4CMW9tx6llWcascXFxKigosD8FW/1+4Ct5eXnKz8+3Hx87dkzx8fEB/55VV1f1SJLL5dLUqVP1yiuvaM2aNbIsS7169dI999yjoKAg5efna+rUqXK73RoxYoRGjRqllJQUPffcc/a1Sb52xx136O2337YfT5w4UYsXL9Ybb7xhXwRXrX///vrjH/+oWbNm+Sxf9XUz1Z9MGjRokO644w5J0vDhw1VQUKAnnnhC0qUL+aZNm6bExEQdO3ZMM2bMkNvtVq9evTRu3DifZa6srFRISIgGDhyojIwMTZkyRZ06dfL7xaOeuN1uTZkyRVlZWSopKVFlZaVGjx5tfy1QaGiopk+frsrKSj388MN+ydi6dWv79MB3/eQnP9GiRYv0+uuv+/1u/Tk5OZddB5eUlKR3331XnTt31rJly+wLt/v27eunlP9fffZpQkKCmjVrZn9IxVeqf7aio6OVnJysKVOmKCYm5rJrQQNFIOet7TjNyckxZg0NDdWkSZP0/PPPKywsTJ06dfJp3rKyMi1fvlzFxcUKDg5WbGysHnzwQaWkpAT0e1ZdccdtXPWOHTumJUuWaPbs2f6OggDy/U8NNWXVn8qdP3++T0eVm9rPVlPL60lZWZnCw8NlWZaWLVum2NhY+4/WQDNr1izde++9Pi9zDXVVjyQB//znP/XWW29pwoQJ/o4COGLTpk3661//qvvuu8+nBamp/Ww1tbzeyM7O1qZNm1RRUaGOHTvat1xB42EkCQAAwOCqvnAbAACgvihJAAAABpQkAAAAA0oSAACAASUJAADAgJIEAABg8P8Al0/OxY5xtrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bandwidth_by_month = {\n",
    "    'Oct': bandwidth_totals['202010'],\n",
    "    'Nov': bandwidth_totals['202011'],\n",
    "    'Dec': bandwidth_totals['202012'],\n",
    "    'Jan': bandwidth_totals['202101'],\n",
    "    'Feb': bandwidth_totals['202102'],\n",
    "    'Mar': bandwidth_totals['202103'],\n",
    "    'Apr': bandwidth_totals['202104'],\n",
    "    'May': bandwidth_totals['202105'],\n",
    "    'Jun': bandwidth_totals['202106'],\n",
    "    'Jul': bandwidth_totals['202107'],\n",
    "    'Aug': bandwidth_totals['202108'],\n",
    "    'Sep': bandwidth_totals['202109'],\n",
    "}\n",
    "\n",
    "colors = ['blue']*9 + ['red']*3\n",
    "plt.bar(range(len(bandwidth_by_month)), bandwidth_by_month.values(), color=colors)\n",
    "plt.title('Bandwidth by month (TB)')\n",
    "plt.xticks(range(len(bandwidth_by_month)), list(bandwidth_by_month.keys()))\n",
    "\n",
    "plt.savefig('bandwidth-by-month.png')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}