diff --git a/exercicios/projeto-guiado/projeto_barbara_reimao.ipynb b/exercicios/projeto-guiado/projeto_barbara_reimao.ipynb new file mode 100644 index 0000000..8058f45 --- /dev/null +++ b/exercicios/projeto-guiado/projeto_barbara_reimao.ipynb @@ -0,0 +1,4757 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('INMET_MS_ITAQUIRAI_2020.CSV', delimiter=';', skiprows=8, encoding='latin1')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) object\n", + "PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) object\n", + "PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) object\n", + "PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) object\n", + "RADIACAO GLOBAL (Kj/m²) object\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) object\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) object\n", + "TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) object\n", + "TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) object\n", + "UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, RAJADA MAXIMA (m/s) object\n", + "VENTO, VELOCIDADE HORARIA (m/s) object\n", + "Unnamed: 19 float64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
02020/01/010000 UTC,6970970969,5NaN23,122,623,122,622,821,698.094.097.011.03,21,9NaN
12020/01/010100 UTC0970,2970,29702,923,721,723,72322,521,697.088.088.010.04,61,3NaN
22020/01/010200 UTC0969,8970,2969,81,62421,824,423,721,921,288.083.088.0345.03,2,6NaN
32020/01/010300 UTC0970,1970,1969,8,624,321,425,1242221,288.080.083.0332.04,81,5NaN
42020/01/010400 UTC0970,2970,5970,1NaN23,821,724,323,721,821,489.083.089.0316.03,3,2NaN
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "\n", + " PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) \\\n", + "0 970 \n", + "1 970,2 \n", + "2 969,8 \n", + "3 970,1 \n", + "4 970,2 \n", + "\n", + " PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) \\\n", + "0 970 \n", + "1 970,2 \n", + "2 970,2 \n", + "3 970,1 \n", + "4 970,5 \n", + "\n", + " PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 969,5 NaN \n", + "1 970 2,9 \n", + "2 969,8 1,6 \n", + "3 969,8 ,6 \n", + "4 970,1 NaN \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 22,6 \n", + "1 21,7 \n", + "2 21,8 \n", + "3 21,4 \n", + "4 21,7 \n", + "\n", + " TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24,4 \n", + "3 25,1 \n", + "4 24,3 \n", + "\n", + " TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) \\\n", + "0 22,6 \n", + "1 23 \n", + "2 23,7 \n", + "3 24 \n", + "4 23,7 \n", + "\n", + " TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) \\\n", + "0 22,8 \n", + "1 22,5 \n", + "2 21,9 \n", + "3 22 \n", + "4 21,8 \n", + "\n", + " TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) \\\n", + "0 21,6 \n", + "1 21,6 \n", + "2 21,2 \n", + "3 21,2 \n", + "4 21,4 \n", + "\n", + " UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) \\\n", + "0 98.0 \n", + "1 97.0 \n", + "2 88.0 \n", + "3 88.0 \n", + "4 89.0 \n", + "\n", + " UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) \\\n", + "0 94.0 \n", + "1 88.0 \n", + "2 83.0 \n", + "3 80.0 \n", + "4 83.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 97.0 11.0 \n", + "1 88.0 10.0 \n", + "2 88.0 345.0 \n", + "3 83.0 332.0 \n", + "4 89.0 316.0 \n", + "\n", + " VENTO, RAJADA MAXIMA (m/s) VENTO, VELOCIDADE HORARIA (m/s) Unnamed: 19 \n", + "0 3,2 1,9 NaN \n", + "1 4,6 1,3 NaN \n", + "2 3,2 ,6 NaN \n", + "3 4,8 1,5 NaN \n", + "4 3,3 ,2 NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB)PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB)PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB)RADIACAO GLOBAL (Kj/m²)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C)TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C)TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C)UMIDADE REL. MAX. NA HORA ANT. (AUT) (%)UMIDADE REL. MIN. NA HORA ANT. (AUT) (%)UMIDADE RELATIVA DO AR, HORARIA (%)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, RAJADA MAXIMA (m/s)VENTO, VELOCIDADE HORARIA (m/s)Unnamed: 19
87792020/12/311900 UTC,4972,6973,3972,6775,923,122,723,321,7NaNNaNNaNNaN97.032.06,61,2NaN
87802020/12/312000 UTC0970,4972,6970,4837,824,222,724,423,122,822,197.089.091.0355.02,8,8NaN
87812020/12/312100 UTC0970,7970,7970,1524,724,92324,924,123,322,593.089.089.0315.04,21,2NaN
87822020/12/312200 UTC0972,4972,4970,7256,524,222,125,124,223,122,189.087.088.0291.04,8,9NaN
87832020/12/312300 UTC0974,1974,1972,49,623,522,524,223,422,52294.088.094.0132.03,9,9NaN
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "8779 2020/12/31 1900 UTC ,4 \n", + "8780 2020/12/31 2000 UTC 0 \n", + "8781 2020/12/31 2100 UTC 0 \n", + "8782 2020/12/31 2200 UTC 0 \n", + "8783 2020/12/31 2300 UTC 0 \n", + "\n", + " PRESSAO ATMOSFERICA AO NIVEL DA ESTACAO, HORARIA (mB) \\\n", + "8779 972,6 \n", + "8780 970,4 \n", + "8781 970,7 \n", + "8782 972,4 \n", + "8783 974,1 \n", + "\n", + " PRESSÃO ATMOSFERICA MAX.NA HORA ANT. (AUT) (mB) \\\n", + "8779 973,3 \n", + "8780 972,6 \n", + "8781 970,7 \n", + "8782 972,4 \n", + "8783 974,1 \n", + "\n", + " PRESSÃO ATMOSFERICA MIN. NA HORA ANT. (AUT) (mB) RADIACAO GLOBAL (Kj/m²) \\\n", + "8779 972,6 775,9 \n", + "8780 970,4 837,8 \n", + "8781 970,1 524,7 \n", + "8782 970,7 256,5 \n", + "8783 972,4 9,6 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "8779 23,1 \n", + "8780 24,2 \n", + "8781 24,9 \n", + "8782 24,2 \n", + "8783 23,5 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "8779 22,7 \n", + "8780 22,7 \n", + "8781 23 \n", + "8782 22,1 \n", + "8783 22,5 \n", + "\n", + " TEMPERATURA MÁXIMA NA HORA ANT. (AUT) (°C) \\\n", + "8779 23,3 \n", + "8780 24,4 \n", + "8781 24,9 \n", + "8782 25,1 \n", + "8783 24,2 \n", + "\n", + " TEMPERATURA MÍNIMA NA HORA ANT. (AUT) (°C) \\\n", + "8779 21,7 \n", + "8780 23,1 \n", + "8781 24,1 \n", + "8782 24,2 \n", + "8783 23,4 \n", + "\n", + " TEMPERATURA ORVALHO MAX. NA HORA ANT. (AUT) (°C) \\\n", + "8779 NaN \n", + "8780 22,8 \n", + "8781 23,3 \n", + "8782 23,1 \n", + "8783 22,5 \n", + "\n", + " TEMPERATURA ORVALHO MIN. NA HORA ANT. (AUT) (°C) \\\n", + "8779 NaN \n", + "8780 22,1 \n", + "8781 22,5 \n", + "8782 22,1 \n", + "8783 22 \n", + "\n", + " UMIDADE REL. MAX. NA HORA ANT. (AUT) (%) \\\n", + "8779 NaN \n", + "8780 97.0 \n", + "8781 93.0 \n", + "8782 89.0 \n", + "8783 94.0 \n", + "\n", + " UMIDADE REL. MIN. NA HORA ANT. (AUT) (%) \\\n", + "8779 NaN \n", + "8780 89.0 \n", + "8781 89.0 \n", + "8782 87.0 \n", + "8783 88.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "8779 97.0 \n", + "8780 91.0 \n", + "8781 89.0 \n", + "8782 88.0 \n", + "8783 94.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, RAJADA MAXIMA (m/s) \\\n", + "8779 32.0 6,6 \n", + "8780 355.0 2,8 \n", + "8781 315.0 4,2 \n", + "8782 291.0 4,8 \n", + "8783 132.0 3,9 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Unnamed: 19 \n", + "8779 1,2 NaN \n", + "8780 ,8 NaN \n", + "8781 1,2 NaN \n", + "8782 ,9 NaN \n", + "8783 ,9 NaN " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = df[['Data','Hora UTC','PRECIPITAÇÃO TOTAL, HORÁRIO (mm)', 'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)','TEMPERATURA DO PONTO DE ORVALHO (°C)','UMIDADE RELATIVA DO AR, HORARIA (%)', 'RADIACAO GLOBAL (Kj/m²)', 'VENTO, DIREÇÃO HORARIA (gr) (° (gr))' ,'VENTO, VELOCIDADE HORARIA (m/s)']]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC,623,122,697.0NaN11.01,9
12020/01/010100 UTC023,721,788.02,910.01,3
22020/01/010200 UTC02421,888.01,6345.0,6
32020/01/010300 UTC024,321,483.0,6332.01,5
42020/01/010400 UTC023,821,789.0NaN316.0,2
52020/01/010500 UTC023,522,393.0NaN141.00
62020/01/010600 UTC022,722,498.0NaN40.00
72020/01/010700 UTC022,9NaNNaNNaN36.0,9
82020/01/010800 UTC022,9NaNNaNNaN68.00
92020/01/010900 UTC022,922,597.02,3358.00
102020/01/011000 UTC024,722,889.0408,116.02
112020/01/011100 UTC026,422,780.01219,6341.01
122020/01/011200 UTC028,623,674.01870,5345.01,4
132020/01/011300 UTC030,323,266.02602,9346.02,5
142020/01/011400 UTC03223,159.02996,3351.02,6
152020/01/011500 UTC032,623,157.03715,37.02,8
162020/01/011600 UTC032,221,453.03284,4338.02
172020/01/011700 UTC033,523,355.03238,710.01,7
182020/01/011800 UTC029,623,269.02380,5128.01,9
192020/01/011900 UTC3,42523,290.0930,1342.02,2
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "5 2020/01/01 0500 UTC 0 \n", + "6 2020/01/01 0600 UTC 0 \n", + "7 2020/01/01 0700 UTC 0 \n", + "8 2020/01/01 0800 UTC 0 \n", + "9 2020/01/01 0900 UTC 0 \n", + "10 2020/01/01 1000 UTC 0 \n", + "11 2020/01/01 1100 UTC 0 \n", + "12 2020/01/01 1200 UTC 0 \n", + "13 2020/01/01 1300 UTC 0 \n", + "14 2020/01/01 1400 UTC 0 \n", + "15 2020/01/01 1500 UTC 0 \n", + "16 2020/01/01 1600 UTC 0 \n", + "17 2020/01/01 1700 UTC 0 \n", + "18 2020/01/01 1800 UTC 0 \n", + "19 2020/01/01 1900 UTC 3,4 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "5 23,5 \n", + "6 22,7 \n", + "7 22,9 \n", + "8 22,9 \n", + "9 22,9 \n", + "10 24,7 \n", + "11 26,4 \n", + "12 28,6 \n", + "13 30,3 \n", + "14 32 \n", + "15 32,6 \n", + "16 32,2 \n", + "17 33,5 \n", + "18 29,6 \n", + "19 25 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 22,6 97.0 \n", + "1 21,7 88.0 \n", + "2 21,8 88.0 \n", + "3 21,4 83.0 \n", + "4 21,7 89.0 \n", + "5 22,3 93.0 \n", + "6 22,4 98.0 \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 22,5 97.0 \n", + "10 22,8 89.0 \n", + "11 22,7 80.0 \n", + "12 23,6 74.0 \n", + "13 23,2 66.0 \n", + "14 23,1 59.0 \n", + "15 23,1 57.0 \n", + "16 21,4 53.0 \n", + "17 23,3 55.0 \n", + "18 23,2 69.0 \n", + "19 23,2 90.0 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 NaN 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 NaN 316.0 \n", + "5 NaN 141.0 \n", + "6 NaN 40.0 \n", + "7 NaN 36.0 \n", + "8 NaN 68.0 \n", + "9 2,3 358.0 \n", + "10 408,1 16.0 \n", + "11 1219,6 341.0 \n", + "12 1870,5 345.0 \n", + "13 2602,9 346.0 \n", + "14 2996,3 351.0 \n", + "15 3715,3 7.0 \n", + "16 3284,4 338.0 \n", + "17 3238,7 10.0 \n", + "18 2380,5 128.0 \n", + "19 930,1 342.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 1,9 \n", + "1 1,3 \n", + "2 ,6 \n", + "3 1,5 \n", + "4 ,2 \n", + "5 0 \n", + "6 0 \n", + "7 ,9 \n", + "8 0 \n", + "9 0 \n", + "10 2 \n", + "11 1 \n", + "12 1,4 \n", + "13 2,5 \n", + "14 2,6 \n", + "15 2,8 \n", + "16 2 \n", + "17 1,7 \n", + "18 1,9 \n", + "19 2,2 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 6\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 6\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 466\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 466\n", + "RADIACAO GLOBAL (Kj/m²) 4049\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 6\n", + "VENTO, VELOCIDADE HORARIA (m/s) 6\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_5 = df.isnull().sum()\n", + "df_5" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 9)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "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", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC,623,122,697.0NaN11.01,9
12020/01/010100 UTC023,721,788.02,910.01,3
22020/01/010200 UTC02421,888.01,6345.0,6
32020/01/010300 UTC024,321,483.0,6332.01,5
42020/01/010400 UTC023,821,789.0NaN316.0,2
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC ,6 \n", + "1 2020/01/01 0100 UTC 0 \n", + "2 2020/01/01 0200 UTC 0 \n", + "3 2020/01/01 0300 UTC 0 \n", + "4 2020/01/01 0400 UTC 0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 23,1 \n", + "1 23,7 \n", + "2 24 \n", + "3 24,3 \n", + "4 23,8 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 22,6 97.0 \n", + "1 21,7 88.0 \n", + "2 21,8 88.0 \n", + "3 21,4 83.0 \n", + "4 21,7 89.0 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 NaN 11.0 \n", + "1 2,9 10.0 \n", + "2 1,6 345.0 \n", + "3 ,6 332.0 \n", + "4 NaN 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 1,9 \n", + "1 1,3 \n", + "2 ,6 \n", + "3 1,5 \n", + "4 ,2 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'] = pd.to_numeric(df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'], errors='coerce')\n", + "df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'] = pd.to_numeric(df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'], errors='coerce')\n", + "df['TEMPERATURA DO PONTO DE ORVALHO (°C)'] = pd.to_numeric(df['TEMPERATURA DO PONTO DE ORVALHO (°C)'], errors='coerce')\n", + "df['RADIACAO GLOBAL (Kj/m²)'] = pd.to_numeric(df['RADIACAO GLOBAL (Kj/m²)'], errors='coerce')\n", + "df['VENTO, VELOCIDADE HORARIA (m/s)'] = pd.to_numeric(df['VENTO, VELOCIDADE HORARIA (m/s)'], errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) float64\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) float64\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "RADIACAO GLOBAL (Kj/m²) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, VELOCIDADE HORARIA (m/s) float64\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# df = df.dropna() # remover linhas com valores nulos ou faltantes\n", + "\n", + "df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'] = df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'].fillna(0)\n", + "df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'] = df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'].fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data 0\n", + "Hora UTC 0\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 0\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 0\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 7934\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 466\n", + "RADIACAO GLOBAL (Kj/m²) 8312\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 6\n", + "VENTO, VELOCIDADE HORARIA (m/s) 7207\n", + "dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.fillna(0)" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC0.00.00.097.00.011.00.0
12020/01/010100 UTC0.00.00.088.00.010.00.0
22020/01/010200 UTC0.024.00.088.00.0345.00.0
32020/01/010300 UTC0.00.00.083.00.0332.00.0
42020/01/010400 UTC0.00.00.089.00.0316.00.0
..............................
87792020/12/311900 UTC0.00.00.097.00.032.00.0
87802020/12/312000 UTC0.00.00.091.00.0355.00.0
87812020/12/312100 UTC0.00.023.089.00.0315.00.0
87822020/12/312200 UTC0.00.00.088.00.0291.00.0
87832020/12/312300 UTC0.00.00.094.00.0132.00.0
\n", + "

8784 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC 0.0 \n", + "1 2020/01/01 0100 UTC 0.0 \n", + "2 2020/01/01 0200 UTC 0.0 \n", + "3 2020/01/01 0300 UTC 0.0 \n", + "4 2020/01/01 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 2020/12/31 1900 UTC 0.0 \n", + "8780 2020/12/31 2000 UTC 0.0 \n", + "8781 2020/12/31 2100 UTC 0.0 \n", + "8782 2020/12/31 2200 UTC 0.0 \n", + "8783 2020/12/31 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 97.0 0.0 \n", + "1 88.0 0.0 \n", + "2 88.0 0.0 \n", + "3 83.0 0.0 \n", + "4 89.0 0.0 \n", + "... ... ... \n", + "8779 97.0 0.0 \n", + "8780 91.0 0.0 \n", + "8781 89.0 0.0 \n", + "8782 88.0 0.0 \n", + "8783 94.0 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# .fillna(df['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "df['UMIDADE RELATIVA DO AR, HORARIA (%)'] = (df['UMIDADE RELATIVA DO AR, HORARIA (%)'] - df['UMIDADE RELATIVA DO AR, HORARIA (%)'].min()) / (df['UMIDADE RELATIVA DO AR, HORARIA (%)'].max() - df['UMIDADE RELATIVA DO AR, HORARIA (%)'].min())" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
02020/01/010000 UTC0.00.00.00.970.011.00.0
12020/01/010100 UTC0.00.00.00.880.010.00.0
22020/01/010200 UTC0.024.00.00.880.0345.00.0
32020/01/010300 UTC0.00.00.00.830.0332.00.0
42020/01/010400 UTC0.00.00.00.890.0316.00.0
..............................
87792020/12/311900 UTC0.00.00.00.970.032.00.0
87802020/12/312000 UTC0.00.00.00.910.0355.00.0
87812020/12/312100 UTC0.00.023.00.890.0315.00.0
87822020/12/312200 UTC0.00.00.00.880.0291.00.0
87832020/12/312300 UTC0.00.00.00.940.0132.00.0
\n", + "

8784 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 2020/01/01 0000 UTC 0.0 \n", + "1 2020/01/01 0100 UTC 0.0 \n", + "2 2020/01/01 0200 UTC 0.0 \n", + "3 2020/01/01 0300 UTC 0.0 \n", + "4 2020/01/01 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 2020/12/31 1900 UTC 0.0 \n", + "8780 2020/12/31 2000 UTC 0.0 \n", + "8781 2020/12/31 2100 UTC 0.0 \n", + "8782 2020/12/31 2200 UTC 0.0 \n", + "8783 2020/12/31 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data'] = pd.to_datetime(df['Data'], format='%Y/%m/%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data'] = df['Data'].dt.strftime('%d/%m/%Y')" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
001/01/20200000 UTC0.00.00.00.970.011.00.0
101/01/20200100 UTC0.00.00.00.880.010.00.0
201/01/20200200 UTC0.024.00.00.880.0345.00.0
301/01/20200300 UTC0.00.00.00.830.0332.00.0
401/01/20200400 UTC0.00.00.00.890.0316.00.0
..............................
877931/12/20201900 UTC0.00.00.00.970.032.00.0
878031/12/20202000 UTC0.00.00.00.910.0355.00.0
878131/12/20202100 UTC0.00.023.00.890.0315.00.0
878231/12/20202200 UTC0.00.00.00.880.0291.00.0
878331/12/20202300 UTC0.00.00.00.940.0132.00.0
\n", + "

8784 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 0000 UTC 0.0 \n", + "1 01/01/2020 0100 UTC 0.0 \n", + "2 01/01/2020 0200 UTC 0.0 \n", + "3 01/01/2020 0300 UTC 0.0 \n", + "4 01/01/2020 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 1900 UTC 0.0 \n", + "8780 31/12/2020 2000 UTC 0.0 \n", + "8781 31/12/2020 2100 UTC 0.0 \n", + "8782 31/12/2020 2200 UTC 0.0 \n", + "8783 31/12/2020 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Data object\n", + "Hora UTC object\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) float64\n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) float64\n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) float64\n", + "UMIDADE RELATIVA DO AR, HORARIA (%) float64\n", + "RADIACAO GLOBAL (Kj/m²) float64\n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) float64\n", + "VENTO, VELOCIDADE HORARIA (m/s) float64\n", + "dtype: object" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
001/01/20200000 UTC0.00.00.00.970.011.00.0
101/01/20200100 UTC0.00.00.00.880.010.00.0
201/01/20200200 UTC0.024.00.00.880.0345.00.0
301/01/20200300 UTC0.00.00.00.830.0332.00.0
401/01/20200400 UTC0.00.00.00.890.0316.00.0
..............................
877931/12/20201900 UTC0.00.00.00.970.032.00.0
878031/12/20202000 UTC0.00.00.00.910.0355.00.0
878131/12/20202100 UTC0.00.023.00.890.0315.00.0
878231/12/20202200 UTC0.00.00.00.880.0291.00.0
878331/12/20202300 UTC0.00.00.00.940.0132.00.0
\n", + "

8784 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 0000 UTC 0.0 \n", + "1 01/01/2020 0100 UTC 0.0 \n", + "2 01/01/2020 0200 UTC 0.0 \n", + "3 01/01/2020 0300 UTC 0.0 \n", + "4 01/01/2020 0400 UTC 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 1900 UTC 0.0 \n", + "8780 31/12/2020 2000 UTC 0.0 \n", + "8781 31/12/2020 2100 UTC 0.0 \n", + "8782 31/12/2020 2200 UTC 0.0 \n", + "8783 31/12/2020 2300 UTC 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df['Hora UTC'] = pd.to_datetime(df['Hora UTC'], format='%H%M UTC', errors='coerce').dt.strftime('%H:%M')\n" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)
001/01/202000:000.00.00.00.970.011.00.0
101/01/202001:000.00.00.00.880.010.00.0
201/01/202002:000.024.00.00.880.0345.00.0
301/01/202003:000.00.00.00.830.0332.00.0
401/01/202004:000.00.00.00.890.0316.00.0
..............................
877931/12/202019:000.00.00.00.970.032.00.0
878031/12/202020:000.00.00.00.910.0355.00.0
878131/12/202021:000.00.023.00.890.0315.00.0
878231/12/202022:000.00.00.00.880.0291.00.0
878331/12/202023:000.00.00.00.940.0132.00.0
\n", + "

8784 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 19:00 0.0 \n", + "8780 31/12/2020 20:00 0.0 \n", + "8781 31/12/2020 21:00 0.0 \n", + "8782 31/12/2020 22:00 0.0 \n", + "8783 31/12/2020 23:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + "[8784 rows x 9 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = df['Data'] + ' ' + df['Hora UTC']" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
001/01/202000:000.00.00.00.970.011.00.001/01/2020 00:00
101/01/202001:000.00.00.00.880.010.00.001/01/2020 01:00
201/01/202002:000.024.00.00.880.0345.00.001/01/2020 02:00
301/01/202003:000.00.00.00.830.0332.00.001/01/2020 03:00
401/01/202004:000.00.00.00.890.0316.00.001/01/2020 04:00
.................................
877931/12/202019:000.00.00.00.970.032.00.031/12/2020 19:00
878031/12/202020:000.00.00.00.910.0355.00.031/12/2020 20:00
878131/12/202021:000.00.023.00.890.0315.00.031/12/2020 21:00
878231/12/202022:000.00.00.00.880.0291.00.031/12/2020 22:00
878331/12/202023:000.00.00.00.940.0132.00.031/12/2020 23:00
\n", + "

8784 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 19:00 0.0 \n", + "8780 31/12/2020 20:00 0.0 \n", + "8781 31/12/2020 21:00 0.0 \n", + "8782 31/12/2020 22:00 0.0 \n", + "8783 31/12/2020 23:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + " Data e Hora \n", + "0 01/01/2020 00:00 \n", + "1 01/01/2020 01:00 \n", + "2 01/01/2020 02:00 \n", + "3 01/01/2020 03:00 \n", + "4 01/01/2020 04:00 \n", + "... ... \n", + "8779 31/12/2020 19:00 \n", + "8780 31/12/2020 20:00 \n", + "8781 31/12/2020 21:00 \n", + "8782 31/12/2020 22:00 \n", + "8783 31/12/2020 23:00 \n", + "\n", + "[8784 rows x 10 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
001/01/202000:000.00.00.00.970.011.00.001/01/2020 00:00
101/01/202001:000.00.00.00.880.010.00.001/01/2020 01:00
201/01/202002:000.024.00.00.880.0345.00.001/01/2020 02:00
301/01/202003:000.00.00.00.830.0332.00.001/01/2020 03:00
401/01/202004:000.00.00.00.890.0316.00.001/01/2020 04:00
.................................
877931/12/202019:000.00.00.00.970.032.00.031/12/2020 19:00
878031/12/202020:000.00.00.00.910.0355.00.031/12/2020 20:00
878131/12/202021:000.00.023.00.890.0315.00.031/12/2020 21:00
878231/12/202022:000.00.00.00.880.0291.00.031/12/2020 22:00
878331/12/202023:000.00.00.00.940.0132.00.031/12/2020 23:00
\n", + "

8784 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "... ... ... ... \n", + "8779 31/12/2020 19:00 0.0 \n", + "8780 31/12/2020 20:00 0.0 \n", + "8781 31/12/2020 21:00 0.0 \n", + "8782 31/12/2020 22:00 0.0 \n", + "8783 31/12/2020 23:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + " Data e Hora \n", + "0 01/01/2020 00:00 \n", + "1 01/01/2020 01:00 \n", + "2 01/01/2020 02:00 \n", + "3 01/01/2020 03:00 \n", + "4 01/01/2020 04:00 \n", + "... ... \n", + "8779 31/12/2020 19:00 \n", + "8780 31/12/2020 20:00 \n", + "8781 31/12/2020 21:00 \n", + "8782 31/12/2020 22:00 \n", + "8783 31/12/2020 23:00 \n", + "\n", + "[8784 rows x 10 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora'] = pd.to_datetime(df['Data e Hora'], format='%d/%m/%Y %H:%M', errors='coerce')" + ] + }, + { + "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", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
001/01/202000:000.00.00.00.970.011.00.02020-01-01 00:00:00
101/01/202001:000.00.00.00.880.010.00.02020-01-01 01:00:00
201/01/202002:000.024.00.00.880.0345.00.02020-01-01 02:00:00
301/01/202003:000.00.00.00.830.0332.00.02020-01-01 03:00:00
401/01/202004:000.00.00.00.890.0316.00.02020-01-01 04:00:00
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 0.0 0.97 \n", + "1 0.0 0.88 \n", + "2 0.0 0.88 \n", + "3 0.0 0.83 \n", + "4 0.0 0.89 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0.0 11.0 \n", + "1 0.0 10.0 \n", + "2 0.0 345.0 \n", + "3 0.0 332.0 \n", + "4 0.0 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Data e Hora \n", + "0 0.0 2020-01-01 00:00:00 \n", + "1 0.0 2020-01-01 01:00:00 \n", + "2 0.0 2020-01-01 02:00:00 \n", + "3 0.0 2020-01-01 03:00:00 \n", + "4 0.0 2020-01-01 04:00:00 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora BR'] = df['Data e Hora'].dt.tz_localize('UTC').dt.tz_convert('America/Sao_Paulo')" + ] + }, + { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e HoraData e Hora BR
001/01/202000:000.00.00.00.970.011.00.02020-01-01 00:00:002019-12-31 21:00:00-03:00
101/01/202001:000.00.00.00.880.010.00.02020-01-01 01:00:002019-12-31 22:00:00-03:00
201/01/202002:000.024.00.00.880.0345.00.02020-01-01 02:00:002019-12-31 23:00:00-03:00
301/01/202003:000.00.00.00.830.0332.00.02020-01-01 03:00:002020-01-01 00:00:00-03:00
401/01/202004:000.00.00.00.890.0316.00.02020-01-01 04:00:002020-01-01 01:00:00-03:00
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 0.0 0.97 \n", + "1 0.0 0.88 \n", + "2 0.0 0.88 \n", + "3 0.0 0.83 \n", + "4 0.0 0.89 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0.0 11.0 \n", + "1 0.0 10.0 \n", + "2 0.0 345.0 \n", + "3 0.0 332.0 \n", + "4 0.0 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Data e Hora \\\n", + "0 0.0 2020-01-01 00:00:00 \n", + "1 0.0 2020-01-01 01:00:00 \n", + "2 0.0 2020-01-01 02:00:00 \n", + "3 0.0 2020-01-01 03:00:00 \n", + "4 0.0 2020-01-01 04:00:00 \n", + "\n", + " Data e Hora BR \n", + "0 2019-12-31 21:00:00-03:00 \n", + "1 2019-12-31 22:00:00-03:00 \n", + "2 2019-12-31 23:00:00-03:00 \n", + "3 2020-01-01 00:00:00-03:00 \n", + "4 2020-01-01 01:00:00-03:00 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "df['Data e Hora BR'] = df['Data e Hora BR'].dt.strftime('%d/%m/%Y %H:%M')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e HoraData e Hora BR
001/01/202000:000.00.00.00.970.011.00.02020-01-01 00:00:0031/12/2019 21:00
101/01/202001:000.00.00.00.880.010.00.02020-01-01 01:00:0031/12/2019 22:00
201/01/202002:000.024.00.00.880.0345.00.02020-01-01 02:00:0031/12/2019 23:00
301/01/202003:000.00.00.00.830.0332.00.02020-01-01 03:00:0001/01/2020 00:00
401/01/202004:000.00.00.00.890.0316.00.02020-01-01 04:00:0001/01/2020 01:00
\n", + "
" + ], + "text/plain": [ + " Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 01/01/2020 00:00 0.0 \n", + "1 01/01/2020 01:00 0.0 \n", + "2 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 04:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) UMIDADE RELATIVA DO AR, HORARIA (%) \\\n", + "0 0.0 0.97 \n", + "1 0.0 0.88 \n", + "2 0.0 0.88 \n", + "3 0.0 0.83 \n", + "4 0.0 0.89 \n", + "\n", + " RADIACAO GLOBAL (Kj/m²) VENTO, DIREÇÃO HORARIA (gr) (° (gr)) \\\n", + "0 0.0 11.0 \n", + "1 0.0 10.0 \n", + "2 0.0 345.0 \n", + "3 0.0 332.0 \n", + "4 0.0 316.0 \n", + "\n", + " VENTO, VELOCIDADE HORARIA (m/s) Data e Hora Data e Hora BR \n", + "0 0.0 2020-01-01 00:00:00 31/12/2019 21:00 \n", + "1 0.0 2020-01-01 01:00:00 31/12/2019 22:00 \n", + "2 0.0 2020-01-01 02:00:00 31/12/2019 23:00 \n", + "3 0.0 2020-01-01 03:00:00 01/01/2020 00:00 \n", + "4 0.0 2020-01-01 04:00:00 01/01/2020 01:00 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Análise de Dados" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
count8784.0000008784.0000008784.0000008784.0000008784.0000008784.0000008784.0000008784
mean0.0302822.4116581.5392760.63272576.901298184.8894580.2030972020-07-01 23:30:00
min0.0000000.0000000.0000000.0000000.0000000.0000000.0000002020-01-01 00:00:00
25%0.0000000.0000000.0000000.4900000.000000133.0000000.0000002020-04-01 11:45:00
50%0.0000000.0000000.0000000.6700000.000000171.0000000.0000002020-07-01 23:30:00
75%0.0000000.0000000.0000000.8200000.000000254.0000000.0000002020-10-01 11:15:00
max40.00000040.00000025.0000001.0000003886.000000360.0000008.0000002020-12-31 23:00:00
std0.6941427.2915064.8890040.241409414.22431181.7847190.753577NaN
\n", + "
" + ], + "text/plain": [ + " PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "count 8784.000000 \n", + "mean 0.030282 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 40.000000 \n", + "std 0.694142 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "count 8784.000000 \n", + "mean 2.411658 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 40.000000 \n", + "std 7.291506 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "count 8784.000000 \n", + "mean 1.539276 \n", + "min 0.000000 \n", + "25% 0.000000 \n", + "50% 0.000000 \n", + "75% 0.000000 \n", + "max 25.000000 \n", + "std 4.889004 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 0.632725 76.901298 \n", + "min 0.000000 0.000000 \n", + "25% 0.490000 0.000000 \n", + "50% 0.670000 0.000000 \n", + "75% 0.820000 0.000000 \n", + "max 1.000000 3886.000000 \n", + "std 0.241409 414.224311 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "count 8784.000000 8784.000000 \n", + "mean 184.889458 0.203097 \n", + "min 0.000000 0.000000 \n", + "25% 133.000000 0.000000 \n", + "50% 171.000000 0.000000 \n", + "75% 254.000000 0.000000 \n", + "max 360.000000 8.000000 \n", + "std 81.784719 0.753577 \n", + "\n", + " Data e Hora \n", + "count 8784 \n", + "mean 2020-07-01 23:30:00 \n", + "min 2020-01-01 00:00:00 \n", + "25% 2020-04-01 11:45:00 \n", + "50% 2020-07-01 23:30:00 \n", + "75% 2020-10-01 11:15:00 \n", + "max 2020-12-31 23:00:00 \n", + "std NaN " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df.set_index('Data e Hora BR', inplace=True)\n", + "\n", + "df[['PRECIPITAÇÃO TOTAL, HORÁRIO (mm)', 'TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)',\n", + " 'UMIDADE RELATIVA DO AR, HORARIA (%)']].plot(subplots=True)\n", + "\n", + "plt.suptitle('Séries Temporais das Variáveis')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAHHCAYAAACY6dMIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5wdVfn/3zO39+29b5JNNr1AEmoAIbQICNJEAmJDxZ/gV5AvfEVRioKCUkWaYigioCI9kFBDIL23zfbebu8z8/vj7t7k5t7dbEufd17zemXPzJw5M3fmzDPnPM/nERRFUVBRUVFRUVFROQIQD3UDVFRUVFRUVFSGimq4qKioqKioqBwxqIaLioqKioqKyhGDarioqKioqKioHDGohouKioqKiorKEYNquKioqKioqKgcMaiGi4qKioqKisoRg2q4qKioqKioqBwxqIaLioqKioqKyhGDarioHFNcc801lJWVHepmHJX88pe/RBCEIW0rCAK//OUvx+zYzz77LIIgUFdXN2Z1jjV1dXUIgsCzzz47pvWWlZVxzTXXjGmdI+VAnaOKyt6ohssACIIwpGX58uWHuqmHjEcffVTtoPbi0ksvRRAEbrnllkPdlPgL5P7770+5/v777z/sX/SHkuXLlyc85xqNhpycHC655BK2bt160Nvz2Wef8ctf/hKn03nQj324sHXrVgRBwGg0HtPXQQW0h7oBhyvPPfdcwt9/+9vfeO+995LKJ02adDCbdVjx6KOPkpWVddh87R1K3G43r7/+OmVlZbzwwgvce++9Qx59OFq4/fbb+fnPf36omzGm/PjHP+a4444jEomwYcMGHn/8cZYvX86mTZvIy8s7aO347LPP+NWvfsU111xDWlpawrrt27cjikf/N+jf//538vLy6O3t5Z///Cff/va3D3WTVA4RquEyAFdddVXC359//jnvvfdeUvnRgqIoBINBTCaT2o4R8MorryBJEk8//TSnn346H330Eaeeeup+9/P5fFgsloPQwgOPVqtFqz26upSTTz6ZSy65JP53VVUV119/PX/729+4+eabD2HL9mAwGA51Ew44iqLw/PPPc+WVV1JbW8uSJUuGZLgcqf2JyuAc/Wb6AUSWZR588EEmT56M0WgkNzeX733ve/T29iZsV1ZWxvnnn8/y5cuZM2cOJpOJqVOnxqeZXn31VaZOnYrRaGT27NmsXbs2Yf9rrrkGq9XK7t27WbhwIRaLhYKCAu688072Te493Da988478Tb9+c9/BuCZZ57h9NNPJycnB4PBQHV1NY899ljS/ps3b+bDDz+MD6cvWLAAGNjXIZUfwmjbMRj/+te/mDJlCkajkSlTpvDaa6+l3M7n8/HTn/6U4uJiDAYDVVVV3H///UnXdjCWLFnCmWeeyWmnncakSZNYsmTJgOf/4Ycf8oMf/ICcnByKiooGrHPx4sUYjcakqYmFCxeSnp5OS0vLkNs3FEZ7n6b63UOhEDfeeCPZ2dnYbDa++tWv0tTUlHTs+vp6fvCDH1BVVYXJZCIzM5Ovf/3rKaeyNm/ezOmnn47JZKKoqIjf/OY3yLKc8pzeeustTj75ZCwWCzabjfPOO4/NmzeP7AIRM2QAampqEsqbm5v51re+RW5uLgaDgcmTJ/P000/vt74NGzZwzTXXUFFRgdFoJC8vj29961t0d3fHt/nlL3/Jz372MwDKy8vjz1v/tdnbx2XVqlUIgsBf//rXpGO98847CILAf//7X2B41zwVTqeTa665BofDQVpaGosXLx5wCueDDz6I/w5paWlccMEFw5py+/TTT6mrq+Pyyy/n8ssv56OPPkp5Hw3Wn6gcPRxdn0cHme9973s8++yzXHvttfz4xz+mtraWhx9+mLVr1/Lpp5+i0+ni2+7atYsrr7yS733ve1x11VXcf//9LFq0iMcff5z//d//5Qc/+AEA99xzD5deemnS8K8kSZx99tnMmzeP3/3ud7z99tvccccdRKNR7rzzzhG1afv27VxxxRV873vf4zvf+Q5VVVUAPPbYY0yePJmvfvWraLVaXn/9dX7wgx8gyzI//OEPAXjwwQe54YYbsFqt3HbbbQDk5uaO6DqOph0D8e6773LxxRdTXV3NPffcQ3d3N9dee22SoaAoCl/96ldZtmwZ1113HTNmzOCdd97hZz/7Gc3NzTzwwAP7bX9LSwvLli2LvyyuuOIKHnjgAR5++GH0en3S9j/4wQ/Izs7mF7/4BT6fb8B6//jHP/LBBx+wePFiVqxYgUaj4c9//jPvvvsuzz33HAUFBftt23AZ7X26L9/+9rf5+9//zpVXXskJJ5zABx98wHnnnZe03Zdffslnn33G5ZdfTlFREXV1dTz22GMsWLCALVu2YDabAWhra+O0004jGo3y85//HIvFwhNPPJHyi/q5555j8eLFLFy4kN/+9rf4/X4ee+wxTjrpJNauXTsiJ+3+l3p6enq8rL29nXnz5iEIAj/60Y/Izs7mrbfe4rrrrsPtdvOTn/xkwPree+89du/ezbXXXkteXh6bN2/miSeeYPPmzXz++ecIgsDXvvY1duzYwQsvvMADDzxAVlYWANnZ2Un1zZkzh4qKCv7xj3+wePHihHUvvfQS6enpLFy4EBj6NU+FoihccMEFfPLJJ3z/+99n0qRJvPbaa0nHBFi6dCnnnHMOFRUV/PKXvyQQCPDQQw9x4oknsmbNmiH9DkuWLKGyspLjjjuOKVOmYDabeeGFF+IG3d4M1J+oHEUoKkPihz/8obL35fr4448VQFmyZEnCdm+//XZSeWlpqQIon332WbzsnXfeUQDFZDIp9fX18fI///nPCqAsW7YsXrZ48WIFUG644YZ4mSzLynnnnafo9Xqls7NzxG16++23k87V7/cnlS1cuFCpqKhIKJs8ebJy6qmnJm17xx13KKlurWeeeUYBlNra2jFtRypmzJih5OfnK06nM1727rvvKoBSWloaL/vXv/6lAMpvfvObhP0vueQSRRAEZdeuXfs91v3336+YTCbF7XYriqIoO3bsUADltddeS9iu//xPOukkJRqN7rdeRdlzn/zmN79Rdu/erVitVuXCCy/c7361tbUKoNx3330p1993330D/hYjvU/3/d3XrVunAMoPfvCDhGNfeeWVCqDccccd8bJUv/WKFSsUQPnb3/4WL/vJT36iAMrKlSvjZR0dHYrD4Ug4H4/Ho6SlpSnf+c53Eupsa2tTHA5HUvm+LFu2TAGUp59+Wuns7FRaWlqUt99+Wxk3bpwiCILyxRdfxLe97rrrlPz8fKWrqyuhjssvv1xxOBzxc+v/TZ555plBz/uFF15QAOWjjz6Kl6X6vfopLS1VFi9eHP/71ltvVXQ6ndLT0xMvC4VCSlpamvKtb31r0GOnuuap6H9ufve738XLotGocvLJJyed44wZM5ScnBylu7s7XrZ+/XpFFEXl6quvHvQ4iqIo4XBYyczMVG677bZ42ZVXXqlMnz49advB+hOVowd1qmiEvPzyyzgcDs4880y6urriy+zZs7FarSxbtixh++rqaubPnx//e+7cuQCcfvrplJSUJJXv3r076Zg/+tGP4v/v/7oLh8MsXbp0RG0qLy+Pf33tzd5fry6Xi66uLk499VR2796Ny+Ua8jUaKmPdjtbWVtatW8fixYtxOBzx8jPPPJPq6uqEbd988000Gg0//vGPE8p/+tOfoigKb7311n7bv2TJEs477zxsNhsA48ePZ/bs2SmniwC+853voNFo9lsvwFlnncX3vvc97rzzTr72ta9hNBoP6ND3WNyn/bz55psASdc21QjE3r91JBKhu7ubcePGkZaWxpo1axLqnDdvHscff3y8LDs7m2984xsJ9b333ns4nU6uuOKKhGdBo9Ewd+7cpGdhIL71rW+RnZ1NQUEBZ599Ni6Xi+eee47jjjsOiI08vPLKKyxatAhFURKOtXDhQlwuV0L7BzvvYDBIV1cX8+bNAxh0v8G47LLLiEQivPrqq/Gyd999F6fTyWWXXZby2INd81S8+eabaLVarr/++niZRqPhhhtuSNiu/1m85ppryMjIiJdPmzaNM888M36PDMZbb71Fd3c3V1xxRbzsiiuuYP369Smn/QbqT1SOHtSpohGyc+dOXC4XOTk5Kdd3dHQk/L13pw/EX6jFxcUpy/f1SRFFkYqKioSyCRMmAHuGr4fbpvLy8pTbffrpp9xxxx2sWLECv9+fsM7lciUYA2PBWLejvr4eiBkQ+1JVVZXQKdfX11NQUBA3Ovrpjxbrr2sgtm7dytq1a7n66qvZtWtXvHzBggU88sgjuN1u7HZ7wj4Dne9A3H///fz73/9m3bp1PP/88wP+viNhX5+U0d6ne1NfX48oilRWViaUpxq6DwQC3HPPPTzzzDM0Nzcn+BftbaTW19fHjabB6ty5cycQM7hSse9vMhC/+MUvOPnkk/F6vbz22mu8+OKLCVNjnZ2dOJ1OnnjiCZ544omUdez73O1NT08Pv/rVr3jxxReTthvpR8L06dOZOHEiL730Etdddx0QmybKyspKuB5DveapqK+vJz8/H6vVmlC+7+/Q//yk+s0nTZrEO++8s18H9b///e+Ul5djMBjiz1hlZSVms5klS5Zw9913J2w/3OdL5chDNVxGiCzL5OTkDPhVve/880Bf2AOVK8NwDB1pm1L5BdTU1HDGGWcwceJE/vCHP1BcXIxer+fNN9/kgQceGNAJcm8GCgOWJCll+YFqx8Hg73//OwA33ngjN954Y9L6V155hWuvvTahbLgRDmvXro2/1DZu3Jjw5TkQRqMRiL2cUtFvCPZv18/BuE9TccMNN/DMM8/wk5/8hPnz5+NwOBAEgcsvv3xEv3X/Ps8991zKsOWhRj9NnTqVr3zlKwBceOGF+P1+vvOd73DSSSdRXFwcP85VV12V0r8DYqMLA3HppZfy2Wef8bOf/YwZM2ZgtVqRZZmzzz57VPf4ZZddxl133UVXVxc2m43//Oc/XHHFFQnnPdbX/EDQLzMQDAZTfog8//zz3HXXXQl9jhpBdPSjGi4jpLKykqVLl3LiiScelAdFlmV2794dH2UB2LFjB0DcuW0s2vT6668TCoX4z3/+k/D1nWpofSADpd9x0el0JmhO7G/0YqTt2JfS0lJgz1f33mzfvj1p26VLl+LxeBJGXbZt25ZQVyqUvhDN0047Le60uje//vWvWbJkSZLhMhx8Ph/XXnst1dXVnHDCCfzud7/joosuik9VDER2djZmsznpfPvZvn07ZrM57uh5ICgtLUWWZWpqahK+uFO16Z///CeLFy/m97//fbwsGAwmRamUlpYO6XftH+XJycmJGx5jwb333strr73GXXfdxeOPPx6PlpIkadjH6e3t5f333+dXv/oVv/jFL+Llqc5vuJpAl112Gb/61a945ZVXyM3Nxe12c/nllydsM9RrnorS0lLef/99vF5vwqhLqucrVTnEnrGsrKxBR1teffVVgsEgjz32WNK9un37dm6//XY+/fRTTjrppP22WeXoQfVxGSGXXnopkiTx61//OmldNBo9IMqODz/8cPz/iqLw8MMPo9PpOOOMM8asTf1f1vsOGz/zzDNJ21oslpR19r80Pvroo3iZz+dLGaI5Fu3Yl/z8fGbMmMFf//rXhCHv9957jy1btiRse+655yJJUsK1BXjggQcQBIFzzjlnwOP0h2hee+21XHLJJUnLZZddxrJly0YVtnzLLbfQ0NDAX//6V/7whz9QVlbG4sWLCYVCg+6n0Wg466yzeP3112loaEhY19DQwOuvv85ZZ501ZF+bkdB/7f70pz8llD/44IMp27vv6M1DDz2UNEp37rnn8vnnn/PFF1/Eyzo7O5NGGRcuXIjdbufuu+8mEokkHa+zs3NY59JPZWUlF198Mc8++yxtbW1oNBouvvhiXnnlFTZt2jSs46S6xyH19el/uQ+1X5k0aRJTp07lpZde4qWXXiI/P59TTjkl6fhDueapOPfcc4lGownyBJIk8dBDDyVst/ezuHfbN23axLvvvsu555476HH+/ve/U1FRwfe///2k5+t//ud/sFqtA44wqxy9qCMuI+TUU0/le9/7Hvfccw/r1q3jrLPOQqfTsXPnTl5++WX++Mc/JghXjRaj0cjbb7/N4sWLmTt3Lm+99RZvvPEG//u//xufAhqLNp111lno9XoWLVrE9773PbxeL3/5y1/IycmhtbU1YdvZs2fz2GOP8Zvf/IZx48aRk5PD6aefzllnnUVJSQnXXXcdP/vZz9BoNDz99NNkZ2cnvUTHoh2puOeeezjvvPM46aST+Na3vkVPTw8PPfQQkydPxuv1xrdbtGgRp512Grfddht1dXVMnz6dd999l3//+9/85Cc/SfLP2JslS5ag0WhShvcCfPWrX+W2227jxRdf5KabbhrSee/NBx98wKOPPsodd9zBrFmzgJi2zYIFC/i///s/fve73w26/9133828efOYNWsW3/3udykrK6Ouro4nnngCQRCSfAPGmhkzZnDFFVfw6KOP4nK5OOGEE3j//fcTfIH6Of/883nuuedwOBxUV1ezYsUKli5dSmZmZsJ2N998M8899xxnn302/+///b94OHRpaSkbNmyIb2e323nsscf45je/yaxZs7j88svj998bb7zBiSeemGSsDpWf/exn/OMf/+DBBx/k3nvv5d5772XZsmXMnTuX73znO1RXV9PT08OaNWtYunQpPT09Keux2+2ccsop/O53vyMSiVBYWMi7775LbW1t0razZ88G4LbbbuPyyy9Hp9OxaNGiQUcrLrvsMn7xi19gNBq57rrrksLWh3rNU7Fo0SJOPPFEfv7zn1NXV0d1dTWvvvpqSt+Y++67j3POOYf58+dz3XXXxcOhHQ7HoPmq+mUG9nXu7sdgMLBw4UJefvll/vSnPyVIPagc5RyKUKYjkX3Doft54oknlNmzZysmk0mx2WzK1KlTlZtvvllpaWmJb1NaWqqcd955SfsCyg9/+MOEslRhrIsXL1YsFotSU1OjnHXWWYrZbFZyc3OVO+64Q5EkaUzbpCiK8p///EeZNm2aYjQalbKyMuW3v/2t8vTTTyeFY7a1tSnnnXeeYrPZFCAhNHr16tXK3LlzFb1er5SUlCh/+MMfBgyHHm07BuKVV15RJk2apBgMBqW6ulp59dVXlcWLFyeEQytKLHT2xhtvVAoKChSdTqeMHz9eue+++xRZlgesuz9E8+STTx60DeXl5crMmTMVRdkTDv3ll1/ut+1ut1spLS1VZs2apUQikYR1N954oyKKorJixYr91rN161blsssuU3JychStVqvk5OQol19+ubJ169akbUd7n6YKgw8EAsqPf/xjJTMzU7FYLMqiRYuUxsbGpHDo3t5e5dprr1WysrIUq9WqLFy4UNm2bVtSqK+iKMqGDRuUU089VTEajUphYaHy61//WnnqqadS3hfLli1TFi5cqDgcDsVoNCqVlZXKNddco6xatWrQ69YfDv3yyy+nXL9gwQLFbrfHw+3b29uVH/7wh0pxcbGi0+mUvLw85YwzzlCeeOKJpGu2d6hwU1OTctFFFylpaWmKw+FQvv71rystLS1J10dRFOXXv/61UlhYqIiimHCuqa6RoijKzp07FUABlE8++SRp/XCueSq6u7uVb37zm4rdblccDofyzW9+U1m7dm3SOSqKoixdulQ58cQTFZPJpNjtdmXRokXKli1bBq3/97//vQIo77///oDbPPvsswqg/Pvf/45fi4H6E5WjB0FRxsi7TuWAcc011/DPf/4zYaRARUVFRUXlWET1cVFRUVFRUVE5YlANFxUVFRUVFZUjBtVwUVFRUVFRUTliUA2XI4Bnn31W9W9RUVFRURkVH330EYsWLaKgoABBEPjXv/61332WL1/OrFmzMBgMjBs3jmefffaAt3N/qIaLioqKiorKMYDP52P69Ok88sgjQ9q+traW8847j9NOO41169bxk5/8hG9/+9u88847B7ilg6NGFamoqKioqBxjCILAa6+9xoUXXjjgNrfccgtvvPFGgrji5ZdfjtPp5O233z4IrUzNUS9AJ8syLS0t2Gy2Yctmq6ioqKgcWyiKgsfjoaCgIEm0bywJBoOEw+FR16MoStK7zWAwYDAYRl33ihUrklJZLFy4MGWG94PJUW+4tLS0JGW2VVFRUVFRGYzGxkaKiooOSN3BYJDysgza2lMnQR0OVqs1yQfyjjvuGFSVeKi0tbWRm5ubUNaf+yoQCByyhJZHveHSnzivsbFxyKnsVVRUVFSOTdxuN8XFxQlJV8eacDhMW3uA+o1XYrfpR1yP2xOmdOrzSe+3sRhtOZw56g2X/iE0u92uGi4qKioqKkPiYLgW2GxabPaRv4YVZODAvd/y8vJob29PKGtvb8dutx+y0RY4BgwXFRUVFRWVwxFZUZBHER8zmn2Hwvz583nzzTcTyt577z3mz59/QI+7P9RwaBUVFRUVlUOAjDLqZTh4vV7WrVvHunXrgFi487p162hoaADg1ltv5eqrr45v//3vf5/du3dz8803s23bNh599FH+8Y9/cOONN47ZNRgJquGioqKioqJyDLBq1SpmzpzJzJkzAbjpppuYOXMmv/jFLwBobW2NGzEA5eXlvPHGG7z33ntMnz6d3//+9zz55JMsXLjwkLS/H3WqSEVFRUVF5RCg9P0bzf7DYcGCBQwm3ZZKFXfBggWsXbt2uE07oIzKcAmFQke997KKioqKisqBQGaUPi6jMHqOZIY1VfTWW2+xePFiKioq0Ol0mM1m7HY7p556KnfddRctLS0Hqp0qKioqKioqKkMzXF577TUmTJjAt771LbRaLbfccguvvvoq77zzDk8++SSnnnoqS5cupaKigu9///t0dnYe6HarqKioqKgc0chjsByLDGmq6He/+x0PPPAA55xzTkoJ5EsvvRSA5uZmHnroIf7+978fcq9jFRUVFRWVw5mRRAbtu/+xyJAMlxUrVgypssLCQu69995RNUhFRUVFRUVFZSCG5ePidruR5eTBKUmScLvdY9YoFRUVlaMBKSoT8IdxuwP4A6NPqKdydKGMwb9jkSEbLq+99hpz5swhGAwmrQsGgxx33HG8/vrrI27IvffeiyAICVkng8EgP/zhD8nMzMRqtXLxxRcnyQ+rqKioHG7IsoLHHaChsZtly7ewZMln/Pvfq9m6tRmvL7kPVTk2OdgCdEcLQzZcHnvsMW6++WbMZnPSOovFwi233MLDDz88okZ8+eWX/PnPf2batGkJ5TfeeCOvv/46L7/8Mh9++CEtLS187WtfG9ExVFRUVA4WLqefxsZunn7qQ5568kOWvreJV17+gocfWcqXX+5WR19UVEbBkA2XTZs2sWDBggHXn3LKKWzcuHHYDfB6vXzjG9/gL3/5C+np6fFyl8vFU089xR/+8AdOP/10Zs+ezTPPPMNnn33G559/PuzjqKioqBwMIuEoLpeftWvr2bKlOV6uKNDZ7uaDZVvp7fEdwhaqHC4ojC6i6NgcbxmG4dLb20s0Gh1wfSQSobe3d9gN+OEPf8h5553HV77ylYTy1atXE4lEEsonTpxISUnJoM7CoVAIt9udsKioqKgcLAKBCNGIxI4dbUnrJEmmq9NDY1PPIWiZyuGGGg49MoZsuJSVlbFq1aoB169atYrS0tJhHfzFF19kzZo13HPPPUnr2tra0Ov1pKWlJZTn5ubS1pbcIfRzzz334HA44ktxcfGw2qSioqIyGgQBEEDUpO5eBQG0GuHgNkrlsERWRr8ciwzZcPna177GbbfdltI5tq2tjdtvv52LL754yAdubGzk//2//8eSJUswGo1D3m9/3HrrrbhcrvjS2Ng4ZnWrqKio7A+TWY9er2Xa1OSPJq1OQ0FhOkVFmYegZSoqRwdDzlX085//nH//+9+MHz+eq666iqqqKgC2bdvGkiVLKC4u5uc///mQD7x69Wo6OjqYNWtWvEySJD766CMefvhh3nnnHcLhME6nM2HUpb29nby8vAHrNRgMav4kFRWVQ4ZWqyHNYWbq1CJOOaWKTz7ZgSwraLQiZeXZLDxrKunpyUEOKsceCqPzUzlGB1yGbrjYbDY+/fRTbr31Vl566aW4P0taWhpXXXUVd911FzabbcgHPuOMM5Kcea+99lomTpzILbfcQnFxMTqdjvfffz8+krN9+3YaGhqYP3/+kI+joqKicrCxp5nR6jRcetk8TjhxPHV1XTjSzEyaWEB2th2DQXeom6hyGDBaP5Vj1cdlWNmhHQ4Hjz76KI888ghdXV0oikJ2djaCMPz5WpvNxpQpUxLKLBYLmZmZ8fLrrruOm266iYyMDOx2OzfccAPz589n3rx5wz6eioqKysHEbDFgthjIybEzfXopGo2IZgC/FxUVlaEzLMOlH0EQyM7OHuu2JPHAAw8giiIXX3wxoVCIhQsX8uijjx7w46qoqKiMFarBojIQo3WwPVadc4dsuHR1dXHbbbfhcrm4/fbbk0ZLxoLly5cn/G00GnnkkUd45JFHxvxYKioqKioqhxIZAZmRR5iNZt8jmSF/BlxzzTXk5uZy0UUXce6556Iox6ipp6KioqKionLIGLLhsmbNGi677DIuvfRS2tra6OzsPJDtUlFRUVFROarpH3EZzXIsMuSpogsvvJBbb72VsrIypk2bRk5OzoFsl4qKioqKylGNoggoysiNj9HseyQz5BGXhx9+mMsvv5yqqiref//9A9kmFRUVFRUVFZWUDHnERRRFrrzyygPZFhUVFRUVlWMGqW8Zzf7HIiMKh1ZRUVFRUVEZHQoi8tAnPlLufywypLM+++yz+fzzz/e7ncfj4be//a0avqyioqKiorIflDFYjkWGNOLy9a9/nYsvvhiHw8GiRYuYM2cOBQUFGI1Gent72bJlC5988glvvvkm5513Hvfdd9+BbreKioqKiorKMciQDJfrrruOq666ipdffpmXXnqJJ554ApfLBcRUdKurq1m4cCFffvklkyZNOqANVlFRUVFRORpQBehGxpB9XAwGA1dddRVXXXUVAC6Xi0AgQGZmJjqdmjBMRUVFRUVlOMiKgDyKkObR7HskM2LnXIfDgcPhGMu2qKioqKioqKgMihpVpKKioqKicgiQRxlVNJp9j2RUw0VFRUVFReUQoPq4jAzVcFFRUVHpQ5b7g0wFRHFkLwVZVhCEWOCCiorK2DOmhoskSWg0mrGsUkVFReWAEwiE8QfCdHS62bWrA61OpGp8Hrk5DiwWw5Dq6PX4aepwUd/eg91sZFxRFlkOK3qd2ieqpEZ1zh0ZY2K47NixgyeffJLnnnuO1tbWsahSRUVF5aDg84fo6fHx5epaXvn3Kvz+MKIgkJ+fxvnnTOfE+eOxWY2D1tHe4+Gtz7eysaYVWYnJgmU5LCw6aTKTSnPRaVXjRSUZBQFlFNM9o9n3SGbEnj1+v59nnnmGk08+merqaj766CNuuummsWybioqKygHH6w3S2ubk5de+xO8PAyArCi0tTt55bxNNTT2D7h+KRFm1rZH1u1riRgtAl8vHe19sx+kNHND2q6gcawx7xOXzzz/nySef5OWXX6akpIStW7eybNkyTj755APRPhUVFZUDRjgSxeUOsHV7K8FgJGGdgoLL5WfLthbKSrMwmfQp6/AFQuxo7Ey5rqnTRWevl+w065i3XeXIR3XOHRlDHnH5/e9/z+TJk7nkkktIT0/no48+YuPGjQiCQGZm5oFso4qKisoBQZEVZAUikWjq9QpEIhKKMnBWGFmBqDRwnt6IJI+6nSpHJ/2Gy2iWY5Ehj7jccsst3HLLLdx5552qA66KispRgV6vxWYxMH5cHqK4sS+qaA9ms55xFTkYjQOrg5sNOkpy02npcietS7eZyVFHW1QGQM0OPTKGfNa//vWvefnllykvL+eWW25h06ZNB7JdKioqKgccQRCwWo0UFaZz2qmJedbsVhPHz6mgojwbURy4qzQb9Rw/qYS8DFtCuU4jcsqMCtLtpgPSdhWVY5Uhj7jceuut3HrrrXz44Yc8/fTTzJ07l3HjxqEoCr29vQeyjSoqKioHDIfdhCgKXHj+TKZMKmTX7g4y0i1UlGdTUpRBRsb+R0yKc9O58qzZbKxpoaXLjdmoZ9q4fMrzMjDq1VxuKqmRldGFNMsDz2Ae1QjKYJO3g+DxeHj++ed5+umnWb16NccffzyXXHLJYRdZ5Ha7cTgcuFwu7Hb7oW6OiorKYYrPHwIBQoEIAV8YURQwWwzYbEZEzdAGp8ORKKFIFFEUsRj1SJKMPxjG7Q8RkSQMOi3pNjNG/dGh/SnLMp5ACG8gFo1lMxmwmQ1HtPjewXhn9B/j7e3fx2Ibmk5QKnyeEGdXPX7Mvd9GbLjszcaNG3nqqad4/vnn6ejoGIt2jRmq4aKiojIYsizT1OrE6fIT8Uf4aNkWana2Y7MYmFhdyLwTx1MxLhfdMI2NcCRKl9PHp5tr+WDNTtp7vRTnpnHG7AmcNKWcDJv5AJ3RwSESlahp6ebjjbvZ3dqNIAhU5mdy8rQKKvIz0Q7R2DvcUA2Xw58xMfunTp3Kgw8+yH333TcW1amoqKgcNHqcflatr6OyKIt/PL+Cml3tAHRqRMKhKB1tLi5ffBLFJcOLnnT7gny4oYZn3vqC/s/DLbXt1LXFptbPnDUBk+HInUZq7/Xw8kfr6XL54mUbalvpcHlZfNYcCjIdh7B1RwaKIqCMYqpoNPseyQzZcPnTn/60320EQeCGG24YVYNUVFRUDibNrb2YjHqaG7rjRguAJMm4PUGMBh9bNzaSm+tAbxhalxkMR+hx+1m2dhf7jmn7A2E+3VTL8VUlR6zhEolKbKptSzBa+mnr8bCtoZPcdBuaQZyaVUBCRBpFZNBo9j2SGbLh8sADD+x3G9VwUVFROZJQFIW2Tjcmg47a3V1J6yNRCVlR6OnyEolEh2y4SFGZcESirceTcn1Th5NwNLV2zJFAOCrRmcJo6afT5SUSldDoj80Xq8qBZciGS21t7YFsh4qKispBRxAEcjJttHW6ycpJ9hHQakVEAdIyLGiHkSxRoxHR6zTkpFmpb0+OuszPsh/R+Yv0Wg2Z9oF9dLLsFnSq3td+kfuW0ex/LDKm5nBzc/NYVqeioqJywCkuzMAfCFNSmkVpWVa8XKMRsNtM2B1mqqcWYRjGtI7RoCPdZubUGZVJ60xGHSdMLifNcuTqu+i0GqaU55FuSz6HnDQrE0ty0ByhzrkHEwVx1MuxyJicdVtbGzfccAPjx48fi+pUVFRUDhrpaWbmTCtF0ApcdtUJHDe3gowMM8WFGUyeUsSii48jLz992PXaLUZOmzGOa845jsJsB3qthnHFWVx99nGcOq3iiPVv6Scvw84lp0ynqjgbvVaDXqehujSXi0+epqoFqxxQhjxV1Nvbyw9+8APee+899Ho9P//5z/nRj37EL3/5S+6//36mTZvGM888cyDbesQSiESIyjIGjQa99ujQb+gnGI4QjETRiiJW08jD+vYmEIogyTIGnfaIHk5XOTLQiCIlRRl4/VZEoLIyl4AvhCAIWKwGbHbTfkcPJEkmEonGhv4VBY1GxKTXkZdp5/x51ZwyrTKu45JhM2EypE7YOFwC4QiSdGieFb1Ww6SSHAqzHHgDIQTA2qfjojrlDg1ZEUYpQKdGFQ3Kz3/+cz777DOuueYa3nnnHW688UbefvttRFHkgw8+YN68ecM++GOPPcZjjz1GXV0dAJMnT+YXv/gF55xzDgALFizgww8/TNjne9/7Ho8//viwj3UoCEQiNDhdrG5uxh0MUWC3MSM/n3y77Yif/41EJVp63KyqaaKmtQuH2ci8iWWU56RjNxtHVKc/GKah08mG2hZ8gTAFWQ6mlOaRl6FGJ6gcWARBQFZkalq62dXYhdVsoDg3DaNoGNRoURSFrl4f/kAITyjM5oZ2ujw+Mu0WZo0rpCjLgcNqwmEd22khfyhMY5eTdbUteINhCjLsTC3NJ/8gR/JoRJF0q4n0MT6/YwU1O/TIGLLh8tZbb/Hss89y+umn86Mf/YiKigpmzJjB3XffPeKDFxUVce+99zJ+/HgUReGvf/0rF1xwAWvXrmXy5MkAfOc73+HOO++M72M2HxmiTeFolLUtrby0fiP+SASA1c3wRWMz35w1g6rsrP3UcHhT29HDn/77CZsb9oSPvv7lVm44/0ROri7HPMwvymA4ytqaZv69YjOhvky963a3sK6mmUtPmUF5XsaYtl9FZW/cviDLVu/i47U1CeHL08YVcP7J1WQNMPXR2e1la00beouOp95eSV1bL4IAWelWvtzZwMUnTmP2uCIMurEbaQ1Foqzd3cy/Vm4m2P+s1LawrraFy06cTkXe8PRmVA4dyihHXI5VHZchm+YtLS1MmhRLQlZWVobRaOSqq64a1cEXLVrEueeey/jx45kwYQJ33XUXVquVzz//PL6N2WwmLy8vvhwp6oC9gSBLd9bEjZZ+2r1ePq6twxMKHaKWjR6PP8iyjTUJRguALxTmxY/W0+X2D7tOp9fPsvW74kZLPy3dblZuq8cfDI+qzSoqg9Ha5eaz9bVJmisbdrWwo6ETSUqO34hEJbbuasVi0fPJ5tq4sJyiQI/Th8sbYvmGGpzewJi2tdcbYNnGmrjR0k9Lj5vPdzTgU58VlaOcIRsuiqKg3cs/Q6PRYDKN3fCgJEm8+OKL+Hw+5s+fHy9fsmQJWVlZTJkyhVtvvRW/f/CXYigUwu12JyyHAmcwSKPLlXJdTU8P3vCR27n4QmE21LWkXFfX2UNr7/CveafbN6AuRF17L77QkXu9VA5vopLMzoZOoimME4BdjV0EQpGk8mAwQnunB41Ww9b6RCNekhX8gTBtTg/dnuEb8oPR7fHR7vKmXFfb3oNffVaOGGTEUS/HIkMev1QUhTPOOCNuvAQCARYtWoRenzglsGbNmmE1YOPGjcyfP59gMIjVauW1116juroagCuvvJLS0lIKCgrYsGEDt9xyC9u3b+fVV18dsL577rmHX/3qV8Nqw4FAFEAUBOQUqaA0gohwRM9NCmgH8NEREEaUo2SweXmNKBzh10vlcEYQYqHPA6ERBVLlDBQEgf7bNpUfjCCA0Lf/WKIRRQQgVZI5jSiSsrEqhyWKQtIo33D3PxYZsuFyxx13JPx9wQUXjEkDqqqqWLduHS6Xi3/+858sXryYDz/8kOrqar773e/Gt5s6dSr5+fmcccYZ1NTUUFmZrI8AcOuttyZkqHa73RQXF49JW4dDuslEZUYGO7u7k9ZNzM7CNkZRBYcCm0nPceOK2VDXmrSuujiHvDTbsOvMtlsoynLQ1JU8SjWuMBur6ci9XiqHNxpRZGJZLh+t3U0onDj9IgCTKvIwG5PvP5NRR1FBBqFghBmVBdS29sTXaTUiZpOekpx0Mm2WMW1vps1McVYaDV3OpHUTCrKwpmirisrRxIgNl7FCr9czbtw4AGbPns2XX37JH//4R/785z8nbTt37lwAdu3aNaDhYjAYMBjGJix3NKSZTJxdNR7XhiAd3j1TIFXZWZxSXoZFf+R2LhajgVMml7O7vZvlm/Y4MxZmOPjGglkj6qjTrCbOnDWBf322id69fAKqS3KZN7EEo/7I1rxQObzJSbdx5twq3lu5PW68aDQiJ0wro6IwEyHFKIZGI1JVkcvWna3MqyqlocPJ6h1NaESBrAwr+Rl2zpwxHodlZFF2A5FmMXHWjAm8+vkmerx7pqEmFeUwr6oEk/qsHDGMdrpHnSoaBW63myVLlvDUU0+xatWqUdUlyzKhARxX161bB0B+fv6ojnEw0IoiE7OzuX7e8Wzp6KTXH6AkzUFFZgbZlrH9AjsUFGWm8d2z5vGVaePZ0dJFutXItLIC8tJsGPXDv610Wg2TSnLJdljZ2tiBxx+kJCed0pz0QaXFVVTGAotJz9zJpZTnZ7C1rh1JkplQmkNBlh3bIIZHusPM5KoC/IEQ3zlnLlecPpNQNIrZoCcnzULGAZC+12k1TCrK4TtnzWVbUwduf5CS7NizkmVTn5UjCTUcemSMynBZtmwZTz/9NK+++ioOh4OLLrpoWPvfeuutnHPOOZSUlODxeHj++edZvnw577zzDjU1NTz//POce+65ZGZmsmHDBm688UZOOeUUpk2bNppmjwm+UJhur58erx+zQU+W1Uya2Yi4l69GMBpBFAQm52Sj02iw6vWYBxhpkWQZly9Il9uHPxwhy24hw2JKOUQ9HAKRCD1BPx0+HzqNhhyzhXSjadSdqSgK5KXbyHFYmFVZiFYjDuj3MlQMOi2FWQ7yM+xIsoxWI6b80lVRGQqSLOP2Bul2+ggEI6Q7zGTYzZhNesKRKE5PgK5eLyCQlW4hzWaivDCT4rx0FEUZsqCbw2bCYTPhC4bp9QfodPtIs5rQa7W0dLpwegNk2M04rCY8wRC+UASHxUggGqHHH8Cs15FtsZBmMg5Zg0Wv01KU6SA/3YYkK+gO4rMSiUo4fQE6XT4kWSbLbiHdah7RB4uKykgY9p3W3NzMs88+yzPPPIPT6aS3t5fnn3+eSy+9dNgPTkdHB1dffTWtra04HA6mTZvGO++8w5lnnkljYyNLly7lwQcfxOfzUVxczMUXX8ztt98+3CaPOb2+AMu37WbFrnq8wTAaUaAqL5tzpldRnpWOKIq0e728vXMnq5qbCUSj6DUapuXmcl5VFcUOR0J9kiyzu72Ht9dsY1drN7KiYDcZOGFiGSdNKsMxwpwm3nCIFc2NLK3dRVfAjwCUpaVz/riJVGfloB+DL0FRFDGOcQZYURQQxSNboE/l0CLJMg0tvby3Yhs1DZ1IsoLVbGD+jHJOmFHO9roOPvhiB+3dsezNeVk2TjtuAlPG5WMcgRS/OxDk0x31fLytlmybhVOrKnhj8xa21rWTZbdw/onVfLqjnoZuF6dPq2R57W6W7qhBo9NgMxqoys7k/EkTqczMQDsMATmNKHIwUwKFoxJbG9t5d80OGvt8bLLtFk6dWsnscUVYVP+aYaGOuIyMIRsur7zyCk899RQfffQR55xzDr///e8555xzsFgsTJ06dUTW/lNPPTXguuLi4iTV3MOBiCSxtr6F9zbtjJdJssKWlg4Arpw/A5NBx0d1dXxcXx/fJixJrGppQa/RcMmUKdj38sPp9QZ4c9VWdrXtceR1B0K8u24HaRYj86pKR6SGuau3h1e2bSIix8I8FaDW2cs/tm7kh7PnUmhzDF6BisoRissT5N3PtrKzvjNe5vWHaOt009jm5N/LNuAP7glxbuvy8J/lG8lOt1JaMDyxQ0mW2drcwZtrtyErCmdPm8C6Hc3886MNmA06FsyopNnp4cl3v+BbZx5HrbOXv32xFklRMOl16DM1bOvsQlK2cu3sWWRbD9+p5E6Xl3+t2JQQ4t3p9vGflZvJsluoLsk9hK078lAUYVQicqoA3X647LLLmDlzJq2trbz88stccMEFSaHQxwKeYIh1Dak1TLa3deIKBHEFg6xra0u5zfq2NpzBYEJZu9NDTXty9JGsKKyva8UbGL5YnTccZnVrc9xoSTiez8v27q5h16micqTQ1eulpjH5Hi8tyGD1lgbc3mDSOl8gzOaaViIRaVjH8oXCrKtvQVYUMq1mLDo9K7bUARCMRCnKTefzHfVoNSIlOel8vLseqc+jPRCOEOwTqdzV1U2rxzPMMz14yLLC9qbOlLo04ajEhroW/Cn0blRUxpohGy7XXXcdjzzyCGeffTaPP/44vb29B7Jdhy2SJA8ohibJCsFIlKgs4xtAYM4XiRCVEjtGTyA0YDy+NxgiKg8/WF9WZLyRgYWoeoPBlBozKipHA/5AGDnFc6PVirg8AZQB7n2vP0Q0hbE/GJKk4O1TqzXotCiKgtsXM4z0Wg2g4PIHMfT/P5BoNEWkPSOi7uDhq6gtKwruQLLB1483EEYa5rU71umfKhrNciwyZMPlz3/+M62trXz3u9/lhRdeID8/nwsuuABFUZCPoZvVoNOS50itU2Ix6LEbDZh0OvJtqbfJt1ox6RLn0HPTbBgHyGWSn27HoBu+v4deo6XAmjo9gigIVKZnIKqOrypHKZlplpSO7V5/iIqirAQn+r0pyk3rMzaGjkGniWsXufxBRFGgNDc23RSOSoQjEmXZ6XhDYcIRiYqsPVNRoiDEn32jVku+ffgaSAcLrUakNDt9wFdlQebI+qpjGVnZkyF6ZMuhPoNDw7AcJ0wmU1wgbuPGjUyePJnc3FxOPPFErrzyykEVbY8WbEYDcytLsKQQkJtXWYLDbMRhNHJiSUmS86tGEDihtIT0fVIlZNktHD++JKk+u8nAnHHFWI3D16UxarXMyS8kz5KcHG5mbj7FdtW/ReXoJSPNzOzJycKTjW29TB6fT2VxcpLT8sJMxhVnD5oNOhVmg57jKopJs5jwhcK0uDwsmFmJyaDDYTayq6mLuRNKyLJb2dnSxSnlZWRaYmHLDrMxnoBxbkkR2ZbDO5y5JDuNyaV5SeX56TamleWj16qRRcNBlfwfGYIy0JjpEJFlmTfeeIOnnnqKt956a0ANlkOF2+3G4XDgcrnGLEFjMBxhR3sXK2saaXW6sRj0TC/JZ1ZZIRl9HY8vHGZjeztfNDXR6fPhMBo5rqiQGXn5OIzJuhDdHj+ra5rYVN+GPxymMMPB8eOLGZefNeLMshFJotbVy6eN9dQ6e9FqRKqzcjihqGTA0RgVlaOFXrefddua2LyrDX8gTH62ndmTi6ksyaazx8MXG+vZ3RzzLasoyuL4KaUUZNsHHI0ZjHBUoqa9m5W7GnD6gpw+uRK3O8DGmlZ8gRBfOa4KRYSNjW1MLMohqET5orEJVzhMtsXMhOws5hQVkHWYazwpikJbr4eV2xvY2RLL71SWm8HcqhJKstNHlO7jcONAvDMGOsaTG/8Xs23kAoV+T5BvT7172G195JFHuO+++2hra2P69Ok89NBDHH/88QNu/+CDD/LYY4/R0NBAVlYWl1xyCffccw/GFO+yg8GoDZd+AoEADz/8MD/72c/Gorox40DdhIqi4AmGCEaiaEURm8mQpI0iyTKeUIiQJKHXaLAbDINGB0WiEp5ACEmWMep12EyjVwCOSFE84TABKYpWEHEYDBi1B05ZU1YUPKEQYVlCL2qwGQzqlJTKISMSlfD6Q0iSjEGvxWo2xCMgA8EIvmAIATCbDJhGEAa9N4oS82XxhyOx591siKvwhiUZvVZDVJaJRiXMRj2CAL5wJPa863Q4jIZBozMlWcYbCCHJCia9lrAkEemr17afffe+Hj5/CFlRMBp0I9aJCoajeIMhFEXBYtAn1OMLhwlEIoiCiM2gH7ZmlCccIhiNohHEWJ8pCLjDIcKShE4UsekH70dHy8E0XP6y8bZRGy7fmXrXsNr60ksvcfXVV/P4448zd+5cHnzwQV5++WW2b99OTk5O0vbPP/883/rWt3j66ac54YQT2LFjB9dccw2XX345f/jDH0bc9tEwrE/5zs5OVq5ciV6v54wzzkCj0RCJRHj00Ue59957iUQih53hcqAQBAG7yYh9EIkVjSiSNowM2jqthowxUr5UFIVmn4vPO+rZ6erCotVTbs9gRmYhhRbHATEmfOEwmzs7+KKliS6/n0yTmeMLC6nOzsGmP/RpGFSOPXRaDekDKC+bjDpMxrEx4vtHYdfUNuMJhjHrdcwoLaAyJ4OdbV2s2d1MjzdAtt3CyRPL0Ok1bGvv5MumZnr9QXKsFo4vKWJiTlZKkUq3P8jmxnbq2nuYUppHq8vDhoY2gpEoeQ4rcyqKqMrPwjRIDrRup4/1O5rZuruNcESiMCeNOZOLKc5LH7LYXj9GvTZJcC4iSdT29LKioZEGpxOjVsvUvLwhjySFpSi7enpY0dRIk9tFttnMueOr6Az4+LKtiQ6fjzSjkTl5RUzLzkuQlDhSUfp8VUaz/3D5wx/+wHe+8x2uvfZaAB5//HHeeOMNnn76aX7+858nbf/ZZ5/F3UEAysrKuOKKK1i5cuWI2z1ahmy4fPLJJ5x//vm43W4EQWDOnDk888wzXHjhhWi1Wu644w4WL158INuqMgzaA15erFnHNmdHvGxDTyvbnB18c/wc8sxj6wQYkSRWt7bwwuYNhPuiphrdLrZ0dXBZ9VROKE72+VFRORqQZJmtrR0s+Wwd/vCecOCy7HQ+39nIm2u3xiOcmntcVBVms9PZwxtbt8fDoptcLra0d3DlrOkcV1yYMKIQCEdYuaOB/67aytfmT2VzcztPfvAFsgIFGXZaet1sbeng8vnTmVVemHI0wu0N8v7K7azcuEdbqqndSU1TF1eeM3vY2jWpqO918uzqtXT69uRm29HVTafPx1erJ6acIt+bXT09PL1uDb3BWK6y6bl5rOto47nNa7Hq9WhFkUZPrE+5uGoKpxaXHdDR4yMJt9ud8PdAOfvC4TCrV6/m1ltvjZeJoshXvvIVVqxYkbLuE044gb///e988cUXHH/88ezevZs333yTb37zm2N7EsNgyONtt99+O+eeey4bNmzgpptu4ssvv+Siiy7i7rvvZsuWLXz/+9/HNIzRBZUDhyTL7HB1Jhgt/Wx3drLd2THmYYvOYJCPG+riRks/YUni44Y6nMHAAHuqqBzZeIIhVuxsSDBasm0W0sxGXl+zOaE8P92G0aDjX+u3EIgkZqIORqN8WluPax+dJ6cvwGfb68lLt2Ex6nlr3XYikowkyzi9AaKyTDASZcXOetz+1D6GHT0eVm9tTCrv6vWydlsTwVHqrwQiEVY2NiUYLf183tBIu9c76P6ecIgVTY1xo8Wm11Ngt/Nu7U6a3K6EfkVSFD5uqqM3OHBo9pHC6CKK9ozWFBcX43A44ss999yT8nhdXV1IkkRubqJQYG5uLm0DaI9deeWV3HnnnZx00knodDoqKytZsGAB//u//zu2F2MYDNlw2bhxI7fffjtTpkzhzjvvRBAEfve733HJJZccyPapjICQLFHrTha062e3p4eQFB1w/UgIRCM0edwp1zV53ASiY3s8FZXDhUA4SrMz8d5PM5uQZIXtfQ6sCeWKzLa2WBqCfWl2ufFHEo0ITyBEl9tHmsVEVJap69yjoRWMRONyFM29bvwD6Ec1tTuJRlN/rLR0ughFRvd8BqNRmt2pn/+wJNHQ6xx0/1A0SvNe/Uea0YSsKOzq7UZSFIL79B+tXg++QXSqjhTGSselsbERl8sVX/YeURkty5cv5+677+bRRx9lzZo1vPrqq7zxxhv8+te/HrNjDJchTxX19vaSlRULITSZTJjNZqZMmXLAGqYycrSCgEU38PyvVacfc+c2jSBi0emSRlwALDodGtVBV+UoRSMKmPU6en17RhUjkoQA2E3GBH+yqCQjCgIOk5FUj4RZr0MrJD6beq0WvVZDJCohCgIWgz4ugqkRRYS+l5dZrx8wqsdqHrg/MBv1aITR9QcaQcCsG3jaxmYYfJpII4gJ+0fk2PWzGQz0BANJ+ZuMWi26A+ige6Rht9uH5JyblZWFRqOhvb09oby9vZ28vOQwd4D/+7//45vf/Cbf/va3AZg6dSo+n4/vfve73HbbbSOKwhstwzrili1b2LBhAxs2bEBRFLZv3x7/u39ROfToNVpmZBZg0SY76pm1OmZkFmDQjK3egsNoZGpO6ht/ak4ejv10XCoqRyoOk5GpRYn3flOPCxmFU6srEqJq6rt6ERRYMKECXYpEolPz87Dv4wuSbjEyqSiHhi4noiAwby/NJ5vJEDdWphTlYjelfs6K89LJz0p+sYmiwJRx+ZhNo0vfYtHrmVVYkNLpv9jhoDR9cN0om8HAzNz8uLhdu9dLKBrlpMJSzDp9Un81NSuXNMOR75qgIIx6GQ56vZ7Zs2fz/vvvx8tkWeb9999n/vz5Kffx+/1Jxomm754eo6DkYTOst9cZZ5yR0NDzzz8fiEXYKIqCIAhIKb64VQ4+BRY7l1RM463GbXQEYvPLOUYrZ5dMpNA89uJzVr2er5RXEohGWNfWSkSW0Yoi03PzOLOiEttREAGgopIKg07L/HEluIMhVtc2E4pGUVBw+gN8dXY1Fr2eTY1tRCUZRYmFYX9t+mTe3r6DzW0dRGUZvUbDrMICTq0ow6xPHLmwm418Zfp4IpLMjuYOFk6bQCgisb21k3SLCaNey4zSAk6aWIZJn3rUI8Nh5tyTq3l3xTYa25wA2MwGTphRzoTSHERxdCOiGlGkOieH8yZOYPnuWjx9I0Jl6Wl8tXoimebBoyW1osjM/Hy6g34+aWjAFwmzurWF08or0Gs1bOxsJyJLaASBKdm5nF0x4aiIKjoU2aFvuukmFi9ezJw5czj++ON58MEH8fl88Sijq6++msLCwrifzKJFi/jDH/7AzJkzmTt3Lrt27eL//u//WLRoUdyAOdgMWcelfq9Mx4NRWlo6qgaNNWMZky8p8pCHVGVFRuzbVlaUA6ploigKCiQdIyhF8UVCBKKR2LC13oRRo41PEx2INvUGAnT4fXQHYuHQeRYrDqMRWVZSdo6SJA9bqXS0xHwChFF31ioHloHumaGiKAqKwkH7nd2BIF0eP50eLw6TkVy7FYfZhCsQpNPto9fnJ8NiJttuwWE20hsI0uHx0hsIkGWxkGO14OgbMZGV2Ld0vzaLLCs4fQE6XF4MOg1WowFJUYhIEia9DoNWu1/dp2hUotcToKPbQzAcITfTTobDPGItl/52wp6+xBcO0+Xz0+r2YNLryLdZyTCZE6aw+s8tVZ/lDYfo9Ptp83qw6PSUp6UjKTKdfh+dfh9pRhN5FivpRtOQdGtGwsHUcXlo/S8xjULHJeAJcsP0Xw67rQ8//HBcgG7GjBn86U9/Yu7cuQAsWLCAsrIynn32WQCi0Sh33XUXzz33HM3NzWRnZ7No0SLuuusu0tLSRtz20TBmAnSHK6O9CSVZpjPoo9bTTYvPTb7ZToU9k2yjJclPRJJlOkMeatydmDV6MgxW2gNeGr1OMgxmJqRlk2OyphwiHgnBaIR2v4+t3R34IhGqMjIpsjniQ6i+SIj2oIcGby/jbFnoBC0tHg9bOjowiFqm5eVRYLNjHUT7YTTIskKPx0dDu5OWLheZDgvl+Zlk2M10OL3saOmkrddDSU4albmZ5KRZD6ywlD9Ia6+HjXWtyIrCtLJ88tPtOCzqNNbhgiTL9Pb6aWjqob3TRXamjdLiTDLSLUM2cMORKN1OH7vqO/H4Q5QWZFCQ4xhQz+VA4Q2GaHd62d3RTabNQklmGlaTHuMgviA9Pj8NThe13T04jEbmlhbhCYTZ3dlDq8vDuJxMKnIy8IXC1Hc5aXW5yXPYKM/KIMtmHvT5CYQi9Hr89Lj9KIpCht2M3WLCNoj/y4DnFgrT7vawpa0TgOq8bHLttkH7ErcviD8UxhuO0Ob10OLxkGU1U5WTTbbFkpQjKibyGaY36Kc3FCQYjWA3GMkwmcg2J/e/Y8XBNFz+uO5XozZc/t+MOw5oWw9Hhu3o8OWXX/LCCy+wY8cOACZMmMCVV17JnDlzxrxxhxpFUajz9vJyzTp27RWlU27L4NLK6YyzZ8WtfkmWqfF08kr9GtL0ZmZnlvHHTR+xobuVXLMNk0ZPvtnG1ytmUJ2em+RsNlyC0ShrOlp5ZccmnKFYWOA7dQInFJSyqLIKo1bLp+27Wda6k0vKpuOLRFi6axtLNq0lIiloBYESWzpXTZ3BgoqKAzKV09zl4j+fbmJnU1e87IcXnkiXx8ef3/mcTQ17wu/mVZVy3VeOY1x+cg6ZscDlC/DBhhqeXfplPAOv1WBg8Rmz+cqM8aRZjvz58qOB5hYnb723kdr6PfdMUWE65501jdLizP2OnkQiEttq2/nv8k30uPxA7Kt+6oQCFp40iZyMg5PE0BMI8tHWWj7YVBOP2DHqtJw1YwInTChNmX+sze3h3xu3sq45ZlhfMn0yLU4Pz368mo+31aIIcP6MiZw0oZylW3bR6nTHRyzKstL52pwpVGZnpByJCIQirN7WyNsrt+Hry2St04icMLWc02aNw2Ed+v3vCYb4uKaOd7fujEf7vL99F2dNGs/JlWXYUpyb0+Nnc20bBquel9dt4rOaekRBICfdyoT8bC6bMZXq3JyEkZleX4Ddzh7+svpL1ra2xq/hSeVlLJ4+i/EZmUe8KreiCCMSkdt7/2ORYb09b775ZubOncuTTz5JU1MTTU1N/OUvf2Hu3LnccsstB6qNhwxPJMTSph0JRgtAraeHpU07cYWDe20bZGnLVpp8vUxNK+TTtlo+ba/FEw3SHfQRVWRa/R7ebNhKb8g/6rZ1BXz8e9eWuNECffoGzXW0er00+138t2kzFfYsrHoju3t6eGLtF/QGg8iKjIxCvaeXlzZvosnlGnV79sUXCPPpxtoEoyU33YrZpOcfn6xnY32iZsDn2+t5a/U2XL4Do/fS0OnkL++sjBstAN5QiCffWUl9e+8ge6ocLLy+EJ99sSvBaAFoau7lkxU78fr2r9vh9ARYumJ73GiB2NTE+u3NrN3SRHiUYb9Dpb7LybvrdySEGQcjUd5eu53WXk/S9qFolJX1TaxpakFWFHQakUm5Oby7cSdvbdiBhIJRp+X4imL+s3YLy7bWENkrzLquq5dlW2oS7u+96ej1JhgtABFJ5qP1Nexq7hqWk2WT08Wbm7cnhCgHo1He3LydJmdyXyLJMtsbO1FEgZX1jXyyqw5ZUYjKMh1OL409Lt7cup3ewJ5n3xcK0+Xz8fr2bXGjpf8aft7QyFs123GHjnwdl37DZTTLsciQDZe//vWvPPTQQ/zpT3+iu7ubdevWsW7dOnp6enjggQf405/+xN/+9rcD2daDjiscTCniBrDN2YE7sufBcUUCbHe3k26wYNDo+LKzIb7OGw3FBd92u7vpHgPDZberh65A6nr80TDre5oJSVGKzGn4wmFWNTfHQ5UjsoSigIJCd8DHutZWQmOss+IJhNjR1JlQNqUiH28ozPJNNSk7ypU7GgcU0BoNgVCY1buaCESSRbZCksQXOxsTOnSVQ4PXG6Rmd2fKdbtqO/B49/+i6ujx0NKR2hDfUd+B13/gf+dAOMLG+raUOi3hqMSmhjYi0cQgBk8ozJa2PX3NuMxMIlGJj3fWxT40FIXy7Ay0osgXuxtx+YNJ0gPb2zpxB5KfH1mW2d7QnvIeVxTYUpt6XSqCkSgbmtuIphCwjMoyG5pjaQj2JhCMUN/ag8mk48v6psR9pFj+pdqe3gTxukA4gicS5svmxO0hZtRsaG/DdZgl9FU5eAx5quiRRx7h7rvv5kc/+lFCuU6n48c//jHRaJSHH36Yq6++eswbeahQUIjIqaOkwn0v/376vyDEPi/vvTuVmPNsbGMZZUxUa0ODRG/JxJz2IDZMLiux9g60bViS4052Y4WiKET36Zw1ooiiKIQiUQwp5vlDkeiYtwNAkhVC0YGvVzgqxUW8VA4diqIQHeC+jkYllCH8RPsaBEl1HASXPnmQ84CYxsu+97nS13/0o9WIKECo39hWYlopCn33q6IknUtESn1+/fsM1h6GeFlkZeA+caA2KChxIy5VO/qdqPcW6kOJlafShVIUhVB07PusQ4GEgDSKqKLR7HskM+QRl82bN3PBBRcMuP7CCy9k8+bNY9KowwWrVk+5LXUOjwpbBlbdHkc0q85AqTUDZ9iPrMhUp+/RdTBr9wg85ZttZBhH7yRY6cjAPECeDoOoZVJ6LiICXSEfFr2Oqbl72qMRxPjtbtPqqc7JxqgdW10Xs1FPSW56Qlltazdmg44544pJ5aowrTwf6yj1JFK2xaBjWll+SsEvAZheno9lFJEVKmOD2aynuDD181ZcmIHFsv/fKCfThsOW2l+jpCAD8xglVRwMk07LhILslOsEAaoKsjHoEp83s15HeUZa/O+67l70Wg2zS4sQhFgEXFOvCwGYUpSH1Zicjb4sKyOlc6xGFBlflD2gOF1lQSbGIWbGNum0VOWkPjeAqtxsjPucm1GvIyfdihSVqc5PzD4sCgJmk54cm4Usy55+Ua/VYNHpqU6Rrdik11GVlYktRTLKIw1lDJZjkSEbLhqNhvAActIAkUjkkMV0HyjsehOnFYzDvo8KrVVnYEHBOBz6PR1kmt7MqXkT0Gu07PJ0cHrheIotaehEDRkGC1pRxCBqOaNwPOljIJyUY7ZyRklFknNapSODXLOFMmsm83PK2dzbiiTLTMzK4txxVehEMeYYLIBDZ+IrleOpzEjt0DcabCYDJ0wpI20vp7+dTV3IksJlJ09PKAfIS7Nx3uyJB8RJVhRFxuVn8tXjJietO2/OJMYXZB8S9UeVRKxWI3PnVGCzJkZZmM165h9fiW0Ag2Rv0u1mTp5dmfSSzsuyM2dyCaaDYKCKokhlXiYzygqS1h0/roSS7PSkcotez/zyEgocMedhTzhMm8fDudOrGJ+biYCA0x9ke1sni2ZMpLogOyEKx2rQc0pV+YACdPmZduZPLkv6Pp9YmsOkstwBjZp9EQSBiqwMji8tSj630iIqspL7Ep1Ww+SKPLzeEKeOK6ckI6YjJQDpNjM2o4EzxleSsZfWi81kIN1oZFHVpAQNGFEUKMtI56yK8aTtJ2mjytHLkMOhFyxYwMknnzxgfoLbb7+dTz75hOXLl49l+0bNaEPbglKUBk8vqzob6Q75yDBYmJNdRJk1HcM+Ix6BaJh6bzcbepuotOWiF7Vs6mmnI+DFoTcyO7uIClsmFt3YdJ6uUJAdvV2s7WglGI1S4UhnZk4+eRYbGlGkO+hju6sdECi3ZeILRdjY1sbG9jaMGh1zCgqZmpdH9hBSzo+EcESiucvF+l3NdDp9FGU7mD6uAJvFSF1HDyu21dPh8lKY6WB+VSmlOekDCmiNBe29HjbWt7F6V2zefGZlIdPL88lNOziRJir7JxSO0tLqZNOWJnp6/djtJqZNLqSwIH3IowJef4j6lh427mjBHwxTkO1gyoQC8rPsB1UzqNPtZWtzJ9ubOxAEgeqiHKoKcsi0pR5xjcoyLS43a5taaHK6yTCbOGdSFR0uDytqGqjr7KUgzc75MyfiDobY2NiGAqSZjVRkZ1KYbk8a7dgbpyfArqZONte1E5VkKgoymVyeS3aaddgfLp1eH1vbOtjWFw49MS+bSXk5ZFtT9yWyrNDp9OILhnFHQqxtbqXD7yPbZmFOcSEVmcmjReFoFHcwRL3LyYqmRjr9XorS0lhQWk6J3YFhjEeJ+zmY4dC/W3M3JusowqG9QW6e9b/HXDj0kA2X//73v1x44YXcdNNN/PSnP41nl2xra+P3v/89Dz74IK+99lpcTfdwYaxuwkA0QkSW0IsaREHAF43gCQeRFBmH3ohBq8EfDaEVRbRoQRCQ5CiyAjKx6RubPnaDBqMRnOEgYSmKRWfAKGrxBENEJBm9VkOayTjkh1JRFLyRMIqiYNTq0Gs0uEMh3KEgVr0BRZDxRIJoRZE0nRmIzQ9rRAHLCHMWhaJRnIEg4aiETiPiMBoHNThCkQiRqIwvECYalTHoRdJsZgLhKJIko9NqBpyq6T8XWQGrXofdYBxVKHlEkmKOiApYjLGcL95gGEEAm9GAzWRI2YlLsozLH8QfiiCKAnaTIWVIq8rYEAxGiEYlNFoNphFO7/gCYWRZxqDXoh/khT5aZFnGEwjhDYRBAKvRgN0cu48kWcbfl3nZbNAN6XkLRaOEotFY/q++l7kvFCYcldCIQnxUxRUM4goFkVGw6Q04hvBsyH3tURQwGXRDHmnZl4gkEYxECEZjXhYWvX5IHx3RqNTnUxfzUdFoRCx7TflEZRlnKIg/EkYjCGQYzWgEgWA0igLoRBFfMEwoIqHViDjMg/c9I+FgGi6/XXM3xlEYLkFvkFuOQcNlyE/z+eefzwMPPMD//M//8Pvf/x6HIzbc53K50Gq13H///Yed0TKWmLQ6TOjwhsO0B9wsa9nB+007GefI4muVk9npbaDW10KG3sZJ2VPZ4W5lq6sJdzRAut7KzPQy5mVNICIrfNpaxxcdjaQbTJyeN45NzR1saenAGwyTZTFzXGkR88qLSTPtf2hcEARs+tgLVJJlanp7eL+2hjyrlWybkQ/bd1Lv70av0VBpy+LkvPFUO/IwaEb2sLsCQVbWNfJFfROdXh9pJiMzigo4pbKMTGvqL8lgKMrqLY2s2dZEj8uPw2Zk2vhC5k0tHVAUTJZl6lxOltbWsK2rk7AkUZ6Wzqll5UzJzh2xT45OoyHNYiIcjbKtpZOPt9ZS29GDRhQYn5/FgupKynLSE14wgXCEzU3tfLK9jsZuJwadlkkFOSyorqAow3HAFDyPZYxGHTC6F5LlAPhL7UskKrGrtYuPN9eyu60bQRCozM/k1MkVlOdlotWI+1W03ReDVpv04WIx6LH0VaMoCo0eF+/X17Cps52gFKXUnsbJRWVMz8kfNNmhKIpYh9meffGFwqxrauXT3fU0O91Y9DqmFORy2oQK8h2Dvzy1Wg1abWqXAn8kzLqONj5urKXB48Kk1TElK5czSispstnxBkOs3NnIypoGOlw+7GYD00vyOamqnGz7gRk1Vjk8GVbvf8MNN3DRRRfx8ssvs3PnTiAmQHfxxRdTXFx8QBp4OBGWJLoCXt5q3MLfd6xCL2q4btLxLGvfwJvNqyizZfDtcdNY3bObF+s/JteYjkNnoS3o5O3WdVRa89nU3cm/a7cgCAJnFk7g090N/G31WvLMVrJNVppdblo2bkFAYMGE8mENh3b5/fxzyya84TAzCnJ5ducK1nc3oxVFStPS2Ca30eJ3ct2EkxhvT3Z62+/5R6N8Ud/EvzZsiXv0t3u8vLN1B7Isc/bkCQlfTxBTMV29tZE3P90Sj8Lq6PGydOV2ZFnm9OMmpPyi7gz4+efWzWzv3qPpsaWrk0a3i+tnH09V1sAOgkOhucfNS5+tx7mXbsza2ha6PX6uPnU2eXtNH+3u6OGlFevxh2NfzsFIlM93NeAOBLl8/owBh/5Vjn7aej28/Ml6utx7pAk21LbS6fSy+CvHUZAx9l/BPcEA/9q5hfUde7SQtvd00eB2YdbpmJ6TP+bH7EdWFLa2dfDS6g3xiJ9QNMpHu+oIRKJcPHPykD64UrG9p4vnt6wj0CfNEIxG+bipDm84xDVTZrG2roXXVm2KRyh1uqMs3bSLcFTivJkTj8gRUFkRkEehxTKafY9khv3ZWlRUxI033ngg2nLY44uE6Q0HeK9xOwBTMvIxakWWtW0iIkvYtBYMop6POjajoOCO+LBojegELdlGB+5IiM/a6lCAEosDPVre27ELgN5QgDSDCY1Gi6LAlw1NzCzOJ8dmHXL7ap297Ojp5ivllbQFXazvbgb6hl+DAUxaLe5IkDXdDRSZ0zClyB49GK5giFUNTSnDEFc1NjOvvDjJcHF5g6zb1kyqCcl125uZPak4peFS73QmGC39eMJhvmhpptiRNuiX5WCEIlHW17UmGC39NHQ52dXWTY7diigK+IJhVtc2xY2WvdnW0kmL060aLscokajExvq2BKOln9ZeD9uaOsg9AGksmj1uNnW2J5UHohG+bG2iIi0jPgo71niCIb6sb0oZpry+qZVTxpWPyHDxhEN80doUN1r2ps7Vi9MfZFVtc0ptnDV1zQOqER/uKIgow9OBTdr/cKe2tpaPP/6Y+vp6/H4/2dnZzJw5k/nz52McoYP1kA2X//znP0Pa7qtf/eqIGnIkIMkyYSlKeyCmfOnQGwnKYZzhmHCSQdQjKwrtQSfQr/USe9AsGgNhSaLd70EQNFh1BmRZodXjiW+7t0HQ7fMPqr2wL7Ki0OxxA2DW6WjxJb70w33aERoBuoNeIrLEcLuXcDRKlze16F2vP5BSKyUSlehJ0bED9LhTn6Oy17mkoivgJypLjHQqIRKV6PL4Blzf5vTEMvaKGiKSRLcndftlRaHN6WFqcV7K9SpHN+GoRLd74Puo0+klEpXQ6Mf25dLm8yAN4JrYGfAnKOqONWFJosuX+nkISxI9Pj+QOex6I5JE9wCCmkadjkAkQvcAfY83GMYXSv6wUDm0LFmyhD/+8Y+sWrWK3NxcCgoKMJlM9PT0UFNTg9Fo5Bvf+Aa33HLLsJMzD9lwufDCCxP+FgQhSWhIEASkQYSXjnREQUCn0ZBttNAZ9OGOhDCKOhw6M+6on5AcQRAEcowOWgI96ERN3P/BL4XRiRqyTVa6ggF80TCiCLlWK+1eb9zpt58MswndAHPBA7Ut3xqb3vBHIuRbHAnr9Zo99acbzGhHkOhRr9GQYTHhSxEW7zAak5KkQSwUMs1mIpCiY0mzpT5HQRDIsw480pRhNI3KQVer1ZAxgD8OQI7DirZPaEarEWPbtncnbScIkOsY+oiYytGFXqshY5DRtky7JUlrZSzItVj7hCWTjZdMowndAYyc0ms0ZJjNNDuTPyx0GpF088imibSihnRj6n1D0QhGnY50iynlKGnM/+fA6/McCGQltoxm/8ORmTNnotfrueaaa3jllVeSXElCoRArVqzgxRdfZM6cOTz66KN8/etfH3L9Q77DZVlOWMxmM7t27UooO5qNFgCLTk+63szphVUAbOppJSwpnJJbjU7U4In6iMgRTsyehICAXWtGK8Q6rvagE7vewLzcMgAavE4iSJwxvhKANL0pIWv07JLCYesUVKSnU5GWzrauTvJNaUxOj811a0QhNg0liFi0BmZllmAe5jQRgMNkYk5xUUqtxlnFBaSnGCK2W43MqCpMWd/0CQU4BvCor0jLoDI9WYzMotNzXEEh5lGElBt1WmaUFWBP4aRYmGFnfF5mXNfFajQwu7wwZZjphLwsCtKPHU9+lUR0Wg1Ty/JIT5GgMMdhZVJxzgEJvy602pmUmezjZdRoOS6/6IBNE0FML2ZOSWHKD4epBXnk2EbmJGvT6zk+vwhDCkOv0OYgzWxkdllhyqSKM0rzj9gkqTEROWEUy+HJvffey8qVK/nBD36Q0v/VYDCwYMECHn/8cbZt20ZFRcWw6j9wMYJHIQatlmyThfNLq9FrNCxt2sEHzTVcVDGDAouDWm8LG5w1nJg9FYvGyDZ3M+5IgHSDlZlpZWQZrZxaEPtC/6KjkS86GzmtMiZwt6U1FlWUbbUwp6SI+eUlSeqa+yPbbOHS6im8V1tDu9vP1ePn8kHbNup93Ri1Gipt2ZySN4ESS2p10v2h12o4vqyIqCLzRV0jnV4f6WYTM4ryWTC+Ih66mXDNdFrmVJcgyTJrtzXT7fLhsJqYPqGAE6aXYx4gDDrLbObr1VN4b/cutu4VVXRaWTnlackCXsOlMMPOpSdM56Mtu/uiikTG52dx2pRx5OwzilKZk8HX507jkx11NHT1YtRpmViQw+mTK8mwqP4txzJ56Xa+ftJ0PtxYQ217DwDj8rNYMLUy6T4aKzJMZi4aPxmr3sDmznYC0QiljjROKS5nQsaBya7ej0YUmZyfwyUzp/DZ7nqaXW7Mej1TC3I5fUIlaSMccREEgYmZ2Vw+aTofNdbS4HZi0uqYmp3LmWXjsOr1zCovJCLJfFHTQLvLi91sZHpJHqdOqkyZkfpI4GjNDr1w4cIhb5uZmUlm5vCmF4es47IvNpuN9evXD9tSOtiMZUx+WIoiKVFAJBCN4o4E0Qki6QYzEhKeiB9JkbFpTehELVFFJqpEERCwak1IioKiyMiAOxxCkiWyTBYiURlXIERUljFqtaSZjZhSOJ76ImFkZEwaPRpBwB8NI6Ng1uzRY5FlGU84Vm7UaPBLYbzRWDIyq86AQ2cadJpIkmX8kQgCAha9jqgsE5Fi2gtajYhFpyMUlXAGAoSi0Vh4scmIeS+n3IgkEZSiaAQhPjISDEVw+YKEw1F0Wg0Oq2lAfY5gJEpEkjBotfijkT4dFwWrXk/aKKeJ9j1XbzBMtG/EUKfRYDcZUqroRiQpQcfF0afjooZCx4hKEsFQFFEUBjRGh0owFCHap2mk14/9t5WiKHiCIWRFwazXp5ziHA6RqITbHyQYiWLW60AjoBVFzPqh6bZALPpOlhWiyCgKGLQa9PtEFAYiEaKyjEGr7dNrCuIMhVAUmTSjCZ0gIskKOo0m5QihoiiEwlGC0SiiIGAxDqzj5IuEkRUFk1aHVhRj+i99Durmvn6hxx8gEImgEzU4TMb9Gg/hqBTTp+m7NqmISBK9oQD+SARREEkzGLEb9tQbikTp8fkJRqIYtVoyrGZkQSaqyBg0WvTi6O+Xg6njcueXv8OYYsRuqAS9AX5x3M1HjI7LG2+8wfLly5EkiRNPPJGLL754RPUc0hGXxx57jMcee4y6ujoAJk+ezC9+8QvOOeccAILBID/96U958cUXCYVCLFy4kEcffTQufnewiMhROoK9bHXX0RVyYdOZmZE2ngJzGr5ohCZ/D5ucDYTkCNkGGxPs+aQbLHSEuqnxNlFhKaLR42JDdzuecJhcs42T88qw6Q3UdPfyaX09oiBQmp5GVWZW0nCoOxyk1tPD2q5mMo1mpmTk0R5ws8XVhqwoTHTkMjW9AEmCXr8frVZEFiRqfe3sdLeSb06jwpaDXWcY0GhRFIVOr48t7Z3s6upiekE+JWlpuEJBtnZ0srWjA7vJyHFFhUzIyiLPnqw2K8kyrT4P6ztbaXC7sOj0zMotoMyejtWg36/yaSgapcnpZk1jC70+P5lWC7OK8yly2NEdAJVMXzBMj8/P1pYOtrd1YdbrmFNexLjcTBz7SKfrNBqybBZQRXYTkGWZzl4vW3e309DWi1GvZcq4Akry07Gah/cVHApHaelwsWl7M053gIw0C1OrCsjLdqDXjY2viNMXYFdHNyt2NeAPhZlUkMPM0gIK0u3Djv6RZZl2t4/Nze2gKFTmZVLX5WRrewcKMCknm6qc7AHVZCFm9HT2eghGJboCftY2txKSo5RmpDOzKJ98u42ILFHndLK6tQVvOESx3cH03HzybTbsBiOhaJTmvuemx+cn02JmZnEBhWn2uJyCLxjG5Q+wramT1bub0WpEZlUUUl2UQ/ZeI0PuUJDd7h7WdrYQjEaZn19Cnt5GfXsvW5s7yLSamTmukDaflwanE51GJM9mQ6sVsRr0KQ35qCzT6vawrrmVFrc7NnpSVEBJelpSFKJOoyHHnHqkKhiN0uhyUdPdjU7UMD4ng+2edjb2NuOPhiiypDMtvZA8syOeG+5wR0ZAHkWixNHse7D5v//7P1599VXOO+88FEXhxhtvZPny5Tz00EPDrmvEIy52u53169dTXl4+kt0BeP3119FoNIwfPx5FUfjrX//Kfffdx9q1a5k8eTLXX389b7zxBs8++ywOh4Mf/ehHiKLIp59+OuRjjNZ6VhSFGm8zr7d8Sk94j0Pa7PQq5qRPpSPo4uldy9nubgVAK4rcPvUCQoqXTzrXcWrObHxBLY9u+oSgJJFrzMCiNXD77DOo6e7h3o8/pDe4x+GsMj2TW046hel5eQiCgC8SZnlrDa/Xbcai03P5uBl83F7DW02bKbA4MGm0ZBosfLN8Lrs6eyl02ECj8H7bRl6u+wKtKGLUask1W1lYMJXzi2aTaUh++7Z7vLy8fhMbWtuYUZDPyeWlBKJRnl+3ns/qG2KJ3gSBfIeNK6ZP4+wJ47EZEl9Mta4entuyjnq3M16m12i4ePxkTiwsxTRAUkiIdW4bW9p4cdUGXMFgvDzNZOSKOTOYkp8zpmGlPV4/rU4Pf/t0NZ/tagBiw9U5NgtXnjCTsyaPOyLDKw82LZ0uXl26ntrmPc7LWo3IwhMmMW962ZBHX6KSzNadrbz23nq8vlC8PM1u4qKFM6gqzxl1Pil3IMjSzbt4dOnnhPYKu507rpgfnXECZSlyCA1Gc4+LF1duoNPj5evHT2X57jpeXb+ZHLuVDKsZjSgwp6iQi6ZWpzReFEVhR0MnvR4/7RE/j3+0Ek8whEYUycuyMyEvkxtOns/W7k7+sXkTQWlPm4tsdq6ePpMyRxqbWtp5YfV6nIF9npvZ05lSkEsoFKXT4+OVFRt44eN1cWkCq8nA10+cxmUnTifLbsEbCfF+Yw1v1G5FUhRyTFYuLpvCxxtr2VzXjt1o4PKTZ7C+o43/bN1Gps0cdz6emJ3FFdOnUZCij93V1c2SNetpdu3pP41aLZdMm8zxJcWDpinoJyrLrG1u5YX16/GGwnxj1jTaok7+uuNzMiwmbAYDAlBkTuOKyuMpt458yuxgjrjc8cX9ox5x+dXx/3NYjrisWrWKOXPmxP+eMGEC69evx9TnC7l+/XoWLFhAb2/vsOseck+Qnp5ORkZGfPF6vcycOTOhLCNjeL4TixYt4txzz2X8+PFMmDCBu+66C6vVyueff47L5eKpp57iD3/4A6effjqzZ8/mmWee4bPPPuPzzz8f9omOFG80wKqebQlGi4BAoSmXnpCPzzp3xI0WgEn2QrSiwD8bP8Qg6nFo0/hP3Sac4QBBKYw74mNGVj6SrPDXdWvoDiR6ydf0dvPf7dvp7Xt5dwa9vNe4nagiMzUjH3ckwGt16/BGQrhCAWRFYUp6AXVOJyEpSkCK0BF08mr9KhQgIstEZZmugJ8VXTvZ7W1HkhPDJaOyzMbWdja0xgStpuTl4A2F2dTezrs7dsWjF2RFocPj5a3tO2hxexLqCEQifNbSkGC0QCxE8t26XQOGOvbjDgRZvnN3gtEC4AwE+XDnbtzB0AB7Dp+oJOP0B1nb0BI3WqBv1Mnj4+0N22lzecfseEcroXCUdduaEowWiF3fD1fvpMc1+G++Nx5vkE9X1yQYLQBOd4DP19bi8Y3+9+90+/jbp2sTjBaAlbsa+XRnHYEUWj0DEYpEWVXXTE1HN1MK83CGQnFhxi6vLx7mv6qpmR2dXUnPHIDHH2JLbRs6k5bX1m3G03ePS7JMj8uHRhBp9/l4Y+eOBKMFoMnjZlVLM73+AMt37k4wWiD23CzftZtQVCIYjVLT1s0/Pt2QoKfkDYR4e812tjV3IskyHX4fSxt2xkOtp2Xl0dHr5bV1m3CFg1QVZBNB5h/rNtLl8cVSB/S5h27r7GJNS0uSvosvFObT2oYEowVioydLd9bQG0iOEkqFMxDkg5oaPKEwZenpmIxaXqldiyscpNvfL48ATX4nn3fsxh8dOCGwysHh+9//Pj/5yU/w+2P9QEVFBb///e/Zvn07Gzdu5LHHHmPChAkjqnvI4+8PPvjgiA4wVCRJ4uWXX8bn8zF//nxWr15NJBLhK1/5SnybiRMnUlJSwooVK5g3b17KekKhEKHQnk7O7R5YD2Qo+KUQDf5EsSeL1ohRNBKWImxxNiesK7Jk0BXqpTXQw7S0CsKSwtbePQqXASlEpSMTTyjIly3NfdL1icN9a1qb8YRCZJhM1Li78fY9hDkmK5t7W4kqsU7QFw0TVRSy9VbWNLYxt7CEgBRhq6c5vg2ApMhIiownEmKHu5VqRxEWcc9USCAcYUdnTPfFrNNh1umJyhLrWlpRUIjKcny0IyLJuEMhtnR0MD4rMz6t5Y2EqXH2pLyG3UE/LV43RTZHyvUA3lCY2q7Ulvfu7h68ofCIQy33JRCOEI5GY0P8+yArCt1eP9taO6nMGfus2UcTgVAkyWjpx+sP09jWS1Fu2pDq8vpD1Denvn/qm3vwB8I4hpAdeiAURWFnezfdA+j3rG9oY+HUCUPOe+MPR9jdETv3bLuFTa3tRPuMk6gkEwiHMfX552zt6GR6QX5SEkF/MEw4HMUfibCrI/E6BkIRcq1W2rweWjzulArannAQTyjE7u4BnpuuXmQllptofV0L0RT6Lm5/kG3N7Rw3roidzi780T3GW47RyurdTSjEfF6yHRZ29/TgCcX6I28ohNWoR9MnHbCzq5uTy8rQ7xUZ5IuE2d2T+ndt83jp8vrITzHtvC/eUIjanth55tms9IR9NPtcQEz+ISrL8ajM3Z4ufNHQiCInDzZy3zKa/Q9XVq5cyf3338+sWbO47777ePrpp7nhhht44IEHkCSJk046ieeff35EdQ/ZcFm8ePGIDrA/Nm7cyPz58wkGg1itVl577TWqq6tZt24der2etLS0hO1zc3Npa2tLXRlwzz338Ktf/WrM2idA0nxp/+yaIAhJ0xeKoqAR+h9cJb6/pEjx+hQlNmoz0NSHVhTjL0zdXsdWUBJ8VIT++vr2URQFQRDiIdiJZwEiAhpBRNh3XlQg3vkoscbFE5qlviYx58PYiQjxa6EdZF55fw61giAgigKkiKjXCCJjaT/01zVQmwRBGHHyuWMJQWDQ6bvhhAILgoCoEZHk5BtAIwr72vbDRhCEQfVNtBpxWEaqIBCfulIUJel+2buu2DM3cEWiIKARhH1E5YTY8z7IMyX09QD9z+6+aMTYFqIA2gH0ZGLPXewYuhT+b/2/b3+/tfczIyAkPJcaMfk8hb4+J3X7GfL0X/9UtaQofUKae44kQEKflrKPO0w5WqOKADQaDbfccgtf//rXuf7667FYLDz88MMUFBSMuu5D3jtXVVWxbt06Vq5cyfXXX8/ixYvZsmXLiOu79dZbcblc8aWxsXFU7bNqTVRYEy+0TwrilXzoBC2zMsoS1tV4OkjX2xlnK6Ar5ESvEZiRVRRfb9aa2Njdht1o4NSSspQP2PziEhx9/iPl9kwyDLGQ2yavi6npBZj6EiRadUY0gkBzwMmMvHza3V7MOj2THIXxbQRinY1O1GDXG6l2FGHSJH6JmHU6pubFHJ4D0SjOQAABgeOKi2Iv8b06F4NWQ6bFxOTcRJ8Dm07PxBTaEhDTnci3DD7/ajPqqcpJvX9VblaSP81oMOl1mPQ6ZpYlP0AaUSTPYWVSfrY62rIfzEY9VWWpc15lOMwUD3G0BcBmMTCuNPXvX1mSPWxH35T15GZSnJl61O/4iiJsw4iGshj0TCqInXtzr5tp+bmY+nw19FpNfORGAKYV5KaMorGZDRj12lgywcJE9WWLSU+Ty02u1ZpSzwgg3WjCYTIM/NzkZCMgYNTrmF1RmHI0KcNmYnpZPnqthnFpmaQZ9ozENvlczCgtQCuK2PQGmrtdVGRkkGUxIwpgMxkQ9zJKJufmJkQXQkz3ZWJOan+TkvQ0sgcRgtwbu2HPeTa6XKTpzIx3xP626vUJfVSVIxerTvVPO1yoqKjgnXfe4aKLLuKUU07hkUceGXWdh9xw0ev1jBs3jtmzZ3PPPfcwffp0/vjHP5KXl0c4HMbpdCZs397eTl7ewBLrBoMBu92esIwGs9bIcRmTKDEnRjJ1BLvIMto4PnMcJ2ZPiJsfdb5OwrLM14tPwyDqaA93cEH5FMpsGVi1Jmw6M9ucnSDA1TNmMmGf+PV5hSWcN6EKR5/4XLbRwqLSamw6A5t729AJGq4edzz5ZjsOvRFRENjmbKPE4SDLYsEgaMgxOFg87iQsGj16jQa9qKHI4uDM/KmUWpJfyBpRZGJuNqdUlKERBNa3tGHW65iYnc1Vs2bEh351okhZRjoXTa4mdx9lW4NWy7z8YmZk5yeYYukGE+dXVJGxn/wldqOR0ydUUpSW+GIpSXdw2vhK7GPoKKsRRdLMRqYW5vHVmdXxTk8URcqz0/na7Clkj1BI61hCp9UwZXwBMycWJUTCWc0Gzpo/kQzH0K+h1WLg5DmV5GUnPq/F+emcMLsCm2VkOU32Jsdm4dunHheLDutDI4pcMKua4yuKk8KPB0On0TCjJJ855YVsa+3EIGq5+riZ2IwGcuxW9BoNWlHkjPGVVGamnnK0mAzMrCoi7I9w0YzJFKXH7n29Tkumw0yG2Uyu1cqiqolkmhJf8FNycplbWES6ycTpEyooTk98borTHZw+oSJupJfmpPPtM4/HvFdkX26alUtPnE5lbiaCIJBtsrCovBprn4TBhq5WbDYD3zpxDpkmMzXt3UTDEtccN5Py7Iy4ISQKAvNLipmWl5s0imnS6TihvITJuYkGbqbZzDkTJ6QUrUyF3WjgzHGVFNhttLg99HhDXFYxi1JbOpkWc/y4U9IKmJtdjlFzZCjp9o+4jGY5XHE6ndx8880sWrSI22+/nYsuuoiVK1fy5ZdfMm/ePDZu3DjiukccVXSgOP300ykpKeGPf/wj2dnZvPDCC/FY7+3btzNx4sRBfVz2ZSw8xGVZpifiocHXTluwm3S9jQprAZl6O/5oFFfER72vi3pfJ9kGGzMzyrHpDPSGPez2NpNrzESQDdS4e+kO+CmyOqhKy8am09Pm9bGxvY1Wj5fKjAwmZmWRb7MlDMEHohHaAh42dbei02iYlpGPJxpki7MNWZGpTsujyJIOsoAzGIwN4WuhJ+ShwddJptFKkTmTDL0V4yDzvs5AkCani22dXUzLzyXLbMYfjdDodLGtsxObwcCMglgY5r5hjP10BXzUunqpcfZg0xuYkpVDrtnW58szOJIs0+7xsqOjmw6Plzy7lfHZWeTYLGOeqA5iIaKeUIimHhfbWzsxGXRMK86nIM2W9OWoMjC9bj9N7U52N3VjNuqYWJ5Ldrp1v+Hv+xKVZLp6vNQ0dNLd6yM3y055cSaZaZYxU6D1BkO0ubysrW/GGwwzq6yQ8qx07OaRGUY9Pj+N3S463F7G5WUSiEbZ1tGJrChU5+VQ5LBjH0QBO+aI6ycUjeIOh9jV3YM3EqY8M52KzAyyrRYikkS7z8fWrg70Gk3sI8VkwW6I6QhJskyHx8fOzi7a3Kmfm0A4gjcQornHzcb6VjSiyLSyfEqy0hLO3R8J0+b3srG7jWA0wszsArL0FtzeIJ0eHzajgbwMGz2BAJs62glGolRlZ1GSljagEaIoCl0+P7t7eqnv6cVhMjKjIJ9Ms2nAKaxURCWZdq+X7Z1dOINB5hQVIGmi7HC344kEKbNmUWbNJNNgGdVo6cGMKrr18wdGHVV0z7wbD8uoogsuuACn08kVV1zB+++/j9Fo5LnnngPg/fff54YbbmDRokX89re/HXbdh9RwufXWWznnnHMoKSnB4/Hw/PPP89vf/pZ33nmHM888k+uvv54333yTZ599Frvdzg033ADAZ599NuRjjOVNKPc5uWoQE6ZJFEXpc8xTiChh3BEvnqgXh86OTWtFK2rRChpkRUFSZLT75CWKylJMcEujGXTOV5JlJEWJj4BEZRkFJWFuOipLBKJhAnIIvaAlJEfwSyHsOhMCIp5wCL2o60tREKAn7MOuM5JjcCDJCgoKigKuUAhfOEyuxYLdEBvZ0YgiWlHEFQzSFfDTGwyQZjCSZbYkpCdQFIWILKMR98xvK4qCMxCk0+vDpNWi02hwBoMEwhEyLWYyLOa486Isy0TlmN9AKonv/RGKRun2+Wl3eQlLEnl2K1lWCxaDnnA0So8/QK8vQIbZHPvtJAmNqMFs0GE26FLml/GGYpov3T4/FoOeLIuZNJNxxCG6bn+Qbo8flz+I3WQk02bGMYpRBalPIMzlj71gJFkm12Ejy2oessPpaIlEJURxYN+tVHh9QQLBCJLc57cg9j1PkozJqMeg02I2Dd2IjEoyLk+ALpePcCRKdpoFh92MJxyiw+tDURRK09MwarTIKHgjYSJ9167XH8Cg0ZBrs2I3GHAHY/tEZZkcq4UMs3nA0N1wVIrd76IYey4VJek+CkYj9AQDdPi8aESRHLOVdKMJvUaDLCtIsowgxPLP6FL43HhCodg96A9g0evJtppJM+65B+PPjSgMeF9KUkxQUhTFBOG9QDSCMxgk5pcnEFVkRAQsOj1GjRZPMERUkglHJdyBIGa9jgyrGYNOi0mvIxiN0hMI0On1Iooi2RYLGabYuQWiEdzhIGFZwihqMGp1uINhOv0+tKJIttlCet+2Q2Hf/qE/+EAnaMZkelc1XMYGm83G2rVrGTduHJIkMW7cOGpra+Prg8Egd955J3ffffew6z6kAnQdHR1cffXVtLa24nA4mDZtWtxoAXjggQcQRZGLL744QYDuUCEKYsKcbj9CX/JFV8TN6t71bHFtJyAF0QgayizFzM+cQ54xF40ookkxO6cVNUNKehjbf+/9EuuKyBLdQS+NgTY8ET+ioGF5xwbOLzgORZFZ1dlIgTGdbJODDb2NvFT7BTpRw0+nLESKiqzvbKPQYmdlcxP/3rEVRYFiq4NZ+fl8dcIkKtMzaPN6eHPXDla3xgSqDBoNM/LyOW9cFYV9D44gCAmdkKIo1Pc4eXPzdnKsFvIddv61YQs1XT2kmYxkWkzMLSvh9AkVZFjMsU51hB/YwUiETS3t/PXzNaxpbEEBStPTuOr4GZw8rowtbR0s21HDSZVl7Gzr5vU1W/AFw9iNRooy7Jw5dTzTS/Ix7vWy7/EHWLZjN5/X1uMJhdGIAhNzszl38kTKM9KGbby0OT28t24H6+taCUWi6LUappXmc9aMCeRnDL/zkWSZXm+Aba2dPL9iHVua2xFEgQl5WXztuCmcMK70oEiiDycpKEBnr5ctu1pJt5ro6vZishh48+PNrN/ejMmkp7Qog/nTyjh19rghTTtFoxK7Grt4b+V26lt7EBC4+MzpSC4n7+/eTaM7FoWSa7Vy3XGzcYWDKChsa+/in5s20+H1kme18dOTTkRWFN7fWUOjM7ZPjtXCV8aP47iSwpSjjXsbAamcvn2RMCtbmnivbicd/lhkU6k9jXMrq5ianYtBo0UcpA/o8fv5YOduVtQ3xO5BQWBSbjbnTdpzDw7ludFoxKTRK284zKrWJkw6HU0eNy9u3UCbz0uexcINs+ZjknVEozFl5Jc/38Cutm7SrWbG5WRw5rTxzKosZF1bG+/s2kW7NyYjUOxwcEFVFeVZ6azsqOeDlp2EpChfL59Ji9PLR/W1+EIxhe5SRxrnTpjAlOzclNFT+7LveWoE8YgRnNsXRYHRDB0cXvMliYwfP54nnniCb3/727z33ntJGaCNRuOIjBYYY8Plzjvv5LTTTuPkk08e0vZPPfXUoOuNRiOPPPLImDjzHGgicoStrh2s7lkfL5MUiRpvHQICX8k9FZvuwGYSdoX9dIVdfNixjpOzp/Ns7VKqHcXYdXo297ay3dXKBGshjb5u/rj5XbxSmO9OOBWtoOOLtkZ8kTD+UJS/rFsV99wXAE27gKLAt2fO4YO63XzauEf7JCRJrGxuwqDR8rWJ1SmdaF2BIG9v2UFddy/HlxbxwuoNrKiN1aEQi+hYun0XDqOBBRMqRpVRt93t5aHlK9jZuSe8tL7Xyed1jZRlpfPPtZsYl52JBpG/LFuJ0x9EK4oYtFpanR5e+WIT2XYrFTkxh8iIJLGmoZn3tu2M1yfJCptbOwD4xpwZw8pX5A+F+XhLLV/s3OM0Ho5KrKppQq/T8NXjqrEM08jwBsK0Oj08ufwLdnf2hZ5KClubO/hbaA3ZNgszSwoOK2fjYCjCyvV1ZKVZ2LylmdKybN78eDPLv9yFTisSiUo0NvcSCkexmPScOmf8fnN3dbv9vPHJFlo6Y8ZGeWEGGpOGB5Z9AjoBe19CT7vRQIffy87ebvLNNh5f+QWBPm2XHJsFRVF44KNPMWp1cYfbDq+PVzZuJsdmoTo3tUPyYNQ6e/nn9o2E9tI5qXc7eXnbRnLNVortA0sFRCSJVY3NvLtjV7xMUhQ2tcXuwatmzyDDPPKcWbud3ex0dlOVkcUjaz7HHY7JSdgNRvRo+O/GrZxXPZG/fbSa3R2x+6vd5UGv0dDQ7SQ93czLmzYSiO45t0aXi1qnk5Amwmt1G4kqMifmlhMIRXlo1WfIikKpNR2jRke9y8k/Nm0i53grxY6Br8PRiDJK5VzlMI6eevrpp/n2t7/NI488wowZM3jyySfHrO4xNVOfeeYZFi5cyKJFi8ay2iMCX9TPLm9tynV1vgbcEU/KdWOJJxKiLdCFVWumO+xhu6eJ6WllRGSFT9p3MTm9mKAUZVV3Hd5oCC0iU9IKicoKn7c2UpWezfL63XHBOUlRcIdDBKWY1kRvMMD69tSh6OvbW3GGginX9fgDbG7tYFx2Jv5IhC/rm+Lr3MFgXF9iXXNrXIRrJMiKwra2zgSjpZ9Jedms2N2I0x+kIjODDfWtOP2x9kZlGVcglg/JFwqzoaGVcN+LzBMMsa65Nak+gG3tnXR6U+uCDITLH2RTfepruKmhHZc/9TUcjFAkQk1H9x6jZS96vH5W1zXjCx1eglxuX5DWLhdGvZYelx8ZhZUb6oHYdI+iKHh9sXtjc01bkjBdKhrbeuNGC0BZQQY7u7tp6nXh8QWR+u6z8ZkZ7OjpItNkZmVjY9xoAZiWm8uW9g4ael14Q6GEL9pQNMqappZ4zp6h4o+EWd3enGC09NMV8LO5K1kUcm88oRDrWlLfM1tHcA/ujS8cZnV7CyX2NFa3tcSNFoApWbk0dDnJsJhp7HZS07Hn/lIUBXcgSHFWGp/U1+NJcU3y7VY+aasl0ndu5dZMVjY3EohGCEnRBM2Y7oCfLZ0dg16Ho5Gj2Tl3xowZrFq1Cp/Px6effsqkSZPGrO4xHXGpra0lEAiwbNmysaz2iEBGxi+lVoGMKhIheeyUXwciJEVxR/0YNDrcET8CAnpRi6yAM+THKOqRUegNxzo6o0aLKIhEZQlfJIyikJB+AGLGQESKJSAMSxLecOoXoCccHrDTCfQlTdRpNHhD4bhQF8SmOeQ+9c191w0XRVHo9qVWazVqdTQ7PSh9/gOufZRGJTmW3A4h5sAZlRT02phR4w2l/u0kWcE3zJeYJCkDGhG+vuMOl4gk0+sf4N6TFDyB0D4aIYceSZKJRGUUOXYdQ+EogVDsWvYPn0uygiwreANDuy9cnsRroNNp6Q3EPhhixlBfuUZDjy9AgdWOcx9D2ajT0eH2xlWnY2OCe14O3lAYSRnePRqVlQGfGwBXXxLRgcYZB70HleHfg4n7x5Ky6kVN0oeHUaulxxvAqNXS60u+v6KyjE6rid17Ke4vnUZDbzAQ15fSCRpcex0jvI9mz/6ug8qRQ/9vfqAY84lBk8nEueeeO9bVHvboBT1ZhtR6C1atBav2wE4TQSz7c74xE3fYT4EpA52ooSfsRRQUKu05dIXcaBGptMWGuv3RMJ5oAJ0oUmC1EZKijE9PDM/W9eU6CkajWPV6CqypVS4LbLYBcxGlmQykm004AzGH2PS9EhgatNr4/HSBw77f6YDB0IgiE3Kz0KZ4YLp8PqpyYwks3aFQfCqoH5NOF3cELs5Mw9Dns2DUalPmXwGw9TnpDgejXkteWuprmJdmxziCbMhGnZby7PSUvhVGnYaiDEf8fA4XDHodJoMWUSPEHHCNenIzY9dFFPv8pHQaNBqBvEzbkK5LUV56gv+G1xekMiMDURAw6HUxgUPAFQwxLiMTVzhIZUZifqIun4/xWVkIQkzfaF+dpUKHHYNmeL+RUasZUDVaACrSMgYVaNzfPZg5zHtwbwxaLUU2O+5wiHFpic9Ep99HRXYG3f4AZSnuL6NOi8cfYnxWVvza7o07GGKcIyv+AvNEg1TsdQzLXv2FAJSnp76Hj2aUMVgORyZPnsyLL75IeBCDHWDnzp1cf/313HvvvcOqf9h3yZdffslNN93E+eefz/nnn89NN93EqlWrhlvNUYdZa2KyYyIGMdk/YYpjIvYD7N8CYNMayDakY9Lq0YtaTs+dwRfdO9GJGk7MraDG3YFOKzI1rZhJafkoAqzqqkOv0XBqUQXbejs5rayCrD7NCL2oIdNkwaDRUpWVRbrRxInFpUmKuhpB4KTi0oTIor1JN5s4obyE3V09gMI5kycCMSe7dLMJrShi0euYW1ackMJ+JJRkpHF2dVVSeXOvi+mFeUzIzWRrawdl2RlMLY7pAVn0+r7MtlCRk0FVfnb8BWgzGphbVpzSIXNuWcmw/FsAHGYj8yaUJCmtakWR+VUlpI0gLNeo11KcnsapkyoSyjWiyIT8bOaUF2HUHV66FnarkSnjCuhy+SgrySQUCHPOydVoRAGdVoMoCqQ5zNjMBuZUlwxJgC4v08b0CXtEBbfVtZNjtnDq+HIcVmP85bq5vYNCqx1JkZmWn0flXjnW1rS0km+38ZXxlZh1ugTF3kKHnRkF+QmOuENBr9EyMzefQmuy8TE1O48yR/qgX6c2g4F5pcVYU9yD80pLyByFf4tBo2V2bgFtPg8TMrKYkrVHr2pTZzvpVhNWnY40i4nTqvfcX3qtFofJSHuvm+OLCylPS05Q6QmGODGvjFxTrO/b6mxnem4elWkZOPSmBK2VKTm5+70ORyOyIox6ORx56KGHuP/++8nLy+Oyyy7jvvvuY8mSJbzyyis8+eST3HTTTRx//PHMmDEDu93O9ddfP6z6hxUOffPNN3P//fdjtVqpqIjdxDU1Nfj9fv7nf/5nRPHYB5qDEdrWT0gKUe9vYotrO93hXqwaC1X2cVRayw64Y24/zpCfnrCbtmDM12WLq4FiSzbllhyafG7CkkyhKYOesJdPOnZQ7+3i6+VzKDJl0+h2ohE1OANBPm6op8vvo8BqZ25hEXPyC8k0m/n/7P13vGdXXe+PP3ffn15O7216L0kmjZCEhEQkAiqCcFHkeqVpBMWL+FVRLur1AvdyvbYr9wdIUaMIBhAIkF4nmUmm19N7+fS6+/798TnzmTlzzplMS0iEVx6feeTssvbaa6/yWmu93693yTI5ND/H3qlJioZBZzTGpqZmNjU1X5B05CpVnp+YZr5YYmNbM6fmUhydncf1fHoTMW7o76GvMUHgKgywE5kcj5wa4enRcQzL5rreLm5d20d/U5LZQomnhsdoCIVoDAbYPzzFRDqPKkusbWngxnW9tCciSzyFDNvm5FyKvaMTTBcKhDWN7e2t7O7uuGTiArWtqGPjc+wbmiRTqpAIBdg10MmW7lYigcsjbiXDZDZX5InTozy3GF9me3cbt27op785eUUGzy8VKobF0HgKRRIpFQ0CQZXTEwu8cGISx/Xo7Wzgui09rOtpJnCRmjAL2RL7j09wYnQOy3K5ZfcALW1R9k1Pc2JhAVEQWNPQwG39fdi4ZKsGJdPisZFRhtMZGgJB3rJ5E42hIM9NTHFyfgHH8+hvSHJTbzc9yctbFXA8j9F8licnxxjKZQirKusSjVzb1kl7OPKiA3bVtjk5n+KZsbN1cEd7K9d0dVyRYe6ZvI3kMuRMA9/3eWJqjNPZDAlN540D6+kKxpjLl1Alif3DUxydmEWTZda1N3HDum56mxJMFgo8OT7OYCaDKAisb2zkpu5uWsMhhosZnpobYaqc53Uda9HReH56mrFcDkWUatd29dAeefFyeDnwcrpD//aTf4F2Be7QZqnKZ2669xXpDg3wxBNPcN999/H4448zNjZGtVqlsbGRnTt3ctddd/HOd76TROLSorLDJRCXv//7v+d973sfn/rUp3jve9+LsjjA2LbN3/zN3/DRj36U//t//y+/9Eu/dMmZeCnxclTCqlPF8iwkQSIgBah6Rk0LwfPqui0hKYAkitieS8mp7eUGJIWgfHXcVE3XpuyYyEJNKdfyXDzfQRRELN9BE1UUQcTxPWRfwgMM38Z0bWRBJKYGa8aptoUoiDXbE98nKCs4vofteyhCTfobavv/VdvGcBwkUSCu6UiSSNE2cDwPTZKJKEtXDxzXW4zy7KPKMrbr1NymJQ/DtWvPk1RCysU1ZNN1KFkWPj4hWV1CesqmVYsCLNS2HTRJqhtHnnHV9nwf2/GwbIeAqqDKEqbrYLgOsigS1TTUxW0B3/cpGCamUzsX0bUrIgOu51GsmtiOiyJLRHTtskTWHM+juGj/oEsytutSMW0QanLr4ZfQDdq0bSqmjeV4KLJIJPDiZeL7NXsWSRKxbQfXB9tykGUR3/URJQHTdvEBTZEJB7UVtyEumC/LoVw18XyfkK4iyxKG6+B5HqZbSzuoKEQ0DcOxsVwPx3WxXBdJEIloGroiY9gORdPExyeq6XjUIq17ro/j1LRQwrp6SYq7hlNrM4bjIAoCEVUjoCiUqybGYrmEA+oyYTbH83A8h6JpY7kOqigTD+irlrftupi2Q9Wy8X0IqgrhFyHFpUU7NQ8f063lL6xoBBUF03Yw7JotjWm7CEItdMa59atsWZRtCwGBsLq0PVYci7JZ08yJLvYhFafmDh3RNHRZxvd9Km61ZuMEWF7N3yYs60jiyn2L47lU3Cqe76GKKkH5yhWWX07i8ltP/J8rJi7/8+bfeMUSl5cKF93i/uqv/oo//dM/5dd//deXHFcUhXvvvRfHcfjLv/zLVxxxeSlhezYzxizHCydYMFPoosa68Fr6wwOMVxc4lBskZeYIy0FubNxCTIlyODfBsdwUpmfTGUxybcMA3aHGFQOcXQx832fOyLMvPcJgcQbX9+mPNLOnYYAWPb5EDMz1PCrFKhPVAk9Mj3A0PUdjOMjGplauaemkMxSjIXBWL6NsWxzPLLB3doL5SomkFuA1Hb2sTzQxms/yw5FhDi/Msj7ZyFs2bGTSSHM4O03RrtIaiHFtUy9rI83oi3vZsiSSDJ1tpKYLGavIocww+zKnMByLNZEObmjcRG+oBfUCst0zpQJ7ZyY4llnA9XzWxJNc395NTyTGbKlMulzGxafiWHRFYgxWSzw6McSaRCO7mjuYLZR4dGSEkXSWt2/fytqGBoayJR4ZHeFkap54IMD13d3c2NlNSziMIAjEAlfeKZ6BJIrEQ1cW7TpbrXJgdobnZ6YpWRYd0Sg3d3XTl0helB7G5cJyXPLlKmMLWR4/NsLoQpbGaIjr1/Wwu7+DZGTlFQDDtCkbFqoskS1WODm5wCMHBsmXTdb1NHHNui42djfTeAUdOYCmymhqbSCcKRQZnEiTDAc4mUpzYGYGz/fpSya4obubNQ3JVVcKdUVGV2Qcz2Msn6NoGtiGy7OnJphI5UgGgmztbmX3mk6aYxe3opqpVnlmeoIT6QVEBN60diNexePgySmm0wUCmsLmvla2r2knsViORcsgZZZ5bmGU5zMT6KJEf7SJPU199IcbUM6zuclXDbKlKgdHp3nm1Dim5bCxo5mbN/bS39qwqh3ZSltR9TJV5Be1Pwup6rItVdtzyRoVji8s8NjoCFPFAh3hKK/t7Wdna3tdX8hwTUbL09iejefL7E+PMVHJ0B9u4abm9YyXMhzNTVGwDVoDUW5tWU9C0zhdGuVUcQTDNWnWG9gSW0dnoA1F/JFKlF00rtRO5ZVq4/JS46K/7tGjR3nTm9606vk3v/nN/MEf/MFVydSrBbPGHN+f/QFFp1Q/1qK3cLwwyvdm9mL5NTfLgFSm6to8Pv88BzLjdSPQyUqGweIc7+y7ib7wpWtDAKStEt+c3M+x/FT92EQlzVgpxdt7b6A1EK8fz+YrzJSLfOHIszw9OVY//lhohKMDffzq5j20LwZDdDyPQwszfPn4AQy39h4TxTyv6ehlNJ/lT598hJOZNJIg8Ob1G3lg6jjfnjxERziKKklMVnIcz8/yzoHr2J7oXLYE7PouWavE92f3c//kk/Xjx4tjHC+M8Z7+n2JdtJOVkKqW+dfTx3hhfrp+bLSQJWtUefPAJp4aGaerIcYPR4b4mTUbGC/k+dNnHqYxGOKu3vWMZLL86cOPMl0scltfHwktwFAmw/986kkm8vl6Xl+YmWG2VORtm7e9aKyllxsly+KhkWG+N3iq3nlNFPIcX5jnV3buZktzywXvvxKkC2VG57P8739/grncWTf/g6MzvPXGbbxh14ZlW16e55EpVggHNXL5Ks8PTvE//+URStWa8d4Th4Z5vHuE//yG67h5S98VGWmfQapU4d8OHmVXdwePDI/ylRcO4AMhVWE8Xwtt8e7dO9nScuGymirmeWpyjHWRBv72+3sZmq+52yd0neG5DLO5Im/es4XEixCuhUqZr504wsH5mmvzzZ09pNIl/n/f3UvAl+sEf3AyRbZY5c5r1oEC80aJfxs/wNfHDtTTUmYHOZCZ4P0bXsva6Nm+o2CYzBdKPHRokH964kDd2efI+CzHxuf4tbv2sLHrpasb5yNvVtk3PcVnnn6Msl371gfmp3huepL3X3MDt/X1o0gig6VxjuROsyG6ni8MPsKp4gyiIHBby2YemT3JNydeoFmPoYoSC0aBnclOjhWPcjB/NiDvnJlitDzFPe230xtaue/4Cf5j4KLXpiVJuqCFsG3bSK/AffSXClW3yvHCiSWkRRNVGrVmHl3YR8E5q60wEO6g7Fh8Z3o/tu8sSSdlFnkhM0rVuXSdDd/3GSstLCEtZzBaXuB4YRpn0eXQcVyyhSrDhcwS0gJQLBscnpvhaGa27naaNw0enxqrkxaA1mCYpmCIxyZGOZ5OAbClqYWIrvCN0YPMlopLtBnKjsnehREK9nJtkqpjkjLzPDj7/JLjnu8zXplnX+YUZWf5fb7vM5rPcuAc0nIGCT3AyYUUmixxIp2iKVgTE3tqeowTmQVe09mL6/k8NjLKUCaL5bpc39WF5bo8PTnB0fn5uoYN1DRXHhsdZbKYX/asHzXSlQpPjI8um3HlTZOnJ8br20dXG2XDolg1eerk6BLSApAtVXns6DALheW6IqWqiWm7WKZDrlzle8+dqJMWAM/zmZzL8uihIXKllV27LwWe7zOUymC7HgjwreMn6mVVtmxKpknOqPLM+MQFy8p0HPbNTtMSDHNodLZOWoBayArH5sDINFOZC9cR3/cZzmbqpEUUBNbHG3n62BjjqRwF01xS9547Pk7ZsCg7FrPVAt+bPLokPdt3OZWfZ+/CCJVz+o6SaZIrVfnu8yeWeChbrstYOsfek+OUqi+9NANA1bHIGlW+N3iyTlrOIGWUeHBokLxpUHYrHMyepCvYxvH8NKeKNd2kjdEOZFHkO5MHyVlVqo6FD6yNtuBh8eDsvmVu8kWnxJH8Kaor9B2vRPgIV/z7ccRFE5ddu3bx1a9+ddXzX/7yl9m1a9dVydSrAZZnkzJTS46F5BCeD+PlGSzvbEONKiEmymnKjlEnEudiqpLF8pxlx188Dy6j5dSq5yfKaUz3zL60g+HYnM4urHhtxbAZKWQwFq+vODaTpaWdcUIPgC9weH4Wf3EYaA6FKToGs5UCjufV9GDOGU4ny1mq7nKdCdf3mDUylJzlg5TpWkxVU/W8nwvH8xjKZVZcIo1rAY7PLxAN6JzOpGgJhXHxOZmpvXNbKIzreZxcqJWZ7/s0h0IgwMmFFD4s0TvxqXlGDGUySwaVVwLmyyWKq0wkJgsFDOfytT0uBHNRk2d4brnYned55CsG0ysM4rbjocgCjutRNiwGJ5fX24phM7GQvypieZbjMpLOkAgGmC+VF+PwnPMsqxYfaTJfwHBWb3uG6zBZyBNTdYbmlgob+tTE5TzPZ3g2jeetXkcs12Uod7bMArKMKkiMzNbSrDr2kjpWNW3KVYuqbTNRzlBZoS0YrsN4+WybhdqKy0KhTK68fOA2bJvxVA7DuvS+5nJg+y4V22Iou1wQ0sNnKJfGsB0M12LeTBOUggwX5+vXNGpRCpbBnFEAoOJa+L5PXA2SsnJkrOKKmjrzZhrTe2UJLq6G/6heRS81Lpq4fOQjH+HP/uzP+K//9b8yNzdXPz47O8vv/M7v8Od//ud85CMfeUky+UqEJIho0nnGp56DAITkAJKwNPBhLcihsGKso6CsXlYwQUkQCF3AuDcoqUiLtjOSKCIJwqr7+bIkElLUuqaKLIgElaX71ZbrAj5xLcAZP1HLddAlGVWsBTireVycfZewrK0YR0RAICTriCvMGERBJCTpK94niSJhdeV3sD2XuK7juB5RTcNya98jptW+U3XR+DJ2xm1bEGrv5FM/dn5uZKlmpHs53+elREhdvc4EFeUliaYN1AMJrhb7SJLEFQ2CRQE8T6jVEUkkGlp+jSQKBPWVg1xeTj7DqorluoRVtRbA8RzIoogg1LaNLhTnRhIEgoqC43lEVzBulUUJBIgGdC5URWr19mx7sj0PQRDqW2qSsLQlCICiSCiSSHQVY3VJEJe1L02WCGkq0goGzZIoEtJUROnlqcsiYq2urNJew4pWD8Sqixqu7xI5511Nz0GXFLTFPkwWaoEnHc8lKOm1b7hC/6FLK/c5P8HLD8dx+NKXvrSEM1wNXPTXfeMb38j/+l//i//9v/837e3tJJNJkskkHR0d/MVf/AWf/vSneeMb33hVM/dKRkAKMBDuW3Ks4BSxvCrXJDejimc7qfHKLK2BOJtiXcuMcEUEtia6L0hAVoMsSmyKdRBewZJeE2W2JbrrWgm6phALBdjW0F5bOTk3HVmiMRJid1NnXUQupmlsb2xdct1EMY/tedzRN1AXNDs0P4eCzA0tfYQUlaiqLelKtiQ7iCrL86dKMi16gg3R7mXnQnKArYk+Qiu8lygIbGlsJq4tPzdezHFNZwfzpRLXtnVyIr2AJsm8prMXVZI4MD+FLIu8pq8XRRSRBYFDc3MokshN3d0E5KUDvipL9CeTrG9oXPasHzWaQyHWNTSseG5HW9uqg8WVIqxraLLEdeu6lhGnoKayrr2J9hUCRQZ1Dddz0TWJaFDnpq1L2w4CNCRC7BzouKIo2WegSBJb2luZL5SIajo72tvq50RBIKrXiPH2tlYi2upGqSFFZVdLO+OlHNcMdC4JHqpJEmFFpSESZG1H4wVdeWVRZFtza51EW67LVKXAdeu7kUSBmK4vqXs9rUlCukJAUukMxdkQW26XEld1djV0Ez6n7whrGslokG09bUsvFmrkfEdf+8sScBMgKCvE9QA3dfUuO6eLCns6u4hoGmE5yJpIN7PGPLuSPWhirQ86np9ElxR2N/QhCyJhRUcAhooLROQIW2MDyzSlANaF+wjKryybtFXhC1f+ewVDlmXe9773YRhXd+vukmjpb/zGbzA0NMSnP/1p3v72t/P2t7+dz3zmMwwODvKbv/mbVzVjr3RIgsRAqJ9rErvQxTOzdYGsleHGxm3sadiEutgAF8wcIj7v6nst/ecY4YZlnTvbt7Ih0rbiSszFoFmP8aau3TTrZweLpBrmns5ddAaXKmHGo0Haw1E+cO1N9MRqvvOaKrO1s413bNxFV/isumdQUbm1s58b2rqXuA+PF7NsaWrhQ9fcSEsoTNaosm96hl9ccw339G6qEx9dUnhNyxqua+xbUWlUlzQa1Rg/330Lm2O9iAiIgkCLnuAtnTezNda3asTslmCEX1i/lfbQWQXaBj3AxmQz7dEIaxsbiSoaGxubSRsVNiab+a3rbubIwjzpSpkdba3ce+P1dESjPD46RtEy2dLcwoduuoGWcM07RFdkbujp4h1bt9Mcenk0eC4FCT3APes2sr6hsU4gQorCHf0DXNPW8ZLptkiSSEMkxPbedn7xlp3EFgXzQprK9Rt6eMt1m0mGl3sVaapMMhLE96ExFuKuazfwMzdtJqDVFG3bGqPcvWcDr9+97rK1bM5HayzMbesHyJYrvG3bVna2txGQZdqiERqDAe5YM8DujguXlSAIrE020hwK0xAL8e5brqEpEqqlE47Q25zgzXs20xR58ejVraEwb924hbZF9el9c1Os6W7iP9++h8ZwaPF50NeW5O7rNxALBYiqOi16lHevuYHN8bZa0FNBZE2kkbf3X8OWRPsSwpMIBmiMhHj7zTvY0duOJNZWuPoak7zpuk3sHOi45CjelwtREEloAe5as4571p7tGyKKzhv6N/LT6zYQ1TQ0SWVXYhOyIBFVVN7V/xqa9SiO7zNaWuDnenZzR9smAov9ackxEXyZN7bfQlewrb7qEpB0rk/uZF2kb8mK9ysZnn/lv1c6rrvuOg4cOHBV07wkAbpXI15qn/yKU6VgF8jaWXRRo1FrIiQHKTsGWatQ275QwoRkvSbuZlWYM/KAQGcgQVDWUEQJH1Zc+vd9v37O870lBOfM36Zrk7MqVN1azJGwrKNLal2D5My1AkItJoxtk7EqzFaKCKJAXyxBgx5CFiUEWDJzzJlV5itlUtUyCT1AeyhCVNUpmCZVx6Zi28iiSELXMX2HeaNIwTZo1iM06eFlWi7Ly8+g7BhYnkNA0mqaDYtLwquROc/zcXyPdLXCdLlmW9MRjtIUCKFKEqbj1oI3+h6m6xCQZSRRJGNUKTsmfdEkrueTqlSYLZZoi0TojEYwHLd2rFwkqCh0RmM0BINLZtkXguf5IKz8Ha8Gzq0LAI7rkjWqzJZLlEyLlnDNeHqlCN1XCs/3l9SNqmlTNi0yxQrzhRKRgEZ7IkoiHFyiCux5Xj3PgiBQNSwEUcCxa/F3Uvky2VKVjsYYrYkImipf1W0u03HIlKs4rouLT8E0USWR5nCYkKqiyTKe77/oN6vYFnnTxHM9qoZDKl9GV2TaElGS4cBFkwHTcUgbFaaLNfuMvlgCXZDJFavMZ0sENYXmZIREOFDX9XE8j4pjkbMqzFYLWJ5DZzBOkx4hpJznveX7eL5PxbLJl6ss5MpULZuOZJSWeKTmrXWRdfTcb+77Pr4PoijUv+nFfifDtSkYJnOlIqlKlcZgkPZIlGQgsKSvKdkVinYJQZAxXIcFo4QqyvSGG7E9jwWjSMGu0qxHaNQjBCWFglMibeUwXIMGNUFMiV6xlsvLqePywUf/5op1XP7qte9/Reu4/PM//zMf+9jH+PCHP8zu3bsJhZaS/G3btl1ymhdNXPbv389HPvIR7r///mUFlM/nefOb38xnP/tZtm/ffsmZeCnxUlXCol1h3swyVpqhSU/SFWgiKKtYvkXOypEy03QE2gAT2zMQhCAZq0TJrrAm0o/rgeN7ZE2D04UUtuuxKdFKRzBGXAtQdSzSZgnbt5AEi7w9g+1V6QttxsVn3pglb+dp0ppoDXTgewqz1QLH83OICGxKtNKqR5FEl4KdZtoYwfd92gN9xJSmJfY5GbPCZCnH6XyKqKqzId5McyC8bKXE9TwWqmXmyiWimkbVszmVn0cSRDYlWmgLRAgqFzdoup5LyixhujaKJBCUNFxPoOCUmajMkzLztAUS9IfbaFCjeB6k8mWGplLkSlW6mhP0tiaILTZ6w7JJ5cqcGp+najhsHmilJREhoC/VgikYBtOFIqlSmfZYlLZoBMN1mKkUmC4VaA9FaQtF0GVl1dhLy+pC1SRXrTKayjKRy9McDbOxtYnmSPiqrHxUHZsFo8jx3ByO73FdQw+iIDJfKTGSzxLTdLoiMRJ64KpvEV1s3TgfBcMgb5qM5rIMZTNsb22lLRhhoVgmFtBQBAlNklAUGVkUyJaqjMxlyBQqdDTG6W6K0xANXnUl1ZJlMV8s1YTkPJ/JbJ5C1aC3IUl3MkZD6Oo/86WG53ksGBWG82mmSgVaQxHWxRtp1IN1BeiyYTFfKHFisuY5t6GjmZZ4eEV7pLxVZbqSZ7yUZV2kGc+CmVyR9a1NFA2TI1OzFKomG9ua6WmI0/giq015q0zRruDioYm17S9d0tAuoNP0o8RPiMvVhbgCwT1DhgVBwF0havqL4aLFEj7zmc9w++23r1g4sViMO++8k0996lN85StfueRMvNqQt0o8tnCQ57Mncfxaob+h7XrWhJuYqEzx3dkHuLPldgZL+5itjrEhejOPLDzC8cIJbm26DVkM4ngC0+UKXzj1DGmzTEDS6AgkeH3HBt7Ys4nDuQlszyammjw09y1S5jx3t72Rqeo4T6UfxvAsZEEjJIV5TeNP8UI6zaMzQ5iL3klPzQ3zgU03kLFPcTT3DLZfc4E8ln+WTbHrWB/dTUAKMVct8s3Ro+xbmKh71DRoQX6+fzvbGtrqA5Tv+4zks3xn5CR72jp5enKEfxo8AAK0h6I06EF+unsjN7f2E34R8uL7PqPlFKcKM7QEwsSUIK4rMGtk+crID3khM4woiMTUADc2r+M9fT/F3GyV+588wny25n4uCLCpt5U33rCJRCTIodPTfOfJ4xQWvSke2X+aazd1c8ee9SSjta2LbLXKD04O8ujQCLbnMZBM8q7rdvDw1BB/d/TZustmVNX4L5uv46d61y8aIq+OTKnCVK7AV555gaeHx/H8mirw7t523nX9Lja1NV9R4DjDsXkhPcE3xg5RsA1+tmc7BdPgcGqOvz34HAvVMgKQDAR4y9rN/NzazTQHr87W1sXWjfORqpSZLhb5+4Mv8N3Bk1zT3kFvNME39h2lOxZnU3MzX338eU7PpHjPbdfSFovytccPUrUcgpqCAKzvbOJnrt9MZ1P8qrwL1FyFHxsdxfdBEyQ+//R+xrN5mkJBGoNBtne18Zbtm+iIrxwQ8ZWK0WKOfzl9iFO5s55avdEEv7B2G2vjjVRMi6dPjvP9g6eoLkaSfvDwILdvWcMtm/uIBs6dxJR5YPI4T8+P8taenbwwPs1X9x3gU/f8NEen5/jMA48zky/WSKcsc8/2Dfzinu20x1ceNNNmgQfnXuBgdqgeBT6mhLi77Vo2RbsvKDL54wDfF/CvwE7lSu59uTAyMnLV07zoHnXv3r0XFKC75557eOqpp65Kpl7J8DyPwdIUz2aO1UlLoxqjRY+QtXP8YO5BkmqCsKzyxPw36A5t4oXcMQ7kDqKICj2hbk4WJnE8+NyJx0mZtYG46prMmwXmjRIz1TxPp07TrOs8k3qIBXOOsByhSW/n6fRjjFYGKTsFfDx6Q/1Mlgv888hTVNyzLoD90SbS5hx7Uw/XSQuA41scyT1F2pzBch32zU+wd358iRtw2qzwrbGjZMxK/VjBMvnB2CAxTSdvG3zp5H4qi1tFC9UyRdvkW+NHmSq/uOZJwa7y+NwJGvUwU5UFNFEjY5b5wex+9meG8PHx8MhbVcZLGVLFEt977nidtAD4PhwdmWVwKkU6V+J7T5+okxYA1/N55sgYx4Zn6+ELTs2nePD0EPai9sPr1vUzUc7zP/Y9SsE6e2/BMvnrw08zkl/u8nsuDMshXazw8Mlhnhwaq7uzWo7DwfEZvn3oBLnKlWmSpMwy3xw/QsE2aA/G6NQTzFcq/NXBvYtlXds+SlerfGvoBPvmprAvYwZzPi6lbpwL03FIVyo8OjbCt0+fwPV97uxbw8GJGb57/BRrmhv4/sFTHJ2YI6yrdDXG+dbeoxydmCNdLOO4tW9zcnKBJ46OUDGunlvrZKHA02MTtITCfP3AUUYzWTzfY75UpmLbHJme48mh8frg/mpAwTJ4aGJwCWmBmiDj98dPUXVsZrNFvvvCiSXvZTkuPzh0ivGFXP2Y63kcy83yyOwgPZEkmq/w9889z67ODjzP568eeobxdA7bcXE8H8d1+cYLR3lmaALTXu5ibXsOR3KjvJAdrJMWgLxd5sG5F8japWX3/LjB96/890pHT0/Pir+uri4OHz58WWleNHGZmpoiEomsej4cDjMzM3NZmXg1oeqZnCqOLznWHmhEEnwyVo7RyhidgU5S5iiyqKJJUY4VjuP6Lm16KyDj+R4jxSwzRp5zd+rKjkFnKM7h3ARJNYTtlxmvjCw+o6s2+JaO4/s+hlvF813iSjPPp8coOWU8/+yA1ROOMV45RdYqLtFVAfDwmK4OU7QNjmRnV3zP6UphCQkpmCYnMgt0RWLsT03gnKOfULItHM/D8lwOZaax3AvrRBQdg6xdRhZEVFHGclyqnsnz6aH6NWfKpVVPkipUGJxZWa/GdlyGp9LkiisThBOjc1SqFmXL4tDM7JKSaImGeXpmjIrr4HjeknNVx+GZuQmqF9BDqZgWVcdm/9jk8nOWzfHZBdLllQf4i8VgYYGsVUujO5QABMaKWUYLWVzPW9JxlSyTgwszlOwrH+xLtsXR7MoujOfXjXNRtEwqjs3TkxNAzTutIxzjmZFx+hqSCAg8N1Q7t6a1Edf1eGGoJqBYMqw6cQE4PZWieJXE0izH4dDMLG3RCBXT4vD02XrvL9q+eJ7Pibn5l0y876VA0TI5np1f8dyJzAKGbXN4fBbLWU5mXc/n8NhMLbYVUHYsDmdqwo49oQTDCxlS5TJ7eroYT+c4MXv2Oee2l73D4xSN5WVWcUxOl5a3DYCUmWemcuGJwU/wHxODg4P83u/9Hp2dnbzlLW+5rDQumrg0NTVx8uTJVc+fOHGCxsZXntvo1YbvU19pOQNREPF9D8ezwa95HDm+jbho2W57dv06qHWUZwb3cwdLHx9JEDBdG0EQ8Xy3TjpEQVwM8ubUr/XxERCxPKf+9xlIAjheTdRqJTMmz6+tQpyvPHkuzHNm7j4+tucintE+WVImZ59se+6LirX59Tz5SIJY8+rzfSxvOUkQBRHHcfFWEJqqpcWKs70zsB13sQyoqaieC6EWqPFMOufDci/8Lv6iIeT55XEGnu8tf+Ylwj5HsPCMgfaZ73J+zs7k5Xyiejk4871Xg7nKO58tk1q5njHgtBx30WjXrw+ikijieX69jM6vp/aLlP+lwPNr2imiIOB4/pJVpNr5WqnZrveKExu8EM6810o40xYv1MZt11tSX85MSERBrLcrSRQwHWdpX3VOGZmOs2KZ+fgrCm6egeW/PEJ4r2T4V+H3akC1WuVLX/oSt9xyC+vXr+epp57iD//wD5mcXJnYvhgumrjccccd/Mmf/MmK53zf50/+5E+44447LisTryZokkJ3cKmmQtrKIwgyDWqSRq2BeXOBJq2XilPE8y36w32IgkjKTCEKtU5hINpARNaXWPcHJI0Fo8ymWCd5s4wuhWnUau7TKXMeWZDpDvYiCAKqqCMKEmU3y6Z4B5qoI3HWEHSuWqEj2E9E0Vf0zmnS2wnJGv3RlbVAYqq+xD06pKj0xZKkKmW2NizViAgqCrIoICKwMd5Sj7myGkKythgVW8DwLBRRQpdUNsbOarqccXFMm3kaoiHaEyvbHciSQH9HI7q6sr1Fb3uSgKYQVBTWNi5912LVZHdzJyKLgmTnnBMEgZ1N7Rc00NVUmYAss6lteZwpVZbpTtSMPa8E/ZHGuhbPvFFCESX6YgmSegBJEJaIngUVhXWJRoIXaVR8IQRk9aLrxtL7FHRZZmtzTQMoZxgULIOt7a1MZnNIosjmxVg5U5k8iiyxvqOpln9VQTpHHK23JUlIX11j5VKgyRIbmhqZL5UJ6yo9yfiS82GtJlLX15BYFijwlYywotIXTa54ri+aRFcU1rU1repFtK69EX0xLpQuyQxEapPPeaPIQHMDQUXh9EKavsYErZGztlPnehRt62wjtIIWji6pdAVXjsEWknU6AivXrx8n/EeX/H/uued473vfS2trK5/97Gd505vehCAI/PVf/zXve9/7aHmROGGr4aKJy+///u9z+PBh9uzZwz//8z9z8OBBDh48yH333ceePXs4cuQI/9//9/9dViZeTVBEmY3RXvpCZwfvycoCabNCQo1xa/MtDBWHAJUdidcyXj7MtcmdtOltFJ0ic8YM3cFmNFnkbf3XIteVaiUSahjDcegOJemNNJG3XK5veC2KqLJgzlGyc9zY+FpCUoSQHEFEYrwywppoA7e1blmiXXA4O0Oz3sHW+O5l79AT3ECz1k1QUbm+uYeuUHzJeVkQeV3HWhr1s94CcU3ntq5+hvJZOkNxbu9YU7tWFGnQgyiixPUtvfREVu5Ez0VUCbCncQ0z1TyNWhxfcIgpIe5s3UmLHgOERXImg+DSEA1x+851qOe5nDbFQ3Q2J2hJhrl5R/+yzrm3Pcn2tR2oiowsiWxta2FTS1P9/NOjE6yNN/KODTsQz1Ma/bmBzaxPNF7QbTSsaySCAV63YQ2d5xArURDoSES5a9O6JdGwLwctgQi3tq1BRGC4kCJtlWkJhPmlTbsWSVUtf7oss7u1gxvaul+UOF4MgrJy0XXjXIQ1jWQgyB19A2xobMLzfR6bGOXGNd20x6JM5PLcvXM9iVCAiXSeXKXKPddvIhrQSEaCdS+sRDjADRt7rpqmiyAI9CWSdMailG2Lt2zfTFBZFFvUdUKKSlMkxE39PS+bQNvVQFTVuLWzn7i61AU4pKjc1jWALsv0NMW5bm3Xsnt39LazprWxTkJUSWZHQyfros0cz82haiJv2bqZ+w8fIaSp/OprrkWWJSRRRJFqysPbO1q5ZV3fqsRlR2KAjsDSlXhJELmxcTMJdXXTg5/g1Y9t27bx1re+lYaGBp566imef/55fvu3f/uqeO1dko7Lvn37ePe7382xY8fqD/d9n02bNvGFL3yBa6+99oozdLXxUri2eb5HysxzsjjOeHkORZTZkVhLZyCJ4RpMVadJmWn6w11UnTRBOYztqwyWJinbBjsSmxEFFd8TGStleT41iY/AhlgLuxu7aNZDFJwqE+U0AVnE9DJMVU6hijIbo7upehaj5UHKToW4mmBzdDumK3EsO0vKKKNKMgPRBvrCDfhUmTFGmamMEZIjNOrtNKithJU4UNurnqnkeSE1zVgpQ1BW2dnYwZpoI9HzOkPDcRgrZMmZBnFd41RhgdP5mjrttoZ2NsVbaFhlQDsfhmszVcnUdCLwiCohHM9n1shwMDuIIAi0BhKsCbfTHmzAsWFsNsPBwWlKVYu2hgjbBtppa4iiyBKFssHwVIojQ7OYlkNPW4It/W00JcP1jtn3feaKJQ7PzHE6lSagyPzM5o0YvsPB1DTPzk4gCALXtnSys6mdttCL1xfbdSlWDabzRfaPTzOVy9MYDnF9fzcDjckVO/RLRd6qcio/z6HsNEFJ4ZbmNfgInMym2DszgeN53NDexdbGVpoCoaumg3IpdeNc2K5L3jSYKRbZOzXJSC7LPWs3kNR0BucydMaj2JbLsYk5PN/nxvU9GJbDkZFZilWTpniYHf3tdDRGUeUrjxJ9LlLlMiOZLPFAgHSpzMHJGQzbpSsRY2dXG53x2Esm3vdSwXQdxos5xvIZBEFEFkWag2F6o4n6imGmWOHE1DzHp+ZRZImt3a30NiVInCcW6Pk+c9UChzLTlB2TTZE2FnIVLNtlc1sLIwtZnhwcpWza7O7tYFd3O02R0Ko6Nq7nMW/mOJYfY6qaIiIH2Bbvpy2QvGK9lZcKL6c79Hsf+jvUK3CHtkpV/u/tv/aKdIfWNI23ve1tvOtd7+KOO+6ocwZFUTh48CCbNm267LQvS4DuwIEDnD59Gt/3WbduHTt27LjsDLzUeCkroe05mJ6NIoiIODh+FQEBQdCxfQ/Bl9AkFc+38Xwbx/fwAEXQkEWZkl1GFCRcH2RBRpVkSk4Vy7PRRIWIHMT2nFrcDaFmxyILCoqoYroGru8hCxKqVJshVhyjFphQ8DBdC0WUCUgaEiIuVWyvjIiAKoZQpEjdBgdqnZ/pOoiCUHdnrgmc1bxtXFxUUUKTFcq2he259U5REUUCskLJMSnZJoIAEVknouirsuuKbeH5LoZXQRJEZFGphUnwBQzLoVSpRcsNairRkF4fTCqGhet5qLKMdt72kO/7VAwLzwddleudqet5FEyTomWhyTWZdoFaLJSyaxEUFXRZwXAdBAQiqnbRLsy262I5Lrbn4rgenl/blogFrm6n7Ps+ZcfC8310WUEVJQy7Vqdc36NkW7i+jy7JxDV9ifjglWKlunFR9zkOlutgux6qJBHWNEzHwbQdVFnG92ueKbIkEVAVKqaF63oosoSuXh03Wc/3KZgGxcXAjQ3BAJ7vkzdMNFlCpBYrR5Pl+pbJ1UTZssiZtbYakBXimr4qMSo7JgWriut76JJKTA0sCxGyGgzHJmNUqTg2siCS1ANEzwuL4XoepuNScSwqjo0gCERVjbCiYLg2JdfEcCwCskZI0nAXhfmCkoLpODi+jyQKWLaLKApUHLumzCsKaLJMxbEoLUZljioB4mqwvk1tew6u51FZDPDo4GJ4NqJQU9I9s6Vds+Eq4ngmkqhheg6O79bUyX2FomXjeB66LBPXAhctDnkpeDmJy689eOXE5e9e98okLlNTU3zxi1/kC1/4AtVqlV/8xV/kne98J3v27OHAgQMvP3F5NeGlroSub2G5GYrWMJOl75Azj6GIIRoDN9ARugtdbmXBOMZkZR9Fe5qE2kdn6HUMlWc5VRwmZxfoDLRzfcO1DBbnOZgbJWXmiShBtsR6uL5hPUntxZdU540sU9UFNFFiX/YAlmtxc9MuVMFDFqqMlh5iwTiBLmq0BbbSFb6ZpLYWSVx5IKraFidTKYq2Rck1UGWJ3miCoXyWfxs6wvHsAglN57aOAd6+bjvzZp7H508zXFxAFATWRJu5tWU9feGmZSsAs6UiFbfMqeJJnk0doOgU6Ik28/rW2whaTTx5ZIzjY3NUTJuOxijXb+xha18bwcuwd7Bcl+OpeR4eHWEom0ESBN6+eRuxkMITc0OcLMzjeB79kQZe27qGDfHWFxVXO4OyaZEpVzg6N8+3j55kMJWmORziljV9vH79GrrisZdczGyuUuKRqSEOpKYpWCYtwQg3tnZzXUvXBVdFfhxguy6nM2keGhnmdDrNztY2dre389z0FCdSC5Qtm45ohBu7erimvX3V4J2Xi+lSgYfGhjm0MEvZsuiIRLmps4fdLR1LAi4CzFbzPDZ3iiPZKcqOSXswzp6mfnYmupep456PnFnl6ekJvjl4jOOZBZKBILd39fMzAxvpjsbr11muy/H0PA9PDDOcz6BLCu/avJ2mkM7JwgwPzBzgdGGWuBrmhqa1vK5lK12hJMai2u/BuVmemZrgjr6BWtwy12VnWzPt4Qgz1QLfmtzHvvQQiiBxbeNa3tCxkw3RNhRJxnE9BtNpiqaJKZr8YOoYpwtzxIMBtibbubV1AwORJGlzmOnqcZr19UxVZzlROEyL3kOPvoOnJ2c4lspgOB5t4Qg3dvRwbVvHVRdd/Alxufp46KGH+PznP8/Xv/51DMPgIx/5CL/6q7/KunXrLiu9i55ibNq0iSeeeIJksmbD8IEPfIBPfOITdU+i+fl5ent7qVSuzP3z1QbLzVKxpziR+UuqTs0d3HazTLnfJqIMUHYLHM9/E9evzfiS2gaOFo7z8PyjhORmJEGhM9DOC9lhvjn1DDElVrPoNws8Mn8Y13e5vWX7igEHz6Bgl3k6dYQ1kXYeWXiSF7JH+ZW+n2fOGKNdT3Ig/WVy9hASEmVBwPGrVJw5tiZ/mZjWuyw9x3WZLhb599On2NPTwcPTp3nn+t2MFXJ8fO8PSBklJEEkZ1VZU84zVk7zL+PPUXbOukQeyEywYJR498CNtAXj9eOZaoW5coFJ6zT/Nv4AtTUocHNgxgUeeO4Ap0ay9Zna8EyGyVQeTZHZPtB+yURgspDny4cOkjVq7tJd0Rii7PN/jjxO2TPRF7cijuZmmark+bX1NzEQfXHvOM/zSZXKnE6l+czDT1BanNHnqwbzpTIly+Jdu3dcsXHuhVC0TL4/cYpHpobrx8aKWSZLOWRR4ua23isSv3u1Y7ZU4quHDjJXLtWMqJub+Nfjx/jh8CA98TgBWWE4m2Uin0eTJK7r6Lxq22w5o8q3h06yd3qifmwol2GimEeTZK5r7agriuatKg9MH+W51FmhrpFSislKFk2U2b2olLwSHNflhblp/mTvwxiLrvsl2+SLR/dTsi3+y9ZraQjU6uBkMc9Xjh8gs9gWNiSb0BWRU4UZ/s/J71JcXC0pOFUWJvMULINfXXMb2arB0dQ8/2PvY/zyll2MF/P89QtP8ye33EHFMyjYCp8+9k2Gi7OLoR3gBzMvMFZe4L9u/hl6w03Mloo8OTzG1p5m/t+xJxgu1OQN5qslTM+mQQ+T0KoczH6LnvC1zBjzPDD3NSRBYmf8dr4zfJLvjh4hLEdIqElG8g4TxTyqJHF9e9ertp5fqWfQq2XV4fbbb+f2228nn8/z1a9+lc9//vN8+tOfZsuWLRw6dOiS07vor33ixAkc56z72le+8hUKhUL9b9/3r3oEyFc6XM/EdnNkzIN10nIGnuegSHEmy8/gLgrABaUGRCHGwdwLmG4R17cIyyEiSoynU0cp2mWc81wED2RHyNsXJoMZq4DjO1TdCgdzx+kKthORNSyvSsWZZs44huO5i43Ep+KUqLhp5o2DON7yb2Z5Ls/PTNMejXAwPc26eDO25/LkzBgz5QKeD67v43oe2xtbeXZhlPFSdlkjmqpkOVWYwz3HHTNdrYBosz99qE5aANZG+ykUHR4/eQz3vDKwbJfnT09RukRND9Nx2D8zXSctABsbm5gs53h+YYqyZS1x68xZVZ5Pj2O4Ly5AVjRNypbNkyNjddJyBgXD5PmJKaYLxUvK76UiY1TYN7/cndD1fZ6dmyBv/Xi1x3PheC6H52eZK9dEztY1NGI6Lg+PDmO6LkXTrLsB257Hs1OTFK2rp9+yUCnzwtz0suOW6/LszCQF62ydSZlFDmYmll1rey7702MU7dXzlTMNvj92uk5azsV3R04yV6m9v+k4PD8/XSctABsaGrF8i6cWTpOxykvurboWh3Nj5C2Dkm3xyPgIiigxkGjg+6On2dHcji86tAaiHMyOMVic4YxjtU/NrXqoNMOBzDiW63Bsdp6mSIjB4lydtEDNTb5gmLQHwgwXD+L6NiG5hWOF53F9h+7gGgxb4pHJYwBUnHJdEsLxPPZOT1C4it/tR4L/oJGhV0IsFuMDH/gA+/bt4/nnn+fWW2+9rHQum6autMP0aovxcaU4Y7tStZcL78liABAo2dP1slLEIC4+GSuNj4/r2wQkHc+HOSOL7dvLyrXoVKm6FxYUy1klVFElZ9cITFgOUus+BEr2TJ2wnOHnju/g+z5VN4O3gpaC6bqM5XLEdZ3Jco6YpuP5PpOlmujYmQ7f9X2iqs5UJYfhOCvWiTmjsET3pmCa+IJLyswuuS6sBEnly1RtE2+FeUS6WMZ2Lk0TxXJdUpWlHXJQUZiq1N7DOE+bAiBllC+oX3IGtuvieh7T+eXkxPd9qo7LfPGlVQYt2uaqYnMpo/yiQoD/kWG5LnOls98+pKpkjSoVuzbAm+dpxKSrlVX1eC4HGaO6anrp6tI6ljUrWN7K3yptllY9BzU7rYnCymKAZdsiVa2VgeW5LJzXFkKKDD7MVLOLk4ulrcEXfCzHwfY8ZkpFYpoO+EwW8yT0AHm7giRKTJbPFZKrpeH5Hvg+M9UspuMwVywRUGVmKsvzarkuIVkma80jizUbpKxVIzdBKUzeNCjaNcLl4eGe05+kquWr+t1ebni+cMW/Vyt27NjBX/zFX1zWva/O9bVXCERBQRBkdHm5VoHrVwGfkNxSJ3S2X0VEIKEkFsPTKxiuiShAkxZHFuRl5C8k6wSkC9t2xJQwlmcTV6JIgkTZrQACPj4hpWXRc6f2L4C0+BxdSiAKy3cLVUmiMxojb5q0BaPkTQNREOkI1/ZQz+RQEgSKlklbMIYmLc87QJMeQT7HCDiiagi+SFJbqgNScao0RIPoilrXcDkXiXAQWb606qpIIsnA0q2aqm3TGqjZDGmyvOxJSS2IIry4wZ8i1dxCz9W2OANBENBliabwxXlYXS7CikZoFdfnBj2IepGGnf8RoYgSTeds01Vsm7iu17cGVUla4uqe0ANX1ZsooQdQVtm+SC7KB5xBXA2uaoSbUEMX/I6qKNMeWdm2IaioNOjBxeskGvSlthQV20EAWgLxxa2o81qDL6DKMooo0hIKL65sCLSHI+RNg6gSxPVc2oOJc26qpSEKIggCrYE4mizTGA5RtRxaAsvzqkgSFdchrjbgeAaiIBBTayYJVbdMVNXqW+Ui4hLZh4bAj3c9/3HFRY8EwmJY+vOP/ThDEjVUMU5S34EuNS09KUjYbpbO0PWIQo14VJwUvl9gW3wnqhRGElSKTomSU+T6xk1E5PCSQR5gW6yXmHJhO4kGLYokiASkIFtjGxgvT1N2LRRRIyR30qitQxakercUlMLoUpxmfRuyuNx2RhMlrunoYCKXZ2djJ6ey8yiiyA2tPTQFwoiCgCQISKLIwdQM1zX20hWKLyMBrYEY66OtS+wGGoJBfE/hmuS2JQTlVGGYWFThpjUbkM8jU4ossXNtx4qRbC8EXVbY3dZOTDt734l0ip5wkm3JNsKquqQORxSNXQ1dF6WDEtFUgorCTf09dT2QM4jqGjs62miPvbTGckk9wK6mjmXHRUHgmubOxRnyjycUSWJbSytNwRp5PJ1OocsKt/T0okkSUU2r1z9ZFLm2o5PoVTTybAqG2N7ctuy4LIpc29ZJ5Bzj3CY9zNZ45/JrBZHdDT2EL2DfFtd0Xt+zdkUvstf3rKUlVCPW2qLOT/ycOnEyk0YVVW5oXEtCXUpqdElhU6yLqKIRVlRu7e6j6tgM5zLc2bOW/bNTyL7CbDXPjkQPfeFmxHNatCyI9ASb2ZHsQZVktrS2MF8qsy7aQnf4rNaTsvgtpspF+iM7EASRijPPpuiuRZ2qQQKqx2s6NgAQlEP1/kESBK5t7SSqvXp0d5bhx0U69yrjor2KRFFky5YtyIszlkOHDrFhwwbUxQboOA5Hjx69rBDVLyVecq8iz8RyMxSsQSaK3yJrHUERIzQFbqAz/EYCcitzxhEmy89StGdJav10hu7gdGmKk8Vh8laRnlAne5LXcbI4w4HsCCmzUPcquqlpI43ai+d7pppmtppClUT2pp/H8V1e07QTVQBJKDFS+CEL5gl0UaU9uI3u8GtJauuRV/EqKtsWx+fnqTg2ebdKSFHoiSQ4mUvxL4OHOZ6Zp1EPcnvnGv7Thp3MGFkemzvFcGkBEYG10RZua13PQKQZ+ZwZke/7zJZKVLwSx/JHeSb1AiWnSG+khTd0vI6A2czjh0Y5NjZL1bRpb4hxw6Yedgy0E7oMMTLTcTi6MM9Do8MMZtJIosi7tu4gHJR5bG6Qk/l5HM+lP9rIba1r2ZRorSvVvhjKhkm6UuXg9AzfPHKCwVSalkiY167t46c2rKMnEX/Jyf1sucgPJwcXvYoMWoMRbmzt4Ya2HmI/5l5FlutyMrXAD4eHOJ1Jc11HJzta29g7OcGJdIqKZdEeiXJzdw/XdXQSucoD4EQhzw/HBjk0P0vZtuiIxHhNZw/XtXUu82CaruR4ePYER3JTVByLtkCMG5oGasRFufB3zBpVHp8a5d8Gj3E0PU+jHuB1PWt4y5rN9MXOroaYjsPR9DwPjQ8xmEsTlBXes3UXyaDG8cIU351+gVOFGZJqhBua1vH6tm10BxsxXYdUtcL+2Sn2zkxyV99ahvMZ8GFnWxPtoShT1SzfGH+W/ZlFr6KGtdzTdQ0bY+1okoLtupxaSFO1LcpCle9MHGGwMEciGGBbYweva93EmmiSlDnIbPUETfoGJipTHCscojO4hi5tB4+PT3IilaXquLSFI9zU0cP17V3L3L6vFC+nV9F7fvD/UK/AgN8qV/j8nb/6ivcqutq4aOLyx3/8xxeV4Mc//vErytDVxtWqhJ5v43kGgqDh+uD4NpIg4/kukiAh4uH4RRyvDEgoUhRFrGmluJ6N6RVwPAtdjNVWY3yHilPbtw1KQWRBxsahbJtYvosiSMTVMIro43suPhaCICEJQYQz2giuhYePiI8gQMV1cP1abGVdUFAlGcuzEBERBR/frwAeghBEFoN1HRfP96k4NVuJgKScE1/GIW8a+IDreyiShCyKVGwL03OJKBqKVJtFqoJK1bVwfBcfD0WUCMuBujeE63sYrlVbUfIFTNfB9R1Mv4rnO4TkILqkIQgChuFRrFh4no+uycRC+kUJkfm+T8Wq2QkF1LPv4XhebZ/cNBEFgaimEVE1So5J3jLw8YnIGjE1cMleJZbjULUdSqZJ1XFQRIlEMED0JVJfNVwL13dRRQVFrJVJybbImVUczyOkKMTVAOaiIX1AUa6ap8zVhGE72J6LIkn17Zur/wwbz4eqY1O2LAKKQlBVsBaNcz3fJyArJAIr6944rovteViegySKhGR1GRG1XRfDdZAEgaBSm8QZjoGHjy5plG2bTLWmzRJRNeKaXo8tFpBUJFHEcT0cx8XEoeQauJ6HLqvElcBF6/FUbKtmw+PYqJJMUyCIR61NBGSl7nXjeB4F06BgmYtbMhohVcV0LYqOSdW1kASRuBIkogTObnO7LlWnpqGiiBKiUEtLkSV830MUREqOQdW2iGshRNED30MV1brOlOt5FE0TVZKpeCYlx0BAIK4GCMs6jm8gIuHh4Ho2giBjeCa2ZxOQAnieStGyFnVcFBKajvYS1J2fEJdXPi76q78UhOTP/uzP+PrXv86JEycIBALceOON/Pmf/znr16+vX3Prrbfy6KOPLrnvve99L3/7t3971fOzEjzfxXRnKVknCcjrqXoiJaeAKMjMm7M4nsPacCeyN4nrjiCLcVS5F4mzxEASFYJiA5ZrkbVznC4OMlQeYWNkA+si6yi7Jaaq44yUTtOgJekNriURaMX3cxh2gbI9SNE8jCwEiOq7iWjbKTgFFEFGEjxydpmxyhhTlUla9R52JXdjeQbD5QkicoQGRcOyhygbT+P6BQLKOoLadahyN1nL5HR+gSPZGTzfZ1O8lfXxJhr1MKos0yQvt+GIawEKdpmJyhwni2O4vsf1DVuQBZHh8iSz1RS6pLIh2kdnoJWCXWWiskBICpKumBxemANPYGtTKxuSTUR1lZyd5nT+CFkrRUiKsDayiZiSRH0R+54zyJWrDM6nOTI5h+d7rG9rYn1rE42RUC0sQSBYdws9g5gaIKZemSS/KsuosnzVBefOR9UxmTZSHM2PUHEMWvQkG6I9NOsJwopKeHHQzFQqPD85zZHZOXwfNrU2s76p8SV1y74UmI7DZD7P/ulp0uUqTeEgu9rb6YxePZVcw3aYzOZ5YXyaXKVKX2OC3X0dzJslfjgzRdEyWBdvYkO8maS23LbF9TzyhkHaLHM0O8PJ3DwxXeeapm7WxpqIqQFcz2O2XOLw/Cwj+SydkSjXdbSRd9IMlUcJiBprI+tZqBpMV4ok1ACKEuP5zBwnCrN4vsctzevRTYXBsQXGZrIENIXdm7pobYxesmZRUFHrxClvGpzIpjgwP43puqxLNLC5oYWWYBhZrNl9nW/7JYsBQsrqbUGRJCzXxfBcXM/BFWx8HLChYBcYKo+QkKNsjq8nbc5wrHCEklOkRW9jQ2QjMaWBnGmgSCJTlRSHs2OIosD6aDtRBSYqQ1ieQUxJUrTnyFjjSIJMR2ArCa0TTapt+cW0K2uvrzT4/soBXi/l/lcDHn30UT796U9z/PhxoCav8ju/8zu85jWvuaz0rrinePTRRymXy9xwww0kEokXv+G8ez/4wQ9y7bXX4jgOv/d7v8frX/96jh07Rih01rDxv/yX/8InPvGJ+t/B4MvXCRvOJJOFfyARuJ2S67E/8yi94Q08m3ma4fJp3tX9DjKlf2W+9E2iSoyYEkcSJALaHmKhX0BetH2xXIu0leFfJr7OgdwBInKEnfHtZK00z2We4NnMU/Ud4luabiMobUARXFLl7zBX/iYAAiIBZYCclaJkF0hoEbJmjgfnv8vR/H5ERN639o8wvQpH88eZM2b5qdbbKBgPMp7+BD5VREQEQSQZfBPh8O/ynclhHp0Zqr/vswvj7Gnq4c29W1eNR1OyKzyVOsQz6SP4+GyM9pE2c3xv9gkqbgVVqG21ZK0CNzWqPDh7kK3xPvYtTPO5I89guS4NWoSnZsd569qtbGsN8Ez6QXJ2uv6MU6UjXN9wKwPhDSjihTvxfMXgB0cHeeT4UH3Ld9/IFLt7O3jTrk00Rl5aI9mXGqZrcyQ/zAOzezEXI2gfLYxwtDDCPe030xOqBSrLVqt85/gpHhserd/77MQk1/d08abNG3/k5MXxPI7Nz/PVAwfJG2ddWJ+dmOI/7djG5paWK14dclyPo9Nz3PfcIYqLz+hqiPPUzBjfGD2ML9XMR59dmKBJD/GuddewMd68ZCVlvlRirlriK6ef48mZmraKIAg8GDvFL67ZxU91bWKhXOErRw4wlMugShK721p4cv55ns3upyEQ5K7WO3h45hT/Ovo8PaFW/tOa6/j62As8MHWEtlCMnckustkK3374COn5Si0uF/Dc0XFef8MGbtzRd1mCi0XL5KHxIb4zerIeAfu5uUm2NLTwi+u30xa+vPhAhu0wXyrh+D6y7FF0S/iCS8kp8I/j/8ackebTO36XU8WT/NPEl3E8G0ms2dXJwtsICTYtwQhH8xN85vg3yVhlmrUov7v5p3li4WlML8V1DbdzMPttBotPE5AiRJUGJiqHGAjvYW30FnTpJ7GNXo34yle+wq/8yq/wsz/7s9x7770APPnkk7zuda/ji1/8Iu94xzsuOc2L7iX+/M//nD/4gz+o/+37PnfffTe33XYbb3zjG9m4cSNHjx69pId/73vf493vfjebN29m+/btfPGLX2R8fJz9+/cvuS4YDNLa2lr/vVxLYq5XJVt9Gs83UaRuRsqnEASJlLnA89mn2RTZikaGsfx9eLgU7QKWV9tyqZp7Me2T+Ith4m3f5nD+KAdyBwDYGt9CSI4wa0zxyPyDdddMWVDoD/VQsqYwnCnmyt+q50cSI+jqRiYqp4ipcQwnxUhlhKP5fYCPLgZo0VvJWzkemvsBNySvx/UWmMn9NZ5fwff9ulu0IAYYLc7y2PTpZe+9d2GMwfzCiqHqAebMDM9mjuIvxiZdE+7kcP4UR/ODVByj7i7dF+rkhewgFddE9FT++dQBTLe2TJ6xSni+R3NIY1/6OdLWwpJnWJ7J4fx+ys6LuxRPZvM8dnJ4mZ3a/tEpTs+mlujIvBpRsEs8lTpcJy1nMGdkeCF7ksqicNhYJscTI2PL7n9mbIKhdGZFd/WXEwXD4KGh4SWkBWqE6+HhEQrmletx5KsGj5wYrpOWxnCQxliQfz19iLFcFsc9WxcWjDKPTg9RsM/q3VRsm1S5wuHMdJ20wJk4V2W+N3mcomXyzPQEQ7maG/DGhiYk2eZ7008xWy7QrrfhuBJfH9tH0SmzLtZI0Ta4f/wAJccib1XZFOngyOlpnj49Qtk261Nn1/V4bP8g6dxS1+WLxWylyA/GB+uk5QyOpOc4sDCNfZk2iAXDoGo7CALk7DKGW9vCeSa9nxljgRsadyKLIt+f+y5Fu1iXdojIUUJSkvlqHstz+Jfxp+uaMdc1rsWnwCPz36dJ6yVnTTFYfBqAqlvE9Krg+wyXniNvzV5Wvn+CHz3+5E/+hP/xP/4H9913H/feey/33nsv9913H//9v/93/tt/+2+XleZFE5f77ruPLVu21P/+2te+xmOPPcbjjz9OKpXimmuuuWg7mNWQz9d8/M+o857BV7/6VRobG9myZQsf+9jHLqjOa5omhUJhye9y4foVyvYImtyC48Nw6TjNejuj5UHAp01voWIdwsPGW7TtMNxKfeA2rSN4fgXP97A8i1PFsyShVWvBdA1GK8O4i/8BJNQEuiTg45E3D8I5Im0BuQvPl8nb84iCj+37jJbOprk2shV8n7xdIGtnCSsBTHsc0xmvX3Mmb6LYz5HMMK6/stjasdwcVWe5RojruwyXpnAXCZkuaQQklcFi7RkVx6ifi6tRjhfGSaphZspF5qtnSYjnewRVCV32OZ0fWZFcZK0UJefC389yXE5ML+B6Kw/KJ2YWqFovLij3SkbGKpKyVtbqmKjMU3VNDNvh2Nz8qmTz+Nw8VftHWw5F02I4k13x3FAmW48ndGXPMBlOndUVaYlGMH2H45l5ypa9rJ4NFdJLtHAqloUv+BzOLBePq8Wl8ihYJqezZ1cHW8MRpquzlJ0qtucRU2KMlDIUFrVHmvUwx/MzOIvtwnJd4mKQ42NzAJQccwnRKFctRqfP1Ua5eJzMpDBW0e85mU1RXUGo7mJQsiwEAUShtpLqCx4eDieLNXK3LtJPyS4yUh7Cx8fza3J0TVoTticQVBSKdoUjubNCe72hBqaqQ7i+S1RJMlM9ueSZplfBw8XHZd4YxPVe3e14Jfi+cMW/VzqGh4e55557lh3/mZ/5GUZGRla448Vx0VtFIyMjbNu2rf73d77zHX7+53+em266CYDf//3f561vfetlZQLA8zw+9KEPcdNNNy0hSO94xzvo6emhvb2dQ4cO8dGPfpSTJ0/y9a9/fcV0/uzP/uyKCdS5EAQJFo1fJUHC932kRXc8j5qWy1n9A4ElWgiCvMTl91xXZ39RIO6MJsGZ62qrIjWXVvG8z+PjgQDiOWmeq2ng4iFQ01AQ/EX1lhV0Wmrv5S2593xIgriKR4yw5L4zs/j6ewhn37hWViIIPvJKkuWLfXXN62j5TFBAWFXq/Ox7gCStfo0krvYerx5cqAxEQVgM7MkFt1leCeVQa0Mr50ESBMSrkD2Bmpu+t7iy4C8GChQv8FzhvPsRWLm+1lJclAM4e973/fM855be77Pye8uL9VZk+TnlEjWLzuCCdUAQl0m1XCxEUVhsokK9zsHZPu2Mge75Gkze4qosAIKALIo4Z74N1PvSWp+3tD86X3uKV3k7/nFFV1cXDz74IGvWrFly/Ic//CFdXV2XleZFtw7HcdDOcRd8+umnufHGG+t/t7e3k0qlVrr1ovDBD36QI0eO8E//9E9Ljv/ar/0ad911F1u3buWd73wnX/rSl/jGN77B0NDQiul87GMfI5/P138TE8ultC8WkhAmrKynak8g47E+up3p6hhrwusREBmvjBNQtyMLAUShJmgVkIKLDU5EV3cgCLUIqZqosTm2uZ72eGUCTVTpD61FE7T64JSx0pQdGwGJmL4LgbNlXrVHEX2TpNaB4/uogsyayMb6+eP55/ERiCkxWgLNZKwcmtxNQD0bhVNY/OS2fYIdjWvQpOVGpSIC25JtBOXle+ySILIm3Ikm1uxYDM+iaFfYFBsAICQH6h1QysywIz7AbDVPSzBET+SsDZQsSpRtl5LlszG+DnmFUatZayMsX3hbUJEktnQ0o8nLSZggwJbOFoJXKdLwjwpJNUJHYOX4SX2hdoJyzbtiW1vLijFbJEFgW1srAeVHWw5RTWNd08rvsaGp8aq4I0cDGutazj5jMpdHRWJXcwdhTa2Thfpz481EznEbD2sqki+yu2l5h6rJMmFVI6bqbG48Kzo5XsjTqrWQUKNokkTaStMTTtCs1+ruZDnP5ngHgUU3+4CkMO8U2D5Q0+AJK/oSwhGPBOhuW7rqfLHYmGxaNejglsYWQiu06YtBSFXxqcXoSqgR8AUEJLbEakHyns8eJSxH2BjdXCOPgoSAwGx1Bkl0KNkWMSXIruRAPc2ThVk6AmtRRY2sNUdncMsS4qOLIUREZEGlWV9bJzn/kfCjWnH5q7/6K3p7e9F1nT179vDss89e8PpcLscHP/hB2tra0DSNdevW8Z3vfOeinvXbv/3b3Hvvvbz//e/ny1/+Ml/+8pd53/vex4c+9CE+8pGPXFb+L5q4DAwM8NhjjwEwPj7OqVOnuOWWW+rnJycnaWhouKxM/Pqv/zrf/va3efjhh+nsXC7EdC727NkDwODg4IrnNU0jGo0u+V0uJFEjEbiOgNKNYZ+kM9BLRI4SUaLc2nw3g6UTlNwg/YlfQxEjxJQEqqgCCuHA69HkgfosVxFkNkTWcUfL65CQOFY4Ts7O0qy18ob2n0Ff1FPxBZ+R8jgRtRdNaqEj+k5Eodax+jiUzQN0BNZSsPLocgM9wR5ubLwLSZDx8RgpDxJXYtzZ8lM8k34WQWygI34vqtyBsDgjElBQpQRdoSbe0L0FTTzbIciCyOs71tMfXf1bNmpxbmnaVScvJ4pjbIj28dqm3QQlvd71TFXn2BrvozvURNWv8M4Nu2kKhJAEkUY1iiJI5E2XXcnddAX7ljwjIsfYnriOkPziBnmtsQh3b1u/xDVSFkVu2zhAf3PyR77ScKWIKiFe07SDuLLUw2tNuJNdifXoi55XnfEYP7VhHeo5XjKyKHLn+jX0JOMvZ5ZXRFTXuXPNAF2xparJPfE4tw/0XxUhsaiuccfGAdpjtXpTqJpMLOR569rtbG1pW0Ls1kQbeW37AJFzoi9rskxzOMzGRCtv6ttSXzmRJZH+RJK39G0npKhc29bBNa0dCMDpTIqyCW/qupW+aDNz5hweBr/Yt4fOQCODhRSKIPGfBvbQFogSVXUO5ydZ29/Em67dtuT5oYDK62/YQEPs8gzKm4Nh3tS/cYmisiQIvKajl62Nl2/8HNf1muo1EFcihOQAAUnnuuRONkXXcKRwkqJd4a6WN9AR6EQRa0rahlfF8or0RZqQkPiF7htYG2kFYF96CMtTeWPHz5G3pgjJjWxN/BSioBCWEyiSjiyqrIu+loiyXJ38J7g83HffffzWb/0WH//4x3n++efZvn07d911F/Pz8yteb1kWd955J6Ojo3zta1/j5MmTfO5zn6OjY7n45Up4//vfzz/90z9x+PBhPvShD/GhD32II0eOcN999/He9773st7honVcPve5z/HhD3+Yt73tbTzzzDPE43GefPLJ+vlPfvKT7N27l29961sXSGUpfN/nN37jN/jGN77BI488wtq1a1/0nieffJKbb76ZgwcPLtm6Wg1XwyffclNU7AkUsRUHjZJTRECg5JYoWgV6w+3oFLHto4CHrm5BkbuQxKUdtO3ZlJwSM9U5hksjNGgNbIpuxMMhbS4wURlGFmX6Q+to1lrx/BK+b2J585TMoyhSkoi2C0VMUnGLizMbF8O1SVt5JqtT6GKQ3fHd2FTJWQVUQSemKjjuLBVzP77vEtKvQZE6kMQIZcdiulLgSKYWJG1zopWOYIzoi4iXVRyDlJnjdGkC13fZHluLLErMG2mKToX+UCcRJYQsSJRcgzkjh+hLmI7P6UyGim2zLtFIbyxBgx6kYOeYN6dJmXOE5AidgV5iSgJJvLhZVskwmc2XODo1hypJbO1qpSEcIKBe3gzzlQbLdUhbeQZLkxTtCh3BJroCzSTUyBJiVjRNZgpFTi+kSQYD9CTjxHSd0CukHBzPY75U4mQqxVyxTHc8yoamZuK6Vo+WfMXPcD3miyVOzqVIFct0JqJsam+m6JqcyC+QqpboCscZiDbSpIdWHMzzhkHZNpmq5jmZnyMoq+xu6qI1EEVb1FZJVSuM5rLMl0t0x+J0xSKUnAKjlTEEX2BtZC1F2+F4bh5ZENmWbKfoGJxctHe5rqEP3VGYWSgwNJEmoMls7G+lMREmoF3+6ljZtpgtFzmcmsN0HTYkm+iOxEnoV+ZKbDkOZcuu2a+ILoLgIwgCJadExswQUyK0BJoo2HmGy0MUnQLtejvdwV6CUoSsVUERRfJ2hcHiDCmzwLpIO2ujjVTdLAU7TVxtxHLLpM1RJEGmJbCOsNyMJr18btAvp47LL33381es4/Kln3rPJeV1z549XHvttfzlX/4lUDPT6Orq4jd+4zf43d/93WXX/+3f/i2f+tSnOHHiBMqPeNX2DC6auAB8/vOf51vf+hatra18/OMfp7W1tX7uAx/4AHfeeSdvectbLvrhH/jAB/iHf/gH7r///iXaLbFYjEAgwNDQEP/wD//AG97wBhoaGjh06BAf/vCH6ezsXKbtshqutBK6voXtFpCEAD42jmciCDI+CqqoUbMjkRbtXxxq9jBnP67ruJhmBcPNYjgZIsFWgmoDju8hCzKSeEYErhY8TETE8/PYbh5ZDCBgg+/hC3F8QcHxShjuAgISYbkDUdSxLRsLCwEBTQlgewYIIiIunu8g4aCKITxkcoaNJIpoigB+EcfNIokikpBEFIJo8qU1Itd3F/f4a5256zlYXpGqm6XqpFGlCGG5BU2K4S7aG9Su81FEER8X061gemU8z0MUVYJShKrpkylWyVcMYkGdhkiQaPDFtVJs1yVvV0mZRQzXoVELk9SCBOWls3nHcynYZcqOgSYpWJ5J0S6jyzpJJUpECb2ofU3ZsMgWK6QLFeJhnZZEGAubolMia+VQRZmkliCuROvlc2ll61B1irh+TT+jJv6loosaAUlHWUWczPN9smaFhWqZsm3TqAdp0IPL1FrPwHRtslaZtFlEFWWatTgl22ahXEYUBJpDYZJ6YMlKzqWiYlkULJOFShnP92kNhYlqOobtULYsRF+gatoUygbhgEZjNEQsqNdsKy4TnufheD6yKNbTcT0P1/eQRWlVuxdYrB+WsejJJRBRNKquQc4u4vguSTVGXAkhIJGpVlkol/CAzkiUiKYiCpxtE76H6/v1mDpnIrWfG5/IdlxEUaiTqKprkrdKZO0CmqiSVKNE5OUkq+SUydsFSk6ZiBwmqkQIy6HF53j4vr9qDCbHc7A8C893MF0DYdG2x8XBcCsYbpUGtR3LVshUDCqmRVssSjIYWKyTJqZrEJZDi8KTLpZXRRYkVDGAtKgzZXsuWdOgYns0B6L4go+z6HUUVjQsr4Lr20iCgirq6GIAX/AQEPF9KLll8nYOy7WIKTGiShRRcOt9qiRqBKRGNCm2zE7mcvBqJC4TExNL8qpp2hLTjvr1lkUwGORrX/sab37zm+vHf/mXf5lcLsf999+/7J43vOENJJNJgsEg999/P01NTbzjHe/gox/9KNJVjO91Kbik3vQ973kP73nPe1Y899d//deX/PC/+Zu/AVgW2voLX/gC7373u1FVlR/+8Id89rOfpVwu09XVxc/93M/x+7//+5f8rMuB65lkzcNoYiOe6DBTeYrTuS9TtqcQBY3m4E2sjb+dBm0TCNIyQ1jbdigXs0wW9nFw8H4KpRkUTaGvdxtbut9EY2AjUPvwZwzbqs4o6fIPiWpbsK0hyuVvEQq/G0HeieNPcyr7j0yWH8PHYX3sbXQHfpFj8wfJlXJcN/BaHMFhpDxIWFYp2TO0a1FKlX8jpG5iwdiBIETojDdStmcYy9/HdOlhBMEmqbbQE7mblvCbUOXlMVZWgyRIdYM/17OpuCmGCj/keO7fqLoZFDFEX/i1bIr/LHGtt04GJKmmRlx2sgwWn+VQ9ruUnDQD4evpV36Gxw/Pcmw8jeW4aIrMtp42Xr9jHa2J1beOXM9lpLTAd6ePMFSYx8MnpgS4sXkNNzevrYvN2Z7DUGmaQ7nTbIr2ciw9yFOpA4iCQFyJMBDu5Oam3XQGWlddWs+Vqjx+aJhnTowhiSLvecO1FN0Kh/LHuH/iIaaMBVRRZndyHT/dcRtrwn0o4sXPVhzPZs4YxvIMcrbDg3OPMVWdISjHaNM7uS65gy2xdQTlpTNRz/cZKWT49uhxjmXmcXyPsKJyY2sPd3StoeE8bZ6KY3IwO8oj80cpOyb3tF/L0fkUD4+NUDBtREGgKxLjDQPr2dbUclFxnJaVlWkwWcjzD0cO8t3BU1Qch/+8fRd39qzh5FyKdQ0NPHV8jAcPnUb0BdqiEbb2tnLXrvV0NyUum7yIooh63ueTRBHpRXbIHc/ldGGe704eYbSY5s2925EllycWDlJwSiiiSFKN8vMdtzNfsPju4ClGczkAmkMhXtc3wJ7OLsKLi1ySICKd8wryCkEBlXNstIp2mX3Z4+zPnKDoVBAR6Am18drmXXQHWuqTnZSZ4Zn0PgaLI9i+jSIqrAsPsKdhFw1ackV7p7PvaFG0y3jYDJdOEFHCuH4FTQxyMPs4w+XDXJO4k6yv8N3jg4ylq4DETb3d/PTmdVhClYfmnqEz0MLaSDcFO4/h5Xli4ftMVIboDvVwT/svoAsBTmTzfGN4Lze0baIlFOd7MwcpO1XeNXAzkOFEfi8OZWRBIq40sTl+Pd3BdYiITBszPJN+lqnqFD4+7Xo7tzdfT8E6zEzlWSyvgIBIXO2nN3InMbXvqpCXVxvON3L9+Mc/zh/90R8tuy6VSuG6Li0tLUuOt7S0cOLEiRXTHh4e5qGHHuKd73wn3/nOdxgcHOQDH/gAtm2vKkybSCQueos+k7l0D7ofqbXTiy32dHV1XfTKykuBqjNDpnqA3uhbSJtHOJT6DK5vAAIeJnOVR3A8k11NHyGiLt/vqxaqpEpDPH34izhuTSvCNm1On34eT7S4oe8DhNX2+vWOlyNV/g6SEELyi6SLf4WuXotBKwHB4VT2Hxkrfw8BCQGZVvUOJnJDfP/g1/nZPb+KrmoczO1HkyTGyofoD62jWP02ResokvwLPDJ6jLdtfT2mm2W6+A3GCv8O1Dr4lDmD59+PIoZoCv8sknjpe+y2V2Gy/CzPp7/AGZch2ytzuvBdBEFiZ8O7CcpnjQ5tt8p05STPLPwjHi4iEl3aHn548CiPHB2kQetAFlVM2+G5wQl0Reanr9lIaBVhroxV4duThxgundWEydtVHpg6QkINsqexH0kUyVhFvj+7ly2xfiars3xr+rG6Jo0iyoyUp/B8nzd23EpCXT7jsl2XA0PTPPjCaXzgtTv6CYU1xirjfO7017F9GwSwPIe9qRNYnsl/Hng7LfrF79OXnSxj5YM061v495nvMGfUXGeLdhpJUHl44RniapR1kaW2QTmzyr+PnuBQ+qzuRcm2+P7EaaKqzus6B5bIyE9W0nxrah+m57Ar0Ydpwf85+CSW49IdakSXFCaKee47foiW4A30nBP75mJgOg7pSoV/PX6Urx2v6TyFVZWdLe185fkD3NTTw9GxOb721KH6PZIocnh0FkEQ+IWbtxMPv7xqqWmzzDfHDzJWzjAQaSKuafzd4LeZNbJ0BuPE1SAROch8pcyXDh+ibJ71iJsvl/nX40dpDoXY2tJ6gaesDNfzOFWc4LH5F/AW25CHz0h5GnfO5S2dt5JUY1ScKvszBzleOFW/1/ZsjhZOoIoKNzftQV/B8P4MSk4ZD4ep6ggL5jQCSXQpwOH8UxzIPUJUaaBR3sg/vbCf56cniMgJkloTm9qacQWXvelDPJc5yq0brmO0PEJY0Xlw7ptMVEaQBJG+0EbwSsyYDp8//gMiapieaBNfHH6QU4UZbm/dgS453D/5DapumbZAkpgSJGcvsD/9IAmlCVEI8FTqaaaNmXq+u4IdzFePMFT4JiE5iCDUvC2z1iBe0WVT/BcJyJdna/kjgS/UfldyP6y44nK14Hkezc3N/N3f/R2SJLF7926mpqb41Kc+tSpx+exnP1v//3Q6zSc/+UnuuusubrjhBqDm3PPAAw8s0Ya7FLzygpi8QuD5NlnzCDFtE45vMlV+fJG0AJzRafbJWyfImqfqQnPnwrBKTGb21UnLGfiez/z8KGnj5JL7bDdNyTxKSOnHsPbi+1UUdScOGpabZaL88OLTfWLqAJIQ48j0c7QnOogGEpieyXhlhJCkU7RTBEWRTOURotouZkoWTaEkggi2l2W69NjZ/Cy+S9EpkDP343gra4a8GCyvxEjxUc4PWerjM1HeS9lZavxlemVGy/vxFl2hG7QePDvMc8MnMb0Slre03A6Pz1KoLD12LmYqeUZKC8uOe/gczE5QdmqiZGPlWfJ2iRY9wYHcybq2jQ+UnCqu7zJemSFt5lZ8TqlqcWh4uv6Wm3pbcLDYlz5M1TWXvL2Hx2BxhpHyGN4KdWQ1LJijhOQ4U9WFOmmp5dHDcIvYns2JwhBVZ2l5pIwyR7Nz5ycHwAsL0xSssyJvVcfiUG4c06vpfnQHm9k3O0XZtrB9l5Jt1CXFc6bBoYU5nEsU8yvbFplqhYdHh+vHtre0IiJwcGaWpkCQp06MLrmnYJjYnseJyXnSxdU1m14qTJazjJVrs8CBSBNj5VlmjQzgU7ANHN+jJ9jCqXSKofx8XbfoDCzX5fmZaSrWpevSVNwqxwsjddJyLiYqc8wbNR2cslNmqDy6YhpD5VFKzurl5nh2fYvoaH4/3aF+stYMsqhxsrAPH+gMrKVkwL6pQXy/pk/VFY+QDOlYnsHj8y+wPb4BH5ecXaDs5BirDC1OACT6Q/2YXpUj6VnmjRwbE11krCInC5P4uGyKtzNaOkHZLeEDRbta17kxvApFJ0fKSi0hLZIg0aonmKzso+QU8fylEgp5a5Sy/eoSqjsj+X8lP2CZQ8pqxKWxsRFJkpibW9pHzM3NLTH9OBdtbW2sW7duybbQxo0bmZ2dxVqljv/yL/9y/ffkk0/yiU98gn/8x3+sC9D94z/+I5/4xCcue2HiJ8RlFfi+i+2VkEQNHwfLXVk4y/ctTDdXH/zOhec5GFZxxftcx8V0CjVtljPHfAMfG1FQ8LzF5wkBfHxsr4Tnnx10ZFEHQaBQyaFIKqIk4vkulmcgULOPEPBwvByiGCRvVgnKGh4e+C6mm1v6HtTsbGw3v2irc+lwfXtZuue+m+2Wl11fdc+WjyyqeJ5IvloBfNzz8lE2zAsOnAW7umqU95Jt4vi1Pf+CXUYWZHx8SvbSDr62V18jW2W3uvK7uB4Vo9ZgBWqaGz4+uTPf+ryVRNtzKNnlFevISvB9j6pbRBJkiisoB9c6bJ+Ka9S+5zmo2PaqZVRcLIP6e/ge5XOIjyxIZM2z71wri7N5LpjGJasQe76P43lkjbPpBmUF03HxffB8yJWXki930TbDcb0fiXhg3j6bV02UKNhn622tbH1kUSFn1MQWV/quRcvCuQylYtf3qLgrk/MzxPrMdVVn5fpZXSTfq8HzvbpAnOHWwoA4fq0+V5wC4KOIGmXLwvZqdcD1XVRZQhJEHN8lZ5cISTq25+D5HmWnWM+jIsqIgoDr+WTNMr7vo0sK+XPaWkCSKTnF+hzH9d0l7cb1HCrnkS9ZkBHwMdzCojDd+eXrY3kr97c/QQ2qqrJ7924efPDB+jHP83jwwQfrqyHn46abbmJwcBDvnLZ/6tQp2traUC/C6P+BBx7g7rvvXnb87rvv5oc//OFlvMVPiMuqEASFkNKN6WaQBI2YurLHkyRGiKm9K+6rKnKARKR7xfvUgEZM60E8xy5GEePIYhzbK6DIi3oH7jySALrcQFCq7UvWbGEWEHyPzoY+CtUclmWiiCoROYaHgCxquEgElH4sZ57OSIxUOYuEhCjoxLT+pe+LgCqqBJVuRPHyluYVMUjivHTPICQ3E5Qbz7s+QFI96/5ecXLIskt3QxMCEqq4dKm7NRFFV1bf3WwLxuoxX1Y6py26aHYGm7BcC9fz6Agu3b7RxFoEYE1UaVDjK6alKRItyZqtjQ/kSrWotgORzpq5z3l7u2FZpyPYdkHBv3MhCCJJtQPDLdOutyCe10wVUUNApFlrQBGW2pwk9SCxVTzCOkJR9HO2iTRJpj1wduun5FYZiJ3dygtI6hJdjb5YclVDz9UgiyJBRWFN8uzy/Vy5jK7IBBQZBOhrWbr9pMkykigSDWokQi9/UL3OYKJuOJu3DbqCzXWROF2SEREpORX6E0l0UVnRiLszWtN0uVSookKTtvJ2nCoqNC+e0ySVRm1lrZcGLYl2gfhekigjCzKiINGktVF1K+hSCAFo0rsQECg6GRJBnYQeQkBEEVXyVQPDddBFjYFQOzNGCl3UUESZpNqMIqgIgOVZVJwqsigwEGtGFEQyZpGOQHKxfQqkzAptga56U1GlpeWoS0Ea1OSS+m15FpbnkVC7UAR1WblLgkZQvvTtuR8p/Kvwu0T81m/9Fp/73Of4+7//e44fP8773/9+yuUyv/IrvwLAL/3SL/Gxj32sfv373/9+MpkMv/mbv8mpU6f493//d/70T/+UD37wgxf1vIaGhhWNfu+///7LllC5LOKSy+XYt28f+/btI7dolPYfDaIgEVUHKFkTCMi0hW4iqp4ZlAUQRARUOkK3E1F7VkxD1wN0JHaRPI+8qLrCQNceotrS44qUJKHfRMk6garuRJHXYFhPIfklZCHCmthbqX0ygbIzQ8UfYXPrNViuS7Y0j4TMhuhW0laW9sB6MnaJptCbyBvPkgxYyLKMabsoUpLuyD0I1DqRM0qYCaWZZOC1yGL8sspMFcMMRO9Al5beL4saa6J3LSMumhRkIHwt0UWNhrw9iyVNc/um7cTURmThbOcrSyLXr+smFlp9375Ji3BtQ++y4zElwDUNvYQW9TLa9AbWR3sYrcyyM7GR2KI+iirKROQAEiJb4mtXtG8BCAc0rl3XXbe1OXhqBsmX2J7YSG+4Y4l2qCbK3Ni0jTb90jrUuNoG+CTUENviW8+Wg6CiS2EatAQbov2o0lLi0qAHubG1Z5lAakhWuKG1ZwmpUUSZzbEu2hbJy+niNJubmlmfaCIs6wRktT6wbGpoZiCRvKAnzkqIqhoNgSBvXr+x7pV0ZGGOnFnljRs3MJbLcuvmASKB2rcRRZFEMIAiiuxZ103iZbZvAWgJRLmmodamj+amSagxrmlYhyJIxNQgkihwujRBZzTCa7rWLFO+bY9E2NnavkRX6GIRlHW2x9cQWcG7b1tsDUmtJrEQlkNsiW1couALte2ULbGNdc+ilSAJEpqkIyCxKbaT6eoETVoPVafIjvitSILMdHUIRalw97pdiIJESI4wV6wyky0iCyqva72eE4URTM+lVW9FFgPsTt60SCZ8ThSPo0kxBmIxdjet5WBqBEVQeF3rdkRB5nBugma9m+7gQK1clWCdiDRrXYTkOHE1wYboWW9TH5/xyiydoetp0NqXEZfWwK5lfcwrHT7CFf8uFW9729v49Kc/zR/+4R+yY8cODhw4wPe+9726we74+DgzM+fYFXV18cADD/Dcc8+xbds27r33Xn7zN39zRdfplfDHf/zHfPSjH+Wee+7hk5/8JJ/85Ce55557+N3f/d3LVrm/JHfo0dFRPvjBD/LAAw/UDWsFQeDuu+/mL//yL+nt7b2sTLyUuBLXNt/3qDjTWG6ekNxOwR5novQgBWuEoNxKQt9Ca/Bawkr7qmmUCiXyxhgjqSfIFScJhxO0N6+jPbabsLrce8d2sxTMFxDRUEWwzKdQpH485XpEUWKhuo+p8qOYTpa24A30Bt/KfGWWsewQ/Y2bSIaSzBizyIKP4aZJKCEE9xT4eWz/BubLCj3JZgShRNF8gbnywzhelqTWR0voNsLadmTx8qOwGk6elHmKkeLDZMxhwkoz3eGb6Qhes8Qw9+z1BRbMMU4XnmHBHKFVX8+60OsZmapyeGyOimmRDIfY1NXCxq5mwvqFjc5SRol96VGO5qaoOBadoQQ3NA7QF2lEO2eQXzByHM4N06THMD2Dw/nT5Kw8MSXC+mgfm6NriKurl4NpOZyeSvHcyXHmc0XecP0muttizJpzPJN6gZOFESJKgB3JDexObKVJa7xkIbyCvUDemsPxdY4WTjFcmkAWNLpCXWyPb6Rdb17RzTpjVNi/MMXzC1MULZO2UIQbWnvYmGgmcJ5XkOt7TJRTPJceZLaa47qGtegE2T8zw2g+hyQKrE82cXNHDx2R6GWJ+ZUtk6xh8sLsNA8Mn2a+XObW7j5+es06xtI5YrpOOldm78lxShWTroY4m7pb2NbXRvxHsOICsFAt8mx6lKPZaTbEWlkXa+B0aZzJyhyKKNOqJ7mhcQuGCU9NTnA6lUYSoTMaY09nF73xxAW9ei4Ey7UZq8xyMHeavF0iJAXoCrawKda7hExXnCqDpRFOFgcp2EWiSoSN0bUMhHsJvIjuSW17pwy4pMzZRSkGB1UKsGBMMlw6QlRJsiZwC8dnSxyeXqBs2WxqaeaO9WuQJJeh8jhpM8fOxAYqTgkPm9HyccbLg4TkEDc03EZUCTFXsXh8ZggJie3NfQyWphkpz3Jtwxq6Q0EmK8fJ2dMogkaL3kt/aDNRNYkoiOStPCeKpxgpj+D5Hu2BdnbGt2B7c8xV91N1MkiCTkLrpyWw86oY5r6c7tD/6dtfvGJ36K+88d0vaV6vBvbu3ctf/MVfcPz4caBmI3PvvffWBWUvFRdNXCYmJrj22mtRFIUPfOADbNxYk5o/duwYf/M3f4PjODz33HMvqnz7cuNqVELHq+J5DpKo4/vWYlBFA0kI1Fj/oqeRIEYQhdrAWi5UqBSrqJpCMBrAF208bFzfRBI1JBQQPBzPxMdDFnTUxbDtnu/geAUEZEQ8wAQieMiAje1XkAQFAR98CZEgVauMi4sq6yiSguFVF2eCbk2zwrcQBR3DFbBcH12WQXCAMvgOshhAFqOLsZnOwnZdiraJJAjIolA35AzKGvoiEXB9B8st4eOhCmEEAUyvhOubiCjocgxJWN2N1vNsbN/C8R1cz0YWVFQxiGG72Lg4vo0sioSVIJ7nU7IsfHxCilqXsbddl1LVQlVEEGuB60Rqcuqu51NxbHx8IqqOIopUHRtR8BdjQwm1Z/sOqigTlAMoF9BdMdwyjmejiBqmCablIkki0aCOh0fVNbA8E1EQCctBNOks2fI8j6JpYTs1m4GIrq1KBlzPwvEtHM8BREzfAwR0USMoX1jTxvZcClbNJkiXZKLq6s8BMBwDw6vgAyEphO0JlG0LRZQIyiIORk0TRNRXDBPxYvA8j4pjU7ZtHNdDl2USuo7h1HRcVElCFiRMx8FyXTRZJhrQLkrp1fU8imbNoFeTJCJa7V0Nx6ZkWSAIhBW1VueBqmNTdWxUScLxXVzfQxEkwkot9EbNvsatXedaaLKCKoqIoliLiuw7KIJMRAkiImK6DkXbxPZcNEkmrgZeNN+WW8HyytTsSYJoUnjZNYZrUnVsLM8lIKmokoDtmTX9J0HGxwbfx/AcXHwUQa152iBQckxM10EWRCKLIQWqtk3ZshAEgbCqoskytmvh4WN5xqINiYCHh+PXdKEUIYDtClQsG8/3CaoKUU3D9X083z3bbgQVD69m2+KX8X0bSdDQpBie51B1LUpOrX3psoLhWXi+T1jSkQQfFxtZkMHzsS0HX6iCCJoSw/EEbL8W5NH1fERBQhclfKr4fgUBEVGMIV3mFvf5eFmJy7euAnG555VPXK42Lnot84/+6I9Yv349DzzwALp+tuN685vfzIc//GHuvvtu/uiP/oj/9//+30uS0R8lZDGAL/jYbg7TnSVjPIEmthBSGrGt53GdSUQxiKZsQVPvZnoww95v7+fY0ye5+ef3sOsNa/G1EnPGPiR0mgObEASJjHmKucpzOL5JXF1LW+h64mo/sqijSmdXJzzfw3Bz5IxDpKrP0Ra6FVFwcL0KcmkLx546yoGHjyDJMonmGFtv2UT/tl4C4bPfyXQNJqpjDJaOY7kmYTlKb2iA1kA3+iqD4EK1xL75SSRBoCUU5Eh+jIlKCl2S6Y+0cFPTBnTJYrZyhHnjJI5vEVM76AzuJKZ2XnxsEUGibOcZKR+mZGfoCV4Lboy8U+HZ9ClGinPc3LqeNcFuDs8tcCqVxvU9BhJJbuvrR0XiwMg0iiwSiis8nxknpKnc2NpP3jJ5dm6SZ2bHWR9v5LbOAYZKKSqOyYZYG0O5NEfScwgIrIs1srO5nd7oyh2g41ksmBOMlg+TtxZQpQDdwU10hNcSqC/ti6hSGFg+EJUMk2PT8+wfnSJbrtIQDnJtfyfrW5sIaeo5zzGw3DxFe5bJ8l7KTgpdTtCsb6ElsJnAi5AWqImbNegX1yHmrQwj5eNMVUdwfZsmrZ2B8FYaAy2UnDynSseZqgzh+A6NWhv94a00aBdvswO1LaCwqtVF8EzH4XgqxdMTExRNkzv6+jFNhwPjM8wXSoQ1le1dbWzvbiMeXH1AKhgGh2bn2D81Tb5q0BIO8YYN6/EEn73TE5zK1OKnrU82cUdvPwXH5FB6mu5InDkzxwuZMUzPpi0Q4/qGAfpDzZiOy4nZBR4+OcyNA13EEyK6JlJ2yxzOnSZrF2jXG7m1eQ+eL3EiO8/js8PMVkp0hmK8tn0N25NtS2IgnYHneRSdGSbKzzBZfhbPd2kLbqMnfDMxpRtJlHE8h4JdZqKc5fH5U3QGE6yLJZiqDtKkJukKdYJfJGU8S8Y4hCaGaNR3ktR3Y7gKg4UUzy6MMlstEFUDvLFzC4Ir8dzEJKfTaUQE1jc3sqeri/ZITXlZkzRKdoGR8ilGy4Mk1UY6tc0cS81xeGEe8OmOJtje1EpAacDFouKUmKpOUrDzdAS6CEgyFXuY+eqz2F6eZn0DneE7KNuzpIwDuJ6BKsVJaOtJaBtQpNp2lulmEH0Ro2hhOnkWSs8ym36O7vY70aJbcHwfD7s+GWnXg/juPJXqIzjuKWQxQlDbQ0C7GVleffX7FYnLtFNZcv+PIS6auHzve9/jvvvuW0JaziAQCPD/Z+/Poyw5yjtv/BO53rx77WtX7/si9aYdCUkgdoNZbAw2HvudGbDBg/F4POPhvHjemcP4Z8/Pv3nNMbbHYwawMcbGgNkkkAQIhHa1Wr0v1d3V1V37dvebe+Tvj7x9q25XdavVEpjte04dqTPzRkZGRkY+GfE8n+e//bf/xjvf+c6XtXI/SvLDedxwkgulTyIjl6HsO5gr/TGufwxD6cTSV6FrG5gZXeBTH/l7jj56ii03b2ToxjbqwTTDhS/iKFPc2PlreLLArHOMEwt/i6oYCBQK7kkW3FNsa/tlOhPbEUvWb92wymz9cY4u/DlD6dfiyznOFD7OjtSf88RXD/LJj3yaMAhRhIkiDB7+u+/z/v/317jhldtRVIUwCjlfO8Pjcw/HjJGGztZOcHPHK9mc2bkMjlZ0bb4+epKR8gJvWruZz55/jAPzI5iqxlA6z4JfZWe+m9HqU0zazzd/V/YnWHBHuKH9HbQZ15b5s+zP8czC/Sx4k2zP3s1sGaRS4JNnH2a0usBAsp282sZfH3qKwzPT9CTy6EJjslJle0cPx85NMVko84rda/jfx7/PcHGWj9zyemq+zz+dOcw/nDlMt5XiLeu38/nzz/Po5Fn+277X89CFYf7hzCEMRWVVqo3z5QIHZsf59e372dLWtayes+4Fnpz/Kk7YiPTxYdoZoZa9nS3ZW6+KJfeCkAPnx/nCs0ebUT9jhRInJ2d4x027uGntKjQ1/tqv+9PUwzkOzP01Zf9SklDBRP0Z1mVezabcG0ioL8/XVS0oc7D4KCO1E81t8940RW+e2zpfw5HSY5yrHm3uW/CmmHYucnvXm+g0rx1UeLmG5+f55MGDlByHu9euZbpU5WPfehwVQVc6jSYEp6ZmKdkO927b0GLYXZLt+zw+epF/Pnac8FKWckUwVavylTMnGK+Wmx4AmlDY2NnOP48e4dae1ZypTvG/Tn0XVwZkDZOBZI6hZAf9RjtHxqf5v7/yEHdtXAumy8G5cda1dfLZC1+jHtkIYP3gq6n6PkcXxvnTo9+jHsTP1cH5izw3N8Z7t97GXf3rWpYnAerhHM8v/B3D5W82t03YB5msH+b2ng+SM1ZR8itcqC3wZyceJKUluKVrkK+OfwkZSX5x6M244TznSp9lsv59jMb4MescZGd7ignH5+/PPUctiKOE1qkac/U6Xzh8nNlKvekTc65Q4GKxzC/dsJPudBo7rHO4+DRHywfRhM6G1G7uHznBl88cosPsIaEmOLUwz7NT4/zHm2/HF1XG7PN8e/pB3tj/89SCClX/HM/N/m8UIdGETndiPzP2s4yUv4amXEo+C3P286zLvZnu5H5k5OEFCyhOjmqtyOjcFzh1/vP0d9+Gq3RSd0oUw1kSWp4jxbPc270JKS8yVvwfOP5xdEXDUHRs7yC5YIxs+ldQ1eXP7s/0k6VrXoSdm5u7qg/LunXrrouA9+OgKAoJZImaf46C8wRt1i143lEc73BzXyArqOzn2OOnOPLoUSBiz6u3o2U8asEkZ2ceoSexB/AIo4Czpa8QErMUIiJCWaceTDNZfwZPLobAhlGIG84zWvk6ENFp7WKu/gjd1p1U5yT3f+LrhEG8fCMjjwhJabbM9/7pCUrzcWhgPahyqnKkxWi5VPap8hHqwfIQwql6hSenR9nR3suEs8CB+REA3DCg6DlszPRhB/OcLD+5LCyxFswxWT9MIK/MXFmsQ8CEPcyCN4mlZjDpoR54nChf4HRpjCiS7OlYz2y1zkMjpyl7dZwwvo6tXV1UbZdvHDnN5oEuhiszHJ2f5IauQdoTKSZqZf753DHCKGR/9yqCKOQr54+yva0PJwj56vnjAHgypOjZhJFk3qnz5OQoNb+VT+CENUaqRxaNliU6Wz1ILShe9TqLdZvHhkeXhSq7QcgTZy5QshuAQlnBk1Wm7ENLjBaAKAYH1p6h+jKyKhbcGc7XTi3bntZzzHmTjCwxWi6p5M8xWjuBL188pwTifEpPXLxIyXHQFIUNbe187/QI87U6C7aNF8ahvFEEj58ZZaG2MpOkYNs8ev5802gB2N7TzZnCHN+/0NrW27t6eH5ugpl6lfaExYPjx5rLnmXPRRUaq81ObD/gK4dOULFdbts4yMHCMOvzvTw5f5hpuxAvl6gJhpKDVH2Pr1w4QtVfxBREwIxT5dsTwxTc1v4vpaTgnWe4/OCya4mNl0PYoU3Fr/HU7DnG6gvs61zLnDvO6cow23JbifCpBxeZqD2KjMIGGygilOBHHXx/6ijVJSHu23J9nJ6d5/Hx8/iyNUT66PQ05xbiayr5BU5VYkBgv7UK21P45vmjBFFALag0WTUJTaMa1HBDh+cKz7A2tT5ealMCzpYexI/qBFGApXWSNno5X/4mdjCFXDL2SHym6k8RSBs3nMNUczh1B9ub5OzFrwHQ230Hc4GHK2uA4GjpLOvTvWjUcdznqHlHiYgIZNjAOBSpu4/jB+dX7Cs/00+Wrtlw6evr4/jx41fcf/To0SsCbH7cJSOPUNaoecMAJNQuXH/xCzUiJJR1fDvJyJERaDAUutfmCSOHgnuOCImlduCFJXxZox5MEfvJLw4mYWRT8cdbXvihDPBlnbI3gqHkUYRGxTtNylhDZb7G2JnxJTWNoDHAnDsyilONy/Gky4K3HMwGsODN4kl32faRygK+lORNk7OV1helHfgNyuUYtaC+IgG57E812RBXky89Fry4/KSawQ9AV1XONc4pCelIZDi9MNdkZsSQt4i2RILxhTILtTrZtMmZUgy460lm8KKA8+VC88uzy0pzsVrADn26rDTj1TLVJcaJE8Zr+ABj1RJO0GrkBdKl6K8MdnNlnWpwdaPd9nwmiuUV940XSk1eSdDwlyq455cdJyOfIHIoeReueq5rlYwkM+54C0vokpJqmml7dBlL55IK3gzBdRouThAwXo7bImOaKAjOzcbtF0iJGyyes2y7zF0BQldxPWaqrWygXCLB6fl5nCBoYc7kEibDpVnSDZbRuctAhZaqYwodx/c5Oj5F0tDJJHVm7RKGqnG+OkkgY5+ovJ5BEzr1wOd0cWYZLM6TAeerBepBa/sEkcu8M8yV5vcX3HN4oUcQwUg17svdiTQT9hgA7UaOQNpUvLPNe3YJwpbQOnClwkh1rAVqmdUTDM/P4clwmeECcHZhgVBKFtxZ/MbzmtIyTFQrTYPMl16zzPaEhSTEky5T9gRZPUc1qKIiKbgjDTaPbET3RBS9MwTSWdbHav4EURTiBNMoQsWtu1TdUYIGO8m0enFDj3l/lrSWZaw+SZthoQiJ458kHjvjT6bYcJWEUQ3fHyaKfvjsn+uXeBn+fvp0zYbLW97yFn73d3+X2dnlL8CZmRn+43/8jy1Jm36SJBp5iDQlnp6XkXtZ5meBEBqKJknn01zqTG4tQAgVszGtHzYc1tTGks6l3y6WoqIrqRYmjECgCBVdSTcBdLqSI5Q2pmWQSF0WZdNwwMy0pVG1xTxIprKyX4ShJJY55ALNsNlASrJ66xKIpij4MiShZJaFY16SrliIa+heilCavJYwClCUGFqWbpxTIPBlQM5MNKeaL53Tl5JMw4EzCGUzo7XXcErMm0t9fIKW/ZnLEg4uvY6UbixzrhSNxHErKWbgXN0xUFUULGNlB+WUaaA28vEIVBBRw09mhTMJtenE/VKlCAVLESxppAAA9q1JREFUXTlsVkYhSS3DlQZGU0m0LGe+GKmK0uJULYQgcylaTIiWaBxFCJJXaDdTVZexUnwpyZomqiJanJEDGfePIJIIBGm99XmQkSREogqFtpSFF4REkWgu9aQbTq8C8CIfiGLWjJFY1kKKEKQ1Y1lUkSq05liwkgw1jSKUxu/j+rlh2LxHvgwQQkNXlpRxKWlp5KEKyOhJlt6zQIZkzQQKYsVnNW0YKEIs8dGCIIqfj0ulxGHH8b/8MEREotF3koRRgC50okb9EXGkaRh5gEBXMihCbeEBAWhKXM/4v6DqGsaStpHSRRUqSSVFGAUkVYswig2VS2PvYv3i/1PQUJQsl/K//VjoX4Dj8pOgax55/uAP/gDHcVi/fj2/+Zu/ycc+9jH+9E//lPe9731s2LAB27b5yEc+8oOs67+YFGGgK21kzRvQlTZK7jEs81aEsIg5KCaakkO3zrPnVTeQzMQD/vHvn0MJUrSZG8gkelnwTsWQOWHSm7wZUFAanvwIBV3J0pXYgaEsvrR01cBUO+hN3oony1T9STqTdzBZe4Bsd5JbXncblx5hITQECkLA/tfuJtsZv+CSapqh5MpguKHkOlIrMB/WZtoZSGU5W15gW25V03gRQpA1ElyozZHR+xmw1i7jKQgUeq1tGMoLO4caSoJVyS2oQqfkz6KpDkEo2dOxAVPVUVA5URxja2cXfZkshqKR1mIj5uzCAl3ZFLuH+rgwXWJPxxCWpnN4bgI/DBnK5NnR0QsIjsxP0Z3IsDXfw5GFCbqtFDvbF2cIs3oCTVERwO6ufjJ6q2GTUFMMWJtWvIbuxGrS+sowsEvKJRPsGOhZcd/2gR5yVqLRHhkUVPqtPSiXuaDpSoqM3k/bFbhB16Nea4i0llu2fc6bpN9aR95Y7i+goDCU2ox5nVEcGcNgd1/c9lXPY96pc+v6mGmU1vUWY2RjbyedmZWNq7yVYHtva5uOLBTY29dPXzrdYjiMFArc1D1EJXCQEm7pXHweNEWhEjjM+mWShs6rtm7EC0OOXZxjR/saLpbn2d+5DUNTEUIw5xaZ9xZI6wav7Nu4rP+nNZO9navIG63toyo6PdYO0vpy3yBNJBlM7ielpbBUjZu61qEKhWPFCdanN2GpFsOVcxhKkqyxBUvrRiAaDtIiXnIRZfZ2bmv58DlfW2DvQD9dVrolGzVAxjTY1RsnEm3Xu+hq+CxNO+N0Jk22dQwAAktNNR2xR8tFDNXEVBJsyW7nfO0cKS2JF6kMpW5t1qnkjhJIh4HU7ehKtsGMWlS7uRVVGCTUXpygQiJpkjKGaM9tAaC48Bw53SKt5Sh4c9zYtoXj5XEkCZKJ21CERTx+ChQEikiga4MYxo7rNqh/ph+cnn32WX7v936Pd77znbz1rW9t+bseXfMdbmtr46mnnuLd7343n/vc5/jt3/5tPvShD/GP//iPvOtd7+LJJ5+kvf3qg/ePszQ1T0LrZ03+N6n75wkw6cp+iIS+mYQ2gCoSOO6zDG7s49c/+m761nfz3MOHmTlVI6l1cdPq9+FEBephEV1JsSn3NgaSt6NgoAidjL6WofQ99Cb3oiqtL01DzTGU/TkGUnczWfs+KWML7Ym9qLkJ3vS+13Hrm25FN+JZnFQuxRt/4z5ufdM+Esm4HFNNsC23mw3prWiNsGRVqKxJbWRnft+KX92dVoq3rd9J1ffwQ/i1DXexJt1Jj5UmoxvUQ5ek1sYN7W8kpy8mmDSVNBuz99Bhrr/mAaTd6OeG/D1YapYp7zj92QR9Zju/tuk+uqwcxwoXsanzwX13srd7TTNU2Q0DskmTd9yyi6rtYgQ6/3r77ahCcKo4RVcixW/uuI393as4V55ntFLgvVtvZ12ug2m3zHu27GF3Zz89iQwZ3SSjG7x2zWZu6OxfNuOiCo2h1DY2Z25uzhAJBN3manbk7iT5ArMgmYTJvds2cMOqvuYL1dBU9q0d5K7Na0k2nE9VxSCp9dBmrmVH+y9hqe3EX64peqzdbM69CUt9+ZLIZbU29rffTZuxSBBOqmkGrfXk9A72tN1Nu7FoHFhqmhva7qQ7MXRdTBeIlwL39g/wqvXrSek6z46Ps22gh3ffdCNr29vQVRVFCDb1dPKGXVvIJ1eeLcwlErx200Z29PagNuoyPDdPl5XifXtupje1+AEwUS2zPtvBO9fv5lx5gVf2buWunk1kdJP+ZJaUblCNHCxd475tG/jlW3bz/eHzdCld6IrKpsxq3jx4J2nNwlJNzlTPk9UNXj+0nTes2kZCjT9A2s0krx3cyuuHti4zfiEmSN/W9QG6ElsWr0Nfxe09v0XeWI0iFLJahq25ft697hUMlyep+oK3DL6FalCmHrok1G52tr+PdjPOoKwpOl2JzSS1JLd3b+KOnvVNQvKJ0hQdKYsP3HQrvenF9uhJp3n7jh30ZuJtaT3L/vY76DH7KftF6tE0v7RlLzf1bCapphCC2FAbWkfeyJAzctyY30uX2U3RL6IraVZlXsWGzL2YSgpVgYJ7gtWZ+xjK3BenKAEEGp2JG+hP34mupjDUdoQQJHMWKauXnev/LZ1tO7kw+T0yUY2klqbPWkVvop28nqUSaOjaOgbyv4OlDWAoOpqSIm3eRDb1HlTlx8xd4adgxuVzn/sct912GydOnOBLX/oSvu9z7Ngxvv3tb5PLLf9ouha9KADdJUVR1Fwy6urquu4B7IehlzMmP5QuMnLwZQkRaehqCkHUSLEhUBQVIUzsakBhqsjE2SmSGYvVu/rQrAhXFnGCIimtC12xGgyXOO9Q7HXfmIFZoT3jxGhlZFRHRUMRBoEsoZLDLiqomoZuqCiaiiIUdHP59HotqFL2C1SCMmktQ1ZvI6WmEUIQRXKZoeHLkKrn4soAQ1GxpcusEydg7LXayBspdCVmUgTSRRKgK0l0kUBXE83kjZeXKxvZwS459QohCKRHPSxR8mdJa20oMk8QScp+jXm/TKeZocdsp+77uGGILgR5K6aYRo18NwuVOumUga8EzLrV+IWkmZRch6rvk9Q0Oq0UvgzxZICh6DhhwGy9iqGq9CYzJDUdU7syc8YJ69SCAmV/HlNNktU6SGrZFVM+SClRGkaKlBFCQLHuMFOpUqjZdKZTdGaSK4b7+qFNGHk4skjNn0VTTNJaLwktvyITR0ZyRfS8jCRENOsRNdp96bGBDKgFJYr+HEEU0KZ3kdayGGqCQPrUwwpFb5Yg8skbXaTU3HWxXC5XxXWZrddZqNdYnW9DFwplO54RaUsnsDQNTVVBxMsVK12jjCKKts1UpUogQ1bn27B0jTCKWHBs5ut1cokEeTNBUtdjB2y3jhsGGKpCJbApezbtZpquRIa8kcT1fcqOx0yliuMHrOrMIJQQSZxHqOiXyepp+hPdRAjKnsOMXWXBrdOZSNGfzNJmJpc9x5faPpQudrhA2Z9ERj5ZY4Ck2oGhpuJ8MCLO9FwPPApejShS6EwkESIglAGGYiJEiC+LuME0qjBIG2tQRQpNMSh7DrNOhVmnStaw6LeymIrOQt1mvFJGFYL+TJbOVLJhRMfPaCgDqmGVojePL126zUHcQInzShGRNUySuo7amJl0Qgdb1qn6FVJaioRqQWTjhfMEskZaX42ptuHLCnY4hxsWMNU2LK0Ts0HXjiJJhCQMbQQKni3xoyJ2MI2ma5jmGkJUfOkiEARRRFpTMUSIlPOE4QSKsNC1VahqF0K8cO6cF9IPlePyz3/z0jkub3nPjzTHZdeuXbz3ve/l/e9/P5lMhkOHDrF27Vre+9730tfXd1303OsyXC4piiK+853vYNs2t912G21tLy7l/Q9DL2cnlNIhkAVUYYK08d0E81MVzh4cxrU91u7cSM/qHjLtL2xFRlEEUQUnDPAih7I/RdEbxVKzdCW2kNI60S6beYlkEcIJouA0gjQYu0B0EER13LBA1T9P1Z/A0nrJm5uwtC6Uq4DfAMJwjjAcxffPoiht6PpWVLUbIQzc0GfWqXCqPE0tcNmRH6DXypLUFusVSI9qsMCMM4Ira3Qn1tOmt4Ocx/GPEUUuCX07mrYKO4yntQMZAWGcibkWUJksM3Zkgo6edtbvGCKdT2FYiwNQKCWzbiWGk7kSU9VIWQoFf4FztVE0RWNLZgOdRjuGunzg8sKAOa/MheosXYkcApVzlVnqoceO3AA9yTRlv8pI7SKe9FmTGqDTbCe9Anb9Wu5rYbrExJlJVF0l1dfGXLVOKpVATWjMVCuoqkp3Jk17MkEmkViG0felTyALOP4YoSxiap2Yaj+akkFZ0idkJCn7ZaacaebcefJGjn6rj7yepxrUmXfnGKmdI6km2ZTZSi3wOFOdxJU+G9J99FntyAim7TLj9SJtZpJ+K0daTzThgm5oUw/LVIMSURSR1vMk1TSJK/jGXI9KNZuJ+TKO6zPYm8eLQoJI4uOgqxGqElD2Z6kGBdqNbrrMATJ6PnbqDALm7DqaECiKwkytypGZaaIo4tVrNxAguVAtcqFapCuRYmO+k24rvcz/5Gqq+CVqQQ07rJPWsqS0NBEKc26Zc9UpOowMa1LdWJq5IrjQlz5lv8iEPYod1ulJDNBrDmAu4fHYQZ2CX+Bc7Sy+9FibXE+v1UcYSYpeBUWECOqUvPNARK+1naTWiSZ0Cl6VcXuesfoceSPFulQvHWYWTbmyr0cga3jhDIG0CTEp+wtUg3nyxirajVWYDX+bsl9l1lkAFKLQ4GK5wrxtsyqTY12unY5kEpB4YR1fFrCDSUwli+YPIiINGdnIcJYomkczFHSjD1XrxJVlKt5ZXFkgpa8io21AOgoL02WqxRqmZZDKJzESBtn2xdlMO6hR8Oep+CXyejtZvQ1d0dGUq49zL0Y/M1xeXqVSKY4dO8aaNWvo6OjgkUceYefOnZw4cYJ77rmnJb3AteqaOS7FYpEPfvCDPPfcc9xyyy38yZ/8Ca9//et5/PHHAeju7ubBBx9k165dL7oSPw6SkYvtH0ZX+oiCM/jBGo49dpSHP/lXVBbmQSR4zMix57Vv5M533Eu++wWWzeQslcCmHtqcKn2d4fK3kUg0JUOftZFdbW+lx9rRNF6icJ7IuR+8J4A4gzFRhdB8BVV/nNOFTzJjPw5EKCJBp3UHG/O/Qt7c1JLIcanCYJy6/U943nNcmnNUlF5SqV8iUrdzuDDOly8+T9GLozq+NXmCfR2ree3ATroSGQLpM2mf5nDxYephEVXo5PV2ysEh6vb9KMRRTbb6PLr1r/GjLCLSEErEifIIhq1y/Jsn+ed/fJDQDeizOhhs7+FVv/gK9r7qBpJZi1BKzlZmGasVUDyd8YUSd29exSMzz/Lliw8TioCEptFh5nhz/2vY3baLpLY4g+HLgFPlcb46foDbu7YwXJ7h02cfox769Cfa6E6kuWCP8szC8/jEURVPLTzP7vw2bunYTVZfyUn2ypoameHBTz+CmTTYdO9OPvvZ77JubTfrdwzw6YcPMloo0duRIZUw2NzTydtu3M7q9kWD35c+djDJxdLfM159mIgAAbQn1rIh/3+RNfc1jZcZZ4ZH5x5jzF6MLNvXtocd2R08vfAM35j4FkEU8s7V7+DxuaN8e/oQQhioQuVQ4TxvX3UHhwvjPDp9Gq8RcZJUDV4zsINbutahCcnZ6mEOFx/Da0S6aUJnS3YfW7P7sbQX1zYrab5c48FnTzNbrHDPTZuYqFYYLRfpyAomnSnWZXM8Ovt1Jp04hUKn2c6q5Fr2tb+KvNbHkdlpxssldvf18/3z5/jYM49T9T3etHELA/ks94+eZKJebiaHHEzn+MUNu9ic77omKu+CN89T89/nfO0sERGGYnJX12sZrZX53uzxZmi+rmi8omsbt3duI7PEmT2QPhfqZ3lq/rtUgziSSiBYl97MvrbbyRnt2EGdY+WjfG3yn5vH9Jr9/MKqX2G4coH+ZDtV/wwH5r+CH9l0GG3k9e9xQ9vPI5Qhvjl5kJPlseaqQbeZ440DN7Ex07+iQ24gayzYj+OF86jqOg4VPsOscwJD7cRQO2kzV7M7/2ZCkeTRmWfj2RTZzv85+gjnSwU6zDxpLcn2zm7+1Y49JPQ6djDG2dJn8GWVLebvARLfLVKrPMGZQx/DtWdRVJVdd/wuZvcGLtYewgnnATDVLrYkf5vjT5zib/7g00ycmQBA1VXuftdreMsHXk/P6m5qQZUjxWc4Vn6+Ge1mKhZ7229lY3r7yzIL+MNWY/L5Jf3+R11tbW1UKjFyY2BggKNHj7Jz506KxSL1+soRgy+ka/7s+N3f/V2eeOIJ3vnOd3LkyBFe+9rXEoYhTzzxBE899RRbt27lwx/+8HVV4sdBQTiP70+h4hKG0xSnXR7+1F9RXpglDkN2CPwqT3/1Sww/N0wYXjmtfCTrBOE8laDOlH2YU+UHkQSAJJBVppxznCo9gBMW4uOjEPzD4H0floRPR/ou3HCBseqDzNiPccn4kJHDvP0Eo5UH8MKVQ3CldHDcR/G8AyxdKJVyCs97lnm3zAPjR5pGC8R5bZ6aG+Fw4SK+DKmHRY6Xv0c9LALQba7FEh4Xi39BPZhoLgWZxi5m3BKmalAPfQp+mZPlC1TPV/jHz3wd23bwZciEPcdCqcR3/v77zFyIlyLLvsNj02fJqkm+fvwEe1f1M+FM8vnRB7BljLT3wpAFr8yXJx5i1p1ruc6iV+OhqcNYqk5as/j7808z5ZQp+zaDqTymGvG5Cw8y4Sw0w7rDKOTZwhHO18YanIxrk111eOabz3Pu0Hk23ryRJw6PcGZkms2b+/nCs0c4OTmL6/nMFav4YcjpmTkeGR6h6i6Go3vSZt5+grHqN4iIB+cImHdGuFj+e3wZX58d2BwqHW4xWgCGkqu5aI/xlfFv4kiPDZn1GEqKz44+zHDlAl4jhHlTtp+R6hxfGj2Au6Sv1kOPB8YPU/EdSv4chwrfbxotAEHkc6z0JLNu63mvR0EoOTIyxZMnRtm2thdbBpyan8XU4em5Iwyk2jhUeJzhyimcMGgs05SYci4yWjvFnF3jm+dOs6Wzi8lqhY898zgFx4EIbl+1mgcvDPPVsyfjFBGNLj5WLfGNC6cpeS/MGPKkx6nKUUZqZ5p9eVVyNRXf4x9GH6bqLz4bvgx4ZPoIF+qtUZfVoMxzhSeaBgnEgbxnqyc5WztFKEMKfoGvT3655ZjNmS2cq40y7c6gCYdn5/8ZV9aQkWTeK2CHNp6MeHLuGCeWGC0AM26J70wfpuS3hopfkhtMMlN7kIS+njOVx5hxjhMR4YazhLJO0RunEixwonSWs9WLdBs9fPnMMYaLs/hRwIJXwo8CRkoFan4JJywwUXuIeed5esxXEtkpvHqAUxvnzJE/x7XjNjETPajWEOdmP03NO9l86w6mXkdhusI//vHnGF+Cdwj9kIf/5gEOPHgI3wuYcsY4UjrQEqLvSpsDC49T8gsveD9/JPVT4ONy55138tBDDwHwjne8gw9+8IP8m3/zb/ilX/ol7r333usq85oNlwceeID//b//Nx/+8If5whe+wJNPPskf/uEfcvPNN7N//37+6I/+iGeeeea6KvHjID8cR9cGkME5VG0t54+epTzfOkjF/ADJ6WcOY1+BPREfWMWRMXtgov78ZTtDfBkvHZW8sebxkX8ZCEzkCIWFH1aYs59adoowqlF0j+A2jIplVYjK+P6VuDwGF2tzzDjLwXQAJ4qT+DKg7M9Q9mea2/NGL65/jECWcMNakzEhlbXUfQ8vlAjgWGmEQaWTI0+eaBp4EREyiij7NWzb5dSzZwn8gIrv4Egf35MU6nXSCZXnC8cJorCx3BaHukZRRMkvM1w918jtE2vGKTNuLzCQ7GDaKXOhNt/cN5Bs40J9grJfoxo4BJcZKWeqozjhcsbNlVQr1Rg5PEpbbx41neDQsQusHepCqoKnT19oGkZ1xycI4nOdmp6l7CyeI5AV5usrP0dF9xy2H4MAa2GdC/Wxlv0JJYGlWhwrHiNoDO6rU6sYqU5R8CoNmFiVKIroTbTz3Pwo5aBOGLUa2U4YUPLqjNXP4EfLrz8iYqw+jNtgblyv6o7HiQvTGLpKez5FJOB8qUjKVJhzyiRUOFU+DlHsyxLnnHIIoxAZhYxXSwghsHSd5yYnYqMFWJXNkzR0npi8QBBJSq7TBKgBnCrMUnRfuO5OWGes3srM6TB6OFG+SCkoLwM6SiJOlC5gB4tttuDNUfBajelLGqufRxIyUjtLJSi17OtPDnGsfJpOM8+MfQpPLtY39vVJIElytHg6/rC5TKO1GQrecliijALK7hEMtY0wUpmyD7fsD2QZBYUQjeHqOXoSnbh+xHMzizBET/q4ocdAOkskPAJZY94+iEAhy05UTcdzPezaCZza4jJAtmMroeIxX3oOGZagYZhnjdVMnp3mzKHh5Y0URRz8ziFsv85oY9brcrnS4aI98qI+Mn6mH57+7M/+rEnV//CHP8zv/M7vMD09zdve9jY+8YlPXFeZ17xUND09zaZNcTjowMAAiUSCVasWke5DQ0MrMl5+UhRFHopIgXRBqHjOSvCt+KEKPI9IXs0UvvT4RQ3ewXLJln0Rlx7yRWmN00nklcqIgsZMzsp1gJVBTUIoeOGVIU6eDIkilsHJ4gR1bqP0xeuPUEGEzWv2ZEASA89bfg5JDJkLvIAoipqDkWwwHBAx4GvFK4oaJM0l5/YbxypCWQbgUhWBJ/3mby9X0KAaX6uiRoI4IxGvt3t+GGP8iZH/ZqQ1z3WpXD8MW88dySv2idi4W2zfyw2OS74yXuTHt1fEyxJey3HxZ5rS4ONcDk9bPEpeET4HL75tVpKMIoJQNpczBJdgYov9J1hiHCwix+L/98OwGU3kLIHWaQ0mjhcGRCzpO826yxba7pUURRHBZW18iSu0tI5L5Udhy/bgKm0YRnEfv9QHl0pBwZc+ilBa2mDpETE51ifCWMaSkUQtAL6leyRBg7EUEV5WdpxyVCGKZJw0UovPc/mzExGhikt0lkvlKBDF/BwZyuZYcElCaPGY1PjoaHRRQMF3rzLe2N4L90fpv+T++C+iSMR/L+X3P+JaGm2sKAr/6T/9p5dc5jXPuEgpUZfwFVRVbfGa/1GOLHo5ZGir8MNpFH0DMphg9fb1GFZrNEjMKhAMbduMmboK40JYGEIACl2JzZftVFCFTkrLk7uU60dYoF3GYYkWUESIpqTJmzuWnULBIKtvbHrvL6uCSKJdXmZDUroMpbtJacvDOQHWpjvRFZWc3kNiCXOm4i9g6tsQGBiK1YwAUaNJNEXFVBSiSLA1u5qJqMDmXetb64QgrSXRVY11u1ajGzopPYEuFExDJakZ+D5szW1oXAMgiAdQIUiqCdakhjCWOOp1WznyepI5t0y/lafNWHSEm3GqrE4OYCo6lmos8wcYtPowlWuPUrDSFoOb+ijNlhF+yOb1vYxPFVAl7Fjdi2i8UBOGHkfLAGs62kkZi+fQlDRtiW0rlp/Su7D0+J5ZSoLeRCvDxA4dgihkU2ZjE0w2686xLt2LqRhNaJgQCgWvwrZ8P0nVXBapowhBRkvSZ629IkSw11qNoazcP65Vlqmztqcd2/Op27Gx351MEYSCtG4RRgprUnEfEcT5hhKqiYqCIlQGMjlsPyCQkht7+zAabTpeKROGEbs6+1CFIG2YLfd2dSbfhBFeTaaaoMdsDa+tBkU2ZgYwFQN9Bcf39enWPtNudF0R8tdt9qEIlTWpteiXRcPMuTOsT6+h6FXpTmxsuQ8CQRTZaCJkfWY5RykuO0feWH5eRRhkjC244TyqUOhMbGzZrympBigTViX7WPBKmJrC5rbFvqYKBVMxmKnXINLQRJK8uYWIAEdcRIYBZtIkkdyCtsRHrFYeRSdFLrUeVU3FsEXACefo39BL58DKOYa23LwFU03Qmxhccb+CyoC1+kUl/fxR0U8DN/dVr3oVn/rUpyiXV3ZbuB69KFLPX//1X/Oxj32Mj33sYwRBwKc+9anmv38Ss0Ivlap0AgqRyCKI6BzIceubfwlViSNlEBoIgzW79rPtth0YK4QjNyUymGqGpJpgILm3xXgRwiSnd7Auc1eT1yGEidD3grrU0IhQ/FOYahuD6deS1BZZKgJB0tjAQOZezBbC76IUJYNp3omiXA7DMtC0tXSaWV7Zs3nZy3wo1c6+zjUYqkZSy7MxcwtKYwCads4SijZ6Mm/DUrPNwdb3n6fbzBJEAbqq0G22025m6N3Wwx137YvbVyi0m1nSmsWee3fRvyF+YeQNi13tg1Qjh3s3beDg+CTrU6u5vXtvgyos0FUVVajc1X0r/Vbry7zNSPOK7m1cqM2CkPz80J74BahonK/MoSsJXtt3K21GqiW6Z01ygE2ZNWgrRIlcSal8kn333Ugym+Ti4QvcuX8jUkZMjhf4+f27yFoJFEWhPZdEVxXyVoI7N6whay2+RHUlSVfyLnJGq0FrqSlWZd6Coca8laSWZFduZws8MCJitHaedal13NyxG4ATpdOYiuD1/bfSZbY3CcqnKuNszvVyR9emZVFNt3auJ60naDd62JBZ7my/KrmR3sTqFUPAX4xMXWP3xgFW97RxenSWlKazvaubmYrD/s5tnK/Os6fjdnJ6HlPV0BSNnJ4hqWXpNgfoTKa5eWAVwwtzrM7l+aXtuxCAHQY8Pz3B69ZsZlt7D1nTpGEzYqka9w5uoM18YcMloVpsye4gvwQuOFofoctM85reW5dlP9+eG2J9urfF6TejZdmZ29t8Ri6pw+hmY2Y7mqLRaXRxV/e9LXTZk5VjbM6sR6CiKW1sz72yuS+rx1FRghq3de6kw2iNJjEVjTu6tpHTVzaYLG0VucQNeMEoGzJ3Yqmxc7imZFGVNIaawlKT7MpvwVR0qmGJN6/fQVo3EQja9Cy6ojWySmfQ1SwDqVdjqp3MBN9Fqg56QsNKDbFmy7uaZO5K8RShM8+a7nejqb3QGFvGqw/R3pvj5//d2zESrQbcpn3buOWN+0gkEgwl17IquXbZ9WzN7qLd6FzxWn/k9VPg47J9+3Z+//d/n97eXt7xjnfw5S9/Gd9/aWkZrjkces2aNdc0qzIyMvKSKvRy6+XluJQJwgU0YULkUislOH/sAiefOozneAxt3cyWm7fTOdjd5GZcSVHkIWWFehhSDwtxbhqhYigJUmobeXMIcwnULIpCkFPgHSYKR0CYCP1GIm0bgaxTDcaZtZ+h7k/QZu6hy7oRQ+tAvwLqPy7TJwzG8LwDcRoDdQBNG0JVu1GUNGXf5mxllsOFMZzAYyjVwa62QXqtXHNwdsIa8+5Fxu0T+NJlbepGOow2gmAYxztEFPkk9J1Y5k2UAjAUHT+UeJFL0a8i531GDo5y4cgYKSPB1r0b2XDjWvLdiwaXHXiM14uEYUS15tGdTqMYHmeqIwxXR0gInT3tu1iV7CezQhRQ1XcYrc1yoTbL2nQvBa/OaHUeU9FZn+miP5lhyp3hTPU8vgxYnexnU2YdOT3zomcSfS9gamSaE08NM7CpnyiT4PxskU3reymHHkcmp6l6Hj2ZNHtX9TPYlsPUWl+AbujgBuMUnadxginy5nZSxnpMtRttSZ/wpc+MM8twdZiSXyatpdmY2UBfoo+iV+Bs9SwjtRF6zB62ZLcz6ZQ5VR7HkyFrUt3sbluHG4YcL00y51QwVY3BZBur0520m/FLrxaUmLTPM26fQxcGg9YGOhMDJF+GiCKIQ92nC1WOjUySz1j0d+eohB4IiUsNSxMg6lysn8KXdXqtIdaktpHWcmiKTsV1GSkW4pxHQnB8doZnJsfQFZVf3LYTn5Dh4hz1wCehaazPdbA200ZSv7aZtDAKmXdnOVc7w7w7G7/Qc3tQRIKz1UlOlseRSDak+9iWG6LNWN4udlhn2hlnpDqMKx06zW7WpDbRbnQ2Z0tKXpHR+ignK8cIZMC27A42pjfhSJ9Zd4GMplL2L7DgnkVXFLoTG+kyN2OoeSbtBY4WzzPvVei32tmQ6afLzLVgCy6XFy5Q886iKnnqocOkfRJbVkmqHaxK3kDO6EOgMectMFIbp8foZr7uM1IsEErIGgk2tXcylM2jiABPVqj74xSc50hrG0mHu4hCAaKMUz+O65xDT2iksxsxktupB9PMu4cIZJ2cvpGOxD7qRZ+RI6McePAglWKVTXs3svdVN9Ix2IGmqQ0/tgIX6+eYdMZQUFiT2ki/NURyBfr39eqHGg79T3/70sOh3/4rP9Lh0BCv2Dz88MN89rOf5Utf+hKqqvL2t7+dd7/73dx1110vuryXxHH5cdAPohOGsobAxKl5MTzNi9dyzaSFkXhx0+exARMRRGVCWQEEmtqGCFNUFmq4dRfN0Mh3pdE0l9jXJQR0UHLNF2sMxwsAgScrhJGNQMVU8y+Y28YPbeqhRyXwkBEkNZOcnkJX4sGiFsRJDU1Fx2gQOauBTdW3EQgyehJNRER4+LKGQMFQUihIInSqYYAT+qQ1CxkF6IoOkQIi9gFQpSC045w1ybSFoi4afVLWiWQJhIUnLTwp8QKJpWmoauyL4fsRNSdAygjL0MklE2jqcsOxHrjNb9paYONKj6SawFJVIkI8KTEUkyCS2KETZwLWEqTUZJwPqcHxkdJGiPhLWL9C7hnXdvHdACNhEIiIsuOgKSqKIlAUQULTSOhXnpWLoggpAyRFpKyDSFOXEaow0IWJHwXUgjq6omOImDSrKzqmalL1PQQREhmDuwQklXgJzg49oigioRpNzocTOlSCKjIKMRWDlJaO79GlexBJvNDDjyKc0MMOPVQR3/eMZr0sy8R+EOJ4PpqqoKqxP1KEABHGzJUoQhFRAwBZIyJEExammkdVDKqe2/B3EbhhiKaIOGeVolILXdwwQFdU2swkSW250RLIADt0qYexs7KhGGT1NIZiEMoAR9qxkzAKtcCHSJDSDCKiuB0jgRP6JDSBjOp40kYRAlPJklBjA9gJbWQkMRSjhTsSRRE130eI2JdNUwKIqghCFJEhEjE0MZ79bIRfiwRRVCGMKghhEUUZImyiqIIiQBFpVCXXnO3wgoCi4+AEIbqi0JG00JQaUpYQIkGIiURFF1ocDBDVABVF6cQOBXXfx1DjtvRliKlotJtJIup4soYhMqhCReIiohRRaBNFGqpi4No+1VKF0A8xrQTZjgy6qRNKF88Ocaoe9bKNlU6Q7UxjV2xUXSPwJZquEJkeflRDVywQWpzIkRBDJHBLPnY5dspO5ZKk21IvuT/+0A2X5EswXOo/HobLUjmOw1e/+lU++tGPcuTIkatG4F5J1z4P/gIaGxvjv/7X/8pf/dVfvVxF/shKVVJMnJ3iO597jKe+/hyluTLrb1jDPb90BzfcvZ1M27V/jcoooOIfZbryJUrOcyT0Afr03+Hsk1We//ZJSnMV3vjeu0gYJUz1STRlDIQB+haEeTeRugYhFFTFJAhdZuwDjFa+QdEdxlDzDKTuYHX6PtLGyuvDdlBmyinynenDHFw4iRNKNmTWcVf3Lna1rSapmS0J6WQkGbfneGr+GGer4/gy4M7uG9meaWfeeYqie4JA1skYq1mVfjNjts3zxTNszqxC4vF88ShFr0hKS7A6uYo97bvoNbtRzeXLDmE4g+N8GynLSP3NnC4V+dK5YzwzM46maLxv+83syPTy5OkLHB+fwXZ9Btuz3LppNTcM9TUx+ktV8Kqcrozy3ZkD9Fod3N29hXn3EGVvhNWpfajqEM8VTjNuzxPKiH6rm9u7dtOfSFFynmKq+jWq3ikMtZ2u5F10pd6ApQ8tO49pmZiWScl2ePLcRZ4evchctU7eSrBn1QB3bFh9dcMFHy84TtX5Foq2nxG7SkSCNekbqUYuj88d4bszz7Hgl1iXGuCV3Xu5tWMXM/UKC26d0do0D0wc5HxtlsFkjlu7N3Jnzw4GrPYWn4iSV+RI6TBna2epBzXyRhtbMlvZnNnc/Iq1Q58Ft8akPc/D089xqjxBRkuwt309d3bvZHWq56qws2uRrqno2mIZxmVDkxdWqQczjNe+y0Tt+3iySruxicHMPfRY+0gbi8a5JOLE3CympnC0NMm3x09R8OqszXVwU9cQd/VupNtaCjWLabjPF4/x1PxBin6JQauXV3bfypbMWi7UT1Ly5+kwNvH03EUOLVzAC1V6k+3c17+dAaudkdI8m9rSzNijHCs+yLR9AkuzWJXay+bcvXSYa2Ky7GUKpeRseZ5vjZ1FFwr3DOQIg0cJgyOYqoOp9ZNO3E7KvAlVyQImUeTh+sep2g/j+sOkrdegaf1Uncdw/XNAiKGtI2vdTULfSc2LeHpsjCdGLzJVrfKGzRu5eVCih49AeJIID1XfRsJ6K4E/guN+lzAcRddvoC5ez9GCQ2cyy8GFCzw+c4YwkvzbzbfhyTqz9lNoisqq1F4c9zgd5utw3ZNMXPg8Ts0jl/t5Dn/3PKefuUi9Al0Dndzwyh3sedVO7KrDzIU5vvv5x3n+O8cIA8n2Wzfx8x98AxCh6ApGT53TM98iZw6i6HlOlg9Q8kvsz78Gec7kwANHmDg5iyIEq7ev4pY37GFo2yCa/rK92n6ml1FTU1N87nOf4zOf+QyHDx/mpptuuq5yXra7Oz8/zyc+8YmfCsOlMFPi63/1MA9++pHmtiOPnmD4uXP81p/9a/bedwPqCl/8K8kORjm38Ie4QRw2mDPeyvAzo3zxLz6PwWp2372Xrr4ak8f+lHQ2oGtVJ6rugPc0kSwgku8BtZtQBsw7Rzk4+/8SRvEXiC9rnC7+QwyFyv8KCa3tsnNXWfCKfGXscZ6YO9Lc/nzhEBP1BUz1Pm5sW93yBVPwKjw09TQjjTDHpGrSa1ocKXwBNxgmqcYzTrqSZsKe5Ytjj7MxsxFdEfzt6Fcp+mWSaoLeRDsnK/ESx+v7X0W70Vo3KSs4zjdwnO+gJ/89YzWf/+9zDzJcmkEoCdrNHHnV4lOPP8vYTJlkA9N/dmaBsYUyCV3jxtX9zbrLSDLvxnTcT418DSEEbxq4mROlhzleepQN6S0Y2gBfHX+Is9VzZPReEmqWkdoYd3btpuIe5PTC/yCM4rBUP6hRK3+GUNYYyP06hrocOOgFIU+dv8g/Hz7W5IhMV6o8cPwUMpK8ZtumK2Y+DoJxZkv/A8u8hYtOnecWHubuvn+LjCK+Pf0sn73wjTihJoJD/jAXatPckNvC6eI8dlTlf564n1KDMzJtlykGdSqBzTuG7qDDzDTuv83B4nMcLh1qnnfWnWHenUMVKttzOxAIJuoLVII6nzj3TSbthWY/KPk15twSv7L2VfRaP7g8ZYF0sIM5RisPcK78leb2KftpKv5F1HaN3tStKEIjimKjZbZWpSLqfPzYo80w92rgMe/V8GXIm4Z2ktETMZnWr/BM4RBfHX+oGZlysnKOfe27OF05xNHiY9zU+Sa+P3OWr1x8EoC0liMkjgp78MIpbuvtxw6n+fbkn1PypwCBKzXq4YNU/Tlu7f51csbyPDrTdpXPDR9islbhN3bspGx/jdHiA+iqwppMG4IRFqoXEUInbd6OEBp+MEGh8mlCOYuurkJTe5krfxzHP4murUERFo5/FD+8SFf2/+a5iQqfP3yUMIroSafZ3Anjs59EY5iedAZNUVBEHt8/Qq32mYbviUqg7OXhi8foSw9yYP48nxp+ggi4rWcdGc3m4Ym/JqX63Nn7a1Sc75PStxAEBY4/90csTB9n643/H574ykG+8w9fwTCTZNu2MnE2xHMDhrYNYldsvvLn3+TpBw4222Pk6AUK00UuHB/jtl/ewVPTn8P2KnSnb+T+iU9TCKbYmNmDmDH5zJ99joWJAh1mL4ZicuLJ08yNz/ML/+HN9K7pXtbWP9O/jMrlMl/4whf47Gc/yyOPPMK6det497vfzT/8wz+wfv36Fy5gBf0sjeZ1aH58ge994cll252ayxNffYby/Mr8k8sVhDUK9vebRosqUqjODg4+8hS2O0coq2zZP4i0nyb0ClQKNbylYYPBMITnAfBkmfHad5tGy1KNVR/BDqeXn1/WmXXKPLtwomV7hKToz3Ng4SzVoLW8CXuuabQArE31I6hwrvI0Zb/W5GWk9E0cLg6z4E6yNtXDycoZin7sVV4PHZwwDvmddKYZr08uC0eWcgHXfQpNW0tAJwfnxhguxcyYKArY2z2A4wQ8eHKYkhsv61ySGwQcODdOdQkfpeq7OKHLgcIJHOmxNbsGQ/E5WX6SMArpTKxn2ilyphqzIpywjIxC2vUseV1hsvZQ02hZqqnaQ3jB8rYFKNo2z4yOrUi3fPbCOMX6yiwRKX1s73n8YAS0Gzleeoah1DYSIk0lqPPQ9NON+7QYGry7bRMl1yWIAp6ZP9M0Wi5ppl7lROkiE/Yix6YaVhiunl5+fiSnK6eoBTHfpho4nCqPNY2WS6oENmP2PKcrYwTyxU/3Xqt8WceTRcaq3122z5ELTNvPNEGLZc/l8Mwknekkj0yeaWHzlF0HLwg4MH+Bghu3Tz2wqQZ1np0/1BJOm9ezdBhZni88QZvRTSgNHps51txfD6sMJvMoqIxWi+RMhYu15xtGC8Rh/yEyksy4J5lxhptco0uSUnKqMMtopcjGfCcZrcZE+fvxNYdySb8OqDlPEsoyUeTheM8RygbUzdiGH4xhe88TRQ5SVrnksRlFkpJT5+mLF5vh31u7u9AZo1Q7TNF28cMQEKj6BlznEWQ4AVGIpq2jFKQZLU+Q1hN8Z/J4s3X2dwwybR+m7M1gqD3oimCu/iBJ5Uaq5cPMTR4k37kTz07z7EPxPfPcOoFfIIpC1mxfRb1cZ258gecebuXIbL99CzMX5uhZ20U1nGR4/jEGszcybo8w704QRRFD5nZOPjPM9Ng0fuTjhPXm+DF7cZ4zB0cIgx9cf3xZ9VPgnNvT08OHP/xhduzYwRNPPMGpU6f4yEc+ct1GC7yMMy4/TSpMFXGqK5M3p0Zm8J1r85iWkYvrTzT/rSoZQk8wPx2/oMPIJp038Ox4MJShJPRbWQZROAFRhIw8qkvKWipf1rCDBdouc7/xI5d5t9LkUixVGPnMu5UmCh7itfhpp5VQaakmbrhAGHn4UmkO/opIMu8tICMPSzWYc1tfem6DuyAQzHsFJBJ1SeRFJKtEUQUh1uNLwcXK0vNK8kaCuUoNJwxwwwAZ0YwcAZiv1vHCxZdWEAUEUcCsWwQgrSVxwlKTCmsoaWaW1FFGPhGSjJ4BfGreBZpwlJY2rOKHKwPGvCBkvrYyiHC+Vm8h1i5VhIsfXEBRMoSolPxZBpIbCYlwpc+Mu9DkZ1yqU4eZww1DVAWm7OKyMu3QJ4wipp0iOxvbnNDBvgJErhyUCKOAMBLIFe47xHwUTwbMuWWCKETjBxOOGkUST1bxZGnZvjDysYPZJvvGD0NsP2bMTNZbwy8l4IYhtcCj0oDEhZEkkD5zXmv/TGspIGLWnWYgOYgnJfPu4geJjEJSmoEnQwIZEOFT8lpzrlxilYSRT8WfJYyClkgsP5JM1eMyU5qOjMr4cvEcseEToQhBEM4hIxcRGQThIvRRiBRucH7JOR0iJAIVRUnhhQHztUUQXdrQkWFMpw2lJJASMAGVMBwnijwgQogUti+RUYQEJusFLn3ntpkGRS821lWRhCjAD+dBCuzaKAC6kcOpelQWilx6ZsLQIYokyUyCetlmbnyBwG99BtK5JFPnZ+jf2EPZGyZCYmoZxu2RxvVBQqaZmRjmUt8PpN8yfsxPLOB7Aar2YxAe/VKNjx8Dw+UrX/kK99577wsGrLwY/WzG5TqU78mRSK7shNs91IWeuLaEX4owMLXFcORQVlD1iLauOLRPFQmqJQ89EU97KqqCetnarVB6EUKgCJ2UtnJKd1WxSKywlKEJk3YzEzvkXf4bodNupNGX+C4IIegy8y3HOdLDVNtRhR6HSHJpacamzcijCANHerQbrb8zFL15bJuRR7msKwolhRBpoqiGpsBAaunvBWXPpSOdxFBUDFVrMVoA2lIW+pLlOk3R0IRKhxFHK9UCOw7lFCYC8GSNriXLVYrQEShUgiqgk9IHWImaoIsk+gptC2BoKm0rZH4GaEtaTe7I5RIYMaVZVlAJyeodOGENBQVD0ehs3oNFkkPBK2OoCjKCHmt5CHxC1VCEoDuxuM9UEiSuEHWW1mJny0uOv13m8jIFAl3RaDcyaD9AhoYQCrqSwlCWOx+qQsfUOlAbHBRdVUlocd9a6scC8WBnqDEPKN2IuFGFiqZoy/pnLawTG4TdeNJBV5SWiCFFqNihj67EofgCnYzRGoovGoA2RWik9c5l4dOaUOi24jLrgY8iMmjKYnSMrqjNUHVN7UARRpxrSl0M/Y2iOrq6OIYIYTYxBFLWMFSN9uRimXU/QFH742tXRCPZpAdIFLUXIXRAEEV1EqqCIuIns3tJnyp5PjkjZq6EkQ1Ci58BEZFIxuypwKuQSOmk84v3TFVNhFCway5WxqKjr22ZcVEr1+le1Um9ZJPWexAouGGVnHEJDQGuUqOzp5NLfV9VtJbxo60nj2787Jv8R0WvfvWrX1ajBV6E4fLWt771qn8f+tCHXtaK/Sirc6Cd239+/7LtpqVz65v2km2/NudcTU2Tt27HUC4NAjVC6wS777oZ02hDVTKcfnYCNXkTqp4lnU+2PpDqOtBiroGhZBlI34kilhtUg6k7SWrL13x1xaLLzHBj+6aW7QJBRsuzp31dc4BvlpXsYii5OECPVCeADKvT+8hoySb3pe4Psyu/ibzRw0h1hq3ZjWQbIbSWamI1sjj3mF0MWn3LIgEUpQPD2EcQnENnjt3dg6zNNgYvdA7MjmNZOvds3kDeaM2wrKsqe9cNkFkS4ZXWTEzVZF/7VkxF52T5PIE02Zy9CVWozLnn6LXaWJtag0CQUDMoQmXBK1HyI3pT9yFY3rbdyXsw1JUNxpyVYN/QwIqQqL2rBsgnVzYaFMUgYexBUwcR4VG25vZxoXYCT1bJainu7d7faAfR/O+zCyfjdkBlX/sGMlpr2d1Wms3ZAfqtjua2jJ5hfboVQHapvE2ZzSTVFCktQVJLsDU3RPdlxktGT9BvtbE5O/iSnXOvJl2Jo4f6U3cs22cqeXqs/c3IuYxhsrO7m7mazd19G5pk3Uv7TFXlxvZB2sw4kiOpmaS1FPvaWnk1Ba/EvFfmxvzNFN0ZdMXntq5FMKClprlYKxARMpjOUfEihlK7yeiLEDVdiSGdneZGehIbl3FvVEVhS1sXg6kcZ0rz1IIU/ZnbG79VyJmX+rVKyrwZVcmhCAPL2IOqxPfR9Y5jaKtJ6NsRwkRRMiw1sHMJi/2Dq5rPx8mZWQJWkU1uJ5dINJJPSkL/DGbilShqHwiVIDhHTq8zkO6h5jvc3bd47c/MjdFj7SKtt+OFUwQS2q17qUdHyGRvpL17G4X5wxjJOnvuie+ZblpoWjtCqJw/coF0zqJzsJ0b7t7e0ibHnzhNz5ouJkdmyGr9rGu7ifHy8wxa62gzehBCcME7xtabN9PZG2fHttTFSKL2vjY27l334zHbAvykIuj27NlDoRDP0u7evZs9e/Zc8e96dM1maS63Mshs6f73vOc911WJHzflu3O86X2vwUonePJrByjOlFl/4xpe9ct3su3WzS/qobH0Nazr+E9MVb9IyX6WovMom/b/B976b97L898+yalnz7D1lntYtf3fYahPoqljIHTQtiLMe0FpzM4oOp3mLm7s/ADnKw9QcIZJqHn603ewJvN6EtryWQFLy9CO5M2Dt5NWkxwonMQNJRsz67irZzdbsv3LqJxteobX9N7E43NHOVsdx5Eek47NDe1vY85+ohFVZOOECwxluvj5wft4vjBMEAl+YdWbOFA4RNEvktKSrE4Osq/9Rtou+9qFGJBnWa9HCB0ZfJ9Vqbfwu7tfwxfOHeHp6XF8GVEI6vzr2/fz+MlRjo9PY3s+A+05bt+0mi193S3GkCIUOow0qhjiV9a8ju/MHODphTPc3f1qOs0OKv4IXjjBG/rv40DhJBfr88gIBqxugkiStfawuf13mKx+lYp3CkNtozv5SnrSb8bQOpbVH8DUNG5ZO0QoI54ZHWO2WqMtabFnVT93bVzbQsy9XLo2SHf+P1KxH2IokcFvewVFb5y80c2rem7CUHS+M32Agl9ifXqQe7r3kdJ1NuU6WPCS/Lutr+NrY88xWp1hMJPn9u5N3N2zk7YlnBtLtdjTthdVqJyrnaEe1MkbbWzNbGVjelMTvjeYbGPe1fn19a/hwcnnOFkeI6Mn2Nexgbu7b1hxNubllKZYJNRO1mbfgKokmKg+GkcVJTYxlH413dZulAbFVhGCbZ3dHJ+dZcDI8N4tt/HQ+GmKfp31uTZu7l7L3X2byDYi5VShktMz7O/YjSIUnmxEFa2yerHUBJuy2zFVkwV3lFf0bERTNJ5fuIAvNboTOTRF8NqhLYyUCmxt7+Wevt/kSOEBpu3jWFqKVam9bM29mrS+MiCtJ5nhnRtv4KGxYZ6cLnDPwOvZrFuEwREs1UVX46iipLkH0Zix0bRB8un3ULUfxPOHCcMZurK/RcX5Hm5wDiKJoa8ja92DoWbZO5jBC0Mev3CB6UqVk3MRNw/8Gmr4bYQ8CZFHJOcxE3chUklc77sEwSi6fIb7hl7P0QWb/R1riYDHps8yZheoBtu5t///Ys5+mmnnCKuSd+H4x0gbN7J9339mbORzFBYe5va3vAUrnWf42THqFRFHFd29nfa+NqyMxVve/1qy7Wme+9YRpJT0reulrSdPrjOLM6uwv/fdnFj4Jq4/z30D7+ZY6WlKfgm6Xd79gXfy7P2HmTgxi6rEUUW3vmkfXYMrP48/kvoJXSp685vfjGmazf9/ucn6P+O4vJSy5yuU5yuEgSSRMsl3ZTGvsIR0NYWRRxAWCGQFgUBTOoiCJNWFKk7dw0qb5DosFKUMUQ3QQMmCyC7rELHRUGxwXDRMNYepXv3FEkgPNwwJI4+IEFUYmIrV+BoDT7qEUYgpTFzpcin/TS1wkUiSaoKcliKI6niyTBSFaIqFoeaQkSCMJFLa8XS+UHClCxEk1ASaoq2ILL8kKW3C0CVwBZFhUgl8ZBSR0HQMAboqcL0IP4wII4kiBGnTxNQ13NCLx4UoIkRiiDglgyM96oGNJ30ymhXzOCKbiAhdyVILHKoN34+kapHRk6hCJZAxGydwfXzXR9V1rFQr+CqKIuzQJQISjTQCju9TrDsxS0RVaU8m0dSY+yGEuGJqhSD0iKIiUVRHigz1UKIKg4Rq4UofO3DQlXiZTFc0LDVOs1D1HNwwxI08XOmhC5WckSR9BeaKEzrUgipBFGIoOik1jaEuGlWB9PBliB+BE7rYgYsiFLJ6XKb6Mk8DX0mhdPFlDU9WmxyXhNqG2kg94IU+QRSiCw0vlI3llzjjtaaoJDUFTUToqo6hJPBliBP4aEKNuS+RTyADgihmpqQ0C13RcUOHelhpLMEY1IKAUEoMVSNvJEmoOjXfww18MoaOxGs6yWsiiaYkrtrHvdCn7HnUA4+8YWGpDogQrwZEGmYqSShivx1DMVGEShSFhLKElLEfj6p0EeER4TcYSQJFWKhKPLPkBgEF28EJ4uWtjqSFoXoQ2cQT73rscyY0ohaOSwd2qFP3PQxNRUYhYRSiKSop1SSMqviyhipMdMVARjZKlCcMKkhZQlEtQjdNpeASBiFGwiDbkcG0DKIowqm71Ms2YRCSSKokrAjNMBFqkmqxhqopRKaLF1UxlCQhAk+6CBRSSganGFAv1UFAOp8inU+1cKCuRz9Ujss/fOalc1x+8Zd/rDguL4f+RRcC//AP/5AvfvGLnDx5EsuyuO222/ijP/ojNm9exJ07jsO///f/ns997nO4rstrXvMa/vzP/5yenp6rlPzDUbYjQ7bj6nC3qymKItxwhpJ3Ai8okdT7sdQ+hFAwEgZmf3vsLS+nwT9I5E2B0oHQ1gHLjRaIv07TylXyJF2mQDp4soYdTDLnHMENF0jpvXQn9hPSybw7S8GbZzC5hknvIqcqR6kHNQaTa9iU3kZWb2+kPQCDNIa6+EVf9R2gRtk7RxQFWPoGxuwJTpZPIYGNmQ2sTa2h3bhyKG1hyubM8+cZPXaRRDrB7W+/mSilUvOnmAmmSCuDRGGK8VKNA2MTaELw6q3riPQYvFYJXI6XLuCHktu7tmBpIfPuGCoaaT2PpZqE4Xn8cAJV28WUfZaR6gk0xaTT6MU0+yFKgoDIj5i+OMmJJ55n5sIYmbYutt+xh771/VipJBW/zoX6NCfLo4RRyOpUHxvSA7QbWXpzmeY9n3HKnFiYYKQyi6nq7MgPsjbdSUaP75sdVJl1x7lYH8YQBquSm8kZJm1GijAKKXgzlP0CWb2DSeciF+ujqEJlW/YGDCXD+do0vgzJGSk6jAxpzSKtX7lPJNQECXX5slUgXcr+JFP2MeywRN4YpDexlQ6js3nPf5hSFRNVMUnQ2l98GTDjLDDnLpBUcoyWypyam6c9kWRnVx/r29pwZIEx+zQFb5ohaztR1MnxwhzjtRK39gwxkLIoBTWGKxeZtOfI6Clu79yNJwOGqxdwQpe8nmFVsodOM4+pts6WpXSDhApOOE/JPcGCcxghVDoSN5Izt2Jpy2dc7MCh6NoML8wxXipz11AvipykVtWZPFNl+nyBNfvX4tgjTMvzCC2iJzFEn7WWjNaGprbDEv8qGRk4wQQl53nccBpdyZFL7MHSBjE1i97M4rMZRS4ymCSSVaQQ2N4x/HASVcmTNG+PCdoNoyelQEJTmXEKnCyfZ8YtkNYstmXX0Wd1kFniGxYGaWbH55kamSGZtVA1D92s0L2qk0SqtY8JIbBSCRJJHcJJIv8QhGPgpsHYQzq3GqEkgSQpWnEJl5TognzXj/EL+yd0xmWp1q1bxzPPPENHR+tMWLFYZM+ePZw7d+5Fl3nNhsvu3btXfFHmcjk2bdrEBz/4QbZtWzk53JX03e9+l/e///3s37+fIAj4z//5P3Pfffdx/PhxUo0v2Q996EN8/etf5/Of/zy5XI4PfOADvPWtb+Wxxx57Uef6UZQTTnOx/E+UvcVwZIFCb/o19CRfiaakQU4R1f8BgsVjIhRIvA7MexDK9aPXw8inFsxR9s5xZP6vsBvRMaow2NfVS8mZ5rnCU7yu9xcYrhznC2N/iytdDMXg2cITrE6u4+cG3km/tWpZ2QWvioLLePVbnC49yh19/5lH577P1ye+jCp0NCXB4/OPc2v7Lbym774VjZe58QW+8X++zalnzgBwx9tvYaJWQaXIodLX2Jl7LYUAnhs7w18+9gRhqPChu1/ByfIIkRJgGgn+9vzDlPwabxm8jaI/yT+PfxlLjUhpCfa334MmT1Ouf4nBtv+H4eoJ7p/4BK50UISFEBqD1npe1/cueow+Lp4c5Yv/v48xe3ExrcXBh+/nLR/8IGtv3cwT88d4bO5QM+PykdI5NmWGeF3fLXQ2llOmnRL/dOFZTpenmmU8Mz/Cq/t2cFf3JlRFcqpygCPFJ4jjOeBE5Vn6rbXc3vlG5r0ZDhWfYGt2H4/OPcwzhcdQUdmQ3saq5CYenXmO05Wp5jKPAG7p2MYre24gq1/7l52MQmad0xwtfhVPxlEpU/ZRRqtPsqPtLXSZGxBXmUX4YUlGktH6BI/NPMfu/A18afgoXz13FFUodJsdVDyPhFHmROVbVMNZBqxNFD2FT5++n4may8+t3oele4zWC3xh7EHG7AkE8IqumxmtT/Lw1NPUQqc5Y2IqOq/pu5Wd2fUY6hK6sAxxwlmGi5/hbPkfm9tVYbAl/69Yk31Hy3KtHTjMORXuP3OSL546yH++9ZVU7e/hV3s5+cgcT375Qd76u/+ak+XHeezZhzEzFj2ruzhvnWDAWs++9lc1HVahQd/1zjJW+SzekqijgvM0A5lfIJfY01xOi6KQ0D9K6D5PpG+hUPoLPP8ICB1FHaTuPEE29XZSiTtRGsbLWH2Gr0w8ylwjKg/gcPEMr+27le25dc3EppPnpvnKn3+TyXOLiAAjoXPfr76SG+/ZiWmtsDwajhLV/y42Wi5dj/cUIvl2IuMmhHjhvFI/04+uzp8/vyId13VdxsbGVvjFC+uaDZe3vOUtK24vFos899xz7N69m29/+9vcfvvt13zyb3zjGy3//tSnPkV3dzcHDhzgzjvvpFQq8YlPfILPfvaz3HPPPQB88pOfZOvWrTz55JPccsst13yuHzXJyKfoHG4xWiBmqEzXvkPO2EZKN8F7vsVoafwanG+Bvh1eguHihhV8WWG08mDTaAFI64OEJHh67n42Zm7Cjzwenv5qjD0nDi3WhcJo/RxHis/SZnRgqYsvxVBKvDBAMMszs5/ltt4PMOcVeGDya0REBJGPEqkoQuGJhSfZnN1MTsu1fMWHQcjwc+eaRksyY9G7cwBPOEwVnyKt5YmCDCXb5e8PPEfd81jf0clQZ5oHJh/ljWtv4fMXH6XoV0mpCbZl+zlY+DYz7gxZLUlXoo823WKy9AXazN3UZcj3Z/4ZV8bhy3FYqMqYfZaKVyTjZHj6/odbjBaAWnGKyXNnSd7QxZPzR5tGyyWdrlxgfbqfNn0bkohDhYstRgvEIbnfnT7JvvbVICqcKD3TNFouadadoBqUOVk+SIfRTckv8Wzh8fj3hAxYQ4zXC3x75mm6zG40kY5niYCnF06yOTv4ogwXJywzUn2iabRckh0WuVB9imwD0PcvrVpg89zCcboTHUxXbb4+ErNWwkhSDirs7G7ncOEppt0L5E2LbnMjj05e4HhxjIyWYks+hyttDhROcKp8nqSmoykaWzLreWLuMEfL5xhM9mA2DBdX+jw5d4TVyV46l2ReDyKHonuKs+V/aqlfGHmcKX2eDmsPptrW/PizpcN4pcQ/n3qeHZ39DKQCytXTRNU1PPSPX2Xn3hsQ+YjHn/86YQR2BSoLVYw+nXH7LH32GtJ6vpkROZAV5u1HW4yW+Pw1ZuvfJqmvw9Rix+FIFgnsh1HMm6i5j8VGC0DkE8l5ImFRtR/ANLZhKEPUAptnCydbjBaIIwqfmDvSnIWyaw7PfetIi9EC4Dk+j335GdbsHKJnqDUDdCRrRO5jLUZLLJfIeRihbQT18kSwPzkSUfz3Un7/o6qvfGURFvnNb36zxU82DEO+9a1vsXbt8qSZ16JrNlz+4A/+4Kr7P/zhD/ORj3yEb33rW9dVEYBSKeY0tLfHXyYHDhzA931e9apXNY/ZsmULQ0NDPPHEEysaLq7r4rqL4LGXM5X2y6lQ2lS9Myvuk5FDxTtNSuskClY+BhzwT4N2/RCfQLoE0mbBPdmyPaMPUg/rTDlnuK3rzRS8eabdRUaFjGQMVBCCkdoZbgrvbDFcHBmzIArOaVxZIa0PcqA4TBhd4sVEyChEaZQxXD3DpvQm0ktCQe2qw8iRC81/t/e3EVkaqmpzoXyKHT23Uax7zFY9Jhr3eKg9TyBcwgZ74mjpAqam0mu1oysR56rDRFFEPXTJaG2IqE7NO0V/5h0UvHlmvYvN80WECCSgktKz1OfqnD/y7IrtqCgBZ8oX8aPlPByAkeoku3IbkBGcrcyseIwdepR9B1uO4jeYJC33RGvDDutMORe4se0VnK2eaQGm5YwOnp6LGRpOaGOpyWaIaBhJhivjrEv3oV9jtmtP1ih6F1bcV/Au4IX1HwnDxQ4dxupT3Nqxh8cnJ1tAhEldI2XAhYXzeJFHu5lFIc2JYgw967LSGGpEGEWcLp9HRrGfVLeRw1AMTlTO4UsfL/Qwl+QWmnELFP3KkrB0CCOHOfsAXGZwAniySME5Rpu5A03ES3b1oM6J2RmcMGBVtg0ZXsDU8oyfnqZSLNK5qp8p5zx+6CJQQRjYFZuwK4dmKEw7F1ib3oGlphrnr1L3V55yt4MLBLKEScNwiSrIcBJFpHDdVgBcFNWICAhlgSC4iKENYYcuY/WVIYvT7gJlvxobLhWHiyfHVzyuMFVkbmxhmeFCVIPgCksFchrCmZ9ow+UnWZcmO4QQ/Oqv/mrLPl3XWbNmDX/yJ39yXWW/bHO973rXuzhy5MgLH3gFSSn57d/+bW6//XZ27NgBxHkNDMMgn8+3HNvT08PU1NQKpcR+M7lcrvm3atXyZYwfDYlmArQV9zamdbkaH0NcGy/myueI66FcBg6LiBovPYUI2QxxXkmKUJthuUtKbuy79JKMml+GK0lFXbYMKYRoSZsQycZisBCoQo3rKERLMsUoilrqcqnel15ml2Z04mMiEEr8UkBetX7N+mhXbu+rcUxUodDI//cCbSmWhcwuKmrwetRGwsHL7lm0tOzlS7pXO+9KElfpnwKVlzlI4LoluNQfWBaSHUURUSQW25+o5R7E/SJqlnGpxEvxCnpj27K+iVjOHYLmUsxKtVSE1tI3FS4xVEBGcYh/RISqx+eMZLTk+RGL9RCXtqiX3WXlKvdLaWD8l9RWXCrzckNWLKmn1txypf4T9xOlURZXTXWy8j7xAmPcTzqP5SczHBrid7qUkqGhIWZmZpr/llLiui6nTp3ijW9843WV/bIZLqqqIuXyr41r1fvf/36OHj3K5z73uZdUj9///d+nVCo1/y5evPjCP/oXkKYkyZor+wRpSoaMsRFECqFdwW9IZEHbtPK+a62DSKApFp1WK8Oi5J7FUhOsTm5nvH6BvNHB6uTizI4q1ObAtymztWW2BSCpGggU2sxNpLUuFpxhNmU2NWFnghiYFwO6BFuzW0heloDOSifYtG/xnLNj80QVH8/XWJvfyZw3Si6p05lKsqGzE4FgeHYOJTIwFA0F2Nu+HiHiNAVOGLE5s6MZxVP0ZpAkyZo3UHUPkzfaGbQWncIFGpcej5JfIJVLsXHvbSu2YxgabMytwlJXjg7alBnCUkxSqsnW3Mpfj1ndIqNb9CZWk1BSy/aX/HmSapoBay0Ff5a1qQ1oSwb1BXeKHfl1qKgk1WRLFIuuaGzJDl3zbAuAqaTpMFeexu0w12K8hCXKl1NJzWJ1qp95b57d3X0YS4wX25cUHcn67GayRgIpAgJZYHfHGgBmnCp1X6AK2JHfgCIEqhAseEXqYZ1d+U2Yqtn037ikAauLvNHqlK8Ki+7kzSgsN14stYv2xC7UJbA/S0uyraubrJHgTGEGqQzgBUX6N3TR2dvD5NlRes3VJI1M48UuSOaSzZf/QHIdxhInfE1JkzY2X35qAFL6BnQl3/y3ULKo6lqQRRJmK49KiDQIDU3tQ9cGG22cYG2qf8WyB61u8o0Q+2TWYt0Nq1c8rndNN50rhSmLNGgr1xt1CJSulff9pOinAPk/MjJCZ+fKOIDr1ctmuHzxi1980c65l/SBD3yAr33ta3znO99hcHAxi3Fvby+e51EsFluOn56eprd3ZeiXaZpks9mWvx9FCaGSM7fRad3aJF1CjNDuT78OU+2Kv6D0HWDcRsutEklE4g0v+aE21SymkmdN5j7azEUjyJVFFGxu7nods+4UAsF9vW+m0+hGESpq4+vxhvw+tuf2YKrLowUSqoGhdnJL969zuvxN8lqSt676RSw1haYYKEJDEzqv7X0Ng8nB5QA6VWHtrtXsf91uVE3Bsz0uPnueNBar8/vRhI5UF2izErxn/z76Mlnmqh4nxwu8sm83C26J1/bvY3WyB08GHC5eZFf+FjalN9NmpHFlnWl3gZ7MOxBymoQIuKv7rbTrfYCGEAZCCLZn99Od6CeRTrH/9XezYc/NLfXsHtrG2l1b6bLaeGX3HhLKovOhKhT2tW9hXbofRVFQFYXt+UFu6liHsuRLKaWZvK5/J2ndJKO1savtdowlLzmBwtrUdlJqlq3ZvdhhDUtNcGfnq9GFgS4Mxp2LdJtp3jJ4N8klhqShaNzTfSPdifyL6huGmmFd+g7SWmv0Xt5YxZr0rZjqj4bhktIs9rVvpxrUyVkav7RlLwlVw1R08kaWIzNzbM/vY0N2KwCTzil2d/ZyZ+92LCXPkYV5dJHixvxmbu28AVXEaSuOlk5zU/sObuvY2UKWbtMz3NF1I1mt1bhUlQQZYz3b2t+HusSZ1FTb2dL266T11hd6UrXoT+f45R03cbFc4FxJkk3ux2yr8ob3vJWZ0Ys4Uw6v3Px2LCNPtiNDJp9CVTQ2Zm6k31rbYpxqSpoO6xWk9A0t5zHVHrpSr0ZbQh0WIodm3UcUjmOZe7ES9xLPACcRaieK0kkm+eYmoddSTfa0bVlmvLQZGe7ouoFMoy1My+SGu3ewef+Glhm5bGeGO99xK7nO5RGYQrEQ5m2g7WjdoXQgEq+DJQbXz/Tjq1qtxv33389f/uVf8rGPfazl73p0zRyXK52gVCpx4MABvv71r/PAAw+0+KO8kKIo4rd+67f40pe+xCOPPMLGja0Uz1KpRFdXF3//93/P2972NgBOnTrFli1brujjcrl+GDH5L0VeWKQejFFxT6MoBjljOwmtF23J11QkCxBcJApOgTAR+g5Qehuhgi9NYeTjyzpOsEDFO48dzpLSesmbmzHUNsp+kVl3ir7EEHZYZaR2hnpYpd8aojcxQF5vvyJcqB44EHnY4RROME/a2ETBrzBcOYOpJtic3khWz2JpVw7VLS9UmRqZZvTYGD1rutiwdx2uKvFkCTucJSHaUKIsMxWb49OzhDLizg1DKHrM9HBlyIXaLJXAZnfbelIaLLjjFP05cloHQ6kNCEqEQRmhDVAJqszY43QlBkjrOTShYygmQgjC0KUwVeDCiREmz44xuGkdq3esJZ3PoukaduAy6xY5XblIEAWsSw/Qm2gnq7e+5IpenbH6AsPlaRKqxs7cKnqsLLqqNfqEQ8mfZ9w+SyA9eq3VtBu9JLUMMpJU/AJ2aJNQk5SDEmP1UQSCDZktGCLFjFtiuDKOKhQ2ZQbpTuRJaS8+MkNGAbVgnnn3HLVgnozWQ7u5hqTWcVUuyQ9bMpIseGXm3AIJkaTshJycn0Mg2NnVy+psnpAq8+44894EPYk16KKHC9Uy58rz7GzvZTCdpBrUmbBnGbNnyelZbsxvJowk4/YMU/Y8/ckuBq0e0pq1Iik4lAG+LFELxliwDyGERod1IyltAGMFlpIXelQ9l/FqmeH5eW7s6aTHcom8iMKkx9jpBQZ3DCLyAdPBKJES0metoc3oIaktNwKiSOKFs9T8s9jBGIbSQdrYgql1L1vGiqKASE4jwxIRCn44hReMoiidWOY+VKUNZYkRHkURC16ZMXumGS6+Pj1Ah5FrmcmLoojCdInx4UkunpognU+ycfc6OgbaMBIrAxdj5MMchCNEwXmEyIG+DdRuxAok8B+0fpgcl1/5u797yRyXv333u39k328ABw8e5PWvfz31ep1arUZ7eztzc3Mkk0m6u7uvKxz6mg2XK3n/ZrNZNm/ezIc+9CFuvfXWF3Xy3/zN3+Szn/0sX/7yl1vYLblcDsuKX2a/8Ru/wf3338+nPvUpstksv/VbvwXA448/fk3n+FE3XC5JRj7iKuvUAFHkc7W17Gs7j6Tk2cy5VWqBR7uZpCeRaSRJ9JBRgK6YaIrZZHvISCKjEFVoSCShDNEV/ZppiKGURISARFNM3NCn7Ncp+BUCGdJhZMkbqWVsjKWqex6V0GHBrSEJ6UvkSWoGmkJcLwmy4a6qKwpCiEa9JQIlTsIm4twrl65HEXFkUyjLhOEcgSxiatsJozqOnMcN5tCUDJbWjaG040dViASaMHGCiKLrUnAculIWkRJQ8asktQRZPYWh6Fe9HgBfhqhCYIclasFCw7ugjZIXO/nmjQSWppO8zOjwQxdX1qkHVSIRkVBSGIqFqS6Czi5la9YUFRkFSCkJcbDDAjV/loSaJ62vouQ5zLplqoFLVyJLp5EmpZnY0sMJHdzQQxUKCVXHUixMzSSQISXfZs6p4EqfDjNDu5HC0la+Xjd0KQcVan6NjJbDD+KcSvXAp+DaJDSN7mSadjOGHnphQMGzmXUqZDSDHi1D4AboZoRQK0g5QxSVUdUuVLUXtRHdIyNJLfBwwwAvDBrQOZ2kZqAIhSiKkFGcENEPy43/N9HVJLqSIPZvkYREaI2+UQ/sRh/yCGQZVxYwlAxJrZcwgnpYp+SX8aVP3mijXW9HFSoSL/YeU67eBy7VO5QSEOhqDJcDiQwVpJToho6MwtgH5io+H25YxgsrDQCkgaak0EQS5SrMnSiSQEAUaQgRxkA6WSAIdMoLCguTFQI/or2/jbbuHLqpNwF9VzNeoygi8ANUVb1mIFz8KvKJZzz/5QzjH6bh8p6XwXD5mx9xw+WVr3wlmzZt4i//8i/J5XIcOnQIXdf55V/+ZT74wQ/y1re+9UWXec2L3iMjIy980IvUX/zFXwDxhS3VJz/5Sf7Vv/pXAPzP//k/URSFt73tbS0Aup80Xdmxb1HiJTrjykhyobbAN8aPcrI0SRBJ7u3bys62PKcqB5hyLsaEXMViU2YbO3J7yei5ONFaYyBRUVGvkBzwSorJqvHvfRlwtjrBd6afZ9yeIwLajQy3d27nxrb1y17SAEXXZqJe5CtjB/ne5Ckc6dNtZbhvYBuvH7iBXiuPqrIsN3FrvZUVtwfhDJX6A9TdJ2lL/waeXGC8dj/ny/+IF86iiCTb2v8DWXM7F6rfo+JfZDD5cxyd8XlkZIQ3bNrLpD/Jw1PP4GKTMUy6E23c3rmLbdl1JK5ivOiKSsEb50jh6zhhlX7rXr45/izPz0+gK22ktRQ3dAzwusFt9CfjL3YnqFIJChwvP8XhwnephxUSappNmX3sbb+XTnMARSjNGYEw8vGDOlIEXKg+yuHCZwmlw+sHP8FIdY7PjHyf706fxI8C+hJ5/uCGt9GdSFENajwy8zTPFY4SRCHdZjv723fyqp7bGa+X+MbEEc5X55BE5I0kd3Rt4tau9U2I3iXVgzrHyic4UT7Jjdk9nJwukzPTDJcK/NOpoyy4Nj2pFLu6+3jzuq1sbu/keHGKb4wfRxMKH1j3CmrFGoYVEoSTFGY/Sal4P4oWkMqsIpt9G+nU29C0foqezSNTwzwxc46K76IKhc25Ht4wuJ21mXiWSEqHGftJpuuP4skioJDR1zCQfg1ZYyOqspjjuuAVOVk+Q18iw0z9cc5VHkEVIXs7fhmZMJjzyjwy823O1YYBQZc5yE3tt3Jj/kYy+rUvpSlCaXm5xx8mKqpGM+PxlZ22Y9nBLKOVBxkpfxU3XEDBoDd1Cxvzv0BOX39F4yU2EAyEgCicI3IeIpADnDms8t1/fJDx4TlQumnv7+O2n9vHrju3k8y+MOBSCIFuvLjxKv4QemFD72f68dLzzz/P//pf/yteLldVXNdl3bp1/PEf/zG/+qu/+oM1XH4QupbJnkQiwcc//nE+/vGP/xBq9JOtkmfzjfGjHC3GIYspzWRDJs93pr/FjHuRjkQKBQVX2hwpHSChJtmZ24umvDSDaanm3TL3TzzNvLcYpr7gVfjG1LO0m1m2ZFujwOzAZ9au8tDEEb45thi1Nl0v87WLh0iqBm9atfv6lkKkQ915lJrzLTS1H01fxVTt25xa+FgcWSEUFKGiqzlOFz9P2Z9gIHU701WNvz3yMNs7h0gmJH9x+lsUvBqGqrImrzLnFvnm5JN0GDlWp64cymkHZU4UH+Zk6Tvc3v1+Hpk8x0PjzwCQMySq0Hh6dhRT0XjL6l0kNY1aUOR87RhPzn2NS555TljhZPkpFKFye9fPkdYWlyWcYAFDyTBtH+bJmf+JJGBvx/uwpcbfjDzEtyaPNmfwdFXDDmzm3YCDxaM8Pv9cs5wJZ5bz9XGmnCJfHjvIeL3Y3Ff06jwwcZh2M8We9tVNozCKIsbsMZ6Yf5KN6Q3UHJWzhWlWt6n86YFHiVAQKIxXKhiqxmA6RyZh8E/nD1Lw6vyH7ffilB081yWRqlJY+DsK81+I710ItWgU+BsUNUPCeifPzV/kwfFF5lEYSY4X4zD+X16/nzbDouSdYqx6f2MGEEBS8c9xsfI1NuTfg9Xw6XFDl0PFY6S1BCX3KMeLX2ncl0FMrZ+yX+bhqfsZqY00HdWnnVG+N+uQ0TLszO94wUi1l0uBdJmuP8PJwqeb2yQeE7XvAYKdHe/FUq7uHBlJm8j9PoTjLMxu45v/52+Zn2hEbkrBwqTOg5/+Lm09eTbv33DVsn6mF6GfAnKuruvN7NDd3d1cuHCBrVu3ksvlrjt45kXNxw0PD/OFL3yhOfvy9a9/nTvvvJP9+/fz0Y9+9JoMkZ/pX04LXo2TpUUey7p0JxKHk+VTlH2b4LKosPO1M9TD+st2/iiKOFedbDFaLsmXAUdLI9QDt2V71feoBg6Pzyzn2ZQ9l4MLo5R9+7rqI6MStnsQgGTiLkJpM1l9kDggPG6LNnMnErhYfZgwckmo63h28iJO4LG1c5DTlfMUvAoAXhhS9bw4B4v0OFE+jxf6Vzy/HZY5W32SNnMVMsry9MziS7ceFAij+LdHChOUPRs3rOFFLmcqB7l8xPKkzcX6aSr+Qst2P6zhRw6jle8jiZegBlN3MetU+O7U8cZ1xmXd2LYaicSVHgcLl0MPoS/RzWh1jpHqchZNGEkOFS5QCxYZNHZoM1w5SxiFdBt9PDsxzsb2Hh6fGKUeeoSRbNRRUnZdOqwkz8+PseDVEUCfkcWpO5iWJJQLVEoPt96/MMJzS7juQSq+zfMLK1M4T5WmmXWqBLLGgnN4idGyqFpwkZq/+PtaWGfcnqLDSDJWe6a5vcfaSgQU/BJnKsdZCgqMiHDCKqerp7GD6+uT1yNflpmoPbrivqn6k9jByuygFkUl8A+BtoGRIxOLRgtAVIEozs91/InT1Ks/vGv7iddPQVTR7t27eeaZ+Bm66667+MhHPsLf/d3f8du//dtN9MmL1TUbLl/60pfYtm0b73rXu9i6dSt/8zd/w9vf/nZSqRQ9PT38l//yX/jjP/7j66rEz/TDUT3wCKLFgdZQNFzp4kc+YSSXGZ5OaCOj5YP89Soiohxc2RCqBU4Mt1v6mwYUrOw7y44PpaQcLDe4rrk+UYCMYqNDESkkPq6cb6mxKixC6RI0wFwCk4obX0NC0yn5tZaxw1+Ctq6FDuFV2i+KAuygiC5MZAQlv7Z4bZHfBMxVfJeQCEnsm1MPqyuVRhj5OJcZmpKIKApbyMiqSFAJ7GXAvISq48r4vJVg+Tl0oVHwqlf8QKn6btMYuXRuu5GsUkWj5NiYmkbBtRs1XiwnkBJdUZh3a406KggEvhc0/DB8gqDVKIs5LREysgkiSc1vNXovKWzsiwgJopXaLla8dNT4jQwJZQBI3LDS3K6JBEHkY4d1Ym+Y1raQhNiBfdX7/nJLRgFuWLzCPncZ/XhlhRDVkNKgvHD5h0XscwNQL9eRwfVjL36mnz799//+3+nri2eeP/rRj9LW1sZv/MZvMDs7y1/91V9dV5nXbLh89KMf5fd+7/dwHIe/+Iu/4H3vex9/+Id/yAMPPMDXvvY1Pv7xj/OpT33quirxM/1w1GakyOqLSypF38ZSU+T0HKaqL3O2azc60K/BufBapQiFVcnullDgpepNtGNcxhrRFRVT1VmdXs6AMFWNwWQ7CfX6lrKEsNDUAQD8YBxVJMgZWy/tBQROOIuuWKT0IRRhIimyOheHoS/YVYZSvc2rEUBSN5pOy32JjpZ8NpdLUxJ0mmupBQvoimQovRh6bCipJhiwP5nFUOLwcU3odJkDK1yLSkrNktFbk9GpwkAVBh2JJYlLwzm6zSydZpalAKtZpxJHzAiNAWs5bqAW1lmT7kK/wtJhn5XDXHL/dKHRYcb3zYlqrM13MG/X2JCPty2FuCV1nYrnsSXXgwCCSBJEIVYqQSQVhEiSSLRGHQohUFQdVekmoej0JVfOgp7WTDoSaRRhYqkrYxQEGiltcZnSUE0sNUEYKeSNRURDPVjAUCza9DYsLd2CMgDQhEGn2XnFNvpBSBUJcubKBO2E2klSvRZsggVqH4ooMbhpAKEsfUbNJiSuZ00XRuKHd20/04+/9u3bx9133w3ES0Xf+MY3KJfLHDhwgBtuuOG6yrxmw+XUqVP8+q//ehPf63leS+jzfffdx+jo6HVV4mf64ajNTHJz17rmq+p8dQ4nULi542byRrJJ8gQwFJONme3L4HIvVf1WB1uyQ8u2d5k5duTWLHvRZwyTrGbx6v4d6EsdDIWg20pzR/dGci8iB89SqUqOVOIOhEhQd76DKgz606/HUDqa9NCSe4xQVtmYezuqSFD2T3JDbw+rs10cnB6hz+xhZ1sccZcxTZK63rjOLjakB6/q52Cpebbm7qUeFHDDce7tuxFdiSMq0loHqtDQhMJt3etoMyxMNYWpptiU3UdSbQ2HTapZNmX3ktbyLdtNLYMqdFan7iDbeDGfLH6edjPFL6y+reGcGV/rs/NnCSNIKAa3d+5u4ZdoQmXSnqXfaufmzuUvyXYjxd6OtS2RRaZqsjmziZye5aJ9ge09XUxWi+zp7mdjvqtpKGcMg6ShIyPJxlwX2/Lx19nhyiRm0iTwdYToIN/+jqaDuhACTVcxEwMkrVeS0ZPc3LWW1AqRTTd3raXDTKEpFh3WXswlGZUvqSOxp+nfApDWkmzJbWLCLjCYvr0J3JusHyaKHLJ6hpvaX3EZqVmjw+hlW24bSe3lfW6uJkPJMpi6G1O9PIOyYG32DSS0FcBvl0vJIYxbIRimf12WLbfsXbKvDdDpGuxg2y2brxjW/DNdh34Klop+ELrmcGhFUZiamqK7uxuATCbDoUOHWLduHRBD4fr7+1fMAvkvqR+XcOgflgpunYMLoxwtTuDLgC3ZXv7/7L13nGVVma//rB1PPpVjVyc656aBbrLSZEGCKKA/RWTU64zOjFwc1FEM12sYFZk7phkGEMcccJzRERAkiEADDZ3oHKpz5XDy2Wn9/qjQdapOVVfqvB8+p6mz1t5rrZ3fs/b7ft9zK+ppyjfSmN5Bzs1QYpQzO7KA+uA0jGHUYCdCS66T1zt2sjN1CEe6NIQqWVE6mymhyqL6GBnbotPK8GbXAf7SsoOsYzElXML5VbOZH68joo8/e6znpcnZm8jkXkJXZxEMXEi3vZ3DqSdJWrsI6VOpC19NzJxDe24rLbmNVJhLSeRqWHOgmZpoKQ2lQTYnd9FpdxPRTKqDZSyMnUVt8Oh6J1mnm4PZNzmUfpMp4QtoTFq81naQpOVRHoxxXsVUFpfVEe3dRsezSDsJWnL72JZcS0e+iTKzlhmRRUwPzyesDZ11yNpdqIpOp7WbPclnaM1uYVXV3XhUsqZ9F881byZhZVhSOo1r65ZRGgiTdTLsTu/n9c43STpppgSrWVG6iDnR6STsPK+1N7K5+yB516EhXMZ55TOZEanEUAtnzBzP4XCuic2JzVQaVahOKY6rkHNdnj/YiEQSM0yqw1EurJtGbTjKoUw3L7bsZm+qnQ/OuhDSLkJxMIIp8vlXSCYfR4oOgqG5hINXYZpnoyhRco7NtkQza1obOZTpJqwZLCubwoqKqZSZfTl9LFLWHlqzr5C296MqAUrNJZQFlhHUCh1Yc26OfZkDGELB9Q5zOLMGx+2kIXIelcHzyLg225Lb2JJ4E0s6TA3OYHHJUhpCUzAmcaZyNNhums78Vg6knyHjNGGIOJWhs6kJnUdQG51QpfTS4LyJ9JK0NVXzxjPb2LVuP44TpGFeA2evXsKUObWo2vFxOj5RHNdw6EcnIRz6jpMvHHr58uWjlst4/fXXj77QIEZtuKiqSlNTE5WVPRdBLBZj/fr1/fouvuFy6uB4Llk3jSNtNKERUnsyCWedNK50MRQDUzXxvGSPX4cIoClDBa/Giu1Z5NwsitBQUMm6Pf4UQdUoCIOWUpLM58m7LoaqEjEMHM9GIsg4NqoQOLJHtUVXVCJaoDfkeux40sPxLJApwEIREYQI4sk8Gdsl74CmaMTMIIriYXuZ3pQFkLUhY3tEjCCqIrGliy3zCCCoBgmoAZJWnpzjYKgqIV0n4+RxpIehaP3GiOvZ5HI58mkbI2BiaRKnd9ti+tBtcz0bTzq40sPFxpUOqtAwlfCwEWCu16MrkveSODKHJoJoRLCkiyc9FJFFwe7V/4jhSInt2eQ8CxUFTdGwPBeQhNQAAkGy1ycpqBpDjEdPurgyj+W60JvPypUuQoDrCIKaSd5zSDl5BIKIXthG1rFJOXmQUGoGcfMu0pOougUiDTgoShBFKcGREstzcT0bRahYnovteeiKSlQPFM7U9Z2Lbgrw8JA9+knYgINAR1Wi/Tl8POmRcbIoQiDI9ywjNAwliifB9vLkvDwSMJUAITU46ht2H3nXIWX3tBHWDIK9ObF6xCF7fIo0JYB+FMFJz/NwZArXy4AQ6CKKNsYZUyk9kAmQYOVV0gkHKXtScISiRw+DPh3wDZeJ84UvfGHUyx4tgXMxRh0OLaVkzpw5/RdlKpVi+fLl/WFOfkTRqUHezdGUa2RP6k2STidhNca0yHzqg2cR0XtOfMftIpF7lVT+dVwvgaHWEQusIqjPQVHGPrshpaTTbmd7cgsHM/uRSGqDdcyLLqLUKJyVyNo2W9vaWLN/PwnL4po5M1H1HLqm05HLEtKCtOQSvNK+h5Sdoz5Uyjnl01kQrx/zzEvWTXMgs4vG9FbyXo4yvYqpodmUGw3s7cjy0t79HOjuJqTrLK2r5ZwpdUQDkLPWk82vwXW7iQSvxJILSOZzbEtuY2tiO6aicXn1apJZnTWHDgGSCxsaOJTvYEviEFnHojoY56LK2UwNRWk90MYbz65j5/odeK5k/vLFLL9sOfEpFf3XV89+dLDdTlJOU0/WYumxL72JDquJgFZCfXABU0OLiOiFr0KklwBrA569Fs3TMIJXcDil8sbBHcwoD1MWaiJtrUHKZkJGGRFzBRFjGSEtToggrbkuXu3YwY7kQVzpMTVUxfKyWdQFyvoTVw7EctNk3W5a80nWdb1JjVlLRaCc3ak9tFttXF97HQcyWf50eAcvt+5BAKsqZ/DWutlMD5ehKApBTSeo6Ugpad7byvpn36SrtZt4eZSKKRVMWzCF8rpS2vMpUk6OdquD1zu30JrrpC5UzrKSuZwVmVLUaAFQFJOUtR+wke5BUrnn8bx2Alo9IXMVQXM5qtKjYRTpVz4ufMAooseoDTI0t9RoOZxJ8ErLXrZ0NeNKycxYOavrZhPR8rTm1tOW34InbaL6FGqD5xA1GgYkXxxwjKVHzj1Ee+6V3qzzgqgxm/LAeQS02lEbU0IoIEoAMEM9H59jh5A9n4msfzIyHmNkLIzacHnkkUeO5Th8jgOudDmQ3cGatidwZE/YaictHMrt4ZzSyzgruhQFm67cs3Skf09fJEHe2U/G2kx17P2EjaVj/kWZcLp5se059mUa+8ta8k0055p4S9WVlBk97+Bdz2NTczP/sX49Gdvm6tmzaHda6cx0EdRjdFtZTNXg/217EsvzMIRGdTDOtsRhbmxYwQWVszBH6ahrexa7kptY2/kcXu92tuYPIYRCZ9Lkx69toiN7JJJpW2sbM8tCCLmOZOaXgIOm1pDySkg5nfyl/WVeal8LwCUVF7CnK8WD619CF1E+vGwVa9p286NdL1EeDFMZCnMo28m55VNob87x23//JZteWd/f166tm9i1aTfv+rtbqJp6ZJrfdttI2U0cyrxE1FzKX1oepcPaixA6AbWS9vx+knYbi0suJ6T1GKHSy+HlX8TL/gZwQZ1Ka9rm568/ybTyOQT1Vl7b/z0sN0HEMKgviZGxtuCGE5QELyfhuDzRtJY3u48cu4PZNvZmmrml4RJqg4VGkutZdFn76LZdfrX/d4S0EHOis/nVgV+zP3OY2xreScJ2+damP/HU4W39RuuLLXvY1HWI/73oMqaEj/hqtB/q5A///jS7NxT6zy24YA5XfHQ1O7MtICwebfxdf1j6tuQ+dib38pbqc7mgfHlRUcOUtZ+EtZuwYnG465u4XjcAplpKznqTEvkuIoHVKJOQVmM42nJpftO4gTfaD/aXdeQznFtRzuHMn2nvDdUHSNoH6LJ2s6DkdmLGUB+xnNvCvsQvSdlHZAPSdiMZez9TY7cS0KqO2Xb4+ByNrq4ufvWrX7Fr1y4+8YlPUFZWxuuvv051dTX19UODDY7GqA2XO+64Y8yN+5xcZN0UO5Pr+42WPjzpsj25jobwXDS6SWT/DBSGPLoySSL3IgF9BpooHr1RDE96NOUOFRgtfTTlDnEgs5e4XoIqVBL5PC/s20fGtglqGjPLY7zQvoaVVYv53f4NXDNlGT/a8yIpJ4+CQFVVOq00MT3Ay207WRCvozo4urGlnQTbUuv7jRboyVpdok7h6R3bac2kCuTV6+MxhOimM/UnNKUnjNjUF9PlGXTZnaxpewnQ0RSNmaE5/G7HTg6kO7iothpDg/85sBFXSjpyGUrMADOjlVSaAXa8uoWNa9YVGIMSlx1vbmD72mWU15Whaiqel8NyO2jLbSKk1XMgs5kOq+dhLqVN3uvC9ErYn3mTaeEl/YYLsgMv/2fo0y7RFrH9UJI9bQe4esEympJ/wHJ7wl9TlkU6b2GEVLqzfyYWuIiDmW42dw91uj+UbWdrYh8VZqwgV03O68by8mxJ7uNw7jDX1V3Hvsx+9qT2IITKwtgCXms7zB8Pb+vd1iNxTU8e3MrV9QuoCcTQVBXP9Wh8c98QowXACBgcTLSjmwovd2zpN1oA8p5Dl53l9c4tLIgNVWO2vSzt2fWUmFPpTv2o32gBsLxuTFlCOvssQWP5MTNcpJQ0JjtYN8BoAVhYWoPltbKt6yXKA4GCZIUZp4WW7AZCWjWaYg5oyyVhbSswWvpIWFtJ2bsw1YoTKqPvc+ayYcMGLr/8cuLxOI2NjXzwgx+krKyMxx57jH379vHDH/5wzG36Z/IZhO3l6bJai9Z12a140sV223EG6FkMJO8cwPPGJj7lSIfm3OFh61tyTdhejyGVsW0OJnoeovFAAKG4pOwMUgqSdg4Pyc5UM9CXl0iSdx0c6XE4203WtYbtZ8i2uFkSg8TaDMVESINdHc1D9GRKg0FUkSWZa+wvk6KMtGPRnGvGlnl6/D9CKOjs7G7Bw6MkYJJwcrTmeh6sjueRdx1K9BDYLru37ikaGOB6OQ7sPEQ+27NNnszjSZeEtRtTq6Q931iwvJQujszg4dCW39//6lZ6KfCa+5ezZDl72luJByOYmkMiV5jKI2PbeFLieO04nsW+TMsQrZI+DmbbyA8S2HO8HBKdg5lDAJQYJRzM9DycJR4Cjc1dA8XNCtve3NVEtrdNK2dxYHvxcydeEWNvog1VCPalm4bU51yLjJOlPd81pM6TOSyZRMEjN+hhL6WHI7O4XivuMNfBZOBIj93JtiF7ttQM0m3tJe1kCzRx+kg5B3FloV6NK/NkrOEjOlNWI54srnHjc2Lpe1U0kc/Jzt1338373/9+duzYQSBw5EfEtddey/PPPz+uNn3D5QxCQcEYxkelr1wRQYZm/elBFeF+p8XRogqFwAh+MaYa6Ncr0RSlP5zY9jzodQoVAgxVRQCx3kzSovejCgUFQVDVxySxrioamiiM/HCliyIgagQKwlyhR1hOoqGrR/wZFGxUoRLSQv3LO9JBCPr1cmzPI6BoGAN8LVRFwfYcFFUlEivu9CyESigSRO3LYSN6EkfqahRPWgTUwlw4PQkae45NUI0emcERBnDk17kqLCJmANt18KSCPiisWhWid99qKKLH8Xk4gqqJOuhXvEAFPMK94cC2Z/WHBvc5NceN4MAVCogbwf6wfFVTCUaK929bTs9xEoJwkeziR86LoesLVBQMJAJVGerQqKAhMBBi/NFqR0MVSr9z9kBsz8VUY6iKMuQcBNBEqHcfH0Ggoo4wM6QpY79ufY4TZ0A49KuvvsqHP/zhIeX19fU0NQ390TEafMPlDCKoRqgPFReqqg+ehaaY6GoloX4RtkLC5lLUMUYXqUJjWngmQXXow0UXOjMjszF6ExHGTJMlNT1aGu2ZDF1ph6mRGlJ2ihmRKtJOnguqZve2qyJQiOgmmqKyIF5f+EA8CiE1ypTQzIIyR9qkZDPnT509xAhq7OzC9aKURs7pL7OdHcQ1lUqjirpgAyDIuFna7CYuqp+FLjT2dSUJKCZnV0wDIKwbmKrK3nQ7Gemy6LwFxEuHvt6KhitYeP58zJDZu70RdCVCVXA5ifxWpoaXo/S/6VVQRABDiRDVKqgwj/hACKUEoR+R1da8nSytqyXvWDQlPWqiK/vrVEUQD/RkmA4ZC9GEyaxoPXF9qPOpJlQWxacT1ArD5U01hoLFgvhcVKGyJ93I3OgcDMVAESptVgvnV02nzDhizPRRYUZYVTm9XwtGN3Xmr5xd1HhpbmxhZmkNKTvP8tK5BXUCiOlBpoXrKTdLho5dCRMzppPzcsSCFw2qC6AqAUxjEaoyCv2TcaIIwYLSGkoGnbM7E22EtanUh6ahKoWGi0ClKrh4SHSRqpiUBpaiFjG0NBGmxFw8qiSuPj7HAtM0SSSGpnnZvn17f5TyWPENlzMIQw0wN3o2M8IL0XpvZAoqDaE5LIyvJKiG0JQ45aHrCOrz6Ts9FBGkJHgZscAqFDF2XZdSo4wLyi+lZICqa1SLcX7FJZQbR07coK5z8bTpXDh1Kqam8drBw8yLzMLD5YLqmaScHOdXzOJt9cuI6SFieoDaQJyzy6ZyWc18omNItBhUwyyOr2RK8Kx+BVdNGEhczp0yjavnzSVi9DxABdAQjxHQSykJv42AsRzQyNtbCSpNlBqlXFf3dqaGewTeNnZtYFl1NXfMvxhdMdjR3sHbG5ZzUfUs6iNRdEXFQ9Kas6lpqOXmD95OTUN9b18KtXVncf0HbqZu1hGVVyEEmlpOXJ9GRG8gpsU5t+LdhLUKdDVORK+l3JjKkpLLCWsDhMhEHCV4NUJfDKhIexO1kTTvPPtqtrVkMLXzmFF2NWE9Sm00iqmbhIyFlIWvRVHClBsxrq09j+rAkTbjepgra85hSmjoTcdUI8T0eqYEK7im9ir2p/ejCIWbptzIlOAUnmv5M3WhGPctv4b58SPbNzdexT8uvZKGcElBe1XTKrjqzrdSVnuk/7LaEhZdNI+qUJzKQJxZkWm8rfYCwloQXShUB+IsiE3n0qpziGlDjS5FqJQG5gMaAXMVZeGbUESPQF1QqyVknE0sdB2qMnpfrvFQHYzwzhlLqQsdmfVJWDkCahmLy24koh9xWjSUKDOiV1JinFXUOT6o1VMfvQlDPWJsmWoVU6I3EdCKqwX7+BwP3v72t/PFL34R2+55BSyEYN++fdx777284x3vGFebo9ZxKcZf/vIXzjnnHExz8kXKJgtfx2UoGSdF0ukgZXcT0qLE9DJCA14vSOnieJ1YbhOul0BXq9HVSrQi0+qjxfZskk6CTqsdT3qUmxVEtFhRoa7uXI6WdJrWdJopsRixoIaDje16CFSEEHTbWTryKcrMMJVmjBIjNOZoJyklaTdJwu7AdvOUmlWYShBTDZDK52lLZ2hKpggbOjXRKKWhIKoQuF4HjtuE63WiqTUgaslLSDspOq1OPBzqgnWoMkh7NktzOsW0WBxdU3Cli65oBDUdXahoikIum6GrrZuWva1IT1A3s5aymjJUTUFKCkKiXS+LK7PkvTRSqti9+jYhLY4uAihCGxKiLKUErxPpNSG9LoRSi0U1HRmHtGVTEfbQlC5c2YKulmCo1WhKWb8zp+U5dFspuq00Qc2kxAgTUIwebZMBr4p69GAUpJRYboq8Z5NwMiTsLFWBClQEihCYwsQB2vJpDqW7qA+VUBOMoQql5xXJoOOYz1p0tyZo2deGlJKqaRWUVMYxgwZ2rxaM5Vk9iQ0FhLUgYTWIqehFw7X7z0k33avLk0d6XXheO5pahqbWog7Y/pHOH5ATcnq1XIf2fIbDmQSO51IXjlNuhjFUyLtdZOwWHJknrFUT0EpH1HJxvRyW10HWOdyTwkCfiioiKEIb87UxECnd/gziZwLHU8flzocmruPyyF0nn47LQLq7u7nlllt47bXXSCaT1NXV0dTUxKpVq/jDH/5AODx2OYEJGS6xWIx169b1q+eejPiGy+lFt5WkJd9Jzs0R0UKUGXFCWrAgsmWsSClJOAlacy205JuJ6lHqAlMo0UtGfPCNlZSdJ+1kacl1EtQUynWboKLg4eBJUBQDTZQh8cg4zbTnNgGC8uAiQloNpjr0/HXcJI5Mk3UOkrS2o4oIcXMRAa0abRIjYjzpkXY6caSF42VI2ofJuUnqQ0uxpU5zrpVOq5MKs5ypoSlImcFyO1GEhqGUgdCxPZtOu4v92X0gYVnJChR00o7FoXSCTW3NaEJlSUUtU6JxosbYfhAl7AxN2U52pZoIqDpzovWUm7Fx57Iafl/kcdwWstZmPC+Bqc/G0KaiFUklcKLIOBk6rHb2Z/ahKTpTQ1Mp0UsxR6mELaULXiuesxvcw6DWoWgzQKk87Y0Y33A5Nrzwwgts2LCBVCrF2WefXZAyaKxMyGPLF53zOZ605Tt5tuVVtiX29Ee6lOoxVtes4qzI1HEbL+1WOy+2vUBj5kiETZlRxkUVl9IQbJgU46Uzn6Y9n+TZlnVENZNzS6Db7SCvBTjU/TC2d4CIeQ7TSz/NofSLbO38EY7s0ZHRlQjzS9/HlMhbMNUjry9stxPLbaUp/SQHU7/Bk3kEGiF9FlNj76Y8uGrSjJdO6xCd+UOoiscb7b+kw2pkYfxtKEoNz7f8mS4ni64EWVl2DkElwcHUEzheB4YSY1rsZhBxdqX28FTz46TdFBE1xuzIAvKuxVP7tvPgppdI2nmEEEwLl3PH/JVcOW02cXN0r/86rRR/at7Aax07+qNxXmjdzNW1Z7OkZMao9X2OhictstZ6OlO/wPF6M25nFcLmuZSEb0IfJonj8SRlp3ijay0butf3Z6l+o3MtK8vPZ150/lGNFykl0tmNl/kF0j1yTUhtFkrwnaDNnNAMjs+ZyUUXXcRFFx3xKXv99de57777+N3vfjfmtnwfF59TAsu12dS9g62J3QXhuZ12ghdaXydpp8fVbt7N82b3pgKjBaDD6uC1jldIu6kJjRt6hPXa8ml2pQ7xctsWFsdL6Ur/iZBWyf7un9Ft7UBKj+rw20nbTWxo/w6W192/nbaX4s3OR0jZB/rb7AtdT1o72Z/8RX+4q8QhY+9if/I35JzmouMZKzk3TWNqPQE1yLbuP9JhNaIrQSoCC1nT/iK705vJOl2E1SB1gRK2dP0Xh7JbcaVLWWA+Lj1aN080/bZ/f64svwCExu7uVr638QWSdu/4paQx1cZPtq6lsbtzdPtXemxLHGBN+7aCEOKkk+XJpnV0DNB4mSiO205X+r+OGC0AeKTza0jnXj7hYcdSSg5mD7Cu641+owUg5+VY0/4SnfYo9qlM4OWeKjBaAKSzEy//FMjJ259nPKd5VNETTzzBPffcw6c//Wl2794NwNatW7nxxhs599xz8byhIf+jYUKGy7/+679SXV199AV9fCZIxs2xO3WgaF1Tro12q2tc7WbdTM+riyIczh0iaU/8Jp128tiezYau3cyI1BBQ0kg8PJmlO7cBT0pc6RLQZ9KcfbVHswWHgXcl18vSkn0dx+uZhfFkGldm6cytZbBYoMTGdlvotrb05J6ZIHk3hS2zeDgczr4JQJkxFQ+N7clNSCSWl6bSrECSoSW7maybw5UuQW0KOSfL/sx+Uk43snesM8Kzydg2rzQfIO1YQ+6/bVaKtS0HydhH1+bJOHm2JoqfG912mj2pyTHgAGz3ELZbvK+s9SaeN3FDdyLkvByNmT0U097JeTn2ZfYedaZcet1IZ0vxOnsrDBDs8/EZjoceeohrrrmGH/zgB3zta19j1apV/OhHP+L888+npqaGTZs28T//8z/jantChsu73/3ucTnW+PiMFYks+AU5GNtzJtBu8XUlEpeJJw3tMx0sz+mRuJcuCIGHg+xtv+dRogwQFxv6cHG9/BFDpPfh48niD3aJi5RWv6EwEWTvfwzYVz1+DgLbs/uX6XHMdXv77BmfQAFkrxPxkW1SUPCkh+U6RX819jj4uqMavaQnweVwWJ49bN1YkcPs755xOJOyvyeClBJnmPMZ6E1WerSf6RLkMPtMjmZ9Hx/453/+Z772ta/R1tbGL37xC9ra2vjud7/Lxo0b+f73v8/8+cVlN0aD/6rI55QgoBrUBYvnW4nrESqK6HWMBlMJUB0o7pdQZpQT0SaeFTuo6iiozInWczjbgUMEpEQTEUL6dIQQqELFdlupCCwF+oTcBvoRKFQEF6MpPbofihJEEToxc8GQ/gQKmlJC1JhbNCHfWDGUEJowUIROhdnjiJ+wmlCFZFp4FiDQlSCddheKCFNiTMVUTBShYLltmGqAhtAUTHFE2K85f5iIbrKssq5XbK2QiBZgYXkVQfXo4w+oBtNCxc8NU9GZEZk8vxNDq0dVSor3pc1EFSf2h5ypmtQFiud+UYXKlFBDQVLTYggRQWgzitdpMxEiUrTOZxycxq+Kdu3axTvf+U4Abr75ZjRN4+tf/zpTpkyZcNu+4eJzShBUAywpmUulWVpQrgmVc8sWE9PHZ2CEtBALY4uI64WaHbrQWVqynOgkGC6mqlFmhpkfn06JEWFPxiIWXE7e7WZK7F2YahQFQXv695QYM5kZuwlFGAXibDNj1xIfoOGhCANdKSNuLqQssGpAbwJNraA6vJqQVjfhsQME1Ah1wbnk3Cxz41dgKCEybgcJaw+ryi8mppURVEvoyHfR7djMiV9DiV6JJlQ681vQBZTqZVxQeVnvDAy81PY8ipDMK63mxhlLCvqL6UGumjqfeWWVqMrRb1GGorG4ZBrTw4WvrRUEF1TMp8KcvGgLVakgFrwSQaGzr65OIRK44JgmZRwNqlCZHp7OtNC0IXWLYospNyqO3ogSRzHfCoMNFBFDCbwFJiCL4DOI09hwyWazhHojpoQQmKZJbW3tpLQ9oXDoUwE/HPr0wZUuLbkOtiX30JxtJ6gFmBebwZRgNaEisu+jxfEc2qw2dqV20GF1EFJDzIrMpjpQQ6CIZPy4xu55JO0c7VaCxvQhpoailGpJQloEy22jO/8GjsxTE7oBlBDtuTdpzb0BKFQFz6bcXEBIL3wwS+nhuN3kvTa68htI5reiKjFKAyuIm/Mx1NLigxkHtmeRsFrwcMi6HTRlNyEQzIy8hbTrsSO1i5STptKsZHF8DpbbRHtuA7aboiK4nJA+g6yb51D2ELvTu5ASzi1bRYlRQWcux4bWw7zWcgBDqJxTPZVllbVUh0dvNHrSoyXXzZbEfvamW4lqARaXTKcuVD5i2oLx4HoJ8vZO0vm1eDKNqU0jaJ6NoU45KUKFpZR02Z00pvdwMHuQgBLgrOhsqswqwkUE+Yq24eWQ7l6k9RrS60Ao5QjjHIQ29ZimQjgZOK7h0P82CeHQHzo5w6EVReFLX/oSkUiPAXzvvffyiU98goqKQuP5b//2b8fc9qQaLtlslmBw/A+QY4FvuJx6SCnJumlyXgakxFRDBHtzBFleHsuzcaVEEyoRLYyUkpSbJedaBBUNVeSQ5BGoGGoMTRndzdpyLRzZ44cSUAM9+i5WnmQ+jwdEDIO4YaIIQdrNkHWySCRBNUhYC+HIPHk3iSddNGESUKOoSuEvc8dzsVynRwnXcxB0IxQNDwFCQxE9kvOuZ+PINCDQRBhV0ZDSwfW68GS6J9+OEkdVonjS6U3CaCHQenPTjDxT4XoeKScP0kWSBmw0RcdU4+hFckulnSyO5yLwMBQNRQg8BJZnDXD2FAQUEyEU8p4NWAgkATXS6xvj4kkHW3ooqJhqEF0xsFwby3PIOx5CCKKGiT7OEHTbc3E8l6xrkXUtBIKIHiCqBSY9hNf1UoCHEGaBonQunSPZkcKxXYygQawsgm4ef8l9V7o4noPjZXpD6wWmGsFUwqPeF9LLAA6gI5ST695+rDiehssH/u3HGMEJGC7ZDA+fpIbL9OnTj3qeCSH6o43GwqRk3srn83z729/m61//+riTJvn4QE+Yb3PuIFsSa2nK7UMiqTYbOKfsLXTb3WzqXk9T7hACwZTgNM4rv5DDuW42de1iUbweIffQlHkJRXYS0UspNRdSHbqEkH50PwdDNTDoUfJ1PY/dXR38ad9utrS34nges0rKeNf8RdgiybqujezPHERKyTlly5gTredg5g2astuwvDQRvZKpoeXUhxZhDkjMqCkqCgIr004y8Rxt+58gn95PqKSOqqmrCcUvByWAquiolBzZL16GnLWOVO4ZbGcvQgQJGIuJBK9AV6f2GmejNdAc9iQ6UJU8HdYmdnS/jOV1UBeqYXpkGQ2RVYS08t7j4dGUa+e19o3sTh/A9hzOLpvP2SXzSTpJ1na+zobujeTdPCtKz+b88gvZmjzM+s49dFkp7jrrciKaheV1sjf1LPtSLyKlQ11oEQtKbiXtRnm5fQs7koewPJv6UAUry+YxJzaFgDpUVfloSCnZnWrhhdZtNKZaUYXCrGg1l1TNZ1q4fFIFBVVlqK9H++FOXvnDG2xds51UV4aKulKWXbaYxRfPJ1JyfP1fpHTpyO9hT3oNnfkDCKFSac5kZnQlpcYUlFHMDokT/OrL59SlsbHxmLU9asMln8/z+c9/nj/+8Y8YhsE//MM/cOONN/LII4/wj//4j6iqysc//vFjNlCfM4Nuu4M1HU/RabX0l3m4tFstPNf6J3Jurr/cxeFgtoXfHnyRc0rnocgDvNb6CI7ME1ANarGxvE4cmWNa9AYMNV6sy6K0ZNL8fOtG9gzQEsk4Nm35Tl7s+DNJtyckVBca5UaUNzqeoD2/FbP3YdtlHex1YNVoCC8reEjkMxkyXX9m75vf7okwArpbdpDq2M1Zyx3Cpe9ADHoo5u3tdKZ+iJQZAKTMkck9j+clKIm8D00dhe9CLwfS3RzKdhLRG/nz4Z/j9UY2pZ00ea8DD4ezopdjqGG67RTPNq9hd7onBFgVKjVmOV1ON39pfZE1Ha/0t1tuVPBy+xaeatpAWAtzTtlsgqok6bTSmHic7YnH+712lJxJvZ3licNv0Jjppu+H2e7UYZqyHdymvZXZ0eJOpiNxKNvJL/e+TJed6S9b17mX9nyK/2/GRVQHj13+oXQiw4u/fZVX/uf1I+PZ1Uzz3lZUTWXFFUtQteP3KilhN7G+67/Iub0h/RIOZjeScTs5u+xmovr4Etz5+JxoRu2ce9999/G9732P6dOn09jYyDvf+U4+9KEP8a1vfYv777+fxsZG7r333mM5Vp/THE+6HM7uLTBaAGoDU9md2kFz7mBBuGltoIENXTvpyHcxPVzKwfTLOL3hxDnXIuvmQUo68xvIu22MFiklOzrbC4wWgPnllezPHGRr9/5+obP6UB2KsNmaeJm0ky7QyPBwOZDZQH6QiJ3ndtJ24Ml+o6UP13FpO/gUrtNRWO4lyeZf7jdaBpKzNuI4h0a9bXnXYXNnE9VBlR3dr/QbLQBZxyZt52nKbiDXa5i15NrZkz7Yv0x9sIqIHiJhJ3i1c21/aGy5UU7cKOP51nUknCSudFlWOp2sm8Bxk+xKPsdAT8LqwGKa80k2dm9EDgrfzbh5NnTtJuOMTcwt79qs79pXYLT0sT/Tzq5UM94k6NoMR3drgk0vDNU/cR2Pjc9vJtl5/DRebC/PoczmI0bLADqtA3Tk9/nK5ycBQk78cyYyasPll7/8JT/84Q/51a9+xZNPPonrujiOw/r167nttttQ1RPvlOZzauNKl267Y0i5rgTosNtwB2lIGEqA1nwnmqKgCI+UXWjw5D0bSY/WSd4dnQorgON5HE4NTcMeNjRac+09+iK9N/2gGiDvpsh7GRzpDNG4yLhduIM0MVwnSz5zuGjf+VRLr1/BAKSNM6zh5WK7xdsqhuU6pGwLgUu33TqkPuc65N0kdu8YWvOdBdsUVE0kHkk7SdpJ02eMhLUQnoTD2fZ+rZCwZpB1U1heEmuQMJupRmnOdeB4+aLaJ51WEmcEbZZi2J5LR354wcDmXHeBsu5kk0lkySSyRes6m7uxc5OnJ3M0PGmTdodeS30kndYCo9XnBHGCooq+853vMH36dAKBACtXruSVV145+krAz372M4QQ3HjjjePreJIYteFy4MABVqxYAcCiRYswTZOPf/zjfs4Kn0lDQSWqlwwpt708JXoZqtAKQoRtL0+5EceREk8qhPTC1yWGoiMAgY6hDm13ODRFoaZIREvadigPlGKoKkrveZ9zcxhqGEMJog0aH0BQjaOKQsdMVQtgBobRHQlVDHWCFDrqsAn8lJ4M1aNEV1VCmo5EJaYPfb1kqhqGEkLvHUP5IH2cnGshUIhoEUJqiD6tmYybRQioCpSiKT37IePYBNUwuhLBGOQgbbkpqsxSNGEUdSSO6xG0MUboaIpKqTG8xkil2ZOB+lgRigYJRotH3MQrY8fVQVcROqERzvmwVo6C/2PzTOTnP/85d999N5/73Od4/fXXWbp0KVdddRUtLS0jrtfY2Mg999zDxRdffJxGOjyjvopd18UwjjjLaZrWH+bk4zMZqIpKXXAGcb28oLw5t5+Z4TlUmrX9OiAAh3P7WVIyixI9xt5MF/XhVSi9RoKp6oRUE4Sg1FxIYAw+IEIIZpeVMzVWUlC+tb2NaaEpzIrV9z8AD2QPI6XB3Oi5hLVQgSEvUHqdcwuvE1UtpbzhCgZffqqqUD7lMhSt0EhRlSghc1XRMFRTX4g+Br2WgKqzoLSGlqzH7Nh5BfszoOqEdYPq4BICvf5A1YFypoWOtH8w20zazhHXY6woXdZvqLXl20janVxSuZSoFkFTVDZ0NRJSY+hKlBmRixkoqNec3Uh1IMqC+ELEIFe7gGKwpGQGIW1s2aEDqs7S0qnE9KHRL3XBUmZFq48qvjYR4pUxFpw/d0i5oggWXTSPSOnxc87VFZO60ELMIhF1cb2WCvPoER8+pyf3338/H/zgB7nzzjtZsGAB3//+9wmFQjz88MPDruO6Lu95z3v4whe+wMyZM4/jaIszaudcKSXvf//7Mc2em0kul+N//a//NUTy/7HHHpvcEfqcUcT1UlaWX87m7tdoyu3vzenjUmlWs7r6WjZ2raMpd7D3gSmZEqzmximX8mb3HhqCUzm74g6a0n9BoYuQVkppYBE1oUvHNOMCUB2KcOu8xTzVuJOtHa3YnocmBOVGGW+reytvdG1gX29UUZuV4Oyya9ifqaI5t428myaqV9IQWk5tcMGQ6A0jFCbMpUxb5NK67w9kU/uJlNZSNe0KgvFrUJShsz2mPpeSyP9HOvsMltOIIoKYxmKiwatRldEbZQANkRIsz8FQFnBh9S1s634R2+ukPlzDjOhypoYvwOg1tkr0KG+tXskr7Rt6o4psDudaWV42n7dUXUpQDbK+eyOWm6cl18wFFRcR10tY37WHNzp3s7J8NlG9inklb8dQg+xLv4iULuWBqZToQa6bcil/advMzuRBLM+hPljByvJ5TA2PLwdaXbCUd0w9jxdaeqKKFKEwO1bDpdXzqZxEIbpihOMhLrrpPFRNZeuaHaS60lTUl7HsrYtYdNE8NH1SgjhHTVyvZUnp9exJraEjvx9FqFQGZnJW5HzC2nAzeD7HlYmKyPWum0gUvto2TbP/WT0Qy7JYu3Ytn/rUp/rLFEXh8ssv56WXXhq2my9+8YtUVVVx11138ec//3nMw9y1axePPPIIu3bt4p//+Z+pqqriD3/4A1OnTmXhwoVjbm/UOi533nnnqBp85JFHxjyIY4mv43Lq4UmPrJsi46ZBSoJahGDvL8eslybr9vgRBNUQITWMBJJ2hpxnEVY1FLJImUcIFUOJo6vjmxn0pCSRz5HI5/GQRA2TuBlAEZB2MuQ9C13RwVMJqjoeOXJeComHLkwCagxNOTJL6XouOc9CQSGgGjh2DkEWISwQKkKJohTRUOlDShvX68Tz0iBUVCWOqow+SsaTNq6XQwgNgUnSziNw8EgBNpowCKgxdLUwBFZKSdrNkrIzSCQB1SSmhXGlS9bLkXUyeEgCiklMj+FIj4SdQUpJRAtgKBqOtHBlrkebRnroSpigWoIre5xx004OSY8PTUwPofWGLUspkbLHl0aIUL/AWz5rkUvnEEIQKQ3jIbE8C0Uo6IpOws6Ssnvqo1qAqB44prMtA8kks706Lg5m0CBaFsUMjj20ezLwpEvOTWL1+iwF1Givjosvmj4cx1PH5a7vTlzH5aG/fs+Q8s997nN8/vOfH1J+6NAh6uvrefHFFzn//PP7y//hH/6B5557jjVr1gxZ54UXXuC2225j3bp1VFRU8P73v5+uri7+8z//c1RjfO6557jmmmu48MILef7559myZQszZ87kq1/9Kq+99hq/+tWvRr29fYz6J8CxMEief/55vv71r7N27VoOHz7Mb37zmwKnn/e///08+uijBetcddVVPP7445M+Fp+TAyk9knYHzblGcl4aTeiUetVopkpADRNRYkS0oTeTkgLfhsm52ShCUBIIUhIY+uohqkewcxn2dycIGy557zCd+R3EjFIqAlMx9bp+o6VnVqabnckD7Ms0YyoaF5YvokzLgLMB6exDKGEwViCZNiQUug8hdDS1irG6JnjSJec00ZXfSNY+gKaEKQksI6JPQ1OiwMgKtUIIIlqIiFZ4g1VRMVSDuF64vzV68gdlnTTNuSQbOw7SlEmwoKSes2JVxPUAQnHZnz3ErtQObGkzNTSNukA9ES3Ur7Xiup04znYs6w3AQ9cvRRFz6GzuYvuru9jwwmbOvmwJ9atq2e810WK3YagGcyMzqQ9WURoufOV4vAhFg4SiJ4dYmyJUQloJoQGaQD6nH/v37y8wsorNtoyHZDLJe9/7Xh588MEhirej5ZOf/CRf+tKXuPvuu4lGj9xrLrvsMr797W+Pq81JmbuUUvL444/z0EMPjcl6SqfTLF26lA984APcfPPNRZe5+uqrC4ymyTogPicnCbuN1zoepyW/t79MQWVRycXMipyDqZ4cD4TOXJYnG3cwrzzG7uSrvNLyBBKP0kCQ2lCU+vBCFsSvJaxX0Jrv4veHXmR3uidseVF8Jq67l47Uz4kqPSJpEiD/MkroHSjGhYgRZl7GSsbez97ET8k6B/rL2nOvMiV6A+WBlaiT2FcfaSfJwXQXj+x4gbVte/rLy4wyvnzuzXQ7B3mp/YX+TMbbklupNKu4rGo11YEaPK+bXO735HJ/pG8+PBT6EG0HOvnlN37Lb7/zBOddezYr3q3w3af+g2QoTdW0SnRTY1P3di6uOJezSxcSUP37hc/pTywWG9XsUEVFBaqq0tzcXFDe3NxMTc1QJ/9du3bR2NjI9ddf31/meT2ReZqmsW3bNs4666wR+9y4cSM/+clPhpRXVVXR1jZ6mYqBTGi+cM+ePXz2s59l6tSp3HTTTeRyuaOvNIBrrrmGL33pS9x0003DLmOaJjU1Nf2f0tLJy7/ic3LheDb7MlsKjBbo0UPZmlhDyhl9SPOxZk+ik4SVR1MTrG19uj+ktzOXJe1YtOS20JrfjuXabE409hstAsHCWA1u/i+0ZtZjeQNDZC283B9Btk/aOB0vQ3v25QKjBcCTeZrTf8Lyhg+ZHS+e9EjbGV5r21tgtADUhkIk7QQvt7/cb7T00ZpvYWtiC7Zn4zj7yOX+RJ/RYprXY2Uddr6xh//67pMAnH/zOWxN7WLzgR0c3tNCNtVz/3Gly6sd6+myh4a0+/icTBxvHRfDMFixYgVPP/10f5nneTz99NMFr476mDdvHhs3bmTdunX9n7e//e289a1vZd26dTQ0NBy1z5KSEg4fHirZ8MYbb1BfP3aRSRiH4ZLP5/nxj3/MZZddxty5c/nyl7/M3XffTUtLC7/73e/GNYiRePbZZ6mqqmLu3Ll85CMfob195Jt6Pp8nkUgUfHxODWwvR2t+X9E6y8vSlj9QtO54k3NsNrc3Ux0O057bhSOtgvqklceVkvZcI1k3x970kTQYIdUkrgky1lZcvB6RvIF4bUh39IJyR8P1MqTsPUXr8m4bOWfkEMjxYHl5sq7Dm50Hh9RNCZfRYbVzMFP8l9ah3CFcmce236QnR04Ppnk+uXSe9c+9iZSSQDhAxaxytjX15DlxbIdUVwrP7TEg026Ww9mhOjU+Pmc6d999Nw8++CCPPvooW7Zs4SMf+QjpdLrfj/V973tfv/NuIBBg0aJFBZ+SkhKi0SiLFi0qiDQejttuu417772XpqYmhBB4nsdf/vIX7rnnHt73vveNaxtGbbisXbuWv/7rv6ampoYHHniAG2+8kf3796MoClddddUxcWK6+uqr+eEPf8jTTz/N1772tX4nH9cdXjjpK1/5CvF4vP8zGovQ5yRBiBG1JdSTIPMu9MyaqEJBSopmA+4LMxVCIIQocArtEXMTiN7tHKz70sMkRp8ICkKeh471WOxTgRA9ejiD8ZCoQmG4SNyeMHOlyLg8hEJ/ZI70PJAU5B7qcTgVg9ry8TmJOQECdLfeeivf+MY3uO+++1i2bBnr1q3j8ccfp7q6J5Jv3759RWdIxsuXv/xl5s2bR0NDA6lUigULFnDJJZdwwQUX8JnPfGZcbY76Drly5Uo+9rGP8fLLLzN37lCtgmPBbbfd1v/34sWLWbJkCWeddRbPPvssq1evLrrOpz71Ke6+++7+74lEwjdeThEMJUBtcCaHc7uG1IXUGBXmlBMwqqGYmsaSyhqePbCHOWWzCKhhcm4aACEgZpioQqEqMJeQGmR2ZArbkz0zSRk3T2vepjawDNveQXCwD4Zaj1BrJ22smogQM+eScYbOZAW1KQTU4kJ4E8FUTEKawdnl03ipZUdB3e5EK1fVL2B6uJ50b1qBgTSEpqIJA6kvIZv9I9Dz+ieT+R2B8F2cffkSfvPP/0M+a7F/3QEWr5jLhn1bMAI60dIwitpjuJToMWqHEfnz8TlZmKhs/3jX/ehHP8pHP/rRonXPPvvsiOv+4Ac/GFNfhmHw4IMP8tnPfpZNmzaRSqVYvnw5s2fPHlM7Axn1T5LVq1fz0EMP8cUvfpHHH3/8hOS5mDlzJhUVFezcuXPYZUzT7HdUGq3Dks/JgSo06oNzmB5eXDATYSohFsUvIayVnLjBDWJqtIRp0TgZO8Sq6uswlSBCQGUwQlgLMDW8knJzJpqiMifWwPKS2Si927QxcRBhrKQuellPOHUfohQ18DZQJs+PS1UClAfPI24uYuBshK6UUBu+EmNYRd7xI4QgrIZZWt7A1fVL+2c+FBRyjkJcj3NRxQVEtMLoqZnhmcyNzkNTNFS1nlDwBqDHcdhxXkY3BTMWNXDHF9+FETD4y69eZbpaz1sWraJhTj2BcM+yYTXIRRXnEDdGjpby8fE5fkydOpVrr72Wd73rXRMyWmAMMy5PPPEE+/fv55FHHuEjH/kI2WyWW2+9FeC4KTAeOHCA9vZ2amsn7xepz8lFRC9jaclbaQjNpyW3t3cWZhZRvQxdOXkiRKKGyaVTZtKaSaMqy7ll5kwSzi4EHlXB2cT1un7F3DIjxuqac1lcMouUk8FUdMJGFaHAHQi3A2Q3CB2UKoRagRCTu50BtZapsXeSts4lYx8gpE8lrM9EVyIo4tiIogW1ELVBhffMuoBLauaTsPNE9RD14RJKjCCaiFFqlLE300jWzVAbqKMqUE1c79GlUZQIZuBSNH02tr0eKV1cdw/xqrO48o63suTShbz5l62ITnjPBTfSQTcH880EVJOzwlMpM0owlOMnse/j43OEgW89jsb9998/5vbHdNdqaGjgvvvu47777uOPf/wjjzzyCJqmccMNN3DLLbdwyy23cPbZZ4+6vVQqVTB7smfPHtatW0dZWRllZWV84Qtf4B3veAc1NTXs2rWLf/iHf2DWrFlcddVVYxm2zylGSIsT0uLUBs9CIIYozxYjbafpdrrptLoIqUFKjVJiemzUomN5N03G6STrdmOqEUJqKQG15xe75SXIOe3YXgpTLcFUyzDUCFHDJGqYOJ4HVNAgzkLiIfBw3A6y+X14SjlJR0FBo8oMEtUsbC+LJAsEkEoI6aYQBEDEsT0H2ztI3m0F6RLQqgmo5ahKEE/mcdwOLLcFAehqNZpainIUQ0cIgalWYATKMfV52J5Lu23RbR3EkR6VZpy4ESGoTlwkLeNk6bZTJOwkITVIdaCcklKFrNuO7XVhqqAJBUONUqVWUWFW4EkPVahDfgApSgRFmYOmzaRHgK7HECmrKaGkKsbMJdNQVAXD1KmmklnedBShHDffFsvtxnI7sLwuDCWOoZZjqKMXBPTxmSzl3JONN954o+D766+/juM4/W4m27dvR1XV/vyHY2XcP7euuOIKrrjiCjo7O/nRj37Eww8/zNe+9rURHWcH89prr/HWt761/3uflXbHHXfwve99jw0bNvDoo4/S1dVFXV0dV155Jf/n//wfX8vlDEEd5WxAl9XFy+2v8Oe2F0jYSTRFY1FsAVdUr2ZKaMpRjZe008GOxAsczGzEljkUoVETmMOikmvwvBT7Un+kM7cFDxtNBKkMLqc+8laCWo8g00AnVM+zyFhvkMg+g2lexuauFrKuR32ojHWdz3Aou5MZkelcWnE1wt2AsF9GkSnUwNuwiZOyG9nV/XM6ra0oCErMWcyI3kRl6Dwy1kY60o9j9UYdGWo9ZeGriRjLUQYnZiy2n+wu2vOdtFsZfn/wJXYkDxDSgkwP1XNJ1VJWlM0mUiTPz2jptpO82rGBjd3byLo5FsVms6J0Ku3ZF0hY25HSQhUBKoJLaYisJqhVogjlqMdHFDkPFEUhECq8D+jK8ZPUzzpNNKWeoDO/Hk/mUYRJibmU2siVBDV/RtjnzOaZZ57p//v+++8nGo3y6KOP9suZdHZ2cuedd447YeOEr/TS0lI+9rGP8bGPfYzXX399TOu+5S1vGdFX5oknnpjo8HxOc/KuxZvdm/mfw4/3RuyA4zms69qAQHDzlBspMUqGXd/x8uxNraUx/Wp/mScdWnK7yDhttGReoD234cjyMsvhzIuoSoiGyOVog15f2e4hOlI/JWgsptkyeLbpad4x7XrWdvyO3entIDSqA7NR3O00d/8rMb2MiDELR1tA1m1ma+f3SNmHQCh4QEd+GyXGFqJ6NS3Jn+PJdH9flnuQ1uQv0EuqCSoji0Dl3Ty7UrsJqjF+vf/Z/hDtlJNib+YQjx/OU2pEWFwyY1yvfh3P4c3uHbzSsR4ATajMjU5hd+KPHMq8RKVZji5UXJmjObMGTQSYGr0KbRQG18mG46VpST9Le+6V/jJP5unIvYIqTOqj16MVSW7o43Mm8s1vfpMnn3yyQIOttLSUL33pS1x55ZX87//9v8fc5qTOqY7lNZGPz2SQcdOs697Qb7QMZGP3m3RYI4vW5dwUTbltQ8pLjXpsL0lLdn3R9Tpyb2J7hRpBnrTIWOvwZBKhzmVT127qQvW4Msue9CYkDqpQmRqsJ519AXDJuSmENgsHg7S1h6S1vV/Mro+w3kB37iVcOTQKx5VJ0vl1eIO0ZAaTdtI4nkNTtqtAVwYg42awPJtN3Y1kBuvKjJKUk2F78oheTE2gEkNx2J9+jYyTxfEKxebac29iuaemxpLtdtOV31i0riu/EbtItJSPT1FOQDj08SaRSNDaOlRTqbW1lWQyOa42faEDn1MaV7ok7eInvyOd/oSMw+FJl7ybHlKuCgNX5nFl8fVtL40nC1+LSuni9hozEoO0k8ZQdfJeBlc6gETHQBESx+sxqDxcEAaedLC8PiOr0HBRhYHtdTDcXcrxkshBKrSDcaWLRJKwh25rX13ayeFKr8jaR8eVXsG+1hUdgUfOTSDpUdMtHHO6Z9tPQTxcXG/ofgRwvBQeIx8LH58+xCR8TnZuuukm7rzzTh577DEOHDjAgQMH+PWvf81dd901bKqfo+EbLj6nNIZiUh8sLhtdosdGfE0EoCkmUX2o3kfOS6IrYULa0PwdAGGtBm2QU6wiDExtes/fspO6YDXdVqInyZ0aA1TyMk/GtQnqPa92dGEgvW50oRPSpyPQGHxZWl6CsD53SHkfAX36USORDNVAIKgPVaANcnY2FANFKFQHSjHGGWVkKjoV5pHQ6pSTRqJRYkxFE2p/tuc+QnoNmpj8HEnHA1UECAzjxxLUalHFqff6y8fnWPH973+fa665hne/+91MmzaNadOm8e53v5urr76a7373u+Nq0zdcfE5pwmqIc8vOJqoPzah8YcUFlOgjR3kE1AjTwsvRRGFETbfVhKHEqQ9fwODLRBUmVaFz0dVCjSAhVEx9HqY+B9vZyPxYPVk3S971WFb6FgQaEo/GzB7M4CXoajVBNY60t6PIBGFtCnWRq/pVdaEnwWTebSViLieozRky/qA+m5A+96iRV2E1TFyPEdODnF+x+MiYEUS1CJVmCUtKZxLQxhdZFNKCLI7PJdDr89Oa76DLdjgr9hZiehxtgEGkCJ2q4LkY6qmpsWSoJVSEVvUamUcQaFSEVqH7kUU+o+UMeFUUCoX47ne/S3t7O2+88QZvvPEGHR0dfPe73yUcHp8v2Lh+Xm3YsIHt27cDMGfOHJYsWTKuzn18JoqqqEwLTeO9097D2s7XOZA5REyPsLRkCQtjCwhrI18YilCpDsxlWanKgcx6Uk4HATVCXWghIa2csLYKTQnRll2P5SUIqpVUhVZQahY3FnS1mrLIe0jl/kJQaePmhqvZl05yVvQCYkYVhzJbcbxubM6huuwfwXoF6XWgegdQjFXMjL+HqDGT1uxaBB7lgcXUhC7BVOupir6b7twLZO0dCARBfQ6xwIXoavVR95OmaEwPT6fd6uDq2vOoDVawuasRIRRmRRs4t2wu9cHycR8HRSjMCDfwtrq3sql7O235TvZnWrmgfCUVRiUd+Q3k3S4CajmVwbMpM+cfMw2ZY40idEoDK1Aw6Mi9juW2Y6hllAVWEA8sQhUTDyv3OUM4TcOhixEOhyfNVhByDBK4r7zyCnfddRebN2/ujwYSQrBw4UIeeughzj333EkZ1GSSSCSIx+N0d3f7KrqnCa6XxZUZQEEVEfJej9NrzsthezaqUIlo4UJV2qPgSQ/LS+N4FqrQMNUIilCRUmJ5OdxenwxdCaAr0aOG8HpeBg8HT2rkXA9HSgxFASxAoikGphIGmSWXtsilbTQjQDBu4pLH9TIIIdFEBF09MpvkehlcmQJAFRFUJTTiOBzbwcpZKGoGoXpoehzLk9ieS96zAUFIMwlrvQq1Xgavd99qSgRljA9hKSVpN4vl2ZiKjiFUwMGRWaT0UBWzV/hucnMkWV4O28sBAlMJoR0H8TlPujheEk/aKEJHU6KTvl0+x5/j8czo6+ND3/oxRnDka3gkrGyGf/v4e07659trr73GL37xC/bt24dlFQYSPPbYY2Nub9Q/eTZv3szq1auZP38+P/rRj5g/f35/+be+9S1Wr17Nyy+/zIIFC8Y8CB+f0eBJl5yzn87sy+ScJuLBy2jJK7yZ2EeXnabciLMoPof6UNWYjBbomTEIqFEG5njMuzkOZPexI7mFjJOlxChhSmgaDaHpBNWR/Rg8DJrzXWxJbKEl10pUjzIlUM/MyEziRgwpPVy7i0O7mlj39Gsc2NaIHogx+5xlLL5kCeW1xWdRVCWEyuhudImOJHa+G8vaRHvbc1TUXkA2PJMNiUMczHUT06PMDNcxJzaVoKqRtffSmVtDxm5EETpRcyEl5jmY2uhz/gghiGg940vbTezLvEFnfieKUCk1zqIisAhdGfpab7x40qXLaqIxvY4O6yAKKtXBs2gILSKmV0xaP8VQhIqhlhzTPnxObybqYHsqOOf+7Gc/433vex9XXXUVTz75JFdeeSXbt2+nubmZm266aVxtjnrG5V3veheO4/DrX/96iM6DlJKbb74ZXdf5xS9+Ma6BHCv8GZfTh6x9gP2JH5J19lERupoWu4rfHPg1jjQIaLUoGCgovKX6XFaULsScgBKsK112prbyfMvT2NLuL1eFyvnllzA/tnhE46gp28TjTX+k0y4Mx14Qm8/FFRcRFFkO7TrEr+//IS17DxxZQERZetlVXPn+K4hXjD/XTiaZpfVAE658hR0bv0VZ9Qpic9/LL3b/Nx2inWBoOopShhCCq2rOY1k8xsHEf5DrFbfrozSwkprITWN+QGeddrZ3/yft+c0F5TF9KvNKbiWiF3d6HitdVhOvtv8nXXZhiPeU0EKWllxJSPP9TXzGxvGccfnw/ROfcfnXu0/uGZclS5bw4Q9/mL/5m78hGo2yfv16ZsyYwYc//GFqa2v5whe+MOY2R+2c+8wzz/DpT3+6qDiVEIJPf/rTBWp5Pj6TiSctEtYGss4+VBFCUWewtvN1sm4W20vgej2huB4eb3RuJWGnJtRfxkmxpXtTgdECPQbNlsQm0s7w+gOWZ7E1uX2I0QKwLbkd28ti5RK8+ZeNhUYLgEyy6YXXaG5smdD4kx0ppOzm8P7/xnWzlNVdzObEXvYn92Hl8zhOG5KebQsogrbsK0OMFoCu3GvknYNj6ltKSbe1b4jRApCw99GR34p3lPDt0eB4Ngez24YYLQAHM1votA5PuA8fH5+JsWvXLt72trcBPZmi0+k0Qgg+/vGP82//9m/janPUhksymaS6engnwJqamnGLyfj4HA3Xy5G19wE9mY1tT9CU7XvQSlyZ6Reh67S6ybi5CfWX9yzarbaide1WK5Y3vOCb5dm0W+1F61zp4nhprJxL0579xZexk+zbcmBCGdjtvI0kQ7JrBwBauJaDqYO97bt4XgZ6dWhKdJOu/B6KefpJXNL2rjGNxZM2Cbtx2PqEtR/HG5/Q3UAcadE1jHEi8WjP7x+itePjc1IhQUzgcyo455aWlvbbBvX19WzatAmArq4uMpnMuNocteEybdo0XnnllWHr16xZw7Rp08Y1CB+foyGEitoro+5hoQgIqkemWAeGpupCQ5tg3hpVKJjDZKM2lcCIzrkKw68LoAgNRYFgZLiIJ5VIWWRCWdcVVQWpohs908fSyRHSe/aXUAQIjb435Jb0MNQIw70x15T4mMYihIouhvdj0ZXQpDixKigYI6QMMNUw4pTwAvA5YzkDwqEvueQS/vjHPwLwzne+k7/7u7/jgx/8ILfffjurV68eV5ujNlxuu+027r777n5raSAbN27knnvu4dZbbx3XIHx8joamhImbyxHoWG4bGgnmxxYCoKCjKZH+h9TMSAOxo4RBH42QGmZaeGbRumnhGYTU4dsPqgFmRc5CKXJ5lRllaEqUYDTMgguWoOqDDSyN8vrpzFg0dSLDJxgxQcaoqutJYpZuWcvisnloio5u6GhqvD95YXMuQ1VoZYF+TB+GWkXYmDWmvhWhUh6Yh6EM9dFRhUFlYDGaMnHxOUMNUh8qHlYdUkuoDsxEHKdM0T4+PsX59re/zW233QbAP/7jP3L33XfT3NzMO97xDh566KFxtTnqn6Wf+tSneOqpp1i2bBlXXHEF8+fPR0rJli1beOqppzjvvPP49Kc/Pa5B+PiMhpA+ndrIDbRmniZtvcqSktVkHIttqYN4mL1aIlO4oGJZf2TLeDHVAIviS8m7OXand+BIBwWVaeEZLC05h9AIhpEQgobQFFaWn8eGrg2k3Z7p0CqzklXlKwmqAQQK0xbM4LL33MLL//0EyfZOwKRm1lLeevvVlNWUTGj88YoYtuUQiN6A46RoOfwXZtaez01zb+aV7o1kZAQhNGoCZdQFK4kZcbzI9bRmnsXxugAIaFOoCb8NU60cc/8hrYpZsetpTD1Fxunx1wmopTSE30JUL650PB7KjXoWxS9jR/Jlsr25j2J6FQtilxDRyo6yto+Pz7GmrOzIdagoCp/85Ccn3OaYdFwsy+Jb3/oWP/3pTwsE6G677TY+/vGPY5ojy46fCPyootML18tiue3knEMYagUuZXTbWZJOhkqzlIgWwlQMFGVyfmmnnRTddidJJ0FYjVJilBJWR/caJ+fmSNhJOq1OSowSSvUSNKEiRM/rFOnlyWWyZFMW+YyNqukEwgGC0SC6MXEdEtd1ySayoCRw3WYUzUYJzCTherTkE+hCoypQSlwPoysarpfBkw4CHQQIVIQQKGJsY5FSIvGQ0iXndpJ2mpHSJazXEFBLJ2W2ZSC2lyPtdJGwW1GERkyvJKTGj4uWi8/px3GNKvr6jzEnEFWUz2b410+cfFFFicToE6iOZ9xjcgQwDIN7772Xe++9d8wd+fhMBqoSJKhMIahP6S+LGSXk3QQpu5n96VfQlAAV5mxCWvm4H5JZ16I1l2RXsoWoHqA+WE/cCBEapSS+7WXJu20ImaYhWIWqqFjuPjqsjbhekoixgJA2EymDpLrS7Fq3B8+TzFo2HVXXJsVwUUSCcKQDKRSEmAYiAEIjbBjUBodqs0gEebeLzvwWcm4bMeMsovp0glr1qAw117PJuO10WXvpsg4Q0aqoDMyjIrDwmAmzdVhJDmXaSTk5KswKKs0YQdWcsI+Tj89x4TRVzi0pKRm1X5zrjt2BfsxXt5SStWvX0tjYiBCCmTNnsmzZsgk5Evr4TISs08nu5J84mFmL7M04vFd5gTmxq6gOLkEbwVG2GDnX5vX2vfzPwfUknZ7oJE0onF85i8trFlJqjuw/Y3tZDmXWsj/9CrOjVwIuidwbbO/4JlnnICAIarXMDX2TN//Uzp9//Rr5TE+UzYu/fZULbziX8649m0jJ+P10pNuKzP0erFcAt+f+JkoQwZuRxrIhSRkdN0eXtYU32x6g29rSW6pSH7maOaV3EjVGdrx3pUO3vZ8NHT/hQOZVeu6ogkpzAcvL30dFYM6kGy9N2U4eP/w6WxL7+yPKKs0Y19Wdx+xo3ZDEjj4+PseHgdIojY2NfPKTn+T9738/559/PgAvvfQSjz76KF/5ylfG1f6YDJdnnnmGu+66i7179xZI/s+YMYOHH36YSy65ZFyD8PEZL550actt40CmMOLN8pLsTP6RmDGFqFI8k+9wtOaS/OHQhn6jBcCRHn9u2c6UUBnnlc9EHeFVVNppYWfiKWqCSzCUCK7sYmfnd0k7e1F6HWDj5nKaGtt48if/jXBK+51IrazF8796iSlz6phzzlljGncfUjpgrQPrpUEVXcjcfyO0BlDrCqosr4sdXT8cYLQAuBxM/Z6wMY2z1Hehj+CQbLspdiX/NOg4SFrzb7K1+/eco1cTmkSfk5xr8VrHDjYn9hWUt+YTPN28nqpACeXm+AX8fHyOB6ercu6ll17a//cXv/hF7r//fm6//fb+sre//e0sXryYf/u3f+OOO+4Yc/ujdgTYuXMn1113HdOnT+exxx5jy5YtbN68mV/+8pdMmTKFa6+9lt27d495AD4+E8H2MrTmtxaty7nddOYbx9zmjmQTCTtbtG5z90Ey7vAaLn2GlCOzRPUaEApZex9Ju2eMfTMDIbGAra9sI5NpRlIoxuY6Hltf3UkuM06tE5lCOm8OM8BWcPYOKc65bbRlXi66SkvmL1he94hd5r0EhzNri9Y1Zd4g53aNuP5YSTs5dqSKa7jsz7TRYfmaUj6nAGdAOPRLL73EOeecM6T8nHPOGVFiZSRGbbg88MADrFq1ij/96U/ccMMNzJ07l3nz5nHzzTfzzDPPsHLlSr71rW+NaxA+PuNHjqjC6srhjYzhsLzh27M9t9/4GG48bq/arkBBAK4sYoBIDdu2kXhQxD/esRykN867kpTACMq0RcYjpdP/mm0wrswfZZvpyb00SGW4j55kk5MrBOchcb3ibUokzjB1Pj4+x5eGhgYefPDBIeX//u//TkNDw7jaHLXh8uyzz/L3f//3ReuEEPz93/+9L/nvc9zRRICSYfwvNBGg1Jw+5jZnRasxhvGPmBmpJKgO7zirCI1ycxYClazbiUQS0qdjqn3OsL2ib0ojs5bOQh+gp1LQz5JpmKFx5lpSgqDOKF4nwqBNH1JsquXEzHlFVykLnI2ujOxvY6gRKgPzi69vziGgTm7OoJBq0hAqHqZdYcQoM/zXRD4nP4KJKeeerK+KBvKtb32Lf/mXf2Hx4sX81V/9FX/1V3/FkiVL+Jd/+ZdxT3aM2nDZt28fixcvHrZ+0aJF7N07dArax+dYoioG1cFFlBjTC8oFClPD5xNSx54huDoQ56LKOUNUV2dHq1lS2oB+lIiVqF7LlPC5tOa24nhZNKWcmfG7UIXZ32Jn7iWmzK7ivMsvQwxyWl104TymLZgy7pBuIQII41xQpw+qUcB8KyhDH/imWspZ8feiicIHftSYx5TIFRjKyIaHqcQ4K3o5Ea16UHkpc+LXEJzkLMphLcC55bOpNAtDKXVF46LKBZQYExMg9PHxmRyuvfZatm/fzvXXX09HRwcdHR1cf/31bN++nWuvvXZcbY5ax0VRFJqamqiqKp7ivrm5mbq6unGFNh1LfB2X0x9PemScFlpyW+iy9qEJg8rAAsrMGZjq+I55l5VhZ7KZTV0HsT2HGZFKFpVMoSoQHVHuv4+s00WHtQvHy1FuzkYVkLG305n5C45METcXUBa8jEy7ya71jex8fQ9SSmYunc7s5TMoqy0d17j7kNIF9zDYG5DuHlCqEPpiUOsRRRRtAfJuF0lrN02ZF8k7bUSNs6gOrSKsTx1VZJbtZei2DnIw/RoJ+yAhrZIp4XMpM2aiq5Or3QLgeC6Hc51sTxwg69qYqk5DqJyGUCVhbfL78zkzOJ46Lh/52o8xAxPQccll+N69J5+Oy7FmTFFFmzdvpqlpaCZWgLa24gnpfHyONRIXQwlSF1xGbXAJitAxlOiEQvRLjBBnl01jXqwWiSSgGuiDXh9JL4snu0DaIAwUpQQheh6YQa2EWnU5jpcBFFRFx1DOI2osAiSqCKEqJmY1nL16CfNXzgEkgUgAVZ14GK8QKmhTkGoVjuWQ7MhgZV00PU+0rEfobjCmWoJuLiNmnIWUHoow0dXR31R1JURFYDYxvQ5P2mgiiMTBlWnyVgseEl0JYahxVDHO12AD0BSV+mApFYaC5WUQKATUGMYxMJJ8fI4Jp6mOy4YNG1i0aBGKorBhw4YRl12yZMmY2x+T4bJ69eqiWWKFEEgpfS0Xn+OO7aXoyK6jNbeGjNOErkQoNRdSHbqYoDZ8NvPRoAiFiF78Iei5bVj5Z3Hya/G8ThS1Et1chW5eiKKU9K/fk7ywFwEqQx/YiqoQjk8sRcFwZBI2G57fwrpnNtF2sINISYh5583mvGuWU143NDxZURQMJuaPYvSGTeecVrJOGwfTz3Ao9QyutCgLLKU+chlVwXMK9804cLwc7fmtHEq/TMLejyoMyszZTAlfTFSv9/MU+Zz8nKaGy7Jly/rf0PTpvA1nOxxTAbo9e/aMuXEfn2OJJ106c2/SmHysP6Q47+ZpyjyPKy0aItdjqJPvpOl5Gazcn7By/3OkzN1PPnMAEBiByxGTMKMwUVzH5c0Xt/PEI3/CdTwAOrIWL/72VWzLYfW7Lz5mBlPeaSfrtrA3+Xt2J37dX34o3UrKPoAidGpCK4smSBwtCWsv27p+jSN7ckG5MkdTdi15L8m8+C0EtfIJb4ePj8/Y2bNnD5WVlf1/TzajvmtMmzaycqaPz/HG9pK059YO0UEB6Mitozp4wTExXKTXgW2tKVaDnV+DZqxAVSc22zMZJDtTbHx+c7/RMpA3X9jKOVcuPWaGi+2lsJwuDqSeKiiXuOTcFloyr1EWWEBAHZ8vj+1laM6t6zdaBtKZ30HKPuQbLj4nPX3RQRNZ/2RkoL1wLGwHP6GHzymLJy3ybkfROlfmsLzRJ/oaC5I80msvPiavDcahHXMssHMOnS3FheMyySyZRHGRvcnAlRlsmS4qXOfKLDm3Fc+zYJzuPJ50yDmdw9RK0k4zlQwfBenj43P8OHToEC+88AItLS14XuEPqb/9278dc3u+4eJzyqIKA0MtIee2DKlThImuHBsve4GJUEqR3tAHp6KUw0nwmghANzXiFTG6W4cacMFIgGD02DmxqiKEJsLoSgx7kAGpigCmWj7mrNMDUVAxhw2xFoS04tGPPj4nE6frjMtAfvCDH/DhD38YwzAoLy8v8IUVQozLcPG913xOWXQlSnngbESRn+1l5mJMdfJy4wxEKKXoxnnFatDNc1GOonlyvIiUhll88XwUZajT/ILz5xCvOHbhk7oSxlTj1IffUlDeE/lT2eucO/7+dTVMdXAZmhhqfJUYM4nqdUXW8vHxOd589rOf5b777qO7u5vGxkb27NnT/xlvmiB/xsXnlEUIlTJzMV40T2t2DVmnCU2JUGouoib0lgk9GEdCUcIYgdWAwLZeQ3qdKEolmrkK3biwPyT6RKPpGgsvnItt2az7U19UUZh5K2ez6roVE8o+fTQMtRwpPWbEbkRVQhxK/aknqii4jCnhy6kMLp2QYy5AzJjGnPhNHMy8RMLqiyqaS0PkkmNmtPr4TCqnaVTRQDKZDLfddtu4BTWLMSmGSyKR4Mc//jEPPfQQr7322mQ06eMzKnQ1SlXwQkrMhbgyh4KGrsRHzGQ8Vhwvh5QOitBRe4XYFLUKI/h2dPNiwAJhoohShBIcsr6UEstN4+GiK0E05fi9SoqWRlh57dnMXzmHfDaPpmtEyyKEokFcz8aVFq600YSJrg4d+3gRQhDQq1CVEHNK3svM2M1IPFQRJKCW9u/HiaDiURlYSJk5D1dmAYEmImiK6Usz+PicJNx111388pe/5JOf/OSktTkhw+WZZ57h4Ycf5rHHHiMej3PTTTeNaf3nn3+er3/966xdu5bDhw/zm9/8hhtvvLG/XkrJ5z73OR588EG6urq48MIL+d73vsfs2bMnMmyf0wxVMQgqk+/T4Hp5UvYh2nLrsdwuTK2cisASwlotqmKgKCFQRo7KyTnddFh7OZBZi+VmqAjMoja4iKheOyoF3snACBhU1B+ZgbC9LDknScpp4mDmNZJ2E1G9hvrQuZQYU9CUyZsx0tUIOhFg7KkXhsPzunGdXUgvgaNUkbIPkrQaEcKgxFxMxJiJMckpBnx8fMbHV77yFa677joef/xxFi9ejK4X+rbdf//9Y25zzIbLwYMH+cEPfsAjjzxCV1cXnZ2d/OQnP+Fd73rXmH/lpNNpli5dygc+8AFuvvnmIfX/9E//xP/7f/+PRx99lBkzZvDZz36Wq666is2bNxMInBzT8T6nJ5506MhvZVf3r7G9ZH95W/YNZsVvocScd1TDI+8maUy/xCttP+jPUr0j+TR1waWcV3EnpebUY7oNxXA8C9vN02nt5oXmb5FwDgM9vic7jD9yfuXfUBua+GucY4XnJbFyT+NYmxCBqzmQ+BEd2ecQIoaq1tGRe5Xq8FupCV2BfoxeFfr4TBpSFs0OP6b1T3K+8pWv8MQTTzB37lyAIc6542HUd6df//rXPPTQQzz//PNcc801fPOb3+Saa64hHA6zePHicQ3gmmuu4ZprrilaJ6XkgQce4DOf+Qw33HADAD/84Q+prq7mP//zP7ntttvG3J+Pz2ix3ASH038uMFoA8m4nh9MvEtbrMY+S8TjttLO+41f9Rksfh7Lr2Zd+hYhehT6JsxujwXLTOOTZ0v27fqMFQOLRbR1iW+IPlJjTCZ+kGiie24SVexLdvJJuew8d2ecAkDKBlBEQJi3p54kbi3zDxeek50yIKvrmN7/Jww8/zPvf//5Ja3PUc9W33nory5cv5/Dhw/zyl7/khhtuwDCO3bv6PXv20NTUxOWXX95fFo/HWblyJS+99NKw6+XzeRKJRMHHx2es2F6KhNVYtC5hN2J76RHX9zyXLms/mWF0Zppzm7Hckds4Fng42F6aQ5l1Q+okLk2ZjVhucuiKJwmusxVkDtQqEvlNBXXSSwEuEoeEtQVPDhUm9PHxOb6YpsmFF144qW2O2nC56667+M53vsPVV1/N97//fTo7hxN/mhz6kjlWVxcqkFZXVw+b6BF6pqXi8Xj/p6Gh4ZiO0+f0RKD0JCosWqciOMoMo5Ajvm5R0E6YA6lADDM2gRDa0bfthNI3bokYsg0Dp6BHcYx8fHyOOX/3d3/Hv/zLv0xqm6M2XP71X/+Vw4cP86EPfYif/vSn1NbWcsMNNyClHKKEdyL51Kc+RXd3d/9n//79J3pIPqcguhqlxCjuBF5inoWujJwgUBEaJcYUSvQpRevrQksxjtLGsUARGroSZlp4VZE6lSmhFZgn8SsWTZ+PEDFwD1Jink2BsaJEARVVBIgbC4c1PH18ThaEN/HPyc4rr7zCo48+ysyZM7n++uu5+eabCz7jYUxhDcFgkDvuuIPnnnuOjRs3snDhQqqrq7nwwgt597vfzWOPPTauQRSjpqYGgObm5oLy5ubm/rpimKZJLBYr+Pj4jBVdiVIfuZSQVltQHtGnUhu6eFQ5kEJqOcvKbiNUoCmiMDt6OVPCK45rWHQfphpBEwHmxq+hJnAknbyCSk1gEXPj157UhotQqjBDN+DaW4noNdRE3oFAR1HKexx0RZDayNUEJpgZ3MfHZ3IoKSnh5ptv5tJLL6WioqLgjUg8Pj6xznGHDsyePZsvf/nLfOlLX+L3v/89Dz30ELfffjv5fH68TRYwY8YMampqePrpp1m2bBnQoxezZs0aPvKRj0xKHz4+w6EIhZgxg3ml76Pb2kHWaSOk1RA3ZhLQRhfaa6gh6kNLuaLuMzRlN5F301QGZlNqTCWsT1548FhQhY6hBCkxpnFJzcdpy+0kYR8iptdSEZhLRKtCOYlnKhQlhGacj6JOx3MPUxe5nrLgW0hYu1AUk5ixgKBWg6YcO3E9H59J4wwQoHvkkUcmvc0JxzwqisL111/P5Zdfzre//e0xrZtKpdi5c2f/9z179rBu3TrKysqYOnUqf//3f8+XvvQlZs+e3R8OXVdXV6D14uMz2XjSI+8myLodICVl5mKUgIGuBBC9IdA9r0g7cb1WPC+BqpSjqhUoA/IjeTIPMkVEDTI7ejF9rzEU5cSGGjsyi+1mMESUqeHzAYkqDBTl5DVYBqIoIRTlLKQ2DZCYuk7UXATD+u74+JycCJiQJ9aZ6sU1pqu8tbWVNWvWYBgGq1evRlVVbNvmu9/9Ll/96lexbZtPfOITo27vtdde461vfWv/97vvvhuAO+64gx/84Af8wz/8A+l0mg996EN0dXVx0UUX8fjjj/saLj7HDCk9EvYBdiWeoj2/E4mLroSpD61gavhCgloJUkocdx/pzG/JW+sBGyFCBMxVhIPXoqpVeF6OlLWOjvQfsNwDABhqLaWhq4iYK1CPIlx3rEjZh2hMPk1bfjOetFBFgKrgUqZF3kpIqTwhYxovA51zJ5Kw0cfnhHEa67iUlpYWDUCIx+PMmTOHe+65hyuuuGJcbY/acHnhhRe47rrrSCQSCCE455xzeOSRR7jxxhvRNI3Pfe5z3HHHHWPq/C1veQtyhB0vhOCLX/wiX/ziF8fUro/PeMm5CXYlnqItv62/zPbSNKaex1AiTI1cADJNOvM78taR9BZSZsjm/oQQEcLBt2O5h2lN/QLX6+5fpqfsl+hqNSFjznHdrp7+k+xLPUdLbl1/mStzHM6sQRdBpkcvRyuSssDHx8dnrDzwwANFy7u6uli7di3XXXcdv/rVr7j++uvH3PaoDZfPfOYzXHvttXz605/m0Ucf5Zvf/CY33XQTX/7yl7nlllvG3LGPz8lI1u2gPb+zaF1LbjN1obNRvE4se33RZSzrDUKBK0hZ6wuMlj48mSZtvUFAm4YyCfl6xkLeTdCW21y0ri33JrWhlb7h4uNzPDmNfVyONpGxbNkyvvKVr4zLcBl1VNHGjRv5zGc+w6JFi/jiF7+IEIJ/+qd/8o0Wn9MK28sgcYepSyORSHJImSu6jCeTSBxcd3jhQ8dLIrEnZbxjQeLgyEzROstLI/EF23x8fI4P1113HVu3bh3XuqM2XDo7O6mo6ImECAaDhEIhFi1aNK5OfXxOVoJqKcYwESkRrRoFFUVEUYdJ6qip9QhMAvqMYfsIaFNRxPH301JFkJBWfNwRvQb1BIzJx8fnzCSfz49bfX9MzrmbN2/uV62VUrJt2zbS6ULZ8iVLlhRb1cfnlCCgllIfOoc9qecKynUlRG3obHQlhJQKwcDFpDK/AY4oQAkRIGBehKIECOqzCepzydrbCtvXZhAyFhZRfT32mGqcutBKdiV+jxwwbkUY1ATPOan1W3x8TkfOhFxFw/HQQw/1S52MlTHdPVevXl3gTHvdddcBPU60UkqEELhu8Wl2H59TAUMNMTV8AYYSpTW3FZCE1DIqggsoN2cihECIIIHApQgRIWe9gud1o6pVBM0LMfRFCKGhq1VURW+jO/cXstY2pJQEjVnEAxdhqMMLKB5LNMWkJrgCVQRoya4j73UTVMupDi6n3JznhxL7+BxvTmMfl74o4cF0d3fz+uuvs337dp5//vlxtT3qO9WePXvG1YGPz6lGQCuhIbyKutByHJlDSg9NCaINyOSsKiUEA5diGsuR2AhMFCXWr/MihIKpTaU8VIkbTNGjlRI5YWHQfRhqlNrQuZQH5uFJB1Xo6EqkQHTO8zwS7SnsnI1uakTLI6jq+DVePOngeCkkDgoBdHVyUh3k3ByWl0dBEFBDaCdYH8fHx+cIb7zxRtHyWCzGFVdcwWOPPcaMGcO/Uh+JUV/p06ZNG1cHPj6nGraXIed00pLbxKHMK1hemlJjNlPC51NmnoXWGw0khIZaIOc/FFUJonJyReooQiWglhStS3Wl2fbqTl7679c4tKuZ6qkVnH/9OcxfNYdo2dgNDsvtpDO/nq7cBhwvTVCrozx4HhF9Juo4o6ocz6E138S25GZa881oQmdaeCZnReYQ14tvl4/PycjpLED3zDPPHLO2R224/Nd//VfR8j4xmdra2qL1Pj6nEq6XJ++0szf1PJu7ftbvC9KWe5PW3GaWld9JVWDhCcvsfCyx8hYbntvMv97zKJlkT9TUjrW7efXxN/irr76XVW9fQSA4emPDdlM0Z56jOf0UfXPaWecASWsb0+PvJW7OH9c426wWnml5ki77SIb6w7mDdFudnFd+IWHt+Cev9PEZF6fxq6JjyagNl5Fk9oUQ3HbbbTz44IOEQid2KtzHZyI4Xoas28nu5JMFDqzgkbL3czD9KiXGNMxRJFk81ehuTfL0T/7cb7T0kc/aPPWj55i/ajaBaaNX17W8DtozLzL47mp73bRnXyGkNYz5tZHl5dmZ2lpgtPSxPbWVOdH5vuHi43OaM+pwaM/zin46Ozv54x//yOuvv86XvvSlYzlWH59jjsQh63aSdduG1LkyR8Laj+MV13A51cmlc+xa31i0bucbe8ilx7bdOacFR6aL1mWdg3jDaOGMhO3ZtOeHHhsAVzq05JvG3KaPzwlDTsLnDGTUhstwxONxLrvsMr71rW/x2GOPTcaYfHxOIAJdBIrmvhFCxVDDiJM4e/JEUFWVWHnx2YpYeRRljA66mhJiuFuMpoTGtR8VFExleL2ZkOpnhfY5hejLVTSRzxnIhA2XPubNm8eBAwcmqzkfnxOCKgIEtDKqA8uG1OlKlKrgEkzl9HwVESuPsPLaFUXrVl23gnjF2F6PBbQqosZZRetKzCVo49iPphpgZmQWSpFbV5lRTlXgxISa+/iMG3+2ZcxMmuGye/du6urqJqs5H58Tgq5GCKoVzC25kergcgQKoBBSa5kdu5660ApUZXxqjyc7kdIIb731Qi5/7yUEwj1OuEZQ59J3ns8V77uUWPnYDBddKaU2ci0RfRZ98Q+qCFEdvoySwLJxZXRWhMKU4DRWlK0kqB7xp6s0q1lVfjFRLT7mNn18fE4tJkX4YN26ddxzzz287W1vm4zmfHxOKKZaQpkwOKfib0g7LdhelrBWTUirxJwkDZKTlbpZNbzrnrdz2W0X0X64g9LqEiobyimtLhlzW4pQiehnMaPkvWSdZhwvRUCrJqBWjGu2pY+QFmZx/GxmhmfjSBtdMQkqQXTF8LVcfE4pzmTl3Ikw6qu8tLS0aAhoOp3GcRyuuOIKvvCFL0zq4Hx8TgRCCHQ1jK6GiehnXph/aXUJpdUlzGbmhNtShIqpVmCqFZMwsiME1AA516E5l2Nveg8RLcisaC3lRgxD9Y0Xn1MFPx56PIz6Cn/ggQeKlsdiMebOncuCBQsma0w+Pj4+I9Ka6+bJpjfY2NWI13vzLjMivK3uXObFpqD7My8+Pqcto766hRDceuutmOb41C59fHx8JgPLdVjXuZv1XYVpSDqsFE81raMmUEplwPd18Tn5OVGvir7zne/w9a9/naamJpYuXcq//Mu/cN555xVd9sEHH+SHP/whmzZtAmDFihV8+ctfHnb548GonXPvvPNOuru7j+VYfHx8fI5K2s2xPXWoaN3hXCctef8+5XOKcAJ0XH7+859z991387nPfY7XX3+dpUuXctVVV9HS0lJ0+WeffZbbb7+dZ555hpdeeomGhgauvPJKDh48OPbOJ4lRGy7yDI0X9/HxObmQUuJ4w2ehtzz7OI7Gx+fU4v777+eDH/wgd955JwsWLOD73/8+oVCIhx9+uOjyP/7xj/nrv/5rli1bxrx58/j3f/93PM/j6aefPs4jP8KYwqFPx/wsPj4+pxZBzaAhVNzZN66HqQmUHucR+fiMl8mZckkkEgWffD5ftDfLsli7di2XX355f5miKFx++eW89NJLoxpxJpPBtm3KykZOMHssGZMH2+rVq9G0kVd5/fXXJzQgHx8fn5EIqiYrymbRmG6hKXckZ5EmVC6qnE+pcXqHrPucRni9n4msDzQ0NBQUf+5zn+Pzn//8kMXb2tpwXZfq6uqC8urqarZu3TqqLu+9917q6uoKjJ/jzZgMl6uuuopIxL8p+Pj4nFimhCq4derFbE8eJO3kMRWN+lAFU8OVBNTTUyDQx2c49u/fTywW6/9+rIJovvrVr/Kzn/2MZ599lkBg+NQbx5oxGS6f+MQnqKqqOlZj8fE5qfC8HFJ2IWUeIQyEKEUZIU/OqYKUeTyvCylzCHSEUoKinFpZ3VWhUBMoIarrZN0sAkFUjxBUT/3j43PmMFlRRbFYrMBwGY6KigpUVaW5ubmgvLm5mZqakdNlfOMb3+CrX/0qTz31FEuWLBn3mCeDMYVD+/icKbhuJ1b+z+Stl3HdNlS1DMNYiWlegqqWn+jhjRvP6yaff5l8/i+4bhOKEscwVhAIXIaqnjo/SizPYm+6kY3dG2jJN6MJnamhqSwtWUalWYUiJi2biY/PaYNhGKxYsYKnn36aG2+8EaDf0fajH/3osOv90z/9E//3//5fnnjiCc4555zjNNrhmZSoIiklf/jDH7jlllsmZVA+PicSz8th5f9MJvtrXPcAkMN1D5HN/oZ8/lk8L3uihzgupLSxrFfJZH6G6zYCOTyvmVzuf8jlHsfzkid6iKOmOdfEs61/4kB2P5ZnkXHTbE1u4S9tL5C0Eyd6eD4+o+MEZIe+++67efDBB3n00UfZsmULH/nIR0in09x5550AvO997+NTn/pU//Jf+9rX+OxnP8vDDz/M9OnTaWpqoqmpiVQqNWm7YayM2nDZs2cPFRUVQ8o++9nPMnXqVG666SZyudykD9DH53gjZRd56yWKiSTk82uQXufQlU4B+mZbYGgocT6/Bs/rOP6DGgd5N8f25Hay7lAD8kB2P6351hMwKh+fU4Nbb72Vb3zjG9x3330sW7aMdevW8fjjj/c77O7bt4/Dhw/3L/+9730Py7K45ZZbqK2t7f984xvfOFGbMPpXRdOmTQMgn8/zq1/9ioceeogXXngB13X5xje+wV133TWqd2w+Pic7UuZx3baidZ7XiqR4qOFJj8zjecUf6lImkd6J+wU1FhzpknCGF5lrt9qYxezjOCIfn/FxopRzP/rRjw77aujZZ58t+N7Y2Di+To4ho55xWbt2LX/9139NTU0NDzzwADfeeCP79+9HURSuuuoq32jxOW0QwhjWj0VRyhGcolErwkBRim+XEBGEEj7OAxofqlCJatFh60uNE6cv4eMzNk6AdO5pwKgNl5UrV2KaJi+//DKvvvoqf/u3fzskFtzH53RAiFIMY2XROsM4F6GcmgJnilKCYayi2GVvGOegKKfGAz+gBpgdmYupDA35rAvUU2X69yUfn9OZUb8qWr16NQ899BAtLS28973v5aqrrvIjjXxOSxQlgGleAnjk8y/jeW0oShmGcR6BwOpTLnS4DyF0TPM8kDZ564VBUUVXoCinzqxpTaCGt1Rexobu9b1RRRpTQ9NYVnI2Mf3U2Q6fMxvh9Xwmsv6ZyKgNlyeeeIL9+/fzyCOP8JGPfIRsNsutt94K+KHSPqcfqlpOIHAtprEKiYXAQCilp6zR0oeilGAGVmOYZyNlDtBRlFKUU+Q1UR+majIrOpvaYC05N4cQgpAaJqSG/PuRj89pzpjEDhoaGrjvvvvYs2cP//Ef/0FrayuapnHDDTfw6U9/mrVr107q4D7/+c8jhCj4zJs3b1L78PEZDkUJomr1aNoMVK3+lDda+lCUAKpai6bNQNOmnHJGSx+qUInpcaoC1VSaVYS1sG+0+PicAYxJOXcgV1xxBVdccQWdnZ386Ec/4uGHH+ZrX/sarjt81tbxsHDhQp566qn+70fLleTj4+Pj43NKMFH/2jPTN3f8hksfpaWlfOxjH+NjH/sY//RP/zQZYypA07SjShH7+Pj4+PiccoxTRK5g/TOQMb0qchyHTZs2sX379oLy3/72tyxbtozPfvazkzo4gB07dlBXV8fMmTN5z3vew759+0ZcPp/PD0nx7ePj4+Pj43N6MGrDZdOmTcyaNYulS5cyf/58br75Zpqbm7n00kv5wAc+wNVXX82uXbsmdXArV67kBz/4AY8//jjf+9732LNnDxdffDHJ5PDS5F/5yleIx+P9n8Hpvn18fHx8fE4KToDk/+mAkCMlIRrA2972NvL5PH//93/PT3/6U376058yd+5c7rrrLv7mb/6GYDB4rMdKV1cX06ZN4/777+euu+4qukw+nyefP6JsmkgkaGhooLu72xfJ8/Hx8fEZkUQiQTweP6bPjL4+PvHX/45pjt/pP5/P8PXv/tUZ93wbtY/Lq6++ypNPPsmyZcu4+OKL+elPf8qnP/1p3vve9x7L8RVQUlLCnDlz2Llz57DLmKaJaQ4VpvLx8fHx8TmZOFGS/6c6o35V1NbWRl1dHQDxeJxwOMyqVauO2cCKkUql2LVrF7W1tce1Xx8fHx8fH5+Tg1HPuAghSCaTBAIBpJQIIchms0OcXydzuuqee+7h+uuvZ9q0aRw6dIjPfe5zqKrK7bffPml9+Pj4+Pj4nBj8eOjxMGrDRUrJnDlzCr4vX7684LsQYlJ1XA4cOMDtt99Oe3s7lZWVXHTRRbz88stUVlZOWh8+Pj4+Pj4nBD8celyM2nB55plnjuU4ivKzn/3suPfp4+Pj4+Pjc/IyasPl0ksvPZbj8PHx8fHxObPw3xSNi1E75/7iF7/Asqz+7wcOHMDzjqSmzGQyx0Q518fHx8fH53REAELK8X9O9AacIEZtuNx+++10dXX1f1+wYAGNjY3935PJJJ/61Kcmc2w+Pj4+Pj4+PgWMyTl3pO8+Pj4+Pj4+Y8B/VTQu/FTLPj4+Pj4+JwI/qmhcjCnJoo+Pj4+Pj4/PiWRMMy5PPPEE8XgcAM/zePrpp9m0aRNAgf+Lj4+Pj4+Pz9Hw3xWNhzEZLnfccUfB9w9/+MMF34U4U32cfXx8fHx8xojX+5nI+mcgozZcBoY++/j4+Pj4+PicCMbs45LP50mn08diLD4+Pj4+Pj4+IzJqw6W1tZVrrrmGSCRCLBZj1apV7Ny581iOzcfHx8fH5/SlL6poIp8zkFEbLvfeey/r1q3ji1/8It/4xjfo6urigx/84LEcm4+Pj4+Pz+mLb7iMi1H7uPzxj3/kBz/4AVdddRUA1113HfPnzyefz2Oa5jEboI+Pj4+Pj49PH6OecTl06BBLly7t/z579mxM0+Tw4cPHZGA+Pj4+Pj6nNXISPmcgYwqHVlV1yHdf+t/Hx8fHx2cc+Mq542JMuYrmzJlToNWSSqVYvnw5inJk4qajo2NyR+jj4+Pj4+Pj08uoDZdHHnnkWI7Dx8fHx8fnzMKfcRkXozZcBqvm+vj4+Pj4+EwA33AZF36SRR8fHx8fH59ThlHPuMycOXNUy+3evXvcg/Hx8fHx8Tlj8GdcxsWoDZfGxkamTZvGu9/9bqqqqo7lmHx8fHx8fM4A/OzQ42HUhsvPf/5zHn74Ye6//36uueYaPvCBD3DttdcWRBT5+Pj4+Pj4jBI/O/S4GLXV8c53vpM//OEP7Ny5kxUrVvDxj3+choYGPvnJT7Jjx45jOUYfHx8fHx8fH2Aczrn19fX84z/+Izt27OAnP/kJa9asYd68eXR2dh6L8fn4+Pj4+Jym+NK542FMyrl95HI5fvWrX/Hwww+zZs0a3vnOdxIKhSZ7bD4+Pj4+PqcvvnPuuBiT4bJmzRoeeughfvGLXzBz5kw+8IEP8Otf/5rS0tJjNT4fHx8fHx8fn35GbbgsXLiQlpYW3v3ud/Pcc88VJFz08fHx8fHxGSN+UNG4GLXhsmXLFsLhMD/84Q/5j//4j2GX83MV+fj4+Pj4jALp9Xwmsv4ZiJ+ryMfHx8fHx+eUwc9V5OPj4+PjcyLwnXPHxagNl1deeYUVK1agqmrR+nw+z29/+1ve9a53TdrgThY878ycjvPx8fE5FZAyiydzCKGhqfETPZyxcWbaHhNi1Dou559/Pu3t7f3fY7FYQV6irq4ubr/99skdXS/f+c53mD59OoFAgJUrV/LKK68ck36K0We0DPShGnieyRHPOoksahEfKSteP3CZYjH7w/09/DgGtzj8iIZvuW+sg9cv2t6A7Rpu3xXsh2JtyiJlg/+SxUY+3KiOlA8e30i9DN7qgnXlkWWG2y9H2/aCP4fdnmIU9nqkn2JtD2632DlVvM/R7OHhWi/WcrH9B8NvTbH907OsHHL9jXw1Fhtr8XN6pPVHWr7wein+99HblsOM9ej9D6w7WhsD9+LRWi92fAe20P+3HHS8Bh2hke53UsoRtnfAPim4di0sdxedmV/R1H0/7cmHyORfx3G7h+nH53Rg1DMug0+4Yifg8Cfl+Pn5z3/O3Xffzfe//31WrlzJAw88wFVXXcW2bduOec6kgTMtYkB54d8Dv/Ug+5cRBQsfKR+wvhCDyvu+9ZUMbb+/TAJCDFhLImVPm8WWH0WLIy83pN0R2huw7OC2Zf//C9ccuB9E7z9y4Pf+ZQasJ4Y/MsXbFgPGN6S1gvWOjKXwvC7YNlGst8HrD17myB7oO0f6j/qg7Rm4T4aeP8W/iYFrCIFE9pYNPreG7PGiWzKaPSwGHKniZ19vj7JvTEeO8dG2pufPwnO779/Bd5zh99VwWygKjgYFf8uCrRlu26WUvefBoL083GlahGL3lEFnSdG/GdD3cNs5/FUxcF/KYZca6Rwc8nfBhhbuv57b1eAdcWSLRt5HR1oa2Ebe3sn+zvtI5db2l2nJ/2Ba+ZeImm9BVcMjNXri8V8VjYtJTTQ09KScOPfffz8f/OAHufPOO1mwYAHf//73CYVCPPzww5Pe10AKXw+N7eQ4+o1+4G1g5IfRiH2LI3V9t6A+Q+goa47IcL96Bt44x9aeLPh74A118GcwPeWyYPnB9cX6Kex/8HJ9/x/tlhT2XOyX39jos8iKPxQH7vXhHzqj7IeCW37R+qMxuq0c7ggO6k2IgiVHv02iiEk0/PpDTZrR9FA4rmL9FV1PFG77cGsVXgejG89QQ6Hw3Bjcd7E+R3/8jl5T/O40/FE4sszk9gvgym66Mv9dYLQAOG4HLd0P4nhtI/Z8UtBnuEzkcwZyUmdItCyLtWvXcvnll/eXKYrC5ZdfzksvvVR0nXw+TyKRKPhMnMk3yEbf7kjLDH2YjrX1sfY+njYLf0GOr4Wx9HO0tcf+0Bxm/YkY6kVXHTy/dHIwmaOZUFtjWnk4U3DoUsdrbw83ezPyOkcvOVqfE92+kQz0sdzBJvt+5HkpErniz4GktQ7HbZqEHo8xvuEyLsaknLt582aamnpOBiklW7duJZVKAdDWNvnWbVtbG67rUl1dXVBeXV3N1q1bi67zla98hS984QuTPpZTiZPrsXeEYz2u8cwG+fic7pzs10Xv28PRLTvgbyEEQuhFlxMojDOjjc8pwJiO7OrVqwus7+uuuw7oOYF63vOe+MvjU5/6FHfffXf/90QiQUNDw5jb6dumPor7oRQy9AIc+ZZxxPdgJEZqY7DnxshrTMYNbLyviib2u290vQ739rzYq6LJOFNHd2TGUiuOUn/0sZwMD6mRfCKO5X1i5Gv02O6Zkc7xI8dm7NfBREc98Wtv5JnFgmN9lGM77FkvxrediohSEnwLqdxrQ+rigQvRteoia51kSCbo4zJpIzmlGLXhsmfPnmM5jqJUVFSgqirNzc0F5c3NzdTU1BRdxzRNTNOccN9CCDzpFXgJFLu4Chz7BP3Oh33rDXb86/OskL2TuIUvB2TvOgO9LwauXTiKnq761qG/VYbtFQaf6YWmT9/LnOKvLOQwvfTVDSyVBU/SwfuhyJ4c5ExTsBd6DcKB+3awSTn4QTmwh6HfBz5KBq89uFU5aEcdKRv6QBi8N4/sk8FHdPBrocL9U7iNxY9cIUP6G+FX7NBHWeHOH3hGDWy92OuXQkPhyNgL92Dhfi1m2RQe08KR9J81nkQoouBMG7ptfX+I/j4ER87qAWdo7/KFZz+9D9/BR2ewm2mx43Skf9nbX99VfmSLBm7+4D08+DzpW3Dw8gXL9jrnDvbXGnguFetnqCk18Foo3N6h+3ngsT5S2/Njb8A5IQpHPLyRP/iM7NsPA0ZUcF/tqVFElHjoWrLWdtrT/9W/dtCYS3X8Q6hKxTA9nkT4zrnjYtSGy6OPPso999xzXLNAG4bBihUrePrpp7nxxhuBHqfZp59+mo9+9KPHvH9VUYeJLCp89BTUicJbw8C/Bz4kxeD1BnwbOS5jwCgE/Q+oga0OHudwf/V8G/yIH7rMkWULH1PFxt4/hmGaGvYWNqRIDNmnxSOVRhrFcM0fWarYbb3w0d739Ct6tI/Sixjy15HlCvdgoZEli6wzfK9F+xPD/4YdWlr8LBxq/g1da6jh28PgeJz+VvrO2SJdDz+unraFogyI4BnOnOq3lYfUDWoNUax+mCi/Yke24FE/wAAb7joudpUNPgvEoBWGPwJi0HiHvyKHMvj8FwP+Ljbyweb98N46hUFFYqRBDN6SATM2g++SDJnJ6fumK9OoK72XsvCN5OytaGoFIXMFulqHqgSO3rnPKcmoDZcvfOEL/K//9b+Oq+ECcPfdd3PHHXdwzjnncN555/HAAw+QTqe58847j0v/itLjv1xMhG6ka7LYzWYU1/AYGS6EcCw9jW9UY32onrwUG/loyya7376ayejr+LQx3KNypPN+fG+KhhqvxdoeWDaa6/PodaM7TuN9/XV0Y3S0bYz3eB/9DjW8YMAYmh7N4uPYh0IoaKKOsFlL2Dwf0FHV4n4vJyX+jMu4GLeOy/Hi1ltvpbW1lfvuu4+mpiaWLVvG448/PsRh91jTZ8D4+Pj4+JyMHN8f1ZOBlMOJlI5+/TORMTnnnijn249+9KPH5dWQj4+Pj4+Pz8nNmAyXOXPmHNV46ejomNCAfHz+//buNKips+0D+B94TEJYFZW4sLkjLggqFT9UKxpHR8BaF3AfK1JxXFDUaVUU5xFnEBDRaaujuNagrVWndrRCpVrAUjGiKC5FwKUJVhSVVoiQ6/3QlzxEwi7gCddv5nzIOfe5z/3PjOTynPucwxhjbQJfKmqUBhUumzZtgo2NTXONhTHGGGs7uHBplAYVLjNmzGj29wMxxhhjjNWk3oXL+/BwOcYYY8xo8BmXRnnv7ypijDHGjBFpCaRtwl1FTdhXyOp9j69Wq+XLRIwxxtg7Q+9gabhdu3bB2dkZEokEXl5eyMjIqLX98ePH0a9fP0gkEgwcOBA//vhjo477rvDDSRhjjLE2IjExEaGhoQgPD8fVq1cxePBgyOVyPHnyxGD7tLQ0BAQEYMGCBVAqlfD394e/vz+ys7NbeOT/w4ULY4wx1hq01PSlgWJiYrBw4ULMnz8f/fv3x1dffQWpVIp9+/YZbB8XF4fx48cjLCwMrq6u2Lx5Mzw8PLBz586mpm80LlwYY4yxVtGyl4o0Gg0yMzPh4+OjW2dqagofHx+kp6cb3Cc9PV2vPQDI5fIa27eEBt0OLUSVk4pfvnzZyiNhjDH2vqv8rWiJG1LKysveyf5v/76JxWKIxeJq7Z8+fYqKiopqr8yxt7fH7du3DR5DrVYbbK9Wq5sy9CYx+sLl1atXAAAHB4dWHgljjDGhePXqVbM9cFUkEkEmk2FH0tYm92VpaVnt9y08PBwbN25sct/vK6MvXLp27YqHDx/CysqqSc+iefnyJRwcHPDw4UNYW1u/wxG+f9pSVoDztgWc2fgzv6u8RIRXr16ha9eu73B0+iQSCfLy8qDRaJrcFxFV+20zdLYFADp27AgzMzMUFhbqrS8sLIRMJjO4j0wma1D7lmD0hYupqSm6d+/+zvqztrZuE38EgLaVFeC8bQFnNn7vIm9LvNpGIpFAIpE0+3GqEolE8PT0RHJyMvz9/QH8+6iT5OTkGl9kPGLECCQnJ2P58uW6defPn8eIESNaYMSGGX3hwhhjjLF/hYaGYu7cuRg6dCiGDx+O7du34++//8b8+fMBAHPmzEG3bt0QGRkJAFi2bBk+/PBDREdHY+LEiVAoFLhy5Qp2797dahm4cGGMMcbaiOnTp+Ovv/7Chg0boFar4e7ujrNnz+om4D548ACmpv+74djb2xvffPMN1q1bh88//xy9e/fGyZMnMWDAgNaKwIVLfYnFYoSHh9d47dCYtKWsAOdtCziz8WtreZtiyZIlNV4aSklJqbZu6tSpmDp1ajOPqv5MiF9CxBhjjDGB4AfQMcYYY0wwuHBhjDHGmGBw4cIYY4wxweDChTHGGGOCIejCJTIyEsOGDYOVlRU6d+4Mf39/3LlzR69NaWkpQkJCYGdnB0tLS0yZMkXvKYBZWVkICAiAg4MDzM3N4erqiri4uGrHSklJgYeHB8RiMXr16oX9+/fXOT4iwoYNG9ClSxeYm5vDx8cH9+7d02vz3//+F97e3pBKpbC1tTXqrL6+vnB0dIREIkGXLl0we/Zs/Pnnn0ad2dnZGSYmJnrL1q2GH/Mt9LwpKSnVslYuv//+u1FmBoCrV69i7NixsLW1hZ2dHYKCglBSUiLIvCdOnMC4ceNgZ2cHExMTXLt2rVqb3bt3Y9SoUbC2toaJiQmKi4tr7K+l8qpUKgQGBqJPnz4wNTXVe1haXXbt2gVnZ2dIJBJ4eXkhIyOj0XlZCyEBk8vllJCQQNnZ2XTt2jWaMGECOTo6UklJia5NcHAwOTg4UHJyMl25coU++OAD8vb21m3fu3cvLV26lFJSUig3N5cOHTpE5ubmFB8fr2tz//59kkqlFBoaSrdu3aL4+HgyMzOjs2fP1jq+rVu3ko2NDZ08eZKysrLI19eXXFxc6PXr17o2GzZsoJiYGAoNDSUbGxujzhoTE0Pp6emUn59PqampNGLECBoxYoRRZ3ZycqKIiAhSqVS6per4jSlvWVmZXk6VSkWffvopubi4kFarNcrMjx8/pvbt21NwcDDdvn2bMjIyyNvbm6ZMmSLIvAcPHqRNmzbRnj17CAAplcpqbWJjYykyMpIiIyMJAD1//rzG/loqb15eHi1dupQOHDhA7u7utGzZslpzVlIoFCQSiWjfvn108+ZNWrhwIdna2lJhYWGj8rKWIejC5W1PnjwhAPTLL78QEVFxcTG1a9eOjh8/rmuTk5NDACg9Pb3GfhYvXkyjR4/WfV69ejW5ubnptZk+fTrJ5fIa+9BqtSSTySgqKkq3rri4mMRiMR09erRa+4SEhFoLl7cJOWulU6dOkYmJCWk0mpqDViHEzE5OThQbG1uvfG8TYt6qNBoNderUiSIiImoPWoXQMn/99dfUuXNnqqio0LW5fv06AaB79+4JKm9VeXl5NRYulS5cuNDgH/LmylvVhx9+WO/CZfjw4RQSEqL7XFFRQV27dqXIyMhqbRuTlzUPQV8qetuLFy8AAB06dAAAZGZm4s2bN/Dx8dG16devHxwdHZGenl5rP5V9AEB6erpeHwAgl8tr7SMvLw9qtVpvPxsbG3h5edW6X30JPeuzZ89w5MgReHt7o127drUk1R8rILzMW7duhZ2dHYYMGYKoqCiUl5fXI61w81Y6ffo0ioqKdI8Srw+hZS4rK4NIJNJ70qi5uTkA4NdffxVU3pbQXHkbQ6PRIDMzU+/Ypqam8PHxafXvidXOaAoXrVaL5cuXY+TIkbpHEavVaohEompzR+zt7aFWqw32k5aWhsTERAQFBenWqdVq3eOQq/bx8uVLvH792mA/lf0b2q+mY9eXkLOuWbMGFhYWsLOzw4MHD3Dq1Km6A0O4mZcuXQqFQoELFy5g0aJF2LJlC1avXm20eavau3cv5HJ5vV9yKsTMH330EdRqNaKioqDRaPD8+XOsXbsWwL/zLoSUt7k1Z97GePr0KSoqKprlbzRrXkZTuISEhCA7OxsKhaLRfWRnZ8PPzw/h4eEYN25cvfc7cuQILC0tdculS5caPYb6EHLWsLAwKJVK/PTTTzAzM8OcOXNA9Xh4s1Azh4aGYtSoURg0aBCCg4MRHR2N+Ph4lJWV1bqfUPNWevToEc6dO4cFCxbUex8hZnZzc8OBAwcQHR0NqVQKmUwGFxcX2Nvb652FMUSIeZuiNfNeunRJL++RI0caPQbW+oziXUVLlizBDz/8gIsXL+r9704mk0Gj0aC4uFivoi8sLIRMJtPr49atWxgzZgyCgoKwbt06vW0ymUxvlntlH9bW1jA3N4evry+8vLx027p166b731ZhYSG6dOmit5+7u3ubzdqxY0d07NgRffr0gaurKxwcHHD58uVaX5Eu9MxVeXl5oby8HPn5+ejbt6/R5k1ISICdnR18fX1r/C6MJXNgYCACAwNRWFgICwsLmJiYICYmBj169BBU3ubU3HnrMnToUL07pOzt7SEWi2FmZmbwe3r72Ow909qTbJpCq9VSSEgIde3ale7evVtte+XEr2+//Va37vbt29UmfmVnZ1Pnzp0pLCzM4HFWr15NAwYM0FsXEBBQr4l927Zt06178eJFoyfnGlPWSgUFBQSALly4UGO/xpb58OHDZGpqSs+ePTPavFqtllxcXGjlypU19le1rTFkrmrv3r0klUoNTuJ8n/NW9a4m57ZU3qoaOjl3yZIlus8VFRXUrVs3npz7nhN04fLZZ5+RjY0NpaSk6N2C+c8//+jaBAcHk6OjI/3888905cqVarfg3rhxgzp16kSzZs3S6+PJkye6NpW3FoaFhVFOTg7t2rWr3rdS2tra0qlTp+j69evk5+dX7XbZgoICUiqVtGnTJrK0tCSlUklKpZJevXplVFkvX75M8fHxpFQqKT8/n5KTk8nb25t69uxJpaWlBvsUeua0tDSKjY2la9euUW5uLh0+fJg6depEc+bMMcq8lZKSkggA5eTk1NqfsWSOj4+nzMxMunPnDu3cuZPMzc0pLi5OkHmLiopIqVTSmTNnCAApFApSKpWkUql0bVQqFSmVSt0t0xcvXiSlUklFRUWtlpeIdH87PT09KTAwkJRKJd28ebPWvAqFgsRiMe3fv59u3bpFQUFBZGtrS2q1ulF5WcsQdOECwOCSkJCga/P69WtavHgxtW/fnqRSKU2ePFnvH2F4eLjBPpycnPSOdeHCBXJ3dyeRSEQ9evTQO0ZNtFotrV+/nuzt7UksFtOYMWPozp07em3mzp1r8Phvn4UQetbr16/T6NGjqUOHDiQWi8nZ2ZmCg4Pp0aNHNfYp9MyZmZnk5eVFNjY2JJFIyNXVlbZs2VJjoSb0vJUCAgL0nsNRG2PIPHv2bOrQoQOJRCIaNGgQHTx4ULB5ExISDPYdHh5e5/EN9d+SeevTxpD4+HhydHQkkUhEw4cPp8uXL+ttb0he1jJMiOoxM5Ixxhhj7D1gNHcVMcYYY8z4ceHCGGOMMcHgwoUxxhhjgsGFC2OMMcYEgwsXxhhjjAkGFy6MMcYYEwwuXBhjjDEmGFy4MMYYY0wwuHBhrBnMmzcP/v7+1danpKTAxMQExcXFLT6mxsjPz4eJiYlusbKygpubG0JCQnDv3r0G9+fs7Izt27e/+4EyxtoMLlwYExiNRtPix0xKSoJKpUJWVha2bNmCnJwcDB48GMnJyS0+FsZY28aFC2Ot7LvvvoObmxvEYjGcnZ0RHR2tt93Z2RmbN2/GnDlzYG1tjaCgIADAmjVr0KdPH0ilUvTo0QPr16/Hmzdvaj3Ww4cPMW3aNNja2qJDhw7w8/NDfn5+nWO0s7ODTCZDjx494Ofnh6SkJHh5eWHBggWoqKgAAOTm5sLPzw/29vawtLTEsGHDkJSUpOtj1KhRKCgowIoVK3RncACgqKgIAQEB6NatG6RSKQYOHIijR4825CtkjLUhXLgw1ooyMzMxbdo0zJgxAzdu3MDGjRuxfv167N+/X6/dtm3bMHjwYCiVSqxfvx4AYGVlhf379+PWrVuIi4vDnj17EBsbW+Ox3rx5A7lcDisrK1y6dAmpqamwtLTE+PHjG3wWx9TUFMuWLUNBQQEyMzMBACUlJZgwYQKSk5OhVCoxfvx4TJo0CQ8ePAAAnDhxAt27d0dERARUKhVUKhUAoLS0FJ6enjhz5gyys7MRFBSE2bNnIyMjo0FjYoy1Ea39lkfGjNHcuXPJzMyMLCws9BaJREIA6Pnz50REFBgYSGPHjtXbNywsjPr376/77OTkRP7+/nUeMyoqijw9PWvcfujQIerbty9ptVrdurKyMjI3N6dz584Z3CcvL48AkFKprLYtJyeHAFBiYmKNx3Rzc6P4+Hi9LLGxsXVmmThxIq1cubLOdoyxtuc/rVw3MWa0Ro8ejS+//FJv3W+//YZZs2bpPufk5MDPz0+vzciRI7F9+3ZUVFTAzMwMADB06NBq/ScmJmLHjh3Izc1FSUkJysvLYW1tXeN4srKy8Mcff8DKykpvfWlpKXJzcxucj/7/xfKVl3xKSkqwceNGnDlzBiqVCuXl5Xj9+rXujEtNKioqsGXLFhw7dgyPHz+GRqNBWVkZpFJpg8fEGDN+XLgw1kwsLCzQq1cvvXWPHj1qdF9VpaenY+bMmdi0aRPkcjlsbGygUCiqzY+pqqSkBJ6enjhy5Ei1bZ06dWrwmHJycgAALi4uAIBVq1bh/Pnz2LZtG3r16gVzc3N88skndV6GioqKQlxcHLZv346BAwfCwsICy5cvb5VJyIyx9x8XLoy1IldXV6SmpuqtS01NRZ8+fXRnWwxJS0uDk5MTvvjiC926goKCWo/l4eGBxMREdO7cudYzM/Wh1WqxY8cOuLi4YMiQIbpxz5s3D5MnTwbwb6H09sRfkUikm8xbKTU1FX5+frozUVqtFnfv3kX//v2bNEbGmHHiybmMtaKVK1ciOTkZmzdvxt27d3HgwAHs3LkTq1atqnW/3r1748GDB1AoFMjNzcWOHTvw/fff17rPzJkz0bFjR/j5+eHSpUvIy8tDSkoKli5dWueZoKKiIqjVaty/fx+nT5+Gj48PMjIysHfvXl2B1bt3b5w4cQLXrl1DVlYWAgMDodVq9fpxdnbGxYsX8fjxYzx9+lS33/nz55GWloacnBwsWrQIhYWFdX11jLE2igsXxlqRh4cHjh07BoVCgQEDBmDDhg2IiIjAvHnzat3P19cXK1aswJIlS+Du7o60tDTd3UY1kUqluHjxIhwdHfHxxx/D1dUVCxYsQGlpaZ1nYHx8fNClSxcMHDgQa9euhaurK65fv47Ro0fr2sTExKB9+/bw9vbGpEmTIJfL4eHhoddPREQE8vPz0bNnT93lqXXr1sHDwwNyuRyjRo2CTCYz+PA+xhgDABOqnGHHGGOMMfae4zMujDHGGBMMLlwYY4wxJhhcuDDGGGNMMLhwYYwxxphgcOHCGGOMMcHgwoUxxhhjgsGFC2OMMcYEgwsXxhhjjAkGFy6MMcYYEwwuXBhjjDEmGFy4MMYYY0wwuHBhjDHGmGD8H/4iuQE/PuYjAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df['Data e Hora'] ,df['TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)'],\n", + " c=df['UMIDADE RELATIVA DO AR, HORARIA (%)'], \n", + " cmap='viridis', \n", + " alpha=0.7, \n", + " edgecolors='w')\n", + "plt.colorbar(label='Umidade Relativa do Ar (%)')\n", + "\n", + "plt.title('Temperatura do Ar x Umidade Relativa do Ar')\n", + "plt.xlabel('Hora e Data')\n", + "plt.ylabel('TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "correlacao = df.corr\n", + "\n", + "correlacao" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8784" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sqlite3\n", + "\n", + "conn = sqlite3.connect('clima.db')\n", + "# cursor = conn.cursor()\n", + "\n", + "# persistindo o DataFrame no banco de dados\n", + "df.to_sql('clima', conn, if_exists='replace')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Data e Hora BRDataHora UTCPRECIPITAÇÃO TOTAL, HORÁRIO (mm)TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)TEMPERATURA DO PONTO DE ORVALHO (°C)UMIDADE RELATIVA DO AR, HORARIA (%)RADIACAO GLOBAL (Kj/m²)VENTO, DIREÇÃO HORARIA (gr) (° (gr))VENTO, VELOCIDADE HORARIA (m/s)Data e Hora
031/12/2019 21:0001/01/202000:000.00.00.00.970.011.00.02020-01-01 00:00:00
131/12/2019 22:0001/01/202001:000.00.00.00.880.010.00.02020-01-01 01:00:00
231/12/2019 23:0001/01/202002:000.024.00.00.880.0345.00.02020-01-01 02:00:00
301/01/2020 00:0001/01/202003:000.00.00.00.830.0332.00.02020-01-01 03:00:00
401/01/2020 01:0001/01/202004:000.00.00.00.890.0316.00.02020-01-01 04:00:00
....................................
877931/12/2020 16:0031/12/202019:000.00.00.00.970.032.00.02020-12-31 19:00:00
878031/12/2020 17:0031/12/202020:000.00.00.00.910.0355.00.02020-12-31 20:00:00
878131/12/2020 18:0031/12/202021:000.00.023.00.890.0315.00.02020-12-31 21:00:00
878231/12/2020 19:0031/12/202022:000.00.00.00.880.0291.00.02020-12-31 22:00:00
878331/12/2020 20:0031/12/202023:000.00.00.00.940.0132.00.02020-12-31 23:00:00
\n", + "

8784 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Data e Hora BR Data Hora UTC PRECIPITAÇÃO TOTAL, HORÁRIO (mm) \\\n", + "0 31/12/2019 21:00 01/01/2020 00:00 0.0 \n", + "1 31/12/2019 22:00 01/01/2020 01:00 0.0 \n", + "2 31/12/2019 23:00 01/01/2020 02:00 0.0 \n", + "3 01/01/2020 00:00 01/01/2020 03:00 0.0 \n", + "4 01/01/2020 01:00 01/01/2020 04:00 0.0 \n", + "... ... ... ... ... \n", + "8779 31/12/2020 16:00 31/12/2020 19:00 0.0 \n", + "8780 31/12/2020 17:00 31/12/2020 20:00 0.0 \n", + "8781 31/12/2020 18:00 31/12/2020 21:00 0.0 \n", + "8782 31/12/2020 19:00 31/12/2020 22:00 0.0 \n", + "8783 31/12/2020 20:00 31/12/2020 23:00 0.0 \n", + "\n", + " TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 24.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 0.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " TEMPERATURA DO PONTO DE ORVALHO (°C) \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "8779 0.0 \n", + "8780 0.0 \n", + "8781 23.0 \n", + "8782 0.0 \n", + "8783 0.0 \n", + "\n", + " UMIDADE RELATIVA DO AR, HORARIA (%) RADIACAO GLOBAL (Kj/m²) \\\n", + "0 0.97 0.0 \n", + "1 0.88 0.0 \n", + "2 0.88 0.0 \n", + "3 0.83 0.0 \n", + "4 0.89 0.0 \n", + "... ... ... \n", + "8779 0.97 0.0 \n", + "8780 0.91 0.0 \n", + "8781 0.89 0.0 \n", + "8782 0.88 0.0 \n", + "8783 0.94 0.0 \n", + "\n", + " VENTO, DIREÇÃO HORARIA (gr) (° (gr)) VENTO, VELOCIDADE HORARIA (m/s) \\\n", + "0 11.0 0.0 \n", + "1 10.0 0.0 \n", + "2 345.0 0.0 \n", + "3 332.0 0.0 \n", + "4 316.0 0.0 \n", + "... ... ... \n", + "8779 32.0 0.0 \n", + "8780 355.0 0.0 \n", + "8781 315.0 0.0 \n", + "8782 291.0 0.0 \n", + "8783 132.0 0.0 \n", + "\n", + " Data e Hora \n", + "0 2020-01-01 00:00:00 \n", + "1 2020-01-01 01:00:00 \n", + "2 2020-01-01 02:00:00 \n", + "3 2020-01-01 03:00:00 \n", + "4 2020-01-01 04:00:00 \n", + "... ... \n", + "8779 2020-12-31 19:00:00 \n", + "8780 2020-12-31 20:00:00 \n", + "8781 2020-12-31 21:00:00 \n", + "8782 2020-12-31 22:00:00 \n", + "8783 2020-12-31 23:00:00 \n", + "\n", + "[8784 rows x 11 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cursor = conn.cursor()\n", + "cursor.execute('SELECT * FROM clima')\n", + "\n", + "col_names = [description[0] for description in cursor.description]\n", + "\n", + "# for row in rows:\n", + "# print(row)\n", + "\n", + "df_db = pd.DataFrame(cursor.fetchall(), columns=col_names)\n", + "\n", + "df_db" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. # Qual foi a média de valores de uma coluna específica?\n", + "A média da coluna 'UMIDADE RELATIVA DO AR, HORÁRIA (%) é de 0,63." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.6327254098360656)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_db['UMIDADE RELATIVA DO AR, HORARIA (%)'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Qual o total de registros após a limpeza dos dados? Após a limpeza dos dados, o DataFrame apresenta 8784 linhas e 11 colunas" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8784, 11)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_db.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quais foram os valores máximos e mínimos identificados?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countmeanstdmin25%50%75%max
PRECIPITAÇÃO TOTAL, HORÁRIO (mm)8784.00.0302820.6941420.00.000.000.0040.0
TEMPERATURA DO AR - BULBO SECO, HORARIA (°C)8784.02.4116587.2915060.00.000.000.0040.0
TEMPERATURA DO PONTO DE ORVALHO (°C)8784.01.5392764.8890040.00.000.000.0025.0
UMIDADE RELATIVA DO AR, HORARIA (%)8784.00.6327250.2414090.00.490.670.821.0
RADIACAO GLOBAL (Kj/m²)8784.076.901298414.2243110.00.000.000.003886.0
VENTO, DIREÇÃO HORARIA (gr) (° (gr))8784.0184.88945881.7847190.0133.00171.00254.00360.0
VENTO, VELOCIDADE HORARIA (m/s)8784.00.2030970.7535770.00.000.000.008.0
\n", + "
" + ], + "text/plain": [ + " count mean std \\\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 8784.0 0.030282 0.694142 \n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 8784.0 2.411658 7.291506 \n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 8784.0 1.539276 4.889004 \n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 8784.0 0.632725 0.241409 \n", + "RADIACAO GLOBAL (Kj/m²) 8784.0 76.901298 414.224311 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 8784.0 184.889458 81.784719 \n", + "VENTO, VELOCIDADE HORARIA (m/s) 8784.0 0.203097 0.753577 \n", + "\n", + " min 25% 50% 75% \\\n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 0.0 0.00 0.00 0.00 \n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 0.0 0.00 0.00 0.00 \n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 0.0 0.00 0.00 0.00 \n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 0.0 0.49 0.67 0.82 \n", + "RADIACAO GLOBAL (Kj/m²) 0.0 0.00 0.00 0.00 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 0.0 133.00 171.00 254.00 \n", + "VENTO, VELOCIDADE HORARIA (m/s) 0.0 0.00 0.00 0.00 \n", + "\n", + " max \n", + "PRECIPITAÇÃO TOTAL, HORÁRIO (mm) 40.0 \n", + "TEMPERATURA DO AR - BULBO SECO, HORARIA (°C) 40.0 \n", + "TEMPERATURA DO PONTO DE ORVALHO (°C) 25.0 \n", + "UMIDADE RELATIVA DO AR, HORARIA (%) 1.0 \n", + "RADIACAO GLOBAL (Kj/m²) 3886.0 \n", + "VENTO, DIREÇÃO HORARIA (gr) (° (gr)) 360.0 \n", + "VENTO, VELOCIDADE HORARIA (m/s) 8.0 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_db.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Quantos registros tinham valores nulos antes do tratamento? \n", + " Antes do tratamento o DataFrame possuia as seguintes 7 colunas com valores nulos:\n", + " PRECIPITAÇÃO TOTAL, HORÁRIO (mm), TEMPERATURA DO AR - BULBO SECO, HORARIA (°C), TEMPERATURA DO PONTO DE ORVALHO (°C), UMIDADE RELATIVA DO AR, HORARIA (%), RADIACAO GLOBAL (Kj/m²), VENTO, DIREÇÃO HORARIA (gr) (° (gr)), VENTO, VELOCIDADE HORARIA (m/s).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Qual foi o impacto da normalização de uma coluna específica? \n", + " Para realizar uma análise mais precisa e comparar informações em escalas diferentes é necessário realizar o processo de normalização dos dados. O que na prática significa ajustar todos os valores para uma mesma base. No DataFrame estudado, a coluna normalizada foi 'UMIDADE RELATIVA DO AR, HORARIA (%)'. A normalização ajustou os dados de percentual para decimais permitindo, dessa forma, que os dados possam ser manipulados com confiança e precisão." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Que padrões emergiram após a análise dos dados?\n", + "A análise dos gráficos apresentados, indica que a cidade de Itaquiraí apresenta baixa precipitação anual de forma regular, porém com alguns episódios de alta precipitação concentradas num curto período de tempo. As temperaturas são elevadas em quase todo o período registrado, padrão que se repete nos registros da umidade relativa do ar.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Como os dados foram agrupados e quais insights foram gerados?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quais visualizações forneceram as informações mais valiosas?\n", + "Através do gráfico que relaciona a umidade relativa do ar com a temperatura pode-se inferir que quando as temperaturas estão no gradiente de baixa temperatura para média temperatura (um pouco abaixo dos 30°), a umidade relativa do ar costuma ser alta, com picos de quase 100% em alguns registros. Esse tipo de dado ajuda a mensurar os impactos da relação temperatura X umidade relativa do ar na saúde humana, no clima, amplitude termica e formação de chuvas. Importante, também, para análises relativas a agricultura e pecuária, pois pode indicar periodos ideais para plantação e colheita, o tipo de cultura que melhor se adapta à região, estratégias para manejo dos animais." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Como o uso de SQL contribuiu para a organização dos resultados?\n", + "O uso do SQL é importante porque permite agregar e transformar dados de várias tabelas e colunas, facilitando a organização, cruzamento e acesso aos resultados dos dados." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# De que forma os gráficos ajudaram na compreensão dos dados?\n", + "Os gráficos ajudam na compreensão dos dados porque através deles é possível identificar padrões, verificar resultados e comparar medidas de forma mais rápida." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}