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mooc-rr
Commit 92bb5d2d authored by 1899a69eca748c9e8b02007d0e49196a's avatar 1899a69eca748c9e8b02007d0e49196a
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module3/exo1/influenza-like-illness-analysis.ipynb
View file @ 92bb5d2d
...@@ -9,16 +9,41 @@ ...@@ -9,16 +9,41 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true "outputs": [
}, {
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting wget\n",
" Downloading wget-3.2.zip (10 kB)\n",
"Building wheels for collected packages: wget\n",
" Building wheel for wget (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9681 sha256=df54105f5bb9f2d741d755365ad8f15b83ebc4494cfc2a3a6f8f1fc8d9cbf40f\n",
" Stored in directory: /home/jovyan/.cache/pip/wheels/90/1d/93/c863ee832230df5cfc25ca497b3e88e0ee3ea9e44adc46ac62\n",
"Successfully built wget\n",
"Installing collected packages: wget\n",
"Successfully installed wget-3.2\n"
]
}
],
"source": [
"!pip install wget"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd\n", "import pandas as pd\n",
"import isoweek" "import isoweek\n",
"import wget\n",
"import os"
] ]
}, },
{ {
...@@ -30,10 +55,8 @@ ...@@ -30,10 +55,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
...@@ -63,11 +86,242 @@ ...@@ -63,11 +86,242 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"using file from cache...\n"
]
},
{
"data": {
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202011</td>\n",
" <td>3</td>\n",
" <td>101704</td>\n",
" <td>93652.0</td>\n",
" <td>109756.0</td>\n",
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" <td>142.0</td>\n",
" <td>166.0</td>\n",
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" <td>France</td>\n",
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" <td>96650.0</td>\n",
" <td>113304.0</td>\n",
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" <td>202009</td>\n",
" <td>3</td>\n",
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" <td>119326.0</td>\n",
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" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202008</td>\n",
" <td>3</td>\n",
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" <td>153522.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202007</td>\n",
" <td>3</td>\n",
" <td>183610</td>\n",
" <td>172812.0</td>\n",
" <td>194408.0</td>\n",
" <td>279</td>\n",
" <td>263.0</td>\n",
" <td>295.0</td>\n",
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" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1842</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1843</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
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" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1844</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
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" <td>308.0</td>\n",
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" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1845</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
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" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
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" </tr>\n",
" </tbody>\n",
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"<p>1846 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202011 3 101704 93652.0 109756.0 154 142.0 \n",
"1 202010 3 104977 96650.0 113304.0 159 146.0 \n",
"2 202009 3 110696 102066.0 119326.0 168 155.0 \n",
"3 202008 3 143753 133984.0 153522.0 218 203.0 \n",
"4 202007 3 183610 172812.0 194408.0 279 263.0 \n",
"... ... ... ... ... ... ... ... \n",
"1841 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1842 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1843 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1844 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1845 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 166.0 FR France \n",
"1 172.0 FR France \n",
"2 181.0 FR France \n",
"3 233.0 FR France \n",
"4 295.0 FR France \n",
"... ... ... ... \n",
"1841 176.0 FR France \n",
"1842 163.0 FR France \n",
"1843 195.0 FR France \n",
"1844 308.0 FR France \n",
"1845 213.0 FR France \n",
"\n",
"[1846 rows x 10 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "local_filename = '/tmp/tmp.csv'\n",
"if not os.path.exists(local_filename):\n",
" print(\"downloading file...\")\n",
" wget.download(data_url, local_filename) \n",
" print(\"download finished\")\n",
"else:\n",
" print(\"using file from cache...\")\n",
"raw_data = pd.read_csv(local_filename, skiprows=1)\n",
"raw_data" "raw_data"
] ]
}, },
...@@ -80,9 +334,73 @@ ...@@ -80,9 +334,73 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1609 198919 3 0 NaN NaN 0 NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1609 FR France "
]
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"metadata": {},
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}
],
"source": [ "source": [
"raw_data[raw_data.isnull().any(axis=1)]" "raw_data[raw_data.isnull().any(axis=1)]"
] ]
...@@ -96,9 +414,226 @@ ...@@ -96,9 +414,226 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
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" <td>202009</td>\n",
" <td>3</td>\n",
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" <td>102066.0</td>\n",
" <td>119326.0</td>\n",
" <td>168</td>\n",
" <td>155.0</td>\n",
" <td>181.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>3</th>\n",
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" <td>3</td>\n",
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" <td>133984.0</td>\n",
" <td>153522.0</td>\n",
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" <td>233.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202007</td>\n",
" <td>3</td>\n",
" <td>183610</td>\n",
" <td>172812.0</td>\n",
" <td>194408.0</td>\n",
" <td>279</td>\n",
" <td>263.0</td>\n",
" <td>295.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <td>143</td>\n",
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" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>1842</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <td>France</td>\n",
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" <td>101414.0</td>\n",
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" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>1845</th>\n",
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" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
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" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1845 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202011 3 101704 93652.0 109756.0 154 142.0 \n",
"1 202010 3 104977 96650.0 113304.0 159 146.0 \n",
"2 202009 3 110696 102066.0 119326.0 168 155.0 \n",
"3 202008 3 143753 133984.0 153522.0 218 203.0 \n",
"4 202007 3 183610 172812.0 194408.0 279 263.0 \n",
"... ... ... ... ... ... ... ... \n",
"1841 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1842 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1843 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1844 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1845 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 166.0 FR France \n",
"1 172.0 FR France \n",
"2 181.0 FR France \n",
"3 233.0 FR France \n",
"4 295.0 FR France \n",
"... ... ... ... \n",
"1841 176.0 FR France \n",
"1842 163.0 FR France \n",
"1843 195.0 FR France \n",
"1844 308.0 FR France \n",
"1845 213.0 FR France \n",
"\n",
"[1845 rows x 10 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"data = raw_data.dropna().copy()\n", "data = raw_data.dropna().copy()\n",
"data" "data"
...@@ -123,10 +658,8 @@ ...@@ -123,10 +658,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def convert_week(year_and_week_int):\n", "def convert_week(year_and_week_int):\n",
...@@ -154,10 +687,8 @@ ...@@ -154,10 +687,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 11,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sorted_data = data.set_index('period').sort_index()" "sorted_data = data.set_index('period').sort_index()"
...@@ -180,9 +711,17 @@ ...@@ -180,9 +711,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [ "source": [
"periods = sorted_data.index\n", "periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
...@@ -200,9 +739,32 @@ ...@@ -200,9 +739,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff92c44bd30>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IA4YF1fPOXzyH6/65puIKlyn+51k+pOMoSLlUdVXiReIRFxFJdEml6PRyHLQhHQYlgUK6i/7pDW3oHsjgtgAMP/6IQ3aLNUGNnbt6UT080qOaG720snOGdAAFST2oaqrE/femrBhrmcO0UrRKpoxY/LApy6GAO3fD5qeuj1uU30C6/BDMkfCcgkIoLH7dOP5Kh825UzYMXwS5c1cd1ho8egezyror9e6mywjxGwn6RMfiX7e7C5kqO+S44u8PQPGHlaKtBUIRzqn7QFIVHnS61mTYopE4iIJL2aCDStw/j7GW1V2peyvH4h/eZkIBJLfy6o4DWHjjc4FQLqWgPmEp/iBSXodtNVNLhMPi13wi5b2cpUEd1RPuERSkdDpZSisCSd2VmmzL2dQzEhSKn8X/ir3XYUdHdXdUJ+wUElvae8uuS2cVO1owLDh+jopvjdfcuetRPBTg7tAglaMyS2kFXqD8akomT7DgPuRyjIqRoEf8OH7+7tVVOXsn9/UEMdbCtkKvJUJh8fvtfOTWSKUVv+6wCCvVkw/GLr8qJy1FlS1+J8RXchNB9znPTVOW4g+dCVA6/Cx+CjRYWB9BhhWH7n2tIUKREMXvgfBkbtXMgqjTUljHURCKqFZnDvM6pRZ/wI1SAIkCh/rTMFIOfiJVW+JAV6/h6+6aoaaKn1v6flQPtzUqHVHgTtKmVy6MCDSOX/FdJS0omVKslLIs516G+tswjiNZ/9bqIDZHnAD6KuesJg1CEdXj9+Lwbyudvlk7qqeiUpSPIOTLR1wpylTQ5JfVXakhUIudu2EaR6R5Gl61J6sglbXZwJVHODh+P8XP0zdXMYR4OPK2+XwrwdWpPoKyghZ/lTj+vEM80Gq1ECaqpxKBAUEgSHHCdm+1RCiievzCOXVXBmXLI1avtHTDPYAC3blbbeeu/Vc2yVeq52tB9YQxDNRPpGobRI7FH8iuxPKrGCkIicWvW67SVI/35+GGIHfuVrodad2S6+IYCHICLmc1OXSqJzyjLH+Wg4zjr1FUTwUs/jD1e60QDo5f0/QJi4UUEjGK4OzcDbBOlXKtxEScP5VNRvKL7QfXbjn3MtRfhmnhqEv1jASO36DW4Zz2Q9XNwVPpJG26+fhDq/k5qhQCV4lu8HPA5VyKPxyhfmGn/nTgOHclK59ancTLfMZDKTAcfx5aip+IFhLReiLaSERXK8q9l4gYES3QqbdU7r7iaZmF6ofzcjBQi1/1XS3COQWJgniROYNRTsTYUO2RMCoimUR+VFClEOzkHr7+rhV8FT8RRQHcDOBCAEcCuJyIjvQoNwbAlwC8pNs4fw66uyarmZZZhbBPCsGMb/9KKuPcVS/txSEQRPv5VAXlVDa034ZJD/lRPbWy+IOl86y/Yer3WkHH4j8ZwEbG2GbGWArAXQAu9Sj33wB+BGBAt3E+yHRfumrqfd3B8ddXdlZWkBIQ5PF5Okvsijp3ZRS/8EVYrMGREcdv/fXrh2rL7JfCoxSYDVx56Cj+GQB2CP/faV9zQEQnApjFGPuXqiIiupKIlhHRsra2NucB6HL81VwaqymO/Of/vHtlxWUpFcFSPdV27qrrFi8HuQIsp6qh/jRcVE84D2IJ0koPVXcLSGVyuOHxDeizDyGqBsp27hJRBMANAL7qV5YxditjbAFjbMGkSZO0k7RxVDWOX1WuolIMHXmrrfy6tCz+8pspuW5RnkBpgDIqG+pvw6iI/Jzq1ZY5SFo1XBNtHnct3Y6bnnwDtyzaVLU2dRR/K4BZwv9n2tc4xgA4GsDTRLQVwKkAHtRx8PKHGpqoHmGQDUdHUDB8tQU/rh2oTB/xGmV1VyyOvwYWf5gsCL/snLVSms6EE0BdQUYIBQl+znSlD5oSoaP4lwKYR0QHE1ECwAcAPMi/ZIx1MsYmMsbmMMbmAFgC4BLG2DLfmu0HoKvQq/nAdKmestthDA+s2BnImaJBgrHCD8WoyDzs83KKCiiI9oOIVgn7Bq5cjuHNA/3KMn7+IUdpVnm2CnZyD5nGt8H1H08RXg34Kn7GWAbAVQAeBbAWwD2MsdVEdB0RXVJO4/wx6Cr+ysfxi/9RlAtw8D/3Rju+cs9K/PDhdYHVGeTu9mq7+vKH88gsT/FzSCz+kKds+MVTG3H6D5/Cjv3y07PyFr/397Wz+IMz+cPmv+BwFH8Vd0fHdAoxxh4C8FDBtWslZd+q2zh/YcLi3K3FuOCHSLf6WGSlINAIiJBF9bgs/kCdu2VY/EP9XZWU6TMb9gIA9nQNYFZLg7KsnGLj3wcqmi+CXGnkn3G4ZgCu+CNhsvgrCf4cBjVpjqqGc6qyUgYoRyJqPYJUpnx+L0jnLjReuEo8D7+onqCpHq96S0XYwzn5NhmVYvE7erHWHH8QYBrGTC3A+7aaFn8ocvX0pDJa1lvlo3pEx2FFm3KQtA+THswEx/EHqPd9onoq6NyVfC+mFAhNyoYh9kO1lGlOg0rwc+7m4+mri7A840qCW/yx6ChR/CKd0Kdh9YeF6glSioSt+IM8VjKYcE6NibgCQQh5q0zH4i//RvmrVo7/aMg/rZIi4v0U0bAo/cI5q40grfSwc/w6zycohILqAaC1eaGaKRuUAy3ACSgeINXDEQwfyuuqbDvSurU4/vLbyZ88VQ7VM7Tfltt7uu3yZxlRvO0OTehzAE7tOP7yEdaonlogFIetA3pKvdLPTaxfrfCCh66DWweBRvVU2bnLq5RzzeLnIJ27Q//t0J27Q2+zlN/r0Kj+HL/Tql6jAaEiHH9wVQYDn0m3EqixxZ+/UZ1EbZW3+EWOvzrO3byCDddw1MmRUhHF7+NUDprqybdbfYu/XPl1f5/VsdY1Of5qJ0oM8gSusFI9VIMUeKFw7gJ6Fm9YlmqV2FQS9Kaw8uvgHxRlKmihSC3PHPMtE2R7OqhVVI9r9aO4Af5dOavqfEJFffmCQJBWeqDHOFYA1RQrNBx/VoOwrWaunmpRPUHGKTsncAVC9fi/cO4UyQE/G40NXEG0GcQh46X81B05Vp78umcT6GTB1d25G+TeCR0E2VpoLf4a5LwOxWHrgF5US6UfnFh99bjt4C2pIKpyJiQl5SUqsQAaFaATT17O4SmqektFKb90GRcBcvyqvsjqKH7S4/irvermE02QkWoh1f9VRYgsfomFJ1wPil/sGcygeyA95N9XwuIPW7xyXvErykg+q/C3V1px05NvaLSvwfEHGE5aTp+V8uxyAU6WupMI7ydVDjC/lY/D8VdZawbr3A2uruEOrZQNlYL4HNKSUZl1vSjBPLmjv/coAGDrDy92y+OqXs/SLRfOwA6gSme5XqVDKwoPRYlqOKm+fPerAIAvnj/Pp33J9Qpt4Koa1eP6XJ78rtWPiuMvwTHrz/FX2eIPyTOuJII8QEkXoYnqkQ3KbAUsfh1U6xmwCrxQQXSTThVB0ha67QdpMbvrLefXtbH4dSOcsg5dojBmHJnUvhUdjv+r96zElXf6J+fVQXieceUQaKoVTdTW4hduVBbVI16vPMev10AlwjkDVdYBRvUoKQRR8QfMnOpQPYFw/DyMsYwHUJpzN/+53Mlel2pzLH4Nf42fb0VH5vtXBHccaSUOYtHp9puefAMvbmrHXVeeFlj7Moy+cE5R8UvIw2xWVPzqgfvipvYy47GFz6pylRiMAdYZRE16cfyVsb5V9VUqjr+cukqZM3THmFZdOe/PheAWv2rFnDdAZBw/r6sUCctHZZK0+Vd6w+MbsGTz/rLau+Gx9fjxI/rp1qu5IAlNVE9G4qnLar7of3u1FR/87Uu4b3kw1kbVonqcJXQAVZVg0fjW5dTpXyaoNkVIs3OKyi7QcM6h11HKpB2oc1c7nNO/TD4bq6wOfaUZJMJGgZaCm57aiFue9j9OsRZUT2gsfpk1Ik4Iqgd3oM+K0lm580Ag8igt3SG3UF5d2RzDw6t2+b58QawetJK0iUosaKpHo83hmJbZ3U55N6CbvoLTWCrjwv8AHH+6aCj42yut6OyTR9gFSquGleO3/1bT+RyanbuyOH5d5+6YujgAoG9w6OmNa8Hxl2JJLd60D5/90wqsau1UyhWEfPmDN/RM/qpZ/KKyCzS/UTlUj/5vxZLliu+eeOXgfjKV0uaTgpxic//VgV8wxua2Hnz57lfxpbtfkZapyC75wGoMBvnV9WhR/DoWvzAhaG0mCshPotZ3QSoc66/OC8UzmPLDmaV1liuUUIfa15FHIAe8a9AgpTh3b3xiA373/BattoOaQ3xXYy6qqty28p9V/c9DpVUTZcZZFcg4fvX3XnhdYqBwDKQtuXZ3DkjLBLlTeyhUaKYKTo1KraZUCD3H745VrrA8LqpHr1zZbdp/dRQnfzllKZydNAsByKfzkpS6mUp15qtVX3H7qjb97vPGJ97Adf9coywTTFpm789eENuRjXn9dvX6Qsfi5ylTpBSbLWop/fTkur3K73W47SCpvaHsPk5VRfFbf6vpOK9tWmah/2VRPe5wTvkDo1okvAgAfmF0InhfyDa7OVRPoBFCqlWWKJv/qD3rx4uU3+so9XCmZdb/sSjzYJlnMJS64lLdo2Px+3H8Gh3VmIgCAJqSUWU5nVc2yOc9lE1o6Yx32VU7O/HY6t1lyVOOXOWipnH8OcYQixAyOaa1gcsvnDNI6OaoKb8d55NvWb7slCn+fKXlyQToxTzr7hzVhftZS9qs0L6Osjh+kb7xa0f4PKB51rS03RIjhFQ0TX6Tl6wt/XZi0QiArPYZE8qJs2CMxdVziRL5TWil/MZbtnf98nkAxbv/hwKH7q1i2FHNnbv8nMm05GlkNOP4A5FHs/pgqZ4SLH67L2SWIiv4W5ZcGpWIRYI4SEYnqspNLwVp8ZdB9YifferRtfizOYZ7l+3wScXgXa+qThn8LP58rh7/drglnw0gsY+b/iuvrqHskve73yCMQGefxajh+Fn+6EFpkjYXJ6qwwofQfqHi0I7qGUJb0rocS8q/Vj45yiKgStmgoi2XslD+YxAWvw6fG0qqp5SoHqHooMLiv3vpDnz9vtdwx4tbtdrVEUHN8astfn5Zp885g5P26VS/NgvbC2qn81CT6jn1CNf8Ai1KaWNUWfxc8Wtx/BpL1VKg3sIu/12QE7OzhNYoy+9RyvHzvyXId8kvn8dfXt7uIVf1qR7XS6bh3A3iPXGs04BSNvhSPUIBHtXihX57UtiucIjrRvXkyw/d4i9FOeWdlWpORafPxSLlWsTOfoYSqvG6BXGlpnNyoB/4bY0ii9/i+AHFzl3huora5gO3lLwXhQNPh2rw+65UlBImxy19P46/FOle29mJbz2wSlGH3uQYBNXjzrwpKxOcBSiirFQfQ3buyq3FMXWW+62rX29zk5bFr0zrYId8+qy0dB4zf0Z+Y4J/ryol9q0qLYUOhrIHxEsZi5E+5UZmWXKVPiGVi/BY/FLnrvhZ3snOgQ1DfAm5PM7nKln8pVjp3LkrC+d0tt0HIZ9GXZW0+OUcf/5zNVM2rN3VhXuW7fD8bqjhnCqLPxmz3gtVOGGpNIjKouSWq18Yrc5z5u34cfxaWUNLXNWoMBTayGuCGEyLxmhw476aVE9No3rAgLjt3JUNEnFGVQ7coVA9Bb/RHQxBPp5SHE5OHL9EGZQaFqZO0+tP9YhfBWn5WJ/9ywQZ9+zXZxf+/DkAwGULZhV95zIYfEaHi+NXWPzRiP/+glJpr3KiekoZp1ldi1/n1D1x5365HP8QaEKv+xWfWxArXT6Oq5l2vuYbuGK+Fr+eVclXA6VQPYW6SttZFijFYP/VKMspHllscalSqcZZ3vegZ40FEs6p0f9BZ+fkNeiK7xWCWVLKBqGoyuKP2M4HnYyaVr0aSlTDueuXHE9vvwBX/OqZWSeFQrBx/Hp1+Z0TIq64tfwUPmX496OI4wciZFk3skGS0Vb8dp0lqL/CjtZ10gVq8dt/S7GkUllvS9FxEmlqMdWLqbNzV3xBAg/nlCqggBU/n+A06+ryOLJz6FSPf0SIalVTqgWrF87p/X0pPLROGmixTRVqcXCN3xkgLueuxj34lclqvGtBo6aKP8cYCGQrfv8XXdWBfhEE3r9x16eru0p9QI+8vhs7O7yjM3hdOm3nnbuSviqR6tHLzyyUKmIAACAASURBVC7/vfhVEPykTtKxIOO6xTZ1+2wgVTzOShFDbEfqq4G/BQ7oBSMUHo/p154fx69jvfIiflSO884qirmo3jLHmGu8KqoSgye8+ky8L51cPr4rH82JMkhoKX4iWkhE64loIxFd7fH9V4hoDRG9RkRPEtFBOvUyZoXTxSOk4PiFgathsZSCIueuy7pQKUX9tlKZHD7zx+X4yG0vq2XQqNLPuZufRHQtfnm5nEZdgUf1iEtxjX0dQVr8fuLzsE9P/0opVI/wWW3I+Ct+d39J2hPLaChYWREdeQD3KtrX4tfg+EVas+w4fs2xI75fXuXEzaZBWvyhonqIKArgZgAXAjgSwOVEdGRBsVcALGCMHQvgPgA/1mmcWfUrLX5x8CgVlcZmEFXdQIE1qfhdKW3wzIMH+lLedTl/9QeQzLmbDw3Vk01pvWlEcQQe1eOzxK5Im5oWP/cceVlvpaxCROWjitjRoUt0JkFdReyn2LmO9lNOuu+rWFZVSlSyqqYfXPkm5lz9L2Vuf1e4sEK2bfvyq3OvPhMnLJ0x6DfBOf40n75dtG4vXtzU7tueDnSiek4GsJExthkAiOguAJcCcNIeMsYWCeWXAPiwTuOMMUTIyu3hx/FHSI+jLMXyLIrjdyWY165GCb4RJxnzTjJSSpK2vHNXvYFL13Io2+LXrEsXOoqsUlSPX11EBDDm6VgvJaxUlF/2HIH8M9ShZywZZO2VtmKWTrj8ex+jIuNS/OrCOmMmralkb7PTb29q78GJs8d7ltGNgvru31/Pl/O4hUy2RIvfhw7SpXo+8fulvm3pQofqmQFADGDeaV+T4QoAD3t9QURXEtEyIlrW1taGHLOiF2IRkqdssK8nYhE9i6UEBVRM9ci/c5WDNRHpwO9hlkLP+O7cLaEuP9n0OGbxpQwinDP/WSdNdxBJ+5w+0xw3XlY6c33We96AeiOejjLQ2fcgtqdM2ZD1sfh1qR4X/61p8SvqTPvQLhxx+4VUTaa6UT1inL4nx58rbdzrpq4YtmmZiejDABYAuN7re8bYrYyxBYyxBZMmTUKOW/wR8k3ZkIjqKf5S4slVO3f90jlENTW/r+IvIYc+t3584/g1lZiOUlE6gEVFrcHXTmlOCr8tLu/KyyRzYGvQQYA+DeREQmlSPV7KupRViHiPMie9KI/qNtyKTFKPZn85ykxWjyYlVoozVmfnrtvRKi/HN4Kq+lSX4z9oQoPz2WtciPeoM+79ord0+zZI6FA9rQDEXSsz7WsuENHbAFwD4BzG2KBO4zlmc/xRucXPZ9RELKq1gauUWbOwo3WXxQzMzv+vYaVzKkFSthR5+YBL+cTx6y56dKgzlXylOloLFWRhPnaxz+WRS/nPSgtW8yXSpXo4vF700qiefFnlrlyNFaxOynLdMe0fx6/3fpXG8dsOZUWxlE+EDUfc3uksy/Jb+HuVfThtbJ1QrrhNHfrJlcjN5zjYnE/fVwI6Fv9SAPOI6GAiSgD4AIAHxQJEdAKA3wC4hDGmPnZHgMPxRyLS5RAfPElNqsdv6eXaCVhQVDcTKJj+CY9+Vk8pDzvjk6unlNS5gJ6zXDeqRCsm20dRuZS6BtWjUri6Fn+p4Zzl5kkSm9EJ51Sek6vRF2I3qg0nnqtH/R76TXJpl+L3S9Km/NqqT1PxRzRSQYvfqOrqFPIjeQ0jcfKX6S3xcq99ZKoMTlRPAH4yXfgqfsZYBsBVAB4FsBbAPYyx1UR0HRFdYhe7HkATgHuJ6FUielBSnQsW1cM5fsmLrqv47c7zU0CqqBDxf34x7roHfvlz/PoP23Hu+p3Apc3xl+dcLDXCxi9O30WD+Ph8Cj8Xwu349J+8fKN6FOGc4k99z9wVvlY6brlc2vfoXUbH4hdj76XOXU2jojSO31/z9wtpj3VYXNVko8vxdw/kFbVnVI+LzpIll8z/7oAi0kgsGzaqB4yxhwA8VHDtWuHz24bSeC5nOXejOhx/LKKOe/ZxTjnlFPSE31ZtsZxuagg/hVgK1ePs3PWL6tHm+OXf8efh1w+F5dXtqS1+F00gEc5vV6V3W0DU43GVsvPVet5MwvHrv7A6fgxAoFZUk5ZGX5Qa8ilr7rWdnUVtemFoHL+8XF8qi3iUkM4yrX5OqfpU4z6tOsQQ0uKCaY3JTWxrw55uLDx6qrQ9x88UJou/ksgxBiLrFC6/oxcTsYhWOJqfAnI5cMtw7pZq8etYZJ2KFLxA3gqWWfylbKsH/KwjHYs//7nU3OpeTes4i3WP4tTJ8VSKpc7nea++Z5LPXnCtkjR8FMpUyiUqdVldfqsjMTbe7zEPJY5fhb5UFk3JmN22f3lV6KSuYZfO5vLnNHgaKP6Tm3i9Z9CH6nEsfmWxQBGCXD2EqCbHr44797dQC78vdu4K5Xy4Qm2O33fpn//+a/euVJZ1du5KT+Cy/gYR1aOzgcilqDWpHv5CeTnhdHws2opf4yXXdfaJ8HI6l7JCF4vqZcvUm9xkfaFDLWV86tF1sBbWpW3xK4r1p7NotBW/zlhUh8gKn336lafF9mrTZfHLxqlQv6/i50bWaLL4IxE7ZYMPV+YXx+9EoQRF9fha/LpUj1+GwvxnvstXBm4FpyTpfEt1VOpMpKqxqGu9OmWEF8rLotfZ/JPJMSTs0D1dqkcmmy7nC/iFc4oWs7IaZ4wR6aVs0J14pWGYGopYNHK8iuhmyBW/J/J3hOtY/APpLBoT+ha/LtWjajqdzY8xrybFVYXf/iMA6PVR/KMuOyd37qo4ft6xyVhUHXeu8aIAcJ3iUxzVU9yuZx1gIACXHj/dFfPrhVLC37hSlMHvzF3+8uueBqe1k1OT489qOCsYg/NCeS3JxTznsnvM5phzhkO5VI+uIgDy1J5qgxAAX66HtxOPRLTk1zknV6xXXUZm8Quctsf3ok/JTzfpRuEB4uEv8jKDmRzqEtaud1V1/PmoqB7x934rqYS9097buZu/JpvcsiUofh2LP2j+v8aK37KcVRw/7+S4ooxVznoAvpEESgpAz7JhDABZVqDfi+Bv8ecriHl5IF11WWX9z9wt3+LXCyfUq8upkzEk49YL5UXt8XS3TcmY0rnLY7ZVtynmupe9UKVw/NyZ70316L+UWc3xzMeFajzrcPx+NI4oE+B9L2IacP9cPVa/18XVhppYVtZ/mWwO2RxDQ1yuhAuhWmXoHuqUzubyVI+Pc1e6ihJ+16sdxy8v47d6KhU1P3PXyscv5/izuRyiEUIsEtGyfvyWg6ooh1KclQQ9usfveYkiNCTUQVbOzl2J1anr58jL5r+CUlkhQwnnVFr8trJuTMqVRjaXc3Zpqtp82w3P5H8j47/FiV5TeXv5JsRb8UvZ4KxgfRSj3yQPFEb1yPursM5C+PHyqRIyZPKJys8nJ7Yre/e5IdDALX4txa+5QlI5zXMMCce4KK5P7FN5Ovn858Wb92mFFOtQ2UGhZoq/qz+NHLOcu2qO30qPEFHk87HK2RaSTwflFINcV5Exxhyl7/ei+6W7FdscVx9X1+U4d4PJ1aO0+PkkEpDFzxgDY0AyLt9az6mexkRM+gK7OX69+5SnePb+7IU81eO19C+dCvFNQcL8Fb9OGKZbqUvqcflW3BXt7R7ARTdZx04eN3MsGNNzOPtRs6L8snJc8dfbit/P7waoN8XpbgbL5ETnrlc9pVn8gDqWX8do9aUYS0TNFP+2/X1IZ3P5E7iknG4OsYg6kRugx0mL5YDih6MbpcJgKQIdqqeUfQUtjQllWcdC8s3Vo5bJKa8xkarq0g2PE7/nWUq97oG/6I3JmNS5m80xhxLTNYKkVpkGVSK2C3jLrbIyi9rkVE/Mh+rJqVd3okyA3AAR3ysppSLUM1jQ3tb2fIpiTtOp+p3XVReP+Dt3bdlktJ5oCAB6UWiqEGV3X8jlyuRyjsXvnaRNx+K3rp84exwAYG+3PIuNzkpdlYpiKKgp1bO7a8DJx69ahkaJEKFgLH7x4RcqPv3Mj3aUB2lYeJr7ChoSUd8XJb9z17tOfjUQi1/DCimF6uFfJxRRPeLSXuXsj0YIESplh7Kkv0SKRjNM0Vvxq52jXvUkfXJP8SrVScfyn2V6QSe6SVxtDxYkFOM0C2Dx9oV1ytrT4fjzfepdjvtp6h3nrry+lM+7AbgpJWX6iqx6VenOx6927s6Z2AjA+8jOwrJKi78E40IHNVX8B/rSiBAsGkcWcpdjiEbJ3r2nkdvEd7D5z9Z+9fAkbTq7d319Do4ikPs5CmXK5pinfKXm6pENWsaYFu9Yik+E90NSkUyLKx3L4pcr/ljEMgS0qR5pOKdoBMh/zxgTLH4vRaBvjTlJB32oHi5bfzqLvd3eYb46jluduHoxgEK1wqhTWMGFdelw/H5+DG7x88lHvdPcpkGVK6Sck1XX7x4SPnH8PLLML46/uc6ib1WRPTqZWEsZYzqoqeIH8vn4ZbTDcxvbcaAvjXjUJ2WDtuKXvwj6HL/+Bi6/TSpZRyFGfXk8Uel47iC1v9alQGQxzzpWotVO/jvdHEmqOH4X1aOI6olGIvYKUNmkA6nFL5bRtLY8LX6RcvGljKy/cc2kg4B8f4dOymKdsFb+LBoSsSKqR2yjLu5vefOJTYfjFw9P8irLnf15xe//jPyonnx8vsriF6keL7lzTl/4pQ8fU2fRVH0peWSPTij6iIrqASxlFSX50Yub23oBWPm2VbO57glcYgcWdqY4FvyssUiEQBp0g+/h1AIF4h8FkZdXdSCI7g7AtCs+21vZq+P4rb9WaKLeZp284ldw/ImoIsrLtvgj+lSPThijTqgw4P0CZjQUcGFdSR+LXxwLXf3e1qK7D2XWvEDjSN4fLkdjIuraS1EoR13cf+NcWojq8d/AJYxnD9m4vFzJKg9scU6nU5dRKXQOVwCBp8UvKH4fi5+nm9Cz+EcJ1QMAA5ksIhKLXxw48Rip85c7DhL1YHOlVC3oTF3qImv7HXSsfp1ol4idr0h1f4DbYvF6UUoN5xTbk4W6qapyHJU+qzGxnoRD9XhZ/FkkYhGrPqXFr6Z6ChWETvoHZTSIoEy8npE4pu54cau0HrHNREy9gUt8H2T8sLhi07H4C5U6B++fhmTMdfoU4H4H60vg+JNxjQ1cPv2fp3ps564Wx682DlUUjlMum99vIovjT0QjyqzCvP4xNtWjtvj531Fk8fenstYJXB43LZ5ck4xaFoRs1udKvCSOv8jit34bU/gcrDbyJ3D5qVgd7jtChIRC2XGksznH0aWmejQtfsnZoboHdPOv/CKuANGXwZfI3nH8yVjEivKSKuucoPi92yq0bOXctnoFxZF2WfweikCo//U3O6X1iLL4rfDE7/olSsO90lDTOI2JaJFSL5TJsvgLFb9o8ftb3k5UTyyKTI75UCrCpJQtvsdSqB7nrAqV8szkN2apOf6c0rmbzuYQi5JynPIho0X1aPjmRpziH0jbFr/HTfcLij8ejYAxufXG89f4KSAXX1tQlosQi8ofKMCpHthUj7I5jTBHey9DNOK7nMvmmGN1FS5pdY+VEyFaWeLAcuduUfcDYJ2O1t6jPnSND+o6ZRx/DslYFPGoOoVH1KbZZLIVKjjZIjDjw917lev3OFRDpMxULzggKH4fqieVzaHRVngDPpY6ILf4+Ts0riEhpXr4BNiQiCGVzbksT3FyGW+HG6stfpvK0qKF1FQPv2+dqJ481SN/jn3prONs9aPZHErIo1wmyxDnFr9snNqyNiajIAL6FIex5P2T0iLO+3LcrHHyQiWg5oo/qojRH0iJVA9XGLIIAOt6KeGchVZnzrH4/VNARzWjenSyc/LU1CrlwxhDJscc6ydVYCG5Qvs06UAZ1aNr8fNvDpvShO37+6TlLJnyCg/wdsINZrJIxiKIRSPqOH5u8Utk4xTBmYdOLLofEX5UA4f4XPb3pjzqkYdDytr0SzOeymTRbG/ok1nqKYmPRgTnlic0JaRUj2PxJ/nYKl7h/PMLZzpKU2WgiOGq1v/1+tWT6imy+KVV+R5SBFhHIE5osiavAcXqhzEos3OmstbeIvXK1LoeIUJjIqZM28DfDR0fxncuPkJaphTUXPHHo1aERmEH3vHiVpx9/SLn/1xhyJw3uopfVJiFViVfAdTFfVJA21YnoLFFP6t+qGnbeohH1c4wfn986Vh47q74P33nrmjxeit79X4Gyz8xoSmpkY/E+usXx5+MRxCPWAdvePUZ5/ijETnVw1/qFh8LVbzuxw1zdHjswExrcO2FbfpRPalMznnWcotfVPze9XDFP74hIVV2Dsdvc+niRMPbSMQijjKUTSCAlxNffo/iCsSLaislZYOTzkTyHFOZHFLZHCbYY6JfMkHz+81Tql4Wv+UkVhko/L2JRggNiaiexa9Bi8UiuvGEatRc8f/nBYdZL3HBQ/2/5zc7nz991sGOxS97sNzSksW4c7T35C22wrrSQhSB367WqBPVIy1mlbULyF7yTM7iC1X0BpA/Do4rs0JFVcouVA7XJCgMYHHQKp27tn+iKRn1zUDI5a23lYuXot2+r8+K8orIaYK8xS+/z4GC+G+/rJTWLlPVS5efdL1yq+vuDQHylm1DIuq7IYlHhMgU9s6OfuezrK5em3pqaVRY/NwPYFv8YjlR4XCOXyaPWD7pE/UCuMeA16rG2blr94Nq9yr3B8reoX6nH5Ku8jL5GxPFq5+83Myx+P2MiigRGpMx5zmoyvpRfwCcPFXloqaK/7ApTTh17gRPZ6r4AM+dPxkJe8OE14N4ev1edAnnZKqst9db8863IoufK6d41DePTYRsxS8tZZe165HJZA0if4ufKxzOsxb2gysUVdu5K9JewmdNHjrHrKVsQ8JSiDpLVa6Mvfp3VWsntrT3OikZvEMnrTh+Ujl3nWRv8kkGyD+b+njUh+qxyo1riHtOcC6L33fFmbco/Sz+ungUyVhESh/9/dU38+0qLP5YhDCmrjhGn6PI4vfw/cSjpVn83JejE3sPqC3+lgbbSpcoz2yOOWVlz5ofeJ6netR+Ez7JefWZ5dy1OH6/dPKRCKE+HvX0DXHwLtKJ6uEr5nJRU8V/7EzLUeGVgC0iZL5MxqPOTOflvPn47Utd/5c9/FQmhzsXbwPAD8LwDl2ri6utsTzV47/s4gNJ+tJlc4hHreyjKquzh1v89ktQ2A+ycEwVRGXnchTan+NROY8O2FYmAdPH1aMvlfXkvwvrr497R/WIk4ZqV6S12oJl8Utk4xY/Xx31SWgoUSZVVA8fJ+PqE+hLZV1HERbeS72Q4sALfBwkY1FlwrPBjEUn1MWLI228IKunL5VFQyJq1aMR1WO1La4E+ViI6Fn8JXL8jmWt4PjH2WNe5jgXaRvZu8+pFk71yBS/qGATkr1D6awV9aNzZGw0QmhMRpVUKB97KpuB3zt/f8pFTRX//7z7aADWcqiwA7nFAFiKIKFw7k5trgMAfOAtswDIHXU7OvIOSK8oGh4XnYxFfPOgOxy/j3HNrZRsjnmGMHLaKBFTO3e7By1l02JbLIVLx7QHVeMHUdl5nSoUj6pTYfN9BdPGWv3fpojs4c9Exp3y/1+2YCZiEfkmr0wuh1gkgqgijv/lLfsBAJPGWMt6WRw8f8b1PnmSuGynzm0BADz0+q6i7/l3M8bVS+sBrH5IRC0/BiC3iHm5unjEU0nxSS+fPti7vd7BDBqTMWvlkMlK/Cb2asyDWuLPIBYlJ1JHbfHzic3f4k9lcs6qTBbHz9/9RCziWO2F6BsUV/sSymvQbQz0p7yfd35ittr0kiuTsxIFxiJyPw1/b/iKWMbx53LMaUP1rnE90uBjWOiiZop//pQxjgURj0aQY+4XnZ+AAwBHTGt2LH4v62dMXQwXHj0VR0xrBgD84OF1nm3+1z/WAADu/+zpiEeoSLHwmbypLuYoWi9kc3mqx4/sEZennnxhjoeGqZ193OI/ZFITAGDDnm53PZkhKH6JxS9aeX4hdPEoOU5I2Q5TsU7H4i+wBLnVNn9qs0P1ePVHjsH2r8ipnusfXQ8gr/i7ByQ7XwVHnnJXuP3c3jKnxa7PPTbS2RzG1MVx9IxmqdOQI2Vb8pGIOtcLL5eMRT0VP2/ngyfPBqDi+C3Fb61ivcdgscVfHNUTj0RQZ7+TspWDeD86zt2ewYyjiL0mk4F0zlk5NCSiUqpHNIL8qJ7m+jjiUZI6zDmtloxFLcXvsb8glbHOhFBy/IJvpDEZlXL8vK+9/Jxe8vud2aGLmil+katqrrduRnxB+cB5zwkzEI9G8lE9Hg+WWzWb23oAAPct31lU5p+vvYlnN7QBAA6fOsb2yBdYnRlLkU1oTGJfj5y2yNkWP48+UaFPeGm9XpiMHRrml5KC9838KWMQi1CRFauTd70Qrg1cHjHt9YmokjayFH/E2Z1YqBC92mqQWPz9wlKWT/Je/ZHK5DfP+O3SntRkW/z93nJxGRoSMa2oHh5e2VOwbM/krKRddRIl7ZI/a+1O9ksWlqd6Ip7GDlcE3AEs5/izaExEHYqj3WNcF3P8xU5/0eKXKU0gv4Llz1BlzPSlss7k7DVh8vBeAMqQyE77+Y5Jyp8jp/saElHUxeSTCJ8Ykwqqhz9va+Opd3u8j+riUdTHY65Viaucfd8NcXW21v5UFkRuJqQc1DyqB4CgOPKd8+qOAwCA//eeYwAIW/0LFAZjDPt6U5jQmMDpdtz2/CljitpYsnmf87kxGbMyERYMkr50FnXxKOZPbcKuzgG0HugvrAZAPmWDbCkoyiY+cG9HEUMsGvHNPrq7y0rUNbk5iaa6WNHATQlLVN0cNq4NXB45XZrr40hlc3jk9d2Yc/W/ipKFpTLWaqXJjgZRRS7wFUm9JLkVf/EbElFncpApg7p41O4v9X3ylchPH9/g+T2fECY1JaXhowCwy77veDSCxkTUWX1xdA+k0ZCIoT7hr/h7By3OPeZH9WQta7cuLrH47b5uqlMfRP7ipnbUJ6KOgt3bVZzwzYlksSeRfpcFbVuu9sQGqC3+vlQWdXbaDUBNC/UMZpzJ2Yu/HxR22tYnouhPeytP/hwnNCXkVI9gMdd55CRy2rTvLRmXUz3c4FGdI8Lpsrp4RGnxO+M+qfb59KWyaIhHtU7900EoFH+zPXjX2/SF+DJwOoi/xB19boulezCDwUwOk8Yk8Y6jpuLkg1ucshy/fmYT/rhku+taYT6Yh1ftwp9f2o59vSmcMHs8AOCNAjoFAJZt3Y9Nbb2IRLifQP4S/Ofdr+LJdXud/3tbD9Yqwy/fzZsH+jG+IY6GRMzT+iklHS6HGJrolbdmus3d/8xWnOt2d7l+z6kevhxXKT2+RyIZj3g61jkHWhePOgdveDllB9I51MWiSMT8nZ7iBrtVO4tTKTz/RjuAPCXkHbqXw9fuXQnAmrRmtTRgS3uP830mm0Nb9yCmj61DMhZFv0IptnUP4q+vtGJnR78TvKDi+JN27LyXM5U/f35qm9c4fHFTO9JZhiWb92Ncg1Xu/hXFq2He99PHWc+7U1ghZQSqR8fiP9CXxriGBBpsY0DmkN24txupTM6JUvN61l39accn1JiISuvqdBR/Uk71DPLQ0Kg1eUtWD3wMJ6Kc6vGOLItFItJUM0B+wquLR9FcF0f3QNpTqfN3cIIdZiob032prBMKHQTCofjtwfvpO5chl2P45O+tKJ3zDp/slDmopRFEwCvbD7h+++JGy5LnL++U5rqi9AE/9OD8YwVx85/90wrn81hnSV9sYbzv14vR1j1oO2QtJSvj5v4mhNsB3tZP72AGDQmL3pBtN2eMYcX2A5huOw69rB8+4Mc1JJSbRThyOYY1b3Y5k6TXLsqptuLnu3ILIwq45eOEvikUf7+g2OMeEUz8hatPRB2l4eXM609nUZ+wrDGvl/z3L2xxPrc0JbDwqKkAgLYet6WbyzHcvWwHAGDmeKtf93QWO6dFQ6MxGcXEpqRLMbb1DCLHgKlj69FcF5PSSgDw+Jo9zue4E7JaPHaef6Mdnf1pJ6rHS9HyZ8zHvZdSFFdo/BkVGkBAfpxzx7R4WlRvKoP6eBSRSN7il9EkANDZn8LY+rhy8gaA7/ztdQBw3lUv+Ve1duLI6WMBWKyA7PhCTntObEpIFf/63V1oTFjPb4ytiL2w0aaLp42rk1I9KR6Jpwh3diz+WBTN9THkmPeKmDMbB01oAOCedEX0pTLOPosgEArFL/L9bT2DeMbm4v/thBnO9bENcRwzYyxWteYV/+7OAXzmj8sB5PnciU0JTx6zEPGCA965D+Hdx093qIbCwViYqdDhojVJdS/qYl9PChObkkjELC7XyyrY3N6Ltbu6cPGx0wB4Wz9ctvENcaSzTLnEBoAfPLwW+3pTzssuvqDcoTV9bL1L7qINb/auY847ysL8Nuzpxuf//AoA62AKa9IttPjzVI+jNFLFk1s2x1AXiyIpeSm/bzvwWxoTaErG8K2LDgcA7O91v1A9Qt2nzp0AwFI0hRBDVBsSsSIrkNNA08bWYcrYOuzpGpAaAiKHmw+NLH5OH77tJQCwLX7vMEyuRMY1JBCPkqeRwsfn246YrIw26hmwjI+WxgSmja1zrY46+9KOD665PoaJTQmsfrNLVpVt8ceFd8jbCOGReN+5+EgkohH0FRgyfakMdnUO4PCpFm07q6UeOyRpQbiybGlMSqme7fv7MGdiI+LRCJrrY1KHP/ftzW5pcN7JQgykLLoxquD4uaGTjEecVBdeRsE/VlrG4VHTm133ImIwk8XfX33TFeJeLkKh+A+d3OR8Fq0JvtnCKTepycnPDwA3PL6+qI6JTUn0DGacF+qR13d7tpmMux0886Y04ezDJuGGy44XrBX34BAtqPpEzOEfvRR/Yaw3kI824bhz8VZsbu/FxKYkJjYlkMrmiiJjtu/rw/k/fQYAcMrBE+y2o0WWFFeCPOa5kIcW8fT6vfjtc5ZlfPQMy6LatZdapQAAG2FJREFU15u38nhd0+ylP0fhZDOYySIey1v8soiWr9/3mmMZNdfFLUd2QZ9xeRsTMceyEemstu5BPLraepb1iahvmm6ucLj/qKfAwhNf/EMnNyFCxZFSgFvxNyVjRVbgzU9tBGD11bSxdcjkGNp7vcNab3tuMwBgwUHjHUeq2KeMMddEcOLs8UjGI94Wvz02G5NRawOdx/PmffD9S47CuIYEjps5Fm+dP6moXHvPIMY3JEBEmNXS4Fjh63Z34e5lOxxZiQjHzxqPv77S6nl/AHCgv1DxF8v+zIY2ZzU8aUzSWsEWlOPWPXdKT2xKoqMv5TmprtjWgfENcTTXyZ27uzoHnBVzc11cujLj+3ziUW+OnzGG3lQGTcmYMivtgb406uOWj4YzGl5hxVObrZUF39PktarhG/W2tPcWfTdUhELxN9fFccWZByMZi7iSfXErnmPupEbs6hxwBvTWfVbZKc1JTLYtCP6bp9fvxea2HpdTV8T4hjgOCMv4zv40JjQmEImQQzUUWgVvCs7e/lTWWal0eGxc2iTwwB8+1Qq5WyNYSq0H+nHt31cDsAY3l7/wmL1v/3WV8/lg+/zOjXt78fLW/a7VAbf4eLjnLsmpTYB7w9u3L7KSPrUJy3v+shZaiYUHRnf0pdDSELcdvLEi/wtHoxB7PKYuhgmNCVfU1KJ1e3GvHYllKYJii//zf16Bq+xVQzIeRcLHv8JXH5zK4isBAHhgxU6c+aOnAAAfP30O6uJRzG5pwBt75Yr/qnMPRX0iWqQM+Ms4Z0KjQxktEvw6gDU+Ft74rDNe//ipUzzprFue3oTDv/uIdY+xCM49fLKnM9n6nc1ZJ2KY3dKAzcJ44+BGBKcum+q8J4g3OwecZz19bB3W7upCLsdwzV8tOkYcG0+steiqZVv3F9UDWIprbH3CcRR70XXrC3xFDR4rWN7v3Dcxtj6OHHOv1ABLmT6xdi/eMqfFk/5jjGHd7i60dvQ7Pqsxdd4Wf0dvykUTJz0s/oF0DjkGZ2+EzNjp6Es7oap5i7+4zbaeQRw+bYzzjLwsfj7ezheo73IRCsUPAJccNx2DmRw++ruXnWuzbd6LY66t1LjV/+aBfrxlzng8943znDKc8/zMH1fgvJ8+g7mTLGV5+yfe4qprQlMS2/b3OZ3a1Z92Oj8Zi2JiU6John2zM6/4T53bglnjLfnOuf5plyXyt1da8Z5bXgQAPPeNc/E/7z4GM8bVO05jLjvHuIY4Jtty7+lyK1e+zAbym0/44OwWViT3LLMU54kHWZaDl/UKuK2O/770KLQ0JjAmGXPosY7eFP78ksUDzxzv7v9CZ/e+nhQm2BPt5DFJ3PHi1iKq6k8vbcOLm6zJd0xdDJEIYXJzEi9t2e/wm5/4/VI8tW4vohFCS2PCmShEi1+0hOtiETQmi/l0L+tLzG3SNZDGHxZvxVfuWelseLpsgbXp77hZ47B40z7XcxzMZJ3J5jNvPQQAihR/IhbB24+cgrp41Inz/+b9+ckaAP61ahfW7bb6rrnOiqnnYZiiArrpyTecz7d/3Bqvh05uwt7uQdfK48YnNjgO54ZEFEdNb3YZFQCwq7Mf//vQWgBwVrBNSe9cQ7s6+53V3Qmzx6NrIIP23kHHQv7KBYc5Zfn7tG2fm3ZhjOGr96xEe8+g2+IvWJmu3HEA/+8hy+f2H+fMde5BnORf2rwP7/zF8wCQt9K5YiywiN95k1Xu1LkTMLEpiRwD1u/Oj9NXdxzAwhufQ/dgBtNEi7/A+t6wpxs/ecy9Ih/XkCgyZjbZPoDxDXE01cWlIaYdfSln0uJ/9xesBBljWLxpHyY1JZ13u7AMYPVZfTyK33zkJM+2hoLQKP6jpjc7LwMA/OpDJzrRIhyczvnGfa9hIJ1F64F+nHHoRJePYN6UJtdv+EA/2X4pOd517DS0dVv+hGyOoXsw4wwuwIp3fuCVVsy75iFsbusBYwytdlKs575xLq4482AcafNyAFzL+y/f/arzeVaLpTxnjK/HE2v3OKsD/nfymCQWHj3N4TwLLbcd+/sxd1Ijll7zNufaty60eGtuve3Y3+dwhW+Z04JxDXE8tMpNcWVzDP92yws49vuPAQB+8v7j8JHT5gCwJpDfv7gV2RzDL57aiGXbOgBYivrfF8zCsTPH4rApTXhjjzuaZVfngDNgTztkAnLMctBzrN/d7ViNh01pwqrvvwOAtbxt7xnEu29+wXmRAGtii0bIWUaL1pdIB/YOZjBjfD227uvDv17bhafXWxa2uILj3DAA/OlTpwAA7np5O75rr7I4jphmlTvjkIno6Evj9B8+hSWb92Hj3m7M/84jTjk+NkWOP5XJYd3ubqcPOK0EAD9+JB9QIFpxR9nOSm40yNJZ89BkvilRnMhvfOINR5YxdXEcNKERHX1pl1I/7QdPOZ/5ZrGmZLzI0s3mGHbs73cU7KwW6++mvb3OmPrkmQc75e//zOkA3LvgAUvB8oihuljUoYdE44QxhktvfsH5/5fPtyaUSWOSLhr1R0LfHdRiTTQ8eumsHy/C9Y9a3y/dut/pv+Nnj8OFR1uO/Oc3tju/37g3P75m2+/ixDFJ9KWyLuX/wd8uwZ9sg+caexU8tdmSixszvYMZXPJLa6I5c95ENCVjRU7igXQWV/15BZ5at9cJ2eU6oPBZb27vxWAmh0OnNGH6uHokYhFsaiumc5Zu3Y8z501ELKAEbYCm4ieihUS0nog2EtHVHt8niehu+/uXiGhOqYLEohG89O3znf8fPq25qMw8++Vfs6sL37z/NTCWf5gchfTE7S9sxcSmpLP05HTJ2YdZXOen71yGQ779EBjLrxYA4JSDrYkinWU476fP4I8vbcdfXt6B8Q1xzGppABFhSnMd3n38dADAcxvasWj9XpfV+eP3Hut8PuMQ60X+91sXY/u+Psfi/+cXzsSkMUlMG1eHWIRw7d9XO1ZNZ18aq1o78fYjp7pkm2FTCr96ehPW7upyokWOmzUOU5rrcPEx0/DS5n1oPdBvJ7DKoq170ImImj62Du87aaZTH3dsv7xlP3qEHcvJWAQ/et+xePCqM3HszHFY1dqJ/lQWA+ksDr3mYQD5TVvfWGhNRk+s3es4bhdvyr+AzYJSnNKc9x1w/wUAJwInFo1gzsRGx3J7cu0ePLAizyvv70s7nPDn/7wCH799KTa19ThW8SXHTcffrzrD9SwT0QieeyMvDwePi+YRTLu7BvCh/3sJb7vhWafM247IL7EbE1G096Rw3/KdWLbNojumezhOb3l6Ez5w62IsWrfX5ZT8rL1ymNKcRDxK+M0zmzCYyaIvZYUlHz2jGV8871CnPA/zu3/5TuRyDPuEyfDyt8xCIhZxwjCfXLunKF+SmL99XEMce7oG0DOYQSqTw5b2Xpz306etPreV1EkHWeP+8t8uKbonwEoSePjUMfjrK63I5hhe2d6BnR19zuoNsIIDohHCIZMa8aKghHfsz69yv3rBYU6o5kEtjY5SHEhnsUKI3BsrUD0cNy/ahL5UBu//9WIAVt6tE2ePx6QxSTTXxbD6zU5nsv36fa8BsAwH/s7PsZmELbaSfb2101nx/tsJM/Dps62VyJTmOgxmck5ddy3d4WyUmzm+ATPG1aG9J+UEowCW/+Kfr1kpPbjhOrY+jrp4xBV00jWQxsdsduN9J85ENEI4dFKTa4JnjOFr967E1n19OD6gA1g4fANDiSgK4GYAFwDYCWApET3IGFsjFLsCQAdj7FAi+gCAHwH491KFaUzGcPEx0/CvVbucGb5AFnz+3ENw86JNjsPjdFuhimVu+9gC/McfliOTY2jrHsQ77WiY575xrjOQ6uJRnHf4ZDwl8LHzBKvyzitOxkdvexkv2XlfvmuHn3HrjuOGy47H3159E1+1l95fOn8eAODr75iPy+zcQQDwuXMPwc+e2IANe3pw9vWLHIqFK/RkLIr/uvQoXPPX13HcdY+52njHUVNc/+d+jD8s2YY/LNnmXP/b5yxr7LiZ4/Cnl7bjjB8+hUJcfOw0XG0raY5/fOFMvOPGZ/GZPy7HtLF1OH7WOPz1c6e7NotcfOw03Ld8J865fpFjhQLAVy6YD8Aa3JxKOPSah3HynBa8LPDAX337fOdzs8ez/ecXznQczYDl93hy3V7MufpfxfdwzDREI3lHHOCeQC4/ebZrtRizncmi4n/bEVNw9YV5mfhkCrgpowuPnopfXH6C8/9TDp6A3z63xaFaeF0cr157AY6/7nEAwJLN+7Fks9UH5xw2CXd88mSnHJG1AW1nR79rZfGF8+bhHfYECORpgnuX78SuzgGcNS8/3j96+hwA+QiZL91lrTS5A/fKs+fiU2fNdcqfcegE3Pb8Fhz9vUdRiE+eYVn1Y+vjdspr67p47/k+mYafPbEBh3z7oaLvjp05Fh865SAAwPtOmoUfPbLOeYZjhBX9VcLkdtDEBrT3pFzPeub4ejz3jXOd/88r2JR55LX5e3joi2cBsPq0ezCDB1a04oEVra68Nk985Rzn8xzb+Lv05hfwg/cc45yT/McrTsEpc/PMADcq+fPk+IG9qfSdx07HTx7bgI/97mX81yVH4bk32vHkunzI7reFSTedZbj12c14Zn2bpQse34CdHf2YPCbprGbn2xPqPUt3oCEZRUdvyslCII6xIKBj8Z8MYCNjbDNjLAXgLgCXFpS5FMAd9uf7AJxPQ9xi9ovLT8Cr117gbOwoxNffcbizjD/9kAmOpSbi/COm4P7PWkrw1LktuPZdRwKwllyi5XnrR05y6rrw6KlOWB9gKeK7/+M0PPGVs52TnADgmwvzygKwltHiAPu5zdOedsgEV7l4NIKnv/ZWnHSQxfPv703hiOnNLuX6wZNnex6tdtxM97UTDxpftDv5w6fOdup6x1FTMbHAMc7x/Xcd5Sw9OeZPHYO3HzkFnf1prNvdjUuPn160Q/C0uRNw0IQG7O3Oh9su/tZ5rpXIoq+91VFUXOnPm9yELT+4yNUf7zp2Ot530kwnhG3GuHqX0gfgTNYiHvjc6dj6w4sxf+oYHDp5DB77z7M973H+1OKd2x897SDX/7/3riNx6OR8uUMmNTmrN45xDXHc8qETXUvs848odrAdJPiixjUk8MDnTnfujeODp8wu+t2nzzq46NqCg8a7/j+luc5Jgvf8xnYnD9XK773dceQX9t3T663nc1jBGCk0kjhe/vb5rvdt3X9fiPeeOBPfufgIvOu46UXlv3j+obj0+OLr//Puo/HgVWc6K6BPnDHH9X33YAZvmTMer3z3Atf4OmGW+54B4O7/OM1VpqUx4UTRiXjiK+e4DJErhYmOO4z/cdWZrt/ME577tx5YhXW7uzF3UiPOnDfR5RM649CJzvvK8c2Fh+NyOz/SnImNONeeZL/34Go8sXYPGAOOnzUOS695m8tK50zD+j3d+NJdr2Lrvj58/PQ5rvvkffqN+1/DVX9+Bd/9+2pMHpPEC1ef5zmmywH5be8novcBWMgY+5T9/48AOIUxdpVQ5nW7zE77/5vsMu0FdV0J4EoAmD179knbtm3DUNA7mMHDr+/GufMnOc5FL/SlMoEkNcrlGB5bswdvnT/JCV0UcaAvhbuW7sCcCY245emNuPzk2c7g8MLzb7Sj9UAfFh49zbWE5cjY+b6zOQZCnqMtRDqbw9pdXThq+lgn94uIHfv7cNfS7ehLZXHOYZNw6twJnvJzPLp6N3oGMnjPiTM8t4b3Dmbw2s5OPLp6N04+uAUXHVOsnPd0DaCzP43Fm/ahPh7F+xfMlG4z70tl8OTavbjAdo4WYsBOofHK9g4cMa1ZKnsmm8MX73oF8WgE11x0hBMhVYjugTR+8PA6HDtjLD4geT65HMOSLfuwcW8PLjhyCqaNLaZx+lIZtHb0Y9u+PiTjEZw1rzhEkqM/lcXa3V04cXaxcsvlGNK5HDbs7sGGPd14y5yWooAGUfZP37kML2/Zjz9+6pQiJd6fyqKzP42bnnoDy7d24OJjp+Ez5xxSlL99MJPFX1e0ork+jmlj69CUjBVZ0zpgjOH11i7MbmnAbS9swRFTx+BCj/HQn8qieyCN+1e04rRDJuC4mWOLxgNjDJvaep2DjWa11Bf59wArgmp/bwr7egbRn87i6BljnclPRCZr0VgzxtfjzQMDLv+QiM6+NLbu68UTa/fgzEMn4pS5EzzLpTI5/PO1NzF1bB1OmzvBJf8be7px/4pW1MUjWHBQC2a3NDghqiJ6BjP4+RMbcOLs8c4egLPmTSzqi2c3tGFTWw9aO/oxq6UB586fXDQmiGg5Y2yBp7CaqKriF7FgwQK2bNky2dcGBgYGBh4IQvHrUD2tAGYJ/59pX/MsQ0QxAGMBeAfQGxgYGBjUFDqKfymAeUR0MBElAHwAwIMFZR4E8DH78/sAPMV0U0QaGBgYGFQVvgQ4YyxDRFcBeBRAFMDvGGOrieg6AMsYYw8CuA3AH4hoI4D9sCYHAwMDA4MQQsvzyRh7CMBDBdeuFT4PAHh/sKIZGBgYGFQCodm5a2BgYGBQHRjFb2BgYDDKYBS/gYGBwSiDUfwGBgYGowy+G7gq1jBRN4D1ACYCkG70sjEWQPHxSEMrF1QZQE/2WsilU67asgddVxjHjW65kS57LeQaTeNmPmOsvBwOjLGa/IMVCur89Sl7q2advuWCKqMre43k0qmrqrLXou9rJFcgfT+cZTfjprJ9r/sMVP+GC9XzjwDLBVWmFFRbriDlr7Zc1ZZdt1y1+344y65blxk3pZcLBLWkepYxxhbwvzURokwY2WuH4Sy/kb12GM7yB6kza2nx31rwdzjCyF47DGf5jey1w3CWPzCdWTOL38DAwMCgNhguHL+BgYGBQUAwit/AwMBglCFwxU9EvyOivfbhLPzacUS0mIhWEdE/iKjZvh4nojvs62uJ6FvCb7ba118loqqd2FKi/Akiut2+vpKI3ir85iT7+kYiummoR1HWSPaniWi93fevElHxeYPByz6LiBYR0RoiWk1EX7KvtxDR40T0hv13vH2d7H7dSESvEdGJQl0fs8u/QUQfk7UZUtmzQr8Xpj8Pg+yH2+NpkIi+VlDXQnvcbCSiqystewXkr6rOGYLsH7LHyyoiepGIjhPqKq3vy40H9Yg1PRvAiQBeF64tBXCO/fmTAP7b/vxBAHfZnxsAbAUwx/7/VgATg5YvYPk/D+B2+/NkAMsBROz/vwzgVAAE4GEAFw4j2Z8GsKDK/T4NwIn25zEANgA4EsCPAVxtX78awI/szxfZ/Up2P79kX28BsNn+O97+PH44yG5/1xPyfp8M4C0A/hfA14R6ogA2AZgLIAFgJYAjh4v89ndbUUWdMwTZT+djGcCFwpgvue8Dt/gZY8/Cyskv4jAAz9qfHwfwXl4cQCNZp3bVA0gB6ApaplJQovxHAnjK/t1eAAcALCCiaQCaGWNLmPVk7gTw7uEge6VllIExtosxtsL+3A1gLYAZAC4FcIdd7A7k+/FSAHcyC0sAjLP7/R0AHmeM7WeMdcC654XDRPaqo1TZGWN7GWNLAaQLqjoZwEbG2GbGWArAXXYdw0X+qmMIsr9oj2kAWALrNERgCH1fLY5/tSDI+5E/yvE+AL0AdgHYDuAnjDGuuBiAx4hoOVmHtNcSMvlXAriEiGJEdDCAk+zvZgDYKfx+p32tFihVdo7b7SXvd4kqT1OJIKI5AE4A8BKAKYyxXfZXuwFMsT/PALBD+BnvY9n1qqBM2QGgjoiWEdESIqq4sSBCU3YZatrvQNnyAzXUOUOQ/QpYq0ZgCH1fLcX/SQCfI6LlsJY0Kfv6yQCyAKYDOBjAV4lorv3dmYyxE2EtaT5PRGdXSVYvyOT/HaxOXgbgRgAvwrqfMGEosn+IMXYMgLPsfx+plrBE1ATgfgBfZoy5Vn/26im08ccByX4QszbnfBDAjUR0SPCSFmM49zsQmPw10Tmlyk5E58JS/N8captVUfyMsXWMsbczxk4C8BdYfBRgDe5HGGNpm254ATbdwBhrtf/uBfBXWJNETSCTnzGWYYz9J2PseMbYpQDGweLpWpFfhgHeB9RXBUOQXez7bgB/RpX6nojisF6APzHGHrAv7+E0iP13r329Fe4VCu9j2fWKIiDZxb7fDMvXckLIZJehJv0OBCZ/TXROqbIT0bEA/g/ApYyxffblkvu+Koqf7KgQIooA+A6AX9tfbQdwnv1dIyxH1zoiaiSiMcL1twN4vbDeakEmPxE12PKBiC4AkGGMrbGXaV1EdKpNk3wUwN+Hg+w29TPRvh4H8E5Uoe/tfroNwFrG2A3CVw8C4JE5H0O+Hx8E8FGycCqATrvfHwXwdiIab0dDvN2+FnrZbZmTdp0TAZwBYE3IZJdhKYB5RHQwESVgnbtdjaikQOSvhc4pVXYimg3gAQAfYYxtEMqX3vcqz+9Q/sGyKnfBcp7shLUk+RIsa3IDgB8iv2O4CcC9sHjoNQC+bl+fC4uDXml/d03QcgYk/xxYqaXXAngC1jKd17MA1sDZBOCX/Ddhlx1AI6wIn9fsvv85gGgVZD8T1pL2NQCv2v8uAjABwJMA3rDlbLHLE4Cb7f5dBSEKCRa9tdH+94nhIjusqI1V9rhfBeCKEMo+1R5bXbACAnbCCmSA/bsN9n1V5Z0NSn7UQOcMQfb/A9AhlF0m1FVS35uUDQYGBgajDGbnroGBgcEog1H8BgYGBqMMRvEbGBgYjDIYxW9gYGAwymAUv4GBgcEog1H8BgYeIKLPENFHSyg/h4SsqAYGYUas1gIYGIQNRBRjjP3av6SBwfCEUfwGIxJ20qtHYG1GOxHWppyPAjgCwA2wNg+2A/g4s3bNPg1rU8yZAP5i7+LsYYz9hIiOh7XjuQHWBplPMsY6iOgkWDmPAOCxKt2agUHZMFSPwUjGfAC3MMaOgLVT8/MAfgHgfczKXfQ7WHnZORKMsQWMsZ8W1HMngG8yxo6FtaP2e/b12wF8gTF2HAwMhhGMxW8wkrGDMfaC/fmPAL4N4GgAj9uZpqOwUlxw3F1YARGNBTCOMfaMfekOAPcS0Tj7Oj/r4A+wsjoaGIQeRvEbjGQU5iPpBrCaMXaapHxvheUxMAgFDNVjMJIxm4i4kv8grFOLJvFrZJ35fJSqAsZYJ4AOIjrLvvQRAM8wxg4AOEBEZ9rXPxS8+AYGlYFR/AYjGethHaixFtb5u78A8D4APyKilbCcuadr1PMxANcT0WsAjgdwnX39EwBuJqJXYWXcNDAYFjDZOQ1GJOyonn8yxo6usSgGBqGDsfgNDAwMRhmMxW9gYGAwymAsfgMDA4NRBqP4DQwMDEYZjOI3MDAwGGUwit/AwMBglMEofgMDA4NRhv8Px21AYfBQqfwAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"sorted_data['inc'].plot()" "sorted_data['inc'].plot()"
] ]
...@@ -216,9 +778,32 @@ ...@@ -216,9 +778,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff92c2cb5c0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"sorted_data['inc'][-200:].plot()" "sorted_data['inc'][-200:].plot()"
] ]
...@@ -251,10 +836,8 @@ ...@@ -251,10 +836,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 15,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
...@@ -273,10 +856,8 @@ ...@@ -273,10 +856,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"year = []\n", "year = []\n",
...@@ -299,9 +880,32 @@ ...@@ -299,9 +880,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff8f9167668>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"yearly_incidence.plot(style='*')" "yearly_incidence.plot(style='*')"
] ]
...@@ -315,9 +919,54 @@ ...@@ -315,9 +919,54 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\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": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"yearly_incidence.sort_values()" "yearly_incidence.sort_values()"
] ]
...@@ -332,9 +981,32 @@ ...@@ -332,9 +981,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff8f91217f0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"yearly_incidence.hist(xrot=20)" "yearly_incidence.hist(xrot=20)"
] ]
...@@ -342,9 +1014,21 @@ ...@@ -342,9 +1014,21 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true "outputs": [],
}, "source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
} }
...@@ -365,7 +1049,7 @@ ...@@ -365,7 +1049,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.1" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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