From feaf831da23ae5e9a42e3e38c67bbfbab6b536cd Mon Sep 17 00:00:00 2001 From: d115e048ca5ccb9a2d354cd5df20434a Date: Sun, 28 Feb 2021 10:26:14 +0000 Subject: [PATCH] update to local data --- module3/exo1/incidence-PAY-3.csv | 1897 ++++++++++++++ .../influenza-like-illness-analysis.ipynb | 2271 ++++++++++++++++- 2 files changed, 4118 insertions(+), 50 deletions(-) create mode 100644 module3/exo1/incidence-PAY-3.csv diff --git a/module3/exo1/incidence-PAY-3.csv b/module3/exo1/incidence-PAY-3.csv new file mode 100644 index 0000000..3308446 --- /dev/null +++ b/module3/exo1/incidence-PAY-3.csv @@ -0,0 +1,1897 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202107],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2021-02-28T10:34:59+01:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202107,3,28994,23212,34776,44,35,53,FR,France 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- "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'wget'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0misoweek\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mwget\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'wget'" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import isoweek\n", + "import os" ] }, { @@ -30,13 +41,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 41, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "if os.path.isfile(\"incidence-PAY-3.csv\"):\n", + " data_path = \"incidence-PAY-3.csv\" \n", + "else:\n", + " myfile = requests.get(data_url)\n", + " with open(\"incidence-PAY-3.csv\", \"w\") as f:\n", + " f.write(myfile.text)" ] }, { @@ -63,11 +86,978 @@ }, { "cell_type": "code", - "execution_count": null, + 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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210732899423212.034776.04435.053.0FRFrance
120210632336519231.027499.03529.041.0FRFrance
220210532242618445.026407.03428.040.0FRFrance
320210432580421491.030117.03932.046.0FRFrance
420210332181017894.025726.03327.039.0FRFrance
520210231732013906.020734.02621.031.0FRFrance
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720205332122016498.025942.03225.039.0FRFrance
820205231642812285.020571.02519.031.0FRFrance
920205132161917370.025868.03327.039.0FRFrance
1020205031684513220.020470.02620.032.0FRFrance
112020493129399923.015955.02015.025.0FRFrance
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1320204731908515285.022885.02923.035.0FRFrance
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1820204233514529812.040478.05345.061.0FRFrance
1920204132787723206.032548.04235.049.0FRFrance
2020204032044316381.024505.03125.037.0FRFrance
2120203931981015900.023720.03024.036.0FRFrance
2220203832556221142.029982.03932.046.0FRFrance
2320203731848514649.022321.02822.034.0FRFrance
242020363103907646.013134.01612.020.0FRFrance
25202035399186842.012994.01510.020.0FRFrance
26202034360843090.09078.094.014.0FRFrance
27202033361063411.08801.095.013.0FRFrance
28202032359183330.08506.095.013.0FRFrance
29202031343512269.06433.074.010.0FRFrance
.................................
186519852132609619621.032571.04735.059.0FRFrance
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1895 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202107 3 28994 23212.0 34776.0 44 35.0 \n", + "1 202106 3 23365 19231.0 27499.0 35 29.0 \n", + "2 202105 3 22426 18445.0 26407.0 34 28.0 \n", + "3 202104 3 25804 21491.0 30117.0 39 32.0 \n", + "4 202103 3 21810 17894.0 25726.0 33 27.0 \n", + "5 202102 3 17320 13906.0 20734.0 26 21.0 \n", + "6 202101 3 21799 17778.0 25820.0 33 27.0 \n", + "7 202053 3 21220 16498.0 25942.0 32 25.0 \n", + "8 202052 3 16428 12285.0 20571.0 25 19.0 \n", + "9 202051 3 21619 17370.0 25868.0 33 27.0 \n", + "10 202050 3 16845 13220.0 20470.0 26 20.0 \n", + "11 202049 3 12939 9923.0 15955.0 20 15.0 \n", + "12 202048 3 13804 10641.0 16967.0 21 16.0 \n", + "13 202047 3 19085 15285.0 22885.0 29 23.0 \n", + "14 202046 3 24801 20503.0 29099.0 38 31.0 \n", + "15 202045 3 42516 36857.0 48175.0 65 56.0 \n", + "16 202044 3 44567 38521.0 50613.0 68 59.0 \n", + "17 202043 3 43737 37523.0 49951.0 66 57.0 \n", + "18 202042 3 35145 29812.0 40478.0 53 45.0 \n", + "19 202041 3 27877 23206.0 32548.0 42 35.0 \n", + "20 202040 3 20443 16381.0 24505.0 31 25.0 \n", + "21 202039 3 19810 15900.0 23720.0 30 24.0 \n", + "22 202038 3 25562 21142.0 29982.0 39 32.0 \n", + "23 202037 3 18485 14649.0 22321.0 28 22.0 \n", + "24 202036 3 10390 7646.0 13134.0 16 12.0 \n", + "25 202035 3 9918 6842.0 12994.0 15 10.0 \n", + "26 202034 3 6084 3090.0 9078.0 9 4.0 \n", + "27 202033 3 6106 3411.0 8801.0 9 5.0 \n", + "28 202032 3 5918 3330.0 8506.0 9 5.0 \n", + "29 202031 3 4351 2269.0 6433.0 7 4.0 \n", + "... ... ... ... ... ... ... ... \n", + "1865 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1866 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1867 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1868 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1869 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1870 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1871 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1872 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1873 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1874 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1875 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1876 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1877 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1878 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1879 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1880 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1881 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1882 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1883 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1884 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1885 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1886 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1887 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1888 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1889 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1890 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1891 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1892 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1893 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1894 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 53.0 FR France \n", + "1 41.0 FR France \n", + "2 40.0 FR France \n", + "3 46.0 FR France \n", + "4 39.0 FR France \n", + "5 31.0 FR France \n", + "6 39.0 FR France \n", + "7 39.0 FR France \n", + "8 31.0 FR France \n", + "9 39.0 FR France \n", + "10 32.0 FR France \n", + "11 25.0 FR France \n", + "12 26.0 FR France \n", + "13 35.0 FR France \n", + "14 45.0 FR France \n", + "15 74.0 FR France \n", + "16 77.0 FR France \n", + "17 75.0 FR France \n", + "18 61.0 FR France \n", + "19 49.0 FR France \n", + "20 37.0 FR France \n", + "21 36.0 FR France \n", + "22 46.0 FR France \n", + "23 34.0 FR France \n", + "24 20.0 FR France \n", + "25 20.0 FR France \n", + "26 14.0 FR France \n", + "27 13.0 FR France \n", + "28 13.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1865 59.0 FR France \n", + "1866 64.0 FR France \n", + "1867 97.0 FR France \n", + "1868 93.0 FR France \n", + "1869 80.0 FR France \n", + "1870 116.0 FR France \n", + "1871 149.0 FR France \n", + "1872 281.0 FR France \n", + "1873 395.0 FR France \n", + "1874 485.0 FR France \n", + "1875 544.0 FR France \n", + "1876 689.0 FR France \n", + "1877 722.0 FR France \n", + "1878 762.0 FR France \n", + "1879 926.0 FR France \n", + "1880 1113.0 FR France \n", + "1881 1236.0 FR France \n", + "1882 832.0 FR France \n", + "1883 459.0 FR France \n", + "1884 207.0 FR France \n", + "1885 190.0 FR France \n", + "1886 198.0 FR France \n", + "1887 224.0 FR France \n", + "1888 266.0 FR France \n", + "1889 219.0 FR France \n", + "1890 176.0 FR France \n", + "1891 163.0 FR France \n", + "1892 195.0 FR France \n", + "1893 308.0 FR France \n", + "1894 213.0 FR France \n", + "\n", + "[1895 rows x 10 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_path, skiprows=1)\n", "raw_data" ] }, @@ -80,9 +1070,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
165819891930NaNNaN0NaNNaNFRFrance
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1658 198919 3 0 NaN NaN 0 NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1658 FR France " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] @@ -96,9 +1150,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210732899423212.034776.04435.053.0FRFrance
120210632336519231.027499.03529.041.0FRFrance
220210532242618445.026407.03428.040.0FRFrance
320210432580421491.030117.03932.046.0FRFrance
420210332181017894.025726.03327.039.0FRFrance
520210231732013906.020734.02621.031.0FRFrance
620210132179917778.025820.03327.039.0FRFrance
720205332122016498.025942.03225.039.0FRFrance
820205231642812285.020571.02519.031.0FRFrance
920205132161917370.025868.03327.039.0FRFrance
1020205031684513220.020470.02620.032.0FRFrance
112020493129399923.015955.02015.025.0FRFrance
1220204831380410641.016967.02116.026.0FRFrance
1320204731908515285.022885.02923.035.0FRFrance
1420204632480120503.029099.03831.045.0FRFrance
1520204534251636857.048175.06556.074.0FRFrance
1620204434456738521.050613.06859.077.0FRFrance
1720204334373737523.049951.06657.075.0FRFrance
1820204233514529812.040478.05345.061.0FRFrance
1920204132787723206.032548.04235.049.0FRFrance
2020204032044316381.024505.03125.037.0FRFrance
2120203931981015900.023720.03024.036.0FRFrance
2220203832556221142.029982.03932.046.0FRFrance
2320203731848514649.022321.02822.034.0FRFrance
242020363103907646.013134.01612.020.0FRFrance
25202035399186842.012994.01510.020.0FRFrance
26202034360843090.09078.094.014.0FRFrance
27202033361063411.08801.095.013.0FRFrance
28202032359183330.08506.095.013.0FRFrance
29202031343512269.06433.074.010.0FRFrance
.................................
186519852132609619621.032571.04735.059.0FRFrance
186619852032789620885.034907.05138.064.0FRFrance
186719851934315432821.053487.07859.097.0FRFrance
186819851834055529935.051175.07455.093.0FRFrance
186919851733405324366.043740.06244.080.0FRFrance
187019851635036236451.064273.09166.0116.0FRFrance
187119851536388145538.082224.011683.0149.0FRFrance
18721985143134545114400.0154690.0244207.0281.0FRFrance
18731985133197206176080.0218332.0357319.0395.0FRFrance
18741985123245240223304.0267176.0445405.0485.0FRFrance
18751985113276205252399.0300011.0501458.0544.0FRFrance
18761985103353231326279.0380183.0640591.0689.0FRFrance
18771985093369895341109.0398681.0670618.0722.0FRFrance
18781985083389886359529.0420243.0707652.0762.0FRFrance
18791985073471852432599.0511105.0855784.0926.0FRFrance
18801985063565825518011.0613639.01026939.01113.0FRFrance
18811985053637302592795.0681809.011551074.01236.0FRFrance
18821985043424937390794.0459080.0770708.0832.0FRFrance
18831985033213901174689.0253113.0388317.0459.0FRFrance
188419850239758680949.0114223.0177147.0207.0FRFrance
188519850138548965918.0105060.0155120.0190.0FRFrance
188619845238483060602.0109058.0154110.0198.0FRFrance
1887198451310172680242.0123210.0185146.0224.0FRFrance
18881984503123680101401.0145959.0225184.0266.0FRFrance
1889198449310107381684.0120462.0184149.0219.0FRFrance
189019844837862060634.096606.0143110.0176.0FRFrance
189119844737202954274.089784.013199.0163.0FRFrance
189219844638733067686.0106974.0159123.0195.0FRFrance
18931984453135223101414.0169032.0246184.0308.0FRFrance
189419844436842220056.0116788.012537.0213.0FRFrance
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1894 rows × 10 columns

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198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1891 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1892 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1893 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1894 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 53.0 FR France \n", + "1 41.0 FR France \n", + "2 40.0 FR France \n", + "3 46.0 FR France \n", + "4 39.0 FR France \n", + "5 31.0 FR France \n", + "6 39.0 FR France \n", + "7 39.0 FR France \n", + "8 31.0 FR France \n", + "9 39.0 FR France \n", + "10 32.0 FR France \n", + "11 25.0 FR France \n", + "12 26.0 FR France \n", + "13 35.0 FR France \n", + "14 45.0 FR France \n", + "15 74.0 FR France \n", + "16 77.0 FR France \n", + "17 75.0 FR France \n", + "18 61.0 FR France \n", + "19 49.0 FR France \n", + "20 37.0 FR France \n", + "21 36.0 FR France \n", + "22 46.0 FR France \n", + "23 34.0 FR France \n", + "24 20.0 FR France \n", + "25 20.0 FR France \n", + "26 14.0 FR France \n", + "27 13.0 FR France \n", + "28 13.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1865 59.0 FR France \n", + "1866 64.0 FR France \n", + "1867 97.0 FR France \n", + "1868 93.0 FR France \n", + "1869 80.0 FR France \n", + "1870 116.0 FR France \n", + "1871 149.0 FR France \n", + "1872 281.0 FR France \n", + "1873 395.0 FR France \n", + "1874 485.0 FR France \n", + "1875 544.0 FR France \n", + "1876 689.0 FR France \n", + "1877 722.0 FR France \n", + "1878 762.0 FR France \n", + "1879 926.0 FR France \n", + "1880 1113.0 FR France \n", + "1881 1236.0 FR France \n", + "1882 832.0 FR France \n", + "1883 459.0 FR France \n", + "1884 207.0 FR France \n", + "1885 190.0 FR France \n", + "1886 198.0 FR France \n", + "1887 224.0 FR France \n", + "1888 266.0 FR France \n", + "1889 219.0 FR France \n", + "1890 176.0 FR France \n", + "1891 163.0 FR France \n", + "1892 195.0 FR France \n", + "1893 308.0 FR France \n", + "1894 213.0 FR France \n", + "\n", + "[1894 rows x 10 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -123,10 +2144,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "def convert_week(year_and_week_int):\n", @@ -154,10 +2173,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -180,9 +2197,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -200,9 +2225,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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ZiYaIhPEE4+eq+ksAVT2sqnFVTQA/xss5gOcNzE5pXgscdPbaNPY+bUQkBFQCTYP01QdVvUNVl6nqspqammx+0oSgIxrzwlPO07BFC3NH2kR4wMJThv/JpnpKgDuBLar6/RT7jJTDPgi87l4/DFzjKqLm4yW8X1TVeqBNRFa4Pq8FHkppk6yMugp4wuU9HgcuE5EqF/66zNmMLOiMxikOBykpCPa+N3JDzwDhKVt7yvA7oSyOuRD4FLBJRDY6298BHxORJXjhoj3A5wBUdbOI3A+8gVd5db2qJq9Wnwd+ChQDj7oHeKJ0j4jsxPMwrnF9NYnIN4GX3HHfUNWm4f3UiUdXNM6UsoLeRHiHeRo5Y6BEeMxEw/A5GUVDVZ8lfW7hkUHa3ATclMa+ATgrjb0buHqAvtYAazKN0ziRzmiMkoKSlOop8zRyxcAltxaeMvyNzQj3Mb0lty48ZTmN3NHdk6AoHOxjCwUtPGX4HxMNH9PZ44lGiYWnck4kFj+heqrAVU956TjD8CcmGj6mMxKnuCBEaUGQYEA41t2T7yH5gnhC6YkrhaG+nkY4GEDV+9ww/IqJhk+JxRNE4wlKCoKICFUlBTR1RPM9LF+QXI6lKHxiTgMgZqJh+BgTDZ/S2eMlvZPltpNLCzjabqKRC7rduU23CRNgeQ3D15ho+JTkhS2ZrK0qDdPcaaKRCyLO0ygMnxieAmx5dMPXmGj4lEiPu7C5u+HJpYUctfBUTojEBvI0nGhY2a3hY0w0fEr/u+HqUstp5IrunmROo7+n4YWnbP0pw8+YaPiU/nfD1aUFtHT22IzlHDCQp5Gc7GeiYfgZEw2fkqzw6Q1PlRUA0NxpZbcjpdeLS1NyCxaeMvyNiYZP6X9hqy71RMNCVCPneJFB/1VuLTxl+B8TDZ+SFI1kyKS6xBONox2RvI3JLxwvMujnabhzbSW3hp8x0fApkX5zCaqT4akOC0+NlONFBicuIwIQs/CU4WNMNHxKpN+s5ePhKfM0RkrmklvzNAz/YqLhU/rnNKp6w1OW0xgpmUpuLTxl+BkTDZ/Sv3oqHAxQWRy2RHgOyOhp2IzwrFFV9h3tzPcwjCGQzXavs0XkSRHZIiKbReRLzl4tImtFZId7rkppc4OI7BSRbSKyMsW+VEQ2uc9uddu+4raGvc/Z14vIvJQ2q9x37BCRVRhZkbywpW4UVF1aYJ5GDrCS29zx9PZG3vGdJ9nZ0J7voRhZko2nEQO+qqpnACuA60VkMfA1YJ2qLgTWufe4z64BzgQuB24TkeT/rtuB1Xj7hi90nwNcBzSr6gLgFuBm11c1cCNwPrAcuDFVnIyBSXdhqy4toMkWLRwxmRYsjCXM08iWTXWtAGw/3JbnkRjZklE0VLVeVV9xr9uALcAs4ArgLnfYXcCV7vUVwL2qGlHV3cBOYLmIzAAqVPV59Xapubtfm2RfDwAXOy9kJbBWVZtUtRlYy3GhMQYhWRba39OwRQtHTsTtDx4I9N0FOelpRC08lTU7nIexv8lCVOOFIeU0XNjoXGA9ME1V68ETFmCqO2wWsD+lWZ2zzXKv+9v7tFHVGNAKTB6kLyMDkViccFAIplzYJlt4KidEehIneBmQuoyIhaeyJRmW2meiMW7IWjREpAx4EPiyqh4b7NA0Nh3EPtw2qWNbLSIbRGRDY2PjIEObOETd3XAq1aUFNHdESdgmQSOiOxY/YY4G2IzwoRJPKLsaTTTGG1mJhoiE8QTj56r6S2c+7EJOuOcGZ68DZqc0rwUOOnttGnufNiISAiqBpkH66oOq3qGqy1R1WU1NTTY/yfdEYokT9nuorSohllAOtnblaVT+wPM0gifYw7Zg4ZCoa+4kEksQDIiFp8YR2VRPCXAnsEVVv5/y0cNAspppFfBQiv0aVxE1Hy/h/aILYbWJyArX57X92iT7ugp4wuU9HgcuE5EqlwC/zNmMDERi8RNCKKfUlAKwq7EjH0PyDZEBPI0Cq54aEsnQ1LK5VRxo6bK91ccJ2XgaFwKfAt4jIhvd433At4BLRWQHcKl7j6puBu4H3gAeA65X1bjr6/PAT/CS47uAR539TmCyiOwEvoKrxFLVJuCbwEvu8Q1nMzIQiZ0Ydz+1pgyANxutvHEkeOc2jadhM8KHRDIJ/p7Tp9ITVw4d687ziIxsCGU6QFWfJX1uAeDiAdrcBNyUxr4BOCuNvRu4eoC+1gBrMo3T6Eu6EMqUsgLKi0K8aZ7GiOjuOdGLAwgGBBETjWzZ2dDO1PJCzppVCcC+o53MmlSc51EZmbAZ4T4lGk/0KbcFEBFOrSnrTT4awyMSS5ywLHqScDBgy4hkyY6GdhZMLWN2VQlgZbfjBRMNn5IupwFeXsM8jZExUHgKvLxGT8xi89lwtD3C9IoiZkwqIhgQq6AaJ5ho+JRITyJtsvbUmjIOHeumPRLLw6j8QWSA8BR4s8JtRnh2dEbjFBcECQcDzJxUxP5mE43xgImGTxnobvhUV0G127yNYeOFp9J7GuFgwHIaWdIRiVFa6KVVZ1QWU99qifDxgImGTxkoPNVbQXXE8hrDZXBPI0DUwlMZiSeUSCxBSUFy6f4wLbbEzbjARMOnRGMnJsIB5kwuIRgQHtlUT8zuiIdFdyx96A+88JR5GpnpjHrh0dICz9Pw1kWzXSXHAyYaPiXdPA3wVr39yqWLeHzzYb5030a8OZTGUPA8jYHDU5bTyExn1Ju6VVJ4fJOw5o6o/T2OA0w0fMpgFT7Xv3sBX3jXqfzutXr2N9mSIkMlY8mthacy0hHp62lUlRQQSyhtVqAx5jHR8CmDxd0BLlk8DYBtto/BkIjFE8QSOrCnEbJ5GtnQ62kkcxpuD/uWDgtRjXVMNHxKZICcRpJF08oB2/xmqET6baPbn8JQgEhPPO1nxnF6PY3CpKcRBqDJkuFjHhMNHxJP6KB3wwBlhSFqq4rZdshEYyhkEo2icJBu24QpIwN5GrZJ2NjHRMOHJHeOG6jCJ8lp08rN0xgiyb3XB5qnURwO0B01TyMTHdG+nkZ1iRMN2yRszGOi4UOSF7bBchoAi6aXs6ux3UpEh0B3z+CC7HkaJhqZ6Iz08zSSomFlt2MeEw0fcjyEMnB4CjxPoyeu7D5is8Oz5bggpz+3RaEg3ZbTyEhynkaJq54qLwoRDIh5GuMAEw0fEnF3w4MlwuF4MtzyGtmTPLcDldwWhQO93ogxMB39chqBgDCpOGw5jXGAiYYPyTY8dUpNKcGAmGgMgaQXMaCnEQ7SZZ5GRjqjMYIB6fM3WlVaYKIxDshmu9c1ItIgIq+n2L4uIgf67eSX/OwGEdkpIttEZGWKfamIbHKf3eq2fMVtC3ufs68XkXkpbVaJyA73SG4Ha2QgU4VPkqJwkLmTS9jRYKKRLdlUT0VjCRK2demgdETilBQEcZcBwEuGN1l4asyTjafxU+DyNPZbVHWJezwCICKLgWuAM12b20QkeUt2O7Aab8/whSl9Xgc0q+oC4BbgZtdXNXAjcD6wHLjR7RNuZKD3wjZAhU8qC2rKevdqNjKTKV+UrKqKWNntoHRGY72zwZNMKgnTYonwMU9G0VDVPwDZ7st9BXCvqkZUdTfeXuDLRWQGUKGqz6u3uMzdwJUpbe5yrx8ALnZeyEpgrao2qWozsJb04mX0I9vwFMCCqWXsPdppFVRZcrzkduCcBmDJ8Ax0ROO9604lqS41T2M8MJKcxhdF5DUXvkp6ALOA/SnH1DnbLPe6v71PG1WNAa3A5EH6MjKQvMvNlAgHTzRiCWXvUaugyobektsMnoblNQanM5LO0yigpbPHFi0c4wxXNG4HTgWWAPXA95xd0hyrg9iH26YPIrJaRDaIyIbGxsbBxj0hiPRkl9MATzQAC1FlSa8XN4CnUexEwzyNwemMxnsrp5JUl4aJxhO9lVXG2GRYoqGqh1U1rqoJ4Md4OQfwvIHZKYfWAgedvTaNvU8bEQkBlXjhsIH6SjeeO1R1maouq6mpGc5P8hXJBfMyzdOA45symWhkR3cGQT4enrJw32B0RuO9s8GTTLJZ4eOCYYmGy1Ek+SCQrKx6GLjGVUTNx0t4v6iq9UCbiKxw+YprgYdS2iQro64CnnB5j8eBy0SkyoW/LnM2IwORnuxzGqWFIWZWFploZEmXm5RWXJBekJPFBzYrfHA6orETPY0SW39qPBDKdICI/BfwLmCKiNThVTS9S0SW4IWL9gCfA1DVzSJyP/AGEAOuV9Xk/57P41ViFQOPugfAncA9IrITz8O4xvXVJCLfBF5yx31DVbNNyE9oIlmuPZXk1Kll7GxsZ8+RDiqKw1S7xeOME+mMxgkGhILgAJ6G8+5s/anB6YykCU+VeX93R9tNNMYyGUVDVT+WxnznIMffBNyUxr4BOCuNvRu4eoC+1gBrMo3R6EuvaAQzh6fAy2vc8/xeLv7+07xzUQ1rPv3Wkzm8cU1XT5yScN/5BakkPRDzNAbH8zT6Xn5qygoBaGyP5GNIRpbYjHAfkilZ25/FMyqIJZSqkjB/2N5Iq9XKD0hXND5gaAosp5ENqupyGn3P4xQnGkdMNMY0GT0NY/yRXBp9oBBKfz50Xi2n1JQRCghX/Ptz/Pcbh7h62ezMDScg6ap+UukNT1n11IBEYgniCT3B0yguCFJeGKKxzURjLGOehg+JxBIUBAMEAulDKP0JBoSlc6s4u7aSWZOKefT1Qyd5hOOXzmic4oKB77VsnkZmuly+pzSN+E4pLzTRGOOYaPiQSM/gW70OhIjwvrdM55kdjRzrthBVOrp6Tqz6ScXCU5lJbsBUUnii+NaUFVp4aoxjouFDIrF4VuW26bhoYQ09ceWNg8dyPCp/0BmN907gS0eRTe7LSGevp3GiaEwpLzBPY4xjouFDunsSwxaNudUlAOxv6szlkHxDpkR4YSiAyPG5MsaJdESSnsaJ59HzNKzkdixjouFDOqOxE2bbZsvMScWImGgMRKZEuIi3R4TlNAZmUE+jrJDWrp7eCkBj7GGi4UO8FUSHJxoFoQAzK4vZ39yV41H5g0yiAW6fcMtpDEivp5HmPNaUJ8tuzdsYq5ho+JCuaCxtZUq21FYVm6cxAN09cYrDgwtycdj2CR+Mzn5bvabSO1fD8hpjFhMNH9KRZomGoTCnuoR9Jhon4E1KG7x6CpynYZswDUibq8yrKA6f8FnS07Bk+NjFRMOHdKZZomEozK4uoaEtYnfL/YjEEiR04MUKkxSGAr1zEYwTSe7OV5lGNKaU26zwsY6Jhg/pSLNEw1CYXV0MQJ3lNfrQNUhYJZWicNASuYPQ0tVDaUGQcJoVC6a4RQvN0xi7mGj4kM7IyDyNOVZ2m5bOnuxEw3Iag9PS2dO7d0Z/CkNBKopC5mmMYUw0fIaq0tkTH1EifHaVE41mE41Uju+lMbggF4UDVj01CK1dPWlDU0lqygttpdsxjImGz+juSaCafomGbKkpL6QwFDBPox/Jqp/BZoRDsuTWPI2BaO2KDioaU8uLeLOxw/YKH6OYaPiM5Lo+I/E0RITZ1SXsPWqikcpgpaKpFIWDNrlvELzw1MCi8f6zZ7D1UBtPbmvotb24u4kfPb2LRzfVj8YQjUHIKBoiskZEGkTk9RRbtYisFZEd7rkq5bMbRGSniGwTkZUp9qUissl9dqvb9hW3Nex9zr5eROaltFnlvmOHiCS3hDUGoTPi7oZHkNMAOG1aOVsO2fpTqSQT4Zmqp2xy3+C0dA0uGh9962zmTi7h249tI5FQntnRyEd+9Dz/79Gt/N2vNo3iSI10ZONp/BS4vJ/ta8A6VV0IrHPvEZHFeNu1nuna3CYiyf9htwOr8fYNX5jS53VAs6ouAG4BbnZ9VeNtLXs+sBy4MVWcjPTkwtMAeEttJfubumjusJm5SbL3NAK29tQAqKrLaQy8pXA4GOCrl53G1kNt/OD32/nnR7Yyu7qYz140n+bOHmJxE+R8klE0VPUPeHt3p3IFcJd7fRdwZYr9XlWNqOpuYCewXERmABWq+rx6gcq7+7VJ9vUAcLHzQlYCa1W1SVWbgbWcKF5GPzoHWXZ6KJw9qxKATQdaRzwmv5AMOZVkmBHuTe4z0UhHd0+CaCwxaE4D4ANnz+CqpbXc+sROttQf4688LaEzAAAgAElEQVRXns7cyV6BRlOn3cjkk+HmNKapaj2Ae57q7LOA/SnH1TnbLPe6v71PG1WNAa3A5EH6MgahIzLwBjdD4UwTjRM4Xj2VwdMIBemJq90Rp6Gly7vgDxaeAi+v9q0PvYUPnTuLdy6q4c/eMoPJbomRJvN+80qut3tNt1WcDmIfbpu+XyqyGi/0xZw5czKP0sf0ehojzGlUFoeZP6WU1+pacjEsXzCU8BRAdyxBWZZb7k4UkrPBJ2XwNABCwQDf/+iS3vfVpV5I66gtZphXhvsXfdiFnHDPyTKHOiB1c+la4KCz16ax92kjIiGgEi8cNlBfJ6Cqd6jqMlVdVlNTM8yf5A96l50ewYzwJG+ZVcmmOvM0kmRbcpv0RKzs9kRau9wSIhk8jXQkZ4sfNU8jrwxXNB4GktVMq4CHUuzXuIqo+XgJ7xddCKtNRFa4fMW1/dok+7oKeMLlPR4HLhORKpcAv8zZjEHo6L0bHrkTeXZtJQdbu21JB0dXT5yicOa914tCJhoDMdi6U5mYXOqFp47axL+8kvHKIiL/BbwLmCIidXgVTd8C7heR64B9wNUAqrpZRO4H3gBiwPWqmvyf83m8Sqxi4FH3ALgTuEdEduJ5GNe4vppE5JvAS+64b6hq/4S80Y/OQfYqGCqLZ1QAsP1wW+/qoxOZbBeCLOzdJ9xEoz+tvTmNgaunBqKyOEwwIJbTyDMZ/weo6scG+OjiAY6/CbgpjX0DcFYaezdOdNJ8tgZYk2mMxnE6sgyhZMOsKm/hwkOt3SPuyw9k2h88yfF9wi0R3p+h5DT6EwgIVSUFtkFTnrEsnc/wFisMZgyhZMO0iiIADh0z0YDM+4MnKQ5beGogWrt6CAdl2J7w5NICC0/lGRMNn9ERjecknwHeHfOkkrB5Go5stnoFKC/yzv8xt9mQcZwWt1ihWxBiyEwuK7DwVJ4x0fAZXdFYTiqnkkyvKKLeRAPwEuHZhKeSpaFNHSYa/WntHHyF20xUlxZY9VSeMdHwGbn0NACmVxZx6JhtxgReeCobT6PKiYYtwXIiLV3RYSXBk0wpK7TwVJ4x0fAZndHYiGeDpzKjsohDrfafFLKvniovDBEOii13kYbWrp5hJcGTTC4t4Fh3jKjtwZ43TDR8Rkcku2RttkyrKOJIe8T+k5J9IlzEq/IxT+NEWkYanipLhv7s3OYLEw2f4XkauQtPzaj0Kqga2iyv0dmTXXgKvNi7Xdj6kkgoDcciI5rz0zvBr8O833xhouEzOiJxSnKYCO8tu53gyfBEQmnvjlGW5erBVSUmGv050h4hGk9Q6+b/DIfJZbb+VL4x0fAZufc03AS/CT5Xo7E9QiyhzJiU3QWvurTAchr9qGvxCipmjUQ0Si08lW9MNHxGZzS3nsZ08zQAqGv2Lni1WYpGVWnYchr96D2HVSXD7iO5PPoRq6DKGyYaPiIWTxCJJXLqaVQUhygKBya8aBwY4l1ydWkhLV09xBNpV/OfkBxwojErS+FNR0VRiMJQgAZbRDNvmGj4iM6e7PZ7GAoiwozKYuoneHgqecGbmW14qiSM6vGlwA2oa+5kUkmY0hHsKikiTKso4vAE/3vMJyYaPqIzkrtl0VOZUVnUG1qYqBxo6aSyOJx9Itxi7ydwoKVrREnwJFPLC2k4Zp5GvjDR8BHJu9rk2ke54i2zKtly8NiEXoDvQHPXkMIqyaVEmi0Z3stQz+FATK0o5LCVgOcNEw0fcbB1aCGUbFk6t4poPDGh9ws/2NI9pKqfqhIrDU1FValr7mLWpOEnwZNMLS+i0TyNvGGi4SMOtiRFoyin/S6dWwXAhj3NOe13vKCqHGgZ2l1ycj6BeRoezZ09dPXEcxOeqiikLRKjMxrLwciMoWKi4SMOtnQRDAhTy3MrGpPLCjmlppQNeybmxonHumK0R2JDuuAlPQ3LaXjUNXcCI5ujkWSa+/u2vEZ+GJFoiMgeEdkkIhtFZIOzVYvIWhHZ4Z6rUo6/QUR2isg2EVmZYl/q+tkpIre6fcRxe43f5+zrRWTeSMbrdw62dDO9oohgDjZg6s+yuVW8vK+ZxAQsIa1rcRe8IXgaReEgJQVBm6vhyEW5bZKpFd5cDaugyg+58DTerapLVHWZe/81YJ2qLgTWufeIyGK8/b/PBC4HbhORZG3o7cBqYKF7XO7s1wHNqroAuAW4OQfj9S0HW7pyHppKsmxeNS2dPbx5pP2k9D+W6b3gDfEuuarEZoUn2dvkCe/s6pHnNJJL29hcjfxwMsJTVwB3udd3AVem2O9V1Yiq7gZ2AstFZAZQoarPq6oCd/drk+zrAeBiGe6WXxOAg61dOU+CJzl39iQAXqubeMnw3ol9Qzy31aW20m2SrfXHmFFZNKIVbpNMLTdPI5+MVDQU+G8ReVlEVjvbNFWtB3DPU519FrA/pW2ds81yr/vb+7RR1RjQCkzuPwgRWS0iG0RkQ2Nj4wh/0vgkkVAOtXafNNGYO7mUYEB4s7HjpPQ/ltl88BhVJeHeMtpsqSkv5JDF3QHYUt/GGTMqctJXZXGYglCARvM08sJIReNCVT0PeC9wvYi8Y5Bj03kIOoh9sDZ9Dap3qOoyVV1WU1OTacy+5Eh7hJ64MrPy5ISnCkIB5laXsLNh4oWn1u8+yvL51UPe13rB1DLebGyf8EuJRGJxdjW2c/r08pz0JyJMLS80TyNPjEg0VPWge24AfgUsBw67kBPuucEdXgfMTmleCxx09to09j5tRCQEVAITs4QnAwdaTs4cjVROnVrGrsaJJRoHW7rY39TF+fNPcHAzsmBqGZFYgn0unj9R2dnQTiyhOfM0wM0KHyeext6jHfzzI1v41J3reWRTfb6HM2KGLRoiUioi5cnXwGXA68DDwCp32CrgIff6YeAaVxE1Hy/h/aILYbWJyAqXr7i2X5tkX1cBT7i8h9GPgy3eXddJFY2aMvYc7SAWnzi7+L2427tHWT6/eshtF03z7qy3H27L6ZjGG1vrvd9/xozceBrgJcPHi2j8xc9e4afP7WFXQztf+Pkr3PS7N/I9pBExEk9jGvCsiLwKvAj8TlUfA74FXCoiO4BL3XtUdTNwP/AG8Bhwvaom16X4PPATvOT4LuBRZ78TmCwiO4Gv4CqxjBOpT84GrzyZolFKT1zZPwHWoYonlM5ojPW7j1JeFBrWXfLCqWUA7JjgorGl/hiFoQDzJpfmrM9pFUUcau1mrN9DbjvUxpb6Y/z9+8/g6b95NyvPnMa9L+0f8+MejGEvUqSqbwLnpLEfBS4eoM1NwE1p7BuAs9LYu4GrhzvGicSBli5KC4JUFOd23alUTnUXwZ0N7cyfkrsLwFjkX5/Ywb8/uZNwMMDbTpk8rLkvpYUhZk0qZvvhiRXS68/WQ20smlZOKJi7Ys1Takppj8Q4dKy7d6OwscivNx4gGBDef/YMwsEAFy2s4fHNh8f8uAfDZoT7hD1HOqitKhlysnYonFrjicZEyGs8vvkwk0oKCAaEy8+aPux+Fk0rm9DhqURCeaP+WE5DUwCnT/c8v2ToayySSCgPbzzIRQumMMVtHnWaC1luOzR2x50JEw0fEE8oG/Y2c97cSSf1eyqLw9SUF7LL5xVUR9ojbKk/xqcvmMemr6/k6mWzMzcagEXTynmzceLkgV4/0MqaZ3f3hl827G2mqSPKhQum5PR7TnOVWFsOHctpv7nkpT1NHGjp4oolM3tti6Z5N17j+Ubi5MUyjFFj26E22rpjw0rWDpVTa0rZ4XPR+OOuowA5udAtnFZONO5VUJ3iPDU/8+3Ht/GH7Y1UlYb54Lm1PLTxAMXhIJcunpbT76ksDjNrUvGY9jR+tn4f5UUh3nvWjF7bpJICplUUsu3Q+P0/ZJ6GD3hxt3eRWz6MstChcu6cKjYdaKW107870j234wjlRSHeMqtyxH0l5yZMhJn0DW3dPLujkXBQ+MeHNrOrsZ1HNtVzyeJpOd8YDLxqrK1j1NNoaOvmsdfruXrpbIr77aS5aFr5SfE0WjtHZ3thEw0f8OKeJmZNKs7JYnCZuOSMacQTylPbGzIfPA5RVZ7deYQLTh1e8rs/i2dUMLW8kMc3H8rB6MY2v321noTC7Z9YiipcdssfaO7s4c/PmZm58TA4fXoFuxo7iMTG3uZg97+0n5648okVc0747LRp5exoaMv5Bf5vH3yNj/zo+ZNemWWiMc5RVV7c3TQqoSmAJbMnMaWsgHVb/CkaWw+1caCli4sW5mZlgUBAWHnmdJ7c1uD7/R8e2niAxTMquGTxNB790tv583NmsmT2JN6xKLf5jCSnzygnntC8r1KQSGif4pBYPMF/rt/HRQum9BaPpLJoWjndPQn2j2DSZzSW4LmdR3pXnX55bzOPbT7EOxfVnNRiGDDRGPfsauzgSHuUt84bHdEIBoT3nD6VJ7c10OPD5O4vNtQRDgrvf8uMzAdnyXvPmk53T4Knt/l3XbSfvbCXV+ta+dB53rJxs6tLuOWjS/j19RdSGApmaD08khVUW/Kc17j7+T1c/L2necntN/PE1gYOtnbzyRVz0x6/KBmyHMFOmDc+/Dqf+Ml6vvHbN1BVbn50K1PKCvns2+cPu89sMdEY5zy/6wgAF5x68vMZSS4+Yxpt3TFeePPoqH3naBCNJfj1xgNccsa0IS9OOBjL51dTVRLmkdf9GaJ6ZFM9/+eh13nP6VNZdcG8Ufve+VNKmVJWwEMbD4zad/YnnlDWPLcHgJsf3Yqqcs8Le5leUcQlZ0xN22bxjApmVxdz+1O70oaoYvFE76ZV4G1g9fs3DnOo1Vv14Zev1PFfL+5n4dQyfvrHPbz1pt/z4p4mvnzJwpOSO+qPicY457mdR5k1qZi5k0e+T0G2vGNhDdWlBax5dveofefJZveRDtY8t5umjihXL6vN3GAIhIIBPnDOTB57vX5EIYmxyKa6Vr5y/0bOm1PFv3/8PMI5nMCXiWBAWP2OU3hmxxFe3pufJeme2tbAvqZO3rGohg17m/mbB17jmR1H+Pj5cwaczFgQCvA3K09nS/0xfvWnEwXvf//iVd71nafYfriN/3huNxfd/CSfvXsDn/6PF9nV2M4//Pp1ls+v5pEvvZ1PXzCPc+dU8f2PnMPHl5+YPzkZmGiMY+IJ5fk3j3LhgsknPY6ZSnFBkP9x4Tye3NbI5oPjvyrooY0HePd3n+Jbj25lZmUR78hRPiOVL7xrAQERvr92e877zhe7j3Sw+p4NTC4t5EefWnpCldBo8MkVc5lcWsC3H9tGV3T0E+I//eMeplcU8aNPLuXUmlIefKWOpXOr+MT5g1/A/+zsGSyZPYnvPt533E9ua+DXGw8SSyh//YtXufmxrbx94RS+ccWZbD3UxpX/9hwBEW756BLCwQBf//Mz+fG1y/jQebUETsKOnekw0RjHbD7YSmtXT84nTmXDp942j/LCELc9uWvUvzuXbNjTxF//4jWWz6/ml1+4gN/95dtzutxFkumVRXzmwvn8euMBXqtryXn/o80r+5r54G3PEYkl+PG1y3pnPI82JQUhvnzJQtbvbuLd332K9aMYMt3f1MkzO47wseVzKC4I8tv/9XZe/6eVPPj5C5ic4XyICH///jM4dKybO599E/BKZv/hV69zak0pf/++M3i1rpVQIMC3rzqba982j6uX1tIWifGPH1g8KpWSA2GiMca4/6X9vPdfnuH5XQP/8UdicX6+fi8/f2EfABecOvqiUVkc5qpltax94/C4rQpqj8T40r0bmTnJu1M8b04VVTnMZfTn8+88lWnlRVz/n6/QMo63ge2JJ/jKfRspLwrxqy9cwOKZuVvyfDh86m3zuG/1CorCAb5070aOdY/OHKIHX6lDBK5y4cziguCQcgpvnVfNyjOncftTu9h66BhfuX8jDW3dfOfqc/jMhfP40HmzuPnDZ/euUfXNK8/i3tUruHppbsOnQ8VEYwzx5NYGvvbL19jV0M7Hf/ICH/nh89zwy9c42t53CegfPf0mf/+r17lvw35On15OTXl+7vLeddpUovEEL+1pzsv3j4RYPMG3Ht3CwdYuvveRJSdVLJJUloS5/ZPncbg1wlfvf/Wkf9/J4pev1LHnaCc3/tmZzM3hyrUj4fxTJvODa86loa2bmx/detK/L5FQHnyljgtOnTyiu/6/vfx04qpc/oNnWLe1gX94/2LOm1NFKBjg+x9ZwvvPPl7FVxQOsuKU0Q1Fp8OWERkDdERifH/tdu764x4Wz6xgzaffyo+efpNNda08+MoBnt15hDWr3srCaeU0tHXzw6d3cckZ0/jMhfPy6qa+dV4VBcEAf9x5hHcuGh87JqoqX/3Fq/zyFS8B+ZkL57F0btWoff+5c6r4q0sXcfNjW3l1fwvnzD6564XlAlWlubOHqpIwHdE4t67byTmzJ3HxANVB+WLJ7Elcd9F8fvzMbsLBAH/3vjMoCOX+vviPO4/w+OZD7G/q4quXnjaivk6pKWPdV9/FE1sO0xNXrn1b+jLdsYSJxhjgW49u5Wfr9/LRZbP528tPp6q0gP/zZ4sB+NO+Zv7n3S/zodv+yD9+YDG/21RPNJbg799/Rt6XJy8pCHHunEk8u/NIXseRpLkjSmE4QKQnQX1rN5sOtBCNKzMqirho4RSKwkEeeLmOX75ygA+fV8vy+VVcee6szB3nmE+umMNtT+7kx8+8yb99/LxR//7B2N/Uyc9e2Et5UYgPnVfLvqZO/v3JnTyzw5slf6i1m/rWLr579Tl5v+NNx99cfjrxBKx5bje7j3Rwx7VLs5on8uyOI9z70j4a2yJMryzi4jOmpZ3J/sTWw1x31wYCIqw4pZqVZw5/BeQksyYV86m3zRtxP6OFiUaO6e6JUxgKZP0fqqUzygMv13HVebV868Nnn/D5uXOqeOiLF3LdT1/irx94jYJQgL9eeVreBSPJRQum8L2122nqiOZ0bsNQ6IjE+JsHX+N3rw28leakkjDn1E5iwx5v9vx3rjp71KpN+lNeFObj58/hx8+8yd6jHWMixNMTT/CD32/nh0+/iQCxhPLd//YqvSqKQnz6gnk8/OpBQgHhP//nClacMnrzgoZCOBjgHz+wmIXTyrjhl5v4y//6E//6sfMG9Tj2HOngc/dsoLggyLzJpbzw5lEe2uj91jNnVvDHXUeJ9MTpjiX49yd3snhGBb/4i7eNypyIsYiMhx2kRORy4F+AIPATVf3WQMcuW7ZMN2zYMOLvbO6IUloYysq9be3q4cXdTdz30j7WbW0gFBAWz6jgo2+dw9K5VcydXEJROP3dzu1P7eLmx7by2Jff3jvDNR3tkRjrthzm7W6OxFjhlX3NfOi2P/JXlyziLy9eQGN7hMmlhSes26SqvUIaicX5zav1PL29kbNmVrBsXhULppZTWRwe8vf/YXsj3/jtG7zZ2M5n334KVSUFFIYC1JQXctasSkoLgmw91Mb9G/azv6mTSSUF/N8rz2J29ejNa0lHfWsX7/nu05QWBvmH9y/mHYuO/7smEooIo3Yn//LeJv7pN2/wWl0rVy2t5auXLaK9O8aT2xqYP6WM5fOrqSwO0xWNk1CltHB8XCz/47nd/NNv3mD5vGo+sWIOh491885FUzltejmxeIJndhxhZ0M7D716gH1HO3nsy+9g5qRiIrE419zxAm8cPEYsoX0m4M2oLOL+z70t738/JwMReVlVl2U8bqyLhogEge14W8fWAS8BH1PVtBvtDlc0OqMx7nl+LwmFjfub+e83DlMUCnLhgil85dJFnDa9nMa2CPWtXcQTSnlRmFNqSrn9qV3cum4HsYRSXVrAh8+bRTAQ4MmtDWxzK1mKeNuwnlJTSnVpAZ3ROF3ROB3RGNsPtbFkziR+/tkVwz5H+SSeUFbfvYF1WxuYWl5IQ1uE5fOq+atLF/Gz9Xtp6YzSE/M24jl3ziSuXjab7zy+lf1NXUwuLeBoh1dFFBD44Lm1XH7WdJo7osTSzJQVgbmTS5hdVUJDW4Rb1+3g6e2NzK4u5p8/+BbefhLmV5xMth46xl/d9ypb6r2VWi8/czoXLJjMret20NYdY2pFIVPLi5jmnqdWFLJk9iRWzJ9MQ1uEfU2d1Ld28VpdK6WFIf78nBnsONzOgZYuCkMBGtoiqMLSuVVUFIeIxZWE0uul3vvSPn6/5TCvHzjGtIpCbvzAmbwvh8unjAV+8+pB/vcvXiUSO77kTVVJGAVa3ErNBaEA//LRJbw35bcfPtbN5+55mSWzJ7HqgnlUFocpCAUoDgdzspDlWMRPovE24OuqutK9vwFAVf9fuuOHKxpH2yMs/b+/B7xQxkeXzSbilpVo7eohKHLChawgGCAaT/CBc2byseWzOW9OVa9HoapsqW9jR0Mbu490sOdIB7uPdNDc2UNJQZCSgiClhSFKC0Jc/+4FvKV25Mtw54tEQlnz3G5eeLOJhdPKWPPsbiKxBJXFYRZMLUNVWTC1jEc2HaI9EmP+lFL+8QOLedeiGhraImw+2MozO47w8/X7iMayX8+qvCjEX75nIddeMPekrW90sumJJ3hlbzN/2NHImmf30NUTZ9ncKpbOreLwsW4OH4vQ0NZNw7EIbRGvtLmkIEhnyoSwwlCAnniC/jobcN7KQKupisCyuVVcungan1wx17fhloMtXTR3Rplc6q02vKOhjUhPgovPmMYFCyZTGAqM27+fXOIn0bgKuFxVP+vefwo4X1W/mHLMamA1wJw5c5bu3bt3yN+TSChdPXECIhSEAr13E62dPdz1/B66e+LMmFTMzMoiCkIBjrRH2LivhbNrJ/Gh82aNyaRgvnj9QCtPb2/kk+fPpbLkeMjpYEsXT29v5Mols9LOHm5o6+ZAcxdTygrThgVjbkXTQ61dlBaGuPDUKaNSKjtaHGrtZveRDs6fX50239LW3cMTWxt44c0mFk0rY8HUMmrKCzm1pozDx7pZt6WBM2ZUcNr0ciKxOFUlBURjCTYdaCUaS/T+TW8/3MaxrhgfOm+WL8MsxvDwk2hcDazsJxrLVfV/pTs+VzkNwzCMiUS2ojEeJvfVAambNNcCB/M0FsMwjAnNeBCNl4CFIjJfRAqAa4CH8zwmwzCMCcmYz3ypakxEvgg8jldyu0ZVN+d5WIZhGBOSMS8aAKr6CPBIvsdhGIYx0RkP4SnDMAxjjGCiYRiGYWSNiYZhGIaRNSYahmEYRtaM+cl9Q0VE2oBtWRxaCeRqg+tc9nUy+ksyBcjFOubj4ffmus9cnbskY/0c2vkbG33B6Jy7KUCpqmZewE1VffUANmR53B05/M6c9XUy+hvqufHD7z0JY8zJuRsv59DO39joa7TO3VC+YyKHp34zRvs6Gf3lmvHwe+0cjq3+cs1Y/r2+Pnd+DE9t0CzWT5mI2LkZPnbuRoadv+EzGuduKN/hR0/jjnwPYAxj52b42LkbGXb+hs9onLusv8N3noZhGIZx8vCjp2EYhmGcJEw0xjEiMltEnhSRLSKyWUS+5OzVIrJWRHa45ypnn+yObxeRf+vX18dEZJOIvCYij4nIlHz8ptEix+fuo+68bRaRb+fj94w2wzh/l4rIy+5v7GUReU9KX0udfaeI3Co+39Esx+fuJhHZLyLto/YDclnKZY/RfQAzgPPc63K8vdQXA98GvubsXwNudq9LgYuAvwD+LaWfENAATHHvv423xW7ef+M4OHeTgX1AjXt/F3Bxvn/fGDx/5wIz3euzgAMpfb0IvA0Q4FHgvfn+fePo3K1w/bWP1vjN0xjHqGq9qr7iXrcBW4BZwBV4Fy/c85XumA5VfRbo7teVuEepu8urwOcbXeXw3J0CbFfVRvf+98CHT/Lw884wzt+fVDX5N7UZKBKRQhGZAVSo6vPqXQXvTrbxK7k6d+6zF1S1fjTHb6LhE0RkHt4dyXpgWvIPyT1PHaytqvYAnwc24YnFYuDOkzjcMcVIzh2wEzhdROaJSAjvP/rsDG18xTDO34eBP6lqBO9iWZfyWZ2zTQhGeO7ygomGDxCRMuBB4MuqemwY7cN4onEuMBN4Dbghp4Mco4z03KlqM965uw94BtgDxHI5xrHMUM+fiJwJ3Ax8LmlKc9iEKOnMwbnLCyYa4xx3wX8Q+Lmq/tKZDzu3H/fckKGbJQCqusuFCO4HLjhJQx4z5Ojcoaq/UdXzVfVteOue7ThZYx5LDPX8iUgt8CvgWlXd5cx1QG1Kt7X4PDQKOTt3ecFEYxzj8g93AltU9fspHz0MrHKvVwEPZejqALBYRJKLlV2KF2f1LTk8d4jIVPdcBXwB+EluRzv2GOr5E5FJwO+AG1T1ueTBLgzTJiIrXJ/XksU5H8/k6tzljXxXEthj+A+8ah7FCydtdI/34VX0rMO7410HVKe02QM0Ae14d3mLnf0v8ITiNby1aSbn+/eNo3P3X8Ab7nFNvn/bWDx/wD8AHSnHbgSmus+WAa8Du4B/w0069usjx+fu2+5vMeGev36yx28zwg3DMIyssfCUYRiGkTUmGoZhGEbWmGgYhmEYWWOiYRiGYWSNiYZhGIaRNSYahjHKiMhfiMi1Qzh+noi8fjLHZBjZEsr3AAxjIiEiIVX9Yb7HYRjDxUTDMIaIW2TuMbxF5s7FW9r6WuAM4PtAGXAE+LSq1ovIU8AfgQuBh0WkHG8p6++KyBLgh0AJ3uS2/6GqzSKyFFgDdALPjt6vM4zBsfCUYQyP04A7VPVs4BhwPfCvwFWqmrzg35Ry/CRVfaeqfq9fP3cDf+v62QTc6Oz/AfyleutZGcaYwTwNwxge+/X4OkA/A/4Ob4OctW7juSCQus/Bff07EJFKPDF52pnuAn6Rxn4P8N7c/wTDGDomGoYxPPqvv9MGbB7EM+gYQt+Spn/DGBNYeMowhsccEUkKxMeAF4CapE1Ewm7/gwFR1VagWUTe7kyfAp5W1RagVUQucvZP5H74hjE8zNMwjOGxBVglIj/CW5X0X1iOUeEAAAB4SURBVIHHgVtdeCkE/ABve87BWAX8UERKgDeBzzj7Z4A1ItLp+jWMMYGtcmsYQ8RVT/1WVc/K81AMY9Sx8JRhGIaRNeZpGIZhGFljnoZhGIaRNSYahmEYRtaYaBiGYRhZY6JhGIZhZI2JhmEYhpE1JhqGYRhG1vx/kucualqJ/l0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'][-200:].plot()" ] @@ -251,10 +2322,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", @@ -273,10 +2342,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "year = []\n", @@ -299,9 +2366,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -315,9 +2405,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2042389\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -332,9 +2468,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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