{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import os\n", "import datetime\n", "import math \n", " \n", "pd.plotting.register_matplotlib_converters(explicit=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.pyplot import figure\n", "figure (num=None, figsize=(16, 6))\n", "font = {'family' : 'normal',\n", " 'weight' : 'bold',\n", " 'size' : 25}\n", "\n", "plt.rc('font', **font)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of weather information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Climate data per station \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Clipping the dataset to the rainy season from May to end of October\n", "seas = [5,6,7,8,9,10]\n", "# seas = [1,2,3,4,5,6,7,8,9,10,11,12]\n", "# dat = dat[dat.Month.isin(seas)]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017510.000000250.71412041.73333330.13333335.9333334.779912-4.779912
312017520.000000253.59838041.83333329.86666735.8500004.831694-4.831694
322017530.000000235.30092640.83333330.50000035.6666674.476502-4.476502
332017540.646667204.38425938.80000027.66666733.2333333.808452-3.161785
342017550.000000253.43287041.26666729.20000035.2333334.804499-4.804499
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" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 0.000000 250.714120 41.733333 30.133333 \n", "31 2017 5 2 0.000000 253.598380 41.833333 29.866667 \n", "32 2017 5 3 0.000000 235.300926 40.833333 30.500000 \n", "33 2017 5 4 0.646667 204.384259 38.800000 27.666667 \n", "34 2017 5 5 0.000000 253.432870 41.266667 29.200000 \n", "\n", " Avg Eref P - E \n", "30 35.933333 4.779912 -4.779912 \n", "31 35.850000 4.831694 -4.831694 \n", "32 35.666667 4.476502 -4.476502 \n", "33 33.233333 3.808452 -3.161785 \n", "34 35.233333 4.804499 -4.804499 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Bogande\n", "ET_Bog = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Bogande.csv')\n", "\n", "ET_Bog = ET_Bog[ET_Bog.Month.isin(seas)]\n", "\n", "Rain_Bog = ET_Bog.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Bog = ET_Bog.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Bog['P - E'] = ET_Bog['Precipitation'] - ET_Bog['Eref']\n", "# Def_Bog = ET_Bog.groupby('Year').sum()['P - E'].mean()\n", "Def_Bog = ET_Bog.groupby('Year').sum()['P - E']\n", "\n", "ET_Bog.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", "2017 -236.113898\n", "2018 -246.985789\n", "2019 -135.764221\n", "Name: P - E, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Bog.groupby('Year').sum()['P - E']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Bogande26.806667
16Bogande79.165667
27Bogande142.474333
38Bogande157.701000
49Bogande59.294333
510Bogande18.361333
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" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Bogande 26.806667\n", "1 6 Bogande 79.165667\n", "2 7 Bogande 142.474333\n", "3 8 Bogande 157.701000\n", "4 9 Bogande 59.294333\n", "5 10 Bogande 18.361333" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Bog\n", "\n", "ET_Bog_mon = ET_Bog.groupby('Month').sum()/len(ET_Bog.groupby('Year').sum())\n", "ET_Bog_mon = ET_Bog_mon.reset_index() \n", "ET_Bog_mon['Name'] = 'Bogande'\n", "ET_Bog_mon = ET_Bog_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Bog_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
1192018510.0249.63888941.328.634.954.721403-4.721403
1202018520.0232.13194441.828.935.354.404802-4.404802
1212018530.0249.75000041.228.534.854.719564-4.719564
1222018540.0204.67708338.829.133.953.838177-3.838177
1232018550.0255.07986141.729.135.404.842224-4.842224
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "119 2018 5 1 0.0 249.638889 41.3 28.6 34.95 \n", "120 2018 5 2 0.0 232.131944 41.8 28.9 35.35 \n", "121 2018 5 3 0.0 249.750000 41.2 28.5 34.85 \n", "122 2018 5 4 0.0 204.677083 38.8 29.1 33.95 \n", "123 2018 5 5 0.0 255.079861 41.7 29.1 35.40 \n", "\n", " Eref P - E \n", "119 4.721403 -4.721403 \n", "120 4.404802 -4.404802 \n", "121 4.719564 -4.719564 \n", "122 3.838177 -3.838177 \n", "123 4.842224 -4.842224 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Boromo\n", "ET_Bor = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Boromo.csv')\n", "\n", "ET_Bor = ET_Bor[ET_Bor.Month.isin(seas)]\n", "\n", "Rain_Bor = ET_Bor.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Bor = ET_Bor.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Bor['P - E'] = ET_Bor['Precipitation'] - ET_Bor['Eref']\n", "# Def_Bor = ET_Bor.groupby('Year').sum()['P - E'].mean()\n", "Def_Bor = ET_Bor.groupby('Year').sum()['P - E']\n", "\n", "ET_Bor.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Boromo86.413000
16Boromo115.558167
27Boromo250.289667
38Boromo288.175333
49Boromo153.011333
510Boromo67.059333
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" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Boromo 86.413000\n", "1 6 Boromo 115.558167\n", "2 7 Boromo 250.289667\n", "3 8 Boromo 288.175333\n", "4 9 Boromo 153.011333\n", "5 10 Boromo 67.059333" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Bor\n", "\n", "ET_Bor_mon = ET_Bor.groupby('Month').sum()/len(ET_Bor.groupby('Year').sum())\n", "ET_Bor_mon = ET_Bor_mon.reset_index() \n", "ET_Bor_mon['Name'] = 'Boromo'\n", "ET_Bor_mon = ET_Bor_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Bor_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
1202018513.246257.99305637.922.430.154.670279-1.424279
1212018520.000266.11805636.324.630.454.832329-4.832329
1222018530.000236.02430634.225.529.854.259170-4.259170
1232018540.000236.10763934.725.730.204.276326-4.276326
1242018555.102275.65972238.520.829.654.9638610.138139
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "120 2018 5 1 3.246 257.993056 37.9 22.4 30.15 \n", "121 2018 5 2 0.000 266.118056 36.3 24.6 30.45 \n", "122 2018 5 3 0.000 236.024306 34.2 25.5 29.85 \n", "123 2018 5 4 0.000 236.107639 34.7 25.7 30.20 \n", "124 2018 5 5 5.102 275.659722 38.5 20.8 29.65 \n", "\n", " Eref P - E \n", "120 4.670279 -1.424279 \n", "121 4.832329 -4.832329 \n", "122 4.259170 -4.259170 \n", "123 4.276326 -4.276326 \n", "124 4.963861 0.138139 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Daffiama\n", "ET_Daf = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Daffiama.csv')\n", "\n", "ET_Daf = ET_Daf[ET_Daf.Month.isin(seas)]\n", "\n", "Rain_Daf = ET_Daf.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Daf = ET_Daf.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Daf['P - E'] = ET_Daf['Precipitation'] - ET_Daf['Eref']\n", "# Def_Daf = ET_Daf.groupby('Year').sum()['P - E'].mean()\n", "Def_Daf = ET_Daf.groupby('Year').sum()['P - E']\n", "\n", "ET_Daf.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Daffiama85.507667
16Daffiama71.179667
27Daffiama118.004000
38Daffiama83.283333
49Daffiama202.653000
510Daffiama108.831667
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" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Daffiama 85.507667\n", "1 6 Daffiama 71.179667\n", "2 7 Daffiama 118.004000\n", "3 8 Daffiama 83.283333\n", "4 9 Daffiama 202.653000\n", "5 10 Daffiama 108.831667" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Daf\n", "\n", "ET_Daf_mon = ET_Daf.groupby('Month').sum()/len(ET_Daf.groupby('Year').sum())\n", "ET_Daf_mon = ET_Daf_mon.reset_index() \n", "ET_Daf_mon['Name'] = 'Daffiama'\n", "ET_Daf_mon = ET_Daf_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Daf_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017510.0231.67245440.76666725.60000033.1833334.316963-4.316963
312017520.0226.54745441.30000027.66666734.4833334.269826-4.269826
322017530.0237.00578741.16666727.93333334.5500004.469464-4.469464
332017540.0184.97569438.06666727.93333333.0000003.441090-3.441090
342017550.0238.67939840.96666728.16666734.5666674.501660-4.501660
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" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 0.0 231.672454 40.766667 25.600000 \n", "31 2017 5 2 0.0 226.547454 41.300000 27.666667 \n", "32 2017 5 3 0.0 237.005787 41.166667 27.933333 \n", "33 2017 5 4 0.0 184.975694 38.066667 27.933333 \n", "34 2017 5 5 0.0 238.679398 40.966667 28.166667 \n", "\n", " Avg Eref P - E \n", "30 33.183333 4.316963 -4.316963 \n", "31 34.483333 4.269826 -4.269826 \n", "32 34.550000 4.469464 -4.469464 \n", "33 33.000000 3.441090 -3.441090 \n", "34 34.566667 4.501660 -4.501660 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dedougou\n", "ET_Ded = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Dedougou.csv')\n", "\n", "ET_Ded = ET_Ded[ET_Ded.Month.isin(seas)]\n", "\n", "Rain_Ded = ET_Ded.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Ded = ET_Ded.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Ded['P - E'] = ET_Ded['Precipitation'] - ET_Ded['Eref']\n", "# Def_Ded = ET_Ded.groupby('Year').sum()['P - E'].mean()\n", "Def_Ded = ET_Ded.groupby('Year').sum()['P - E']\n", "\n", "ET_Ded.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Dedougou81.05725
16Dedougou167.00975
27Dedougou187.75500
38Dedougou263.47100
49Dedougou143.34600
510Dedougou64.03000
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Dedougou 81.05725\n", "1 6 Dedougou 167.00975\n", "2 7 Dedougou 187.75500\n", "3 8 Dedougou 263.47100\n", "4 9 Dedougou 143.34600\n", "5 10 Dedougou 64.03000" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Ded\n", "\n", "ET_Ded_mon = ET_Ded.groupby('Month').sum()/len(ET_Ded.groupby('Year').sum())\n", "ET_Ded_mon = ET_Ded_mon.reset_index() \n", "ET_Ded_mon['Name'] = 'Dedougou'\n", "ET_Ded_mon = ET_Ded_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Ded_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017510.0254.37384342.80000029.60000036.2000004.859340-4.859340
312017520.0258.60879642.83333328.86666735.8500004.926574-4.926574
322017530.0251.32523142.20000028.80000035.5000004.774353-4.774353
332017540.0233.47106540.40000027.36666733.8833334.375115-4.375115
342017550.0259.68981541.60000027.53333334.5666674.895196-4.895196
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 0.0 254.373843 42.800000 29.600000 \n", "31 2017 5 2 0.0 258.608796 42.833333 28.866667 \n", "32 2017 5 3 0.0 251.325231 42.200000 28.800000 \n", "33 2017 5 4 0.0 233.471065 40.400000 27.366667 \n", "34 2017 5 5 0.0 259.689815 41.600000 27.533333 \n", "\n", " Avg Eref P - E \n", "30 36.200000 4.859340 -4.859340 \n", "31 35.850000 4.926574 -4.926574 \n", "32 35.500000 4.774353 -4.774353 \n", "33 33.883333 4.375115 -4.375115 \n", "34 34.566667 4.895196 -4.895196 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Dori\n", "ET_Dor = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Dori.csv')\n", "\n", "ET_Dor = ET_Dor[ET_Dor.Month.isin(seas)]\n", "\n", "Rain_Dor = ET_Dor.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Dor = ET_Dor.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Dor['P - E'] = ET_Dor['Precipitation'] - ET_Dor['Eref']\n", "# Def_Dor = ET_Dor.groupby('Year').sum()['P - E'].mean()\n", "Def_Dor = ET_Dor.groupby('Year').sum()['P - E']\n", "\n", "ET_Dor.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Dori43.65150
16Dori51.71675
27Dori112.41375
38Dori184.79225
49Dori38.49025
510Dori6.19775
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Dori 43.65150\n", "1 6 Dori 51.71675\n", "2 7 Dori 112.41375\n", "3 8 Dori 184.79225\n", "4 9 Dori 38.49025\n", "5 10 Dori 6.19775" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Dor\n", "\n", "ET_Dor_mon = ET_Dor.groupby('Month').sum()/len(ET_Dor.groupby('Year').sum())\n", "ET_Dor_mon = ET_Dor_mon.reset_index() \n", "ET_Dor_mon['Name'] = 'Dori'\n", "ET_Dor_mon = ET_Dor_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Dor_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
472018510.0263.52430640.821.731.254.837437-4.837437
482018520.0266.73611140.116.928.504.757786-4.757786
492018530.0248.04861140.916.428.654.431829-4.431829
502018540.0260.53819440.416.728.554.649819-4.649819
512018550.0268.69791738.317.427.854.757665-4.757665
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "47 2018 5 1 0.0 263.524306 40.8 21.7 31.25 \n", "48 2018 5 2 0.0 266.736111 40.1 16.9 28.50 \n", "49 2018 5 3 0.0 248.048611 40.9 16.4 28.65 \n", "50 2018 5 4 0.0 260.538194 40.4 16.7 28.55 \n", "51 2018 5 5 0.0 268.697917 38.3 17.4 27.85 \n", "\n", " Eref P - E \n", "47 4.837437 -4.837437 \n", "48 4.757786 -4.757786 \n", "49 4.431829 -4.431829 \n", "50 4.649819 -4.649819 \n", "51 4.757665 -4.757665 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fari\n", "ET_Far = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Fari.csv')\n", "\n", "ET_Far = ET_Far[ET_Far.Month.isin(seas)]\n", "\n", "Rain_Far = ET_Far.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Far = ET_Far.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Far['P - E'] = ET_Far['Precipitation'] - ET_Far['Eref']\n", "# Def_Far = ET_Far.groupby('Year').sum()['P - E'].mean()\n", "Def_Far = ET_Far.groupby('Year').sum()['P - E']\n", "\n", "ET_Far.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Fari54.379333
16Fari132.038667
27Fari181.133667
38Fari225.954000
49Fari77.060000
510Fari50.140333
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Fari 54.379333\n", "1 6 Fari 132.038667\n", "2 7 Fari 181.133667\n", "3 8 Fari 225.954000\n", "4 9 Fari 77.060000\n", "5 10 Fari 50.140333" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Far\n", "\n", "ET_Far_mon = ET_Far.groupby('Month').sum()/len(ET_Far.groupby('Year').sum())\n", "ET_Far_mon = ET_Far_mon.reset_index() \n", "ET_Far_mon['Name'] = 'Fari'\n", "ET_Far_mon = ET_Far_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Far_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
12020175119.68196.04861139.922.231.053.58793016.092070
1212017520.00256.72916737.124.630.854.689111-4.689111
1222017530.00227.23958335.425.830.604.140059-4.140059
1232017540.00269.49652836.924.630.754.917370-4.917370
1242017550.00246.13194438.726.732.704.575914-4.575914
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "120 2017 5 1 19.68 196.048611 39.9 22.2 31.05 \n", "121 2017 5 2 0.00 256.729167 37.1 24.6 30.85 \n", "122 2017 5 3 0.00 227.239583 35.4 25.8 30.60 \n", "123 2017 5 4 0.00 269.496528 36.9 24.6 30.75 \n", "124 2017 5 5 0.00 246.131944 38.7 26.7 32.70 \n", "\n", " Eref P - E \n", "120 3.587930 16.092070 \n", "121 4.689111 -4.689111 \n", "122 4.140059 -4.140059 \n", "123 4.917370 -4.917370 \n", "124 4.575914 -4.575914 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Finkoloni\n", "ET_Fin = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Finkoloni.csv')\n", "\n", "ET_Fin = ET_Fin[ET_Fin.Month.isin(seas)]\n", "\n", "Rain_Fin = ET_Fin.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Fin = ET_Fin.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Fin['P - E'] = ET_Fin['Precipitation'] - ET_Fin['Eref']\n", "# Def_Fin = ET_Fin.groupby('Year').sum()['P - E'].mean()\n", "Def_Fin = ET_Fin.groupby('Year').sum()['P - E']\n", "\n", "ET_Fin.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Finkoloni76.191000
16Finkoloni85.512667
27Finkoloni259.270667
38Finkoloni240.746000
49Finkoloni194.300667
510Finkoloni85.017556
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Finkoloni 76.191000\n", "1 6 Finkoloni 85.512667\n", "2 7 Finkoloni 259.270667\n", "3 8 Finkoloni 240.746000\n", "4 9 Finkoloni 194.300667\n", "5 10 Finkoloni 85.017556" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Fin\n", "\n", "ET_Fin_mon = ET_Fin.groupby('Month').sum()/len(ET_Fin.groupby('Year').sum())\n", "ET_Fin_mon = ET_Fin_mon.reset_index() \n", "ET_Fin_mon['Name'] = 'Finkoloni'\n", "ET_Fin_mon = ET_Fin_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Fin_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017512.376667247.31770836.86666723.43333330.1500004.472515-2.095849
312017520.000000262.43923636.73333325.86666731.3000004.801832-4.801832
322017533.510667255.71875036.96666724.63333330.8000004.655490-1.144823
332017540.226667269.18402835.23333325.30000030.2666674.873872-4.647206
342017552.783000275.09722238.56666724.23333331.4000005.038407-2.255407
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 2.376667 247.317708 36.866667 23.433333 \n", "31 2017 5 2 0.000000 262.439236 36.733333 25.866667 \n", "32 2017 5 3 3.510667 255.718750 36.966667 24.633333 \n", "33 2017 5 4 0.226667 269.184028 35.233333 25.300000 \n", "34 2017 5 5 2.783000 275.097222 38.566667 24.233333 \n", "\n", " Avg Eref P - E \n", "30 30.150000 4.472515 -2.095849 \n", "31 31.300000 4.801832 -4.801832 \n", "32 30.800000 4.655490 -1.144823 \n", "33 30.266667 4.873872 -4.647206 \n", "34 31.400000 5.038407 -2.255407 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Gaoua\n", "ET_Gao = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Gaoua.csv')\n", "\n", "ET_Gao = ET_Gao[ET_Gao.Month.isin(seas)]\n", "\n", "Rain_Gao = ET_Gao.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Gao = ET_Gao.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Gao['P - E'] = ET_Gao['Precipitation'] - ET_Gao['Eref']\n", "# Def_Gao = ET_Gao.groupby('Year').sum()['P - E'].mean()\n", "Def_Gao = ET_Gao.groupby('Year').sum()['P - E']\n", "\n", "ET_Gao.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Gaoua86.681333
16Gaoua134.502750
27Gaoua172.942500
38Gaoua251.573583
49Gaoua220.121667
510Gaoua117.634333
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Gaoua 86.681333\n", "1 6 Gaoua 134.502750\n", "2 7 Gaoua 172.942500\n", "3 8 Gaoua 251.573583\n", "4 9 Gaoua 220.121667\n", "5 10 Gaoua 117.634333" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Gao\n", "\n", "ET_Gao_mon = ET_Gao.groupby('Month').sum()/len(ET_Gao.groupby('Year').sum())\n", "ET_Gao_mon = ET_Gao_mon.reset_index() \n", "ET_Gao_mon['Name'] = 'Gaoua'\n", "ET_Gao_mon = ET_Gao_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Gao_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
492018510.0282.50347242.224.933.555.277252-5.277252
502018520.0290.09375040.927.334.105.445320-5.445320
512018530.0274.72916740.626.833.705.138866-5.138866
522018540.0289.15972241.823.432.605.355019-5.355019
532018550.0292.17708341.424.532.955.428436-5.428436
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "49 2018 5 1 0.0 282.503472 42.2 24.9 33.55 \n", "50 2018 5 2 0.0 290.093750 40.9 27.3 34.10 \n", "51 2018 5 3 0.0 274.729167 40.6 26.8 33.70 \n", "52 2018 5 4 0.0 289.159722 41.8 23.4 32.60 \n", "53 2018 5 5 0.0 292.177083 41.4 24.5 32.95 \n", "\n", " Eref P - E \n", "49 5.277252 -5.277252 \n", "50 5.445320 -5.445320 \n", "51 5.138866 -5.138866 \n", "52 5.355019 -5.355019 \n", "53 5.428436 -5.428436 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Kourounikoto\n", "ET_Kou = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Kourounikoto.csv')\n", "\n", "ET_Kou = ET_Kou[ET_Kou.Month.isin(seas)]\n", "\n", "Rain_Kou = ET_Kou.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Kou = ET_Kou.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Kou['P - E'] = ET_Kou['Precipitation'] - ET_Kou['Eref']\n", "# Def_Kou = ET_Kou.groupby('Year').sum()['P - E'].mean()\n", "Def_Kou = ET_Kou.groupby('Year').sum()['P - E']\n", "\n", "ET_Kou.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Kourounikoto16.609333
16Kourounikoto93.006667
27Kourounikoto322.386333
38Kourounikoto342.661667
49Kourounikoto168.909333
510Kourounikoto41.664333
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Kourounikoto 16.609333\n", "1 6 Kourounikoto 93.006667\n", "2 7 Kourounikoto 322.386333\n", "3 8 Kourounikoto 342.661667\n", "4 9 Kourounikoto 168.909333\n", "5 10 Kourounikoto 41.664333" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Kou\n", "\n", "ET_Kou_mon = ET_Kou.groupby('Month').sum()/len(ET_Kou.groupby('Year').sum())\n", "ET_Kou_mon = ET_Kou_mon.reset_index() \n", "ET_Kou_mon['Name'] = 'Kourounikoto'\n", "ET_Kou_mon = ET_Kou_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Kou_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
242018510.0228.30555638.528.733.604.255101-4.255101
252018520.0259.77083339.228.934.054.860977-4.860977
262018530.0209.51388937.429.733.553.903108-3.903108
272018540.0266.26736139.828.834.304.993463-4.993463
282018550.0253.84375040.629.635.104.793115-4.793115
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "24 2018 5 1 0.0 228.305556 38.5 28.7 33.60 \n", "25 2018 5 2 0.0 259.770833 39.2 28.9 34.05 \n", "26 2018 5 3 0.0 209.513889 37.4 29.7 33.55 \n", "27 2018 5 4 0.0 266.267361 39.8 28.8 34.30 \n", "28 2018 5 5 0.0 253.843750 40.6 29.6 35.10 \n", "\n", " Eref P - E \n", "24 4.255101 -4.255101 \n", "25 4.860977 -4.860977 \n", "26 3.903108 -3.903108 \n", "27 4.993463 -4.993463 \n", "28 4.793115 -4.793115 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mandouri\n", "ET_Man = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Mandouri.csv')\n", "\n", "ET_Man = ET_Man[ET_Man.Month.isin(seas)]\n", "\n", "Rain_Man = ET_Man.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Man = ET_Man.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Man['P - E'] = ET_Man['Precipitation'] - ET_Man['Eref']\n", "# Def_Man = ET_Man.groupby('Year').sum()['P - E'].mean()\n", "Def_Man = ET_Man.groupby('Year').sum()['P - E']\n", "\n", "ET_Man.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Mandouri49.030000
16Mandouri84.407000
27Mandouri94.951667
38Mandouri154.959000
49Mandouri124.901333
510Mandouri57.084000
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Mandouri 49.030000\n", "1 6 Mandouri 84.407000\n", "2 7 Mandouri 94.951667\n", "3 8 Mandouri 154.959000\n", "4 9 Mandouri 124.901333\n", "5 10 Mandouri 57.084000" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Man\n", "\n", "ET_Man_mon = ET_Man.groupby('Month').sum()/len(ET_Man.groupby('Year').sum())\n", "ET_Man_mon = ET_Man_mon.reset_index() \n", "ET_Man_mon['Name'] = 'Mandouri'\n", "ET_Man_mon = ET_Man_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Man_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017510.0227.16782441.16666729.26666735.2166674.310843-4.310843
312017520.0249.31365742.06666729.16666735.6166674.746443-4.746443
322017530.0230.78240741.13333327.20000034.1666674.341056-4.341056
332017540.0189.41319438.46666726.90000032.6833333.516144-3.516144
342017550.0243.39351840.66666726.93333333.8000004.563743-4.563743
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 0.0 227.167824 41.166667 29.266667 \n", "31 2017 5 2 0.0 249.313657 42.066667 29.166667 \n", "32 2017 5 3 0.0 230.782407 41.133333 27.200000 \n", "33 2017 5 4 0.0 189.413194 38.466667 26.900000 \n", "34 2017 5 5 0.0 243.393518 40.666667 26.933333 \n", "\n", " Avg Eref P - E \n", "30 35.216667 4.310843 -4.310843 \n", "31 35.616667 4.746443 -4.746443 \n", "32 34.166667 4.341056 -4.341056 \n", "33 32.683333 3.516144 -3.516144 \n", "34 33.800000 4.563743 -4.563743 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ouahigouya\n", "ET_Oua = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Ouahigouya.csv')\n", "\n", "ET_Oua = ET_Oua[ET_Oua.Month.isin(seas)]\n", "\n", "Rain_Oua = ET_Oua.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Oua = ET_Oua.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Oua['P - E'] = ET_Oua['Precipitation'] - ET_Oua['Eref']\n", "# Def_Oua = ET_Oua.groupby('Year').sum()['P - E'].mean()\n", "Def_Oua = ET_Oua.groupby('Year').sum()['P - E']\n", "\n", "ET_Oua.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Ouahigouya293.455750
16Ouahigouya158.352417
27Ouahigouya102.617250
38Ouahigouya133.577000
49Ouahigouya99.862417
510Ouahigouya124.538250
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Ouahigouya 293.455750\n", "1 6 Ouahigouya 158.352417\n", "2 7 Ouahigouya 102.617250\n", "3 8 Ouahigouya 133.577000\n", "4 9 Ouahigouya 99.862417\n", "5 10 Ouahigouya 124.538250" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Oua\n", "\n", "ET_Oua_mon = ET_Oua.groupby('Month').sum()/len(ET_Oua.groupby('Year').sum())\n", "ET_Oua_mon = ET_Oua_mon.reset_index() \n", "ET_Oua_mon['Name'] = 'Ouahigouya'\n", "ET_Oua_mon = ET_Oua_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Oua_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
442018510.0274.61805643.224.033.605.127697-5.127697
452018520.0276.97916743.126.534.805.225907-5.225907
462018530.0273.09375043.624.133.855.110558-5.110558
472018540.0277.83333343.721.632.655.142934-5.142934
482018550.0277.22916743.220.731.955.097760-5.097760
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "44 2018 5 1 0.0 274.618056 43.2 24.0 33.60 \n", "45 2018 5 2 0.0 276.979167 43.1 26.5 34.80 \n", "46 2018 5 3 0.0 273.093750 43.6 24.1 33.85 \n", "47 2018 5 4 0.0 277.833333 43.7 21.6 32.65 \n", "48 2018 5 5 0.0 277.229167 43.2 20.7 31.95 \n", "\n", " Eref P - E \n", "44 5.127697 -5.127697 \n", "45 5.225907 -5.225907 \n", "46 5.110558 -5.110558 \n", "47 5.142934 -5.142934 \n", "48 5.097760 -5.097760 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Oussoubidiagna\n", "ET_Ous = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Oussoubidiagna.csv')\n", "\n", "ET_Ous = ET_Ous[ET_Ous.Month.isin(seas)]\n", "\n", "Rain_Ous = ET_Ous.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Ous = ET_Ous.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Ous['P - E'] = ET_Ous['Precipitation'] - ET_Ous['Eref']\n", "# Def_Ous = ET_Ous.groupby('Year').sum()['P - E'].mean()\n", "Def_Ous = ET_Ous.groupby('Year').sum()['P - E']\n", "\n", "ET_Ous.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Oussoubidiagna5.833333
16Oussoubidiagna182.966333
27Oussoubidiagna132.484000
38Oussoubidiagna189.573000
49Oussoubidiagna86.775000
510Oussoubidiagna18.813833
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Oussoubidiagna 5.833333\n", "1 6 Oussoubidiagna 182.966333\n", "2 7 Oussoubidiagna 132.484000\n", "3 8 Oussoubidiagna 189.573000\n", "4 9 Oussoubidiagna 86.775000\n", "5 10 Oussoubidiagna 18.813833" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Ous\n", "\n", "ET_Ous_mon = ET_Ous.groupby('Month').sum()/len(ET_Ous.groupby('Year').sum())\n", "ET_Ous_mon = ET_Ous_mon.reset_index() \n", "ET_Ous_mon['Name'] = 'Oussoubidiagna'\n", "ET_Ous_mon = ET_Ous_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Ous_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302017510.0229.20138939.00000027.10000033.0500004.267069-4.267069
312017520.0233.85532439.13333327.96666733.5500004.373291-4.373291
322017530.0189.22453737.26666726.93333332.1000003.491863-3.491863
332017540.0202.17013936.93333324.93333330.9333333.688477-3.688477
342017550.0233.82754638.60000024.26666731.4333334.287268-4.287268
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min \\\n", "30 2017 5 1 0.0 229.201389 39.000000 27.100000 \n", "31 2017 5 2 0.0 233.855324 39.133333 27.966667 \n", "32 2017 5 3 0.0 189.224537 37.266667 26.933333 \n", "33 2017 5 4 0.0 202.170139 36.933333 24.933333 \n", "34 2017 5 5 0.0 233.827546 38.600000 24.266667 \n", "\n", " Avg Eref P - E \n", "30 33.050000 4.267069 -4.267069 \n", "31 33.550000 4.373291 -4.373291 \n", "32 32.100000 3.491863 -3.491863 \n", "33 30.933333 3.688477 -3.688477 \n", "34 31.433333 4.287268 -4.287268 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Po\n", "ET_Po = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Po.csv')\n", "\n", "ET_Po = ET_Po[ET_Po.Month.isin(seas)]\n", "\n", "Rain_Po = ET_Po.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Po = ET_Po.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Po['P - E'] = ET_Po['Precipitation'] - ET_Po['Eref']\n", "# Def_Po = ET_Po.groupby('Year').sum()['P - E'].mean()\n", "Def_Po = ET_Po.groupby('Year').sum()['P - E']\n", "\n", "ET_Po.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Po138.806250
16Po132.104000
27Po237.401250
38Po304.892667
49Po195.327333
510Po114.310333
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Po 138.806250\n", "1 6 Po 132.104000\n", "2 7 Po 237.401250\n", "3 8 Po 304.892667\n", "4 9 Po 195.327333\n", "5 10 Po 114.310333" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Po\n", "\n", "ET_Po_mon = ET_Po.groupby('Month').sum()/len(ET_Po.groupby('Year').sum())\n", "ET_Po_mon = ET_Po_mon.reset_index() \n", "ET_Po_mon['Name'] = 'Po'\n", "ET_Po_mon = ET_Po_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Po_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
1202018510.000252.17013939.828.534.154.734415-4.734415
1212018520.000253.26388939.728.934.304.761115-4.761115
1222018530.000194.04861136.829.733.253.614272-3.614272
1232018540.000226.35069438.128.633.354.219726-4.219726
1242018550.017244.87500040.529.134.804.623034-4.606034
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "120 2018 5 1 0.000 252.170139 39.8 28.5 34.15 \n", "121 2018 5 2 0.000 253.263889 39.7 28.9 34.30 \n", "122 2018 5 3 0.000 194.048611 36.8 29.7 33.25 \n", "123 2018 5 4 0.000 226.350694 38.1 28.6 33.35 \n", "124 2018 5 5 0.017 244.875000 40.5 29.1 34.80 \n", "\n", " Eref P - E \n", "120 4.734415 -4.734415 \n", "121 4.761115 -4.761115 \n", "122 3.614272 -3.614272 \n", "123 4.219726 -4.219726 \n", "124 4.623034 -4.606034 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pusiga\n", "ET_Pus = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Pusiga.csv')\n", "\n", "ET_Pus = ET_Pus[ET_Pus.Month.isin(seas)]\n", "\n", "Rain_Pus = ET_Pus.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Pus = ET_Pus.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Pus['P - E'] = ET_Pus['Precipitation'] - ET_Pus['Eref']\n", "# Def_Pus = ET_Pus.groupby('Year').sum()['P - E'].mean()\n", "Def_Pus = ET_Pus.groupby('Year').sum()['P - E']\n", "\n", "ET_Pus.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Pusiga88.955667
16Pusiga162.105000
27Pusiga295.875333
38Pusiga241.363500
49Pusiga243.730667
510Pusiga101.851667
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Pusiga 88.955667\n", "1 6 Pusiga 162.105000\n", "2 7 Pusiga 295.875333\n", "3 8 Pusiga 241.363500\n", "4 9 Pusiga 243.730667\n", "5 10 Pusiga 101.851667" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Pus\n", "\n", "ET_Pus_mon = ET_Pus.groupby('Month').sum()/len(ET_Pus.groupby('Year').sum())\n", "ET_Pus_mon = ET_Pus_mon.reset_index() \n", "ET_Pus_mon['Name'] = 'Pusiga'\n", "ET_Pus_mon = ET_Pus_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Pus_mon\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
912018510.0275.07638941.225.733.455.143357-5.143357
922018520.0247.40625039.624.331.954.562544-4.562544
932018530.0277.86458341.123.332.205.136403-5.136403
942018540.0229.11111138.526.732.604.251027-4.251027
952018550.0262.81250039.327.733.504.916228-4.916228
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "91 2018 5 1 0.0 275.076389 41.2 25.7 33.45 \n", "92 2018 5 2 0.0 247.406250 39.6 24.3 31.95 \n", "93 2018 5 3 0.0 277.864583 41.1 23.3 32.20 \n", "94 2018 5 4 0.0 229.111111 38.5 26.7 32.60 \n", "95 2018 5 5 0.0 262.812500 39.3 27.7 33.50 \n", "\n", " Eref P - E \n", "91 5.143357 -5.143357 \n", "92 4.562544 -4.562544 \n", "93 5.136403 -5.136403 \n", "94 4.251027 -4.251027 \n", "95 4.916228 -4.916228 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Selingue\n", "ET_Sel = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Selingue.csv')\n", "\n", "ET_Sel = ET_Sel[ET_Sel.Month.isin(seas)]\n", "\n", "Rain_Sel = ET_Sel.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Sel = ET_Sel.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Sel['P - E'] = ET_Sel['Precipitation'] - ET_Sel['Eref']\n", "# Def_Sel = ET_Sel.groupby('Year').sum()['P - E'].mean()\n", "Def_Sel = ET_Sel.groupby('Year').sum()['P - E']\n", "\n", "ET_Sel.head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Selingue59.678667
16Selingue73.167333
27Selingue92.954667
38Selingue106.336333
49Selingue59.423667
510Selingue36.850000
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Selingue 59.678667\n", "1 6 Selingue 73.167333\n", "2 7 Selingue 92.954667\n", "3 8 Selingue 106.336333\n", "4 9 Selingue 59.423667\n", "5 10 Selingue 36.850000" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Sel\n", "\n", "ET_Sel_mon = ET_Sel.groupby('Month').sum()/len(ET_Sel.groupby('Year').sum())\n", "ET_Sel_mon = ET_Sel_mon.reset_index() \n", "ET_Sel_mon['Name'] = 'Selingue'\n", "ET_Sel_mon = ET_Sel_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Sel_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
482018510.0270.79513942.224.433.305.057124-5.057124
492018520.0270.22916741.022.531.754.974500-4.974500
502018530.0265.91319441.520.330.904.854355-4.854355
512018540.0247.37847241.021.731.354.536190-4.536190
522018550.0269.88541741.520.931.204.941599-4.941599
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "48 2018 5 1 0.0 270.795139 42.2 24.4 33.30 \n", "49 2018 5 2 0.0 270.229167 41.0 22.5 31.75 \n", "50 2018 5 3 0.0 265.913194 41.5 20.3 30.90 \n", "51 2018 5 4 0.0 247.378472 41.0 21.7 31.35 \n", "52 2018 5 5 0.0 269.885417 41.5 20.9 31.20 \n", "\n", " Eref P - E \n", "48 5.057124 -5.057124 \n", "49 4.974500 -4.974500 \n", "50 4.854355 -4.854355 \n", "51 4.536190 -4.536190 \n", "52 4.941599 -4.941599 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sirakoro\n", "ET_Sir = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Sirakoro.csv')\n", "\n", "ET_Sir = ET_Sir[ET_Sir.Month.isin(seas)]\n", "\n", "Rain_Sir = ET_Sir.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Sir = ET_Sir.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Sir['P - E'] = ET_Sir['Precipitation'] - ET_Sir['Eref']\n", "# Def_Sir = ET_Sir.groupby('Year').sum()['P - E'].mean()\n", "Def_Sir = ET_Sir.groupby('Year').sum()['P - E']\n", "\n", "ET_Sir.head()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Sirakoro13.7260
16Sirakoro54.2575
27Sirakoro259.4445
38Sirakoro322.1415
49Sirakoro266.4055
510Sirakoro54.9400
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Sirakoro 13.7260\n", "1 6 Sirakoro 54.2575\n", "2 7 Sirakoro 259.4445\n", "3 8 Sirakoro 322.1415\n", "4 9 Sirakoro 266.4055\n", "5 10 Sirakoro 54.9400" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Sir\n", "\n", "ET_Sir_mon = ET_Sir.groupby('Month').sum()/len(ET_Sir.groupby('Year').sum())\n", "ET_Sir_mon = ET_Sir_mon.reset_index() \n", "ET_Sir_mon['Name'] = 'Sirakoro'\n", "ET_Sir_mon = ET_Sir_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Sir_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
1202017511.43159.17708339.728.834.252.992467-1.562467
1212017520.00280.05902839.725.932.805.197384-5.197384
1222017536.86236.27083338.925.332.104.3560602.503940
1232017540.00278.97569438.425.131.755.126115-5.126115
1242017550.91257.26736141.126.633.854.819737-3.909737
\n", "
" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "120 2017 5 1 1.43 159.177083 39.7 28.8 34.25 \n", "121 2017 5 2 0.00 280.059028 39.7 25.9 32.80 \n", "122 2017 5 3 6.86 236.270833 38.9 25.3 32.10 \n", "123 2017 5 4 0.00 278.975694 38.4 25.1 31.75 \n", "124 2017 5 5 0.91 257.267361 41.1 26.6 33.85 \n", "\n", " Eref P - E \n", "120 2.992467 -1.562467 \n", "121 5.197384 -5.197384 \n", "122 4.356060 2.503940 \n", "123 5.126115 -5.126115 \n", "124 4.819737 -3.909737 " ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Somo\n", "ET_Som = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Somo.csv')\n", "\n", "ET_Som = ET_Som[ET_Som.Month.isin(seas)]\n", "\n", "Rain_Som = ET_Som.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Som = ET_Som.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Som['P - E'] = ET_Som['Precipitation'] - ET_Som['Eref']\n", "# Def_Som = ET_Som.groupby('Year').sum()['P - E'].mean()\n", "Def_Som = ET_Som.groupby('Year').sum()['P - E']\n", "\n", "ET_Som.head()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Somo22.382000
16Somo81.475333
27Somo133.468667
38Somo177.306667
49Somo45.984000
510Somo9.990667
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Somo 22.382000\n", "1 6 Somo 81.475333\n", "2 7 Somo 133.468667\n", "3 8 Somo 177.306667\n", "4 9 Somo 45.984000\n", "5 10 Somo 9.990667" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Som\n", "\n", "ET_Som_mon = ET_Som.groupby('Month').sum()/len(ET_Som.groupby('Year').sum())\n", "ET_Som_mon = ET_Som_mon.reset_index() \n", "ET_Som_mon['Name'] = 'Somo'\n", "ET_Som_mon = ET_Som_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Som_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302019513.316296.81597236.426.231.305.418255-2.102255
312019520.000306.80902837.325.431.355.603469-5.603469
3220195329.644236.14236137.921.929.904.24879425.395206
332019540.000144.48263930.124.427.252.522435-2.522435
342019550.000307.21180636.325.831.055.593954-5.593954
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" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "30 2019 5 1 3.316 296.815972 36.4 26.2 31.30 \n", "31 2019 5 2 0.000 306.809028 37.3 25.4 31.35 \n", "32 2019 5 3 29.644 236.142361 37.9 21.9 29.90 \n", "33 2019 5 4 0.000 144.482639 30.1 24.4 27.25 \n", "34 2019 5 5 0.000 307.211806 36.3 25.8 31.05 \n", "\n", " Eref P - E \n", "30 5.418255 -2.102255 \n", "31 5.603469 -5.603469 \n", "32 4.248794 25.395206 \n", "33 2.522435 -2.522435 \n", "34 5.593954 -5.593954 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tamale\n", "ET_Tam = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Tamale.csv')\n", "\n", "ET_Tam = ET_Tam[ET_Tam.Month.isin(seas)]\n", "\n", "Rain_Tam = ET_Tam.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Tam = ET_Tam.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Tam['P - E'] = ET_Tam['Precipitation'] - ET_Tam['Eref']\n", "# Def_Tam = ET_Tam.groupby('Year').sum()['P - E'].mean()\n", "Def_Tam = ET_Tam.groupby('Year').sum()['P - E']\n", "\n", "ET_Tam.head()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MonthNamePrecipitation
05Tamale90.0190
16Tamale69.8140
27Tamale69.4870
38Tamale93.0895
49Tamale169.7260
510Tamale72.3905
\n", "
" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Tamale 90.0190\n", "1 6 Tamale 69.8140\n", "2 7 Tamale 69.4870\n", "3 8 Tamale 93.0895\n", "4 9 Tamale 169.7260\n", "5 10 Tamale 72.3905" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Tam\n", "\n", "ET_Tam_mon = ET_Tam.groupby('Month').sum()/len(ET_Tam.groupby('Year').sum())\n", "ET_Tam_mon = ET_Tam_mon.reset_index() \n", "ET_Tam_mon['Name'] = 'Tamale'\n", "ET_Tam_mon = ET_Tam_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Tam_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
332017510.000252.16319438.526.832.654.668595-4.668595
342017521.515268.41666740.123.431.754.927065-3.412065
352017532.04074.47222230.723.226.951.2971330.742867
362017540.000246.16319437.323.830.554.464578-4.464578
372017550.000267.85763939.027.333.154.982079-4.982079
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" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "33 2017 5 1 0.000 252.163194 38.5 26.8 32.65 \n", "34 2017 5 2 1.515 268.416667 40.1 23.4 31.75 \n", "35 2017 5 3 2.040 74.472222 30.7 23.2 26.95 \n", "36 2017 5 4 0.000 246.163194 37.3 23.8 30.55 \n", "37 2017 5 5 0.000 267.857639 39.0 27.3 33.15 \n", "\n", " Eref P - E \n", "33 4.668595 -4.668595 \n", "34 4.927065 -3.412065 \n", "35 1.297133 0.742867 \n", "36 4.464578 -4.464578 \n", "37 4.982079 -4.982079 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Tanguieta\n", "ET_Tan = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Tanguieta.csv')\n", "\n", "ET_Tan = ET_Tan[ET_Tan.Month.isin(seas)]\n", "\n", "Rain_Tan = ET_Tan.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Tan = ET_Tan.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Tan['P - E'] = ET_Tan['Precipitation'] - ET_Tan['Eref']\n", "# Def_Tan = ET_Tan.groupby('Year').sum()['P - E'].mean()\n", "Def_Tan = ET_Tan.groupby('Year').sum()['P - E']\n", "\n", "ET_Tan.head()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Tanguieta103.727333
16Tanguieta163.666000
27Tanguieta239.011000
38Tanguieta159.212833
49Tanguieta164.595333
510Tanguieta77.485167
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" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Tanguieta 103.727333\n", "1 6 Tanguieta 163.666000\n", "2 7 Tanguieta 239.011000\n", "3 8 Tanguieta 159.212833\n", "4 9 Tanguieta 164.595333\n", "5 10 Tanguieta 77.485167" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Tan\n", "\n", "ET_Tan_mon = ET_Tan.groupby('Month').sum()/len(ET_Tan.groupby('Year').sum())\n", "ET_Tan_mon = ET_Tan_mon.reset_index() \n", "ET_Tan_mon['Name'] = 'Tanguieta'\n", "ET_Tan_mon = ET_Tan_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Tan_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDayPrecipitationRsiTemp_maxTemp_minAvgErefP - E
302016510.00000263.38888942.57527.30034.93754.984491-4.984491
312016520.04500248.06510442.00029.10035.55004.718084-4.673084
322016530.31025233.51822941.35026.22533.78754.376049-4.065799
332016540.00000221.86979239.67526.62533.15004.134229-4.134229
342016550.00000240.93316041.50027.70034.60004.546668-4.546668
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" ], "text/plain": [ " Year Month Day Precipitation Rsi Temp_max Temp_min Avg \\\n", "30 2016 5 1 0.00000 263.388889 42.575 27.300 34.9375 \n", "31 2016 5 2 0.04500 248.065104 42.000 29.100 35.5500 \n", "32 2016 5 3 0.31025 233.518229 41.350 26.225 33.7875 \n", "33 2016 5 4 0.00000 221.869792 39.675 26.625 33.1500 \n", "34 2016 5 5 0.00000 240.933160 41.500 27.700 34.6000 \n", "\n", " Eref P - E \n", "30 4.984491 -4.984491 \n", "31 4.718084 -4.673084 \n", "32 4.376049 -4.065799 \n", "33 4.134229 -4.134229 \n", "34 4.546668 -4.546668 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unimaid\n", "ET_Uni = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Files\\Weather_Unimaid.csv')\n", "\n", "ET_Uni = ET_Uni[ET_Uni.Month.isin(seas)]\n", "\n", "Rain_Uni = ET_Uni.groupby('Year').sum()['Precipitation'].mean()\n", "Eref_Uni = ET_Uni.groupby('Year').sum()['Eref'].mean()\n", "\n", "ET_Uni['P - E'] = ET_Uni['Precipitation'] - ET_Uni['Eref']\n", "# Def_Uni = ET_Uni.groupby('Year').sum()['P - E'].mean()\n", "Def_Uni = ET_Uni.groupby('Year').sum()['P - E']\n", "\n", "ET_Uni.head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", "2016 -235.872862\n", "2017 -379.160140\n", "2020 -62.147910\n", "Name: P - E, dtype: float64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Def_Uni" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Avg 29.556024\n", "dtype: float64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Uni[['Avg']].mean()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "483.80333333366667" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(ET_Bog.groupby('Year').sum()['Precipitation']).mean()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "502.5326666666668" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(ET_Uni.groupby('Year').sum()['Precipitation']).mean()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Year\n", "2016 -235.872862\n", "2017 -379.160140\n", "2020 -62.147910\n", "Name: P - E, dtype: float64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Uni.groupby('Year').sum()['P - E']" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# c = boo_con.groupby('Year').sum()\n", "# c = c.reset_index() \n", "\n", "# Rain_Uni = ET_Uni.groupby('Year').sum()['Precipitation'].mean()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthNamePrecipitation
05Unimaid30.956250
16Unimaid63.701750
27Unimaid170.282500
38Unimaid167.886333
49Unimaid65.307667
510Unimaid4.398167
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" ], "text/plain": [ " Month Name Precipitation\n", "0 5 Unimaid 30.956250\n", "1 6 Unimaid 63.701750\n", "2 7 Unimaid 170.282500\n", "3 8 Unimaid 167.886333\n", "4 9 Unimaid 65.307667\n", "5 10 Unimaid 4.398167" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Uni\n", "\n", "ET_Uni_mon = ET_Uni.groupby('Month').sum()/len(ET_Uni.groupby('Year').sum())\n", "ET_Uni_mon = ET_Uni_mon.reset_index() \n", "ET_Uni_mon['Name'] = 'Unimaid'\n", "ET_Uni_mon = ET_Uni_mon[['Month','Name','Precipitation']]\n", "\n", "ET_Uni_mon" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30 5.418255\n", "31 5.603469\n", "32 4.248794\n", "33 2.522435\n", "34 5.593954\n", "35 5.686051\n", "36 4.499168\n", "37 4.713312\n", "38 5.293129\n", "39 4.478217\n", "40 3.025061\n", "41 5.470888\n", "42 4.077291\n", "43 4.621554\n", "44 2.772514\n", "45 3.691495\n", "46 4.885390\n", "47 4.497516\n", "48 4.404125\n", "49 4.757883\n", "50 4.843815\n", "51 4.470932\n", "52 5.156764\n", "53 4.936423\n", "54 3.367381\n", "55 4.472966\n", "56 4.472817\n", "57 4.898060\n", "58 3.314057\n", "59 4.036664\n", " ... \n", "550 2.905933\n", "551 3.655052\n", "552 1.886238\n", "553 3.410145\n", "554 3.799538\n", "555 3.611072\n", "556 4.238749\n", "557 2.515986\n", "558 2.761356\n", "559 3.561697\n", "560 4.520344\n", "561 3.875080\n", "562 3.850556\n", "563 4.059134\n", "564 3.832619\n", "565 4.169186\n", "566 3.608853\n", "567 4.449016\n", "568 3.256575\n", "569 4.703652\n", "570 4.735144\n", "571 4.646500\n", "572 4.629104\n", "573 4.175359\n", "574 4.252889\n", "575 4.296273\n", "576 4.453928\n", "577 4.484481\n", "578 4.505016\n", "579 4.557260\n", "Name: Eref, Length: 368, dtype: float64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ET_Tam[\"Eref\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Year\n", "2019 154.950565\n", "2020 -350.545101\n", "Name: P - E, dtype: float64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Def_Tam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compilation of datasets in a sigle block " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "ET_WA = [ET_Tam[\"Eref\"], ET_Gao[\"Eref\"], ET_Daf[\"Eref\"], ET_Tan[\"Eref\"], ET_Man[\"Eref\"], ET_Pus[\"Eref\"], ET_Po[\"Eref\"], ET_Sel[\"Eref\"], ET_Bor[\"Eref\"], ET_Uni[\"Eref\"], ET_Far[\"Eref\"], ET_Fin[\"Eref\"], ET_Ded[\"Eref\"], ET_Sir[\"Eref\"], ET_Bog[\"Eref\"], ET_Som[\"Eref\"], ET_Oua[\"Eref\"], ET_Kou[\"Eref\"], ET_Dor[\"Eref\"], ET_Ous[\"Eref\"],]" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "Rain_WA = [Rain_Tam, Rain_Gao, Rain_Daf, Rain_Tan, Rain_Man, Rain_Pus, Rain_Po, Rain_Sel, Rain_Bor, Rain_Uni, Rain_Far, Rain_Fin, Rain_Ded, Rain_Sir, Rain_Bog, Rain_Som, Rain_Oua, Rain_Kou, Rain_Dor, Rain_Ous]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "Eref_WA = [Eref_Tam, Eref_Gao, Eref_Daf, Eref_Tan, Eref_Man, Eref_Pus, Eref_Po, Eref_Sel, Eref_Bor, Eref_Uni, Eref_Far, Eref_Fin, Eref_Ded, Eref_Sir, Eref_Bog, Eref_Som, Eref_Oua, Eref_Kou, Eref_Dor, Eref_Ous]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "Def_WA = [ET_Tam[\"P - E\"], ET_Gao[\"P - E\"], ET_Daf[\"P - E\"], ET_Tan[\"P - E\"], ET_Man[\"P - E\"], ET_Pus[\"P - E\"], ET_Po[\"P - E\"], ET_Sel[\"P - E\"], ET_Bor[\"P - E\"], ET_Uni[\"P - E\"], ET_Far[\"P - E\"], ET_Fin[\"P - E\"], ET_Ded[\"P - E\"], ET_Sir[\"P - E\"], ET_Bog[\"P - E\"], ET_Som[\"P - E\"], ET_Oua[\"P - E\"], ET_Kou[\"P - E\"], ET_Dor[\"P - E\"], ET_Ous[\"P - E\"],]\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 20, 20, 20)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ET_WA), len(Rain_WA), len(Eref_WA), len(Def_WA)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [0,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", "len (a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax) = plt.subplots(2, 1, sharex = True, figsize=(20, 15),)\n", "\n", "# ax = subplot(figsize=(20, 10),)\n", "ax.boxplot(ET_WA,)\n", "\n", "# x = [1, 2, 3]\n", "# y = [0, 0, 0 ]\n", "# my_xticks = ['Q_0.75','Q_0.95', 'Q_0.9']\n", "# plt.xticks(x , my_xticks) \n", "\n", "ET_WA_name= ['Tam (9.4,-1)', 'Gao (10.39,-3.2) ' ,'Daf (10.4,-2.5)' , 'Tan (10.6,1.3)', 'Man (10.9,0.8)', 'Pus (11.1,-0.1)', 'Po (11.2,-1.1)', 'Bor (11.7,-2.9)' , 'Uni (11.8,13.2)' , 'Sel (11.6,-8.2)', 'Far (12.2,-10.7)', 'Fin (12.3,-5.5)', 'Ded (12.5,-3.5)', 'Sir (12.7,-9.2)', 'Bog (13,-0.2)', 'Som (13.2,-4.8)', 'Oua (13.6,-2.4)', 'Kou (13.8,-9.6)', 'Dor (14,-0.1)', 'Ous (14.2,-10.5)']\n", "\n", "#ET_WA_name= ['Djo (9.7,1.6); 2002-20016', '\\n\\nBenin', 'Bel (9.8,1.7) / 2002-20016', 'Nal (9.7,1.6) / 2002-20016', 'Nya (13.6,2.6) / 2005-2015', '\\n\\nNiger', 'Ban (13.5,2.6) / 2005-2015', 'Kob (14.7,-1.5) / 2005-2011', '\\n\\nMali','Ago (15.3,-1.5) / 2005-2011', 'Bam (17.1,-1.4) / 2005-2011']\n", "ax.set_xticklabels(ET_WA_name, rotation=75, fontsize=10)\n", "\n", "# ax1=ax.twinx()\n", "# ax42.plot(On_d['Station'], On_d['dif_Onset 2017'], '--r', label='dif_Onset', marker='^' ) \n", "ax1.scatter(a, Rain_WA, marker='o', label='Precipitation' )\n", "ax1.scatter(a, Eref_WA, color = 'red', marker='^', label='Reference evaporation')\n", "ax1.set_title('Seasonal rainfall/Evaporation in the semi-arid zone of WA', fontsize=22)\n", "ax1.set_ylabel('Precipitation/Eref (mm/season)', fontsize=22)\n", "ax1.legend()\n", "\n", "ax.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "\n", "ax.set_title('Daily Reference Evaporation in the semi-arid zone of WA', fontsize=22)\n", "ax.set_ylabel('Eref (mm/day)', fontsize=22)\n", "#ax2.set_xlabel('Sowing date ')\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\ET0_comparison_boxplot3.png')\n", "\n", "plt.subplots_adjust(hspace=0.1)\n", "\n", "\n", "\n", "#plt.xticks([1, 2, 3], ['ET_Benin', 'ET_Niger', 'ET_Mali'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StationYearCDD3CDD5CDD7CDD10
0Bogande2018.037.00000015.6666678.3333335.333333
1Boromo2019.024.0000009.0000005.0000001.000000
2Daffiama2019.031.66666713.0000007.3333332.666667
3Dedougou2018.528.25000011.0000005.5000002.750000
4Dori2018.539.25000017.50000011.5000005.750000
\n", "
" ], "text/plain": [ " Station Year CDD3 CDD5 CDD7 CDD10\n", "0 Bogande 2018.0 37.000000 15.666667 8.333333 5.333333\n", "1 Boromo 2019.0 24.000000 9.000000 5.000000 1.000000\n", "2 Daffiama 2019.0 31.666667 13.000000 7.333333 2.666667\n", "3 Dedougou 2018.5 28.250000 11.000000 5.500000 2.750000\n", "4 Dori 2018.5 39.250000 17.500000 11.500000 5.750000" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Consecutive Dry Spells analysis \n", "\n", "dry_spell = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\CDD-Stats.csv')\n", "\n", "dry_sp = dry_spell.groupby('Station').mean()\n", "\n", "dry_sp = dry_sp.reset_index()\n", "\n", "dry_sp.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "dry_Bog = dry_spell.loc[dry_spell['Station']=='Bogande']\n", "dry_Bor = dry_spell.loc[dry_spell['Station']=='Boromo']\n", "dry_Daf = dry_spell.loc[dry_spell['Station']=='Daffiama']\n", "dry_Ded = dry_spell.loc[dry_spell['Station']=='Dedougou']\n", "dry_Dor = dry_spell.loc[dry_spell['Station']=='Dori']\n", "dry_Far = dry_spell.loc[dry_spell['Station']=='Fari']\n", "dry_Fin = dry_spell.loc[dry_spell['Station']=='Finkoloni']\n", "dry_Gao = dry_spell.loc[dry_spell['Station']=='Gaoua']\n", "dry_Kou = dry_spell.loc[dry_spell['Station']=='Kourounikoto']\n", "dry_Man = dry_spell.loc[dry_spell['Station']=='Mandouri']\n", "dry_Oua = dry_spell.loc[dry_spell['Station']=='Ouahigouya']\n", "dry_Ous = dry_spell.loc[dry_spell['Station']=='Oussoubidiagna']\n", "dry_Po = dry_spell.loc[dry_spell['Station']=='Po']\n", "dry_Pus = dry_spell.loc[dry_spell['Station']=='Pusiga']\n", "dry_Sel = dry_spell.loc[dry_spell['Station']=='Selingue']\n", "dry_Sir = dry_spell.loc[dry_spell['Station']=='Sirakoro']\n", "dry_Som = dry_spell.loc[dry_spell['Station']=='Somo']\n", "dry_Tam = dry_spell.loc[dry_spell['Station']=='Tamale']\n", "dry_Tan = dry_spell.loc[dry_spell['Station']=='Tanguieta']\n", "dry_Uni = dry_spell.loc[dry_spell['Station']=='Unimaid']" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "62 5\n", "63 9\n", "64 8\n", "Name: CDD7, dtype: int64" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dry_Uni['CDD7']" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "Cdd7_WA = [dry_Tam['CDD7'], dry_Gao['CDD7'], dry_Daf['CDD7'], dry_Tan['CDD7'], dry_Man['CDD7'], dry_Pus['CDD7'], dry_Po['CDD7'], dry_Sel['CDD7'], dry_Bor['CDD7'], dry_Uni['CDD7'], dry_Far['CDD7'], dry_Fin['CDD7'], dry_Ded['CDD7'], dry_Sir['CDD7'], dry_Bog['CDD7'], dry_Som['CDD7'], dry_Oua['CDD7'], dry_Kou['CDD7'], dry_Dor['CDD7'], dry_Ous['CDD7'],]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b = [12.98, 11.74, 10.42, 12.46, 14.03, 12.16, 12.26, 10.39, 13.85, 10.86, 13.57, 14.25, 11.18, 11.07, 11.65, 12.68, 13.24, 9.40, 10.63, 11.81]\n", "len (b)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CDD7StationIndLat
08.333333Bogande012.98
15.000000Boromo111.74
27.333333Daffiama210.42
35.500000Dedougou312.46
411.500000Dori414.03
58.333333Fari512.16
64.000000Finkoloni612.26
73.000000Gaoua710.39
87.666667Kourounikoto813.85
910.000000Mandouri910.86
109.500000Ouahigouya1013.57
119.333333Oussoubidiagna1114.25
123.250000Po1211.18
133.333333Pusiga1311.07
1410.000000Selingue1411.65
156.666667Sirakoro1512.68
168.333333Somo1613.24
1710.500000Tamale179.40
183.750000Tanguieta1810.63
197.333333Unimaid1911.81
\n", "
" ], "text/plain": [ " CDD7 Station Ind Lat\n", "0 8.333333 Bogande 0 12.98\n", "1 5.000000 Boromo 1 11.74\n", "2 7.333333 Daffiama 2 10.42\n", "3 5.500000 Dedougou 3 12.46\n", "4 11.500000 Dori 4 14.03\n", "5 8.333333 Fari 5 12.16\n", "6 4.000000 Finkoloni 6 12.26\n", "7 3.000000 Gaoua 7 10.39\n", "8 7.666667 Kourounikoto 8 13.85\n", "9 10.000000 Mandouri 9 10.86\n", "10 9.500000 Ouahigouya 10 13.57\n", "11 9.333333 Oussoubidiagna 11 14.25\n", "12 3.250000 Po 12 11.18\n", "13 3.333333 Pusiga 13 11.07\n", "14 10.000000 Selingue 14 11.65\n", "15 6.666667 Sirakoro 15 12.68\n", "16 8.333333 Somo 16 13.24\n", "17 10.500000 Tamale 17 9.40\n", "18 3.750000 Tanguieta 18 10.63\n", "19 7.333333 Unimaid 19 11.81" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# dry_sp = []\n", "# dry_sp_7\n", "dry_sp.loc[dry_sp['Station']=='Bogande']['CDD7']\n", "dry_sp['Ind'] = a\n", "dry_sp['Lat'] = b\n", "dry_sp[['CDD7','Station', 'Ind', 'Lat']]\n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StationYearCDD3CDD5CDD7CDD10IndLat
17Tamale2019.50000038.00000016.50000010.5000006.500000179.40
7Gaoua2018.50000021.7500006.5000003.0000001.250000710.39
2Daffiama2019.00000031.66666713.0000007.3333332.666667210.42
18Tanguieta2018.50000019.5000008.2500003.7500002.2500001810.63
9Mandouri2019.00000036.33333316.66666710.0000005.333333910.86
13Pusiga2019.00000027.6666679.3333333.3333331.0000001311.07
12Po2018.50000023.7500008.7500003.2500001.5000001211.18
14Selingue2019.00000037.33333317.66666710.0000005.0000001411.65
1Boromo2019.00000024.0000009.0000005.0000001.000000111.74
19Unimaid2017.66666731.66666715.0000007.3333334.6666671911.81
5Fari2019.00000026.66666713.6666678.3333334.666667512.16
6Finkoloni2018.66666721.3333338.3333334.0000002.000000612.26
3Dedougou2018.50000028.25000011.0000005.5000002.750000312.46
15Sirakoro2019.00000023.3333339.3333336.6666673.0000001512.68
0Bogande2018.00000037.00000015.6666678.3333335.333333012.98
16Somo2018.66666732.33333315.6666678.3333335.0000001613.24
10Ouahigouya2018.50000036.00000015.5000009.5000005.2500001013.57
8Kourounikoto2019.00000029.00000011.6666677.6666674.000000813.85
4Dori2018.50000039.25000017.50000011.5000005.750000414.03
11Oussoubidiagna2019.00000035.33333317.3333339.3333335.6666671114.25
\n", "
" ], "text/plain": [ " Station Year CDD3 CDD5 CDD7 CDD10 \\\n", "17 Tamale 2019.500000 38.000000 16.500000 10.500000 6.500000 \n", "7 Gaoua 2018.500000 21.750000 6.500000 3.000000 1.250000 \n", "2 Daffiama 2019.000000 31.666667 13.000000 7.333333 2.666667 \n", "18 Tanguieta 2018.500000 19.500000 8.250000 3.750000 2.250000 \n", "9 Mandouri 2019.000000 36.333333 16.666667 10.000000 5.333333 \n", "13 Pusiga 2019.000000 27.666667 9.333333 3.333333 1.000000 \n", "12 Po 2018.500000 23.750000 8.750000 3.250000 1.500000 \n", "14 Selingue 2019.000000 37.333333 17.666667 10.000000 5.000000 \n", "1 Boromo 2019.000000 24.000000 9.000000 5.000000 1.000000 \n", "19 Unimaid 2017.666667 31.666667 15.000000 7.333333 4.666667 \n", "5 Fari 2019.000000 26.666667 13.666667 8.333333 4.666667 \n", "6 Finkoloni 2018.666667 21.333333 8.333333 4.000000 2.000000 \n", "3 Dedougou 2018.500000 28.250000 11.000000 5.500000 2.750000 \n", "15 Sirakoro 2019.000000 23.333333 9.333333 6.666667 3.000000 \n", "0 Bogande 2018.000000 37.000000 15.666667 8.333333 5.333333 \n", "16 Somo 2018.666667 32.333333 15.666667 8.333333 5.000000 \n", "10 Ouahigouya 2018.500000 36.000000 15.500000 9.500000 5.250000 \n", "8 Kourounikoto 2019.000000 29.000000 11.666667 7.666667 4.000000 \n", "4 Dori 2018.500000 39.250000 17.500000 11.500000 5.750000 \n", "11 Oussoubidiagna 2019.000000 35.333333 17.333333 9.333333 5.666667 \n", "\n", " Ind Lat \n", "17 17 9.40 \n", "7 7 10.39 \n", "2 2 10.42 \n", "18 18 10.63 \n", "9 9 10.86 \n", "13 13 11.07 \n", "12 12 11.18 \n", "14 14 11.65 \n", "1 1 11.74 \n", "19 19 11.81 \n", "5 5 12.16 \n", "6 6 12.26 \n", "3 3 12.46 \n", "15 15 12.68 \n", "0 0 12.98 \n", "16 16 13.24 \n", "10 10 13.57 \n", "8 8 13.85 \n", "4 4 14.03 \n", "11 11 14.25 " ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dry_sp = dry_sp.sort_values(by=['Lat'])\n", "dry_sp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "# Deficit_WA = [Def_Bog, Def_Bor, Def_Daf, Def_Ded, Def_Dor, Def_Far, Def_Fin, Def_Gao, Def_Kou, Def_Man, Def_Oua, Def_Ous, Def_Po, Def_Pus, Def_Sel, Def_Sir, Def_Som, Def_Tam, Def_Tan, Def_Uni]\n", "Deficit_WA = [Def_Tam, Def_Gao, Def_Daf, Def_Tan, Def_Man, Def_Pus, Def_Po, Def_Sel, Def_Bor, Def_Uni, Def_Far, Def_Fin, Def_Ded, Def_Sir, Def_Bog, Def_Som, Def_Oua, Def_Kou, Def_Dor, Def_Ous]\n" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "Deficit_WA = [Def_Tam.values, Def_Gao.values, Def_Daf.values, Def_Tan.values, Def_Man.values, Def_Pus.values, Def_Po.values, Def_Sel.values, Def_Bor.values, Def_Uni.values, Def_Far.values, Def_Fin.values, Def_Ded.values, Def_Sir.values, Def_Bog.values, Def_Som.values, Def_Oua.values, Def_Kou.values, Def_Dor.values, Def_Ous.values]\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.int64" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(dry_sp.at[1,'Ind'])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "import matplotlib.patches as mpatches" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20]" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "import matplotlib.patheffects as path_effects" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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42jTtJEmSJEmS1EMdS6xFxB4RcV5E/Dwiro6IDxZTLfvVnwDPrpRNOVotIj4WEa+LFgskRMRfM3m0GsC1letzq82AAytlb2jSfbWdJEmSJEmSeqjWVNCIeCVwfnH5ELB1Zv4uIp5PY221dZhYdP/5wEuAV3Yo1k47unK9DLhwmjbbAx8AboyILwKXAXfTGF12APDOJm0mbVyQmZdFxDXA7qXif42IR4ClwAhwQqWPqzPz8mlikyRJkiRJUhfVXWPthUzsBHpxZv6uOD8G+AMaO1eWdwh9eUS8LjO/NutIOygingfsUyk+MTNXz7CLHYCPzKDe+Zn51SblbweuZGI30iHgiy36eBA4bIZxSZIkSZIkqUvqTgUdH2WVwHcBImId4FVFWZSOcW+cZYxz4QOV6xXA2TNo90CNZ5wOHNLsRmZeD7wCuHOaPu4EXlnUlyRJkiRJUh+pm1h7Zun8x8XrrkyMYlsGvA64urgO4LltRzcHImJr4PWV4lMy8+Hp2mbmW4A9geOAi4BbgFXA48B9wHXAJ4DnZeY7puozM3/AxNTSy4F7gd8Xr5cX5dsX9SRJkiRJktRn6k4F3ax0fnvxumOp7LTMvCAiHgX+pyh7arvBzYXMvI3GWnDttr+SxjTOTsTyII0NE6bcNEGSJEmSJEn9p25ibXHp/PfF6/alsvEpi78qlfXzzqCSJHVci82jZyQzp68kSZIkqS/UnQpantr49OJ111LZTU36HasblCRJgywzWx4zuS9JkiRpMNRNrN1eOv+XiPgAjY0LAB4Blhfn41NGk8bGAJIkSZIkSdK8Uncq6DXADsX584ojaCTQfpCZq4t7zyq1uW1WEUqSJEmSJEl9qO6ItbOalI3PWzm7VLZP6dxdLSVJkiRJkjTv1EqsZeZ3gBNpjFIrHxdk5ucBImJd4E9Lza7oTKiSJEmSJElS/6g7FZTMPDoizgH2LtpfWyTcxi0C3ly6/t7sQpQkSZIkSZL6T+3EGkBmXgtc2+Le/cD/zCYoSZIkSQtLRLTd1l2VJUm90lZiTZIkSZI6aarkWESYPJMk9aVZJdYiYjNgK2BDGmutNZWZl83mOZIkSZIkSVK/aSuxFhFvAz4A7DCD6tnucyRJkiRJkqR+VTvhFREfB94/ftnZcCRJkiRJkqTBUCuxFhEjwFE0RqFRem3ZpJ2gJEmSJEmSpH5Xd8Ta20rnUyXNcpr7kiRJkiRJ0kBbq2b9FxavAVwPjJTuJbAf8HfA74E7gH2BZ8wyRkmSJEmSJKnv1B2xtk3xmsAJmfnDiEkD036Vmd+JiGHgH4AzgefOOkpJkiRJkiSpz9QdsbZR6fz6JvfXLl6/Vrw+nUaCTZIkSZIkSZpX6ibWHiqdP1C8Plwq27R4fbBU9rq6QUmSJEntiIi2DkmSpHbUTayNls6Hitf7SmWvKV5fWbwG8IdtxCVJkiTVlplNj6nujd+XpIWo3Q8k/FBCaqi7xtoKGtM7ATYrXn8FbEEjifYPEfFnwLOZ2Bn0sQ7EKUmSJEmSOmyqDxciwg8fpGnUHbFWXldt++L18uI1aayx9hxgHRpJtQSum02AkiRJkiRJUj+qm1gbT5IFsH9xfgbw++I8S8e4T7QdnSRJkiRJktSn6k4FvZCJZNyjAJl5U0QcCZzKxK6g407IzP+eXYiSJEmSJElS/6mVWMvMW4GTmpSfFhGXAwcBT6OxFtvXMvPajkQpSZIkSZIk9Zm6I9ZayswbgA93qj9JkvrZ8PAwY2NjbbVtZxetoaEhRkdHp68oSZIkqWs6kliLiLWAdTNzVSf6kySp342NjXV1lyy3tJckSZL6T9uJtYjYB/gbYG9g06LsGcAwsGtRbUVmfnOWMUqSJEmSJEl9p3ZiLSKeBHwKePt4UfE6/rH9IuCs4vqRiHhKZj4wyzglSZIkSZKkvrLW9FXWcDJwGGsm1BoXmVcBPy/uLwL+dDYBSpIkSZIkSf2oVmItInYH3kkjmZZMJNeqLiydv6y90CRJkiRJkqT+VXfE2hHFa9BIrH2R5sm1q0rnu7URlyRJkiRJktTX6ibWXlK8JvAvmXloi3o3F68BbNtOYJIkSZIkSVI/q5tYe2rp/Lwp6j1UOt+45jMkSZIkSZKkvlc3sbZ26fzhKeqVE3CP1nyGJEmSJEmS1PfqJtbuLZ3vOkW9/UvnK2o+Q5IkSZIkSep7T6pZ/zpgKxprp30oIi6r3F8rIg4G3kVjHTaAa2cXoiRpUOWxG8OSxd17liRJkiR1Ud3E2tdpjEZL4FnA8sr9HwKLmdgpNIELZhOgJGlwxXEryczpK3biWRHkkq48SpIkSZKA+lNBvwDcVrr+g9J5AJsUr1kcy4EvzyZASZIkSZIkqR/VSqxl5iPAwcCq8aIWRxR13pSZj3UsWkmSJEnqU8PDw0RE7QNoq93w8HCP37Ekqe6INTLzSmBv4Mc0EmjNjh8DL83MqzsXqiRJkiT1r7GxMTKza8fY2Fiv37IkLXh111gDIDOvA54XES8GXgo8rbh1B3BJZn6vQ/FJkiRJkiRJfamtxNq4IoFmEk2SJEmSJEkLTu2poNOJiM0jYoNO99sJEfHWiMgax59N0dfOEfGJiPh5RNwfEQ9FxC0R8aWI+JMZxrNORBwREf8bEXdExCMRcW9EXBURx0bEpp1795IkSZIkSeqkWiPWImI9YJdS0Y8y89Hi3vuAY4Dh4voS4PDMvKkzofaPiPgw8I/A2pVbTy+OQyLi68Ahmfm7Fn1sD3wd2L5y68nF8QLgfRHxl5l5YSfjlyRJkiRJ0uzVHbH2Z8APiuN/gMcBIuIg4EQaCaHxDQz2BS4qknHzRkQsAT7Imkm1qv2BCyNijT/jiNgCuIw1k2pVmwDnRcR+bYQqSZIkSZKkOVQ3sfYCGkkzgP8/Mx8vzt9XvGbpANgGePtsApxjHwC2neK4uFw5Ip5DY1Re2XeBFwO7Ap+s3HsZcEST534CeErp+hHgXcCOwGuAX5fuPQk4PSLWnembkiRJkiRJ0tyru3nBSOn8coCI2BjYg0YyLZq0+TPg1Laim3v3ZuYtNeq/l8l/ZiuB12Xm/cX1uyJiN+BFpTrvj4hPZ2YCRMQ2wAGVfk/IzPGk3C8i4mHgotL9rYGDgC/UiFUzENHsr+zMFF9SSZIkSZK0QNUdsbZV6fznxetupX4uBoaA/yyuA9ip7ejm3j9GxN0R8VhEjEXEdRFxYrH+2STRyMAcWCn+v1JSbdxXKtfbAc8tXR/Imn/u51auvw2MVsoOavku1LbMbHnM5L4kSZIkSVq46ibWNiud31O87lAq+0qRaPpUqayfd7b8I2BzGqPQNqGRJPxb4GcR8XeVujsAG1fKftakz+ublO3e4hxgNfCLckExuu3nlXrVdpIkSZIkSeqhuom19Uvn41Miy6O7xhNEd8/iGf3gScAJEfE3pbJtm9S7Z4Zl27Y4BxjNzN/PoJ8tXGdNkiRJkiSpf9RdY+1BGiO7AJ5DI5G2R+n+TcXrouI1gXvbjm5urAC+RmPa6i9ojBh7FvD3TF5DDuCfI+KczBwDFjfp66EmZaualG1SOq/206yPqfq5q1oYEYcDhwNsvfXWLbqTJHVSHrsxLGn2rWEOn6eucP1NSZIkzVTdxNpyJtYLOyUiDgX2Kq7HMvOO4vyppTYrZhFfp/0P8KXMfLRSfn1EfI3Ghgx7lsoXA68GvkTzjRlmWpZT3G/10/t0/UwUZp4GnAYwMjLiT/SS1AVx3MquJlEiglzStcctaFN9XSPC5JkkSZKeUHea5mWl8ycDryrOk8YIsHG7ls6XtRHXnMjMFU2SauP3Hgf+vya3dite72tyb70ZlpU3OKj206z+TPqRJEmSJElSD9VNrH0KeKw4r35ce2rpfL/S+ffqBtVDNzcpG5/GubzJvc2blD2lSdnyFucAQxGxzgz6uSszH25ST5IkSZIkST1QK7GWmcuAQ4AxGlMVg8YaYUdl5mUAEbE5E4m1YLASa89oUjZavC4DVlbu7dSk/i5Nyq5pcQ6Nr8GzygXRWNyl2ne1nSRJkiRJknqo9o6dmfnfwJbA82lsXLB5Zv5Hqcq9wBCwEbBRZl7biUBnKyIWR8RXIuKZLe6vDfxDk1vXAmTmauC8yr1XRMQmlbKDKtc3AdeVrs9jzdF+1TavZPKGBwDnNolNkiRJkiRJPTJlYi0iDoqIDavlmflYZl6Xmddk5u8q91Zn5u/Gj04HPAtBI4G1LCIujIi3R8RzI2KniHg98APgRZU2dwPfLF2fBDxeut4I+FpEvCgidomITzJ58wOAE7O0ynFm3sKaCbq/i4h3RcQOEfEa4HOV+7dhYk2SJEmSJKmvxDQ7X60GHgG+A5wPXJCZ93Qpto4qRpaN1WiyGnhDZk5KgkXEEuDYGfbxXWC/YrRbuY8tgB/RfD22qt8Dr87Mb8/kgSMjI7l06dIZhrcwDA8PMzZW50s/O0NDQ4yOjk5fUVoAurmDYrd3a5zvz1Nzfh0Gl1+7wTYoXz+/N2i+8e+YNCEirs3MkWr5TKaCLqKx++engTsi4oqIODoitut0kHPscWDVDOuOAQdXk2oAmbkEOJ7JI9ea+QawfzWpVvRxF7A30++Yej/w+pkm1dTc2NgYmdm1o5tJPEmSJEmS1DvTJdYOBf6bRkIqivp7AicAN0bE9RFxfEQ8f27DnL3MfIDGCLE30kgSXg2soLHL6SPAncBFwFHAdpnZcuplZn4QeC7wSeAXwIPAwzSmbH4Z+LPM3H+qqbDFRhC7AO8E/o/GtNPHaGyWcA1wXBHHBe2/a0mSJEmSJM2VKaeCPlEpYhHwCuC1wP7AZqXb4x3cDlxAY8roJZk53YguzQGngq7JIflS7zgVdHCfp+b8Ogwuv3aDbVC+fn5v0Hzj3zFpwmymgpKZj2TmNzLzHcAWwEuBfwdupjGSLYCtgL+hMerrnoj4fEQcEBHrd+pNSJIkSZIkSf1iRom1smy4PDOPysztgOfQWMz/R0wk2YaAv6Cxk+W9pV04hzsYuyRJkiRJUksR0fYhzUTtxFpVZv40Mz+Smc8DtgHeB1xCY1fNANYF/hT4LHDkbJ8nSZIkSZI0E1NtPDeT+9J0Zp1YK8vM2zLz5Mx8GY2NAt5GY921h2gk2SRJkiRJkqR54Ulz1XFmjgJnA2dHxLrAH9PYXVSSOmI2w7P9BKp7ujWMfmhoqCvPkSRJkqRxc5ZYK8vMh2mMXJOkWoaHhxkbG+t4v62SPUNDQ4yOjnb8eQtVOwlMd5+SJEmSNCjaTqxFxFNpbFwwNF0/mfn5dp8jaWEbGxvr+rb1kiRJkiTNRO3EWkTsBJwC7F2jmYk1SZIkSZJ6YDazQNr54NlZIFpIaiXWImI74HJgMTPfjMD5PJIkSZIk9YizQKS5U3fE2rHAJjSSZTP5V+m/JkmzksduDEsWd/d5kuY9P7mXJElSJ9RNrL2ciYSaSTNJcy6OW9n1T9dySdcepwHXzU9j3fW0s/zkXpIkSZ1QN7FW/qn+28DRwLJi109JkhaMdpMy7noqSZIkzR9r1ax/OxMj1f4uM39iUk2SJEmSJEkLUd3E2n+XztfvZCCSJEmSJEnSIKmbWPsXJkatfSwintz5kCRJkiRJkqT+V2uNtcwcjYhXABcCewK/joj/BZYD903R7sOzilKSJEmSJEnqM3U3LwDYElhEY9TausBrZtDGxJokacGYbgfIqe67sYEkSZI0OGol1iJiF+AbNBJq4z/5T7d/vL8hSJIWFJNjkiRJ0sJQd8TaB4H1mJwsm+q3h+mSbpIkSZIkSdJAqptY25vJI9USGAVWAas7GJckSZKkeWZ4eJixsbG22k43zb6ZoaEhRkdH23qeJEkzUTextlHp/HPA0Zm5soPxSJIkSZqnxsbGujpdvp1knCRJdaxVs/6NTEzvPMmkmiRJkiRJkhaquom1z5fOt+xkIJIkSZIkSdIgqZtY+wRwCY1Ra5+IiJ07HpEkVURE146hoaFev11JkiRJ0oCou8baZ4G7aWxasD3w44j4KXAzcF+LNpmZh7UfoqSFrJvrsEiSJM1GHrsxLFnc3edJknoq6vzSGhGrmbwr6LhWnQSNxNra7YWnukZGRnLp0qW9DqOvRETXF8k1GSS1z1lom34AACAASURBVH9D6ga/Nyw88+FrMJuF+Pvlvc/3f3vz/XkaXP7dbG5Q4lR/iIhrM3OkWl53xFpZ+W9fs+/y/u2UJEmSOmSqX/785VCSpN5oN7E2k4/L3NtakiRJkiRJ81bdxNplOBJNkiRJkiRJqpdYy8x95igOSZKkrnGBcUmSJHXCbNZYkyRJGkhx3MruL+K8pGuPkyRpQRkeHmZsbKyttu1sDDM0NMTo6Ghbz9P8Y2JNkiRJkiQNrLGxsa5/YCaNm1ViLSLWA4am6yczb5vNcyRJkiRJkqR+UzuxViTT/g74C+CZM2iS7TxHkiRJkiRJ6me1El4RsTFwKbAr4NhHDQQXqJb601RD6Ke6181h/pIkSfOBvxNJc6fuSLJ/BJ5TnM/kNxuTb+o5F6iW+pMJMkmSpO7wdyJp7tRNrB3A5ISaiTNJkiRJkiQtSHUTa39YOv81cCKwDHioYxFJkiRJkiRJA6BuYm0U2JLGqLW3ZeZ3Ox+SJEmSJEmS1P/Wqln/ktL5vR2MQ5IkSZIkSRoodRNr/ww8XJy/p8OxSJIkSZIkSQOj1lTQzPxZRBwKfB54e0Q8F/gCsBy4b4p2l80qSkmSJEmSJKnP1F1jDeB7wHXAnsBzi2Mq2eZzOioihoCXAHsDLwCeCQzTiO0+4EbgYuCzmfmbKfqps0fxeZn5+in62hA4AngN8CxgCBgDfgFcCHw6M39X43mSJEmSJEnqkloJr4jYFLgc2JZGwizmIqg5ciuwUYt7mxbHi4GjI+LIzDxzLoOJiD2B82hsBlG2eXHsDbw/Ig7MzCvnMhZJkiRJkiTVV3eNtWOAZ5Suc5qjn8z0va4PnB4Rr5qrQCJiZ+Ai1kyqVT0V+L+ivlRbRLR9SJIkSZKkqdWdovlaJhJmg/qb98XAV4FrgEeA5wFLaIzCGxfAccC3punrKuDgKe63msZ5BrBh6XoMOBL4IfB84BRgk+LehsDpwB7TxCKtIbN1fjsipryv3ptNgtOvrSRJkiTNvbqJtS1K59cAH6GxccEqYHWngpoj3wQ+nJk/rZRfHxGXANczearoCyJi/cxcNUWfD2fmLXWCiIi9gd0rxUdl5peK819ExPrAaZVYXpKZl9d5lqTBZmJUmlvdHJ07NDTUtWctBMPDw4yNjdVu187XfGhoiNHR0drtJEnSwlA3sXYHE+urvSszl3Y+pLmRmQdNce+2iLgc+JPKrY1oJA1b2TUilgF/SOPP5G4aCcf/BL6emc2SjdU4ksZaa2XnAp9h8qjAg2isbydJkmap3cS0Se3+MDY21rWvg8sjSJKkqdRdY+38OYmiP1R/aloF3DNNmyHgj4B1gfWAbWgkwM6nsTbapk3aVEer/TozV5YLMvM+4PZp2kmSJEmSJKmH6ibWjmci4fNPEbFOh+PpiYj4Q+BlleIv5+w+Cn0ZcGFErF0p37Zy3Sp5Vy2vtpMkSZIkSVIP1U2s7Q+cVTq/NSL+IyLeExGHtjo6GnGHRcQGwJeBRaXi+2msH9fM4zQ2NXg38CJgB2A/4JPFvbI9ger7X1y5fqjFc6pTUDdpWguIiMMjYmlELF2xYkWratKCcM4557Dzzjuz9tprs/POO3POOef0OiRJkiRJ0jxVd421s5i8K+gWNBJM0/l8zed0RTFV8+vAC0vFDwMHZubyFs2enpnVaZrLgIsj4noaCbayQ4AzpwpjhuUtR89l5mkUmx2MjIy48IsWrHPOOYdjjjmG008/nb322osrrriCww47DIBDDjmkx9FJkjS9djdmADdnkCSpF+qOWBsXNBI9WZxPdfSliNgO+AGTk2oPAvtn5sWt2jVJqpXvfQqo/mSyW+X6/sr1ei26q5ZX20mq+OhHP8rpp5/OvvvuyzrrrMO+++7L6aefzkc/+tFehyZJ0oyMb8zQraPdJJ4kSWpoN7GWlfNWR1+KiD1pJNW2KxXfBeyTmd+eZfc3V66rUzirI+E2b9HPU6ZpJ6nihhtuYK+99ppUttdee3HDDTf0KCJJkiRJ0nxWdyoo9PEotJmIiAOA/6Sxk+e4nwN/kpm3duARz6hcV0ewXQPsUbreKiIWZ+YTI9IiYgh4WpN20hqcMjJhxx135IorrmDfffd9ouyKK65gxx137GFUkiRpXB67MSypLjk8x8+TJGkO1UqsZWa7I9z6QkS8D/g3Jo/U+w6NNdXum0H7j9EY6XZ+sx1DI+KvgeFK8bWV63OBI8vNgAOBM0plb2jy+HOni08L0/iUkW5pJxnXLccccwyHHXbYGmusORVUkqT+EMet7PrPLbmka4+TJC1A7YxYG0gR8R/AeyvFFwN/DWwSEc123bwrMx8uXW8PfAC4MSK+CFwG3E1jdNkBwDub9DFp44LMvCwirgF2LxX/a0Q8AiwFRoATKn1cnZmXT/X+JE1sUPDud7+bG264gR133JGPfvSjblwgSZIkSZoT0c1PjHopItp5o/tm5iWlPs4H/rxG+/Mz83VNYtkZuBLYYAZ9PAjsmZnXz+SBIyMjuXTp0hohzn8R0f1PRn2e5phfB6k3/LfXH7r5dZjv32d9ns/TwjDf/27O9+epP0TEtZk5Ui0f6KmdPfBAjbqnA02HyRRJslcAd07Tx53AK2eaVJMkSZIkSVL3tJwKGhFnAv+cmb/sxIMi4o+Af8zMt3Wiv17IzLdExKnAq4A9aUwN3RxYRCPpthy4AjgzM6+bpq8fRMT2NKaPvgbYkcYOovcBNwAXAp/OzAfn6O1IkiRJkjTw3BhFvdRyKmhErAYeB74KfA74TrMF+6d9QMQ+wBHA64G1MnPttqPVtJwKuqb5Pix4vj9Pzfl1kHrDf3v9wamgPs/n9eZ5Glzz/e9mtzdYGxoaYnR0tKvPVO+1mgo63eYFa9HYofINwF0R8VUaC/ZflZm/afGgpwEvAF4CHAQ8dfwW4P/6kiRJkiSpY0wwq5emSqw9AGxEIxkWwJbAkcVBRNwPrABGizpPBjalMZ1x3HjaePxv+cpOBS5JkiRJkiT10lSJtWcCxwF/VdQbT46NJ8s2KY5q+bhkIin3OPBZ4NjZhyxJkjR3pptOMtV9PzGXJElaWFom1jLzXuBdEXEScDSNHS43GL/drEnpPIpjFXAO8K+ZeWNHIpY0iQt1SlJnmRyTJEnSTE23xhqZuQw4PCKOAt4EvJrG+mlDLZrcT2NnzG8CX8xMp39KcyiOW9n9hUiXdO1xkiRJA6Wbi6gPDbX6lUyS1C3TJtbGZeYDwGeAz0Tju8Uf0Vh3bTMao9PuAe4ClrWze6gkSZIkDbJ2fw1yd09JGlwzTqyVFYmzZcUhSZIkSZIkddRsRgF36wOLthJrkiRJkiRpcDhNWYNoquRYv4z2NbEmSZIkSdI85jRlae6s1esAJEmSJEmSpEFkYk2SJEmSJElqg1NBtSC4noAkSZIkSf1neHiYsbGxttq287v+0NAQo6OjbT2vGRNrmla7Sal+mYvvegKSJEmSJPWnsbGxrv7u3emBNybWNK1Wf8FNPEnSwjQI255LkiRJ3WBiTZJ6aNCHPWthGoRtzyVJkqRuMLEmST006MOeJUmSJGkhq5VYi4jtMvNXcxWMJEmStJDlsRvDksXdfZ4kSWpb3RFrN0bE5cAZwLmZ+dAcxCRJkiQtSHHcyq4+b2hoiNElXX2kJEmTDPqHSnUTawG8pDhOjoj/As7MzKs6GpUkSZK0AE23huFc9CtJUi/FcSu7vjxOLulcf2u1GwewMfBXwPcj4vqIeF9EbNq50CRJkiSNy8y2D0mSNDfaSawFkMUxfv1s4N+A30TEuRHx6nCFbEmSJEmSJM1jdRNruwMfB26lkVArJ9kC+APgAOAbwG0R8ZGIeGbnwpUkSZI0yCKia8fQ0FCv364kaZ6rlVjLzGsz8+8z8xnAC4ETgd/QSKrBRIItgKcB/wgsi4jvRsRBjmKTJEmSFq7ZTGVtp93o6GiP37Ekab5rd401MvPqzDw6M58O7AV8Arhz/DaTk2x7A/8F/DgidptdyJIkSZIkSVLv1d0VtKnM/H5E3AM8Bry71G95pdQAdgYujYjnZeZNnXi2JEmSJEmS1Attj1gDiIh1IuLgiPgOcCPwt0xO1kXpGLchcMxsnitJkiRJkiT1Wlsj1iJiB+Bw4FBgeLy4eM3S+Y9pTBFdBhxPY0poAPu0F67UWdMt+zfVfbeulyRJkiRpYauVWIuIN9NIqL14vKh4La+pthq4EDg5My8ttf1z4G4aO4duNbuwpc4wOSZJkiRJktpVd8Ta55k8Im08KxHAGHA6cEpm3lZtmJn3R8SdwNOBtdsLV5IkSZIkSeoP7W5eUN7x82c0pnt+ITMfmqbd420+T5IkSZIkSeor7STWxqd7foPGdM+LZ9owM7dr43mSJEmSJElS36mbWLsfOBP4RGYun4N4JEmSJEmSpIFQN7G2VWb+bk4ikSRJkiRJkgbIWnUqm1STJEmSJEmSGmol1iLi1RHxw+L4XkRs3KTO4oi4olTvVZ0LV5IkSZIkSeoPtRJrwGuB3YDnAD/JzJXVCpl5P3Btqd5rZxukJEmSJEmS1G/qJtb2LJ2fP0W9r7doI0mSJEmSJM0LdRNrW5TOb5qi3q2l8y1rPkOSJEmSJEnqe3UTa4tL5+tNUW/d4jUqbSRJkiRJkqR5oW5irbym2kumqFe+90DNZ0iSJEmSJEl9r25ibXnxGsAxEfGH1QoRsRVwDJCVNpIkSZIkSdK88aSa9b8HjNBImm0JXBcRp9HYBRTg+cBfAcM0km8JXNGZUOefiAjgYOANNP5cNwNWAbcBFwGnZuatrXuQJEmSJElSr9RNrJ0BvLd0PQz8faXOeEJt3JltxDXvRcTmwIXAHpVbi4Ah4DnAuyPivZl5WrfjkyRJkiRJ0tRqJdYy86cRcSbwNiaSZ1GtVnr9fGb+ZHYhzj8RsT5wCbDjNFXXBT4TEY9m5llzGdPw8DBjY2O12zUG3dUzNDTE6Oho7XbSfJTHbgxLurfHSx67cdeeJUmSJEnzXd0RawDvBrYCXkEjeZaV++OZlouBd7Uf2rx2HJOTagksAc4Fngr8O7BL6f5JEfGtzLxrrgIaGxsjs/qlnBvtJOOk+SqOW9m1f3vQ+PeXS7r2OEmSJEma1+puXkBmrgL+BHgP8CsaibTy8cvi3quLuiqJiA2AIyrFX8jMD2fmDZl5MfB6JicsN6axdp0kSZKkARQRLY+Z3Jck9ad2RqyRmY8DpwCnRMRTaYyyCuD2zLyjg/HNR68CNqqUnVu+yMxlEfFjYLdS8UHAR+Y4NkmSJElzoJsj1CVJ3dNWYq2sSKSZTJu53ZuU/axJ2fVMTqw9OyLWdxSgJEmSJElSf6g9FVSztm2TsntmULY2sHXnw5EkSZIkSVI72hqxFhEbAa8BdgWGpuknM/Owdp4zTzXb/u+hJmXNRqZt0uFYJEmSJEmS1KbaibWIeCtwErDhTKrTWITfxNqEZquPzrSs6cIMEXE4cDjA1ls7qE2SJEmSJKkbak0FjYg/Bk6nsfh+dTfQ6qHm7mtStt4My+5v1mFmnpaZI5k5stlmm80qOEmSJEmSJM1M3TXWPsDEKLTxoxWTa80tb1K2eZOyp1SuHwd+3flwJEmSJEmS1I66U0Gfz0QyLWiMvrqJxhphj3cwrvnsmiZlOwE3V8p2qVz/PDN/NzchSZIkSZIkqa66ibVFpfOvAG/OzN93MJ6F4FvAAzSm0447CPj6+EVEPIvGxhBl5859aJIkSZIkSZqpulNBlzMxxfNjJtXqK0adfaZS/OaI+FBE7BgRL2fNJNpK4LNdCVCSJEmSJEkzUjex9rXS+bqdDGSBORa4oXQdwHHAz4FvAztX6r83M+/qUmySJEmSJEmagbqJtY8Bdxbn/xQRdaeSCsjMVcC+wNXTVH0YeGdmnjXnQUmSJEmSJKmWuomx3YATgH8H/hhYFhGfBX4B/LZVo8y8rO0I56nMvDsiXggcXBzPBzajsRHErcD/Aqdm5q29i1KSJEmSJEmt1E2sXcLkXUG3AY6fpk228ZwFITMTOKc4JEmSJEmSNEDaTXgFkxNskiRJkiRJ0oLSbmItW5xXmXSTJEmSJEnSvNROYs1kmSRJkiRJkha8uom1beckCkmSJEmSJGnA1EqsuUOl1J8iujeQdGhoqGvPkiRJkiSpn7lbpzTgGpvL1hcRbbeVJA2u2XwY4/cNSZKkyWaVWIuIZwN7A1sC6wEfzcz7OxGYJEmSOm+q5JgfukiSJNXTVmItIp4FfBp4SeXWKRHxp8DxxfVtmblP++FJkiRJkiRJ/Wmtug0i4gXAlTSSalE6xn0d2BzYBnhJRDxv9mFKkiRJkiRJ/aVWYi0i1gfOAzYuirI4npCZDwDfLBW9ajYBSpIkSZIkSf2o7oi1w4Gn0UimVUeqlX27dP6iNuKSJEmSJEmS+lrdxNprSuc/AbajeXLtp6XzHesGJUmSJEmSJPW7uom1nUrnH8rMm1vUW1G8Bo311iRJkiRJkqR5pe6uoJuUzm+Yot76pfM/qPkM9UAeuzEsWdy9Z0mS+trw8DBjY2NttY1otVJEa0NDQ4yOjrb1PEmSJKlX6ibWHgSGivPhKeqVR7atrPkM9UAct5LMnL5iJ54VQS7pyqMkSW0aGxvr2vcFaC8Zp4XLDwQlSVK/qJtYu5WJxNrBwFXVChGxLvC3xWUCv2o7OkmSJKnCDwTnp+kS7FPd7+YHAZIkldVNrF0G7EZj7bT3RMTalftvB/YHnlsqu7z98CRJkiQtBCbHpN4wqS3NTt3NCz5bvCaN5Nq7SvcC+CCNpFqW6p0+mwAlSZIkSdLcyMy2D0k1E2uZ+TPgFBpJtCy9jitfJ3BKZt7YgTglSZIkSZKkvlJ3xBrAUcA5NJJo47J0RHF8GTh6tgFKkiRJkiRJ/ah2Yi0zH8vMvwAOAC4GHmUimfZYUXZgZr4pM3/fyWAlSZIkSZKkflF384InZOb5wPkRsRbw5KL4t5m5uiORSZIkSZIkSX2s7cTauCKRtqIDsUiSJEmSJEkDY9aJNUmSJEnSwhYR01dqwd0lJQ2ylom1iDijdHl0Zo5WymYqM/OwNtpJkiRJkgbAVMmxiDB5JmnemmrE2ltp7PIJsAQYrZTNRBT1TaxJkiRJkiR10HwZLTqb91HX0NBQR/ubbiroeGKsWfl0+ucrJEmStIAMDw8zNjbWVtt2frAdGhpidHS0ree1q1s/gHf6h29JkjppPowWbTfGfnl/c7nGWvfSjZIkSXrC2NhYV3/Q7OanzNDeD+D98sO3JEmaX6ZKrF3GxKizh5uUSZIkSZIkSQtWy8RaZu4zkzJJkiRJkiRpIZrLqaCSpBkY5IU6JUmSJGkhM7EmST006At1SpIkSdJCViuxFhHbAvuXis7MzAcqdRYDf1kqujAzb2k7QkmSJEmSJKkP1R2xdijwoeL8isw8uVohM++PiP2BlxVFQ8Bx7YcoSZIkSZIk9Z+1atZ/OTC+GNCZU9Q7u1Rv37pBSZIkSZIkSf2ubmJtm9L50inq/ah0vm3NZ0iSJEmS+szw8DARUfsA2mo3PDzc43csSdOrOxV0s9L5w1PUG78XwOY1nyFJkiRJ6jNjY2Nd3TypmzunS1K76o5Ye7R0vsMU9cr3Hqv5DEmSJEmSJKnv1U2s3QOMf0Tx3inqvafSRpIkSZpz7U5Fk6SptDOV1f9bpIWhbmJtKRObErw8Ir4cEduP34yI7SPiv4BX0EjAJVOvxSZJkiR1TGa2dUjSVKb6v8P/W6SFre4aa18D3licB/B64PURsaooW79UN2gk1r42qwglSZIkSZKkPlR3xNp5wI3FedJIngWwQXGMX4+PVlsGfLUjkUqSJEmSJEl9pFZiLTMfB94ErBwvanEE8ADwpqJNz0XEUES8JiL+NSIui4jbI+KhiHgsIlZExBURcVxEbDVNP1njmDKpGBEbRsRREXFpRNwdEY8Wr5cW5Rt09k9BkiRJkiQNkuHh4bbX+Gun3fDwcI/f8WCpOxWUzLwuIvYGzgCe36LaUuAd/4+9Ow+37KrrxP35kgRCyHSLDIAxAyAYCZNewBDQBEUFGQIRIbRD6ABCC4gydCO/dApF/dE0tgxNYzQMgiQMEQQFUaEYogaoiB0CBGxICB0ISahLQggJGVb/cc6lTp06dzp3n3OHet/n2c/dZ+299lq77q0hn6yhtXbRajrXsa8mOWCBa4f0jxOSvLCqntNae9MkO1NVx6c3AvCuQ5cO6x8/leR3quqU1toFk+wLAAAAsD7Nzc1Ndc0+G2+szIqDtSRprX02yYP64dBJSe6W3ii1K5Jsa639S3dd7MxyR+ftl+TsqvpGa+3vJtGRqjouyd8n2X+JW++W5B+q6vjW2sWT6MtQvybdRJJkZmZmKu0AAAAATNJYwdq8foC2HkO0xXw4vXXfPp3kpiQ/nmRrkmMG7qkkL0uyVLD2ySRPWeT6dxcof2N2DdXmkjwnyb+mNwrwdUkO7l/bP8nZSR6yRF9WZZz0u6rsdAMAAADssVYVrG0wH0zye/3RdoMurqqPJrk4u04VfXBV7ddauyELu7G1dtlKOtGfRvugoeIXtNbe3j+/pKr2S3LWUF8e3lr7xEraAgAAAGByFgzWqurIgY//t7V221DZsrXWLh+nXpdaa09a5NrlVfWJJI8eunRAksWCtftV1ZeS/HB6mzZ8M72RcH+Z5P2ttdtG1BnuR0tvrbVB70ryp+mNnBusJ1gDAJbUzjww2XrQdNsDNj1/tgDsbrERa5dl5y6fd09y+UDZSrQl2lkvhhcYuyHJVUvUmekf847uH09K8pGqenJr7ZqhOsOj1b7WWrtusKC19u2quiLJ4A6lw/UAAEaql1039UWO29apNQeskXrZdUvf1KGZmZns2DrVJgFWbKnAa9Rq9ptue4iq+uEkjxgqfkdb3b9IH5Hkff0pnLcOlB8zdN9C4d1V2TVYG64HAAAwNeP+55G1mYHNbKmdMkf96ddWcKx7VXWnJO9IcoeB4muT/P4CVW5Nb1OD5yZ5aJJ7J/nZJK/vXxt0fJJfGyobHjv9vQXaGZ6CevDIu5JU1TOrantVbb/66qsXug0AAGAiqmrBYznXATaq5YxYGw7IpvInX1WdluRNq3jEo1pri+7qWVWHJHl/kp8cKL4xySmttUsXqHZUa+2KobIvJflwVV2cXsA26NQs/h4L/XoOly8YVLbWzkp/s4PZ2dkNEWgCAACbhxFpwJ5qsWDtpIHzK0eUbWhVdc/0dgq950Dx9Ume0Fr78EL1RoRqg9f+V1W9PMmWgeIHDN12bZLDBj7fcYHHDZdfu1C7AAAAAEzfgsFaa+1jyymboO8mWTDEWoYbF7pQVccneV+SQwaKr0zymNbahatoM0m+kl2DteEpnJdm12DtsIx2+Ih6AAAAAKwTCwZrVfWV/mlL8vDW2ter6r8O3PInw7tZdqm19q4k7+r6uVX1xCR/mWTfgeLPJ3l0a+2rHTRx96HPO4Y+fzrJQwY+H1FVB7XWfjAirapmkvzQiHoAAAAArBOLbV5w9MAxH8BtTXJm/1hwMf31qqqen15YNxiqfSTJCcsJ1arqv1XVE2qBFTar6tnZdbRakgyPgBsOCyvJKUNlvzzi8Z2HjAAAAACMb6nNC+YNBnCjNjRY96rqT5L81lDxh5M8O8nBVTUqKLyytTY4pfReSV6U5ItV9bYkH0/yzfRGlz0xybNGPGOXjQtaax+vqk8nedBA8X+vqpuSbE8ym+QVQ8/4VGvtE4u9HwAAAADTtViwdlt27kx5TJLLJt6byRoO1ZLkZ9Lb0XMhJyX56Ijyeyf5/WW0+d7W2rtHlP/HJBckuVP/80ySty3wjOuTnL6MtgAAABjTli1bMjc3t+J6C0xoWtTMzEx27BheNQjYiBYL1r6T5MD++bur6qKh6+dW1YIbBAxorbWfGat36893VnDv2UmeM+pCa+3iqnpkkvOS3HWRZ3wjySmttYtX0C4AAAArNDc3l9amMzlrnDAOWJ8WC9a+lN50xZbeiKqfGrhW2XUB/oVsyGmjC2mt/WpV/c8kv5Dk+PSmhh6W5A7phW6XJjk/yZtaa59Z4ln/UlX3Sm/66OOSHJveunXfTvKF9HYtfUNr7foJvQ4AAAAAq7BYsPae7FwHbMOHY621Tv6XQGvtgvSmcXbxrOuT/Pf+AQAAALCLduaBydaDptsey7ZYsPYnSU5O8uAFrhu7CgAAADBB9bLrpjZNOelNVW5bp9bchrdgsNZau7GqHpbkyelN+9w/yWnZOXrtr9JbWB8AAAAA9jiLjVhLa+2WJH/ZP1JVp2XnumkvaK1dPukOAgAAAMB6tGiwNsLH+l9bkuXsCAqsoaV2G1rs+jSHGgPQvWnuODczMzO1tgAA1pMFg7WqurV/2pLcvT867bLsnAp6y2S7BqyWcAxgzzTun/9V5e8OAIAVWGzE2qj/zfnr2RmsbU2yo+sOAQAAAMBGcLslrs+HaHsNlNkNFAAAAIA93mLB2m0D5/cbcd08AQAAAAD2WItNBf12kpn0Rqi9s6q+PnT9n6pqOeustdbaPcbtIAAAAACsR4sFa59L8vD0Rqbtk+SogWuV5IhltmFkGwAAAACbzmJTQc8dOG/ZPSBryzgAAAAAYFNaLFj70yTvSW902vwxqJZxAAAAAMCmtOBU0NbabUlOqaoTkjwkyf5JtmbnSLTXpLcOGwCwybQzD0y2HjTd9gAAYIOp1pY/Y7Oq5ncKbUmOaa1dPpFeMbbZ2dm2ffv2qbRVVVnJzw/QHb//mLRp/4z5mV4ffB+APdk0/wz05y0r4d9lo63Br8uFrbXZ4fLFNi8Y5S0D59evrktsFFULz+pd7NpG+I0IAACQTHe0tpHasHmsKFhrrT1tUh1h/RKQAQAAm1297LrpjljbOpWmgAlbbPMCAAAAAGABKxqxVlUfGaON1lr7mTHqAezRZ4bqRQAAIABJREFUFptqvdR1I03pwlI/g12amZmZWlsAANCVla6xdmJ27gq6HLXC+wHoE46xlsb9+dsoi90CAEAXVhqsLWXwf237VzUAAAAbxrRGaxupDZvHOMHaYn/SCNMAAADYcMYZcW2kNrDSYO0ti1w7JMm9kvxI//NtSd6Z5MYx+gUAwARYvxEAoDsrCtZaa09b6p6qemSSv0hyWJI7tNZOHbNvAMA6JJjZ2HwPAAC6c7uuH9ha+4ckv5PelNHHV9VTum4DAFg7rbWxDwDYiKpq5LHYtWnurs3mt9jPWdeHNQBXpuvNC+Z9ZuD8mUnOnVA7AAAAMFH+5xBryW7t61vnI9b6HtH/WkkeMKE2AAAAAGDNrGjEWlX92iKX90qyX5Jjkzw9vR1CK8m+Y/cOAAAAANaplU4FfXN6gdlSBieTf2WFbQAAAADAujfuGmtLrcI4GL6dM2YbAAAAALBujRusLTVqbT5425bklWO2AQAAAADr1jjB2mKj1W5N8u0kFyV5e5I3tdZuG6djAAAAALCerShYa61NahdRAAAAAPiBqsVXIlvsemvL2SJg9cadCgoAAAAAEzOtcGw1jEADAAAAgDF0FqxV1f5VdXhVGQUHAAAAwKa36mCtqp5QVRcluTbJ15PcUFUfqaqfWXXvAAAAAGCdWjRYq6oTquqq/vHNqjph6Pp/SPLuJPdJb7fQSm/dthOTfKiqTp9MtwEAAABgbS01Yu0nkhzSP25srf3T/IWq2i/J/0gvTEuSNnTcLslrquqIrjsNAAAAAGttqWDt/v2vLckHh649Mb3AbXCLhvlRa/P2TfK01XQQAAAAANajpYK1Hxs4P3/o2mMGzivJjUlekeR12TlqLUkesZoOAgAAAMB6tNQOnocMnF8ydO2h6YVn1f/6ktbaa5Kkqr6X5EX9++7dQT8BAAAAYF1ZasTaloHzufmTqjo0yfDaaecOnL934Pzg8boGAAAAAOvXUsHaAQPndxo4nx267/+01q4a+Hz1wPlSo+KmoqpOq6q2guMxSzzvuKp6bVV9vqqurarvVdVlVfX2qnr0Mvu0T1X9RlV9qKq+XlU3VdU1VfXJqjqzqg5Z+ikAAAAArIWlQq/rs3PE2bFJLuqfnzhwT0vyyaF6gyHcd8ft3HpVVb+X5HeT7DV06aj+cWpVvT/Jqa21ke9fVfdK8v4k9xq6dOf+8eAkz6+qX2+tva/L/gMAAACwekuNWLts4Pw/V9URVfVj6e30Ob++WrL7xgbH9L+2JN9YbSfXk6ramuSM7B6qDXtskvdV1W6/xlV1lyQfz+6h2rCDk5xXVT87RlcBAAAAmKClgrV/Gji/f5KvJvlsdt3UoCX5u6F6Pzlw/uWxezdZL0ovAFzo+PBwhaq6f5KXDhVvS3JCkvslef3QtUck+Y0Rbb82yeEDn29K8pvpjQp8XJKvDVzbO8nZVbXvcl4KAAAAgOlYairo2Un+U/+8BsoHdwP9+9ba5UP1HjdwfuGqejg517TWLlthnd/Krr9m1yV5Qmvt2v7n36yqB6S3Y+q836mqN7TWWpJU1dFJnjj03Fe01uZDuUuq6sYkfz9w/cgkT0ry1hX2FwAAAIAJWXTEWmvt35L8cXaGaPPHvO8mecFgnaqaTfKjA/d9oqvOdux3q+qbVXVzVc1V1Weq6o/7a5/tpqoqySlDxf8wEKrNe+fQ53smeeDA51Oy+6/7u4Y+/2OSHUNlTxr5FgAAAACsiaWmgqa19qL0wrNr0gvY5o/PJnlka+0LQ1Ve2P9a6W1+8LHOetutH0lyWHoj0A5O8oAkv53kc1X14hH33zvJgUNlnxtx38Ujyh60wHmS3JbkksGC/ui2zy/yDAAAAADW2JLBWpK01v5HkrskuU9664ndo7V2/9ba8G6gSfKcJHftH8e01m7pqrNTsneSV1TVfxoqP2bEvVcts+yYBc6TZMcCv0bDz7mLddYAAAAA1o+l1lj7gf4oquHRaaPuu2ZVPZqsq5O8J72NCS5Jb7TYjyb5z0lmh+79w6o6p7U21/980IjnfW9E2Q0jyg4eOB9+zqhnLPacK4cLq+qZSZ6ZJEceeeQCjwMAAACgS8sasbYWquq0qmqrOH5h6JF/m+SI1tpvtNbe2Vq7qLV2cWvt3entYvovQ/cflORRg10a1c1llrVFro+6fznP2VnY2lmttdnW2uyhhx66wOMAAAAA6NK6Dda61lq7urX2/QWu3Zrkj0ZcesDA+bdHXL/jMssGNzgYfs6o+5fzHAAAAADW0LKngq6B7ya5YhX1b1zh/V8ZUTY4hfPSEdcPG1F2+IiyS4fOHzzweaaq9mmt3bzEc65sra30nQAAAACYkHUbrLXW3pXkXVNs8u4jynYMnH8pyXXZdWfQ+4yoc98RZZ8eOn/ywOfbpbfO22fnC6qqRjz70wEAAABg3dgjpoJW1UFV9c6quscC1/dK8l9GXLpw/qS1dluS84auP7KqDh4qe9LQ5y8n+czA5/Oy+1ppw3V+LruOlkumGzICAAAAsIQ9IlhLbyOAJyX5UlW9r6r+Y1U9sKruU1W/lN7GBQ8dqvPNJB8cKnt1klsHPh+Q5D1V9dCqum9VvT7J8UN1/ri/o2qSpLV2WXYP6F5cVb9ZVfeuqscl+fOh65dHsAYAAAAMqKoFj+VcZ/VqIPPZtPqjyuZWUOW2JL/cWhsOwFJVW5OcucznbEvys/3RboPPuEuSf8vo9diG3ZLkUa21f1xOg7Ozs2379u3L7B4AAAAAS6mqC1trs8Ple8qItVuT3LDMe+eSPGVUqJYkrbWtSV6eXUeujfI3SR47HKr1n3Flkp9Kb922xVyb5JeWG6oBAAAAMD3rdvOCLrXWvlNVhyd5dJKTkvx4kmPSW8fstvQ2Kfhskg8leXNrbcdCz+o/74yqemeSZyV5RJIj0vu1vCq9aaVvba397RLP+FJV3TfJ05KckuR+SbYk+U5667J9IMnrWmvXjPXSAAAAAEzUHjEVdE9iKigAAABAt/b0qaAAAAAA0CnBGgAAAACMQbAGAAAAAGMQrAEAAADAGARrAAAAADAGwRoAAAAAjEGwBgAAAABjEKwBAAAAwBgEawAAAAAwBsEaAAAAAIxBsAYAAAAAYxCsAQAAAMAYBGsAAAAAMIZqra11H+hQVV2d5KtTau6YJJdOqa214P02Nu+3cW3md0u830bn/Tauzfxuiffb6LzfxrWZ3y3xfhud9+vWUa21Q4cLBWuMraq+21q701r3Y1K838bm/Tauzfxuiffb6LzfxrWZ3y3xfhud99u4NvO7Jd5vo/N+02EqKAAAAACMQbAGAAAAAGMQrLEaf7XWHZgw77exeb+NazO/W+L9Njrvt3Ft5ndLvN9G5/02rs38bon32+i83xRYYw0AAAAAxmDEGgAAAACMQbAGAAAAAGMQrAEAAADAGARrAAAAADAGwRoAAAAAjEGwBgAAAABjEKwBAAAAwBgEawAAAAAwBsEaAAAAAIxBsAYAAAAAYxCsAQAAAMAYBGsAAAAAMAbBGgAAAACMQbAGAAAAAGPYe607QLcOOeSQdvTRR691NwAAAAA2jQsvvPCa1tqhw+WCtU3m6KOPzvbt29e6GwAAAACbRlV9dVS5qaAAAAAAMAbBGgAAAACMQbAGAAAAAGMQrAEAAADAGDrbvKCqfrerZw1rrf3hpJ4NAAAAAOPoclfQlydpHT5vkGANAAAAgHWly2BtXnX8vEmFdQAAAAAwtkmssdZVECZQAwAAAGDdmsSItSR5b5LvrqL+nZKc3FFfAAAAAKBzXQdrld5Is+e31i4f+yFVR0WwBgAAAMA61vVU0K6nb5oOCgAAAMC61HWwVul284KuN0IAAAAAgE50ORX00IHzHat81uVDzwMAAACAdaWzYK219q0On9WSdPY8AAAAAOha11NBAQAAAGCP0PWuoGOpqgOTPGf+c2vtD9ewOwAAAACwpHURrCWZSfLy7NwFVLC2jlSNt4dEb0YvAAAAwOa0XoK1eZWd4RrrxEIBWVUJzwAAAIA9ljXWAAAAAGAMnY5Yq6qnjln1kC77AQAAAACT1vVU0Ldl/KmcLb2poAAAAACw7k1qjbVxAjKLdQEAAACwYUwqWBOSAQAAALCpTWrzghrjAAAAAIBlO/roo1NVPzi2bt061fa7HrG2I8mW9Eas/VqSC5ZZ74gk2zruCwAAAABMTNfB2qeT/Hz//PDW2peXU6mqbum4HwAAAAAwUV1PBf30wPmDxqhvbTYAAAAANoRJBWuVZHaM+tZaAwAAAGBD6DpY+3iSZ/SPP1pBvSuTPLJ//FzHfQIAAABgD9Fay5/92Z/l+OOPz0EHHZQDDjggxx9/fN74xjemtW4nS3a6xlpr7dokZ49R76YkH+6yLwAAAADsWb7//e/nMY95TD7wgQ/sUn7BBRfkggsuyAc+8IGcc8452WeffTppr+sRawAAAACwJl796lfvFqoNOu+883LGGWd01p5gDQAAAIBN4YYbbsi9733vvOc978lFF12Us846KwceeOAu97zqVa/KFVdc0Ul7nU4FXY6q2i/JyfOfW2tvn3YfAAAAANh87nCHO2Tbtm25613vmiS5733vmzvf+c455ZRTfnDPLbfcknPPPTcveMELVt3e1IO1JIcmeVuS+dXiBGsAAAAArNqjHvWoH4Rq804++eRs2bIlO3bs+EHZBRdc0El7azkVtDp9WNXeVXViVb2oqt5ZVZdWVRs63rzEMy4bUWehY3tH/T6hqs6uqn+vquv7x79X1Z9X1UO7aAMAAABgT3DMMcfsVna7290uRx111C5lV155ZSftrcWItUk5Ism2te7EclXV3kn+V5Knj7h8z/5xelX9eZJntdZunWb/AAAAADaaqtHjuFpry7pvpTZTsLbR/HmSX1/GfU9P7/v0tMl2BwAAAGBj+8pXvrJb2W233ZbLL798l7LDDz+8k/Y2266gVyf5uyS/n+RxSb6+imddkeSYRY7HjvvgqvrF7B6qvSvJbP9499C106rq0eO2BwAAALAn+OAHP5hvfOMbu5S9973v3WV9tSR5yEMe0kl7azlirS19y4p8tbV22GBBVb12Fc+7pbV22eq6tKDhbSe+nOSprbVbkqSqnppewHb0UJ0PTKg/AAAAABveTTfdlJNOOimveMUrcve73z0XXHBBXvjCF+5yz957752nPOUpnbS3lsFapcNwrQ1Pll29w6rq4vRGp+2V5JokF6Y3muzc1trN4zy0qg5JcuJQ8XvnQ7Ukaa3dXFXvTfL8gXtOqqo7t9a+NU67AAAAAJvd3nvvnS9+8Ys5+eSTF7znt3/7t3PEEUd00t5aTAW9Mskj+8fPrUH7y3XHJPdJsl+SOyT5ofSml/5Fkk9X1d3HfO5sdt8R9XMj7rt46HP16wIAAAAwwvOe97yccMIJC15//OMfn5e//OWdtTfREWtV9dCBj99srX25tXZTkg9Pst0puH+SD1XVbGvt2hXW3X3f1+SqZZaNqgsAAABAkgMOOCAf/ehH8/rXvz5vfetbc8kll6S1lvvc5z55xjOekdNPP72zHUGTyU8FPT87p3v+p/TWElvPbkvyiSTvS/Kp9EbX3SXJzyf5nST7Dtx7z37ZmSts46ARZd8bUXbDiLKDRz2wqp6Z5JlJcuSRR66wO8B6tZo/7LufHQ8AALD+XHbZZSPLn/e85+V5z3vexNufdLB2fZL90wvXPjnhtrrw8NbaFUNlX0ry8ao6P7tvHnBqVh6sjfov5eWWjfwv5dbaWUnOSpLZ2Vn/NQ2bxGLhWFUJzwAAANbYpNdYGxyhdtOE21q1EaHa4LUPJvnXoeIfqar9VtjMt0eU3XGZZSuddgoAAADAhEw6WHvHwPnPTritafjKiLKR0zMXcemIssNGlB2+zLoAAAAArIFJB2t/nOQz6U1rfFlVbfRwbdROoHMrfMb27D6l8z4j7rvv0OfWrwsAAADAOjDpNdZemOQj6YVEB6e3k+an0wvbvp7k1lGVWmt/OOF+7aaqXpzkmiR/0Vq7ZcT1RyX58aHiz7fWvjd035uT/PpA0cdaayfOf2itXVNVH01y0sA9J1fVf2mt3dx/xu2TPH6orW2ttW+t6KUAAAAAmJhJB2svz87RWS29kWsPTvKgJeqNFaxV1dFDRcPvt//QPTe21q7sn98tySvSG1n31vQCwa+lN03z59ILCYe9aZx+JnlVdg3W7p7k7VX1R+n9Gr0kyVEj6gAAAACwTkw6WBs2H7KN2vFy+J5xLLUG2Sn9Y97Hkpw4dM8R6QVbL1niWZ9K8tqVdG5ea+1vq+ot2XVk2y/1j1He0lob3pEUAAAAgDU06TXWFtIWONbSd1Zw7/uTPKq1tpqdTp+e5Oxl3Hd2/14AAAAA1pFJj1j7etY+MFuW1toZVfWuJI9OckKSH01y1yT7JvluetNC/yXJW1trH++gvVuSPL2q3pTk9CQ/neQu/cvfSPLxJGe31v5ptW0BAAAA0L1qbUPkXizT7Oxs2759OpuHVlX8/MDa8PsPAABgeqrqwtba7HD5Wk0FBQAAAIANbaLBWlWdVlX7TbINAAAAAFgLkx6x9sYk36iqs6rqJyfcFgAAAABMzTSmgu6f3uL8/1RVn6uq36mqQ6fQLgAAAABMzLTWWKv+cWySVyb5v1X17qr6xaqyzhsAAAAAG86kQ63r0gvUkqT1j0qyT5InJHlfksur6uVVdY8J9wUAAAAAOjPpYO3QJI9P8pdJrs+uIVv6n++W5CVJvlRVH62qX6mqfSfcLwAAAABYlYkGa621m1tr72+t/WqSw5I8Mck7ktyQnSFbsnOq6MOTvCXJlVX16qo6YpL9AwAAAIBxTW19s9baTa2197bWTk0vZPvlJO9OcnN2nSZaSQ5M8pwkX6yq06bVRwAAAABYrrXaOKAluVOSI9Jbb22wfP5IkjsmObuqHjbd7gEAAADA4qYarFXV/avqdUm+keSNSR6SnSFasnN66Pf65/Oj2H53mv0EAAAAgKVMPFirqv2q6vSq+mSSf03y7CQHZfc11m5I8vokP5beVNE/GbjnAZPuJwAAAACsxN6TfHhVvSHJqUn2z65B2uB6al9J8j+TnN1au26g7ouTPL1f9/BJ9hMAAAAAVmqiwVqSZ2ZniDY/5XM+UPtIktckeX9rrQ1XbK3dUlVXpResAQAAAMC6Mulgbd58uHZDkr9M8prW2ueWUe/T6a3HBgAAAADryjSCtUpyeXrTPf+stfbt5VZsrZ06sV7BHqCqlr5pASMGkgIAAAADJh2sfTy96Z7vba3dNuG2gCGLhWNVJTwDAACAVZhosNZaO3GSzwcAAACAtdJZsFZV+82ft9ZuWG/PAwAAAIAu3a7DZ32nf1xXVUeu5kFVddTg8zroGwAAAAB0qsupoOOvkj6d5wEAAABAZ7ocsZYkVkIHAAAAYI8wqc0L/v+qun4V9ffvrCcAAAAAMAGTCNYqyZM7eE6L6aAAAAAArFNdTwVNTAcFAAAAYA/Q9Yg1I8wAAAAA2CN0Gaz9QYfPAgAAAIB1rbNgrbV2RlfPAgAAAID1bhJrrAEAAADApidYAwAAAIAxCNYAAAAAYAyCNQAAAAAYg2ANAAAAAMYgWAMAAACAMQjWSJJs2bIlVbWiI8mK61RVtmzZssZvCwAAALB6e691B1gf5ubm0lqbSlvzoRwAAADARmbEGgAAAACMQbAGAAAAAGOYSrBWVQdU1UOr6vFV9biqOr6q9p9G2wAAAMDynXPOOTnuuOOy11575bjjjss555yz1l2CdWuia6xV1SOTvCTJCSPauqWqzk/yR621f5xkPwAAAIClnXPOOXnpS1+as88+Ow972MNy/vnn5/TTT0+SnHrqqWvcO1h/ahIL1lfV3kn+PMmvzhctcntL8hdJntFau6XzzuxhZmdn2/bt21dcr6qmunnBtNpiYb4PG5vvHwAAk3Dcccflta99bU466aQflG3bti3Pfe5zc/HFF69hz2BtVdWFrbXZ3conFKz9VZLHZ2egtlAjg9f/qrX2pM47s4cRrLFcvg/rw5YtWzI3Nze19mZmZrJjx46ptQcwbePuPu7vRICevfbaKzfeeGP22WefH5TdfPPN2XfffXPrrbeuYc8YZdy/9xJ/963UQsFa52usVdXzkpzc/9iyM1SroWPweiV5YlU9u+v+AKxnc3Nzaa1N7ZhmiAewFhb682+xa/7DAmCnY489Nueff/4uZeeff36OPfbYNeoRi1nq7zZ/901ep8FaVd0+yUuz6wi1SvL1JOcmeVWSP07yjiRXZteArZKc0Z9GCgAAAEzZS1/60px++unZtm1bbr755mzbti2nn356XvrSl65112Bd6jrEemKSQ7MzKLs+yXOSvK21dtvgjVV1uyS/luQ1Se7ULz48yROSvKvjfgEAAABLmN+g4LnPfW6+8IUv5Nhjj80f/MEf2LgAFtB1sPaI/tdKcmuSx7bWPjbqxn7Q9uaqujzJ3w9c+pkI1gAAAGBNnHrqqYI0WKau11j70f7XluS8hUK1Qa21jyQ5LzunhY41cbuq9q6qE6vqRVX1zqq6tKra0PHmRerfrqruX1XP7de/uKq+XVW3VNX1VfV/qurcqnp8rWZ1wF5bR4/o22LHf19NewAAAAB0r+sRa0cMnP/VCuqdl2R+R9AjFrtxiba3jVk3SZ6f3hpwo9wpyT36x5OTfKKqntBa+9Yq2gMAAABgA+t6xNrBA+dfWEG9S/pfK8lMd91ZkZX8Wjw8yftWO3INAAAAgI2r6xFrdxw4v24F9Qbv3W8V7V+d5MIkn+4fb0hytzGe8dYk/5DksiSHJfmVJE/PzumqSfLQJL+Q5IOr6O+885K8cJHr13bQBgAAAAAd6jpY22fg/LYF79pdGzgft09fba0dNlhQVa9dQf1vpTcd9A2ttZsGyi9J8vGq+l6S5w3VOTHdBGvXt9Yu6+A5AAAAAExJ18Ha7bIzJPvyCmdKtvRGhI01vbK11pa+a9H6b1rilrdl92DtwNW0OeDnq+rS9EbXfT/JN5L8S5I3tdY+2lEbAAAAAHSo62BtXo3x7FUFY1MwKvC7tKNn32Xg/PZJfqR//FpVvSPJ01pr3+uoLQAAAAA6MKlgbb2HZOM4bejzbUneNYV2n5zeSMBfnkJbAAAAACxT17uCzqsxj3Wpqk5J8qyh4te11lYzYu2m9DYteEaSByf50fQ2Qzh3xL1PqqqTFunfM6tqe1Vtv/rqq1fRJQAAAACWq+sRa/+cTTZaraqeluSs7Br8fTDJC1bx2CuT/FBr7VtD5V9M8qGqujzJi4eunZpk26iHtdbO6vcxs7Ozm+rXHwAAAGC96jRYa609rMvnrbWq2prkzKHiv07y5NbaLeM+t7V2Y5IbF7nlD7J7sPaAcdsDAAAAoHuTmgq6oVXVPlX1puweqr0hySmttZsm2X5r7bok1wwVHzzJNgEAAABYmUltXrBhVdWB6a199rMDxS3JS1prr5hiH+48VLxjGm0DAAAAsDwTD9aq6ugkj0rywCSHphdSXZ3kM0k+2Fr76qT7sFxVdUSSv01yv4HiG5P8emvtnct8xpuT/PpA0cdaaycO3fPnSd7WWvvoAo95aXbfzOHC5bQPAAAAwHRMLFirqrsleWWSJ2fhHT9bVZ2b5MWtta930ObRQ0XD77f/0D03ttau7Nc9LsnfJfmhwetJTk/yqRHP3qX+Cs0mOb2qLkxyTnqbPuxIclSS09LbqGBQS/KWMdoBAAAANrAtW7Zkbm5urLpVC8UxC5uZmcmOHSbNLddEgrWqekCSv0ly1ywcqqV/7dQkP11Vv9hau2iVTV+6xPVT+se8jyU5sX/+S9k1VEuSfZP85SLPG6w/jp/oH0t5TWvtU6toBwAAANiA5ubm0lqbWnvjhHF7ss43L6iqQ5K8P8nd0gvO2hJHpRdovb+qZrruzzr1nWXed2uSP0ry2xPsCwAAAABjmMSItdelF5QNxqkLTgXNznDtiCSvSfKrE+jTenNikp9O8nNJHpzkR5Ickt7349ok/57eaLizW2v/vkZ9BAAAAGAR1eVwwv7i/5dm50i4SvKtJH+W5KNJrsjOEWonpbd+2Z2zM1y7OcnRrbVvdNapPczs7Gzbvn37iutV1dSGlk6zLRbm+7A+TPv74PsO7Kn8+QfARuW/GdaHqrqwtTY7XN71iLVTk+yVnaPVPp7kia214VXvLk7yoap6ZZL3JHnYQH+emuRVHfcLAAAAADrV9RprD+5/rSRXJzl5RKj2A621byV5QpJrsjOMe0jHfQIAOlRVYx8AALCZdB2s3aP/tSU5q7X27aUq9MO1s7JzHbZ7LHI7ALDGWmsLHsu5DgAAm0XXwdphA+fbVlDvI/2vleTw7roDAAAAAJPRdbB20MD5FSuoN3jvQQveBQAAAADrRNfB2u0Hzm9cQb3Be+/QUV8AAAAAYGK63hV0n+zchODkqrpmmfUOGTjfq9suAQAAAED3ug7W5lWS/7HCOi07NzAAAAAAgHVtUsFasvKQzFZhAAAAAGwYkwrWhGQAAAAAbGqTCNZM5wQAAABg0+s6WHtGx88DAAAAgHWp02CttXZ2l88DAAAAgPXqdmvdAQAAAADYiCa5KygAsEFt2bIlc3NzY9WtWvlyqzMzM9mxY8dY7QEAbGbtzAOTrQdNtz2WrfNgrarenuSQ/sf3tdZet8T9z0nyuP7Hq1tr/6HrPgEAKzM3N5fWprfJ9zhhHADAnqBedt3U/13Wtk6tuQ2v02Ctqn4qyVOStCS3JDl9GdXel+RV832pqje01j7RZb8AAAAAoGtdr7H2uIHzv26tfW2pCq21y5P8dZL5/1X9+I77BAAAAACd6zpYO37g/G9XUG/w3uMXvAsAAAAA1omug7UfGTi/aAX1Ptv/Wknu2V13AAAAAGAyug7WBrepuG4F9a4dOD+4o74AAABrvytUAAAgAElEQVQAwMR0Hax9f+D8ziuoN3jvzR31BQAAAAAmputgbcfA+cNWUO/hCzwDAAAAANalroO1f+9/rSS/VVV3WqpC/57nJWn940sd9wkAAAAAOtd1sHZ+/2tLckSS91bVgmumVdVBSf4qyQ+nF8YlySc67hMAAAAAdG7vjp93bpIz+ueV5BFJLqmqNyT5SJL/2792RJKfSfLMJIelF8RV/+u5HfcJAAAAADrXabDWWrukqt6f5HHZGZYdll7YdsaIKjVYPcn7W2tf7LJPAAAAADAJXY9YS5JnJ3loejt9tn5ZLXDv4PVr+nUB9hjtzAOTrQdNtz1YBj+brGdbtmzJ3NzciutVLfRP0oXNzMxkxw57a7F64/z8zWutLX0TAGuiJvGHdFU9JMn7khyaneHZgrenF6o9prX2qc47s4eZnZ1t27dvX3G9qpraX9jTbIuF+T6sD9P+Pvi+s1x+NlnP/LuFzcbPGbAY/y5bH6rqwtba7HB515sXJElaa59M8pAkf51ecLbY8d4kDxGqAQAAALCRTGIqaJKktXZZkidU1b2T/GKSB6Y3gq2SXJXk35L8jTXVAAAAANiIJhaszesHZ8IzAAAAADaViUwFBQAAAIDNTrAGAAAAAGOY+FRQAAAAAMZXVVNra2ZmZmptbQaCNQAAAIB1qrU2Vr2qGrsuy2cqKAAAAACMQbAGAAAAAGMQrAEAAADAGARrAAAAADAGmxeQJGlnHphsPWh6bQEAAABscII1kiT1suumtltIVaVtnUpTAAAAABPTabBWVQ/tn17VWvs/XT4bAAAAANaTrtdYOz/JJ5J8sao+WFWP6Pj5AAAAALAuTGrzgkryc0n+oar+tapOraq9JtQWAAAAAEzdpIK1ll64VkkekORtSb5cVc+vqv0n0WBV7V1VJ1bVi6rqnVV1aVW1oePNy3zWUVX136rq36pqR1XdWFVfq6r39EPC6rDfx1XVa6vq81V1bVV9r6ouq6q3V9Wju2oHAAAAgG5NavOCSi9cG/x8ZJJXJfmvVfWnSV7TWvtGh20ekWTbah9SVb+R5E+S7Dvi+UckOTnJ86rq8a21q1bZ1u8l+d0kw6P5juofp1bV+5Oc2lr77mraAgAAAKBbkxyx9q30ArX5z/Oj2A5O8uIkl1bV2VV1nwn1YcWq6rQkb8juodqwn0yyraruuIq2tiY5I7uHasMem+R9VTWp7xUAAAAAY5hEWDMfph2f5NeS/O/snBY6GLDdPslpSS6qqr+pqhM7aPvqJH+X5PeTPC7J15fd6aq7Jnn1UPFFSX42yY8l2ZpdR+HNl61YVd0/yUuHirclOSHJ/ZK8fujaI5L8xjhtAQAAADAZkxwFdXNr7W2ttR9PbyODv8/ogK2SPCrJh6tqe1U9ecz2vtpaO6y19qjW2n9trb0/yc0rqP+MJAcOfG5Jntha+3Br7QuttZclOWeozrOq6k5j9PW3sus03OuSPKG19s+ttc+21n4zyT8P1fmdLtd2AwAAAGB1pjK9sLX2j621X0hyXJK3pBd4jQrYfjzJ28dsoy1916KeNPT5X1trXx4qe+fQ5wOT/PxKGumHY6cMFf9Da+3aJdq6Z5IHrqQtAAAAACZnqut2tdY+31p7WpKjk7wiybeza8C2Jvqjzn5sqPhzI269eETZg1bY3L2z68i4SbYFAAAAwISsyYL4rbUrW2svSfLDSX47yVezc222tXBkdv+1GLXj56iyY1bY1qj7J9UWAAAAABOy99K3TE5r7YYkr66q16Y3FfMFSX5iDbpy0Iiy740ou2FE2cFr3VZVPTPJM5PkyCOPXGF3AAAAVmc1y0GvflWfyRv3/dbLu23ZsiVzc3NTa29mZiY7duyYWnt7sqV+Nhe7vl5+PrN1VEwy6TaHV+Ma3ySCtRV/Z1prtyV5R5J3VNXDu+/Skkb9pC23bKXv23lbrbWzkpyVJLOzs+vkdwYAALCnWOw/0Ktq/fwH/JgW6v9Gebe5ubmp9tO+e9OzEX7+llIvu27qP59ta3fPm8RU0FX9DmqtfaKrjqzAt0eU3XGZZSuNOafZFgAAAAAT0vWItT8YOB8VIK1Xlye5LbsGjYeNuO/wEWWXrrCtUfdPqi0AAAAAJqTTYK21dkaXz5uW1tp3q+rzSY4bKL7PiFvvO6Ls0yts7ktJrsuuO4NOqi0AAAAAJmRNdgVdp9419PmBVXXPobJfHvp8XZIPDRZU1daqagPHZYPX++vJnTf0nEdW1fDGBE8a+vzlJJ9Z7AUAAAAAmJ5NFaxV1dGDR3Yfkbf/0D13Gbj2Z0m+M/i4JOdV1SOq6tiqOjPJk4ee96ette+O0dVXJ7l14PMBSd5TVQ+tqvtW1euTHD9U54/bZliVEAAAAGCTmMSuoGtpqTXITukf8z6W5MQkaa19o6p+K8kbB67fL8mHF3jWF5JsHaeTrbX/XVUvT3LmQPGJSf5pgSrbkrxhnLYAAAAAmIxNNWJttVprb0ry7CQ3LnHrJ5Oc2Fq7YRVtbU3y8uw6cm2Uv0ny2P4UUgAAAADWCcHakNbaG5Icm+SVSS5Kcm2S7ye5Isn7kjw1yfGttas6aOuMJA9M8voklyS5Pr1Q7/Ik70jymNbaY8ecbgoAAADABJVluzaX2dnZtn379hXXq6pM62dhmm2xMN+H9WHa3wffd5bLzybrmX+3sNls9p+zzfx+G+Xd/L3OerZRfj6r6sLW2uxwuRFrAAAAADAGwRoAAAAAjKGzXUGr6qkDH9/TWvteV88GAABgbW3ZsiVzc3Nj1a2qFdeZmZnJjh07xmpvHOO+30Z4N2ByOgvWkrwtyfwk1WOSXF5V3+9/bknu2Vr7WoftAQAAMCVzc3NTXwdpmqb5ftN+N2ByugzWkqSyM1wbfH7rXwMAAACATaHrNdbmQ7V9R5QBAAAAwKbR5Yi1m5Lcvn/+W1X19v75/Ci2n6iqI5bzoNbaP3fYLwAAAADoXJfB2jeT/HD//Fn9Y14lefcyn9M67hcAAAAAdK7LqaAXZOc6apXd11SrFRwAAAAAsK51Gay9Jslt/fOW3ddWa8s4AAAAAGBD6CxY66+L9uQkX834I9YAAAAAYEPodC2z1tp5Sc6rqsOSHJDk37NzJNpJSa7osj0AAAAAWCsT2SSgtXZVkquqKtm5K+hlrbXLJ9EeAAAAAEzbpHfffOTA+ZUTbgsAAAAApmaiwVpr7cPDZVW1T5LD+h+vaq3dPMk+AAAAAMAkVGvT2Yyzqp6a5NlJHpydgd6tST6V5H+21s6ZSkc2udnZ2bZ9+/YV16uqTPFnYWpt7Qm2bNmSubm5qbU3MzOTHTt2TK29zW7avx/8/mO5+ss5TI0/W6ZnNd/bdfPnx9aDptzetdNtjz3Ohvn7edq/95Lp/v7bzH+2bPbv3SI2xd97m9xG+XdnVV3YWpvdrXzSPyhVtV+Sc5I8Zr5oxG0tyd8keUpr7XsT7dAmJ1jb8whmNjbfPzYbP2Mb20b5/vl3C5vNRvk52+z/btnMf7Zs9vbGtVH6yWhr8HM9Mli73RTafkOSx6YXqM1vZDB8VHrB2xum0B8AAAAAWLWJBmtV9eAkv5LdQ7TBIwPlv9KvAwAAAADr2qR3Bf2PA+eVZC69aaFf6ZfdPcmpSWaG6nxqwv0CAAAAgFWZdLB2Qnqj0ZLkX5Oc1Fr7zuANVfW7SbYleWB64dvDJtwnAAAAAFi1Sa+xdkR2Tvc8YzhUS5LW2nVJzhiqAwAAAADr2qSDtf0Gzi9f5L7Ba3ecUF8AAAAAoDOTDtauHThfbFOCwWvXLngXAAAAAKwTkw7WvtT/WkleWVW7rZ9WVT+V5JXZuWvol4bvAQAAAID1ZtKbF3w4yUPTC8y2JPlYVX0pyZf71++R5F7ZuQ5bS/KPE+4TAAAAAKzapIO1P03y4iS3Ty80qyT3Ti9MS3YN1CrJ95OcNeE+AQAAAMCqTXQqaGvt6+kFa4MB2nyIVgOf56+9qF8HAAAAANa1SY9YS2vttVW1V5I/SnKH+eL+1/nA7ftJ/ktr7XWT7g8AwGa3ZcuWzM3NjVW3qpa+acjMzEx27NgxVnsATMc4f76Pa2ZmZmptwVqbeLCWJK21P6mq85KcnuSnk9ytf+nrST6W5OzW2tem0RcAgM1ubm4urbWlb+zINP9jDYCVG/fvhKqa6t8nsBFNJVhLkn5wtnVa7QEAAADAJE10jTUAAAAA2KwEawAAAAAwBsEaAAAAAIxBsAYAAAAAYxCsAQAAAMAYBGsAAAAAMAbBGgAAAACMQbAGAAAAAGMQrAEAAADAGNY8WKuqLVW171r3AwAAAABWYu9JPrwfmP3YQNHFrbXv9689J8n/l+TQJK2qPpzkWa21SyfZJwCAza6deWCy9aDptgdky5YtmZubG6tuVa24zszMTHbs2DFWe7CZ+L23eS31/Vnsemut6+6MNNFgLcljkryjfz6X5PAkqapTkrxm4L5K8sgkf19V922t3TjhfgEAbFr1suum9o/JpPeP2rZ1as3BujU3Nzf133uA33ub2TS/r+Oa9FTQB6cXmiXJB1prt/bPn9//2gaOJLl7kqdNuE8AAAAAsGqTDtZmB84/kSRVdUCS49ML02rgmPfYCfcJAAAAAFZt0sHaDw+cf67/9QED7W5Lb421c/qfK8l9JtwnAAAAAFi1SQdrhwycX9X/+qMDZee21r6V5PUDZYdOuE8jVdVpVdXGOE4bo603r7CN/SfwygAAAACswqQ3L7jTiLbuNVB2Sf/rlQNlkw77WMC0FmCcmZmZSjsAAAAAkzTpYO36JAf3z++bXpD2oIHrX+l/vUP/a0tyzYT71LWrlr5l/Rtnp42q2hA7dAAAAABMwqSDtUuTPLB//rqq+pUkP9X//O3W2hX987sN1Ll6wn1ayLuTfHSJe/46yf9j777j5ajr/Y+/PikkdE7ovUgxGJoEEKUFUKxYEAXFCqJc5YoVMBZQ0asXEb1y9ec1gKJG7IKCoBhKAIGEGgihBgih5xAgkP75/fH5DjvZnLpnZ3Zn834+HvvYPbOzO985szPz/X6+bdfc3/cBf2/CtvcH5vTx/oImbENERERERERERJqo6MDaNdQCaxsCb02vHbgit94uudf3FpymHrn7C0QLux6Z2T6sGFQDOMvdlzdh83PcfXYTvkdEREREREREREpS9HhmPwaWpNf1fQbzExYcmnt9baEpatwX6v5+Gji/Sd/9FzN71swWm9mTZnalmZ1iZmOa9P0iIiIiIiIiItJkhQbW3H0WcAwwH7D0WASc7O5XApjZBtQCawZMLTJNjTCz7YB31C0+x91fatImdgXWBUYSLfsOBL4NzDKzNzRpGyIiIiIiIiIi0kRFdwXF3X9nZhcRwaMRwAx3fz63SjewUW79+UWnqQGfBYbn/n4JOKeE7W5AtGbbz92nl7A9EREREREREREZoMIDawDuvgi4qZf3lhEt2tpS6o754brFP3f3oU6yMAv4IzEO3YPA2sB44CvAprn1RgNnEa3Yekvj8cDxAFtttdUQkyUiZTOz0rbV1dVV2rZERESk83R6vqWs/VOeTKRzmHv90GcFb9BseyJwtDpwVQq6tS0zmwh8M7doOfBKd294kgUz2zw3I2r9e5sBM4D6K+0WvX0mb/z48T5t2rRGkzYoZkbZvx9ZWdnHQce9Peg4SLvSb7M9dPq9oczt6Tctg9HJ58JQVCWdjejkfYPq7J/OPSmDmU139/H1y0tpsWZmo4HPAycAm+Te2tbMxgJHpL/nuPvXy0jTQJjZKOBTdYv/MpSgGkBfATJ3n2tm5xHdT/N2A/oNrImIiIiIiIiISDkKD6yZ2YbApcAexOQEmSy8Owv4aHrPzexcd59TdLoG6BhWDAQCnFnCdh/oYdl6JWxXREREREREREQGqNBZQZPfAa8mBc6oBdQAcPfZwLXUZg19Wwlp6pdF5/r6VmPXuvt1JWx+ux6WzSthuyIiIiIiIiIiMkCFBtbM7F3AAdQCar2NBPm33OsDikzTILwZ2LluWZ+t1cxsGzPzusdBdescZmbfMrN1e/mOzYCP1C124JbBJV9ERERERERERIpUdIu196VnA54FPkHPwbWbc693KThNA/X5ur/vAS5qwveuDpwKzDGz/zOzd5rZq8xsTzP7BDF7av3EBZe4+xNN2LaIiIiIiIiIiDRJ0WOs7Z2eHTjV3X9qZj/pYb256dmAzQtOU7/M7NXAQXWLz3L35U3czFrAcenRl2eAk5q4XRERERERERERaYKiW6xtmHs9tY/18gGrNQtKy2B8oe7vp4CfN+m7F7Di/vblLuBgd7+vSdsWEREREREREZEmKbrF2hJgVHo9so/1tsm9XlBYagbAzLYC3l23+EfuvrAZ3+/u/zCzLYHDifHkxgFbAmsDi4AngOnAH4Hfu/uSZmxXRERERERERESaq+gWa4/nXu/fx3r5wfrn9rpWCdz9YXcf6e6We3x9gJ+dXfc5c/cre1hvrrv/xN3f5+67unuXu49w9zXdfTt3P9LdJyuoJjJ4kydPZty4cQwfPpxx48YxefLkVidJREREREREOlTRLdZuALYnxk77upk9XPf+Tmb2BeBIYhw2gH8XnCYR6VCTJ09m4sSJTJo0if3224+pU6dy7LHHAnD00Ue3OHUiIiIiIiLSaczd+1+r0S83exPwNyJoZrnnTLZxy/39Bne/orBEdbjx48f7tGnTStmWmVHk70cGpuzj0M7Hfdy4cfzP//wPEyZMeHnZlClTOPHEE5kxY0YLU9Z87XwcZNWm32Z76PR7Q5nb029aBqOTz4WhqEo6G9HJ+wbV2T+de1IGM5vu7uNXWl70j8HMriK6gdYH1fKyRFzp7ocUmqAOp8Daqkc3kZrhw4ezcOFCRo6sDem4ZMkSRo8ezbJly1qYsuZr5+Mgnc+st9t5//S7LUen3xsUWJN21cnnwlBUJZ2N6IR964j7+mnrtmCb88vfprRUb4G1oruCAhwFTAF2pBZAq2fAPcAxJaRHRDrU2LFjmTp16got1qZOncrYsWNbmCqRztM2mWgREREZso64ryvIJS1U9OQFuPtjwGuA/yVmvbS6xyLgx8C+aV0RkYZMnDiRY489lilTprBkyRKmTJnCsccey8SJE1udNBEREREREelAZbRYw92fBT5lZp8H9gY2T2/NBW5w94VlpENEOls2QcGJJ57IzJkzGTt2LGeccYYmLhAREREREZFCFD7GmpRLY6ytejSWx6pJx0FE+tLp9waNsSbtqpPPhaGoSjob0cn7JiIr6m2MtcK7goqIiIiIiIiIiHSipnUFNbP35f78k7u/VLdswNz9101KloiIiIiIiIiISCGaOcbaL6nN+rkt8HDdssFQYE1ERERERERERNpasycvMHoOpNkgvkMd1EVEREREREREpO01O7DWW1BsoMGywQTgREREREREREREWqaIFmsDWSYiIiIiIiIiIlJpzQysjcxeuPuy+mUiIiIiIiIiIiKdpGmBtVwwrc9lIiIiIiIiIiIinaDZXUFFREREpA2YlTcaR1dXV2nbEhEREWknhQbWzGx1YNvcovvdfVHdOqOB7XKLZrv7i0WmS0RERKSTuTc2ybqZNfxZERERkVXRsIK//zjgjvS4COipa+gy4E+59T5ScJpERERERERERESGrOjA2uupzQr6/9x9af0K7r4E+HFuvTcUnCYREREREREREZEhKzqwtnPu9VV9rDe1l8+IiIiIiIiIiIi0paIDaxvlXj/Tx3rd6dmAjYtLjoiIiIiIiIiISHMUHVjLT46waR/rbdLLZ0RERERERERERNpS0YG1ebnX7+1jvaN6+YyIiIiIiIiIiEhbKjqwdnt6NuAEM/t4/Qpm9jHgBMDT446C0yQiIiIiIiIiIjJkRXe7vBx4IxEwGwb8r5mdCtyS3t8D2JLajKCePiMiIiIiIiIiItLWig6snQ+cBqxNBM0M2IoIpsGKATUDngfOKzhNIiIiIiIiIiIiQ1ZoV1B3fxb4JCsG0LIgmtX97cCJ6TMiIiIiIiIiIiJtrfAZON39V2Y2CvghsEa2OD1nAbeXgE+7+wVFp0dERESkL2bW/0q9cPf+V5KmGMpxGoyurq5StrOqGDNmDN3d3aVtr6uri3nzNDdaGfo7J/t6vwrXzr7SX/V9E5GhKTywBuDu55rZpcBxwARgMyKo9igwBZjk7nPLSIuIiIhIX/oqBJmZCkltoJFjoGPXHrq7u0s9DmUFYKXzA0idvn8i0rhSAmsA7v4Y8I30EBERERERERERqbRCx1gTERERERERERHpVAqsiYiIiIiIiIiINKCUrqBm9gHgfcCuQFc/23V3H1VGukRERERERERERBpVaGDNzFYDLgJeny0awMc0KqSIiIiIiIiIiLS9olusfQF4Q+7v/oJmmrZHREREREREREQqoejA2tHpOQuoKXAmIiIiIiIiIiIdoejA2nZEUM2ApcAk4G7gJWBZwdsWEREREREREREpTNGBtZeA0URw7TPufk7B2xMRERERERERESnFsIK//9bc6ysK3paIiIiIiIiIiEhpig6s/Sz3eteCtyUiIiIiIiIiIlKaQruCuvtkM3sP8HbgbDN70t2vLHKbIiIiIv0ZM2YM3d3dDX3WbPBzMXV1dTFv3ryGtifSSfxr68Bp65a7PRERkQIVGlgzs58CzxNjrG0CXGFmdxMTGDzTy8fc3T9eZLpERERk1dbd3Y27979ikzQSjBPpRHb6c6Wfe35aaZsTEZFVUNGTFxxHBNWgNjvoWOCVvaxvaT0F1kREREREREREpK0VHVirlwXZeqq2La/qSkREREREREREZIjKCqwNpP+D+kiIiIiIiIiIiEhlFB1Yu44KtUQzsw8D5w3iI29z978OYXtrEd1eDye6x3YB3cQYdBcBP3H3BY1+v4iIiIiIiIiIFKfoWUH3K/L7q8zM9gX+AGxa99ZG6XEA8FkzO8Ld/112+kREREREREREpG/DWp2AVZGZjQMuZ+WgWr3NgH+k9UVEREREREREpI2UPXlB1XwB+H0f7z/R4PeeC6yV+7sb+BRwM7An8CNgvfTeWsAkYJ8GtyUiIiIiIiIiIgUoLbBmZtsCHyO6OG4KjAb2BoYDW6TV5rv7nWWlaQCedvfZzfxCMzsA2Ktu8efc/dfp9d1mtgbw09z7e5vZ/u5+TTPTIiIiIiIiIiIijSulK6iZnUwMyH8ysC+wLbAJEVR7JXBNekw1s1FlpGmAvmRmT5jZEjPrNrNbzOwsM9txCN95ZN3fToy1lvc7Vp70of5zIiIiIiIiIiLSQoUH1szsVODbwEjA6t9398uBh9N76wCHFZ2mQdiBmEhgBNE1c3fgM8CdZvbFBr+zvrXaI+7+XH6Buz8LPNrP50REREREREREpIUKDaylll1fJ1pf1bfAyvtz7vUhRaapSUYA3zGz/2jgs9vW/f1kL+vVL6//nIiIiIiIiIiItFDRY6x9kuju6USLtOuJrqD1rgc+nV7vWXCa+vMU8CfgCqL76nKiu+rJwPi6db9lZpPdvXsQ379u3d8v9bLei3V/r9fjWoCZHQ8cD7DVVlsNIikDY7ZSQ8MBvefeVyy1PfSV/v5UYf9E2lmj55/OPWkG/9o6cFr9Lbng7bWJ/s69qt/bO1mn5FuGsh+D1dXVVdq2YNW+toiIrKqsyJusmd0BvIoIrJ3r7h8zs+XpbQe2dfeHzWx3YkZMgCfdfZPCEtUHM9uQmEBhcQ/vDSfGgasPDL4/N/HAQLaxEMiPIzfV3ffvYb2pwOtyixa6++r9ff/48eN92rRpA02O9MHM2ioT2puy01mV/0un6+Tj0Mn7Ju1D185VT6cfg07fv6rQtUVEpHOZ2XR3r29wVfgYa/nmUz/rY70Xcq97bZlVNHd/qqegWnpvGTFWXL3dB7mZ+XV/9xYsq19e/zkREREREREREWmhogNr+ZZZfXWX3DD3ellBaWmGB3pYNthA4IN1f2/Uy3ob9/M5ERERERERERFpoaIDa/Nyr3fsY72Dc6+fKSgtzbBdD8vm9bCsLzfV/b2Fma0wEIOZdQGb9/M5ERERERERERFpoaIDa3fkXp9sZqPqVzCzvYHPUps59LaC09QjM1vXzH5rZq/o5f3hwCk9vDU9t842ZuZ1j4Pq1v9d/VcDR9Qte08P26n/nIiIiIiIiIiItFDRs4JeBrw+vX4tK3elvBjYmQjwGRFYu7TgNPXGgCOBI8zsb8CfgVuAxcBY4IvAXnWfeYJBptfdrzazm+q+60wzWwRMI2Ye/U7dx25092sGsx0RERERERERESlW0YG1nwFfArqIwNWmufcM2CW9zqayeQb4RcFp6s8w4G3p0ZflwCfd/YV+1uvJR4F/A2umv7uAX/ay7gvAsQ1sQ0REREREREREClRoV1B3f44IInk/DyMmLTi2wUBVMywDXhzgut3AUe7+h0Y25O4ziJZ8j/Wz6mPAG9L6IiIiIiIiIiLSRopusYa7X2Rm7wR+ysozXWaeBI5394uLTk9v3P15M9sYeDMwAXg1sC0x6+dyYpKCO4juree7+2AnLajf3vVmtiPwCeBworvpesCzwEzgIuAnLQw0ioiIiIiIiIhIH8zd+1+rGRsyW4MYw+xAajNezgWuBH7v7gtKSUiHGz9+vE+bNq3VyegIZkZZ58dQlJ3OqvxfOl0nH4dO3jdpH7p2rno6/Rh0+v5Vha4tIiKdy8ymu/v4+uWFt1jLuPuLwM/TQ0REREREREREpNIKHWNNRERERERERESkU5XWYk1ERESknZhZadvq6uoqbVtSbWPGjKG7u7uhzzbym+7q6mLevCENHSx1dG0REVm1FBpYM7PFDXzM3X1U0xMjIiIikjQ6JpHGM5KidXd3lz5GlzSPri0iIqueolusNfL9uqOIiIiIiIiIiEjbK6Mr6GACZaoyExERERERERGRSmGfdlMAACAASURBVCgjsNZfsEwt1EREREREREREpHKKDqx9rI/3NgB2BN4DrAksB74BPFJwmkRERERERERERIas0MCau0/qbx0zOxn4O/Bq4N3pWUREREREREREpK0Na3UC3P1p4OT051jg0y1MjoiIiIiIiIiIyIC0PLCWPJV7fXTLUiEiIiIiIiIiIjJA7RJYOzY9G7BDKxMiIiIiIiIiIiIyEIWOsWZmP+3j7eHAGkT3z12I2UENzRIqIiIdaPLkyZxxxhnMnDmTsWPHMnHiRI4+Wo20RURERESqrOhZQY+j/0CZ5V47cEdxyRERESnf5MmTmThxIpMmTWK//fZj6tSpHHtsNNZWcE1EREREpLrK6gpqfTw89wA4p6Q0iYiIlOKMM85g0qRJTJgwgZEjRzJhwgQmTZrEGWec0eqkiYiIiIjIEJh7cT0vzWw5tS6e/XHg2+7+5cIStAoYP368T5s2rdXJ6AhmRpHnR9Octm4Ltjm/tE2NGTOG7u7u0rbX1dXFvHnzStteX8wGcunsWbv8dss8fu107OoNHz6chQsXMnLkyJeXLVmyhNGjR7Ns2bIWpkx60gnnnqxM9/W+tlnefX1VpmuLiEi1mdl0dx9fv7zorqDX0XdX0KXAs8DtwG/cfWbB6RHpPB2eGe7u7i41MzmUTG+zdUImuszj107Hrt7YsWOZOnUqEyZMeHnZ1KlTGTt2bAtTJb3phHNPqstOf670+56fVtrmVmm6toiIdKZCA2vuvl+R3y8iIlIFEydO5Nhjj11pjDV1BRURERERqbaiW6yJiIis8rIJCk488cSXZwU944wzNHGBiIiIiEjFFTrGmpRPY6w1T2XGYulwZR8HHffmKvP/qWMnIn2pyjVC9z0REZH21JIx1sxss2Z9l7vPbdZ3iYiIiIiIiIiIDFXRXUHn0PfkBQPlqNuqiIiIiIiIiIi0kTKCVe07TZuIiIiIiIiIiEiDygisDbXFmgJzIiIiIiIiIiLSdspsseb0HiTr6z0REREREREREZG2U3Rg7fXADsAPgJHADOB84EEikLYN8GFgHLAA+BQxLpuIiIiIiIiIiEhbKzqwNp0IpI0ALgbe4XXzeZvZ94GLgLcAXwb2cPcXCk6XiIiIiIiIiIjIkAwr+Pu/BGyeXp9VH1QDSMvOTH9uB5xacJpERERERERERESGrOjA2ttzr/tqhZZ/74iC0iKrqDFjxmBmg34ADX1uzJgxLd5jERERqbJG8h+NPrq6ulq9uyIiIpVWdFfQLanNCnok0TW0J+9NzwZsVXCaZBXT3d1ND40lC5MF5UREREQGq9E8i5mVmt8RERGRUHRg7QVgFBEw+4KZrQOcR0xe4ETXz48Cx1ObGVTjq4mIiIiIiIiISNsrOrB2HXA4taDZx9OjXtbEx4HrC06TiIiIiIiIiIjIkBU9xtr3qHUFzYJrPT3y63y34DSJiIiIiIiIiIgMWaGBNXe/BvgCK7ZI6+mRvf8Fd7+2yDSJiIiIiIiIiIg0Q9Et1nD3s4A3AXfSe4u124HD3P37RadHRESkGRqdcbjRh2YcFhERERFpP0WPsQaAu18GXGZmuwD7ABsTLdWeAG5w9xllpENERKRZNOOwiIiIiIiUEljLuPsdwB1lblNERERERERERKQIpQbWAMxsFLARsDpwv7svKzsNIiIiIiIiIiIiQ1VaYM3MjgH+AxgPDCe6gm5nZlsCB6bVHnf3c8tKk4iIiIiIiIiISKMKD6yZ2ZrAhcQEBlCbATQzH/gmEWhbamYXufvTRadLRERERERERERkKAqfFRQ4H3gztYDaCiM9p4kLpqf3RwBvLSFNIiIiIiIiIiIiQ1JoizUzOwQ4glowzagLrCUXA3um1wcRwTgREfxr68Bp65a7PWmaMo9f2cdOv02R9tTXDLp9vVfmLL+N6m924Krvn4iISBUV3RX0I+nZgEXA94FTelhvWu71rgWnSUQqxE5/rtTCgJnhp5W2uY5X5vEr/didNr/EjYnIQHVyAKmT901ERKSqiu4K+tr07MBX3P1Lvaz3cHo2YOuC0yQiIiIiIiIiIjJkRQfWNs69vryP9RbnXq9dUFpERERERERERESapujAWr69+vI+1tsi9/qlgtIiIiIiIiIiIiLSNEUH1p7KvR7fx3rvyb1+oqC09MnMuszscDM708yuNrNHzewlM1tiZk+Z2VQzO93Mtuj/2/rd1vlm5oN4rNWMfRQRERERERERkeYpevKCm4gx0ww43cxuqXt/HTP7HHActdZtNxacpt48RO/dUDdIj9cBnzezT7n7eaWlTGQV198saM3U1dVV2rZERERERESk2ooOrP0ReDcRNNsSuDm9zkrJtxCt5rK/HfhDwWnqzUBb760BTDKzx9z970UmSEQanwHNzDR7moiIiIiIiBSq6K6gvwPuTK+zgFoWRDNgeHr29Lgd+HPBaerPFcAJRNfVXYAPAQ/WrWPA6U3c5v7Atn08FjRxWyIiIiIiIiIi0gSFtlhz92Vm9h7gSmBDVpzMIM+AJ4GjvHVNTC4Fvu7ud9Qtn2FmVwIzWLGr6N5mtoa7v9iEbc9x99lN+B4RERERERERESlJ0S3WcPeZwN7AJdRarNU/LgX2dfdZRaenj3Qe2UNQLXvvYeCaHt7qbUy2wfqLmT1rZovN7Ekzu9LMTjGzMU36fhERERERERERabKix1gDwN0fAt5qZlsDBwKbp7fmAldVpLVW/ejpLxKt7Jph19zrDYn/0YHA58zs/e5+eZO2IyIiIiIiIiIiTVJoYM3MXpv78wl3vx/4RZHbLIKZbQkcXLf4whK6rW5AtGbbz92nF7wtEREREREREREZhKJbrE2lNq7afwD3F7y9pjOzNYELgVG5xfOBbwzxq2cRs6ZeQ0yOsDYxYcJXgE1z640GziJasPWWxuOB4wG22mqrISar8/jX1oHT1i13eyLyMrP6Br/F6OrqKmU7IiIiIiIiGSuy0ZWZPQesRQTX9nT3WwvbWAHMbAPgYuA1ucULgbe6+xVD+N7N3f3RXt7bjJgoob6EuEVvn8kbP368T5s2rdGkdSQzo8w5McrenvRMx6G6dOxERERERKTdmNl0dx9fv7zoyQvyLdQWFbytpjKz7YHrWTGo9gLwtqEE1QD6CpC5+1zgvB7e2m0o2xQRERERERERkeYqOrB2Ye71oQVvq2nMbF8iqLZ9bvHjwEHu/s8SkvBAD8vWK2G7IiIiIiIiIiIyQEUH1s4CbiFm1DzdzNo+uGZm7wL+RUwckLkLeE2JEwhs18OyeSVtW0REREREREREBqDoyQs+TwSpdiFaXF1mZjcRwba5wLKePuTu3yo4XT0ys5OA77FiwPFfwBHu/uwAPr8NMRFB3gR3vzK3zmHERATfcff5PXzHZsBH6hY78T8TEREREREREZE2UXRg7ZvUZgV1ouXa3sBe/Xyu9MCamZ0NfLpu8RXACcB6ZtZTV8zH3X3hIDe1OnAqcKKZ/Qa4BLiHmP1zL2JW0PqJCy5x9ycGuR0RERERERERESlQ0YG1elmQzQawTtnqg2oAhxBBr95MAK5scHtrAcelR1+eAU5qcBsiIiIiIiIiIlKQosdY64338lgVLACWD3Ddu4CD3f2+AtMjIiIiIiIiIiINKLrF2lxWnYDZgLj7P8xsS+Bw4ABgHLAlsDawCHgCmA78Efi9uy9pVVpFRERERERERKR3hQbW3H2LIr+/mdy9r+6pA/2O2fTdzTVbby7wk/QQEREREREREZEKalVXUBERERERERERkUpTYE1ERERERERERKQBZc8KKtISZkPu6TtgXV1dpW1rVdffce3rfXcN/9hqfR0fHTsREREREamCpgbWzGxeeunAbu4+J/fef+ZWPdfdX2jmtkV6o0J459KxrTYdPxERERERqbpmt1hbLz07K3czPZvaDKF/BhRYExERERERERGRyip7jLXy+uOJiIiIiIiIiIgUSJMXiIiIiIiIiIiINECBNRERERERERERkQYosCYiIiIiIiIiItIABdZEREREREREREQaUGRgzRt8T0REREREREREpO2NKOA7nZj9c7ZZj5OA9vUegLt7EekSERERERERERFpmiIDWL1Gzvp5T0REREREREREpO0VFVhrtKunAm4iIiIiIiIiIlIJRQXWFCATEREREREREZGO1uzA2tVoYgIREREREREREVkFNDWw5u4HNfP7RERERERERERE2tWwVidARERERERERESkihRYExERERERERERaYACayIiIiIiIiIiIg0wd8010EnM7CngoZI2ty3wYEnbagXtX7Vp/6qrk/cNtH9Vp/2rrk7eN9D+VZ32r7o6ed9A+1d12r/m2trdN6xfqMCaNMzMFrj7mq1OR1G0f9Wm/auuTt430P5Vnfavujp530D7V3Xav+rq5H0D7V/Vaf/Koa6gIiIiIiIiIiIiDVBgTUREREREREREpAEKrMlQ/LHVCSiY9q/atH/V1cn7Btq/qtP+VVcn7xto/6pO+1ddnbxvoP2rOu1fCTTGmoiIiIiIiIiISAPUYk1ERERERERERKQBCqyJiIiIiIiIiIg0QIE1kVWImQ1vdRpEREREREREOoUCayKrEHdflv/bzHQNEBERkbZlZtbqNJTBzIatKvsqItJpVKgWWUWY2XVm9k0z2zxb5u7LW5mmMqSMqq51IiIiFeRppjULHRd8yvIo7r7cNauctLlOPQ87mXoslUOFTWmYghXVYWb7AK8BvgRMMLM1zOxMM3uTma3R4uQVwsxGw8sZ1eW55R2ZGejEfepknXC8zGxdM1u71ekok5ltaWbfMbM3m9n6rU7PUJnZWWl/xrU6LUXK8itm9l9m9r9mdqiZjWx1uqR/ZvYtM3uHmW3gYXku0Dasyvf0XD76QDN73Mx+bGavbWmipGFV/R0ORLZvZja8t/OwtSmUnuR+k38wsx+Y2UFmNqKliWoyM1sr5UdXOv9SELi087Kj/rFSLndfbmbrAi8CXcBS4Dl3X9ralEkP3pyebwD+BbwB+CywP/A5YGqL0tV0KVD4EWDX1DrvQWL/rnX3OZ3WSs/MRrv7wk6v5TYzy2fiqn4c3d1T8HdJfRftCvk+cKSZ/Q64CrgeeNDdl7Q2Wc2XChPLgNcCXwDeCfwQ+FFLEzYEZrYmcBLwEvAoMMPMfgzcA1wL3O7uC1uYxKbJXS8+CGwC7ATcATzRskSVIBV2var3BzPbCTgFWA50m9kdxLXmX8CN7r6obv2XW36VndYGZQW+fYGNiLzZ1cB1nXCfg9q928w2AN4K3ATcV3/sOkHazzXdfUFqITTc3Re3Ol3NkPbt7cBhZrYZMAO4HJha5d9pdp6Z2c7AwcS1ZWZVr5n10nHbEDg8LdrE3a9sYZKaIndd2ZQo870C+LuZXZLOv5HuvqTs46jAmgxK7oe8OnAo0QLqlcBM4E7gNjObCTwEPEUE2jri4lRxB6fntdLzm9Lz48BjAKn2fmmVj5eZrUMUdI9Ji5zIuH4CmGdms4gg2xRgmrt3tyShQ5A7B9cA3ggcnVqb3E4UOK4F7nH3l1qZzkaZ2Sjg/cB2RAbnmuzmmGVYq5iJqwsMvho4ChgFjDCzB4njNr1imfAJwJrAh9MD4h5wJbVzbG5LUtZ82XVxr/T8EFEArnKg98D0PB+YaWbbAB8HngOeBx42s9uAfxNBjLtbkchmMbMtgLWJ/fuDu3dcUC3dF3Yl8l531f8uU2G/St0Ns0pBB9YHDkqPrwBzzWw6cZ+Y4u4zqnYe5ipVtkzPNxL3cahdc6puOFHx/l7gB8Q+PmxmM4DbiADN7Ar9JntkZqsBrwNOMrPxwF3AzWZ2E1FGegx4vkoVT7n85vrAJ4HTqOWrDyfKgMvNbCrwN+ASd7+zVelt0DAicP8J4FPAvcAcM7uZqCyc5u4PtzB9DcvlO3dOi+4DLqh7r6qGAcuAw4gGIl3AfHf/nZm9Cvi0mR1BXF/OA35TRkWhVft/KmXLau3N7IPA94iMTr2nSTcUUrDN3aeVmEzJSYGKm4gA6AjiIvMKYHXg18Bn3P2pus9kTborUdOd+10eCVxIZOKGU6sNrreUyORsW7WMeK527avAqURwpt69ROb8CuI8vK/dj2Nuv/YBzgXGEteOPcxsPeBo4Aji2P0FOK8qrWnqgmrfJDKo66a3lxNBjJnAj939gipkeMxsY1JQvg8vEdeefwFXAndUMZidZ2YnA98maurf6+7zq3C8emJmJwD/TQRBPwy8G/gxtYITwBKiVfozwAPAdKJgfEPVgqapBfNkYD/gQ+lcy1oiVp6ZHQx8C9gC2Iy4rlwD/BX4u7vPbl3qGmNm/w18gLifd9H7EDaLgNlEQfifRAv1h8pIYzOkPPX5RLr3T8sqeV2pl8ufXUFUxmReIMoLc4g8y+1EoO0ud38yfbbt/we5/TsKOJM49+o9TuTFrgNuJfZxdnmpbExu304ggmobEsGM4cQ5NyK9zrvJ3fcpNaFDkAsePghsnRY7sBhYQDQSmQVMI47f7e7+dEsS26DUqusSYEfgze5+Vdaiq8VJa1juuF1KBNfuAt7t7neb2b+ICpjMYuCd7n5p0dcUtViTwcp+jMcRQbV8BjyzAXBAegCcTVyQpDWGEV1AszF0smcnWq5tk7pXXEfcEO+uWrAp5y3peTnwMFGwH0X8JtemlgEYATxUxf1MwafRwBdZOUMDcVx3SI/j0rJDieBGO8uuI+8igmpPA99Iy75IdAeCyNS9GphHBFHbXi6odirwH9SCahD7vQ4xBuJrzGwtd/9xBQoUDnwa2AbYgzhmG9etszor3gueNLNridZ5P/RqDhvwd+BEonLiRagd36pJv7O/A+u5+zNmti3Rem0Zcb0cmR7rpse2RJe1NYFziP9D28udSyOJiqX9SAWoqgfVchUSuxEVElsR9z8njuGb0wMzm0sEhC8HLqzI7/YsIr27EC3xtgc2JVp4DSOun0bc53dKjw8Rh/0z7v6DViR6IHLHbkviWM0BtjWzXYlKiCocn37lzrHXEAVcI/JgaxK9KLYhutg/R3TNnmVmfwXOrUgeLTtOJxBBtZ7KRZuQOxeJANwXS0nd0GT//2OIoNpzwN3EeZYNBbQGEWSDOA+vLTmNQ5KCM11EPux5It8ygtiXUcAYIj/9BiLfudzMDnP3mS1KciPeQAQJVyd6g1yVD6qZxRhkVbrmpOO2ARFUWwpcnoJqHwT2zq26HFgNeIOZXVF0rxC1WJNBSy2gHicuQg8Bk4jAxeuA3Vk5YPsed/99qYmUFZjZnsR4SLtR6w6av/F3RKsEM7ucCCJdQmSulxGF/t3SY0diHJNXAKe6+3dalNSG5DLiBxO18kuJYzaFOAd3otalJG/9qrQUMrN7iMLTH4lxE3YGfkVkBpYQ1xcDfg+8v91r3HK1ahsRFQxbpLdmUCtUZJYRx3NPd3+01IQ2IGXG1iECalsTrWJ3JwrBO6b3erLA3Ssz6YGZjXD3pWb2OeB0ohDRRXSp+GqVWsb0JR3PnYlM6b7EcdySKECNJgL5WaHxY+4+qUVJbYiZvUgULrqIzPapxHVmrld0vKfcb/N0onskxL4tYcXWzPnC/iJ3X73EZDaNxcD+/w94VVr0OLXWbPk8zXDgEHefUm4KBy537H5FtMjOghS3Af9FtPZ9HFhYkQBTn8zsbcRQHT3lUeqDUS8Q5+Yn3X1BCckbklQueooIFj4J/Ja4/+1JVDrVV4K+191/V2oiG2QxPtcTRFD0x8BcotJzNWAiMW7lTkSL0W2AQ9293StyV5B66exL5F/2Ja4vmxJBtRHENTXfWnZku1fK5PKeOxE9Ih4hzj0nArsXA7PqeyxVQa4l5XuA3xBjaX+cyGNfQDSyuI6ofD+TOHZTgMOKDh6qxZo0Yn0ioPYKolDxy3RRWp04afcmmmC+jojyX9GidDYsFTA2JcbT2YQoBN9UdKS7KO4+3cy+CBxCTFrQRdwkhxHXgY5olUDUbB9KjFX1THprSnpgZlsTLfb2J7oEVU12Q9g+Pc8gWnJdTZx7mxMBjV2J/dwLmNPuQbVcBmAHYt9eAP7l7s+b2dFEZu0p4pgdTxQYtyeuRY+3JtUDlo0D8XoiqNZN7Mf33P1Bi0GAjyG6h25JZOTGEYPJt7WUQZmfHveY2RTi2rI5ccx2JgLa44j7xWrpo5UoUORk592biILvcOKYvh8YZ2b/IFqE3pp1YaqKfMvI9HxnepxnMbnBrkA2q/RYomJiY+Cy1qR4cHKVEftSCw4uI+57Xye6l19jZjcQXUkeB16oUGvKLOCSzSQ5B/gD0bJiS+J+sC2Rj8lmQf1VmQkcinRfH+nuiy3GzTmJKPT+igiMbkFcZw5Pj6VEq5q1SWMgtrGsYH5Qeh5B5Mt2A35B/B5vAP5tZrcTXe+fbPcCfV6uADyeCB5uQhTozySupYcSY69tASwk/gcjiEqnDxLX1V+0IOmDtQlxfDYmWqJdSOzfesS9cE/iGrpn+vufrUjkYOS6ye+fFj1ATPiyM3Evn+Xu3zazy4gu5zOIwGnbBrN7kwLXWWv6cyzGlHs10dLrGOK4zifKSFMrcg5mYxtmY2pvRFxfRhKtKw8F7rIYF30mMf7abHd/vgVpbVTWtXwhcD9xjh1IxCi+mpadQFT6Pl9GizwF1mRQUiZ1rpn9BfgyqYYpXZQWEE2E7zazC4gbyubtXqivZzH476nA56ldhEYQzdN/DZztFRwY3t3/TWTQvpoW/YvICOzMyq0S1kjr3Fp2OgfLagOHb03tRvKqntZNLUseIgZZrZzcTeES4ti9RIxDtpAYo+ReM7uaOJYbEzeTea1I6yBlwadsMPXngDvMbDtigoaFREuhSURmZz9glLu3e1AtL8vcTAd+lIJqI1KL0O+a2RgiQ76UyHxXInABteBMCkY8lR63mtk/iUDbZkRgbWciM1epoHYuE539PvMtgfZIjy8C8y0Gcb6ZOMZtXxPcV0YztRS5Pj2ylgt7ATu5+5xyUjhk2f4dlJ5H5t5bndQFO/09lwhmXERFZntNQcPhxHXegV+5+6kAFrO2b0lUcI4lgmwHEK19KyFVuGTn3yeIcQCvAr6bfoNziMk1zjWzbxAtaG4kugW1deE37VvWIgZqFQ8Qv9Ospf3xREvmx4lZNavUQjY7/04hhnn4G9EKLbt+XGYxjt7FxLXlH8T9b0x6/zjgF9bGE8SktD2UutWfALyUKuEXA88Cs9MQCJOIANzGFSsX7ZeeHyL2Z8f0dxZAe5HIj24D/LRK3Qnr5fIyzxC/xX+Y2S+Je8IGxHiVVamYyK5/2RA5Wb7FiYYTWd5lCXEdfQ74P+B/S0xjQ3LX9g2IPPM2RFnhvURQ/o9E67WdiPMQSuqirMCaDEruxnYvEUg7zcwWET/Yp7IWXenC2p0ebS/XYmZT4GTgP4mTdU1qzdN3Jlp7rWNmp1Wx60jqjnYeEUA7z2MAy7WILj9VbZWQHZ+fEbX2i4B3mdkkIiNzP9Bd1daG9VIrkvcRLRV2JwLYL89ul87R7Nyryix+2XVle+KmP4a4IU4gMnHXEC1f1yeNa0XUnLa9XAbglel5faLml9QNaEQKSF1PZE43pf9JAdpKPiOdxgvqAu509xeI1oePADdYzNp7LtFlpFJS2k8muiTvTvw+6yfvWZfIxL6FuB5VSrr/vZY4x65392fz76dA4SXpUQm53+YfiMLDbsR9bheiVVPepkQQ+CkqElhLnLg+HkmuO527Z61JZwB/SoHR7YjgfmXkrqHvTM+rkVoqW4w3Sqpcyu4Js9z97FIT2bgXiPvc1tS6YO9BjGeVtz4xpEOVgmpZ4HdN4tg5MYbVXHh5Fs1h7v5kCkrtSoypdytR8TsWWM3MdnL3WS3ZgQHIlYumEXmZU8xsHnGePe/uy9OQFc+mRyXyZbnzLpuMYR7RzXWH9Hc2edTmRCVv1hvk0rLSOBS5ct+axNiUc4ly7dLcOsPc/bbUqutQ4BvuflNrUjw4uXvfYcRxeQsxxt/OdauOICpgshazlZBaM99ItDofQfSwykx29+fM7CDingcxPm7hFFiTQUk3wvcQgads4NFvkaYkThef2cTF9/l2rzHMyVrMvJeYgQpq58cyan3r1ycKV9nU0pWSMjD/SQTOnk/LXqDCrRJyv7H9iVrexcSN8QNEJvUWYsrzO4l++M94xWb0gRWa5b+dGH9lDtHi4lIz+yFRiz/L3V/s42vaUi4D8ABRMBwN/JTaTf7P7j7LzN5Jrbb0r+WmsnEWs5pmret2AI4xsz+6+7O5LmfjqBX0KzX4L4CZ7U5MzLAnUUgck2p6zwAedfcX3P05ola0clIm7QdE8GwzooZ0LLWurttTa+n7mLs/0op0NiK1eDoa+C5xTVk3LZ9JFJIupjrdX3rk7veY2b1EgG0MUZDIKpTGE8cyu+dXoqtyLij/EaLA9DxwcG+BiBQYbftWlD0xsy2I3+ZyotXyWKIyNz8z9MZEoOqdZvaddmzRbGavAJ5OQU9ShV/WHWsKMS7XJtTGOnxNer0OMeZaZeS6me9O3NeXE9fLNYnyweK03jAi6DYKOMnd35SGFdiJCHa3dQ8RMxtBtCR8P5EH3QP4AXE8r095z8eIe9/iKrXoMrO1qV0X1yBmx9yJOJYfSveOg6kF2x4sPZGNy3q4fJwYM+4i4CYzu5nYj3kew5GsT/w2hxEtEit1HqbA71Xp8UWLiRoOJQJthxL5mewYV6ExBfByi99/AB8lekRk+/B3d78sNSSZQMQpbnH3u8pIlyYvkAHJjZPwZuCXRCuZrJYmP6BjN9GV4jbih1yJwY1zNRc3EQXDhcTU0jcTGbgJREAjqw3+hbt/OD8+TdVYDLa6zKszlkyvzGwPVq6Frx8Mdz4xjsA9VGRA3DyrDXT8W6I7zFJi/4YTtaAPEoGpGUQw8W7gvnbtPtETizHwsOcIowAAIABJREFUriJqDzNzgZ1TYOMcomvIi8DYdiw41ctdW84kWrwuI4Js5xO1bU60ZvsC0Uphtrtv18vXtSUzew3wa2oTMSwmWpVcSlS87E1USvzc3e9tRRqLkK6h61MbUy67V1zv7l9qYdIGJHdfP5joOrEOK1Yk5a+fy4nBgP8OfLuq9708MxtJFPLXJ8Yh253o9vT+KtwfcteWbGyn7LybRQQHbyZ6Fzzs1Ro3ZyWpwu831MbUWUx0ab0cuJ0IPp1FVBo+6u49DZDfUilI8Xcif/wzd7/ZYibex+oChFmwezRpSBVizOI73f3ykpM9ZGa2FzHm2DZp0UzgJ8Rxm01cN/+P2M8p7n6ImX0J+CYxe/u2Zad5IHLXzzcS9/ONqF0/89fOp4g82Q3EWM2VqRQEsBj8/ljgYWK8u1nUZgDPBvV3Yvy/TVuSyAbkjt8/ieBgZinxG72J+H3uQ7T6GkEq+5Wc1CHJd6POVcbk39+aCLLt5e4faUUaG5Xu4YcR4zF2EQ1eLnb3+83sE0S31gXAt9z926WkqQPyRlKC3AXoAqJWZjmRsRndx8eud/fXlZLAJrAVZ775DnBaXRenA4G/EIWPa4Cj3L1SXbbSRej9xDgrLxBNux8kgk0PuPsTfXy8baXuIHsRNdkHE7W89V0pskDbPHffoNwUNo+ZzWbFwFP9bEULiMDNMuBz7l6ZlpWpafchxFg6exEZm++5+/UpeHM5USP8B3c/pnUpHbhc4fe9RPfANXNvZ9121yUybQuIMRy/svI3tZfcfmXjjmTTmy8kaneNKBj9N9FFayvgSHf/QyvS2ywW4/5tTAz0+0w+cG0xPufGwLNegTF0csH6HxGtDSHufy+RWq3lZNeZtgxaDFQKhq5e3801vTcaGN3Te+0md/5tRRT+luUeqxPj5jxGVLbcTUxIMRv4p1dsGIvcvp5ItATKW8rKs/b91t2PKjONA2Fmbwf+RByXo4gxq35NBKxnpceDRKBted1nRwJLqxrQNrOLiFZdy4jKwHlEPmUToqV2Ntv36e5+upn9lKhEa8tjCT2Wi6AW3O5NpcpFPTGzzxMVZiOonX8vEBUupQQvmsnMlrDyrK15nh7DgI+4+89LSVgTpeDvVsR+LibKfLdnLWc7gZmN9OhynZXnv0n0XPoT0YW3lC7Y6goqA5LrApI1972ZyOAsIIIYBxDNn/ODOl9UWgKHIBfNzwYwXkC0GvGU0V6emqxPJ2rY9iMyA5UIQuUypesDnwHqW1I8Q2Tw7rHc7DDu3vYTF2RSbe81xOxu5xBBtXHEsTqIaIWYddOqTKCpFwcR4yC9nvjNbsOK592aRLNoiBrGykiFhn+mB2Y2yt0XmdnqREFkLaIFxgWtS+XgZAUhd78w1dx/Nvf2MKJFQpap+xNR810F+dlO906vLyAKS6cQAcPbiTEPs4zpDj1+UwWkWt0TiVnCdib2/y4z+xtwvrvP9OiGXZmuMLma6/Hp+S4i+PsicV15JdHFdUtq3ZSrMnDzClJg+yji9/hUqqC4hxiD8xF3fz7dRxb2/i3toa6l/CbEudbFioXDEURBaivinrGQqDwbV2JSmyK3r78muu8eRa2CYjgRVMtaCt1HjK3ajrLKoHuJluUfIO7l+xDdBB9O7800s7uJ3+dsd38mKzBW2CnEb/R11MZRHUNtgHUjrp2/spj9dZ+0/C8lp3PAcuWibLKsO4gxjF8iykN7U5tBM1OJclE/fkXkOT9ItDK8hsi3VGZSlEyqaHk3cQxfS1TOb1K3WjaL9K1UZPy4TOr2eQFRFlonLX6WuM78wcz+x91fMrPVvALjUFttlu9XENfP6cR142GPoUaAGPbAzE4m7hnziGtpKRRYkwFLNWbXEDeL37p7lsH+cyr4bkG0MtkfeAfVuQBlmbatiKBaF7EvWcAmk7Vog8jsVKWLXVYAPoYYS6De+unxaqImYyHwZ+DDJaWvKbLCRiosPpYe/0jB0c2IjM7biYxPZbn7bKLlwa8BzGwXouB0MFFA3phUg+julRjgP11b3kq0rHiKqP1clLWs8JiF9ySLMbvWpiJjXFgMBn8wUfC70d0/b2Y3EC3ydiFmNBpOBKOuBT7r1Rv/723p+ToicJ/V3N9LHM/NiYzN1qzYWq/t5TJx2xPdzN6a3lpCXFd3To83mNlx7j7damMhVoLFuKn3Etf/X3ka9N1i3KBNiYF/X0mMq/M6KjL+WCYVnI5n5ZZOi4lu5vcTgYy7iAqmf7V7i658qyV3vxFY32J2yUOAw4lxczbOfWQ50bvgmTLT2Wzu/oyZnULcI95DVCAtI1rNjCK61n+JGE6gHV1EHJdb3b3bzD6cljuR7+wixiBbToxT/CDx25wD/NXdp5Wf5OZw97vM7HRi1tYDc29lweBsiI77zOz/iEDH48QkBm0rXV+mE93Iz3P3s1N+ZhQRONyauLbuRcwM3vbloh6CFzdRq4BYkHrqnAGckVqsD3P3J1uY5Iala/1fLCbP+BnRnXdnIrC7F3E+jiDue2dXYT9zx29b4HRi/M18eXU9Yt/2IsoOb65CUC0ZRuzLx4iZ2O8lKiTuTg1D7iZ+q4+lluel3wsUWJN+5Vp0bUYMjruUullFUsH33vT4tZmdnI8et7NcJvVmai1/Tk83jIuIWt4HiRtMNr5HKbOLNEl2QT2GCKA9RVx89iJq0obl1jOiVqMSrfHyUqu8tYnC+xO5lkILie4wD1gMGF/JrhSZ+jESUvDsDuB/cl1i30YFZvfJBSHeSgwq/iDRyul6YLqZPUD8Xl9y92VVKliY2URioNt1iELtfDP7FZHRuZqYAW11oiXlo+5+TavS2ohc8Gi39Hy/u883sw+lv+8mMjy7Umst2rYzu/UkV3nyZaJlXk+WEP+D75nZ0V6R4QFyrZ6GE+P/vJ9oEQq83JrtkfS4KlWebe7u97UivYOVu7ZMICYcyltG3Pu2SY9DiFYmD7j7LiUmc1BSIX4CMK0+AO/u84jC3+/Suq8gxp45nGjZvA7wj1ITXIC0nxMtxqx8DVFhtoDIvz3g7o+2Mn19cfcLWLG19Z+I/PT4ulWNaDWzCTEBE8R9sTL3v564+z+Bf6Yxuw4kjt9yIj893d0fNLPdiPzAk8Q4dG2ZF82Vi9YkWvouIVr3kloXLiEqCB82s+uJa+sYd3+gRUkejP6CF/cSebVHKlgR2KMUYHsyPWaY2e+JANRSojL3eY9xfqswrnY2vt/R1GZSXkiMM50fA285USn4GXf/fonpG4osT/aW9Lw90RPiECI+MYdotXx3rrLsljK7vGqMNemXrTjAYzY2wGrEoICTiaBFd7vX8vYnBdKuIgZSdSKjPZtoNrsTcWMcRXS72NfdS2ta2qhcN9DNiAvOQmKQ2LuJGvwRRMu0DxEZ9mwcsoPd/cpWpLkRafyA44hMzotES4SZRHeLu6tQyzQYZrYNMbvU3BYnZUhyYzydy8otJJ34nd5EBNpuI47rk3UtSduOmZ1E1OiuTuxHNoaHEdfNT1cgc9YvM9sYuIQo3F7j7gea2XJin7/s7t82s88QwcS1iJmGKzF5Qe7auQlxjxtNBHkvIApMXUTr7X2I4zsSeKNXZHDx3H39q0St9R5EK98TiAG2Kze7cF5u/7JrSzfRomkXopIwLzt+17j7gbQpM3srUdl3GdGlfC6xP9NT5WZfn92HGOKhsq3WUguMbYGngedS6+36ddq24JsLxtQvH0Wcg4cTwdCeJq/ZzCswWU9vLMYC3JEIyMzpqbyQyw9sR3Q/v9/bdGb63PXlCuIeMIooM3yHCG7PBV6sUM+Wl+VaPN1BtBzMTwS2QvCCCCo+SLTCrERjip6Y2duICsDFRAv7WVU933J5l+z4zQHOJHpFLCa6hX6MGC5nBDGxyPtgxdbQ7SpdL5+kNjxFpn7CuseJe/u73L1+crvCKLAmA2Zmi4jM52Jqg8XeR9R230xtcNynq1qLYWYfAH5EXGDzM/vkT9hvu/vEFiRv0HI3/6OIroMziAvseOBTxCxTu5jZYcRYFucBV7h7ZcZKMLMjgR+ycteXbuLC+iC1m//FVQ5GpZre04iuyhsSQd8bgSuA69z9qdalrnFm9i+iYNGX+URXppO8jWfVSkHPK4mu5fUTS0CM8bS7u88yMyPuw5XLfMPLg73/EngXkdG5kuiiNZsIdD9CjLuyC1G7vXVLEtqA3LXzGGImtIeIa/9P0/ujiQqXLwNHpI99xd3PaEmCG2RmLxJBw2xQ8YeIcSizmYUfAB6vQoa7J2b2BHGtPJ/ornwKEbj4EPG7/Ay1roSnufvXW5PS/pnZeUS6ryLOufcC5xAFpruICogb3X1myxJZgFSQ+gbRajRrIfsMcSwvbfd7eq6guwGwprs/lJYPI67/y+rW34QYy/FNROuthe5ePxlTJaR9/izRin5HogwxB5hC5DevhhVaB1dKXbloNaIgP4M4F/9NlIseJVo89Rn8biftHrxoFjPbE/gvIjiatdh+lMhX/9jd/9lbQLwd5a41mxL7sYiYEfMbuXWGE8MFXEAMR3IjcEQ7t/btSSoPvYm4tuxHnIeZ/O90XS9zVmx310OPfh9Ebfbyfh7dRMb1fCLz0PJ0N7CfqxHdRhb0sH9LiADOGq1O5yD2Z3h6PjvtwyVEIfDi9PcP0/s7EJmBfxOFrGGtTns/+5VVCmxAtGTKH6elPRy7F9Lx263VaW9gX4el532Jrjy9nX9PEwG2M4B9Wp3uQe7j9sSN/hRifL9H6s67xbnjekCr09vPvpyQS/uNRODlb0RLyuVErfYHW53OJu7v13s4/15I15Ibcvv83VandZD7lV07f5b2YSowNi0bkVvvtblj+7+tTvcA9y27puyeO26LcvuxnAhc3AL8FvgaEdAZ1eq0D3D/svvDjrl9+SRwavYbTe9vQAQSLya6pq3X6rT3s1+3p/Sfk/6+ktoM7YuIipb7033iO8RYt1u0Ot1DPIabUsu/5O/vS4jKwXem/8P3gJGtTnc/+/RZIm/5DDHe6/Dce2sTQcMxPXxuo1anfZD7mV1ftiAmkshfY5bl/n4IeEv+eGev83+364MYO62/ctHT6Xw8k5hxuOXpHuQ+7gScROQtF9XtW/5Yrt3qtA5in7Lf5+uIwG62Lz2VHY7Pf6bdH7l9e0dK/7PAcWmZ1a17aVrnvlane5D7OKKHZUaUIS6mVmZYTlTolpo+jbEmAzWTyHi+mhiM+zVELXDeOmn5ju7+4VJT1wSpVmIx8B0z+39ExudAIuh0I3ERut4r1EXGazWhW6XnZ4jx016Z/s7eH0u0QtybCFy0e3em/IyEuxAF+WnEb/DVrFhbsYxogejuflv5SW2aE4jZdyECFdl4gMOoDX58INGld0MiqFEJHuM23WdmVxIFjE2JQv/7qA28Oiyte3WLkjlQb0/PVwOnuPu/AczsZ8BHifNsw7SsMjWhfTibGJ9r2/T3MKIL7N7UxsO4mjTZRlXkrp0bEfsxnjRWnEd3pWwWrdWJFnqvJMY6bHu539xwYuDtHYhrZ77GNz+Y+ruJLvU/LzOdQ5DdH7KZvucQFX87pb9vTM+LifvGYcBXPQY7bkupddMZRCXn1NTadVdqLQ2NOH7rEOPG7QccCzxqZvOJgeEr8ftMsmP4ASKoC1HhsjYx9tG9xHVlE+K+OAb4Lm06PmxqQfIO4nrxEnBbdo0xs72BrxDdtkZbTHBzLjFhgXt1h7L4NHHtyBi1vNlSorvnWWb2kLvPyFbyVFKugIeJcax2JoI0uxO/w7xsUpHx7v75cpPXuKxbrrvPIsZGPTtdcw4hjusbiWM5ghgjtrwWQUOUu/99jtrss0uImXk3qFv9K2Z2k7vfUlb6hig7d9Yn9mcd4AAzuxB4IdfNdzviHIQSZ8xsBk9jTFtMvDScqChbQozf+AjRIn1jYtiVa8tOnwJrMiAeYxpdA1xjZucQBcNxRObtIGBPagNU/60VaRyq7GKbbijPAj9PD8xspFd0unMzW4torg216ehfQVyA359ulgemZRBdJqvi8PR8HTGuxRuJwNqPiAvqD4nm3WtQkZkk66Wb4BpE159syu+HiX1dLb3equ5jlTwHgWXu3g10m9k9RBfz9Yjg6W+pxuDNB6Xnq4FbrTbZxC1Ed9Z1qUgApi+pO8Fyd59nZocS3SkOIY5X1v11GNGC5jPufldrUtq4tI8ziO5YI4lM9n8T3e0WpwH930ft/LuiNSkduDQu3lPuvtyj685eafnexIDAbybu5/XuLC+VQ5YVnLZPz88T18n3pb+zyrFtiLzMGkQlTdsWnlL+5ML0IP32vkwEY/YgghTrES3OhxMVL9nMhBDduqokP+nSusR15ETgm8Q9fhaRV8m6ho4gZmBsq8BarvJkApFffh74nqfx4cxsV+KYbk1tAqm3Ea29HqMa97wVpDzLMKLCZW2ild45REF/LeL68nqiYL8D0ep3RjuPj9cTjyFvLkzDAqxHVAi+igjW7EOUkUan1f/SkkQ2qN2DF42qGzs1a9V1F1FBOJsIRE0grjtrEbOa70Yb3xvycufPzdQaF7yNqFiaDMxJeYBjqVXUt315IXfc1gK2d/dbPTeTaSrHjvAYYuUW4l5/m7tfVnZaFViTAct+2OmC+1h6/CPdVDYjMndvJ8ZNqBQz25AoVKwPzDOzx4na0aeAZ6oaVEuWEhm3rHZ+IRFo25ionflParWIj3gFBhfPtSbJZsy6hbhB7p3+/o27X29mRxA3yeOoYGDNajPbHUAU/h4ixntahwgqPkG0gvoaUWN6A1FYbPsCfsbMXgvMcPfn6jLV7u53m9llRMb7cWLijbZlZvsSwc6niRY++UkW1iKOG8T4K/ma07bW0xhBudYW5jGb25eI3+DriFZe8/j/7Z15mF1VlbfflZEkJBAIM8hkFDCGGRk1AVEUGQREBJpuEW0bHOgGkZZWHBhUbMAWaT4BG6RlcAIhIMjQDAIRCEkgjEkgYZAQAgkhJCGkan1//Pauc6pSSaoKrHvOzXqf5zw3de+tPPvUOWfvtdfwWzJUL0jO0trh0ljLWa5vo2fuY6jj1Gw0Z+6L5s/JXvGGNma2FiptfdjU3e1ZYIa7v+zuD6BMrtPTmj4GrYn7oGy82xoz6u7RIQv0VXQfDkYbjdzNfC8zuxU5nnLGwsxeHWg3KOn9HYyE3x9y6TVdmD4fjOyvndOxDdrkD0nHDK9od8XO6KATNAqVoP3C3W82s+vS16a4+xtmNhw5SoejwEVVOTi9PgCMg7bn8evIqbaYwgkDcj59BziwTg6n0vO3K8omnAOc6+4/zJ+j7Objke0JsH2dg9dpnZ+VjommjpLrIGf3aBSs+FXjRtg16uS8eAfkTNiPpp9nAGe4+9XQdn/ejUooT0Nr+y5I4qhOTEVr2igUmPhqOp5Ln+dg4AtIIqjq5Ot2DHCBmb2GHLrXA3929+eBt81sY2R/Anwe+H1vDzQca0GXSRPuUGSovZwX+rSoPAM8Y2Z/qIsBkDGzkcgZuHvp7ddQeuwjwGQzexJ1+ZnpFe9IWCYZOYuR2PFdpfcvQiL4bW8hJ+KFvTrAHlBKZd4cOQbnI+NtMDJkcgQK5Ez7DNIomd6I8b5L7J1en0DGW4403eTud5hZCxIDfh44yd0XNGCMXaZ0DbcB7kCZXU8gx+B41FQjG9mbIefwR1D0u8qbp0+l1z7AZ9J8+Sgy0kai52xC2hDmBjBe9TmzNL5jgB+Y2WLk0B2XHWzuPt3M3kRO7SXL+a/qyJ+Qvtqe6Hqthkp+yvwNOawqSWljvn86Pooi2DOAp9L69jTKCprh6u52czpylltlyyTLlDLP+6BI/ACUCbvQzB5H80l/5DAEbZzy/FlJSoGkPwCY2VmokcabySGxEG0y7k2fj0BZXbugMtdaOEVL5JLBHCh7A9lhH0TX8xWU4dQXORAHoyzMpxox2BVRcvLul14fQFUD+b38/mrI3lwDZav1BdYxsy3c/ZleGu67Qc6SGZNeX0HVLpTK56eb2fmozHcEsEldnWpm1p8i+24uavTyPLLFnk9lvb+uSalkbZwX74Bsy+T93iKUlV4OZM8xs6tQN+mNUPl2bUhVEgvM7CTkxM8SD44cavlvsAAFPutQpZTnlY+l1+EoE+8AADN7AdlpQymcpg0JXIdjLegSZrYfyvoZgqKDf0sb4SkoM2M21EobAWjrfPMT2jvVWlH5xK4UGi0z0KJ5DPUqiRmYotyD0YTzBrpm56JshUNQtuFAVMpV+QWyZKhuhSbbhej6jEzvzwZGp/szb4A/hcpCa0VpQzU6vb6MItv5XO9Jr1kIeH3qcX/2QWM+EG2UtkcbwUPQc/acmU1HJSRHobVqQ3evslMNikV/GLrn9kLXbB7KSgCV8raVWtQFW7FG0IdQ1uQoYICZ3QVc7u51iIR2SilDe76ZHQmchzpQDWbZzmjnIZHuqpI3TIeVfl4/HbuibOaX0IZ/kpnd4u63QZsTvBbZTma2P9L7mZTWiWnAOcmJDXARctAPKf3aAuAyr7iOlZm9L/1zLvCcu7+Zft4vlfE+DVzj7ktSidqf03FGykCsE9mOHI7mzhHp2Cm9PwNpA25AUbZcuVLzUgbQjihjeT5yvCwysyGog/IG6esPA7u7SswnI/mDDRsy8HdAyWbZGDmbtiZ1bE/nljPTRqCsmhGorLfs2KgFpo70/4IcwIORTfOkmd2MOkpOT+dTB6ca1Mh50VNK+4dBaE38AJpDpqSs4Hx/boyCuBtRo3LsNOcsBXD3W81sDMpU2wMlHoCu88vA/7j7jxsy0G5SspffQ6Er2vYxul5HpJ/z57f22gBLhGMtWClp8fgv0uKYyF1AZwHPpkjws8ANXvHW59BuAd8TLRotKOugL8saM46i3JtRpNFWklImUH/kpDidolHBUmSkvoiy1y4CfgdsUJeIqJltijZOS5FD7WnU+e0J5MQAlfNeiIzvbCDMoqaY2TBkyLSg6O8simu6i5n9GZ37MHQ/b0IFNxkdyMbN2PRqaD0amo5N0LlAIbBa6WYMqXwib/JyNtpaFBpHebP44RQNnY42h08BT1Q1KGFd0wi6mvYaQYcBW5jZbHevjVFaJm2I+7v72+7+gpn9Oyrh3RutBXORcXoR8H9V3hCWxpY7Eq7d4SsD0PXbFGVyHWtmVwKnu/trvTbQd84N0JbRdWZyYLSVmLn7ODP7GNLP2QxlCf0RaXJWktLz9/H01nMUjoh1kf1yHHC3u19R/j3QRrJOWfbQLkA7gUKv8Wto8wQKHg1FTo28TozrtQF2n9xkotwgZB/kqAeVY52bHE+DkUP4g6jDXy1ss06YTLHHPNnMWoE7vWgQ8g8UzURu7u3BvVPM7CDgpyg4kTtI9kFl2NsA+5jZ4e4+1WrSpKhOzot3gYdRUxQHTkplrtcDb5rZ9mi+yRqddTrHnEyRdf9eR8/XPWj+GYju0994zXRv0772e6iUfBs0f2Stv7I/qy9yhl7X8f/oDcKxFnRKKdI2AgnkdnSq9UHG+drI4783emDHIwdV1cmRmax7MRm4FmU4HYrK02YiDYG+6FmZWfV07tLifRbSsBiE9En6o/PIkd9tkePty+5+Ux00PMxsbbTwTTCzC9A1+zaaXKemA3StPpCO/PeobeYMuobPUujJLUDX8m3gs8gJ9XF03m/WYbEs3af/SYrUo8Uyi2+X16Z+KJvm1705xh7QB/h3lFWxF0WpRKYFGXEj07EQOUrfRPNnpTNm6J5GkFNDjaBkXP8jivC2oO6Ll7j7467Oteea2XmkDozu/lYDh9sTvorW8q1RlsWuaC0Y1uF7w4ETkMTDeb05wJ7SIaPreZcGGSija2fgSbSZuN/MpgBrptKmupDLzKdSBPg2oshengztuvlVfiPfBaZTNCjYhWIO/VR6b1uKBil/atAYl0tpzpuD5vuhwJfN7P1oPc9OtttRdiEosJvt01oIpi+HSel1CZpnfo/Kzl9Da8Xe6Fq+QMq8r3JwApbZF30XOdVA92DuwAiyzbYFfmxmn6uTY7suzot3gUcograjUBde0DrRDznVWoH7vMLaqaV7sj/waXTtssM6lydPQ7pxl7v7k40Z6TsnBcj+CG37wc1Q0733pWNDZHc/DZzcqPU9HGvB8silI/uiyNkCNIkOQyVb5VKYFlJ5jLtPXva/qiR5Ac/aVfciTYSj0s8XuPt1SdNjBMrIqEXmRSpb+iKFLsBAVLpV1gloRdGnH5rZkzWJiu6H7sWBSLNiHjDOzG5MC8vv0XmPLv1OH2TQVc7o7iru/rKZfRV1qH0eGejPIsNnONJMytzR+yPsPqms8CNow/ETd3/VpLc2FjmmtkFaMzkq+hUqrIEE4NKl+hGAma2JDOuPoDlmZ5bV6RiIHFKvVrkMbRXTCPoyKpMfmH7eCTjYzHZA1/JQ5LCfBfzRzK6pQXlyG+7+KspYe9zMxiFH9sZoztwVbfa3Tl/vixoZXOwV12xMlDO6noROM7quTBuRN4A36pBJUhrfR9LrExRO+JEUWRV5g1vp8+kqyUG40MxORo78gRQZNDlACMqg/WnOoK0o45HjZSjaCL4XbXqzHf2LVL4L2uR/IP37+l4e57vJw0iw/xiKbO2tOnxnLnCJuy+uw7NI+33RtuhZux0F5eejIOcnKaon9kbz67Rl/qeKUhfnRXdJJZH3pHJPc/e7zOxS4J/TV3LCyFYU+8NpqJttZSk575eXTLFOOnYDjjKzE939D40Y67uBSe91dsmWmVB6fyCqaCo31ur1gG441oKVcWB6vQ/4LdpE7YBKJ+5FJaKrI8dabbouJkfMOijl+S1UHrk6hZF6X3q9GXWGedDdr+31gXYTM9sQZSUMQ4bbZOAWFOXuhzaKY5FB3oKMuMORg63qWSWfTK/TUJSznSaHu88ysz2RqPoOaDF5BPhunTJLTILMuyHNh3kArq6ozELyAAAXoElEQVSKt5ZKfS9GC2mZ8cA5vTva7mNmp6ESnjWQ1tFcM7sCODVn26Xy1/cCS9x9SsMG20PSdcsNQ76fnsud0bM3Bjky8iaxsg7DVUkjyMzeg7LvBlI4dJeijcX/Q2Vbq5d+ZWdkvFba8M6USwPT61uolPVlU4e33yJH7xcouvW9hbRZ6tD1rUsZXRTXtfJdeUvP364U3Yaf8kJfbQt0jqD5v/Ln1BU66ATdbmZjkV2TpQ4yLyHHTGVLeRNzkM18OkVWbz+UufUzd7+/9N3DUNDsNerx3LUj37Pu/raZfQVlZB+BgoAdtSkvpbBZqmx7duSA9HoPslsmApjZINQw5XzkVMud6mvjWIN6OC+6Qwrk/gbYxMxaS+M9HgWpj0Jzadbe7IuqCX7g7lf19ni7SzeTKb5jZhO9Hg0LADU9QZUEuyB7bAOT3t8twI3u/rQnHdhUdQA0TvM9HGtBp5RSsnP52USKRQLU+e1+MzsUbRaPoyaOtVJkbBTaGGXdqg3QM/EiRYrwYArNoMo61krntDeFwO91wPE5Epo2Vr9HxunZKCrqwJfM7Mc1MMj3Ta9PoQ0hlKLz6W+wwNTxdEhyRuXPKr/4l9gfXbsXzOwBFBG9F5iay5vc/YdmdivKoNkJZRFd7O4zGzTmLmFmJwLfQgu+ozKRYSgjbYCZfcPd30zZXw83bqTvLi7dyT+iLKd+yIj7EOpYe8WKfrcirAoaQR9HEXlQZuh8tEY4Kpsv680sRRkz55jZ1WkDUmnK83taC0xve2v6bAHSrvo3M9sa/T2GIAd4HWjGjK7cHTNv5HO34dWQdk7WdLy7VPqar6/XaM3rSEedoHloE/UXCp0gUGlv5Uub0nX4kZnNQE6mnG13s7ufmb9nZnsg+ZHFFF0Ya4MlPUMzG+Tui5I99j1kn3wUZaIvRDb2lcAtOehZh3u1tC/aPr3OosiO7ZOewSlmdi3KWqvL3Fk750U3GYOeuVPd/Xulcnk3swvR/vZDqNx1CYXDu/LazJ0kUzyCEkJyMsXO6PxzMsVoJCFTh2QKzGw7FJA4qMNH2yAN7dPM7JfAGe4+vwrnE461YBlKWTGbo8loPoq4DUbRwlYKcfQH0eZwXXef3ojxdpfSBmN1dF5D0fnkDpJrAT9NWRefS+/Vpd3ywWgD+CBwjrvPMbOB7v5WOu9cPpk3+hshw30TpClXSUyaJCNQ2cfkUiaXp0U+d5nE1c59Sbm0oAqTbTfYJ71unI5D0s9TzexO4E5gvLtPIEUS64CZbQaciJ6lnHY/oPSVf0J6TlOz4Vaz69YlUibG0+motFOt9PdfFTSCPp1e70GG3CLgMqRX0hedy2soG3Z4+u5C5NCpdGlFMr53AR529+c6BlFKjracKZQ3kINJQvlVxsx2o8joeroJM7o6dhveEzlC10zvv2JmGwML3H1e3c7RVt50qSl0gtz9GpNkxTBgbnl9MzWBOS39OA1JP9SGFGQ/xSSDcJ+ZXebud7lkDi43s1+7+1IrNROpI2a2PsrkBWXzrov0l1uzwwbNpYvR+lj5hIM6Oi+6yWfT6zFmdqW7Zz1mXDIHt6KKkEE5QGFmo8zsWHfvWBlSNZo2mcLMtkQ6zGMpAmK5HDsnv6yB9hXrpsD8K70+0A6EYy1YhtLDthVaIBaiDou5pGI2MNrMnqBwRn0KlYXWiadQV8xRKOqUz3sgSg0+qvReZcu1oN01ywb4nShyD4rAtJEcbQ+b2f0oEw9UelBZxxpFZgzA0SnlfiLwiEsYtk30NpVSQr0yE8pkzbRsfPZDz2EWvf8i0JLKt+5E9+bEGkTXPoFKr0HZaNcj58xYlJlg6eepNTTcmp1VQSMo623eBdzv7m+ZOtmBMmWOd/cpKbJ/I3KAr06Fgy6l4MIXgZOBv5rZdOBRVBr5hLvP6ZDNlnV1QI6aOuim5jLQPsBhZjaQJsjoSpv1zroN58ZRefxj0abqOVNjholUvNtwGV9506Wm0QlKjpfOOu066nw9CLgKORFrgZkdhYJiORNvJHoOd0YC/8eiPcMC4CYzu9zdX2zMaN8x81H54Gh0T/7IzM539/HJcTgGdR0eCvyt7MSpInV1XnSTnDm4OZIh+bfyh6mKoNUlcTEMJVScjZxSVacpkykS30Lar6B78hmURb8e7SsIQFqODwAXWoP1GsOxFrTDzDZF9fNLkUPtaVQe8wSFIOfawIXI2ZYdOVXf1C+Dq9PLiWY2PKWsX4Mm3fJGqQ/6G9zQiDF2hZzOa9JhyVl4k1Mkpi3rJG8mKBxtWXR7CNXv5Jo3ToPQBng3tGDMTJmF44EH3P0pr3hnqRVhEtveAhk4eeGYgwy4Mn1RlGontGHGzD7m7rf10lB7Qo6G3o1S8scDmNklyPDuRzrPRi+MwTI0tUZQJxlPb6Xsma3RBmMc6mjX11XuOhGthwNQ+WTVOQjN83ujDLvXUYBsppk9DkxBWdtzkKzDNun3bu/9ofaIZs7oMpbfbdjQ5netdOyA/hZ16jYMNL9O0Mpw90eBR83sHKQtunRlv1MFUlnyv9JeQ20pmm9+ioIsZa3N0cAwM/t2HTPXXA01Hkdzaj+kM3q4mc1FDrfhaP1rQYH7qlNL50U3+Tnw4fTvI83sf9Lz1k7P0cz2Ak5F8+ZAKp6JnshNe+6kiZIpUsXcIWj+nw2cguyUiWg+2RtlIu6DAjB9gH8xs+tc0isNIxxrQRspUn09MMHMLkAR7W+jUpip6QDdNx9IR55Yb+rd0faMzmrK3X1uSuF+0MwOQpGZkchJ9Qrwea92x6nOdFgOS5Hup4DHOslKGEKRzr7IK9xOOpGdun2RwbIamlw3QiVORwFzUjbGBNR8YrLXTKMEnddf0MYQtAG+ES2aOyAdiJFoE5XJxmzVN/hj0uvdwKRS2cREdJ5roEyaoGKkObOZNYI6Zjw5cnCDjM+H8ibQpCG3FDnVFrj7pN4ebFcpZTxtV3rbKBwxW6FMhfnonNahyIJaiBoaVJpuZnTNNLPHqFFGl6uDadN1Gy5j3W+6VCudoO7g7gsbPYZusheyTUAB9meQTdaKNr19St9diuzqU4BfU9/1/ny0zm2afm5FDrXhFE6Ne9E5VpY6Oy+6yU3p2B8FJk4C/qlUgr4xcuqfiDINs1P7zgaMtcukgOAQmjOZ4hNoTzADOMvdfwVte/gXkYzKFWb2NZQt24p8EmvQ6HNz9zjiwN2hKH18Etin9L6l1/WBSek75eO3wBqNHn8Xzm8waq18BHIW9lvO97ZE3U9HAf3Lf4MqHkggHJQK3Iom0bfRZPs4WlD+EwmT7ogM1fWBq9P3r230Oazk/HZN43wTGdXle6+ldL4t6ViMDLxZwHsbPf4enO+AdL3K53kZKr9bP92bJ6FW9hPSOb/Q6HGv5Jx2S+cxGziyw2ffLF3XoY0eaxwrvZb9kFPGOrw/Os01rUhA9+ONHms3zqnj3Dkb6Tq1oiyEbyGjbUB6Bq9Mn93U6LF34dyGpmfst2lO7Lh+Z021pel4K71/ObB2o8ffhfMbtpLzW9Lh5wXpmk5B2rANP4d3cO4bosyZ81nWNvtNo8fXhfFn2+Xo9Ny1ou59I8rfQZmHn0KOmLzuP5N/P46GXsML0jV5KF2jLVE5fb4PH0OZP9NL124h8JVGj70H59qXYj+0RbpX56R5s/zszQC2a/R4u3A+x6fxPgMcV3q/49r+tdIa0Qps3eixd+Mc8x7uQORYakXZ9Hul949GJdgd143bGj32LpzbmWmsc9Iz9iUUdBnRyXeHlJ7VZxs99i6c2x/TWP8AbJHey+uFAX07+Tu8Cnyh0WOPjLWgzCfT6zTU5Y1U+tIC4O6zzGxPVLa1A4puPwJ811Nnn6qSdHG+iDzbs1DJ59T02bWolHCcuz/masLQrhGDp6e3ivjydVjKWQl7o43iLHTeiyg6vP6ZapPvywdRSQwonXk/VKpVnsda0KS7LrDY3evW5jyXmp2KDNJvICfwoSgT4Ux3v9nUxGA4Stf/ALq2VaacEfQZMxuKNknzUBaCARPc/Y2sd0ENNJBWRbzJNIKWM3eOoHD2bgx8Hc0505DjPpfOjOvVwfYA73rGU2440QfpNp7m9eh2Op8mz+haHl7/bsOZZtYJanay/u144K/u/krSUgMF/k5x9/8DMLOLgS+gZ3BYeq/SJYVmNgJ1mZ+Z90Ipa+YZM/sWsDtqYrAeWhdnAj/3QnO0yuQywknAHdCukYghR0aLu/+Xma2H7O+56Jyf6PR/rBieMs3d/XozuwvZomsC30zXbx+07i9BgbMZaJ94aUMG3D3KEggHoHLX2cCMpIH+CAogTUYBtlxpUNks+xK5Q/vbJKkpb9+MrqXkn5ievjeMkt52owjHWlBm3/T6FPBy+ne5fLCPS4vsIrTQzC19VvV0/B2Bz6d/P47afLea2UgU8T0IONvM5qFo2/XAn7z6gvCZFemwgJ719dKxLVpEcop+XRxrM5Cw/RzgfuAkM1sLbaAOQO3cN6DQhbi3l8f5jik5sd8GfpnE089AmQmfBTY2s6+4BMVfSseklPJdZTpqIO2F5ph5FOUUk6DNcRPUDK+pRhCaBzubO/M80kIhnr4d7efOWmjIZVzdlO9Kx/dTCd5OaKO0HfAiOqe/uPvfarCut2M557czKiMcg7Iq83WtdEOi7uI16jac8eZvutTUpOdrc5TlOt0LUfssZXEH8FBpA/woyhhdnepLV2SOAX5gZotRUsG4kp02LTkRr3Z1o68btXVedAUz2wjN/5Nccj7fQEkh6yOHcNYDBDnVfg6c5+7P9P5ou0c3kymeRgHBWiRTmNl7UFYrwChfTnm8F3rahuaV4VTAaRiOtQAAM3s/8ma/gWq154Em1xy5IDnZ0gKypBxpqoHxfQAyql8AznV1koTCoFuIomhrUjjaHgW2rcPmohtZCRlDz/+slKFXSdLisQO6954A5qb7sS/q4vMaEoj9Xfr+5sjBdiz16OjTRvk+y8+Wu19mZncA56B7eA/U2e9U4L9zpmjFI74rMgCg0EH6sJldhQy4CdREAyloz/KMoKrSg4ynWsydXSFlPF2fjo5zUOXXvZXRRBldTUe+v6y5my41OzkYvwQ40Myyk2Ioyt6akGzTTH9kZ4O0ZKtuu2yAsikHIcfE5FLW2i6omc8HgQEpG+pyd6+L3nStnRdd5DjU3Gu8mT2LnLkvoEB1C9pH5Ez77wB3eH2any0vIJipczLFHIrkni3N7PvA2Z46e6e1vCWtH0NRUHA4FdG8DcdakPlE6d9Hm9kgJGD5SHJCtU02ZpYjvpVdEDvh4PR6G0pZz+yfXp1ig78UPRsvmdka7v46NaKLUfv+6etVz+raLb2+DJQ7frZlw6T70dx9qatL2MXpqA2lTUZ2PA00taofgqJrG6JrthBpBe6PdJ7qUMq0MgOgBT17I9OxkBp2tQvqT5PNnd2m7Eiru1OtI3XM6GpyVoWmS81OlngYiIIR21PsFVqAD5nZTPTMtQKbofnzSXd/tQbO+7Eo++4N4Ccp6wkzGw1cg7LtW9G9fBiwhZnNdveHGjPcblFr50UXyd2w90HX6WUKv0dOFrkWlSs/15AR9pBmDQimOWGhmWW7vx/w5fTZpakkO3dxHYBkco5N372x1wfcCeFYCzJ5gRyEHszdUJnWTDObjJxRD7h72blRC1K551ZocXwgZTllcrfJO1EZzCaoLGYNZAAMo4iQ1pKVRO1/1cixdYHbkLNlS2ScLZNJUb4fS9lsXqf7NBkv30X321jkPFsN3Y+gaGnf9D5IpLPKBmkbPTAAmkYDKag3NZ87g6AONLNOULNzaHodgOyRoRQNCkYg+ZX9ULOQuRTdQ29Or30pBUkrSA7IP0DS00zyI19HNspiZKeBzn9HlPl0YJWdhs3gvFgZtmw3bJBUTNZO7YOCuO8DzjGzp9Cc8iQ1q5RopoBg6e9+OfBp5BgdAfwH8B9mNgVVtMxGGoFboj3ELNRMpOFYje6d4O+Imb2FHjynEIDvQ1GDvhBFOHKZ1n0oLfr5hgy4G5jZccAvkEbX1939hvT+KGS0LQKOdfdrzGxTlO30USSku6cn8csg+HthZnsA96QfnUJI/HXk5F2MFn1Dz9+lSDei1hP4cgyAzO/c/fBGjCsIgiD4+5I2v/NLb+UNb2YJyzZd2gtlPp3g7v/dOyMNOmJm/YF/QEH4MWiDWyZncmVbJncfHgh8zN1vK+l3VRIzm4/KlM8GznD3RWZ2JJLm2CB97RFko22MHIV/BY6qiU7XWOA65LwoP3edOS+GoOfwBHe/tpeH2m3MbBhqUre8Soml6JzzeberlGiWoG4nAcGL8x64yiQJgJ8D/5zeegs5gPuWvpb9Ff1Q8P7sFMhvKJGxFpA0Lvojo2U12t8XeeM+BEWjNkfZJsem393Tq995cZv0uh7KwsPM+rv7FDPbAUUspqTvLKbosDgnnGpBL5EbNCxC0ZeXUJON8ajd9HzgFXefb2YDXJ1DrfP/qj6EBlIQBMEqSzM3XWpqkm38S9RkaRCyo/dCmei7s+y1bNPGJXWMrrhTbSfkVJuPyucWpVLkwymcag8Duyd7bDLSW9uwIQPuGXchSZGOzotR6YDCeQHKIrq9NwfYU6JSQtRVAsHVXPBrqHz3eIpM5bwn709R3no9cLpXpIFIONYCKDb1DyIjB6QXsB+wNe3vk5zNti6wuAZONdCD+DaaSNeEdi2YJ6GuitlJsRVyHkLFU2aDpiKXw+RnrT+KnK2OND4mAJjZ0iwyW/dstY7U1QAIgiAIuo83adOlVY2kyzU5HRes5Fo+mhxRlc5WQ06yFlSinMvo9qHQo86N0JaY2WBgWvqdPnXIVoN6Oy+6QxdLJZuyW3Sdcfe3zewHwNXAEcAJwNrI2f06ksT5GXBNTjaowr4oSkEDzOwhpH3wK+Bkd59T+mwttDAegMojNyj96u3uvi8Vx8yOBS5JPz4EHJEXvqQf0FrSEzgfZeOtDuzs7hMaMORgFaKTcpjcrSizCHU/m4o6o+bj+boJrgZBEATByliJTMDv3f0zjRhX0H3StdwROAS4z90vNrN+2e6uImZ2APC/qFJnGnK47EaRyXU58A13n2Nm7wV+jDTZxrn7gQ0Yco9JDcBGsqzzYgntnRd1aDjRLepaKrmqkhppbIpKd2flRIMq3ZfhWFvFKW3qW4HTgJ+kf/dFDqfWDt/fHDnYjkWtpS/q3RF3HzPbD7iJoqb+KuCH7v5Y6TvDUTTqKnTuU939/Q0YbrCK0QUtiI7MQ3qBLyMtj9dW/PUgCIIgqCex+Q16GzNbB5iISjuz7u1StD8wYA93vz9992CUeTkS+JK7X9Lpf1oT6uC8CIKqEo61VRwz2xe4BWk6neDu13Xynb7oXqlsdGlFJF2EG1DUs5wN9AgShH8+fbYLSn1+Bfipu5/V22MNVm26WA6TBZ7nuftavTvCIAiCIAiC5iXJw5wCnE7R+ROkw/wzd/9m6bv/i7TX5gPb16GpWxAEfx/CsbaKkxaPLVDXl6fdfcaKohLp+32RxFOV9RHaYWb7I+caSD+gD+3L7XKL8P4oxft77j6jN8cYBB1ZSTnMb939s40YVxAEQRAEQTNjZocDn6PQH7vZ3c8sfZ47ui8GrnP3I3t/lEEQVIVwrAWrDGZ2EHAWasgASut2CmFSUMebY9z9pV4eXhCskCiHCYIgCIIg6D2S7TUMmFtOOjCz0cAPUaO3KUhz7ZbGjDIIgioQjrVglSEtjmOBo4H9gbWQMPwbKNp0IXBJaFYFQRAEQRAEQdAZZvZB1JBhDNJnviJ1SA2CYBUlHGvBKktaFLcCngUeBxa5u4dAZxAEQRAEQRAEK8LMBgNL6qpDHQTBu0c41oIgCIIgCIIgCIIgCIKgB/Rp9ACCIAiCIAiCIAiCIAiCoI6EYy0IgiAIgiAIgiAIgiAIekA41oIgCIIgCIIgCIIgCIKgB4RjLQiCIAiCIAiCIAiCIAh6QDjWgiAIgiAIgiAIgiAIgqAHhGMtCIIgCIIgCIIgCIIgCHpAONaCIAiCIAiCIAiCIAiCoAeEYy0IgiAIgiAIgiAIgiAIesD/B16IvbuheFC+AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig2, (ax1, ax2) = plt.subplots(2, 1, sharex = True, figsize=(20, 15),)\n", "\n", "# ax.boxplot(ET_WA,)\n", "\n", "# ET_WA_name = [Tam, Gao, Daf, Tan, Man, Pus, Po, Bor, Uni, Sel, Far, Fin, Ded, Sir, Bog, Som, Oua, Kou, Dor, Ous]\n", "ET_WA_name= ['Tam', 'Gao ' ,'Daf' , 'Tan', 'Man', 'Pus', 'Po', 'Sel', 'Bor' , 'Uni' , 'Far', 'Fin', 'Ded', 'Sir', 'Bog', 'Som', 'Oua', 'Kou', 'Dor', 'Ous']\n", "\n", "\n", "# ET_WA_name = ['Bog', 'Bor' ,'Daf ' , 'Ded', 'Dor', 'Far', 'Fin', 'Gao' , 'Kou' , 'Man', 'Oua', 'Ous', 'Po ', 'Pus ', 'Sel', 'Sir ', 'Som', 'Tam', 'Tan', 'Uni']\n", "\n", "ax2.set_xticklabels(ET_WA_name, rotation=75, fontsize=20)\n", "# ax1.set_xticklabels(ET_WA_name, rotation=75, fontsize=20)\n", "\n", "# ax3=ax2.twinx()\n", "# ax42.plot(On_d['Station'], On_d['dif_Onset 2017'], '--r', label='dif_Onset', marker='^' ) \n", "# ax1.scatter(a, Rain_WA, marker='o', label='Precipitation' )\n", "# ax1.bar(a, Deficit_WA, ) #color = 'red', marker='^', label='Reference evaporation')\n", "# ax1.set_title('Average seasonal water deficit in the semi-arid zone of WA', fontsize=30, fontweight='bold')\n", "ax1.set_ylabel('Sufficiency (mm/season)', fontsize=25, fontweight='bold')\n", "# ax1.legend()\n", "\n", "# ax3.bar(a, dry_sp['CDD7'], )\n", "# ax2.bar(dry_sp['Station'], dry_sp['CDD7'], )\n", "\n", "ax1.boxplot(Deficit_WA,)\n", "ax2.boxplot(Cdd7_WA,)\n", "\n", "ax2.set_xticklabels(ET_WA_name, rotation=75, fontsize=20)\n", "# X + 0.25, data[1], color = 'g', width = 0.25\n", "# ax.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "\n", "# ax3.bar(dry_sp['Ind'] +0.25, dry_sp['CDD3'], )\n", "# ax2.set_title('Average frequency of dry spells in the semi-arid zone of WA', fontsize=30, fontweight='bold')\n", "ax2.set_ylabel('Frequencies of 7 CDD [yr-1]', fontsize=25, fontweight='bold')\n", "# ax2.tick_params(labelrotation=75, zorder=True)\n", "#ax2.set_xlabel('Sowing date ')\n", "\n", "# ax1.legend()\n", "\n", "ax1.text(20, 900, r'a', fontsize=25)\n", "ax2.text(20, 15, r'b', fontsize=25)\n", "\n", "# red_patch = mpatches.Patch(color='white', label='a')\n", "# white_patch = mpatches.Patch(color='white', label='b')\n", "# ax1.legend(handles=[red_patch])\n", "# ax2.legend(handles=[white_patch])\n", "\n", "plt.subplots_adjust(hspace= 0.05)\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Deficit_dry_spell6.png', dpi=400)\n", "\n", "# plt.subplots_adjust(hspace= 0.0)\n", "\n", "\n", "\n", "#plt.xticks([1, 2, 3], ['ET_Benin', 'ET_Niger', 'ET_Mali'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 154.95056537, -350.54510135])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Def_Tam.values" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[56 5\n", " 57 16\n", " Name: CDD7, dtype: int64, 23 3\n", " 24 2\n", " 25 2\n", " 26 5\n", " Name: CDD7, dtype: int64, 6 4\n", " 7 14\n", " 8 4\n", " Name: CDD7, dtype: int64, 58 3\n", " 59 7\n", " 60 3\n", " 61 2\n", " Name: CDD7, dtype: int64, 30 4\n", " 31 15\n", " 32 11\n", " Name: CDD7, dtype: int64, 44 5\n", " 45 2\n", " 46 3\n", " Name: CDD7, dtype: int64, 40 3\n", " 41 3\n", " 42 3\n", " 43 4\n", " Name: CDD7, dtype: int64, 47 7\n", " 48 12\n", " 49 11\n", " Name: CDD7, dtype: int64, 3 4\n", " 4 5\n", " 5 6\n", " Name: CDD7, dtype: int64, 62 5\n", " 63 9\n", " 64 8\n", " Name: CDD7, dtype: int64, 17 3\n", " 18 13\n", " 19 9\n", " Name: CDD7, dtype: int64, 20 5\n", " 21 4\n", " 22 3\n", " Name: CDD7, dtype: int64, 9 6\n", " 10 5\n", " 11 5\n", " 12 6\n", " Name: CDD7, dtype: int64, 50 5\n", " 51 4\n", " 52 11\n", " Name: CDD7, dtype: int64, 0 10\n", " 1 9\n", " 2 6\n", " Name: CDD7, dtype: int64, 53 9\n", " 54 6\n", " 55 10\n", " Name: CDD7, dtype: int64, 33 9\n", " 34 13\n", " 35 7\n", " 36 9\n", " Name: CDD7, dtype: int64, 27 9\n", " 28 7\n", " 29 7\n", " Name: CDD7, dtype: int64, 13 10\n", " 14 12\n", " 15 11\n", " 16 13\n", " Name: CDD7, dtype: int64, 37 8\n", " 38 10\n", " 39 10\n", " Name: CDD7, dtype: int64]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Cdd7_WA" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([ 154.95056537, -350.54510135]),\n", " array([ 97.88650818, 514.72930331, 455.23100729, 176.38195098]),\n", " array([ 275.3555385 , -307.32975958, 192.27539546]),\n", " array([430.79468487, 236.1181011 , 436.0435763 , 400.39012834]),\n", " array([ 269.50521277, -362.9624686 , 26.51608236]),\n", " array([314.4503197 , 561.72870508, 961.89715339]),\n", " array([422.19915348, 390.55240165, 575.05863401, 706.75472744]),\n", " array([ 150.47534394, -384.88555822, -343.94015458]),\n", " array([337.17924508, 500.33946538, 197.21539973]),\n", " array([-235.87286196, -379.16013989, -62.14791019]),\n", " array([ 635.40116569, -100.63972552, -200.71993801]),\n", " array([136.33098882, 130.13929817, 349.7055605 ]),\n", " array([-103.50877652, 191.81030056, 677.31280009, 275.41073155]),\n", " array([747.115647 , 354.24270021, 19.58360702]),\n", " array([-236.11389845, -246.98578917, -135.76422076]),\n", " array([-389.50003553, -352.72687133, -198.62170982]),\n", " array([-109.76896705, 355.63729769, 270.4743812 , 629.32996651]),\n", " array([205.94969301, 449.51418026, 209.9862076 ]),\n", " array([-524.36869386, -343.07818504, -239.44787951, -144.05619323]),\n", " array([ 180.85960772, -422.34418818, 134.04420034])]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Deficit_WA" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "# Def_WA[16]\n", "# ET_Tam.loc[ET_Tam['Month']==5]['P - E']" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "Def_WA_May = [ET_Tam.loc[ET_Tam['Month']==5]['P - E'], ET_Gao.loc[ET_Gao['Month']==5]['P - E'], ET_Daf.loc[ET_Daf['Month']==5]['P - E'], ET_Tan.loc[ET_Tan['Month']==5]['P - E'], ET_Man.loc[ET_Man['Month']==5]['P - E'], ET_Pus.loc[ET_Pus['Month']==5]['P - E'], ET_Po.loc[ET_Po['Month']==5]['P - E'], ET_Bor.loc[ET_Bor['Month']==5]['P - E'], ET_Uni.loc[ET_Uni['Month']==5]['P - E'], ET_Sel.loc[ET_Sel['Month']==5]['P - E'], ET_Far.loc[ET_Far['Month']==5]['P - E'], ET_Fin.loc[ET_Fin['Month']==5]['P - E'], ET_Ded.loc[ET_Ded['Month']==5]['P - E'], ET_Sir.loc[ET_Sir['Month']==5]['P - E'], ET_Bog.loc[ET_Bog['Month']==5]['P - E'], ET_Som.loc[ET_Som['Month']==5]['P - E'], ET_Oua.loc[ET_Oua['Month']==5]['P - E'], ET_Kou.loc[ET_Kou['Month']==5]['P - E'], ET_Dor.loc[ET_Dor['Month']==5]['P - E'], ET_Ous.loc[ET_Ous['Month']==5]['P - E'],]\n", "Def_WA_Jun = [ET_Tam.loc[ET_Tam['Month']==6]['P - E'], ET_Gao.loc[ET_Gao['Month']==6]['P - E'], ET_Daf.loc[ET_Daf['Month']==6]['P - E'], ET_Tan.loc[ET_Tan['Month']==6]['P - E'], ET_Man.loc[ET_Man['Month']==6]['P - E'], ET_Pus.loc[ET_Pus['Month']==6]['P - E'], ET_Po.loc[ET_Po['Month']==6]['P - E'], ET_Bor.loc[ET_Bor['Month']==6]['P - E'], ET_Uni.loc[ET_Uni['Month']==6]['P - E'], ET_Sel.loc[ET_Sel['Month']==6]['P - E'], ET_Far.loc[ET_Far['Month']==6]['P - E'], ET_Fin.loc[ET_Fin['Month']==6]['P - E'], ET_Ded.loc[ET_Ded['Month']==6]['P - E'], ET_Sir.loc[ET_Sir['Month']==6]['P - E'], ET_Bog.loc[ET_Bog['Month']==6]['P - E'], ET_Som.loc[ET_Som['Month']==6]['P - E'], ET_Oua.loc[ET_Oua['Month']==6]['P - E'], ET_Kou.loc[ET_Kou['Month']==6]['P - E'], ET_Dor.loc[ET_Dor['Month']==6]['P - E'], ET_Ous.loc[ET_Ous['Month']==6]['P - E'],]\n", "Def_WA_Jul = [ET_Tam.loc[ET_Tam['Month']==7]['P - E'], ET_Gao.loc[ET_Gao['Month']==7]['P - E'], ET_Daf.loc[ET_Daf['Month']==7]['P - E'], ET_Tan.loc[ET_Tan['Month']==7]['P - E'], ET_Man.loc[ET_Man['Month']==7]['P - E'], ET_Pus.loc[ET_Pus['Month']==7]['P - E'], ET_Po.loc[ET_Po['Month']==7]['P - E'], ET_Bor.loc[ET_Bor['Month']==7]['P - E'], ET_Uni.loc[ET_Uni['Month']==7]['P - E'], ET_Sel.loc[ET_Sel['Month']==7]['P - E'], ET_Far.loc[ET_Far['Month']==7]['P - E'], ET_Fin.loc[ET_Fin['Month']==7]['P - E'], ET_Ded.loc[ET_Ded['Month']==7]['P - E'], ET_Sir.loc[ET_Sir['Month']==7]['P - E'], ET_Bog.loc[ET_Bog['Month']==7]['P - E'], ET_Som.loc[ET_Som['Month']==7]['P - E'], ET_Oua.loc[ET_Oua['Month']==7]['P - E'], ET_Kou.loc[ET_Kou['Month']==7]['P - E'], ET_Dor.loc[ET_Dor['Month']==7]['P - E'], ET_Ous.loc[ET_Ous['Month']==7]['P - E'],]\n", "Def_WA_Aug = [ET_Tam.loc[ET_Tam['Month']==8]['P - E'], ET_Gao.loc[ET_Gao['Month']==8]['P - E'], ET_Daf.loc[ET_Daf['Month']==8]['P - E'], ET_Tan.loc[ET_Tan['Month']==8]['P - E'], ET_Man.loc[ET_Man['Month']==8]['P - E'], ET_Pus.loc[ET_Pus['Month']==8]['P - E'], ET_Po.loc[ET_Po['Month']==8]['P - E'], ET_Bor.loc[ET_Bor['Month']==8]['P - E'], ET_Uni.loc[ET_Uni['Month']==8]['P - E'], ET_Sel.loc[ET_Sel['Month']==8]['P - E'], ET_Far.loc[ET_Far['Month']==8]['P - E'], ET_Fin.loc[ET_Fin['Month']==8]['P - E'], ET_Ded.loc[ET_Ded['Month']==8]['P - E'], ET_Sir.loc[ET_Sir['Month']==8]['P - E'], ET_Bog.loc[ET_Bog['Month']==8]['P - E'], ET_Som.loc[ET_Som['Month']==8]['P - E'], ET_Oua.loc[ET_Oua['Month']==8]['P - E'], ET_Kou.loc[ET_Kou['Month']==8]['P - E'], ET_Dor.loc[ET_Dor['Month']==8]['P - E'], ET_Ous.loc[ET_Ous['Month']==8]['P - E'],]\n", "Def_WA_Sep = [ET_Tam.loc[ET_Tam['Month']==9]['P - E'], ET_Gao.loc[ET_Gao['Month']==9]['P - E'], ET_Daf.loc[ET_Daf['Month']==9]['P - E'], ET_Tan.loc[ET_Tan['Month']==9]['P - E'], ET_Man.loc[ET_Man['Month']==9]['P - E'], ET_Pus.loc[ET_Pus['Month']==9]['P - E'], ET_Po.loc[ET_Po['Month']==9]['P - E'], ET_Bor.loc[ET_Bor['Month']==9]['P - E'], ET_Uni.loc[ET_Uni['Month']==9]['P - E'], ET_Sel.loc[ET_Sel['Month']==9]['P - E'], ET_Far.loc[ET_Far['Month']==9]['P - E'], ET_Fin.loc[ET_Fin['Month']==9]['P - E'], ET_Ded.loc[ET_Ded['Month']==9]['P - E'], ET_Sir.loc[ET_Sir['Month']==9]['P - E'], ET_Bog.loc[ET_Bog['Month']==9]['P - E'], ET_Som.loc[ET_Som['Month']==9]['P - E'], ET_Oua.loc[ET_Oua['Month']==9]['P - E'], ET_Kou.loc[ET_Kou['Month']==9]['P - E'], ET_Dor.loc[ET_Dor['Month']==9]['P - E'], ET_Ous.loc[ET_Ous['Month']==9]['P - E'],]\n", "Def_WA_Oct = [ET_Tam.loc[ET_Tam['Month']==10]['P - E'], ET_Gao.loc[ET_Gao['Month']==10]['P - E'], ET_Daf.loc[ET_Daf['Month']==10]['P - E'], ET_Tan.loc[ET_Tan['Month']==10]['P - E'], ET_Man.loc[ET_Man['Month']==10]['P - E'], ET_Pus.loc[ET_Pus['Month']==10]['P - E'], ET_Po.loc[ET_Po['Month']==10]['P - E'], ET_Bor.loc[ET_Bor['Month']==10]['P - E'], ET_Uni.loc[ET_Uni['Month']==10]['P - E'], ET_Sel.loc[ET_Sel['Month']==10]['P - E'], ET_Far.loc[ET_Far['Month']==10]['P - E'], ET_Fin.loc[ET_Fin['Month']==10]['P - E'], ET_Ded.loc[ET_Ded['Month']==10]['P - E'], ET_Sir.loc[ET_Sir['Month']==10]['P - E'], ET_Bog.loc[ET_Bog['Month']==10]['P - E'], ET_Som.loc[ET_Som['Month']==10]['P - E'], ET_Oua.loc[ET_Oua['Month']==10]['P - E'], ET_Kou.loc[ET_Kou['Month']==10]['P - E'], ET_Dor.loc[ET_Dor['Month']==10]['P - E'], ET_Ous.loc[ET_Ous['Month']==10]['P - E'],]\n", "\n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "618.3860983402719" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Def_WA[16].max()\n", "# type(Def_WA)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No handles with labels found to put in legend.\n", "No handles with labels found to put in legend.\n", "No handles with labels found to put in legend.\n", "No handles with labels found to put in legend.\n", "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, (ax5, ax4, ax3, ax2) = plt.subplots(4, 1, sharex = True, figsize=(20, 20),)\n", "\n", "# ax = subplot(figsize=(20, 10),)\n", "# ax.boxplot(Def_WA_Oct,)\n", "# ax1.boxplot(Def_WA_Sep,)\n", "ax2.boxplot(Def_WA_Aug, )\n", "ax3.boxplot(Def_WA_Jul,)\n", "ax4.boxplot(Def_WA_Jun,)\n", "ax5.boxplot(Def_WA_May,)\n", "# x = [1, 2, 3]\n", "# y = [0, 0, 0 ]\n", "# my_xticks = ['Q_0.75','Q_0.95', 'Q_0.9']\n", "# plt.xticks(x , my_xticks) \n", "\n", "# ET_WA_name= ['Tam (9.4,-1)', 'Gao (10.39,-3.2) ' ,'Daf (10.4,-2.5)' , 'Tan (10.6,1.3)', 'Man (10.9,0.8)', 'Pus (11.1,-0.1)', 'Po (11.2,-1.1)', 'Bor (11.7,-2.9)' , 'Uni (11.8,13.2)' , 'Sel (11.6,-8.2)', 'Far (12.2,-10.7)', 'Fin (12.3,-5.5)', 'Ded (12.5,-3.5)', 'Sir (12.7,-9.2)', 'Bog (13,-0.2)', 'Som (13.2,-4.8)', 'Oua (13.6,-2.4)', 'Kou (13.8,-9.6)', 'Dor (14,-0.1)', 'Ous (14.2,-10.5)']\n", "ET_WA_name= ['Tamale', 'Gaoua ' ,'Daffiama' , 'Tanguieta', 'Mandouri', 'Pusiga', 'Po', 'Boromo' , 'Unimaid' , 'Selingue', 'Fari', 'Finkoloni', 'Dedougou', 'Sirakoro', 'Bogande', 'Somo', 'Ouahigouya', 'Kourounikoto', 'Dori', 'Oussoubidiagna']\n", "\n", "# #ET_WA_name= ['Djo (9.7,1.6); 2002-20016', '\\n\\nBenin', 'Bel (9.8,1.7) / 2002-20016', 'Nal (9.7,1.6) / 2002-20016', 'Nya (13.6,2.6) / 2005-2015', '\\n\\nNiger', 'Ban (13.5,2.6) / 2005-2015', 'Kob (14.7,-1.5) / 2005-2011', '\\n\\nMali','Ago (15.3,-1.5) / 2005-2011', 'Bam (17.1,-1.4) / 2005-2011']\n", "ax2.set_xticklabels(ET_WA_name, rotation=75, fontsize=10)\n", "\n", "# # ax1=ax.twinx()\n", "# # ax42.plot(On_d['Station'], On_d['dif_Onset 2017'], '--r', label='dif_Onset', marker='^' ) \n", "# ax1.scatter(a, Rain_WA, marker='o', label='Precipitation' )\n", "# ax1.scatter(a, Eref_WA, color = 'red', marker='^', label='Reference evaporation')\n", "# ax1.set_title('Seasonal rainfall/Evaporation in the semi-arid zone of WA', fontsize=22)\n", "# ax1.set_ylabel('Precipitation/Eref (mm/season)', fontsize=22)\n", "# ax1.legend()\n", "\n", "# ax.set_ylim([-10,60])\n", "# ax1.set_ylim([-10,60])\n", "ax2.set_ylim([-10,60])\n", "ax3.set_ylim([-10,60])\n", "ax4.set_ylim([-10,60])\n", "ax5.set_ylim([-10,60])\n", "\n", "ax2.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "ax3.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "ax4.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "ax5.yaxis.grid(True, linestyle='--', which='major', color='Black', alpha=0.5)\n", "\n", "ax5.set_title('Rainfall deficit in the semi-arid zone of WA', fontsize=20)\n", "\n", "ax2.set_ylabel('P - Eref (mm/day)', fontsize=15)\n", "ax3.set_ylabel('P - Eref (mm/day)', fontsize=15)\n", "ax4.set_ylabel('P - Eref (mm/day)', fontsize=15)\n", "ax5.set_ylabel('P - Eref (mm/day)', fontsize=15)\n", "#ax2.set_xlabel('Sowing date ')\n", "\n", "ax2.legend(title=\"August\", fontsize=10)\n", "ax3.legend(title=\"July\", fontsize=10)\n", "ax4.legend(title=\"June\", fontsize=10)\n", "ax5.legend(title=\"May\", fontsize=10)\n", "\n", "\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Rain_deficit_boxplot2.png')\n", "\n", "plt.subplots_adjust(hspace=0.1)\n", "\n", "\n", "\n", "#plt.xticks([1, 2, 3], ['ET_Benin', 'ET_Niger', 'ET_Mali'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# ET_WA\n", "# Rain_WA" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Probability of dry spells\n" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "from matplotlib.dates import DateFormatter\n", "\n", "import matplotlib.dates as md\n", "\n", "from scipy.interpolate import interp1d" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StationStarting dateMd>7Md>10Md>15CMd>7CMd>10CMd>15
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" ], "text/plain": [ " Station Starting date Md>7 Md>10 Md>15 CMd>7 CMd>10 CMd>15\n", "75 Dedougou 21-Aug 0 0 0 0 0 0\n", "76 Dedougou 1-Sep 0 0 0 0 0 0\n", "77 Dedougou 11-Sep 25 0 0 25 0 0\n", "78 Dedougou 21-Sep 50 25 0 50 25 0\n", "79 Dedougou 1-Oct 75 50 25 75 50 25" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob = pd.read_csv(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Dry_Spell_Prob.csv', )\n", "\n", "prob.tail()\n", "# On" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Station', 'Starting date', 'Md>7', 'Md>10', 'Md>15', 'CMd>7', 'CMd>10',\n", " 'CMd>15'],\n", " dtype='object')" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob.columns" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'21-May'" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob.at[2,'Starting date']" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "for i in range(len(prob)):\n", " prob.at[i,'Starting date'] = datetime.datetime.strptime(prob.at[i,'Starting date'], '%d-%b') #.strftime('%Y')" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StationStarting dateMd>7Md>10Md>15CMd>7CMd>10CMd>15
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" ], "text/plain": [ " Station Starting date Md>7 Md>10 Md>15 CMd>7 CMd>10 CMd>15\n", "0 Dori 1900-05-01 00:00:00 75 75 75 25 0 0\n", "1 Dori 1900-05-11 00:00:00 100 50 25 75 0 0\n", "2 Dori 1900-05-21 00:00:00 100 0 0 100 0 0\n", "3 Dori 1900-06-01 00:00:00 75 50 0 75 50 0\n", "4 Dori 1900-06-11 00:00:00 75 50 0 75 50 0" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob.head()" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "prob_Gao = prob.loc[prob['Station']=='Gaoua'].copy()\n", "prob_Ous = prob.loc[prob['Station']=='Oussoubidiagna'].copy()\n", "prob_Dor = prob.loc[prob['Station']=='Dori'].copy()\n", "prob_Uni = prob.loc[prob['Station']=='Unimaid'].copy()\n", "prob_Ded = prob.loc[prob['Station']=='Dedougou'].copy()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "prob_Gao.iloc[:,2:] = prob_Gao.iloc[:,2:]/100\n", "prob_Ous.iloc[:,2:] = prob_Ous.iloc[:,2:]/100\n", "prob_Dor.iloc[:,2:] = prob_Dor.iloc[:,2:]/100\n", "prob_Ded.iloc[:,2:] = prob_Ded.iloc[:,2:]/100" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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StationStarting dateMd>7Md>10Md>15CMd>7CMd>10CMd>15
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" ], "text/plain": [ " Station Starting date Md>7 Md>10 Md>15 CMd>7 CMd>10 CMd>15\n", "16 Gaoua 1900-05-01 00:00:00 0.75 0.25 0.00 0.75 0.25 0.00\n", "17 Gaoua 1900-05-11 00:00:00 0.50 0.00 0.00 0.25 0.00 0.00\n", "18 Gaoua 1900-05-21 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "19 Gaoua 1900-06-01 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "20 Gaoua 1900-06-11 00:00:00 0.00 0.00 0.00 0.00 0.00 0.00\n", "21 Gaoua 1900-06-21 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "22 Gaoua 1900-07-01 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "23 Gaoua 1900-07-11 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "24 Gaoua 1900-07-21 00:00:00 0.25 0.25 0.00 0.25 0.25 0.00\n", "25 Gaoua 1900-08-01 00:00:00 0.25 0.25 0.00 0.25 0.25 0.00\n", "26 Gaoua 1900-08-11 00:00:00 0.00 0.00 0.00 0.00 0.00 0.00\n", "27 Gaoua 1900-08-21 00:00:00 0.00 0.00 0.00 0.00 0.00 0.00\n", "28 Gaoua 1900-09-01 00:00:00 0.00 0.00 0.00 0.00 0.00 0.00\n", "29 Gaoua 1900-09-11 00:00:00 0.00 0.00 0.00 0.00 0.00 0.00\n", "30 Gaoua 1900-09-21 00:00:00 0.25 0.00 0.00 0.25 0.00 0.00\n", "31 Gaoua 1900-10-01 00:00:00 0.50 0.50 0.25 0.50 0.50 0.25" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prob_Gao" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig4, ((ax3, ax4), (ax, ax2)) = plt.subplots(2, 2, figsize=(30, 20)) # sharex = True,)\n", "\n", "# Oussoubidiagna\n", "ax3.plot(prob_Ous['Starting date'], prob_Ous['Md>7'], label='Md>7',markersize=10, marker='o', color = 'gray' ) #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "ax3.plot(prob_Ous['Starting date'], prob_Ous['Md>10'], label='Md>10', markersize=10, marker='v', color = 'black' )\n", "# ax3.plot(prob_Ous['Starting date'], prob_Ous['Md>15'], marker='o', )\n", "# ax3.plot(prob_Ous['Starting date'], prob_Ous['CMd>7'], '--', label='CMd>7', markersize=20, marker='*', color = 'gray' , markerfacecolor='none') #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "# ax3.plot(prob_Ous['Starting date'], prob_Ous['CMd>10'], '--', label='CMd>10', markersize=20, marker='x', color = 'black')\n", "# ax3.plot(prob_Ous['Starting date'], prob_Ous['CMd>15'], marker='o', )\n", "\n", "\n", "ax3.legend(loc='upper center', fontsize = 25, )\n", "ax3.set_title('Oussoubidiagna', fontsize = 35, fontweight='bold' )\n", "ax3.set_ylabel('Ratio', fontsize = 35, fontweight='bold' )\n", "ax3.tick_params(labelrotation=35, zorder=True)\n", "\n", "ax3.xaxis.set_major_locator(md.WeekdayLocator(interval=3))\n", "\n", "\n", "# Dori\n", "ax4.plot(prob_Dor['Starting date'], prob_Dor['Md>7'], label='Md>7', markersize=10, marker='o', color = 'gray') #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "ax4.plot(prob_Dor['Starting date'], prob_Dor['Md>10'], label='Md>10', markersize=10, marker='v', color = 'black' )\n", "# ax4.plot(prob_Dor['Starting date'], prob_Dor['Md>15'], marker='o', )\n", "# ax4.plot(prob_Dor['Starting date'], prob_Dor['CMd>7'], '--', label='CMd>7', markersize=20, marker='*', color = 'gray') #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "# ax4.plot(prob_Dor['Starting date'], prob_Dor['CMd>10'], '--', label='CMd>10', markersize=20, marker='x', color = 'black')\n", "# ax4.plot(prob_Dor['Starting date'], prob_Dor['CMd>15'], marker='o', )\n", "\n", "\n", "ax4.legend(loc='upper center', fontsize = 25, )\n", "ax4.set_title('Dori', fontsize = 35, fontweight='bold' )\n", "ax4.set_ylabel('Ratio', fontsize = 35, fontweight='bold' )\n", "ax4.tick_params(labelrotation=35, zorder=True)\n", "\n", "ax4.xaxis.set_major_locator(md.WeekdayLocator(interval=3))\n", "\n", "\n", "# Gaoua\n", "ax.plot(prob_Gao['Starting date'], prob_Gao['Md>7'], label='Md>7', markersize=10, marker='o', color = 'gray' ) #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "ax.plot(prob_Gao['Starting date'], prob_Gao['Md>10'], label='Md>10', markersize=10, marker='v', color = 'black')\n", "# ax.plot(prob_Gao['Starting date'], prob_Gao['Md>15'], marker='o', )\n", "# ax.plot(prob_Gao['Starting date'], prob_Gao['CMd>7'], '--', label='CMd>7', markersize=20, marker='*', color = 'gray' ) #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "# ax.plot(prob_Gao['Starting date'], prob_Gao['CMd>10'], '--', label='CMd>10', markersize=20, marker='x', color = 'black' )\n", "# ax.plot(prob_Gao['Starting date'], prob_Gao['CMd>15'], marker='o', )\n", "\n", "\n", "ax.legend(loc='upper center', fontsize = 25, )\n", "ax.set_title('Gaoua', fontsize = 35, fontweight='bold' )\n", "ax.set_ylabel('Ratio', fontsize = 35, fontweight='bold' )\n", "ax.tick_params(labelrotation=35, zorder=True)\n", "ax.set_ylim([0,1])\n", "\n", "ax.xaxis.set_major_locator(md.WeekdayLocator(interval=3))\n", "\n", "\n", "# Ded\n", "ax2.plot(prob_Ded['Starting date'], prob_Ded['Md>7'], label='Md>7', markersize=10, marker='o', color = 'gray') #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "ax2.plot(prob_Ded['Starting date'], prob_Ded['Md>10'], label='Md>10', markersize=10, marker='v', color = 'black' )\n", "# ax2.plot(prob_Ded['Starting date'], prob_Ded['Md>15'], marker='o', )\n", "# ax2.plot(prob_Ded['Starting date'], prob_Ded['CMd>7'], '--', label='CMd>7', markersize=20, marker='*', color = 'gray') #label='Local Onset',)# c=On_Gao['Year'], cmap='Accent') s=50,\n", "# ax2.plot(prob_Ded['Starting date'], prob_Ded['CMd>10'], '--', label='CMd>10', markersize=20, marker='x', color = 'black')\n", "# ax2.plot(prob_Ded['Starting date'], prob_Ded['CMd>15'], marker='o', )\n", "\n", "\n", "ax2.legend(loc='upper center',fontsize = 25, )\n", "ax2.set_title('Dedougou', fontsize = 35, fontweight='bold' )\n", "ax2.set_ylabel('Ratio', fontsize = 35, fontweight='bold' )\n", "ax2.tick_params(labelrotation=35, zorder=True)\n", "\n", "ax2.xaxis.set_major_locator(md.WeekdayLocator(interval=3))\n", "# ax8.set_xlim([\"1900-05-01\", \"1900-07-15\"])\n", "# ax8.set_xticks(p)\n", "\n", "\n", "# ax.text(20, 900, r'a', fontsize=20)\n", "# ax1.text(20, 15, r'b', fontsize=20)\n", "\n", "\n", "\n", "# Define the date format\n", "date_form = DateFormatter(\"%b-%d\")\n", "ax.xaxis.set_major_formatter(date_form)\n", "ax2.xaxis.set_major_formatter(date_form)\n", "ax3.xaxis.set_major_formatter(date_form)\n", "ax4.xaxis.set_major_formatter(date_form)\n", "\n", "plt.legend()\n", "# plt.subplots_adjust(wspace=0.2)\n", "plt.subplots_adjust(hspace=0.25)\n", "# plt.savefig(r'C:\\Users\\sagoungbome\\Pictures\\WR-Papers\\Tahmo Data\\Prob_dry_spells5.png', dpi=400)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }