diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..a643ed98ff332fbd2af1ac981ac694df35114b24 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2073 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210932262117527.027715.03426.042.0FRFrance
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220210732239318303.026483.03428.040.0FRFrance
320210632318319134.027232.03529.041.0FRFrance
420210532242618445.026407.03428.040.0FRFrance
520210432580421491.030117.03932.046.0FRFrance
620210332181017894.025726.03327.039.0FRFrance
720210231732013906.020734.02621.031.0FRFrance
820210132179917778.025820.03327.039.0FRFrance
920205332122016498.025942.03225.039.0FRFrance
1020205231642812285.020571.02519.031.0FRFrance
1120205132161917370.025868.03327.039.0FRFrance
1220205031684513220.020470.02620.032.0FRFrance
132020493129399923.015955.02015.025.0FRFrance
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1520204731908515285.022885.02923.035.0FRFrance
1620204632480120503.029099.03831.045.0FRFrance
1720204534251636857.048175.06556.074.0FRFrance
1820204434456738521.050613.06859.077.0FRFrance
1920204334373737523.049951.06657.075.0FRFrance
2020204233514529812.040478.05345.061.0FRFrance
2120204132787723206.032548.04235.049.0FRFrance
2220204032044316381.024505.03125.037.0FRFrance
2320203931981015900.023720.03024.036.0FRFrance
2420203832555921141.029977.03932.046.0FRFrance
2520203731848514649.022321.02822.034.0FRFrance
262020363103907646.013134.01612.020.0FRFrance
27202035399186842.012994.01510.020.0FRFrance
28202034360843090.09078.094.014.0FRFrance
29202033361063411.08801.095.013.0FRFrance
.................................
186719852132609619621.032571.04735.059.0FRFrance
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1897 rows × 10 columns

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France \n", + "27 20.0 FR France \n", + "28 14.0 FR France \n", + "29 13.0 FR France \n", + "... ... ... ... \n", + "1867 59.0 FR France \n", + "1868 64.0 FR France \n", + "1869 97.0 FR France \n", + "1870 93.0 FR France \n", + "1871 80.0 FR France \n", + "1872 116.0 FR France \n", + "1873 149.0 FR France \n", + "1874 281.0 FR France \n", + "1875 395.0 FR France \n", + "1876 485.0 FR France \n", + "1877 544.0 FR France \n", + "1878 689.0 FR France \n", + "1879 722.0 FR France \n", + "1880 762.0 FR France \n", + "1881 926.0 FR France \n", + "1882 1113.0 FR France \n", + "1883 1236.0 FR France \n", + "1884 832.0 FR France \n", + "1885 459.0 FR France \n", + "1886 207.0 FR France \n", + "1887 190.0 FR France \n", + "1888 198.0 FR France \n", + "1889 224.0 FR France \n", + "1890 266.0 FR France \n", + "1891 219.0 FR France \n", + "1892 176.0 FR France \n", + "1893 163.0 FR France \n", + "1894 195.0 FR France \n", + "1895 308.0 FR France \n", + "1896 213.0 FR France \n", + "\n", + "[1897 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020210932262117527.027715.03426.042.0FRFrance
120210832090316885.024921.03226.038.0FRFrance
220210732239318303.026483.03428.040.0FRFrance
320210632318319134.027232.03529.041.0FRFrance
420210532242618445.026407.03428.040.0FRFrance
520210432580421491.030117.03932.046.0FRFrance
620210332181017894.025726.03327.039.0FRFrance
720210231732013906.020734.02621.031.0FRFrance
820210132179917778.025820.03327.039.0FRFrance
920205332122016498.025942.03225.039.0FRFrance
1020205231642812285.020571.02519.031.0FRFrance
1120205132161917370.025868.03327.039.0FRFrance
1220205031684513220.020470.02620.032.0FRFrance
132020493129399923.015955.02015.025.0FRFrance
1420204831380410641.016967.02116.026.0FRFrance
1520204731908515285.022885.02923.035.0FRFrance
1620204632480120503.029099.03831.045.0FRFrance
1720204534251636857.048175.06556.074.0FRFrance
1820204434456738521.050613.06859.077.0FRFrance
1920204334373737523.049951.06657.075.0FRFrance
2020204233514529812.040478.05345.061.0FRFrance
2120204132787723206.032548.04235.049.0FRFrance
2220204032044316381.024505.03125.037.0FRFrance
2320203931981015900.023720.03024.036.0FRFrance
2420203832555921141.029977.03932.046.0FRFrance
2520203731848514649.022321.02822.034.0FRFrance
262020363103907646.013134.01612.020.0FRFrance
27202035399186842.012994.01510.020.0FRFrance
28202034360843090.09078.094.014.0FRFrance
29202033361063411.08801.095.013.0FRFrance
.................................
186719852132609619621.032571.04735.059.0FRFrance
186819852032789620885.034907.05138.064.0FRFrance
186919851934315432821.053487.07859.097.0FRFrance
187019851834055529935.051175.07455.093.0FRFrance
187119851733405324366.043740.06244.080.0FRFrance
187219851635036236451.064273.09166.0116.0FRFrance
187319851536388145538.082224.011683.0149.0FRFrance
18741985143134545114400.0154690.0244207.0281.0FRFrance
18751985133197206176080.0218332.0357319.0395.0FRFrance
18761985123245240223304.0267176.0445405.0485.0FRFrance
18771985113276205252399.0300011.0501458.0544.0FRFrance
18781985103353231326279.0380183.0640591.0689.0FRFrance
18791985093369895341109.0398681.0670618.0722.0FRFrance
18801985083389886359529.0420243.0707652.0762.0FRFrance
18811985073471852432599.0511105.0855784.0926.0FRFrance
18821985063565825518011.0613639.01026939.01113.0FRFrance
18831985053637302592795.0681809.011551074.01236.0FRFrance
18841985043424937390794.0459080.0770708.0832.0FRFrance
18851985033213901174689.0253113.0388317.0459.0FRFrance
188619850239758680949.0114223.0177147.0207.0FRFrance
188719850138548965918.0105060.0155120.0190.0FRFrance
188819845238483060602.0109058.0154110.0198.0FRFrance
1889198451310172680242.0123210.0185146.0224.0FRFrance
18901984503123680101401.0145959.0225184.0266.0FRFrance
1891198449310107381684.0120462.0184149.0219.0FRFrance
189219844837862060634.096606.0143110.0176.0FRFrance
189319844737202954274.089784.013199.0163.0FRFrance
189419844638733067686.0106974.0159123.0195.0FRFrance
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189619844436842220056.0116788.012537.0213.0FRFrance
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1896 rows × 10 columns

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38521.0 50613.0 68 59.0 \n", + "19 202043 3 43737 37523.0 49951.0 66 57.0 \n", + "20 202042 3 35145 29812.0 40478.0 53 45.0 \n", + "21 202041 3 27877 23206.0 32548.0 42 35.0 \n", + "22 202040 3 20443 16381.0 24505.0 31 25.0 \n", + "23 202039 3 19810 15900.0 23720.0 30 24.0 \n", + "24 202038 3 25559 21141.0 29977.0 39 32.0 \n", + "25 202037 3 18485 14649.0 22321.0 28 22.0 \n", + "26 202036 3 10390 7646.0 13134.0 16 12.0 \n", + "27 202035 3 9918 6842.0 12994.0 15 10.0 \n", + "28 202034 3 6084 3090.0 9078.0 9 4.0 \n", + "29 202033 3 6106 3411.0 8801.0 9 5.0 \n", + "... ... ... ... ... ... ... ... \n", + "1867 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1868 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1869 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1870 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1871 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1872 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1873 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1874 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1875 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1876 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1877 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1878 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1879 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1880 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1881 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1882 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1883 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1884 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1885 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1886 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1887 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1888 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1889 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1890 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1891 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1892 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1893 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1894 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1895 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1896 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 42.0 FR France \n", + "1 38.0 FR France \n", + "2 40.0 FR France \n", + "3 41.0 FR France \n", + "4 40.0 FR France \n", + "5 46.0 FR France \n", + "6 39.0 FR France \n", + "7 31.0 FR France \n", + "8 39.0 FR France \n", + "9 39.0 FR France \n", + "10 31.0 FR France \n", + "11 39.0 FR France \n", + "12 32.0 FR France \n", + "13 25.0 FR France \n", + "14 26.0 FR France \n", + "15 35.0 FR France \n", + "16 45.0 FR France \n", + "17 74.0 FR France \n", + "18 77.0 FR France \n", + "19 75.0 FR France \n", + "20 61.0 FR France \n", + "21 49.0 FR France \n", + "22 37.0 FR France \n", + "23 36.0 FR France \n", + "24 46.0 FR France \n", + "25 34.0 FR France \n", + "26 20.0 FR France \n", + "27 20.0 FR France \n", + "28 14.0 FR France \n", + "29 13.0 FR France \n", + "... ... ... ... \n", + "1867 59.0 FR France \n", + "1868 64.0 FR France \n", + "1869 97.0 FR France \n", + "1870 93.0 FR France \n", + "1871 80.0 FR France \n", + "1872 116.0 FR France \n", + "1873 149.0 FR France \n", + "1874 281.0 FR France \n", + "1875 395.0 FR France \n", + "1876 485.0 FR France \n", + "1877 544.0 FR France \n", + "1878 689.0 FR France \n", + "1879 722.0 FR France \n", + "1880 762.0 FR France \n", + "1881 926.0 FR France \n", + "1882 1113.0 FR France \n", + "1883 1236.0 FR France \n", + "1884 832.0 FR France \n", + "1885 459.0 FR France \n", + "1886 207.0 FR France \n", + "1887 190.0 FR France \n", + "1888 198.0 FR France \n", + "1889 224.0 FR France \n", + "1890 266.0 FR France \n", + "1891 219.0 FR France \n", + "1892 176.0 FR France \n", + "1893 163.0 FR France \n", + "1894 195.0 FR France \n", + "1895 308.0 FR France \n", + "1896 213.0 FR France \n", + "\n", + "[1896 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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lbpS0XdIrkhaW0udKeiHPrcqtgMntgh/M9I2SZpbKLJPUna9lpfRZmbc7yx7bmEtiA9WoZZT9fQofjZ8OR3vPzNpLPT2Ug8D8iHhP0jjgbyVVtu69PSL+tJxZ0myKLXzPBqYBT0j6RG4DvBpYDjwLPA4sotgG+EpgX0ScIWkpcBvwJUmTgJuBDiCALZIei4h9mef2iFgr6btZx+rBXwobrHIgWHnxpwZcvt5P4aPx0+Fo7ZlZe+q3hxKF9/Lbcfmq9QCwxcDaiDgYEa8C24F5kqYCEyJiQxQPELsHWFIqsyaPHwYuyt7LQqAzInoyiHQCi/Lc/MxLlq3UZcPkrJvWMfOGn3Dfxp1EFIFg5g0/4ayb1vVfuMSfwmsbjT0za091zaFIOgbYApwB/O+I2CjpM8C1kq4AuoCv5h/96RQ9kIpdmfZ+Hlenk19fA4iIXknvAJPL6VVlJgNvR0RvH3XZMHnm+guPuEf7QPhTeG2jsWdm7amuVV4RcSgi5gAzKHob51AML30cmAPsBr6V2dVXFTXSB1OmVl2HkbRcUpekrr179/aVxQapkYHAn8JHHj+iZPQZ0CqviHhb0tPAovLciaTvAz/Ob3cBp5aKzQDeyPQZfaSXy+ySNBY4EejJ9E9XlXkaeAs4SdLY7KWU66pu853AnVA8vn4gP6/1r1H7cPhT+MhztHNr1n763Q9F0hTg/QwmxwM/pZgQ3xIRuzPPHwLnRcRSSWcD9wPzKCblfwacGRGHJG0G/iuwkWJS/n9FxOOSrgE+FRFfyUn5346I381J+S3Ab2Zzfg7MjYgeSX8F/LA0Kf+LiPhOrZ/F+6GYDT3vcT/y1LsfSj09lKnAmpxHGQM8FBE/lnSvpDkUQ007gC8DRMQ2SQ8BLwG9wDW5wgvgKuBu4HiK1V2V2du7gHslbafomSzNunok3Qpszny3RERPHq8A1kpaCWzNOsysyRo1t2btp9+AEhG/AM7tI/3yGmX+BPiTPtK7gF8Zz4iIA8AXj1DXD4Af9JH+/yh6QWbWQrzIYvTynfJm1nDe43508p7yZmZWk/eUNzOzYeWAYmZmDeGAYmZmDeGAYmZmDeGAYmZmDeGAYmZmDeGAYmZmDeGAYmZmDeGAYmZmDeGAYjaEvCeIjSYOKGZDqLwniNlI54dDmg2B6j1B7tu4k/s27vSeIDaiuYdiNgSeuf5CvjBnGuPHFf/Fxo8bw+I503hmxYVNbpnZ0HFAMRsC3hPERiMHFBtRWmkSvLInyCNXX8Bl553O3vcONrtJZkOq34AiabykTZKel7RN0h9n+iRJnZK68+vEUpkbJW2X9IqkhaX0uZJeyHOrJCnTj5P0YKZvlDSzVGZZvke3pGWl9FmZtzvLHtuYS2LtrJUmwb93eQcrl5zD7GkTWLnkHL53eb/bSZi1tX432Mo/+h+JiPckjQP+FrgO+G2gJyK+IekGYGJErJA0G3iAYnveacATwCci4pCkTVn2WeBxYFVErJN0NfAbEfEVSUuBiyPiS5ImAV1AB8Xe9VuAuRGxL/et/1FErJX0XeD5iFhd62fxBlsjV/UkeIUnwc2OXsM22IrCe/ntuHwFsBhYk+lrgCV5vBhYGxEHI+JVYDswT9JUYEJEbIgiit1TVaZS18PARRnIFgKdEdETEfuATmBRnpufeavf30YhT4IPr1YaWrTWUdcciqRjJD0H7KH4A78ROCUidgPk15Mz+3TgtVLxXZk2PY+r0w8rExG9wDvA5Bp1TQbezrzVdVW3fbmkLklde/furefHtTbkSfDh1UpDi9Y66roPJSIOAXMknQQ8IumcGtnVVxU10gdTplZdhydG3AncCcWQV195bGSoTIJfOu807t+0k73+9Nxwvr/GahnQjY0R8bakp4FFwJuSpkbE7hzO2pPZdgGnlorNAN7I9Bl9pJfL7JI0FjgR6Mn0T1eVeRp4CzhJ0tjspZTrslGqPOm9ckmtzzw2WM9cfyErH3+Zn277Jw68/wHjx41h4dm/ztc+98lmN81aQD2rvKZkzwRJxwP/Gfgl8BhQWXW1DHg0jx8DlubKrVnAmcCmHBbbL+n8nAO5oqpMpa5LgCdznmU9sEDSxFxFtgBYn+eeyrzV729mQ8RDi1ZLPT2UqcAaScdQBKCHIuLHkjYAD0m6EtgJfBEgIrblCqyXgF7gmhwyA7gKuBs4HliXL4C7gHslbafomSzNunok3Qpszny3RERPHq8A1kpaCWzNOsxsiHlo0Y6k32XDI8lQLhve8+4Brn1gK3dceq4/rZk1UDv+32rHNtfSsGXDVh+vejEbGu34f6sd29wI7qEcJd9QZzY02vH/Vju2uR7uoQwT31BnNjTa8f9WO7a5kRxQjtJAVr347mJrB63ye9qOK8rasc2N5IDSAPU+VXa0jqtae2ml39N2fGJzO7a5UTyHMgxG6riqjSz+PbUj8RxKCxnt46rWHvx72npaZfixXg4ow6Cdx1Xb7RfaBq+df09HqlYafqzHgJ7lZYPXrncXl3+hV178qWY3x4ZYu/6ejjTt+hBOz6FYnzyebtY8e949cMSHcDajx+g5FDsqHk83a552HX70kJf1qV1/oc1GinYcfnRAsSNqx19os5GiHff38RzKKDbSnohqZkPDcyjWr3Zbkmhmrc1DXqNQuy5JbDXu4Zkdrp4tgE+V9JSklyVtk3Rdpn9d0uuSnsvXZ0tlbpS0XdIrkhaW0udKeiHPrcqtgMntgh/M9I2SZpbKLJPUna9lpfRZmbc7yx7bmEsy8nkFV2O4h2d2uHp6KL3AVyPi55JOALZI6sxzt0fEn5YzS5pNsYXv2cA04AlJn8htgFcDy4FngceBRRTbAF8J7IuIMyQtBW4DviRpEnAz0AFEvvdjEbEv89weEWslfTfrWD34SzF6eAXX0XEPz6xv/fZQImJ3RPw8j/cDLwPTaxRZDKyNiIMR8SqwHZgnaSowISI2RLES4B5gSanMmjx+GLgoey8Lgc6I6Mkg0gksynPzMy9ZtlKX1WE0PxH1aLmHZ9a3Ac2h5FDUucBG4ALgWklXAF0UvZh9FMHm2VKxXZn2fh5Xp5NfXwOIiF5J7wCTy+lVZSYDb0dEbx91WR3acUliq3APz6xvda/ykvRR4IfAH0TEuxTDSx8H5gC7gW9VsvZRPGqkD6ZMrbqq271cUpekrr179/aVxWzA3MMz+1V19VAkjaMIJn8ZET8CiIg3S+e/D/w4v90FnFoqPgN4I9Nn9JFeLrNL0ljgRKAn0z9dVeZp4C3gJEljs5dSruswEXEncCcU96HU8/Oa9cc9PLNfVc8qLwF3AS9HxLdL6VNL2S4GXszjx4CluXJrFnAmsCkidgP7JZ2fdV4BPFoqU1nBdQnwZM6zrAcWSJooaSKwAFif557KvGTZSl1mZtYE9fRQLgAuB16Q9Fym/RHwXyTNoRhq2gF8GSAitkl6CHiJYoXYNbnCC+Aq4G7geIrVXesy/S7gXknbKXomS7OuHkm3Apsz3y0R0ZPHK4C1klYCW7MOMzNrEj96xczMavKjV8zMbFg5oJiZNcFI3F7bAcVsFBmJf8Ta1Uh8dI8fDmk2ipT/iK28+FPNbs6oNJIf3eNJebNRoPqPWMVI+CPWbhq5X/xwPfHak/Jm9q/8/LHW0chH97TasJmHvMyabDg+Zfr5Y63laLfXbtVhMwcUsyYbrnmNo/0jZo1ztI/ueeb6C484bNZMDihmTTLcnzL9/LGRo1V7nJ5DMWsSz2s0zmhcDt2KT7x2D8WsSVr1U2Y7Go3LoVuxx+mAYtZEntc4Oq06OT1a+T4UM2tbjbynw47M96GY2YjnYcPW4iEvM2trHjZsHR7yMjMbwRpx46yHvMzMbFgfz1LPnvKnSnpK0suStkm6LtMnSeqU1J1fJ5bK3Chpu6RXJC0spc+V9EKeW5V7y5P7zz+Y6RslzSyVWZbv0S1pWSl9VubtzrLHNuaSmJm1v7NuWsfMG37CfRt3ElGsgJt5w08466Z1/RcepHp6KL3AVyPik8D5wDWSZgM3AD+LiDOBn+X35LmlwNnAIuA7ko7JulYDy4Ez87Uo068E9kXEGcDtwG1Z1yTgZuA8YB5wcylw3Qbcnu+/L+swMzOac+NsvwElInZHxM/zeD/wMjAdWAysyWxrgCV5vBhYGxEHI+JVYDswT9JUYEJEbIhi4uaeqjKVuh4GLsrey0KgMyJ6ImIf0AksynPzM2/1+5uZjXrNWAE3oFVeORR1LrAROCUidkMRdCSdnNmmA8+Wiu3KtPfzuDq9Uua1rKtX0jvA5HJ6VZnJwNsR0dtHXWZmxvCvgKs7oEj6KPBD4A8i4t2c/ugzax9pUSN9MGVq1XV4Y6TlFMNsnHbaaX1lMTMbkYb78Sx1rfKSNI4imPxlRPwok9/MYSzy655M3wWcWio+A3gj02f0kX5YGUljgROBnhp1vQWclHmr6zpMRNwZER0R0TFlypR6flwzMxuEelZ5CbgLeDkivl069RhQWXW1DHi0lL40V27Noph835TDY/slnZ91XlFVplLXJcCTOc+yHlggaWJOxi8A1ue5pzJv9fubmVkT1DPkdQFwOfCCpOcy7Y+AbwAPSboS2Al8ESAitkl6CHiJYoXYNRFxKMtdBdwNHA+syxcUAeteSdspeiZLs64eSbcCmzPfLRHRk8crgLWSVgJbsw4zM2sS3ylvZmY1+U55aymjcQMks9HGAcWGxXA+/sHMmsNPG7Yh5Q2QzEYP91BsSHnfdLPRwwHFhpQ3QDIbPTzkZUPOGyCZjQ5eNmxmZjV52bCZmQ0rBxQzM2sIB5QRyjcSmtlwc0AZoXwjoZkNN6/yGmF8I6GZNYt7KCOMbyQ0GzwPFR8dB5QRxjcSmg2eh4qPjoe8RiDfSGg2MB4qbgzf2Ghmo96edw+w8vGX+em2f+LA+x8wftwYFp7963ztc590754G3tgo6QeS9kh6sZT2dUmvS3ouX58tnbtR0nZJr0haWEqfK+mFPLcqtwEmtwp+MNM3SppZKrNMUne+lpXSZ2Xe7ix7bD0XxcysLx4qbox65lDuBhb1kX57RMzJ1+MAkmZTbN97dpb5jqRjMv9qYDnFHvNnluq8EtgXEWcAtwO3ZV2TgJuB84B5wM25rzyZ5/aIOBPYl3WYmQ1aZaj4kasv4LLzTmfveweb3aS20+8cSkT8TbnX0I/FwNqIOAi8mnvEz5O0A5gQERsAJN0DLKHYU34x8PUs/zBwR/ZeFgKdlT3kJXUCiyStBeYDl2aZNVl+dZ1tNDP7Fd+7/MMRnZVLzmliS9rX0azyulbSL3JIrNJzmA68VsqzK9Om53F1+mFlIqIXeAeYXKOuycDbmbe6LjMza5LBBpTVwMeBOcBu4FuZrj7yRo30wZSpVdevkLRcUpekrr179x4pm5mZHaVBBZSIeDMiDkXEB8D3KeY4oOgtnFrKOgN4I9Nn9JF+WBlJY4ETgZ4adb0FnJR5q+vqq613RkRHRHRMmTJloD+qmZnVaVABRdLU0rcXA5UVYI8BS3Pl1iyKyfdNEbEb2C/p/JwfuQJ4tFSmsoLrEuDJKNYyrwcWSJqYQ2oLgPV57qnMS5at1GVmZk3S76S8pAeATwMfk7SLYuXVpyXNoRhq2gF8GSAitkl6CHgJ6AWuiYhDWdVVFCvGjqeYjF+X6XcB9+YEfg/FKjEiokfSrcDmzHdLZYIeWAGslbQS2Jp1mJlZE/nGRjMzq6neGxtHVUCRtBf4xz5OfYxibqaduM1Dr93aC27zcGm3Nh9te0+PiH4noUdVQDkSSV31RN9W4jYPvXZrL7jNw6Xd2jxc7fXThs3MrCEcUMzMrCEcUAp3NrsBg+A2D712ay+4zcOl3do8LO31HIqZmTWEeyhmZtYQIzKgHGEPl38raUPuyfJ/JE3I9HGS1mT6y5JuLJV5Ovd1qez7cnKLtPlYSX+R6c9L+nSpTJ/7zrR4m4flOks6VdJT+e+8TdJ1mT5JUmfur9NZetjpgPf3afE2t+R1ljQ5878n6Y6qulryOvfT5iG/zoNo729J2pLXcouk+aW6GneNI2LEvYD/CPwm8GIpbTPwn/L494Fb8/hSikfuA/waxZ3/M/P7p4GOFmzzNcBf5PHJwBZgTH6ruEO0AAADpklEQVS/Cfh3FA/RXAd8pg3aPCzXGZgK/GYenwD8PTAb+CZwQ6bfANyWx7OB54HjgFnAPwDHDOd1bnCbW/U6fwT498BXgDuq6mrV61yrzUN+nQfR3nOBaXl8DvD6UFzjEdlDiYi/oXiMS9lZwN/kcSfwO5XswEdUPGzyeOBfgHeHo51lA2zzbOBnWW4P8DbQoeIZaxMiYkMUvymVfWdats1D1ba+RMTuiPh5Hu8HXqbY+mAxxb465NfKNfvX/X0i4lWgsr/PsF3nRrV5KNrWqDZHxD9HxN8CB8r1tPJ1PlKbh8sg2rs1IioP0d0GjFfxzMWGXuMRGVCO4EXgC3n8RT58kvHDwD9TPIZ/J/Cn8eEzwwD+Irut/2Moh4+O4Ehtfh5YLGmsiodwzs1ztfadGS4DbXPFsF5nFZvGnQtsBE6J4gGm5NfKEMVg9vcZMkfZ5opWvM5H0srXuT/Ddp0H0d7fAbZGsRFiQ6/xaAoovw9cI2kLRRfxXzJ9HnAImEYxRPBVSf8mz10WEZ8C/kO+Lh/eJh+xzT+g+IfvAv4M+L8UD+Mc0F4xQ2SgbYZhvs6SPgr8EPiDiKjVG23InjyN0IA2Q+te5yNW0Udaq1znWobtOg+0vZLOpthC/cuVpD6yDfoaj5qAEhG/jIgFETEXeIBibBmKOZS/joj3cyjm78ihmIh4Pb/uB+5n+IcO+mxzRPRGxB9GxJyIWAycBHRTe9+ZVm3zsF5nSeMo/gP+ZUT8KJPfzK5/ZZhlT6YPZn+fVm1zK1/nI2nl63xEw3WdB9peSTOAR4ArIqLy96+h13jUBJTKSgtJY4CbgO/mqZ3AfBU+ApwP/DKHZj6WZcYBn+fDfV+a2mZJv5ZtRdJvAb0R8VLU3nemJds8nNc5r8ldwMsR8e3SqfKePOX9dQazv09LtrnFr3OfWvw6H6meYbnOA22vpJOAnwA3RsTfVTI3/BoPdja/lV8Un4x3A+9TROArgesoVkL8PfANPryp86PAX1FMVL0E/Pf4cBXHFuAXee5/kqtlWqDNM4FXKCbinqB4Emilng6KX+B/AO6olGnVNg/ndaZYlRP5Xs/l67PAZIoFA935dVKpzNfyWr5CafXLcF3nRrW5Da7zDooFHu/l79LsNrjOv9Lm4brOA20vxYe7fy7lfQ44udHX2HfKm5lZQ4yaIS8zMxtaDihmZtYQDihmZtYQDihmZtYQDihmZtYQDihmZtYQDihmZtYQDihmZtYQ/x/rFzgxQKYDrwAAAABJRU5ErkJggg==\n", 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