total downloads.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "from datetime import datetime\n",
    "import intake\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.diagnostics import ProgressBar\n",
    "pbar = ProgressBar()\n",
    "pbar.register()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_count = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/6 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://anaconda-package-data/conda/hourly/2016/01/2016-01-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/02/2016-02-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/03/2016-03-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/04/2016-04-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/05/2016-05-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/06/2016-06-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/07/2016-07-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/08/2016-08-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/09/2016-09-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/10/2016-10-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/11/2016-11-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2016/12/2016-12-*.parquet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 17%|█▋        | 1/6 [00:03<00:17,  3.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://anaconda-package-data/conda/hourly/2017/01/2017-01-*.parquet\n",
      "[########################################] | 100% Completed |  4.9s\n",
      "2017 1\n",
      "s3://anaconda-package-data/conda/hourly/2017/02/2017-02-*.parquet\n",
      "[########################################] | 100% Completed |  3.8s\n",
      "2017 2\n",
      "s3://anaconda-package-data/conda/hourly/2017/03/2017-03-*.parquet\n",
      "[########################################] | 100% Completed |  3.8s\n",
      "2017 3\n",
      "s3://anaconda-package-data/conda/hourly/2017/04/2017-04-*.parquet\n",
      "[########################################] | 100% Completed |  5.1s\n",
      "2017 4\n",
      "s3://anaconda-package-data/conda/hourly/2017/05/2017-05-*.parquet\n",
      "[########################################] | 100% Completed |  5.8s\n",
      "2017 5\n",
      "s3://anaconda-package-data/conda/hourly/2017/06/2017-06-*.parquet\n",
      "[########################################] | 100% Completed |  5.0s\n",
      "2017 6\n",
      "s3://anaconda-package-data/conda/hourly/2017/07/2017-07-*.parquet\n",
      "[########################################] | 100% Completed |  4.5s\n",
      "2017 7\n",
      "s3://anaconda-package-data/conda/hourly/2017/08/2017-08-*.parquet\n",
      "[########################################] | 100% Completed |  5.7s\n",
      "2017 8\n",
      "s3://anaconda-package-data/conda/hourly/2017/09/2017-09-*.parquet\n",
      "[########################################] | 100% Completed |  5.1s\n",
      "2017 9\n",
      "s3://anaconda-package-data/conda/hourly/2017/10/2017-10-*.parquet\n",
      "[########################################] | 100% Completed |  5.7s\n",
      "2017 10\n",
      "s3://anaconda-package-data/conda/hourly/2017/11/2017-11-*.parquet\n",
      "[########################################] | 100% Completed |  7.4s\n",
      "2017 11\n",
      "s3://anaconda-package-data/conda/hourly/2017/12/2017-12-*.parquet\n",
      "[########################################] | 100% Completed |  7.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 33%|███▎      | 2/6 [02:30<05:52, 88.20s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017 12\n",
      "s3://anaconda-package-data/conda/hourly/2018/01/2018-01-*.parquet\n",
      "[########################################] | 100% Completed |  6.0s\n",
      "2018 1\n",
      "s3://anaconda-package-data/conda/hourly/2018/02/2018-02-*.parquet\n",
      "[########################################] | 100% Completed |  6.0s\n",
      "2018 2\n",
      "s3://anaconda-package-data/conda/hourly/2018/03/2018-03-*.parquet\n",
      "[########################################] | 100% Completed |  6.2s\n",
      "2018 3\n",
      "s3://anaconda-package-data/conda/hourly/2018/04/2018-04-*.parquet\n",
      "[########################################] | 100% Completed |  6.0s\n",
      "2018 4\n",
      "s3://anaconda-package-data/conda/hourly/2018/05/2018-05-*.parquet\n",
      "[########################################] | 100% Completed |  6.9s\n",
      "2018 5\n",
      "s3://anaconda-package-data/conda/hourly/2018/06/2018-06-*.parquet\n",
      "[########################################] | 100% Completed |  7.0s\n",
      "2018 6\n",
      "s3://anaconda-package-data/conda/hourly/2018/07/2018-07-*.parquet\n",
      "[########################################] | 100% Completed | 10.1s\n",
      "2018 7\n",
      "s3://anaconda-package-data/conda/hourly/2018/08/2018-08-*.parquet\n",
      "[########################################] | 100% Completed | 10.3s\n",
      "2018 8\n",
      "s3://anaconda-package-data/conda/hourly/2018/09/2018-09-*.parquet\n",
      "[########################################] | 100% Completed | 10.2s\n",
      "2018 9\n",
      "s3://anaconda-package-data/conda/hourly/2018/10/2018-10-*.parquet\n",
      "[########################################] | 100% Completed | 12.3s\n",
      "2018 10\n",
      "s3://anaconda-package-data/conda/hourly/2018/11/2018-11-*.parquet\n",
      "[########################################] | 100% Completed |  9.7s\n",
      "2018 11\n",
      "s3://anaconda-package-data/conda/hourly/2018/12/2018-12-*.parquet\n",
      "[########################################] | 100% Completed |  8.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 3/6 [05:32<06:32, 130.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018 12\n",
      "s3://anaconda-package-data/conda/hourly/2019/01/2019-01-*.parquet\n",
      "[########################################] | 100% Completed | 10.5s\n",
      "2019 1\n",
      "s3://anaconda-package-data/conda/hourly/2019/02/2019-02-*.parquet\n",
      "[########################################] | 100% Completed |  8.4s\n",
      "2019 2\n",
      "s3://anaconda-package-data/conda/hourly/2019/03/2019-03-*.parquet\n",
      "[########################################] | 100% Completed | 10.8s\n",
      "2019 3\n",
      "s3://anaconda-package-data/conda/hourly/2019/04/2019-04-*.parquet\n",
      "[########################################] | 100% Completed | 10.4s\n",
      "2019 4\n",
      "s3://anaconda-package-data/conda/hourly/2019/05/2019-05-*.parquet\n",
      "[########################################] | 100% Completed | 10.5s\n",
      "2019 5\n",
      "s3://anaconda-package-data/conda/hourly/2019/06/2019-06-*.parquet\n",
      "[########################################] | 100% Completed | 11.1s\n",
      "2019 6\n",
      "s3://anaconda-package-data/conda/hourly/2019/07/2019-07-*.parquet\n",
      "[########################################] | 100% Completed | 11.9s\n",
      "2019 7\n",
      "s3://anaconda-package-data/conda/hourly/2019/08/2019-08-*.parquet\n",
      "[########################################] | 100% Completed | 10.1s\n",
      "2019 8\n",
      "s3://anaconda-package-data/conda/hourly/2019/09/2019-09-*.parquet\n",
      "[########################################] | 100% Completed | 10.3s\n",
      "2019 9\n",
      "s3://anaconda-package-data/conda/hourly/2019/10/2019-10-*.parquet\n",
      "[########################################] | 100% Completed | 14.2s\n",
      "2019 10\n",
      "s3://anaconda-package-data/conda/hourly/2019/11/2019-11-*.parquet\n",
      "[########################################] | 100% Completed | 11.9s\n",
      "2019 11\n",
      "s3://anaconda-package-data/conda/hourly/2019/12/2019-12-*.parquet\n",
      "[########################################] | 100% Completed | 12.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 67%|██████▋   | 4/6 [09:17<05:36, 168.00s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019 12\n",
      "s3://anaconda-package-data/conda/hourly/2020/01/2020-01-*.parquet\n",
      "[########################################] | 100% Completed | 13.8s\n",
      "2020 1\n",
      "s3://anaconda-package-data/conda/hourly/2020/02/2020-02-*.parquet\n",
      "[########################################] | 100% Completed | 13.3s\n",
      "2020 2\n",
      "s3://anaconda-package-data/conda/hourly/2020/03/2020-03-*.parquet\n",
      "[########################################] | 100% Completed | 16.2s\n",
      "2020 3\n",
      "s3://anaconda-package-data/conda/hourly/2020/04/2020-04-*.parquet\n",
      "[########################################] | 100% Completed | 21.2s\n",
      "2020 4\n",
      "s3://anaconda-package-data/conda/hourly/2020/05/2020-05-*.parquet\n",
      "[########################################] | 100% Completed | 13.3s\n",
      "2020 5\n",
      "s3://anaconda-package-data/conda/hourly/2020/06/2020-06-*.parquet\n",
      "[########################################] | 100% Completed | 12.6s\n",
      "2020 6\n",
      "s3://anaconda-package-data/conda/hourly/2020/07/2020-07-*.parquet\n",
      "[########################################] | 100% Completed | 13.2s\n",
      "2020 7\n",
      "s3://anaconda-package-data/conda/hourly/2020/08/2020-08-*.parquet\n",
      "[########################################] | 100% Completed | 16.6s\n",
      "2020 8\n",
      "s3://anaconda-package-data/conda/hourly/2020/09/2020-09-*.parquet\n",
      "[########################################] | 100% Completed | 12.4s\n",
      "2020 9\n",
      "s3://anaconda-package-data/conda/hourly/2020/10/2020-10-*.parquet\n",
      "[########################################] | 100% Completed | 17.8s\n",
      "2020 10\n",
      "s3://anaconda-package-data/conda/hourly/2020/11/2020-11-*.parquet\n",
      "[########################################] | 100% Completed | 14.9s\n",
      "2020 11\n",
      "s3://anaconda-package-data/conda/hourly/2020/12/2020-12-*.parquet\n",
      "[########################################] | 100% Completed | 12.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 83%|████████▎ | 5/6 [13:48<03:25, 205.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 12\n",
      "s3://anaconda-package-data/conda/hourly/2021/01/2021-01-*.parquet\n",
      "[########################################] | 100% Completed | 12.7s\n",
      "2021 1\n",
      "s3://anaconda-package-data/conda/hourly/2021/02/2021-02-*.parquet\n",
      "[########################################] | 100% Completed | 13.4s\n",
      "2021 2\n",
      "s3://anaconda-package-data/conda/hourly/2021/03/2021-03-*.parquet\n",
      "[########################################] | 100% Completed | 20.6s\n",
      "2021 3\n",
      "s3://anaconda-package-data/conda/hourly/2021/04/2021-04-*.parquet\n",
      "[########################################] | 100% Completed | 31.0s\n",
      "2021 4\n",
      "s3://anaconda-package-data/conda/hourly/2021/05/2021-05-*.parquet\n",
      "[########################################] | 100% Completed | 41.6s\n",
      "2021 5\n",
      "s3://anaconda-package-data/conda/hourly/2021/06/2021-06-*.parquet\n",
      "[########################################] | 100% Completed | 27.5s\n",
      "2021 6\n",
      "s3://anaconda-package-data/conda/hourly/2021/07/2021-07-*.parquet\n",
      "[########################################] | 100% Completed | 34.8s\n",
      "2021 7\n",
      "s3://anaconda-package-data/conda/hourly/2021/08/2021-08-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2021/09/2021-09-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2021/10/2021-10-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2021/11/2021-11-*.parquet\n",
      "s3://anaconda-package-data/conda/hourly/2021/12/2021-12-*.parquet\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [17:45<00:00, 177.66s/it]\n"
     ]
    }
   ],
   "source": [
    "for year in tqdm([2016, 2017, 2018, 2019, 2020, 2021]):\n",
    "    for month in range(1, 13):\n",
    "        s = f's3://anaconda-package-data/conda/hourly/{year}/{month:02}/{year}-{month:02}-*.parquet'\n",
    "        print(s)\n",
    "        try:\n",
    "            df = dd.read_parquet(s,storage_options={'anon': True})\n",
    "        except IndexError:\n",
    "            continue\n",
    "        except FileNotFoundError:\n",
    "            continue\n",
    "        datetime_count[(year, month)] = df.loc[(df.data_source=='conda-forge')]['counts'].sum().compute()\n",
    "        print(year, month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(2017, 1): 0,\n",
       " (2017, 2): 0,\n",
       " (2017, 3): 701645,\n",
       " (2017, 4): 7375445,\n",
       " (2017, 5): 8261383,\n",
       " (2017, 6): 7535179,\n",
       " (2017, 7): 10039857,\n",
       " (2017, 8): 12408943,\n",
       " (2017, 9): 11690260,\n",
       " (2017, 10): 13837261,\n",
       " (2017, 11): 13064540,\n",
       " (2017, 12): 11730379,\n",
       " (2018, 1): 17343201,\n",
       " (2018, 2): 18564943,\n",
       " (2018, 3): 21064271,\n",
       " (2018, 4): 16748850,\n",
       " (2018, 5): 24040170,\n",
       " (2018, 6): 23831192,\n",
       " (2018, 7): 28815251,\n",
       " (2018, 8): 32511378,\n",
       " (2018, 9): 37050898,\n",
       " (2018, 10): 47325248,\n",
       " (2018, 11): 50874921,\n",
       " (2018, 12): 42107025,\n",
       " (2019, 1): 43062229,\n",
       " (2019, 2): 46763206,\n",
       " (2019, 3): 55879507,\n",
       " (2019, 4): 48684136,\n",
       " (2019, 5): 66728803,\n",
       " (2019, 6): 62462089,\n",
       " (2019, 7): 65210830,\n",
       " (2019, 8): 61969868,\n",
       " (2019, 9): 62608046,\n",
       " (2019, 10): 78757103,\n",
       " (2019, 11): 85271670,\n",
       " (2019, 12): 90225074,\n",
       " (2020, 1): 99275455,\n",
       " (2020, 2): 99000914,\n",
       " (2020, 3): 115990884,\n",
       " (2020, 4): 122603668,\n",
       " (2020, 5): 117375623,\n",
       " (2020, 6): 120825988,\n",
       " (2020, 7): 126489061,\n",
       " (2020, 8): 150563060,\n",
       " (2020, 9): 162411810,\n",
       " (2020, 10): 174860758,\n",
       " (2020, 11): 157774277,\n",
       " (2020, 12): 157887143,\n",
       " (2021, 1): 191561916,\n",
       " (2021, 2): 226465813,\n",
       " (2021, 3): 261409705,\n",
       " (2021, 4): 243629532,\n",
       " (2021, 5): 236837474,\n",
       " (2021, 6): 264760615,\n",
       " (2021, 7): 271182169}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datetime_count = {k: v for k, v in datetime_count.items() if v}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [k[0] for k in datetime_count]\n",
    "months = [k[1] for k in datetime_count]\n",
    "counts = [datetime_count[k] for k in zip(years, months)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf = pd.DataFrame({'year': years, 'months': months, 'day': [1] * len(years)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf['idx'] = pd.to_datetime(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf = cdf.set_index('idx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf['counts'] = counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yZAjjx4/n9NNPb9J927Vrx69+9SsuvPBCunTpwq9//Wv279/P9ddfT/fu3enWrRtvvvkmzz//fDTetoiIiIRhe0EpFzwyl4f9/oPj+nfhteuP4ZQxvRo915xz0Y6vVZg8ebJbuHDhQeVlZWUAZGRkxDqkhKLPWUREJLpmr8zlxheW1DYXX3H0IG4+deRBtYNmtsg5d9AAALX1iYiIiLRhldUB7n1zNX95bx0AmRkp3HPeOL4URu1gMCWFIiIiIm3U3uIKrnxqIQs37QVgXL/OPHDRRPpntW/ytZQUioiIiLRRf3x7TW1CePn0HH765VENDiZpiJJCIFH6VcaTcw4zi3cYIiIihwznHDOX7wTgu8cN4SdfHtmi6yX86OOUlBQqKyvjHcYhr7q6ut65DkVERKTplm8vZMc+byDnmeP6tPh6CV9TmJKSgpmxd+9e0tPTMTPVaEWQc47q6moqKyvp0KF1TgouIiLSHOVV1ZRXBeiUkRqX+7+1YhcAfbu0Y1TvzBZfL+GTQoDMzEwCgQCVlZVqSo4wMyMtLY0OHToo2RYRkUNGVXWAM+//kJ2FZbx8zXQGdY99xUdNUnjSqOyI/I1VUuhLSkoiPT093mGIiIhIG7BiRyGrdu0H4IF31nLv+eNiev9tBaWs2FEIwMmjmzb1TH3UyUtERESkieZvyK99/vKSbWzJL4np/d/+3KslzExPYcqgrIhcU0mhiIiISBPNXf9FUlgdcDz8/rqY3r+m6fi4ET2aPQVNKCWFIiIiIk0QCDgWbPSSwmHZHQF4YeFWcgvLYnL/wrJK5q7PA+Dk0T0jdl0lhSIiIiJNsGrXfvaVetPZ3XXu4XRul0pFVYBHP1gfk/u/v3o3ldWOlCRjxvDsiF1XSaGIiIhIE8zza+myOqQxvn8XLp+eA8Cz8zazt7gi6vevaTqeMiiLzu0jNx2OkkIRERGRJpjvNx0fkdMVM+Oyo3LokJZMSUU1j3+4Iar3rqwOMHtlLhDZpmNQUigiIiISNudc7cjjqYO6AdClfRqXHDkQgCc+2sj+suitlLZgYz6FZVUAnDRKSaGIiIhIXKzbXcyeIq+JeOrgL6aCueKYQaSlJFFYVsXTczdF7f41Tccje2XSP6t9RK+tpFBEREQkTPM2eP0JMzNSGNmrU215dmYGFxzRH4C/frCB0orqiN/bOccsf37CSDcdg5JCERERkbDN8+cnnJKTRXLSgUvLfee4IaQkGXnFFTy/YHPE7716VxFb8kuByDcdg5JCERERkbAE9yesaxWRvl3a8dUJfQF45P31VFQFInr/mlrC7Mx0xvbtHNFrQwyTQjP7nZktN7NCM9tuZo+aWb3rspjZDDNzZlYUtH0Uq3hFREREgm3OL2GnP0H11MHd6jzm6hlDSDLYsa+MlxZvjej93/T7E540uidJIbWUkRDLmsJq4BKgGzAO6Ac83tg5zrmOQdtR0Q5SREREpC7z/FrC9mnJHNanU53HDO7RkdPG9gbgz++to6o6MrWFuYVlLN1SAMDJUWg6hhgmhc65W5xznzjnKp1zu4EHgBnRvKeZdTOz4WY2vKqqKpq3EhERkUNcTX/CSQO7kpJcfwp1zfFDAdiUV8Jrn+2IyL3f9ucmbJeazLQhdddStlQ8+xSeCHzayDHJZrbFzHaa2WtmNq6J97gOWAWsys3NbVaQIiIiIgDzN3ojj4+sp+m4xqjenThplLf83IOz1xIIuBbfu2YqmmOHdycjNbnF16tLXJJCM/sacCVwQwOHrQTGA4OAkXgJ5Dtm1qcJt7ofGAGMyM6O3NqAIiIikli2F5TWjvyta5BJqJrawtW7injLHyDSXCUVVcxZuweAk0f3atG1GhLzpNDMzgMeBc50zi2u7zjn3E7n3FLnXJVzrsA591MgH/hyuPdyzuU551Y751anpKS0PHgRERFJSDXzE6anJHF4v8ZH/k4Y0JXpQ70axQdnr8W55tcWfrBmDxVVAZIMjh/Ro9nXaUxMk0Izuxx4GDjDOTe7GZcIAJEfbiMiIiLSgJqpaCYO6Ep6SnjNtzW1hZ9u3ccHa/Y0+941TceTBnalW8f0Zl+nMbGckuZ64B7gS865D8M4/gQzG2pmSWbW0cxuB3oCM6McqoiIiMgBaietDqPpuMa0wd2YOKALAA/MXtus+1YHHO/4g0yisYpJsFjWFP4R6ATMDp57sGanmV0c/Bpv2pq3gf3AeuBI4GTn3JYYxiwiIiIJLnd/Gev3FAMHrnfcGDPj2hO82sL5G/JZsDG/yff+ZPNe8ou9tZajsYpJsFhOSWPOudSQeQc7Bu1/NuT1H5xzA51zHZxz2c65U51zC2IVr4iIiAh80XScmmxM6N+1SecePyKb0b29OQ0feKfptYU1TceDe3RgcI+OjRzdMlrmTkRERKQBNU3H4/p1oV1a06aDMbPavoXvrd7NzOU7m3R+zcjlaDcdg5JCERERkQbV1BQ2pek42KmH9WJEz0wAvvvMIv44a01Ycxeu213E+t1es3W0VjEJpqRQREREpB75xRWs2rUfgCmDmreSSHKS8bfLj2Bs3844B3+YtZqrnl5EYVllg+fN8puOu3VIY8KApjVbN4eSQhEREZF61AwOSU4yJg1sfmLWt0s7XvzuNL42sR8Asz7fxdkPfsja3P31njPLbzo+YWQ2yUnRn5FPSaGIiIhIPWr6Ex7WpxMd01u2EEZGajL3nHc4vzxrDClJxvrdxZz1wIe8sezgfoZ5ReUs2rQXgJNi0J8QlBSKiIiI1KtmveOpjax3HC4z49JpOfz9qiPp3jGd4opqvvvMIu6ZuYrqoH6G76zMJeC8FVSOGdY9IvdujJJCERERkToUllWyYnshAFNymjfIpD5H5GTx3+uOZkLQ5NZXPLmAfSVeP8OapuOjh3anfVpslupVUigiIiJSh4Ub8wk4MIMjmrCSSbh6dc7g+auO5KKpAwB4d9VuznxwDku3FPD+am9ZvFg1HYOSQhEREZE6zfOnohnVqxOd26VG5R7pKcn85qtj+e05Y0lLTmJTXglffehDSiurAThxZHZU7lsXJYUiIiIidWjOesfNdcGUAfzjO0fSq1MGNV0Lx/fvQnanjKjfu4aSQhEREZEQxeVVLNu2D4AjmzlpdVNNGNCVV687ujYJPW9yv5jct0Zsei6KiIiItCGLN++lyq+yOyLCg0wa0iMznX9cdSQ7C8voFcNaQlBSKCIiInKQmqbjYdkd6dYxPab3NjN6d24X03uCmo9FREREDtLS9Y7bIiWFIiIiIkHKKqtZsqUAaP56x22RkkIRERGRIEu2FFBRHQBgagxGHrcWSgpFREREgtT0J8zp1p6eMR7sEU9KCkVERESCzNvgr3ecQE3HoKRQREREpFZFVYDFm/cCiTXIBJQUioiIiNT6bFsBZZVef8JYrGTSmigpFBEREfHVrHfct0s7+nVtH+doYktJoYiIiIivZpBJIo06rqGkUERERASoqg6waFNi9icEJYUiIiIiAKzYUUhReRWQWJNW11BSKCIiIsIXTcfZmenkdEus/oSgpFBEREQE+GKQydTB3TCzOEcTe0oKRUREJOEFAo4FG72kMNGmoqmhpFBEREQS3sqd+9lXWgnAkUoKRURERBLTfH9pu6wOaQzN7hjnaOJDSaGIiIgkvJr+hFNyshKyPyEoKRQREZEE55xjfu0gk8RsOgYlhSIiIpLg1u0uIq+4AoCpCTg/YQ0lhSIiIpLQ5vrzE3bKSGFEr8w4RxM/SgpFREQkodX2JxyURXJSYvYnBCWFIiIiksC8/oTeyONEbjoGJYUiIiKSwDbllbCrsBxI3EmraygpFBERkYQ1z68l7Jiewpg+neIcTXwpKRQREZGEVdOfcNLArqQkJ3ZalNjvXkRERBLavPWJvd5xMCWFIiIikpC27i1hW0EpAEcm8KTVNZQUioiISEKqWcUkIzWJsX27xDeYVkBJoYiIiCSkmqbjiQO6kpailEifgIiIiCSkeZqf8ABKCkVERCTh7CosY2NeCaBBJjWUFIqIiEjCqZmKJi05iQkDusQ3mFZCSaGIiIgknHnrvabj8f27kJGaHOdoWgclhSIiIpJwakYeq+n4C0oKRUREJKHkFZWzJrcIgKman7CWkkIRERFJKDW1hClJxqSBXeMcTeuhpFBEREQSSs0gk8P6dqZ9Wkqco2k9lBSKiIhIQqlJCtV0fCAlhSIiIpIw9pVUsnJnIQBHatLqAygpFBERkYSxYGM+zkGSwaQc9ScMpqRQREREEkbN0naj+3SiU0ZqnKNpXZQUioiISMKo7U+opuODxCwpNLPfmdlyMys0s+1m9qiZNdjD08xO9c8pNbNlZnZKrOIVERGRQ0tReRXLtu0DNGl1XWJZU1gNXAJ0A8YB/YDH6zvYzAYDLwF3Ap39x3+bWU7UIxUREZFDzsKN+QSc93xKjpLCUDFLCp1ztzjnPnHOVTrndgMPADMaOOWbwCLn3DPOuQrn3LPAYr88LGbWzcyGm9nwqqqqFsUvIiIibVtN0/HIXpl07ZAW52han3j2KTwR+LSB/eOARSFli/3ycF0HrAJW5ebmNi06EREROaRoveOGxSUpNLOvAVcCNzRwWCawL6SsAOjUhFvdD4wARmRnZzclRBERETmElFZU8+nWAkCDTOoT86TQzM4DHgXOdM4tbuDQ/Xh9CYN1AQrDvZdzLs85t9o5tzolRcvYiIiIJKrFm/dSWe11KFRNYd1imhSa2eXAw8AZzrnZjRy+FJgYUjbBLxcREREJW01/wsE9OtAjMz3O0bROsZyS5nrgHuBLzrkPwzjlKWCymV1oZqlmdiEwCXgymnGKiIjIoWfeem/SajUd1y+WNYV/xOsPONvMimq2mp1mdnHwa+fcOuAc4Fa8JuNbga865zbGMGYRERFp48oqq/lkSwEAU9V0XK+YdbRzzlkj+58Fng0pewN4I5pxiYiIyKHt0637qKgKADB1sJLC+oRVU2hmJ5jZtKDXV5jZAjN70syaMhpYREREJKZqmo4HZLWnd+d2cY6m9Qq3+fheIBvAzIYBDwEL8fr43RWd0ERERERabp7mJwxLuM3HQ4Bl/vOzgFnOuav92sMXoxKZiIiISAtVVgdYtGkvoP6EjWnOQJPjgZn+8214axmLiIiItDqfbdtHaWU1AEcOVsrSkHCTws+Aa8zsaOAEYJZf3h/YHY3ARERERFpqrt+fsHfnDPp1VX/ChoSbFN4CXAa8BzztnFvhl58JzI9CXCIiIiIt9vE6LymcNrgbZg1OhJLwwupT6Jx7z8x6AJnOuYKgXX8GSqIRmIiIiEhLVFQFWLjR60945BA1HTcm7HkKnXPVQEFI2cYIxyMiIiISEZ9uLajtTzhN/QkbVW9SaGbvhHsR59wJkQlHREREJDJqmo77dW1H/6z2cY6m9WuopnBd0PMk4Fy8msJ5gAFHAF3RlDQiIiLSCn28/ov+hNK4epNC59yVNc/N7Ld4yd93/GZkzCwZr09hQZRjFBEREWmS8qrq2vkJp6k/YVjCHX38LeDumoQQavsY/h64PBqBiYiIiDTXks0FlPvrHSspDE+4SWE6MLiO8kFAWuTCEREREWm5mqbjnG5a7zhc4Y4+/ifwVzP7GfCxX3Yk8GvghWgEJiIiItJctfMTqpYwbOEmhdcC9+D1IUzFG2hSATwK/Dg6oYmIiIg0XVllNZ9sLgC0tF1ThDt5dSneMnc/Bob4xeucc8VRi0xERESkGRZv2ktFtd+fUElh2MKevBrATwI/jVIsIiIiIi1W059wSI8OZHfKiHM0bUfYSaGZHQ9cAAwkZHCJJq8WERGR1kL9CZsnrNHHZnYpMBPoCRwPFAK9gIkcOMm1iIiISNyUVFSxdGsBANMGd49vMG1MuFPS/Ai40Tl3Nt4Akx8AY/AmtN4endBEREREmmbhxr1UVjsAjhycFedo2pZwk8LBwP/85xVAB+ecA/4AXFnvWSIiIiIxVNOfcETPTLp1TI9zNG1LuElhAVCzkvR2YLj/vAOQGeGYRERERJpF/QmbL9yBJnOBY4HlwCvAfWY2GTgLmBOl2ERERETCVlRexWfb9gGan7A5wk0Kfwh09J//CugMnI6XJN4YhbhEREREmmTBhnyqAw4z9SdsjnAnr94Y9LwMb4UTERERkXoFAo7yqgDt0pJjcr+a/oQje3WiS/u0Ro6WUOH2KQS8uQrN7Bp/Oy5aQYmIiEjb5pzj2r8vZuztM/lgze6Y3HOunxRqFZPmCXeewt5m9jHwNnC7v802s4/MrFf0whMREZG26Nl5m/nfZzupCjh+87+VBAIuqvcrLKtkmd+fUINMmifcmsI/AqnAaOdcD+dcD2AUXvPzfVGKTURERNqgjXuK+fVrn9e+/nxHIW+u2BXVe85fn0/AQZLBlEHqT9gc4SaFpwDXOOdW1hQ451YB1wGnRiMwERERaXuqA44fvriU0spqundMZ9LArgD88e01Ua0trOlPOKZPZzq3S43afQ5l4SaFKUBJHeWlNGH9ZBERETm0PfL+ehZt2gvAb88Zy0+/PBKIfm2h5idsuXCTwg+B35lZ55oCM+sE3AF8EI3AREREpG35fEchf3hrNQDnT+7HSaN7Mjkni2OGeWsQR6u2sKCkgs93FgIaZNIS4SaF3wcOA7aa2TwzmwtsBcaheQpFREQSXnlVNTf+YwkV1QH6dmnHz78yunbfDScOA6JXWzh3fT7OQXKScYT6EzZbWEmhc+5zYAReAjgHr+bwRmBEcD9DERERSUx/nLWGlTv3Ywb3nj+OzIwv+vVFu7awZiqasX070zFdvdqaK+xPzjlXCjwWxVhERESkDVq0aS9/eW8dAFdMH1TnEnM3nDiMD9bsqa0tPPWwyM1op/6EkVFvUmhmR4V7EefcR5EJR0RERNqSkooqfvjCEgIOhmZ35KYvjajzuJrawg/W7OGPb6/hlNE9SUqyFt8/r6icVbv2A+pP2FIN1RTOARzQ2E/MAbFZv0ZERERalTv/t5KNeSWkJBl/OH88Gan1pwTRqC2cuz4fgNRkY3JO1xZfL5E1lBQOilkUIiIi0ua8v3o3T8/dBMB1JwxjbL/ODR4fjdrCj9fvAWBcvy60T1N/wpao99Nzzm2KZSAiIiLSduwrqeTH//wUgHH9OvO944eEdV6kawvVnzBywp2SBvMMMrMjzeyo4C2aAYqIiEjrc9sry9hZWEZ6ShL3nj+e1OTwUopIjkTOLSxj3e5iQP0JIyGsn6CZTQRWAmuBj/D6G9ZsmrxaREQkgfzvsx28vGQ7ADefOpKh2R2bdH6k5i2cu8HrT5iWnMTEgepP2FLh1hT+BdgMHAsMxutvWLMNjk5oIiIi0trk7i/jZ//+DPBq5y47KqfJ14hUbWFN0/GEAV0aHOAi4Qk3KRwDXOuc+9A5t9E5tyl4i2aAIiIi0jo45/jpvz5jb0klmekp3HP+uGYPFIlEbWHNpNXqTxgZ4SaFqwGtGyMiIpLAnp67ibdX5gLwf2eMpm+Xds2+VktrC3fuK2PDHvUnjKRwk8LrgDvNbJyZtXymSREREWlTVu3czx2vfQ7AaWN7ce6kfi2+ZktqC2umoklPSWL8gC4tjkXCTwpnA8cAi4FKM6sI3qIXnoiIiMRbWWU11/19MRVVAfp0zuDOrx5OJOqIWlJbWNOfcHJOV9JT1J8wEsKd5fFKvJVLREREJMH85n+fs3pXEUkG910wgc7tUyN27ebOW/hxTX9CNR1HTFhJoXPuiSjHISIiIq3QWyt28dTH3pjSa08YxpRBkR1i0JxVTrbuLWFLfimgQSaRFFZSaGYPAO8A7znn8qIbkoiIiLQGuwrL+PE/lwIwaWBXrj9haFTuE1xbeMp975NsRlUgQMBBdcB9sTnvsaIqAED7tGQO79clKjElonCbj/sDjwGdzGwZXoI4Gy9JLIxWcCIiIhIfgYDjBy8sqZ1+5r6vjyclzFVLmiq4tnBtblHY580Y0SPslVSkceE2H59lZknAJOB44ATg20A7M1vsnJsaxRhFREQkxh5+fz0frvUaB399zlj6Z7WP6v1+f/54Xv5kG1UBR3ISJJmRkmQkJxnJSUkkJ1H7mGRGRmoy04d2j2pMiSbcmkKccwFggZntAnKBvcDXgJFRik1ERETiYOmWAu59cxUA507qx5nj+kT9nj0y07nyWC2SFk/hrn18kZk9amZrgVXAN4DlwAxAPTxFREQOEUXlVVz//CdUBRw53dpz+5lj4h2SxEi4NYXP4NUO3gU85Jwri15IIiIiEi+3/Wc5m/JKSEky/nThBDqmh92oKG1cuL0zrwHeB34CbDGzf5rZ98xMTcciIiKHiP8s2ca/Fm8F4KYvjdDI3gQTVlLonPuzc+5851w2cCIwBzgVWGpm26IZoIiIiETflvwSbv33MgCmD+3GVceof1+iadI4bjMbChwJTAWmAKlAaRPOv8DMPjCzQjOrauTYGWbmzKwoaPuoKfGKiIhI46qqA9zw/CfsL6+ia/tUfn/++EYnkJZDT7iTVz+NN6ikL7AVb47Cm4F3nHNbmnC/vcBDQDvgkTCOr3bOdWzC9UVERKSJ/vT2GhZvLgDg7nPH0bNTRnwDkrgIt/doCvBLYLZzbm1zb+acmwleLWBzr9EUZtYNf3T0uHHjYnFLERGRNmVzXgkPzPb+tF86bSAnje4Z54gkXsKdvPrCaAdSj2Qz24LXTL0IuMU5t7QJ518H3AaQm5sbhfBERETatv8s2UbAQVaHNG45bVS8w5E4CrtPoZmNMbMnzWyBvz1hZqOjGNtKYDwwCG+C7E+Bd8ysKTNo3g+MAEZkZ2dHPEAREZG2zDnHy0u88aJfObw3GanJcY5I4incyatPB5YAw4F3/W04sMTMvhyNwJxzO51zS51zVc65AufcT4F8IOz7OefynHOrnXOrU1I0z5KIiEiw5dsLWbe7GICzxveNczQSb+FmSncAv3fO3RxcaGa/BX4NvB7pwOoRADQcSkREJAJe/sSrJRyQ1Z6JA7rENxiJu3Cbj0cBj9VR/jcg7CZkM0s2swwgzX+d4W8HJXpmdoKZDTWzJDPraGa3Az2BmeHeT0REROpWHXC8snQ7AGeN70Mdf4olwYSbFObjJYahRvj7wvUNvHkNZwLJ/vNSYKCZXWxmRUHHjgPeBvYD6/HmRzy5iVPgiIiISB3mrs8jd385oKZj8YTbfPwc8JiZ3QJ8CDhgOl6z8tPh3sw59wTwRD27NwLPBh37B+AP4V5bREREwlfTdHxY304MzdaUwBJ+UvhTvFrFB/CmhzGgwn/9s+iEJiIiItFQVlnNG8t2AnDWONUSiifceQorgR+Y2a3AEL94rXMu7CXuREREpHWYvTKX/eVVmMEZ45oy05scypo6T0t7YLtzLi8awYiIiEj01cxNOG1wN3p11pJ24ml0oImZZZvZ38xsL7ALyDWzvWb2VzPTjNAiIiJtyL6SSmav3A3A2RpgIkEarCk0sw7AHLz1g58GVvi7xgAXAdPNbKJzriSqUYqIiEhEvL5sBxXVAdJSkjh1bK94hyOtSGPNx9cA6cBY59z24B1mdifwMfA94J7ohCciIiKRVNN0fOLIbDplpMY5GmlNGms+PgO4MzQhBPDLfgucFY3AREREJLJ27Ctl3gZveuGzxmuAiRyosaRwJPBBA/s/8I8RERGRVu6VJdtxDjIzUpgxQsMC5ECNJYWdgYZGGuf5x4iIiEgr958lXsPfaYf1JiM1Oc7RSGvTWFKYDFQ3sD/gHyMiIiKt2Jpd+1mxoxCAsyao6VgO1thAEwNeNLOKevanRTgeERERiYKaASa9OmVw5KBucY5GWqPGksInw7jGhkgEIiIiItHhnKttOj5zfB+SkizOEUlr1GBS6Jy7PFaBiIiISHQs2rSXrXu9lWnP1LJ2Uo9GVzQRERGRtq2m6XhodkfG9OkU52iktVJSKCIicgirrA7w2qc7ADh7fB/M1HQsdVNSKCIicgj7YM1u9pZUAnCW1jqWBigpFBEROYS9/Ik3wGTSwK70z2of52ikNas3KTSzd8ysi//8UjNLj1lUIiIi0mLF5VW8tWIX4DUdizSkoZrCo4F2/vPH0colIiIibcqbK3ZSWllNSpJx+uFKCqVhDU1Jsx64wcxex5vEepqZ7a3rQOfc+9EITkRERJqvpun4mGHdyeqg9SakYQ0lhT/BqyH8MeCAf9dznENL3YmIiLQqe4rKmbN2DwBnT9AAE2lcvUmhc+5l4GUz6wVsByYCu2MUl4iIiLTAf5dupzrgaJ+WzMmje8Y7HGkDGlvmDufcTjM7HljmnKuKQUwiIiLSQi/7y9qdMron7dMa/XMv0nhSCOCce8/M0s3sG8Bov3gZ8HfnXEXUohMREZEme/vzXSzZUgDAWWo6ljCFlRSa2QjgDaAH8LlffDVwm5md6pxbHaX4REREJEz7Siu5478reHHRVgD6dmnH0UO7xzkqaSvCrU++D1gFTHLO5QOYWRbwjL/vtGgEJyIiIuF5Z+UufvrSZ+wqLAdgfP8u3Hv+OFKTtU6FhCfcpPAY4KiahBDAOZdvZj8F5kQlMhEREWnUvpJKfvnfFfxrsVc7mJ6SxA9PGc4VRw8mOUnrHEv4wk0Kq4CMOsrTgerIhSMiIiLhemvFLn7278/I3e/VDk4a2JW7zj2cIT06xjkyaYvCTQrfAh40s4ucc2sAzGwI8EfgzWgFJyIi0pZUBxx/eW8d76zM5bKjcvjK4b0xi3xt3d7iCn7x6vLaEcbpKUn86EsjuHz6INUOSrOFmxTeALwCrDSzPXgTVvcAFvv7REREElp+cQXf/8cS3l/tTem7aNNeXli4hV+cOYbBEay5m7l8Jz/79zL2FHm1g0fkdOWuc8cxqHuHiN1DEpM558I/2OwkYJT/coVz7u2oRBUFkydPdgsXLox3GCIicghauqWA7z27mG0FpQAM6dGBdbuLAUhLTuK7xw3me8cPJSO1+QuArc0t4o9vr+HVpV7tYEZqEjefOpJvTsshSbWD0gRmtsg5N/mg8qYkhW2ZkkIREYk05xzPztvML19dQUV1gPSUJH511mGcN7kfry/byS9fXcHOwjIABnZrzy/OHMOMEdlhX7+8qpqZy3fx3LxNzF1fO9aTqYOyuOvcwxnYTbWD0nRKCpUUiohIBJVWVPOzlz/jpcXbABiQ1Z6HLp7IYX071x5TVF7FfW+t5vGPNlId8P7enja2Fz//ymh6d25X77U35RXz3PzN/HPhVvKKv1gjolenDK45fggXTx2o2kFpNiWFSgpFRCRCNuwp5upnFrFy534AThqVzb3njadz+9Q6j1+xvZBbX/6MxZsLAOiQlsyNJw/nsqNySPHnEaysDjBrxS6em7+ZD9bsqT3XDI4b3oOLpw7k+BE9ao8XaS4lhUoKRUQkAmYu38lNLyxlf3kVSQY/PGUEVx83pNGau0DA8eKiLdz5+koKSioBGNkrk5tOGcGSLQX8Y+EWdvtTywD0yEzn65P78/Uj+tM/q31U35MklmYnhWaWijd59SfOub1Rii/qlBSKiEhLVFUHuPvNVTz83noAsjqk8acLJnD0sKYtI5dfXMFvX/+cFxZurXP/McO6c9GUAZw0uqdWI5GoqC8pDGdKmirgdWAk0GaTQhERkebavb+c6/6+uHawx4QBXXjo4okN9gusT1aHNO46dxznT+7PrS8vY+XO/XTrkMa5k/tx4REDyNHUMhInjSaFzjlnZiuA/sCG6IckIiLSelRWB7jo0bmsyS0C4LKjcrjltFGkpbSsFm9yThb/ve5oVu7cz7CeHUlPaf50NSKREO7k1T8E7jKzG4GFzrnKKMYkIiLSavx78bbahPC+r4/n7Al9I3btlOSkA0Yri8RTuEnhTCAJmANgZgesd+ycS4twXCIiInFXWR3g/tlrAG8qmUgmhCKtTbhJ4bejGoWIiEgr9NLirWzJL8UMbjhxeLzDEYmqsJJC59yT0Q5ERESkNamoCnD/O2sBOG1sb0b0yoxzRCLRFXYvWTPrYWY/MLM/m1l3v2y6mQ2KXngiIiLx8dLirWzdW1NLOCze4YhEXVhJoZkdDqwCrgKuADr5u04G7ohOaCIiIvERXEt4+tjeDO+pWkI59IVbU/h74DHn3EigPKh8JjA94lGJiIjE0T8XbWVbgWoJJbGEmxROBh6po3w70DNy4YiIiMRXRVWAB2d7tYRfObwPw1RLKAki3KSwCqhrivUhQH7kwhEREYmvFxdtCaolHBrvcERiJtyk8HXgx2ZWs9q38web3AG8GpXIREREYqy8qpoH/b6EZ47rw9Bs1RJK4gg3KfwRMBFvmbsM4GX/eSfglqhEJiIiEmMvLNzK9n1lJBlcd4L6EkpiCXeewp1mNgG4EJiEl0z+CXjWOVcWxfhERERioryqmodmB9cSdoxzRCKxFe6KJvjJ3+P+JiIickh5YcEWdvi1hNdrxLEkoKZMXj3BzJ4ys4VmtsB/Pi6awYmIiMRCWWU1D85eB8DZ4/syuIdqCSXxhDt59YXAQrzRxu8A7wKDgUVm9vWoRSciIhIDLyzcws5Cr5bw2hM04lgSU7jNx3cAv3HO/Ty40Mx+CfwG+EekAxMREYkFr5bQ60t49gTVEkriCrf5uDfwVB3lT/v7RERE2qTn529mV2E5yUnG9RpxLAks3KTwY7xRx6EmA/PDvZmZXWBmH5hZoZlVhXH8qWa23MxKzWyZmZ0S7r1EREQaU1ZZzUPven0JvzqhLznd61qnQSQx1Nt8bGZHBb18ArjHzEbhJYgARwLfBn7ahPvtBR4C2lH3snnB9x8MvARcBbwAnAf828zGOOc2NuGeIiIidfr7/M3k7vdqCa9TX0JJcA31KZwDOMCCyn5ex3FP4DUjN8o5NxPAzGaEcfg3gUXOuWf818+a2Xf98l+Ecz8REZH6BNcSnjOhLwO7qZZQEltDSeGgmEVRt3HAopCyxX55WMysG9ANYNw4zZ4jInKoW7KlgA17ishISSY9NYn0lGTSU5LISPUe02vLk3hx4VZ219YSqi+hSL1JoXNuUywDqUMmsC+krAAY04RrXAfcBpCbmxuZqEREpFV6+L113Pn6yiaf97WJfRnQrX0UIhJpW8Je0cTMsvD6EWYTMkDFOfe3CMcFsB/oHFLWBShswjXuB54DyM7OXhWZsEREpLV5dt6m2oSwQ1oyAGVVAaoDrsHz2qclq5ZQxBdWUmhm5+BNSdMeKMHra1jDAdFICpcCx4eUTQDeDvcCzrk8IA9g8uTJkYtMRERajf8s2catLy8DYOqgLJ781hQyUr3EsKo6QHmVt5VVVvvPqymv9F73z2pPny7t4hm+SKsRbk3hPcBjwK3OuaLm3szMkoFUIM1/neHvKnfOhf537ingR/5qKv8EzsWbFufS5t5fREQOLW8u38kPXliKczCuX2ce++bk2oQQICU5iZTkJDqkxzFIkTYi3HkKuwN/aklC6PsGUArMBJL956XAQDO72Mxqr++cWwecA9yK12R8K/BVTUcjIiIAH67dw7XPfUJ1wDG8Z0eeuHwKmRmp8Q5LpM0Kt6bwNeBYYH1LbuacewJvCpu6bASeDTn+DeCNltxTREQOPYs27eXKpxZSUR1gYLf2PHPFVLp2SIt3WCJtWrhJ4dXAc2Y2AfgUqAze6Zyrawk8ERGRiFu+fR+XPz6fkopqenfO4JkrppLdKaPxE0WkQeEmhScBM4BTgeqQfY6610UWERGJqHW7i7j0r/MpLKuiW4c0nr5iKv2zNJ2MSCSE26fwHrwRxtnOudSQTfX1IiISdVv3lnDJY/PIK64gMyOFp66YwtDsjvEOS+SQ0ZSBJr93zu2JZjAiIiJ1yS0s4+LH5rFjXxntUpN54vIjGNMndCpbEWmJcJPC/wJHRTMQERGRuhSUVPCNv85nU14JaclJPHrpZCYNzIp3WCKHnHD7FM4B7mpgoMlzkQ5MRERk9/5yvv3kAlbt2k9ykvHARRM4elj3eIclckgKNyn8k/94Yx37HP5SciIiIpFQURXgiY828Ke311JUXoUZ3HPe4Zwyple8QxM5ZIWVFDrnwm1mFhERaTbnHO+szOWO1z5nw55iADIzUvjFmWP46oR+cY5O5NAWbk2hiIhIVK3NLeKX/13B+6t3A2AGFxwxgB+eMpzuHbVOnUi0hZUUmtktDe13zv0mMuGIiEii2VdayR9nreGpjzdSFXAATMnJ4v/OGM1hfTXCWCRWwq0pvDLkdSrQG2/d4lxASaGIiDRJdcDxjwVbuOfNVeQXVwDQp3MGt5w+itPH9sbM4hyhSGIJt0/hoNAyM+sFPAk8EOmgRETk0DZvfR6/eHUFK3YUApCRmsR3jxvCd44dQru05DhHJ5KYmt2n0Dm308x+BjwDvBq5kERE5FBVXlXNr1/7nKc+3lRb9pXDe/PT00bRt0u7OEYmIi0daFIO9I1EICIicmjbkl/CNc8t5tOt+wAY06cTt50xhimDNBG1SGsQ7kCT0NVMDOgD/AhYGOmgRETk0DJrxS5+8MISCsuqALjm+CHceNJwUpI145lIa9GUFU0cXjIY7EMOHoQiIiICQFV1gLvfXMXD760HoEv7VP5w/niOH5kd58hEJFS4SWHoQJMAsNs5VxbheERE5BCRW1jGtX//hPkb8gEY178LD140gX5d28c5MhGpS7ijjzc1fpSIiIjno7V7uP75T9hT5E01c9lROdxy2ijSUtRcLNJaNZgUmtmx4VzEOfd+ZMIREZG2LBBwPDh7LX+YtZqAgw5pydx17jhOP7x3vEMTkUY0VlP4LnX3JcQvr3nUcnkiIgkuv7iCG/+xhPf8ZepG9srkoYsnMrhHxzhHJiLhaCyZ69/AeVcB3wf2RDIgERFpe5Zt28dVTy1k+z6vq/m5k/rxq7MO00TUIm1Ig0mhc25baJmZfQX4Hd6UNL8E7otKZCIi0iZUVge47u+fsH1fGekpSfzqrMM4/4j66hREpLUKu9nXzCYC9wBHAw8Dv3DOqZZQRCTBvbhwKxv2FAPw3JVTmTRQk1GLtEWNDgMzs/5m9gywACgAxjjnrlNCKCIiZZXV/PHt1QCcOa6PEkKRNqzBpNDMfgusAoYAxzrnznHOrYlJZCIi0uo9+dFGdhWWk5Jk/ODk4fEOR0RaoLHm4x8DpUARcJtZXYOQwTl3SoTjEhGRVm5faSUPvbsOgK8f0Z+c7h3iHJGItERjSeFTfDH1jIiISK1H31/PvtJKMlKTuP7EYfEOR0RaqLHRx5fFKA4REWlDdu8v569zNgBw2VGD6NkpI84RiUhLab0hERFpsgfeWUNpZTWZGSlcfdyQeIcjIhGgpFBERJpkS34Jz83fDMB3jxtC5/apcY5IRCJBSaGIiDTJH95aTWW1o3vHdC6fnhPvcEQkQpQUiohI2Fbt3M+/l3iLXV1/4lDap4W9BoKItHJKCkVEJGx3z1yFc9A/qx0XHDEg3uGISAQpKRQRkbAs2rSXWZ/vAuAHJw8nLUV/QkQOJfoXLSIijXLOcdcbKwEY2SuTM8f1jXNEIhJpSgpFRKRR76/Zw7wN+QDcdMoIkpPqXuFKRNouJYUiIjGwbNs+zv/Lx9z5+ufkFZXHO5wmCQQcd8/0agknDujCiaOy4xyRiESDho2JiESZc45bX17Gki0FzN+Yz1MfbeLSowZy1TGD6dYxPd7hNep/y3awbFshADefOhIz1RKKHIqUFIqIRNnH6/NYsqUAgIzUJEorq3n4vfVecjhtIFceO5juzUgO1+zazxvLdjJ7VS6ZGamcM7EvXxrTi4zU5IjFXlkd4N43VwNw3PAeTB3cLWLXFpHWRUmhiEiU/fnddQAc3q8zT31rCn+bs4HHP9zI/vIqHn5/PU99HF5y6Jxj+fZCXl+2gzeW7WTd7uID9r+3ejeZGSmcMa4P50/uz7h+nVtcq/fPRVvZsMe7z4++NKJF1xKR1s2cc/GOISYmT57sFi5cGO8wRCTBfLq1gDMf+BCAv1wykVMP6w3AvpJK/vrhBh6fs4H95VUAtEtNPig5DAQcizfv5Y1lO3lj+U627i094PrdO6Zz8uie7NxXynurdxMI+pU+LLsj503ux9kT+pKdmdHk2Msqq5lx97vsLCzjK4f35oGLJjbnIxCRVsbMFjnnJh9UrqRQRCR6vvv0It5YvpMhPTrw1o3HkRQyare+5PCiqQMor6pm5vJd7N5/4MCUvl3a8eXDenHqYb2YMKBr7UjgXYVlvLR4Gy8u2sL6oFrE5CTj+BE9OHdSf04YmX3Q/ILOOYrKqygoqWRvSQV7SyrZW1zBR+v28MLCrSQnGbN+cByDuneIxkckIjGmpFBJoYjE2NrcIk7+w3s4B/ecN45zJ/Wr99i6ksNgQ3p04MuH9ebUw3oxpk+nBpuFnXMs3lzAPxdt4dWlOygKul5WhzQmDuhKYVklBSUV5BdXsq+0gsrq+v8WXDhlAHeeMzbMdy0irZ2SQiWFIhJjN724lH8u2krfLu1490czSE1ufBawfSWV/O3DDby4cAtdO6Rx6phefHlsL4ZmZzYrhtKKat5YvoMXFmzl4/V5YZ/XuV0qXdunktO9A/ecN65ZA2FEpHVSUqikUERiaFtBKcfdNZuqgOP2M0Zz2fRB8Q6JLfklvPzJNrbvK6VL+zS6tk/1H4Ofp9K5XSopYSSwItI21ZcUavSxiEgUPPr+eqoCjm4d0vj6EQPiHQ4A/bPac92Jw+Idhoi0UvqvoIhIhOUVlfP8gs0AXD49h3ZpkZs3UEQkWpQUiohE2OMfbqSsMkDH9BS+MS0n3uGIiIRFSaGISATtL6vkyY83AnDJkQPp3C41vgGJiIRJSaGISAQ9O28z+8uqSEtJ4ltH58Q7HBGRsCkpFBEJMnP5Tm5/ZTk795U1+dyyymoe+2ADAOdP7tesVUREROJFo49FRHz/WbKN7/9jCc7B68t28NdvHsFhfTuHff6Li7ayp6ic5CTjO8cOiWKkIiKRp5pCERFg1opd/OCFpdRM3bqrsJzz/vIxbyzbGdb5VdUBHnl/HQBnjutD/6z20QpVRCQqlBSKSML7eF0e33tuMdUBx/CeHXnu21Pp3TmD0spqvvvMIv787joam+j/v5/uYEt+KQBXz1AtoYi0PUoKRSShLd1SwLefXEBFVYABWe15+oqpHDW0O/+5Zjrj+nlNx797YyU3vfgp5VXVdV4jEHA89O5aAE4a1ZPhPZu3JJ2ISDwpKRSRhLV6136++fh8iiuqyc5M59lvT6VnJ29wSHanDP7xnWmcPrY3AP9avJVvPDaf/OKKg67z9spcVu8qAuB7x6uWUETappgmhWaWbGZ3m9luM9tvZv8ys+71HDvDzJyZFQVtH8UyXhFpubyi8kabXuNhc14Jlzw2j4KSSrq0T+WZb089qB9gRmoy9184getPGArA/I35nP3gh6zN3V97jHNf1BIeOTiLiQO6xu5NiIhEUKxrCn8CnAVMBfr5ZU83cHy1c65j0HZU1CMUkRYrq6zmpcVbOfvBD5l0xyy+/eTCepte42FXYRkX/3UuufvL6ZCWzJOXT6m3yTcpyfjBKSO47+vjSUtOYnN+CV996CPeX70bgLnr8/lkcwEA1xw/NFZvQUQk4mI9Jc1VwC+dc+sBzOzHwFozy3HObYz0zcysG9ANYNy4cZG+vIiE2JJfwrPzNvPCwi0HNLO+vTKXa55dzEMXTyItJb69VvYWV3DJY/PYkl9KWkoSj33zCMb179LoeWdP6Ev/rHZc9dQi8ooruPyJBdx+xmjeXLELgLF9O3P00DobPkRE2oSY/XY2s87AAGBRTZlzbh1QCBxez2nJZrbFzHaa2Wtm1tTM7jpgFbAqNze3OWGLSCMCAcfsVblc8cQCjr17Nn95bx35xRUkGZw8uiffOHIgALM+z+WG5z+hqjoQt1iLyqu47PH5rMktIiXJ+PPFE5k2pFvY508amMXL10xneM+OVAccP//Pcj5YsweA780YgplFK3QRkaiLZU1hJ/9xX0h5QdC+YCuB8cByoCNwM/COmY11zm0P8573A88BZGdnr2pivCLSgL3FFby4aAvPzN3M5vyS2vJuHdK4YEp/Lpo6kL5d2uGco1O7FB6cvY7Xl+3kxheWct/Xx5OcFNsEqqyymm8/uYClW/dhBveeP44TR/Vs8nX6Z7XnX1cfxXV//4R3V3lNyIN7dOBLY3pFOmQRkZiKZVJY0zM7dHmALni1hQdwzu0EamaNLQB+ambnAl8G/hrODZ1zeUAewOTJk5scsIgcbMe+Uu59czWvLt1OedUXtX6TB3blG9MGcuphvUhPSa4tNzNuOmUEFVUBHv1gA68u3U5qsnHPueNIilFiWFkd4NrnFjN3fT4AvzrrMM4a37fZ18vMSOWxSydz18xV/GvRVm49fVTM3ouISLTELCl0zhWY2WZgIrAEwMwG49USfhrmZQKAfvOKxMm+kkoufnQe6/cUA9AuNZmzJ/TlG0cOZHSfuir8PWbGLaeNorLa8cRHG3lp8TbSkpP4zVfHRj2ZWr59H/e+uZp3VnpdSG4+dSSX+E3aLZGSnMQtp43iltNGtfhaIiKtQawHmjwC3Gxms/Fq8H4HzKxrkImZnQBsBtYD7YGbgJ7AzJhFKyK1KqsDXPPcYtbvKSY12bj51JGcf0R/OmWkhnW+mXHbGaOpqA7w3LzNPL9gC6nJSfzyrDER74tXVR1g1ue7+NuHG5m/Ib+2/OoZQ7TaiIhIPWKdFP4W6AosANKBt4BLAMzsYuBh51xH/9hxwONAd6AYWAyc7JzbEuOYRQT41X9XMGetN6jijrMP4+tHDGjyNcyMO846jMqqAC8u2srTczeRmpzEz78yKiKJ4b6SSv6xcDNPfrSJbQWlteU53dpz5bGDuWhK02MWEUkU1honlY2GyZMnu4ULF8Y7DJE26em5m/j5y8sAuOLoQfz8K6NbdL3qgOOHLyzh5SXemLHvHjeEm08d0ezEcG1uEU98tIF/LdpGaeUX8yEePbQ7l0/P4fgR2erzJyLiM7NFzrmDBlvEuqZQRNqYj9bu4fZXlgNw3PAeEelDl5xk3HPeOCqrHa99toO/vLeOtJQkfnDy8LCvEQg43lu9m799uKF2WhiAjNQkzpnYj8uOytEaxCIiTaCkUETqtWFPMVc/u5jqgGNodkfuv2hCxKaSSUlO4r4LxlNZHeDNFbv409trSEs2rj1hGOD1YdxVWMbOfWVs31fGzn2lbC/wXu/YV8rWvaXkBU2Q3adzBpcelcMFR/SnS/u0iMQoIpJI1HwsInXaV1rJVx/6kPW7i+nSPpX/XDOdgd06RPw+5VXVXP3M4trRwaN7d2JPUTm7i8oJ59fTlJwsLpuewymje5KSHN/VUkRE2gI1H4tI2Kr8ef3W7y72V/6YFJWEECA9JZmHLp7IlU8t5IM1e1ix46BpS+mQlkzvLu3o3TnD39rRp0sGY/t2aXAqHBERCZ+SQhE5yB2vfV7bT++XZx3WpKXgmiMjNZlHvjGZxz5YT2V1gN5d2tGrcwZ9Orejd5cMMtNTtISciEiUKSkUkQM8N28zT3y0EYDLjsrhoqmxmcalXVoy1504LCb3EhGRg6kDjojU+nhdHv/3H2/qmWOH9+DW07Vah4hIolBSKCIAbMor5upnF1EVcAzu0YH7L5yggRsiIglEv/FFhMKySq54ciEFJZV0bpfKX795BJ3bhbd8nYiIHBrUp1AkgTnneHf1bn73+krW5haRnGT8+eKJDOoenZHGIiLSeikpFElQizbt5XdvrGT+hvzasl+cOYajhnaPY1QiIhIvSgpFEszqXfu5e+Yq3lqxq7Zs8sCu/PjUkUwZlBXHyEREJJ6UFIokiC35Jdw3aw0vfbK1dqWQkb0y+fGpIzh+RLbmARQRSXBKCkUOcXuKynlw9lqenbuZiuoAAP2z2vHDk0dwxrg+EVvLWERE2jYlhSKHqP1llTz2wQYe+2A9xRXVAHTvmMb1Jw7jgiMGkJaiyQdEROQLSgpFDjGb80p46uON/GPhFvaXVQGQmZ7Cd44bzOXTB9EhXf/sRUTkYPrrIHIIcM7x8fo8Hv9wI7M+31XbZzAtJYnLjsrh6uOG0LVDWnyDFBGRVk1JoUgbVlZZzcufbOOJjzaycuf+2vLuHdO55MgBXDx1ID0y0+MYoYiItBVKCkXaoB37Snn64038ff5m9pZU1pYf3q8zl0/P4fSxfdRnUEREmkRJoUgbsmRLAY9+sJ43lu2kOuC1EScnGace1otvTc9h4oCumlpGRESaRUmhSBuwY18pd/5vJa8s3V5b1rV9KhdOGcA3pg2kd+d2cYxOREQOBUoKRVqxsspqHvtgPQ/OXkdppTetzLDsjlxx9CDOntCXjNTkOEcoIiKHCiWFIq2Qc46Zy3fx6/+tYEt+KeDVDP7wlBFcOGWAJpwWEZGIU1Io0sqs2bWfX7y6gjlr9wBen8FLpg7gxpOH06W9ppUREZHoUFIo0krsK63kvlmreerjTbWDSI4a0o3bzhjDiF6ZcY5OREQOdUoKRaKgpKKKssoAaSlJpCYbqUlJJNXT5FsdcPxjwRbueXMV+cUVAPTt0o5bTx/FqYf10mhiERGJCSWFIhHknOOR99dz71urqagKHLAvNdlITU6q3dL9hLGsMsDOwjIAMlKT+N6MoVx17GANIhERkZhSUigSIZXVAX7+8jKeX7Clnv2OyupqoLrO/V85vDc/PW0UfbtoehkREYk9JYUiEbCvtJLvPbuID9fmAXDqmF587/ghVFYHqKhyVFYH/OcBKqoDfoLova6sDjBxYFcmDuga53chIiKJTEmhJKySiioKSirp3TmjRf32NueVcPkT81m3uxiA7x43hB9/aUS9fQhFRERaIyWFklA255XwzspdvLNqN3PX51FRFeCInK7cePJwjhrSvcnXW7Qpn6ueWkRecQUpScYdZx/GBVMGRCFyERGR6FJSKIe0yuoACzbmM3tlLu+szK2tzQu2YONeLnp0HlMHZXHjycM5cnC3sK79ytLt3PTiUiqqAmRmpPCXSyYxfWjTE0sREZHWQEmhHHJ27y9n9qpcZq/MZc6aPewvrzpgf6eMFI4d3oMTRmaTmZHKA++sYenWfczbkM8Fj8zlqCHduPHk4RyRk1Xn9Z1zPPDOWu59azUA/bPa8fhlRzA0W3MJiohI26WkUNqkyuoA2/aWsiGvmI17itmUV8KGPcVszPOehxrRM5MZI3twwohsJg3sSkpyUu2+k0ZlM3tVLr9/azXLthXy0bo8Plr3MUcP7c6NJw9j0sAvksPyqmp++tJnvLR4GwCTBnblkW9MolvH9Oi/aRERkSgy51y8Y4iJyZMnu4ULF8Y7DGmGz3cUMm99Hhv9xG9TXjFb95ZSFaj/u5ueksT0od05fmQ2x4/oQb+u7Ru9j3OOWZ/n8oe3VrNiR2Ft+bHDe3DjScPI6daB7zyziPkb8gE4Y1wf7j73cM0nKCIibYqZLXLOTT6oXEmhtFYlFVXc9cYqnvx4I/V9Tc2gT+d2DOregZzu7cnp1oFhPTOZOiir2cmac46Zy3dx36zVrNy5v7a8c7tU9pVWAnD9icO48aRhWm1ERETanPqSQjUfS6v00do93PzSp2zJLwWgR2Y6I3pm1iZ+Od28JLB/VnvSUyJbU2dmnHpYL04Z3ZM3lu/kvlmrWb2riH2llaQmG7/72uGcM7FfRO8pIiISb0oKpVUpLKvkzv+t5O/zNwOQkmRce8JQvjdjKGkpSY2cHVlJScZpY3tz6phevPbZDt5ZmcvFUwcwuZ4BKCIiIm2ZkkJpNWavyuWWlz5jxz5vHeCxfTtz17mHM6p3p7jGlZRknDGuD2eM6xPXOERERKJJSaEcZH9ZJQs37mXu+jyWbd9Hh7QUumem071jOj06ptG9YzrdM9Pp4T92SEtuUd+6gpIKfvnfFbUjetNSkvj+ScO46pjBB4wSFhERkehRUigUlVexYGM+c9fnMXd9Psu27aO6gZG9oTJSk7yEMTOdAVntGdKjo7dle33/Ghrw8cayndz68jL2FJUDMGFAF+4+93DN+SciIhJjSgrbqEDA8fnOQt5fvYf3V+9my94SurZPI6tDGt06ptGtQxrdOqaT1SGN7h3TyOqQ7pel4Rx+Euglgp/VkQSawahenZg0sCtVgQC791ewp6icPUXl7N5fTnlVoPbYssoAW/eWsnVvKZ9sLjjoOv27tmdIjw4MrkkWe3SgR2Y69761mtc+3QF4ieWPvjSSy47KIVlrBouIiMScksI2ZE9ROR+s2c0Hq/fw/po9tbVrNbbuLW3R9Uf17sSRg7OYNrgbUwZl0aV9Wp3HOecoKq9iT5GfKO73ksXc/eVs2FPMut3FrN9dRHlVAOdgc34Jm/NLmL1qd53Xmzooi9997XByundoUfwiIiLSfEoKW7GKqgCLNu3l/TW7eX/1bpZvLzzomOzMdI4d3oPD+nSisKyK/GIvUcsrqiC/uIK84nLyiyuoqzV4ZK9MjhzcjSMHd2PqoCy6dqg7CQxlZmRmpJKZkcqgehK5QMCxraCUdbuLWLe72HvM9Z7XJLMd0pL5yWmjuHjKAJJUOygiIhJXSgpbmR37Spm9cjfvrMzl43V7KK6oPmB/WnISUwZlcezw7hw7vAcjemY2OsgjEHAUlFaSX1zOnqIKyiqrObxfF7LCTAKbIynJ6J/lzSM4Y8SB+/aVVLIpv5gBWe3rrY0UERGR2FJSGGfVAceSLXt5Z2Uu76zczec7Dq4NHNKjA8cO78Gxw3tw5KButEtr2mTNSUlGVgevv+HQ7EhF3nyd26dyePsu8Q5DREREgigpbKLi8ip2Fpaxy9927y8nPSW5doBH947egI4u7dPqHTBRUFLBe6t3M3tlLu+t3s3eksoD9rdPS/bW7R2RzbHDu4e1bq+IiIhISygpDFFZHWDOmj1syitmZ2E5uYVlQUlgOUXlVWFdJ8mga/uakcDpZHVMI6t9Gqt27mfhpvyD+vgN7Nae40dkc8LIbKYOzor40m0iIiIiDVFSGGRXYRnXPLuYhZv2hnV853ap9MhMp6IqQF5R+QH9/wIO8ooryCuuAIoOOjclyZgyKIsTRmZz/MhsBnfv0KIJoEVERERaQkmhb976PK557pPakbH9s9rRq1MGPTtl1D727JxBz8x0enXOIDsz46C+fWWV1V4iWFTuP4Y8Ly4nOzOd40dkc/Sw7mRmpMbjrYqIiIgcJOGTQuccf52zgTtfX0l1wNEhLZm7zxvHaWN7N/laGanJ9O3Sjr5d2kUhUhEREZHoSeiksLi8ih//69PaVTWG9OjAw9+YpCXWREREJOEkbFK4bncR33l6EWtzvf5+p43txV3njqNjesJ+JCIiIpLAEjIDemPZDm568VOKyqtITjJ++uWRXHH0IA30EBERkYSVUElhVXWAu99cxcPvrQege8c0HrhoIkcO7hbnyERERETiKymWNzOzZDO728x2m9l+M/uXmXVv4PhTzWy5mZWa2TIzO6W5964KOL7x1/m1CeHEAV3473XHKCEUERERIcZJIfAT4CxgKtDPL3u6rgPNbDDwEnAn0Nl//LeZ5TTnxmtzi/h4fR4Alx2Vw/NXTaNX54zmXEpERETkkBPrpPAq4HfOufXOuX3Aj4FT60n0vgkscs4945yrcM49Cyz2y8NiZt3MbLiZDa+sDpCRmsR9Xx/P7WeOIS0l1m9dREREpPWKWWZkZp2BAcCimjLn3DqgEDi8jlPGBR/rW+yXh+s6YBWwKpkA//7edM6e0LdJcYuIiIgkglhWl3XyH/eFlBcE7QuW2YRj63M/MAIYMaxnJqN6N+VUERERkcQRy6Rwv//YOaS8C15tYV3Hh3tsnZxzec651c651akpCTXQWkRERKRJYpYUOucKgM3AxJoyfzBJJ+DTOk5ZGnysb4JfLiIiIiIRFOvRFo8AN5vZIDPrBPwOmOmc21jHsU8Bk83sQjNLNbMLgUnAk7ELV0RERCQxxDop/C3wKrAA2AYkA5cAmNnFZlZUc6A/COUc4Fa8JuNbga/Wk0CKiIiISAuYcy7eMcTE5MmT3cKFC+MdhoiIiEhcmdki59zk0HJN1iciIiIiSgpFREREREmhiIiIiKCkUERERERQUigiIiIiKCkUEREREZQUioiIiAhKCkVEREQEJYUiIiIigpJCERERESGBlrkzs93Apiac0gUoiEowbSsGiH8cyUBPYBdQHcc4IP6fhWL4Qmv5XnQh/p8FtI44WkMM+l4cqAvxj6M1xKDvxYGGO+c6hRYmTFLYVGb2nHPuokSPoTXEYWbDgVXACOfc6njF4ccS95+JYqiNoVV8L1rDZ9Fa4mglMeh70criaCUx6HsRRhxqPq7fv+IdAK0jBmg9cbQGreGzUAytS2v5LFpDHK0hhtaitXwWrSGO1hBDa9FaPos641BNobR6reV/eNK66HshddH3Quqi70V4VFMobUEe8Av/UaSGvhdSF30vpC76XoRBNYUiIiIioppCEREREVFSKCIiIiIoKRQRERERlBSKiIiICEoKRURERAQlhSIiIiKCkkIRERERQUmhiIiIiKCkUGLIzH5nZsvNrNDMtpvZo2aWFXLMpWa2zsxKzGyemU0K2tfOzF40szVmFjCzW+u4x1Qze9/MCsxsl5k9bWbdYvH+pOli9J043MzeNrO9ZrbDzH5pZhaL9yfNE4HvxZFm9pr/O2CfmS0ys7NDzs82s5fMbL+Z7fbvqb+JrViMvhd3mNknZlZhZrNi9NZaDf0DkFiqBi4BugHjgH7A4zU7zexo4M/A1UBXvAW7/2dmnfxDHPARcBUwP/TiZpYM/Bf4EOgBjAL6AH+KztuRCIj2d6Iz8AYwE+87cQJwGfDDqLwbiZSWfi+ygH8Ah/n7fwX83cyOCLrHs/5jP2Aq8FXgR1F6PxIZsfherAP+D3gkqu+ktXLOadMWlw04HdgX9PpJ4Omg1wZsBr5Zx7nvAreGlGXhJQnDg8q+AyyL93vVFrfvxGlAPv6Snn7ZbcD6eL9XbbH5XgQdswC40X8+yP9dMSRo/xXAhni/V23x+16ElN8OzIr3e4z1pppCiacTgU+DXo8DFtW8cN6/zE/88kY55/KBh4ErzSzdzHoAFwD/jljEEm0R/U7gtYaENhUnAYOCag+k9WvR98LMegFjgq4xDi+ZWBd02GIgR9+LNiXS34uEp6RQ4sLMvgZcCdwQVJwJ7As5tABoyi/pF/GagYqBXCAA3NnsQCVmovSd+AjvO/BTM0szs8OAb/n79Me/DWjp98LMOuA1I77inHu7kfOp6xrS+kTpe5HwlBRKzJnZecCjwJnOucVBu/YDnUMO7wIUhnndYcDrwB1AO//cdXh9yqQVi9Z3wq89Ph04FdgBPAX8DS9R3NuyqCXaWvq9MLNMvN8JucClYZxfs09asSh+LxKekkKJKTO7HK+J9wzn3OyQ3UuBiUHHGjDeLw/HOGCvc+4J51ylc24fcD9wjD/gQFqhKH8ncM7Ndc4d65zr5pybCLQHFjjnilscvERNS78X/qjUt4HtwHnOuYqQ8zub2eCgsgnARv/3hrRSUf5eJDwlhRIzZnY9cA/wJefch3Uc8ihwjpmdaGZpeCNEMwjqE+j3FczA++6mmFmGmaX6uxfi/aK/xMyS/f8NXos3qEC/6FuhGHwnMLOJflm6X8NwFfCzKL4taaGWfi/8vmLvAZ8DFzvnqoJPds5tAGYBd5lZJzMbBNyMl2xIKxXt74V/TKr/+yQFSKr53RGdd9QKxXuki7bE2fBG+1UCRcFbyDGXAuuBUrwpRiaF7N/oXyd4eyJo/+l4o8kKgDy8puPD4v3etcX1O/EIXlNxMTAPOCXe71tbdL8XeCPMnf8zD77GLUHHZAMv4TU57gHuApLi/d61xf178UQdv082xvu9x2oz/0MQERERkQSm5mMRERERUVIoIiIiIkoKRURERAQlhSIiIiKCkkIRERERQUmhiIiIiKCkUETixMzON7NP/FUHpAXM7Ggzc2aW08AxG83s1hiGFXNmNsP/HPo1cEwHM9thZhNiGZtIW6CkUOQQZWZP+H8gnZlVmtkeM5tjZjebWcdmXK/KzC6LUGypwO+A21zIZKnmWe3HPCQS92uNzGyWmT0R7zhaysze9b9jv6tj353+vnejcN+1ZnZ7U89z3vKGd+OtjCEiQZQUihzaPgB6AwOB44HngGuAT/wln+LlHCAdeK2OfcfjLU31NHBlrALyl7aS5tkMfNPMUmoK/Off9Pe1Nk8BR5vZ4fEORKQ1UVIocmircM7tdM5td8595px7CJgGdAN+W3OQmZ3s1/jkm9k+M3vfzI4M2r8RSAYer6l9DNo3xczeNrNiv1nuKTPr0UhcFwH/dc5V17HvKryE8AngsuB1jP37tTezv5lZoV/7+Tv/9btNPOZdv+zXZrYTWOOXDzez//ifwx7/+aCQGG4ys+3+e/6Xmd0Q8pkMMrOX/GNKzGyZmX0zaP8TwIl4iVRNbe4Mf19vM3vazPL8GN42s3Eh97/EzDaYWamZvQ0cEF8D2pnZX4M+l9+aWZJ/zV+Y2crQE8zs72b230au+zZQDpwRVHY63pJkb4dcz8zsp35zdoWZrTKzy0OOcWZ2jZk9a2ZFZrbFzH4ctP9dYAhwW9DnlxN0idHm1YqXmtlyMzsl+PrOuT3Ah8DFjbwvkYSipFAkwTjntgHPAl+tSQiAjsCDwJHAUcBq4HUz6+7vPwKoBr6PV/PYG8DMDgNmA+8AE4HTgF7Ay2YN9hU8Fm9d0gP49zsbrybnA7w1Ss8MOexu4MvABcAxQGfga804BuA8IAuvdvLLfu3pHLz1lKf7ce4HZplZOz/G84A7gV8DE/z3/n8h1+2IlwydCowFHgb+amYn+vtv8N/fC3zxeX7k32M2kAqcjPe5fwK8a2Y9/ftP8T+fp4HxeMnzXXW8t7pcB2zzr3uj//oGf99jwFAzO6bmYDPLAr4KPNrIdQPA48C3g8quBP7m7wuN4efAL4HD/Gs/ZmanhRz3f8C7eO/x98DvzOw4f985eD+je/ni89sSdO7dwB3AOLzP7x9m1jnk+vPxfu4iUiPeiy9r06YtOhtesjCrnn1X4y30nl3P/iRgL3BxUFkVcFnIcU8Cz4WU9favPbmea3fx93+5jn03AR8Hvb4deCPodUe8GqlvBZUlAxuAd8M9xi97Fy/5TQoq+wXwUUhMqXiJ4bn+64+Ap0KOedr7ddrgz+M/wKNBr2cBT4Qcczlec2tySPkq4Cb/+XPAnJD9v/E/05wG7r+xnvO2Br3+L/Bk0OsbgO1ASgPXfRcvoRwAVAD9gL7+z6C/vy/4c98K3BlyjeeBD4JeO+BPIcesBX4V8vr2kGNm+OeeE1TWzy87MeTY64Hd0fr3p01bW9xUUyiS2ByAmQ00syfN67xfCBTi1a4NbOT8ycDX/Ca+IjMrwm+GBYbVc047/7Gsjn3fxks0azwFnBTUNDgESAPm1r4Brwl6YdA54RxTY6FzLrgmazJwRMj72Qt0CHo/o4Kv7TvgtZm185ull5nXJF+EV4sazufZB9gXEsOQkPt/FHLeh41ct0Zd5/U1s07+64eBc4Nq1b4NPO6cq2rsws65zXi1o5cB3wLeds4F197h36cvXm1ssDnA6JCyT0JebwN6NhZHHedu8x9Dzy3li++iiAApjR8iIoeg0XiJX57/+jUgH28Qyha8Gp85eMlVQ5Lwasl+W8e+XfWcswcvGc0KLvT71I0AHjCzB4J2JeMlJ9GYTqUk5HUSXmJzbR3H5jfhunfjNbveiFfLV4zX1BnahBkqCfjcPzdUYQPnNXdan9Dz/of3Pi8ys0+AMcBZTbjeY3jv3YAfNnCca2Bfjco6zgm3IqP2XOec83syhJ6bBewO83oiCUFJoUiCMbM+eB3s/+2cC5hZN7w//qc552b6x/QFskNOrcBL0IItAg4H1jnnwvlDj3Ou0syW4fUn+1fQrquAN4AfhZzyFeA6M7sNWOfHMRVY4ceaBEzii1Gu4RxTn0XApXhNqnXVZIKXtE0FHgoqmxJyzHHAM865F4LuP5wDE+X6Ps9LgULnXG4991+B1/cz2LR6jg1V13nbnHOF4NWomtlf8ZLwJXi1fevDvDbAK3ifiwNeDd3pnCs0s214/TX/F7RrOrC8CfeBuj+/phhL3bXHIglLzccih7Y0M+tlZn3MbKyZXY3X1JkH/MQ/Zi9ejcmV5o28nYbXx6s05FrrgeP8a9UMQLkTr9bxCTObbGaDzRvJ/KiZZTYQ1+t4iRMAfmJ6Dl5fvWXBG/AIXoL6FedcEV5t1B1m9mUzGwk8gDea2uuMFsYxDbgfrw/hS2Z2lHmjiI8zs9+bWU3z7R+AC83sajMb5n+mp4dcZxVwlnkjs0f776FPHZ/nBP8z627eKOtngU3AK2Z2opnlmNk0f2RwzQCQP+JNp3K7//O6CK8vYjjGh5x3A14NZrDH8AZoXELjA0wO4JyrxGvmHu4/r8vvgBvM7FL/8/sB3oCfumqbG7IemGZm/f3PL+y/Z/4gqGOoe0okkYSlpFDk0HYMsAOvhuxdvD/0DwITnHM7Afw+defh9Vv7FG+Ayv3+ecF+gNfnbQN+s5tzbjlwNF7C9TawzD+3FG+gQX0eAY6xL1aeuBRvlGpdtUv5eIMyrvKLfgTMxBu5OwfYh1frFFyzF84xB3HO7cIbfV3ox/I53gjaTnjJM865F4Gf4Y2OXQKchDcRcvC1b8RL7mpGZu8A/hlyu3vwkvNP8T7P6c65UrwRz58Cz+All//Aq2Xc7t9/vv95fdM/7iq+SPAb8wDe9DWL8H5OD+AlmcGfwVa8GttC4OUwrxt8fmFNzWMDMfwa+BVe7eB3gCudc/9r4Jy6/B/eoKXVeJ/fgCacewzez/SFJt5T5JBmYbb4iIhElJk9Dux1zv2ghdcxvCbVt5xz1zf3mBbG8DAwzTl3SEyGbGbzgPedc6FN+YcEM3sV+NA519TaSZFDmvoUiki8/BRv8mYLtz8igJmNxZsf8GO80aPX4jVZXtyUY5rLb+b9AV/UPH4Zr/n2ppZeO97MrCvevJATgfPjHE5UmFkHvDkK74tzKCKtjmoKRaRN8SfM/ive1CwOrwnyVufcO005pgX3T8HrizYJb6qadcBfgAebkty2RuatypIP3OKcezje8YhIbCkpFBERERENNBERERERJYUiIiIigpJCEREREUFJoYiIiIigpFBEREREUFIoIiIiIsD/AxV8CEjAUa1vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for context in [(\"dark_background\", \"seaborn-talk\"), (\"seaborn-talk\", )]:\n",
    "    with plt.style.context(*context):\n",
    "        fig, ax = plt.subplots()\n",
    "        cdf.plot(y='counts', ax=ax)\n",
    "        ax.set_title(f\"Downloads per Month\")\n",
    "        ax.set_xlabel(\"Date (Aggregated by Month)\")\n",
    "        ax.set_ylabel(\"Number of Downloads\")\n",
    "        fig.savefig(f\"plots/downloads-{'_'.join(context)}.png\", bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.525450666"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf['counts'].sum()/1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "idx\n",
       "2017-03-01      0.701645\n",
       "2017-04-01      7.375445\n",
       "2017-05-01      8.261383\n",
       "2017-06-01      7.535179\n",
       "2017-07-01     10.039857\n",
       "2017-08-01     12.408943\n",
       "2017-09-01     11.690260\n",
       "2017-10-01     13.837261\n",
       "2017-11-01     13.064540\n",
       "2017-12-01     11.730379\n",
       "2018-01-01     17.343201\n",
       "2018-02-01     18.564943\n",
       "2018-03-01     21.064271\n",
       "2018-04-01     16.748850\n",
       "2018-05-01     24.040170\n",
       "2018-06-01     23.831192\n",
       "2018-07-01     28.815251\n",
       "2018-08-01     32.511378\n",
       "2018-09-01     37.050898\n",
       "2018-10-01     47.325248\n",
       "2018-11-01     50.874921\n",
       "2018-12-01     42.107025\n",
       "2019-01-01     43.062229\n",
       "2019-02-01     46.763206\n",
       "2019-03-01     55.879507\n",
       "2019-04-01     48.684136\n",
       "2019-05-01     66.728803\n",
       "2019-06-01     62.462089\n",
       "2019-07-01     65.210830\n",
       "2019-08-01     61.969868\n",
       "2019-09-01     62.608046\n",
       "2019-10-01     78.757103\n",
       "2019-11-01     85.271670\n",
       "2019-12-01     90.225074\n",
       "2020-01-01     99.275455\n",
       "2020-02-01     99.000914\n",
       "2020-03-01    115.990884\n",
       "2020-04-01    122.603668\n",
       "2020-05-01    117.375623\n",
       "2020-06-01    120.825988\n",
       "2020-07-01    126.489061\n",
       "2020-08-01    150.563060\n",
       "2020-09-01    162.411810\n",
       "2020-10-01    174.860758\n",
       "2020-11-01    157.774277\n",
       "2020-12-01    157.887143\n",
       "2021-01-01    191.561916\n",
       "2021-02-01    226.465813\n",
       "2021-03-01    261.409705\n",
       "2021-04-01    243.629532\n",
       "2021-05-01    236.837474\n",
       "2021-06-01    264.760615\n",
       "2021-07-01    271.182169\n",
       "Name: counts, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf['counts'] / 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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