{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import datetime\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import subprocess\n", "import requests\n", "import json\n", "from scipy import stats\n", "from ipywidgets import interactive\n", "import ipywidgets as widgets\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "datasource = 'https://github.com/CSSEGISandData/COVID-19.git'\n", "testsource = 'https://covidtracking.com/api/states/daily'\n", "nation = 'US'\n", "series = 'Confirmed'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/home/leon/Covid-19/COVID-19',\n", " '/home/leon/Covid-19/COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "covid19_dir = os.path.join(\n", " os.environ['HOME'],\n", " 'Covid-19',\n", " 'COVID-19'\n", ")\n", "ts_file = os.path.join(\n", " covid19_dir,\n", " 'csse_covid_19_data',\n", " 'csse_covid_19_time_series',\n", " 'time_series_19-covid-{series}.csv'.format(series=series) \n", ")\n", "[\n", " covid19_dir,\n", " ts_file\n", "]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def run(cmd):\n", " result = subprocess.run(cmd, capture_output=True)\n", " if result.stdout:\n", " print('\\nStdout:\\n', \n", " result.stdout.decode(), \n", " '\\n')\n", " if result.stderr:\n", " print('\\nStderr:\\n', \n", " result.stderr.decode(), \n", " '\\n')\n", " return result" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Stderr:\n", " fatal: remote CSSEGISandData already exists.\n", " \n", "\n", "\n", "Stderr:\n", " From https://github.com/CSSEGISandData/COVID-19\n", " = [up to date] master -> CSSEGISandData/master\n", " = [up to date] web-data -> CSSEGISandData/web-data\n", " \n", "\n", "\n", "Stdout:\n", " Already up to date.\n", " \n", "\n" ] }, { "data": { "text/plain": [ "[CompletedProcess(args=['git', 'remote', '-v', 'add', 'CSSEGISandData', 'https://github.com/CSSEGISandData/COVID-19.git'], returncode=128, stdout=b'', stderr=b'fatal: remote CSSEGISandData already exists.\\n'),\n", " CompletedProcess(args=['git', 'fetch', '-v', 'CSSEGISandData'], returncode=0, stdout=b'', stderr=b'From https://github.com/CSSEGISandData/COVID-19\\n = [up to date] master -> CSSEGISandData/master\\n = [up to date] web-data -> CSSEGISandData/web-data\\n'),\n", " CompletedProcess(args=['git', 'merge', '-v', 'CSSEGISandData/master'], returncode=0, stdout=b'Already up to date.\\n', stderr=b'')]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.chdir(covid19_dir)\n", "[\n", " run(['git', 'remote','-v', 'add', 'CSSEGISandData', datasource]),\n", " run(['git', 'fetch','-v', 'CSSEGISandData']),\n", " run(['git', 'merge', '-v', 'CSSEGISandData/master'])\n", "]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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479NaNEast Timor-8.5500125.5600000000...0000000001
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100CaliforniaUS36.1162-119.6816000000...22128234042655769875195211771364
101MassachusettsUS42.2302-71.5301000000...108123138164197218218328413525
102Diamond PrincessUS35.4437139.6380000000...46464646474747474949
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399New Castle, DEUS39.5393-75.6674000000...0000000000
414AlabamaUS32.3182-86.9023000000...056122939467883131
417Puerto RicoUS18.2208-66.5901000000...003555551421
431GuamUS13.4443144.7937000000...0003335121415
448Virgin IslandsUS18.3358-64.8963000000...0011122333
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247 rows × 64 columns

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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "98 Washington US 47.4009 -121.4905 0 0 \n", "99 New York US 42.1657 -74.9481 0 0 \n", "100 California US 36.1162 -119.6816 0 0 \n", "101 Massachusetts US 42.2302 -71.5301 0 0 \n", "102 Diamond Princess US 35.4437 139.6380 0 0 \n", ".. ... ... ... ... ... ... \n", "399 New Castle, DE US 39.5393 -75.6674 0 0 \n", "414 Alabama US 32.3182 -86.9023 0 0 \n", "417 Puerto Rico US 18.2208 -66.5901 0 0 \n", "431 Guam US 13.4443 144.7937 0 0 \n", "448 Virgin Islands US 18.3358 -64.8963 0 0 \n", "\n", " 1/24/20 1/25/20 1/26/20 1/27/20 ... 3/12/20 3/13/20 3/14/20 \\\n", "98 0 0 0 0 ... 442 568 572 \n", "99 0 0 0 0 ... 328 421 525 \n", "100 0 0 0 0 ... 221 282 340 \n", "101 0 0 0 0 ... 108 123 138 \n", "102 0 0 0 0 ... 46 46 46 \n", ".. ... ... ... ... ... ... ... ... \n", "399 0 0 0 0 ... 0 0 0 \n", "414 0 0 0 0 ... 0 5 6 \n", "417 0 0 0 0 ... 0 0 3 \n", "431 0 0 0 0 ... 0 0 0 \n", "448 0 0 0 0 ... 0 0 1 \n", "\n", " 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 \n", "98 643 904 1076 1014 1376 1524 1793 \n", "99 732 967 1706 2495 5365 8310 11710 \n", "100 426 557 698 751 952 1177 1364 \n", "101 164 197 218 218 328 413 525 \n", "102 46 47 47 47 47 49 49 \n", ".. ... ... ... ... ... ... ... \n", "399 0 0 0 0 0 0 0 \n", "414 12 29 39 46 78 83 131 \n", "417 5 5 5 5 5 14 21 \n", "431 3 3 3 5 12 14 15 \n", "448 1 1 2 2 3 3 3 \n", "\n", "[247 rows x 64 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nation_frame = ts_frame[nationrows]\n", "nation_frame" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Province/State WashingtonNew YorkCaliforniaMassachusettsDiamo...\n", "Country/Region USUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUSUS...\n", "Lat 9531\n", "Long -22951.8\n", "1/22/20 1\n", " ... \n", "3/17/20 6421\n", "3/18/20 7783\n", "3/19/20 13677\n", "3/20/20 19100\n", "3/21/20 25489\n", "Length: 64, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nation_frame.aggregate(np.sum)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[pandas.core.series.Series,\n", " 1/22/20 1\n", " 1/23/20 1\n", " 1/24/20 2\n", " 1/25/20 2\n", " 1/26/20 5\n", " 1/27/20 5\n", " 1/28/20 5\n", " 1/29/20 5\n", " 1/30/20 5\n", " 1/31/20 7\n", " 2/1/20 8\n", " 2/2/20 8\n", " 2/3/20 11\n", " 2/4/20 11\n", " 2/5/20 11\n", " 2/6/20 11\n", " 2/7/20 11\n", " 2/8/20 11\n", " 2/9/20 11\n", " 2/10/20 11\n", " 2/11/20 12\n", " 2/12/20 12\n", " 2/13/20 13\n", " 2/14/20 13\n", " 2/15/20 13\n", " 2/16/20 13\n", " 2/17/20 13\n", " 2/18/20 13\n", " 2/19/20 13\n", " 2/20/20 13\n", " 2/21/20 15\n", " 2/22/20 15\n", " 2/23/20 15\n", " 2/24/20 51\n", " 2/25/20 51\n", " 2/26/20 57\n", " 2/27/20 58\n", " 2/28/20 60\n", " 2/29/20 68\n", " 3/1/20 74\n", " 3/2/20 98\n", " 3/3/20 118\n", " 3/4/20 149\n", " 3/5/20 217\n", " 3/6/20 262\n", " 3/7/20 402\n", " 3/8/20 518\n", " 3/9/20 583\n", " 3/10/20 959\n", " 3/11/20 1281\n", " 3/12/20 1663\n", " 3/13/20 2179\n", " 3/14/20 2727\n", " 3/15/20 3499\n", " 3/16/20 4632\n", " 3/17/20 6421\n", " 3/18/20 7783\n", " 3/19/20 13677\n", " 3/20/20 19100\n", " 3/21/20 25489\n", " dtype: object]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nation_series = nation_frame.agg(np.sum)[4:]\n", "[\n", " type(nation_series),\n", " nation_series\n", "]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3/17/20 1.21212\n", "3/18/20 1.75729\n", "3/19/20 1.39651\n", "3/20/20 1.3345\n", "dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array(nation_series[-4:])/nation_series[-5:-1]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[pandas.core.indexes.base.Index,\n", " Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n", " '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n", " '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n", " '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n", " '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n", " '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n", " '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n", " '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n", " '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n", " '3/19/20', '3/20/20', '3/21/20'],\n", " dtype='object'),\n", " list,\n", " [Timestamp('2020-01-22 00:00:00'),\n", " Timestamp('2020-01-23 00:00:00'),\n", " Timestamp('2020-01-24 00:00:00'),\n", " Timestamp('2020-01-25 00:00:00'),\n", " Timestamp('2020-01-26 00:00:00'),\n", " Timestamp('2020-01-27 00:00:00'),\n", " Timestamp('2020-01-28 00:00:00'),\n", " Timestamp('2020-01-29 00:00:00'),\n", " Timestamp('2020-01-30 00:00:00'),\n", " Timestamp('2020-01-31 00:00:00'),\n", " Timestamp('2020-02-01 00:00:00'),\n", " Timestamp('2020-02-02 00:00:00'),\n", " Timestamp('2020-02-03 00:00:00'),\n", " Timestamp('2020-02-04 00:00:00'),\n", " Timestamp('2020-02-05 00:00:00'),\n", " Timestamp('2020-02-06 00:00:00'),\n", " Timestamp('2020-02-07 00:00:00'),\n", " Timestamp('2020-02-08 00:00:00'),\n", " Timestamp('2020-02-09 00:00:00'),\n", " Timestamp('2020-02-10 00:00:00'),\n", " Timestamp('2020-02-11 00:00:00'),\n", " Timestamp('2020-02-12 00:00:00'),\n", " Timestamp('2020-02-13 00:00:00'),\n", " Timestamp('2020-02-14 00:00:00'),\n", " Timestamp('2020-02-15 00:00:00'),\n", " Timestamp('2020-02-16 00:00:00'),\n", " Timestamp('2020-02-17 00:00:00'),\n", " Timestamp('2020-02-18 00:00:00'),\n", " Timestamp('2020-02-19 00:00:00'),\n", " Timestamp('2020-02-20 00:00:00'),\n", " Timestamp('2020-02-21 00:00:00'),\n", " Timestamp('2020-02-22 00:00:00'),\n", " Timestamp('2020-02-23 00:00:00'),\n", " Timestamp('2020-02-24 00:00:00'),\n", " Timestamp('2020-02-25 00:00:00'),\n", " Timestamp('2020-02-26 00:00:00'),\n", " Timestamp('2020-02-27 00:00:00'),\n", " Timestamp('2020-02-28 00:00:00'),\n", " Timestamp('2020-02-29 00:00:00'),\n", " Timestamp('2020-03-01 00:00:00'),\n", " Timestamp('2020-03-02 00:00:00'),\n", " Timestamp('2020-03-03 00:00:00'),\n", " Timestamp('2020-03-04 00:00:00'),\n", " Timestamp('2020-03-05 00:00:00'),\n", " Timestamp('2020-03-06 00:00:00'),\n", " Timestamp('2020-03-07 00:00:00'),\n", " Timestamp('2020-03-08 00:00:00'),\n", " Timestamp('2020-03-09 00:00:00'),\n", " Timestamp('2020-03-10 00:00:00'),\n", " Timestamp('2020-03-11 00:00:00'),\n", " Timestamp('2020-03-12 00:00:00'),\n", " Timestamp('2020-03-13 00:00:00'),\n", " Timestamp('2020-03-14 00:00:00'),\n", " Timestamp('2020-03-15 00:00:00'),\n", " Timestamp('2020-03-16 00:00:00'),\n", " Timestamp('2020-03-17 00:00:00'),\n", " Timestamp('2020-03-18 00:00:00'),\n", " Timestamp('2020-03-19 00:00:00'),\n", " Timestamp('2020-03-20 00:00:00'),\n", " Timestamp('2020-03-21 00:00:00')],\n", " array([1, 1, 2, 2, 5, 5, 5, 5, 5, 7, 8, 8, 11, 11, 11, 11, 11, 11, 11, 11,\n", " 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 15, 15, 15, 51, 51, 57, 58,\n", " 60, 68, 74, 98, 118, 149, 217, 262, 402, 518, 583, 959, 1281, 1663,\n", " 2179, 2727, 3499, 4632, 6421, 7783, 13677, 19100, 25489],\n", " dtype=object),\n", " '3/21/20']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "strdates = nation_series.keys()\n", "dates = [pd.to_datetime(sd) for sd in strdates]\n", "[\n", " type(strdates),\n", " strdates,\n", " type(dates),\n", " dates,\n", " np.array(nation_series[:]),\n", " strdates[-1]\n", "]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "nations_widget = widgets.Dropdown(\n", " options = nations,\n", " value = nation,\n", " description = \"Nation: \"\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['Confirmed', 'Deaths', 'Recovered'])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load series into a dict of pandas dataframes\n", "series_options = ['Confirmed','Deaths','Recovered']\n", "#series_options = [re.match(r'time_series_19-covid-(.*).csv',file)[1] for file in os.listdir(os.path.join(\n", "# covid19_dir,\n", "# 'csse_covid_19_data',\n", "# 'csse_covid_19_time_series'))\n", "# if re.match(r'time_series_19-covid-(.*).csv',file)\n", "#]\n", "series_frames = dict();\n", "for series_to_read in series_options:\n", " ts_file = os.path.join(\n", " covid19_dir,\n", " 'csse_covid_19_data',\n", " 'csse_covid_19_time_series',\n", " f'time_series_19-covid-{series_to_read}.csv' \n", " )\n", " series_frames[series_to_read] = pd.read_csv(ts_file, parse_dates=True, date_parser=pd.to_datetime)\n", " \n", "series_frames.keys()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "nation_series_cache = dict()\n", "def get_nation_series(nation,series):\n", " if nation in nation_series_cache and series in nation_series_cache[nation]:\n", " nation_series = nation_series_cache[nation][series]\n", " else:\n", " ts_frame = series_frames[series]\n", " nationrows = ts_frame.loc[:, 'Country/Region'] == nation\n", " nation_frame = ts_frame[nationrows]\n", " nation_series = nation_frame.agg(np.sum)[4:]\n", " if nation not in nation_series_cache:\n", " nation_series_cache[nation] = dict()\n", " nation_series_cache[nation][series] = nation_series\n", "\n", " strdates = nation_series.keys()\n", " dates = [pd.to_datetime(sd) for sd in strdates]\n", "\n", " return (nation_series,dates)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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5SUlJ/vqwAQAA8D/lFU53vJyu5dvz9OTYfjqnQxOvI50ypnkAAADAE3/5cL0+Xpet317VU6N6tvA6zmnx5cg0AAAA/O29Vbv19NzNGjuojb435Gyv45w2yjQAAABq1KbsAv2/VzPUp01DPXRFD6/jnBHKNAAAAGpMQXGpJk1fqvjYaD3zvf6qWye8F2U5GeZMAwAAoEZUVDj95NXl2pZTqBkTBuuspHivI50xRqYBAABQI56eu0kfrtmjX1zaTUPaN/Y6TlBQpgEAABByc9dn6y8fbdBVfVvqlnNTvI4TNJRpAAAAhNS2nEO6a2a6urVooD98p7fMzOtIQUOZBgAAQMgUlpRp8vSliooy/WPcAMXH+vuCw6NRpgEAABASzjn97F8rtWFPgZ68vp/aJNfzOlLQUaYBAAAQEs/N/1Kzl+/UTy/uovM7N/U6TkhQpgEAABB0n23epz+8u06X9GyhHwzv4HWckKFMAwAAIKhyDh7WHS+nq32T+vrTNX0i6oLDo7FoCwAAAILq+QVfKrewRDMnDlFC3cium4xMAwAAIGgKiks1beE2jerRQl1aJHodJ+Qo0wAAAAial7/IVEFxmX4wInLnSVdHmQYAAEBQFJeW69n5X2pYxybq3bqh13FqBGUaAAAAQfHGsh3aW3C41oxKS5RpAAAABEF5hdM/5m1W79ZJOqdDY6/j1BjKNAAAAM7Yu6t2aVtOoW4f0SGib4V3NMo0AAAAzohzTn9P26z2Tevrou4tvI5To8KqTJtZfTNbamaXe50FAAAAgZm3cZ9W7zyg287voKio2jMqLYW4TJvZ82aWbWarjto+yszWm9kmM7uv2lM/k/RqKDMBAAAguP6etkktGsTpqn6tvI5S40I9Mj1V0qjqG8wsWtLTki6R1F3SWDPrbmYXSlojaU+IMwEAACBIlmXm6vMt+zXhvHaKrRNWkx5qREjXd3TOzTOzlKM2D5K0yTm3RZLMbJakKyUlSKqvyoJdZGZznHMVocwHAACAM/NM2mYlxcdo7KC2XkfxhDnnQnuAyjL9jnOuZ9XjMZJGOecmVD0eJ2mwc+6Oqsc3S9rnnHvnOPubJGmSJDVv3nzArFmzQpr/WA4ePKiEhIQaPy4Cw/kJX5yb8MW5CV+cm/DFuZF2HqzQ/fOLdGWHGF3dKdbrOF8T7PMzcuTIpc651KO3h3Rk+jiONSv9q0bvnJt6ojc756ZImiJJqampbsSIEcHMFpC0tDR5cVwEhvMTvjg34YtzE744N+GLcyP9v1eXKz5mlx68YYSS64dXma6p8+PFxJYsSW2qPW4taacHOQAAAHCaduQV6d8ZO3T9oDZhV6RrkhdlerGkTmbWzsxiJV0v6W0PcgAAAOA0PfvpFknShPPae5zEW6G+Nd5MSQsldTGzLDMb75wrk3SHpPclrZX0qnNu9Snud7SZTcnPzw9+aAAAAJzQ/kMlmrVou67s20qtGsZ7HcdTob6bx9jjbJ8jac4Z7He2pNmpqakTT3cfAAAAOD1TP9uqotJy/WBE7R6VlsJsBUQAAACEt0OHy/TiZ1t1Uffm6tgs0es4nqNMAwAAIGAzF2Uqv6hUt43o4HWUsECZBgAAQEBKyir07Kdfakj7ZPVv28jrOGHBl2WaCxABAABqVkWF02MfbdDuA8W6fURHr+OEDV+WaefcbOfcpKSkJK+jAAAARLw9B4o17vkv9Pe0zbqqb0ud16mJ15HChhcrIAIAAMAnPl63Rz99bYUKS8r0x+/20rWpbWR2rAWtayfKNAAAAL7hcFm5Hnl3nV5YsFXdzmqgp8b25e4dx0CZBgAAwNdsyj6oO2ema82uA7r5nBTdd0lXxcVEex0rLPmyTJvZaEmjO3Zk8jsAAECwOOf02pIsPfj2asXFROnZ76fqwu7NvY4V1rgAEQAAADpQXKofzUzXvf9aob5tGurdu86nSAfAlyPTAAAACJ70zFz9aGa6duUX656Lu+i24R0UHcVFhoGgTAMAANRim7ILNO65RWpYL0av3TaUxVhOEWUaAACglsovKtXEaUsVFxOlVycPVcuG8V5H8h3KNAAAQC1UXuF058x0ZeUW6uWJQyjSp8mXFyCynDgAAMCZefT9dfpkw179+oqeGpiS7HUc3/JlmeZuHgAAAKfv3xk79I9Ptuh7Q9rqhsFtvY7ja74s0wAAADg9K7Pyde/rKzQoJVm/uryH13F8jzINAABQS+w7eFiTpy9R4/qx+tv3+iu2DlXwTHEBIgAAQC1QUlah219apv2FJXr9tnPUJKGu15EiAmUaAACgFvj17NVatHW/nri+r3q24rqzYGFsHwAAIMLN+GKbZnyRqcnD2+vKvq28jhNRfFmmuTUeAABAYBZv3a8H/71awzs31b0Xd/U6TsTxZZnm1ngAAAAntzOvSD94aanaJNfTk2P7KTrKvI4UcZgzDQAAEIEKS8o0efpSFZdWaNakAUqKj/E6UkTy5cg0AAAAjq+4tFyTpi3V6p35euL6vurYLNHrSBGLkWkAAIAIUlpeoR/NTNf8Tfv0f9f00be6Nfc6UkRjZBoAACBCVFQ43fPacn24Zo9+fUUPjRnQ2utIEY8yDQAAEAGcc3rg36v0VsZO3XNxF910TorXkWoFyjQAAIDPOef0yHvrNOOLTN02vINuH9HB60i1BmUaAADA5/6Wtln/+GSLvjekrX42qovMuAVeTfFlmWbRFgAAgEpTF3ypP72/Xlf3a6WHr+hJka5hvizTLNoCAAAgvbZkux6avUYXdW+uP43prSgWZalxvizTAAAAtd2clbv0s3+t0HmdmuipG/qpTjS1zgt86gAAAD6Ttj5bd81KV7+2jfSPcQNUt06015FqLco0AACAj3y2eZ9ue2mpOjVL1PM3D1S9WNbg8xJlGgAAwCf+u3aPbn5hsdo0qqdp4wcpKT7G60i1HmUaAADAB2Yv36nJ05eqS/NEvTJ5qJok1PU6EiTxcwEAAIAwN3NRpu5/c6UGpiTruZtSlRjHiHS4oEwDAACEsX/O26LfzVmrEV2a6u83DlB8LBcbhhPKNAAAQBhyzumxDzfoyY836bJeZ+mx6/oqtg4zdMMNZRoAACDMVFQ4PfzOGk39bKuuS22j33+nl6JZkCUs+fJ/b1hOHAAARKryCqef/WuFpn62Vbee206PfJciHc58WaZZThwAAESikrIK/WjmMr22NEt3fauTHri8m8wo0uGMaR4AAABhoKikXLe9tFSfbNirX17WTRPOa+91JASAMg0AAOCxnIOHNWn6Ui3LzNUfv9tL1w1s63UkBIgyDQAA4KGNewp064uLlX3gsP52Q39d0ussryPhFFCmAQAAPPLpxr26/aVlqhsTrVcmD1XfNg29joRTRJkGAADwwPTPt+mht1erU7MEPXfzQLVqGO91JJwGyjQAAEANKq9w+u1/1uiFBVt1QddmenJsPyXUpZL5FWcOAACghhw8XKY7Z6br43XZuvXcdvrFZd24h7TPUaYBAABqwI68Io2fulgbsw/qt1f11PeGnO11JAQBZRoAACDEMrbnacKLS3S4tFwv3DxQ53du6nUkBAllGgAAIIT+s2KXfvJqhpo1qKuZEwerU/NEryMhiCjTAAAAIfLaku26918r1L9tI00ZN0CNE+p6HQlBRpkGAAAIgTfTs3Tvv1ZoWMcm+uf3UxUXE+11JIRAlNcBAAAAIs3by3fq/726XEPaNdaUcRTpSEaZBgAACKI5K3fp7lcylHp2sp67OVXxsRTpSObLMm1mo81sSn5+vtdRAAAAvvLB6t26c2a6+rZpqOdvGah6scyojXS+LNPOudnOuUlJSUleRwEAAJAkfbxuj3748jL1aJWkqbcMZFXDWsKXZRoAACCcfLJhr26bvkxdWzTQtFsHKTEuxutIqCGUaQAAgDOwYNM+TZq2RB2bJWj6+EFKiqdI1yaUaQAAgNP0+ZYcjX9xsdo1qa+XJgxWw3qxXkdCDWMyDwAAwGnYkFuuxz9erDaN6umlCYOVXJ8iXRsxMg0AAHCKlm7L1V+WFKtFgzjNmDhYTVjZsNaiTAMAAJyCxVv36/vPfaGkuqaXJw5Rs8Q4ryPBQ0zzAAAACNAXW3J0y9TFatEgTj/qWaEWSRTp2o6RaQAAgAAs3Jyjm19YrJYN4zVr0hA1iqNGgTINAABwUgs27dMtUxepTXK8Zk4comYNGJFGJco0AADACczbsFe3Tl2slMb19fLEIWqayMWG+B/mTAMAABxH2vpsTZq+VB2aJmgGt7/DMVCmAQAAjuHjdXt02/Rl6twiQS+NZ0EWHBvTPAAAAI7y4Zo9mjx9qbqelagZ44dQpHFcjEwDAABU896q3frRzGXq3jJJ024dpKT4GK8jIYwxMg0AAFDlvVW7dMfLy9SrVZKmj6dI4+QYmQYAAJA0f+M+/Whmuvq0aagXbx2khLrUJJwcI9MAAKDWW749T5OmL1GHpgl6/uaBFGkEjDINAABqtU3ZB3XL1MVqnBDLHGmcMso0AACotXblF+mm5xcpyqTptw5mZUOcMn6GAQAAaqW8whJ9/7lFyi8q1axJQ5TSpL7XkeBDjEwDAIBap7CkTLdOXaxtOYX65/dT1bNVkteR4FNhU6bNrJuZPWNmr5vZD7zOAwAAIlNpeYVun7FMGdvz9OTYvhraobHXkeBjIS3TZva8mWWb2aqjto8ys/VmtsnM7pMk59xa59xtkq6VlBrKXAAAoHaqqHC657XlSlu/V7+7updG9TzL60jwuVCPTE+VNKr6BjOLlvS0pEskdZc01sy6Vz13haT5kv4b4lwAAKCWcc7pN/9Zo7cyduqei7to7KC2XkdCBAhpmXbOzZO0/6jNgyRtcs5tcc6VSJol6cqq17/tnDtH0o2hzAUAAGqfv6Vt1gsLturWc9vp9hEdvI6DCGHOudAewCxF0jvOuZ5Vj8dIGuWcm1D1eJykwZJel/QdSXUlrXDOPX2c/U2SNEmSmjdvPmDWrFkhzX8sBw8eVEJCQo0fF4Hh/IQvzk344tyEL85NcHyyvVQvrC7R0LOiNbF3XUWZnfE+OTfhLdjnZ+TIkUudc9+YiuzFrfGO9d3rnHNpktJO9mbn3BRJUyQpNTXVjRgxIpjZApKWliYvjovAcH7CF+cmfHFuwhfn5sxt3ntQ0z6Yp+Gdm+rZm1IVEx2cH8xzbsJbTZ0fL+7mkSWpTbXHrSXt9CAHAACoBf78wXrF1YnSn6/tE7QiDRzhxXfUYkmdzKydmcVKul7S2x7kAAAAEW759jzNWblbE85rryYJdb2OgwgU6lvjzZS0UFIXM8sys/HOuTJJd0h6X9JaSa8651af4n5Hm9mU/Pz84IcGAAAR40/vr1dy/VhNOK+d11EQoUI6Z9o5N/Y42+dImnMG+50taXZqaurE090HAACIbPM37tP8Tfv0wOXdlRgX43UcRCgmDgEAgIjjnNOj769Tq4bxunEw95NG6FCmAQBAxHl31W6tyMrXjy/spLiYaK/jIIJRpgEAQEQpK6/Q/72/Xp2aJeg7/Vt7HQcRzpdlmgsQAQDA8by+NEtb9h3SPRd3UXTUmS/OApyIL8u0c262c25SUlKS11EAAEAYKS4t1+MfbVS/tg317e7NvY6DWsCXZRoAAOBYpi3cqt0HivWzUV1lQVgyHDgZyjQAAIgI+UWlenruZg3v3FRD2jf2Og5qCco0AACICFPmbVZ+UanuHdXF6yioRXxZprkAEQAAVJd9oFjPz9+qK/q0VI+WXFOFmuPLMs0FiAAAoLqnPt6k0vIK/eTbnb2OglrGl2UaAADgiG05hzRzUaauH9RGKU3qex0HtQxlGgAA+NqfP9igOtGmOy/o5HUU1EKUaQAA4Furd+br7eU7deu57dSsQZzXcVALUaYBAIBv/en99UqKj9Hk4R28joJaypdlmrt5AABQu+3OL9bv56xV2vq9un1EByXFx3gdCbVUHa8DnA7n3GxJs1NTUyd6nQUAANScVTvy9dz8LzV7+U5VOKcr+rTUTeekeB0LtZgvyzQAAKg9Kiqc5q7P1j8/3aLPt+xX/dhojRt6tm49t53aJNfzOh5qOco0AAAIS0Ul5frXsiw9P/9Lbdl3SGclxen+S7vquoFtmYiwW2gAACAASURBVNaBsEGZBgAAYSXn4GFN/WyrXvp8m3ILS9W7dZKeHNtPl/RsoZhoX17uhQhGmQYAAGFjW84h3fDPL7Qzv0jf7tZcE85rr4EpjWRmXkcDjokyDQAAwsKm7ALd+OwXOlxWobduP1d92jT0OhJwUr78WQm3xgMAILKs2XlA1/3jc5VXSK9MGkqRhm/4skw752Y75yYlJSV5HQUAAJyhjO15un7KQsXWidKrk4eoS4tEryMBAWOaBwAA8MwXW3I0/sUlSq4fqxkTBnOrO/iOL0emAQCA/83bsFc3vbBIzRvU1auTh1Kk4UuMTAMAgBr34Zo9+uGMZerQLEHTxw9Sk4S6XkcCTgtlGgAA1KjZy3fq7lcy1KNVkqbdMkhJ9ViABf7FNA8AAFBjXluyXXfNSlf/to300niKNPyPkWkAAFAjpn++TQ+8tUrndWqiKeNSFR8b7XUk4IxRpgEAQMityMrTr/69Shd2a6anb+yvunUo0ogMvpzmwaItAAD4R1l5he5/c6WaJtTVY9f1pUgjoviyTLNoCwAA/jH9821ateOAHhzdQ4lxzJFGZPFlmQYAAP6wO79Yf/5gg0Z0aapLe7XwOg4QdJRpAAAQMg+/s1ql5RV6+IqeMjOv4wBBR5kGAAAhMXddtuas3K07v9VJbRuzuiEiE2UaAAAEXVFJuR749yp1bJagiee19zoOEDLcGg8AAATdUx9vVFZukV6ZNESxdRi7Q+TiuxsAAATVhj0FmjJvi8YMaK3B7Rt7HQcIKco0AAAImooKp1++uUoJcXV0/6XdvI4DhBxlGgAABM3ry7K0aOt+3X9JNyXXj/U6DhBylGkAABAU+w+V6A9z1mpgSiONGdDa6zhAjfBlmWY5cQAAws8f5qxVQXGZfnd1L0VFcU9p1A4nLNNmlnyiXzUV8mgsJw4AQHj5YkuOXluapYnnt1fn5olexwFqzMlujbdUkpNkktpKyq36fUNJmZLahTQdAAAIeyVlFfrlW6vUulG87rygk9dxgBp1wpFp51w751x7Se9LGu2ca+Kcayzpcklv1ERAAAAQ3v756RZtzD6oh6/sofjYaK/jADUq0DnTA51zc448cM69K2l4aCIBAAC/SM/M1ZP/3ahRPVrogq7NvY4D1LhAV0DcZ2a/lPSSKqd9fE9STshSAQCAsFZe4fT3tE167KONatEgTg9e0d3rSIAnAi3TYyU9KOlNVZbpeVXbAABALZOVW6ifvLJci7bu1+g+LfXbq3oqKT7G61iAJwIq0865/ZLuMrME59zBEGcCAABh6u3lO/WLN1fKOemx6/roqr6tZMZt8FB7BVSmzewcSc9KSpDU1sz6SJrsnLs9lOEAAEB4KCgu1YP/Xq030neof9uGevy6fmrbuJ7XsQDPBTrN4zFJF0t6W5Kcc8vN7PyQpQIAAGFj6bb9+vErGdqRW6QfX9hJd4zsqDrRvlz3DQi6QMu0nHPbj/oxTnnw4wAAgHBRVl6hpz7epKc+3qhWjeL12m1DNeBsz9ZsA8JSoGV6e9VUD2dmsZLulLQ2dLEAAICXsnILdefMdC3LzNN3+rXSr6/socQ4LjIEjhZomb5N0hOSWknKkvSBpB+GKhQAAPBOcWm5bp26WLvyivXE9X11Zd9WXkcCwlagd/PYJ+nGEGcBAABh4JF312nDnoN68dZBGt65qddxgLAW6N082kn6kaSU6u9xzl0RmlgAAMALn2zYq6mfbdXN56RQpIEABDrN4y1Jz0maLakidHEAAIBX9h8q0U9fW65OzRJ03yVdvY4D+EKgZbrYOfdkSJOcAjMbLWl0x44dvY4CAEBEcM7p/jdWKq+wRFNvGai4mGivIwG+EOhNIp8wswfNbKiZ9T/yK6TJTsA5N9s5NykpKcmrCAAARJTXlmTpvdW7dc/FXdSjJf++AoEKdGS6l6Rxki7Q/6Z5uKrHAADAx7blHNJDs1draPvGmjCsvddxAF8JtExfLam9c64klGEAAEDNKiuv0I9fyVB0lOnP1/ZRVJSd/E0AvhLoNI/lkhqGMggAAKh5f527SemZefrd1b3UsmG813EA3wl0ZLq5pHVmtljS4SMbuTUeAAD+tSwzV099vElX92ulK/q09DoO4EuBlukHQ5oCAADUqEOHy3T3Kxlq0SBOv76yh9dxAN86aZk2s2hJDzjnLqyBPAAAoAb85p01ytxfqFkTh6hBXIzXcQDfOumcaedcuaRCM+M+OQAARID3Vu3WrMXbddvwDhrcvrHXcQBfC3jRFkkrzexDSYeObHTO3RmSVAAAICSyDxTr52+sUM9WDXT3hZ29jgP4XqBl+j9VvwAAgE8Vl5br7lczVFRarsev66fYOoHe1AvA8QRUpp1zL4Y6CAAACJ3cQyWaOG2JlmzL1aNjeqtjswSvIwER4YRl2sxedc5da2YrVbni4dc453qHLBkAAAiK7fsLddMLi5SVW6Snb+ivy3qf5XUkIGKcbGT6x1X/vTzUQQAAQPCtyMrTrVOXqLS8Qi+NH6xB7ZK9jgRElJOV6Xck9Zf0W+fcuBrIAwAAgmTuumz98OVlalQvVrMmDVbHZoleRwIizsnKdKyZ3STpHDP7ztFPOufeCE0sAABwJmYuytQv31qlbmcl6vmbBqpZgzivIwER6WRl+jZJN0pqKGn0Uc85SZRpAADCiHNOj324QU9+vEnDOzfV327sr/p1A715F4BTdcI/Xc65+ZLmm9kS59xzNZQJAACchpKyCv38jZX617IsXZfaRr+9uqdiorn9HRBKgd4a7zkzO0dSSvX3OOemhSgXAAA4BQXFpbp9xjJ9unGf7r6ws+78VkeZmdexgIgXUJk2s+mSOkjKkFRetdlJokwDAOCxzJxCTX5pqTbuKdCfxvTWNaltvI4E1BqBTqJKldTdOfeNe00DAABvOOf02pIs/Xr2akVFmZ67eaCGd27qdSygVgm0TK+S1ELSrhBmAQAAAdp/qEQ/f2OF3l+9R0PaJ+vP1/ZVq4bxXscCap1Ay3QTSWvMbJGkw0c2OueuCEkqAABwXGnrs3XP6yuUV1ii+y/tqgnD2isqivnRgBcCLdMPhTLEEWZ2laTLJDWT9LRz7oOaOC4AAH5QVFKuR95dqxcXblPn5gl68ZZB6t6ygdexgFot0Lt5fGJmzSUNrNq0yDmXHch7zex5VS5Hnu2c61lt+yhJT0iKlvSsc+4R59xbkt4ys0aS/k8SZRoAAEmrduTrrlnp2rz3kG49t53uHdVFcTHRXscCar2Abj5pZtdKWiTpGknXSvrCzMYEeIypkkYdtb9oSU9LukRSd0ljzax7tZf8sup5AABqtQrn9PTcTbrq6QU6eLhML40frF+N7k6RBsJEoNM8fiFp4JHRaDNrKukjSa+f7I3OuXlmlnLU5kGSNjnntlTtb5akK81sraRHJL3rnFsWYDYAACLSjrwiPbKoWBty1+uyXmfpd1f3VMN6sV7HAlCNBXK3OzNb6ZzrVe1xlKTl1bed5P0pkt45Ms2jalR7lHNuQtXjcZIGS9og6SZJiyVlOOeeOca+JkmaJEnNmzcfMGvWrEAiBNXBgweVkJBQ48dFYDg/4YtzE744N+Ent7hCv/+iWAUlFRrXva7OaVmHRVjCDH9uwluwz8/IkSOXOudSj94e6Mj0e2b2vqSZVY+vkzTnDPIc628D55x7UtKTJ3qjc26KpCmSlJqa6kaMGHEGMU5PWlqavDguAsP5CV+cm/DFuQkvOQcP67opn6uoIlo/G1RXt1z5La8j4Rj4cxPeaur8nLBMm1lHSc2dc/eY2XckDVNlEV4oacYZHDdLUvXlmVpL2nkG+wMAICIcKC7VTS8s0vb9hZp26yAVZa70OhKAEzjZBYiPSyqQJOfcG865nzjn7lblqPTjZ3DcxZI6mVk7M4uVdL2kt89gfwAA+F5RSbnGT12s9bsL9My4ARrcvrHXkQCcxMnKdIpzbsXRG51zSySlBHIAM5upypHsLmaWZWbjnXNlku6Q9L6ktZJedc6tDjS0mY02syn5+fmBvgUAgLB2uKxck19aqqXbcvX4df00skszryMBCMDJ5kzHneC5gNYsdc6NPc72OTrNedfOudmSZqempk48nfcDABBOysor9ONZGZq3Ya8e/W5vXdb7LK8jAQjQyUamF5vZNwqrmY2XtDQ0kQAAqD0qKpzue2Ol3l21Ww9c3l3XDmxz8jcBCBsnG5n+saQ3zexG/a88p0qKlXR1KIMBABDpnHN6+J01en1plu6+sLPGD2vndSQAp+iEZdo5t0fSOWY2UtKRpcD/45z7OOTJTsDMRksa3bFjRy9jAABwRv7y4QZN/WyrJgxrpzu/xb9pgB8FdJ9p59xcSXNDnCVgzJkGAPjdlHmb9dTHm3T9wDb6xWXdWJAF8KlAF20BAABBcKC4VDM+z9Qf31uny3ufpd9d3YsiDfgYZRoAgBDbd/CwPlyzR++t2q3PNu9TabnThd2a6bHr+io6iiIN+BllGgCAEMjKLdT7q/fo/dW7tWTrflU4qW1yPd1ybjtd3KOF+rVpqCiKNOB7vizTXIAIAAhHm7IP6v3Vu/Xeqt1auaNyYbGuLRL1ows6aVTPFuraIpEpHUCE8WWZ5gJEAEC4cM5p4ZYc/T1tsz7duE+S1K9tQ/38kq66uEcLpTSp73FCAKHkyzINAIDXKiqcPlq7R39L26yM7XlqkhCrey7uou/2b60WSSdaQBhAJKFMAwBwCkrLKzR7+U4988lmbdhzUK0bxes3V/XUNQNaKy4m2ut4AGoYZRoAgAAUl5brtSXb9Y95W5SVW6TOzRP02HV9NLp3S9WJjvI6HgCPUKYBADiOQ4fLlJVbpP+u26Pn53+pfQdL1L9tQz00uocu6NqMu3EA8GeZ5m4eAIBgKC4t1868Im3PLdL2/YXanluorNwiZe0v1PbcIu0/VPLVa8/v3FS3j+igwe2SuSMHgK/4skxzNw8AwLFUVDilb8/T3oJi5ReVHvWrTPlFpTpQ9Su/qFQ51cqyJMVEm1o1jFeb5Hq6uGWSWjeq/H23Fonq1DzRo68KQDjzZZkGAKC6/MJSvbZ0u6Z/vk3bcgq/9lx0lKlBXB0lxceoQXyMkuJj1KpRvJLiY9Qssa7aNKqnNsn11CY5Xs0S41iREMApoUwDAHxr9c58TV+4TW9l7FBxaYVSz26kuy/srE7NE5RUVZwT6tZhWgaAkKFMAwB8paSsQu+t3q1pn23Vkm25iouJ0lV9W2nc0LPVo2WS1/EA1DKUaQCAL+zOL9bLizL18heZ2nfwsM5uXE+/vKybrhnQRkn1YryOB6CWokwDADxz/5sr9e7KXQG99kBxmSqc08guzTRu6Nka3qkpt6YD4DnKNADAE+UVTv9O36H2TRPUr23Dk76+Yb1YjenfWm0b16uBdAAQGF+Wae4zDQD+tzG7QIdKynXLuSn6Tv/WXscBgNPiy/VPnXOznXOTkpK40AQA/Co9M0+S1K9tI4+TAMDp82WZBgD4X0ZmnhrWi1EK0zYA+BhlGgDgiYzteerTuiH3gAbga5RpAECNKygu1YbsgoAuPASAcEaZBgDUuJVZ+XJO6tuGMg3A3yjTAIAal7698uJDyjQAv6NMAwBqXHpmnto3qa+G9WK9jgIAZ8SXZdrMRpvZlPz8fK+jAABOkXNOGdvzGJUGEBF8Waa5zzQA+FdWbpH2HTzMxYcAIoIvyzQAwL8yvpovzWItAPyPMg0AqFEZ2/NUt06Uup6V6HUUADhjlGkAQI1Kz8xVr1ZJionmnyAA/sffZACAGlNSVqFVOw9w8SGAiEGZBgDUmLW7DqikrEL92jJfGkBkoEwDAGrMVxcfcicPABGCMg0AqDEZ2/PULLGuWibFeR0FAIKCMg0AqDHpmbnq26ahzMzrKAAQFJRpAECNyD1Uoq05hUzxABBRKNMAgBqRkVU5X7ofi7UAiCC+LNNmNtrMpuTn53sdBQAQoPTMPEWZ1Lt1ktdRACBofFmmnXOznXOTkpL4CxkA/CJje546N09U/bp1vI4CAEHjyzINAPCXigqnjMxc9WO+NIAIQ5kGAITclzmHdKC4jJUPAUQcyjQAIOQyMqsuPmTlQwARhjINAAi59O25SqhbRx2aJngdBQCCijINAAi5jO156tMmSdFRLNYCILJQpgEAIVVUUq51uwqYLw0gIlGmAQAhtWpnvsoqnPqyWAuACESZBgCE1JGLDxmZBhCJKNMAgJBK356r1o3i1TSxrtdRACDoKNMAgJDKyMzjlngAIhZlGgAQMnsOFGtnfjFTPABELMo0ACBk0pkvDSDCUaYBACGTsT1PMdGmHi0beB0FAELCl2XazEab2ZT8/HyvowAATiA9M1fdz2qguJhor6MAQEj4skw752Y75yYlJSV5HQUAcBzlFU4rd+Rz8SGAiObLMg0ACH8b9hSosKSc+dIAIhplGgAQElx8CKA2oEwDAEIiY3uuGtWL0dmN63kdBQBChjINAAiJjO156tumoczM6ygAEDKUaQBA0BUUl2pj9kEuPgQQ8SjTAICgW5GVL+eYLw0g8lGmAQBBl56ZK0nqQ5kGEOEo0wCAoMvYnqcOTesrKT7G6ygAEFJ1vA4AAPDW3oLDWr+/XPW+3B+0faZn5mlEl2ZB2x8AhCvKNADUUvmFpfr7J5v1woIvdbisQlq0MKj7H9SOiw8BRD7KNADUMsWl5Zq2cKuenrtZB4pLdVXfVuoQvU/9+vYN2jHqRJkGnE2ZBhD5KNMAUEuUVzi9mb5Df/lgvXbmF2t456a6d1QX9WiZpLS0NJ3bsYnXEQHAdyjTABDhnHNKW79Xf3xvndbtLlCvVkn6v2v66BzKMwCcMco0AESw9MxcPfLuOn3x5X6d3bie/npDP13a8yxFRbEqIQAEA2UagO/syi/Sf9dma/HW/Sord17HCVt5RSVasClHTRJi9fCVPXT9wLaKrcMdUQEgmCjTAMKec06rdhzQR2v36L/r9mjVjgOSpLOS4lS/Ln+NHU+0me76VidNPL+9EvicACAk+NsVQFgqLi3XZ5v36aO12fp4bbZ2HyhWlEn92zbSfZd01YXdmqlD0wSZMV0BAOAdyjSAkNuy96A+WLNHGetK9Fnh2pO+/st9hzR/4z4VlZarfmy0zu/cVN/q1lwjuzRV44S6NZAYAIDAUKYBhMTGPQWas3K33l21S+t2F0iSYqOl6B3bTvre5PqxGjOgtS7s3lxD2ierbp3oUMcFAOC0UKYBBIVzTuurCvSclbu0KfugzKSBZyfrwdHdNapnC61P/0IjRozwOioAAEFDmQZOwDmnzP2FKqvgjhHHc6CoVB+t3aN3V+7Wln2HFGXSoHbJ+v7QHhrVo4WaNYj76rXrPcwJAEAohE2ZNrP2kn4hKck5N8brPIBzTve/uUozF2V6HSXsRUeZhrZvrPHntdNF3VuoaSLzmgEAtUNIy7SZPS/pcknZzrme1baPkvSEpGhJzzrnHnHObZE03sxeD2UmIFDPL9iqmYsydePgthrULtnrOGErNjpKg9s3VnL9WK+jAABQ40I9Mj1V0l8lTTuywcyiJT0t6duSsiQtNrO3nXNrQpwFCNjc9dn63X/W6OIezfWbK3uyWhwAADimkC6F5ZybJ2n/UZsHSdrknNvinCuRNEvSlaHMAZyKTdkFuvPldHVt0UCPXdeXIg0AAI7LnAvthVVmliLpnSPTPMxsjKRRzrkJVY/HSRos6UFJv1PliPWzzrk/HGd/kyRNkqTmzZsPmDVrVkjzH8vBgweVkJBQ48dFYM7k/BwscXr48yIVl0kPDo1T43iWXg4m/uyEL85N+OLchC/OTXgL9vkZOXLkUudc6tHbvbgA8VjDfM45lyPptpO92Tk3RdIUSUpNTXVe3GYrLS2N23uFsdM9PyVlFfr+818or6RYsyYNUf+2jYIfrpbjz0744tyEL85N+OLchLeaOj9eDLtlSWpT7XFrSTs9yAF8xTmnB99erc+37Ncfv9uLIg0AAALiRZleLKmTmbUzs1hJ10t624McwFde/Kzyzh0/GNFBV/dr7XUcAADgEyEt02Y2U9JCSV3MLMvMxjvnyiTdIel9SWslveqcW32K+x1tZlPy8/ODHxq1zrwNe/XwO2v07e7Ndc9FXbyOAwAAfCSkc6adc2OPs32OpDlnsN/ZkmanpqZOPN19AJK0KfugfvjyMnVunqjHuXMHAAA4RdyqALVWXmGJJry4WHXrROnZm1JVv27YLAgKAAB8gvaAWqm0vEK3z1imnXnFmjlpsFo3qud1JAAA4EOUaUQE55wWbs7Riwu3KnNXkf6x4fMTvj63sETrdhfo/67powFns1Q4AAA4Pb4s02Y2WtLojh07eh0FYeDzLTn6y4cbtOjL/WqaWFeN6kjlFSdejKhBXIweuLy7xgzgzh0AAOD0+bJMcwEiJGnRl/v12IcbtHBLjpom1tWDo7tr7KC2+nzBpxoxYqjX8QAAQC3gyzKN2m3J1v167KMNWrApR00S6uqBy7vrxsFtFRcT7XU0AABQy1Cm4RtLt+Xq8Y826NON+9QkIVa/vKybbhx8tuJjKdEAAMAblGmEvR15Rbr/jZX6ZMNeNa4fq/sv7arvDTlb9WL59gUAAN7yZRvhAsTao6ikXBNeXKLt+wv1s1Fd9f2hZ3M/aAAAEDZ8uWiLc262c25SUlKS11EQQs45/eLNlVq3+4CeuqGffjCiA0UaAACEFV+WadQO0xZu0xvpO3T3hZ01skszr+MAAAB8A2UaYWnJ1v36zTtrdGG3ZrpjJNN5AABAeKJMI+xkHyjWD2YsU+tG8frztX0VFWVeRwIAADgmJqAirJSUVej2Gct0sLhML40frKT4GK8jAQAAHJcvR6bNbLSZTcnPz/c6CoLs93PWasm2XP1xTG91aZHodRwAAIAT8mWZ5m4ekenN9CxN/Wyrxg9rpyv6tPQ6DgAAwEn5skwj8qzZeUA/f2OlBrVL1n2XdPU6DgAAQEAo0/BcXmGJJr+0REnxMXr6hv6KiebbEgAA+AMXIMJTFRVOP34lQ7vzi/XK5KFqmljX60gAAAABYwgQnnr8vxuVtn6vHhzdQ/3bNvI6DgAAwClhZDpC/XftHmXuL/Q6xgnlHirRkx9v0pgBrXXj4LZexwEAADhlvizTZjZa0uiOHVkZ71j2FhzWhGlL5JzXSU6uf9uG+u1VPWXGwiwAAMB/fFmmnXOzJc1OTU2d6HWWcPTJhr1yTnpl0pCwv1dzg7gYVjgEAAC+5csyjRObuz5bzRLralC7ZEZ8AQAAQogLECNMWXmF5m3YqxFdmlKkAQAAQowyHWGWZeapoLhMI7s08zoKAABAxKNMR5i567NVJ8p0bqcmXkcBAACIeJTpCDN3XbZSUxqpQVyM11EAAAAiHmU6guzKL9K63QVM8QAAAKghlOkIkrZ+ryRpZFfKNAAAQE3wZZk2s9FmNiU/P9/rKGFl7rpstWoYr07NEryOAgAAUCv4skw752Y75yYlJSV5HSVsHC4r14JN+7glHgAAQA3yZZnGNy3ZmqtDJeXMlwYAAKhBlOkIMXddtmKjo3ROx8ZeRwEAAKg1KNMRYu76bA1un6x6sawQDwAAUFMo0xEgM6dQm/ceYooHAABADaNMR4C0DdmSuCUeAABATaNMR4C567KV0rie2jWp73UUAACAWoUy7XPFpeX6bHOORjDFAwAAoMZRpn1u4ZYcHS6rYIoHAACAByjTPpe2LltxMVEa3C7Z6ygAAAC1ji/LNMuJV3LO6f+3d+8xcpXnHce/D15fMMY24Et9K5dgkxjqGFiRVE0TOxBq0kARwSRpSSlNQEVK1SiNlEZFbSOlKq2qKIlIlFCVkkQRxJCixAmUkHStFBogBszFYBtjStbYxTZgYxt82336xxyX6XRvnp2dM7P7/UijnXnPe86+Mz+f42dn3nOma+NOfuttM5g0flzZw5EkSRpz2rKY9uvEK7bs2s+vXn2DZU7xkCRJKkVbFtOq6NpQuSTeskUzSx6JJEnS2GQx3cbWbNzJwllTWHDy5LKHIkmSNCZZTLep/QeP8PALr3gVD0mSpBJZTLepBzfv4nBPsuwsp3hIkiSVxWK6Ta3ZtJMpEzvoPNVL4kmSJJXFYroNZSZrNuzgPWfOYEKHEUqSJJXFSqwNbXp5H9v2HGD5253iIUmSVCaL6TbUtbG4JN5ZnnwoSZJUJovpNtS1YQeL50xl9tRJZQ9FkiRpTLOYbjOvHzjM2hdfc4qHJElSC7CYbjMPPLeLnt5kuVM8JEmSSmcx3Wa6Nuxg6qQOli6YXvZQJEmSxjyL6TbS25us2bST9y6aScc4o5MkSSpbR9kDENzz1HZuf+RXg/Y7eKSXnXsPOsVDkiSpRbRlMR0RlwKXnnnmmWUPZdh6e5O/u/dZ3jzUw6+fPHnQ/u9bNJOLFs9uwsgkSZI0mLYspjNzNbC6s7PzurLHMlwPvfAK3a++yZc/spTLz51X9nAkSZJ0DJx4W7I7127lxEkdrDjn18oeiiRJko6RxXSJXj9wmHue2s5l75zLpPHjyh6OJEmSjpHFdIl+9MR2Dh7p5arOBWUPRZIkSXWwmC7RqrXdnDX7RJbMn1b2UCRJklQHi+mSbHp5L+u6d7Oycz4RUfZwJEmSVAeL6ZLcubabjuPCK3hIkiS1MYvpEhzu6eXux1/iwnfMYsaUiWUPR5IkSXWymC5B14Yd7Np3yBMPJUmS2pzFdAlWrd3KzBMn8r5FM8seiiRJkobBYrrJduw9QNfGHVxx3jw6xvnyS5IktTOruSa7+7GX6OlNVp7vFA9JkqR2ZzHdRJnJqrXdnH/qSZw5a0rZw5EkSdIwWUw30ePdu3l+536u6pxf9lAkSZLUABbTTXTn2m6OHz+O310yt+yhSJIkqQEsppvkjUNHWP3Edj74G3OYMrGj7OFIkiSpunz23AAAColJREFUASymm+Tep/6bfQePOMVDkiRpFLGYbpJVa7s57ZTJXHD6yWUPRZIkSQ1iMd0EL76yn4dfeJWVnQuIiLKHI0mSpAaxmG6Cux7dynEBV5w3r+yhSJIkqYEspkdYT29y16Nb+e2FM5kz7fiyhyNJkqQGspgeYQ9s3sX2PQe4qtNvPJQkSRptWuYabRFxAvB14BCwJjO/W/KQGmLV2m6mTx7PRYtnlT0USZIkNdiIvjMdEbdGxI6IeLqmfUVEbIyIzRHxF0XzFcBdmXkdcNlIjqtZdr9xiPvXv8zlS+cxsWNc2cORJElSg430NI/bgBXVDRExDvgacAmwGPhYRCwG5gPdRbeeER5XU/xg3TYO9fSy0mtLS5IkjUojOs0jM38eEafVNF8AbM7MLQARcQfwe8BWKgX1Olp4LvfTL+3hH9ce4NYtjwza95ltr3P23KmcPXdaE0YmSZKkZovMHNlfUCmmf5SZ5xSPrwRWZOYni8cfB94FfA64GTgAPNDfnOmIuB64HmD27Nnn33HHHSM6/lpbdvfw7fVvctxxg0/bCOBDbxvPubNaZmr6mLBv3z6mTJlS9jDUB7NpXWbTusymdZlNa2t0PsuXL380Mztr28uo8vr61pLMzP3AtYOtnJm3ALcAdHZ25rJlyxo7ukEsA86YvoZm/14N3Zo15tOqzKZ1mU3rMpvWZTatrVn5lDGdYitQfZ24+cC2EsYhSZIkDUsZxfQvgYURcXpETAA+CvywhHFIkiRJwzLSl8a7HfgFcFZEbI2IT2TmEeBTwH3As8CqzFx/jNu9NCJu2bNnT+MHLUmSJA3RSF/N42P9tN8D3DOM7a4GVnd2dl5X7zYkSZKk4WrZS9BJkiRJrc5iWpIkSaqTxbQkSZJUp7Yspj0BUZIkSa2gLYvpzFydmddPm+bXdEuSJKk8bVlMS5IkSa3AYlqSJEmqk8W0JEmSVKe2LKY9AVGSJEmtoC2LaU9AlCRJUitoy2JakiRJagUW05IkSVKdLKYlSZKkOllMS5IkSXWKzCx7DHWLiJ3AiyX86hnArhJ+r4bGfFqX2bQus2ldZtO6zKa1NTqfUzNzZm1jWxfTZYmItZnZWfY41DfzaV1m07rMpnWZTesym9bWrHyc5iFJkiTVyWJakiRJqpPFdH1uKXsAGpD5tC6zaV1m07rMpnWZTWtrSj7OmZYkSZLq5DvTkiRJUp3GVDEdEbdGxI6IeLqm/Tcj4p8i4gMR8WhEPFX8fH+xfHJE/DgiNkTE+oi4qWb9ORHxk4hYGhG/KPo8GREfqepzekQ8HBHPRcT3ImJCc551+4mIBRHRFRHPFq/ln1UtO5rVKUWffRFxcx/b+HxE/EFEfCYininy+FlEnFrV55oij+ci4ppmPb92M9w8IuJvI6I7Ivb1sW33nWEYYjZ9HteKPmbTJBExKSIeiYgnitfyC1XLBs2q6OdxrUGGmEefx7WwJlCtzBwzN+C9wHnA0zXtXwA+DJwLzC3azgFeKu5PBpYX9ycA/wFcUrX+tcCfA4uAhUXbXGA7ML14vAr4aHH/G8ANZb8erXoD5gDnFfdPBDYBi2uyOgF4D/AnwM19bKMLmAksByYXbTcA3yvunwxsKX6eVNw/qezn3oq34eYBvLvYxr4+tu2+M/LZ9HlcM5umZxXAlOL+eOBh4N1Dzapo87jW3Dz6PK5hTeCt5jam3pnOzJ8Dr/ax6ELgp5n5eGZuK9rWA5MiYmJmvpGZXcU2DgGPAfOr1l8B3JuZmzLzuaLfNmAHMDMiAng/cFfR/1vA5Q1+eqNGZm7PzMeK+3uBZ4F5xeKjWe3PzAeAA7XrR8RUYEJm7szMrsx8o1j0EG/l9jvA/Zn5ama+BtxPJUfVGG4emflQZm7vZ/PuO8MwxGz6PK4V65hNk2TF0U8Axhe3oyctDZqVx7XGGmIefR7XrAlUa0wV032JiBnA4czcU7Pow8DjmXmwpv904FLgZ8XjccBZmflMTb8LqPzF+jxwCrA7M48Ui7fy1n94GkBEnEbl3ZqHB8iq1kUU+dT4BHBvcX8e0F21zEyGoM48+tuW+04DDTGbPo9rfWzLbEZARIyLiHVUiqr7M/NYsvK41mDHmEd/27AmEB1lD6AFXAz8pLohIs4G/r5YVt3eAdwOfDUztxTN76Ly8VB1vznAd4BrMrO3+Cu0lpdRGURETAG+D3w6M1+PiN+nJqt+rAD+pWZbVwOdwPuONvWxnpkMYBh59Md9p0GGkk1/x7V+mM0IyMweYGlRgN0dEecASxhaVh7XGmyoefTHmkBHjfl3poFLgH87+iAi5gN3A3+Ymc/X9L0FeC4zvzzA+lOBHwM3ZuZDRfMuYHqx40Hl46BtqF8RMZ5KcfDdzPzXovn/vNYDuAB4pGpbFwF/CVxW9S7PVmBB1TpmMoBh5tEf950GGEo2gxzX+mI2IygzdwNrqBTIQ83K49oIGSiPQVgTCBjjxXTx1+ESYF3xeDqVf/Sfz8wHa/p+EZgGfLpmMxfy1sc7E6gcBL+dmXce7ZCZSeXEkSuLpmuAHzT6+YwWRS7/DDybmV+qavvfrAZY92xgQ/GOAxFxLvBNKv/h7Kjqeh9wcUScFBEnUXkH6L6GP5lRYDh5DMJ9Z5iGks1Ax7UBmE2DRcTMIgsi4ngq0zY2MoSsPK413lDyGGR9awK9JVvgLMhm3ah8HLMdOEzlL/jPAbdVLb8R2E9lRzp6m0Xlr8akcnLP0fZPUjmr+t+r1r+62Hb1+kuLZWdQeVdhM3AnMLHs16NVb1TOnk7gyarX8a+qsyr6/ReVE0r3FXkuBj4L/FFVn58CL1dt54dVy/64yGMzcG3Zz7tVb8PJo2j/h+Jxb/Hzb9x3mpdNf8c1s2l6VkuAx4usni5y6hxKVh7Xysmj6Pf/jmtYE3iruY3pb0CMiBuBzZl5R53rXw3Mz8ybBu2sYRlqVhFxP5WPR/u7QoEawH2ndZlN+/C41lrcd1SvMV1MS5IkScMxpudMS5IkScNhMS1JkiTVyWJakiRJqpPFtCRJklQni2lJGgUioici1kXE+oh4IiI+ExEDHuMj4rTi2xIlSXWymJak0eHNzFyamWcDHwA+CPz1IOucBlhMS9IweGk8SRoFImJfZk6penwG8EtgBnAq8B3ghGLxpzLzPyPiIeAdwAvAt4CvAjcBy4CJwNcy85tNexKS1IYspiVpFKgtpou214C3A3uB3sw8EBELgdszszMilgGfzcwPFf2vp/LtiF+MiInAg8DKzHyhqU9GktpIR9kDkCSNmCh+jgdujoilQA+wqJ/+FwNLIuLK4vE0YCGVd64lSX2wmJakUaiY5tED7KAyd/pl4J1UzpU50N9qwJ9m5n1NGaQkjQKegChJo0xEzAS+Adyclbl804DtmdkLfBwYV3TdC5xYtep9wA0RMb7YzqKIOAFJUr98Z1qSRofjI2IdlSkdR6iccPilYtnXge9HxEqgC9hftD8JHImIJ4DbgK9QucLHYxERwE7g8mY9AUlqR56AKEmSJNXJaR6SJElSnSymJUmSpDpZTEuSJEl1spiWJEmS6mQxLUmSJNXJYlqSJEmqk8W0JEmSVCeLaUmSJKlO/wOp18/K35IKHQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_single_series(nation,series):\n", " nation_series, dates = get_nation_series(nation,series)\n", " nation_series.plot(\n", " x=dates,\n", " logy=True, \n", " title='Cumulative {series}: {nation}'.format(series=series, nation=nation),\n", " grid=True,\n", " figsize=(12,8)\n", " )\n", " plt.grid(True)\n", " plt.ylabel(series)\n", " plt.xlabel('Date')\n", " plt.legend([series])\n", "\n", "# Plot all the series\n", "def plot_all_series(nation):\n", " for series in series_options:\n", " plot_single_series(nation,series)\n", " plt.ylabel('Cases')\n", " plt.legend(series_options)\n", " plt.title(f'Cumulative: {nation}')\n", "\n", "plt.figure()\n", "plot_single_series(nation,series)\n", "\n", "plt.figure()\n", "plot_all_series(nation)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fe1815d568e4403c8f2c109e5e74e709", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Nation: ', index=155, options=('Afghanistan', 'Albania', 'Algeria'…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interactive(plot_single_series,nation=nations_widget,series=series_options)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "16ea337a2b8b44e9b1f4000bc516fbfd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='Nation: ', index=155, options=('Afghanistan', 'Albania', 'Algeria'…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def do_linear_regression(nation,series,n_points,days_back):\n", " nation_series, dates = get_nation_series(nation,series)\n", " days = np.arange(0,nation_series.shape[0])\n", " y = np.log10(np.float64(nation_series+1))\n", " if days_back >= 0:\n", " s = slice(-n_points,None)\n", " else:\n", " s = slice(-n_points+days_back,days_back)\n", " lr = stats.linregress(days[s],y[s])\n", " \n", " fig = plt.figure(figsize=(12,8))\n", " plt.plot(days,y)\n", " line = lr.slope*days[s]+lr.intercept\n", " plt.plot(days[s],line)\n", " plt.grid(True)\n", "\n", " plt.xlabel('Days')\n", " plt.ylabel('log10(Cases+1)')\n", "\n", " str = f\"\"\"Slope: {lr.slope:.3f}\n", "Doubling time: {np.inf if lr.slope==0 else np.log10(2)/lr.slope:.3f} days\n", "Daily increase: {(np.exp(lr.slope)-1)*100:2.0f}%\"\"\"\n", "\n", " plt.text(days[s][0]-25,line[0],str)\n", "\n", " return lr\n", " \n", "interactive(do_linear_regression,\n", " nation=nations_widget,\n", " series=series_options,\n", " n_points=widgets.IntSlider(value=4,min=2,max=nation_series.shape[0]),\n", " days_back=widgets.IntSlider(value=0,min=-nation_series.shape[0],max=0)\n", " )" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "testresponse = requests.get(testsource)\n", "testresponse" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'date': 20200321,\n", " 'state': 'AK',\n", " 'positive': 14,\n", " 'negative': 758,\n", " 'pending': None,\n", " 'hospitalized': 0,\n", " 'death': None,\n", " 'total': 772,\n", " 'dateChecked': '2020-03-21T20:00:00Z'},\n", " {'date': 20200321,\n", " 'state': 'AL',\n", " 'positive': 124,\n", " 'negative': 28,\n", " 'pending': None,\n", " 'hospitalized': None,\n", " 'death': 0,\n", " 'total': 152,\n", " 'dateChecked': '2020-03-21T20:00:00Z'},\n", " {'date': 20200321,\n", " 'state': 'AR',\n", " 'positive': 118,\n", " 'negative': 567,\n", " 'pending': 154,\n", " 'hospitalized': None,\n", " 'death': None,\n", " 'total': 839,\n", " 'dateChecked': 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5520200321WY23.0438.0NaNNaNNaN4612020-03-21 20:00:00+00:00
\n", "
" ], "text/plain": [ " date state positive negative pending hospitalized death total \\\n", "0 20200321 AK 14.0 758.0 NaN 0.0 NaN 772 \n", "1 20200321 AL 124.0 28.0 NaN NaN 0.0 152 \n", "2 20200321 AR 118.0 567.0 154.0 NaN NaN 839 \n", "3 20200321 AS NaN NaN NaN NaN 0.0 0 \n", "4 20200321 AZ 104.0 240.0 122.0 NaN 1.0 466 \n", "5 20200321 CA 1279.0 11249.0 NaN NaN 24.0 12528 \n", "6 20200321 CO 363.0 3317.0 NaN 44.0 4.0 3680 \n", "7 20200321 CT 194.0 2106.0 NaN NaN 4.0 2300 \n", "8 20200321 DC 77.0 506.0 0.0 NaN 1.0 583 \n", "9 20200321 DE 45.0 36.0 NaN NaN 0.0 81 \n", "10 20200321 FL 658.0 6579.0 1002.0 158.0 12.0 8239 \n", "11 20200321 GA 507.0 2557.0 NaN NaN 14.0 3064 \n", "12 20200321 GU 15.0 108.0 NaN NaN NaN 123 \n", "13 20200321 HI 37.0 124.0 9.0 NaN NaN 170 \n", "14 20200321 IA 68.0 1049.0 NaN NaN NaN 1117 \n", "15 20200321 ID 31.0 880.0 NaN NaN NaN 911 \n", "16 20200321 IL 759.0 5488.0 NaN NaN 6.0 6247 \n", "17 20200321 IN 126.0 707.0 NaN 1.0 4.0 833 \n", "18 20200321 KS 55.0 417.0 NaN NaN 2.0 472 \n", "19 20200321 KY 54.0 714.0 NaN NaN 2.0 768 \n", "20 20200321 LA 585.0 2180.0 NaN NaN 16.0 2765 \n", "21 20200321 MA 525.0 4752.0 NaN 61.0 1.0 5277 \n", "22 20200321 MD 190.0 94.0 NaN NaN 2.0 284 \n", "23 20200321 ME 73.0 2264.0 NaN NaN NaN 2337 \n", "24 20200321 MI 787.0 2069.0 NaN NaN 5.0 2856 \n", "25 20200321 MN 138.0 3952.0 NaN NaN 1.0 4090 \n", "26 20200321 MO 73.0 369.0 NaN NaN 3.0 442 \n", "27 20200321 MP 0.0 NaN NaN NaN 0.0 0 \n", "28 20200321 MS 140.0 695.0 NaN NaN 1.0 835 \n", "29 20200321 MT 20.0 1146.0 NaN NaN NaN 1166 \n", "30 20200321 NC 184.0 5092.0 NaN NaN 0.0 5276 \n", "31 20200321 ND 28.0 1141.0 NaN 3.0 0.0 1169 \n", "32 20200321 NE 38.0 356.0 NaN NaN 0.0 394 \n", "33 20200321 NH 55.0 973.0 942.0 NaN NaN 1970 \n", "34 20200321 NJ 1327.0 294.0 40.0 NaN 16.0 1661 \n", "35 20200321 NM 57.0 3771.0 NaN NaN NaN 3828 \n", "36 20200321 NV 124.0 2384.0 NaN NaN 2.0 2508 \n", "37 20200321 NY 10356.0 35081.0 NaN 1603.0 44.0 45437 \n", "38 20200321 OH 247.0 140.0 NaN 58.0 3.0 387 \n", "39 20200321 OK 53.0 560.0 144.0 10.0 1.0 757 \n", "40 20200321 OR 114.0 2003.0 433.0 NaN 3.0 2550 \n", "41 20200321 PA 371.0 3766.0 NaN NaN 2.0 4137 \n", "42 20200321 PR 21.0 143.0 71.0 NaN NaN 235 \n", "43 20200321 RI 66.0 862.0 290.0 NaN NaN 1218 \n", "44 20200321 SC 152.0 1255.0 NaN NaN 1.0 1407 \n", "45 20200321 SD 14.0 691.0 268.0 NaN 1.0 973 \n", "46 20200321 TN 371.0 3272.0 NaN NaN NaN 3643 \n", "47 20200321 TX 304.0 6218.0 NaN NaN 5.0 6522 \n", "48 20200321 UT 136.0 2424.0 NaN NaN 0.0 2560 \n", "49 20200321 VA 152.0 2638.0 NaN 25.0 2.0 2790 \n", "50 20200321 VI 6.0 NaN NaN NaN 0.0 6 \n", "51 20200321 VT 29.0 779.0 NaN NaN 2.0 808 \n", "52 20200321 WA 1524.0 21719.0 NaN NaN 83.0 23243 \n", "53 20200321 WI 281.0 4628.0 NaN NaN 4.0 4909 \n", "54 20200321 WV 11.0 330.0 2.0 1.0 0.0 343 \n", "55 20200321 WY 23.0 438.0 NaN NaN NaN 461 \n", "\n", " dateChecked \n", "0 2020-03-21 20:00:00+00:00 \n", "1 2020-03-21 20:00:00+00:00 \n", "2 2020-03-21 20:00:00+00:00 \n", "3 2020-03-21 20:00:00+00:00 \n", "4 2020-03-21 20:00:00+00:00 \n", "5 2020-03-21 20:00:00+00:00 \n", "6 2020-03-21 20:00:00+00:00 \n", "7 2020-03-21 20:00:00+00:00 \n", "8 2020-03-21 20:00:00+00:00 \n", "9 2020-03-21 20:00:00+00:00 \n", "10 2020-03-21 20:00:00+00:00 \n", "11 2020-03-21 20:00:00+00:00 \n", "12 2020-03-21 20:00:00+00:00 \n", "13 2020-03-21 20:00:00+00:00 \n", "14 2020-03-21 20:00:00+00:00 \n", "15 2020-03-21 20:00:00+00:00 \n", "16 2020-03-21 20:00:00+00:00 \n", "17 2020-03-21 20:00:00+00:00 \n", "18 2020-03-21 20:00:00+00:00 \n", "19 2020-03-21 20:00:00+00:00 \n", "20 2020-03-21 20:00:00+00:00 \n", "21 2020-03-21 20:00:00+00:00 \n", "22 2020-03-21 20:00:00+00:00 \n", "23 2020-03-21 20:00:00+00:00 \n", "24 2020-03-21 20:00:00+00:00 \n", "25 2020-03-21 20:00:00+00:00 \n", "26 2020-03-21 20:00:00+00:00 \n", "27 2020-03-21 20:00:00+00:00 \n", "28 2020-03-21 20:00:00+00:00 \n", "29 2020-03-21 20:00:00+00:00 \n", "30 2020-03-21 20:00:00+00:00 \n", "31 2020-03-21 20:00:00+00:00 \n", "32 2020-03-21 20:00:00+00:00 \n", "33 2020-03-21 20:00:00+00:00 \n", "34 2020-03-21 20:00:00+00:00 \n", "35 2020-03-21 20:00:00+00:00 \n", "36 2020-03-21 20:00:00+00:00 \n", "37 2020-03-21 20:00:00+00:00 \n", "38 2020-03-21 20:00:00+00:00 \n", "39 2020-03-21 20:00:00+00:00 \n", "40 2020-03-21 20:00:00+00:00 \n", "41 2020-03-21 20:00:00+00:00 \n", "42 2020-03-21 20:00:00+00:00 \n", "43 2020-03-21 20:00:00+00:00 \n", "44 2020-03-21 20:00:00+00:00 \n", "45 2020-03-21 20:00:00+00:00 \n", "46 2020-03-21 20:00:00+00:00 \n", "47 2020-03-21 20:00:00+00:00 \n", "48 2020-03-21 20:00:00+00:00 \n", "49 2020-03-21 20:00:00+00:00 \n", "50 2020-03-21 20:00:00+00:00 \n", "51 2020-03-21 20:00:00+00:00 \n", "52 2020-03-21 20:00:00+00:00 \n", "53 2020-03-21 20:00:00+00:00 \n", "54 2020-03-21 20:00:00+00:00 \n", "55 2020-03-21 20:00:00+00:00 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "latest = tests.loc[tests.groupby('state').dateChecked.idxmax()]\n", "latest" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datestatepositivenegativependinghospitalizeddeathtotaldateCheckedposrate
020200321AK14.0758.0NaN0.0NaN7722020-03-21 20:00:00+00:000.018135
120200321AL124.028.0NaNNaN0.01522020-03-21 20:00:00+00:000.815789
220200321AR118.0567.0154.0NaNNaN8392020-03-21 20:00:00+00:000.140644
420200321AZ104.0240.0122.0NaN1.04662020-03-21 20:00:00+00:000.223176
520200321CA1279.011249.0NaNNaN24.0125282020-03-21 20:00:00+00:000.102091
620200321CO363.03317.0NaN44.04.036802020-03-21 20:00:00+00:000.098641
720200321CT194.02106.0NaNNaN4.023002020-03-21 20:00:00+00:000.084348
820200321DC77.0506.00.0NaN1.05832020-03-21 20:00:00+00:000.132075
920200321DE45.036.0NaNNaN0.0812020-03-21 20:00:00+00:000.555556
1020200321FL658.06579.01002.0158.012.082392020-03-21 20:00:00+00:000.079864
1120200321GA507.02557.0NaNNaN14.030642020-03-21 20:00:00+00:000.165470
1220200321GU15.0108.0NaNNaNNaN1232020-03-21 20:00:00+00:000.121951
1320200321HI37.0124.09.0NaNNaN1702020-03-21 20:00:00+00:000.217647
1420200321IA68.01049.0NaNNaNNaN11172020-03-21 20:00:00+00:000.060877
1520200321ID31.0880.0NaNNaNNaN9112020-03-21 20:00:00+00:000.034029
1620200321IL759.05488.0NaNNaN6.062472020-03-21 20:00:00+00:000.121498
1720200321IN126.0707.0NaN1.04.08332020-03-21 20:00:00+00:000.151261
1820200321KS55.0417.0NaNNaN2.04722020-03-21 20:00:00+00:000.116525
1920200321KY54.0714.0NaNNaN2.07682020-03-21 20:00:00+00:000.070312
2020200321LA585.02180.0NaNNaN16.027652020-03-21 20:00:00+00:000.211573
2120200321MA525.04752.0NaN61.01.052772020-03-21 20:00:00+00:000.099488
2220200321MD190.094.0NaNNaN2.02842020-03-21 20:00:00+00:000.669014
2320200321ME73.02264.0NaNNaNNaN23372020-03-21 20:00:00+00:000.031237
2420200321MI787.02069.0NaNNaN5.028562020-03-21 20:00:00+00:000.275560
2520200321MN138.03952.0NaNNaN1.040902020-03-21 20:00:00+00:000.033741
2620200321MO73.0369.0NaNNaN3.04422020-03-21 20:00:00+00:000.165158
2820200321MS140.0695.0NaNNaN1.08352020-03-21 20:00:00+00:000.167665
2920200321MT20.01146.0NaNNaNNaN11662020-03-21 20:00:00+00:000.017153
3020200321NC184.05092.0NaNNaN0.052762020-03-21 20:00:00+00:000.034875
3120200321ND28.01141.0NaN3.00.011692020-03-21 20:00:00+00:000.023952
3220200321NE38.0356.0NaNNaN0.03942020-03-21 20:00:00+00:000.096447
3320200321NH55.0973.0942.0NaNNaN19702020-03-21 20:00:00+00:000.027919
3420200321NJ1327.0294.040.0NaN16.016612020-03-21 20:00:00+00:000.798916
3520200321NM57.03771.0NaNNaNNaN38282020-03-21 20:00:00+00:000.014890
3620200321NV124.02384.0NaNNaN2.025082020-03-21 20:00:00+00:000.049442
3720200321NY10356.035081.0NaN1603.044.0454372020-03-21 20:00:00+00:000.227920
3820200321OH247.0140.0NaN58.03.03872020-03-21 20:00:00+00:000.638243
3920200321OK53.0560.0144.010.01.07572020-03-21 20:00:00+00:000.070013
4020200321OR114.02003.0433.0NaN3.025502020-03-21 20:00:00+00:000.044706
4120200321PA371.03766.0NaNNaN2.041372020-03-21 20:00:00+00:000.089679
4220200321PR21.0143.071.0NaNNaN2352020-03-21 20:00:00+00:000.089362
4320200321RI66.0862.0290.0NaNNaN12182020-03-21 20:00:00+00:000.054187
4420200321SC152.01255.0NaNNaN1.014072020-03-21 20:00:00+00:000.108031
4520200321SD14.0691.0268.0NaN1.09732020-03-21 20:00:00+00:000.014388
4620200321TN371.03272.0NaNNaNNaN36432020-03-21 20:00:00+00:000.101839
4720200321TX304.06218.0NaNNaN5.065222020-03-21 20:00:00+00:000.046611
4820200321UT136.02424.0NaNNaN0.025602020-03-21 20:00:00+00:000.053125
4920200321VA152.02638.0NaN25.02.027902020-03-21 20:00:00+00:000.054480
5020200321VI6.0NaNNaNNaN0.062020-03-21 20:00:00+00:001.000000
5120200321VT29.0779.0NaNNaN2.08082020-03-21 20:00:00+00:000.035891
5220200321WA1524.021719.0NaNNaN83.0232432020-03-21 20:00:00+00:000.065568
5320200321WI281.04628.0NaNNaN4.049092020-03-21 20:00:00+00:000.057242
5420200321WV11.0330.02.01.00.03432020-03-21 20:00:00+00:000.032070
5520200321WY23.0438.0NaNNaNNaN4612020-03-21 20:00:00+00:000.049892
\n", "
" ], "text/plain": [ " date state positive negative pending hospitalized death total \\\n", "0 20200321 AK 14.0 758.0 NaN 0.0 NaN 772 \n", "1 20200321 AL 124.0 28.0 NaN NaN 0.0 152 \n", "2 20200321 AR 118.0 567.0 154.0 NaN NaN 839 \n", "4 20200321 AZ 104.0 240.0 122.0 NaN 1.0 466 \n", "5 20200321 CA 1279.0 11249.0 NaN NaN 24.0 12528 \n", "6 20200321 CO 363.0 3317.0 NaN 44.0 4.0 3680 \n", "7 20200321 CT 194.0 2106.0 NaN NaN 4.0 2300 \n", "8 20200321 DC 77.0 506.0 0.0 NaN 1.0 583 \n", "9 20200321 DE 45.0 36.0 NaN NaN 0.0 81 \n", "10 20200321 FL 658.0 6579.0 1002.0 158.0 12.0 8239 \n", "11 20200321 GA 507.0 2557.0 NaN NaN 14.0 3064 \n", "12 20200321 GU 15.0 108.0 NaN NaN NaN 123 \n", "13 20200321 HI 37.0 124.0 9.0 NaN NaN 170 \n", "14 20200321 IA 68.0 1049.0 NaN NaN NaN 1117 \n", "15 20200321 ID 31.0 880.0 NaN NaN NaN 911 \n", "16 20200321 IL 759.0 5488.0 NaN NaN 6.0 6247 \n", "17 20200321 IN 126.0 707.0 NaN 1.0 4.0 833 \n", "18 20200321 KS 55.0 417.0 NaN NaN 2.0 472 \n", "19 20200321 KY 54.0 714.0 NaN NaN 2.0 768 \n", "20 20200321 LA 585.0 2180.0 NaN NaN 16.0 2765 \n", "21 20200321 MA 525.0 4752.0 NaN 61.0 1.0 5277 \n", "22 20200321 MD 190.0 94.0 NaN NaN 2.0 284 \n", "23 20200321 ME 73.0 2264.0 NaN NaN NaN 2337 \n", "24 20200321 MI 787.0 2069.0 NaN NaN 5.0 2856 \n", "25 20200321 MN 138.0 3952.0 NaN NaN 1.0 4090 \n", "26 20200321 MO 73.0 369.0 NaN NaN 3.0 442 \n", "28 20200321 MS 140.0 695.0 NaN NaN 1.0 835 \n", "29 20200321 MT 20.0 1146.0 NaN NaN NaN 1166 \n", "30 20200321 NC 184.0 5092.0 NaN NaN 0.0 5276 \n", "31 20200321 ND 28.0 1141.0 NaN 3.0 0.0 1169 \n", "32 20200321 NE 38.0 356.0 NaN NaN 0.0 394 \n", "33 20200321 NH 55.0 973.0 942.0 NaN NaN 1970 \n", "34 20200321 NJ 1327.0 294.0 40.0 NaN 16.0 1661 \n", "35 20200321 NM 57.0 3771.0 NaN NaN NaN 3828 \n", "36 20200321 NV 124.0 2384.0 NaN NaN 2.0 2508 \n", "37 20200321 NY 10356.0 35081.0 NaN 1603.0 44.0 45437 \n", "38 20200321 OH 247.0 140.0 NaN 58.0 3.0 387 \n", "39 20200321 OK 53.0 560.0 144.0 10.0 1.0 757 \n", "40 20200321 OR 114.0 2003.0 433.0 NaN 3.0 2550 \n", "41 20200321 PA 371.0 3766.0 NaN NaN 2.0 4137 \n", "42 20200321 PR 21.0 143.0 71.0 NaN NaN 235 \n", "43 20200321 RI 66.0 862.0 290.0 NaN NaN 1218 \n", "44 20200321 SC 152.0 1255.0 NaN NaN 1.0 1407 \n", "45 20200321 SD 14.0 691.0 268.0 NaN 1.0 973 \n", "46 20200321 TN 371.0 3272.0 NaN NaN NaN 3643 \n", "47 20200321 TX 304.0 6218.0 NaN NaN 5.0 6522 \n", "48 20200321 UT 136.0 2424.0 NaN NaN 0.0 2560 \n", "49 20200321 VA 152.0 2638.0 NaN 25.0 2.0 2790 \n", "50 20200321 VI 6.0 NaN NaN NaN 0.0 6 \n", "51 20200321 VT 29.0 779.0 NaN NaN 2.0 808 \n", "52 20200321 WA 1524.0 21719.0 NaN NaN 83.0 23243 \n", "53 20200321 WI 281.0 4628.0 NaN NaN 4.0 4909 \n", "54 20200321 WV 11.0 330.0 2.0 1.0 0.0 343 \n", "55 20200321 WY 23.0 438.0 NaN NaN NaN 461 \n", "\n", " dateChecked posrate \n", "0 2020-03-21 20:00:00+00:00 0.018135 \n", "1 2020-03-21 20:00:00+00:00 0.815789 \n", "2 2020-03-21 20:00:00+00:00 0.140644 \n", "4 2020-03-21 20:00:00+00:00 0.223176 \n", "5 2020-03-21 20:00:00+00:00 0.102091 \n", "6 2020-03-21 20:00:00+00:00 0.098641 \n", "7 2020-03-21 20:00:00+00:00 0.084348 \n", "8 2020-03-21 20:00:00+00:00 0.132075 \n", "9 2020-03-21 20:00:00+00:00 0.555556 \n", "10 2020-03-21 20:00:00+00:00 0.079864 \n", "11 2020-03-21 20:00:00+00:00 0.165470 \n", "12 2020-03-21 20:00:00+00:00 0.121951 \n", "13 2020-03-21 20:00:00+00:00 0.217647 \n", "14 2020-03-21 20:00:00+00:00 0.060877 \n", "15 2020-03-21 20:00:00+00:00 0.034029 \n", "16 2020-03-21 20:00:00+00:00 0.121498 \n", "17 2020-03-21 20:00:00+00:00 0.151261 \n", "18 2020-03-21 20:00:00+00:00 0.116525 \n", "19 2020-03-21 20:00:00+00:00 0.070312 \n", "20 2020-03-21 20:00:00+00:00 0.211573 \n", "21 2020-03-21 20:00:00+00:00 0.099488 \n", "22 2020-03-21 20:00:00+00:00 0.669014 \n", "23 2020-03-21 20:00:00+00:00 0.031237 \n", "24 2020-03-21 20:00:00+00:00 0.275560 \n", "25 2020-03-21 20:00:00+00:00 0.033741 \n", "26 2020-03-21 20:00:00+00:00 0.165158 \n", "28 2020-03-21 20:00:00+00:00 0.167665 \n", "29 2020-03-21 20:00:00+00:00 0.017153 \n", "30 2020-03-21 20:00:00+00:00 0.034875 \n", "31 2020-03-21 20:00:00+00:00 0.023952 \n", "32 2020-03-21 20:00:00+00:00 0.096447 \n", "33 2020-03-21 20:00:00+00:00 0.027919 \n", "34 2020-03-21 20:00:00+00:00 0.798916 \n", "35 2020-03-21 20:00:00+00:00 0.014890 \n", "36 2020-03-21 20:00:00+00:00 0.049442 \n", "37 2020-03-21 20:00:00+00:00 0.227920 \n", "38 2020-03-21 20:00:00+00:00 0.638243 \n", "39 2020-03-21 20:00:00+00:00 0.070013 \n", "40 2020-03-21 20:00:00+00:00 0.044706 \n", "41 2020-03-21 20:00:00+00:00 0.089679 \n", "42 2020-03-21 20:00:00+00:00 0.089362 \n", "43 2020-03-21 20:00:00+00:00 0.054187 \n", "44 2020-03-21 20:00:00+00:00 0.108031 \n", "45 2020-03-21 20:00:00+00:00 0.014388 \n", "46 2020-03-21 20:00:00+00:00 0.101839 \n", "47 2020-03-21 20:00:00+00:00 0.046611 \n", "48 2020-03-21 20:00:00+00:00 0.053125 \n", "49 2020-03-21 20:00:00+00:00 0.054480 \n", "50 2020-03-21 20:00:00+00:00 1.000000 \n", "51 2020-03-21 20:00:00+00:00 0.035891 \n", "52 2020-03-21 20:00:00+00:00 0.065568 \n", "53 2020-03-21 20:00:00+00:00 0.057242 \n", "54 2020-03-21 20:00:00+00:00 0.032070 \n", "55 2020-03-21 20:00:00+00:00 0.049892 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posrate = latest['positive']/latest['total']\n", "latest['posrate'] = posrate\n", "latest = latest[latest['posrate'].notna()]\n", "latest" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AK 772 0.018134715025906734\n", "AL 152 0.8157894736842105\n", "AR 839 0.14064362336114422\n", "AZ 466 0.22317596566523606\n", "CA 12528 0.10209131545338442\n", "CO 3680 0.09864130434782609\n", "CT 2300 0.08434782608695653\n", "DC 583 0.1320754716981132\n", "DE 81 0.5555555555555556\n", "FL 8239 0.07986406117247238\n", "GA 3064 0.16546997389033943\n", "GU 123 0.12195121951219512\n", "HI 170 0.21764705882352942\n", "IA 1117 0.06087735004476276\n", "ID 911 0.03402854006586169\n", "IL 6247 0.12149831919321274\n", "IN 833 0.15126050420168066\n", "KS 472 0.11652542372881355\n", "KY 768 0.0703125\n", "LA 2765 0.2115732368896926\n", "MA 5277 0.09948834565093803\n", "MD 284 0.6690140845070423\n", "ME 2337 0.03123662815575524\n", "MI 2856 0.2755602240896359\n", "MN 4090 0.03374083129584352\n", "MO 442 0.16515837104072398\n", "MS 835 0.16766467065868262\n", "MT 1166 0.017152658662092625\n", "NC 5276 0.034874905231235785\n", "ND 1169 0.023952095808383235\n", "NE 394 0.09644670050761421\n", "NH 1970 0.027918781725888325\n", "NJ 1661 0.7989163154726069\n", "NM 3828 0.014890282131661442\n", "NV 2508 0.049441786283891544\n", "NY 45437 0.2279199771111649\n", "OH 387 0.6382428940568475\n", "OK 757 0.07001321003963012\n", "OR 2550 0.04470588235294118\n", "PA 4137 0.08967851099830795\n", "PR 235 0.08936170212765958\n", "RI 1218 0.054187192118226604\n", "SC 1407 0.10803127221037669\n", "SD 973 0.014388489208633094\n", "TN 3643 0.10183914356299753\n", "TX 6522 0.04661146887457835\n", "UT 2560 0.053125\n", "VA 2790 0.05448028673835126\n", "VI 6 1.0\n", "VT 808 0.03589108910891089\n", "WA 23243 0.06556812803854924\n", "WI 4909 0.05724180077408841\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lplot = latest.plot(\n", " x='total',\n", " y='posrate',\n", " kind='scatter',\n", " title='Positive test rate',\n", " figsize=(12,8)\n", ")\n", "for i in range(len(latest)):\n", " try:\n", " row = latest.loc[i]\n", " lplot.annotate(row['state'], (row['total'], row['posrate']))\n", " print(row['state'], row['total'], row['posrate'])\n", " except KeyError:\n", " pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" } }, "nbformat": 4, "nbformat_minor": 4 }