diff --git a/Team JM 4-notebook .ipynb b/Team JM 4-notebook .ipynb new file mode 100644 index 00000000..0555eeac --- /dev/null +++ b/Team JM 4-notebook .ipynb @@ -0,0 +1,4787 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6c7e849a", + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-11T09:24:53.643384Z", + "start_time": "2021-06-11T09:24:53.622385Z" + } + }, + "source": [ + "# Regression Predict Student Solution\n", + "\n", + "© Explore Data Science Academy\n", + "\n", + "---\n", + "### Honour Code\n", + "\n", + "I {**JOSEPH, OKONKWO**}, confirm - by submitting this document - that the solutions in this notebook are a result of my own work and that I abide by the [EDSA honour code](https://drive.google.com/file/d/1QDCjGZJ8-FmJE3bZdIQNwnJyQKPhHZBn/view?usp=sharing).\n", + "\n", + "Non-compliance with the honour code constitutes a material breach of contract.\n", + "\n", + "### Predict Overview: Spain Electricity Shortfall Challenge\n", + "\n", + "The government of Spain is considering an expansion of it's renewable energy resource infrastructure investments. As such, they require information on the trends and patterns of the countries renewable sources and fossil fuel energy generation. Your company has been awarded the contract to:\n", + "\n", + "- 1. analyse the supplied data;\n", + "- 2. identify potential errors in the data and clean the existing data set;\n", + "- 3. determine if additional features can be added to enrich the data set;\n", + "- 4. build a model that is capable of forecasting the three hourly demand shortfalls;\n", + "- 5. evaluate the accuracy of the best machine learning model;\n", + "- 6. determine what features were most important in the model’s prediction decision, and\n", + "- 7. explain the inner working of the model to a non-technical audience.\n", + "\n", + "Formally the problem statement was given to you, the senior data scientist, by your manager via email reads as follow:\n", + "\n", + "> In this project you are tasked to model the shortfall between the energy generated by means of fossil fuels and various renewable sources - for the country of Spain. The daily shortfall, which will be referred to as the target variable, will be modelled as a function of various city-specific weather features such as `pressure`, `wind speed`, `humidity`, etc. As with all data science projects, the provided features are rarely adequate predictors of the target variable. As such, you are required to perform feature engineering to ensure that you will be able to accurately model Spain's three hourly shortfalls.\n", + " \n", + "On top of this, she has provided you with a starter notebook containing vague explanations of what the main outcomes are. " + ] + }, + { + "cell_type": "markdown", + "id": "05600c92", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Table of Contents\n", + "\n", + "1. Importing Packages\n", + "\n", + "2. Loading Data\n", + "\n", + "3. Exploratory Data Analysis (EDA)\n", + "\n", + "4. Data Engineering\n", + "\n", + "5. Modeling\n", + "\n", + "6. Model Performance\n", + "\n", + "7. Model Explanations" + ] + }, + { + "cell_type": "markdown", + "id": "997462e2", + "metadata": {}, + "source": [ + " \n", + "## 1. Importing Packages\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Importing Packages ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to import, and briefly discuss, the libraries that will be used throughout your analysis and modelling. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "475dbe93", + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-23T10:30:53.800892Z", + "start_time": "2021-06-23T10:30:50.215449Z" + } + }, + "outputs": [], + "source": [ + "# Libraries for data loading, data manipulation and data visulisation\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import rc\n", + "import seaborn as sns\n", + "from statsmodels.graphics.correlation import plot_corr\n", + "\n", + "\n", + "# Libraries for data preparation and model building\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.linear_model import Ridge\n", + "from sklearn.linear_model import Lasso\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "# Setting global constants to ensure notebook results are reproducible\n", + "# PARAMETER_CONSTANT = ###" + ] + }, + { + "cell_type": "markdown", + "id": "f22a6718", + "metadata": {}, + "source": [ + "\n", + "## 2. Loading the Data\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Loading the data ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to load the data from the `df_train` file into a DataFrame. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fbbb6c18", + "metadata": { + "ExecuteTime": { + "end_time": "2021-06-28T08:49:35.311495Z", + "start_time": "2021-06-28T08:49:35.295494Z" + } + }, + "outputs": [], + "source": [ + "df_train = pd.read_csv(\"df_train.csv\")\n", + "df_test = pd.read_csv(\"df_test.csv\")" + ] + }, + { + "cell_type": "markdown", + "id": "81132ab3", + "metadata": {}, + "source": [ + "\n", + "## 3. Exploratory Data Analysis (EDA)\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Exploratory data analysis ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to perform an in-depth analysis of all the variables in the DataFrame. |\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e6ef4be6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8763, 49)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# confirm the dimensions of the training dataset\n", + "df_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f2b48d6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Unnamed: 0', 'time', 'Madrid_wind_speed', 'Valencia_wind_deg',\n", + " 'Bilbao_rain_1h', 'Valencia_wind_speed', 'Seville_humidity',\n", + " 'Madrid_humidity', 'Bilbao_clouds_all', 'Bilbao_wind_speed',\n", + " 'Seville_clouds_all', 'Bilbao_wind_deg', 'Barcelona_wind_speed',\n", + " 'Barcelona_wind_deg', 'Madrid_clouds_all', 'Seville_wind_speed',\n", + " 'Barcelona_rain_1h', 'Seville_pressure', 'Seville_rain_1h',\n", + " 'Bilbao_snow_3h', 'Barcelona_pressure', 'Seville_rain_3h',\n", + " 'Madrid_rain_1h', 'Barcelona_rain_3h', 'Valencia_snow_3h',\n", + " 'Madrid_weather_id', 'Barcelona_weather_id', 'Bilbao_pressure',\n", + " 'Seville_weather_id', 'Valencia_pressure', 'Seville_temp_max',\n", + " 'Madrid_pressure', 'Valencia_temp_max', 'Valencia_temp',\n", + " 'Bilbao_weather_id', 'Seville_temp', 'Valencia_humidity',\n", + " 'Valencia_temp_min', 'Barcelona_temp_max', 'Madrid_temp_max',\n", + " 'Barcelona_temp', 'Bilbao_temp_min', 'Bilbao_temp',\n", + " 'Barcelona_temp_min', 'Bilbao_temp_max', 'Seville_temp_min',\n", + " 'Madrid_temp', 'Madrid_temp_min', 'load_shortfall_3h'],\n", + " dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# look at the column names \n", + "df_train.columns" + ] + }, + { + "cell_type": "markdown", + "id": "649decf4", + "metadata": {}, + "source": [ + "1. From the results of the foregoing codes, the dataset contains 8763 rows and 49 columns. The columns contain data regarding various weather features recorded '3-hourly' from specific spanish cities. The features which include wind speed, wind direction, humidity, clouds quantity, pressure,snow levels, weather Id, rain levels, and temperature are factors which determine to varying degrees the amount of renewable energy available for generation and by extension the load shortfall in relation to fossil fuel energy sources. To enable a much closer look at the data within each column it is necessary to break up the dataframe along the column axis into a number of parts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a7bad589", + "metadata": {}, + "outputs": [], + "source": [ + "# transpose data to enable full view of data\n", + "df_train_trans = df_train.transpose(copy = True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "93368695", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Unnamed: 08763.04381.0000002529.8045380.0000002190.5000004381.0000006571.5000008.762000e+03
Madrid_wind_speed8763.02.4257291.8503710.0000001.0000002.0000003.3333331.300000e+01
Bilbao_rain_1h8763.00.1357530.3749010.0000000.0000000.0000000.1000003.000000e+00
Valencia_wind_speed8763.02.5862722.4111900.0000001.0000001.6666673.6666675.200000e+01
Seville_humidity8763.062.65879322.6212268.33333344.33333365.66666782.0000001.000000e+02
Madrid_humidity8763.057.41471724.3353966.33333336.33333358.00000078.6666671.000000e+02
Bilbao_clouds_all8763.043.46913232.5510440.00000010.00000045.00000075.0000001.000000e+02
Bilbao_wind_speed8763.01.8503561.6958880.0000000.6666671.0000002.6666671.266667e+01
Seville_clouds_all8763.013.71474824.2724820.0000000.0000000.00000020.0000009.733333e+01
Bilbao_wind_deg8763.0158.957511102.0562990.00000073.333333147.000000234.0000003.593333e+02
Barcelona_wind_speed8763.02.8704971.7921970.0000001.6666672.6666674.0000001.266667e+01
Barcelona_wind_deg8763.0190.54484889.0773370.000000118.166667200.000000260.0000003.600000e+02
Madrid_clouds_all8763.019.47339228.0536600.0000000.0000000.00000033.3333331.000000e+02
Seville_wind_speed8763.02.4250451.6728950.0000001.0000002.0000003.3333331.166667e+01
Barcelona_rain_1h8763.00.1289060.6347300.0000000.0000000.0000000.0000001.200000e+01
Seville_rain_1h8763.00.0394390.1758570.0000000.0000000.0000000.0000003.000000e+00
Bilbao_snow_3h8763.00.0319120.5572640.0000000.0000000.0000000.0000002.130000e+01
Barcelona_pressure8763.01377.96460514073.140990670.6666671014.0000001018.0000001022.0000001.001411e+06
Seville_rain_3h8763.00.0002430.0036600.0000000.0000000.0000000.0000009.333333e-02
Madrid_rain_1h8763.00.0378180.1526390.0000000.0000000.0000000.0000003.000000e+00
Barcelona_rain_3h8763.00.0004390.0039940.0000000.0000000.0000000.0000009.300000e-02
Valencia_snow_3h8763.00.0002050.0118660.0000000.0000000.0000000.0000007.916667e-01
Madrid_weather_id8763.0773.52759477.313315211.000000800.000000800.000000800.6666678.040000e+02
Barcelona_weather_id8763.0765.97968788.142235200.666667800.000000800.333333801.0000008.040000e+02
Bilbao_pressure8763.01017.73954910.046124971.3333331013.0000001019.0000001024.0000001.042000e+03
Seville_weather_id8763.0774.65881871.940009200.000000800.000000800.000000800.0000008.040000e+02
Valencia_pressure6695.01012.0514079.506214972.6666671010.3333331015.0000001018.0000001.021667e+03
Seville_temp_max8763.0297.4795278.875812272.063000291.312750297.101667304.1500003.204833e+02
Madrid_pressure8763.01010.31692022.198555927.6666671012.3333331017.3333331022.0000001.038000e+03
Valencia_temp_max8763.0291.3372337.565692269.888000285.550167291.037000297.2483333.142633e+02
Valencia_temp8763.0290.5921527.162274269.888000285.150000290.176667296.0566673.104267e+02
Bilbao_weather_id8763.0724.722362115.846537207.333333700.333333800.000000801.6666678.040000e+02
Seville_temp8763.0293.9789037.920986272.063000288.282917293.323333299.6203333.149767e+02
Valencia_humidity8763.065.24772719.26232210.33333351.33333367.00000081.3333331.000000e+02
Valencia_temp_min8763.0289.8676486.907402269.888000284.783333289.550000294.8200003.102720e+02
Barcelona_temp_max8763.0291.1576447.273538272.150000285.483333290.150000296.8550003.140767e+02
Madrid_temp_max8763.0289.5403099.752047264.983333282.150000288.116177296.8166673.144833e+02
Barcelona_temp8763.0289.8554596.528111270.816667284.973443289.416667294.9090003.073167e+02
Bilbao_temp_min8763.0285.0179736.705672264.483333280.085167284.816667289.8166673.098167e+02
Bilbao_temp8763.0286.4229296.818682267.483333281.374167286.158333291.0341673.107100e+02
Barcelona_temp_min8763.0288.4474226.102593269.483333284.150000288.150000292.9666673.048167e+02
Bilbao_temp_max8763.0287.9660277.105590269.063000282.836776287.630000292.4833333.179667e+02
Seville_temp_min8763.0291.6333568.178220270.150000285.816667290.816667297.1500003.148167e+02
Madrid_temp8763.0288.4194399.346796264.983333281.404281287.053333295.1546673.131333e+02
Madrid_temp_min8763.0287.2022039.206237264.983333280.299167286.083333293.8845003.103833e+02
load_shortfall_3h8763.010673.8576125218.046404-6618.0000007390.33333311114.66666714498.1666673.190400e+04
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" + ], + "text/plain": [ + " count mean std min \\\n", + "Unnamed: 0 8763.0 4381.000000 2529.804538 0.000000 \n", + "Madrid_wind_speed 8763.0 2.425729 1.850371 0.000000 \n", + "Bilbao_rain_1h 8763.0 0.135753 0.374901 0.000000 \n", + "Valencia_wind_speed 8763.0 2.586272 2.411190 0.000000 \n", + "Seville_humidity 8763.0 62.658793 22.621226 8.333333 \n", + "Madrid_humidity 8763.0 57.414717 24.335396 6.333333 \n", + "Bilbao_clouds_all 8763.0 43.469132 32.551044 0.000000 \n", + "Bilbao_wind_speed 8763.0 1.850356 1.695888 0.000000 \n", + "Seville_clouds_all 8763.0 13.714748 24.272482 0.000000 \n", + "Bilbao_wind_deg 8763.0 158.957511 102.056299 0.000000 \n", + "Barcelona_wind_speed 8763.0 2.870497 1.792197 0.000000 \n", + "Barcelona_wind_deg 8763.0 190.544848 89.077337 0.000000 \n", + "Madrid_clouds_all 8763.0 19.473392 28.053660 0.000000 \n", + "Seville_wind_speed 8763.0 2.425045 1.672895 0.000000 \n", + "Barcelona_rain_1h 8763.0 0.128906 0.634730 0.000000 \n", + "Seville_rain_1h 8763.0 0.039439 0.175857 0.000000 \n", + "Bilbao_snow_3h 8763.0 0.031912 0.557264 0.000000 \n", + "Barcelona_pressure 8763.0 1377.964605 14073.140990 670.666667 \n", + "Seville_rain_3h 8763.0 0.000243 0.003660 0.000000 \n", + "Madrid_rain_1h 8763.0 0.037818 0.152639 0.000000 \n", + "Barcelona_rain_3h 8763.0 0.000439 0.003994 0.000000 \n", + "Valencia_snow_3h 8763.0 0.000205 0.011866 0.000000 \n", + "Madrid_weather_id 8763.0 773.527594 77.313315 211.000000 \n", + "Barcelona_weather_id 8763.0 765.979687 88.142235 200.666667 \n", + "Bilbao_pressure 8763.0 1017.739549 10.046124 971.333333 \n", + "Seville_weather_id 8763.0 774.658818 71.940009 200.000000 \n", + "Valencia_pressure 6695.0 1012.051407 9.506214 972.666667 \n", + "Seville_temp_max 8763.0 297.479527 8.875812 272.063000 \n", + "Madrid_pressure 8763.0 1010.316920 22.198555 927.666667 \n", + "Valencia_temp_max 8763.0 291.337233 7.565692 269.888000 \n", + "Valencia_temp 8763.0 290.592152 7.162274 269.888000 \n", + "Bilbao_weather_id 8763.0 724.722362 115.846537 207.333333 \n", + "Seville_temp 8763.0 293.978903 7.920986 272.063000 \n", + "Valencia_humidity 8763.0 65.247727 19.262322 10.333333 \n", + "Valencia_temp_min 8763.0 289.867648 6.907402 269.888000 \n", + "Barcelona_temp_max 8763.0 291.157644 7.273538 272.150000 \n", + "Madrid_temp_max 8763.0 289.540309 9.752047 264.983333 \n", + "Barcelona_temp 8763.0 289.855459 6.528111 270.816667 \n", + "Bilbao_temp_min 8763.0 285.017973 6.705672 264.483333 \n", + "Bilbao_temp 8763.0 286.422929 6.818682 267.483333 \n", + "Barcelona_temp_min 8763.0 288.447422 6.102593 269.483333 \n", + "Bilbao_temp_max 8763.0 287.966027 7.105590 269.063000 \n", + "Seville_temp_min 8763.0 291.633356 8.178220 270.150000 \n", + "Madrid_temp 8763.0 288.419439 9.346796 264.983333 \n", + "Madrid_temp_min 8763.0 287.202203 9.206237 264.983333 \n", + "load_shortfall_3h 8763.0 10673.857612 5218.046404 -6618.000000 \n", + "\n", + " 25% 50% 75% max \n", + "Unnamed: 0 2190.500000 4381.000000 6571.500000 8.762000e+03 \n", + "Madrid_wind_speed 1.000000 2.000000 3.333333 1.300000e+01 \n", + "Bilbao_rain_1h 0.000000 0.000000 0.100000 3.000000e+00 \n", + "Valencia_wind_speed 1.000000 1.666667 3.666667 5.200000e+01 \n", + "Seville_humidity 44.333333 65.666667 82.000000 1.000000e+02 \n", + "Madrid_humidity 36.333333 58.000000 78.666667 1.000000e+02 \n", + "Bilbao_clouds_all 10.000000 45.000000 75.000000 1.000000e+02 \n", + "Bilbao_wind_speed 0.666667 1.000000 2.666667 1.266667e+01 \n", + "Seville_clouds_all 0.000000 0.000000 20.000000 9.733333e+01 \n", + "Bilbao_wind_deg 73.333333 147.000000 234.000000 3.593333e+02 \n", + "Barcelona_wind_speed 1.666667 2.666667 4.000000 1.266667e+01 \n", + "Barcelona_wind_deg 118.166667 200.000000 260.000000 3.600000e+02 \n", + "Madrid_clouds_all 0.000000 0.000000 33.333333 1.000000e+02 \n", + "Seville_wind_speed 1.000000 2.000000 3.333333 1.166667e+01 \n", + "Barcelona_rain_1h 0.000000 0.000000 0.000000 1.200000e+01 \n", + "Seville_rain_1h 0.000000 0.000000 0.000000 3.000000e+00 \n", + "Bilbao_snow_3h 0.000000 0.000000 0.000000 2.130000e+01 \n", + "Barcelona_pressure 1014.000000 1018.000000 1022.000000 1.001411e+06 \n", + "Seville_rain_3h 0.000000 0.000000 0.000000 9.333333e-02 \n", + "Madrid_rain_1h 0.000000 0.000000 0.000000 3.000000e+00 \n", + "Barcelona_rain_3h 0.000000 0.000000 0.000000 9.300000e-02 \n", + "Valencia_snow_3h 0.000000 0.000000 0.000000 7.916667e-01 \n", + "Madrid_weather_id 800.000000 800.000000 800.666667 8.040000e+02 \n", + "Barcelona_weather_id 800.000000 800.333333 801.000000 8.040000e+02 \n", + "Bilbao_pressure 1013.000000 1019.000000 1024.000000 1.042000e+03 \n", + "Seville_weather_id 800.000000 800.000000 800.000000 8.040000e+02 \n", + "Valencia_pressure 1010.333333 1015.000000 1018.000000 1.021667e+03 \n", + "Seville_temp_max 291.312750 297.101667 304.150000 3.204833e+02 \n", + "Madrid_pressure 1012.333333 1017.333333 1022.000000 1.038000e+03 \n", + "Valencia_temp_max 285.550167 291.037000 297.248333 3.142633e+02 \n", + "Valencia_temp 285.150000 290.176667 296.056667 3.104267e+02 \n", + "Bilbao_weather_id 700.333333 800.000000 801.666667 8.040000e+02 \n", + "Seville_temp 288.282917 293.323333 299.620333 3.149767e+02 \n", + "Valencia_humidity 51.333333 67.000000 81.333333 1.000000e+02 \n", + "Valencia_temp_min 284.783333 289.550000 294.820000 3.102720e+02 \n", + "Barcelona_temp_max 285.483333 290.150000 296.855000 3.140767e+02 \n", + "Madrid_temp_max 282.150000 288.116177 296.816667 3.144833e+02 \n", + "Barcelona_temp 284.973443 289.416667 294.909000 3.073167e+02 \n", + "Bilbao_temp_min 280.085167 284.816667 289.816667 3.098167e+02 \n", + "Bilbao_temp 281.374167 286.158333 291.034167 3.107100e+02 \n", + "Barcelona_temp_min 284.150000 288.150000 292.966667 3.048167e+02 \n", + "Bilbao_temp_max 282.836776 287.630000 292.483333 3.179667e+02 \n", + "Seville_temp_min 285.816667 290.816667 297.150000 3.148167e+02 \n", + "Madrid_temp 281.404281 287.053333 295.154667 3.131333e+02 \n", + "Madrid_temp_min 280.299167 286.083333 293.884500 3.103833e+02 \n", + "load_shortfall_3h 7390.333333 11114.666667 14498.166667 3.190400e+04 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# provide full view of summary statistcs of data features \n", + "df_train.describe().transpose(copy = True)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fd597dce", + "metadata": {}, + "source": [ + "2. It is clear from the views of the data above and accompanying statistical summaries that some recorded features are sources of concern. First, the column 'Valencia_wind_deg' - which like corresponding data from other cities is supposed to carry wind direction data measured in degrees - contains categorical data measured in levels. Second, Columns depicting hourly rain data in various cities seem incompartible with the time schedule of recording which is every 3 hours. Third, the columns containing weather_Id need to be understood more clearly to elicit its bearing on the corresponding weather feature combination for each city. A close look at these features shortly. \n", + "\n", + "3. For other features, rain, pressure, wind and others, we have to plot feature interractions to tell each feature's influence on load shortfall, if any. In order to this, we will need to produce a condensed dataframe where each column contains the average quantity of each weather feature across all cities. Since we are concerned with the shortfall of the whole country and not just for each city, it is safe to analyse for instance, the mean rainfall, mean temperature or mean pressure with respect to load shortfall for each observation of load shortfall for the whole of Spain. Moreso, we expect the values of a feature to correlate perfectly across the given cities, giving causes for multicollinearity. But before going into all that, first is a little data engineering. We have to ensure there are no missing values in the datasets and that all features are numerical in dtype. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ab18dffb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8763 entries, 0 to 8762\n", + "Data columns (total 49 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 8763 non-null int64 \n", + " 1 time 8763 non-null object \n", + " 2 Madrid_wind_speed 8763 non-null float64\n", + " 3 Valencia_wind_deg 8763 non-null object \n", + " 4 Bilbao_rain_1h 8763 non-null float64\n", + " 5 Valencia_wind_speed 8763 non-null float64\n", + " 6 Seville_humidity 8763 non-null float64\n", + " 7 Madrid_humidity 8763 non-null float64\n", + " 8 Bilbao_clouds_all 8763 non-null float64\n", + " 9 Bilbao_wind_speed 8763 non-null float64\n", + " 10 Seville_clouds_all 8763 non-null float64\n", + " 11 Bilbao_wind_deg 8763 non-null float64\n", + " 12 Barcelona_wind_speed 8763 non-null float64\n", + " 13 Barcelona_wind_deg 8763 non-null float64\n", + " 14 Madrid_clouds_all 8763 non-null float64\n", + " 15 Seville_wind_speed 8763 non-null float64\n", + " 16 Barcelona_rain_1h 8763 non-null float64\n", + " 17 Seville_pressure 8763 non-null object \n", + " 18 Seville_rain_1h 8763 non-null float64\n", + " 19 Bilbao_snow_3h 8763 non-null float64\n", + " 20 Barcelona_pressure 8763 non-null float64\n", + " 21 Seville_rain_3h 8763 non-null float64\n", + " 22 Madrid_rain_1h 8763 non-null float64\n", + " 23 Barcelona_rain_3h 8763 non-null float64\n", + " 24 Valencia_snow_3h 8763 non-null float64\n", + " 25 Madrid_weather_id 8763 non-null float64\n", + " 26 Barcelona_weather_id 8763 non-null float64\n", + " 27 Bilbao_pressure 8763 non-null float64\n", + " 28 Seville_weather_id 8763 non-null float64\n", + " 29 Valencia_pressure 6695 non-null float64\n", + " 30 Seville_temp_max 8763 non-null float64\n", + " 31 Madrid_pressure 8763 non-null float64\n", + " 32 Valencia_temp_max 8763 non-null float64\n", + " 33 Valencia_temp 8763 non-null float64\n", + " 34 Bilbao_weather_id 8763 non-null float64\n", + " 35 Seville_temp 8763 non-null float64\n", + " 36 Valencia_humidity 8763 non-null float64\n", + " 37 Valencia_temp_min 8763 non-null float64\n", + " 38 Barcelona_temp_max 8763 non-null float64\n", + " 39 Madrid_temp_max 8763 non-null float64\n", + " 40 Barcelona_temp 8763 non-null float64\n", + " 41 Bilbao_temp_min 8763 non-null float64\n", + " 42 Bilbao_temp 8763 non-null float64\n", + " 43 Barcelona_temp_min 8763 non-null float64\n", + " 44 Bilbao_temp_max 8763 non-null float64\n", + " 45 Seville_temp_min 8763 non-null float64\n", + " 46 Madrid_temp 8763 non-null float64\n", + " 47 Madrid_temp_min 8763 non-null float64\n", + " 48 load_shortfall_3h 8763 non-null float64\n", + "dtypes: float64(45), int64(1), object(3)\n", + "memory usage: 3.3+ MB\n" + ] + } + ], + "source": [ + "# Examine columns with missing values and data types\n", + "df_train.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1146cc05", + "metadata": {}, + "outputs": [], + "source": [ + "# replace missing values with the most frequent value in 'Valencia_pressure'\n", + "df_train['Valencia_pressure'] = df_train['Valencia_pressure'].fillna(df_train['Valencia_pressure'].mode()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "384e6694", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timeValencia_wind_degSeville_pressure
02015-01-01 03:00:00level_5sp25
12015-01-01 06:00:00level_10sp25
22015-01-01 09:00:00level_9sp25
32015-01-01 12:00:00level_8sp25
42015-01-01 15:00:00level_7sp25
............
87582017-12-31 09:00:00level_6sp23
87592017-12-31 12:00:00level_6sp23
87602017-12-31 15:00:00level_9sp22
87612017-12-31 18:00:00level_8sp23
87622017-12-31 21:00:00level_9sp25
\n", + "

8763 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " time Valencia_wind_deg Seville_pressure\n", + "0 2015-01-01 03:00:00 level_5 sp25\n", + "1 2015-01-01 06:00:00 level_10 sp25\n", + "2 2015-01-01 09:00:00 level_9 sp25\n", + "3 2015-01-01 12:00:00 level_8 sp25\n", + "4 2015-01-01 15:00:00 level_7 sp25\n", + "... ... ... ...\n", + "8758 2017-12-31 09:00:00 level_6 sp23\n", + "8759 2017-12-31 12:00:00 level_6 sp23\n", + "8760 2017-12-31 15:00:00 level_9 sp22\n", + "8761 2017-12-31 18:00:00 level_8 sp23\n", + "8762 2017-12-31 21:00:00 level_9 sp25\n", + "\n", + "[8763 rows x 3 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine the 3 non-numerical columns \n", + "cols = [col for col in df_train.columns if df_train[col].dtype not in ['int64', 'float64']]\n", + "df_train[cols]" + ] + }, + { + "cell_type": "markdown", + "id": "4b21baf9", + "metadata": {}, + "source": [ + "4. Following from the above results, the 'time' column can easily be converted to 'datetime' datatype and some other features engineered from it. The other two features poses a bigger problem. For one thing, they seem like different notations for angle and pressure (perhaps the whole azimuth plane is divided into levels and may be 'sp' stands for static pressure or standard pressure pressure where 1 sp equals 1 atm = 100 kPa, especially seeing that temperatures were most likely given in kelvin)). Let us go ahead and examine the unique values of these features." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cc3dc185", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n", + " [Text(0, 0, 'level_5'),\n", + " Text(1, 0, 'level_10'),\n", + " Text(2, 0, 'level_9'),\n", + " Text(3, 0, 'level_8'),\n", + " Text(4, 0, 'level_7'),\n", + " Text(5, 0, 'level_6'),\n", + " Text(6, 0, 'level_4'),\n", + " Text(7, 0, 'level_3'),\n", + " Text(8, 0, 'level_1'),\n", + " Text(9, 0, 'level_2')])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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yG3dJmkcqKMuBIyNixSDbNTOzQWi1WHy2nY1GxG003+exax/9zwHmtDODmZm1rtWjoa7pdBAzM6uvloqFpCdYeQTSGsDqwFMRsX6ngpmZWX20umYxqvha0n74xDgzs2FjUFedjYhLgbe2N4qZmdVVq5uh3lF4uRpp53Q7zrkwM7MhoNWjofYpPF8O/JF0zSYzMxsGWt1n8YFOBzEzs/pq9dpQkyT9QNJSSUskfV/SpE6HMzOzemh1B/d3SJfdmEC64usPczczMxsGWi0W4yLiOxGxPD/OBnxZVzOzYaLVYvGwpIMkjciPg4C/djKYmZnVR6vF4hDgAOAvwGJgf9LF/8zMbBho9dDZzwOzI+JRAEljSDdDOqRTwczMrD5aXbN4dW+hAIiIR4BtOxPJzMzqptVisZqk0b0v8ppFq2slZmY2xLW6wP8K8BtJF5Mu83EAvr+Emdmw0eoZ3OdK6iFdPFDAOyLi7o4mMzOz2mh5U1IuDi4QZmbD0KAuUW5mZsOLi4WZmZVysTAzs1IuFmZmVsrFwszMSrlYmJlZKRcLMzMr5WJhZmalXCzMzKyUi4WZmZVysTAzs1JdKxb59qy3SvpRfj1G0hWS7st/i5dEP17SAkn3Stq9W5nNzIarbq5ZHA3cU3h9HHBlREwDrsyvkbQ1MAuYDuwBnCFpRMVZzcyGta4UC0mTgL2AbxU6zwTOyc/PAfYrdL8wIp6OiAeABcD2FUU1MzO6t2bxn8CxwHOFbhtHxGKA/Hej3H0i8FChv4W524tIOlxSj6SeZcuWtT20mdlwVXmxkLQ3sDQibm51kCbdolmPETE3ImZExIxx48YNOqOZmb1QN+6jvTOwr6Q9gbWA9SWdDyyRND4iFksaDyzN/S8EJheGnwQsqjSxmdkwV/maRUQcHxGTImIKacf1VRFxEHA5MDv3Nhu4LD+/HJglaU1JU4FpwE0VxzYzG9a6sWbRl5OBeZIOBR4E3gUQEXdJmke6pety4MiIWNG9mGZmw09Xi0VEzAfm5+d/BXbto785wJzKgpmZ2Qv4DG4zMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWqvJiIWmypKsl3SPpLklH5+5jJF0h6b78d3RhmOMlLZB0r6Tdq85sZjbcdWPNYjnwyYh4BbADcKSkrYHjgCsjYhpwZX5Nfm8WMB3YAzhD0ogu5DYzG7YqLxYRsTgibsnPnwDuASYCM4Fzcm/nAPvl5zOBCyPi6Yh4AFgAbF9paDOzYa6r+ywkTQG2BW4ENo6IxZAKCrBR7m0i8FBhsIW5W7PxHS6pR1LPsmXLOpbbzGy46VqxkLQe8H3gYxHxv/312qRbNOsxIuZGxIyImDFu3Lh2xDQzM7pULCStTioU342IS3LnJZLG5/fHA0tz94XA5MLgk4BFVWU1M7PuHA0l4NvAPRFxSuGty4HZ+fls4LJC91mS1pQ0FZgG3FRVXjMzg5FdaHNn4GDgDkm35W4nACcD8yQdCjwIvAsgIu6SNA+4m3Qk1ZERsaLy1GZmw1jlxSIifkXz/RAAu/YxzBxgTsdCmZlZv3wGt5mZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMyvlYmFmZqVcLMzMrJSLhZmZlXKxMDOzUi4WZmZWysXCzMxKuViYmVkpFwszMys1ZIqFpD0k3StpgaTjup3HzGw4GRLFQtII4HTgbcDWwIGStu5uKjOz4WNIFAtge2BBRNwfEc8AFwIzu5zJzGzYUER0O0MpSfsDe0TEYfn1wcDrI+IjDf0dDhyeX24J3PsSmh0LPPwShm+XOuSoQwaoR446ZIB65KhDBqhHjjpkgPbk2DQixjV2HPkSR1oVNen2oioXEXOBuW1pUOqJiBntGNdQz1GHDHXJUYcMdclRhwx1yVGHDJ3OMVQ2Qy0EJhdeTwIWdSmLmdmwM1SKxW+BaZKmSloDmAVc3uVMZmbDxpDYDBURyyV9BPg5MAI4KyLu6nCzbdmc1QZ1yFGHDFCPHHXIAPXIUYcMUI8cdcgAHcwxJHZwm5lZdw2VzVBmZtZFLhZmZlbKxcLMzEq5WAxSPirLCiQ1Ox9mWJE0VtKELmcY1c32e+XL9Hi+yCTVYnk72M+jFuGHGklbAedKGl2HL4Kk9fLfEV1oexNJu0maHl06WkLSyyWdJOmdkkZ3I0POsRVwM/D2/LryeUPSdOAqSa+U1LWjHSVtAZwkadOIiC5NiymS9pe0U9VtN+TYECAinutWwZA0WdKukrYb7PfUxWKA8gLhm8DVEfFotxaQhTxbA5dIOh04TtKUCtveApgP7A6cKelTkrapqv2cYUvgJ8Ao4DOki01WLs8Xc4FrgSMlbVH1vCFpMnAe6dDyOyNieZXtN/hn4D3AUZJengtGZcubPF/8GNgZ+Lmkd1bVdkOO6cDVkr4E3SkYed78BfBO4KeS3pO7D6iAu1gMgKQxwG3ADyLivyStIWl6t66Am/N8D7iIdJKigAvzF6XTbY8EPgJ8JSL+BfgYaeHwYUmv73T7OcPqOcPpEXEM8G/AqyVtK2njKjLkHBOA75MW0gcDPwN2zO9Vuba3ITA/Ir6R580PSNpTUjcuQ3EVcA3wJ1LBeFlEPFdFw5I2In0eX42IjwMfBTaXNKmK9gs5RgHfBnqAdSSdDNUWDEnjgPOBL0XEh4H3AXMkbTLQHzMuFgMQEY8A3wIOzjPkd4ETgB9JOkbS2Ioj/R34NWkh9XPg30lfknMlbdbJhvOv1mXAFElrRcTNpAXECNKvuY5vhomIZ0mXgnlrXhDMIV1A8lTg45Je28n2C0YBR0bE2fn1Q8D7c8YVFW6C2RB4fd7scTbwVlIB/5CkvSvK0OtOYHXgDmAJ8EVJ35U0oYIF5f8CH4qIb+Vi/WnSPHmJpE/k727HRcQTwKeAzwNnAeOLBaOKDKRl/CkRcZYkRcTPgBsZxAnZLhYt6p3B85Vu5wN/AR6MiPcCB5Aumb5DhXlGkD6/zYHjc7bngC8DlwKHd2onfGHhdwNppjtV6YZUrwEuAN4n6RWd3AxTyPADYDFwCnB7RLwdOBQYB1SySSwi7o2I+b2ZIuKrwNOSTsyvK9kcFRHXAdeTFkxL8lrOJ4H7gS2qyADPf1eWAatHxNWk+eTtwATgmU4vKCPi/4Df5JfbAhdExEzgQ6RNMR3/nhbmhWsi4kHgFuA0UsH4Yu5no06vAUfEEtImqOJ8uBZpuYGk8ZLWbmVcLhYtKq465lXbd5B+NRARPcCVQMd/sRR2lq2IiKdIl2R/v9LlUHpniGuBsfneH21XmOnmA5eQFkZrA4dExJXAL4GOHi3WmyEi/icijgDOBZ6QNDIi7iP9sp2urJNZipkKv5q/CWzUqYLdqPA/fhd4AthD0gZ5YbEUeKWkEVVMi4h4LiKeBO6Q9GHSmt45pO/IZyStW0GG3vmjJyJ6i/atpE2EHd9E2fgDIb++nXQTt7UlXUr6hd/xAzIi4mF4ftMxpO/mU5K2I22+bm25FRF+9PMARvT3OnfbGbgH2LnDWaYDt5K2Pxa7b5/b/1R+/QbSjDiefEmXdk+LxvECq+W/u+Qs21TxeRS6/wPwFeAkYC/gPmDXquaLPvLcB3ywygykH4DbAv8FXAEcCCwA/rGqHIV54TDSPWU+ml9vB2zZjc8j97MzcHcnv6ct5vgwqaC/vRsZSAeCnErahL1fy+Pt1EQb6g9gfNnEJ22C2S5/GffqcJ5RpNX5b5J+nZzc8P60XCC+CTwAzKxiWvQWjbyQmg78Gdi36s8jfxbvBr5G+rXUkc+jxfmid2G5TycKVisZ8nuHAB/sVKFo4TNZC9ipE20P8PNYOxeK/+n2fEH6Ff/73kJBe3/MtTpfnAI81ztftJrBFxLsg6Srgb9FxF759YiIWNGkv6nABhFxW96B1Mnt9G8mFYJxwFHA4og4rvD++qSdiqMjYkG78rQ6LfJ70yLivnZPi/4yNLYlaVREPNGJz2Mg06IwTGXTokoln8lqUdg30fi6igyFfkT6IbN6RNza7flC0pSI+GNhv0ZbspRl6P2/Je0MPBcR1w9kWrhY9CF/kDcCf4iIA3O3ZjNi8QsyMjpwbHuThaGA15IKxpKIOFbSP5BmgKWdaJ+SadE447d7Wgwgg3oXSp1YiA5gvlitkKOtC8pBzpttX1jXIUddvqctzp+rkb4iXfuONLY7kAzewd1EYeH8emALSRfB84dBjij0NyJ3W1/pTNWOnATVWPnz68adZdcDY9rddqvTgrTpJToxLQaY4blChrYXigHMF8UcbV04DnLebHuh6HaOunxPBzB/9v6y79p3JE+L5YPJ4GJR0PvrmHRXvtdEsh3pXIIXTPzCDLgB6eif8R3I0+cJXRGxPCJuIu1M3hX4RET8vo1td31a1CFDXXLUIUNdctQhQ11yVJohOrjzaSg+gP1IO5LPBS4ENsvdbwR+2NDvBqTDAd/Q5gxd31lWo2nR9Qx1yVGHDHXJUYcMdclRVYa2Trih/gCm5gm5DunwtpuAjQrv3w5sm5+vT7o3eCdmwKuBHxde93dkw5T8t3d7/SozLeqQoS456pChLjnqkKEuOarM0NaJN5QfpBNVxpLOgD6KdAbo5vm9Nzbpf0dghw5lUf7QLyh0e9Gx7BSKAzByVZoWdchQlxx1yFCXHHXIUJccVWdo6wQcqg9WXjJjY9KlEu4AXpXf2410ItzmDcO0dZNP43hzwbgZuKjw3ojG56RfC5uuStOiDhnqkqMOGeqSow4Z6pKjGxm6dr37mtmMdJntrwPzgAAOk3Qv6YqV/xIRfygOEHnqt0vhaIZpktaJiNuA7STdKOmiiHh3FI5siJU7qq4g/ar4U5uidH1a1CRDXXLUIUNdctQhQ11yVJ5hWJ9nIWndSNdXQul+EKtHxOFKJ628lnzWdERcVViYdzLPfsBxpDNN1wBOiIj7Jd0ILI2IfQr9bkC6LtNJEfGrNrTd9WlRhwx1yVGHDHXJUYcMdcnR1QztXDUaSg9ga+Bk4Nj8elPgS8DELuXp2s6yOkyLOmSoS446ZKhLjjpkqEuObmcYVudZ9B6TnP8+TDrcbGdJ55OuHfN60uXGq861BunCYreSLr52EHBgRCyV9EaAiNgm0lUzIV264KPxEtYo6jAt6pChLjnqkKEuOeqQoS456pCh17AqFhERkv6JdF35fYH7Il3n/ibSOQtbAR+RNK5wsktHSdqcdM+BEaQzsD9IunHLHyTtBpyW+ym6ISJueCnt1mFa1CFDXXLUIUNdctQhQ11y1CFDMcwq/yAfVgq8hXTG8xtI9zs4raG/twO7VJztH0n3hRgF7EG6DeOppE1R9wB7r2rTog4Z6pKjDhnqkqMOGeqSow4ZXpSpika69SDtB9ggP1+TdAvUXUiXFe8BJuX3RjcM15HDYhvaWLfw/HRgbn6+M+lohhOAt7YrTx2mRR0y1CVHHTLUJUcdMtQlRx0y9Jmt0w1080E63vjR3glL2hdwHen8hYm52/7AEeR7EFSUq/IdVXWYFnXIUJccdchQlxx1yFCXHHXI0Ge2KhvrxoO0aed+0s3sNwV+TtrEM4J0R7E7gLdVkKN4st1GpOu5XAacD7yHdCvUj6/q06IOGeqSow4Z6pKjDhnqkqMOGZrmqrrBbjyAPUm3d1w3fxCnk24peC1tvKNcCzn+iXQnt8OA6bnbUcDHSPdJ/gPpxkYdW6Wsw7SoQ4a65KhDhrrkqEOGuuSoQ4YXZepGo135R9N9me8G1suvpwIT8vNOLpzrt6OqS9OibhnqkqMOGeqSow4Z6pKjDhmKj2FzuY+I+LGk54A/SHpFRDxQeC/a3Z7S7VYfiYjHJa0J7ERalfw78H/AF3N/oyPi0Yj4QX7d8TPFq54Wdc1Qlxx1yFCXHHXIUJccdchQNOwu9yFpL9J9aq/ucDu7Ad8jXVv+UUkHAR8inaG9b0T8WdL+pKtGzo0O3J+4hYyVTIu6Z6hLjjpkqEuOOmSoS446ZIBhWCx6VfELXtIewBmka7ZsAMwl7dT+L+DVpJuVHBsRP+1kjjJVTIuhkKEuOeqQoS456pChLjm6nWHYFouqSNoT+CqpYLwR2Ad4DbAC+EpEXNa9dGZmrRk2+yy6JSJ+kk/D/y2wfUT8LO/PeDoiFnX714KZWStcLCpQtx1VZmYD5c1QFarLjiozs4FysegCb3oys6HGxcLMzEoNq/tZmJnZ4LhYmJlZKRcLMzMr5WJhZmalXCxsSJI0X9LuDd0+JumMfvqf0eYMP5G0YTvHmcf7LUlbD3CYJwfQ79n5umRmLXOxsKHqAmBWQ7dZuXslImLPiHisA+M9LCLubvd4zV4KFwsbqi4G9s6Xf0fSFGAC8B5JPZLukvS5ZgNK+idJ10u6RdL3JK2Xu/9R0udy9zskbZW7ryfpO7nb7yS9s9D/2Pz8Ukk353YP7yu0pAMknZKfHy3p/vx8c0m/ys+fXwuS9KSkOZJul3SDpI1z96n5f/itpM/3N6GUfF3S3ZJ+TLpTY+9720m6Jmf/uaTxufvr8v96vaQvSbqz5POwVZyLhQ1JEfFX4CbSXcQgrVVcBPxrRMwgXdX3zZJeXRwuL9xPBHaLiNcCPcAnCr08nLt/Azgmd/s08HhEvCoiXg1c1STSIRGxHTADOErSy/qIfi3pgpLkv3+VNJF0U6zrmvS/LnBDRGyTh/1g7n4q8I2IeB3wlz7a6vV2YEvgVXn4nQAkrU66c+P+OftZwJw8zHeAIyJiR9JFL22Yc7Gwoay4Kap3E9QBkm4BbgWmA43b/nfI3X4t6TZgNuk+x70uyX9vBqbk57uRbmsJQEQ82iTLUZJuB24AJgPTmgWOiL8A60kalfv7b+BNpMLRrFg8A/yoSaadWbnJ7bxmbRW8CbggIlZExCJWFrstgVcCV+RpcSIwKe+HGRURv8n9/XfJ+G0Y8IUEbSi7FDhF0muBtYFHSWsDr8s3nDobWKthGAFXRMSBfYzz6fx3BSu/HwL6vNSBpLeQCsqOEfE3SfObtFt0PfAB0j2WrwMOAXYEPtmk32cLl4YpZqK/TE0061fAXXntYWVHafQAxmvDhNcsbMiKiCeB+aTNJxcA6wNPAY/nbftvazLYDcDOkl4OIGkdSVuUNPUL4CO9L5osTDcAHs2FYivS2kt/riUVtWtJa0C7kC5Z/3jJcEW/ZuVa1XtbaG+WpBF5n8Quufu9wDhJO0LaLCVpel5zekJS7//ReCCBDUMuFjbUXQBsA1wYEbeTFr53kQrIrxt7johlwPuBCyT9jlQ8tipp4wvAaEl35k1NuzS8/zNgZB7f5/M4+3MdaRPUtRGxAngI+FXJMI2OBo6U9FtSserPD4D7gDtI+2KuAYiIZ4D9gf+X/6/byPszgEOBuZKuJ62BDKSQ2SrIFxI0sxeRtF5ec0PSccD4iDi6y7Gsi7zPwsya2UvS8aRlxJ9Ia2M2jHnNwqxDJN0IrNnQ+eCIuKND7b2KFx8Z9XREvL4T7dnw4mJhZmalvIPbzMxKuViYmVkpFwszMyvlYmFmZqX+P1qE/0CAUUPAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x = 'Valencia_wind_deg', data = df_train, palette=\"hls\")\n", + "plt.title(\"Distribution of wind direction\")\n", + "plt.xticks(rotation = 45)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "66d61bf1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24]),\n", + " [Text(0, 0, 'sp25'),\n", + " Text(1, 0, 'sp23'),\n", + " Text(2, 0, 'sp24'),\n", + " Text(3, 0, 'sp21'),\n", + " Text(4, 0, 'sp16'),\n", + " Text(5, 0, 'sp9'),\n", + " Text(6, 0, 'sp15'),\n", + " Text(7, 0, 'sp19'),\n", + " Text(8, 0, 'sp22'),\n", + " Text(9, 0, 'sp11'),\n", + " Text(10, 0, 'sp8'),\n", + " Text(11, 0, 'sp4'),\n", + " Text(12, 0, 'sp6'),\n", + " Text(13, 0, 'sp13'),\n", + " Text(14, 0, 'sp17'),\n", + " Text(15, 0, 'sp20'),\n", + " Text(16, 0, 'sp18'),\n", + " Text(17, 0, 'sp14'),\n", + " Text(18, 0, 'sp12'),\n", + " Text(19, 0, 'sp5'),\n", + " Text(20, 0, 'sp10'),\n", + " Text(21, 0, 'sp7'),\n", + " Text(22, 0, 'sp3'),\n", + " Text(23, 0, 'sp2'),\n", + " Text(24, 0, 'sp1')])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x = 'Seville_pressure', data = df_train, palette=\"hls\")\n", + "plt.title(\"Distribution of Seville_pressure\")\n", + "plt.xticks(rotation = 45)" + ] + }, + { + "cell_type": "markdown", + "id": "f9336086", + "metadata": {}, + "source": [ + "5. The above plot may suggest we drop the columns altogether because at best each level in the 'Valencia_wind_deg' column only provides a range of angles and not a specific angular direction of wind. The sp notation for 'Seville_pressure' will require us multiplying each value by 100000 Pa giving us unrealistic atmospheric pressure values. Thereafter, we can go ahead as discussed earlier to average out the values of each weather feature across all cities and store the resulting averages into new dataframes. This average in fact will minimise any effect of the columns dropped. The new dataframe will aslo contain the time data from df_train but will leave out the 'unnamed' column since its just a replica of the of the index." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4d8b9e34", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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71.000000 \n", + "8761 7.333333 67.666667 79.000000 \n", + "8762 7.000000 78.666667 68.666667 \n", + "\n", + " Bilbao_clouds_all Bilbao_wind_speed Seville_clouds_all ... \\\n", + "0 0.000000 1.000000 0.000000 ... \n", + "1 0.000000 1.000000 0.000000 ... \n", + "2 0.000000 1.000000 0.000000 ... \n", + "3 0.000000 1.000000 0.000000 ... \n", + "4 2.000000 0.333333 0.000000 ... \n", + "... ... ... ... ... \n", + "8758 56.666667 4.333333 80.000000 ... \n", + "8759 26.666667 8.000000 75.000000 ... \n", + "8760 63.333333 8.333333 33.333333 ... \n", + "8761 63.333333 2.666667 51.666667 ... \n", + "8762 20.000000 1.666667 33.333333 ... \n", + "\n", + " Madrid_temp_max Barcelona_temp Bilbao_temp_min Bilbao_temp \\\n", + "0 265.938000 281.013000 269.338615 269.338615 \n", + "1 266.386667 280.561667 270.376000 270.376000 \n", + "2 272.708667 281.583667 275.027229 275.027229 \n", + "3 281.895219 283.434104 281.135063 281.135063 \n", + "4 280.678437 284.213167 282.252063 282.252063 \n", + "... ... 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load_shortfall_3h \n", + "0 265.938000 6715.666667 \n", + "1 266.386667 4171.666667 \n", + "2 272.708667 4274.666667 \n", + "3 281.895219 5075.666667 \n", + "4 280.678437 6620.666667 \n", + "... ... ... \n", + "8758 278.483333 -28.333333 \n", + "8759 280.150000 2266.666667 \n", + "8760 281.150000 822.000000 \n", + "8761 280.816667 -760.000000 \n", + "8762 280.483333 2780.666667 \n", + "\n", + "[8763 rows x 47 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop columns\n", + "df = df_train.drop(columns = ['Valencia_wind_deg','Seville_pressure'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f0d8d9fc", + "metadata": {}, + "outputs": [], + "source": [ + "# create subsets of training data in terms of weather feature\n", + "df_wind_speed = df[['Madrid_wind_speed','Valencia_wind_speed','Bilbao_wind_speed','Barcelona_wind_speed','Seville_wind_speed']]\n", + "df_wind_deg = df[['Bilbao_wind_deg','Barcelona_wind_deg']]\n", + "df_humidity = df[['Seville_humidity','Madrid_humidity','Valencia_humidity']]\n", + "df_rain = df[['Bilbao_rain_1h','Barcelona_rain_1h','Seville_rain_1h','Seville_rain_3h','Madrid_rain_1h','Barcelona_rain_3h']]\n", + "df_clouds_all = df[['Bilbao_clouds_all','Seville_clouds_all','Madrid_clouds_all']]\n", + "df_pressure = df[['Barcelona_pressure','Bilbao_pressure','Valencia_pressure','Madrid_pressure']]\n", + "df_snow = df[['Bilbao_snow_3h','Valencia_snow_3h']]\n", + "df_weather_id = df[['Madrid_weather_id','Barcelona_weather_id','Seville_weather_id','Bilbao_weather_id']]\n", + "df_temp_min = df[['Valencia_temp_min','Bilbao_temp_min','Barcelona_temp_min','Seville_temp_min','Madrid_temp_min']]\n", + "df_temp = df[['Barcelona_temp','Bilbao_temp','Madrid_temp','Seville_temp','Valencia_temp']]\n", + "df_temp_max = df[['Barcelona_temp_max','Bilbao_temp_max','Madrid_temp_max','Seville_temp_max','Valencia_temp_max']]\n" + ] + }, + { + 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" + ], + "text/plain": [ + " Barcelona_temp Bilbao_temp Madrid_temp Seville_temp Valencia_temp\n", + "count 8763.000000 8763.000000 8763.000000 8763.000000 8763.000000\n", + "mean 289.855459 286.422929 288.419439 293.978903 290.592152\n", + "std 6.528111 6.818682 9.346796 7.920986 7.162274\n", + "min 270.816667 267.483333 264.983333 272.063000 269.888000\n", + "25% 284.973443 281.374167 281.404281 288.282917 285.150000\n", + "50% 289.416667 286.158333 287.053333 293.323333 290.176667\n", + "75% 294.909000 291.034167 295.154667 299.620333 296.056667\n", + "max 307.316667 310.710000 313.133333 314.976667 310.426667" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_temp.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d83775ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Barcelona_temp_max Bilbao_temp_max Madrid_temp_max Seville_temp_max \\\n", + "count 8763.000000 8763.000000 8763.000000 8763.000000 \n", + "mean 291.157644 287.966027 289.540309 297.479527 \n", + "std 7.273538 7.105590 9.752047 8.875812 \n", + "min 272.150000 269.063000 264.983333 272.063000 \n", + "25% 285.483333 282.836776 282.150000 291.312750 \n", + "50% 290.150000 287.630000 288.116177 297.101667 \n", + "75% 296.855000 292.483333 296.816667 304.150000 \n", + "max 314.076667 317.966667 314.483333 320.483333 \n", + "\n", + " Valencia_temp_max \n", + "count 8763.000000 \n", + "mean 291.337233 \n", + "std 7.565692 \n", + "min 269.888000 \n", + "25% 285.550167 \n", + "50% 291.037000 \n", + "75% 297.248333 \n", + "max 314.263333 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_temp_max.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "4ac735f3", + "metadata": {}, + "source": [ + "6. We have paused here to examine these datasets above to examine how some weather features are distributed across the cities. This may help us observe any anormalies within the data or inform the decisions regarding possible location of renewable energy infrastructure. It can be observed for instance that the city of Seville is observed to have recorded the highest temperature levels; Seville provides the highest potential of supplying the highest amounts of solar power. The rain data collected from some cities are observed to be have been denoted as '1h' in constrast with the load shortfall which is denoted '3h'. We may take off this data as they may be a source of distortion in our machine learning process. We can also deduce that Valencia recorded the highest windspeed of 52 units. However, the value suggest the presence of a contextual or point outlier when compared to other maximums. In the same vein, Barcelona may be regarded as the city with the overall highest amount of pressure but unlike the Valencia's maximum wind speed, its maximum value is certainly a point outlier because a pressure of about 1 million units at sea level is a very unlikely. More on these outliers later. For now, we may move ahead with putting all these mean feature data within one dataset but not before taking off those 1-hourly rain data." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "82e89cbf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Seville_rain_3h Barcelona_rain_3h\n", + "0 0.0 0.0\n", + "1 0.0 0.0\n", + "2 0.0 0.0\n", + "3 0.0 0.0\n", + "4 0.0 0.0\n", + "... ... ...\n", + "8758 0.0 0.0\n", + "8759 0.0 0.0\n", + "8760 0.0 0.0\n", + "8761 0.0 0.0\n", + "8762 0.0 0.0\n", + "\n", + "[8763 rows x 2 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# drop 1h rain feature data\n", + "df_rain = df_rain.drop(columns = ['Bilbao_rain_1h','Barcelona_rain_1h','Seville_rain_1h', 'Madrid_rain_1h'])\n", + "df_rain" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8ba3a192", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 272.086456\n", + "1 272.799533\n", + "2 277.224046\n", + "3 283.351587\n", + "4 283.134500\n", + " ... \n", + "8758 284.216667\n", + "8759 288.550000\n", + "8760 288.927933\n", + "8761 287.550000\n", + "8762 286.750000\n", + "Length: 8763, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# append extra column with mean value of weather feature across all cities\n", + "ave_wind_speed = df_wind_speed.mean(axis = 1)\n", + "ave_wind_deg = df_wind_deg.mean(axis = 1)\n", + "ave_humidity = df_humidity.mean(axis = 1)\n", + "ave_rain = df_rain.mean(axis = 1)\n", + "ave_clouds_all = df_clouds_all.mean(axis = 1)\n", + "ave_pressure = df_pressure.mean(axis = 1)\n", + "ave_snow = df_snow.mean(axis = 1)\n", + "ave_weather_id = df_weather_id.mean(axis = 1)\n", + "ave_temp_min = df_temp_min.mean(axis = 1)\n", + "ave_temp = df_temp.mean(axis = 1)\n", + "ave_temp_max = df_temp_max.mean(axis = 1)\n", + "ave_temp_max" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "84218394", + "metadata": {}, + "outputs": [], + "source": [ + " # creating a time feature and load_shortfall series\n", + "time = df['time']\n", + "load_shortfall_3h = df['load_shortfall_3h']" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8951b17d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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42015-01-01 15:00:00800.0000001.933333222.50000058.1111110.00.6666671030.9166670.0283.134500283.134500283.1345006620.666667
..........................................
87582017-12-31 09:00:00775.0833332.133333155.16666785.3333330.060.5555561020.1666670.0282.283333283.219333284.216667-28.333333
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8763 rows × 13 columns

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" + ], + "text/plain": [ + " Time Ave_weather_id Ave_wind_speed Ave_wind_deg \\\n", + "0 2015-01-01 03:00:00 800.000000 2.400000 133.000000 \n", + "1 2015-01-01 06:00:00 800.000000 2.066667 180.000000 \n", + "2 2015-01-01 09:00:00 800.000000 1.533333 270.166667 \n", + "3 2015-01-01 12:00:00 800.000000 1.866667 236.333333 \n", + "4 2015-01-01 15:00:00 800.000000 1.933333 222.500000 \n", + "... ... ... ... ... \n", + "8758 2017-12-31 09:00:00 775.083333 2.133333 155.166667 \n", + "8759 2017-12-31 12:00:00 791.833333 3.933333 216.666667 \n", + "8760 2017-12-31 15:00:00 726.500000 6.200000 270.000000 \n", + "8761 2017-12-31 18:00:00 684.125000 5.400000 235.000000 \n", + "8762 2017-12-31 21:00:00 800.666667 3.800000 205.000000 \n", + "\n", + " Ave_humidity Ave_rain Ave_clouds_all Ave_pressure Ave_snow \\\n", + "0 71.333333 0.0 0.000000 1011.333333 0.0 \n", + "1 71.333333 0.0 0.000000 1012.500000 0.0 \n", + "2 67.111111 0.0 0.000000 1013.333333 0.0 \n", + "3 58.555556 0.0 0.000000 1019.166667 0.0 \n", + "4 58.111111 0.0 0.666667 1030.916667 0.0 \n", + "... ... ... ... ... ... \n", + "8758 85.333333 0.0 60.555556 1020.166667 0.0 \n", + "8759 69.111111 0.0 62.222222 1019.750000 0.0 \n", + "8760 61.111111 0.0 60.555556 1016.083333 0.0 \n", + "8761 63.888889 0.0 57.222222 1019.583333 0.0 \n", + "8762 65.777778 0.0 17.777778 1021.250000 0.0 \n", + "\n", + " Ave_temp_min Ave_temp Ave_temp_max load_shortfall_3h \n", + "0 272.086456 272.086456 272.086456 6715.666667 \n", + "1 272.799533 272.799533 272.799533 4171.666667 \n", + "2 277.224046 277.224046 277.224046 4274.666667 \n", + "3 283.351587 283.351587 283.351587 5075.666667 \n", + "4 283.134500 283.134500 283.134500 6620.666667 \n", + "... ... ... ... ... \n", + "8758 282.283333 283.219333 284.216667 -28.333333 \n", + "8759 286.550000 287.598000 288.550000 2266.666667 \n", + "8760 286.794600 287.975933 288.927933 822.000000 \n", + "8761 285.216667 286.430667 287.550000 -760.000000 \n", + "8762 284.283333 285.504667 286.750000 2780.666667 \n", + "\n", + "[8763 rows x 13 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a dictionary containing all feature series with their names and keys in a desirable order\n", + "features_ave = {'Time': time, 'Ave_weather_id': ave_weather_id, 'Ave_wind_speed': ave_wind_speed, 'Ave_wind_deg': ave_wind_deg, 'Ave_humidity': ave_humidity, \n", + " 'Ave_rain': ave_rain, 'Ave_clouds_all': ave_clouds_all, 'Ave_pressure': ave_pressure, 'Ave_snow': ave_snow, 'Ave_temp_min': ave_temp_min,\n", + " 'Ave_temp': ave_temp, 'Ave_temp_max': ave_temp_max, 'load_shortfall_3h': load_shortfall_3h}\n", + "\n", + "# convert dictionary of feature series into dataframe named df_train_clean\n", + "df_train_clean = pd.DataFrame(features_ave)\n", + "\n", + "df_train_clean\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f516a53", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "78f39e0f", + "metadata": {}, + "source": [ + "7. Having significantly condensed the dataset without necessarily impacting on its ability to effectively train our model, we may now go ahead and draw out some inferences from each feature's interractions with load shortfall and with one another.The essence of this is to check for possible linearity between a feature and the target variable (in this case, 'load_shortfall) and possible collinearity between any pair of features over time. We'll make use of scatter plots and heat maps to elicit such interractions. But first, some column manipulation to make the response variable come first." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2fb74182", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot relevant feature interactions\n", + "fig, axs = plt.subplots(3,4, figsize=(14,6),)\n", + "fig.subplots_adjust(hspace = 0.5, wspace=.2)\n", + "axs = axs.ravel()\n", + "\n", + "for index, column in enumerate(df_train_clean.columns):\n", + " axs[index-1].set_title(\"{} vs. load_shortfall_3h\".format(column),fontsize=12)\n", + " axs[index-1].scatter(x=df_train_clean[column],y=df_train_clean['load_shortfall_3h'],color='blue',edgecolor='k')\n", + " \n", + "fig.tight_layout(pad=1)\n" + ] + }, + { + "cell_type": "markdown", + "id": "545bd833", + "metadata": {}, + "source": [ + "8. From the foregoing plots , it is quite clear that no obvious linearity exists between any of the features and the load shortfall. This means that no feature exists which changes the shortfall by the same amount with each change in its own quantity. There is however a suggestion from the plots that the variance with respect to shortfall of the three temperature features are similar. Though this may be expected, we will still have to confirm their correlation as a potential source of multicollinearity using a heatmap. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "363b6bd2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Ave_weather_idAve_wind_speedAve_wind_degAve_humidityAve_rainAve_clouds_allAve_pressureAve_snowAve_temp_minAve_tempAve_temp_maxload_shortfall_3h
Ave_weather_id1.000000-0.249671-0.040275-0.265155-0.037892-0.596429-0.034199-0.0669250.1769900.1951690.2095140.135499
Ave_wind_speed-0.2496711.0000000.230740-0.262193-0.0097040.3422270.0218900.0786820.1104640.1106670.100607-0.157644
Ave_wind_deg-0.0402750.2307401.000000-0.002791-0.0166760.1197690.025140-0.046898-0.089915-0.090755-0.092286-0.168674
Ave_humidity-0.265155-0.262193-0.0027911.0000000.0424820.3295420.0184770.006639-0.726230-0.722368-0.692962-0.118548
Ave_rain-0.037892-0.009704-0.0166760.0424821.0000000.041851-0.0028900.001751-0.045953-0.062838-0.080205-0.037829
Ave_clouds_all-0.5964290.3422270.1197690.3295420.0418511.0000000.0335030.064971-0.211687-0.228930-0.241156-0.147201
Ave_pressure-0.0341990.0218900.0251400.018477-0.0028900.0335031.000000-0.001804-0.027319-0.030635-0.033640-0.034161
Ave_snow-0.0669250.078682-0.0468980.0066390.0017510.064971-0.0018041.000000-0.085402-0.094102-0.102383-0.031910
Ave_temp_min0.1769900.110464-0.089915-0.726230-0.045953-0.211687-0.027319-0.0854021.0000000.9862640.9439460.194317
Ave_temp0.1951690.110667-0.090755-0.722368-0.062838-0.228930-0.030635-0.0941020.9862641.0000000.9845650.184345
Ave_temp_max0.2095140.100607-0.092286-0.692962-0.080205-0.241156-0.033640-0.1023830.9439460.9845651.0000000.168071
load_shortfall_3h0.135499-0.157644-0.168674-0.118548-0.037829-0.147201-0.034161-0.0319100.1943170.1843450.1680711.000000
\n", + "
" + ], + "text/plain": [ + " Ave_weather_id Ave_wind_speed Ave_wind_deg Ave_humidity \\\n", + "Ave_weather_id 1.000000 -0.249671 -0.040275 -0.265155 \n", + "Ave_wind_speed -0.249671 1.000000 0.230740 -0.262193 \n", + "Ave_wind_deg -0.040275 0.230740 1.000000 -0.002791 \n", + "Ave_humidity -0.265155 -0.262193 -0.002791 1.000000 \n", + "Ave_rain -0.037892 -0.009704 -0.016676 0.042482 \n", + "Ave_clouds_all -0.596429 0.342227 0.119769 0.329542 \n", + "Ave_pressure -0.034199 0.021890 0.025140 0.018477 \n", + "Ave_snow -0.066925 0.078682 -0.046898 0.006639 \n", + "Ave_temp_min 0.176990 0.110464 -0.089915 -0.726230 \n", + "Ave_temp 0.195169 0.110667 -0.090755 -0.722368 \n", + "Ave_temp_max 0.209514 0.100607 -0.092286 -0.692962 \n", + "load_shortfall_3h 0.135499 -0.157644 -0.168674 -0.118548 \n", + "\n", + " Ave_rain Ave_clouds_all Ave_pressure Ave_snow \\\n", + "Ave_weather_id -0.037892 -0.596429 -0.034199 -0.066925 \n", + "Ave_wind_speed -0.009704 0.342227 0.021890 0.078682 \n", + "Ave_wind_deg -0.016676 0.119769 0.025140 -0.046898 \n", + "Ave_humidity 0.042482 0.329542 0.018477 0.006639 \n", + "Ave_rain 1.000000 0.041851 -0.002890 0.001751 \n", + "Ave_clouds_all 0.041851 1.000000 0.033503 0.064971 \n", + "Ave_pressure -0.002890 0.033503 1.000000 -0.001804 \n", + "Ave_snow 0.001751 0.064971 -0.001804 1.000000 \n", + "Ave_temp_min -0.045953 -0.211687 -0.027319 -0.085402 \n", + "Ave_temp -0.062838 -0.228930 -0.030635 -0.094102 \n", + "Ave_temp_max -0.080205 -0.241156 -0.033640 -0.102383 \n", + "load_shortfall_3h -0.037829 -0.147201 -0.034161 -0.031910 \n", + "\n", + " Ave_temp_min Ave_temp Ave_temp_max load_shortfall_3h \n", + "Ave_weather_id 0.176990 0.195169 0.209514 0.135499 \n", + "Ave_wind_speed 0.110464 0.110667 0.100607 -0.157644 \n", + "Ave_wind_deg -0.089915 -0.090755 -0.092286 -0.168674 \n", + "Ave_humidity -0.726230 -0.722368 -0.692962 -0.118548 \n", + "Ave_rain -0.045953 -0.062838 -0.080205 -0.037829 \n", + "Ave_clouds_all -0.211687 -0.228930 -0.241156 -0.147201 \n", + "Ave_pressure -0.027319 -0.030635 -0.033640 -0.034161 \n", + "Ave_snow -0.085402 -0.094102 -0.102383 -0.031910 \n", + "Ave_temp_min 1.000000 0.986264 0.943946 0.194317 \n", + "Ave_temp 0.986264 1.000000 0.984565 0.184345 \n", + "Ave_temp_max 0.943946 0.984565 1.000000 0.168071 \n", + "load_shortfall_3h 0.194317 0.184345 0.168071 1.000000 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# evaluate correlation\n", + "correlation = df_train_clean.corr()\n", + "correlation" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6722a031", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# view correlation with a heatmap\n", + "fig = plot_corr(correlation, xnames = correlation.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "71afb40b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5cPOnxMhj13NVoM4880yeffZZABYuXEh5ecuVd3fv3k1aWho/+MEPuOmmm1ixYkXTfc8//3zT98be7fPOO4+HHnqoaZ/GDxPPP/98HnzwwaY3y43DwH1jWbt2LWvWrAn4Ovzxqc1xATASay7jyGa7+c5lvA5rLmMgx/7DGDPO+6UJcYR6p+gdqt3VXDK45bmIzU3sNZFVpauo9+jQy2DIy8tj165dx2yiHe9nWyMiTuBS4PnGbcaYOmNMmff2cmArMLRDwStblM+dR+26dfT63W9xdjAHyb7mWjxlZRx5590gR6daEsqkeCkwREQKRCQJmAHMb7bPfOBKb9W+U4AK75BoAZ4ANhhjmk9Imw9c5b19FfB66C4hdObOncsllxz7InfZZZcxb948LrzwQhYuXMiFF14IQG5uLnPmzGHmzJmMGTOGU045pdVlUSZNmsS+ffs488wzgaPD+Bp7MJ599lmeeOIJxo4dy6hRo3j9detXePvtt3PFFVdwxhlnkJOT03S+iy66iFdffZVx48bx6ad+R/AA1hvI733ve5x66qmccMIJXH755QEl082NHz+e6dOnM27cOC677LIWh1IGW029h5TEBDJSEqnUOcUqFKoPQmo3SPfO+qgqbX3/CGDnc1UgbrvtNhYtWsT48eN59913Wx0Z9PXXX3PyySczbtw47rrrLn7/+9833VdXV8ekSZO4//77+cc//gHAAw88wLJlyxgzZgwjR47k0UetIrB/+MMfcLlcjBkzhtGjR/OHP/wBgJ/+9KdUVlYyZswY7rnnHk4++eSAr6MFHa7NEeCxKsK9s+Md+mX048QeJwZ8zMm9TqbOU8fq0tUhjCx+TJw4kcLCQrZv3w5Wj3DA72cDOP05wEZjTFPlRRHJ9X6ohYgMxPrAa1sLx6sI01Bby4FHHyVt0iQyfOpWtFf6ad8gcUB/yp97LojRqVYZY0L2hTWcazPWp1y/8267Hrjee1uwPsneCnwNTPBuPx1r2MkaYJX3a4r3vu7AB0Ch93t2W3GcdNJJprn169cft01Fn2D+HWf860tz+SOfm9P/8oH55dwVQTtvpACWmRC29858+WujMemvQ415bZYxFbuNuS3TmCX/bvMQfa4KvQEDBpjS0tKwPqa/v2vzNgpcjjXsufHnHwIPNdvnTeB0n58/ACa0dixwO1DkfY19EuhmtI1GnDp3nZnwnwnmrsV3teu4iroKM+bpMeahlQ+FKLL489Zbb5khQ4YYoLY972e9983FGhLtwupR/pHPfXMaz+Gz7TJgHbAaWAFcZNpon0bbaMQoe/oZs37YcFP51VedPteBf//brB823NTt2BGEyOJDZ97rhnJOMcYakrWg2bZHfW4bYJaf4z7D//wMjDWk5OzgRqoU1Lg8ZKQ4SU10UOvyv5ZyvGtrfqPPfhOBxcB0Y8xLYQwxstUdgeRMSPPODWxcokkp/zpTm6O1Yx8B/uT9+U/A34Dj1vCJ9NocsW516WpqPbWc2tt/sbqWZCZlMqzbMFbuXxmiyOLPlClTmDJlCiKy1hhzFwT2ftZ738yWzmuMudrPtpexpg+qKGMaGjj49NOknnQS6Z0fKUTmBRew/6/3cnjBAnKuvz4IEarWhDQpVqF1ySWXNA7nafKXv/yF888/36aIAjNp0qSmiteN/vOf/3DCCSfYFJGl1uUhNyOZ1EQHNS6PrbFEogDXHm/c7y/AO+GPMoIZA65qSEqzKk6ndI2K4dPBEO7nqrKyMs4++/jPTj/44AO6dz++WFFRUVFI4giCztTmSGrpWGPMvsaNIvI4Vm/zcYwW8bHVl7u/xCEOJvSa0O5jR+eM5u3tb9NgGkiQUM6UU0o1qlm5EldJCbm//EVQzpfYpw+p48dz+C1NisMhrpNiY4wtVY6D5dVXX7U7hA756quv2t4pANYHs8FT524gJdFBiibFLWmaowggIo1zFNc32+/nWJ9yTwxveBHOVQMYSPJWYk/PhaoDAR2qz1Xt0717d78V9iNFO567mmpzACVYcxm/12yf+cAN3vY4iaO1OUpbOlZEepuj8x0vAdZ25npUaHy5+0tOyDmBjKSMdh87Omc0L25+kR2Hd1DQteXlFZVSwVPxxhtISgpdvh28Aa2ZU6aw7847qduyheTBg4N2XnW8uP34MCUlhbKysqAnVio8jDGUlZWRkpIStHPW1HtITUwgNclBrSbF/rS0DmMTEemL9Sb7UdogIteJyDIRWVZaGgc9pvVV1vdEb1KclhNQT7E+V8WW9jx3GWPcwA1Yoy42AC8YY9aJyPUi0thtsACrCM8W4HHgZ60d6z3mHhH5WkTWAGcBvwraBaqgqKirYF3ZOk7t076h041G54wGYO0B/bxDqXAw9fUcWfg2Gd/+No4u7VuGtDUZZ38bgMqPPw7aOZV/cdtTnJeXR3FxMXHxZjxGpaSkkJeXF7Tz1bo9Vk+xU5PiFgQyv/E+4GZjjKetns24G5rp8ibFSWnW9/QcKNva5mH6XBV72vPc1dHaHC0d693+w/bEq8Lvqz1fYTAdTooHdh1IqjOVtQfWctGgi4IcnVKqucrPPsdTUUHmRRcG9byJvXuTPGIERz76mO4//nFQz62OFbdJcWJiIgUFOqRIHWUtyeQgNUmHT7cgkPmNE4B53oQ4B5giIm5jzGthiTCS1Vdb332HT+9c3OZh+lylVPxZvGcx6YnpTT2+7eVMcDIie4T2FCsVJofffANHVhZdTj896OfOOOtbHHj0X7jLy3F26xb08ytL3A6fVspXQ4M5dk5xvVaf9qPNtceNMQXGmHxjTD7wEvAzTYi9jhs+nQ015VYBLqWU8vHl7i+Z2GsiiQmJHT7HyO4j2Vy+GU+DfsirVCh5Kqs48uFHZFwwGUnseJttSZezzoKGBqo+/TTo51ZHaVKsFFaRLYCUxARSEx3UaU/xcQKc36ha0jR82psUp3YD47GWaVJKKa9dR3ZRXFnc7qWYmhvabSi1nlqKK4uDFJlSyp/KD97H1NbS9aLQTFVIGTUKR24ORz76KCTnV5a4HT6tlK/GOcSpiQ5SEhN0+HQL2prf2Gz71eGIKWo0DZ/2zilOybK+15RDSqYtISmlIs+Xu78E6PB84kZDug0BoLC8kAGZAzodl1LKv4o33iSxb19STzwxJOeXhAS6fPObHHnnXYzbjTg1fQsF7SlWCqvIFmDNKU504G4wuDw6hFoFUfPh06neeUG1h2wJRykVmRbvWUyv9F7kZ+Z36jyDsgYhCIXlhcEJTCl1HPeBA1R98QWZF14Y0qUTu3zjGzQcOULtunVt76w6RJNipbCKbIHVU5ya5LC2aW+xCiZ/w6fB6ilWSinA0+Dhqz1fcWrvUzv9BjvVmUr/zP5sLt8cpOiUUs0dXrAQGhroGuSq082lnXIKAFVftl2gU3WMJsVKAbWuo3OKUxId3m2aFKsgaj58OjXL+q5JsVLKa13ZOg7XH+700OlGQ7KGUHhIe4qVCpWKN98kecQIkgcPDunjOLOzSR42jKrFmhSHiibFSnG0VzjZW30aoFYrUKtgamn4dM0hW8JRSkWeRcWLSJAEvtHnG0E535BuQ9h5eCc17pqgnE8pdVTdtm3UrllD1wtD20vcKP2UU6hZsYKG2tqwPF680aRYKWiqNt1YaAt0+LQKMlcVJCSCM8n6WYdPK6WaWVS8iLG5Y+ma3DUo5xvSbQgGw7ZD24JyPqXUUYdeehmcTrpODU3V6ebSTj0FU19PzYoVYXm8eKNJsVIcTYBTEh0kOaxmUe/WnmIVRPXVR4dOAySmgiNZk2KlFAD7q/ez4eAGzsw7M2jnHJLlrUCtQ6iVCipTX0/Fa6+Rcda3cObmhuUx0yZMBIeDqq+WhOXx4o0mxUpxdE5xaqKDJKc3KfZoT7EKovqqo0OnG6V20+rTSikAFm5fCMC3+307aOfsl9GPpIQkth7aGrRzKqXgyEcf4zl4kKzLLw/bYzq6pJMyfDg1K1eG7THjiSbFSnG0qFZKYkJTUlynPcUqmNy1kJhy7LbUbtpTrJTCGMNrW17jhJwTGJg1MGjndSQ4GJg1UHuKO+ntt99m2LBhAKNFZHbz+8XygIhsEZE1IjLe574nRWS/iKxtdsztIlIiIqu8X1N87rvFe65NInJ+KK9Ndcyhl17C2asX6aefHtbHTR03jpqvv8a43WF93HigSbFSHB0+nZroINmpw6dVCHjqrOHSvlKztNCWUopPij9hy6EtXDH0iqCfe1DWIO0p7gSPx8OsWbNYuHAhwDpgpoiMbLbbBcAQ79d1wCM+980BJrdw+n8YY8Z5vxYAeM89AxjlPe6fIuII1vWoznPt3k3VZ5+RdemliCO8f5rUceMwNTXUbtoU1seNB5oUK8XRnuLkRAfJTusJTpNiFVTuOnA2T4q7aVKsVJw7WHuQu5fcTf+M/lw4KPhVbAdnDWZv1V4q6yuDfu54sGTJEgYPHszAgQMBDDAPmNZst2nAM8ayGMgSkd4AxphFwMF2POQ0YJ4xps4Ysx3YApzc2etQwXPo5VcA6HrppWF/7NQTTwSgZtWqsD92rNOkWCn8D5+u92hSrIKoxaRYh08rFY+MMbyx9Q0ufu1i9lfv567T7yIxITHojzM4y1o/dcuhLUE/dzwoKSmhX79+vpuKgb7NdusL7GpjH39u8A63flJEurX3XCJynYgsE5FlpaWlATyc6izj8XDolVdI/8Y3SMoL5E8cXIl9++DIzaFm5aqwP3as06RYKaxCWwkCSY4ErT6tQsNTD46kY7elZGlSrFQcOlx/mJ9/+HN++9lv6ZfZj+cvfJ5xPcaF5LEGZQ0C0CHUHWSM8bu52c8SwD7NPQIMAsYBe4C/tfdcxpjHjDETjDETcsNUATneVX3xBe49e8i6IvhTHQIhIqSOGUvt2rVt76zaxWl3AEpFghqXh5REByJytKdYk2IVTO5aSMs5dltqN2v9Ynf90fWLlVIxzd3g5saPbmTl/pXcPPFmZg6fiSMhdPMS+3bpS6ozVXuKOygvL49du3YdswnY3Wy3YqBfG/scwxizr/G2iDwOvNnRc6nwOfTyKzi6dSPj22fZFkPKiBFUfvghDVVVJKSnt32ACoj2FCuFNXw6NdF6U6LDp1VIuOv9DJ/Osr7rskxKxY1XCl9h6d6l3Hbqbfxg5A9CmhADJEgCA7sO1KS4gyZOnEhhYSHbt28Hqxd3BjC/2W7zgSu9VahPASqMMXtaO2/jnGOvS4DGrr/5wAwRSRaRAqziXbowbQTwVFRQ+eGHZF54IZJk3wfZKSNHgDHUbtpsWwyxSJNipbCGT6c0T4q1p1gFk6eFOcWgQ6iVihPuBjePrXmMsbljmTaoea2m0BmcNViT4g5yOp089NBDnH/++WBVhH7BGLNORK4Xkeu9uy0AtmEVxXoc+Fnj8SIyF/gSGCYixSLyI+9d94jI1yKyBjgL+BWAMWYd8AKwHngbmGWM8YT8QlWbDr/9Dqa+nq5Tp9oaR8qIEQDUblhvaxyxRodPK4XVU5ySaCXDjXOKdZ1iFVTuFpZkAq1ArVSc+HL3l+yr3sfsk2cj4m/qaGgMzhrM61tfp6Kugq7JXcP2uLFiypQpTJkyBRFZa4y5C8AY82jj/caaeDzL37HGmJktbP9hS4/nfYy7Ohe1CraK+fNJGjSIlNGjbI3D2asXjqwsajdssDWOWKM9xUrRmBR7e4q10JYKBXfd8fOGtadYqbjy1va3yErO4pt53wzr4zYW29LeYqU6pn7nTmqWL6frtGlh/UDLHxEhZeQI6tZrUhxMmhQrxdFCWwAJCUKiQ3ROsQoudx04U47d1pQUt2cJS6VUNPI0ePi85HPO6HsGiY7gL73UmiHdhgCwpVyTYqU6omL+GyBC14uCv5Z4RySPGEFdYSHG7bY7lJihSbFSHFtoC6zeYu0pVkHlqTt+SSbtKVYqbmw4uIFDdYc4re9pYX/snmk96ZLYRXuKleoAYwwV8+eTNmkSib17t31AGCQPHoJxuajfuavtnVVANClWisZCW0ebQ5JTk2IVRMb47ylO7grigOoye+JSSoXN5yWfIwin9jk17I8tIgzKGqRJsVIdULNyFa6dO20vsOUrebA1JaJuq7bpYNGkWCmOnVMMmhSrIPO4AHP8nOKEBKu3uFqHTyv/RGSyiGwSkS0iMtvP/SIiD3jvXyMi49tx7E0iYkQkp/l9KvhWla5iUNYgslOybXn8wVmD2Xpoqy2PrVQ0q3j1VSQ1lYzzzrM7lCbJAwcCUL9V23SwaFKsFC0kxTqnWAWLp8763rz6NEBats4pVn6JiAN4GLgAGAnMFJGRzXa7AGsd0yHAdcAjgRwrIv2Ac4GdIb4MhTX8ck3pGsbkjrEthkFZgyivK6esRkemKBWohpoaDi9YQOZ55+Hokm53OE0S0tNJ7NOHui2aFAeLJsVKYRXaaj6nuM6tywKqIHF7k+Lmw6cBUrO1p1i15GRgizFmmzGmHpgHNF/cdhrwjLEsBrJEpHcAx/4D+A1gQn4Vih2Hd3C4/jBjcuxLigdnDQa0ArVS7XHkvfdoqKqi66WX2h3KcZIGD6JOe4qDRpNipfA3p9ihw6dV8DQlxUnH35fWXZNi1ZK+gG8VlWLvtkD2afFYEZkKlBhjVgc7YOXf1we+BrC1p1iTYqXaxxhD+bPPkdivH2kTJ9gdznGSBw2mfts2jEc7cYJBk2IV94wxxyzJBNbw6TpNilWweFrpKU7rpsOnVUv8LYbZvGe3pX38bheRNOB3wK1tPrjIdSKyTESWlZaWthmsatma0jWkOdMY2HWgbTHkpObQNbmrJsVKBah68WJqVq+m+7XXIAmRlzIlDx6EqavDVVJidygxIaR/4U4WCHlSRPaLyNpmx9wuIiUissr7NSWU16BiX2Py65sUJ+uSTCqYGnuKmy/JBEeHTxsdxaqOUwz08/k5D9gd4D4tbR8EFACrRaTIu32FiPRq/uDGmMeMMROMMRNyc3M7eSnxbXP5ZoZ2G4ojwdH2ziEiIgzqOkjXKlbKR+3mzZTc9L8Ufe/77Pu/u6ndsAGAhupq9v7pTpy9e0fk0GmA5EHeCtQ6rzgoQpYUd6ZAiNccYHILp/+HMWac92tBUANXcafOdXxSrIW2VFA1DZ9uodCWpw5c1eGNSUWDpcAQESkQkSRgBjC/2T7zgSu9HzKfAlQYY/a0dKwx5mtjTA9jTL4xJh8reR5vjNkbtquKM8YYCssLGdptqN2hMKL7CDaVb8Ld4LY7FKVsV718OUVXfJfKRYsAKH/uObZfcinbLr2U7ZddTn1REX3uupOEZD+v3REgqSkp1g+6giGUPcWdKRCCMWYRoGMKVcjVuKy5GL6FtpJ1SSYVTJ5667u/pDjVuzyLzitWzRhj3MANwDvABuAFY8w6EbleRK737rYA2AZsAR4HftbasWG+BAXsq97HEdcRhnQbYncojMkZQ427RodQq7jnLi+n+Oe/ILF3bwYteIv8555lyKeL6Pnb3yIOJ46sLPo98k/Sv/ENu0NtkSMjA2ePHtRv22Z3KDHBGcJz+yvyMSmAffoCe9o49w0iciWwDPh/xpjyTsaq4litNyk+ttCWJsUqiNy11ne/SzJ1t75Xl0FWv+PvV3HNOxpqQbNtj/rcNsCsQI/1s09+56NUrdlcvhkgMpJib6GvNaVrGJ493OZolLJP6QMP4KmooP9TT+HMsZZqd2RlkX3lD8m+8oc2Rxe4pAEDqN+xw+4wYkIoe4o7UyCkNY9gzYkah5U8/83vg2uBEBUgfz3FOnxaBZW7safYX6Etb0+xFttSKiZFUlLct0tfslOyWV2qhcdV/HLt28ehF1+i2/TvkjLM/mkNnZGUP4D6oiK7w4gJoUyKO1MgpEXGmH3GGI8xpgFrqNjJLeynBUJUQI72FB+7TrH2FKugaewp9rckkw6fViqmFZYX0iu9F5lJmXaHgogwJncMa0rX2B2KUrYpf24uNDSQfe21dofSaUn5+XjKy/FUVNgdStQLZVLcmQIhLWqcc+x1CbC2pX2VCkRjT3GyDp9uUwAV5ad5K8mv8o7UON2OOCNO45JMfodPN/YU6ywQpWJRY+XpSDE2dyxFh4uoqNM30YF6++23GTZsGMDoIK6m8lcR2ejd/1URyfJuzxeRGp9VVh5FBY2pr+fQ88/T5dtnkZSXZ3c4nZY0YACADqEOgpAlxZ0pEAIgInOBL4FhIlIsIj/y3nWPiHwtImuAs4BfheoaVHxorD593PBpTYqPEWBF+Q+AscaYccC1wL/DGmSkcrdWaKub9b26LHzxKKXCwuVxUVRRxJAs+4dONxqTY80r1iHUgfF4PMyaNYuFCxcCrCN4q6m8B4w2xowBNgO3+Ny31WeVlev9HKs6qOrLL/EcOkTWZZfZHUpQJOXnA5oUB0MoC211tkDIzBa2R8/sdxUV/A6fdiZQp3OKm2uqKA8gIo0V5dc37mCMqfTZP522awTEh6bh036SYkciJHfV4dNKxaDth7fjNu6ImE/caHTOaBziYNX+VZyZd6bd4US8JUuWMHjwYAYOHAjWa9pxr334rKYCLBaRLBHpbYzZY4xZJCL5zc9rjHnX58fFwOUhuwjV5PCCBSRkZtLltNPsDiUoEvv1AxHqizQp7qxQDp9WKir4XZLJO6fYen1TXi1Viz+GiFwiIhuBt7B6i/2Kq2J4nlYKbQGkddNCW0rFoMYiW5E0fDotMY0R2SNYsX+F3aFEhZKSEvr1O2ZlAH+vfQG9PrbiWmChz88FIrJSRD4RkTNaOiiuXkeDwNTXc+SDD8k49xwkyU+NjyiUkJxMYp8+WmwrCDQpVnGv1jt8unlPMYDLo0mxj4CqxRtjXjXGDAcuBv7U0sniqhhe05JMLbwIp2ZrT7FSMaiwvBBngpP8zHy7QznGuB7jWHtgLS6Py+5QIl4LH44HYzUV60CR3wFu4Fnvpj1Af2PMicCvgedExG+Vtrh6HQ2C6pWraKisJOPb37Y7lKDSZZmCQ5NiFfdaWpIJ0GWZjtWuavHGmEXAIBHJCXVgEa+1OcVgrVVcfSB88SilwmLLoS0UdC0g0ZFodyjHGN9zPHWeOtYfXN/2znEuLy+PXbt2HbOJIKymAiAiVwEXAt/3Dr3GGFNnjCnz3l4ObAUiZ6hBFKv6/HNwOkmbNMnuUIKqcVkmHd3YOZoUq7hX66/6tMObFGuxLV9tVpQXkcEiIt7b44EkQCtIeepAEiChhTIO6TlQpb8mpWLNlvItDM4abHcYxzmxx4kArNy30uZIIt/EiRMpLCxk+/btYPUIB2s1lcnAzcBUY0y1z/Zcb2FLRGQgVvGubUG7oDhW9cUXpI4di6NLF7tDCaqk/HwaKivxHNQRZ52hSbGKe7UuDyKQ7PRdksnqNdak+KgAK8pfBqwVkVVYlaqnG/3o0ho+7UgG8TfCDisprj4A+qtSKmZU1leyu2p3RFWebpSTmkO/jH6s3K9JcVucTicPPfQQ559/PsAogreaykNABvBes6WXzgTWiMhq4CXgemOMZjud5C4vp3bdOtJP+4bdoQSdLssUHCGtPq1UNKh1eUhxOhCfhCXRYd126fDpYwRQUf4vwF/CHVfEc9e3PHQaIC3HSpzrqyA5tj7BVipebTm0BSAie4rB6i3+rOQzjDHHvP6p402ZMoUpU6YgImuNMXdBUFZT8fuPYYx5GXi581ErX9VLl4IxpJ9yqt2hBF3Tskzbi0gbP771nVWLtKdYxb1aVwMpicc2hcY5xXXaU6yCwV3belKc7i2QUqXVQ5WKFY1JcSQtx+TrxB4ncrD2IDsOa++Sin01q1cjiYmkjB5ldyhBl9i3Lzid2lPcSZoUq7hX4/IcU2QLjg6l1uHTKig89dbw6Zake2uRVeu8YqVixZZDW0h1ptKnSx+7Q/FrfA+rR0mHUKt4ULN6NSkjR5IQI0sx+RKnk6S+fXVZpk7SpFjFvVqX55jlmECrT6sgc9e1PXwaoEorUCsVKxqLbCVIZL7VKuhaQFZylibFKuYZl4vatetIHTfW7lBCJnFAf+qLd7W9o2pRZD5TKxVG/pLiRK0+rYKpraS4sadYh08rFTMKDxVG7HxiABFhXO44TYpVzKvdtBlTW0vq2NhNipPy+uHauUuXZeoETYpV3PM7p9ibFGuhLRUUngCTYl2rWKmYUFZTxsHagxE7n7jRiT1PpOhwEQdrtbixil01q1cBxHRSnNivn7Us06FDdocStTQpVnGvprXh09pTrILBXdf6nOKkdHCm6vBppWJEpFeebtS0XrH2FqsYVrthA45u3XD2icz5/cGQ1L8fAK7iYpsjiV6aFKu4V+un0JZWn1ZB5a4DZxvFPdJzNSlWKkZEeuXpRiO7j8QhDtYdWGd3KEqFTN2mzSQPGxbTS48l5nmT4l06r7ijNClWcc/fnOJkLbSlgslTB86U1vdJ767Dp5WKEYXlhWQlZ9E9pbvdobQq2ZFMQdcCCssL7Q5FqZAwHg91hYWkDBtqdyghldQvD4D6nZoUd5QmxSruWXOK/RfacmlPsQoGdx042ugpTsvRnmKlYkThoUKGdBsSFT1TQ7oNYVP5JrvDUCokXLt2YWprSR46zO5QQiohLQ1HTo5WoO4ETYpV3LN6ipsV2tKeYhVM7kB6inX4tFKxwBjTtBxTNBjabSh7qvZwuP6w3aEoFXS1mzYDkDwstpNigKS8PFy7dE5xR2lSrOJejb85xbokkwomT30Ac4q9w6d1OQWlotqeqj1Uu6ujJike1s1KFjYf3GxzJEoFX92mTZCQQPLgQXaHEnKJ/ftRv2un3WFErYCSYhF5WUS+IxKhK9Ar1UHGGGpcHtKS4q/6tLbrMHLXtl59GqyeYnct1FeGJyYVVpdddhlvvfUWDQ2x+5yiLNFSZKvR0G7WXMvN5fGRFGtbjC91hZtJGjCAhJQ2RmvFgKS8frj37MXU19sdSlQK9M3wI8D3gEIRuVtEhocwJqXCps7dgDGQkuR/TnGMD5/Wdh0u7vq2h0+nedcq1iHUMemnP/0pzz33HEOGDGH27Nls3LjR7pBUiDQml9HSU9wjrQdZyVlxkxRrW4wvdVu3xUUvMVhrFWMMrt277Q4lKgWUFBtj3jfGfB8YDxQB74nIFyJyjYgkhjJApUKput4DQFocDp/Wdh1G7toAhk97k+LqstDHo8LunHPO4dlnn2XFihXk5+dz7rnn8o1vfIOnnnoKl8vV4nEiMllENonIFhGZ7ed+EZEHvPevEZHxbR0rIn/y7rtKRN4VkdhdvNMGmw5uom+XvmQkZdgdSkBEhIFdB7K9YrvdoYRFR9uiij7G7aZ+1y6S8gvsDiUsGtcqrtdlmTok4GGTItIduBr4MbASuB/rzfR7IYlMqTCocVlJcWqznuKEBCHRIbHeU6ztOhwaGqDBFUChrcae4tLQx6RsUVZWxpw5c/j3v//NiSeeyC9/+UtWrFjBueee63d/EXEADwMXACOBmSIystluFwBDvF/XYY0AaevYvxpjxhhjxgFvArcG8zrj3ebyzVEzdLpRftd8ig4X2R1G2LS3Laro5CopAZeLpPx8u0MJi8a1ijUp7phA5xS/AnwKpAEXGWOmGmOeN8b8HOgSygCVCqWa+sak2HncfUmOhJjuKdZ2HSYe79yeQJZkAh0+HaMuvfRSzjjjDKqrq3njjTeYP38+06dP58EHH6SyssV55CcDW4wx24wx9cA8YFqzfaYBzxjLYiBLRHq3dqwxxrfMcDqg1d2CpNZdS9HhoqbiVdEiPzOfg7UHqairsDuUkOtIW3z77bcZZlUvHt2BERtPish+EVnb7JhsEXlPRAq937v53HeL91ybROT8oF18nKkvKgIgqSDf1jjCxZmbgyQnawXqDjo+E/Dv38aYBb4bRCTZGFNnjJkQgriUCoumpLjZ8GmARGcCrtjuKdZ2HQ7uWuu7s61CW43DpzUpjkU//vGPmTJlyjHb6urqSE5OZtmyZS0d1hfw/ci/GJgUwD592zpWRO4CrgQqgLP8PbiIXIfV+0z//v1bilH52FqxlQbT0FS8KloMyBwAwI7DOxiTO8bmaEKrvW3R4/Ewa9Ys3nvvPQYNGrQOa9TFfGPMep/dfEdsTMIasdHY3uYADwHPNDv1bOADY8zd3kR7NnCzd0THDGAU0Ad4X0SGGmM8nbrwONSUFMdJT7EkJJDYL08rUHdQoMOn7/Sz7ctgBqKUHRqHTzevPg2x31OMtuvwaOwpbispTkqHxDTtKY5Rv//974/bduqpp7Z1mPjZ1rxXt6V9Wj3WGPM7Y0w/4FngBn8Pbox5zBgzwRgzITc3t61YFUeXNRqWHWU9xV3zASspjnXtbYtLlixh8ODBDBw4EKw21J4RGxhjFgEH/Zx6GvC09/bTwMU+2+d5P6DeDmzBGvmh2qlu+3YSunbF0a1b2zvHiKS8ftpT3EGt9hSLSC+sT5tTReREjr7IZmINuVQqqlXXuwFI8dNTnOSMzaRY23WYueus720tyQTWEGpNimPK3r17KSkpoaamhpUrV2K861AfPnyY6urqtg4vBvr5/JwHNC8r2tI+SQEcC/Ac8BZwW1vBqLZtLt9MqjOVvC55fu93uVwUFxdTW1sb5shaZ4zh/pH3k16ZzoYNG+wOJyRKS0vZv38/FRUVvPzyy03bKysrOXTo0DHXnZKSQl5eHomJiZSUlNCvn29TateIjT2thNTTGLMHwBizR0R6+JxrsZ9zHUdHc7SuvmgHSfkDEPH3GWHbIrW9tsZz9VU0VFfHbDtu5NtGg6Wt4dPnYxXhyQP+7rP9CPDboEWhlE1qW+spdiZQF5vDp7Vdh1NjUtxWoS2whlDr8OmY8s477zBnzhyKi4v59a9/3bQ9IyODP//5z20dvhQYIiIFQAnWkMrvNdtnPnCDiMzDeqNe4X2DXdrSsSIyxBhT6D1+KqBr0gTJ5vLNDMkagiPh+NcUgOLiYjIyMsjPz+/wG/VQSSxPJMWZQr+Mfm3vHIWWLFnCnDlz2L9/Pw899FDT9oyMDO69915GjBgBWB8QlJWVUVxcTEFBQdMHWc0EOmKjIwI+lzHmMeAxgAkTJmhtgGbqi4pIn9TxTvZIbq8tcR84gGvvXlKGDEGcgc6SjS7N22iwtPrbMsY8DTwtIpcZY15ubV+lolF1K3OKY3X4tLbrMPM0JsVtFNoCKymu3BfaeFRYXXXVVVx11VW8/PLLXHbZZe061hjjFpEbgHcAB/CkMWadiFzvvf9RYAEwBWuIZTVwTWvHek99t4gMAxqAHcD1nb1OZb1R21S+iXP6n9PiPrW1tRH7BjvJkURd4/NVDAq0LYoI3bt3p7TUWgkgLy+PXcdW823PiI3W7BOR3t4PsXoD+ztxLtVMQ3U17r17SepE0hTJ7bUl4u05NS5XzCbFzdtosLQ1fPoHxpj/Avki8uvm9xtj/u7nMKWiRqtzimO00Ja26zBrLLQV6PDpfevb3k9Fjf/+97/84Ac/oKioiL///fim5dt77I+3GN6CZtse9bltgFmBHuvd3r7sXAVkf/V+Kuoq2pxPHKlvsJMcSVS7qjHGRGyMndGetuh7/RMnTqSwsJDt27eD1Ysb8IiNNkKaD1wF3O39/rrP9udE5O9YhbaGAEsCvEzl1bgsUdKAAZ06T7S1BUm0PoA3LhekptocTeiE4u/S1kcI6d7vujyLikmN1adT4qvQlrbrcHIHWGgLjg6fNgai7IVY+VdVVQXQ2rJLKkZsKt8EEHWVpxslJSTRYBrwGA9Oib0epo62RafTyUMPPcT5558PVkXoPwU6YgNAROYC3wJyRKQYuM0Y8wRWMvyCiPwI2Alc4T3fOhF5AVgPuIFZWnm6/VwlJQAk9vU7HTtmSdLRnmLVPm0Nn/6X9/sd4QlHqfBqbUmmWC20pe06zJqGTweYFLtrob4SkjNCG5cKi5/85CcA3Hab1rGKdZvLrcrT0ZoUJyZYb6brPfU4E2IvKe5MW5wyZQpTpkxBRNYaY+6Cdo3YmNnC9jLg7Bbuuwu4q92BqiauEmvEeWKfPjZHEmYOB4hoUtwBAS3JJCL3iEimiCSKyAcickBEfhDq4JQKtWqXh0SHkOg4vikkOROoj8Hh0420XYeJux1JcZp3rWKtQB1zfvOb33D48GFcLhdnn302OTk5/Pe//7U7LBVEm8s30ye9DxlJkf+B1quvvoqIsHHj0RprSQ5r2KWrITRvpm+99Vbef//9dh2Tn5/PgQOBPR/efvvt3HvvvW3up20xPrhKSpDkZBzdu9sdSqf5a68tEREkMbHNpLg9bas95s+fz9133+33vi5drAGKu3fv5vLLLwdg1apVLFhw3CwfWwT6UeB5xpjfiMglWAUArgA+AvRZREW1mnqP315igMTYHT7dSNt1OLRnSaZ071qwVQcgO3gVFZX93n33Xe655x5effVV8vLyePHFFznrrLP4wQ/0c6hYsfng5nb1Ev9lyV/YeDC4hb+HZw/n5pNvbnO/uXPncvrppzNv3jxuv/124Nie4lD44x//GJLztpe2xfjg2r2bxD59gjb3dO+f/0zdhuC21+QRw+n127YX/fDXXlsjiYmY+tC047ZMnTqVqVOntrpPnz59eOmllwArKV62bBlTpkwJR3itCqinGGhcBGoKMNcY428RcqWiTq3LQ6qf+cQQ+z3FaLsOj/b0FKd7P9HWZZlijsv7qf2CBQuYOXMm2dnZNkekgqnOU0fR4SKGZkf+0OnKyko+//xznnjiCebNm8fChQv57ne/iyPBgSPBwSeffMJFF10EWAnkqaeeyvjx47niiitanI+7ZMkSLr30UgBef/11UlNTqa+vp7a2loEDBwJw9dVXN70Rzs/P57bbbmP8+PGccMIJTT1gZWVlnHfeeZx44on85Cc/aWk5pCZ33XUXw4YN45xzzmHTpk1N27du3crkyZM56aSTOOOMM5rOv3XrVjZv3szEiRP505/+xPPPP69tMUa5du+OifnELbXXRh9//PFx7fWUqVOZOWtWm/PnH3zwwePaYPMRF6NHj6aoqIiioiKGDx/Oj3/8Y0aPHs33v/993n//fU477TSGDBnCkiVWLbg5c+Zwww03ALB9+3ZOPfVUJk6cyB/+8IemcxYVFTF69Gjq6+u59dZbef755xk3bhzPP/88Q4YMaaoq3dDQwODBg0PSo+1PoD3Fb4jIRqAG+JmI5AJtrmQtIpOB+7GWg/i3MebuZveL9/4pWIUJrjbGrPDe9yRwIbDfGDPa55hs4HkgHygCvmuMKQ/wOpQ6RlW9h7Qk/80gOfZ7ijvUrlU7tWtOcWNPcXCXGVD2u+iiixg+fDipqan885//pLS0lJSUANauVlFh66GteIynXT3FgfTohsJrr73G5MmTGTp0KNnZ2XTv3p3FixdTVVVFUkISr730GtOnT+fAgQPceeedvP/++6Snp/OXv/yFv//979x6663HnXP8+PGsXLkSgE8//ZTRo0ezdOlS3G43kyZN8htHTk4OK1as4J///Cf33nsv//73v7njjjs4/fTTufXWW3nrrbd47LHHWryO5cuXM2/ePFauXInb7Wb8+PGcdNJJAFx33XU8+uijDBkyhK+++oqf/exnfPjhh/zyl7/knHPOYfPmzVRVVeFwOLQtxihXSQkpI0cG7XyB9OiGQmvtNT09neeff/649ppUWcVf7v4//va3v7U6h95fG2zNli1bePHFF3nssceYOHEizz33HJ999hnz58/nz3/+M6+99tox+//yl7/kpz/9KVdeeSUPP/zwcedLSkrij3/8I8uWLWtaO3zjxo08++yz3Hjjjbz//vuMHTuWnJyc9v/iOiCgnmJjzGzgVGCCMcYFVAHTWjtGRBzAw8AFwEhgpog0/++8AKvU/BDgOuARn/vmAJP9nHo28IExZgjwgfdnpTqkstZFRor/pDhWC2016ki7Vh3QnuHTXXpa34/sDV08yhZ33303X375JcuWLSMxMZH09HRef/31tg9UUaGxyNawbq0vxxQJ5s6dy4wZMwCYMWMGL774IpMnT+aNN95AjPDhOx8ybdo0Fi9ezPr16znttNMYN24cTz/9NDt27PB7TqfTyeDBg9mwYQNLlizh17/+NYsWLeLTTz/ljDPO8HtMY8/ySSedRFFREQCLFi1qGsb8ne98h27durV4HZ9++imXXHIJaWlpZGZmNg3ZrKys5IsvvuCKK65g3Lhx/OQnP2HPHmt1pC+//JJXXnmFL7/8kjVr1iAi2hZjUEN1NZ7y8pjoKW6tvbrdbt56663j2uuEc87m2fnz2eFtVy3x1wZbU1BQwAknnEBCQgKjRo3i7LPPRkQ44YQT/B7/+eefM3OmVWfuhz/8YUDXe+211/LMM88A8OSTT3LNNde0cUTwtKe84AisdU19j3mmlf1PBrYYY7YBeNdum4ZVYr7RNOAZb9W+xSKS1biQuTFmkYjk+znvNKzS9gBPAx8D9nzcqqJeZZ2bLsn+m0GiIzbXKW6mve1atVd7hk87k63e4ori0MakbLFhwwaKiopwu91N26688kobI1LBsungJlIcKfTL6Gd3KK0qKyvjww8/ZO3atYgIHo8HEeGpp57i4YcfRtKEUSeOokuXLhhjOPfcc5k7d25A5z7jjDNYuHAhiYmJnHPOOVx99dV4PJ4Wi18lJ1vPiQ6H45g20Z45oP72bWhoICsri1WrVrV43IYNG9iwYQMul6tpSLe2xdjh2h0blafbaq/Z2dlMnDiRjIyMY9qrp7KS+qIikgpar03irw06nU4aGo6+962trT1uf4CEhISmnxMSEo5pw77aO6e7X79+9OzZkw8//JCvvvqKZ599tl3Hd0ag1af/A9wLnA5M9H5NaOOwvsAun5+Lvdvau09zPRsXRPd+79HG/kq16Ehty0lxrPcUd7Bdq/Zqz/BpgMy+cLgkdPEoW/zwhz/kpptu4rPPPmPp0qUsXbqUZcuW2R2WCpLC8kKGdBuCI8F/jYpI8dJLL3HllVeyY8cOioqK2LVrFwUFBTidTlasWMFzc55j8rTJuBvcnHLKKXz++eds2bIFgOrqajZv3tziuc8880zuu+8+Tj31VHJzcykrK2Pjxo2MGjUq4PjOPPPMpjfBCxcupLy85dlxZ555Jq+++io1NTUcOXKEN954A4DMzEwKCgp48cUXATDGsHr1agBOOeUUzjrrLG666Sb+85//0NDQoG0xBjUlxX2jOyluq70+/vjjTJ8+HeCY9iqJiVTX1LBpw4Z2P2Z+fj4rVqwAYMWKFWzfvr3D8Z922mnMmzcPoMXkNiMjgyNHjhyz7cc//jE/+MEPrFoHjvA9pwbaUzwBGGnaqnhwLH8fDTQ/PpB9OkRErsMakk3//v2DcUoVgyrr3HRpbfh0bPcUt7tdB1An4PscHblRCfzUGLM6SPFGJ7e3AmQgw6cBuuZB2dbQxaNssWzZMtavXx+0Sqgqchhj2FS+ibP7+11yNqLMnTuX2bOPnXV22WWXMW/ePC688ELmzJnDH+77A/UN9eTm5jJnzhxmzpxJXZ314d6dd97J0KH+501PmjSJffv2ceaZZwIwZswYevTo0a7/+dtuu42ZM2cyfvx4vvnNb7b6/m38+PFMnz6dcePGMWDAgGOGaT/77LP89Kc/5c4778TlcjFjxgzGjh3Lfffdx5gxYxg9ejTnn38+hYWFPPjggwHHp6KDq8T6YDnah08H0l6ffvppgOPaq6mt5Y7f/56RE9rX13HZZZfxzDPPMG7cOCZOnNhiew/E/fffz/e+9z3uv/9+LrvsMr/7nHXWWdx9992MGzeOW265henTpzN16lSuueaasA6dBqwn87a+gBeB3oHs63PMqcA7Pj/fAtzSbJ9/ATN9ft7k+zhYxbTWNjumaR+gN7CprVhOOukko5Q/Y+94x9z62td+7/v7u5vMgJvfNB5PQ5ijCg1gmelEu8ZKhLcCA4EkYDVWUu27zzeAbt7bFwBfBXLumG6j791uzB3dA99/wW+Muatv6OJRtrj88svN7t27W92neRuNpK+YbqOdtK9qnxk9Z7R5dv2zbe67fv36METUcbXuWrO2dK0prym3O5SQqKqqamqLc+fONVOnTm1xX39/K22j0WHfvfea9aNPMA0eT6fOE+nttTU16zeYuuJiu8PokKVLl5rTTz+9zf2C3UYD7SnOAdaLyBKgziehbm0hqqXAEBEpAEqAGcD3mu0zH7jBO994ElBhvEOjWzEfuAq42/tdqyOoDjHGUFnbek8xQL2ngZQIHxLXQe1t123WCTDGfOGz/2IgL9hBRx1PfeBDp8EaPl1/BGorIKVr6OJSYXXgwAFGjhzJySeffMy8rPnz59sYlQqGxiJb7ak8Hama1ipusGeN01Bbvnw5CxcuJC8vj8zMTMaPH99UoEvbYuxwlewmsXdvJCHQlWdjjyQ6oYV5vpHs7rvv5pFHHgnrXOJGgSbFt7f3xMYYt4jcALyD1cP0pDFmnYhc773/UWAB1nJMW7CWZGrqJxeRuVgFtXJEpBi4zRjzBFYy/IKI/AjYCVzR3tiUAqhzN+BuMHRJTvR7f5LDejJ1eRpISYzJpPj2du7vrwaA/7U2LD8CFrZ0Z9xMcXDXti8p7uod7lVRoklxDLn99tvtDkGFyKaD1vq40bBGcVsSJAFnghNXg6vFfS655JLj5hn+5S9/4fzzzw9JTGVlZZx99vFD0z/44AO6d+/ernOdccYZvPXWW8EKTUUoV0lJ1BfZ6jSnE+Nyhb29dtbs2bOPGzIeLgElxcaYT0RkADDEGPO+iKRhJbptHbcAK/H13faoz20DzGrh2JktbC8DIn/ijop4R2qtT9Da7CmO0WJbHWjXAdcAEJGzsJLi01t5/MeAxwAmTJgQlFoCEcldF/h8YoCu3uq1FcXQM3hrLCp7ffOb32THjh0UFhZyzjnnUF1djcfjsTssFQSbyzfTO703mUmZAe1vjInoueWJjkRcnpaT4ldffTWM0UD37t1brSTdXoG0RevtqYpWrt27ST/T/1Jg7RXp7bUlkphIQ21t2NtruISijQZaffp/gJew5gCD1WP0WtCjUSqMjtRaL/oZrVSfBmK22FYH2nUx4LveSB6w2895xwD/BqZ5P8SKb556cCYFvn/2QOv7QS22FUsef/xxLr/8cn7yk58AUFJSwsUXX2xvUCooNpdvDnjodEpKCmVlZRGddCUlJMXs8Glouy0aYygrKyMlJcWmCFVnNNTV4S4tDUpPcTS015aI04lxu6My9raEqo0GOnx6FtZ8wq+8wRSKiC6FpKJaZZ23p7ilpNgR2z3FtL9dt1knQET6A68APzTGtLx2Rzxx14KzHU/cad2tYdNlW0IXkwq7hx9+mCVLljBpkjXjYMiQIezfv9/mqFRn1Xvq2V6xnbP6nRXQ/nl5eRQXF1NaWhriyDrucP1hquqrcKe7o7KHrC1/+9vfeP7555kxYwYbvEvW7Nq1q+k2WMlQXp6WxIhG7j1WaaLEPp2vPB0N7bUlDVVVeCoqcAISxmWNwiUUbTTQpLjOGFPf+OQoIk6CtHSSUnapbGP4dKLz6JziGNWudh1gnYBbge7AP73ndRtj4nvtY3c9ONrRUywC3YfAgcLQxaTCLjk5maSko/8HbndsJhzxZsuhLXiMh2HZwwLaPzExkYKCghBH1TkvbHqBP636E+9d/h690nvZHU7Qde3albFjx5KSksKIESNwu91Nt1X0q29ajqnzPcXR0F5bcuTDjyiedQP5L75Aqv5vByTQsmyfiMhvgVQRORdrKZc3QheWUqF3JMCe4rrY7Slud7s2xiwwxgw1xgwyxtzl3fZoY60AY8yPjTHdjDHjvF/xnRBD+3uKAboP1rWKY8w3v/lN/vznP1NTU8N7773HFVdcwUUXXWR3WKqTNpRZvYsjs2Nn/n+fLlYysaeqrcVAolNH2uLbb7/NsGHDAEaLyHFVgMTygIhsEZE1IjLe577JIrLJe99sn+3Pi8gq71eRiKzybs8XkRqf+x5t/niqZa7d1qyuYPQURzNnD2vgn1tHJAUs0KR4NlAKfA38BKt41u9DFZRS4VBRY80p7prqv/p0cowX2kLbdXi0d0kmsJLiw8VQXxWamFTY3X333eTm5nLCCSfwr3/9iylTpnDnnXfaHZbqpA0HN5CRmEFeRuwMtW1MiksqS2yOJDTa2xY9Hg+zZs1i4cKFAOuAmSLS/FOQC4Ah3q/rgEcARMQBPOy9f6TvscaY6Y0fIAMvY009arTV58Pl64Nw2XHDtXs3OBwk9uppdyi2cvbIBTQpbo9Aq083iMhrwGvGmOgbWK+UH4eqrUIi3dL9D22Ng+rT2q7DwV3X/qWVug+yvh/cBr1OCH5MKuwSEhK4+OKLufjii8nNzbU7HBUkG8o2MLz78JgaCt87vTcAuyuPq6MYE9rbFpcsWcLgwYMZOHAgWFOM5gHTgPU+u00DnvGuqrJYRLJEpDeQD2wxxmwDEJHjjhXrn+e7wLeDcX3xzlVSgrNnD8QZ6AzR2OTs3h0SEnBpUhywVnuKvcNBbheRA8BGYJOIlIrIreEJT6nQKa92kegQ0pP8FyBIbFqnOLamz2u7DjN3XfuHT+cMsb4f0Fpl0c4Yw+23305OTg7Dhw9n2LBh5Obm8sc//tHu0FQnuRvcbCrfxIjs2Jqvl+pMJTslO+aS4o62xZKSEvr18114gWKs1Rp89QV2+dmnpe2+zgD2GWN8C0kUiMhKEflERFpcW0hErhORZSKyLBqLQYWCa/dukuJ86DRYxbWcOTm492lSHKi2hk/fCJwGTDTGdDfGZAOTgNNE5FehDk6pUCqvqicrLanFT/iPLskUc2uJ3oi26/Dx1LVvSSaA7EGAwAGtQB3t7rvvPj7//HOWLl1KWVkZBw8e5KuvvuLzzz/nH//4h93hqU4oqiiizlPHiO6xlRQD9O3SN+aS4o62xRaWtGm+0d8bCdPKdl8zgbk+P+8B+htjTgR+DTwnIn4XwTbGPGaMmWCMmaAjUCyukt1BKbIVC5w9e+rw6XZoKym+EphpjNneuME7BOQH3vuUilrl1fVkp7WcrMTwkkzarsOpI4W2ktIgq5/2FMeAZ555hrlz5x5TwXTgwIH897//5ZlnnrExMtVZGw5aRbZiracYrCHUsVZoq6NtMS8vj127dh2zCWj+iUEx0M/PPi1tB5pWfbgUeL5xmzGmzhhT5r29HNgKBLYQdpwzLhfufftI7Ks9xWAV29KkOHBtJcWJxpgDzTd65x/6r06kVJQor3aRldbyv3FjT3EMVp/Wdh1O7V2SqVHOUDiwKfjxqLByuVzk5OQctz03NxeXy2VDRCpY1petJ8WRQn5mvt2hBF1jT3GDiZ3Xv462xYkTJ1JYWMj27dvB6vmdAcxvttt84Erv9KRTgApjzB5gKTBERApEJMnPsecAG40xxY0bRCTXW6ALERmIVbxrW7svOA659u2HhgYS+2hPMVjFtjQpDlxbSXF9B+9TKuKVV9WT3UKRLTjaUxxrc4rRdh1enrr2V58GyBlmDZ9uiJ03pfHId23i9tzXqKXlXHzu78hSMH8VkY3e/V8Vkax2X5hiw8ENDMsehiPBf12KaNa7S2/qG+opqymzO5Sg6WhbdDqdPPTQQ5x//vkAo4AXjDHrROR6EWmsDL0AK3HdAjwO/AzAGOMGbgDeATY0Hutz+hkcO3Qa4ExgjYisBl4CrjfGHAz0OuOZq3GNYk2KAUjs0QPPoUM01Otbu0C0VZptrIgc9rNdgHaOB1QqshyorOPkguwW74/h6tParsOpI4W2wCq25a6Bil3QbUDw41JhsXr1ajIzj58OaIyhtra21WN9lnM5F2sY5lIRmW+M8a1667sUzCSspWAmtXHse8Atxhi3iPwFuAW4uXNXGl8aTAMbD27kooGxudZ03y7W8NPdVbvJTYuNuaqdaYtTpkxhypQpiMhaY8xd3uOa1g/2Vp2e5e9YY8wCrKTZ331X+9n2MtYSTaqdmtYo1uHTwLFrFSflxc6ycaHSalJsjIm9jz+VAmpdHsqrXfTKbDlZOZoUx1ahLW3XYWRMx+YUgzV8GuBAoSbFUczTuUJ9J9PGci50YCkYY8y7PscvBi7vTJDxaNeRXVS5qhjZvflytbGhT7rV07a7cjdjc8faHE1wdLItqijQ2FPs7N3b5kgigzO3ca3iUk2KA9DW8GmlYtL+w3UA9OwaQFLsibmeYhUuDW4wDR0bPp07zPquxbbiWSDLuXRmKRiAa4GF/h5cl3tp2YYyb5GtGKw8DdCny9GkWKlo4dq9G2ePHiQEMDUlHjQlxfr8HRBNilVc2nvYGirVWk9xosNaSSEGh0+rcHF7h+R1JClO6w6p3bTYVnwLZDmXDi8FIyK/A9zAs/4eXJd7admGgxtITEhkUNdBdocSEmmJaWQlZ2lSrKKKq6RE5xP70KS4fdqaU6xUTGpKilvrKW5ckin2Cm2pcHFbIxI6NHxaxFtsqzC4Malo0upyLm3sk9TasSJyFXAhcLZpYSFW1bINZRsYnDWYREfsFuzvnd6bkqoSu8NQKmCu3btJHTPG7jAihiM7GxwOTYoDpD3FKi7tq7CS4p4ZLScrIkKSI0F7ilXHdaanGKxiWzp8Op61tZwLdGApGBGZjFVYa6oxpjpcFxMrjDFsOLghZucTN+rbpS97KmNrrWIVu4zHg2vvXu0p9iEJCTi7d9ekOECaFKu4tPdwLSmJCWSmtj5YIsmpSbHqhM70FINVbKuqFKp1NY541NJyLkFYCuYhIAN4T0RWiUhTFV3Vtr1VezlUd4gR2bE5n7hRny592F25Gx1IoKKBu7QUXC4S+2pS7MuZm6tJcYB0+LSKSzvKqumfnYaIv2l3RyU5E6jXipWqozrbU9xUbKsQ+k8KTkwqqvhbzqWzS8EYYwYHOcy4sv6gVfw7VotsNerTpQ+1nlrK68rJTml5+UKlIoEux+SfMzcX1969docRFbSnWMWl7QcqKchJb3O/RIfgcuun5KqDmpLijvYUD7G+a7EtpSLG+rL1OMTBkG5D7A4lpHyXZVIq0jUux6TDp4+lPcWB06RYxR23p4GdB6spyOnS5r5WT7EOn1Yd1DR8uoM9xVkDrIS6VJNipSLFurJ1DMoaRKoz1e5QQqpxWaaSSi22pSKfq8TbU6xJ8TGcubl4Dh7EuN12hxLxNClWcafkUA0uj6EgJ63NfbXQluqUzs4pTnBYQ6j3rWt7X6VUyBljWHdgHaNzRtsdSsj17tIbQIttqajgKinBkZ1NQmpsf1jVXs4euWAM7rIyu0OJeJoUq7iz/UAVQIA9xQ7qNClWHdXZnmKAHqNg//rgxKOU6pTdVbs5VHeIUd1H2R1KyGUmZZKRlKE9xSoquEpKSMzLszuMiNO0VvF+HULdFk2KVdT6tLCUr4sr2n3c0aS47TnFSQ7BpcOnVUd1dk4xQM+RULkPqvRTXqXstvbAWgBG5cR+UgzWvOI9VdpTrCKfq6REK0/70ZQU67ziNmlSrKLS+t2H+eETS7j4n59TU9++6tDbD1SRkewkp0tSm/vqkkyqU4LSU+ytcKu9xUrZbt2BdSQmJDI0a6jdoYRFny59tKdYRTzT0IBr926StPL0cTQpDpwmxSoqvb3W+uTa02BYVNi+hr79QBUFueltLscEWmhLdVIweop7eHukNClWynbrytYxrNswEh2JdocSFn279NW1ilXEc5cewLhcuhyTH87u3QFNigOhSbGKSit2HmJIjy4kOoRVuw6169htpVUBDZ0GLbSlOqmzhbYAMnpBajbs/To4MSmlOqTBNLC+bH3cDJ0G6J3em2p3NYfrD9sdilItcpUUA+icYj8kKQlHt26aFAdAk2IVlTbtO8LYflmM6J3Jqp2HAj6u1uVhd0VN4EmxM4E6d/uGZyvVpKmnuBPDp0Wg91jYszo4MSmlOqTocBGVrsq4qDzdqG8Xq+ct3odQv/322wwbNgxgtIjMbn6/WB4QkS0iskZExvvcN1lENnnvm+2z/XYRKRGRVd6vKT733eLdf5OInB/q64t2TWsUa0+xX7pWcWA0KVZRp7yqntIjdQzrmcHwXhkU7q8M+NgdZdUYE1iRLYDURAe1Lu0pVh3U2FPs6ERSDNBnHOzfcPR8SqmwW3fAWhotHipPN9JlmcDj8TBr1iwWLlwIsA6YKSIjm+12ATDE+3Ud8AiAiDiAh733j/Rz7D+MMeO8Xwu8x4wEZgCjgMnAP73nUS1oSop1jWK/NCkOjCbFKups81aPHtQjnYG5XThQWcfhWldAx27xJtCDcttejgkgJdFBrUt7ilUHuWtBHOBwdu48vcdCg0vXK1bKRuvK1pHqTGVg14F2hxI22lMMS5YsYfDgwQwcOBDAAPOAac12mwY8YyyLgSwR6Q2cDGwxxmwzxtS3cGxz04B5xpg6Y8x2YIv3PKoFrpISHLk5JKR0YqpSDNOkODCaFKuos6eiBoA+WakM9Pb4biutCujYwv1HEGlfUlyjSbHqKHdt5+YTN+o9zvquQ6iVss3aA2sZkT0CR0L8dNplJmWSnpjO7qrddodim5KSEvr16+e7qRhoPk63L7DLzz4tbW90g3e49ZMi0q2Nc6kW1BcXk9RHf0Utcebm4j5wANOgIx9bo0mxijq7D/kkxd7kdltpYEOot+yvJK9bKqlJgb2pSUl0UKfDp1VHues6N5+4Ubd8SOkKe1Z1/lxKqXZzN7jZeHBjXBXZAhAReqf3juue4hYqbzff6G85C9PKdrCGWA8CxgF7gL+1ca7jiMh1IrJMRJaVxnFPoKtkt84nboUzNxfcbjyHDtkdSkTTpFhFnd2HaumS7CQzJZH+2Wk4EiTgnuIt+ysZHGAvMVhzius9DXgadDkK1QHB6inWYltK2Wrroa3UeeoY3T1+imw1GpA5gJ2Hd9odhm3y8vLYtWvXMZuA5l3nxUA/P/u0tB1jzD5jjMcY0wA8ztEh0i0e05wx5jFjzARjzIRc73q08cZ4PLj27NGkuBXOHrpWcSBCmhS3VHHP5/6gVutT8WH3oRr6ZFmJRpIzgbxuqWwvazsp9jQYth2oYnCPwJPilESriei8YtUhweopBisp3rcO3PXBOZ9SKmDryrxFtuKspxigoGsBO4/sxNUQWO2OWDNx4kQKCwvZvn07WL24M4D5zXabD1zpfV97ClBhjNkDLAWGiEiBiCT5Huudc9zoEmCtz7lmiEiyiBRgFe9aEqLLi3ru0lJwuXQ5plY4vR+YuPdrUtyaTlZ/aZlPxb1zsT71Wioi840x6312863WNwlrKMmkAI79hzHm3lDFriLbnopaendNbfq5ICed7QH0FBeXV1PvbmhnUmwNs651eUhPDllzUbEqWD3FYM0r9tRD6UboPSY451RKBWTtgbVkJGXQP6O/3aGEXUHXAtwNbkqOlJDfNd/ucMLO6XTy0EMPcf7554NVEfpPxph1InI9gDHmUWABMAWrKFY1cI33PreI3AC8AziAJ40xjRUT7xGRcVhDo4uAn3iPWSciLwDrATcwyxijn8y3wFXsXaNYe4pb1JQUa09xq0LZUxxIxb1gVutTccLqKT42KS4qq2pp3k+TxsrTg3tkBPxYqd6kWIttWQIY/TFcRL4UkToRucmOGCNKUHuKx1nfdV6xUmG3unQ1J+ScgIi/6Z6xrSCzAIBtFdtsjsQ+U6ZMYfPmzQBrjTF3gZUMexNivO9jZxljBhljTjDGLGs81hizwBgz1HvfXT7bf+jdd4wxZqq3Z7nxvru8+w8zxiwM35VGn/qmpFiXY2qJJsWBCWVSHEj1vGBW61NxoNbloayqnj5dj/a+DcxJp7rew/4jra/hWtiUFAfeU5zcNHxai20FsN4iwEHgF4CO5ADw1AWvpzh7ICRl6LxipcKsylXFlkNbGJs71u5QbNHYO7y9Yru9gSjlR/2OHeBwkKQ9xS1KSE0loUsXTYrbEMqkOJDqecGs1nfsibUiX0zaU1EL0KynuLECdetDqDfvO0KPjGS6piYG/HipPsOnVdsjOIwx+40xS4H4nHzWXDB7ihMSrHnFu1cF53xKqYB8feBrGkwD43LH2R2KLTKSMshNzdWkWEUk144dJPbpgyQl2R1KRNO1itsWyqQ4kOp5wazWdwytyBeb9niXY+qddbT3LT8nDYDtB1pPiteWVDC6b9d2PV6KJsW+grp2Ylx8cOWqCV5SDN5iW2vB4w7eOZVSrVq1fxWCcELuCXaHYpuCrgVsP6xJsYo89Tt2kjRggN1hRDxNitsWyqS4xYp7PoJZrU/FgZLGNYp9Cm316ZpKkjOBolYqUNfUe9iyv7LdSXHjesY6pxhox9qJgYiLD65cNZCYFrzz9RlnFe86sCl451RKtWp16WoGZQ0iIynwehSxpqBrAdsrtrdZu0OpcDLGUL9jhybFAdCkuG0hK6fbUsW9UFXrU/Ghcfh0L585xQkJQkH3dLaVVrZ43Po9h2kwMLpPZrseL8XZ2FOsc4ppx9qJyivYSXFv75zG3augZ/wtDaNUuDWYBtaUruHcAefaHYqtCroWcKT+CGW1ZeSk5tgdjlIAeA4epKGykqQB8VcVvr0ak2JjTFwWDAxESNeYMcYswEp8fbc96nPbALMCPda7/YdBDlNFkd2HasjpktQ0rLnRiN4ZfLG1rOlT7H9+vJXPtxzgxnOGcnJBNl9uPQDA+AHtq8vWuE6x9hQDPiM4gBKsERzfszekCOeqgqQgJsXdB0NiulVs68TvB++8Sim/iiqKOFx/OG6LbDUanDUYgE0HN5HTV5NiFRnqd+wA0J7iADhzczG1tTRUVuLIiN9RL60J5fBppYKupNlyTI3G9cti/5E69lTU8uaaPfz1nU0s21HO1U8tYePew3y4cT+j+2aS06V98zt1TvFRxhg30DiCYwPwQuPoj8YRICLSS0SKgV8DvxeRYhFpX/d8LKmvhsTj/187LMEBvU7QZZmUCpPVpVa197E94jspHp49HIANBzfYHIlSR9Xv2AloUhwIZw9dlqktmhSrqLL7UM0x84kbTcjPBmD+6t3c8cY6xuR15eObvkWXZCcXP/w5K3Ye4sIx7V/DrjEprtOkGPC/3mKztRr3GmPyjDGZxpgs7+3D9kZtk4YGcNdYPbvB1Gcc7P0aGvR/Mh4EsDa4iMgD3vvXiMj4to4VkStEZJ2INIjIhHBdSzRaVbqKzKRM8jPz7Q7FVl2Tu9InvQ8bD260OxSlmtTvKAKHg0RdjqlNTWsV79ekuCWaFKuoYYyh5FANfbsdnxSP6pPJoNx07l64kfJqF/936Qn0yUrlXz88iZREB2PzuvLDU9r/SaIW2lId5raKwgV1+DRY84pd1XCgMLjnVREnwLXBLwCGeL+uw1q2sK1j1wKXAotCfQ3RbuX+lYzNHUuC6Nul4dnDNSlWEaV+xw4S+/ZFEgNfajNeNSXF2lPcIn2WV1HjYFU9ta4G+voZPi0i3D51FMN7ZXDbRSMZ1ceqMn1i/26s/MO5vH7D6aQnt38KfYrTaiJaaEu1m8ubFAez0BZA73HW9z2rg3teFYnaXBvc+/MzxrIYyPKu0tDiscaYDcYYLWHehtLqUrZXbGdir4l2hxIRhncfzo7DO6hytb78oVLhUr+9SIdOB0iT4rZpUqyiRnG5lWTk+ekpBjhjSC5v33gmV56af8z2zlTZczoSSHQI1fXaU6zaqd77xjHYSXHOUHCm6rzi+BDI2uAt7dPpdcXjYi3xVizbtwyAk3udbHMkkWFE9gjAKrallN2Mx0P9tm0kDx5sdyhRISEjA0lO1qS4FZoUq6jRuEaxv+HToZSe7KSqzh3Wx1QxoKmnOMj/rw4n9BptLcukYl0ga4O3tE+n1xWPi7XEW7Fk7xK6JHZhWPYwu0OJCCO7W6Pv1x5Ya3MkSoFr1y5Mfb0mxQESEV2ruA2aFKuoUdLYU5wV5J63NnTRpFh1ROMQw6QgF9oCawj13jVWMS8VywJZG7ylfXRd8U5atncZJ/U8CWdCSFevjBo90nrQt0tfVuxfYXcoSlG3ZQsAyUM0KQ6UJsWt06RYRY2SQzVkJDvJTA3vG5QuyU4qNSlW7VVfbX0Pdk8xWMW26ivh4Nbgn1tFkqa1wUUkCWtt8PnN9pkPXOmtQn0KUGGM2RPgsaoF+6v3U3S4SOcTN3NSz5NYuX8lxrRr0IFSQdeYFCcNHGRzJNFDk+LWaVKsosaOsir6dkvt1BzhjkjXpFh1RNPw6RD0FOd5V9HZ8Xnwz60iRiBrgwMLgG3AFuBx4GetHQsgIpd41xM/FXhLRN4J42VFhaV7lwJoUtzM+B7jOVh7kKLDRXaHouJcXeEWnH164+gSgtfYGKVJces0KVZRY0tpJYN7dAn74+rwadUhjcOnQ9FTnDscuhXA+teDf24VUQJYG9wYY2Z57z/BGLOstWO921/1riGebIzpaYw5P/xXFtmW7l1KRlIGw7rpfGJf43tay2Cv2BdfQ6jffvtthg0bBjA6iOuF/1VENnr3f1VEsrzb80WkRkRWeb8eDcMlRp26rVt1PnE7OXNzaThyhIbaWrtDiUiaFKuoUOvyUFxeY1tSrD3Fqt1cIVqnGEAERl0M2z6BiuLgn1+pOGaM4YvdXzCx50QcCQ67w4ko+Zn5ZKdks2TvErtDCRuPx8OsWbNYuHAhwDqCt174e8BoY8wYYDNwi8/5thpjxnm/rkcdw7hc3srTQ+wOJaroskyt06RYRYWtpZUYgy1JcXqyQ5Ni1X5NSzKFaGjXSddY37/8Z2jOr1Sc2l6xnT1Vezg973S7Q4k4IsJpfU7ji91f4GmIj6UKlyxZwuDBgxk4cCBYFdyDtV74u95pDgCLsYrhqQDUbd2Kqa8nZcQIu0OJKs4ePQBw79tncySRSZNiFRUK91UC9iTFXZITqaqLjxd/FUSuEBbaAug2AE64HJbPgeqDoXkMpeLQpyWfAnB6H02K/Tkz70wO1R3i6wNf2x1KWJSUlNCvn28h95CsF34tsNDn5wIRWSkin4jIGR2NPVbVrlsHQMqoUTZHEl0S86x/vfpiHWHmjybFKiqs2nWI1EQHg3PtSIodVNW7aWjQapuqHZoKbYVwCbHTbrTmLn/1r9A9hlJx5rOSzxjUdRC9u/S2O5SI9I2+38AhDhYVL7I7lLBoodJ20NYLF5HfAW7gWe+mPUB/Y8yJwK+B50Qk018QInKdiCwTkWWlcTQktnbdOhLS00nKH2B3KFElsW9fEMG1S5NifzQpVlFh5c5yxuR1xekI/79slxQnxkC1S3uLVTvUHbGGTieE8H+250gYNgWW/AvcdaF7HKXiRLWrmuX7lnNa39PsDiViZSZlMq7HOD4u/tjuUMIiLy+PXbt2HbOJIK0XLiJXARcC3zfe7NsYU2eMKfPeXg5sBYb6i80Y85gxZoIxZkKud75oPKhZt46UESOQUL6+xqCEpCScvXrh0p5iv/S/SUW88qp6vi6pYFJBti2Pn55srYusFahVu9RWQIrfD/eDa8KPoKYcCt8L/WMpFeOW7l2Kq8HF6X116HRrzul/DoXlhWyr2GZ3KCE3ceJECgsL2b59O1g9v0FZL1xEJgM3A1ONMdWNJxKRXG+BLkRkIFbxrtj/RQfIuN3UbdxEyujRdocSlZLy8nT4dAs0KVYR74ON+2kw8O0RPW15/C7epFiLbal2qTsMyWFIigd+C9JzYc3zoX8spWLcpyWfkupM5aSeJ9kdSkQ7L/88BOGd7bG/xLXT6eShhx7i/PPPBxhFkNYLBx4CMoD3mi29dCawRkRWAy8B1xtjtHCEV93WrZi6Op1P3EGJ/frhOnbkg/Jy2h2AUm159qsdFOSkM6ZvV1sevykprtWkWLVDuHqKHU4YdSmseNoasp2cEfrHVCoGNZgGPtr1Eaf0PoUkR5Ld4US0Hmk9mNBrAguLFnL92OsR8Td1NnZMmTKFKVOmICJrfdcLb7zfO/R5lr9jjTELsJLm5tv9LrJrjHkZeDkogceg6uXLAUg9cZy9gUSppH55VOzfT0NtLQkpKXaHE1G0p1hFtE8LS1m58xBXnTqAhAR7XnS7piYCUFHjsuXxVZSqDVNPMcDoS8FdC5sWtr2vUsqvdQfWsb96P+cMOMfuUKLC5PzJbK/YzubyzXaHouJIzbJlOHv2tIpGqXZLzLOmuLtKSmyOJPJoUqwiVp3bw22vryO/exozTu5vWxxZaVaPQXl1vW0xqChUdzg8PcUAeSdDZl9Y+0p4Hk+pGPT+zvdxipNv5n3T7lCiwrkDzsUhDt7a/pbdoag4YYyheuky0iZMiPnRCaGS1M9aDrt+x06bI4k8mhSriPXEZ9vZdqCK26aOIiXRYVsc2elWUnywSpNi1Q61hyElTEP+ExJg5MWw5X2oORSex1Qqhhhj+GDnB0zoNYGuyfZM1Yk23VK6cVrf03hr61t4GnR1BhV6rl27cJeWkjZB5/x3VNKgQQDUbd1icySRR5NiFZFKDtXw4AdbOG9kT84a1sPWWLqmJiIC5dU6fFq1Q7gKbTUafSk0uGCj9too1V5bD21lx+EdnN3/bLtDiSrTBk1jf81+Fu9ZbHcoKg5UffEFAGknn2xzJNHLkZGBs1cv6goL7Q4l4mhSrEJuy/5K/vDaWl5aXox3Gb5WGWO4Y/46DIY/XDgyDBG2zpEgdE1NpFx7ilWg3PXWHN9wDZ8G6HsSdCuwCm4ppdrl3R3vIgjf7v9tu0OJKt/q9y26Jnfl9S2v2x2KigOVnywisW9fkgYOtDuUqJY8eDB1W7SnuDlNilVI7Ttcy3f/9SX/WbyDm15czd1vb2zzmLlLdvHu+n3ceM5Q+mWnhSHKtmWnJXFQ5xSrQFUfsL6ndQ/fY4rAKT+FXV/Bzq/C97hKRTljDPO3zufk3ifTI83ekUnRJsmRxJSCKXyw8wMq6irsDkfFsIa6OqoWL6bLN7+p84k7KXnwYOq3bsN4dNqDL02KVUjdvXAj1fVu3vvVmfzglP7865NtzPl8e9P9H23czwX3f8r4P73H9H99yaxnV/C7177mjCE5/M8ZkfNJYLf0JA5WalKsAlRVan1Pzw3v4574A0jJgi8eCO/jKhXFVu5fSUllCVMHTbU7lKg0bfA06hvqeaco9tcsVvapXrIEU1NDl29pIbzOSh4yBFNXp8W2mtGkWIXMjrIqXl9Vwg9PGcCQnhncMXU0547syR1vrufhj7bwk/8s45o5S6lzeThvZE/q3A0s31HOjIn9eOyHE3DYtASTPz0yktl3pNbuMFS0sCspTkqHST+BjW/CntXhfWylotT8rfNJdaZyTn9diqkjRmaPZHDWYB1CrULq8FsLSMjIIG3SJLtDiXopo0cDUPv1GpsjiSxOuwNQsevRT7bidCQ09fg6EoQHZpzIVU8u4a/vbCI9ycFN5w3lujMHkeSM7M9nendN5eNNpRhjdNiOalulTUkxwCk/g6/+BR/eBd9/IfyPr1QUqayv5O2itzl3wLmkJUbGdJ1oIyJcPPhi7l12L9sObWNgVuSM8lKxoaGmhiPvvkvGBZNJSE62O5yolzx4EAnp6VSvXEnXadPsDidiRHYmoqLWnooaXlpezHcn5NEjM6Vpe2qSg3nXncJ7vzqTxb89mxu+PSTiE2KAPlkp1Lg8VNRoBWoVALt6igFSs+C0X0DhO7BrSfgfX6ko8uqWV6lyVfG9Ed+zO5So9p2B38EpTl7c/KLdoagYdOT9D2iorqbrRTrFIRjE4SB17FhqVumIMl+Rn42oqPSvT7ZhDFz/zUHH3ZeQIAzpmUFGSqINkXVMn6xUAHYf0iHUKgBV+8GRDMkZ9jz+pOshvQe8cSO4auyJQakI52nw8NyG5zixx4mM6j7K7nCiWk5qDpMLJvNK4SscqT9idzgqhhhjOPif/5A4oD9pJ0+0O5yYkTpuHHWbN+M5fNjuUCKGJsUq6DbtPcKzX+3g8pPyyOsWG8PR+nqT4uLyapsjUVHh0E7I6mdVhLZDUjpc/E/Yvw7mzoDDe+yJQ6kI9sa2NyiuLOaqkVfZHUpM+OHIH1LtruaVwlfsDkXFkJoVK6hds4bsq65CEjRtCZb0M06HhgYqF31qdygRQ/+7VFBt2X+E/3lmGRkpifzv+cPsDidoBvXoAsDmffoJuArAwW2QbfO8uiHnwrR/wo4v4P6x8OavoXyHvTEpFSHqPHU8vOphRnUfpWsTB8nI7iOZ2Gsiz6x7hhq3jlBRnWeMYf8//oEjO5usiy+2O5yYkjp2LI6cHCo//MDuUCKGJsWq06rq3MxbspMrHv2Cc/6+iMO1Lp64agLdu8ROMYQuyU76Zaeyca8mxaoNxkBZBCTFACd+H2YtgbEzYOV/4JFvwHqtEKvUE18/wd6qvdx40o1aPDGIbhh3A/tr9vP0uqftDkXFgCPvvEPNsuXk/uLnJKTFxsjDSCEJCWScdRZHPv4ET2Wl3eFEBE2KVYc0NBhW7iznlle+5uS73mf2K19TXu3ipvOG8t6vvsmJ/bvZHWLQjeiVyZriCrvDUJHu8G5wVUH28fPpbZFdAFMfgJ+vgNzh8MKVsOB/da6xilvrDqzj8TWP852B3+GU3qfYHU5MGd9zPOf0P4cnvn6CrYe22h2OimKuvXvZe9vtpIwcSdbll9sdTkzK+u53MdXVVLz8st2hRISQJsUiMllENonIFhGZ7ed+EZEHvPevEZHxbR0rItki8p6IFHq/x172FYB6dwOlR+rwNJignK+8qp7nl+7k1y+s4jcvrebJz7azpvgQbk8DtS4Pa4oPMXfJTn7/2tdc+s/POeH2d7jkn1/w6spiJo/uzcs//Qbv/epMbvj2EHIzYqeH2NfpQ3LYebCa7Qeq7A7FNp1p03Gj2Fvxue9J9sbRXFY/uGaBtWTTksfgwQmw4hmoPmh3ZKoV+joaXHur9vKLD39BTloOsyce9+tUQfDbSb8l1ZnKTZ/cREVdbHyQ/PbbbzNs2DCA0eFohyJyi3f/TSJyfqivL9K4y8vZ9ZPraXC56PO3exGnriAbCqknjCbt5JM58Oi/cJeX2x2O7UL2XyYiDuBh4FygGFgqIvONMet9drsAGOL9mgQ8Akxq49jZwAfGmLu9Ty6zgZtDdR2RxBjD8h3lPPfVTt78eg/17gbSkhycM6In3xnTm28OzSUl0dHmOSrr3Ow7XMf+w7Vs3neEDzeV8sWWA7gbDLkZyRhjeGFZMQAJAr55d0aykxG9M7liQj9O6NuV80b1jKoq0p1x1rAeiKzjxWW7+M3k4XaHE3adadPhjtVWmxZCSlfoPcbuSI7nTIbJ/wfDpsC7v4f5P7e+ug+G3uOgzzgrmc+bCI74aNeRTF9Hg2vV/lX8v0/+H1XuKp6e/DRZKVl2hxSTctNy+cuZf2HWB7P40Ts/4q/f/CsFXQvsDqvDPB4Ps2bN4r333mPQoEHrgJmhbIciMhKYAYwC+gDvi8hQY4wnTJdsq+oVK9l9y2zce/aS98g/SS6I3v+daNDzd79j++WXU/LLG8l7+CEcGTatmhEBQvnRy8nAFmPMNgARmQdMA3yfRKYBzxhjDLBYRLJEpDeQ38qx04BveY9/GviYTr6Y7zpYTYMxGG/yZ7CSx6O3G/dsvk/T1qO3jfUzxxx3/HbTtN343G4887H7lFfVs7akgre+3sPW0iq6JDu54qQ8hvbMYOPew7y9di/zV+8mNdHB0F4Z9M5MIS3JQaIjgTq3h1pXA+XV9ew/Use+w7VU1x/7vDowJ50fnzGQ75zQm9F9MxER9lbUsmzHQTbuOYLTIQztmcGoPpn065ZGQkJ8zr/ql53GRWP68O9Pt5ObkcyEAdmkJjlIdAiCvb8TESu+EOtwmzbGdLz8cYPHquaMTyPz1fSzOfZ28/ta/Lkj++J/3wObYe3LcNI1kZ1UFpwB//MRFC+F7Ytg90rY+SWsfcm6PyULcoZCSiYkpkJiGnTpATnDrLWXHYne6xOO+7239ntDIMFpJeeJqeBMBYfT+4RnwDQcPUert7HOk+AAcYAkeG+LN6YIlOC0euvbJypeR10eF3ur91p/osbXMIzP66hp2n7MPsb43b/xZ999Onp8jbuGoooiFpUs4vOSz+md3punJz/NsOzYKQQZiU7tcyoPfvtBfrPoN1w6/1LO6X8Ok3pPom+XvnRL6YZTnCQkJOAQBwkkRESzTUpIomd6z+O2L1myhMGDBzNw4ECwnsxC3Q6nAfOMMXXAdhHZgvVc8GVHr62hthZ3aanPc+3RNnTM65nPfRifNmV87vfed9w5/L0OePc77jy+5zAG98Fy6rdvo3LRp9SsWIGzd2/6P/UkaSdF2IirGJQybCh9/nwXu2ffwrbvXEjXyy4l9YQxOLtnW/O4ExJAEpAE8d62r7EmpKTgzM0N2flDmRT3BXb5/FzM8T1G/vbp28axPRvfYBtj9ohIj84Geta9H+MO0jDkUBGBSQXZ/M8ZA7lobB/Sk4/+6f44bTRfbTvI+xv2sXnfEbaWVlLj8uDyNJDsdJCSmEBWahKj+mTy7eE96JmZTM/MFHpkpNAvO9Xvskm9uqZw4Zg+XBiBnV12+tO00ew9XMsdb6xve+cwSnYmsOnOC0L9MJ1p0x1Pimsr4IFxHT487HqeAGf91u4o2paQAP0nWV+NKvfDzsVQ+K71QURNuTVHur4KjuwBT7198Ua7rP5w49ftPSoqXkeLDhdx6fxLO3OKkOuT3oefjv0pV4+6mrRELdgTDqf1PY3Xpr3GE2ufYMG2Bbxd9LbdIbVqbO5Y/jvlv8dtLykpoV+/Yz7QCnU77Ass9nOuDqtZsYKd1/6oM6cIi+Thw+nxvzfRbcYMEtLT7Q4nbnS96CIS8/Iove9+yv71GDQ02B2SXxnnnkPegw+G7PyhTIr9fZTQPPNsaZ9Ajm39wUWuA64D6N+/f6v73nP5GIyxEs/GD0AEOebDkMbqlIL/fXy3c9x2P8cKR3sX5egFi4jPbWuf9GQHQ3pm0CXZ/58r0ZHA6UNyOH1ITqvXqTqva1oiz193Cpv3VVJUVkWduwGX2/4nD0d4eu8706aPP1mgbTQpHS5+tPGgYx+mxZ/bs68Eb9+kdO/Q4yid/9SlB4ycan011+CB8iLrQwqP69gE2beH9rjb0PR7Mw3Q4AZ3HbhrrWJfDW7v/T6fQDfdlhZue+NpcFvnbPxqiODRhUkdeoMXFa+jPdJ6cNfpd3mDOfqw1uuZNG0/+lp49EVP8Nmn2f5t7uN7vhaOT0xIZEDmAHJTc7XKtA1y03KZffJsbp54M8VHitlbvZeKugrcxk1DQwMe4zk6CsBm3ZL9T633HYHgu7nZz8Fsh0F/HU0aPJjed/9f4zHHvuH1fV0Tn/utnY+GI3LMG1m/+/m8gRXf+/ydx+ccCZmZJA0YgKNLlxavQYVW2oknMuDpOXgqq6jfugV3eTmmpsbq6W+wRmkZj72vsYm9+4T0/KF851YM+H60lgfsDnCfpFaO3dc4HNM7NGW/vwc3xjwGPAYwYcKEVp+ALh2f1/qVKOVDRBjWK4NhveJu3kVn2vRxAm6jzmQYN7MD4aqgSnBA9wipqB0/ouJ1tGtyV6YO8vNBilJeIkK/zH70y2z3FALb5eXlsWvXrmM2Edp2GPTX0cQePXSdXxUQR5d0UseOtTsMW4Sy+vRSYIiIFIhIElbRgPnN9pkPXOmt2ncKUOEdStLasfOBq7y3rwJeD+E1KKWO6kybVkq1n76OKmWziRMnUlhYyPbt28Hq4wx1O5wPzBCRZBEpwCretSREl6eU8gpZT7Exxi0iNwDvAA7gSWPMOhG53nv/o8ACYAqwBagGrmntWO+p7wZeEJEfATuBK0J1DUqpozrTppVS7aevo0rZz+l08tBDD3H++eeDVRH6T6Fsh95zv4BVjMsNzIqXytNK2UlamCsRUyZMmGCWLVtmdxhK2UpElhtjJtgdhz/aRpXSNqpUpNM2qlRk60wbDeXwaaWUUkoppZRSKqJpUqyUUkoppZRSKm5pUqyUUkoppZRSKm7FxZxiESkFdoT4YXKAAyF+jEgQD9cZq9c4wBiTa3cQ/oSpjYZLrP7/tCTerhdCd82R3kariK2/dSz97+q1hEekt9FIeh2N5L9jR8XaNcXi9aR3tI3GRVIcDiKyLFKLLwRTPFxnPFyjCp14+/+Jt+uF+LxmiL3rjqXr0WtRkSYW/46xdk16PcfS4dNKKaWUUkoppeKWJsVKKaWUUkoppeKWJsXB85jdAYRJPFxnPFyjCp14+/+Jt+uF+LxmiL3rjqXr0WtRkSYW/46xdk16PT50TrFSSimllFJKqbilPcVKKaWUUkoppeKWJsVKKaWUUkoppeKWJsXtICIOEVkpIm96f84WkfdEpND7vZvPvreIyBYR2SQi59sXdfuISJaIvCQiG0Vkg4icGmvXKSK/EpF1IrJWROaKSEqsXaMKPxGZ7P0f2SIis+2OJxRE5EkR2S8ia322tdh2op2I9BORj7zPhetE5Jfe7TF7zY1E5K/e14E1IvKqiGT53Of3OVFEThKRr733PSAiYkvwbYi2ttqR/8NIf92Kh/dT8Sza2pg/IlLkfT5bJSLLvNui5rm/va/X0dDOWrim20WkxPt3WiUiU3zua9c1aVLcPr8ENvj8PBv4wBgzBPjA+zMiMhKYAYwCJgP/FBFHmGPtqPuBt40xw4GxWNcbM9cpIn2BXwATjDGjAQfWNcTMNarw8/5PPAxcAIwEZnr/d2LNHKx24Mtv24kRbuD/GWNGAKcAs7x/11i+5kbvAaONMWOAzcAt0OZz4iPAdcAQ71fz/xXbRWlbbdf/YZS8bsXD+6m4FKVtrCVnGWPG+ax9G03P/XMI8PU6itrZHPy/rvzD+3caZ4xZAB27Jk2KAyQiecB3gH/7bJ4GPO29/TRwsc/2ecaYOmPMdmALcHKYQu0wEckEzgSeADDG1BtjDhFj1wk4gVQRcQJpwG5i7xpVeJ0MbDHGbDPG1APzsP53YooxZhFwsNnmltpO1DPG7DHGrPDePoL1Jr4vMXzNjYwx7xpj3N4fFwN53tt+nxNFpDeQaYz50lgVPJ8hMn8vUddWO/B/GNGvW/HwfirORV0ba4eoee5v5+t1VLSzFq6pJe2+Jk2KA3cf8BugwWdbT2PMHrBetIAe3u19gV0++xV7t0W6gUAp8JR3WNO/RSSdGLpOY0wJcC+wE9gDVBhj3iWGrlHZIp7/T1pqOzFFRPKBE4GviJNr9nEtsNB7u6X/9b7e2823R5qobqsB/h9G+jXeR+y/n4pnsfI3M8C7IrJcRK7zbov25/5YbWc3eKf6POkzJLzd16RJcQBE5EJgvzFmeaCH+NkWDWtfOYHxwCPGmBOBKlofGhJ11+ltLNOAAqAPkC4iP2jtED/bIvoalS30/ySGiUgX4GXgRmPMYbvjCRYReV+s2grNv6b57PM7rOG7zzZu8nMq08r2SBMtcR6nHf+HEXuNcfR+Kp7Fyt/sNGPMeKxh4LNE5Ey7AwqhaP6bPQIMAsZhdXb9zbu93dfkDGpYses0YKp38nYKkCki/wX2iUhvY8we79Cx/d79i4F+PsfnYQ3RjXTFQLEx5ivvzy9hJcWxdJ3nANuNMaUAIvIK8A1i6xpV+MXz/0lLbScmiEgiViLyrDHmFe/mmLhmY8w5rd0vIlcBFwJne4dEQ8v/68UcHWLtuz3SRGVbbef/YSRfY7y8n4pnMfE3M8bs9n7fLyKvYg29jfbn/phrZ8aYfY23ReRx4E3vj+2+Ju0pDoAx5hZjTJ4xJh9r0vaHxpgfAPOBq7y7XQW87r09H5ghIskiUoBVcGRJmMNuN2PMXmCXiAzzbjobWE9sXedO4BQRSRMRwbrGDcTWNarwWwoMEZECEUnCep6Yb3NM4dJS24l63ueIJ4ANxpi/+9wVs9fcSEQmAzcDU40x1T53+X1O9A7FOyIip3h/b1cSmb+XqGurHfg/jNjXrXh5PxXnoq6NNSci6SKS0XgbOA9YS/Q/98dcO/Mm940uwfo7QUeuyRijX+34Ar4FvOm93R2reluh93u2z36/A7YCm4AL7I67Hdc3DlgGrAFeA7rF2nUCdwAbvQ3nP0ByrF2jfoX/C5iCVaV3K/A7u+MJ0TXOxRqe5ML6FPZHrbWdaP8CTscabrUGWOX9mhLL1+xz7Vuw5mM1XvejPvf5fU4EJnifV7cCDwFi93W0cG1R1VY78n8YDa9bsf5+Kp6/oq2N+Yl/ILDa+7Wu8Rqi6bm/va/X0dDOWrim/wBfe58f5wO9O3pN4j1IKaWUUkoppZSKOzp8WimllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3NKkWCmllFJKKaVU3HLaHUA45OTkmPz8fLvDUMpWy5cvP2CMybU7Dn+0jSqlbVSpSKdtVKnI1pk2GhdJcX5+PsuWLbM7DKVsJSI77I6hJdpGldI2qlSk0zaqVGTrTBvV4dNKKaWUUkoppeKWJsVKKaWUUkoppeKWJsVKKaWUjd5++22GDRsGMFpEZje/XywPiMgWEVkjIuN97pssIpu898322Z4tIu+JSKH3ezfv9u+LyCqfrwYRGReGy1RKKaUiVlzMKVbRyeVyUVxcTG1trd2hRJWUlBTy8vJITEy0OxQVJtpWootvG/V4PMyaNYv33nuPQYMGrQNmish8Y8x6n0MuAIZ4vyYBjwCTRMQBPAycCxQDS32OnQ18YIy525sszwZuNsY8CzwLICInAK8bY1aF58pVc9p2I5O+jqpg07YeXKFoo5oUq4hVXFxMRkYG+fn5iIjd4UQFYwxlZWUUFxdTUFBgdzgqTLStRI/mbXTJkiUMHjyYgQMHAhhgHjAN8E2KpwHPGGMMsFhEskSkN5APbDHGbAMQEd9jpwHf8h7/NPAxcHOzcGYCc4N/lSpQ2nYjj76OqlDQth48oWqjOnw6Ch05WEvNkXq7wwi52tpaunfvrk8e7SAidO/eXT+JjHHGGHZv3oi73noe0LYSPZq30ZKSEvr16+e7SzHQt9lhfYFdfvZpaTtAT2PMHgDv9x5+wplOK0mxiFwnIstEZFlpaWlblxZVXPuraaj32B2Gtt0IpK+jkWHfvn14PPa30WDRth48oWqjmhRHoWd++wVzZn9udxhhoU8e7ae/s9i37pMPmPuHm/hwzr+atunfPXr4/q2szt/jNN/o749rWtkeSAyTgGpjzNqW9jHGPGaMmWCMmZCbG5FLs3aI8TSw7+/LOfjsBrtDAbTtRiL9m9jr4MGDPPLII7z33nt2hxJU+n8VPKH4XdqeFIvIr0RknYisFZG5IpLSUoEQ7/63eAuKbBKR8+2M3U4NnoDe9yilYtDGzz8BYPOXn9HQEDufpMejvLw8du3adcwmYHez3YqBfn72aWk7wD7vEGu83/c3O+cM4nXodIP1+lm7tcLmQJRS/lRVVQHWkGOlwsXWpFhE+gK/ACYYY0YDDqwX6sYCIUOAD7w/IyIjvfePAiYD//QWGokbLfQqqBB69dVXERE2btwY9sd+9NFHeeaZZ8L+uCpyGWPYt30rCQ4HddVVlO9pnj/ZK1zt5eOPP+bCCy8Myrluv/127r333qCcyzeuOXPmcMMNN7S6/8SJEyksLGT79u1g9fzOAOY3220+cKW3CvUpQIV3SPRSYIiIFIhIUrNj5wNXeW9fBbzeeDIRSQCuwJq/HMf09dSXna91Svmj73mDT9t5y2zvKcYq9pUqIk4gDetT7mlYhUHwfr/Ye3saMM8YU2eM2Q5sAU4Ob7g20+eHsJs7dy6nn3468+aF5v1ja3Nmrr/+eq688sqQPK6KTlXlB6k9cpgRZ5wFwIGdRfYG1Eyo20uscTqdPPTQQ5x//vlgfeD7gjFmnYhcLyLXe3dbAGzDes17HPgZgDHGDdwAvANsaDzWe8zdwLkiUohVnfpun4c9EyhuLNClFNjXdt1ud0w+llKRSF+jW2Zr9WljTImI3AvsBGqAd40x74rIMQVCRKSxQEhfYLHPKfwVJAGsAiHAdQD9+/cP1SWEXbx+avbpC5s5sKsyqOfM6deFM747tNV9Kisr+fzzz/noo4+YOnUqkyZN4qmnnuKFF14ArF6hv/3tb7zxxhu8++673HbbbdTV1TFo0CCeeuopunTp4ve8+fn5XHvttbz77rvccMMNHDlyhMcee4z6+noGDx7Mf/7zH9LS0rj99tvp0qULN910E9/61reYNGkSH330EYcOHeKJJ57gjDPOCOrvREW+smJrqO2wU89g/aIPKd1RRPesnKb7P5rzGPt3BDfX6TFgIGddfV2b+4WqvSxdupRf/vKXVFVVkZyczAcffHDM/QcPHuTaa69l27ZtpKWl8dhjjzFmzJhj2g/A6NGjefPNN8nPz+euu+7imWeeoV+/fuTm5nLSSScB8MADD/Doo4/idDoZOXJki28clixZwo033khNTQ2pqak89dRTjWsNt9uUKVOYMmUKIrLWGHMXgDHm0cb7vVWnZ/k71hizACtpbr69DDi7hWM+Bk7pULAxJbLm9x16Yyv1u6uCes6kPulkXTSozf1C+Vo3ffp0PvroIwCee+45Bg8ezNVXX012djYrV65k/Pjx/OxnP2PWrFmUlpaSlpbG448/zvDhw3nxxRe54447cDgcdO3alUWLFrFu3TquueYa6uvraWho4OWXXyYxMZELL7yQtWutKfL33nsvlZWV3H777XzrW9/iG9/4Bp9//jlTp07lW9/6Fr/+9a+prKwkJyeHOXPm0Lt37yD9xpVq28KFC9m7d29Qz9mrVy8uuOCCVvcJVTufPXs28+fPx+l0ct5553Hvvfdy9dVXk5mZybJly9i7dy/33HMPl19+OcYYfvOb37Bw4UJEhN///vdMnz6dn/3sZ0yePJmpU6dyySWX0K1bN5588kmeeOIJtm/fzp133hnU35c/dg+f7obV+1sA9AHSReQHrR3iZ5v/KiWxWiCkwe4I4strr73G5MmTGTp0KNnZ2XTv3p3Fixc3zXd5/vnnmT59OgcOHODOO+/k/fffZ8WKFUyYMIG///3vrZ47JSWFzz77jBkzZnDppZeydOlSVq9ezYgRI3jiiSf8HuN2u1myZAn33Xcfd9xxR9CvV0W+I2VWFeBuvfuS0T2Xiv3BfWHtjFC0l/r6eqZPn87999/P6tWref/990lNTT1mn9tuu40TTzyRNWvW8Oc//7nN0RXLly9n3rx5rFy5kldeeYWlS5c23Xf33XezcuVK1qxZw6OPPtriOYYPH86iRYtYuXIlf/zjH/ntb38b6K9JRYz4/JDZn1C+1mVmZrJkyRJuuOEGbrzxxqbtmzdv5v333+dvf/sb1113HQ8++CDLly/n3nvv5Wc/+xkAf/zjH3nnnXdYvXo18+dbMwMeffRRfvnLX7Jq1SqWLVtGXl5em9d36NAhPvnkE37xi1/w85//nJdeeonly5dz7bXX8rvf/a6DvzWlokso2vnBgwd59dVXWbduHWvWrOH3v/9903179uzhs88+480332T27NkAvPLKK6xatarp9fx///d/2bNnD2eeeSaffvopYK3KsH69tSrhZ599FrYOILvXKT4H2G6MKQUQkVeAb+AtEOLtJfYtENJaUZG4EK89xW316IbK3Llzm17EZ8yYwYsvvsjkyZN54403uPzyy3nrrbe45557+OSTT1i/fj2nnXYaYL2RP/XUU1s99/Tp05tur127lt///vccOnSIysrKxqGUx7n00ksBOOmkkygqKur8Baqoc+TgAQC6dMsmMze3KUluFEiPbqiEor1s2rSJ3r17M3HiRMB6g93cZ599xssvvwzAt7/9bcrKyqioaLmI0qeffsoll1xCWloaAFOnTm26b8yYMXz/+9/n4osv5uKLL27xHBUVFVx11VUUFhYiIrhcrpZ/MUoFIJAe3VAJ5WvdzJkzm77/6le/atp+xRVX4HA4qKys5IsvvuCKK65ouq+urg6A0047jauvvprvfve7Ta9/p556KnfddRfFxcVceumlDBkypM3ra3y93bRpE2vXruXcc88FrOlL2ksceRorC8fqe962enRDJRTtPDMzk5SUFH784x/zne9855haHxdffDEJCQmMHDmSffv2Adbr9cyZM3E4HPTs2ZNvfvObLF26lDPOOIP77ruP9evXM3LkSMrLy9mzZw9ffvklDzzwQGh/MV52J8U7gVNEJA1r+PTZwDKgCqswyN0cWyBkPvCciPwdq2d5CLAk3EHbKUafHyJSWVkZH374IWvXrkVE8Hg8iAhPPfUUDz/8MNnZ2UycOJGMjAyMMZx77rnMnRt4Mdf09PSm21dffTWvvfYaY8eOZc6cOXz88cd+j/n/7N15fFx1vfj/13sy2ZemTdOFpqV7oSwtUCjILkuhAgXRKyCLXr2IFhX0XgSvCyr4Ra8i8gNFUFkEWUTQAmVfRJFSSluWtkD3Nl2TtGnWSSaZ9++PcyadppN9Zs6ZzPv5eLQzOdu8Z5LPnPM+ny03NxeArKws6xuVoRp21ZBfXEIwJ4eSsnI2r+xyRp2USlZ5UdUep16Id+EkIgSDQSKRvc1rYuc07OqYzzzzDK+//joLFizgJz/5CStWrCAY3P9U+f3vf59TTz2VJ598kg0bNnDKKaf0+F6MP9h5dF/JPtfFlrXY59FzYCQSobS0lOXLl++371133cVbb73FM888w8yZM1m+fDmXXHIJs2fP5plnnmHOnDn8/ve/Z+rUqV2W9djXUlUOOeQQ3nzzzV7Hb8xgkKxyHgwGWbx4MS+//DKPPPIId9xxB6+88gqw95oV9p6nu7rRMWbMGHbv3s1zzz3HSSedxK5du3jssccoKiqiuLg4AZ9AzzxtPq2qbwGPA0uB99147qaLAULcAUQeA1YCzwHzVTWj5iMZrHfN/Ojxxx/n8ssvZ+PGjWzYsIHNmzczYcIEgsEgS5cu5Z577um4+3zsscfyxhtvsGbNGgCampr4+OOPe/1a9fX1jB49mnA4zEMPPZSU92MGh4ZdNRSVOX2IS8pH0LCrxhffC8kqLwcddBBbt27taOJcX1+/3w2hk046qaPcvPbaawwfPpySkhLGjx/P0qVLAVi6dGl0hGdOOukknnzySZqbm6mvr+epp54CnIvzzZs3c+qpp/Lzn/+8o+VGPHv27GHMGGdIi/vuu6+/H5sxnkv2ue7RRx/teIxX21RSUsKECRP4y1/+AjjXOe+++y4Aa9euZfbs2fz4xz9m+PDhbN68mXXr1jFx4kS+8Y1vcN555/Hee+8xcuRIdu7cSU1NDS0tLTz99NNxY5k2bRpVVVUdSXE4HGbFihVxtzVmMElWOW9oaGDPnj3MnTuX2267Le7NrVgnnXQSjz76KO3t7VRVVfH6669zzDHOmMnHHXcct912GyeddBInnngiv/jFL1I6do7no0+r6g9V9SBVPVRVL3NHlq5R1dNUdYr7uCtm+5tVdZKqTlPVZ72M3Qs+uPbNGA8//DAXXHDBPssuvPBCHnnkEc455xyeffbZjmYi5eXl3HfffVx88cUcfvjhHHvssX0a7v4nP/kJs2fP5owzzuCggw5K6Pswg0t9TTXFw8oAKB4+AtUIGvF+sIFklZecnBweffRRvv71rzNjxgzOOOOM/WqBbrzxRpYsWcLhhx/O9ddfz/3339/x+rt27WLmzJn89re/ZepUpxvGkUceyec+9zlmzpzJhRde2HHSbW9v59JLL+Wwww7jiCOO4Nprr6W0tDRuXNdddx033HADxx9/fLcjyBvjd8k+17W0tDB79mx+/etf86tf/SruNg899BB/+MMfmDFjBocccgh//7vTQPB//ud/OOywwzj00EM56aSTmDFjBo8++iiHHnooM2fO5MMPP+Tyyy8nOzubH/zgB8yePZtzzjmny/NoTk4Ojz/+ON/5zneYMWMGM2fO5N///ndfP7KUEJFrRWSFiHwgIg+LSJ7XMZn0laxyXl9fzznnnMPhhx/OySef3GUZj7rgggs4/PDDmTFjBp/85Cf5+c9/zqhRowA48cQTaWtrY/LkyRx55JHs2rUrpUmx+KGGIdlmzZqlS5Ys8TqMhAg1hvnDt52O6PPv+qTH0STXqlWrOPjgg70OIy3F++xE5B1VneVRSN0aTGU02e788iVMnf0Jzvivq9nw7lL++tMfcOb/3sxhh8/wOjTTB1ZGvRNpaWfrD/8NQaHiphM8jWWwn+fGjx/PkiVLGD58eM8b+4yXZVRExgD/AqararOIPAYsVNX7utpnMJXRLVu2cM8993DAAQdw5ZXejZORSIO9rHsh0WXU85pi0zeZcBPDGBNfJNJOqL6OgiFDASgcOgzAFzXFxqQdO50afwsC+SISBArIsIFljUk1rwfaMn1kUzKllwsuuKCjH2PUz372sy5HlzamOy3utAn57lyBhaVOcjxYkmI/lpd7772XX//61/ssO/7447nzzjs9isgMnGXDidZV2bVZEvpHVbeIyC9wBqRtBl5Q1Rc6byciVwJXAowbNy61QaaAVQT5ix/P0YlkSXGasS+I9PLkk096HYIZREIN9QDkFTkjMeYXFSOBwD6jrqYzP5aXL37xi3zxi1/0OgyTSHYaTTg/lt10JiJDgXnABKAW+IuIXKqqD8Zup6p34wxQy6xZs+wv2yTVYC/n1nw6zWRaTbHdBOi7vnxmInKWiHwkImtE5Po460VEbnfXvyciR7rLx4rIqyKyyh0I5Jsx+9woIltEZLn7b25C3pgh1OiMhBxNiiUQoGBIKRqJWFlJI/a7MrHs78F/fPA7OR1Yr6pVqhoGngA+4XFMZoB88Hc1aCTjs7SkOM1kUoHKy8ujpsYf082kC1WlpqaGvLyeB6kUkSzgTuBsYDpwsYhM77TZ2TjzgU/BaaL1W3d5G/BtVT0YOBaY32nfX6nqTPffwgG9KdMh1BBNios6lhUOGUrLnlorK2miL2XUDH52nvMfn5TRTcCxIlIgzuTOpwGrvAwolXqamz4dWVlPnGSVUWs+nWYyqTBVVFRQWVlJVVWV16Gklby8PCoqKnqz6THAGlVdByAij+A011oZs8084AF1/vAWiUipiIxW1W3ANgBVrReRVcCYTvuaBIs2n84t3JsUF5SWsuGNVxk1eaqVlTTRhzJqksFHp1E7z/mT12VUVd8SkceBpTg3oZfhNpPOJIPpmtfKemIlo4xaUpxuBs/3Q4+ys7OZMGGC12EMZmOAzTE/VwKze7HNGNyEGEBExgNHAG/FbHe1iFwOLMGpUd7d+cUH+wAhyRBNivPd5tPg1BRXf7DcyooxacjOc6YrqvpD4Idex2ESw8q6/1nz6TQzmO6aGc/Fa5/U+Q+s221EpAj4K3CNqta5i38LTAJm4iTPv4z34qp6t6rOUtVZ5eXlfQw9M0WbT3euKW6qrbXvBmN6y8qKMb5m5zPjBUuK00ymDbRlkqoSGBvzcwX7z4PY5TYiko2TED+kqk9EN1DVHararqoR4B6cZtomAUKNDeTk55MV3NvIp3DIUCLtbR2DcBljjDHpzJJi4wVLitOMfVGYBHobmCIiE0QkB7gIWNBpmwXA5e4o1McCe1R1mzvwxx+AVap6a+wOIjI65scLgA+S9xYyS6ihvmPk6aiC0lIAmmr3a6FujDHGGGN6wZLiNGM1xSZRVLUNuBp4HmdUy8dUdYWIXCUiV7mbLQTWAWtwan2/5i4/HrgM+GScqZd+LiLvi8h7wKnAtSl6S4NeqKF+n6bTAAUlQwBoqtvjRUgmAZ577jmmTZsGcGhfpkZz18WdVk1EhonIiyKy2n0cGrPucBF5051O7X0RyaihsO3esjH+Fq0Asoogk0o20Faaif2CUNVBOWy9SR13uqSFnZbdFfNcgflx9vsX8fsbo6qXJThM4wo1NJBf1CkpHlIKQNMeS4rTUXt7O/Pnz+fFF19k0qRJK3CmRlugqrEjucdOjTYbp9/+7Jhp1c7A6erwdsy+1wMvq+otbrJ8PfAdEQkCDwKXqeq7IlIGhFP1fo0xpieWDBsvWE1xmtk3KfYwEGNMyoUaG8gr7NR8uqOmuNaDiMxALV68mMmTJzNx4kRwBrGLTo0Wq2NqNFVdBJS63RQ6plVT1dZO+84D7nef3w+c7z4/E3hPVd8FUNUaVW1Pzrszxhhj0oMlxWkmtvm03UkzJrPE61OcX1wCQLM1n05LW7ZsYezY2LHsOqY9i9XV1GhdLQcY6c4njvs4wl0+FVAReV5ElorIdV3FJiJXisgSEVkyqObWtHOnMb5m17fGC5YUp5l9aooj9qVhTKZQVUINDeR1aj4dyMoir7jEmk+nqS4u/no7NVpvplXrLAicAHzefbxARE7rIjabNs0Yk3LWp9h4wZLiNBP7/WDfFcZkjnBLiEh7234DbYHThNqaT6eniooKNm/evM8iej81WnfTqu2IjgTvPu6MOdY/VLVaVZtwxhQ4kkxk51BjjDEuS4rTTGztsNUUG5M5Qg3OPMSdm08DFAwZYjXFaeroo49m9erVrF+/Hpya315PjUb306otAK5wn18B/N19/jxwuIgUuINunQzEDuo1+Nmp0xhfi9YQ22CyJpVs9Ok0YzXFxmSmUEM9APnxkuKSUqo2bUhxRCYRgsEgd9xxB3PmzAE4BPhJdGo06BgNfiEwF2dqtCbgi+66NhGJTquWBfxRVVe4h74FeExEvgRsAj7r7rNbRG7FSagVWKiqz6Tm3RpjTM+s2bTxgiXFacb6FBuTmfbWFO/ffDq/ZIgNtJXG5s6dy9y5cxGRD1T1Zujd1Gjuuv2mVXOX1wBd9RV+EGdaJmOMMcZgzafTT6d5io0xmSHU6NQUx20+XTKEUEM97W1tqQ7LmPRjp05jfM2ub40XLClOM/tMyRTpejtjzOASbT4dd6CtIaUANNfXpTIkY9KUXXAb42eWFBsvWFKcZiJWU2xMWlj67FPc/z9X01i7OyHHizafjtuneMgQAJr21CbktYzJDHYONcYY47CkON3sU1NsJ3Rj/EhV+fdjD1K9aQMfvPpiQo4ZamwgKxgkmJu737qCEjcptn7FxvTMTp3G+JpV+hgvWFKcZvYZaMu+M4zxpYZdNbQ0NQKweeX7CTlmqKGevKLiuFNU5LtJsQ22ZUzP9p46bboXY/zIkmLjBUuK08w+UzJZTbExvrR72xYAhowcxY71axNyzFBDfdz+xLC3T7HNVWxMX9g51BhjjMOS4jQTmwjbnTRj/Gn3tq0ATDpqNqH6uoQMgNXS2BB35GmAvIJCAllZNNXVDvh1jBn07NRpjK9Fr2/jtYwyJlksKU4z+85T7GEgxpgu1e7YRlZ2NuMOPRyAXVu3DPiYzQ0NcecoBpBAgPySIVZTbEyvWFZsjJ9ZpY/xgiXFaWaf5tP2pWGMLzXW7qawdBjDxowFYNfWzQM+ZqihPu7I01EFJUOsptgYY4wxph8sKU4zsYlwxPoUG+NLzXV7KCgpoWT4CBChrqpqwMcMdVNTDE6/Yhtoy5hesFOnMb5mlT7GC5YUp5l9mkzbd4YxvtRcX0d+cQlZwSBFQ4dRXzOwpLi9rY1wqLnLgbYA8otLbEomY3rDzp3G+JolxcYLlhSnGaspNsb/mur2dEyTVFw2nPrqgSXFLY0NAF0OtAVOTbH1KTamD+wUaowxxmVJcZrZ5+6ZndCN8aXmurq9SfHwEQOuKW5uqAd6SIpLhhAONRNuCQ3otYwxxhgvWU2x8YIlxWkmtvm01RQb4z/hlhBtrS0UuElxyfBy6qqrBnSSDzU4NcX53TSfjs5V3Fw38OmfjBnU7ILbGF+zpNh4wRdJsYiUisjjIvKhiKwSkeNEZJiIvCgiq93HoTHb3yAia0TkIxGZ42XsKac2T7ExfhZNSvOLSwAoLiunPRwe0CBYod7UFA9xknDrV2yMMcYY0ze+SIqBXwPPqepBwAxgFXA98LKqTgFedn9GRKYDFwGHAGcBvxGRLE+i9kDEBtoyCSQiZ7k3l9aIyPVx1ouI3O6uf09EjnSXjxWRV92bWCtE5Jsx+3R5QysTRJPS/JiaYoC6AfQrjvYpzu1m9On84mhSXNvv1zHGGGO8Fq30ERGPIzGZxPOkWERKgJOAPwCoaquq1gLzgPvdze4HznefzwMeUdUWVV0PrAGOSWXMXoqtHVZrPm0GwL2ZdCdwNjAduNi96RTrbGCK++9K4Lfu8jbg26p6MHAsMD9m37g3tDJFqN6tKXZrdYvdpHggg2111BT3ovm0DbaVfp577jmmTZsGcGhfbk656+Le2Orq5pSIjBeRZhFZ7v67KxXv0Vfs1GmMr1lLSOMFz5NiYCJQBdwrIstE5PciUgiMVNVtAO7jCHf7McDmmP0r3WX7EJErRWSJiCypSsAcob4RO86WfWeYgTkGWKOq61S1FXgE56ZTrHnAA+pYBJSKyGhV3aaqSwFUtR6ndceYmH3i3dDKCK3uQFc5+fmAM/o0MKDBtpob6kGE3MLCLrfpaD69p7bfr2NSr729nfnz5/Pss88CrKAPN6d6uLHV3c2ptao60/13VZLemm/ZqdMYY0xnfkiKg8CRwG9V9Qigke5rluK1pdjvHKeqd6vqLFWdVV5enphIfUCtT7FJnN7cYOpxGxEZDxwBvOUu6uqGFp32G5Q3rsIhJynOzs0DnL7Fwewc6mqq+33M5vp68gqLCAS67imSnZtHMCfX+hSnmcWLFzN58mQmTpwIzrms1zen6P7GVkbfnDLGpC+7vjVe8ENSXAlUqmr0gvpxnCR5h3vSx33cGbP92Jj9K4CtKYrVc7FNpu07wwxQb24wdbuNiBQBfwWuUdU+DXs8WG9chVtaAMjOc5JiEaF4+HDqB5AUhxrqyS/uepCt6OsUDBkyoAG9TOpt2bKFsWNjT2l9ujnV3U2r7m5OTXBbZv1DRE7sKrbBeuPKTp7G+JslxcYLnifFqrod2Cwi09xFpwErgQXAFe6yK4C/u88XABeJSK6ITMBpTrY4hSF7ap9piu1LwwxMb24wdbmNiGTjJMQPqeoTMdt0dUMrI4RDzcDepBicJtQDaT4daqgnr7D7pBicwbaspji9dPE93tubU71qOdXJNmCc2zLrW8Cf3bE94sU2KG9cGZMO4s3M4nVMxgxmnifFrq8DD4nIe8BM4KfALcAZIrIaOMP9GVVdATyGkzg/B8xX1XYvgvbCPhdQka63M6YX3gamiMgEEcnBGdV9QadtFgCXuwP9HAvsUdVt4gwJ+QdglareGmefeDe0MkLY7VOcnZPbsay4rHzANcV5PdQUg9Ov2PoUp5eKigo2b968zyJ6f3OquxtbcW9OuYNU1rjP3wHWAlMT8maMMYkUb2aWjGCVPsYLQa8DAFDV5cCsOKtO62L7m4GbkxmTX2lMImxfGmYgVLVNRK4GngeygD+q6goRucpdfxewEJiLM8p7E/BFd/fjgcuA90Vkubvsu6q6EOcG1mMi8iVgE/DZFL0lXwi3tBDMzUUCe+85FpcNp3HXLiLt7QSy+j6DXHN9PcPGjO1xu4KSUqo2bejz8Y13jj76aFavXs369evBqfm9CLik02YLgKtF5BFgNntvTlXh3tgCtnTaN3pz6hZibk6JSDmwS1XbRWQiTmurdcl8j75jp07jczEzs3wBnJlZgFYvY0olu741XvBFUmx6b9+BtjwMxAwKbhK7sNOyu2KeKzA/zn7/In7TTdxaqLg3tDJBONTcMchWVHFZOaoRGnbv6pi3uC9CDXUdUzx1p2DIEJr31KKqNr9jmggGg9xxxx3MmTMH4BDgJ729OdXVjS330F3dnDoJ+LGItAHtwFWquisFb9UY03uxM7PMAN4BvqmqjbEbiciVOCPSM27cuJQHacxgYklxmrGaYmP8LRwKxUmKo9MyVfc5KW5va6O1ubl3zadLhnRsn1tQ0KfXMd6ZO3cuc+fORUQ+cFtC9ermlLtuvxtb7vK4N6dU9a84YwEYY/wrOjPL11X1LRH5Nc7MLN+P3UhV7wbuBpg1a9aguSiMXt/azV2TSn7pU2x6yWqKjfG3cEsLOXldJcV9H2wr1FAPQF4vaorzS9y5iutq+/w6xmQMO3ka/+tqZpaMYJU+xguWFKeZfZLiiH1pGOM3rfGaT7u1w/0ZbCuaFPeu+XQpAE17bARqY4xJV93MzGKMSRJrPp1m9rl5ZjmxMb4TbmkhOy93n2W5BYXk5Of3q6a4uQ81xQVWU2xM79k51PhbdGaWHJzB8L7Yw/aDhtUUGy9YUpxmYmuH7UvDGP8Jt4TIL96/33BxWTn11f2oKa53a4qL404lu49oTXGz1RQb0zU7dZo00M3MLIOeXd8aL1jz6TQT+z1h3xnG+E+80afB6Vc8kObTeUVFPW67t0+xJcXGGGOMMb1lSXGa2XegLcuKjfGbcEsL2bm5+y13kuKBNJ/uuaY4mJ1NbkGhNZ82xhiTtmz0aeMFS4rTzT41xZYUG+M34VCI7Lz8/ZYXl5XTtKeWtnC4T8drrttDICtITv7+x4wnv6TEBtoypht26jTG36LXt3ada1LJkuI0E4ntUxzpZkNjTMqpatx5imHvtEwNu2r6dMymPbUUlg7t9R3zgpJSmq2m2Jiu2YW2McaYTiwpTjfWqdgY32oPh1GNxG8+7U7LVFe1o0/HbKzd3TGAVm8UDBliNcXGGGPSljWfNl6wpDjNxNYOR6ym2BhfCbeEAOI2nx466gAAardv69MxG/fUUlha2uvtC0pKbaAtY4wxacuaTRsvWFKcZmygLWP8KxyKJsXxB9rKys5m9/atfTpm055aCoYM7fX2BUOG0FxXh9pdM2Pis1OnMcaYTiwpTjP75MF2YjfGVzpqiuP0KZZAgNKRo9m9rfdJsUYibp/i0l7vk18yBNVIx6jVxhhjTDqxSh/jBUuK04xGrKbYGL+K1hTnxGk+DVA66gBq+1BT3NxQj0YifaspducqbrYm1MYYY9KQXd8aL1hSnGZsnC1j/Ks12nw6zkBbAKWjRlO7Y1uvmzY31e4G6FufYndQrqY9tb3ex5iMYidPY4wxnVhSnGb26VMcsRO7MX7SXfNpcAbbag+Hqa+p7tXxGmtrASjsR01xU11dr/cxxhhj/MJqio0XLClOM6oQHaHevjOM8ZfuRp8GGDraGYF617YtvTpe0x6nprigPzXFNlexMcaYNGRJsfFCQpNiEfmriHxKRCzZThKNKJLlZMX2pWGiLrzwQp555hkiNuKwp7obfRqgrGIcADWbN/bqeHVujXLRsLJex5BXXAwiNlexR/pTFp977jmmTZsGcKiIXN95vThuF5E1IvKeiBwZs+4sEfnIXXd9zPJhIvKiiKx2H4d2OuY4EWkQkf/u1xtNZ3bqNClk18bGpIdEF9DfApcAq0XkFhE5KMHHz3iqSiDL/bXZid24vvrVr/LnP/+ZKVOmcP311/Phhx96HVJG6qmmuLB0KAVDSqnauKFXx6uvriKvqLjLgbviCQSyyC8qptlqij3R17LY3t7O/PnzefbZZwFWABeLyPROm50NTHH/XYlzrkVEsoA73fXTO+17PfCyqk4BXnZ/jvUr4Nl+vk1jTO/ZtXEfWaWP8UJCk2JVfUlVPw8cCWwAXhSRf4vIF0UkO5GvlalUIRCwmmKzr9NPP52HHnqIpUuXMn78eM444ww+8YlPcO+99xIOh70OL2N01BR30acYoPzACVRtWt+r49VV76Rk+Ig+x1EwpNRqij3S17K4ePFiJk+ezMSJE8G51fkIMK/TZvOAB9SxCCgVkdHAMcAaVV2nqq2d9p0H3O8+vx84P3owETkfWIeThBtjksiujfsuen0r0f6CxqRAwptyiEgZ8AXgy8Ay4Nc4XwQvJvq1MlJE9ybF1lLWxKipqeG+++7j97//PUcccQTf/OY3Wbp0KWeccYbXoWWMcEsICQTICga73Kb8wAnUVG4i0t7e4/Hqq6soHl7e5zgKSobQZFMyeaYvZXHLli2MHTs2dlElMKbTZmOAzXG26Wo5wEhV3QbgPo4AEJFC4DvAj3p6HyJypYgsEZElVVVVPW2eNuyGskk1uzY2xv+6vnLrBxF5AjgI+BNwbvSEDDwqIksS+VqZKqLWp9js79Of/jQffvghl112GU899RSjR48G4HOf+xyzZs3yOLrMEQ6FyMnL7/budvm48bSHw+zetqWjj3E8qkpd9U7GHnJ4n+PIH1JK1cbe1UabxOprWezie7zzwnh/UNrN8u78CPiVqjb0VAujqncDdwPMmjXLTjjG9INdG/edXd8aLyQ0KQZ+r6oLYxeISK6qtqiqXZkngEas+bTZ35e//GXmzp27z7KWlhZyc3NZssTOuanSGgp1OUdxVPmBEwDYuX5tt0lxS2Mjrc3N/awpLqHZ5in2RF/LYkVFBZs3b95nEbC102aVwNg42+R0sRxgh4iMVtVtblPrne7y2cBnROTnQCkQEZGQqt7R+3dpjOkDuzbuo+j1rV3nmlRKdPPpm+IsezPBr5HZVAl01BR7HIvxje9973v7LTvuuON63K+rkWtj1nc36u0fRWSniHzQaZ8bRWSLiCx3/83tfNzBKtwSIjuv6/7E4IxAnZ2bx9bV3Q/AtNudtmnoqAP6HEdBSSmhxgba26w/ear1tSweffTRrF69mvXr14NT83sRsKDTZguAy93yeCywx61tehuYIiITRCSn074LgCvc51cAfwdQ1RNVdbyqjgduA36acQmxnTtNatm1sTFpICE1xSIyCqcfU76IHMHeJl0lQEEiXsM4YgfasqzYbN++nS1bttDc3MyyZcs67qrW1dXR1NTU7b4xI9eegVMT9baILFDVlTGbxY56OxtnFM3Z7rr7gDuAB+Ic/leq+ot+v7E0FW4JkZ3b/UjRgawsRk+ZxpaPVnW73a6tlQAMGzO22+3iKSx1Zt9prK2lpB81zabv+lsWg8Egd9xxB3PmzAE4BPiJqq4QkasAVPUuYCEwF1gDNAFfdNe1icjVwPNAFvBHVY0OnnUL8JiIfAnYBHw24W/aGNMluzbuP6spNl5IVPPpOTgDCFQAt8Ysrwe+m6DXMESnZLKaYuN4/vnnue+++6isrORb3/pWx/Li4mJ++tOf9rR7x8i1ACISHbk2NinuGPUWWCQipdEmmar6uoiMT+T7SXfhUKjLOYpjjTloOov++iitzU3k5Me/NqrZsplAVpDSkaP6HEdRmTOvccOuakuKU2QgZXHu3LnMnTsXEflAVW+GjmQY97kC8+Pt6zbLXBhneQ1wWnevq6o3dhuYMWYg7Nq4nywZNl5ISFKsqvcD94vIhar610Qc08SnEZCO0aftSyPTXXHFFVxxxRX89a9/5cILL+zr7vFGrp3di23GANvo3tUicjmwBPi2qu7uvIGIXIkz5yrjxnXdtzadhFtC5BUW9bjdAdOmoxph6+qPGH/4EXG32bWlkqGjDyCQldXnOIrLnES4rrqKA6Ye3Of9Td8NsCyaVLILbpMCdm08cJYcm1RKVPPpS1X1QWC8iHyr83pVvTXObqYfVNVJisXO6wYefPBBLr30UjZs2MCtt+5fzGJrrOLozci1/Rnd9rfAT9ztfgL8EvjP/Q4yCEe2DYdCFJcN73G7A6ZMI5AVZNP7y7tMiqs2rmfU5Kn9iiMaQ31Ndb/2N303wLJojBlk7Nq4/6z5tPFCoppPF7qPPVeRmAFRBRFnQnP7sjCNjY0ANDQ09Gf3rka07es2+1DVHdHnInIP8HR/gktHTp/i7gfaAsjJL6Di4ENYt/RtTvr8F/db31i7m7qqHRx59rn9iiO3oJDsvHzqawbP3LJ+N8CyaIwZfOzauJ/s+tZ4IVHNp3/nPv4oEcczXVNVRASxmmIDfOUrXwHghz/8YX927xi5FtiCM3LtJZ22WYDTFPoRnKbV0VFvuxTtc+z+eAHwQXfbDybhUO+SYoBJRx3Dq/ffQ+2O7fv1G46OTD16yrR+xSEilAwvp77aaopTZYBl0aSSnTtNCti18cBZcmxSKaFTMonIz0WkRESyReRlEakWkUsT+RqZTiPaUVNsWbGJuu6666irqyMcDnPaaacxfPhwHnzwwW73UdU2IDpy7Srgseiot9GRb3EG8FmHM+rtPcDXovuLyMM400pME5FKd5RbgJ+LyPsi8h5wKnBtAt+qr4VbWnqckilq0qxjAVj1r1f3W1e54n2ysrMZMX5Sv2MpLhtuzac90J+yaIwZvOzauO8sGTZeSPQ8xWeqah1wDk6zy6nA/yT4NTKaqjPQlogz6JYxAC+88AIlJSU8/fTTVFRU8PHHH/N///d/Pe6nqgtVdaqqTood9TY68q065rvrD1PVJTH7Xqyqo1U1W1UrVPUP7vLL3G0PV9XzeqpZHiwikXbaWlt6XVM8ZMRIDjz8CN5/+QUikfaO5arK2nfeYtyhMwjm5PQ7HicptubTqdbfsmhSyK63TWrZtXEfWZ9i44VEJ8XZ7uNc4GFV3dWbnUQkS0SWicjT7s/DRORFEVntPg6N2fYGEVkjIh+JyJwEx+97Tk2xQMD6FJu9wuEwAAsXLuTiiy9m2LBhHkeUedpaWgB6XVMMMOP0s6mvqWLVP1/rWFa58n327NzB1GNPGFA8xWXlNO2ppc392zCpYWUxHdi506RUv66NjTGpleik+CkR+RCYBbwsIuVAqBf7fROn+WbU9cDLqjoFeNn9GRGZjtPv8RDgLOA3ItL3+UrSmFNTjPUpNvs499xzOeigg1iyZAmnnXYaVVVV5PUhOTMDF3aT4pw+fO6Tjz6WUZOm8PpD91JXvZP2tjb+9cifyC8ZwrRPnDigeKIjUDdYE+qUsrJojOmkv9fGGctqio0XEpoUq+r1wHHALFUNA43AvO72EZEK4FPA72MWzwPud5/fD5wfs/wRVW1R1fU4/RyPSdgbSAPRmmIbfdrEuuWWW3jzzTdZsmQJ2dnZFBYW8ve//93rsDJKa6gZoNfNpwEkEOCsr11LW2srf7r+Gh664Rq2fryKUy//Mtk5uQOKJzpXsTWhTi0ri/5np06TSv25Ns50dn1rvJCoKZliHYwzJ1vssR/oZvvbgOuA4phlI6P9EFV1m4iMcJePARbFbFfpLssYqkogYKNPm/2tWrWKDRs20NbW1rHs8ssv9zCizBIOOTf++5IUA5RVjOPin/wf/3z4fpr21HLW167l4BNPHXA8JSOcr809O3cw9pABH870gZVFY0wnfb02NlhybFIroUmxiPwJmAQsB6IjxyhdFHwROQfYqarviMgpvXmJOMvilhgRuRK4EmDcuHG9OHSa6BhoSyBiXxbGcdlll7F27VpmzpxJVpbTo0BE7EI8hcL96FMcNXzsgVxw3Q8SGk/J8BFIIEDtju0JPa7pnpVFY0ysvl4bd9o3C1gCbFHVc5IVo99YMmy8kOia4lnAdO39X/PxwHkiMhfIA0pE5EFgR3SuUxEZDex0t68ExsbsXwFsjXdgVb0buBtg1qxZg6Z0OfMUO/2KLSc2UUuWLGHlypXOzRLjiXA/mk8nU1YwSEn5CGp3ZMTg375hZTEN2LnTpFZfr41jRcfcKUlsSP5mfYqNFxI90NYHwKjebqyqN7hTuYzHGUDrFVW9FFgAXOFudgUQ7ZC1ALhIRHJFZAIwBVicqODTgSrWp9js59BDD2X7dqsR9FK4xW0+7aNBlUpHjqZ2uyXFqWRlMR3YudOkVJ+ujaO6GHMno9h1rkmlRNcUDwdWishioCW6UFXP6+NxbgEeE5EvAZuAz7rHWSEijwErgTZgvqq2d32YwUcjCuL0KbaqYhNVXV3N9OnTOeaYY8jN3TtA04IFCzyMKrMMpPl0spSOOoAP//Wa28LEai5TwcqiMaaT/l4b38b+Y+5kBEuGjRcSnRTf2N8dVfU14DX3eQ1wWhfb3Qzc3N/XSXeqEAg4/YrtO8NE3XjjjV6HkPH81nwaYOio0bQ0NRJqqCe/OKNa33nGymIasHOnSa0b+7pDb8fcGazj51jzaeOFhCbFqvoPETkQmKKqL4lIAZBR8wgnmzrtp0Hsy8LsdfLJJ7Nx40ZWr17N6aefTlNTE+3tGdWIwnP9HX06mYaMHA1A7fZtlhSniJVFY0ysfl4bxx1zx+1iGHvsQTl+jjFeSGifYhH5L+Bx4HfuojHA3xL5GplOI25OLFZTbPa65557+MxnPsNXvvIVALZs2cL555/vbVAZZm/z6YHNL5xIQ0c5SfHu7XHHIzRJ0J+y+NxzzzFt2jSAQ0Xk+s7rxXG7iKwRkfdE5MiYdWeJyEfuuutjlg8TkRdFZLX7ONRdfoyILHf/vSsiFyTkjRtj4urPtXE3Y+5kBKspNl5I9EBb83HubtUBqOpqYES3e5g+UVV3Sib7sjB73XnnnbzxxhuUlDi1gVOmTGHnzp097GUSqTXUTDA7h0DAP41jSkeNJpCVRc3mjV6HkjH6Whbb29uZP38+zz77LMAK4GIRmd5ps7NxBpacgtNU8rfQMV3Lne766Z32vR54WVWnAC+7P4Mz6M8sVZ0JnAX8rtPcqYOfnTpNatm1cR/Z9a3xQqKT4hZVbY3+4J5o7S87gTSie0efjngdjfGL3NxccnJyOn5ua2uzgZVSLNzSQtBHg2wBZAWzGXZABdWWFKdMX8vi4sWLmTx5MhMnTgTnfPkIMK/TZvOAB9SxCCh1pys8Blijquvcc2/svvOA+93n9wPnA6hqk6q2ucvzsHO0Mck2oGtjVX0tk+YojmXJsUmlRCfF/xCR7wL5InIG8BfgqQS/RkZTdeYoloBg7adN1Mknn8xPf/pTmpubefHFF/nsZz/Lueee63VYGaWtJUSOz5JigLKxB1pSnEJ9LYtbtmxh7NixsYsqcZpXxhoDbI6zTVfLAUaq6jYA97GjZkpEZovICuB94KqYJNkYk3h2bdxHlgwbLyQ6Kb4eqMI50X4FWAh8L8GvkdGiU6uI5cQmxi233EJ5eTmHHXYYv/vd75g7dy433XST12FllNZQs68G2YoqHzeeuqqdtDQ1eR1KRuhrWezi4q/zwnhVzdrN8m6p6luqeghwNHCDiMT9wxWRK0VkiYgsqaqq6umw6cNOnia17Nq4j6xPsfFCokefjojI34C/qeogOoP6R3TwaUTsy8J0CAQCnH/++Zx//vmUl5d7HU5GCre0+GqO4qjh4w4EoHrzRsZMO9jjaAa/vpbFiooKNm/evM8ioPPIaJXA2Djb5HSxHGCHiIxW1W1uU+v9Ojar6ioRaQQOBZbEWT8oR7YdNG/EpAW7Nu4/u841qZSQmmJ3ZMwbRaQa+BD4SESqROQHiTi+2Wtvn2K72W2cE8aNN97I8OHDOeigg5g2bRrl5eX8+Mc/9jq0jBMOhXxZUzx87HgAqjet9zaQQa6/ZfHoo49m9erVrF+/Hpya34uABZ02WwBc7p5rjwX2uE2i3wamiMgEEcnptO8C4Ar3+RXA3wHcbYPu8wOBacCGAb35dGPnTpMCdm3cf5YMGy8kqvn0NTgj6x2tqmWqOgyYDRwvItcm6DUM0dGn3SmZIvalkeluu+023njjDd5++21qamrYtWsXb731Fm+88Qa/+tWvvA4vozhJsX+mY4oqKR9BXnEJ29eu9jqUQa2/ZTEYDHLHHXcwZ84cgEOAx1R1hYhcJSJXuZstBNYBa4B7gK8BuH2BrwaeB1ZF93X3uQU4Q0RWA2e4PwOcALwrIsuBJ4GvqWp1oj4HY0yHa7Br436x5tPGC4lqPn05cEbsiVVV14nIpcALgF2dJ4o6CbEErKbYwAMPPMCLL77I8OHDO5ZNnDiRBx98kDPPPJNrr7XzbqqEW0Jk5+V7HcZ+RIQDpkxj2+qPvA5lUBtIWZw7dy5z585FRD5Q1ZsBVPWu6Hp1rgznx9tXVRfiJM2dl9cAp8VZ/ifgT71/Z8aYfrJrY2PSSKJqirPj3Wl2+05kJ+g1DNHm086FrmXFJhwO73MRHlVeXk44HPYgoswVbvFn82mA0ZOnUVO5iVBjg9ehDFpWFtOInTtNati1cT9ZTbHxQqKS4tZ+rjN9FFEgYH2KjSN2PtS+rDOJFw6FyM7zX/NpgNFTDgJg+5qPPY5k8LKyaIzpxK6N+8mSYeOFRDWfniEidXGWC+DPqpN0pUrAqSq2Lw3Du+++S0lJyX7LVZVQKORBRJkr3BIix4fNpwFGTZ4KImz5aBXjZxzpdTiDkpXFNGKnTpMadm08QHada1IpIUmxqmYl4jimZxrBbT5tNcUG2tvbB7S/iJwF/BrIAn6vqrd0Wi/u+rlAE/AFVV3qrvsjcA6wU1UPjdlnGPAoMB5nVNv/UNXdAwrU59rbwkTa233bfDq3oIBRk6aw6f3lHP8fn/c6nEFpoGXRGDO42LVx/1kybLyQqObTJkVU1W0+baNPm4ERkSzgTuBsYDpwsYhM77TZ2cAU99+VwG9j1t0HnBXn0NcDL6vqFOBl9+dBrdWtCfTj6NNRBx52BNvWfGT9io0xxvia9Sk2XrCkOM10DLRlo0+bgTsGWKOq61S1FXgEmNdpm3nAA+pYBJSKyGgAVX0d2BXnuPOA+93n9wPnJyN4PwlHk2KfNp8GGH/4EWgkwuYV73kdijHesnOnMWnBkmKTSpYUpxmNTslk7afNwI0BNsf8XOku6+s2nY1U1W0A7uOIAcbpe+EW/9cUj546jZz8fNYvW+J1KMZ4zM6dxviZJcPGC5YUpxlVRWz0aZMYEmdZ57+q3mzTvxcXuVJElojIkqqqqkQc0jN7a4r92acYICuYzcQjj2H124tob2vzOhxjjDEmLms+bbxgSXGacWqKsdGnTSJUAmNjfq4AtvZjm852RJtYu487422kqner6ixVnVVeXt6nwP1mb02xf5tPA0w99nhC9XVsXvm+16EY4x07dRpjjOnEkuI0o6pu82lnJGpjBuBtYIqITBCRHOAiYEGnbRYAl4vjWGBPtGl0NxYAV7jPrwD+nsig/agjKfbpPMVR42ceRXZePh+/+U+vQzHGGGPisppi4wVLitNMx5RMAaspNgOjqm3A1cDzwCrgMVVdISJXichV7mYLgXXAGuAe4GvR/UXkYeBNYJqIVIrIl9xVtwBniMhq4Az350Gto/m0T6dkisrOyWXy0cfy8aI3OhJ5Y4wxxk/s+tZ4ISHzFJvUsT7FJpFUdSFO4hu77K6Y5wrM72Lfi7tYXgOclsAwfS+aFOf4ePTpqMM/OYdV/3yVj/79Tw499QyvwzEm5ezcaUx6sOTYpJLVFKcRVQW3T7HNU2yMf0TnKQ76ePTpqDEHH8KwMWN596VnvQ7FGI/YudMYP7Nk2HjBkuJ04n5H7K0pti8NY/xgb59ifzefBueG2owzzmb7mo+p/HCF1+EYY4wx+7A+xcYLlhSnkYj75bC3T7HHARljAKf5tAQCBLNzvA6lVw775JnklwzhrSce9ToUY1LPzp3GGGM6saQ4nbijTTs1xdap2Bi/CIeaycnLd8plGsjOzWPWORew4d2lbFvzkdfhGGOMMR2spth4wZLiNKIdNcU20JYxftIaak6LptOxZp45l/ySIbz2wB/swsNjzz33HNOmTQM4VESu77zenRLtdhFZIyLviciRMevOEpGP3HXXxywfJiIvishq93Gou/wMEXlHRN53Hz+ZivdojDG9ZUmx8YIlxWmk47tBABtoyxjfaA2F0mLk6Vg5+QWceMkVbP1oJav++arX4WSs9vZ25s+fz7PPPguwArhYRKZ32uxsYIr770rgtwAikgXc6a6f3mnf64GXVXUK8LL7M0A1cK6qHoYzj/ifkvXefMtOncYYYzqxpDiNRJNgEUECVlNsjF+EQ81kp1lSDHDoyaczevI0Xnvg9zTs3uV1OBlp8eLFTJ48mYkTJ4KTrj0CzOu02TzgAXUsAkpFZDRwDLBGVdepamunfecB97vP7wfOB1DVZaq61V2+AsgTEf8Pm55QdvI0xs+spth4wZLiNKKxA22J2JeFMT4RDoXISbPm0wASCDDnq9cQbmnh2TtvRSMRr0PKOFu2bGHs2LGxiyqBMZ02GwNsjrNNV8sBRqrqNgD3cUScl78QWKaqLf1+A8YYk2B2fWu8YElxGtF9BtqymmJj/CId+xRHlVWM5dQvXMmm95ez+O+Pex1Oxuni4q/zwngjuGk3y3skIocAPwO+0s02V4rIEhFZUlVV1ZvDpgc7dxqTFiw5NqlkSXEaibjNpwM2+rQxvpKuzaejDvvkmRx0/Mm88eiDbHx/udfhZJSKigo2b968zyJga6fNKoGxcbbpajnADreJNe7jzuhGIlIBPAlcrqpru4pNVe9W1VmqOqu8vLxP78sYY/rLkmHjBUuK00hHn+JoTbG1dDTGF1pDIXLy0zcpFhHOuPJqho2p4Jnb/4/6mmqvQ8oYRx99NKtXr2b9+vXg1PxeBCzotNkC4HJ3FOpjgT1uk+i3gSkiMkFEcjrtuwBnIC3cx78DiEgp8Axwg6q+kbx3ZowxxqQPS4rTSGxNMQHrU2yMXzjzFKdn8+monLx8zv3WDbS1tvLUr/4f7W1hr0PKCMFgkDvuuIM5c+YAHAI8pqorROQqEbnK3WwhsA5YA9wDfA1AVduAq4HngVXRfd19bgHOEJHVwBnuz7jbTwa+LyLL3X/x+hsPXnbqNMbXYq9v7VrXpIrnSbGIjBWRV0VklYisEJFvusvjzrHorrvBnZPxIxGZ4130qbW3phjrU2yMT6gqraFQWjefjiobM5azvvpNtq3+iH/86Y9eh5Mx5s6dy8cffwzwgareDKCqd6nqXe5zVdX5qjpJVQ9T1SXRfVV1oapOddfdHLO8RlVPU9Up7uMud/lNqlqoqjNj/u0kg9ip0xh/s6TYeMHzpBhoA76tqgcDxwLz3XkW486x6K67COeO+lnAb9y5Gge9jtGn3T7F9kVhjPfaWltAlezc9K4pjpp67Akc9anzWfbcU6x64x9eh2OMMSaD2bWuSRXPk2JV3aaqS93n9ThNwMbQxRyL7vJHVLVFVdfjNCc7JqVBe6Rj9Gmx0aeN8YvW5mbAaX48WJx4yRcYc9B0XrrnDup3Wf9iM8jYydP4XFetKDOFJcLGC54nxbFEZDxwBPAWXc+x2N28jLHHGnRTSXT0Kc5ya4oj9qVhjNfCoRBAWg+01VlWMMhZX72WSFs7r953t9fhGJNYduo0/tdVK8qMYM2njRd8kxSLSBHwV+AaVa3rbtM4y/YrMYNxKomOPsUiSJYlxcb4QWvIqSlO13mKu1I6ajTHXngRq9/6N2vfecvrcIwxJmN004rSGJMkvkiKRSQbJyF+SFWfcBd3Ncdid/MyDmqxo08HAtLxszHGO9Ga4sEw0FZns869gLKKcbz8x7sIt7Z4HY4xxmScTq0oO68bdK0iwWqKjTc8T4pFRIA/AKtU9daYVXHnWHSXXyQiuSIyAZgCLE5VvF7aZ/TpgKDt9kVhjNfCoWif4sFVUwyQFczmtC99lfrqKpY+8/eedzAmHdhFtkkTPbWiHIytIsESYeMNz5Ni4HjgMuCTMXMmzqWLORbdORgfA1YCzwHzVbXdm9BTq2OgrWhNsX1pGOO5vc2nB19NMcDY6Ycx+ehjeetvf6GxdrfX4RgzcHbqNGmgi1aUGccSZJMqnifFqvovVRVVPTxmzsSFXc2x6O5zszsn4zRVfdbL+FMpdkqmQJbVFBvjBy1NTQDk5hd4HEnynHjJF2kPt/Lvxx7yOhRjjBn0umlFmREsETZe8DwpNr0X26c4OiWTfXEY462WpkYAcgsLPY4keYYdMIYZZ87l/VdeoGbL5p53MMbP7LRp/K+rVpQZwfoUGy9YUpxGojXD0ZpiwEagNsZjLU1NIDKo5imO59gLPkdWTjZvPfGo16EYMyB2kW38rqtWlF7HlSpWRo0XLClOI9E+xAF3oC3ARqA2AyIiZ4nIRyKyRkSuj7NeROR2d/17InJkT/uKyI0isiVT7m63NjWSk5ePBAb312nBkFJmnvkpPnzjdXZt3eJ1OMYYYwYpqyk2XhjcV3GDzD7zFEeTYutXbPpJRLKAO4GzgenAxSIyvdNmZ+OM8D4FuBL4bS/3/VWm3N1uaWokt2DwNp2OdfS5nyYrO5u3nnjE61CM6T87bRrja5YUGy9YUpxGOo8+DTazhBmQY4A1qrpOVVuBR4B5nbaZBzygjkVAqTtveG/2zQgtTY2Duj9xrIIhpcw4cy6r/vUPqy026ctOnMb4miXCxguWFKeRvfMU760pthGozQCMAWJHTap0l/Vmm572vdptbv1HERka78VF5EoRWSIiS6qqqvr7HjzX0tREbsHgHXm6s2ht8SKrLTbGGJMEVlNsvGBJcRqJHX06YH2KzcBJnGWd/6C62qa7fX8LTAJmAtuAX8Z7cVW9W1Vnqeqs8vLyXgXsR5nUfBqgsHQoR5x1Dqv+9RrVmzd6HY4xfWenTWN8zRJh4wVLitNIbE2xjT5tEqASGBvzcwWwtZfbdLmvqu5Q1XZVjQD34DS1HrRamhrJGcRzFMdz9HkXkpOXb/MWm7RkF9zG+JvVFBsvWFKcRiIdSbGNPm0S4m1giohMEJEc4CJgQadtFgCXu6NQHwvsUdVt3e3r9jmOugD4INlvxEstTU3kFhZ5HUZK5ReXcNSnzmf14n+zfe1qr8NJe8899xzTpk0DODSBo8APE5EXRWS1+zjUXV4mIq+KSIOI3JGK9+c7dto0xtcsETZesKQ4jaju33zaaopNf6lqG3A18DywCnhMVVeIyFUicpW72UJgHbAGp9b3a93t6+7zcxF5X0TeA04Frk3Ve0o1VaW1qTGj+hRHHfWp88kvLuGV+36HRiJeh5O22tvbmT9/Ps8++yzAChI3Cvz1wMuqOgV42f0ZIAR8H/jvpL0pY4wZAKspNl4Ieh2A6b2O0adtSiaTIO50SQs7Lbsr5rkC83u7r7v8sgSH6VttLS1E2tszqk9xVG5BASdf9iWe+82vePel55h55qCejjppFi9ezOTJk5k4cSI4dZjRkdxXxmzWMQo8sEhEoqPAj8cdBR5ARGL3nQec4u5/P/Aa8B1VbQT+JSKTk/zW/Msuso3xNUuEjRespjiN7NOn2JpPG+O55oY6wGlOnImmn/RJxh06g9cfupeaLZt73sHsZ8uWLYwdG9s9P2GjwI90uzrgPo5IYNjpzU6bxvia1RQbL1hSnEY6Rp/OipmSyZJiYzzTXJfZSbGIcNbXriWYk8OCX/6U1uYmr0NKO11c8CViFPgBGyzTphlj0ouqkpWV1fHcmFSwpDiNdNQUy97Rp62m2BjvNNc7SXFecbHHkXinuGw453zzOnZv28Lf/u8mwq0tXoeUVioqKti8eZ9a9oSMAg/siA565z7u7Gtsg2XatP3YadMYX4tNiiM2ZoVJEUuK04jGGX3aaoqN8U5zQz2QuTXFUeMOncHZX7uWzSvfZ8Evf0pba6vXIaWNo48+mtWrV7N+/Xpwan4TMgq8+3iF+/wK4O9Jfitpw2qejPE3S4qNFywpTiOxzaetT7Ex3sv05tOxDj7xVM74r6vZsPwd/v7Lmy0x7qVgMMgdd9zBnDlzAA4hcaPA3wKcISKrgTPcnwEQkQ3ArcAXRKQyzmjXxhjjGUuKjRds9Ok00t7mJMBZwQDi3s5QG33aGM8019eBCHlFmTVPcVcOP20OoLx49x38/Zc3M+/b/0swJ8frsHxv7ty5zJ07FxH5QFVvhoSMAl8DnNbFPuMTEXfastOmMb5mSbHxgtUUp5FIu/PFkJUVsJpiY3ygub6OvKJiAoEsr0PxjcNPO4szrvy6U2P8i5usxtj4T7T5dLxhyowxnrOk2HjBkuI00h52vhgCWTbQljF+0FxfZ02n4zj8tDmc+ZVvsOG9ZTxz+89Ru6gxfmKnTWN8zZJi4wVLitNIe7t2TMcUyHJ+dRFrPm2MZ5rr9lhS3IXDPnkmp1z2Zda8vYg3//qI1+EYY4xJE5YUGy9YUpxG2tsiBILOrywr23mM1h4bY1KvYfcuioYO8zoM3zpy7nkccvJpvPn4n1n99pteh2OMw5pPG+NrlhQbL1hSnEYibUqW22w6aEmxMZ5SVRpqqikuK/M6FN8SEU7/8nxGTZrCs//fL6nauN7rkIzBZmQyxt9UlUDAvc5tb/c4GpMpLClOI+3tEbKiNcXuY5slxcZ4oqWpkXBLiKJhw70OxdeCOTnM++/vkVtYyJM//zGNtbu9DskYY4xPRWuGs7Oz9/nZmGSzpDiNRMIRAkGnprij+XSbfVkY44WGmmoAisssKe5J0bAyzv+f79NcV8eTP/sxoYYGr0MymSxaVWw1xsb4TjQJDgaD+/xsTLJZUpxG2tuVLHeALWs+bYy36nfVAFhNcS+NnDiZc675DtWb1vPYj66nrrrK65BMprJk2BjfsqTYeMWS4jQSaYt01BBHH9vC1tfCGC/Ud9QUW5/i3pp01DGc/50fUrtzB3+67uuseXuR1yGZTGbJsTG+Y0mx8YolxWmkvS3SMT9xtE+x1RQb4409O7cTyMqisNRGn+6L8YcfwWU/+zUlI0by91/cxCv3/Y62cNjrsEwmsWTYGN+KDqxlfYpNqllSnEba27UjGRYRAkGhvc3O7sZ4YffWLQwZMYos92626b2how7g4p/8giPPPo9lzz7Fw9/7b3Zt3eJ1WCZT2PDTxviW1RQbr1hSnEYibXtHnwYIBgNWU2yMR3ZtrWTYmAqvw0hbwexsTv3Clcz7n+9TV72Th757DR+/9YbXYZkMEJsSqyXIxviKJcXGK5YUp5G2cIQsd/RpcPoVt9no08akXHtbG7XbtzJ09BivQ0l7k2fN5rKf/ZphY8by1K3/j9f+9Afa29q8DssMZtrFc2OM52xKJuMVS4rTSLilney8vU01s3OzCLfYxaMxqVa9aQPtbW2MnDjZ61AGhZLhI/jcjT9j5pxP8c7TT/LoD79D5YcrvA7LDFZWO2yMb1lNsfGKdYZLI62hNnJyszp+zivKIVRvA9QYk2rb1nwMwOjJUz2OZPAIZmdz2n9+lTHTpvPan/7Aoz/8DmUV4zjw8CMoHzee8gMnMKxiLNk5uV6HatKcxrawsvzYGF+xpNh4xZLiNBIO7VtTnF+cTWNti4cRGZOZNr63jKJhZZSUj/Q6lEHnoONPZtKs2az4xyt8/OY/ee+l52hrdb7nRAIMP3A8Bx9/Moeffja5BQUeR2vSUvveTFjbI0hWVjcbG2NSKezORpCTkwNYUmxSx5LiNNHeHqGlqY28ouyOZfnFOVRvbvAwKmMyT2tzExveW8ohJ5+OiPS8g+mz7Nw8Zp45l5lnziUSaad2+3aqN2+gauMGNr6/jNcfupe3F/yV4z5zMTPOmEvAkhrTBxozQKWGI5Bjfz/G+EVraysAeXl5gCXFJnXSsk+xiJwlIh+JyBoRud7reFKhuc65c1ZQktOxrHBIDs11rYRb2r0Ky6S5nsqSOG53178nIkf2tK+IDBORF0Vktfs4NFXvJxWWv7CQtpYWDjn5k16HkhECgSyGHTCGqbOP5/j/+DyX/OQXfP6nv2L4uPG8cu/veOC6r7Px/eVehzkgzz33HNOmTQM4NBXlUERucLf/SETmJPv9+U1s82m1GRyMT2XitS5AS4vTMqioqAjYmyQbk2xpV1MsIlnAncAZQCXwtogsUNWV3kaWXLt3NAJQMjyvY9mYqUN559mNvPqnVRx42HCyc7OQgCACEhACImRlC9m5QQJBISsYIJgdICs7gIjQ1tpOS3Mbba0RgtkBgjkBgtlZZAUDSJaQnRMgkJWW901ML/SyLJ0NTHH/zQZ+C8zuYd/rgZdV9Rb3RH498J1Uva9kaW1uYvXiN/n3Xx5i4pFHM3ryNK9DylijJk3hs9+/mTVLFvGPB37P4zd9j/Ezj2L6CacwavJUhowYlTa1x+3t7cyfP58XX3yRSZMmrQAuTmY5FJHpwEXAIcABwEsiMlVVM+bu6r5Jcca8bZNGMvVaFyAUCgGQn59PXl4ee/bsoa3TjASdW2nFa7UVXWYtukxvpV1SDBwDrFHVdQAi8ggwD+j3F8X9N7xBe7SPUadRKTt+jK7e+6SL7XTvqs6bqnaxvItjx/yoESWYE2DkhCEdr1kxbSiHnjSGVW9uY/WSnd2/yX4KBAURQQDE+XIJZAkSENC9czxq9LnuDT/gJuciAt18J6kqGnEfFUQgEHD20XYlEnEWdhzGfU40Juc/+vu9t8+vvPOgK9HjRj8Dn8rKDnD5zZ/o6269KUvzgAfU+UUvEpFSERkNjO9m33nAKe7+9wOvMYCkuLmhnvu//bV95hN1/tZ037IV/Vt0/wg1pvCqst/66H7Oz3v/jqMr9/497/vdMHryNOZ89Zr+vh2TICLClKOPY8KMo3hn4d9Z/txTLFz+Tsf6YHYOwby8fS6Iuro4SsVFU3HZcD7/01/tt3zx4sVMnjyZiRMngvPHl+xyOA94RFVbgPUisgbnu+DN/r63cFUTVb97r7+7d61Pg2D1fuNI494L7B2/fIdAQdA5ufRwrjKDW86YYoZ/4RCvw4hK6LXuhg0b+Mtf/tLl+p7m607l+tbWVrKyshg6dCgjR45k6dKlLF26tNv9+6OnxLq7RDtRP3e3jSX1+5s8eTLnn39+0o6fjknxGGBzzM+VOHfO9yEiVwJXAowbN67bAx542HBUde+5MPqH2PEznX6WLpZHH2JOrF3su98+XbxY7OKxBw8jN3/vr0wCwsmXTOP4z06mviZEWziCRvYmmJGI0h6OEG5pp70tQqQtQnubdmwXzAmQkx8kmJNFezhCW7idttYI7eEIqkq4pZ221nbneO5rasQ5rrZrR7IYTRw73rfs3TYaS5eUfWq3ESACEVWIKJIlToJMnMTbTXSiz/dJkHr4Etlvrez/NPa4PX2hey0r0K8vzd6UpXjbjOlh35Gqug1AVbeJyIh4L97bMpoVDDLpKPfQEnuCkH1vkCB713X8PUZPKu5C94f9EqXYmx77rJeOv/Ngdg4jJ03hwENnIAFrQeEXwZwcZp//WY4570J2rF9L9aYN1FVXEW4JEW5p2f/OJp3Kc4qKdl5xcdzlW7ZsYezYsbGLkl0OxwCL4hxrP70to4HcLPIPKety/YAk6YKwYEY5LRv20L6n1flqUCDi7+95k1xZw/J63ih1EnqtW1hYyEEHHdR5324D8HL9+PHjCQaDfPrTn+aDDz4gEonsdx3W08+92SYVx+jrMfdWNtn3UWejRo1K6vHTMSmOV4r2+8tR1buBuwFmzZrV7V/WKZekdzPIYHYWQ0cVeh2GST+9KUtdbdOrctid3pbRnLx8zrjy6r4c2mQgCQQYNWkKoyZN8TqUPuniwieZ5bDX+/S2jGaV5DL0gvT63AFyY1peGeMzCb3WLS8v59xzz01cdCkyZMgQjj/+eK/DMBkiHas7KoHY2+oVwFaPYjEmnfWmLHW1TXf77nCbduI+JqdtvzGDQEVFBZs3b95nEckth3YONcb/rJwak2LpmBS/DUwRkQkikoMzYMgCj2MyJh31piwtAC53R789FtjjNsnsbt8FwBXu8yuAvyf7jRiTro4++mhWr17N+vXrwakdSnY5XABcJCK5IjIBZ/CuxUl6e8aY/rFrXWNSLO2aT6tqm4hcDTwPZAF/VNUVHodlTNrpqiyJyFXu+ruAhcBcYA3QBHyxu33dQ98CPCYiXwI2AZ9N4dsyJq0Eg0HuuOMO5syZA86I0D9JZjl0j/0YzoA9bcD8TBp52ph0YNe6xqSeZEJH7lmzZumSJUu8DsMYT4nIO6o6y+s44rEyaoyVUWP8zsqoMf42kDKajs2njTHGGGOMMcaYhLCk2BhjjDHGGGNMxrKk2BhjjDHGGGNMxsqIPsUiUgVs7ONuw4HqJISTKBbfwGRifAeqanmCj5kQXZRRv/+OemLxeysd40+3MtpXfvqd+CkW8Fc8Fkt8w4HCQVJG/fS5dicd4rQYEycRcfb7PJoRSXF/iMgSvw6mABbfQFl8/pfun4HF7610j38w8tPvxE+xgL/isVji81MsA5Uu7yUd4rQYE8frOK35tDHGGGOMMcaYjGVJsTHGGGOMMcaYjGVJcdfu9jqAHlh8A2Px+V+6fwYWv7fSPf7ByE+/Ez/FAv6Kx2KJz0+xDFS6vJd0iNNiTBxP47Q+xcYYY4wxxhhjMpbVFBtjjDHGGGOMyVgZlRSLyDAReVFEVruPQ7vY7iwR+UhE1ojI9THL/09EPhSR90TkSREpdZePF5FmEVnu/rurj3HFfb2Y9SIit7vr3xORI3sRa6/eazLjE5GxIvKqiKwSkRUi8s2YfW4UkS0xn9ncVMfnrtsgIu+7MSyJWe6Hz29azOezXETqROQad13CPj8/6e59icgN7mf0kYjM8TLO7vT0+/ajeOUgkWUg0UTkjyKyU0Q+iFnWZbzp8reTDkTks+73eUREZnVaF/dzFpGj3L+vNe53nbjLc0XkUXf5WyIyPmafK9zf5WoRuSJm+QR329Xuvjn9eA8JK6OJ+ltMxGckXZxzPYrnyyKyWETedWP5kZefjbssS0SWicjTXsfiB+Lj64hEltFkki6uIb3U1+8kr3QRp7d/k6qaMf+AnwPXu8+vB34WZ5ssYC0wEcgB3gWmu+vOBILu859F9wfGAx/0M6YuXy9mm7nAs4AAxwJv9SLWHt9rCuIbDRzpPi8GPo6J70bgvxPwO+13fO66DcDw/vytpCK+TsfZjjP/WsI+P7/96+p9AdPdzy4XmOB+pllex9uf37cf/8UrB4kqA0mK9yTgyNjv3a7iTZe/nXT5BxwMTANeA2bFLO/ycwYWA8e533HPAme7y78G3OU+vwh41H0+DFjnPg51nw911z0GXOQ+vwv4ah/jT2gZTdTfYoI+o4OIc871MJ4Kd1028BbO+c2rWIYC3wL+DDzt8e9pqNfl2I3tRnx4HUEanUfp4hrS45h6/Z3kwzg9/ZvMqJpiYB5wv/v8fuD8ONscA6xR1XWq2go84u6Hqr6gqm3udouAigTE1OXrdYr7AXUsAkpFZHQP+/bmvSY1PlXdpqpLAVS1HlgFjOlnHAmPr4fjev75ddrmNGCtqm7sZxzpbh7wiKq2qOp6YA3OZ+s3vfl9p4tElYGEU9XXgV2dFncVb7r87aQFVV2lqh/FWRX3c3a/y0pU9U11rnoeYN/fTfR39jhwmlvzNgd4UVV3qepu4EXgLHfdJ91toX9/lwkto4n4W0zgZ3REF+dcr+I50d0u2/2nHsZyCfAp4Pfs5VUsZ2G6M5jOoynXx+8kz3QRp6cyLSkeqarbANzHEXG2GQNsjvm5kviJ3H/i3CWMmuA2y/mHiJwYZ/uu9Ob1utqmu317816THV8HtynRETh3i6OuFqe58B8H0JRjoPEp8IKIvCMiV8Zs46vPD+fO88OdliXi8/OjeO+rt+XSa+kSZ2fxykGiykCqdBVvuv5O0k1356nKOMv32ce94bwHKOvmWGVAbczN6f78LlPx99DXv8VEfkbAfudcr+KpEJHlwE6cxNDLWL4CXAdEYpZ7/nvyAT9eR/j9M4vV1TWk36TT+dyzv8lBlxSLyEsi8kGcf729yyRxlmmn1/hfoA14yF20DRinqkfgNs8RkZJEvV432/Rm34EaSHzOSpEi4K/ANapa5y7+LTAJmInz+f3So/iOV9UjgbOB+SJyUj/j6EoiPr8c4DzgLzHrE/X5pVwPZbSr95WKv/VESJc4O0t2OfBSuv5OPNPP82h/zlN93ScRv0sv/x5S8Rl1dc71Ip6Iqs7EaVV3jIgc6lEsU4F6VX2nm9dPVSyx+yRdP8+3Xkun7+zBfO70gqd/k8FUvlgqqOrpXa0TkR3RZr1uM5idcTarBMbG/FwBbI05xhXAOcBpbvMZVLUFaHGfvyMia3G+hHvT6b7b1+thm5xu9u3Ne+2NgcSHiGTjnJwfUtUnohuo6o7ocxG5B3jai/hUNfq4U0SexGm28zo++fxcZwNLYz+zBH5+KdddGY3V6X315nP0g3SJcx9dlINElYFU6SretPydeKm3ZbSTrj7nSvbtahT7+Uf3qRSRIDAEpzldJXBKp31eA6pxupcE3dq4/vwuU/H30Ne/xYR9Rl2ccz2LB0BVa0XkNZxmw17EcigwTkQ2AHlAiYg86PXnkgr9PN96LW2+s7u5hvSbtDife31tO+hqinuwALjCfX4F8Pc427wNTBFnhMscnGarC8AZDQ/4DnCeqjZFdxCRchHJcp9PBKbgDKbQG12+Xqe4LxfHscAet/lDd/v25r0mNT4REeAPwCpVvTV2B9m3z+wFwAf0z0DiKxSRYjeeQpyB1D6I2cfTzy9m/cV0ajqdwM/PV7p5XwuAi8QZ4XMCThlbnOr4eqE3v29f6aYcJKoMpEpX8abL3066i/s5u99l9SJyrHtOuJx9fzfR39lngFfcm83PA2eKyFBxms+dCTzvrnvV3Rb693eZijLap7/FRH5GxD/nehHPWcCbACKSD5wOfOhRLKXAFFUdj/P7fkVVL/X49+Q5H19HpMV5tIdrSL9Ji/O553+T6oOR0lL1D6d/x8vAavdxmLv8AGBhzHZzcUZtXAv8b8zyNTj9HJa7/6IjDV4IrMAZIW8pcG4f49rv9YCrgKvc5wLc6a5/n31H/Owq1rjvtZ+fW7/iA07AafLyXsxnNtdd9yd32/dwCutoD+Kb6P7O3nV/f776/Nx1BUANMKTTMRP2+fnpX3fvC/hf9zP6CHfETz/+66pM+vVfV+UgkWUgCTE/jNO0KoxTq/Cl7uJNl7+ddPiHc6FSidM6agdOstrt5wzMwrm4WQvcAYi7PA+nW8ganBsVE2P2+U93+Rrgi53+Xhe7y/8C5PbjPSSsjCbqbzERnxFdnHM9iuf7wDI3lg+AH7jbePLZxCw/hb2jT3sai9f/8PF1BGlwHqWba0iP4+rTd5LP4vT0bzJamI0xxhhjjDHGmIyTac2njTHGGGOMMcaYDpYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYY4zJWJYUG2OMMcYYk+ZE5I8islNEPkjQ8dpFZLn7b0EijmmMX4mqeh2DMcYYY4wxZgBE5CSgAXhAVQ9NwPEaVLVo4JEZ439WU2yMMcYYY0yaU9XXgV2xy0Rkkog8JyLviMg/ReQgj8IzxtcsKTbGGGOMMWZwuhv4uqoeBfw38Js+7JsnIktEZJGInJ+U6IzxiaDXARhjjDHGGGMSS0SKgE8AfxGR6OJcd92ngR/H2W2Lqs5xn49T1a0iMhF4RUTeV9W1yY7bGC9YUmyMMcYYY8zgEwBqVXVm5xWq+gTwRHc7q+pW93GdiLwGHAFYUmwGJWs+bYwxxhhjzCCjqnXAehH5LIA4ZvRmXxEZKiLRWuXhwPHAyqQFa4zHLCk2xhhjjDEmzYnIw8CbwDQRqRSRLwGfB74kIu8CK4B5vTzcwcASd79XgVtU1ZJiM2jZlEzGGGOMMcb4iIhcC3wZUOB94IuqGvI2KmMGL6spNsYYY4wxxidEZAzwDWCWO99wFnCRt1EZM7hlxEBbw4cP1/Hjx3sdhjGeeuedd6pVtdzrOOKxMmqMlVFj/C7FZTQI5ItIGCgAtna3sZVRYwZWRjMiKR4/fjxLlizxOgxjPCUiG72OoStWRo2xMmqM36WqjKrqFhH5BbAJaAZeUNUX4sRzJXAlwLhx46yMmow3kDJqzaeNMcYYY4zxCREZijMg1gTgAKBQRC7tvJ2q3q2qs1R1Vnm5LxuZGJM2LCk2xhhjjDHGP04H1qtqlaqGceYT/oTHMRkzqFlSbIwxxhhjjH9sAo4VkQIREeA0YJXHMRkzqGVEn2JjjDH9Fw6HqaysJBSy2UASIS8vj4qKCrKzs70OxRjjQ6r6log8DiwF2oBlwN3eRmXM4GZJsTHGmG5VVlZSXFzM+PHjcSotTH+pKjU1NVRWVjJhwgSvwzHG+JSq/hD4oddxGJMprPm0GZBIJEx7u9UeGeNHqkp9WzuqOqDjhEIhysrKLCFOABGhrKzMat0znKrS3NrudRjGmIFoa4UBnl+Nf1hSbPqlra2ejz6+kdf/eSSv/eMwPvr4R6jaCd4YP1BVHt22i1lvrmTKP9/nE2+tYsmexgEd0xLixEnEZykiZ4nIRyKyRkSuj7NeROR2d/17InJkzLoNIvK+iCwXEZvDJcVC4XYuunsRh/zwOX73j7Veh2OM6Y8Nb8DPJ8DvT4dws9fRmASwpNj0WWPjOt56ay6VlQ8xovwsRo/+NJWVD7B58/1eh2ZMxmuLKNd8uJlvfriJUbnZ3DBhNBGFS95by4bmFq/DMwkgIlnAncDZwHTgYhGZ3mmzs4Ep7r8rgd92Wn+qqs5U1VnJjtfs657X1/HW+l0cNKqEnz33Iau21XkdkjGmr176IbQ2wJYlsKjz16tJR5YUmz4Jh3ezbPlltEdCzDrqL0yf/n8cfNAtlA07iQ0bf0N7u90tGwysFip9/e/qSh7dvotvjR/JU0dO4ZvjR/KXmZOIKPxwzRavwzOJcQywRlXXqWor8AjOnKax5gEPqGMRUCoio1MdqNlXe0S5/82NnDKtnIf/61jys7O45/V1XodljOmLmrVQ+TbM+SlMPBXe/gNErLVkurOk2PTJmjU/p7W1hpkz/siQITMBpynggQd+lXB4Nzt2POVtgGbArBYqfT1XtYf7t9bwtbEjuG7CaAJuM91x+bl8fdxInq+uY2VD+t64evLJJxERPvzww4Qd87bbbqOpqSlhx0uRMcDmmJ8r3WW93UaBF0TkHRG5sqsXEZErRWSJiCypqqpKQNhmyYZdVDe08JmjKhhSkM1nZ43l6fe2sac57HVoxpjeWvOS8zjtbDjqC1BXCWtf9TQkM3A2+rTptebmSrZt/ytjxlxKSclh+6wrLT2a/Pzx7NjxNAcc8B8eRWgSpKMWCkBEorVQK2O26aiFAhaJSKmIjFbVbakP1wCEI8qP125lakEe3524f4Xg5WPK+NXG7dy3pZqfTxvb79f5/upKPkhwYn1oUT4/mVLR43YPP/wwJ5xwAo888gg33nhjQl77tttu49JLL6WgoCAhx0uReJ2SO4/20t02x6vqVhEZAbwoIh+q6uv7bax6N+40MLNmzbLRZBLghZU7yAkGOGXaCADmzTyA+/69gZdW7uDCo3ouA8YYH9i0CIaMhWEToaQC8ofB8odgyuleR2YGwGqKTa9t2/4kqhEOHPfl/daJCCNGnMXu2kWEw7WpD84kktVCpaFnqmpZ19zCdyeOJhjYPx8amh3kU+WlPLWzlnAk/fKbhoYG3njjDf7whz/wyCOP8Oyzz/If/7H3Btxrr73GueeeC8ALL7zAcccdx5FHHslnP/tZGhoa4h7z9ttvZ+vWrZx66qmceuqp3e47fvx4vvvd73Lccccxa9Ysli5dypw5c5g0aRJ33XVXRwwnnXQSF1xwAdOnT+eqq64iEokk4+OoBGLvbFQAW3u7japGH3cCT+LcCDMpsGhdDUeNG0pRrlMnMXNsKWNK83n6vc6/PmOMb21dBgcc4TwP5sBhn4UPn4Hm3d7GZQbEaopNr6gqO3Y8RWnp0eTlHRB3m/Lhp7Fx413s2v0mI0ecneIITQJZLVQaenT7LsbkZnPm8JIutzmnfAh/3bGbN2sbOGlYcb9epzc1usnwt7/9jbPOOoupU6cybNgwysrKWLRoEY2NjRQWFvLoo4/yuc99jurqam666SZeeuklCgsL+dnPfsatt97KD37wg/2O+Y1vfINbb72VV199leHDh/e479ixY3nzzTe59tpr+cIXvsAbb7xBKBTikEMO4aqrrgJg8eLFrFy5kgMPPJCzzjqLJ554gs985jOJ/jjeBqaIyARgC3ARcEmnbRYAV7stPWYDe1R1m4gUAgFVrXefnwn8ONEBmv01tLSxalsdV586uWOZiPCpw0fzx3+tZ09zmCH52R5GaIzpUfNu2L0ejrx877KZl8Di38EHT8DRX/IuNjMgVlNseqWxaQ1NTWsZOeKcLrcpLj6MrKxCdu9elMLITBJYLVSa2dES5rVd9fzHqGEd/YjjOWVYCQVZAZ6pqk1dcAny8MMPc9FFFwFw0UUX8Ze//IWzzjqLp556ira2Np555hnmzZvHokWLWLlyJccffzwzZ87k/vvvZ+PGjb16jZ72Pe+88wA47LDDmD17NsXFxZSXl5OXl0dtbS0AxxxzDBMnTiQrK4uLL76Yf/3rX4n9IABVbQOuBp4HVgGPqeoKEblKRK5yN1sIrAPWAPcAX3OXjwT+JSLvAouBZ1T1uYQHafazfFMtEYWjxg/bZ/mcQ0bRFlFe/XCnR5EZY3pt+/vO4+gZe5eNngEjDoFlf7J5i9OY1RSbXokmumVlJ3W5TSCQTWnpLHbvfjNVYZnksFqoNPPqrjoUOGdEabfb5WcFOKG0iNd316ckrkSpqanhlVde4YMPPkBEaG9vR0S49957ufPOOxk2bBhHH300xcXFqCpnnHEGDz/8cJ9fp6d9c3NzAQgEAh3Poz+3tbUB+89BnKz5nVV1IU7iG7vsrpjnCsyPs986YEbn5Sb5lmzchQgcMa50n+VHjC1lRHEuz6/YzvlHdO6pYozxlerVzmP5QXuXicAx/wVPXwMfPw/TzvIkNDMwVlNseqV291vk5R5AXl73TSeHlh5LU9NaWlrsjne6slqo9PParnpG5ASZXpjX47YnDStmfXMrm0OtKYgsMR5//HEuv/xyNm7cyIYNG9i8eTMTJkwgGAyydOlS7rnnHj73uc8BcOyxx/LGG2+wZs0aAJqamvj444+7PHZxcTH19fX92jeexYsXs379eiKRCI8++ignnHBCf96yGYSWbapl2shiSvL2bSIdCAhnTB/Jax9VEQrbtC7G+FrNGsgugJJOXQmPuNQZeOulH0K7jSafjjxJim0O1PSiquyufYvSobN7rPUYOvRYAGprF6ciNJMkqrpQVaeq6iRVvdlddle0Jsqd+3S+u/4wVV3iLl+nqjPcf4dE9zXJE1Hl9d31nDKsuFe1kicMLQLgn2lUW/zwww9zwQUX7LPswgsv5JFHHuGcc87h2Wef5ZxznK4d5eXl3HfffVx88cUcfvjhHHvssd1O4XTllVdy9tlnc+qpp/Z533iOO+44rr/+eg499FAmTJiwX9wmc63cVschBwyJu27OIaNoDrfzz9XVKY7KGNMnNWugbJJTOxwrKxvOvBmqPoS37oq/r/G1lDefjpkD9Qycfolvi8gCVY2d7iV2DtTZOHOgzo5Zf6qq2pkjRZqbNxEO76J0yFE9bltUNJ2srAJq9yxh5Miu+x8bYxJjTVMLu8LtfKK0qFfbTyvIY2ROkH/uqueS0WVJji4xXnvttf2WfeMb3+h4fscdd+yz7pOf/CRvv/12r4799a9/na9//es97rthw4aO51/4whf4whe+EHddQUEBjz76aK9e22SO6oYWqupbOHh0/AHujp1YRnFekOdXbOeM6SNTHJ0xptdq1uzbnzjWtLNhyhx47RY49ML9a5ONr3lRU9wxB6qqtgLROVBjdcyBqqqLgFIR2X/iTZMS9Q3O/Yri4kN73DYQCDKk5Ahqa60S35hUWFrXCMCRJYW92l5EOH5oMf+ubUBtQBBjUmLVtjoApo+OPzp8TjDAaQeN4OVVO2hrT8o0XsaYgWprhd0boWxy/PUicPYtTvPpl204lXTjRVJsc6CmmYb6FYgEKSqa2qvth5TOoqHhQ9ra0qd5pjHpalldE8VZASYX5Pa8sesTpUXsaG1jXXNLEiPzlwsuuICZM2fu8+/5559P6GuccsopPP300wk9phkcoknxwV0kxeA0od7dFGbxhl2pCsv4lIhMc7sJRv/Vicg1XseV8Wo3grZ3nRSD06/4yMud6Zls3uK04sXo0zYHapqpr19BYeFkAoHeXXSXDpkFKLV73mF42SlJjc2YTLesromZJQXdTsXUWbSp9b9rG5hU0PPgXOCMLZCskZRT4cknn/Q6hA5WQ595Vm2rZ1RJHkMLc7rc5uRp5eQGA7ywYgefmDQ8hdEZv1HVj4CZ0NHtcAvOFIfGSzXOIIzdJsXgDLr19j2w4m8w64tJD8skhhc1xTYHapqpb1hJcdH0Xm8/ZMhMRLLYY02ojUmqUHuElY3NHFFc0Kf9JuTnMConm3/vbujV9nl5edTU1FgylwCqSk1NDXl5vbsZYQaHVdvquuxPHFWQE+TEKeW8sGK7lTUT6zRgrar2bsJ1kzzRpHjYxO63Gz0DSipg7SvJj8kkjBc1xTYHahppaa2mtbWaouLeJ8VZWQUUFx1C7Z53khiZMWZ1U4g2hUP7mBSLCJ8YWsQbu+t7VQNcUVFBZWUl1hUlMfLy8qio6H56OzN4tLS1s2ZnA588aESP2845ZCQvrdrB+1v2cHhFafKDM+ngIiDu5OluN8IrAcaNG5fKmDJT9WooKIOCYd1vJwITT4aPFkIkAgGbATcdpDwpVtU2EYnOgZoF/DE6B6q7/i6cOVDn4syB2gRE2x6MBJ50L+CCwJ9tDtTkamp07ooVFk7p036lpUdTueVPRCItvW52bYzpmw8bQwAc1Iv5iTv7RGkRT+zYzbrmlh6bUGdnZzNhwoR+xWhMpltf3UhbRDmom/7EUacfPJKsgPD8iu2WFBtEJAc4D7gh3nrrKphiNWt7bjodNf5EWP6QM0XTyN5XLBnveHLrwuZATR+NTesAKCzooalIJ0NKjyISaaWu/oNkhGWMwUmKs0WYkN/3G0+x/YqNMcmzZqdTxiaX9zxt2tDCHI4ZP4xnP7Am1AZwpihdqqo7vA7E4M5R3MukePThzuOOFcmLxySU1eebbjU1rSMQyCc3d1Sf9nMG28KmZjImiT5qDDG5IJfsQN8HwOprv2JjTP+s3dnotKYs7920aefOOIB1VY18sKUuyZGZNHAxXTSdNinWUg8N26FsUu+2L5sCgWzYYZVD6cKSYtOtpsa1FBZMRKRvfyo5OWUUFEy0wbaMSaIPG5uZ1o+m07C3X7HNV2xMcq2paqBiaD552Vm92v5Th40mJyvAX5dWJjky42ciUgCcATzhdSwGp+k09L6mOJgD5QdZTXEasaTYdKuxaT0FBf3rS1g6ZBa1e95BNZLgqIwxDW3tVIbC/epPHJWJ8xUbk2prdzYwqRdNp6OGFGRz+vQRPPXuVsLtdv7MVKrapKplqrrH61gMMdMx9WGMnREHOX2KTVqwpNh0qb09RChUSUFhL5uKdFJaOou2tj00Nq5OcGTGmI/dQbb6W1MMe/sVv2FNqI1JikhEWVfd0Kv+xLE+fUQFNY2tvP6xjfhujC/UrAUEhvWhomjoeKjbAu3hZEVlEsiSYtOl5uaNgPZ5kK2o0tKjAWxqJmOSYHWTU7s7ZQBJcUe/Yhtsy5ik2FLbTCgcYdKIviXFJ08rZ1hhDk8s3ZKkyIwxfVL9MQwZC9n5vd9n6HjQCOzZnLSwTOJYUmy61Njk9J8o6GdSnJc3lpycEdTWvp3IsIwxwIbmFgLAuLycfh/D+hUbk1xrqpwbTn1pPg2QnRXgvBkH8OKqHexqbE1GaMaYvqj+GMqn9m2f0gOdx90bEx+PSThLik2Xmhqd6ZgKCsb3a38RobR0lg22ZUwSbGhuoSIvh5zAwL7GP1FaxM7WNtZav2JjEm5tdDqmPtYUA1wyexytbREeW2K1TMZ4KhJx+hQP72NSPNRNimstKU4HlhSbLjWHKsnJKScrq6DfxygdMotQy1ZCoa0JjMwYs765tV/zE3d2vNuv+LVd9QM+ljFmX2urGhhakM2wwr636Jg6spjjJpbxpzc30h6xlhzGeKZuC4SbYHgfBtkCKBkDgaDVFKcJS4pNl0KhLeTljRnQMTr6FVsTamMSakNzC+Pz+990OmpCQS4HF+bx9x21Aw/KGLOPdVWNTOxj0+lYlx93IFtqm3nlw50JjMoY0yfVHzuPfa0pDmRB0Sio3574mEzCWVJsuuQkxQcM6BhFRdPIzh5GdfUrCYrKGLM73EZtWzvjE1BTDHD+iKG8XdfI5pD1XTQmkTbvauLAYf1vbXXG9JGMHpLHA29uSFxQxpi+qXZnUelrUgxQNAIaLClOB5YUm7hUI4RC2wZcUyySxYgRZ1FV/TLt7U0Jis6YzLbe7f+biObTAOePLAXgbzt2J+R4xhhoaWtnW12IcWX9T4qDWQEuOWYc/1xdzfrqxgRGZ4zpteqPIW8IFJb3fd/iUVC/I/ExmYSzpNjE1dpajWrrgJNigJEjziUSaaaq6qUERGaM2djs1OiOLxh482mAA/NzOWZIIQ9tqyFio1AbkxCbdzWjCgcOICkG+NzRYwkGhD+/Zf0SjfFE9cdOLbFI3/ctGgkNlhSnA0uKTVyhkDM3Yn4CkuLS0lnk5VVQueWhAR/LGLO3pvjAvMTUFAP855jhbGhu5aWauoQd05hMtnmX0zpq3ACaTwOMKMnjzENG8pd3KgmF2xMRmjGmL6JJcX8Uj4KmamgPJzYmk3CWFJu4oklxImqKRQKMHfsF9uxZwp49ywZ8PGMy3frmFg7IzSY/K3Ff4Z8qL2VUTjb3VFYl7JjGZLKNNU5z53HDCgd8rM/PPpDapjAL39824GMZY/qgudap6e1vUlw0wnlssMHy/M6SYhNXIpNigANG/wfB4BDWrf81as0zjRmQTc2tjMtLTNPpqOyA8F9jy/nn7gbeqm1I6LGNyUSbdjVTkJPF8KKBl9VPTCpj3LACnly2JQGRGWN6rWaN89jvpHiU82hNqH3PkmITV3NoK8HgEILB/k8lESsYLGTChK+za9c/qa5+MSHHNCZTVYZaqUhwUgzwxTHDKc8J8rP1NlKm34nIWSLykYisEZHr46wXEbndXf+eiBzZaX2WiCwTkadTF3Vm2bSrkXHDCpD+9EPsREQ4d8Zo/r22huqGlgREZ4zplf5OxxRVPNJ5tKTY9ywpNnElYo7izirGXEZh4VQ+Xn0TbW02iqYx/dEWUba3hpOSFBdkBfjmgSP5d20Dr++qT/jxTWKISBZwJ3A2MB24WESmd9rsbGCK++9K4Led1n8TWJXkUDPaxpqmAfcnjnXujANojyjPfmA3rYxJmeqPIZANQw/s3/7RmmKbq9j3LCk2cSVijuLOAoEgB037CaHQFtatvy2hxzaJZbVQ/rWjNUy7wpi87KQc/7IDyhiXl8P/rq6kNRJJymuYATsGWKOq61S1FXgEmNdpm3nAA+pYBJSKyGgAEakAPgX8PpVBZxJVZdOuxCbF00YWM2VEEU8t35qwYxr/EpFSEXlcRD4UkVUicpzXMWWk6tUwbCJk9fOcWzjceWyqTlxMJiksKTb7UVVCoa0JrykGZyTqMQdczObN91FX917Cj28Gzmqh/G1LyJmOqSI38TXFALmBAD+dWsHqphZu32gDg/jUGGBzzM+V7rLebnMbcB3Q7V0PEblSRJaIyJKqKhuArS+q6ltoaYsMaI7izpwm1Afw9sZdbN8TSthxjW/9GnhOVQ8CZmDnVG9UfwzDp/R//2Au5BRB0+7ExWSSwpJis5+2tjra2xsSMh1TPJMnf4ecnOGs+vC7RCI2RL0PWS2Uj1W2OGVmTBKaT0edXlbChSOH8ssN23mhek/SXsf0W7xOqp1HMIy7jYicA+xU1Xd6ehFVvVtVZ6nqrPLy8v7EmbG21DYDMKY0P6HH/dTho1HFRqEe5ESkBDgJ+AOAqraqaq2nQWWi9jDsWjewpBigYBg01SQmJpM0lhSb/SR65OnOgsFipk29kYaGVWze/MekvIYZEKuF8rFoTXGymk9H/XxaBYcW5fOfH6znprVbWdNkNVM+UgmMjfm5AujcprarbY4HzhORDTg3vD4pIg8mL9TMtM2tyR09JLFJ8aTyIg4eXcIzlhQPdhOBKuBetyvS70Vkv7m97DyaZLs3QKSt/4NsRRWUWVKcBiwpNvsJhSoBEt6nONaIEXMoH34G69b/mqamjUl7HdMvVgvlY5WhVoZlZ1GYlZXU1ynMyuKvR0zmvBFD+c2mnZzw1oec+NYq/rXbBuDygbeBKSIyQURygIuABZ22WQBc7vb/PxbYo6rbVPUGVa1Q1fHufq+o6qUpjT4DbHVrig8ozUv4sc85fDTvbNzd8RpmUAoCRwK/VdUjgEZgv/E97DyaZNHpmMoGWFOcbzXF6cCSYrOf5iTXFEdNnXYjEGDtul8k9XVMn1ktlI9taQkzJkn9iTsrCWbxm+kH8vZx0/nplDEocMm763i3viklr2/iU9U24GrgeZx+ho+p6goRuUpErnI3WwisA9YA9wBf8yTYDLVtT4j87CyG5Ce+Rcc5h48GrAn1IFcJVKrqW+7Pj+MkySaVqlc7j2WTBnacgjJo3jXweExSeZIU28i2/hYKbSUQyCM7e1hSXycvdxTjxn2JnTsXsqfu3aS+lukTq4XysS2h1qQ3ne5sTF4O/1lRzoIjpzAsO8j/flyJaufGAyaVVHWhqk5V1UmqerO77C5Vvct9rqo6311/mKouiXOM11T1nFTHngm27WlmdGleQuYo7uzAskIOGzOEp96zpHiwUtXtwGYRmeYuOg1Y6WFImalmjZPQFgzwerigDJosKfa7lCfFNrKt/0XnKE7GybyzA8d9mezsYaxZ8zO7yPYJq4Xyt8pQa8pqijsblh3k2xNGsqSuiX/tbvAkBmPSwdbaEAckuD9xrE8dPpp3N9eyqcZabQxiXwceEpH3gJnAT70NJwPVrBl402lwkuKWOmhrHfixTNJ4UVNsI9v6XDLmKO5KMFjMhPFXU1v7FjW7/pGS1zQ9s1oof6pra6e+PUJFEkee7slnRg5jSDCLh7fbXW9jurJtTzOjhyS+P3HUvJkHEAwI97+5IWmvYbylqsvd/sKHq+r5qmpz+qRa9Woomzzw4xQMdR6b7VfoZ14kxTayrc8la47irowZczH5eeNYu/YXqHb7azUmo21tce4yj85NbfPpWPlZAc4fUcrCqlqa2q28GtNZuD3CzvoWRid4OqZYo4fk86nDR/PI4k3sarTaJ2MSLrQHGnfC8EQkxWXOow225WteJMU2sq2Ptbc3EQ7vStocxfEEAjlMmPANGhpWUV39Uspe15h0s6OlDfA2KQY4u3wIoYjaSNTGxLGjLoQqHJDEmmKA+adOpqUtwk1Pr7TuR8YkWqJGngZLitNE0IPXHMjItp/BGdl2LpAHlIjIgzaQT+KEQs6vIi+vIqWvO3Lkuazf8P+xfv0dDB9+Rkr6MxuTbra3hAEY5XFSfFxpEYVZAV6qqePM4UM8jcUYv+mYoziJNcUAU0cW87VTJ3P7y6tpbY9wzIRh7GkKU5KfzTmHj6asKDepr2/MoLbbnS506PiBHyvfHajLkmJf8yIp7hjZFtiCM0LtJZ22WQBcLSKPALNxR7YFbnD/ISKnAP9tCXFihTqmY0pNn+KoQCDI+PFfY9Wq71Bd8wrlw09L6esbkw52tDpJ8Ygcb5Pi3ECAk4cW88quOk/jMMaPOpLiJNcUA1x7+hRQ5a7X1/F0zGjU/98ra3j8quMYP7ww6TEYMyjVOdfDDElAJVG0ptimZfK1lDeftpFt/S1VcxTHM2rkPPLzxrFhQ+fBxo0x4NQUlwazyM/yfor5TwwtojIUpjJk/RmNiVVV3wLAiOLk19SKCN86cxrv33gmi//3ND6+6Wye/voJtEciXPvYcmtWbUx/7amEnGLIS0BrqOiUTo1WU+xnnlxZ2ci2/hUKbUUkSG7uiJS/diCQTcXYy6mrW0Z9vc24ZUxnO1rDjPS46XTUsUOcGqi3am1qJmNiVdW3kJ0lDMlPXVnNDWYxojiPnGCAQ8cM4fqzD2LZplr+ubo6ZTEYM6jsqXRqiRPRnS+YC9mFNvq0z3lf3WB8JRTaQm7uaJzppFNv9KgLCARy2bL1YU9e3xg/294SZpTHTaejDi7KpyQYYNGeRq9DMcZXqhtaGF6U6+nYGOcfMYZhhTn8+a1NnsVgTFrbszkxTaejCoZZ82mfs6TY7COVcxTHk51dyojys9mx4ykikbBncRjjR9tbwozM9WIoiP1liXDMkCIWWU2xMfuoqneSYi/lBrOYN/MAXvlwJ43uqPXGmD6I1hQnSv5QaLKk2M8sKTb7CIW2pHQ6pnhGjDibtrY6du9e5GkcxvhJRJWdrW2+qSkGmD2kkNVNLdS02kW3MVHVDS2Up6A/cU/OmD6S1vYI/1pjTaiN6ZPWJmek6EQnxdZ82tcsKTYdIpFWWlp2eDLIVqxhw04gK6uAqqrnCVBUQAAAUetJREFUPY3DGD/ZFW4nrOqbPsUAR5YUALC8vsnjSIzxD6f5dI7XYXD0+GEU5wb5x8dVXodiTHpJ5MjTUdZ82vcsKTYdWlq2A+p5UpyVlUdZ2SlUVb9kI2ca44pOx+T1HMWxZhQXIMDyOkuKjQGIRJTqhlbPm08DZGcFOPLAobyzwWqnjOmTPZXOY0JriodZ82mfs6TYdGj2aI7ieMqGnURraxWNjau9DsUYX9je4ibFPmo+XRTMYkpBntUUG+OqbQ7THlFfNJ8GOOrAoXy8s566kI3RYUyvJSMpLhgGoVqIRBJ3TJNQlhSbDiEP5yjubOjQ4wDYvfvfHkdijD/scJNiPzWfBphZks+yuiZr1WEMTtNpwBc1xeAkxaqwfFOt16EYkz72VAICxQmsJMofChqBlj2JO6ZJKEuKTYdQaCsAeXmjPY4E8vMryM8bx67db3odijG+sN1tPj0ixx+jT0cdUVJIdbiNLS1WE2VMVb2TFPulpnjG2FJEYJklxWlHRDaIyPsislxElngdT0bZUwnFoyCYwLEB8oc5j9aE2rf8dXVlPBUKbSEnZwSBgD9O5kOHHsvOqmdRjSBi929MZtveEqYsO0hOwF9lYWaxM9jWsromKvK8H1zIGC/5raa4KDfI+LJCPtxe53Uopn9OVVUbPjzVEj1HMTjNp8FGoPYxf11dGU85cxR733Q6akjpUbS11dPYtNbrUIzx3I7WMCN9VksMML0oj2wRltlgW8bsrSn2SVIMcNCoYj7cXu91GMakjz2VUJLg6+H8oc6jJcW+ZUmx6eAkxd4PshU1pOQIAOr2LPc2EGN8YHtL2Hf9iQFyAwEOLsrjgwZLio2pamghJytASb5/bmAdNKqEDTWNNNl84ulGgRdE5B0RudLrYDKGqjMlU6Jriq35tO9ZUmwAUI0QCm0nPy/BXwIDUFAwgWCwhD11y70OxRjP7Whp89V0TLEOLcrng4ZmG2zLZLzq+laGF+UgIl6H0uGg0cWowsc7GrwOxfTN8ap6JHA2MF9ETuq8gYhcKSJLRGRJVZXNR50QTTXQFoIhYxN73I7m05YU+5UlxQaA1tYqVFt91XxaJEBJyQzqLCk2Ga5dlZ2tYV9NxxRrelE+u8Lt7LCaKJPhqhtaGO6TQbaiDhpVDMBH1q84rajqVvdxJ/AkcEycbe5W1VmqOqu8vDzVIQ5OezY7j4muKc4bAog1n/YxS4oNEDsdk3+aTwMMKZlJQ8PHtLU1eh2KMZ6pbm0jAozwcU0xwAcNzR5HYoy3qupbfNWfGKBiaAHZWcL6auvikC5EpFBEiqPPgTOBD7yNKkMkY45igECWkxhb82nfsqTYANDsozmKY5UMmQlEqKt/z+tQjPHMDnc6plE+HGgLnJpigBX1lhSbzFbd0OKbkaejsgLCgWWFrK+25tNpZCTwLxF5F1gMPKOqz3kcU2boSIoT3HwanCbU1nzat/x5hWVSbu8cxf5KioeUzACgvu59hg09zuNojPHGDncO4JE+bT5dEsxiXF4OKxotKTaZKxJRahpbGV7sv6nJxpcVsr7aWlylC1VdB8zwOo6MtKcSgvl7+wAnUv4wqyn2MaspNoDTfDoYLCUYLPQ6lH1kZw8lL/cA6htWeh2KMZ7Z6fbV9WvzaXCaUFtNsclku5taaY+o75pPA0wsL2RDTRPtERsMz5hu7dkMQ8ZAMgbLyx9qfYp9zJJiA0AoVEm+z2qJo4qKp1Nfb0mxyVzR5tPlPm0+DU4T6nXNLTS2t3sdSkYQkbNE5CMRWSMi18dZLyJyu7v+PRE50l2eJyKLReRdEVkhIj9KffSDU3VDK4DvBtoCmDC8kNa2CFtr7caVMd3aU5n4/sRR1nza1ywpNoDTfNpvg2xFFRcfQlPTOhtsy2SsHS1hhgazyA349yv70KJ8FPiwIeR1KIOeiGQBd+JM1TIduFhEpnfa7GxgivvvSuC37vIW4JOqOgOYCZwlIsemIu7BrrqhBcB3fYrBSYoBa0JtTE/2JGGO4qj8YdBkNcV+5d8rLJMyqkootMV3/YmjiosPAZSGxg+9DiVjWC2Uv+xsbfN102mA6UV5gI1AnSLHAGtUdZ2qtgKPAPM6bTMPeEAdi4BSERnt/hwdcSnb/WdtahOgqt5Jist9WlMMlhQb0622FmjYnpxBtsCpKW6th/Zwco5vBsSSYkNbWy3t7U3+TYqLnAoQa0KdGlYL5T87WsOM9HHTaYCxeTkMCWaxwpLiVBgDbI75udJd1qttRCRLRJYDO4EXVfWt5IWaOfxcUzyiOJfCnCxLio3pTp0z6GzyaoqHOo/Wr9iXBpwUi8hfReRTImIJdpry63RMUbm5o8jOHkZ9/QqvQ0k7F154Ic888wyRSKQvu1ktlM/saAkzwqcjT0eJCAcX5rHSkuI+6WcZjTcCTOdy1uU2qtquqjOBCuAYETk07ouIXCkiS0RkSVVVVV/iy0hV9S3kBAOU5PnvBpaIMK6skI01lhSnml0np5HodEwlSboejibFNgK1LyWigP4WuARYLSK3iMhBCTimSaFQR1Lszz7FIkJx8SE0WE1xn331q1/lz3/+M1OmTAEY08vymZJaKLvg7h1Vpaq1jZE+bz4NzmBbqxpDRNTug/RWP8toJRDbvq8C2NrXbVS1FngNOCvei6jq3ao6S1VnlZeX9yKszFbV0EJ5US6SjFFrE2B8WQEba5q8DiMT2XVyutjjXtaUjkvO8aPTPNlgW7404KRYVV9S1c8DRwIbgBdF5N8i8kURiXsVZ/0V/cWvcxTHKi6aTkPjx0QirV6HklZOP/10HnroIZYuXQrQSi/KJymqhbIL7t7Z3dZOq6rvm0+DkxQ3tkfYHLJy2lv9LKNvA1NEZIKI5AAXAQs6bbMAuNw9nx4L7FHVbSJSLiKlACKSD5wO2IANCVDd0MrwIv/NURw1rqyAzbttWqZU6891svFIrZsUJ7um2JpP+1JCmnKISBnwBeDLwDLg1ziF/8U421p/RZ8JhbYQCOSTnT3U61C6VFw8HdUwjY2rvQ4l7dTU1HDfffcBDKeH8ulKSS2U6Z0dLc6AHH5vPg17B9uyfsV909cyqqptwNXA88Aq4DFVXSEiV4nIVe5mC4F1wBrgHuBr7vLRwKsi8h5Ocv2iqj6djPeVaarqW3w5yFbU+LJCwu1q0zJ5oC/XycZDezZB0UjIzkvO8fPdmmJrPu1LiehT/ATwT6AAOFdVz1PVR1X160BRnF2sv6LPREee9muTL4iOQG2DbfXVpz/9aU488USamprAKXc9lU+wWihf2dnaBpAWzaenFeYhwEqblqnX+llGUdWFqjpVVSep6s3usrtU9S73uarqfHf9Yaq6xF3+nqoeoaqHq+qhqvrjFLzNjFDd0OLLQbaiDhxWAMCmXdaEOpX6cZ1svFK7KXkjT4M1n/a5RNQU/15Vp6vq/1PVbQAikgugqrPibG+jZvpMKLSF/Hz/Np0GyM8/kKysIhtsq4++/OUvs3LlSm644QaAMPRYPq0Wymd2tDo1xSPToKa4MCuLCfm5rGq0mqje6k8ZNf7THlF2Nbb6Oyl2p2XaYINtpVpfr5ONV2o3Q2kSk+KcIghkW02xTyWik9pNOBfIsd7EaRYSz4D7KwIz3dqoJ0XkUFX9YL8XEbkSp+k148bt32E+HA5TWVlJKGQ1GtnBawhIPqtWrfI6lC7l5eVRXHQU9Q2WFPfF9773PebOndt5cXflE3BqoehUrqM1UO5zBebH2e894Ij+xmv2F20+nQ59isFpQm1zFfdef8uo8ZfdTa20R3Sf5tN+u85Qhd/PG01R9h5fn+/9IC8vj4qKCrKzE3Izsq/XySYB+lz+VOGYn0NuMSSzfJz5CGTnJ/c1MkCCyygwgKRYREbh1N7mi8gR7E1kS3CaiHQlYf0VReQ1nP6K+yXFqno3cDfArFmz9mtiXVlZSXFxMePHj/d1s+FkU22nvr6N3NyR5OaO8DqcuFSVmpoaGho+R3XNt1Ftx+mabrqyfft2tmzZQnNzM8uWLcPJYSkQkVPovnwan9nZGqYwK0BhMD3+5qcX5fN01R4a29rTJmYvWBkdXOLNUezH64ys7fXkZQc4sKzQ61B8K3rNUVlZyYQJE/p9nAFcJ5sE6HP5a2+FHS3OHMWFSRz8cycQzIVhE5P3GoNcospoZwOpepiDM2hABXBrzPJ64Lvd7NfRXxHYgtNf8ZJO2ywArhaRR4DZxPRXBMJuQhztr/iz/gQfCoV8daLySiTi1EIFAv4dMVNEKCsrY/v24UQizTQ1raewcLLXYfna888/z3333UdlZSXf+ta3oosrgG/Rffk0PrOjtS0tmk5HTS/MB+DDxhBHDbEL765YGR1cquudEddjR5/243VGbjBAS1uf5sTOONFrjgRMFdjf62STAH0uf+3O9TBZSb4eDmRBpD25rzHIJbCM7qPfSbGq3g/cLyIXqupf+7Bfm4hE+ytmAX+M9ld019+F08xkLk5/xSbgi+7uo93XzMLpD/3YQPor+ulE5ZXoFEfOeEr+JSJEZy6or19hSXEPrrjiCq644gr++te/cuGFFwIgIh+r6nkeh2b6aGdLmBFp0nQa9o5AvbKx2ZLiblgZHVyq3MHlOo8+7bfrjJxggIaWNlTVd7H5SSI+m/5eJ3eKIwtYAmxR1XMGHFSG6dPvsc1p7ZH0pFiC0N6S3NfIAMn4/hpI8+lLVfVBYLyIfKvzelW9Nc5u0XXWX9EnnAHAIRDwf02USJBAIJf6+hWMGtV5wHIT68EHH+TSSy9lw4YN3HprR1EcGS2r3ZVP4y87WsMcXpw+Le3G5uVQlBVghY1A3S0ro4NLR02xj6dkAicpjqjSFlGysywpTqaBXCfH+CbOgJcliY7PdJKqmuKsLAhbTbEfDaT6IVoFYMPJp7FIJAwiiPi/JkpEKCo6mLr6/bqQm04aG53RRRsaGmIXB4BiL+Ix/bczzZpPiwjTi/JZZYNtdcvK6OBS3dBCTjBAca6/z6U5QWfSkda2CNlZiZiAxHRjQNfJIlIBfAq4GadbhUmm9laQLKd5czJJECJtzsBe1lrDV/r9jaiqv3MffxTvX+JCHNyefPJJRIQPP0zcVK633XZbdM7LHkUirQQkJ6XNqJYsWcI3vvGNfu1bXDydhoaV0UFpTBe+8pWvAPDDH/6w4x+wzcpnemlsa6exPZJWzafBGWxrZUOzldNuWBkdXKrqWxhRnOvLJsmx1xm5biI80H7FfbnOyFQJuE6+DbgO6PKXJSJXisgSEVmS6P6VGae9NeG1xEVFce6HBIKAgva+DN5444384he/6NNr92efzn7605/u8/Ptt9/OwQcfzOc///ku93nttdc45xynpf99993H1Vdf3eW2d911F4cddhgzZ87khBNOYOXKlfsdI5UGfJtQRH4uIiUiki0iL4tItYhcmojgMsHDDz/MCSecwCOPPJKwY/blZKXamvJBtmbNmsXtt9/er32Liw6hra2eUGhzzxsbrrvuOurq6giHwwBTrXymlx2tbQCMzE2fmmKA6YV51LdH2Bxq9ToU37MyOjhUNbTs15/YL2KvM7KDAQSnpnggLCnuvf5cJ4vIOcBOVX2nu+1U9W5VnaWqs8rLkzhiciZoa3FGhU62aE20jwfbUlUikch+SfFvfvMbFi5cyEMPPZSQ17nkkkt4//33Wb58Odddd13soJOeSET1w5mqep2IXIAzldJngVeBBxNw7JT4+OOfUN+Q2PnCiosOZurU73e7TUNDA2+88Qavvvoq5513HrNnz+bee+/lscceA5w7Jb/85S956qmneOGFF/jhD39IS0sLkyZN4t577417B+r2229n69atnHrqqQwfPpxXX321y33Hjx/PZz5zJv/85zu0twt33303N9xwA2vWrOF//ud/uOqqq3jttdf4wQ9+QFlZGR999BEnnXQSv/nNbwgE4t9PKSoqYv78+bz00ksMHTqUn/70p1x33XVs2rSJ2267jfPOO4/XXnuNX/ziFzz99NPceOONbNq0iXXr1rFp0yauueaabmuRi4sPAaCufgX5+fvPP2329cILL/Dzn/+cJ598EqAVmEqalc9MtqM1OkdxmiXFRc4I1KsaQ4zL92ei4BdWRgeHnXUtHFjWdd//Hz21gpVb6xL6mtMPKOGH5x7S7TbxrjNu/+3d/O5e56I2FdcZl1xyCa+++irhcDhl1xkbNmzgsssu6+imcMcdd/CJT3yCJ598kjvvvJMXX3yR7du3c/LJJ/P6668zatSoAf42utWf6+TjgfNEZC6QB5SIyIOqajfM+uPZ62H7+91soNDa4NQUZ/XynDXqMDj7ll5tqqpcd911PPvsswgRvjf/cj73pWk0NLQyb948du/eTTgc5qabbmLePGfMnJtvvpkHHniAsWPHUl5ezlFHHdXl8W+//XbuuusugsEg06dP76hoW7lyJaeccsp+19e33norf/zjHwH48pe/zDXXXMOGDRs4++yzOfXUU3nzzTeZOXMmzc3NzJw5k0MOOYTi4mLWrVvHeeedx3/+539y/PHHc80119Dc3Ex+fj733nsv06ZN691n5yop2dtVvrGxcZ+WNg0NDXzmM5/hgw8+4KijjuLBBx9MekucRHQoiV6tzQUeVtVdCThmRvjb3/7GWWedxdSpUxk2bBhlZWUsWrSo40v80Ucf5XOf+xzV1dXcdNNNvPTSSyxdupRZs2bFDsyyj2984xsccMABvPrqq7z66qs97nvAASN4/fXnOPHEE/nCF77A448/zqJFi/jBD37Qsc3ixYv55S9/yfvvv8/atWt54oknunxPjY2NnHLKKbzzzjsUFxfzve99jxdffJEnn3xyn2PG+vDDD3n++edZvHgxP/rRj6I1JnEVFU1FJEh9/YpuP1vjiH6WCxcuBNhl5TO97Ghxfn/ladZ8+uBCdwRq61fcIyujg4Nfa4rjXWe8u3QJtXX1QGquM8aOHcubb76Z0uuMESNG8OKLL7J06VIeffTRjmTgggsuYNSoUdx5553813/9Fz/60Y+SnRBDP66TVfUGVa1Q1fE4U5e+YglxEkW7+khy+tk/8cQTLF++nHfffZeXFj7F/9z0a7Zt3UJeXh5PPvkkS5cu5dVXX+Xb3/42qso777zDI488wrJly3jiiSd4++23uz3+LbfcwrJly3jvvfe4666OcYvjXl+/88473Hvvvbz11lssWrSIe+65h2XLlgHw0Ucfcfnll7Ns2TLuvfde8vPzWb58OQ899BB33XVXR7m/9tprOeigg3j99ddZtmwZP/7xj/nud/s3y9idd97JpEmTuO666/ZpRbps2TJuu+02Vq5cybp163jjjTf6dfy+SMSV1lMi8iHQDHzNnUs4rYYd7alGN1kefvhhrrnmGgAuuuj/b+/O49y6ygTv/x6pVLtqcy1eykvF8V7ed7I6QMdJyMbygidAOt3TGZowE3j7HUjDdDczDXxIupvO0B2gE5YQGkIIEDpbQxIIiZ3NW+I98Vq2y2uVy7WopFoknfePK9nlclW5Vp17pef7+egj+epe6blyPdJ57jn33E/w5JNPsnbtWp555hk++tGP8txzz/HAAw/wyiuvsHv3bq644goAurq6WL169aDe48033xxgW8ONN16Lz5fN/PnzCYVCBINBgsEgubm5NDc3A7BixQouu8y5yPi6devYsGEDH/3oR/t8v+zsbNauXQvA/PnzycnJIRAIMH/+fOrq6vrc5qabbiInJ4ecnBwqKys5deoU1dXVfa7r8+VQUDCTNp1sa1BuvvlmZs+eTV5eHkCbF/Mzk51MFMUTPDZ8uiDLz7S8bHZpUXxJmqPe1x2L09TeNWBRfKke3bHSVztjzfs/yO9++xxz/+LTKWhnwC23OFcZS2U7o7u7m8997nO88847+P1+9u7de277f/mXf6G2tpZVq1axbt26Qe3jCHm+nex5l+rR7WiFpgMwbgbkjP78wRs2bGDdunX4/X6qJkzkmlVL2LRpEzdMmsaXv/xlXn31VXw+H8eOHePUqVOsX7+e22+/nfx8Z/RJMof6s2DBAu644w5uu+02brvttnPL+2pfb9iwgdtvv52CAmceuA9/+MOsX7+eW265halTp7Jq1apB7VNLSwt33nkn+/btQ0QG7NAayD333MM999zDz372M772ta/x4x//GHC+E5K1wKJFi6irq+PKK68c1nsM1oiLYmPMfSJyP9BqjImJSDug18u5hDNnzvCHP/yBnTt3IiLEYjFEhB/96Ec89NBDlJWVsXz5coLBIMYYPvjBD/L4448P+X0utW1OTjY+XwCfz0dOzvkfdJ/PRzTqnM/Ye7jCQMMXAoHAued7vmbP17s4hvPv6/f7+10vKRicR2Pj7/U6i4PwzW9+ky996UsUFRWRlZVlAM1PDznR2U2+30dx1hjPhjkG5hbksUcvy3RJmqPedybknDvvtp7i/toZ//zQv/Gd73yH2mkTU9TOON8OSFU745//+Z+pqqpi27ZtxONxcnNzz21/7NgxfD4fp06dIh6P9ztMe7SMtJ1sjPkj8McxCk8BRBO/VWN0TvEFk04mzyk2MX7605/S0NDAli1bCAQCTJs2jY4OJ5ahtG+fe+45Xn31VZ5++mn+/u//nl27nNGUfbWvB5oAM1koD8bf/M3fsGbNGp566inq6uq49tprB71tXz7xiU/wl3/5l+f+PdTaYDSM1jfBHODjIvJp4KPAn4zS66atX/7yl3z605/m8OHD1NXVcfToUWpqasjKymLr1q088sgjfPzjHwdg1apVvPbaa+zfvx+AcDh8wVHP3oLBIG1tbYPY1kkMkYEn2tq4cSOHDh0iHo/zxBNPjPmRmksJBufR3d1EZ+dJq3F4xZ49e3jiiScAxqH56SnHO7uZmBPw5MGfuYV5HIx0Eo6NbEKfTKA56m0NbZ0AVBS6qyjur52Rm5PNuzu38cj3v5+CdsbgjHY7o6WlhQkTJuDz+fjJT35CLOZMahSNRrnrrrv42c9+xpw5c/odIj4GtJ3sZrFOZ+i0b2xOVbr66qt54okniMViNJw5y6tvbWXFkoW0tLRQWVlJIBDg5Zdf5vDhw+fWf+qpp4hEIrS1tfHMM8/0+9rxeJyjR4+yZs0aHnjgAZqbm3tf6u+iWH7zm98QDodpb2/nqaee4qqrrupz3UAg0G8PcEtLC5MmTQKcWaaHY9++feceP/fcc8yYMWNYrzNaRvy/LyI/AaYD7wDJqdQM8NhIXzudPf7449x3330XLPvIRz7Cz3/+cz70oQ/x6KOPnhtCUFFRwaOPPsq6devo7HR+fL/2ta8xc+bMPl/77rvv5oYbbmDChAm8/PLL/W7r9LT6EBm4F2r16tXcd9997Nixg6uvvprbb799pLs/IkWJybba2naRmzvBaixu96lPfYoDBw6waNEigHxgOZqfnnGis8tzQ6eT5hbmYoB32yMsKRr80edMoznqfQ2JERFu6ynur53xm189yVXvv55nf/k4//4T589srNoZgzXa7YzPfvazfOQjH3GGi69Zc64H7Bvf+AZXXXUVV111FYsWLWL58uXcdNNNzJkzZ0TvNxBtJ3tAcubpMToAffvtt/PGG2+wcOFCRIQH/tcXGF9Rxh133MHNN9/MsmXLWLRoEbNnzwZgyZIlfPzjH2fRokVMnTq136IVIBaL8clPfpKWlhaMMXzhC1+gpKSk3/WXLFnCn/7pn7JixQrAmWhr8eLFfZ7iePfdd7NgwQKWLFly0YzTX/ziF7nzzjv51re+xXXXXTf0DwVnAryXXnqJQCBAaWnpubrHFhnpdSRFZA8w17j4gpTLli0zmzdvvmDZnj17xvRL0AvC4cPE450UFvb/w9Vzpmjbkv9nsViYP76ykJppn+Oyy+61HZarzZkzh927dyMiiMgWY8wy2zH1pa8cVbD09V1cUVrIt+dMtR3KkNVFOln15h7+adZk7pg4znY4rqU56n1PbDrCl361gw1fWkN16fkZqN3azojFDbuOtzC+KJfKotxLbzDG3NTO6Kmv/7/h5Giq2smaoxcaUv6d2gWBfCirGdugkk7uhJwglHrvt91NRitHk0Zj+PROYMyn7lOjL27hGsWjwe/Pp6BgOm0hnYH6Umprazl5UoeZe1HMGE52dTMxx3s5CjAlN5sCv09noL4EzVHvSw6fLnfZ8On++H1CwO+jc4TXKlaDpu1kNzNxiHWl5hrFSb4siI/9ObJqaEZj8Hw5sFtENgKdyYXGmIGnSlMjdvvtt3Po0KELlt1///1cf/31g9rexLvwBQYe1njttdf2efL8ypUrzw2TSvrJT37C/PnzB/XeIxUMzqOp6TWdbOsSGhsbmTt3bnKYzOUi8jRofnrB6a5uYsZ7M08n+USYU5CrM1Bfguao9zW0dVKUm0VuwDsT4mX7fXQNoigeaTtjMNzazhhF2k52s6gzUR5ZKRw14fNDPHbp9Xq55557Lro00b333stdd901WpGNia9//es8+eSTFyz72Mc+xle+8hVLEfVtNIrir47Ca1jh9YLqqaeeGva28XgUY+KIDK/B/dZbbw37vYej96ij4qIlnDz5Gzo66snLm5zSWLzkq1/96rnHL7zwwkngn6wFo4bkRIczucVEjxbF4Ey29R+nmz3/XTuWNEe973Rb/9coduvffnaWj1DnpXuqRtLOGKlUtzN6GuWRzl8dzRdTgzeo/IsmDtymtCjOgu6hHzB+6KGHxiCYsfeVr3xl1AvgsTgbYcTDp40xrwB1QCDxeBOwdaSvO9Zyc3M5c+bMmHyoXhA3zpExn8/9w72MMZw5c+aCSyoUlywFoKXF9X9qVl1zzTVMmzYtOXtgCI/kp3JmngaYmOvN4dMA84N5tERj1EW6bIfiWpqj3tfQT1Hs5nZGdpaP7liceNx9sdnWV5tjhK/nyXay1w06/7qTl2NKcVFsht5TrByjnaNJozH79F8AdwNlOLPrTQK+B7x/pK89lqqrq6mvr6ehocF2KFbEYmG6u8+SnW3w+dzfE5Wbm3vuIt4AhQUz8PsLaW7ZyvjxeknP/jzyyCM8/PDDNDU1JRd5Ij+Vc41i8O7waeDcrNNbW9upyXf/ATgbNEe9ryHUyYLqkouWu7mdEe6K0dTeBc05BPxje51eL+rd5hgJr7aTvW7Q+dfeCLFuaHkvNYEBdDRDRxs0+cdsxut0N5o5mjQaw6fvAVYAbwEYY/aJSOUovO6YCgQC1NSkaJY5Fzp46F84dOj/cu01u/D7vddYFfFTXLSIlpYttkNxtYceeoiNGzeycuVKwDv5qeB4Zxe5PqE0yzvnKfY2Kz+XPJ+Pra1hPjK+zHY4rqQ56m3GGE63dvZ5jWI3tzO21zfzF4+/xnfvWMINc/TShmPMk+1krxt0/v3rChh3Oaz72dgHlfTGQ/C7L8OX6iCvNHXvqwY0GocHO40x58bGiUgWzvXXlItFInXk5kzwZEGcVFyylFDoPaLRNtuhuFZOTg7Z2eeH32p+eseJzm4m5ARceT7iYGX5hIXBPLa2hm2H4lqao97WGokS6Y4xodj+pY2GYmZVEL9P2H2i1XYomUDbyW4V7YSmA1A5O7XvmyyEI2dT+75qQKNRFL8iIl8G8kTkg8CTwDOj8LpqDEXCh8nL9/b10YqLlwBxWlreth2Ka11zzTV84xvfIBKJABSh+ekZxzu7meDRyzH1tKSogF2hCJ1xvfxLXzRHve1EqzNZzoQSbxXFuQE/MyoL2XGsxXYomUDbyW51Zr9zaaTKual93/xxzn24aeD1VEqNRlF8H9AA7AD+G/A88L9G4XXVGApHDpOX5+2iuKR4KSLZNDVtsB2Ka33zm9+koqIieQmLcjQ/PeNoRxfVud49nzhpSVE+Xcawq00vzdQXzVFvO9HiTNLjtZ5igHkTi9l5rMWVE4GlGW0nu9XpPc59RYp7igsqnPvQ6dS+rxrQaMw+HQd+A3zWGPNRY8wjRr9hXS0abaO7u4l8jxfFfn8eJSXLtCgegM/n47bbbuM73/kOwEHNT2/oiMU52dnN1Fzvnt6QtKQoH4CtbTqEui+ao952MlEUjy/OsxzJ0M2fVERjqItTrZ2XXlkNm7aTXazhXRA/lM9I7fsWVjn3oVOpfV81oGEXxeL4qog0Au8C74lIg4j87eiFp8ZCOHIYwPPDpwHGlV1JqP09Ojv1aFtPxhi++tWvUl5ezuzZs5k1axbAQs1Pb6jv7MIAU/K8P3x6Ym4247MDel5xLyPNURFZKyLvich+Ebmvj+dFRL6deH67iCxJLJ8sIi+LyB4R2SUi947unmWWEy0diEBlP9cpdrPaScUAOoR6jGg72QNO74GyyyArxfmb7Clud9/M9JlsJD3FnweuAJYbY8YZY8qAlcAVIvKF0QhOjY1IOFEUe7ynGKCs7EoA7S3u5cEHH+S1115j06ZNnDlzJnm5lz0MMj+1wW3XkcR1fad4+BrFPS0tzmdjS8h2GK4ykhwVET/wEHADMBdYJyK9T4q7AZiRuN0NfDexPAr8lTFmDrAKuKePbdUgnWiOUBn05mWNaicVk+33salOz2scI59nmO1kEckVkY0isi3xW/q/UxBv5jmxHcbPT/37ZmU7k21pT7GrjORb/NPAOmPMoeQCY8xB4JOJ55RLRRI9xfl5UyxHMnKFhXMIBMo40/Sq7VBc5bHHHuPxxx/vfTmCLgaRn9rgtu9IR6IoToOeYoDVJYXUd3RztKPr0itniJHkKM7lXfYbYw4mZrX9OdD7gu23Ao8Zx5tAiYhMMMacMMZsBTDGtOEU4pNGYZcy0snWDk8OnQZnsq3FU0p4/UCj7VDS1UjayZ3AdcaYhcAiYK2IrBqrQDNSuAlajsCEhXbev6BSi2KXGUlRHDDGXPRNaoxpALw/O0waC0cOk51did+fbzuUERPxUV7+fhobXyYe1/Oikrq7uykvL79o+SDzUxvclh2JdJHjE6qy0+OrdHVJIQBvNmtvcdIIc3QScLTHv+u5OM8uuY6ITAMWk7h+am8icreIbBaRzQ0NOsyvLydaOphQ5L1JtpLeN72cXcdbaQl32w4lHQ27nZz4bU1+YQYSNz0PeTSd3O7cT1hg5/0LKyGk36tuMpKieKBD/gN2B+jQTLsi4cOen2Srp8rKtcRiIZqaXrcdimv0vO5pHy7VXacNbsuOdHRSnZONz8PXKO5pdkEuxVl+LYp7GGGO9vWH0bvBPOA6IlII/Ar4vDGmz4vVGmMeNsYsM8Ysq6iouERImccYw8mWDs9djqmn1dPHYQy8cfCM7VDS0bDbyeCM2hKRd4DTwIvGmIt+S/V3dAROJIri8ZZ6igurtKfYZbJGsO1CEenrh1SAfn8hegzN/CBOQ3qTiDxtjNndY7WeQzNX4gzNXMn5oZlbRSQIbBGRF3ttqy4hHDnMuHFX2w5j1JSVvo+srCJOn/5PysvX2A7HFbZt20ZRUVHvxYtFpI0B8jMhZQ1u4GGAZcuW6RHwHo50dKXN0GkAvwgrigt4o7nddiiuMcIcrQcm9/h3NXB8sOuISAAnP39qjPn1EENXCS2RbkKdUSaVeHP4NMCiySUEc7J4+d3TrK0dbzucdDOsdnKSMSYGLBKREuApEak1xuzstY7+jg7XiW1QVA0F4+y8f2GlTrTlMsPuKTbG+I0xRX3cgsaYgYaF6NBMi2KxMF1dp9Oqp9jny6ai/AM0NL5IPK7nLALEYjFaW1svuAFvDyI/QRvc1h2JdKXNJFtJq0sKORjp5FSnDtOEEefoJmCGiNSISDbwCeDpXus8DXw6MfJqFdBijDkhIgL8ANhjjPnWKO9WRjnS5MyoPrnMu6ciZWf5WDO7khf3nCIai9sOJ62MoJ3c+3WagT8Ca8cq1ox0cru9odPgFMVdIejSg8VuYWO6RB2aaVE4cgRIj5mne6qsvJFotFVnoR4d2uC2qLk7SnM0xtQ8713iZSCrSgoAeEOHUI+YMSYKfA74Hc7B4V8YY3aJyGdE5DOJ1Z4HDgL7gUeAzyaWXwF8CrhORN5J3G5M7R6kh2RRPMXDRTHA2trxNLV3sfnwWduhqAQRqUj0ECMiecAHcC7rpEZDVzs07rM3yRY4E20BhPSSom4xkuHTw6VDMy06dzmmNLhGcU9lZVcSCJRy8tTTlJdfZzscTzPGREUk2eD2Az9MNrgTz38Pp8F9I06DOwzcldg82eDekTgXCuDLxpjnU7gLnnYg7EwYd3l+ehXFCwrzyff7eKM5xG1VpbbD8bxETj3fa9n3ejw2wD19bLeBvn9j1RClQ08xwDUzK8jO8vG7XSdZdZmloaSqtwnAjxOnHPpwDnw9azmm9HFqF2BgvM2e4irnPnQaymoGXlelhI2iWIdmWhSJ1AGk1fBpAJ8vQGXlDZw48RTRaDtZWQW2Q/I0bXDbsz9RFE9Ps6I4yyesKi5gw1ntKVbp4WhTmHEF2RTm2GhKjZ6CnCyunlHO73ae5G8/NBdJkwn+vMwYsx1nNKQaCye2OfdWh08nJi/UybZcw8bwaR2aaVE4cphAoIysrKDtUEZdVdUtxOMRGht/bzsUpYZtf7iDgAhTctOrKAa4blwRByKdHI7o5dOU9x1tini+lzjpT+aN53hLBzuP9Tn4Tqn0Ur/Z6aktsjgt0bmeYi2K3SLlRbGeC2VXul2OqaeS4qXk5Ezg5Knex1iU8o4D4U6m5WUT8KVfb82aMudg3B+a2ixHotTIHWkKe/584qQPzKnCJ/C7XSdth6LU2KvfBNXLweaoiIIKED+0nbAXg7qAjZ5ijDHPG2NmGmOmG2O+nlj2veTwzMSs0/cknp9vjNmcWL7BGCPGmAXGmEWJm56rOAThSF3anU+cJOJjfNXNNDWtp6uryXY4Sg3LvnBH2g2dTrosL4cpudm8fEZ7o5S3RWNxjjVH0qYoLivIZkVNmRbFKv2Fm6DpAFQvsxuHzw/BCdByzG4c6hwrRbGyIxptp7PzJPn5l9kOZcxUVd2CMVFON/zWdihKDVk0bqiLdHF5/iUvYelJIsKasiAbmkN0xvXyL8q7jjSFicUNU8elR1EMcP288ew7HeJgg573r9JY/Wbnvnq53TgAiidBqxbFbqFFcQY5N8lWGhfFhYWzKSiYwamTOoRaec/Rji66jUnbnmKA948rIhyLs6lFr82ovGv/aadwnFGVPvNz/Mm88QD8bpee46jSWP0mEB9MdME8ZkVaFLuJFsUZpD18EICCNC6KRYSqqptpbtlER0fvSc2Vcrd94Q4AZqRpTzHAFSWF5PiEFxp1CLXyrn2Jonh6Rfpc6WBSSR7zJxXrEGqV3uo3QdU8yHZB7hZPgtbjYPTKsW6gRXEGCYcPAUJemk60lTS+6mYATp16xnIkSg3NvjS9HFNPBVl+ri0L8mxDM3FtCCiPOnA6xITiXIK5AduhjKoPzq1iW30zZ0I6Q7xKQ/E4HNvijqHTAEXVEO2A8BnbkSi0KM4o4fBBcnMn4fenby8UQF7eFIqKFnNSi2LlMXtCEcZnBygNePu6p5dyc0UJxzu72dIath2KUsOy73SIyysLbYcx6q6ZWYExsGF/o+1QlBp9p3ZAZytMXmU7EkfRROe+pd5uHArQojijhMMHyc+vsR1GSoyvuplQaA+h0F7boSg1aHvaI8wpTO+DVgDXlxeT4xOeOd1sOxSlhiweNxxoSM+iuHZSMSX5Adbv06JYpaFD6537mqvsxpFUnLhOcque7ucGWhRnCGMM4XBdWk+y1VNl1U2AT4dQK8/ojhv2tncytzDPdihjLqhDqJWHHW+JEO6KpWVR7PcJV1xezvp9DRjNTZVu6tZD2fTzPbS2FVU79zrZlitoUZwhOrtOEYu1p/UkWz3lZJdTWrKCxjMv2w5FqUHZH+6g2xjmFqR/TzHArZWlHO/sZqPOQq08JjnJ1uUV6VcUA1w9o5xTrZ3n9lOptBCLwuHX3dNLDFBQAb6ADp92CS2KM0S43Zl5OlOGTwOUlV1BKLSHri6dwEC53552Z+bpTOgpBrh+XBF5Ph+/PnXWdihKDcnu487M6bMnFFmOZGxcOaMCgFf3NliORKlRdGKbcz5xzdW2IznP53N6rbWn2BW0KM4QzszT6X2N4t5Ky64A4OzZNyxHotSl7QlFCIik9czTPRVk+bmhophnTjfTFY/bDkepQdt1vIUpZfkU56XXzNNJk0rymF5RoOcVq/RS96pzP81FPcUAJVPgbJ3tKBRaFGeMcPggfn8+OTnjbYeSMkXBWrKygjQ1vWY7FKUuaXeogxn5OWT7Mudr+cNVpZyNxni5qc12KEoN2q7jrcybmJ69xEmrp49jc10T0ZgesLJBRCaLyMsiskdEdonIvbZj8rxD66FiNhRW2o7kQmU10HTIdhQKLYozRjh8kPy8GkTEdigpI+KntGQVTdpTrDxgT3skY4ZOJ11TGqQs4OdXOoRaeURrRzeHz4SpnVRsO5QxtbJmHO1dMXYlhoqrlIsCf2WMmQOsAu4RkbmWY/KuaBccedNdQ6eTSmsg3AidenDYNi2KM0R7+FBGnU+cVFKygo6Oo3R2nrYdilL9Otsd5XhnN3MyrCgO+IRbK0t5obGFtmjMdjhKXVLyfOK5ad5TvLKmDIC3DumcHDYYY04YY7YmHrcBe4BJdqPysONbobvdfUOnwekpBu0tdgEtijNALNZJR0d9Rp1PnFRcvBiAlpa3LUeiVP/2hBKTbGXIzNM9faSqlI644fmGFtuhKHVJyZ7T2onp3VNcWZRLTXkBGw812Q4l44nINGAx8JblULzr0HpAYNqVtiO5WGmiKD6rRbFtWhRngEikDjDkF2ReURwMzsXny6alZYvtUJTq1+72CEDG9RQDLC3KZ2puts5CrTxh17EWKoM5VATTf0K8lTVlbDzURCyu1yu2RUQKgV8BnzfGXDSWXUTuFpHNIrK5oUFnC+9X3atQVQv5ZbYjuZj2FLuGFsUZIBR6D4DCgpmWI0k9ny+HYLCWllbtKVbutSsUoSzgpyo7y3YoKScifLiqlPVn2zjd2W07HKUG9E59Mwuq07uXOGnlZWW0dkR596SeV2yDiARwCuKfGmN+3dc6xpiHjTHLjDHLKioqUhugV3R3wNGN7jyfGCC3GPLKtKfYBbQozgDt7fsQ8ZOfP812KFYUFy+htXUn8Xin7VCU6tPOtggLCvMzaiK8nj5cVUoceLah2XYoSvWrOdzFwYZ2Fk8ptR1KSqysGQegQ6gtEOfH4AfAHmPMt2zH42n1myDaATUuPJ84SWegdgUtijNAe/s+8vKm4fOl/3CvvhQXLcaYLtpC79oORamLdMbjvNvewfxg5g2dTppRkMuU3GxePauzbyr3evtoMwCLp5RYjSNVJpbkUV2ax1sHtSi24ArgU8B1IvJO4naj7aA8qW49iA+mvs92JP0rrdGeYhfQojgDhNr3ZuTQ6aRgsBaAtrZdliNR6mLvtXfQbQzzg/m2Q7Hq6tIgrzeHiOr5i8ql3j7SjE9gYXWJ7VBSZmXNODbWNWGM5mUqGWM2GGPEGLPAGLMocXvedlyedOBlmLjYGabsVmU10FLvXDpKWaNFcZqLxTqIRI5QUDDDdijW5OZOIiurWIti5Uo72pxJthZkcE8xwJWlhbRG42wPhW2HolSf3j5yllnjiyjIyZxz/1fUlNLU3sWBhnbboSg1dJFmOLYFpl9nO5KBlU0HE4ezdbYjyWhaFKe5cPgAYCgozNyiWEQIBudqUaxcaXtbmKIsH1Nzs22HYtWVpUEAXjsbshyJUheLxw3vHGlmSYYMnU5aPs2ZrXdTnQ6hVh5Utx5MDC5bYzuSgVUkRnM2vmc3jgynRXGaC4X2AmR0TzFAMDiP9vb3iMd1dlvlLjtCEWozeJKtpPLsLKbn5bClVXukBkNE1orIeyKyX0Tu6+N5EZFvJ57fLiJLejz3QxE5LSI7Uxu1d+1vCNHWGc2YSbaSasoLKC/MZpNOtqW86MDLECiA6uW2IxlYeaIobtCi2CYtitOcM/N0gPy8abZDsSpYOI94vIv28AHboXiCNrhTIxo37A5FMnqSrZ4WF+WztTWs5y9egoj4gYeAG4C5wDoRmdtrtRuAGYnb3cB3ezz3KLB27CNNH28fca6jnWk9xSLCsqllbDqsRbHyoAN/cGadznL5SKycIBRNgsa9tiPJaFaKYm1wp057+z7y82vw+QK2Q7Hq/GRb+mdzKdrgTp194Q464oYFhVoUAywtLuB0V5R6vV7xpawA9htjDhpjuoCfA7f2WudW4DHjeBMoEZEJAMaYVwGtcoZg6+FmSvID1JQX2A4l5ZbXlHG0KcLJlg7boSg1eE0HnRmd3T50Oql8pvYUW5byolgb3KkVat+b8UOnAfLzp+H3F+h5xYOjDe4U2RFyJtmqzfCZp5OWFDmfw1YdQn0pk4CjPf5dn1g21HXUIL199CyLJ5dk5GkOKxLnFW/U84qVl+x51rmf5ZGSoWIWNO6DeNx2JBnLRk+xNrhTpLu7hY6OeoLBebZDsU7ER2HhbC2KByclDW4RuVtENovI5oaGhmEF6nU72sLk+YTL8zPzGuK9zS3II9cnbG3VGagvoa/KrPeY88GsM/CbaI4C0NrRzb7ToYw7nzhpzoQgBdl+Pa9Yecuep2H8AiidZjuSwSmfCd3t0HrMdiQZy0ZRrEe4UyRZAGpR7AgG5xIKvYcxehTuElLS4DbGPGyMWWaMWVZRUTGUTdPGjrYI8wrz8Gdg71NfAj6htjCPd7QovpR6YHKPf1cDx4exzoA0Rx3bjjZjDCzJ0KI4y+9jydRSnYFaeUfDXqjfBHN797m5WMUs515noLbGRlGsR7hTpC2UKIoLe49Oz0zBwrnEYiEikSO2Q3G7lDS4M13cGHaEIszXodMXqA3msysUIa6TbQ1kEzBDRGpEJBv4BPB0r3WeBj6dmKNjFdBijDmR6kDTwdbDzYjAwsnFtkOxZvm0Mt471UZLWM/3Vx7w2oOQlQdL/9R2JIN3bgZqnWzLFhtFsR7hTpG2tl3k5EwgO7vMdiiuUFg4B4C20B7LkbieNrhTYF+4k/ZYnIU68/QFagvzCMXiHO3osh2KaxljosDngN8Be4BfGGN2ichnROQzidWeBw4C+4FHgM8mtxeRx4E3gFkiUi8if57SHfCYt4+eZWZlkGBu5k5YuXxaGcbAliPaW6xcrvkobH8Clt4JBeW2oxm8ggrILdEZqC3KsvCe5xrcwDGcBvd/6bXO08DnROTnwEq0wT0sbW27dOh0DwUFMxHxE2rbTVXlDbbDcS1jTFREkg1uP/DDZIM78fz3cBrcN+I0uMPAXcntEw3ua4FyEakH/s4Y84PU7oX7JSeTWlqUebPZDmReYibunaEIU/P0XOv+GGOex8nDnsu+1+OxAe7pZ9t1Yxtd+ojHDW8faeaG2vG2Q7Fq8ZQSAn5h46GzXDe7ynY4SvXv9X9x7ld/zm4cQyWSmGxLi2JbUl4Ua4M7NaLREOHwIaqqbrEdimv4/Tnk50/XnuJB0Ab32NvaGqYoy8d0nWTrArMLcvEBO9si3FRRYjscleEOnWmnJdKdsecTJ+UG/MyfVKznFSt3a2+ErY/Bgo9DyeRLr+82FbOdCcKMcYpklVI2eoq1wZ0CodC7gKFIe4ovEAzO5WzTG7bDUIqtre0sDhbg0x++C+T5fVyen8uuxOWqlLJp6+GzgNNTmumW15Txww2H6OiOkRvw2w5HqYu99T2IdsAVn7cdyfBU1cLWH0PrcSjW+YVTzcY5xSoFWlrfBiBYtMByJO4SLJxLZ9cpuroabYeiMlh7LMaeUMe56/KqC9UG87QoVq6w9UgzwdwsplcU2g7FuuVTy+iOGd452mw7FKUu1t0Bm34As2+Cipm2oxme8bXO/amdduPIUFoUp6mW5i3k5U0hJ9tDkwykwPnJtt61HInKZNtaI8RBi+J+zCvM41hnN03dUduhqAy3ua6JpVNL8fl0RMeyac4Qcr1e8dgTkR+KyGkR0eposHb9GiJNsOJu25EMX1VidOfJHXbjyFBaFKchYwzNLVspLl5iOxTXCQadojjUtttyJCqTJSfZWqKTbPWpNjHZ1m7tLVYWnW3vYt/pEMun6RUcAErys5lVFWRTYki5GlOPAmttB+EpWx6F8llQc7XtSIYvtxhKpmhPsSVaFKehSOQI3d1nKC5eajsU1wkESsnJmaCTbSmrtraGmZaXzbhsK9M6uN7cwlzAmWxLKVs2J4o/LYrPW15TytbDZ4nF9TriY8kY8yqgXfKD1XoCjr4FCz7m/QmqqubDSS2KbdCiOA21tGwFoESL4j4Fg/No055iZYkxho0t7SzTXuJ+VWQHGJ8dYKf2FCuLNtc1ke33saC62HYorrF8Whmhzih7TrTaDkUBInK3iGwWkc0NDQ22w7Hn3Wed+zlpcMWV8bXQdAC6wrYjyThaFKeh5uaNZGUFKSi43HYorhQsnEM4fJBYTBvcKvX2hTtp7I7yvhKduGcg8wrztChWVm2sa2JBdbHOtNzDihqn13yjnlfsCsaYh40xy4wxyyoqKmyHY8+eZ6B8pnOdX6+rqgUTh9M6ojHVtChOM8YYmpo2UFq6GhH9Ie9LYXAOECfUrhdIV6n3enMIgPeValE8kAXBPPa2dxCOxW2HojJQpCvGzmMtLK/RodM9TSjOY3JZHm8cPGM7FKUc4Sao2wBzbrYdyeg4NwO1TraValoUp5lIpI6OzuOUlV5pOxTXChbOBXSyLWXH680hJuYEmJqbbTsUV1sYzCeOTral7HjnaDPdMcPyxIzL6rwrL6/gjQNn6NYDVsoN3nseTCx9iuKSaZBbAse22o4k42hRnGbONG0AoKzsCsuRuFdubjV+fyFtIS2KVWoZY3j9bIj3lRQiXp8MZIwtLHJmoH6nTc+rUqn3xoFGfAJLp2pPcW/XzCwn1BnV6xWPIRF5HHgDmCUi9SLy57Zjcq09z0DxZJiwyHYko8Png+plUL/ZdiQZR4viNNPUtIHc3Gry8qbaDsW1RIRgcC5tbXq+hkqtvXo+8aCNzw5QkZ3FNi2KlQXr9zeyoLqE4ryA7VBcZ/X0cvw+4dW9GTyx0xgzxqwzxkwwxgSMMdXGmB/YjsmVOlrhwB9g9oe8P+t0T5OWwend0NlmO5KMokVxGonFIjQ1bWDcuGu1F+oSgoVzCYXexZiY7VBUBvnDGWfG1qvLgpYjcT8RYWEwn+16WSaVYi2RbrYdbeaqGeW2Q3Gl4rwAiyaXaFGs7Nv3AsS6YO6ttiMZXdXLAaNDqFNMi+I0cqbpVeLxDiorrrcdiusFg/OIxyOE2vfZDkVlkJfOtDKnIJdqPZ94UBYG89jX3kF7VA9eqdR548AZ4gauvFyL4v5cNaOc7cdaONveZTsUlcl2/wYKx8PklbYjGV2Tljj39ZvsxpFhtChOIw2nXyArq4SSkhW2Q3G9kpLlADQ36xeOSo3WaIy3WkJ8YFyR7VA8IznZll6aSaXSK3sbyM/2s3iKTrLVn6tnVmAMbNjfaDsUlam62mHfS84EW740K2fyy2DcDDj6lu1IMkqa/RVlrlisk8Yzv6ei/Dp8vizb4bhebm41OTnjaW7eaDsUlSH+2NRG1KBF8RAsDOYDsLVVzytWqRGLG17cfYprZ1WQnaVNpP4smFRMUW4W6/fpEGplyb4XIBpJv6HTSTVXQ91rENXRGKmi3/hporHxJaLRNqqqbrEdiieICCUlK2hu3oQxxnY4KgM8c7qZ8kAWS4sKbIfiGVU5AS7Ly+GNxLWdlRprW4+cpTHUydraCbZDcbUsv48rZ5Tzyt4G4nH9DVUWvPO4M3R6ymrbkYyN6Wugu12HUKeQFsVp4sSJX5KTM56ysvfZDsUzSktW0NXVQCRSZzsUlebaojFePNPCrZUlZPl0EryheF9JIW+2hIjpwSuVAs/vOEG238eaWRW2Q3G9D8yp4lRrJ9vqm22HojJN63HY/yIs+i/gT9PRkdOuAvHBwZdtR5IxtChOAx2dJznTtIEJ429HxG87HM8oKXEmZmhqes1yJCrdPd/QQkfc8OEqPUdxqN5XWkhrNK7nFasx1xmN8R/vHOe62ZUEc/VSTJfy/tlVZPmE3+48aTsUlWne+RmYOCz+pO1Ixk5eiXNppr2/sx1JxtCiOA0cq/93wDBhwsdsh+Ip+fk15OfX0NDwou1QVJr7xckmpuRms6Qo33YonpO8pvP6Jr1eoxpbL+4+RVN7F+tWTrEdiicU5wd43+Xl/HbXST0NSaVOPAZbH3N6UsdNtx3N2Jp7C5zcDmcO2I4kI2hR7HGxWJj6Y49TUfFB8vOn2g7HU0SEivIPcrb5Tbq7W22Ho9LUnlCE15pDfGriOL1++DCMzwkwvzCP/2xssR2KSmPGGB57/TCTSvK4Si/FNGhr543n8Jkwu0/ob6hKkXefhebDsOJu25GMvbm3Ofe7nrIaRqbQotjjjh9/kmi0mSmT/9x2KJ5UUfEnGBOlsVF7i9XY+OGxRnJ9wh0Tx9kOxbNuqihmS2uY4x06C6caG28cOMPGuibuvvoyfHre/6BdP6+KgF/45ZZ626GoTGAMvPZtKK2B2TfZjmbslUx2rsG8/Qln39WY0qLYw6LRdg7VPURJyQqKi5faDseTiooWkp9fw7Fjj9sORaWhI5FOfn6iif9nfBllgTSdDCQFPlRZAsCvT521G4hKS7G44f7fvsv4olw+vnyy7XA8ZVxhDmtrJ/CrLfV0dMdsh6PS3eHX4NhmWH0P+DJkDp2ld0HjXp1wKwW0KPawI0e+T3f3GS6f/iUdljlMIj4mTbqDlta3aW3dbjsclWb+se4kPoHPT62yHYqnXZ6fy+qSAn50rJGoXv5FjbIfvXaIbfUt/PWNs8kNZEhDexTdsXIKrR1RfrVVe4vVGDIGXvoqBCfAojtsR5M6tR+Ggkp4/V9tR5L2tCj2qFDoPeoOf5eqyg9RXLzIdjieNnHCRwkEStm//36dLESNmg1n2/jFybP81+oKJuZm2w7H8/6iuoJjnd384lST7VBUGtlc18T9v32XD8yp4paFE22H40kra8pYOrWUb/9+H5Eu7S1WY2T7L5xr9q75CmRn0KSVWTlOz/iB38OBP9iOJq1pUexB3d0t7Nj5P8jKKmLmzL+zHY7nZWUFqam5l7PNb3LsuA6jViPX0NXNvXuOUJOXzV9NG287nLSwtryY5UUFfP3ACU51dtsOR6WBncda+IvHNlNdms8/fWyhjrgaJhHhvhtmc6q1k68/v9t2OCodNR2E//yfUL3CuTZxpln5Gec86qfvhfZG29GkLStFsYisFZH3RGS/iNzXx/MiIt9OPL9dRJYMdtt019V1hm3b/pxI5DC1tf+X7Owy2yGlhepJd1BWdhV79/4fTp582nY41mmODt+pzm7u2HaQpu4Y3507jXy/HnscDT4RHphVTTgW55PbD2Z8Yaw5OjLP7zjBuoffJD87i0fvWk5xvl6XeCSWTyvj7qsv49/fPMKDL+0lrqc5jJjmacKJ7fDjW0H88OF/y5xziXsK5MJHfwDtp+GHa6Fug068NQZS3loTET/wEHADMBdYJyJze612AzAjcbsb+O4Qtk1L3d3N1B/7GW9t/BBtod3UznuQstLVtsNKGyI+aud9m+KiReza/QW2bf9vnDnzCtFou+3QUk5zdHjOdkd59FgjH9j8HvvCnXy/dhqL9LrEo2pOYR7fr53G/nAnaza9y7cPn+JwpDPjTnvQHB2e7lic9fsauOtHG/nsT7dyWWUhT35mNVPHFdgOLS188fpZfGRJNQ++tI/bvvMav9xSz6nWDttheVIm5ykAnSF49zn45Z/Bw9dAvBs+9Wsou8x2ZPZMWgqf/BV0tsGjN8G3F8PzX4S9L0BYTysaDTamQ10B7DfGHAQQkZ8DtwI9x9zcCjxmnJbOmyJSIiITgGmD2HbIduz87xgTAxINK2Mw9GxkmXNHZJzlpv91zfnnzq17wbb0s23vdc8v6+pqpKPjOGAoKlrMrFmPUBSsHckuqz4EAkUsXvwYR458n8NHvk9j40uAj9zciWRnjyMrqwgRH4IfET+ID3DHcDufL0DtvAdH6+VclaOhaIx73z0CnD8wej5remSQ4cJsMhev0/s1DD3zrY/XSD7fM8UvWt9wqitKfUcXBlhWlM/9syYzrzBvuLusBvD+cUX857IZ/N2+43zj4Am+cfAEpVl+JudmU5jlp8DvQwARJzsFOfdvm8oDWdw/a9RmNnZVjh5rjvC1Z3efywfolUsG6HN5P/nbI+Eues2eed3z+6Cf5cnXaol0c6QpTHfMUJIf4EtrZ/Nfr6ohoCM5Rk2W38c/fmwBV84Yx4Mv7eP/e3IbAGUF2VQGcygryCbL7yPgE/yJm5vUlBfwxbWzbYeRNJgcH7yTO+GV++nZzgR6PDZj8HiYr99+GpqPOv/OKXLOp73iC1CglzVk2pXwP96GHU86Bw22/hg2/pvzXMlUCI6H3JJEb7okfgidX8K0UL0Mrrh3zF7eRlE8CTja49/1wMpBrDNpkNsCICJ34xwdZ8qUKQMGFA4fwpgo55tP9Pgjcv4tSI+/KbnEuvRogfW3rvPYl/yjPfcuFz4PkJc3hYkTPsa4cdcQDM7X857GkM+XzbRpn2XKlD+nqel1Wlu3E4kcpqu7iWi0zTl4YuIYE8UQtx3uOT7fqE7k5KocjRnD/nCns01y256PhXP5dcFXv/RcX3qt3/N15Nzyi14jub70eo0L1heWFeXzifFlXDeuiEXBPM3RMTa7II8nFk2nLtLJH860sqe9g5Od3bRFY5zs7D53QCNZaPU8kGFLe/aofl+4Kke7onEONIScbZALDkAkc+GCnJEL8+58zvaTpxctP5+s5359fcnlcr4d2COeimAOH5w7niVTSrh6ZoXOMj1GRITbF1dz26JJbKtvYcvhs+w/3UZDWydnw920d8WIxeNEY4a4y0Z45GS56gDJoPJ00G3d7jA07ktuxIXtzB4/fv20VUf+WMDX4/MdaP3SqbDokzBlFUxZDVk6UeUFsvNh6Z3OrTsCRzc6l6k6tcs53zh0Ekw88cMXd27pomhsJ0O0URT31Vrs/c3Y3zqD2dZZaMzDwMMAy5YtG/Cbd+WKZwd6WmUgny+H8vI1lJevsR2KDa7K0eJAFq+scM3Re+Ui0/Jy+LPqCtth2OCqHK0pL+CFL1zT39MqA4kIiyaXsGhyie1QvGpQeTrotu7kFXDPm6MWnHKJQB5cdo1zUyNmoyiuB3qOIasGjg9ynexBbKuUGhnNUaXcTXNUqfQ2mBxXSo0iG2NFNgEzRKRGRLKBTwC9p/t9Gvh0YvbMVUCLMebEILdVSo2M5qhS7qY5qlR60zxVKsVS3lNsjImKyOeA3wF+4IfGmF0i8pnE898DngduBPYDYeCugbZN9T4olc40R5VyN81RpdKb5qlSqSeZcCmLZcuWmc2bN9sOQymrRGSLMWaZ7Tj6ojmqlOaoUm6nOaqUu40kR1011Z5SSimllFJKKZVKWhQrpZRSSimllMpYWhQrpZRSSimllMpYGXFOsYg0AIdT8FblQGMK3mcseX0fvB4/jN0+TDXGuPKirinIUa//XXg5fi/HDqmNP5NzNMlrfy9eitdLsYI7483EHHXj/8NgaNyp46aYh52jGVEUp4qIbHbrBAyD5fV98Hr8kB774DZe/0y9HL+XYwfvx+81Xvu8vRSvl2IF78Wbrrz6/6Bxp44XY+6LDp9WSimllFJKKZWxtChWSimllFJKKZWxtCgeXQ/bDmAUeH0fvB4/pMc+uI3XP1Mvx+/l2MH78XuN1z5vL8XrpVjBe/GmK6/+P2jcqePFmC+i5xQrpZRSSimllMpY2lOslFJKKaWUUipjaVGslFJKKaWUUipjaVE8SCIyWUReFpE9IrJLRO7t8dx/F5H3Essf6LH8r0Vkf+K56+1Efl5/+yAii0TkTRF5R0Q2i8iKHtu4Zh9EJFdENorItkT8/zuxvExEXhSRfYn70h7buCb+RDz97cM/iMi7IrJdRJ4SkZIe27hqH9zG67mpeWkvfs3H1PNSvnotN72Ui5p79gznMxaRpSKyI/Hct0VEEstzROSJxPK3RGRaj23uTPzN7RORO1O4f2sT8e8XkftS9b493r+/740h5+FwPvdRiN8vIm+LyLNeintUGGP0NogbMAFYkngcBPYCc4E1wEtATuK5ysT9XGAbkAPUAAcAv0v34QXghsTyG4E/unEfAAEKE48DwFvAKuAB4L7E8vuA+90Y/yX24U+ArMTy+928D267eT03NS/txa/5aOUz90y+ei03vZSLmnv2bsP5jIGNwOrE/9t/9vj7/yzwvcTjTwBPJB6XAQcT96WJx6Up2Dd/Iu7LgOzE/sxN8efb3/fGkPNwqJ/7KMX//wI/A55N/NsTcY/GTXuKB8kYc8IYszXxuA3YA0wC/hL4pjGmM/Hc6cQmtwI/N8Z0GmMOAfuBFRe/cuoMsA8GKEqsVgwcTzx21T4YRyjxz0DiZnDi/HFi+Y+B2xKPXRU/9L8PxpgXjDHRxPI3gerEY9ftg9t4PTc1L+3Fr/mYel7KV6/lppdyUXPPnqF+xiIyASgyxrxhjDHAY1z4N5T82/ol8P5Er+D1wIvGmCZjzFngRWDtWO8bzt/EfmPMQWNMF/DzRIwpM8D3xpDycJif+4iISDVwE/D9HotdH/do0aJ4GBLd/YtxjmzOBK5KDAN4RUSWJ1abBBztsVl9Ypkr9NqHzwP/ICJHgX8E/jqxmuv2ITGs4x3gNM4X7ltAlTHmBDhfRkBlYnXXxQ/97kNPf4ZzZA1cug9u5fXc1LxMPc1He7yUr17JTS/louaeKwzmM56UeNx7+QXbJArtFmDcAK811lz1d9Lre2OoeTicz32kHgS+CMR7LPNC3KNCi+IhEpFC4FfA540xrUAWztCQVcD/BH6ROOrR15EPk7JAB9DHPvwl8AVjzGTgC8APkqv2sbnVfTDGxIwxi3CObK4QkdoBVndd/DDwPojIV4Ao8NPkor5eYsyD9CCv56bmpR2aj3Z4KV+9lJteykXNvbEjIi+JyM4+brf2WGewn/FAn/1wthlLrvk76eN7o99V+1g23M992ETkQ8BpY8yWwW7STwwpjXs0aVE8BCISwPkD/6kx5teJxfXArxNDgTbiHF0pTyyf3GPzas4PsbKmn324E0g+fpLzQ5JcuQ8Axphm4I84w3FOJYZrkLhPDrtzbfxw0T4gzkQUHwLuSAw5AZfvg1t4PTc1L+3Hr/mYOl7KV6/mppdyUXNv9BljPmCMqe3j9h8w5M+4nvNDrHsuv2AbEcnCOZ2gaYDXGmuu+Dvp53tjqHk4nM99JK4AbhGROpxh59eJyL97IO7RY1xwYrMXbjhHNx4DHuy1/DPA/0k8nokzLECAeVx4AvpB3DHJU1/7sAe4NvH4/cCWxGNX7QNQAZQkHucB63G+1P+BCycBeMCN8V9iH9YCu4GKXuu7bh/cdvN6bmpe2otf89HKZ+6ZfPVabnopFzX37N2G8xkDm3BGcSQnTroxsfweLpw46ReJx2XAIZzRH6WJx2Up2LesRNw1nJ9oa16KP9/+vjeGnIdD/dxHcR+u5fxEW56Je8T7bTsAr9yAK3G6+LcD7yRuNyaS7t+BncBW4Loe23wFZza290jMvObSfbgS2JL4434LWOrGfQAWAG8n4t8J/G1i+Tjg98C+xH1Zj21cE/8l9mE/TiMw+f/yPbfug9tuXs9NzUt78Ws+WvnMPZOvXstNL+Wi5p6923A+Y2BZ4v/pAPCvgCSW5+KMltiPM+PwZT22+bPE8v3AXSncvxtxZnw+AHzFwufb3/fGkPNwOJ/7KO3DtZwvij0T90hvySCVUkoppZRSSqmMo+cUK6WUUkoppZTKWFoUK6WUUkoppZTKWFoUK6WUUkoppZTKWFoUK6WUUkoppZTKWFoUK6WUUkoppZTKWFoUK6WUUkoppZTKWFoUK6WUUkoppZTKWP8/IFkSUFbgDbsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# have a look at feature distributions\n", + "features = df_train_clean.drop('Time', axis = 1) # create a list of all numerical features\n", + "features.plot(kind='density', subplots=True, layout=(3, 4), sharex=False, figsize=(16, 12));" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "5f54ceb9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ave_weather_id 3.633779\n", + "Ave_wind_speed 3.708942\n", + "Ave_wind_deg -0.640068\n", + "Ave_humidity -0.951951\n", + "Ave_rain 173.988275\n", + "Ave_clouds_all 0.228047\n", + "Ave_pressure 3688.345400\n", + "Ave_snow 799.972093\n", + "Ave_temp_min -0.667229\n", + "Ave_temp -0.678971\n", + "Ave_temp_max -0.645494\n", + "load_shortfall_3h -0.118999\n", + "dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# examine measure of presence of outliers\n", + "df_train_clean.drop('Time', axis = 1).kurtosis()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "17e257b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create the subset for features with high kurtosis\n", + "df_outliers = df_train_clean[['Ave_weather_id','Ave_wind_speed','Ave_rain','Ave_pressure','Ave_snow']]\n", + "\n", + "#melt dataframe into long format\n", + "df_melted = pd.melt(df_outliers)\n", + "\n", + "#plot boxplots for variables\n", + "sns.boxplot(x='variable', y='value', data=df_melted) " + ] + }, + { + "cell_type": "markdown", + "id": "98061d97", + "metadata": {}, + "source": [ + "9. We can deduce from the plots above that no signiicant correlation exists between any independent variable and the target variable. However, we notice some interesting correlations elsewhere. We can deduce that the weather_id feature is majorly characterised by rain and cloud quantities which have weak negative correlations with the load_shortfall. We can also confirm as suggested by our earlier scatter plots that while the temperature features provide the highest correlation with demand shortfall, very strong correlation exists amongst them. As expected too, the temperature features together produce a strong negative correlation with the humidity levels. We also do not expect a significant effect of pressure and snow on the target variable seeing how thinly distributed their levels are as shown in the density plots and confirmed by the heat map. This is obviously as a result of strong presence of outliers in their data. These will be handled in the data engineering section." + ] + }, + { + "cell_type": "markdown", + "id": "acf5dcb2", + "metadata": {}, + "source": [ + "\n", + "## 4. Data Engineering\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Data engineering ⚡ |\n", + "| :--------------------------- |\n", + "| In this section you are required to: clean the dataset, and possibly create new features - as identified in the EDA phase. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "764b5fba", + "metadata": {}, + "source": [ + "10. For our feature engineering, there is a need to derive other time features such as the hour of day and month of year to provide the model a perspective of the shortfall's variance with the time. Seeing that the time feature as its given in the data may not do justice to this functionality, it is necessary to derive from it, an 'hour of day' feature which will reflect in the model how the demand shortfall changes between day and night, and the 'day of year' or 'week of year' which helps reflect the variation of demand shortfall over the various seasons in the year. For better organisation though, we may reindex the columns such that the target variable along with all the derived time features come first in the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "c313d4d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2015-01-01 03:00:00\n", + "1 2015-01-01 06:00:00\n", + "2 2015-01-01 09:00:00\n", + "3 2015-01-01 12:00:00\n", + "4 2015-01-01 15:00:00\n", + " ... \n", + "8758 2017-12-31 09:00:00\n", + "8759 2017-12-31 12:00:00\n", + "8760 2017-12-31 15:00:00\n", + "8761 2017-12-31 18:00:00\n", + "8762 2017-12-31 21:00:00\n", + "Name: Time, Length: 8763, dtype: datetime64[ns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert time data type to datetime\n", + "df_train_clean['Time'] = pd.to_datetime(df_train_clean['Time'])\n", + "df_train_clean['Time']" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "84eea17b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TimeAve_weather_idAve_wind_speedAve_wind_degAve_humidityAve_rainAve_clouds_allAve_pressureAve_snowAve_temp_minAve_tempAve_temp_maxload_shortfall_3hHour_of_dayDay_of_yearWeek_of_year
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" + ], + "text/plain": [ + " Time Ave_weather_id Ave_wind_speed Ave_wind_deg \\\n", + "0 2015-01-01 03:00:00 800.0 2.400000 133.000000 \n", + "1 2015-01-01 06:00:00 800.0 2.066667 180.000000 \n", + "2 2015-01-01 09:00:00 800.0 1.533333 270.166667 \n", + "3 2015-01-01 12:00:00 800.0 1.866667 236.333333 \n", + "4 2015-01-01 15:00:00 800.0 1.933333 222.500000 \n", + "\n", + " Ave_humidity Ave_rain Ave_clouds_all Ave_pressure Ave_snow \\\n", + "0 71.333333 0.0 0.000000 1011.333333 0.0 \n", + "1 71.333333 0.0 0.000000 1012.500000 0.0 \n", + "2 67.111111 0.0 0.000000 1013.333333 0.0 \n", + "3 58.555556 0.0 0.000000 1019.166667 0.0 \n", + "4 58.111111 0.0 0.666667 1030.916667 0.0 \n", + "\n", + " Ave_temp_min Ave_temp Ave_temp_max load_shortfall_3h Hour_of_day \\\n", + "0 272.086456 272.086456 272.086456 6715.666667 3 \n", + "1 272.799533 272.799533 272.799533 4171.666667 6 \n", + "2 277.224046 277.224046 277.224046 4274.666667 9 \n", + "3 283.351587 283.351587 283.351587 5075.666667 12 \n", + "4 283.134500 283.134500 283.134500 6620.666667 15 \n", + "\n", + " Day_of_year Week_of_year \n", + "0 1 1 \n", + "1 1 1 \n", + "2 1 1 \n", + "3 1 1 \n", + "4 1 1 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create new features\n", + "df_train_clean['Hour_of_day'] = df_train_clean['Time'].dt.hour # add hour of day feature\n", + "df_train_clean['Day_of_year'] = df_train_clean['Time'].dt.day_of_year # add day of the year feature\n", + "df_train_clean['Week_of_year'] = df_train_clean['Time'].dt.isocalendar().week # add week of year feature\n", + "df_train_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "767f5645", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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35075.6666672015-01-01 12:00:001211800.01.866667236.33333358.5555560.00.0000001019.1666670.0283.351587283.351587283.351587
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" + ], + "text/plain": [ + " load_shortfall_3h Time Hour_of_day Day_of_year \\\n", + "0 6715.666667 2015-01-01 03:00:00 3 1 \n", + "1 4171.666667 2015-01-01 06:00:00 6 1 \n", + "2 4274.666667 2015-01-01 09:00:00 9 1 \n", + "3 5075.666667 2015-01-01 12:00:00 12 1 \n", + "4 6620.666667 2015-01-01 15:00:00 15 1 \n", + "\n", + " Week_of_year Ave_weather_id Ave_wind_speed Ave_wind_deg Ave_humidity \\\n", + "0 1 800.0 2.400000 133.000000 71.333333 \n", + "1 1 800.0 2.066667 180.000000 71.333333 \n", + "2 1 800.0 1.533333 270.166667 67.111111 \n", + "3 1 800.0 1.866667 236.333333 58.555556 \n", + "4 1 800.0 1.933333 222.500000 58.111111 \n", + "\n", + " Ave_rain Ave_clouds_all Ave_pressure Ave_snow Ave_temp_min Ave_temp \\\n", + "0 0.0 0.000000 1011.333333 0.0 272.086456 272.086456 \n", + "1 0.0 0.000000 1012.500000 0.0 272.799533 272.799533 \n", + "2 0.0 0.000000 1013.333333 0.0 277.224046 277.224046 \n", + "3 0.0 0.000000 1019.166667 0.0 283.351587 283.351587 \n", + "4 0.0 0.666667 1030.916667 0.0 283.134500 283.134500 \n", + "\n", + " Ave_temp_max \n", + "0 272.086456 \n", + "1 272.799533 \n", + "2 277.224046 \n", + "3 283.351587 \n", + "4 283.134500 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# engineer existing fetures\n", + "\n", + "# create a list of features we need to bring forward\n", + "first_columns = ['load_shortfall_3h', 'Time', 'Hour_of_day', 'Day_of_year', 'Week_of_year']\n", + "\n", + "# create the order of columns\n", + "new_columns = first_columns + [col for col in df_train_clean.columns if col not in first_columns] \n", + "\n", + "# create a new dataframe named 'df_train' with the new column index \n", + "df_train = df_train_clean.reindex(columns = new_columns) \n", + "\n", + "# quick look at the resulting dataframe\n", + "df_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dd30d47c", + "metadata": {}, + "source": [ + "10. We may fare better by removing feature pairs which have a high collinearity such as the temperature features. However, we will choose to leave them since we are considering using a Lasso regressor which inherently performs variable shrinking and selection. As for features with high measure of outliers (kurtosis), we shall use knowledge of their respective distributions and box plots in the EDA section to write a function that sets lower and upper boundaries of a dataframe's field and then replace a detected outlier with the median value. An observation may be classified as an outlier if its value is greater than the 3rd quantile(75th percentile) by 1.5 times the Interquantile Range(5*IQR) or more. The presence of outliers may mislead the training process of our machine learning model leading to less accurate models and poorer results.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "31386e1d", + "metadata": {}, + "outputs": [], + "source": [ + "# define outlier detector function\n", + "def handle_outliers(dataframe, col):\n", + " '''\n", + " define function which takes as argument a dataframe and a column\n", + " as input, calculates boundaries for outliers and replaces each outlier\n", + " by it closest boundary value, and then returns the\n", + " '''\n", + " iqr = dataframe[col].quantile(0.75) - dataframe[col].quantile(0.25) # calculate interquantile range for column\n", + " tail = dataframe[col].quantile(0.25) - (iqr*1.5) # set lower boundary\n", + " head= dataframe[col].quantile(0.75) + (iqr*1.5) # set upper boundary\n", + " dataframe.loc[dataframe[col] > head, col] = head # detect and replace each outlier with nearest boundary\n", + " dataframe.loc[dataframe[col] < tail, col] = tail \n", + " return dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b7c0eee8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ave_weather_id 0.807169\n", + "Ave_wind_speed 0.117803\n", + "Ave_wind_deg -0.640068\n", + "Ave_humidity -0.951951\n", + "Ave_rain 0.000000\n", + "Ave_clouds_all 0.228047\n", + "Ave_pressure -0.102126\n", + "Ave_snow 0.000000\n", + "Ave_temp_min -0.667229\n", + "Ave_temp -0.678971\n", + "Ave_temp_max -0.645494\n", + "dtype: float64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# call handle_outliers for features with high kurtosis \n", + "handle_outliers(df_train, 'Ave_pressure')\n", + "handle_outliers(df_train, 'Ave_rain')\n", + "handle_outliers(df_train, 'Ave_snow')\n", + "handle_outliers(df_train, 'Ave_wind_speed')\n", + "handle_outliers(df_train, 'Ave_weather_id')\n", + "\n", + "df_train.drop(first_columns, axis = 1).kurtosis()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8051d326", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " load_shortfall_3h Time Hour_of_day Day_of_year \\\n", + "0 6715.666667 2015-01-01 03:00:00 3 1 \n", + "1 4171.666667 2015-01-01 06:00:00 6 1 \n", + "2 4274.666667 2015-01-01 09:00:00 9 1 \n", + "3 5075.666667 2015-01-01 12:00:00 12 1 \n", + "4 6620.666667 2015-01-01 15:00:00 15 1 \n", + "\n", + " Week_of_year Ave_weather_id Ave_wind_speed Ave_wind_deg Ave_humidity \\\n", + "0 1 800.0 2.400000 133.000000 71.333333 \n", + "1 1 800.0 2.066667 180.000000 71.333333 \n", + "2 1 800.0 1.533333 270.166667 67.111111 \n", + "3 1 800.0 1.866667 236.333333 58.555556 \n", + "4 1 800.0 1.933333 222.500000 58.111111 \n", + "\n", + " Ave_rain Ave_clouds_all Ave_pressure Ave_snow Ave_temp_min Ave_temp \\\n", + "0 0.0 0.000000 1011.333333 0.0 272.086456 272.086456 \n", + "1 0.0 0.000000 1012.500000 0.0 272.799533 272.799533 \n", + "2 0.0 0.000000 1013.333333 0.0 277.224046 277.224046 \n", + "3 0.0 0.000000 1019.166667 0.0 283.351587 283.351587 \n", + "4 0.0 0.666667 1030.916667 0.0 283.134500 283.134500 \n", + "\n", + " Ave_temp_max \n", + "0 272.086456 \n", + "1 272.799533 \n", + "2 277.224046 \n", + "3 283.351587 \n", + "4 283.134500 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# last look of the engineered training dataset\n", + "df_train.head()" + ] + }, + { + "cell_type": "markdown", + "id": "43b2d523", + "metadata": {}, + "source": [ + "\n", + "## 4. Modelling\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Modelling ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to create one or more regression models that are able to accurately predict the thee hour load shortfall. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2344b3e0", + "metadata": {}, + "outputs": [], + "source": [ + "# split data into predictor and response variables\n", + "\n", + "X = df_train.drop(columns = ['load_shortfall_3h','Time'])\n", + "y = df_train['load_shortfall_3h']" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "9c58df02", + "metadata": {}, + "outputs": [], + "source": [ + "# apply data scaling to predictor variables\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7bdd9689", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " lm_scores rr_scores lr_scores dt_scores rf_scores\n", + "rmse_train 4894.368041 4894.368067 4894.485843 4894.485843 2945.016428\n", + "rmse_test 4936.614832 4936.626081 4936.067923 4936.067923 4501.985059" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e19664f", + "metadata": {}, + "outputs": [], + "source": [ + "#Choose best model and motivate why it is the best choice\n", + "\n", + "'''The best model as may be observed from the results dataframe is the random forest regressor because since\n", + "it produces the lowest root mean squared value for an unseen test data, it is likely going to produce the\n", + "least amount of error form any other unseen dataset '''\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8ad0c0d", + "metadata": {}, + "source": [ + "\n", + "## 6. Model Explanations\n", + "\n", + "Back to Table of Contents\n", + "\n", + "---\n", + " \n", + "| ⚡ Description: Model explanation ⚡ |\n", + "| :--------------------------- |\n", + "| In this section, you are required to discuss how the best performing model works in a simple way so that both technical and non-technical stakeholders can grasp the intuition behind the model's inner workings. |\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "5ff741c2", + "metadata": {}, + "outputs": [], + "source": [ + "# discuss chosen methods logic\n", + "'''The Random Forest algorithm is essentially a Machine Learning(ML for short) algorithm or model; a statistical way of teaching\n", + "a computer how to predict the outcome of an activity. It is a randomised combination of yet another ML algorithm called Decision\n", + "Tree. A Decision Tree starts out at the base(root node) and represents data by dividing it into two different branches based on\n", + "possible ways of answering a question posed to it. The division continues with each branch further branching out until it\n", + "reaches a point where only one answer is possible. The collected answers or predictions from each decision tree is then\n", + "aggregated by their mean value to produce a much better prediction'''\n", + "\n" + ] + } + ], + "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": "3.9.7" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}