diff --git a/Assignment Colab/.ipynb_checkpoints/Stella Esther Nabirye-checkpoint.ipynb b/Assignment Colab/.ipynb_checkpoints/Stella Esther Nabirye-checkpoint.ipynb new file mode 100644 index 0000000..7e457d1 --- /dev/null +++ b/Assignment Colab/.ipynb_checkpoints/Stella Esther Nabirye-checkpoint.ipynb @@ -0,0 +1,2712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stella Esther Nabirye\n", + "### Kaggle assignment\n", + "**Importing Libraries** \n", + "Python libraries contain functions/ methods helpful in analysis and in order to access those functions, we need to import those libraries.\n", + "For the assignment, I import\n", + "- the numpy library which contains functions for mathematical computations\n", + "- the pandas package which contains functions for manipulations of data structure and operations \n", + "- the matplot library which provides for plotting of the data.\n", + "- seaborn for data vusualization\n", + "\n", + "\n", + "
\n", + "So far so good.\n", + " \n", + " For your final report please just imagine this:\n", + " Different people are going to have a look at your report to try and see if you have some sort of proof of understanding the concepts you applied.\n", + " Your explanation and elaboration in the notebook will tell them, so you can't assume anything.\n", + " \n", + " Give the explanations more meat.\n", + " \n", + " But so far so good." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "import os\n", + "#for dirname, _, filenames in os.walk('/kaggle/input'):\n", + "# for filename in filenames:\n", + "# print(os.path.join(dirname, filename))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#import the necessary libraries you are going to use\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "#The necessary libraries are loaded because they contain the different functions that will be used \n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading in data\n", + "\n", + "Since the data is in CSV format, we read it in using the read_csv function" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "", + "_uuid": "" + }, + "outputs": [], + "source": [ + "# Loading the train and test datasets to be used\n", + "Train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../AMP Data Sets/Test.csv\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3038, 12), (758, 11))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the dimensions of the data\n", + "#This returns the number of columns and rows for both the train set and the test set\n", + "#This helps in knowing how much data we are working with\n", + "Train.shape, Test.shape\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "What do the dimensions tell us?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "A good conclusion would befit what you just did above. Some sort of summary about what you have learnt from the data, and concepts deployed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Descriptive Statistics\n", + " Using the describe function, we are able to get a summary of the statistical details regarding the shape of each attribute of our data such as the mean, standard deviation,count, minimum and maximum value of objects of each column that help in further understanding of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 2.073879 8.945091 0.973046 73.821891 \n", + "std 4.230615 7.814449 0.110676 8.029524 \n", + "min -13.000000 0.000000 0.699000 47.000000 \n", + "25% -0.500000 2.721750 0.894000 68.740250 \n", + "50% 2.000000 7.500000 0.965000 74.069500 \n", + "75% 4.000000 14.230250 1.053500 79.284750 \n", + "max 30.000000 44.118000 1.431000 102.929000 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 0.994212 -2.397922 0.084433 15.570067 \n", + "std 0.032370 1.597138 0.278219 11.362589 \n", + "min 0.889000 -7.844000 0.000000 0.060000 \n", + "25% 0.973000 -3.457250 0.000000 5.709000 \n", + "50% 0.994000 -2.238000 0.000000 15.057000 \n", + "75% 1.013000 -1.306250 0.000000 25.290250 \n", + "max 1.182000 2.017000 1.000000 50.098000 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n", + "count 758.000000 758.000000 758.000000 \n", + "mean 73.844204 5.904492 1.250189 \n", + "std 8.915193 0.656911 0.218102 \n", + "min 47.000000 3.843000 0.841000 \n", + "25% 68.346000 5.471250 1.096000 \n", + "50% 73.646000 5.935500 1.188000 \n", + "75% 80.069250 6.375250 1.378500 \n", + "max 102.929000 7.588000 2.283000 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Test.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification\n", + "\n", + "This helps us know how data is distributed with regards to the class, that is, whether it is balanced or imbalanced." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.groupby('CLASS').size().plot(kind='bar',color=('orange', 'blue'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data correlation\n", + "This helps us to know how the relationship between the variables and to identify which variables are positively or negatively correlated and perhaps where there is no correlation at all. This is important because the performance of algorithms such as linear and logistic regression is affected if attributes in data are highly correlated.\n", + "\n", + "
\n", + "Great!" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
FULL_Charge1.000000-0.612996-0.490977-0.434603-0.058725-0.2837580.0880680.355477-0.365374-0.0905700.2329290.534602
FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using the cor() function and the pearson coefficient's method\n", + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# to visualize this the data correlation, we plot a heat map\n", + "plt.figure(figsize=(6,6))\n", + "sns.heatmap(Train.corr(method='pearson'), cmap='PiYG')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_AURR980107', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'CT_RACS820104'],\n", + " dtype='object')" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.iloc[:,[2,4,5,6,10]].columns" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for correlation with regards to the class\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Distribution\n", + "Knowing whether the data is skewed or not is important in the data preparation. This is done by calculating the skew for each attribute with using the skew() function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots\n", + "\n", + "To further learn more about the data, we visualize it by using plots such as\n", + "- Histograms\n", + "- Density plots\n", + "- Box whisker plots\n", + "- Scatter plot matrix\n", + "\n", + "\n", + "
\n", + "\n", + " ## ??\n", + " \n", + " Please try talk more about these concepts and what they mean to you." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histograms\n", + "Histograms are useful for analyzing continuous numerical data and can be really helpful in identifying useful patterns. They are also helpful in indicating the data distribution.\n", + "\n", + "
\n", + "Good explanation, so what did you learn after plotting them.?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram\n", + "plt.figure(figsize=(15,15))\n", + "Train.hist( color = 'violet')\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Density plots \n", + "These are also helpful in visualization of the data distribution. The peaks of the density plots show where values are most concentrated over the interval. Density plots do a better job at determining the distribution shape,an advantage they have over histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Density plots\n", + "Train.plot(kind='density', subplots=True, layout=(3,4), sharex=False)\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Box plots\n", + "These give an indication of how the data values are spread out. They display the minimum, first quartile, median, third quartile and maximum of the data. The advantage is that they takes up less space when compared to histograms and density plots which is useful for data when comparing distributions between many groups.\n", + "\n", + "
\n", + "??" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Box plot\n", + "Train.plot(kind='box', subplots=True, layout=(3,4), sharex=False, sharey=False)\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocessing Data\n", + "This is the step where the data is transformed in such a way that it can easily be interpreted by the algorithm.\n", + "This can be done by;\n", + "- Splitting the data into train/validation and test sets. \n", + "ML algorithms have to first be trained on the available data distribution, validated and then tested before it can be applied to real world world data\n", + "- Transforming the input variables through pre-processing such as `rescaling, normalizing and binarizing` " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.457 0. 0.348 0.545 0.33 0.483 0. 0.005 0.508 0.415 0.182]\n", + " [0.435 0.116 0.322 0.489 0.276 0.458 1. 0.011 0.421 0.586 0.475]\n", + " [0.467 0.116 0.246 0.523 0.288 0.565 0. 0.011 0.442 0.273 0.666]\n", + " [0.457 0.089 0.275 0.399 0.403 0.647 0. 0.011 0.405 0.153 0.424]\n", + " [0.511 0.183 0.323 0.373 0.342 0.595 0. 0.011 0.5 0.196 0.536]]\n" + ] + } + ], + "source": [ + "# Rescaling data\n", + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array [:,11]\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "rescaledX = scaler.fit_transform(X)\n", + "set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7.697e-01 -1.123e+00 -1.900e-01 1.376e-01 -6.067e-01 -7.206e-01\n", + " -3.117e-01 -1.331e+00 -2.257e-02 -3.610e-01 -9.251e-01]\n", + " [ 5.079e-01 -4.109e-01 -3.763e-01 -2.432e-01 -1.181e+00 -9.245e-01\n", + " 3.208e+00 -1.303e+00 -5.924e-01 9.021e-01 1.037e+00]\n", + " [ 9.006e-01 -4.109e-01 -9.163e-01 -8.651e-03 -1.054e+00 -4.632e-02\n", + " -3.117e-01 -1.304e+00 -4.590e-01 -1.409e+00 2.318e+00]\n", + " [ 7.697e-01 -5.741e-01 -7.115e-01 -8.701e-01 1.594e-01 6.274e-01\n", + " -3.117e-01 -1.302e+00 -7.015e-01 -2.300e+00 6.989e-01]\n", + " [ 1.424e+00 2.041e-03 -3.670e-01 -1.050e+00 -4.790e-01 2.003e-01\n", + " -3.117e-01 -1.302e+00 -7.713e-02 -1.977e+00 1.447e+00]]\n" + ] + } + ], + "source": [ + "#We then standardize the data to a standard Guassian distribution with a mean of 0 and standard deviation of 1\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "array2 = Train.values\n", + "\n", + "X = array2[:,0:11]\n", + "Y = array2[:,11]\n", + "scaler = StandardScaler().fit(X)\n", + "rescaledX = scaler.transform(X)\n", + "set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.048 0. 0.009 0.711 0.009 -0.035 0. 0.003 0.698 0.054\n", + " 0.01 ]\n", + " [ 0.04 0.054 0.009 0.72 0.01 -0.04 0.01 0.006 0.686 0.066\n", + " 0.015]\n", + " [ 0.054 0.053 0.009 0.724 0.009 -0.025 0. 0.006 0.683 0.049\n", + " 0.017]\n", + " [ 0.053 0.044 0.009 0.699 0.011 -0.014 0. 0.006 0.71 0.046\n", + " 0.015]\n", + " [ 0.076 0.087 0.009 0.658 0.01 -0.021 0. 0.006 0.742 0.046\n", + " 0.016]]\n", + "\n" + ] + } + ], + "source": [ + "# Normalizing data using the sklearn Normalizer\n", + "from sklearn.preprocessing import Normalizer\n", + "array3 = Train.values\n", + "\n", + "X = array3[:,0:11]\n", + "Y = array3[:,11]\n", + "scaler = Normalizer().fit(X)\n", + "normalizedX = scaler.transform(X)\n", + "set_printoptions(precision=3)\n", + "print(normalizedX[0:5,:])\n", + "print(type(normalizedX))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Did you use all the transformations above to solve your issue?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Selection\n", + "This is where we reduce the number of features/variables by automatically selecting the features that contribute most to the prediction variable or output of interest.\n", + "This is important because;\n", + "- It enables the machine learning algorithm to train faster\n", + "- Reduces the complexity of the model making it easier to interpret\n", + "- Improves accuracy of the model" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 5\n", + "Selected Features: [False False True False True True True False False False True]\n", + "Feature Ranking: [2 6 1 5 1 1 1 3 7 4 1]\n" + ] + } + ], + "source": [ + "# Using recursive feature elimination with the logistic regression algorithm\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array_1 = Train.values\n", + "X = array_1[:,0:11]\n", + "Y = array_1[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 5)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scatter plot matrix important in identifying structured relationships between variables. \n", + "Plotting the selected features to visualize if indeed they would be better to use for classification.\n", + "\n", + "\n", + "
\n", + "What did you learn from the results?" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot matrix \n", + "sns.pairplot(Train, hue='CLASS', vars=['FULL_AURR980107', 'FULL_GEOR030101', 'FULL_OOBM850104', 'CT_RACS820104'])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.085 0.138 0.102 0.091 0.051 0.078 0.035 0.285 0.051 0.033 0.051]\n" + ] + } + ], + "source": [ + "# To compare with the recursive feature elimination, i do the feature importance as well\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Algorithm performance and Evaluation\n", + "This is so we can evaluate the performance of the machine learning algorithm.\n", + "\n", + "First we going to split our data into train and test sets.\n", + "Then train the models and finally predict the required solution.\n", + "\n", + "Evaluation of the models will be done by using the Matthews correlation coefficient(MCC) which will measure the quality of the classifications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression algorithm\n", + "Logistic regression is a predictive analysis algorithm of machine learning that is based on the concept of probability and is used to handle classification problems." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.62512462612163\n", + "MCC: 0.8342865299822478\n", + "[False True]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "#Splitting the data\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.33\n", + "seed = 42\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed)\n", + "\n", + "#using logistic regression\n", + "model = LogisticRegression()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "#Applying matthews correlation coefficient\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "#Reporting the results\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_logistic1.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (83.5359128018065, 27.08521320979506)\n", + "MCC: 0.8342865299822478\n", + "[False True]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# Logistic regression with K- fold cross validation\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "num_folds = 10 \n", + "seed =42\n", + "\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_nb.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Guassian Naive Bayes Algorithm\n", + "Naive bayes is also a classification algorithm and can be used on binary and multi-class classification problems. It is good to use on categorical or two-class input values.\n", + "The Guassian naive bayes is an extension of naive bayes and only requires to estimate the mean and standard deviation from the training data which makes it easy to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.880815746048289\n", + "MCC: 0.8407203694376205\n", + "[ True False]\n", + "False: 370\n", + "True: 388\n" + ] + } + ], + "source": [ + "#Naive Bayes\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = GaussianNB()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_nb.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Vector Machine(SVM)\n", + "This is a supervised machine learning algorithm mostly used for classification problems although it can also be used for regression problems. It performs classification by finding a hyper-plane that differentiates the data classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9081618030224075\n", + "MCC: 0.8187103751493263\n", + "[False True]\n", + "False: 397\n", + "True: 361\n" + ] + } + ], + "source": [ + "#Support Vector Machines model with default\n", + "from sklearn.svm import SVC\n", + "\n", + "num_fold = 10\n", + "seed = 42\n", + "kfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\n", + "model = SVC()\n", + "scoring = 'accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_svm.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9193579555323954\n", + "MCC: 0.8414394511707155\n", + "[False True]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "#Support Vector Machines model with kernel set to linear\n", + "from sklearn.svm import SVC\n", + "\n", + "num_fold = 10\n", + "seed = 42\n", + "kfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\n", + "model = SVC(kernel = 'linear')\n", + "scoring = 'accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_svm_linear.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K- Nearest Neighbors\n", + "This is a supervised machine learning algorithm that assumes close proximity of similar data points. It is used to solve classification and regression problems. K-nearest neighbor works by finding the distance between a query and all examples in the data, selecting the specified number examples(K) closest to the query and then votes dor the most frequent label when dealing with a classification problem. In case of regression, it averages the labels. " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8027933385443807\n", + "MCC: 0.8690586462107053\n", + "[False True]\n", + "False: 402\n", + "True: 356\n" + ] + } + ], + "source": [ + "#K-Nearest Neighbors\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = KNeighborsClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_kNN.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification and Regression trees(CART)\n", + "Classification and and regression trees are types of decision trees and are both used for constructing prediction models from data.\n", + "Classification trees are used where the target variable is categorical and the tree is used to identify the category within which the target variable would likely fall into where as regression trees are used ehre the target variable is continuous and the tree is used to predict its value.\n", + "CART incorporates both testing with with a test data set and cross validation to assess the goodness of fit more accurately." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.727965954490186\n", + "MCC: 1.0\n", + "[ True False]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "#Classification and Regression trees\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = DecisionTreeClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_CRT.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Discriminant Analysis\n", + "This is a classifier that uses dimensionality reduction technique. It has a linear decision boundary that is generated by fitting class conditional densities to the data using Bayes' rule." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8535044293903076\n", + "MCC: 0.8377162908048379\n", + "[False True]\n", + "False: 401\n", + "True: 357\n" + ] + } + ], + "source": [ + "#Linear Disriminant analysis\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = LinearDiscriminantAnalysis()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_LDA.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boosting Algorithms\n", + "\n", + "Boosting algorithms improve boosting accuracy and often outperform simpler models.\n", + "\n", + "Some of them include Adaboost,Stochastic gradient boosting,Stochastic X gradient boosting(XGB) etc\n", + "\n", + "AdaBoost is a short form for 'Adaptive boosting'. It is a boosting algorithm that focuses on classification problems and aims to convert a set of weak classifiers into a strong one.\n", + "\n", + "Gradient boosting combines predictions from multiple decision trees to generate final predictions.\n", + "\n", + "XGB is an improved version of the gradient boosting and here the trees are built sequentially trying to correct errors of the previous trees.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7701895518499218\n", + "MCC: 0.8730261590913062\n", + "[ True False]\n", + "False: 386\n", + "True: 372\n" + ] + } + ], + "source": [ + "# AdaBoost Classification\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "num_trees = 30\n", + "seed=42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "\n", + "model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(results.mean())\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_ADA.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.788623632100052\n", + "MCC: 0.9355236060852458\n", + "[ True False]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "# Stochastic Gradient Boosting Classification\n", + "\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "num_trees = 100\n", + "seed = 42\n", + "\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_SGBC.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.787307842626368\n", + "MCC: 0.9269836637327226\n", + "[ True False]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# XGB\n", + "# Stochastic X Gradient Boosting Classification\n", + "from xgboost import XGBClassifier\n", + "num_trees = 100\n", + "seed = 42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "model = XGBClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_XGB.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Voting Ensemble\n", + "Ensemble methods create multiple models and combine them to produce improved results. They are also good at improving accuracy.\n", + "Voting ensemble is one of the ensemble methods used for classification." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8142988969949625\n", + "MCC: 0.9269836637327226\n", + "[ True False]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# Voting Ensemble for Classification\n", + "from sklearn.ensemble import VotingClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "\n", + "# create the sub models\n", + "estimators = []\n", + "model1 = LogisticRegression()\n", + "estimators.append(('logistic', model1))\n", + "\n", + "model2 = DecisionTreeClassifier()\n", + "estimators.append(('cart', model2))\n", + "\n", + "model3 = SVC()\n", + "estimators.append(('svm', model3))\n", + "\n", + "model4 = XGBClassifier()\n", + "estimators.append(('xgb', model4))\n", + "\n", + "model5 = RandomForestClassifier()\n", + "estimators.append(('rfc', model5))\n", + "\n", + "# create the ensemble model\n", + "ensemble = VotingClassifier(estimators)\n", + "results = cross_val_score(ensemble, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_VE.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing Machine Learning algorithms\n", + "\n", + "This is done to evaluate each algorithm the same way on the data. This is done by forcing each alogorithm to be evaluated on a consistent test harness.\n", + "The following classification algorithms used above are each compared to the dataset:\n", + "\n", + "- Logistic Regression.\n", + "- Linear Discriminant Analysis.\n", + "- k-Nearest Neighbors.\n", + "- Classiffication and Regression Trees.\n", + "- Naive Bayes.\n", + "- Support Vector Machines." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.835359128018065, 0.2708521320979506)\n", + "('LDA', 0.8535044293903076, 0.2571395669719574)\n", + "('KNN', 0.8027933385443807, 0.2521136100771112)\n", + "('CART', 0.7332323692895606, 0.2870258927134236)\n", + "('NB', 0.880815746048289, 0.11642272449162755)\n", + "('SVM', 0.8350280093798853, 0.25836507020625044)\n", + "('ABC', 0.7747861299287824, 0.29219846937739774)\n", + "('GBC', 0.7899437641132534, 0.2887124941351131)\n", + "('XGB', 0.787307842626368, 0.2908368966924707)\n", + "('VC', 0.8146256730936251, 0.28735567966189307)\n" + ] + }, + { + "data": { + "image/png": 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2+mSqTVhuc7eGcftu8fg9KR+3uZuZTWAOdzOzEnKbu1mJVLtcdSL9cMf2cbiblYiD2wa5WcbMrIQc7mZmJeRwNzMrIYe7mVkJOdzNzErI4W5mVkIOdzOzEnK4m5mVkMPdzKyEHO5mZiXkcDczKyGHu5lZCTnczcxKyOFuZlZCDnczsxJyuJuZlZDD3cyshBzuZmYl5HA3Myshh7uZWQk53M3MSihXuEtaJOk2SVskXTDMeKdLCkmdjSvRzMxGqma4S2oDLgZOAeYDSyTNrzLeocC5wI8bXaSZmY1Mnj33FwNbIuKuiNgFXAGcVmW8vwc+AexsYH1mZjYKecL9KODeTPfWtN9ekl4IzIqI7w43IUlnS1onad22bdtGXKyZmeWTJ9xVpV/sHShNAj4DvKfWhCLikojojIjOGTNm5K/SzMxGJE+4bwVmZbpnAvdlug8FOoDrJN0NvBRY7ZOqZmbNkyfcbwbmSZor6WBgMbB6cGBEPBwR0yNiTkTMAW4CTo2IdWNSsZmZ1VQz3CPiCeAc4BpgM7AqIjZJukjSqWNdoJmZjdxBeUaKiDXAmop+Fw4x7on1l2VmZvXwL1TNzErI4W5mVkIOdzOzEnK4m5mVkMPdzKyEHO5mZiXkcDczKyGHu5lZCTnczcxKyOFuZlZCDnczsxJyuJuZlZDD3cyshBzuZmYl5HA3Myshh7uZWQk53M3MSsjhbmZWQg53M7MScribmZWQw93MrIQc7mZmJeRwNzMrIYe7mVkJOdzNzErI4W5mVkIOdzOzEnK4m5mVkMPdzKyEHO5mZiXkcDczK6Fc4S5pkaTbJG2RdEGV4edLulXSLZL+S9LRjS/VzMzyqhnuktqAi4FTgPnAEknzK0b7GdAZEc8HrgI+0ehCzcwsvzx77i8GtkTEXRGxC7gCOC07QkT0R8RjaedNwMzGlmlmZiORJ9yPAu7NdG9N+w2lG7i62gBJZ0taJ2ndtm3b8ldpZmYjkifcVaVfVB1RejPQCXyy2vCIuCQiOiOic8aMGfmrNDOzETkoxzhbgVmZ7pnAfZUjSToZ6AFOiIjHG1OemZmNRp4995uBeZLmSjoYWAyszo4g6YXAl4FTI+KBxpdpZmYjUTPcI+IJ4BzgGmAzsCoiNkm6SNKp6WifBJ4KfEPSBkmrh5icmZmNgzzNMkTEGmBNRb8LM89PbnBdZmZWB/9C1cyshBzuZmYl5HA3Myshh7uZWQk53FtcX18fHR0dtLW10dHRQV9fX7NLMrMCyHW1jBVTX18fPT09rFixgoULFzIwMEB3dzcAS5YsaXJ1ZtZM3nNvYb29vaxYsYKuri4mT55MV1cXK1asoLe3t9mlmVmTOdxb2ObNm1m4cOF+/RYuXMjmzZubVJG5mcyKwuHewtrb2xkYGNiv38DAAO3t7U2qaGIbbCZbvnw5O3fuZPny5fT09DjgrTkioimP4447Lqw+K1eujLlz58batWtj165dsXbt2pg7d26sXLmyKfUkm9PEtWDBgli7du1+/dauXRsLFixoUkVWRsC6yJGxDvdRWrlyZSxYsCAmTZoUCxYsaFqgFqWOCIf7pEmTYteuXfv127VrV0yaNKlJFVkZ5Q13Xy0zCkW6SmXJkiW+MqYgBpvJurq69vZzM5k1TZ5vgLF4tPKeuw+/q2OC77kXrZnMyomce+5Kxh1/nZ2dsW7duqbMu15tbW3s3LmTyZMn7+23e/dupkyZwp49e5pYWXNJolnbU1H09fXR29vL5s2baW9vp6enx0dW1lCS1kdEZ63x3CwzCj78tqG4mcyKoiUvhWz2tcQ9PT10d3fT39/P7t276e/vp7u7m56ennGtw8xsKC23516Ek5mD81m6dOnew+/e3l7vsZlZYbRcm3tHRwfLly/fr0mkv7+fpUuXsnHjxkaWaCPkNnezsZe3zb3lwt0nM4vL4W429vKGe8u1ufsn92ZmtbVcuPtkpplZbS13QtUnM83Mamu5NncrLre5m4290ra5m5lZbQ53M7MScribmZWQw93MbIw145YpLXe1jJlZK2nWLVO8526jJmm/x1D9bGJq9g3+ilJHb28vK1asoKuri8mTJ9PV1cWKFSvo7e0d2xnnuen7WDxa+c86zIqu2X+/WJQ/LilCHY3++0X8H6pmE1MRAq0o/1ZWhDoaXUNDwx1YBNwGbAEuqDL8EODKdPiPgTm1pulwNxsbRQi0ovxZeBHqaPSXbcPCHWgD7gT+FDgY+Dkwv2KcdwJfSp8vBq6sNV2Hu9nYKEKgFeELpkh1NLKZrJHh/jLgmkz3B4EPVoxzDfCy9PlBwIOktzYY6uFwNxsbRQi0IjQNFamORmpkuJ8OXJrp/hvg8xXjbARmZrrvBKYPN12Hu9nYKEqgNfukbtHqaJS84Z7nOvdq17NV3h0qzzhIOhs4G2D27Nk5Zm1mI1WUO6cW5c/Ci1LHeMsT7luBWZnumcB9Q4yzVdJBwGHA9soJRcQlwCWQ3BVyNAWbWW0TNdBsnzw/YroZmCdprqSDSU6Yrq4YZzVwRvr8dGBtevhgZmZNUHPPPSKekHQOyUnTNuArEbFJ0kUkbT+rgRXA1yRtIdljXzyWRZuZ2fBy3VsmItYAayr6XZh5vhP4P40tzczMRsv3ljEzKyGHu5lZCTXtP1QlbQPuqXMy00l+MNVMRagBilFHEWqAYtRRhBqgGHUUoQYoRh2NqOHoiJhRa6SmhXsjSFoXOf4otuw1FKWOItRQlDqKUENR6ihCDUWpYzxrcLOMmVkJOdzNzEqo1cP9kmYXQDFqgGLUUYQaoBh1FKEGKEYdRagBilHHuNXQ0m3uZmZWXavvuZuZWRUtE+6SHq3Sb5mkX0vaIOlWSQ29U1KOed4h6ZuS5leMM0PSbklva2QNkl6VznN2Wsdjkp4xxLgh6VOZ7vdKWjbCeR8h6QpJd6brd42kY9Jh50naKemwzPgnSnpY0s8k/ULSP6f9z0rX1wZJuyT9d/r846NaKTmWseJ9+oWkL0pqyPYuqUfSJkm3pNO/WtLHKsY5VtLm9Pndkm6oGL5B0sY663hdug6em3bPkfSHdNo/l/QjSc/JjP9iSddLui1dJ5dKenKdNRwuaaWkuyStl3RjWtfgtrAhXU//r2JbPUXSOkmbs9vKKOY/S9IvJU1Lu6em3UdLmifpu+n2u15Sv6RXpuOdKWlbWt8mSVfVuy4yNV0n6S8r+r1b0hckHZN+jraky75K0uGNmO8B8twXuAgP4NEq/ZYB702fzwMeASaP1zzT7jcC9wMzMv3eCdwAXNeoGoCTSO6T/6xMHb8C/qlavcBO4Jek99UH3gssG8F8BdwIvD3T71jg+PT5T9JlPDMz/ETgu+nzJwG/AF5RMd27qXGv/xHUOOQyVmwbk4ABoKsB83xZul4OSbunAycAd1WM93Hg/2aWeQMwK+1uT7s31lnLqvQ9GFzmOdlpAm8D/i19fjjJ70oG/1RHJDf5O7yO+VfbRo4Glma3hbT/x4CPps870m35uWn3QcA766jj/cAl6fMvk/yh0BTgduDUzHgdg9srcCaZ/6UAVgJnNWi7fBvw1Yp+NwHHA3cAf5Xp3wV0NGK+lY+W2XOvJSLuAB4Dpo7zfK8ErgX+OtN7CfAeYKako+qdh6TjgX8FXh0Rd2YGfQV44+BeS4UnSE7enDfK2XYBuyPiS4M9ImJDRNwg6VnAU4EPkyzrASLiDyQBVvfyDyPvMh5M8mHf0YB5PhN4MCIeB4iIByPiB8DvJL0kM94bgCsy3atIdgQgWWd99RQh6anAK4Buhr5R39PYt8zvIgn6G9O6IyKuiojf1lHG/wJ2VWwj90TE8opaBRyaqeX9QG9E/CJ9zRMR8YU66vgM8FJJ7wYWAp8C3gTcGMmNDQdr2xgRl1W+WMltyp9CY7YPgKuA10g6JJ3+HOBI4Ji0pu9kauqPiLqO4IZSmnCX9GfAHRHxQBNm/1Ng8NB4FnBERPyE/T/Qo3UI8B/Aawc/DBmPkgT83w3x2ouBN2WbTkagA1g/xLDBcLoBeE72cHuQpKkkR1PXj2LeIzHcMp4naQPwG+D2iNjQgPldC8ySdHt6mH1C2r+PNGQlvRR4KN3hGHQV8L/T538FfIf6vBb4XkTcDmxPt3+AZ6VNDXcC5wOfTvsP936O1gKSbX8ox6fr/1fAySTbasNriYjdwPtIQv7dEbErR22Q7BhtAH4NTKP+92SwnodIjmwXpb0WA1emNTX6PRhSGcL9PEm3AT8mORRvhuw/US0mCXVI9tzqPQ+wG/gRyR5aNZ8DzpD0tMoBEfEIcDlwbp01VFoMXBERfwS+yf53BD1e0i0kTVXfjYj7Gzzv/dRYxs9ExLHAM4CnSKr7VtQR8ShwHMk/im0DrpR0Jsl7fXrarr+YA/fMtwM70ho2kxxl1mMJ+44MstvZnRFxbEQ8C3g343npnXRx2tZ/c9rrhrSWWcBXgU+M4exPIfkS7xiitm9J2ijpm5neV6bbxxHAf5N8QTTK3i97qm8PY64M4f6ZiHgOyR7y5ZKmNKGGF5J8YCH5kJ0p6W6SPzF5gaR5dUz7jySH+C+S9KHKgRHxO5L2wncO8fp/IflieMoI57uJJMT2I+n5JHvk30+XcTH7f4HdEBHPB54HvEPSsSOc72gMu4zpnt33gFc2YmYRsScirouIjwDnAK+PiHtJ2tZPAF7Pvi/4rCtJjjTqbZL5E5ImkUvT9+B9JNt/5d9drmbfMld9P+u0CRg8YiAi3kVybqjafU/GrJZ0G/tz4KUkO3vPrFLb60ja2Q9owoyk8fs7NGj7SH0bOCk9onpSRPyUsXkPhlSGcAcgIr4JrGPfP0KNC0mvB/4C6EuvTHhKRBwVEXMiYg7JiaS69hgj4jHgNSTND9X24D9NchLngPvzR8R2kqAZas9/KGuBQyS9dbCHpBcBnyU5gTcnfRwJHCXp6Ir53k6y7B8Y4XxHrNYypm2+Lyc5iVcXSc+p+LI+ln03wOsjaRq4MyK2Vnn5t0j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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare Algorithms\n", + "from pandas import read_csv\n", + "from matplotlib import pyplot\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "\n", + "#adding models to list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('ABC', AdaBoostClassifier()))\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('XGB', XGBClassifier()))\n", + "models.append(('VC', VotingClassifier(estimators)))\n", + "\n", + "# evaluation of each model\n", + "results = []\n", + "names = []\n", + "scoring = 'accuracy'\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=10, random_state=7)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "- https://medium.com/@swethalakshmanan14/how-when-and-why-should-you-normalize-standardize-rescale-your-data-3f083def38ff\n", + "- https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51\n", + "- https://datavizcatalogue.com/methods/density_plot.html\n", + "- https://towardsdatascience.com/data-preprocessing-concepts-fa946d11c825\n", + "- https://www.kaggle.com/startupsci/titanic-data-science-solutions\n", + "- https://towardsdatascience.com/feature-selection-techniques-in-machine-learn\n", + "- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html\n", + "- https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761\n", + "- https://www.datasciencecentral.com/profiles/blogs/introduction-to-classification-regression-trees-cart\n", + "\n", + "- https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\n", + "- https://towardsdatascience.com/boosting-algorithm-adaboost-b6737a9ee60c\n", + "- https://www.analyticsvidhya.com/blog/2020/02/4-boosting-algorithms-machine-learning/\n", + "- https://www.toptal.com/machine-learning/ensemble-methods-machine-learning" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "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": 4 +} diff --git a/Assignment Colab/Stella Esther Nabirye.ipynb b/Assignment Colab/Stella Esther Nabirye.ipynb new file mode 100644 index 0000000..7e457d1 --- /dev/null +++ b/Assignment Colab/Stella Esther Nabirye.ipynb @@ -0,0 +1,2712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stella Esther Nabirye\n", + "### Kaggle assignment\n", + "**Importing Libraries** \n", + "Python libraries contain functions/ methods helpful in analysis and in order to access those functions, we need to import those libraries.\n", + "For the assignment, I import\n", + "- the numpy library which contains functions for mathematical computations\n", + "- the pandas package which contains functions for manipulations of data structure and operations \n", + "- the matplot library which provides for plotting of the data.\n", + "- seaborn for data vusualization\n", + "\n", + "\n", + "
\n", + "So far so good.\n", + " \n", + " For your final report please just imagine this:\n", + " Different people are going to have a look at your report to try and see if you have some sort of proof of understanding the concepts you applied.\n", + " Your explanation and elaboration in the notebook will tell them, so you can't assume anything.\n", + " \n", + " Give the explanations more meat.\n", + " \n", + " But so far so good." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "import os\n", + "#for dirname, _, filenames in os.walk('/kaggle/input'):\n", + "# for filename in filenames:\n", + "# print(os.path.join(dirname, filename))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#import the necessary libraries you are going to use\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "#The necessary libraries are loaded because they contain the different functions that will be used \n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading in data\n", + "\n", + "Since the data is in CSV format, we read it in using the read_csv function" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "", + "_uuid": "" + }, + "outputs": [], + "source": [ + "# Loading the train and test datasets to be used\n", + "Train = pd.read_csv(\"../AMP Data Sets/AMP_TrainSet.csv\")\n", + "Test = pd.read_csv(\"../AMP Data Sets/Test.csv\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((3038, 12), (758, 11))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the dimensions of the data\n", + "#This returns the number of columns and rows for both the train set and the test set\n", + "#This helps in knowing how much data we are working with\n", + "Train.shape, Test.shape\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "What do the dimensions tell us?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "A good conclusion would befit what you just did above. Some sort of summary about what you have learnt from the data, and concepts deployed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Descriptive Statistics\n", + " Using the describe function, we are able to get a summary of the statistical details regarding the shape of each attribute of our data such as the mean, standard deviation,count, minimum and maximum value of objects of each column that help in further understanding of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "count 3038.000000 3038.000000 3038.000000 3038.000000 \n", + "mean 2.060237 8.521520 0.971410 73.668760 \n", + "std 3.819929 7.586652 0.107413 8.527489 \n", + "min -16.000000 0.000000 0.684000 42.750000 \n", + "25% 0.000000 2.516000 0.895000 68.294000 \n", + "50% 2.000000 7.143000 0.963000 74.059500 \n", + "75% 4.000000 13.158000 1.041000 79.343750 \n", + "max 30.000000 46.667000 1.451000 101.682000 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "count 3038.000000 3038.000000 3038.000000 3038.000000 \n", + "mean 0.994007 -2.432927 0.088545 15.683233 \n", + "std 0.031333 1.707223 0.284133 11.575665 \n", + "min 0.866000 -10.432000 0.000000 0.041000 \n", + "25% 0.974000 -3.606000 0.000000 5.587500 \n", + "50% 0.994000 -2.296500 0.000000 14.988500 \n", + "75% 1.011000 -1.283250 0.000000 26.807750 \n", + "max 1.196000 3.576000 1.000000 51.280000 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n", + "count 3038.000000 3038.000000 3038.000000 3038.000000 \n", + "mean 73.650828 5.911361 1.235255 0.500000 \n", + "std 9.166092 0.693689 0.210012 0.500082 \n", + "min 42.778000 3.533000 0.785000 0.000000 \n", + "25% 67.556000 5.459250 1.082000 0.000000 \n", + "50% 73.697000 5.925500 1.184000 0.500000 \n", + "75% 79.778000 6.382000 1.351000 1.000000 \n", + "max 103.167000 8.662000 2.192000 1.000000 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#we use the describe() function\n", + "Train.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104
count758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000
mean2.0738798.9450910.97304673.8218910.994212-2.3979220.08443315.57006773.8442045.9044921.250189
std4.2306157.8144490.1106768.0295240.0323701.5971380.27821911.3625898.9151930.6569110.218102
min-13.0000000.0000000.69900047.0000000.889000-7.8440000.0000000.06000047.0000003.8430000.841000
25%-0.5000002.7217500.89400068.7402500.973000-3.4572500.0000005.70900068.3460005.4712501.096000
50%2.0000007.5000000.96500074.0695000.994000-2.2380000.00000015.05700073.6460005.9355001.188000
75%4.00000014.2302501.05350079.2847501.013000-1.3062500.00000025.29025080.0692506.3752501.378500
max30.00000044.1180001.431000102.9290001.1820002.0170001.00000050.098000102.9290007.5880002.283000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 2.073879 8.945091 0.973046 73.821891 \n", + "std 4.230615 7.814449 0.110676 8.029524 \n", + "min -13.000000 0.000000 0.699000 47.000000 \n", + "25% -0.500000 2.721750 0.894000 68.740250 \n", + "50% 2.000000 7.500000 0.965000 74.069500 \n", + "75% 4.000000 14.230250 1.053500 79.284750 \n", + "max 30.000000 44.118000 1.431000 102.929000 \n", + "\n", + " FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n", + "count 758.000000 758.000000 758.000000 758.000000 \n", + "mean 0.994212 -2.397922 0.084433 15.570067 \n", + "std 0.032370 1.597138 0.278219 11.362589 \n", + "min 0.889000 -7.844000 0.000000 0.060000 \n", + "25% 0.973000 -3.457250 0.000000 5.709000 \n", + "50% 0.994000 -2.238000 0.000000 15.057000 \n", + "75% 1.013000 -1.306250 0.000000 25.290250 \n", + "max 1.182000 2.017000 1.000000 50.098000 \n", + "\n", + " AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n", + "count 758.000000 758.000000 758.000000 \n", + "mean 73.844204 5.904492 1.250189 \n", + "std 8.915193 0.656911 0.218102 \n", + "min 47.000000 3.843000 0.841000 \n", + "25% 68.346000 5.471250 1.096000 \n", + "50% 73.646000 5.935500 1.188000 \n", + "75% 80.069250 6.375250 1.378500 \n", + "max 102.929000 7.588000 2.283000 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Test.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification\n", + "\n", + "This helps us know how data is distributed with regards to the class, that is, whether it is balanced or imbalanced." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.groupby('CLASS').size().plot(kind='bar',color=('orange', 'blue'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data correlation\n", + "This helps us to know how the relationship between the variables and to identify which variables are positively or negatively correlated and perhaps where there is no correlation at all. This is important because the performance of algorithms such as linear and logistic regression is affected if attributes in data are highly correlated.\n", + "\n", + "
\n", + "Great!" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
FULL_Charge1.000000-0.612996-0.490977-0.434603-0.058725-0.2837580.0880680.355477-0.365374-0.0905700.2329290.534602
FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n", + "
" + ], + "text/plain": [ + " FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\n", + "FULL_Charge 1.000000 -0.612996 -0.490977 \n", + "FULL_AcidicMolPerc -0.612996 1.000000 0.794796 \n", + "FULL_AURR980107 -0.490977 0.794796 1.000000 \n", + "FULL_DAYM780201 -0.434603 0.541481 0.548253 \n", + "FULL_GEOR030101 -0.058725 0.115201 0.346139 \n", + "FULL_OOBM850104 -0.283758 0.513344 0.462712 \n", + "NT_EFC195 0.088068 -0.143168 -0.169540 \n", + "AS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \n", + "AS_DAYM780201 -0.365374 0.449621 0.456260 \n", + "AS_FUKS010112 -0.090570 0.002334 0.032958 \n", + "CT_RACS820104 0.232929 -0.213543 -0.403599 \n", + "CLASS 0.534602 -0.598816 -0.584111 \n", + "\n", + " FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\n", + "FULL_Charge -0.434603 -0.058725 -0.283758 \n", + "FULL_AcidicMolPerc 0.541481 0.115201 0.513344 \n", + "FULL_AURR980107 0.548253 0.346139 0.462712 \n", + "FULL_DAYM780201 1.000000 0.010118 0.334778 \n", + "FULL_GEOR030101 0.010118 1.000000 0.319157 \n", + "FULL_OOBM850104 0.334778 0.319157 1.000000 \n", + "NT_EFC195 -0.090058 -0.230417 -0.230561 \n", + "AS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \n", + "AS_DAYM780201 0.894191 -0.029085 0.275640 \n", + "AS_FUKS010112 0.055915 0.040480 -0.452769 \n", + "CT_RACS820104 -0.326792 -0.151935 0.155304 \n", + "CLASS -0.554838 -0.260470 -0.453287 \n", + "\n", + " NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\n", + "FULL_Charge 0.088068 0.355477 -0.365374 \n", + "FULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \n", + "FULL_AURR980107 -0.169540 -0.426097 0.456260 \n", + "FULL_DAYM780201 -0.090058 -0.408793 0.894191 \n", + "FULL_GEOR030101 -0.230417 -0.160269 -0.029085 \n", + "FULL_OOBM850104 -0.230561 -0.336297 0.275640 \n", + "NT_EFC195 1.000000 0.178683 -0.036844 \n", + "AS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \n", + "AS_DAYM780201 -0.036844 -0.322378 1.000000 \n", + "AS_FUKS010112 0.145924 0.025580 0.045562 \n", + "CT_RACS820104 0.080898 0.171524 -0.256060 \n", + "CLASS 0.260702 0.693552 -0.437168 \n", + "\n", + " AS_FUKS010112 CT_RACS820104 CLASS \n", + "FULL_Charge -0.090570 0.232929 0.534602 \n", + "FULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \n", + "FULL_AURR980107 0.032958 -0.403599 -0.584111 \n", + "FULL_DAYM780201 0.055915 -0.326792 -0.554838 \n", + "FULL_GEOR030101 0.040480 -0.151935 -0.260470 \n", + "FULL_OOBM850104 -0.452769 0.155304 -0.453287 \n", + "NT_EFC195 0.145924 0.080898 0.260702 \n", + "AS_MeanAmphiMoment 0.025580 0.171524 0.693552 \n", + "AS_DAYM780201 0.045562 -0.256060 -0.437168 \n", + "AS_FUKS010112 1.000000 -0.445284 0.033432 \n", + "CT_RACS820104 -0.445284 1.000000 0.267652 \n", + "CLASS 0.033432 0.267652 1.000000 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using the cor() function and the pearson coefficient's method\n", + "Train.corr(method='pearson')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# to visualize this the data correlation, we plot a heat map\n", + "plt.figure(figsize=(6,6))\n", + "sns.heatmap(Train.corr(method='pearson'), cmap='PiYG')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['FULL_AURR980107', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n", + " 'CT_RACS820104'],\n", + " dtype='object')" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Train.iloc[:,[2,4,5,6,10]].columns" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FULL_Charge 0.534602\n", + "FULL_AcidicMolPerc -0.598816\n", + "FULL_AURR980107 -0.584111\n", + "FULL_DAYM780201 -0.554838\n", + "FULL_GEOR030101 -0.260470\n", + "FULL_OOBM850104 -0.453287\n", + "NT_EFC195 0.260702\n", + "AS_MeanAmphiMoment 0.693552\n", + "AS_DAYM780201 -0.437168\n", + "AS_FUKS010112 0.033432\n", + "CT_RACS820104 0.267652\n", + "CLASS 1.000000\n", + "Name: CLASS, dtype: float64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking for correlation with regards to the class\n", + "Train.corr(method='pearson')['CLASS']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Distribution\n", + "Knowing whether the data is skewed or not is important in the data preparation. This is done by calculating the skew for each attribute with using the skew() function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gfLa2fa/K8S62PUjurwOeavt5ku4HnFr77wNYp/YKW/SH5izFAJIWAHe2GG9tSXez/fsm3vrA3SrH+IPtLwJfbNa/K7A3cLik02y/qHK8JwMvBm4dWS6gehOXpJtneomSpGp7PPBu4GzgCNuW9FTbL2shFixLCgDPB55s+zeSPgWcWzOQ7XcB75L0rtqJfAad7ivAhra/3Dx+r6RzgC9LegnLknBNHwfuDfyj7f8DkPQT29u0EAvKQXjgGcBnAGz/oq2LvGlK7h8ATgQ2k/QOyhnZm1uM99/A1yX9F2Xn+mvg6Moxlv6vNmfqxwPHN5dtz6scC8pZ0W22v7HChkiXthDvRuAxgy/PSLyrawezfXbTHvxq4HRJb6SdxDCwvqTtKc2ba9v+TbMdf5D0xzYC2j5I0hbA1gx9f21/s3KorvcVSbq37ZsAbJ/RNKt9FtikdjDbr5a0A3CspJOAD9HuvnKjpOcAPwOeCOwHIGkd2jnRmZ5mGQBJDwWeRkmKX7d9ScvxdgGe3sT7iu3TKq//H2y/t+Y655PmHsXJtldoM5V0qO03thh7C+DfgMW2H9BSjDNGFr3I9s8l3Qc4rY2iUJLeTbm6+wEwOIDY9lTfOJb0IuAK22eOLF8IvMX2K1qKuxbwKmBP4IG2799SnAdTTlDvB7zf9seb5X8OPNP2G6rHnJbkLmnc0fsW239oIdbalC/n02uvez6QdF9gC8qZyrXjzqxj9TX7z91s3zbrm1d93ZcCjxg0F7btrrKvSNoc2N72KZPellqmqVnmXGAr4NeUM+mNgJ9Lug54he1zagWy/UdJtw1fJrahaX45iNIEM6jPfB3weeDdtm+sHG974COUtsafNYu3lHQj8Pe2q7YTNzHvTblZvDRBUA6cVf+2oVidfZ5DcRdT9s07gMts/5BlNz1ruwJYF2g1uU9iX1nJthxpe/8W1vtQYDeG9s2m3b16i4CkVwD/Y/uypifVUcAewJXAvrbPqx1zmpL7l4ETB00jkp5JSRrHU7pJPrZyvN8BF0r6KvCbwULbr6kY43jgdMqd818ANHfP96XccKndn/i/gL+xfdbwQkmPa16r2vNI0l8BhwBfYVmC2Al4p6R/sf2JmvHo+POU9GfAYZR7CzsA3wE2lvQH4CW2q99XoBw0zpf0dYYSfOX9ErrfV2ZqVxfw7JqxmnhvpPRSOw4YNBtuSWmDP872uyuHfC3lJi5N3EcA2wDbU5prnlw53lQ1y6xQ2H6wTNL5th9VOd6+45bbrnZTVdKlth+yqq+tQbzLbG87w2uX235Q5XiXAo8dPWNu+jCfZfvBteN1/HmeR2kvvV7SNsD7bO/e3NT9R9vPrBmvidn6ftnE6Xpf+SNlZrbhriNunm9he73K8X4EbDfarCtpPeDimf72NYi3NEc1vanOsv3vzfOqXawHpunM/YbmaHtc83wv4NdN+2b1LpG2j266Jy603UbvAICrJP0TcPRQd6z7Ai8F2jjrO1XSlyiDJwbr3wr4K8qVUW1ifA+EO1n+S1xL15/n2ravbx7/lNKDBdtflfT+FuJ1tV9C9/vKFcDTbP909IU2elZR9sH7s+JUn5vTThfrO5t2/V9TOoW8Y+i1VnrLTFNyfxHlEv+k5vm3m2VrAy+sHUzSrsB7gfWAbSQ9Cnhr5V4JewEHAt/QsmHI/wecTAt/k+3XSHoWy9oZBVwDHN7SjaR3AOdK+grLEsRCSvPI21qI1+nnCSyR9DHg65TP9H8AJN2Dsl9W19F+OYl95f3AxpSD5Kh/bSHe6yhdnS9j+X3zQZTeM7UdDCyh7Bcn274YljbtXdFCvOlolmnOzt9t+x87jHkOsDPlJshgROyFtv+0q23og6YJ5s9ZPkGcZvvXE92wCiStC7wCeBhlOPlRzc349YHNbK/OBPCzxcx+WUnTDXJHlt83z7bdyhiFpk/7PYf3/cGJgO1basebijP35gvTdf2MO2zfpOVHj3V2JJT0Mtv/VXmda1OGdm9JGfL83aHX3my7du0cmh35uFnf2LI2Ps+mvfbDY5b/lhUv92vpZL+cxL6ykm15hu2v1l6v7Tspg7VG421oe3Rkbo14d1CaZWh6zOxEaX3YFbhv7XjTVDjsPEknS3qJhopDtRjvIpWBFWtL2lbSB4HvzvZLFf1LC+v8D+DPgF8BH5T0vqHX2vwsV6BSV6dLbXyeM5J0akur7mq/nDf7Ch0XtaMMEGuFpMdK+nfKwf9k4FvAQ1uJNQ3NMgAqZQBG2ZWLQQ3Fuwfwz8Cgx8NpwNtt/65ijAtmegl4sO2qtWwkXWD7Ec3jdShnnZtSumad6frFmWZKAqLUflkww+urG6/rz3OmHg4Cvmh785rxmpjD+6Uo++Xbau6XTZyu95WuC5W9fiXx/tl21ZIHKiVTXki5p3AspZTKErdXy2Z6knuXVIqSbQ1c3tbAlybO/1Hao0fbnwV8t/ZQaEk/tP3QkWUHN9uwWQvdv/4AfJLxzQYvsH3PyvG6/jz/CHyD8T1/Hme7lV4QXZjAvvJrZi5U9mnbVZstJP0OeA9l4NmoA2xXLcEt6XrgUsqN4y/a/p2kK9xSaQyYkjZ3AEl3pxTb2Q64+2B57TN3SS8H3gn8mNIbYX/bM51VrKkvUqrhnT9mO/6nhXhLJO3iZdX3sP1WSddSRiPWdgHwXtsXjb4gqY3SDl1/npdQBvpcNiZeG933BqNh3wQsYvnCYY+oHKrrfaXrQmXnAid5zMj2JgfUdj/K1dY+wPtV6hKtL2mdpi2+uqk5c5f0GeCHlBsQbwX+ErjE9msrx7kI2KkZmPIA4JO2H18zxl2FpCcDV83Qd3mx7SUT2KxqJL0AuHBcf3NJz7N90phfW9OYlwL/CFzIUH/sNnrm9JmkhwA3DI1TGH7tvm6xhk5zovocSqJ/EqUIYu3y3lOV3M+zvf2gLbDphnaa7Z0rx1lutNjo89qau+aD7liD2ivfc0v/MRpTT4PS77bVCptd6frz7Jqkb9t+UkexOt9XdBcpVDYg6V6U2liHVV/3tOzzkr5ne0dJ3wT+HvgF5Utbtc1KpRDZcNe9vYefu2IND5X6OB8GLmOoOBNlIMXf2/5KrVhNvOF6GtcMxdsbqF5Po7kRtx+wO2U04CBBfB742OjQ7wrxuv48Xw/cZPtjI8tfTem7XH2UqqSnUf4PR2vLfK5ynK73lbGFyih1e6oXKtOEiszNsC3VZyWD6UruL6cU7n8EpXDRhsDBto+oHGds7Y4B160tcwnwLNtXjizfBjjF9p/UitWst+t6GsdSvpxHs3yC2BfYxPZeleN1/XleBDza9u0jy+9GGQxTux0cSf9N6Tp3McuaZar3GpvAvnI+Mxcq+w9Xnk5T0mmUInNHe8Uic0+33dkk4JKutr1V7fVOzQ1V2//ZPPwG0Nod5prJew7WYVnSG/YzSlnX2rqup/For1is6xrgTLUzGXHXn6dHE3uz8PdN81AbHuluRqN2va9sMJrYAWyfKamNuX0X2T50JNYvgEMltdK9eiVaOcOemuTenA3twYq9BN5aOc4XWMmH7bo1PI4CzpZ0HMvXt9iLdgZudF1P49eS9gQ+24wGHAz53pMVuyvWMO7z3IrSlNDKQJhxN9+aduO2nCnpYbZbG2jT6Hpf6bpQ2VXqsMicyqC9cXlFtDA6FaarWebLwE3AOSybXozaNyJUCvnMaFxXrTWM9zDguSxf3+Lktr686rCehqRFwKGUWijDk6ycDhxo+yctxPwTVix21crnqVKv/jXAG1g2IfYOlEJXh7dxFdg0PT0Q+AmlzV2UK4g2moC6rr0yrlDZyW6hUJlKzaMDm3ijReYOtX1D5XjbUpL46IFja8qN48trxoPpSu4X2X54xzHXAwY1xy+tfQNwhpib2v5li+tfCNxs+8Ym+S6mdCm9uK2YTdz7UPa31v62SWgS0oHAwylnZhdTbsi1Un5A0tbjlrfRFXJS+0ofSfoi8CbbF4wsXwwcYnvX2jGnqbbMdyV1VvlO0lMpvS4Op/TA+JGkp1SO8SxJP5H0bUnbS7oYOEvSNU2viKokHUi5Z3Fmc4P6y8CzgOM183DsNY25o6TH2P4VsJmk1zcJsY1Yuww9vrek/5R0gaRPtdVUYvtU239m+z62N20et1VXZpDEN6IUm9oV2KilxN75vrKSbTmypfU+VNLTRtv0h/ejihaNJnaAZqzHohbige15/UMZrHEBpZjPHyhDeC8YLG8x7jnAQ4aePxg4p3KM84E/AR5PKdD0uGb5nwDntvA3XUyZGOA+wC3Agmb5BsBFLcQ7hDLycAnwLkpzzMHANyn1O2rHO3fo8X8Cb6dc9h5AGY1YO95Xhh4f1MZ+OCbma4GLKAP53tp8D17dg31lkxl+7gNc00K81zS55CTKPKa7jduPKsa7fHVeW5Ofabih+pwJxV3XQyMPbf+oGThV051uBoRIus32mU2sS5r2ztr+aPu3km4Hfks5oGD7Ny117ngB8CjgbpRxCVvavlnSe4CzWH42mtoWe9nUi/82WxfX1TRc+GxPygGsbftRpi78DYCkQ4H/BT5YOU7X+8r1zDzN3mZjf2PNvALYwfatTZPTCZIWuUx918YfeLakV9j+6PBCSftRTiSrm4bkvhmwqUcudVVmpLmW9upmD2bZOaZ5/pfU/0+4UdLfAPei9Cw5gDLJ89NZsYBSDeeqzN+4AWUQzNHNjeqdaafM6R0uN99uk/Rj2zdDqXcuqY3udJs1TQYC7iVJbk6NaKcJchI3rMRQh4LmcRvJqOt9petp9tZ2U7Pd9pVNM+wJzT2NNj7P1wEnShrOI4spM2rt3kK8qUju76F0Txp1CXAkZWdrw98Br6RcvonSlLDCxAxraF/gzZQkMSgqdBrlgPWKyrGgTL6wZxPvBEpPiBdRLk8PbyHe7ZLuYfs2Si8SYOnowDaS+0eBQaXJoyklaq9vBqesUEysggeolKrV0OOlXHnqu8Z/Ue7LnNg8fx7tdPPsel/pepq9X0h6lJsic80Z/HMo3Wmr39tz6W75BEk7UW6+A3zJ9um1Yw3M+94yWskUYpK+78oj16IeSXez/fsxyzcFNrfd9YQdVXXdbXYo7qMpBacEfNP2eW3E6TNJW1KuLH8x5rUn2v7OBDarqmlI7pfbftCqvrYG8Waa8AGoX1q1OZLvQRmwcQelh85Hbf+4Zpwm1rnA54Bj21j/DDE7K+QlaRPKAJtrKWezb6LcrL4EeKcrz9sq6V6DpqYxry0c18SwBrFWOnmE6/fLnsS+0mmhshm6ev7QY0pUT6Np6Ar5NUnv0MhdHEn/Qul9UdudlHbMYygzp+w68lONpHdTRuCdSekJdAWljvwJKiM7a9uY0o3uDEnfk3SApKoTWAxTKeR1GfD/gGcDf0GZ7u6y5rXa/pvSRrwDcAalhvahlBuCH28h3v8MHkj6+shrtcv9/pLStLSk+Tln6KeN0sld7ytvpBQpE/A94Ozm8bFNt8za8Wbq6vnprrt6tqaNLjiVuxBtQJmW6seUwmGfBS6n7AgbthTzoZQkdC4lYTwbWKeFOBcOPV4H+E7zeGPa6W423FXwyZR7CL+gJML9W4h3CaV/7+jybSiDYWrHO7/5V8DPxr1WOd554x6Pe14h1r8D32/+z55Mc9Xd1s8E9pUfUXqojS5fD7ishXiddvWcxM+8P3O3/Rvb+wDPoJx9fRx4pu29PTRDuaTtKsb8oe1DXOq4f4FS7+KAWusfcufQ5fb9gbWb+IOh+q2x/S3bf0+5BD6U0nxRW9eFvNZSGVa+FbBhc6k9GB27XgvxPMPjcc/XLFCZlOZRwGeAl1AmjP9XlYqXrepoXxkUKhvVVqGyP9r+LaVq6XJdPVuINRHT0FsGANtXUJotZnIMUGVSDUlbUIpN7U6piXIAZULb2t5J+ZJeSrla+Lsm/gLKWVptK1RidOmq+GXaKc7UdSGvd1Fm6wL4a+A/JRl4GOVKrLbhrpebDV3Oi+X7wFfhcmp5hqTzKJ/h22ju0dSORff7SteFymbq6vk02unq2bl5f0N1rtTM1FRhPd+gdKc7ntIFbLkbVa5/42oTSgnjVifjnhR1WMiribc2Zb++Q2WykEdRmmh+3kKsQ1b2uu1qB5RmiPxulIo8fcObAAAWOklEQVShCyg3Oz9tu5W5WidB3Ra1W4flu3o+ltIV+aeUom9Tfwbfp+ReZTo8SVey7JJ6+MMZVN9rrZb80DY8BPgH21X7us92o8j2+2rGm2Vbqnc3a7oIzsiVZ/OZK0kH2V6j0auSfkM5Sz+Wcs9puS+u68/E1Om+IukewB/cFOdrvgPPBq603cZV82j8dSn9z39m+7q243UhyX2CJD0CeC+lrfEkyhDyD1POIg6z/W+V491J6XFxKsvKxS5V80yzibc2pcfRFsCXbV/UDBR5E7B+jSutkXh3Um6UDSY9Xm4ouyvPtztXNfZNSR9n5nZ8u/5MTF3vK98E9rN9maQHUXrMfJLSpPY92wdVjncE8EHbFzeD6v6X0ktuE8qJ1bE1403CVCd3Sfe3fW3z+Ezbj6u47t2B023f1DzfCHiqK85oL+ksyryR/wvsAvwT8CngLbZ/VyvOULxHUdpqd6F0oTuWMvN6W5Nxf5zSxv49ygHrKsrNuANrfo5D8Q6gjBm4idKb6sThm+6TUqvJsEsT2FeWDlaU9DbKNIyvVCm7fY4rzz4l6WLb2zWPX0f5bj+vGc186rT9f40z7cm9lYllm3Wf72WFpwbLqn5JR2Oo1NBY1EYb45jYT6C0MT4deKPtk2f5ldWJcRHwCNt3Sro7pa/2gzxmVGDluNtQ/rbdKAeUd7oZZj4Jlc7cX2z7v2dqLmmzSa2jfeUCNwMEJX0HeM/gBKCNkejD32WVGaA+Y/vjo69Ns6npLTODNrsLjusmWvvzurvKrO+Dv+NW4BGDAVtttRE3vXG2p9TQuIYy63sbbnczvZ7t30n6UduJvYn1E0mfp/RjfgmlXPPEkjt19tNBzfF7rvRdlXW4r1wg6b2UbrIPAr7SxN+opXg3Nk2EPwOeSKm2ObjRun5LMTuVM/eZ130UpQ/s4ZS2zlcDG9t+acUYZ6zk5eptxJJeRultcXdKD4Hj27x5JOk2ys0/KAnugc3zVqaGk/QASlPCbpTudMcBX2yjiauJ9yrbH5rD+95k+51tbENbJrCvrE+pVb85cJTt7zfLnwA80PYxK/v91Yj3YOADlFHM7x86a/9zyjiaN9SMNwnzPrlL+iAzTyy7r+17tRR3A+AtlEtRUc4k3j7NXaSam2QXsqzy3miPi6pVDDXDlHBD8aqWa27+vguAzwM3s+LfV7uHR+c38Zsz6Vew4kTxbdxQ7XJfeYbtr87w2qG231gz3l3BNDTLrKxuRhs1NYClI9Wq17SYC0nPAP7J9jMqr3qnyutbqUHybtrAt6MkiEuaAWlteCvLktCGLcWYtM8D3wK+xvJ13WvrdF8BDpd0gO0vDRY0/d6PopxdVyXpX4ErbB8xsvwA4H59OJjM+zP3lZH0Xtv/UHmd77f9OklfYMwVQ80zFkk7A0ewrCvkOymlDgS8o4W+y/9E6WLZ+g3bJt69KNPdLaa0eQt4JKX3xX6eoaLitJB0B3DbuJcozU7VryrH3ehvwwT2lUWUka9vsv255gb8CZQrsH1deXJ6ST8AHj64JzS0fC3K9J0PH/+b02MaztxX5oVA1eTOspmX3lt5veMcBuxP6Qr5LEp1yLe4TPXVhq2BcyS9svYAohl8gDKUe+/Bl6i5WfwW4EOUipjVNAlhL0rJiC8A/wg8hVJ07m22f1kzHqXwW9e9Kr4o6dm2T2k5Tqf7istsSE8HTpO0GeVG+Fm226rQ6NHE3iy8c9ChYdpN+5n71ba3mvR2rK7RNluVqege2HLMR1MGS/2Q0sd+6Q5eu3eOpMtsb7uqr61BvOMppZM3oKmsSUnyTwIeZbvqfLxddpmTdAvL5hTdALid8rdCe1cJXe4rg+/B5pSr168yNANTC/HOBl5k+7KR5dtSatgvrhlvEub9mbtmnqRAtNAVUtKFrKSiX+UeHhtJev7y4Zc9r90s06zzXEn/TCmd/ECWL7VQewRn12dAD7P98KY72zW2BzMlfVlSG4XYPtPCOsey3WkXyCZml/vKYUOPLwDuO7SsjXgHA6dKejvLz2l6EKWI2dSb98md8sEPzlhG3d5CvMHZ3Subf4cnyB7XvromvsHyE4AMPzelOFQ1zeXuYZRCZTsPupu16DuSDqY0iSw9YEp6C6UJqrbbAVyKhl078lobbcfrNn/fOLb9thZi0pwAPImyj3yrpdG+ne4rtru+2X+qpOdRmu5e3Sy+GNjDUz7948BUN8u0SdJ3bD9xtmXTRNIVwLsp0/i1/h/f3FD9GKUU8/mUZLQ9cB7wcleuginpOpbN5rNX85jm+Qtt37dyvHF9oe9BmVz6Prar99iR9GHKIJ9B7ZO9gB/bfuXMv7VacbreV54/ssg0s0/ZvqXt+EPbcXdgV9udXZW1Zd4nd61Y6c/AL91yqVNJ5wOvsv3t5vkTgA/X7KkwZij5YIf+tu2f1IozFG+B7evHLN+KctPzPbVjNut/IKUAlICL3dKcnJL2Xdnrto9uI24T+56UQTj7UcpFH9bGoB9JF1N6ebh5vhblxm61yWqa9Xa6r0j6rzGLNwEeQelZ1caUmoPYawPPpJRY+HPK1dAL2orXlWloljlszLJNVAoK7eP2aobsBxylUjEOymjVqgNFGD+UfBHwz5L+n+3jxry+2oa/rJI2pdSz3odStbF6WVU19VBs/1jS/YZ7Xcx1dOeqGE7ekjYsi9oddNbcE3o9pdnuaODRrjwR94hLKZNYDAaAbUVpo66q633F9svGLW8Gwh1PKTxXlaSnAC+izO37PUoZgm1s125+nYh5f+Y+E0mLgffZfkrLce5F+ZxuajPOSMxNgK/VHv3YnF3uTtmhH0z5ku5le8uacYbiLe0NNKZnUCujOyX9HeWm2KAWy63AobY/3EKs9wDPB46kTPDQegVKlclkHkNJRjSP/5fmflCtcRhd7yuzbEv1fUXSNZTRtx8BTrJ9i6Sf2G592sKuTMOZ+1i2lzRnZ1Vphup7g66v7mBCC9s3tNTX9jpKUngzpenHKqWN26IZHo97vubBpDcDT6CUb72iWfYA4N8lbWL77ZVDvoFS6/zNlKutpZtCS90TKb08utD1vjKWyqQdv29h1Z8Fnke5Z/FHlUJz03mmO4OpTe6S7ks7/xkTqb43rBm52sal/ZsohbU+AnxK0qdbiDGsswmkGy8BHumhQmG2r5D0QsqctFWTu+3OJ5i3/Q1YekU5XFum6vSPdLyvzDAifBNKv/cX145n+7Uqddx3ojQ3vQe4V7OvnNLFVVjb5n2zjMYXDtuEcob2Wttf6H6r6pihT/0mwLWUIdeXtBT3AZQdem9gW+AQysQWK0yKvIZxBlUhhytC0jx/gO0NZvrd1Yx3qe2HzPDaD20/tGa8SZC0P2Vi7N9SBhW1Ov1jh/vKn40sMvAr4DLbbXR5Ho2/LmVikn0oVSE3bTtm26YhuY/2gBj8p5/dRm+EobhHUw4eNzbPN6b0gKh2U1UrVk008Ku2bwKObMOfUtpVX1h7dOyYv285rl8V8uuUiTm+PrJ8Z0pZh66LYVUn6TLg8a5fSmEusdvcVx5nu42xD6tM0lNsf3PS27GmpiG5L7T909nfWT3uCkPLuxhurlJq+HmUodF/0VKMjShnYQA/6vJmcZskbUepmvhtlg1+ewylF8Ruti+e4OZVIenLwPO76tHR1b4ycvP9f20/vo04Q/E6nd93Eqahzf0kyiAYJH3W9h4dxV1L0saDbm1ND5ZWPq+mW+ezKWdFu1Bu9hyx0l9a/ThHUg4eP6Fc0m8t6UTgb2tf/krajzIX5nua5z+j3MsQpaTxR2rGc5ns+OGUz3G7Js43gb9xSxN2TMBBwHdV5t9deqPR9mtqBul6X2H5G+x3r7zucT7Gsvl9PyCp1fl9J2Eakvvwf3or7YozOIzyJTqBcgb4QkpJ3mpU6rYPBk6cQSl1sONMfX4reDOwLrDVYNRf0+XtcEqlxrdUjve3lIPVwHW2t2hGAX6FcrOuKpfp/M6g9PYY1I/vS2IH+A/gdMpEGitUNayo631lrabpc62hx0u/+y3cMF7MBOb37dI0NMvM2Fe6g9gPoxQsEmXm9x9UXv+dlIkXXjoYkSrpihZvjl1EOXjcNrJ8Q+BMV65hLekc2zsMPV863Zyks20/pnK8Qf34HSjlDtaiR/XjASR91/YTOojT9b5yJctuEI+qfsO4q3EXkzQNZ+6PlHQz5T99/eYxtNuXGMrKfwD8oGkH313Seyq3g+9A6YXwNZVaHscBa1dc/6g7x7XV2r5VUhtH+XsPPxlK7GsB92khXqf14yfkjKbHzBdYvlmm9pltp/uK7UVzeZ+k7SrdO3mopMHIXgEPbJ63Mr/vJMz7M/dJmaEd/HNtdb2U9ERKE80elLPOE20fWTnG94GnMv7s6Azbj6wc78PADbbfPLL87cCmtv+2crxO68dPgqRxNYfaOLPtdF+Zq1pn2F335JqEJPcRY9rBPw18cK5nFhXirwU8g3L2+bJmWZWzlQlc+m5AaSZ5DGUQEZRmkiWUqpBVB4pIutz2g2Z4rRfJfRxJ67VwM/xKOtxX5qqLHmsj8VrvudOWJPcRXbeDz3Gbur7XUOvSd7C+B1B6rwD8wCNVISsevI5m2ZR6o/XjH2z7JWsaY75ompt2olxZ7urK5YxXYTuq7itziNf1d6HTg0lN09Dm3rWu28HnousZjY6h6X5ag0udlys6iPdqShe3y1VKNg/Xj9+vwvonTtJjKQl9d8po5ldSJpyYlKr7yjw0tWe/Se4jbJ9HSQZvHGoHX0/SqbTQDj7Xzeo4XtcHkyrxmt4we2r5+vFvbOtKoUuS3kHpjvtTykQdbwWWuMUa9XPU9b7SeimCvui88NE0sf0d26+ijGJ7P2WQA7B0NGRfdX0wqRrP9o9tf8H2yaOJvXHMmGXz3f7A/1HGBvy37V8xP84qq2yDpK21bO4EJO0k6d8lvb7p3FCC2Y+rEW9VNq3jeNUkuc+B7TttnzYyuKjVBCHp/kNPc7ZS1zR+Ye8HvAN4LqXZ6RhK1+C+XH0fT1ORVdKjKJOP/5RyA76NWvxfmeNbp/Y+TV92jEloO0GcSZlxp5OzFUn3tz2YVLqLKnydxhsxH854V4ntPwKnAqc2IyqfQ5mv9WeSvm77RRPatFr/d+sP7Q8vBo6yfVjTe6yN2dYWzOVNti9qIXYnktxXX9sJouuzy04PJhOI1xtNOYUTgBOakgCjk0uvsaYf+I1uCoVJ2olSZ+Yq4EODrpcV/++G9/edKTV0aMoDtPFduLdWnJR7KdufayFmp5Lc56/cRK0ZbLJXCtVIuhtloNsi2v3+Hk/pkXPTUDPJu1jWTPLyyvFOl3Q88HNgY0r9HCRtDrRRG+jelKufsf34gST3u5LaCULjJyKBssNttKbrX0VTfRN1DvpypfB54CZKvZw2pp8b6LqZ5HWUKe82B55k+w/N8m0pXT5ru8oV52aYj5LcV03tBLFkNV9bLV0fTObZwWsab6KOs6XtXWZ/2xrrtJmkGXR2HJQbqpJeS+n6+RNKT7XaHiLpiba/M7xQ0pOBa2foZTVVktxXTdWdemV9lCW9t2asRqcHkwnEW5mpu4k6g+9K+lPbF7Ycp9NmEkkPpgwe3Icy09qnKSPo25o96yzgljHLf0s5mOzaUtzOJLmvmi4TxAuBf6i5wq4PJl3Hm2dXCm15EvDSpoDY72mvimHXzSQ/pJT92NX25QCSDmghzsBmti8YXWh7iaRFLcbtTJL7iHmUILpuRqh+MJlAvPl0pdCWZ3URZALNJHtQztzPUJlK8Dja/Q6sbLan9VuM25kk9xV1liBUpu4b+xI9773SRrwJNHN1blCKVtJmtDgdXdfNJLZPBE7UsjmEDwDuK+kjlLIfcx10NFdnS3qF7Y8OL1SZGvKcyrEmIlUhV4Gk99qudrbZXFqb7krwruxg8n3bW05zvFm25ae2F3YVry2SnkuZAvL+lKkEt6ZMJVi1HMZQddT9hppJOq2O2uw/ewJ72d658rrvC5xI6fU2SOaLgfWA3d2D6faS3FfBtCeICRxMOo03y7ZcbXurruK1pZlEY2fga7a3bwYX7WN7/8pxdqecuT8BGDST/KftbWrGmbTm8xtMGXix7dMnuT01JbmvgtoJQtJoqVQDv7R9da0YdyXz6UqhLZKW2F7cJPntm66J37O9Y0vxBs0k+1AOKkfTTjNJVJY29xEdt4MfNmbZJk0VvH1sVx0s0vXBZAIHr3OY+UphakeljrhRZZLqbwGflHQdcEdbwWz/BvhkE2vQTHIgkOQ+z+XMfcR8aEqQtBh4n+2nVF7vGWMWb0JpZ2zjYNJpvLuC5kz6t5SKrn9JGUb/yaYEcMRSSe7zVJfTibV1MOk63l2lmasp6rWt7a9Jugewtu1xA3LiLizNMiPmQ4Jo7uR3dtRtBm5s2IN4nTZzTYKkV1Am7tgEeCBlIpkjgKdNcrti/klyX1FnCWKGAVObUHoovLZWnDlsR6cHk7bizdQHu7lS+ADQyZVJy14J7EgZPo/ty5o+7xHLSXIf0XGCGB0UZcqAkdfbvq5iHKD7g8l8OXh1fWXSst/bvn1Qu6uZiSltq7GCJPc5ailBnGH7p5XXuTKdHkwmEG+srq9MWvYNSW+iTLH3DODvgS9MeJtiHsoN1TlqEsQptneouM6lN00lfdb2HrXWPUO8hV0eTCYQb6VXCranPgk29dT3A55J6dF1GmVwUb7IsZwk9xFdJghJ59nefvRxWyZwMOk63r4jiwZXCmd3eaUQMR+kWWZFXTYleIbHbRnuu9/F0P+u43XdzNUZSSuUpx3WQsnfmHJJ7ivqMkE8UtLNlCS4fvMYltXovlfleF0fTLqOdxLQ2ZVCx+6kfIaforSx/3aymxPzXZplRnTdlNAlSX8EfkNzMAFuG7xECweTCcTrtJmra5IeSqnxsivwA0qi/4rt1soPxPTKmfuKum5K6Izttfscj+6vFDpl+4fAIcAhkvYCPgEcCrxnohsW81KS+4p6nSB6rutmrk5J2oJShnd34NeUCS1OnOhGxbyVZpkRXTclRMyFpG8A9wSOB04Abhh+3fYN434v7rqS3COmgKQrWXYlOfylHZx09KoJMdZckntEj0jazvbFk96OmLy1Jr0BEVHVMZPegJgfktwj+qX2bGExpZLcI/ol7awBJLlHRPRSkntEv/RlIvBYQ+ktEzEFmnlTb7R9U/N8J+B5wFXAh2wnqcdycuYeMR2OBzYAkPQo4DPAT4FHAh+e4HbFPJXyAxHTYX3b1zaPXwwcZfuwZvKOqZ/4O+rLmXvEdBju4rgz8HUA23eS7o8xRs7cI6bD6ZKOB34ObAycDiBpc+B3k9ywmJ+S3COmw+uAvYDNgSfZ/kOzfFvKNJARy0lvmYgp09xQfRHwQuAnwOdsf3CyWxXzTc7cI6aApAdTarnvQ5nT99OUk7OdJrphMW/lzD1iCki6E/gWsJ/ty5tlV6TUb8wkvWUipsMewC+AMyR9VNLTSC+ZWImcuUdMEUkbUEam7kPpEnk0cKLtr0x0w2LeSXKPmFKSNgH2BPayvfOktyfmlyT3iIgeSpt7REQPJblHRPRQkntERA8luUdE9FCSe0RED/1/qcxo78lXaY0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Train.skew().plot(kind='bar')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plots\n", + "\n", + "To further learn more about the data, we visualize it by using plots such as\n", + "- Histograms\n", + "- Density plots\n", + "- Box whisker plots\n", + "- Scatter plot matrix\n", + "\n", + "\n", + "
\n", + "\n", + " ## ??\n", + " \n", + " Please try talk more about these concepts and what they mean to you." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Histograms\n", + "Histograms are useful for analyzing continuous numerical data and can be really helpful in identifying useful patterns. They are also helpful in indicating the data distribution.\n", + "\n", + "
\n", + "Good explanation, so what did you learn after plotting them.?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Histogram\n", + "plt.figure(figsize=(15,15))\n", + "Train.hist( color = 'violet')\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Density plots \n", + "These are also helpful in visualization of the data distribution. The peaks of the density plots show where values are most concentrated over the interval. Density plots do a better job at determining the distribution shape,an advantage they have over histograms." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Density plots\n", + "Train.plot(kind='density', subplots=True, layout=(3,4), sharex=False)\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Box plots\n", + "These give an indication of how the data values are spread out. They display the minimum, first quartile, median, third quartile and maximum of the data. The advantage is that they takes up less space when compared to histograms and density plots which is useful for data when comparing distributions between many groups.\n", + "\n", + "
\n", + "??" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Box plot\n", + "Train.plot(kind='box', subplots=True, layout=(3,4), sharex=False, sharey=False)\n", + "plt.subplots_adjust(bottom=1, right=2, top=3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocessing Data\n", + "This is the step where the data is transformed in such a way that it can easily be interpreted by the algorithm.\n", + "This can be done by;\n", + "- Splitting the data into train/validation and test sets. \n", + "ML algorithms have to first be trained on the available data distribution, validated and then tested before it can be applied to real world world data\n", + "- Transforming the input variables through pre-processing such as `rescaling, normalizing and binarizing` " + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.457 0. 0.348 0.545 0.33 0.483 0. 0.005 0.508 0.415 0.182]\n", + " [0.435 0.116 0.322 0.489 0.276 0.458 1. 0.011 0.421 0.586 0.475]\n", + " [0.467 0.116 0.246 0.523 0.288 0.565 0. 0.011 0.442 0.273 0.666]\n", + " [0.457 0.089 0.275 0.399 0.403 0.647 0. 0.011 0.405 0.153 0.424]\n", + " [0.511 0.183 0.323 0.373 0.342 0.595 0. 0.011 0.5 0.196 0.536]]\n" + ] + } + ], + "source": [ + "# Rescaling data\n", + "from numpy import set_printoptions\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array [:,11]\n", + "scaler = MinMaxScaler(feature_range=(0, 1))\n", + "rescaledX = scaler.fit_transform(X)\n", + "set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 7.697e-01 -1.123e+00 -1.900e-01 1.376e-01 -6.067e-01 -7.206e-01\n", + " -3.117e-01 -1.331e+00 -2.257e-02 -3.610e-01 -9.251e-01]\n", + " [ 5.079e-01 -4.109e-01 -3.763e-01 -2.432e-01 -1.181e+00 -9.245e-01\n", + " 3.208e+00 -1.303e+00 -5.924e-01 9.021e-01 1.037e+00]\n", + " [ 9.006e-01 -4.109e-01 -9.163e-01 -8.651e-03 -1.054e+00 -4.632e-02\n", + " -3.117e-01 -1.304e+00 -4.590e-01 -1.409e+00 2.318e+00]\n", + " [ 7.697e-01 -5.741e-01 -7.115e-01 -8.701e-01 1.594e-01 6.274e-01\n", + " -3.117e-01 -1.302e+00 -7.015e-01 -2.300e+00 6.989e-01]\n", + " [ 1.424e+00 2.041e-03 -3.670e-01 -1.050e+00 -4.790e-01 2.003e-01\n", + " -3.117e-01 -1.302e+00 -7.713e-02 -1.977e+00 1.447e+00]]\n" + ] + } + ], + "source": [ + "#We then standardize the data to a standard Guassian distribution with a mean of 0 and standard deviation of 1\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "array2 = Train.values\n", + "\n", + "X = array2[:,0:11]\n", + "Y = array2[:,11]\n", + "scaler = StandardScaler().fit(X)\n", + "rescaledX = scaler.transform(X)\n", + "set_printoptions(precision=3)\n", + "print(rescaledX[0:5,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.048 0. 0.009 0.711 0.009 -0.035 0. 0.003 0.698 0.054\n", + " 0.01 ]\n", + " [ 0.04 0.054 0.009 0.72 0.01 -0.04 0.01 0.006 0.686 0.066\n", + " 0.015]\n", + " [ 0.054 0.053 0.009 0.724 0.009 -0.025 0. 0.006 0.683 0.049\n", + " 0.017]\n", + " [ 0.053 0.044 0.009 0.699 0.011 -0.014 0. 0.006 0.71 0.046\n", + " 0.015]\n", + " [ 0.076 0.087 0.009 0.658 0.01 -0.021 0. 0.006 0.742 0.046\n", + " 0.016]]\n", + "\n" + ] + } + ], + "source": [ + "# Normalizing data using the sklearn Normalizer\n", + "from sklearn.preprocessing import Normalizer\n", + "array3 = Train.values\n", + "\n", + "X = array3[:,0:11]\n", + "Y = array3[:,11]\n", + "scaler = Normalizer().fit(X)\n", + "normalizedX = scaler.transform(X)\n", + "set_printoptions(precision=3)\n", + "print(normalizedX[0:5,:])\n", + "print(type(normalizedX))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Did you use all the transformations above to solve your issue?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Selection\n", + "This is where we reduce the number of features/variables by automatically selecting the features that contribute most to the prediction variable or output of interest.\n", + "This is important because;\n", + "- It enables the machine learning algorithm to train faster\n", + "- Reduces the complexity of the model making it easier to interpret\n", + "- Improves accuracy of the model" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Features: 5\n", + "Selected Features: [False False True False True True True False False False True]\n", + "Feature Ranking: [2 6 1 5 1 1 1 3 7 4 1]\n" + ] + } + ], + "source": [ + "# Using recursive feature elimination with the logistic regression algorithm\n", + "from sklearn.feature_selection import RFE\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "array_1 = Train.values\n", + "X = array_1[:,0:11]\n", + "Y = array_1[:,11]\n", + "# feature extraction\n", + "model = LogisticRegression()\n", + "rfe = RFE(model, 5)\n", + "fit = rfe.fit(X, Y)\n", + "print(\"Num Features: \", fit.n_features_)\n", + "print(\"Selected Features:\", fit.support_)\n", + "print(\"Feature Ranking: \", fit.ranking_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scatter plot matrix important in identifying structured relationships between variables. \n", + "Plotting the selected features to visualize if indeed they would be better to use for classification.\n", + "\n", + "\n", + "
\n", + "What did you learn from the results?" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot matrix \n", + "sns.pairplot(Train, hue='CLASS', vars=['FULL_AURR980107', 'FULL_GEOR030101', 'FULL_OOBM850104', 'CT_RACS820104'])" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.085 0.138 0.102 0.091 0.051 0.078 0.035 0.285 0.051 0.033 0.051]\n" + ] + } + ], + "source": [ + "# To compare with the recursive feature elimination, i do the feature importance as well\n", + "from sklearn.ensemble import ExtraTreesClassifier\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "# feature extraction\n", + "model = ExtraTreesClassifier()\n", + "model.fit(X, Y)\n", + "print(model.feature_importances_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Algorithm performance and Evaluation\n", + "This is so we can evaluate the performance of the machine learning algorithm.\n", + "\n", + "First we going to split our data into train and test sets.\n", + "Then train the models and finally predict the required solution.\n", + "\n", + "Evaluation of the models will be done by using the Matthews correlation coefficient(MCC) which will measure the quality of the classifications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression algorithm\n", + "Logistic regression is a predictive analysis algorithm of machine learning that is based on the concept of probability and is used to handle classification problems." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 91.62512462612163\n", + "MCC: 0.8342865299822478\n", + "[False True]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "#Splitting the data\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "array = Train.values\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "test_size = 0.33\n", + "seed = 42\n", + "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed)\n", + "\n", + "#using logistic regression\n", + "model = LogisticRegression()\n", + "model.fit(X_train, Y_train)\n", + "result = model.score(X_test, Y_test)\n", + "print(\"Accuracy: \", (result*100.0))\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "#Applying matthews correlation coefficient\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "#Reporting the results\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_logistic1.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: (83.5359128018065, 27.08521320979506)\n", + "MCC: 0.8342865299822478\n", + "[False True]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# Logistic regression with K- fold cross validation\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "num_folds = 10 \n", + "seed =42\n", + "\n", + "kfold = KFold(n_splits=num_folds, random_state=seed)\n", + "model = LogisticRegression()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_nb.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Guassian Naive Bayes Algorithm\n", + "Naive bayes is also a classification algorithm and can be used on binary and multi-class classification problems. It is good to use on categorical or two-class input values.\n", + "The Guassian naive bayes is an extension of naive bayes and only requires to estimate the mean and standard deviation from the training data which makes it easy to work with." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.880815746048289\n", + "MCC: 0.8407203694376205\n", + "[ True False]\n", + "False: 370\n", + "True: 388\n" + ] + } + ], + "source": [ + "#Naive Bayes\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.metrics import matthews_corrcoef\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = GaussianNB()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_nb.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Support Vector Machine(SVM)\n", + "This is a supervised machine learning algorithm mostly used for classification problems although it can also be used for regression problems. It performs classification by finding a hyper-plane that differentiates the data classes.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9081618030224075\n", + "MCC: 0.8187103751493263\n", + "[False True]\n", + "False: 397\n", + "True: 361\n" + ] + } + ], + "source": [ + "#Support Vector Machines model with default\n", + "from sklearn.svm import SVC\n", + "\n", + "num_fold = 10\n", + "seed = 42\n", + "kfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\n", + "model = SVC()\n", + "scoring = 'accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_svm.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9193579555323954\n", + "MCC: 0.8414394511707155\n", + "[False True]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "#Support Vector Machines model with kernel set to linear\n", + "from sklearn.svm import SVC\n", + "\n", + "num_fold = 10\n", + "seed = 42\n", + "kfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\n", + "model = SVC(kernel = 'linear')\n", + "scoring = 'accuracy'\n", + "results = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\n", + "print(results.mean())\n", + "\n", + "test_set = Test.values\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_svm_linear.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## K- Nearest Neighbors\n", + "This is a supervised machine learning algorithm that assumes close proximity of similar data points. It is used to solve classification and regression problems. K-nearest neighbor works by finding the distance between a query and all examples in the data, selecting the specified number examples(K) closest to the query and then votes dor the most frequent label when dealing with a classification problem. In case of regression, it averages the labels. " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8027933385443807\n", + "MCC: 0.8690586462107053\n", + "[False True]\n", + "False: 402\n", + "True: 356\n" + ] + } + ], + "source": [ + "#K-Nearest Neighbors\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = KNeighborsClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_kNN.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification and Regression trees(CART)\n", + "Classification and and regression trees are types of decision trees and are both used for constructing prediction models from data.\n", + "Classification trees are used where the target variable is categorical and the tree is used to identify the category within which the target variable would likely fall into where as regression trees are used ehre the target variable is continuous and the tree is used to predict its value.\n", + "CART incorporates both testing with with a test data set and cross validation to assess the goodness of fit more accurately." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.727965954490186\n", + "MCC: 1.0\n", + "[ True False]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "#Classification and Regression trees\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = DecisionTreeClassifier()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_CRT.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Discriminant Analysis\n", + "This is a classifier that uses dimensionality reduction technique. It has a linear decision boundary that is generated by fitting class conditional densities to the data using Bayes' rule." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8535044293903076\n", + "MCC: 0.8377162908048379\n", + "[False True]\n", + "False: 401\n", + "True: 357\n" + ] + } + ], + "source": [ + "#Linear Disriminant analysis\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "model = LinearDiscriminantAnalysis()\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_LDA.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boosting Algorithms\n", + "\n", + "Boosting algorithms improve boosting accuracy and often outperform simpler models.\n", + "\n", + "Some of them include Adaboost,Stochastic gradient boosting,Stochastic X gradient boosting(XGB) etc\n", + "\n", + "AdaBoost is a short form for 'Adaptive boosting'. It is a boosting algorithm that focuses on classification problems and aims to convert a set of weak classifiers into a strong one.\n", + "\n", + "Gradient boosting combines predictions from multiple decision trees to generate final predictions.\n", + "\n", + "XGB is an improved version of the gradient boosting and here the trees are built sequentially trying to correct errors of the previous trees.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7701895518499218\n", + "MCC: 0.8730261590913062\n", + "[ True False]\n", + "False: 386\n", + "True: 372\n" + ] + } + ], + "source": [ + "# AdaBoost Classification\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "X = array[:,0:11]\n", + "Y = array[:,11]\n", + "\n", + "num_trees = 30\n", + "seed=42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "\n", + "model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "\n", + "print(results.mean())\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_ADA.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.788623632100052\n", + "MCC: 0.9355236060852458\n", + "[ True False]\n", + "False: 385\n", + "True: 373\n" + ] + } + ], + "source": [ + "# Stochastic Gradient Boosting Classification\n", + "\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "num_trees = 100\n", + "seed = 42\n", + "\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_SGBC.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.787307842626368\n", + "MCC: 0.9269836637327226\n", + "[ True False]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# XGB\n", + "# Stochastic X Gradient Boosting Classification\n", + "from xgboost import XGBClassifier\n", + "num_trees = 100\n", + "seed = 42\n", + "\n", + "kfold = KFold(n_splits=10, random_state=seed)\n", + "model = XGBClassifier(n_estimators=num_trees, random_state=seed)\n", + "results = cross_val_score(model, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_XGB.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Voting Ensemble\n", + "Ensemble methods create multiple models and combine them to produce improved results. They are also good at improving accuracy.\n", + "Voting ensemble is one of the ensemble methods used for classification." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8142988969949625\n", + "MCC: 0.9269836637327226\n", + "[ True False]\n", + "False: 383\n", + "True: 375\n" + ] + } + ], + "source": [ + "# Voting Ensemble for Classification\n", + "from sklearn.ensemble import VotingClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "kfold = KFold(n_splits=10, random_state=42)\n", + "\n", + "# create the sub models\n", + "estimators = []\n", + "model1 = LogisticRegression()\n", + "estimators.append(('logistic', model1))\n", + "\n", + "model2 = DecisionTreeClassifier()\n", + "estimators.append(('cart', model2))\n", + "\n", + "model3 = SVC()\n", + "estimators.append(('svm', model3))\n", + "\n", + "model4 = XGBClassifier()\n", + "estimators.append(('xgb', model4))\n", + "\n", + "model5 = RandomForestClassifier()\n", + "estimators.append(('rfc', model5))\n", + "\n", + "# create the ensemble model\n", + "ensemble = VotingClassifier(estimators)\n", + "results = cross_val_score(ensemble, X, Y, cv=kfold)\n", + "print(results.mean())\n", + "\n", + "model.fit(X, Y)\n", + "output = model.predict(test_set)\n", + "\n", + "mcc = matthews_corrcoef(model.predict(X), Y)\n", + "print('MCC: ',mcc)\n", + "\n", + "myreport = pd.DataFrame(output)\n", + "myreport.columns = ['CLASS']\n", + "myreport.index.name = 'Index'\n", + "myreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n", + "\n", + "myreport.to_csv(\"myreport_VE.csv\")\n", + "\n", + "print(myreport['CLASS'].unique())\n", + "print('False: ',myreport.groupby('CLASS').size()[0].sum())\n", + "print('True: ',myreport.groupby('CLASS').size()[1].sum())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing Machine Learning algorithms\n", + "\n", + "This is done to evaluate each algorithm the same way on the data. This is done by forcing each alogorithm to be evaluated on a consistent test harness.\n", + "The following classification algorithms used above are each compared to the dataset:\n", + "\n", + "- Logistic Regression.\n", + "- Linear Discriminant Analysis.\n", + "- k-Nearest Neighbors.\n", + "- Classiffication and Regression Trees.\n", + "- Naive Bayes.\n", + "- Support Vector Machines." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('LR', 0.835359128018065, 0.2708521320979506)\n", + "('LDA', 0.8535044293903076, 0.2571395669719574)\n", + "('KNN', 0.8027933385443807, 0.2521136100771112)\n", + "('CART', 0.7332323692895606, 0.2870258927134236)\n", + "('NB', 0.880815746048289, 0.11642272449162755)\n", + "('SVM', 0.8350280093798853, 0.25836507020625044)\n", + "('ABC', 0.7747861299287824, 0.29219846937739774)\n", + "('GBC', 0.7899437641132534, 0.2887124941351131)\n", + "('XGB', 0.787307842626368, 0.2908368966924707)\n", + "('VC', 0.8146256730936251, 0.28735567966189307)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare Algorithms\n", + "from pandas import read_csv\n", + "from matplotlib import pyplot\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.svm import SVC\n", + "\n", + "#adding models to list\n", + "models = []\n", + "models.append(('LR', LogisticRegression()))\n", + "models.append(('LDA', LinearDiscriminantAnalysis()))\n", + "models.append(('KNN', KNeighborsClassifier()))\n", + "models.append(('CART', DecisionTreeClassifier()))\n", + "models.append(('NB', GaussianNB()))\n", + "models.append(('SVM', SVC()))\n", + "models.append(('ABC', AdaBoostClassifier()))\n", + "models.append(('GBC', GradientBoostingClassifier()))\n", + "models.append(('XGB', XGBClassifier()))\n", + "models.append(('VC', VotingClassifier(estimators)))\n", + "\n", + "# evaluation of each model\n", + "results = []\n", + "names = []\n", + "scoring = 'accuracy'\n", + "\n", + "for name, model in models:\n", + " kfold = KFold(n_splits=10, random_state=7)\n", + " cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n", + " results.append(cv_results)\n", + " names.append(name)\n", + " msg = (name, cv_results.mean(), cv_results.std())\n", + " print(msg)\n", + "\n", + "# boxplot algorithm comparison\n", + "fig = pyplot.figure()\n", + "fig.suptitle('Algorithm Comparison')\n", + "ax = fig.add_subplot(111)\n", + "pyplot.boxplot(results)\n", + "ax.set_xticklabels(names)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "- https://medium.com/@swethalakshmanan14/how-when-and-why-should-you-normalize-standardize-rescale-your-data-3f083def38ff\n", + "- https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51\n", + "- https://datavizcatalogue.com/methods/density_plot.html\n", + "- https://towardsdatascience.com/data-preprocessing-concepts-fa946d11c825\n", + "- https://www.kaggle.com/startupsci/titanic-data-science-solutions\n", + "- https://towardsdatascience.com/feature-selection-techniques-in-machine-learn\n", + "- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html\n", + "- https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761\n", + "- https://www.datasciencecentral.com/profiles/blogs/introduction-to-classification-regression-trees-cart\n", + "\n", + "- https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\n", + "- https://towardsdatascience.com/boosting-algorithm-adaboost-b6737a9ee60c\n", + "- https://www.analyticsvidhya.com/blog/2020/02/4-boosting-algorithms-machine-learning/\n", + "- https://www.toptal.com/machine-learning/ensemble-methods-machine-learning" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "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": 4 +} diff --git a/Assignment Colab/Stella Esther Nabirye_updated.ipynb b/Assignment Colab/Stella Esther Nabirye_updated.ipynb new file mode 100644 index 0000000..5a44b45 --- /dev/null +++ b/Assignment Colab/Stella Esther Nabirye_updated.ipynb @@ -0,0 +1 @@ +{"cells":[{"metadata":{},"cell_type":"markdown","source":"## Stella Esther Nabirye\n## 2019/HD07/24851U\n### Kaggle assignment\n\nFor this assignment, we were provided with two datasets named train set and test. We were to train a number of machine learning algorithms on the train dataset until we identify an algorithm that is able to best predict the outcome in the test data.\nI begin by importing the necessary libraries that shall be used in the machine learning pipeline.\n\n**Importing Libraries** \nPython libraries contain functions/ methods helpful in analysis and in order to access those functions, we need to import those libraries.\nFor the assignment, I import\n- the numpy library which contains functions for mathematical computations\n- the pandas package which contains functions for manipulations of data structure and operations \n- the matplot library which provides for plotting of the data.\n- seaborn for data vusualization"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n","execution_count":81,"outputs":[{"output_type":"stream","text":"/kaggle/input/amp-data-set/AMP_TrainSet.csv\n/kaggle/input/amp-data-set/Test.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#import the necessary libraries you are going to use\nimport warnings\nwarnings.filterwarnings('ignore')\n\n#The necessary libraries are loaded because they contain the different functions that will be used \n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns","execution_count":82,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Reading in data\n\nSince the data is in CSV format, we read it in using the read_csv function"},{"metadata":{"_cell_guid":"","_uuid":"","trusted":true},"cell_type":"code","source":"# Loading the train and test datasets to be used\nTrain = pd.read_csv(\"../input/amp-data-set/AMP_TrainSet.csv\")\nTest = pd.read_csv(\"../input/amp-data-set/Test.csv\")\n\n","execution_count":83,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Dimensions of data\nThis is informative in knowing the number of rows and columns in the data. It also helps in knowing how much data we are working with."},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking the dimensions of the data\n\nTrain.shape, Test.shape\n\n","execution_count":84,"outputs":[{"output_type":"execute_result","execution_count":84,"data":{"text/plain":"((3038, 12), (758, 11))"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"The Train dataset has 3038 rows and 12 columns while the Test dataset has 758 rows and 11 columns."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Previewing the data\n#The train set has 3038 rows and 12 columns so we choose to use the head() function to limit the rows displayed\nTrain.head()","execution_count":85,"outputs":[{"output_type":"execute_result","execution_count":85,"data":{"text/plain":" FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n0 5.0 0.000 0.951 74.842 \n1 4.0 5.405 0.931 71.595 \n2 5.5 5.405 0.873 73.595 \n3 5.0 4.167 0.895 66.250 \n4 7.5 8.537 0.932 64.720 \n\n FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n0 0.975 -3.663 0 0.282 \n1 0.957 -4.011 1 0.600 \n2 0.961 -2.512 0 0.593 \n3 0.999 -1.362 0 0.614 \n4 0.979 -2.091 0 0.616 \n\n AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \n0 73.444 5.661 1.041 1 \n1 68.222 6.537 1.453 1 \n2 69.444 4.934 1.722 1 \n3 67.222 4.316 1.382 1 \n4 72.944 4.540 1.539 1 ","text/html":"
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
05.00.0000.95174.8420.975-3.66300.28273.4445.6611.0411
14.05.4050.93171.5950.957-4.01110.60068.2226.5371.4531
25.55.4050.87373.5950.961-2.51200.59369.4444.9341.7221
35.04.1670.89566.2500.999-1.36200.61467.2224.3161.3821
47.58.5370.93264.7200.979-2.09100.61672.9444.5401.5391
\n
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#To limit the number of rows to visualize for the test set we also use the head() function as it has 758 rows and 11 columns\nTest.head()","execution_count":86,"outputs":[{"output_type":"execute_result","execution_count":86,"data":{"text/plain":" FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\n0 4.0 3.704 0.873 73.519 \n1 4.0 4.444 0.892 62.444 \n2 2.0 0.000 0.901 47.000 \n3 4.5 0.000 0.869 69.222 \n4 -4.0 21.591 1.061 71.682 \n\n FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\n0 0.987 -4.833 0 0.382 \n1 0.931 -0.584 0 0.320 \n2 1.039 -5.664 0 0.164 \n3 0.982 -5.423 0 2.010 \n4 0.976 -2.002 0 2.758 \n\n AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \n0 74.556 7.225 1.234 \n1 56.056 4.942 1.853 \n2 47.000 5.969 1.174 \n3 69.222 5.462 1.138 \n4 66.000 5.582 1.453 ","text/html":"
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104
04.03.7040.87373.5190.987-4.83300.38274.5567.2251.234
14.04.4440.89262.4440.931-0.58400.32056.0564.9421.853
22.00.0000.90147.0001.039-5.66400.16447.0005.9691.174
34.50.0000.86969.2220.982-5.42302.01069.2225.4621.138
4-4.021.5911.06171.6820.976-2.00202.75866.0005.5821.453
\n
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Looking at the names of the columns\nTrain.columns","execution_count":87,"outputs":[{"output_type":"execute_result","execution_count":87,"data":{"text/plain":"Index(['FULL_Charge', 'FULL_AcidicMolPerc', 'FULL_AURR980107',\n 'FULL_DAYM780201', 'FULL_GEOR030101', 'FULL_OOBM850104', 'NT_EFC195',\n 'AS_MeanAmphiMoment', 'AS_DAYM780201', 'AS_FUKS010112', 'CT_RACS820104',\n 'CLASS'],\n dtype='object')"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Looking at the data type \nInorder to know how to further deal with the data, knowledge of the data type of each attribute is important hence i examine the data types using the dtypes function"},{"metadata":{"trusted":true},"cell_type":"code","source":"Train.dtypes","execution_count":88,"outputs":[{"output_type":"execute_result","execution_count":88,"data":{"text/plain":"FULL_Charge float64\nFULL_AcidicMolPerc float64\nFULL_AURR980107 float64\nFULL_DAYM780201 float64\nFULL_GEOR030101 float64\nFULL_OOBM850104 float64\nNT_EFC195 int64\nAS_MeanAmphiMoment float64\nAS_DAYM780201 float64\nAS_FUKS010112 float64\nCT_RACS820104 float64\nCLASS int64\ndtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Test.dtypes","execution_count":89,"outputs":[{"output_type":"execute_result","execution_count":89,"data":{"text/plain":"FULL_Charge float64\nFULL_AcidicMolPerc float64\nFULL_AURR980107 float64\nFULL_DAYM780201 float64\nFULL_GEOR030101 float64\nFULL_OOBM850104 float64\nNT_EFC195 int64\nAS_MeanAmphiMoment float64\nAS_DAYM780201 float64\nAS_FUKS010112 float64\nCT_RACS820104 float64\ndtype: object"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# To see if there are any null values \n\nTrain.isnull().sum()","execution_count":90,"outputs":[{"output_type":"execute_result","execution_count":90,"data":{"text/plain":"FULL_Charge 0\nFULL_AcidicMolPerc 0\nFULL_AURR980107 0\nFULL_DAYM780201 0\nFULL_GEOR030101 0\nFULL_OOBM850104 0\nNT_EFC195 0\nAS_MeanAmphiMoment 0\nAS_DAYM780201 0\nAS_FUKS010112 0\nCT_RACS820104 0\nCLASS 0\ndtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Test.isnull().sum()","execution_count":91,"outputs":[{"output_type":"execute_result","execution_count":91,"data":{"text/plain":"FULL_Charge 0\nFULL_AcidicMolPerc 0\nFULL_AURR980107 0\nFULL_DAYM780201 0\nFULL_GEOR030101 0\nFULL_OOBM850104 0\nNT_EFC195 0\nAS_MeanAmphiMoment 0\nAS_DAYM780201 0\nAS_FUKS010112 0\nCT_RACS820104 0\ndtype: int64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"There are no null values in both the Train and Test data therefore we can infer that there are no missing values in our data. Also most of the data types are floats and intergers."},{"metadata":{},"cell_type":"markdown","source":"## Data Descriptive Statistics\n Using the describe function, we are able to get a summary of the statistical details regarding the shape of each attribute of our data such as the mean, standard deviation,count, minimum and maximum value of objects of each column that help in further understanding of the data"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Using the describe() function\nTrain.describe()","execution_count":92,"outputs":[{"output_type":"execute_result","execution_count":92,"data":{"text/plain":" FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\ncount 3038.000000 3038.000000 3038.000000 3038.000000 \nmean 2.060237 8.521520 0.971410 73.668760 \nstd 3.819929 7.586652 0.107413 8.527489 \nmin -16.000000 0.000000 0.684000 42.750000 \n25% 0.000000 2.516000 0.895000 68.294000 \n50% 2.000000 7.143000 0.963000 74.059500 \n75% 4.000000 13.158000 1.041000 79.343750 \nmax 30.000000 46.667000 1.451000 101.682000 \n\n FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\ncount 3038.000000 3038.000000 3038.000000 3038.000000 \nmean 0.994007 -2.432927 0.088545 15.683233 \nstd 0.031333 1.707223 0.284133 11.575665 \nmin 0.866000 -10.432000 0.000000 0.041000 \n25% 0.974000 -3.606000 0.000000 5.587500 \n50% 0.994000 -2.296500 0.000000 14.988500 \n75% 1.011000 -1.283250 0.000000 26.807750 \nmax 1.196000 3.576000 1.000000 51.280000 \n\n AS_DAYM780201 AS_FUKS010112 CT_RACS820104 CLASS \ncount 3038.000000 3038.000000 3038.000000 3038.000000 \nmean 73.650828 5.911361 1.235255 0.500000 \nstd 9.166092 0.693689 0.210012 0.500082 \nmin 42.778000 3.533000 0.785000 0.000000 \n25% 67.556000 5.459250 1.082000 0.000000 \n50% 73.697000 5.925500 1.184000 0.500000 \n75% 79.778000 6.382000 1.351000 1.000000 \nmax 103.167000 8.662000 2.192000 1.000000 ","text/html":"
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
count3038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.0000003038.000000
mean2.0602378.5215200.97141073.6687600.994007-2.4329270.08854515.68323373.6508285.9113611.2352550.500000
std3.8199297.5866520.1074138.5274890.0313331.7072230.28413311.5756659.1660920.6936890.2100120.500082
min-16.0000000.0000000.68400042.7500000.866000-10.4320000.0000000.04100042.7780003.5330000.7850000.000000
25%0.0000002.5160000.89500068.2940000.974000-3.6060000.0000005.58750067.5560005.4592501.0820000.000000
50%2.0000007.1430000.96300074.0595000.994000-2.2965000.00000014.98850073.6970005.9255001.1840000.500000
75%4.00000013.1580001.04100079.3437501.011000-1.2832500.00000026.80775079.7780006.3820001.3510001.000000
max30.00000046.6670001.451000101.6820001.1960003.5760001.00000051.280000103.1670008.6620002.1920001.000000
\n
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"Test.describe()","execution_count":93,"outputs":[{"output_type":"execute_result","execution_count":93,"data":{"text/plain":" FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 FULL_DAYM780201 \\\ncount 758.000000 758.000000 758.000000 758.000000 \nmean 2.073879 8.945091 0.973046 73.821891 \nstd 4.230615 7.814449 0.110676 8.029524 \nmin -13.000000 0.000000 0.699000 47.000000 \n25% -0.500000 2.721750 0.894000 68.740250 \n50% 2.000000 7.500000 0.965000 74.069500 \n75% 4.000000 14.230250 1.053500 79.284750 \nmax 30.000000 44.118000 1.431000 102.929000 \n\n FULL_GEOR030101 FULL_OOBM850104 NT_EFC195 AS_MeanAmphiMoment \\\ncount 758.000000 758.000000 758.000000 758.000000 \nmean 0.994212 -2.397922 0.084433 15.570067 \nstd 0.032370 1.597138 0.278219 11.362589 \nmin 0.889000 -7.844000 0.000000 0.060000 \n25% 0.973000 -3.457250 0.000000 5.709000 \n50% 0.994000 -2.238000 0.000000 15.057000 \n75% 1.013000 -1.306250 0.000000 25.290250 \nmax 1.182000 2.017000 1.000000 50.098000 \n\n AS_DAYM780201 AS_FUKS010112 CT_RACS820104 \ncount 758.000000 758.000000 758.000000 \nmean 73.844204 5.904492 1.250189 \nstd 8.915193 0.656911 0.218102 \nmin 47.000000 3.843000 0.841000 \n25% 68.346000 5.471250 1.096000 \n50% 73.646000 5.935500 1.188000 \n75% 80.069250 6.375250 1.378500 \nmax 102.929000 7.588000 2.283000 ","text/html":"
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104
count758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000758.000000
mean2.0738798.9450910.97304673.8218910.994212-2.3979220.08443315.57006773.8442045.9044921.250189
std4.2306157.8144490.1106768.0295240.0323701.5971380.27821911.3625898.9151930.6569110.218102
min-13.0000000.0000000.69900047.0000000.889000-7.8440000.0000000.06000047.0000003.8430000.841000
25%-0.5000002.7217500.89400068.7402500.973000-3.4572500.0000005.70900068.3460005.4712501.096000
50%2.0000007.5000000.96500074.0695000.994000-2.2380000.00000015.05700073.6460005.9355001.188000
75%4.00000014.2302501.05350079.2847501.013000-1.3062500.00000025.29025080.0692506.3752501.378500
max30.00000044.1180001.431000102.9290001.1820002.0170001.00000050.098000102.9290007.5880002.283000
\n
"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Classification\n\nThis helps us know how data is distributed with regards to the class, that is, whether it is balanced or imbalanced."},{"metadata":{"trusted":true},"cell_type":"code","source":"Train.groupby('CLASS').size().plot(kind='bar',color=('orange', 'blue'))","execution_count":94,"outputs":[{"output_type":"execute_result","execution_count":94,"data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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the above plot, it can be observed that the data classes are equally distributed."},{"metadata":{},"cell_type":"markdown","source":"## Data correlation\nThis helps us to know how the relationship between the variables and to identify which variables are positively or negatively correlated and perhaps where there is no correlation at all. This is important because the performance of algorithms such as linear and logistic regression is affected if attributes in data are highly correlated."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Using the cor() function and the pearson coefficient's method\nTrain.corr(method='pearson')","execution_count":95,"outputs":[{"output_type":"execute_result","execution_count":95,"data":{"text/plain":" FULL_Charge FULL_AcidicMolPerc FULL_AURR980107 \\\nFULL_Charge 1.000000 -0.612996 -0.490977 \nFULL_AcidicMolPerc -0.612996 1.000000 0.794796 \nFULL_AURR980107 -0.490977 0.794796 1.000000 \nFULL_DAYM780201 -0.434603 0.541481 0.548253 \nFULL_GEOR030101 -0.058725 0.115201 0.346139 \nFULL_OOBM850104 -0.283758 0.513344 0.462712 \nNT_EFC195 0.088068 -0.143168 -0.169540 \nAS_MeanAmphiMoment 0.355477 -0.431590 -0.426097 \nAS_DAYM780201 -0.365374 0.449621 0.456260 \nAS_FUKS010112 -0.090570 0.002334 0.032958 \nCT_RACS820104 0.232929 -0.213543 -0.403599 \nCLASS 0.534602 -0.598816 -0.584111 \n\n FULL_DAYM780201 FULL_GEOR030101 FULL_OOBM850104 \\\nFULL_Charge -0.434603 -0.058725 -0.283758 \nFULL_AcidicMolPerc 0.541481 0.115201 0.513344 \nFULL_AURR980107 0.548253 0.346139 0.462712 \nFULL_DAYM780201 1.000000 0.010118 0.334778 \nFULL_GEOR030101 0.010118 1.000000 0.319157 \nFULL_OOBM850104 0.334778 0.319157 1.000000 \nNT_EFC195 -0.090058 -0.230417 -0.230561 \nAS_MeanAmphiMoment -0.408793 -0.160269 -0.336297 \nAS_DAYM780201 0.894191 -0.029085 0.275640 \nAS_FUKS010112 0.055915 0.040480 -0.452769 \nCT_RACS820104 -0.326792 -0.151935 0.155304 \nCLASS -0.554838 -0.260470 -0.453287 \n\n NT_EFC195 AS_MeanAmphiMoment AS_DAYM780201 \\\nFULL_Charge 0.088068 0.355477 -0.365374 \nFULL_AcidicMolPerc -0.143168 -0.431590 0.449621 \nFULL_AURR980107 -0.169540 -0.426097 0.456260 \nFULL_DAYM780201 -0.090058 -0.408793 0.894191 \nFULL_GEOR030101 -0.230417 -0.160269 -0.029085 \nFULL_OOBM850104 -0.230561 -0.336297 0.275640 \nNT_EFC195 1.000000 0.178683 -0.036844 \nAS_MeanAmphiMoment 0.178683 1.000000 -0.322378 \nAS_DAYM780201 -0.036844 -0.322378 1.000000 \nAS_FUKS010112 0.145924 0.025580 0.045562 \nCT_RACS820104 0.080898 0.171524 -0.256060 \nCLASS 0.260702 0.693552 -0.437168 \n\n AS_FUKS010112 CT_RACS820104 CLASS \nFULL_Charge -0.090570 0.232929 0.534602 \nFULL_AcidicMolPerc 0.002334 -0.213543 -0.598816 \nFULL_AURR980107 0.032958 -0.403599 -0.584111 \nFULL_DAYM780201 0.055915 -0.326792 -0.554838 \nFULL_GEOR030101 0.040480 -0.151935 -0.260470 \nFULL_OOBM850104 -0.452769 0.155304 -0.453287 \nNT_EFC195 0.145924 0.080898 0.260702 \nAS_MeanAmphiMoment 0.025580 0.171524 0.693552 \nAS_DAYM780201 0.045562 -0.256060 -0.437168 \nAS_FUKS010112 1.000000 -0.445284 0.033432 \nCT_RACS820104 -0.445284 1.000000 0.267652 \nCLASS 0.033432 0.267652 1.000000 ","text/html":"
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FULL_ChargeFULL_AcidicMolPercFULL_AURR980107FULL_DAYM780201FULL_GEOR030101FULL_OOBM850104NT_EFC195AS_MeanAmphiMomentAS_DAYM780201AS_FUKS010112CT_RACS820104CLASS
FULL_Charge1.000000-0.612996-0.490977-0.434603-0.058725-0.2837580.0880680.355477-0.365374-0.0905700.2329290.534602
FULL_AcidicMolPerc-0.6129961.0000000.7947960.5414810.1152010.513344-0.143168-0.4315900.4496210.002334-0.213543-0.598816
FULL_AURR980107-0.4909770.7947961.0000000.5482530.3461390.462712-0.169540-0.4260970.4562600.032958-0.403599-0.584111
FULL_DAYM780201-0.4346030.5414810.5482531.0000000.0101180.334778-0.090058-0.4087930.8941910.055915-0.326792-0.554838
FULL_GEOR030101-0.0587250.1152010.3461390.0101181.0000000.319157-0.230417-0.160269-0.0290850.040480-0.151935-0.260470
FULL_OOBM850104-0.2837580.5133440.4627120.3347780.3191571.000000-0.230561-0.3362970.275640-0.4527690.155304-0.453287
NT_EFC1950.088068-0.143168-0.169540-0.090058-0.230417-0.2305611.0000000.178683-0.0368440.1459240.0808980.260702
AS_MeanAmphiMoment0.355477-0.431590-0.426097-0.408793-0.160269-0.3362970.1786831.000000-0.3223780.0255800.1715240.693552
AS_DAYM780201-0.3653740.4496210.4562600.894191-0.0290850.275640-0.036844-0.3223781.0000000.045562-0.256060-0.437168
AS_FUKS010112-0.0905700.0023340.0329580.0559150.040480-0.4527690.1459240.0255800.0455621.000000-0.4452840.033432
CT_RACS8201040.232929-0.213543-0.403599-0.326792-0.1519350.1553040.0808980.171524-0.256060-0.4452841.0000000.267652
CLASS0.534602-0.598816-0.584111-0.554838-0.260470-0.4532870.2607020.693552-0.4371680.0334320.2676521.000000
\n
"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# to visualize the data correlation, we plot a heat map\nplt.figure(figsize=(6,6))\nsns.heatmap(Train.corr(method='pearson'), cmap='PiYG')","execution_count":96,"outputs":[{"output_type":"execute_result","execution_count":96,"data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# checking for correlation with regards to the class\nTrain.corr(method='pearson')['CLASS']","execution_count":97,"outputs":[{"output_type":"execute_result","execution_count":97,"data":{"text/plain":"FULL_Charge 0.534602\nFULL_AcidicMolPerc -0.598816\nFULL_AURR980107 -0.584111\nFULL_DAYM780201 -0.554838\nFULL_GEOR030101 -0.260470\nFULL_OOBM850104 -0.453287\nNT_EFC195 0.260702\nAS_MeanAmphiMoment 0.693552\nAS_DAYM780201 -0.437168\nAS_FUKS010112 0.033432\nCT_RACS820104 0.267652\nCLASS 1.000000\nName: CLASS, dtype: float64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"There are five features with a positive value for the correlation coefficient with regards to the class indicative of a high correlation with the class and the higher the value, the more correlated they are to the class. The features with a negative correlation coefficient have a lower correlation with the class."},{"metadata":{},"cell_type":"markdown","source":"## Data Distribution\nKnowing whether the data is skewed or not is important in the data preparation. This is done by calculating the skew for each attribute with using the skew() function.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"Train.skew().plot(kind='bar')","execution_count":98,"outputs":[{"output_type":"execute_result","execution_count":98,"data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Most of the features are positively skewed while a few a negatively skewed.\nThis skewedness can be corrected for differently depending on whether its positively skewed or negatively skewed by square root, cube root and log.\n"},{"metadata":{},"cell_type":"markdown","source":"## Data Visualization\n\nWhen exploring a dataset, visualization can be helpful in identifying patterns, corrupt data, outliers to mention but a few. Data visualization is also key in expressing and identifying key relationships in the data.\n### **Plots**\nOne of the ways of data visualization is the use of plots. \nPlots are graphical representations of relationships between variables.\nSome the key plots important in basic data visualization include;\n- Histograms\n- Density plots\n- Box whisker plots\n- Scatter plot matrix \n\nDetails on the above plots are provided below as i apply them."},{"metadata":{},"cell_type":"markdown","source":"## Histograms\nHistograms are useful for analyzing continuous numerical data and can be really helpful in identifying useful patterns. They are also helpful in indicating the data distribution."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Histogram\nplt.figure(figsize=(15,15))\nTrain.hist( color = 'violet')\nplt.subplots_adjust(bottom=1, right=2, top=3)\nplt.show()","execution_count":99,"outputs":[{"output_type":"display_data","data":{"text/plain":"
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"From the histograms, we can see that most of the data is normallly distributed and only four features have their data positively skewed."},{"metadata":{},"cell_type":"markdown","source":"## Density plots \nThese are also helpful in visualization of the data distribution. The peaks of the density plots show where values are most concentrated over the interval. Density plots do a better job at determining the distribution shape,an advantage they have over histograms."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Density plots\nTrain.plot(kind='density', subplots=True, layout=(3,4), sharex=False)\nplt.subplots_adjust(bottom=1, right=2, top=3)\nplt.show","execution_count":100,"outputs":[{"output_type":"execute_result","execution_count":100,"data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"From the plots, we are able to see some features having a tail on the right side of the data which further informs that those features have positively skewed data."},{"metadata":{},"cell_type":"markdown","source":"## Box plots\nThese give an indication of how the data values are spread out. They display the minimum, first quartile, median, third quartile and maximum of the data. The advantage is that they takes up less space when compared to histograms and density plots which is useful for data when comparing distributions between many groupshence their use."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Box plot\nTrain.plot(kind='box', subplots=True, layout=(3,4), sharex=False, sharey=False)\nplt.subplots_adjust(bottom=1, right=2, top=3)\nplt.show()","execution_count":101,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"For most of the features, I observe a number of data points are away from the median and some data points lie beyond the whiskers indicative of outliers."},{"metadata":{},"cell_type":"markdown","source":"# Preprocessing Data\nThis is the step where the data is transformed in such a way that it can easily be interpreted by the algorithm.\nThis can be done by;\n- Splitting the data into train/validation and test sets. \nML algorithms have to first be trained on the available data distribution, validated and then tested before it can be applied to real world world data\n- Transforming the input variables through pre-processing such as `rescaling, normalizing and binarizing` "},{"metadata":{"trusted":true},"cell_type":"code","source":"# Rescaling data\nfrom numpy import set_printoptions\nfrom sklearn.preprocessing import MinMaxScaler\n\narray = Train.values\nX = array[:,0:11]\nY = array [:,11]\nscaler = MinMaxScaler(feature_range=(0, 1))\nrescaledX = scaler.fit_transform(X)\nset_printoptions(precision=3)\nprint(rescaledX[0:5,:])","execution_count":102,"outputs":[{"output_type":"stream","text":"[[0.457 0. 0.348 0.545 0.33 0.483 0. 0.005 0.508 0.415 0.182]\n [0.435 0.116 0.322 0.489 0.276 0.458 1. 0.011 0.421 0.586 0.475]\n [0.467 0.116 0.246 0.523 0.288 0.565 0. 0.011 0.442 0.273 0.666]\n [0.457 0.089 0.275 0.399 0.403 0.647 0. 0.011 0.405 0.153 0.424]\n [0.511 0.183 0.323 0.373 0.342 0.595 0. 0.011 0.5 0.196 0.536]]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Then standardize the data to a standard Guassian distribution with a mean of 0 and standard deviation of 1\nfrom sklearn.preprocessing import StandardScaler\n\narray2 = Train.values\n\nX = array2[:,0:11]\nY = array2[:,11]\nscaler = StandardScaler().fit(X)\nrescaledX = scaler.transform(X)\nset_printoptions(precision=3)\nprint(rescaledX[0:5,:])","execution_count":103,"outputs":[{"output_type":"stream","text":"[[ 7.697e-01 -1.123e+00 -1.900e-01 1.376e-01 -6.067e-01 -7.206e-01\n -3.117e-01 -1.331e+00 -2.257e-02 -3.610e-01 -9.251e-01]\n [ 5.079e-01 -4.109e-01 -3.763e-01 -2.432e-01 -1.181e+00 -9.245e-01\n 3.208e+00 -1.303e+00 -5.924e-01 9.021e-01 1.037e+00]\n [ 9.006e-01 -4.109e-01 -9.163e-01 -8.651e-03 -1.054e+00 -4.632e-02\n -3.117e-01 -1.304e+00 -4.590e-01 -1.409e+00 2.318e+00]\n [ 7.697e-01 -5.741e-01 -7.115e-01 -8.701e-01 1.594e-01 6.274e-01\n -3.117e-01 -1.302e+00 -7.015e-01 -2.300e+00 6.989e-01]\n [ 1.424e+00 2.041e-03 -3.670e-01 -1.050e+00 -4.790e-01 2.003e-01\n -3.117e-01 -1.302e+00 -7.713e-02 -1.977e+00 1.447e+00]]\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Normalizing data using the sklearn Normalizer\nfrom sklearn.preprocessing import Normalizer\narray3 = Train.values\n\nX = array3[:,0:11]\nY = array3[:,11]\nscaler = Normalizer().fit(X)\nnormalizedX = scaler.transform(X)\nset_printoptions(precision=3)\nprint(normalizedX[0:5,:])\nprint(type(normalizedX))","execution_count":104,"outputs":[{"output_type":"stream","text":"[[ 0.048 0. 0.009 0.711 0.009 -0.035 0. 0.003 0.698 0.054\n 0.01 ]\n [ 0.04 0.054 0.009 0.72 0.01 -0.04 0.01 0.006 0.686 0.066\n 0.015]\n [ 0.054 0.053 0.009 0.724 0.009 -0.025 0. 0.006 0.683 0.049\n 0.017]\n [ 0.053 0.044 0.009 0.699 0.011 -0.014 0. 0.006 0.71 0.046\n 0.015]\n [ 0.076 0.087 0.009 0.658 0.01 -0.021 0. 0.006 0.742 0.046\n 0.016]]\n\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"The score i got when i used the raw data was better than the score obatined while using the transformed data thus i opted to use the raw data."},{"metadata":{},"cell_type":"markdown","source":"# Feature Selection\nThis is where we reduce the number of features/variables by automatically selecting the features that contribute most to the prediction variable or output of interest.\nThis is important because;\n- It enables the machine learning algorithm to train faster\n- Reduces the complexity of the model making it easier to interpret\n- Improves accuracy of the model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Using recursive feature elimination with the logistic regression algorithm\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import LogisticRegression\n\narray_1 = Train.values\nX = array_1[:,0:11]\nY = array_1[:,11]\n# feature extraction\nmodel = LogisticRegression()\nrfe = RFE(model, 5)\nfit = rfe.fit(X, Y)\nprint(\"Num Features: \", fit.n_features_)\nprint(\"Selected Features:\", fit.support_)\nprint(\"Feature Ranking: \", fit.ranking_)","execution_count":105,"outputs":[{"output_type":"stream","text":"Num Features: 5\nSelected Features: [False False True False True True True False False False True]\nFeature Ranking: [2 6 1 5 1 1 1 3 7 4 1]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"Scatter plot matrix important in identifying structured relationships between variables. \nI plot the selected features to visualize if indeed they would be better to use for classification.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# scatter plot matrix \nsns.pairplot(Train, hue='CLASS', vars=['FULL_AURR980107', 'FULL_GEOR030101', 'FULL_OOBM850104', 'CT_RACS820104'])","execution_count":106,"outputs":[{"output_type":"execute_result","execution_count":106,"data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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zAAl/ZPMz5z2czccHFf8r6vYPO/Dx3dPSWSY6AtzfKoepKiKC3c9Pdu1BMJo+c8Mmkwy97daxgSrVXV7d/d1Wl99NYWe9L9LzlaWvO8tbab31q4U2vXCQQjRi2BjFQnv5s4mG0LruSl+cNx280tk6zX5BFRobvLZszHeSMGsGDTDiblZhrGQ/T4aCKvrC/QtagNHoV1cy5j+axccjI9vJFfTLk/yORlHxrKS9B6gvUvrz0Pp01p8VyNPiMn5WbKUDRJl5BQD4QQwqNpmi+R9zitiEq4Dhp9dOc7u4Mwge9Ai6/e+bqYAV5Xi92tK7K8vP3NER5+7RtG/6AnDqvcTZIcP9qr06CqGoFQOO4iKbObg43zLqeby8qrXxzirpED6dfdiaqBxSRYMfti/vT2nhgvhdNm6rQ+emcXexJJe7TmeYu3m9+WB6wj12kuY3yworbV8awogoFeN+vnXg5orJh9MWZFsHBcthEeEz1+QLoLt83M4cqADOvrAlRVo9QfjPEoPDn9IswmgdWkMCo7HYfVRLm/nkAwQljVePj6C3j8jd0x/WJSBCl2Gyl2Gy/NH04gGObrompDnjeaTN8U+TyTHA2J9kCUCiHeEkLcJoTwJPhepz7VhyFcB0l9ju58xaTnTlR8H/NxbTDMF4U+ftC7ZZ6DIgQzLz2Loqo6nt3W0nMhkSSa1qrlRhdOe4v9cRMJ95b4mbzsQx5+7WuuOq8HCzfv5MrH3uPmZz+hoDzAC598z31jssjJ9BiLLrMiOp3HEC/ZtCOhTxJJa3SmWnBbHrD2rhPPc9HWeFZVjT0lNfzm5Z3sK63lluc+5crH3zOqH+dkeozj0eC6JXp1+KbysJKjo8wfbOFp/c/ndU/BLc99yn1jzsXnD7GrqJppT33EFYvf5Zcvfskvrz03pl8cVj3BOvpcdVjNLNqSbxgZvkBIPs8kXUKiDYivgT8CVwF7hRCbhRA3CCEc7Zx3ZlLWIOHa2RoQTXGngy/WgNj+vY+wqnFer6S4p5zbK5mL+6Xy5D+/paS6/ujvLZEcI00XPEVVddyx6jP+9PYeHpk0OK47HmBSbmZcV/6k3Ezu3biDJdNzeHH+MGxm5agWPJ1Z7EkkHaGp5y0aWhQvBr0tD1zUY9DadaIG+MSlsWM+1WFpdTxHjZXWwpbmjRhgFKZ78NV8GQbThbQnv1tQHuD78lru3RjbL1HVpeYJ1lGaP7825RWwbGaufJ5JjplEJ1GHNE3bAmxpMBrGAzcATwohtmqaNj3B9z+1iNaAOBoJ1yjuHnD4i5iPPt5XhiJgUI/4BgTAjRf35d5NO/jjW7v53cQLjv7+EslR0jxUY+O8yymsCFBYEeCxrbsMtZH0ZBv/Z90Xxo5amsva5osXQCC46dlPjipptb0wK4nkaGivQnp0PhRV1rUZptTaddpK1G5tPEcXsa2FuZzbM4n1cy/HahJGgm/T72UYzNHTWjhaVH7X2RBe3Fq/rJtzGT2S7C2eS/GeX6kOi3yeSY6ZRHsgjBGpaVpA07T1mqZdD5wNbE3wvU89yvaCYtFzGY4Wdw/wF0Oo8SGT930FZ6W5cFpbtxd7eRxcfV4P/vZJAXuOVB/9/SWSDtJenYa6UMTYJfu8wMfc1XncveELvQp1je4py0h1kJ5ka1X3PrrQOtY8htbCrCSSROELBCmqrMPjtLDq1ksYlZ0ONO4YmxTarCHR1phvbTxHF7Gthbl8U1TN1OUfcqS63mhP0+9lGEzHUVWNcr/+/DtQ7kfTNJ675eJWPa21wQi1wUir/TLtqY/YU1ITdyw072+zWZHPM8kxk2gDYm28DzVNq9Q0bWWC733qUf7d0Uu4RnH30H/7CgC9+vTOg5X07+5q99TrL+qDzaLwe1lcTpJg4oVXHPbV4XU37qSaFMHiybGhS4snD8ZpU2JCNnqnOOIqjmzfX8bzt19KMBxBCCEXPJJTBlXVOOyrY+HmnUxc+gE3PfsJ/3nlQF65czjr5lyGxSTaDcfrSO5OcyM+Gt60Ka+g1bDBwooAc1fncf/YbBkGc5Soqsb+Mr+Rz/CjR9/l+r98QH1YZfHkwbw0fxirbr2ElR/sM+R3z0pzclaas8Uz8fEpQ4x+iYaRdVWRQomkLRIawqRp2mOJvP5px7FIuEZJihoQ34N3EIUVAarqwvRLa9+ASLZbmHBhH1745AAffFvKsHOOwRMikbRBvPCK5nUaFCF4+LVvYgolPfr6LpZMz6FPqjPmegO9bp6//VIqanUJV5tF4boLM3jw1Xwm5WaS5rLyq7HZ9E9zsvz9/XLBIzmpiSfR+Z/Pb2fRhPNJdVmZveLTdsPxorHvzdWbmiZYR0MGvW4bd40cSP/uLtJcVh6eNJhQWGX93MsJR9QYFZ/oPU2KkGEwR0mZP9iiVo3XbaO4qp7Mbk72ltTwwscHmJSbyZwfDTDqPwCkOM387Y7LiGgaBeW1PPzaNzH9EgxH4qp29Ui2EQjKvpJ0HYmWcU0B/hv4CRAtrVwMbAYelhKvTVBVqPgOBl17bNdxNeyyNiRSf3WoEqBDHgiAMT/oyZv5Rfzvm7ulASFJGMFwBK/b1qKKat80pxEHXBuMUFJTz9zVecZ50Z23cn89HkfjS7AiEDIKzeVkenh86hCeeHs3NzcrgLV8Zi63/vBsFEUxXqKyyrTkZKO18KN+3V1UNhRMbP5dIBThYEVtzBhuLddBVTWKqurw14dZPHkwihDcveGL2KrtSTZMaAiTYkiLRslIdaAIIefKURIMR2Jq1USL9jUv1rfyg3384pos0t22mGdUz2Q7xTX1/PLFL/G6bSyflYvHYaE2GMGsiLi5L9HNGVk4TtJVJDqEaT1QAYzQNC1N07Q04MqGzzYk+N6nFtWHIFx/bAnUAM5uYLIYUq47D1ahCOjbzdnOiTpWs8KYH/Tis+8r+Ppw1bG1RXLG05or3WE1cd+YLBZtyWfaUx+xaEs+943Jwl8fNgop2S0Kf5g6JMZd/5cZF3HIV0dBeS37y/xxC2rNGzGAcn8wrpLM3DV5KIpixP22plQjXf6SE0lr4Uc2kzAWmM2/21tcEzOGw2E1ZtGZ6rBQ5g9ypDLA10VVTF3+IZOXfUhdSDWMB2icJ18dquLbEj8lNfVxQwkLKwKdnisytEbHajbF5DNEi/Y1V7369fgfcE53F3tKamKeUXtKakh321h16yUxz9GFm3dSUhOMCQWNXq9pEvYdqz6j1C8VFyXHRqJVmPppmvZI0w80TSsCHhFC3Jrge59alH+n/046RgNCKODqYRST++pQJX08DqzmjtuKVwzysv6zAlZ/9D0PSUUmyVHSVgGssKq1kCO8d+MO1tx2KfPXbjdCKgb1cLNuzmUEIyr7S2t5YPNXlNTUs3TGRdjMCmX+eoJhlbCq8db/uYKn3tuLp2Gh1Jo6U9PE6baUatpTZ5JIEkVr4Ud2q8nIT4guOEdlp/OrsdmEVY1/3n0FRVV1/H17AdflZBh1BUZlp3PXyEHMW5PH4smDqQupPD5lCL5ACI8zvuKS02ri7g1f8NwtF/PrzV+1CCX85bXndmqutPU8ONN2wtNcViOf4d6NO1pVvaoLqewuqeGJt3bHeGyLKuvolWLDYlJaPEfnrcnjD1MvpLw2aPTXprwCQ82p6bWbel8tZgWzImSYk6TDJNqA+F4IcR+wUtO0IwBCiB7AbKAgwfc+tSjrAgnXKG6vEcL05cFKzuuV3LnT7WaG9kvltS8P89vrfoDZlGhHleR0pLXF+fq5l6NpWtwXZkVtkMWTB+O2mfnp2u3GQmPx5MExVaXnr93Ok9NzEEIwv8lxS2dcRERV2ZRXwC+vPa/dKr2yyrTkZKS18COAX1yTxR/e1GWNM1IdaBrMaAjfi3rpbhrWn1Uf7DPG9qTcTOatycPrtuG2mbl3Y+OcWXXrJa3KhxZWBDAJETeUMPp9R+eKNNYbURRBvzQXHqeFdXMuQ4O4fXC4MsCqD/fzq7HZVAVCMc/E5TNzCastn6Netw2H1cSi9fkxz8U1H34fc22rSbQw6BZPHsyjr++ipKb+jDXuJB0n0SvDaUAa8J4QolwIUQ68C3QDpib43qcW5Xv10CNXF+QduHtAxfcUV9VRWhPscP5DUy7p342K2hCf7C8/9vZIzkhaW5wf8gWIqFrcMIzi6nqq6sLGizJ6zr0b9SJWoMcLLxyXTZrbZhgP0ePmr91OjyQ7d141kHWffM+T0y9qUylGVpmWnKzEk1qNGha/mziY83snk+q0Mq9ZsvWf39lDIKRyw6Vnsea2S8jJ9Bg73PNGDGgxtx5+7WuWzoidJ09Ov4hl7+4lI9VBUVVdixCmqCJTZ+aKNNZjURRBN5fev/GU5B6fMoSX/32Im4f157sSf4t+m7smD6e15fPrrpEDW4yJ+Wu3c+0FvYxrL548mEAo0sKgiz5nZWFASUdItApTBbCg4UfSFuX7dAUm0QU2nTsdAuXkHzgMQP8OKDA1Z0iGB5tZYevOIoYNkMnUks7TWmGkMn+Qp/61l+Wzco0Qi6a7X78ae16T5MJk7h/hJd0p8CRFuCG3D+NzMliwaQePTxkS3+0fVvn15q+YN2IAioDnbrkEm1nBbmnplm9PqUYiOdloWjjuYEVtzBzIyfRw87D+zF6hF00cne1l5bR+WKnmlVsGobhsLebMG/nFLByXzcPXX4DFpOALhFAElNTU88ikwazYto/5V57D+jmXEYxo7Cv189jWxl3qjs6V1p4H0lhv9Ditn3s5h3wByvxBXsor4L+v6E5doBy3x4XXHRvmVFgRIBjRQ9GaJsD3TXPGfS727ebk7buvoKS6HrtFwVcbPxnf47A0Xv8MNe4kHSPRIUytIoS4RdO0FSfq/icdFfsbFZSOFZcueFW4bw8AfdM6lkDdFLvFRHavZN7fU9o1bZKcccRbnD8yabAhB7lowvlGiIbFpBBRVR6ceD5JdgsZqQ68bgvPjHGR9soUPafH05ffTXuexduPUFgRMIpdNV+QRFTNKDwXZduCK+OGScgq05KTic4ogqmqhhCiYa7YmDdiAAO8LgrKA3jdNrxuCw8NN5O8Zgz4DnCBpy/BKWsZne1la36JcZ2MVAffl9Uy86+fGH+vue1SVsy+mLpQhEm5mSz957f8buJgeqZYcdnMLJme0+m5Io31tlEUQc9kO5WBEE//61seGm4m9flrjWff+qlryQucyyOv7zZqQyTbzSTZzSyacD5OqwlfIMRhXyDuc9FuUSj3B6kMhHjktb3MGzGgzcrX0riTtMcJMyCA/wtIAyKK7wD0+4+uuVaDIVJ2aC+9Uga1WYG6LbJ7J7P24wMUV9WR3qBBLZF0lOjifN2cyzhcWUeZP2gYDxmpDiIa9GiyqA+HVcprQzy45SuWTM+hv91PytprDUEAfAcwrZvO/Jmv8/F+D8ve3RuTTKrHf+ey8bMDMe2I+yJUVagtgXAQxWzF6/KCcmbFYUtOLjqSZNzUwIioGms/2s9fZlxETX3YSKaNGupp+Eh7ZWLM/LFumMH/3rSV0YdrGufMzFz+/PZuAEZne/nfcRnYKOXrsiAPvFVESU3IWOg39X50Fmmst0/0/9GSCX2xrLgmpu8s62fguvZF7hmdxcoP9vFfVw2kpDqIqmnc8tynhrc2w27iH7dlce8/DrI1v8Tw7tbUh/nzO3t4I78YgGXv7jWSuJt7gaVxJ+kIia4DsaO1r4Aeibz3KUWgAuqrGovAHStu3YCoK9lPv/Sco75MdkPy9YfflTHhwj5d0jTJmYWiCMyKoD6sGlry0QWOqdm6oTwQZO5qPdGzPqRSq9aS4os1BvAdoMbvZ96IAcxdncdjW3exaML5nO11EQyrvP7lYX6U1YN9ZbVGAbn0JBupDW55QDceivPhbzcau3vc8AKkZ4MiBQMkJ4b2kozjGRiPTBpMWFVbKPEs2LSDN249u3EBGsV3ABthQ82nm8vKxs/0gmU/u+ocBooCrKtGGx6LjdOepyp5ICkOW5cs9I/FADmT0ML1cfvOY1X56fodrLpVD8uc9tRHLByXzehsLw8NN8d4a5dOe55vr/kh+8sDRmL02tsvJf9wtaFy17eb0xC1iKowHY13SXJmkmgPRA9gNHrdh6YI4IME3/vUoaFmA+4uMiAc3dAJSvbYAAAgAElEQVSEgqvuMP2OInwpSr80F06riY++K5cGhOSoURSFlR/si5GBXPnBPu4fm42qaiiKIBxW8deH8bptPDp5MLc89ylPjOtNL0/f2Beppy9VIcWQd60NRuiTaicYjnDNH94HYO5/9ONnIwcZlXwzUh0sn5VLVnoSZrOiex6ixgPov/92I6Fb3qRYTZYvT8lxJepVqA3qC/tl7+5tUVkY4hsYCzbt4G9zLjMkWaPnFlYEiAiLbhw3mz+qYmXRlny8bhuLpwxm+fv74f39vH57FtYtM1p4/Ny3vInc7zt+lPmDFJUGuSBO3xXX6qpLJdX1ZHZzsHBcNulJNp4Yn4l95agWfaeNe4m5q3cZlzArgpfvHM5hX13M8zHG09X5lEnJGUqit9u2AG5N075v9rMfXY2pVYQQzwohioUQO1v5foYQYkfDzwdCiCFd3/zjRHTSd5UBoZiot3Wntyij31EoMBmXUQTnpLv5okAWDJccPWkuKz+7elBM0bibh/XnwVfzKfMHUVWNQ5UBSquD/M+PzzUq7T74bgll41fqiyAAT18qf7IKs7s7NWWH6S1K6S4qEZqGrzYM6OFKMy7rZ7wcczKTeWJcb7qFjhCpPoIaiUA4GHd3r8RXJYvJSY4rqqqxv8zPzoOVFFXWYTUp/Pq6bHIyPUBs+J1evd3CplkDeH/uOWyaNQCvW68JEJ1X94zOIifTQ0aqg1pLKqEpa2Pmj3bDC5SoSTw7eyj3jcmioFxfQN6Q24dBaVb4yV9g2hrIGKqf4ztAOFjHgfJaiqvr5JxIINEie7XBMA+8VaQ/+7LG6v1x61a0m17GlpzOqOx0FCGo8IdYtCWfiUs/oKKqOu4zrX+qpcVYCqua8XyERk+XL1APNUfQfAWEKosorjyzi/1J2ifRKky3tfHd9HZOfw5YAqxq5ft9wBWaplUIIa4FngIuPZp2nnB8UQ9Ezy67ZLmpO31EKa5jMCAAzu7uZsuOQ9SFItgtMqFK0nkURdAjycaK2RdjUgQRVePpf31HSbUey13oqyWiadgsCvPXbjf07T8vqOL21+H+azbQy63gcDg5EnLRM/AdntduBt8BMj198U1YSWr389i24EqsZpMhF5mTmdwiCVud9jwk94q7M3u4RgXObH16yfGlIlDPkao6Fm7eGROH/sD48/DVhunf3YWiaBRX1WFRYMWPXXg2TzHG/ooJK/kmonsooh6JRRPOp5vLymtfHuaa7AGkzNqKSQ0SERaOqEmUV9bTI8XGvRt34HXbeHL6hZynFKKsHNsY0nfdEnjnt1BTzLflIcavePeMLvyWaMJhlUOVAYqr60lxWCipCbF4Oyz60QIsG2aC7wDC05eMCSu5b0wWTquZqcs/MoyAQzVqXG+tqlhZPGUwBeUBzkpz6sU1fbHKXQBet4WUqj2wbjrCdwCLpy+m8Sv5n21hfn7NubLPJXFJeMCv0LlUCHG9EGJiw7/bHYmapv0LaLUIgaZpHzTIxAJ8BGR0UZOPPxXfg9UFNneXXbJQTaOvUkqy3dL+wW1wttdFWNX4+nBVF7VMcqahqhpHquu55blPuerx97jluU+ZlJvBb67LZtpTH/GjR99l1l8/IblBqz6aHB01In625RAlSjpHIkn0c9bh2XxzjKves/lmksI+eqU48CbZDLnI+0d4SXsl9lhl3XRQI3rOQ5Od2bLxK3nw3UZlGilhKEk0qqoRqG+Zv3Dvxh10d9tZuHknv1j3b3YX1XD9Xz6gvORQ3LHv0SqNaxZWBMjs5uAfOw5yUb80pj39Mecv/pxrnv2OfXUu3t9dQqrLiqbBwnHZANjqy7FuiA1d4uU74YoFhKau5YG3ioxry9oAXY+qauwqrmb6Mx8zedmHLN76DUtnXMSMwUmG8QAY/R2oOEIwHFtALp63tmz8SvYHHFz9v/9i4eadBIIRSmvqQYO/zbmMF+64lHVzLmP5rFwe+3FvTOumx9wr7ZWbmZObLPtc0iqJTqIeBSwF9gAHGz7OAM4RQszXNO2NLrrVbcBrbbRjDjAHoG/fvl10yy7EdwBcXRtjuiuYRi7l7FfDoBx9N5/d4MH48mAlOX1Tu6p5knY46cdsJyjzB416D6AvRO7e8AWLJpwf85mmaQ1Gg4/Htu5i8eTB9EyxowhBSXU9IFpNLjRpIQoraumd4iDVYeEvM3PpHj4S99hIuB7hPQ/l9rcgHCQkLPzP5gN8XtBoJEsJw6PjdBq3iabMH6QurLZSXE3/fOG4bMPAcJkiccezy6QbujmZHu4aORCAyUP7snjrNy0KzN01cpBRIyIj1cGS6Tn0s1fGvW7Qcw5HlHTgi2ZtO70M6xMxZpuqaQkhYp6PUZWkpePSW02kDqPFSLB+XlDF099042c3bUWoIQKqiac/reSHg3RJ1mjhuUUTzueW5z5lVHY6v7z2PCoDIawmhb4p5rj3ykg2nZZ9LukaEp1E/QRwdUPOg4EQoj/wD+C8Y72BEOJKdAPih60do2naU+ghTgwdOvTkC+ir2G8oJ3UF1UGNr+q6Y7KoWANHCLqOPgG6m8uKx2FhR2Fl+wdLuoyTfsx2gtYq0DqtsQv0oso6Q1Ywyqy/Ni52/jLjIjBZ44Yf1UZMPPhaPneNHER6ko0t/y5k3sUpcY/NL67HXOcnq0c6iiIwqRo/v8bJV02kLaWE4dFxOo3bRBMMRyiqjK/Zf7hS/ztaQRqguFYjM8549gUVcjI93Dcmq4WUa0m1vnM8b8QAftAriaRIBZumZ1JYFWFDfh0CqMeMPc5164SNRa9+zT2js2Lkl083w/p4j9nmalob510et7hfaHwfzHH6JYiZdCp567az2VMW4oG3ivAm2Rl7YQbXPN2YGL10xkVYzYKcTI+RWO+0mpiam8G8EQMIhCK4bWYUIYiICJY490p2u1gx+2Ic1tOrzyVdQ6JDmMxAYZzPDwLHFlsDCCEGA88AEzRNKzvW650QNA0qD3SdhCvwVWmEQ1oaALaag+0c3TZCCDK7OdlVVN0VTZOcgURDipqSkeqgNhi7q7Vi2z68STYWTTifP95wYYvQjiXv7MYcroEJS1skVv/lkwr+MLYPGUoptvpSfjy4F4+8VxrXrf/AW0Uxbvmm+vTbFlzJS/OHy5hfScKIJssCrPpwP49PGWLMj6hi2KoP9wMYxRJBD1Opmbg6ZjzXXr+a7j1686cbc3hu23c8Ma437889hyfG9WbVB99x96hB3DM6ixfzDuCo2EXK2mvp8deh5L45hUXDFBQ07nvtUIt5Epn6PK9/F2JSbiY2s8KjkwczKjtdGtZdQJk/yB/e3MXCcdmsm3MZKQ4Lo7JjNxBHZadzKOTGNyG2X+omraGfSyXpzXuxl3zJBY4yNt2YwaLrsvlps8To+Wu3c9hXz7wRA4DGYnLzrzyH6rowDouJxVu/YcKT2/jZKwWEpz4fcy/fhJVMf34vCzfv5EiVTKaWtCTRHohngU+FEH8DCho+6wtMA/56LBcWQvQFXgRmaZq2+5haeSLxl0Io0KUJ1DtLIxzUugNg8x/iWJf+fVIdvLerxJDclEg6Q2sVaG1mxdh9zUh18F8jB/H/dhczMrsnYVVrsSs3JzcZy/OTdG/d6IfAkQqhWipMXm4/txjX6tFGEuhZE1byk4syuf31b1g183V81TUU12ps2F7L/SO8pDsFyZFy1Eg6iskk9eklx4Wmu8/Dzk7jzqsGsuSdPSwcl02ay0qPZDtWk2DBtedx4yVn8dqXh5t45RT2KX3h2hfxWFV8QQWPuxcmTWBWtJg6AJmevjw0fiWR7k6OFB3iTz/ugW3NuNjCZBtmwLUvsjW/hOLqZEOsIC0lib1+B+f1hp+u3R4jhTzQ65bvgGNEVVVuHtY/pgDm0hkXAbrnISPVwa/GZjPjmY/xui3cf80G0p0CX1BhUHIKpn/8HC6dq+ep+A6gePrinbYWr9sS88yMehyc6Bs4T07PQQjBzL9+3MJLtTW/hP/Ey8PTX8OhhKnXzBysdzFvRD3L3t3LHas+48X5w0hPkgVlJY0kWoXp90KIzcB1wOXo9R8KgRmapuW3da4Q4gVgBNBdCFEI/JoGr4WmacuAB4A0YGlDTnZY07ShCfpPSRyGAlPXhTB9WRKhzhY1II7NAwH6zkUgFOGgL0Bmt6OvKyE5M2mtAi3AS/OHEwiGCUY0Hn39a97IL+b8jFSq68ItQjt6uxV9AeQ7AOtmGp/3unMHtk0tk0sH37SVzwuq+KzUwsLNB/G6LS1UmcJTn4ce2Sgm6aKXJJ6mtRxGZvdgyTt7mJSbicdhIRRRKaoM8Iv1X1BYEWBUdjq/GpuNAFbdeglWk+CGpz825oQetpTKvRt38OYd5+JoJhiQ9srNaDe9TPpr1+vyrK3E04MeQz9ptZ4DpNdXCRrKUNAQQ786TyqTdQERDcN4gEZvwfN3XMacHw2gZ4qdspoghRUBCisCRr8A7L73fLjwRsN4ABrEIWbw22tfZPyK2Dyu9GQbFpPCMzfl4rZbuOGpj2Luu2DTDhaOy2bu6jy25pcw/8pBKCLWcHxk0mAe27qLQDAiNxElMSTaA0GDoWAYC0KI7pqmlXbgvBvb+f524PZjb+EJprLBMePqSgNCJdNjI1ybjM1/6Jivl+HRjYZvi2ukASE5Klrb4fcm2ThSqRLRIryRX0xOpodUl5WNnx7gH7dl4ff7qQwpmM0WeiSLuDkNaPGTS81aiI//+ypsFoWnbxqKpb6CtNAufTEVqIBtf8S8fjqhW95ESek6D6BE0hqNEsMeBnhdvJFfbCTNLp+Va1Rrz8n0cPOw/sx4pnG3eOUtQ3lmUl9cpgjFtRohexr3NIT5mbRQ3DkgahqEBAIVceeOL6hHMedkJnP/CC+93Qp2R4BDQVfcvKVIJEyosgKzFkKYreD0yurtnUTTWnpXvW4L6aKSdFc9QtSjuJLj5sYIRQGXN25fD0yzkpHqwOu28NurezIwzcJ+XxEPf1DOnSOzDKOkKYUVATwOi3H97m4r0+IYGYsmnE9E1fAFgnRzNTzHVVUvyhkOghwLZyQJ7W0hxLVCiH1CiP8nhMgRQnwFfCyEKBRCjEzkvU8ZqhoW+K7uXXK5mqDGvkqVASkQsnfH2gUGRJ+GGNzdR2QehKTrsZgVLCbBprmX8cL0s+ljquTBYYLkNWPoteJizt0ykXO0Ayj/eqRF/oNvwkr2VaqNn0Xx9GVXaZDq+gjl/hADvU4G2Hzw6t3w3FjY+j9w1QPgTkdEpESh5PhgNZsYlZ3OPaMbi7hFiSZM52R6eHTy4Jhdaq/bQpr/W87dMpHMlZeQ++YUcu2H8br1xZ8/Yoo7B/A3SBNv+6Ne26HJ3AlPXYsztQejs708M8ZF7ptT6LXiYlKfv5aBooDR2d6Yy43O9tLNvxfLimsQfzwfnrkaivP1haSkwzTPCcvJTGbFj13YV47C/uQQbM+NIrl6D0/NzInJjVk64yLqwujRCnH6+mB1mA1zLmXj9R4ueO167EuGcO6WiTw03MySt3fhtMbPRYvm2Tx901DMiohrZPRNc/Lwa18TiOatqare989cDXIsnLEk2lz8PfBj4F7gLeA2TdMGANcAixN871ODyoNgtoO1a2pA5JdF0IBzUiBkT8Pmj5fD3jncNjOpTgu7j9QcewMlkiaEwyqhsEpdMMIgazH20i+xawHMzfTP2TwfBo2Bt38DYx9HvesLamZtpTp5EOYkL8FmFXeDU9aiuLpjUqC6LoTmL0Wsa3bNBq17zSSTQiXHhzSXlfvHZrNg0w7+9PYeo94JQG0wYhgX0WrsUe4f4W1RA8KyYQZrpvYlJzOZF3bUoE2NTbDWpq6GA5/ofxd+pheGG/s42l1fUD/7DcpcA/AHVf58XWaLeinWDTN47Md9Yhawi3/cp2W9iL/dqO9CSzpMNCcs+v/2t1f3bNG37pdm0dceYNGE81k35zKemDqEQa5aXbJXMcPEp2L7etpakrr1orLscNx6Dg+P6Q1oLJ1xUUyfPjld//uFOy6jR7INRVHiGhmHfQHeyC8mEs2jri3R+16OhTOaRIcwqZqmfQ0ghKjVNO0jAE3TvhZCSF8XQNVB3fvQfm29DvFlib5DcE4KhHzdcZXt1JWejvH6fVKd7JEeCEkXEg6rfHOkmpLqejLtAZK0Ut1D0Eq8No5UfSG0dgpFt3zKsL98ZcToPvO5yoxrX2RQmpV6zNz7j4Nszf/AqOwrIsG419S6DcDkjt1pjYt010u6AEURmBp2eQsrAjy2VVfj8Tgs9EtzGsmz0WrsUSMi3Sni14AIHGbFj134zG5EuAJmvaQXSqw6hHjvUcLXPIj567/r59YUE3H3oljx8k2Rnz+9/W9Kaur55x0D4l7bLsL8cdqFdHNZ2VNcQyBQTXK8eRmWHrzO0DQnLBAM010tifv/v6qmhlue20NOZjLPjHFhX3lzY6Xwyc/B+D+ByQKhWkKungQjGt3txL2W0xTBigmH1cSqWy+h3B+kzB/kyX/u4b+uGkhdKIwioFeKwxC8iA2FqmR0the7RUFVNbRQPSY5Fs54Ev0G9Akh5goh7gUqhBC/EEL0EULcDMjtbIDKQnCmddnldpaqpNmhmx2C9u6YIgHMQd8xXzfD42BPcY2UcpO0SlSe8mBFLSXVbcv+6YWU6impruesNCdnpwjdy9A0Xrspnr765w3/djicrJtzGQvHZbPyg32MvqA3uNP5vMrNj/+6i635+k5YtLJvRLHEv6bV3X4CtXTXSzpBe/OgaQjL5wU+5q7O4+4NX2CzKLrKSLNq7ICeq9BKiJLno8c4y1wGL86BP+fC2skgFPAfoSwQ4ctrX6Tuzi+omvk6898McPnD77Jw805+c90PGHZ2GvWaOe61w8JCmT/InuIaPTejKhK/DWbpwess0Zwwh9XM3vJQ3P+vVSF9eXb/CG8LDxEbZ0OwRg/HXDuF0gof5f4Qbpcr7rV2FgWY8czHlPtDLHt3L2X+IB6HhRsvOYsUpwWzSUFp2GTM6pHEy3cOaxEK9eTVDro5zOw6Uk1+cX2nxkJn3g2SU4dEGxA3AxcBA4BRDZ9tBaYCdyT43qcGVQf13cwu4suSCANS9H+HHA1KTMdYCwIalZgOVQbaP1hyxhGVp5y4dBvDH/knE5duY9eR6rgvinBY5UhVgJIaXenl7vVfxCZC734dpsTqnzPxKbCnwK1bUW96mac+9THtqY9YtCWfm4f1J6unmz4eG71S7HFjeEvU5Baa6uq0taCpUHOkbWNAuuslHaQj86B5CIseQnIphRV17C3xx1RjXzgum43zLiejdx+0abFhely3RM9tuPDGVsPzCqsijF+xm8Na9xaG9dJ/7ua3V6djERCesqZFblGVycOgHm5cVhNLpufwVF5Vi3oR3PBCl76/zjTSXFZcqT2onriqxf//kK0bOZmeRu9TxlCYtgZmv6rLWKdkGMcfrlF58p3dKIqJyNTYviwbv5IH3y0x1J6uu7C3cf9gRKW6LsyVj73HlOUf8vXhKnyBIKlqZYtQKPP66aj+Uu5Y9RkPvFXU4bHQmXeD5NQi0TKuBcDcJh/9oeFHAhAJ64uX/j/qksvVhjT2+lQuGaT/HbI3FJPzH8Sfdv4xXTuaSL3nSA0ZqVKJSRJLU3lK0Bcod6z6rIXsYziscrAygKpp/Ont3Tx8/QWcleZiX+VhBnj66gmCF0yFfz2mvyRdXjR3DzDbEUe+hLf/L0pNMbePX8lH+5P5vKCKBZt2sOrWS6gLqXxfVsvobC9zcpNJdwqKazWeyquiPqKx5DOFX818HZsII7QIljfvh12vNr780rPjhyWF44c/SXe9pDnN54HXbSFcdQTN7gOLDZxeFEVpIWsciqjMW5PHsLPTWDrjIuav3c7nBT4Wbcnn+dsvxl39LeK9h405gTNNNx4KP2tVlSeSOoAH1x0iI9VBpJnyT05mMg8NN2NfOUo/N2ss4ZmbKQtEKK4FhzOdRzd/ZdQl+MPUISz6yWCE0AjOfgNFDaGZrJjc+n+P5OixmE3UerIonvB3MtwChImiSpVXdxxm8ZQhpJmrIGtsTO0HPH31TZassYR+9Es2fFDLoz+yYF0xUn+Gjn0crdsAdlVoPLe9qqH2TTrFtRo905xMe7pR3WvpjIuMatVz1+SxaML5DPfWYY0zppRIo7zs7a9j1KhIT03Gltwj7vOzo+8GyalHwme+EOJKIcQSIcRmIcQmIcTDQogBib7vKUFNkb4D6uqaHZymCdSgqzABXaLEFJVy3VMs8yAkLYnKUzalsCJAMNxYbVpVNXYVVzPjmY+pqQtz2w/P5pcvfskhX4B7/nGIyp+sgisW6C/JXa/qtR6eHY1YdR2irhI+e85QTkp75WbuH+E17iMElPuDbP3yEEtG2sl9c4qhVrNkpJ0Nn3zPhIsy+KbagT9iwrJmgn4PMDwKoeri+LtiZqsM3ZB0iKbzIBq7fsFr12N64oKY0LdoCEufVCfeJBuhiBpTGyJapXjhuGxStSos62fEzAnWXK+LCnj66gZ2nPG5x6dRUhNi8eTBFFXWxSTHtgiL2fUq5jUT8CS5sXt68ujW3Ya8bGGFXpvCZIIjNSGuWv415zz6JVcuy2dXsV/uJB8DZf4g3xb7KfcH8aiV2NZOwLZkMOe+ch33XGxB+I9gVuthzO9b1H5gw83UXvN7dmuZTMl2kvL3m/TPG/LExOqfkGQzsehKD7lJPjLrdpO747ekB/Ya6l1Rr8TdowYZfzutJnaVBuOOqYhiaRJ+V8Wk1Xu5cf1BSrWUVnPCOvJukJyaJFrG9WHgJuAjIAR8B+wFNgohpiTy3qcEUQlXZ9dIuBoJ1B7974gliYjJ1iXF5Nx2qcQkaZ3m0oSgh2ZYzY35BWX+IHNX51FYEcBuMXH3Br1gli8QoqQmxO8/1VDTBsbf7a/z6TtwHy+H4T8H3wHdtd9wH4tJoS4UYcEV3fUKu83Uaq7pZ+LejTvwJtkI1gfi3qPEVxXfte706h4KGbohaYem8yBu7PrfbqS+6ggl1fWEw6oRF25WBKOy0/E4LLyRX8zc1XlMe+oj5q7Ow0L8Gg94z4XRDyF2bY2rwNTN24vVt11CRqqDFIeF5TNzjbYZRRmbXdMaqSUYChvGQ5TCigB1QTXuTnKZX3rijhZVVRmQ7qKfvTZ2rLjTMfuPMGDzT7AuuRCqD8ftL5+/nsLKes5KMcf9vrdT1TdLnh2tS1dfOhfLvx42Nl9A78fMbk6Wz8plVHY6vkCIB94qIjLt+RbPvDprKosnD45V55o8GIe19TyyjrwbJKcmiVZhGqtp2gUAQoi/Ae9pmnavEGIj8D6wIcH3P7mpbJBY7aIaEF+WqqTaoFvUKygEIXv3LjEgAHp7HOwtlgaEpCXRuO7oAiOqK57msjYkTAepDYaNxUdNfeO/l727lyenX8hZ4f0oZXviF4vzl+gvwNEP6WpMnr4U12qGCpMQ4E2yUheoiPsiTXfqyjeVgRBlVRF6xLnH4RqVn22J41pXFFTveURueRMRCcrQDUmrNJ0HrSknFVdUsej9Qu4aOYh5a/KM+fKXmbn461tWYK/XzNjjzYmSb3SPxLQ1iPcebZwbgQrEe49SMPgBfrblEMtm5qIoghSHmbW3X0pJdT12R23ceSbK95LpscctYhZWWxZAkzvJR4+qalTXh6kNRnBSi6NpXwz/eaOoBOjPvzj95UlyM8STjDOittqfLXJjRj9Eur1RlTEj1YGqaizaks+ymbnkH/Rx18gsih0u0m55E0uTooFJCHokh1k04XycVhO1wQg9ku14HK17Y9t6N0hObRL9BlSFEN0a/t0bMAFomlYByHroVQ0L+64yIBoSqJsqtuoGxLGHMIFuQHxbUoOmSZe1JJam0oTbFlzJS/OHk9UjCcBIoPumqJrR2V42zRpAlqOSV24ZxA25vbh/RBrnOXx4wqWQvxmmxiYUMvk5vU7KT/7SkKdgIjhlLande/HKLYPIsvvoplVSEwhzqCZ+UbmosdHNZaVXr0xCzepGNE00bL4g0kOv/Fy5LF+GbkjapOk8SE9NjjsW05PsPDKmF39+e1fMbv5P1+RxVpqTZU08BRmpDoojSYSnvwgzNugJtDM26HNi2x8brnkWXHijYTyw7Y+w61XDaI4aKTc89TGVgRCaBoeCLtRmybZctwTeewSnKWLUB4i24ZFJg41/NyUj1UFE1eRcOArK/EEKyvUQou8rw7FjxZHauPDPGKo//2Y2jIGMoUbth1A4giNYzqb8WiKzXoZbt+qJ1llj0aatgfceib2p7wC4vPiCCjmZHlbMvpiVt16CBnjdNuatyeOyc7qzcPNOLn/4Xa5als83gRTCdi8l/hCHKwO47WaGZKaQkerg/D4p9EtzoSixy7mmqktl/iADve4W74bm50hOPRLtgXgI+FwIsQs4F/gpgBDCC3yR4Huf/FQd0ovIWVzHfKlAQwL11HNiPw/Z00gq/fcxXx+gd4qD6rowJTX1pCfZu+SaktObpgl0/8wvYslIO5YNU8B3gAuyxvL7K+5DrJ/SmBh43RK9bsmMTRAoh0gQwnXwyl1Gsqc2+ncEgxqZprKYROicaWtZttdC1sTVuF+aZVwzNGUtTtGTVbdm8Oe39/DBd2U8Of1Czp75OtV+P4drVB58vYTPC6riutZlEqCkM0TzG1B76KFuUQUvT1+YsBTb32/FVlPMQ+NXUlytCwEADDs7jYiqkWQ3s27OZVjMgmBYw2YCxV+n10iJXmfiU/rNssYCmu6dazqH9rxFWpKd9+eeQ3Gthj3FRmFFgH98cZCfXZ6KUIOolnSUsY+DxakbHu/8Vq8Voejx8StmX0xEVTEpCsGIrlIWTfCO7iQ/MmkwD76az6/H/wCr2USayyoXhh0kGI7gtJoorAjw4LslPDN+ZWMYU6jBQ+RO1/O+miRPa9PWgNOL+Mc9pOx6FbLGMpGnVr0AACAASURBVPOK+xCrG595zNiIMFlh1O9070U04b4hZ8Zt78aaG11UVfs4VFzOU3lV3DM6i8e27iIU1lo861644zJufPqjGA9Ca0aAqmrsL/PzfVmt4aU4K80Z19CQnNokWoVpnRDiTeBs4FtN03wNn5cA0xN571OCykI9gboLisjll0VQtcb8hyghe3cs9eUo4QCq2RH/5A7S26MbDd8W10gDQhJDVKqvuZu6m9OC123hiXG9uaB3MpbSnbonIVABVjdi/ayWLvbxf9KNiEhINx6iC6eMoXDpXMSq63A3XSz5j0DhZyjrZjB31sscog/7rn2RdCc4HU7u+cdBtuZvMxY8e4pr+M/n/81jU4aQZHfzsy15MW1OdVgoqa43FHJUVZWhG5LOoyi6x+y2t9BCtYiyPXol9cLPAHQhgGs2MGl1FVNzM5h5+Vnc8NRHMSFNNrMg3e5HaSapyUtz9KJxJqteC6Dpdx8vhxELcK4Zh9N3gExPX8JT13Lf1QO447wgllWjG43xK+5rnIOevoSnPs9dLxewNb+EjFQHy2bm8tL2Ayx/f78hNxstfOcLhHhs6y4+L/Bx2w/P5u4NX7S5sJTEYjWbMAt45ZZBpDvB7k5Gu3kLovqwvnEy8SkIVrdInhbrZsLYxxtFIC68MfY56k6HmuLGEKjoc/Lj5WgjfkmVrQd96/dien46Lt8Benn68tD4lfzPtu+4a+RAmnddYUWAUETF67YZCkxtbaD4AkGOVNWxcPNOYywvnjwYj9NCN5fccDmdSLQHAk3TyoHypp8JIbKAezRNO7NrQXRhEbmdpfoO0cCU2M+jtSCs/sPUpZx9TPfo49ENkL0lfoYN6JqwK8npQbxd+j+++Q1Lrstk47RemGqKdBmFpruoM1+MnxyakgG1pWjdByJCTRKeh/+8pRJJQ0wvDTr4Jv8R6lWFYDhCkt3O3rJaiqvrjTYt2LSDheOymbs6j57JdjI8jhg5zVSHhT0lNTGGUDS5sGliqUwClBi0V6XcX4II1ujhJMN/HhNq1MutHzfnigHMXvFJi5CmTfMub11GWNP0qtPNv7vwRmM+RI81r5/B3NmvYXrtvthciS83oc5+jZLqACluNyVaMsXV/zbaMG9NHqtuvYRP9vv4vMDHt8V+vahcs/wIXyAkPXOdJM1pppvtEKbN02M8VLz9G/2AUb+DtIF6f0U9CKAf2+0c+NkOvX5O9LMozfMnGp6T6s2vMun57/nt1Qe54LVYgzTtlZuZc80GUtOclNY0JsXnZHq4a+RAVA0enTyY+zbu4PMCX5sbKIFghHs37ogZy/du3MG6OZfBsQdbSE4iEmpACCEGA4+h5z/8HfgzsBS4FHg8kfc+Jag6BD2PrT5DlJ2lETxWSGvmGGhaC+JYDYhuLisOi0kmUkta0FyqL6ozb3luVOzL0Z2u/+07ABX74idMl3yjh2RMXQP2pMZjmsYFR4l+Hj03EqS/zYfp7/oC6gJPX54Zv5LbX9dlBwsrAgzq4WbF7ItJcZqoCIQM4yHNZY1rCM1dncfa2y8l/3C1TAKUxBKtUt40TKlpTZFoEcIJT8LI38TuCk9YirdbKtsWXNlqgnJtKEJxWZAL4s0TBMQTHWilLoSihlrWErhuCWFNYdLzBSwcl82iLR/zyKTBhmfB67ahCMFjU4egqhoRVWXt7Zfywkf7uaafid5uBZfLxUP/LDLaHAxHDOGEpnNLeiViUQKlsL6ZZ2nzfH2sCBO8eEesB+Gd3xphSAgBK8fp38/YEDsGWnlOhiNhPi+owmNNb/ToNjFo+3rs1JsV+nSzs2L2xXicFlIcFh5+7WujHkh0bJTU1CNEy7yHMn+w1bEckWkypx2JTqJ+GngemASUANvRpVzP0TTtzC4oFwnpReS6qAbEzpIIZ6e0jIaK1oLoCiUmIQS9PXb2lvx/9q40PIoyW79f9ZJek87O2oiIKG6oqKPMuMIEjJKgkgiEsDhuuAwzo+I4XrdxHFG54ygC7mQBJiCrIiB4Fb24ongVRdwJAUlCkk7S3Vm6u+r+OP2llq4KSxIIpM/z8ITurq6q7j5V31ne877xBCJuatNS9elSWK6eQQsWt82zCc+rp67rKwdbWgAIFkokPF6q2urpMfDnx84FmADT0oKY6hqnLeyXbEc4ImHJJ7uwp65FRx1VH65UG2jF33NOx7t3XYoVMy6KwzTiRnYglfJwKyXN7t6xVeHVM2CBCKvZBIGxNoKB928+CcsnD0LW0HRERElf9Xd8EbDxAepqjJ2rfs2Rqn+dCCbdDp6ZiSiefj7e/qayrUt3y6WDcHZ/D+4ZPQQFL3+MK+ZsxrSFn6ImEMKSj37BXWeLOHfjePR+9Twklo7G3eeIOLt/IlEqm4W48vDBmFFnKbEPsOqW2E7riJnyb7/hb/Lrm2dTcYb/5nx+QmkeL0IsAWf396AqKNHszOUPUKFmYTaw4T6kogGSJKLWH8J/rd6OcfM+QOErn2DKRQNxdn9Pm2/cecVgzL72TJgUtz+l2vTu2qDusL3NEmetO96sq3/RBEmSFkqStFOSpH8DEAHcK0lScxcft/tb4z4AUqdAmFoiEr6vEzEoKfa1UEIKJAidxsTUO8mOH+IdiLhpjFP1HYhnvq1bAAD+KrBwK5A7H9KdXxCmW5kB+8oB3y56LusxgjaNVwdSUn4p0GcYMOV1oHIHJHdvQxpXXkEzC8DdWae0sdMA8rBgWJR0F7+qxhZMW/gpCl7+GAwsnjzEjaw9lXJ/JSCYqfMQrNHfLkJV+g9/qMJzI+0xAoiB5hAy3AmoRjKkqWuB2z6hGSHGCANfsZUq05NXEkMTh7tok4qceQCz6J5DJByCPbgXD16RjtdvuxDprgR47BbcecXgGCjKrOVf4ubzkmBeGguBeWRkL7xYOBxmgcX1Ig7GjAQqJUnfVzJPo99eMMvzDwD5wNsPQZq6FtIdnwMJbuCaF1W/f31uMf68dg9uuXQQHn23GuFRj8Ykk0LZJKShAbeUfoZ0VwKen3wu5ow/C61hEc9OGIbSG87Hk9ediYFpTpgEBotZDh+Vnds5b32HOePPUrF4PT/5XKS0Q/UKgLp5/krAt5v+iuLhfKtxO4LW1TMQNsbY2ZApW/0AzmTR3pckSZ938fG7r7VRuHa8A/F9rYiwBN0EAoIJIVtKp2lB9PXY8b8/7EegJQxnQpeP0MTtGDElfWVrOII0oUEfnhQKyv/PXUBVUZMTrHhsbLveX0XdhaY6mWWm33AKkpzpkBL7gK27t42FScorQXWLGRkGfOn/ddVQvLezElcP64fmkL466n5/K2ZfeyZmLf9SxTTz1IadbdvEh6fj1mY8CNT6uRgGXrqSAr7X7ySf1dkuxCyIiBKyBpphXqQOyi3LJiHp2rVR5jLN9dHSSFVkTt8aCQOrbpXfX/0tDdp6BlASbnGA1f+iew6Wuu/Re9F4wOPFkPGL8MjYU5HsSkA4At1rxC6EdQPcoRkJYElu/FrfFCcdOBizpwJ5JYBiiF3MKwXMNgh6PlXzA2C2A7bE2N/RXwUmiUBJbsx90m/rhanLdmPb7gZM/+0gVPtDiECA2QDmlu5KwF1ZQ1T3wPmTzkHxh79g2oiB+FPZF6j2t+DFycPhsRM0rTUcQboroW3A3mpmKL3hAuz3t6Am0Ip/b/oOfxo1xLhzeyAoYNy6pXX1L/MrgP8GzTvMAbBP8fipLj529zYuItcJHYiva+jGfGKi/ushWxoS/J0nJgcAP1UHOmV/cTt+jNNX9k12wOLOiFEyjeSVIph6GnUbrn4GsDqB4H5gaWFsu/6SWTKciVdUh2RTG9+ZDrgywD5+Ua7E+crBNj+BVIeASMFKFV9647hi7KlvxZmuBvzpohQ8+/ZO1ARadTsN+xqa8dSGnfivq4bitVsuxMJp57fhwfk28eHpuLWZnkp5/iIZYmKKVv23PE0Js+p6WIzbV5fj4ifeRTAY1A3K+zgkWVmdB4XmBCCxL3CFAoLy9iNqNWp/FeBIo4Q93AKsvwd4635EtNoPOfNkrQBfOazvPY4TbY1IC1ejl6kBWUOpwHV2/0QsnzwIH946GCaTKUofqzCPFyZLAgSBxZWH2zGlPkLIvx/gAoDR7pGweTZ1lzS+wjU6sHoG4K9WQ5Y8XtLOCTUTXC6/FBj5ML321t8QCEm4/9J0fHjrYJyV3IqnrjsDP/sUuhP9hlMH947PYRVElOSfgOIPflIP9C/6HNee2x93v0bwtoq6JtxYshX7Ay2obmyBIAAP55yGv7/xDfJf+Ah3LPkCv9Y34R9rd+Dmks/w1jdV7XehDgQFjFu3tK6mcb2sK/d/TFtDFFLUCR2I7dUROMxAbwOGg5AtFbbGXR0+DqCgcq1uxBn99FoecevxFmWlkRypiEx5E/v9zahoiODRldXYtrsCO+86HQkmC1XecufrDvQh7WRg+Q0y88j3m4BLZ8nsMnxR3f0BbROleDUtvLLt9UheKULO3rC1+HDKYrmC+9jVRXjy832GnYZtu324ueQzAMC7d12Kaj+xOMWHp+MWY5yq9Q+bZBYmUZQT26Y6CrbPv5EgeJOWAy31QFMdgpIZyXYzlk8ehBSHmZJeHsxHE2ULE+XrQ6MHoCIl2LmW2vyTlgOBKsCZAWx6SA118XjxfVMiAqOW4dQMGywWKyw1OynYbKoDvlsPnJEH56Kr2o7xXN5izEkdgD+c0orU12W9FimvhI4X7f6F8xZDsKdBQFx52Mi0VNcf3joYvQOV6o0cyeQ/CW5gyhtA/W5Zo4PfCyMtxNSUPQdS6mAwwUxaDy2NwJgnqSgDUKI5di7SI43I3Dip7bc7NbcY//xUwr05RfB89BTwu79QolmSC+Yrh8vjxRO5xSqNkoq6Jnjslra//LlgSwQFL38cHcD/RpV03P2azHjHnzPsQrUHBYxbt7WjgkFhjI0CcI8kSaOOxvG7hTXsIQEfa8d5zbbXRDAwETH8zdxCtlQkVn5MlH9Cx6pAvRJtEBjwY1W8AxE3HVO0os3RBSshpwiPvhtoW4x2+4GTEvvQAsEDLC07TH4p0P8iueOQ2CeW755rRrT6gYxTgZJxqtdNSwsgTH0TbPG1MXjt8aOW4dFopyHVaYU32YbG2l/xzJVpqAqm4tF3q1HtD8GRYFLRvMbZZOIWY4IAuDLlx/5KGWLy3Xrg4ruBZYXqxHfzbLidmfj7xbPahBXbBOJMVuC1qdRly5lPz+tRGK+eIVMYAxTMX3IPXSf9htPsReVXbftuHb8If11VgW27G3D9uX3x2G8FWaCRD+e+95SG/nUi7p66DsLCMarn2dLJ8E98HXW/eRBVQQkvbGrAo+PCSHebYuCM8euGTMvwVh+2oLeWmSuvGML6KCxz0jKZ9pobJ42o2AosGg82fQMlCpEWIMkL1P6kpsoeXwRB85smrSrEjTmrYPPYIY15HKz6W/Vxotv886qVGP0S3bM5VW/W0HSc7GrC+zefBF+rAH9zqyq5UJoy2eD7MOxCGUEBzT076ezu1qUQJsbY5Yyx7xhjfsZYKWNsKGNsK4DHAczvymN3e2vY0ynwpYgo4dsa/QFqbiF7OgQpDGtTpfFGB2lmk4BeSbb4IHXc9E2nFe1ZPQWleV5cf25vvD7tZAxwA5JgpsRhy9PA6H/SApg7nxIHVwZQVgDpvOkE0Xglizp2ehWqpH60TeM+3ddZuEX3+d4uAdt2+/D3N75BqtOC9KafMGh1btsA66tXOlEy/TykOSxIZz70ZTVIZz4IiDPJxO0ApoQ1nX6dnDwAakadYRNgWaZgDHNlkHBYgpsS49FPAJ+XkF5K+qmULPQbLh9HS0rg8QJ2D20THaxtKXgDu6d8gsaC9djvIPw7ANw43A2hbJL6vJZNoZkKpUVx8XrXUF2gBb97/gdcW/IjNnxTraouK+GM6e6EHp88ALFU15AiscxcSwvl30DLruTxAtctBKwu6njd9iklroFqICGR5m60+zP4TU9MBGxFWWC+cipk6vy+gz2sjVlr9rVnYtsvNZh7hQ3Ji8egf9H5OGPdNThF2I3rz+0LX1NIF7YWbI20/b/dLpQeFPD6JfR83LqtdXUHYg6AmwB8CGAMgI8A/FeUkalnW/0ewNlxMbaf60U0hYET20sg2qhc96LV2afDx+yTZI9TucZN3wxa0c7WGvxzhAC29Bp6/cI7gTGPE52xJAJbF7bBIfgQNWuqk+EbNg8wfQMtllxUyeMlLQneyTAaZtV5Ps2TiC2zLoPVbEIafGAvxyY9STdsAqveFR/si1usKcXjTFbq7Iaa5P/bkoAbNkJqDVCQpjQe+CckyqJugpmoXoM1dD04UsnnT80GSq+JJRjg/q8kJRhfBARrgazHgZdHAv4qbK9swh/f2Iu/55wOiymIf+UNQ68kG/qiSj8h10JqPV6a49C5hqqCcjIdn3E4sPHZEJ5EJCdo2JY4jDP9FCqkbHmaoEpT3gAgAWYb0Pirums0cRmQciLABJqd4JA2bga/Kav9Ub5vmhN0f19T3Y8onXA69obcSHUIuCgtEYxDRKP7Ni+dhFkT12H6sh9jIKEvFg5HZmJC23223S6UHhRQK8gYt25nXf3rSJIkvStJUoskSasAVMeTh6jVV9CQWwft6xqiOhtkMEANqMXkOsP6eOz4uSaAcCROsxY3jRlRE1ocYJxt5OwC4PRrCGrx7DlA8ViCMPUbrh6itnuAGR8TrnfxeOpEbLiPsOBDsmlwkGPG9agrx84FPnw25nlfThH2tDrRO8mOdHcCWEQ/6WHhpvhgX9xijcP0XhoJPH06BetVOwh2pPz/vi8pUDPSZIAkD0Fz0bB1d5Ovi2GqUPPnAXX3grOYOdKAOz4DJr1G18t7T1EQOSQbNVcXYdMvYay7YQguSmtCf6sfVjMw4cWPEBKZ7nlJrkzVtSLmL0atkAJRQ4gQzluMFz6T4S3xGYcDm5Lq+uz+iUi1m9SDzFyXYe5w+T7nzCQ/aPyVoJpKwglXBhVUSsYBz5xNfnTFQ+oulcdLXQol9XVeCfDNanq85Wligxr3gvreec2LwObZaGlqwoqtu5Dc+D1Ywx7d+6RDiGDbbh+KPvgZi/9wAbbMugwrZ4zAkEw3UpyH0IXiUEBPf/obTx66vXV1B8LDGLtG8ZgpH0uStKKLj989LdxKF34ndCC+2R+BRQC8buNtWu10HGsnMjGFIxLKa4M4Md3VKfuM23FivBWtrNrnFQMJTrk6duEdFCRpAyOO5/aVA8kDgQ33E9PMsimx205cRsOi/ip6nvPhZ88Bkk8AqnfKldpgHcQpawEpghaWgMc3VeF/f/oUK2eMQLo7wRh/y0zxwb64xZoeY8zqGaTFULUD+Gg+BfkWB7DpQUpgtfM99uTYmR7lTMOyKZQU6Plf5mnk5/+3GDgjT74+eFIBCeKYJyAJybjL8RNMpaMBXzkGeLxIyilCusuCn+tFnJIzL0YZu6I5AVWjlqG3S0CS24Xl3wRxWnI5BqS4kTz1TQgAmNkKwZ6GR8eF8cDV8RmHdk3RqRLMVgzJSMPKGSOQGKmFed1fZN/Qm3FZcztQuJruN850ILBf7Q8jZuqKEyJ7DhCl5UV+KXXFClYAzT6gcR/Y5ieA4VOBbaV0f/zsFeDC2+l9Fgd1tQQL4MyE3WHHXRdZwYquNKQihtmKLbMug91qaqN1bfvs/qp4R+E4tq5OIDYDuNrgsQSgZyYQjb8CkDqFgenb2gj6uwFzO9elZLIhbHF3ohYEMTH9WB2IJxBxU8M5zFYg/RSIU9dBCDcRxOjNu2ghyZlHLXnBIDDneG6Plzp0O9cCox7W35YxwGIHClaSIu/OtYC/CpKrFyQJELhuhMcL/OZWCMunAf4qtOQU4YbfnojxQ21IDu0D/DaqwGmTnuuX0P7jg31x05oRY0zjPqocj51LXP2RMHXSrA4K4AQzAAkwJQDh5vavAV85XSd6/geQHsDQHODj52UYVFMd8OFzwOjHwZiEVLEGDJIsLlexFZ7VU3D/qGX46/oKLBybhiRF0NhoTcOdK39sIzvIGpqOZ6+wwbpMZvAR8xeDZZ4GQRCQ7o5Dlto1HW0D4folSM8YCjSE6Z4VqCTfQBR+pPwttzwNNPnIDyw2Kjoq/cGerO9DqYOBO78gWNMnLwEfPiMnl1E/kH7/dzC+r6E5wOK8GD+TCtfgB78Npzvr6dxcvSghUTDhSXkleHlrPRZ/tgMvFpIuhNFnbxf+qV1D4snGMWFdTeM6rSv3f8wap3DthCHqnbUiTvUceLuQLQ02f0WHjwfIWhA/VPkxamjmAbaO23FtOguFmL8YEWcmhI330AAfp4n8aD4FVGJEPzDicwwc5+3xGm8bqKYKLq+yXflUFPYhQgjuB3Keo8qbIw3Y8q82CkTPR08h6dJ7wRS0hpH8xQgkDYb7hk0EZ+ILGKCfWBzKYF98YTz+zKhjxWd21txOuPWmOjUjDk+gnZk0/2O0D/7/hr2x3Yu8Upq1cPciTPzv/kJwKeWMRCgA1hoEmmooOTAn0ED2+nuAiq04rbcbxfluWBBCs/0UEhsVgcKyn9qSBwC46dxEWJepO4VC2UTCqbvi9/0DmpG2wR82UQGEJ4PNDVRM1DIy5cwj2NJHC4CsR4Evlqj9IRTU96FICFh9KxVtlFTXq26hBGXDfZTEFq6mbYwSEQAI1kBKSQXLfZ7oYYO1wDUv0H21vgJs8xO44MwH8ERdE57e+C3m5nghSCFIjIG985j+Z9f6TlxE7pi1Lv11GGN/1vz7E2NsMmNsYFcet9tbJ6lQ17dI2BeQMKCd+QdurY5esDX83KHjcXNYzUh2WPB9VWOn7C9ux7DpLJJC2URI4Waaa+AY7w330ePUwVQZU4pecfGtPudSG52rUBvMMCBnHglpRcWX8OVrgH8fUJQN9sxZwGvTKHmItNKid+EdMi542AQwDfuM6d1/whSoRKi1GZJJEeQrB/tmbqe/h7KoabHyL42kx2J8duiYtUgYkCRKWrXzNluepse8Q8Znfvhzq2cQtvz3j1BleXxRrF9veVre39sPy7C82z8Fpr5JScOS6wknv/BKCiJdGfIx1txOw9mhICUvC7PpbygIXPMScOGdMDfvh7t0NGxzz4KtNBuWllp812hrY2ji1sclxCF8HbH2tA2YiX7jS2ZRAthUow9HEiNUhNlwP90/P36eiiN3fE7U1XnFsX646SGan3BlyPAovk9nOjB2Lti6e4jI5a2/Afu/052HYZXbccYvxWAt9XQfDVQDn7xIycdnxZTc7FyLDAexNT02wgzLq6OAp08He3WMPNem/exaC8RF5I5V62oIkx4y/wQAf2OMPSRJ0n+6+Pjd03gC0cEh6u/riCJtQDvzD9xanL2RWPkRWKQFkimhQ8cFgP7JDuzcF08gerwZLJJmRPQxvQUrgOfOA2Z+JVfAAtXAu48Th32CGxj9OFVp68uBsyZQC79wNTHUNO6jKu7v/yF3IApWyEw1/FgrbqTAq+hqdVfDmR7LfHLBzSrxLFX1S8vxfyjWXgUyXsE99iwSBiq3U2LgyiD/Sh0M1HyvFvryeI0hSpIk65UMySbfbWkkv2yoIN+3eYCND8pMSxYn+X3yCcDi64znJtqOoUMPunoGMHkVpPNvgLnoanUCXTYRZ0x9CwsKzsUtpZ+1segkul26FW7JZEV82uEgrD1tA2aKQtQGA9PWUddAz188XmLl4nCnKx6ke2RJruxDhWuoc9WwR/bDyq9kv1BCQ20eKsoMm0DsWmOfA3asAcYXx2qVfLUUGH4DWPFY9fPvPUXQ0lY/4PHC5XSiOP9EuKOzNm3nrpxrU352pYki7SeeqB6T1tUQpof1nmeMpQDYBMAwgWCMvQLgKgBVkiSdrvM6A/BvAFcCCAKYKknS551x3l1u9XtoUbA6OrSb72qpknnCQSQQrY5eYJBgayxHk2dwh44LAP1THNi4oxLhiAizKd5m7LF2qAPIba+bgZpvCWIB0OK4dDIw5XUg1EwVuVW3qodDNz0gB1WBapn2EEz/WHzffDHLnkOwQY+XAsARM0nx2rdLHvA+3CBfD6oUV1c9vsy/T+4q+MppUHVINnDxXfIwPw+yIOhfF5w+E6CgkAd6tkS6Zt7/F6lWj3yI9FGa6ymA/Go5MHyKvj9ptSDEsD6eXhLBwhH9hF9swTs7GvHUdWfgtKRW2IQw6ppDsE1cAdNimUbWl1MESUhCMuJ2QNMjlLh+Cc1cVX9L9zdXBrHMWfWTNURC1LH1eOneJ0bUTEyBSkpgPQPUx+Z+wfcxKUr32tIInDtdDXvLKyXa4Smvk69Vfk2JyIiZagILVwbp9Yx8kM7Jloxw3mLcu34v7v9dEtx6vslRFlGoKLOnoaaxpU1gMA0+maksPmt2zNlRifwkSaoFDljEWAhgdDuvjwEwOPrvJhxLwnQNewBnx+cfvqsTYTcD6fYDb9viIP2HzoIx9U9xoDUs4peaYKfsL27HqOkIANVcXYQwzPr0lUwgMaRAlQyx4JSFrgzC2FrsQLhFFs7ylRN+l9NXjp1LCr+c9rB6h/6xOJ4ciC6WAyiQum4h4Y033Ac8dz6dx+UPyO12HuSLIqkK+3bTXyPokRFUyWRAaRtfGI9N06sSByqJTW/ySuDObeSz//MICcJpoXdK2mFuPMiq/QlI7E9duNfvpC5d0VXkQ2IYOG8a+ZmePym1IPKKKeng/s2vryseoqKVyaS7j7DEcPkp6TjX9ivcpaNheeZMZCy9Cog0o6FwI2pv/Bx1E9dhj2Uggq0iRDEuqHhAM4JANtXISQUP0kOBWH/JXUBFRkEg/xqSDST2kX2QU7+u/Qvdx/h9tN9w2S/yF5G+yNq/AM+eS8lHpFUNe1taAECkbm3l17Sfiq3q2QjtsUpyIbXUY842hg3fVJMmiI5f+W29sHvKJ/hqzArUJw7GzqoAxs3bghGz38HfDI3scgAAIABJREFUVn6JSKiZrgntZ89fFBeROwbsqCQQjLHLAdS1t40kSe8BqG1nkxwAxRLZRyDK2N6deJpdZw17O2mAOgKvm+C2B7JWRy8AgL3hpw4fFwC8KVTdjcOYergpFklp5na0THkLVfYTEYJJVlHtN5wqYAUrqG3uTIvFh3PthwQ3BU5KzQeeRGSeTjjwj58HTh4tQ6T0NCDyS4k1hCcFHi91GraVAuGmWIiHEivMEx29pCASVicVkTDQuJfa8MqE5z9R6FVcXfX4MS6oxu3sAqoeL8ym4KzmBzn4qq+QGZJu+4S6Xy2NcqeCm8dLa8Hm2UA4qL4uOM//+/9NgV2rn6B8Q7Ll9+YV0zVz5zaaCdr8JM1X6EGYIi0As8SqG+fMw94gwwkJDbBEgio/NpVNxN5aP3Y0JeHqV79D9twPkP/CR9hZ2QgxEjFOsA82+T7eTU/bQNmZdPei39lsl/1l6lpSIrfYZd8qGQdcfDd1VflvZ0T9esksuv9lnknzMFrK7JU3yfc6/py/is7DmU7JyqRllDC3cyxWVoC7L0rEu7edgdREFyJ5irmgIdmQCtfAKkhohRmO5EwIjOHG4q2oqGvC2f09mHLRQOyobqVj/88j8mfPnkOJUnyAuttbl0KYGGNfgehalZYCYC+AKR3cfV8AuxWPK6LP/apzHjeBuhTwer3al4+81VcAfYZ1eDc7ayMYnnFw24oWB0JWD+yd1IHo67FDYMDOfQ3IPvPYyNuOJet2PtueCQJERwZ2Vja2LRDv33k2nM50Yuww2+S2u8dLA9R6iqkpg4C3/ksfR7vhvqj4FqMqrRJjzjUgsh4jjvyaH4C1f6aFKXcB8OFzCFx0D3aHk+Cc8gkynRZYjaAgvMNRXw6suEl9Lu88Blx6L1AmMzhJeSXEq65R0UbFVmLL6WHqqseU3x6quXqR7/IZiBF/AkrHyT6yeTYF56tnUIfskrspoB/5IMGd+g2PZVUaX0QJsL+KklYl7MjqIuayC26OZWK65G5KLsLN5Fsb7qfh7J1rgQtnqK8tQJ6/+PRFiOdMhqCgbw07M5EWDsK5aLwa6x7144EeC/7w8pdtCsoVUcad+aPsQNnE2Nkh4Jhi1TniPss7k64MwJlB3aG3H1H/zpOWxerfLCsEpq0n0beVNxmzJ6WcBPzvHJofc6TGQtkqtlLiwo1DTnMX0JxZ1Q5ifLriAUpQlxYaHkto3IsTIiHA6gQ+fRnInQ/JMwAsUA1WPBZWXzkGebwIXlMCf8LJbT50y6WDMGv5l0h3WfDS1UVIfX0KzUq0wbxSuu77j1unWVcPUV+leSwBqJEkKdAJ+9aru+v2VSVJegHACwAwfPjwo9t75SJyHexA7G8SUdt8cAPU3DqTiclqFtA7yY4d8Q5El1i38tkDmSgi0lgFT2sD/n1VHyz7Joi+qAa2vQacfwO1xpUL4dLJstgRN4+XWmk716r3zSEeOfOIXclfRUGcu7caN1uxlZIM7X4/fA6R0Y/D729BoLkFf323Gvdfmo5z9TC3Sf2p+tawl5Ie7YI5bIKcPETPjS2dTAv0zrWxCY/Z2rEh7GPQjim/PRQTRYKeOFKoCyaYqaOl9JGKrTTgX/g6MdUEq4BLZ9F2HMPOE11nOvnw8unk05NeI+iRUr9kyhsUxGkryEsLyM9dmfQenrAOnypD9/T8u3EvMHgkRFsypDQ7pEgIzaKAurAV/ZdmqecmQkEa2F19GySTtS3w43bTuYkQyjTnxWeHgGOKPOCI+qwoUicqZx51Qut3y92iQKXsG65M/eSgfnfUB9fRsLze71z1NfDze8A5UwmupPSpsXOp02FLpoTWX0WinOEmmYhiSDYNSbc0ki9c8yLg7qN/rEA17b9wNbHdBarAqnfI9MXR83asmAzb1HXIGpqODd9Uw2O3oKKuCRV1TfjDeuD+UcuQ4WDISE5EQuJBqlDHKbKPunX1EPUu7XOMMSdjbBKAiZIkZXdg9xUA+ise9wN1Nrq3+fcBkDrMwMQHqA8pgXD2hmv/lx06rtL6p9jx7b6GA28Yt+PXovh/y38moLevHL09XpyTXwr25WvA4JHEHqO3EKYNoSobVz5NGgCA0XObZ6sZbewpxGvOn1s6mQL9GI78YhKt4xZlWDIVZSPTV45MjxcvXV2El74NY1j+YpiU1dO8EmD9X+VOwvgiWkiVCY2WwYl/FuUQK094jKBK8UXv2DM9nvr8RRScaYMqfxUAiZIHfyUFUgMvJvhesIYCri+WABfeRtuPfJgSCZMVWKRgWHJl0PYRg0H81MHA+08RJK/fcFndunA1sPMt8l+lQnVeMfDpS8DQHJhDjdSl2zwbLn8VnPmldI5n5MVcT/XXLkWLkIR+yXZVEnFAitc4eYC+Baupa+XKoCQiUK0ugnDGojs/Nw7YV9wIXPsKkNiLulFLZWE33nHF2LnkD8oOGS9wFKwg8c1xL1BXymKjJNWVQdCppH4kALp5Nvnz+CJg96dq1rwvlhCkautLtF8xQl3bJC/N/+h1KwJVWHBVOmouT4HJ2tSWTGzb3YBrSxrQL9mOpTd7IdU3H1jdPK4d0S2sqzsQAADGmBXEljQRNBi9HMCCDu52DYDbGWP/AXABgHpJkmLgS93O6rkGRAcTiLpDTyBaHL2Q3PIOTK0NiFgPQjziANY/2YGPfqpFoCUMZ8IRcaW4dTfTUpW6MsD8lcDwaUDtDzJmV7mgXHgnVc8SEuXF6KI7gC9KSRV13PME5/BXUqVMmTxw5iVmApIHEIWhJEYD+RQ1xpzjdhWV1dTQftz1uzOwt9UJ35gVOD3dDBYJEXc6TxZ85RR8Fawklhy+QLky9Rd15bC2x0sLsFsHwxtf9LqfHUxC11RL1fvc+TIMpGwSPdYmsfml1J0oK6CgXS8gyy+lDleoibY1Wamfrtw/H67Nekzf58QIEKwj5pwEdyxE8Kvlimp2Bm1z/k0qFWEOU2JlBcCk5cCiazWdjkLsz1mFZIHhrRtPQX2jH3v9Il74rAFJBhSvbQQBcVYdfePzD75yur+5egHTN9B9kMOLPF6508rhcpfMApIHEvz57MlEwuKvpt+18HW6X5qtlBCMfIiIWkwW/UQu0krdVEkkv8wYSse4/AG1L3MY23tP0fGLc9TQu+0raBbt5/eIVeqLJcCoRwiWpff7J7jBisciLbqP5/IW4zZQEtEv2Y4FBefioTXb8dY3VeiXbMeLhcMxJNOtn0TEKbK7hXX1DMQoABMAZAF4B0AJgPMPRqGaMbYEwKUA0hhjFQAeBGABAEmSFgB4E5SU/ACicT02VK87SQNiZ20EbguQfAiSDq0OmlWwN/wMf9pZHTo+oBikrmzEOd44sV+PNOVAIGfq0AZUymrokGzgzOvUi9HYucC3bwKX/o1a6WIYCOyj5IMxIPtfBAP5bn1slfSaF2lBFEPUQVAGdDx40pyTKa8Yvc0uVFlTIKERLFBF8AFexeVBnDmBqnVgtDi7ekHMX0xqvNF9ieNLILz3BH1+nhDoJQ9AfNHrbnYwCV0kTPdspaI0D6zcvWlm5+pngKS+BFsKNVEQl1cMQCBGJe373p1N0CZlMM9VqrmAoi2RXuMEAUqfz5kHlH9I6sSRMOD7RU1DzGF1Sv79aespYFUmKUqefpNZN9j0Jpoh+H+AqWwiHNEO4/z8xUBiRvsq7R1VcD9ezayYfzBZ5aRNCS+65B6iVQUD/vA20Pir2lfySmjmRTl39fHz5FNgdK/8Ygk9vvl9esyTkyHZxHCnhDXlLwJ+/2jszBf3D0DucvDXeHKbPgSYuBTY8TrNcCi7K0pV7fFFpG2i2Id56UTMnbYRVVefDsZYW/IA0JzNjcVbsXLGCKS7dYKcOEV2t7CuLhtvAPA+gN9KkvQzADDG/n0wb5QkacIBXpcA3NbhMzzS1hBFWXWwA7GzNoIBB8nAxK3F2RcAYK//vnMTiH3xBKLHmlIHQo8VpKyAAqxrX6GFhTFiFtEuVIWriSNfueiMe4GYYwL7acEzEozLnQ8pZRBYoJL2P3kliGBOpMqZ9pyWFsKcPQdOxvBzPcMgk4kGGbXHbg3I+HO+0KYPRbBwA3yNfvzqF7HpizDuyHoK9jGzwQ4ESYovet3LDiah8++Tgze+DdcUsbror38fUHot+Tf3Iz4HYxSQafepFIRbczt1BIzmJkJBSjC0STifhdDC6lwZpCSslwSln0KwQWbSrRpHwGDhCXP0XIWyiTIlqRFBQA8jDzho47TXjXvp3qX1j0nLqeMKAFc/C0CK9RWjuauyAtnvxs6lRHXYBPnxx8/TbIP2Hlo2ieZtjOCZgllfV8SZDtT9AljddJxXr5ST2LcfomsjeSB1Jwzm2yxSCH2THdhTF2xLHrhV1DWhNRzR/x7bE+mLw0SPmHX1t3ougI8AbGKMbWSM3QDA1MXH7N7WsJcqq1bnYe9CkiR8VytiwCGikFodmRAFKxx13x72sZWW5k6A3SLg21/jcxA91hzpkDhVqREriLsXtdOLrpIXGO02YjiWenLlTbRIpZ0sY8L13pvYF4wxIPd54KMFUdrDHKCpnhYwvfdYHPAmmXHXm3shufvoH7u+PGahFZprYPP0hiVlAHr18WL6xYNh8/QGU9I0GlFY8kVPaXFox5Exvd/kYBI6I4XglEEQHWnE2hGopo7Dta/IftTetXCgWRpfOV0vhWvkJGLDfVQ5Xv9XWjv0kholDbESVnfJLP3tL5lFwd3av1CVe3yRit41PL4UZsFApPFASa8efWnc6HtIP4X+5c6nrqdSfyZQRb/3ZfdT4O6vbN9XlI+Vf9fcTkG98vHoxwjqpLc/STTWGEnsq68r4u5NsLwPngVESf15KrYSmYVgou3rK3T3L5no3uewCnh92sl4/+aTsHzyIJzdPxH9ku2wmg3CRR39oTaRPj367Z5KI9zF1tVD1NsAbAMwizE2AgRnsjLG1gFYGWU/6FnWUNHh7sOvAQn+0KHNPwAAmIBmVz846nZ26PjcBMbgTXHi63gC0WNNBMMu0wD0LlgLGwvpV4WsLmBxXhR/G5Jb+CNmyowvFrvxwubbRTCkUBO14PnC2FRHrfqa72mx4pXVQCUtYK9NpTkGvXNyZsBmkvDIyF7ysbTHtifHPhduhSAw/bY60D4sxkiZ1pGurppZ7IRxj8QraJ1iRr+JM/3AWH2u/aDZRjLbsKs2iBPQQDh0gIJwvp0RE1JCEkHjOIEAr+b6q+Sg3+Mln3ZmUFAfCtJrvMOQ9aixv3q8xKoT3E+c+qEgERbobZ88kAQao508aeqbkKasRTgShiAIMG+4j641vc9hsRv7ORCvABuZKFLSpvzeeDfIX0W/19kFQNpJdK8MVLc/d9VvOCWCznTyKSkaKPvK6blAtTw3FgnRfUVLDuHx0kzaxGXqjmteMdEEA/q6Ite8SI8vuBkoytb/PJCoc5HUD1L+IjAFBTbyigExBDEcgqfxBySvo++kv8eLV3OKUOcajFSnQXGFJ2LT1tHnMllonkQp0sfPNQ4T7TI7YpOvkiRtAbCFMXYngFEArkeUOo0xdpokSV8fqXM5qtYJInKHw8DErcXVH87a7R06vtIGpDrwvz/shyhKxowJcTturSbQismvfIoleX3R/8tnYtlmLr2X5hP4DV0wUVDUGlBDhvJL9Rc2wawePr3kHllsi+OBP40uZNp2vq+cFmS9QdfPSyB8+AzO8HghTV1rHOwp7WC6BQeCxehBOwA5GFPCYLSBWTwIOzwz+k1ufOfAWH2u/bD5CQqmOcWmOQH94QerraK5nRU3qQee9WYXxs4FbG5iJlPCiXLmUVdh3d2xAdik5fT8iJnE2BQKytVirb8m9acuSLMPWHWr+hrRu7bqK2RyAl85WMMesFeyYOXnFKjU/RzS9UvAxIjxd9q4L04UYGR6vsghcc4M8q3ewygZ9VfSPTR3gZzocSjlu49TYqC9V+QukOlZHanAtpLYubS8Ejq2cobirQdIP4Qnn011xGhXsRW48wv9BNSRCox9LpZmeM3twHWvEt0sGDGGbVsE6cLbwHLn02es+xl48y4wfxWQvwgsFFDN53hWT0HSDZvABIMgRxQJPlX3k8zkF2qm6+hwOmZxOyw74tQ5kiSJoNmIDYqnSwCcc6TP5ahY/R6g1xkd2sV3dYQLPJwEotnlRfLezbA0VSNk7/hQ2wlpTrz1TSV+qQngxHRXh/cXt2PLWsMRVNQ1ISBagTOvl/G1PFCHRJSRPOBhAnHX8+FSIAoPKiAc7vCpcsKQVwK8P0cOcmzJcqWLv49Tup48Wi2SNOV1ar0zgQYSp79FSr6MUXJzWi6w+wMAAJMk/WDPYpfP+2AHQQ8Ei9HqQoiiWsnalhRb7YtX0DqGazb6TQ5G6M9kBjJOixEQZONegDn5BEoeUk6MTRoqthLmvGAFBfSN+6IY9EdkxiR+HqtnkO9n/4sGa9fcJvu8yRIbJOaVUBCprObmlwLfrgMGXUrHy3pMvh6WTgamrFVfWznzgC//IxMHhILU8VKeE0/EozMYUubpgMUB5kynwXKj7zReATY2I19MPoGSPmcmMPqf9D2abVQwkURKMHigzBjwm1uI3YvPwfD9rLpFTkb2fknaDDE6IpPp/jjqYepIfPisLEDY7COYErch2VT0USasvOshidHKv44oqD1ZPaOTVwLRZIEgmNUzGAB1JXLn03GV7GCRdoL+plpKsrSJeNrgOAPYEbTuwr3ZM0rXkRBVFZyXdWg3O2tFpNgA92FcEy0uks5w+HaivhMSiIFpNMvx9d6GeALRA81qNqFfsh12swSs1AS+ZQUU3G+eLQdWTXXGGPBAdIhu3Au0SL4/R+a5v2QWtdn13tdUpx4UdEYXtJJcdbt885Ny1S1nHgllNdcD+3cCWxeqhwQ/fh4Y8wS1yCXp4IPW9ob7tKYHrckr1l+Qe3IFraP0t4fym+gdO1AVIyCIlTfRjMLav8idB72BZ6W2yHULAUj6PhwJydA1pQaKYIpNKJdOJlYlPqRaX0GUyAMuUENQlIPV9eV0rnnFhBNv9VPCr1e91g5i8xmMGzaBuTPb/06NrtGe7L9KM/reqncSJGf4DfKsmMdL5BLKJIFvn/Oc8XyOZwDweSmx3YWC+tsEa4EXL4vec0qB8/5AcCWXgoJ1SDYpnq+7V75/63VIOYOY0m9rf4zxWdPUN2Pvbfx1V6bse7wj0971GWrSh1VNfTPOAHYErbv0FI8fxdL2rJGLyHUMwrSzNoIBhxmrN7tp6Kiz5iD6eewwCwxf743PQfRES3VaUTL9PPRPslDgNHWtPEjnK6fg218lB1a9zpQXKaV5vIDNQ0HOs+fQojmsgPDAVzxEz4th/fdZ3Qr40j/U6q5AdAErJAgKf7x6Bi206adQgnPBzeohwUvuBl6bArw6hpKMg614Gw336S1genCGpYWULGk/o9FiajSwfTyZEQQpWH1w7z/cgUueuERa9IMePuDKOw/KgWeTlYLpMY8Dd3xOvt/SQNANPR+u3kGV2XALXTu8q8CHXvsNByavAm7/lLoVTCDfDe4nITAxEtvZ4IPVHDPP/SvSooYQ8u1X3aIexA4FVd8Xcyp82J5KCdT0DTL88PolgNmu+/laYEZ1YwtEsWcs9Yam54tj5xJFNdf+UP4m/qpY3+O6HlzhXGkeL7D/O8B7PrEwOVLk34gPa3u81M3gx1haQF1Zi5NogadtAGZup3tpoJqgbPz+rSQK4O9fPUO+Z/EZHMESsxawcAslu5OWqV8bkk3rRM48es6VAaQMaj/oN0pUpYjcVZy5XWYLi8PnusS6SweiZ1gbhevhZ8MRUcIPdSJGDzjM91sTEbJ64PB1DhOT2SSgf4oDX++t75T9xe3YMgESTrAFwPy1am5x3g1gjCqvr02ljsSkZcBP79NCoeQ21+EJx8qbqAPw6hh6bLED44uBZYWK9xXLJ6Ot2itNWVHljyMhGmhUJjgcztHSKFfUDgWCIQi6sBgRDDWNLWgNR2SVVUM4w8CDg071FGG6jtLfGvwmB5xXCURfn/KGftU4EE1glJ2HjKEE7dj4oNx5KFhB10bufOCdR/Xhcv/ziBzMTV4J1P5E51r3CwVYI+6UaT+VcxOmBPrLoP8dOdPl/fPngjU0yG0EPfF4EcpbBObuDfPM7bHdN71B4PxFlNAwIaYC7MspwrRFP6DaH2pfHKwnWNQXQ9M2or7RjxSnFcL6e6m4occypzdEfcksSgRdGca+lP2v6HDz1bH35IvvVh/DV04JRdkkShD8leoOA99nWQFw2yf6fpZyEiUqSV5KOLT6Jx8/T36UkAisvk1932eCWg8jZx5BOdu7h5nt+tckB7PE4XJHxI7aKsMY66N42DP6mw0V9LcDHYjdjRKaI4c3/8CtxdWv06hcAWBAigPb99SDpDni1mMsEgZ8u8BaA/o0kaMepuG8lgbgmheAmV/RQN35N5KQYu58WpCy5xjyhCMSooUSoCrre0/KnY6sx+gxN4+XKrihoH5lTkltOSSb8LvuXhTgOTPpM6y6lc7tyzL1eRwKBENDYSmCYWdlI8bN24IRs9/BuHlbsLOykSgM9c7T4qDE6c4v6G/6KfSattPQ0cr8sWKdQX+rRysabiXfyi+VK6KuDPm3DjdFq5oiBTXKqvH4Ihpw5cY7DxzjrVQ13/ggwURCQTlZLVgJ3LmNkgWlmI+vnHRPTAnAhnvpb9ajsZoBq2dQ9yHSAoBRx0PvO7KnyDAm/lyoybDT1eLqj89GLUO1fRAERwp9x+FW8ilRjJ3Z4Z3GsknEgKNI1iJ//ApfjVmBaW8GsG13Q5s4WE2gZyz3hiYIqBITMXzuTty3aT+kS++VGZO0v+EXS+QOT34pBelpQ8hPlYnr1LXUoeLD97akWP0bfk9W3jMBdYfKlRnbYeCdrLML5Lkw7fslkTrEUli/E5b1D0qQOAEGfy24P/a51TPoXt+eOdMpaVVek7kLCDJ4vN3/urEdzTLVR/w/kiT95iiex5GzThCR68gANbdm9wA4fN+BiaHD34nCTkhzoi4Ywr6G5k7ZX9yOAePV7+KxavpKbr5yGsjbuZYWvKR+lAxEWgmP7a+kQP3zUgqS9BZPj5deG/c8LZAAVbfKCghqVFZA+xdMtLAWrAB2rCF4hTbgyyuWA74h2VSFK7qKYCul11AwNXN7NCl5ihSvlS1/oxmGg4AP1QRacWPxVlTUNQGQVVZ9QpI+tCbURF2XZ4bR36pvqPX/6/8RbOHX/6MKtSj2DLz5ocDCDsUsdmDMk1SNB+jvmCfpeUAWV3v/KQpYsueQH2bPoQDtkrvlcxqSTf4niXJgzW3nWsCeBLj70vBq7gJKXv2VwMqbqSJ7+QMydMnmkTsGdg8FZkYdNbONqrevTYv1+dwFFOj7q+TncuZRFdiVIXe6oq+Fxpfi+rJy/PGNvbBZTBCqd8jwrtf/RAUw3y5K0iOtdD3x81b6XTRZ24c0XP3qd9i2W4a3tisO1oOMz47957NfsSvsoUTviyUyFA6gv5fOAn75kO5XG+4DXsmi+9aYJylhGPkwbfvhPAq6x71AcwCSwb0BoHtoKEi/26Rl5LdWV1Sluln/fUn9aSDbV07bT1om+2teKbDxAbonN+zVf7/JQp9Rq31hcRjcw5rav68KAl2TymLSpgfoWjve7n/d2I4mhKnn9TAb9tLiZDl8ETlO4ertQALR5B4IQQzB7vsBwZRTD39HUWsbpN7TgN5J9g7vL25H30RRQk2gVQ25UcIOgtVUdXRlUCKg105u3EeLUksjDaLqqUyffwPaKqh5JWqK1vFFtNjxipbRwF7dz2odiM9eAc7MJ+iJJNK/rQsJJnDhDDrfGGaSAhpY5Mw1lV/Jqq56weohwIc4U5XSKuqaEGwVkayF1jATDTcqz+3dx4GL7+q5jCNGEKTOgGm1BmK/V24WOz1ePYMeX/RHCoTqd1PCu+WZ6FzPWUBznZqBTDnA7PFSkm22qyElOfOA0U8A6++RB0ddvWhfIx8mMoDgfvqr9zsnJMnQD1+5rP6bOpi6Gt+uB066jLockiQz7vz8XlRBu5KCr4a9gM2D2lACMtwtKM4/Ea5wlezb/YYDF94mK8jzc//dX4joYMRMuk40fseDZKXvtysO1oMs1WnFi4XDcWPxVpjC0YHgyx8gqA8fwndlACYbcPJIoGSc/Pu7MshvlTChvBIq0Gx6iN4vRvR9pu7nqG5EBpD93xoYaTHNe+m9z5VJbHoqNrBi8te1f5E7bnqQqyHZlMTy+Q7l9cG7xdrjMQEQW4HaCqC5kZjOADUTGxPUau/8vcfb/a8b29HsQPQ8vEvDHgpe2OHnTjtqIujlAOwdSP2aEwcCAJy1Xx3+ThTmTXGAAdgen4M4LkwUJV3IjWoAMtwKDLyYKmFvPxJbORs7lypqWY8C9buMVaYjrRSQVX5FVd3c+XKV1+7Rp7xUDuzlzKMhaP76mtuB06+jSpoYoYDcnECMJHxIutmnX/XyV8lDpL5yIPP02CE83nVo2HPQ8CEeSCmtLZDSQmtCTbHnNmxC7HDl6hkUMHRFZb47WlcoGxsxuYSiAa89hZiUsucAZ00AfL8gAka+teJGCprKCihBNVKG5tdCqFkfqtFUQ9v5yinwT3CTpsTCbEoORJECorxi9e+cM4/8WukrXP23YQ8lKgN+Q/MRJeOAucMpaT4jT+4+ONLoszpSgeB+JLrcePYKG9ylo8F4UgLQ+XEdAu25c20MHb/jQTL3/X7JdrxYONxYHKwHmSAwDMl0Y+WMEchITpShbb9/hDpLJislffXldB9T/s4jZuozczXXAb//Oz0XrI71mbFz6V6ZMgiAjs8uK6SijkaRHHmlNJysR0whhtTQ0y1PU+dL+f6sR2PvX1wJ3ZkRu33OPOqoFecSTDYUoAJU3c/UfX1tKnXGWhp7zv2vm1qXdiAYY89CP1EEjLLWAAAgAElEQVRgADxdeexuafV7OszA9E1NBAMTO3YarY5MREw2uGq/RmegBW0WE/om2/FlRTyBOB7MCHKzcsYIWYHZYgdG/AkojVbGApVU+UrsSxA9CcDoxwh+YdSmlkQaEuXdg5x5QOpJRAEYbgGaG/TflzyQ5gMEEy00vBvR9voAqpatvJFmG0Y9Qi36ySsJEmUkxBWolgetPV7A6ojVbOBdh9z5sefG8fO+3aoqubLaWFHXFBtIKTUOGIsV/TKivVUyjsRVfw/d2mNyAeh7TDmRxN/CrZBMVlSGnOidEqEAGyBcuNF+0k+ha+J/HqGOgt42Fgf983hJfdpsl4ebfeUUuF/7CuDOBK5+BkjsQ37fsJdgbHp+3Ma4NJmE6LSBW/YcGabFCQqGZMOW9Q8wiyXKoiMQTGXzbIIfKimOeZfO4iA4lGcA+W3DHpUPKoNkw05mD7Y2RftIukwqUbWDCi8X3y3TUE9apv6d7cn6vuTKVFNXT3lD1o/gSub+KkpOjHQ8XJnAZ8XUjZUkqvJ/+gpwbqH+9lzZetiEaOKTQEmwUrfCCE6VfAJ1F3aup6KRGKb7trLDvHqG3C1Tdpm/Wkoicmkn05wYixZj4ve/I2pdDWHaepivHZ/m2wX0Ofuw3x4MSfilXsJFHSUYYAKa3SfAWdN5itSD0l3YVl4HSZLAOtBhidvRNyPITRt2WRSphR7cLy8MFVtpAew3nFRIpQhVyANVxm1qrjINyFXNqWtlFd0ZH+u/T4zQQJ49RcZ3K1+v/Jq6DdctpGBLqVaa1B/YtjiWvWR8Ec0+DJtgXMlSDi031anPjavCLrwyBtIkADjF1YTNN52EMLMgaElGkj2BAildLQiNUqzTACJmtscK08Xt4M2IycWsD8NkDOiVmAA0u8lPnekUiDfsMYaLlBXQY62/8G04VWruAsJw+6tkATdATmhqflSLLwIUuOUVqyF+WsYl7b2YJ+CREM0vcYjSBTeDbfgbMfcor4txL9CchR7DWigIZJwO+Pepz0EB5WsLkuMmm1YUkZmAL1+joL3FTxV7pfbD5tlUXPlofrTjkyEnd1oopzJZXP9Xgj4qoUM58+i+rQc18ngpUD/lSuqwcZXprMdk+mHt9i2NJHbHu2uTlgHLp6u30yZA/L2iSBC4rH8AH78InDaWkgSl+copMQm3yI8/fj72cx2P7HPHgHXpty1JUpHRPwAdk2M+1qw1SBdtBxb7b2sjkAAMTOr46TQnDoSjbgdl/Z1gJ2W4UBcMYXdt04E3jlu3tnYhNwAtfsEa+r9y8LktiM6mwVOTldrlngGxA575i9Qq0/2GR7G7YWKoGZJNg3R60KhIK11LoUBs+zt3AVVIfeWEoW1pJIzuwmz6G9gPnDedKljKATzBTItSUj9ijLInU2AYHeITRQmRkEIPQMn9D1A7Xk9BOlANVH0D9vJImP59BhIW/h7JjT9A4I1ZXS2IyaQfMH0DndvWIkoqtK36DtBBxw0y9Eb1vS6mpNO3m3xFoxMh1P4E1riPktznzie/EkVKVrVD+0le+n+/4ZQEalljcubRNumnRjt6M6mzlXGqeoDfkUZEBPml6vdffDd1ALLnyGxmWsYlLTOex0sD+Xwwu99wChTNCcAVD8Qy96y8iSCG2i7GqEeoWxhuioUZHo9MYJ1lvGCg1B5p2Avs/oDuh9bofKQWmvblf2igesN9wHPn0b3siocUg8zFwDer1YxigUpKILPnkBZJ7nyq7tdX6A9s55eS71vsJLJptsoBPBcD1fp4sw/4ajl1rW7fKjNE9Rsun4vZFuu7Y+cS9PWCmymZHTySCkLK9YRvm5CkZtDTg3TGfe6o2NEcos4DcNdRPP6RNe7sHUggdtSIAIATOwhhAoAm9wlIjTTD3vATmjwnd3h/J2WQst223XXwpjo6vL+4HT1rF3IjioSbtjjohs+HTH3lchA98GJi7OCCbgMvBn77JxKeYgJVWcMtNMwJ0GJz+QPqymdelFJz85OxCtGjHqYF8MIZVC1WtsvNNvmDONPVw4e8yzF5FSm+MiYvpqP/Sd2S1iaqCL46uu1cpOuXYJdpAPy1LTiDV9I4hWL2HGqj8/0rzVdOAVZ7WgNGGgdgVCl2plNw9+mLdKyUQQTHcsZb9TGV3EOFL2iHs01WSjj5EPukZfKANUB/63fFPrfqFoIXKXVEImHALFEyarbJvP258wmGZLIAoRaiOBbM5L96Vf6L76ZEuXQcXUeFq+n6CVQTHedvbpW7eJc/oGZcyiuh43CfVe73igcoSb/gZplQYPoGY5iV9jlIQN0uCjZ1/T7OhKNr2oKBK4M6ODnzqWDBmMz+pfxezy6gYkbufBlGtnoGwZT276REUassnjOPkk8Om16YTX+3PB07sO1MA/73X8C2aKCfPUcexG6q09fKiYRJ/+F0t1oBfdwLdC29NlV+buJSeWA/UC0nupVfUfLx8fNUNNHTtTAn0DlzM4J0tgaj7H7xe+ORsjgL05Gyul/obwcSiG9qInBagIxOIDqSB6m/7pQEon+yAwlmAdvKfcgZ1rfD+4vb0TND7LIUoUVOsFJwBBGARNSUkSglsCuDgp5wKwXPAy+mwU0eyCsxrLkLZPVbbeVzaQEtjhfdQVXQNqhRMWFqR/+T/jb7SPUUoKSEs8KUFRjTnIphqmBxlqW8EuCTl6gKmDMfaKql16JYb/bOY8gY+U84HECoYDUsG+8neJG/CnD3oSpy0AASwEzGAZa/Ut4uBkZjpXuFmA4kuKhKHZ9xkO0wRPT0mcUUEDB/pTzTA+jP7hjN8yT1pa6EM5MSXIBgH2YbDUXzmYaF2VEaYR1oiXL2Yc3tFFgluIA37yF/TDuZMOJK6ApnCysrkAO8zNPIv7YtAk4fJ7ORQSSf/d1f6DUtVMYI1uKMVpS1cBmLw/g9RrTHHUn4jgdTFgz0CifjiyiYL1hB3/Hm2eRTFofsR0qoWiBajLF79IkWprwOrL9PhmYqix+XzKLB/bpfiE64Yiud04iZVLwwWYBJrwGbHpYD+7ICuVthSiDdBy7syY+78iZKnJWFn7f/TvfsV7LU34evnEgJhk0gccKtC2MLRlmPqhNjI9a/yu0ya14cznRErKuHqFOMXkJPSyB8u+ivu9dh72JHdIC6M0YMWpx9IApWuGq+wv4Tx3V4fyaBYWCaE1/s9nX85OJ21C0Gu8wDtncfJ0pHzsri8VJlqfQaYMpaaqsrqSonr4ztAqy5Xebtzp5DN3u9Ic3GX4nlI+sxum7syRSQ11fQ4ujKoONpVU9tiRSkWWz6Cw3nKufDiEsnUwV58Ei1IipPdM7Ig3PRVXBGnw+NL0Xtxf+Ax2lDQmKmPLynUeDF9Utk4aUY/G8YeOlK+gzKLo52/iI+46BvB1KR1hhnFtN21VSqyNpukN7cgnKehwdbznQiDbjmJepgKOlc84pjFZ+NWLW0sw/NPoIoaecSlBSx3I8BWcyuYAWw5jZ6vGMVXWOAPISaVwKceiUApj4vDsvTHmvTQ3Sdvf0QBXLXLyZ2qlAT6Q9o35O/qEO0x8e1cVFEX3ls4YTTsyoTy7xSwJEsU+gC8j00ew75aFmBcfdIjJC/fbde/Tv5qwhayiFLI2bSNmfkxc7A/OYWomud/hYViqQIAAnYcD/NL2iP68rQn5sRdDorHi/gSKEuw5rbdTrRxVQUmvIGQVervwW2/AuRvFKYlhbEXhP8PjBtHRV3epJvHQXr6g7EZ6Bbq17I27N6nHW76CKxHR75lChJ+LZGxBX9O+l8mIBm9wA4a7/upB0SjGnD1/vQEo4gIc71fXxZIKr7kPVYLKVjIDpMzRA7B8BfUxoPevxVVE3yV+vDNzgrEg+q8kvpGuIQkqzH9FVTp66lCu+6e+UuB983H1blrXn+vsQ+wKLrYvc1cVmMZoRlWQGqxqyAkJiCdL5AGWkVALGJRf4iYMPf5Goz5+9PO5kW855YmT1UM4J+GUBnOLNYusuCf1/VBxkOBl9DJeoTrUh2RmFvyuAOoIBam9y5Mimo2zw7NrDng/iqTloh/bbK4VAjCAZPBIBoYJUKhIP6isI82VAOYvNrZ+ODchdOC0HiCfOUN6gSpfy8FVvpupv6JnUatVCTySuJ+clsJ/iiHcBl9wHvPKbQLsgkogKt/x5iwnfcmrLQoGVT0qVnjerT6PlL8ok0MzF9A8UVesF51Td0b+XFkKzHiB2s8VcgWKvu7haskJNffoyVN1FxpaWROnTKa2HsXEoUtMe9ZJa+GvXUtbHXU8488jWLgzotWpiUPQUI1gFg0S4agGAdQs7eEG7YBBYKUudBOfvjK6ciU5Ov5yWoR9i6NIGQJGlgV+7/mDLfLsriD7N9sKtBRDCMDlO4Kq0p8QR4ft1CFybr+EV2UoYLb3wp4Zu9DTjbm3zgN8Tt6NvBwApEkRRtlVV7pXEWGj3FXCOIQ1J/opg0mdUwCr7YFKygQGjYBPl9znT1MYzoDMMtchWP08s602kxWn0rJS5KthqPNzo4q7Mvwaz7/JA0K8xaPnujboE2sRBFNU0r5++fub1nBVMdMW2wDxhDZ0DMYukuC14a7UTq65QQ9vd4EclfDNhPk7tI+YsoUeZVWouDAiiLnZLMzbOB3/6Z8NraqjCHxSl/W1+5rPjMgyZHqv65KxOB8UWUwJw71TjZ4FCS5noKzjhVZ8VWmg9S7pez2PD31++m7oGWxemy+2gbPahJU53cxeCBf8ZQ4Op/HRiWdIgJ33Fr2kKD0g+M7mdGtNP1uwni9kpWdBhep3vEq/I86dxwH/0F1CJsvnIixtBNVAbQvAOfaeDPr7kdKHw91odSBhl3Q95+SANteogojssKCG61/zsFY56XCkZZjwLr721jpZPySmF1JoM111I8ZU5QH8vjpXVnxY09L0E9wtbVEKZzNE9JAPZLkrS7K4/bLa3uF2rtHaZ15gA1t2b3QJh2b4St4Rc0J53Y4f2dlE6D1F/s9sUTiGPBDhZWEKwmbQZetdcuZl8soYBHb6Hjr6na8sUEf1g8Xl9PwVdOUKWsfxAHORCt/mYQBpwfw4gakymgGZxeFgDu+AwY/ThV6zY+2KYSLOWVgBliuRN0n7eYTWCGzVWNaRMLf+UhBb9x0zEjyJiBiJTVbMIjI3sh9XV1hdVUNhG4YRP9jOFW8rFpG2hwue5nUon2V9G+ARpmbdhLv6ee32qZsbgPTXmDArSGPcDXK6mLoYRg5C4gaMjtnwHVO4CtLwMnj5aZabS+4u5NgVgoCKy+LfZ1fm3kzKMq9bq7Y1/nic6k5TLdstmmHrxWvsfmkWcheOB/sBC7Q0z4jmvj35koqn3YiO66YW9sJzVnHgXfl8yixxVbqcMwcRl9p5Vfx1blnelyUqGnS2J0D6z82rhr1lRLAf+k5VSEEcxUjNTbj2CiLgO/H/PnuW5JJKxWhR/3AkH4inPovAOVNJO2eTbYpffKiT7fduP96uJQT0xQj7B1dW9njubffwN4gzG2gzE2rIuP3X1MkqIJRAcGqPdHIDDA6+6802pqG6TuHD2IVFcC0lxWfPpLbafsL25dbEawAi0dXrhVpvHTo/+7dBawfSVgccbSjf7mVgqGOGVq9hzAlizDgnigozSPF6j6mnjqz7iOKvOFa4Cdb0FFCcshJsrj5ZXKbXXNPsMwA69Np4V4+FRId3wOf8F67BOi16VWuTV/EQVqWqrPsXPB3ryHki9RPPTvnQe/yn3GFVQPzZSV3JnbYxXDNZbqtGJImlU/CIq0ksKtbxew70tKHhypNLh+7cuUYGQMJfYrdx+CYPCum9I8Xlmzgz8eX0TXTuNeCqC+WAL0v4CgSdlzZBrhTQ9QIsC3OSOPKsQrb4r18Zx59HxZAfDW/TrXQAnQZxjtO3kAXQ/KIdTrFtJnmbqWOnyhQFT5ejyw8QEScNTuM3eBDI06nMA/7vOxpvThP/4f0fdq70Hji0iYLbEPQcumb6COGCRKAhLcdJ8akk0+s3i8rIOjFNj0eGkfHz9Pz/NkRWm82KO512HL03JyoTSPl/zI6gSsbupSF11FnQqt/4wvJq2HS+6hc+XPc9ptj5f0erTwKTEkdztGzKTXhk2Qkwfltte8JAs3RotDPTJBPYLW1RCmy/SeZ4wNB/AMgIu78vjdxprq6OLqQALxf9U0QJ3QiaMFLc6+EAULXLVfo2bg2E7Z59DeifjgxxqIohRXHO3udrCwArNVpvEb+xwFXJzZRYpQt+DcQsD3C7DjTaqCmSxE5RcKEl0rpwfMLwUgqXHmMR2KEsCWBFz3ClWdwkHCXfc/jypeJjPhsSWJjlG4hv7v20Xdh9qfdLG2giUBTYUbYGVhmCwJkOxpaAqGYbJEIEouCO/+MxbLbTLTIj9tHeFqtbjww2mRG81LxLG6h2aHMGAuCAwWqw5ee0g2/abKyuf4oijMSDNwZvNAaq4nFep3HiU/5QJaPHn9blNUydlMv+tPm8n/z5oIfPIC8Pu/k1qwK4MGRrloIk9YP32FKFZ5gu0rl2dk+NDrypvpfPJLCQoSCQE3vEU0loxRBfjNWbIQYX4pDcCGm6j7FW5WEw+ML5I7C8MmEJGAK0ONRZcitL8Rdx5e4B/3eX3jPhzYD+z/Hvjg2eicwqn0+sYH5N8xrxR462/qxAAA/vgVMRwVXUW/qd4wfH4pFXjGPA5ccjdV+7XQowtupmIP99/qHfK9TnefiyiBiDRTKToUJL+p2Cr7bOpJ9Ht/+CytATtWUbfiwhlyx8uZCWT/N7D2z+rPxZN7/n8+I6TshnAiA3uynHzz5IH7aZz9q8vsqNC4SpK0lTHmOhrHPirWQQpXUZKwrTKC3/XpvFMCAAhmNLu8cNZ81Wm7PK1PEt77fj927GvAaX06QfEubl1nHFbgypBvwqEg4b2V5kgncbe6nyj4sCfTAhOoIp2GYROiQXcvEmkrHhtlF5pPFIA8EBHMtK+q7XIgV7GVgp5rXqR9NP5KWFmuksshT5ufAIZPpXb5wmx9CsScecRcwwRg04MxWFvh9/+APRKC5O4NJPWDIAhId0czcvdpxlhuQaAERZeC8DBb5HF2pSNujAvHKWFPWf+QfQ2QZxkK1xBkRxtoCGYZMtLSQEFS8kDy2R/fBbznqznx80sJElX7A2mj8GP4ytUDoxx69+EzwJAsdZLDZ2SmrqXr05mpHuAekq1WA25LgpIpaCsroM/z6avA+X8g3QClngCf3SgrkIMzX7kaajI1GsQm9Tt8dpu4zxubGJEHmneuJa0apfK4r5wgb9qBfI8XgEQzLkrYJvet9FMIihcKUjAeaiZYUKAakEy0P3uyDOvcuRY4ezK9rpyRaBuwX0v3PMFE/vNqlhpGNPoJgv0pfZbrT/DPwZjse/4qmndr8sldMiW7GX/sr5K71a5Med3So8G98kkq/PAk91DZv+IJx0HbUflWGGOZAJdi7QHWQQrXn3wi/CFgyOEROLVrze4TiIlJq1h6mHZaHxrS+PDHmk7ZX9y60HhicMVDtFhwtebGfbHQnHAzvfbCJVTpCgUpebjgZnrvK1kUiLU0RG/iT1G3IBDVOrC6yP8DlaRWqmyVh5uJjakklxYJ7bDe0kJKUngy4PECl90fy06zegYtxFz4qKyAPlNZgSy+tXoGWN1PsTAtHtx4+tNf7YLBky2lxVvkx5bxKvgNm0iZd/IqxFCZAjK0LlhN10FjJRWBGirATGZZVffth6lLJUmUuHr6xtKzlhUQV/7q2+g5PksEyPM5q24lesq9X6jnF5Tm8QLJg4D0oVRtVvr+sAly8sCPu2wK6T30Gx59XgJ+czOdJ1dm33AfBWCuDDmJ4cGZ9tihIAVecWrMzjdRpO6Q0g9NFoOB5hPl32dINt2/A1XyY0BDLywB9lRKJP37qLv07Dk0YMwYweteGkmwzvNvJDVpewpV8nMXxEJV191LnYma72N9feVNQFONDDXifqM0j5c6uUrfMyfQ2jB5FSUco5+Q15TSa2gtmfYmaVZkz6FOSs48mv/QrgHLplCSz+/hBwvTVf4WWqXww4Wq9gDr6iHqZxGbKKQAuAjAH7vy2N3Kan6kv67DSyC2VUUAAEO6YC65OfEEpOx5Gwn+3Whxew/8hgNYqisBfZJs2PLDfvzhdyd2whnGrctMEAhDqxTP4jfYaeto8TFb6QrmN2G+OIGpoRb8vWVRAbj1fyX+8CseiqXte/MuqqIWrqb9CCaZ7tWIicSZTrMTny0kDLctUX87k8WYz54P1lkch945OMSh3bh1UxMEGpguyaUKba8z9Qc+LQ59koGxc4HvN1GwY06gweh3Z8vicbrsM1HmMMEkzxJpO2dvP0Tbcwpj7dDspOVAMJoUa4kHjK4ZMQJc8SAlLzU/UKeEU7vybaJ6Ai2u/sDUt5Dg1vHz/EWEn7enxJOHzjbuY4171X5oRBBRvzsqFng6bcPv3bxTmzKI9GyU/pW7gIb/tdSqSwsJCurbTffNpjrgs2LgvBvIb0KBaMAeZUUy26mrxaFEej5ncdA/DrlikD8H93WLgxKHNbeT4Fxgf/uiipwK2ZRA18+Vc2juwojIIKK4tx8q+1ecbviQrKshTBqwHiQANQD+LElSVRcfu/vY/u+pwmp1HHhbHfuiKgKnGejXBaAv5SB1ZyQQADC0TxI++HE/QhERFlN8wenWFjG4wdZXUAXI46VgyVdOi9CImRQ0Ve8gTLb2va4MCphGPUxQopLc2C4Bh0tUfkXJhr+ShvCU1VftwunuTWrRQ3Ooy9FUo79dKKhutzfsVc8t8G0OtXMQx3AfP8aDii1PUzKql2xGWgn3rw0mOB1mSS75Lg/IHclU8dfzSQ7NECPyLBGHLrl7U+WW49r/5xEK3pxpMrwkIYlEEbnIofYaMbpm/JWA5wSCiNRXEJe/zrUeSR6EO9+sxKPjzkI6n/mJ+/mRsf9n77zD46quvf3uadJIo2ZZcpNlm2biC4RiIFznQmgxYMCYYgMGY9Mh3ITcdJIPCDfhQhJuSEIcWgIuYGyKwcSYYkK5MdUE0wymY8tFkmXJ6mXm7O+PNcfTzqjOSCNpv8+jR5qZM2eO5uy29lrrt+wFa6A0th1uWCaJx3aVZzs85+XfSZjRf76V6AFYMS8SMhT9/ONXSluN3gCyvbnurEio1OQZEtKnQ+Dzi4ctWpK4sFw8Hru3SHJzsvG3ZH8pKtqwFZQnYoTYsq2NVZE5IG9sJHfDvl6nooreHPHUzbhNPC6FE2U9FX0NZVPFK6FD0vZzSnqu/mXkhntEug2IF7TWm7s+bIizc5PEjvaSDZUh9i2EdOQktwXGo5WbQM377JpwSkrOecDYfNZ+WMm7FXUcNiFZMXJDRpBsgG0Ku3jrNktewuQZMPWS2Cq75yyS5+1JpmyqeBxsoyFZddToHSxtyc5me1Nk99VpQff0zyRcasReMnk9dplzomBOsXxuU7Wofhx4VmyRull3izejN54DE8M9NLDbfMV6aUuv3xWpdu7LE+Mht0QWQ8nab93miEIMSIgIOAgChIvOgSSS2otCuwjcvFURAwPCEqmtUtTQjvF+5OJYr0O8h81psXnGnZKgumhG5LkLHnPs6182wLUn7k+xXdfEoZ1blqamqZ32YAifx01xrs+IZKQCe8Fat1mMxwsekw2apmpYf29E1CF/rIxnR14hGy9WKLnXKdmu/OQZzsUPA6Xyc+QVsblnUdKpe84D4slqrUsUqph1t4hatNTBXf8RaXN/PSHx/7ZD5nSS640vqmhLvY7YWxTQsvKkjdreskBporf73GVizPTEc2zkhntEug2Ix4FDAZRSj2qtz0rz52UeWktxlL2O69XbWzo0H+2yOHufFF9XGO3y0hoYn9KK1FPG5qOAf35SYwyITMdfHFs8Kzrcx0ZbcOJNiaFOD18kE0Tle/L4mJ/EVlJNpiueMzKSGNdcA0//BGbdBS/eIvHc2fmiBNLRLDtPq74TUT2a+6gY5PE7uR3NkFsKdV/J5667Xd6z6zM5V/NO6GgmVDQRlVuKy+yoZj7pSmaMDkfbXSFVy0NtEjtth+TNfViOdWq/9o6/1rGvv71UquZG11VAi2ei8j15PXtErIJZsDVxIVY0Sf6efrMs9gKl0mdsw3jd7dL25z4azjOKW2xmF8rnx+cSPXdDQv0JPecBJubk4HK1oAjgVNfECoVo3V1JsKGRHY0Wd79Vz7Un7s/kUXnGiOgr0QvWivXiLTjxV5FNjy9elvGrpQ4OOlvC5abfLAt1p7bpcif3Rk3/VWLRTjuBHmLzCQKl0idmLpT1i53wXP2hXNv5D0tItq2Ap5C+9MqfIsU/6zZLArfT9eSWytyRrEhefHX1f9wU2dh64jviIYv3Ct9/inPoUU88aiZUtUek24CIHl326tEblToJ+APgBu7VWt8S93o5sAgoDB/zU631U3273DRQv012VwvGd32sA+/tDBHS6UmgtmnNm0hg54bwQND3CSEv28vepQGe27iD752wbwqu0JAWLEuSN1+8JVa+9M2/xUoFun3QWuu8U9TWEAkVio/FTpaL8PxNslvkL4S/hxPuQkFZyEXv3p5+h8gKRrvctSUSr7YEob2TO3uJJIfakof2pLNptUgGhpVAKhe8iVeNiKgvGTKT7hY57A3hhYe+ZC0WClfDdlRbAzx2eaT9enNEycvJy9XRLL/bGhI9Dt+4Sqqd2/2nsFz6x7xV4c92y26wywPKK2F+uaWx4Up2HRN/UcQLEa3sZHvqtCV5GNGLzZkLxeD+9q8T++um1XDczyW8pLUO/IWoNT/FbfcZp+83fB9ylp9PTt1mxhSWc/Npi7juuY8k5CkvrgqwoWfEL1gbq2QcvuQ5WTc0hTdZHr9S2sKsuyRnYPcWqVHy6CWxHgDLStwQmnW3eGaVq2uPMCRXuPPmiMJS3WYZw0++Rdrgrs/Ey9ZYJeNyW0NEFvilWyNjdXMNNwwAACAASURBVKBUNplG7C1e7bU3SC6ckxxyoBS++7bMK2tvkHPPuhvQsQt621tWtyV56FFPPMcmVLVHpNuA0En+7hSllBv4M3AiUAG8qZRapbXeGHXYL4AVWuu/KKWmAE8BE/t+ySmmKnzJ8Zri3WRDGhOobVryJ1G07UV8zdtozx2XknMePqGIZW9uYWtdC+MK/V2/wdD/RCeM2WFIdljFh49HBvTAKFHdcNop8heJa33fE2T3P/qYznIRKt+TgkgV62Vn9cFzYrXnc0vhX0ukOFL8Au61O8W1PuM2mYy0juilQ2ys+jPXieERvt76Dhd5wVD/fceG3pHmZEYLxaZGP8H6Sg5c45CYbCt5/eMmaacFZbKbuvq/wgulJWIAuzxw0ZPhzRcXPHpxrPFdt1navq00U/uVhN/tWSwtgfceFflXKyTJrJvfECOho9lZaWbVNbJ721oP/3dbbJhL/VYpMJYzMrn3pKVWPis7X4zrg88TY9/p+22uxrX8/JjPL37yIi4/8WHaTT/qO50tWHdvlftoh40GSuW+/W26LNDPvFfy07QFKGhvgMbtIjYRnfycM0LG0vIjku/2B9sir0271lnhzh6vy6ZKuNP9M2LH5Y5mEciIrgbdWCXGy7xVYrRG152wj3npNxEvcUutGB0Hnydjt63o11QdybsoKE9c0CfzyLh7EXpkQlW7TbrNqq8rpeqVUg3AQeG/65VSDUqp+k7edwTwqdb6c611O/AQMDPuGA3kh/8uALal/OpTwfZ35PeIHjlg9vDG9hBjc6EwjRs9rXYidU3qwpgOnyihS89+sCNl5zSkmGQJYy5PbIXfgvEiHxhfXXTOUvFWvPpHmQg82YmVVL9xFTTvkklv+QWx8bRev0xGtuKGLWt5/wzZxT384sSJbPkFMrnYOuNLzhA/Z3Syn31sbolcs13pdOZCxvk7yPGZ3aSMJ83JjDVN7Vy2eD2FPkvOGy+dalc5b6ySIqBLz5T2VrFejl9xIaBE1hIlCzs7tC6awnLoaJEFULA9YjzY/8+KC2Vht/yCiBSy1xfpTyP2cv4eGitlN9iWN84bKzvTfztJ+k/DNucK1gXjZUd31TXhsI84Odf47zfJfRgTcOHzGC9eSkgmIe3yhAtkbo54Beq+klyG466XtvKnQyU0DUS8wl8smzEPnBOpLv7A2dLG1t2eKM06626R1S7eJyJPnEzVy+2Vv50MjOUXSPuz+8eqa8T4nbkQHlkAle8nqkDZ1aU3rRbjwZbc3rQ64n1r3hlWzWuT0Dyv39kb4HI7t3eXaaPpJN2VqHt798YBW6IeVwBHxh1zI/CsUuo/gVzAIVNHUEpdDlwOUF5enuyw9LDjXVHa8OX2+K0hS/P69iD/3jv1127TGihHKxeBXe9TW/7tlJxzTKGfsiI/z3ywgwXTJqXknMOJfmmznSWMRe/AWJbstmYFJPwh1Cq7Y/4RYjyATByLTpOJ7qLVEj9b+4Wobky7NkkuRLFMMqjE1xurSKrRH51gZycOOpxf549D1VfIjmxYASSvsQp9yVogu2/fncGRlLXbNCcztgdDVNS2UNWsGV9Ynhhu11glY/b8p6QtJ1vEH3yeLNyadzpLtM5eLP3nkfmJXg77PCP3Ey+dXVzLXxTpTxc+ntyTYBvcheVw9n0S2mcfm6SYIifdEjHi23ZHriEs55rw/Sa5D4V5AbJzU3MvBpoBXR90Rm5JxDNgL9oDpWIE2F4J2xtg56d1JlxRsR7WXi+ehKKJ4rmo3yoCFd/6qeQ0LFgj7bWzvIToStDxnxH9uGiSeMkaq5IbJXYyte0ltj8r1JEYRjVnqRhITrlRHS3SvuPb+9n3h6uvm3CkdJCp36RTIH58CNR5wP1a6zLgFGCJUsrx/9Fa3621nqq1nlpS0s/JMNvfhRG9W0B/WGPR0A4HFaf4muLQbh9tuWUEat5N6XmnThjBG1/sYleTkUDrKf3SZu342+hdm/iEMTsWfdV3oXqTuKGrN4V3o3Tsri3IZKFcsvNj79jai7Poz5m9RHbYRoTVAWYulJ21OUtlErxgpVQTjT+/02RTvy3x/HOWolyeyI6s7f2o24wKmfaYLlLWbrvTNvuAz+OmrMjPr16spua0RZFwpRm3STGt0/4Ia34kC6xQ0LkdNlXLYsUKyt/Rif3zV8u5sgsjyczJCsTVfRXxBBx/YzgkBVkcerMlJjy+72xYFnl81l/FGM8KRHaRkxVTbNgReV9DlHe4brOEA8Z/vw73wZrzINkFo4ZMAvWArg86w+WSRe/sxZFFe8V6GYPtxXi8N8AWrogmesysWC+yrfVbZfz1+kW69cVb4Lb9oL0Z3rgnttBnYbnkVYw6UMbm7MLujcvVH4mXbPrNYrDMfVj6xZyl0rZto2T24tj2fMadMn84eTmaa5wLvbl9zu3dCpqicGkk3TkQvaUCiE4aKCMxROkS4CQArfWrSqlsYCSQOfUlmnfJLuzE/+jV21/bHgTggDQbEADNBXuTX/WWTF7OdliPOXxiEY9v2MraDyuZPbV3OSCGNNKdhDE7Ft1WhLHzCuo2w1WvOCdJK2RyilYXsRdnhRNE1eOl38jurSdL4nzffSgxiXruIwmqMZyzGF7+rVyb7aZ+/sZwYbpVknjYsA3yx4nKTRp3sQ1pJM3JjMW5Pu6ZN5XLFq/n0qebuOnkxzigxIOqfF8SVu1d+qZqCaGIb+e2Jv8Rl8kxG5ZFjrG9AjMXxi72nEQFoovI2bHmZ/wlIon82OWy6zzjNtnR3V0hORMn/lL6S1Y+tO6OyG/a1YlDbYkJ3vZnxX8uyHO+QOL363AfXGYXt/9wecQ47GiNjGUNOyJ/d0e4YvYSGW8hNiG67itRAXvk4sg52naLV3nLK7G7+TkjZFHvyZYEaidP20tR4/KcpaIYVbFecnoKykXkIrotBkpFRezNe2UuOOpqMShcLrB0khDGFufcqMtecC6A+MzP05ZHZQCldbdzm/sNpZQH+Bg4HtgKvAmcr7X+IOqYNcByrfX9SqmvAc8D43QX/9DUqVP1+vXx9e3SxKanYdkcmH4LjD6gx2+/9OlmNu4Mcs9xabi2OAq3vsi4jXfz9unP0Vqwd0rOqbXm2uUbmDw6jyWXxEegDUnSsiXXr202nrotsntjFyiyY3Ffv0sqUT9/UyQOu6VWFlIn3iQTQevuWHnK0++A91bAfieF47ZHwz9/D9O+D7WfRyYYG3ti8o+QnSSl4M37JJ43t0Ter8KqNtFKILaaDKRPyWdoMfTabTeIr20wkjrUX0+QtnLIBXDUf4oXzO0ToYDyIyLtfPMbcMjcSFhHsC0iYZlbImGrwbbYWgwQKXZVOEEWh9FF5Gy+u0E2caILbIG0X7vAVmF5pKiXXQgs6jg9/ymU1y8Lz7ovJSTE5ZbzhjrEwLYL0w3OfjE82qxlidHY0SKL7ZWXw6SjZcxs3ineAFuhy2byDPj2TWjlRoU6pOCaFRKjUrnE4PX4xaj49n9LHoXNnKWRDSKbwvLIojsUhMaw50pbcl6l5Pp2b4kkbhdOlGKfVkjybpza8vzVcPuBif/zNevlnEtmObznKZmP4rn2fWnT0aFNlgX/u7/zsb0UtUkBQ8NtFyYjPRBa66BS6hrgGUSi9W9a6w+UUjcB67XWq4AfAPcopb6PhDfN78p46He2vCaTxMieS5mGLM0b24Mcleb8B5vmArnGvOp/pcyAUErx73uPZNU7W6lqaKU0z8SdDzrsGOjo8IvsfJl4mqrgmB/FKmucswjeWiQJ0Ku/LwuekfvJbtd7KxJVlU6/Az54DA44y3nHqb1Jzn/R38U4OHxBRNHp2Z9HVEHOvl9+4neqjSSfIQkul4qVIbXC4TrvLIcDzoyVTrV3cW3J0zkPSDGrPepht8BJ/yNeL5dPDOxX/yiGSLQnoLFKqv+u/aX0IaekaysErTs7jzOv2ywGQZFDNfi6zVha484dKYmtdj0VO8eisUp2bE2/yHxcLlEAa62TcffMu8UotXMeJs9IlEE96juw8gpJkLaNxMkz4MSb0LhoD5ThcXtwn/Z7WTlFe2k/fjrRcxUdOqhc8vn12yJSsfNXS5XohMX+GmmjwVbntmwFnT3E1R9FRAyiN6DmPCDGfHQ9lIr1Ea9yvHpSY6XxQKeZjDQgAMI1HZ6Ke+76qL83AtP6+7p6xJfrJK7U03MJpQ93WdS3w4H9EL4E0J47hqAnl7ydG6je55yUnfeb+4zk8Q1befKd7VzyTZNMPejwF0cWT2ffL7v90QbDvFVw4UpZqDRVw/sr4bB5gJKdVrsK73HXw5SZzpKUM25LLsNnVyBt2B6OM9+RuEPWWJWY+G1jJPkM3cUO1zny8sSiVCsulN3Pk2+NLLjrt8prgVJZpNu7rLZhvOUVKR5X/ZEY0qVTZPc/2CKhT+2NzkUcn7tevHiTZyR690Idck2F5VD7pcSWO/SboPLitiwJd4kPHckbI149YzAMDlwuCSGq/VLGWXv8K5sq7UNbssHSsku8FWuvh8Yq1K7PIu1i02qofI/GC57mwN9uYN1PjmVcQY7s0kdXcz7oXAnNi64LVDA+HFYUzodr2JboLXYyEBSyiG+qch7bLcshFGop5BTBt38VKVTXWCkeDisoVa2j+8rrd8Gx1znnRpmicGknYw2IQU/rbtj6Fhxwdq/e/to2yX/oLwMC5aKlYG8C1f9K6WnHFfnZqySXlW9XGANiMNJSE8lXyClKrGTauEMKB3lzxIV8wJkRhRB7wfLuQ+EKqftIcqodSmHviBZNknOccafEn0dPEHYF0o4WWUi9uhDrnCW4Hr7QTAqG1ONyyUIl2Y5ptCCG7Z1zkrV8/S7R6dch8Sg0VYPLK7LD0X2jaGJsrLldJ2XGbYnevdmLI9Wvz7wHPlwNh5wv53ntL3vCp4K5pbT5ishyqqXxxNVwydrEYnHdqfidrsrghu7hC4gResZfwrmK7tjx0pastgu6PfXD2PfXbSbgamf9NZMp9DRBQ4NsCOWNljYRiqrmbMtiT54hRjNIWNELN0uuQnT/sL3T8QaCtsTwDpQ652W8tUiMbLueT8N2ePNu+Pp5UtPE5RYj/dlfSB+LDtWzN58WrBEJY0dpV1esB9rrl75Yv9W03xRhDIh08flLMnmMO7TrYx14cUuQ8gCM7McabC0F+1Dy+UpcHY1Y3kDKzjtt75Esee0rPq1qZJ/S1J3X0A8E22Uy2bRaXNXRk0TZVHFn24meZ9wVca1DZMEyb1UkydOe6OzqojMXiofu8SslEXr+U+Lyrv0iUoho5kJwe2jLGcv7B13P2g1B/mvBc3h1h5kIDKnH7XVeENk6+Db+4nABrZbEfvEfP5Dd4OgQjLP+CjP/HDGen79R5FedYs6jPX0Q9oLMkz544eNijHzjKrhvusTERwkQeArLyTt3mSTfOhlC0Spk3a34nc7K4Iausb1J9vc/9+FYL4A91l64Eqo+lA0Zh/A4VfMJI1+6VRL0o9vmuctEBji+HR95RcSosDd1tBXbP5zCjWYulGuu2yw/tjqZLduqXDB5uvzklsKz/0+Kg55wU6JRdNJvpI05tWWtO29/tgc6uv1GV8T2BcTTYtpwrzDfWrr45Bnw5kLJ5B6/talD8/q2EIeVpuG6OqG5YF8Umrzqt1N63qP2Lsal4LF/VaT0vIZ+wN5lhUQZymnXRhY5066VpD6nQb6xMnGim3Zt5O9gu9RqmDpfjO7Hr5QE1BN+KZPO8zcSyinl3OWb+d7ft3H6oeW48xwKLxkMqSAwWnZI46VTA3EJaS01ojSTNyaxX7TURBZUIL8fvUQ809Eyk96cRLnacxZJP3BUoWmVpNdFM8QbECgVL7cdsx4+Tj0Urk8xeUbsOeJjwJNV/G6ujn1fd48zpIf479+b49w+GnZI23rtTqw5D8S2q5kLJaR02rWJbfOh88TDEN+OnUJO3b5YmdfGKlmEn/GXiHxxoDTsdQ4fY9csefwqkYq972RR3wNAywbVtGsjxoP9eU9cLX3J43eWjo1uy5YlIV51m2HXF1KrKBSM/f4CpRJOu/oH0o/+aqRd+4LxQKSDUAd8+HcYf4QkUfeQdVuDdFhweD+HbrcUTkYrNwU7XmH32N5JzzpRlOPjkPFFPLy+gu+fuB9et1nwDRqi40jX3S6JeSsvl0E6uqCQvyiiQR6/m9oUt8iITwZt3CETy7nLZKKw9byjzmF5c7nj/MPwedwU5/qGjAa9IQNxe2DUARIeEeoQz0NgtDwfje2dyykSeeGH50X6RbIwqNxwqJ296+sfIT/zV4vR7M2WRVDNp859affWyLmWXyCLtsAo58+q3wrH/Fge28nf8eF+3a34nebK4IYuiP/+k4UN2c8fex2M3B89fzWqbrOEKa28QhbyyYq6KXdszkCygnG5JRJuNPcRWd94ssR7nFMk16kUPPVj8YrNXhwnsrEYXv2TPG6tE2N67sPyWrLr8uZAe0OilyO6LVsW7Ppcrism8XoplP5b5Puz5cjjjScj7dorjAGRDj5/STrHxG/26u0vfBUkxwNTRqT4urrA8mTTXLAPBdtfSfm5j9u/lN8+u4m1Gys5+cAxKT+/IU3Ex5G6PBEFGnuHqW5zJMnTKSnOTqS2iS46VFgukpaXro1MBg6Jb968UsYZT4Ohv3B7RP2mM2zv3NtL5fFFfxfDwe2VxGmnBV7+WPjeOxKPHR16p1zw3A0w/Vey4HKKGz/jTkmQtanbLHlHNZ8kN9yfuU4Moejk7+h+1N2K32muDG7ogvjvP5lKUW7JnrHU5XKFw0OvkoWzHdKUzPiwx/oFayT8zi4YF38cOqK6VFgum0qe7MgCvG6LGKxNlXDan6RfhDqkb7z6J+kvheXSv659X/rCuQ/K4t/p8zqaZY7w+iO5GvFtubk6UQrcNrIXrIl8f8mMFGMI9wozI6eDD1ZK+NLYnuc/aK35x+Ygh5SAdwDuTtOIA8jd9T6e1l0pPe/B4wspzvXx4Bubuz7YkFnYcaSF42Vx1Lo7HGNbLTtMheUyoX3jKkkenX5zuJr0YxLKd8yPEl3p626PTHr54yKhSNEGy7Xvy28TZ23IRKKrNL+9FHZuknoka34q+REzF8a2+1l3i3ch2J64kA+MFm9BS7jwnF180a5qPX81vPrn2JoRheVixL90q0Ol98XSx+w48WThft2t+J3myuCGLoj//u3wt9P+KGPtgjXiNcsfE3uf7ffZmzv2WB3fNu176XJJUrIvECkYF33cnKXw7PWxi/SVl4dVksJhQPZivWI9PPmfUPeVeBAePCdiPJy7TD6ncDzkjhQvwdhDZT6InyuK9pI5InckJAtdDbYnD+sKdUS+h45m51AotzGEe0NGFpJLJ2kvFBNsh9/tA+MOg2/+V4/f/mFNiJMfaeJ7X4dvl3d9fKrJrv+cvV//BZ/++2+p3vuslJ77kbcqePRfFbz8o2MpL85J6bkzhKFf3Mh2FXtzoOp9KP0abNsgOzv54yRsAmI15y9aLQsrt1cGc2+2hG3UfSWTRu7Izj/TkG6GfrtNF9HKRF6/GNd/OjRcSfoG8ThYIWn7j10m/WHGbTDm64khE6Gg7MJGS8iCLHAWPC25RNH6/OcskuJgtojBtGulH3Y0hwvYnSbHdRWeMThVmIZfm7Us8Sq1NzoXzkx2L+z7ZlmSW6N1RJHIaTc/2XuUAjT83qGQ23ffBl9uYsKy3VYvWCle62SfF/8/BlskpMrr757kcGMlbH/HuRjpgjXi7bAskZRt2B4bVjVzocxj/TMPDanYWxPClGo+XSuTyMTe5RA8/5Uk/Uzt5wRqm9a8SbRnFzNi87MpNyCOnVzCyrcrWPLal/x8xpSUntuQZqIXD1kB2XECMQg8WaKqBJKgFh12cc5imYBsCT5byWPtLyVO19/PcXoGQyqJrzNihSK7r4tOi1SeLpoEp/9ZFkZZec5Jm26PGOFO2vWBUdLnZtwWqfabWwKPzI+EOtkVqmcuFIWn7noJulsrxdRUGVhcLtmBt0pkDHYqnJnsfT29b07vCQWlgFxhuSh/HfWf4gGzDRI7DCg+7LUnxqb9P/aUnBLxVCSEdS2NiB+4XDJfPfXDWOnk528UhTRTG6XHGAMi1bzzoMQOjj2kV29/8rMOpoyAEQNVtFkpGkqPoKhiLZ62OoJZhSk7dXEgi2/sVcwDr2/mmmP3pSDH2/WbDOmjJzuP8TtKsxfD+vsjyZn2ouX1u2S3CS1x4C//Fk7930jsqlKys3Ta7wd6B9NgSD25JbGFueLlMk+/A176LXzrp84hRZ0tvkbsBdl5kec1sgMdLZEJkf5oJ7hqCxOtPIRIMFot2YFPt2eopQYatsL5K0QNLL5Se3RoUH8bm3b/8BdKuJ8VkrCkwKhY8QOPz1Gkg50fQ1ujCZftIeabSiXNu2DT07DXMb1SX/p4V4hNuyyOHpuGa+sBtWOPxmV1MPKLJ1J+7tO/Ppbm9hB/XfdFys9t6AG2UXDvCXD7AfI7mZydk4TjinlSuMp+/MTVMOsukWJtqxc5vmeuk4VSbmkkdrWgLDFO12AYKtgGwPyn4My7E+UyV10j/Wb5XKnoW7dFFn/dkZGMzkUKjIoYK/aC6PGrwnHgW+CBsyWU6r6TjUzlUKYn43hfCbZLUTdfrkONkgvFK5YOLAsaKqUSd90WySNy+v9cLglDKiyXgo8F4xKV05xyeU6/Q8LBjCxxjzEzeCp5/1GwOmDv43v19pWfdOACpg2wSFFb3gSa8/dmzIf3iXJCCplQnMuRk0Zwz8ufU1XfmtJzG3pAT3Tdk0k42jue9mPlkgJX7Y2RGg6mSI9huOFyhXc6q5P3m7rNonQTv+jryYIwXnBg/lMSPhuvpb98rlkYDVX6sz6HvXsfSjIfhDpS/5l2f/jrCfCHr0t+UNWHkofXGyMp2sCfv1rmKLv6u1Fj6jFmZk8lGx4UN9qIvXr81o6Q5uFNHRwxegDDl6LYOWkm2Y2bKfn8sZSf+7wjyukIWdyw6gOGWxJ/xtATXXe3z1m5wpZitR/Xb4MlZ0js9/0zxANhjAfDcCSnJOwtSNJvouuj2Iu+pmrxSrQ3ysKmbGrXC8Jor4THJ7HoRqZy+NCf9Tns3XvLSqJklIaQZCcD6YmrJSyvpZdKkbaB//hV4rmzlc2MLHGPMbN7qqjeBNv+BXsd16u3P/dVkJ0tmpMGQHnJiYaSw2gqnMyEt27B21yV0nOPys/mnMPKWPP+Dh56c0tKz23oJp4kRkH8AGpZItkaL/s3e7FIA+55vCS2WqiReTQMZ1wuKBifKEt5+h0RSc11t0eOr9sshsN9J8Pfpovxfdz1ESOiOwvCzowWszAamnR3HE8F9u597sjuVWpPBckMpJZdsmHV21AtI0ucEkwSdap4e4kkh+51TI/fqrXmLxvaGJMLhw6Q+lICSrH9a5ew1xv/j/1fvJwPvr0My+NP2elPPWgsH2yv5xcr36fQ7zXF5VJNVwnS0RWmnSp72jRXw9JZkhA6/WYYuZ/Ir755r8RyH3W1xL621cPzvxSVmJH7JRbKMhiGG3ZFazsh2hYQOOU3Uqk3vqbDrs8S8yWm3yzGRHcWhC4X5JfBvFWSV9FULcbKsdeZhdFQpbNxPB2yuy4X5IyArPyuK7WngmQFDO0iib2tIN0XpSjDHowBkQraGuGtRVB+VGxceDd5aUuI96otvnsQuDNIJbgtUEbFAdcw/p3/Zd//+x4fH30HOkUFV1wuxfeO35db1nzEVQ/8i5+evD9XHL0XSmXQFzBYcVJNitcK7+4Aau8A2VU9y6bKzugXL0eKAtkKTI1VUhyooNwMxAYDOKvRWJYs6ivfi60ivPr7scfVbY4kSnfHALAsKWYX3e/nPAAl+5v+OFRJNo5D13NAX+hOpfZU4GQgnX6H5C30NVTLyBL3GWNApIIND8gO7L+d0eO3Bi3Nr19rZXQOHNcP/bGnNJQexvb95zP2o/vY76Xv8PExd6DdWSk5d47Pwy9mTOHOlz7jljUfsXFbPTefeSCBLNMs+0SyxLr43ZruDKDxO0AV60WqdcEa0f92+0QLvLua5AbDcMdp0afcYoBHU1gui7S8sd3rU079fvnc3u/SGgYHTuN4Y2X35oBMx+4rC9aI6EBTdSTp2YTmDThmpu8rVgheWwglX5Odnh5y33vtfFJrcckU8LrTcH0poHb8iWzbfwEjtj7P5BevwBVMnXqSz+PimuP2YfbU8fz93W2c9qd/8uH2+pSdf1iSysQ6p1jRY6+TRU3heJFnzR0ZkZY0xoPB0DXxkqz+Ec4x2d01HqB/E2oNmc1Qagsul/QDX0DClmzjweQsDDhmq7evvPOQ6BN/62c9fuvGmhC/eaONb4yGo9KQf5RKasefiHZ5GLvxXia/cBmbjr07ZTkRLqWYdcg4Jo/O445/fMIZf17HL0//N+YcPt6ENPWGZHGjvdmtMbGiBkP6SUU/S2W/NwxuhlpbMPNQRmK+/b7QUieJoyMnQ/m/9+itVU0Wl6xpJt8H/3mQ5NdlOnXjjmXrv11BwY5X+draC/G07Ezp+aeMyed/zjyIyaPy+Olj73Hx/W8ab0RvSLXCRPxuqRm0DYbU09d+ZpRlDDZDsS2YeSjjMB6I3qI1rPmxVEU85qc9sgCaOzQXP91Mbavm1n+HwtSkFPQLu8cejXZnMe79v3DQU2fwyTf/l4ZRR6Ts/AV+Lz85eX+efn8Hj7xVwcl/+D/2GxXgW5NLOXziCA4pL2RkYBB9YQOB2a0xGIYfpt8bbExbMPQDxoDoLa/+Gd5dDl8/H0bu2+23NbaL8bBxp8UvDod9CtN4jWmiftSRtPtLGP/uHzjg2XOpnjSTbVMup3nE11JyfpdSnHLgGI7et4SXPq5mw5Za/vbPL7j75c8BmDqhiLMOK+OMg8fh92Vo4shAYxQmDIbhh+n3BhvTFgxpxhgQveGNe+DZn8OEeJ4XNQAAIABJREFUafD1c7v9ttpWiwVrmnmv2uKHh8KRGZ730Bmt+Xvx6VG/oeTzlRRvfpqSL56gsfggdk48lZqJp9Ke0/d/LpDtYcZBY5hx0BjagxZf7Gzig227eeWzGn722Hv89plNXPLNScw7agJ52WmogmkwGAwGg8FgSMAYED3BsuDFm+Hl38L4I+E/fgiqey7Bt3YEuWZtCztbND+bmvlJ091Bu7Oo2vdcdk48laJtL1Ow4xUmvnUzE976H+pHHUHNxFOpKT+JYHZxnz/L53ExeXQek0fnMeuQcWza0cDj72zlt89s4s6XPuOioyZy+sFj2bc0YBKvDQaDwWAwGNKIMSC6S90WePJ78NnzsO+34cirpAJjF1Q1WSzc0M7SD9oZ6YffToN9B2HYUmdY3gA1E06hZsIp+Jq2U1D5Kvk7XmOv1/8fk964kbox36Ru3LdoHHEAzSOm9Fm9SSnF/mPy+emYfD6vbuSJDdu444VPueOFTxlbkM039ipm6sQRHD6xiL1LArhcxqAwGAwGg8FgSBXGgOiKus3w5r0StqQtOPJqmHxyp0nTda2a17YHWf1ZB89+GSRowQnj4eIpEBjikTbtuWOo3utMqifNIqtxCwU7XqGg8jWKtr0EgMZFS/4kmooPpLH4AJqKD6RpxIFYnuxefd5eJQG+f+J+1DS2saGijncrdvP8R1U89vZWQJKyj5g0QoyKCUWMLfRTlOOlLWjR3B6itSNE0NIAFOV4yc/2GoPDYDAYDAaDoROMAQHiXfjwSQi2QrANOppg91bY8R7UfCJhShOmwWHzITCKHU0Wf/+snbYQtAU1rUFoCmq2NWq+qAvxZb0sSAt8cHwZzNobxuYO7L/Y7yhFW145VXnlVO0zB0/bLvz1X5Ld8AX++i8o3PYSJV88DoClPDQXfY3W/Am0+0cRzCpEu7xYLg81E06lI6e0y48rDmRx/P6jOH7/UWitqaxvY1NlPR9tb+C9it08t7GyW5ftcSlG5WcztjCb0QV+SvOyyPK4yPK4yfK68LldLJg20YRJGQwGg8FgGLYorfVAX0O/opSqBr4CRgKpLWTQfcxnD83P3qm1PinVJ41qs/3FQN6nZGTaNWXa9UDvr2kwtdtM/N6dMNeZOpyucTC12VSQyffJXFv3SUu7HSiGnQFho5Rar7Weaj7bfLYhlkz8rjLtmjLteiAzrynVDJb/0Vxn6hgM15huMvk7MNc2fDFVRQwGg8FgMBgMBkO3MQaEwWAwGAwGg8Fg6DbD2YC423y2+WyDI5n4XWXaNWXa9UBmXlOqGSz/o7nO1DEYrjHdZPJ3YK5tmDJscyAMBoPBYDAYDAZDzxnOHgiDwWAwGAwGg8HQQ4wBYTAYDAaDwWAwGLqNMSAMBoPBYDAYDAZDtzEGhMFgMBgMBoPBYOg2w86AOOmkkzRgfsxPOn7Sgmmz5ifNP2nBtFvzk8aftGDarPlJ88+QYtgZEDt3ZlJVc4Oha0ybNQxGTLs1DDZMmzUYus+wMyAMBoPBYDAYDAZD7zEGhMFgMBgMBoPBYOg2xoAwGAwGg8FgMBgM3cYYEAaDwWAwGAwGg6HbDKgBoZT6m1KqSin1fpLX5yql3g3/vKKU+nrUaycppTYppT5VSv20/67aYDAYDAaDwWAYvgy0B+J+4KROXv8COEZrfRDw38DdAEopN/Bn4GRgCnCeUmpKei/VYDAMBOu/3MX/rPmQlvbQQF+KwWAwGAwGwDOQH661flkpNbGT11+JevgaUBb++wjgU6315wBKqYeAmcDG9FxphmJZ0FwNwXbw+CCnBFwDbRMaDKmjqr6Vs+98FYDSvGwu+eakAb4ig8Fg5h5DDKY9DEsG0x2+BFgT/nscsCXqtYrwc44opS5XSq1XSq2vrq5O4yX2I5YFVRvh3hPg9gPkd9VGed4w6BmSbbYXvLCpCoAcn5v71n2B1kOuFs+QwrTbYcAQm3tMm+0jQ6w9GLrPoDAglFLHIgbET+ynHA5LurLQWt+ttZ6qtZ5aUlKSjkvsf5qr4aHzoG6zPK7bLI+bzQA4FBiSbbYX/OOjKopzfcyeOp6K2ha21rUM9CUZOsG022HAEJt7TJvtI0OsPRi6T8YbEEqpg4B7gZla65rw0xXA+KjDyoBt/X1tA0qwPdJhbeo2y/MGwxBhw5Y6pozJZ79ReQC89VXtAF+RwTDMMXOPIRrTHoYtGW1AKKXKgceAC7XWH0e99Cawr1JqklLKB5wLrBqIaxwwPD4oLI99rrBcnjcYhgD1rR1U1rdRVuSnfEQOfq+L9V8aA8JgGFDM3GOIxrSHYctAy7guA14FJiulKpRSlyilrlRKXRk+5HqgGFiolNqglFoPoLUOAtcAzwAfAiu01h8MwL8wcOSUwLnLIh23sFwe5xgXrGFo8EllIwDjinJwuxQTinP5cHv9AF+VwTDMMXOPIRrTHoYtA63CdF4Xr18KXJrktaeAp9JxXYMClwtKp8Cla43ygWFI8mlVAwBlRf49v9/4Yhdaa5RySoMyGAxpx8w9hmhMexi2DKgBYegFlgVN1RBsAeUGrx/yx5nOahhyfFbdhM/toiSQBUBZUQ5rP6yiqqGNUfnZA3x1BsMQJ1qa0+sHKwShqAViYNRAX6FhIImXbvUXQ0uNPG6uNkbEMMAYEIMJWy7NVjwoLIeZCyFvDIzYy3RWw5CioraZkXk+XC7xNtieiI8rG4wBYTCkk+i5JlAKx98IT1wdmXfOXSa7zmbOGZ7Er0Umz4BjfgwrLjRtZBhh7uxgoslBLu2Jq6H2cyOZZhhyVNS2MDI3a8/jcYViQHxa1ThQl2QwDG0sCxoroX5rZK6Zdm3EeAAj0zmccWofAAefFzEewLSRYYIxIAYLlgXtjc5yad4cI5lmGHJsq2uhOBAxIAr8XvxeF1/VNA/gVRkMQ5TogmB1X0XmGn+Rkek0JG8fYNrIMMUYEIOF5mrY9ZmzXFpHs5FMMwwpWjtC7GxsZ2Qg0q6VUpTmZ7N5lzEgDIaUE10QrKU2MtdE/21jZDqHH8naB5g2MkwxBsRgIdgOG5+A2Uti5dJmLoSivYxkmmFIsX13KwAjozwQAKPysvmypmkgLslgGNpEFwRbdzucfofMMetul3nGyHQOb5K1D4ANy2D2YtNGhhkmiXqw4PHBlJnw0m/gzHsgtxRcbtAasvPlmMZKI6NmGBJsrW0BiPFAAJTmZ7FhSx2WpfckVxsMhhRgFwSr2wwV6+EfN8HcR6Fll6gvnfZHyApAVgH4cpMr7cSr85i5aGgQ3T4AtCXtw5stORFv3gvTb5Zwpo5myBvtfN9N+xgyGANisJBTAiP2hqZKCLbBkpkRtYM5D0DzLlg6yyggGIYEVQ3igSjKiTMg8rJpD1lUNrQypsA/EJdmMAxN7IJgdphKY5VsUv1turxeNhWOux4euTj5POOkFGjmoqGB3T5euBmOvAJWXRO5x6ffAdUfwdtLI8df+37iOUz7GFKYOzZYcLnAF4BjfhLpuCC/l88VJSajgGAYIuxsbAOgIMcb83xJnhgU2+pa+v2aDIYhTXRBsGvfl9++QCQsZdq1iXNP/DzT7KAUaOaioYHdPk75TWI7WHWNtA+bZPkPpn0MKYwBMZjIDXshkikxxT9nFBAMg5Sdje343C78XnfM8yPCsq52joTBYEgB0fKcIMVJA6Nkzjl3mSwIu6O0Ex0nn+wYQ2Zh3/u6LfLbspIf63JJ2LTTPc4N5zt0lv9g2seQwhgQgwU7btDlhrkPizvZxlZiiqawHNxGAcEwONnZ0EZBjhelYvMcRuRKm95hDAiDITVEy3M+Mh+2vwO1X0BDpbxueyUKJ3SttGPHyXd2jCFziL73tx8gv6s2dm5ExN/jsqmyJgmMEs/VZS8kD0ky7WNIYQyIwUB0J//D12H1D6QyaNlUqQA5b5VUorYNC1udyeXu8tQGQyZS1dBGfnZiilauz02Wx2U8EAZDX7F3nndvhoZtMOloyXFY/QP406Hw1/BiEmRxmD8u4o0A553mnJKujzFkDl2FFDl5J6LvcdlUWYus/gH88WC4/xRo2JH880z7GFKYJOrBgFMnf+JqmL9GFDIWnx5JSJq9GNoa4Pkb4ez7B/KqDYZeU93YRmFO4q6UUoriXJ/xQBgMfcEpmfWCx2DpmYmLyUvXigERnSORTEGnO8cYMofOQoo6S3iOvsf3n5K8zcRj2seQYkDvmlLqb0qpKqWUQ7o+KKX2V0q9qpRqU0r9MO61L5VS7ymlNiil1vfPFQ8QyTq5DkoCdXTnXTEPWneLgoZxCxoGKTsb2yjwex1fK8r1sX23SaI2GHqN06ZUc03X8ekulywMC8dHjIp4unOMITPoLKSoM++EfY/t56PpKqfBtI8hw0DfufuBkzp5fRfwXeB3SV4/Vmt9sNZ6apLXBy/RrkOlnDu5cidPZjJuQcMgJWRpapvakxoQI3J8JoTJYOgLTptSTdXpjU/vSbKuoX/oLKSoOwnPyQwQMPd4GDCgBoTW+mXESEj2epXW+k2go/+uKgOIT2x66seJFajPXQZev3PnLSgzusqGQcuupnYsTVIDoiDHS01jO1rrfr4yg2GI4LTw27BMagqlIz69N8m6hvTjJN1rrx26k/DsZIDMXCjJ+OYeD3kGcw6EBp5VSmngLq313ckOVEpdDlwOUF5enuywzCHedbhptfxesEYk1Oy4QYgt/GMP+Hlj5TVTmXrQMujabArZUwMimQHh99IesmhsC5KX7XyMYWAYzu0244muAOz1J84dx14HJfunJz49WThMslj5fmTYt1k7pMhuH/Vb5d77i53XF9EGZbQB0tECOz+W/MuKcFR5htxjQ3oYzAbENK31NqVUKfCcUuqjsEcjgbBxcTfA1KlTM3/b0sl1uGk1nHyrxA1G45SQBKba4yBn0LXZFGIbEIVJDIj8sNFQ09huDIgMYzi324zGMWl6JVyyFkJxxkI6FnsZrP9v2izJE6a7Y1DabaZuCzxwTuxrGXKPDelh0K4mtdbbwr+rgJXAEQN7RSmkG65Dy9JUN7SxdXcr1boQK78skpBkqj0aBjFdeSDyw8/XNLX12zUZDIMapzlh6SxQ9E8yq9H/z2ySrRlaaiAwCiu/jGpdKOuNhjYsy8HOMvd42DEoDQilVK5SKs/+G/g24KjkNCjpQivZsjSbKhuYtXAd0259gVkL17GpsiHSqTN4t8dg6IqdDdJOC3KShzCBVKs2GAxdo5PMCbq/5gSj/5/ZdLJm6HK9YWPu8bBjQEOYlFLLgG8BI5VSFcANgBdAa32nUmo0sB7IByyl1LXAFGAksDJcpdYDPKi1frr//4M0Ea+V7PWDFdoTm7hbFXDZ4vVU1IqUZUVtC5ctXs/Kq6dRkpcV2QmIHhCidwKiY2FNfoQhw6hubMPrVvi9zoUQ7QJzNcaAMBi6RVB58TrMCRqFsqzej//dnUuM/n9m08maYXdLG8H6SpbNHkdVs+ZXL1bHrjds7Ht82QuSD6FD4PH3//9i6DcG1IDQWp/Xxes7gDKHl+qBr6flojKF6MSmuNjE/DkPUhLw7jEgQIyI9mBIHtg7AU7JT50VhzGDuSED2NnQRqHfR3iDIIE9IUyNJoTJYOgOteTjPm0RxU9eFBn3T78D15ofS/J0b8b/ns4l6cqvMPSdZGsGfzH5VR9y4JrzoW4z4wvLufe0RVz6dFNkvRFPww6zvhgmmDua6TjEJrqXn89NJ4yOOaysyI/PE96x7UyazeRHGDKc6sY28v3J9za8bhe5Pjc1TcYDYTB0B+Vyc926II3nPwkXPwPTb4Z/3CTiHL0d/81cMnRItmZoqcG9/PyYe1z85EXcdMLoyHojGtMmhhVp9UAopa7XWt+Uzs8Y8iSJTZw80kdZkZ+K2hbKivzcM28qxblRyUrJdntMfoQhw6luaKPA33niXV62h13GgDAYukVxro9rT9yfhpYdBO6bHvtib8d/M5cMLZzWDJ2sPzy5DmO0aRPDinSHMF0KGAOiLySJTfT6sll59TTagyF8HjfFuT5caGis6jzGtKv8CINhgNnZ2MaB4zqPnc3J8rC7ZXjVlzQYeovLpZg8Ko9QQ3Pqxn+3mUuGPJ2sP5QrKsTUzoUJv27axPCgzyFMSqn6JD8NwNgUXOPwJomygcotoSQvi3FFOZTkZYnx0J1Kn0YpwZDBhCzNrqZ2CpMoMNnk+owBYTD0BJdL4c0rTc34b1nQ1iBVh81cMnTpZP2xh+gq44/MN21iGJEKD0QdcLjWujL+BaXUlhScf3jjckkxlwVrINQBbi8ERid6Frpb6dOoYRgymNrmdiydvAaETW6Wm8p6k0RtMPSIVI3/zdVSRyJQCjP/DPljZSHpy03PdRsGhu60l+i1R6AUrCDMfRS82eDNAf8Is74YoqTCgFgMTAASDAjgwRScf3hjWVD9UdeqBt2JPYyX3MsfZzq2IaPoqoicTa7PQ31LY39cksEwtOhMDam7sqz2fBMoBa1hySyjujNY6eqed6WeZbeFsqlw3PWw6po4JacR6f8fDANCn3u41voXWus3krz2k76ef9jTXVWDrqpARrsZOwtxMhgGkD1F5Lr0QEgIk9YOFVENBkPP6ckcYc83066NLBjBqO4MNlKxLjBtYdiSki0CpVSBUmqOUuq/lFLfD/9dmIpzD3u6q2rQVW6DkVczDAKqG1sBKOzSA+EmaGlaO4wBbDCkhJ7MEfZ8k1tiVHcGM6lYF5i2MGxJRRL1POBfSEXpHCAXOBZ4K/yaoS905VmwSabjDNBYCe3Nov1dNjXyHtO5DRnGHg9EV0nUWRJ9aRKpDYYU0RMJTnu+KShznp/cPpl36rbIb+PpzkxSJbuaN1pCoruzVjEMGVKRA/Fz4DCtdV30k0qpIuB1JEfC0FucKkTOeUAG5MbK2HjF+FhFp0qhp98hBYQq1pvObcg4dja24XUr/F6HIkVR5PgiBsToguz+uDSDYWjTlcS3U6x83ljnCsZtDZJkbfIiMpu+yLpbFjRVQ3sj7PoMNj4hCkxPXB17340C05AlFQaEApwCka3wa4a+EK+CYAXhmZ9LBdGuBmYn9+Sqa8QT8cx1pnMbMo7qxjYK/F6U6nzoyM0SA6O+1XggDIaU4LRZZc8RTptR9twTr9Kj3HDPsV0rAhoGns7ueWck25x89yGYcRuM3A+8fqPwOMRJhQHxa+BfSqlnAVu2tRw4EfjvFJzfYHsWGivh3lO6PzAnc0+OOkDeYzq3IcPY2djeZQI1RIUwNRsDwmBICZ1JdjZWdi4THj3/1G0xsfCDhd7K+na2OfnAORJGbYzFIU+fDQit9SKl1CpgOjAO8Tq8CPxMa13b2XuVUn8DTgWqtNYHOLy+P3AfcCjwc63176JeOwn4A+AG7tVa39LX/yWjsSzoaOnZwOzxweQZcPB54C+CllrYsAx8OaZzGzKSqvpWCvxdu89zfSYHwmDoLZalqWlqpz0YwudxU5zrw+VSySU7k21GtTeLwRC98OxLWIyh/+lCptWxrSRrDyX7w8VPg3IltgvDkCMVHgjChsJDvXjr/cAdJM+T2AV8Fzgj+kmllBv4M+LlqADeVEqt0lpv7MU1ZD62u7BhW48GZiu7GHXMj1ErLtzjZtSzl6Czi1Mjv2UwpJidje0cOM7f5XEmhMlg6B2WpdlU2cBli9dTUdtCWZGfe+ZNZfKoPDEinEhmFNR8IjvO0SFNvQ2LMWQcydrK/gEfyqk97K4AlwfuO8nkvwwD0npHlVLvdfa61vplxEhI9nqV1vpNIH6VcATwqdb6c611O2K8zOzr9Q4odlK0k2qF7S586VaJM+xmmfhQ086I8QBQtxm14kJCTTvT/M8YDD3HsjS1Td0LYcoxHgiDoVfUNLXvWRACVNS2cNni9dQ0hT3ZTnORk0z4zIUyJ0Gs/GcyRUCzgBx0RLeVQ8bn84dTx1LYvoMQrsT2cPod4HJHkqjByMUPcfrsgVBKnZnsJWB0X8+fhHFE8i1AvBBHJjtYKXU5cDlAeXl5ssMGjk4S1CwUuqMNd91mee0fN0mcob8ICid0Wk1ahdoc3Ywq1LtYVEdXJrp7lUv7QFJ3+xAm49tsGqhtbiekdbcMCLdLlJqMAZFZDMd2mxFYFrqpGivYRlB5afYWkZflo7alI2HcbA+G9hgPNhW1LbQHQ45zkTXnQXbm7I03bx8K5z+FqvtKZDtXXiFqfjbR4bRdVS/OIIZim7Uszc6mNlo7QriVIjfLTXtI0xG0uj+HWhb5oV0smz2OppCbMVkdFDx6TmSNcsFKuGQtdDRD5fuyNjnhlyb/ZRiRihCm5cADOCsxpUtf0anlJy1Jq7W+G7gbYOrUqZlXutYpIemFm9Gn/IZgMESHBbmTZ4jyUsV6WH6BdOBL13a6WNfuLEe3s3b3PBbVyZW55OLDmRj6CuWkzJEiI6JX7vYhQMa32TRQ3dgGQGEXNSBscrPc1LcE03lJhh4yHNvtgGNZ6KqNqIfOw123GXdhOS0zF/FV7j7c+vQmnt1YFTNu+jxuyor8MUZEWZEfn8cdmYsCpXs2qlyN29nVHODSR77g2cv2J+fxq+S1xqrY6xikeQ5Drc3Gz5nfnlLKNcfty9UP/Kv7c6hlwa7Pyar9nPHeHNDN4Bop7cLezFw6S9YgvhxRdazbLHmWJv9l2JCKVd67wO+01gvif4C6rt7cSyqA8VGPy4Btafqs9BOfkFQ2FY68AnXfyfj+dBC5D5yKdfSPJSEauh1T6g6UEJz9YIybMTj7QdyBuPd1Fj4Vxsnt3bhrR8R4gLS4K7t0txuGDHuKyHXDAwGixGQ8EIZhiz1u129NGIcLn7iI5tpKzjpMpsnocbM418c986ZSViS5RvaCsjjXJ3NRoBSOu14WhffPgNU/YF9/PSUBL9//ewXWnAdFjKMH4bSGFNPJnB0/Z5512Pg9xgN0cw5t2QUN22H1D/a0Adqb4PgbIsfYnoXo8LZ1t0tom2kXw4JUeCCuBeqTvDYrBed34k1gX6XUJGArcC5wfpo+K/3EJ6hNu1Yk0aImBNfDF9J4/pPUfuMGSovyycof1eUuv8vthlFT6FjwHCrUjnb7cAdK5HmbLsKn7NChkNYJbu9Cn5V2d2Wn7nbDkKK6sRWAwm4aEDk+N7tbjCFpGIZEj9tn/MVxHC70WTR7I33JHjddLsXkUXmsvHpaYlioxwfH/CRh/nGvuIBfnPgwZy35jJ0zj6X0tN/LNSxYA1onhK8Ox7DTfqOzmhwuV8KcWej39nwO7WhJzGV44mq4cGXkGNuzEC8F6/VLaFMofWHNhswgFTKu/9fJa+uTvQaglFoGfAsYqZSqAG4AvOH33qmUGg2sB/IBSyl1LTBFa12vlLoGeAaRcf2b1vqDvv4vA0a8akVuieOEUNvUxnkrtrLy6omUJOuQcdVCXTkluPJKI8+17Izt0E7hUw+dh75kLZsa/Xt2Mu6bf3iC27uu3cX4NLsrO3W3G4YU1Q0SwlTQ3RAmn8eEMBkGB05VnPuyqIoet5OEjdS1u9Aei0cv3JvSHEVdu4scn3ymy6UoyctKPG9OCYzY23H+Kc1RlBX5US53l7KfwzHstN9IMmfbNTni58y6lo6ezaGWJQVrnTYHddjTEe9ZGEQ5L4bU0WezUCnlUUpdoZR6Win1rlLqHaXUGqXUlUqpTlcCWuvztNZjtNZerXWZ1vqvWus7tdZ3hl/fEX4+X2tdGP67PvzaU1rr/bTWe2utf93X/2NAiVetKCiLuABtCssZEchmycWHi6sZEt2YoaDsTNx7Atx+gPyufB9qPo19rmpjxOWZRM/ZCrbFuEH/+Pwn/Pbsg2Lc3oERo9HxSgwpdld26m43DCmqG9rI8rjwe7tnHJoQJsOgwN4xjh+XQ30wfqPH7XW3J4QT1c1cRFHJaP7NU8Fhz53D+EVHcOCaMyls+DQ2RDV+DgHwBRznn1H52Tx72f4U53S+72jCTtOMHWY2ZynMXy2/A6V7PP/xc+ajb21h4dxDuzeHhnMfQDu2Aby58L13YP5TkkifSroRSm3ILFIRwrQEyXW4EclNAMlJuAhYCsxJwWcMfaIteMtK1NE+/Q5y1/6Micdeh2IKWDrRjTlvVeLOxPK5Ulo+WQXRJPreQRVxex4yvpArv7U32V43yy//Bh6XwuVyUZzrk2vpaRXLHn0tnbjbDYOTt5fCawvh4AvgqKv3PF3d0EZhjhelundvc31GhckwCHDaMV4+V8brwgm9Gy+jx+2K9aKAM+M2dPF+tLuyaFX5lOoGfCvnxcp4R4/9yUJhSvZPnH9mLsS38mJ8jVVdCmWYsNPUEh8ONtLrRx1/YyTEyJbU9YqBYM+ZKy7/Bs1hFab61g5uOfNAvG4XZUV+xhT4nefQ5mqo/RzW3w9n3AmPXxn5jDkPSGjT0lmpF03pIizLkJmkwoA4VGs9Oe65CuA1pdTHKTj/8MP2SCxYI4VZmqplgqhYj6p8TyYASJyUGiud3Y7enMTn7DyFJEV/mr1FlBX5KQlk8cPpk/nJo+8mcUertLsuk7rbDYOPza/DE9+B7AJ45meyc3bg2YCoMHU3gRrEA9HSEaIjZOF1m0nGkKEkq9rbWCkKNr0ZP+PH7cYqdN5YvgwVc+E9b1JR28KrV+3LmM5y1DoLhbE94h0tsPNjeP7GiGRrtBHigAk7TR1O4WBrL9+fbKf8hEvW7nmfy6XwetzMv/u1hPuw8uppyTfggu2yXti0GpoqI5LxLbWQUxwpEGd/bhdtodt0EZZlyExSYUDUKqXOAR7VWgLklFIu4BygNgXnH75oLbGI0URPAPGTQ1O1s4RaR3PscdF5CvEJUB4fln8kHc0dLL3kSIKWZv59byS4o1dePc0s6g095/W/SIjEGXfB2uvhuevha6eDx0dVfRtFOd0PTcvNkuGrvqWD4oBpi4YMJVkV56ZqqePTW3JL4KInQbmt13hkAAAgAElEQVTB66eOABfe8cqesXpbo8WYJDLeCpIbNh3hBWdglISTPHBO4jGdCGXYITTxORAm7LTnOIWD1dY3OhuGcfWdou9DSSCL7x6/L5NG5qLRWJZ2NiLcPlkvFJZHJONBHs9/Sj6nbKoIvdiGRSpCjZK1RVM/IqNJhQFxLnArsFApVYvUaCgE/hF+zdBT7FyG5XNjQpj4x02ivW0v/uMnh81viNci2CaGR1O1TDIev0jAblrtnKcQFT4Vv+PxyJVHpcQdbVQ5DDTsgI2rYMpMyArAwXNh7Q3wzjI47CKqG9uYODK326fL8cmO5m5jQBgyGX+xhCs1VsqYvGEZHHkFvH4XjD+i5+dzmh/OXUaHf6+YsfpXL1Zz/1krKGjdIrvKHc005IxnZ2sOE3I1yu1DORk2VgjqvoKC8cmNn06EMkzYaeqww8EOGZ/PL75VQmmOIs/vi8znNuF7Ej/P7lsSYNU109he18oVS9+KreGU3SxFZe3QY21B627IGSkhUdEhUucuA18uXPw0eLJhxbzY0CbbS9BboYBetDPDwJMKFaYvCec5KKWKAaW13tnX8w5bLAt2b4lMDiC/V10juQx5Y2VCaq6BCx+HXZ/BS7dC7ij0IXNRu76I7fhn3gPrfglH/xCOvx6USzplc3VsBw+rhOiONoL1bZQEJAeipqm9z+7o7qhyGANjGPDFy6BDMOkYeTz2UFF8eeVPtB90AXXNHd2WcAVRYQJoaDVKTIYMxbKg+qPYENFzFsH7K+HY63ouOGFZ0LhDfs74i+wAr7sdHjqPkfPXsPbKA8jSrVQ1ax7e2IzLahUN//Bn58xeSqhpFw25Xna15lA0cxGFT1wUG0u/8nIJiZrzAPX5+5A/ewlqxYWRY2YvkTmoE0zYaWrwedxMn1LCzdM8FD8ZqQKtZy9Bjdgbyo+QTcLAKKzsYsd5dlR+FlcsfYuSgJc/nDqWssIsits/Rz1wQeR85y5D+QvhgbMkrPT4G0Sy1eURDxdajAuIGA8Qyee5ZK0Yx73NYUgSSm3qR2Q2qfBAoJQ6AtBa6zeVUlOUUvOAD7XWa1Jx/mFFc3XyXIbifcVtGD8hzX0U7fWjqj+MTBb2ex67TOIYX/6dGBEPX5TYwWFPApO7bjMHFpZz72mLuPRpuPPFz7j1rIMSciB64o5Opsphh0EZ2b9hwpf/lF2soonyWCmYcjr88/c0bHwO6L6EK0BOlhix9a0mkdqQAThJtTrFdj98ESx4WhZn9Vu73q21LCns1dEifaapJsYosL3TrvoK9tEd8PyNjG+s4usXPIFn6cyEeg77zLiN0O56fvxcC1UNbTx0wd/J0q3idYjKdVDL5+I+/0nUS7+JjYV/6Tdw2u9NbHo/UJzr4/enlpGzeHpsMvyKC+Giv8POTRJtUPMJKtTBqn+1J8yzj135De49exL7+utxrzhH7qVdOdo+30PnyYakXWV60WkSqnTc9ZGaIIXlMHtxpBq1Td1mCLb0LYfBIZTa1I/IfPpsQCilbgBOBjxKqeeAI4EXgZ8ppQ4d9BKr/U2wPXkuQ80nUDgxtqMGSqGxEuX2ipvayfDwF8HB50WMB/t5u4NDQucvfvKiPYWDFr3yBSuuOAqtNT6PmyK/t0fegq5UOboyMHqL8WpkGF+tC+9IRXmvJh4N6+/Hs/5u4BIK/T3IgfDZORDGA2EYYJKpyGQXJIntboUlZ3S9W2vLajZsF8+yw+Jvj3e6qVpem34zLL8AT3NVUlEN9/Lz+f6pK/mqLUA7IbJaah1zHbJdloTKRIfLAJx8a9+/M0NywsaoK9iOX4Wc72OoPcaQVDMX8qNvTqK2OchDb23lkPGF/OSk/Shu/oxSazus+EFkPeB0PivYZUFbVsyTthbdVgrLQXn6nsNgakkMOlJh3p0NTAOOBr4DnKG1vgmYjpFw7Tken8TIzl4cW1/h9DskVMnllgnC1n8+9hcysTRVR5Kfoiksl12jJMXpQh1t6CQJTF8rzeaRK4/iFzOmUBrIYlxRDsW5Pj6pbmTWwnVMu/UFZi1cx6bKBixLJ/2XbFWOaKLDoNIh+2d7NXpynYY00lgl9UhGHRD7vNsLk08if8s/mKB2UNgTD0Q4B6LBeCAMA020p6FsqozR7Y3ymtOYvOuzxM2c5uqYwyxL09FQJbKadlhqssVf0SQJZbKPgchGVPxnt9RC3WYmFnr4779vZN7yzwnmlDoe247b+RwmNj19xNUNUTs/dr4HtV8kKDG5rBC/OqGYH52wDz+cPpncYB3u5efHbi7ahQfjz9dUHVtPJMmagaJJsWuT2YvB7THtZBiSCgMiqLUOaa2bgc+iCr21AKYSSE/JKUEfe53sUM24TQyF6TdHEqiVkl2m+2fI74Iy6dTrbpe41JkLYzv3GXeKQRJwniA2VrXRYjlPEp/t6uDsO1/l/Htf55Pqxj07+qLq4OXRC/dm2exxBOsr2d3SlvRf6qoYXFcGRm8wxYwyDFsC0g6Zi2a/k7GUm/nuZ3qWA2GrMBkDwjDQBNth0tHw3Q0w439lbP7bdFjzE8kZiF9wvRS3gx+3W2tvgOysq+/e4m93hfQx20AA2LAMa84DiRtR626HwnIqGhV/OHUsfzyllK1NEDz/kZhjd5+xmK3BAupmLop5XpvY9PQSH/b20q0J87qes9S5DVkhPA1bufpQH//ctINCnyXPR7cbh8KDDbMWY+WOksT+6TfDxc9g5Y1N3tZm3AbXrJc8nLYGCHWIFy2NRWUNmUcqciDalVI5YQPiMPtJpVQBxoDoOS4XdXn7UFNdyaSCVty7v5IdpWN+AgUT4JlfiDFgx6RqHVFkePrH4eSnx9HKhdIh6dwHnwfvPQIXrITmnXuUQGqO+CHXP72D0rws/jLnQVzLz9/jDq2buYjrn9oBxIYUtQdDlAS83HtS7p6krvGF5YTmPAj+f3OMWYxW5dBWiCLq8ejdqOYWyClJi+yfKWaUYVR+IL/t/IdockbwccE0zq19gXd1DRZju3XKLI8LlzIhTIYMwJcLh18qYabReWibVotQwPw1YHWIiAUKcuNCNezd2jgxi90eL2Os5khoib34i45Ln7lQchfi/m4/+qf8/l8uzp31BOUBjdr12Z6NqNDcxyhp2sU+z0XUdHafsZisBc/j6mjE4/bg1j5+9/ePqWpo4xcnPkxpjqKu3UVZ3niKTGx6+oiPCKhYD5tfRS9YgworLCq317kN7foUHjgHNXkGP57+a4IW8J03RWHpnEUSxlyxHl6/i+CFT1DTHKKqGUbkjaXV4yLvpNvYVd9AfYeXglbF6NlLUSvCydaTZ8D0X8u5Qh3w1mLY9wQxOk77vclhGIakwoA4WmvdBmDXgQjjRapRG3pIc7vFj1Zv5sHT8/FHJ8vNWQqn/E4MgBURBQVmL5E3blotRbpmLkQHRqMB14p5YVWFG2MqSAbPWUqDVcwfT2miqllTFyhnxKVr0cF22rSHBQ9+yttb6vdck7349nnc3HTCaIqfPDM2OW/5+TJ42ImDcYOIy6UoyfVC1ScJccKu0indlv3rbl6DKWaUYVS+D3lj9lRLjWdN3jlcU7uOSe/9gc/+vXvx1Uopcn0e44EwDDyhcO5a0STZ3Fl3uzx//A2QlQ/3nxy74D/+enk9WlrbX5wgZvH/2bvy+KaqtP2cm31pm1JatraKiFXGDcH9U3RcChYoiG0VSkFEUFxGxwXHUXFhGBH99HMQAR0FWkBaWQVZxBGcQUVBFBwUAdG2bC1t0y1ptnu+P96c3JvkphQpgprn9+uvTe6a5txz3u153voh89BkOxN2IasZNP5QtIJI2DojlbXeMgdcZ0SAM8i5b4AzHWSDBQmmGnTVN4Ktm0qBpBv/Bm7vhIBkRtKy8Dk8aVkR5KIVkILEa7sjE1MGzcXYNR4MK94b+qibJnZDctvVluM4VghJU3ua0m8h+Qww50+kkKUmNAPKGBLOY3pf4NLxYPMGw6Am2u9eDxQuBZckBHRWtBgdcOjq0NHug4fX48FllRjzPz3w/OrDmJNjR9KioO2Q8xLQMQu8xQk2b3BsNbE4h+F3B8b58deEM8YYgEsAdAPAARwA8Dlvj5O3M/r27cu3bNlysm+jVVQ3euBxHkT64kHRROrhZcCCvOj3RyymOsTD/6XFq6kK3w1cCrtJj24JEticnOhjBBnKkQm5YAGkTn9AdbMP3+yvx5PLv4kyvpdMuAIdbSb468ph/Mf50Tf+wDfgLfWk6KCWhxPkwKbDVNcZeR9tVGo4FrWmk6TsdEJO/GsYs0fFP/pSJ9M/PqG5+a51LlxzeC4K5PexfeBKuJLPadNpH1i0DZefkYJXbu3dnnf7e0N83B4PZBk4vIOabgnjashM0st314RnJABl7k09GwALV2zSmB+/G7gUYDqc4ZBgYBzMYAFsqZDB4HR74fYGEOAcJh1DQv33sC5RJFd50QrF6FOd01+4AvrpF0Z/ljFrqfRKte/WoJgGQOvAgrGXIj3ZerIFKX67YzaSOO8sB0aUaY+jEYuB5ioKziwdRw5mQUk40V7sGyTgfzdwKV7+pBavXW+BvlSpOqgZNBe7kQGb34nzVt8cfnyM6/PbV4MldG17pkFLqez3laX4Tam4HPc3xxi7EcBuAE8DuAlADoBnAOwObovjGJFi1aObNYbygqTTfr+5ihoALSqkScRZjrM66JBoNqCmKUaXR4M19Le0aDjgqobXH8CrH+7G1GHnh3EWXh9xEZpa/JBlDqY3RddGZuWAA2DeJpqo0vuG5OF4c5AceAzdJmWZo7rRg/11LlQ3esL4F23hNajLpjZNvBZLJ1wZl4U9WfC1EGlUq3wpiGoXx3uWXAQMNpy29e9tPrXVqEdDvA9EHCcTrmrFeQDo97K7yHmIpYxnsFIpSGI3Cp5IUsz50aYLYOy7+7Bf7gC/zgy/rwW+xiocdLqw61AjCmZ/hqtf2ICqQ/sV5yF4LFNLgqf3JeNyyOvQ6SN4b+l9yUi0JNM+6X1D5+hiJzMhPdmCqcPOx+RVO+NcshMJSQJMCYrzAMQeR81VxIes2kkcSYB4kWqhleBaLAj4aRaG5/t3VZwHALCnIUU+gouSGvGHVA1FpRjXl2X52JwHFTkcb15Pr9ujk3UcJwXtUcL0fwCuDzaUC4Ex1h3A+wBihhIZY28BGAiginN+rsZ2Fjz/TQBcAEZzzr8MbgsA2BHctZxzPvj4P8qpAcl9hAyuyDSmzwWAaUu8JmUQwfqO9cDax4CmKkiHv0Hi2sfhK1yufYwg2wEhRSaDPgnVTR68uHYXnhzYCw6LAS5vAE0eP+6e/yXeufMyHK43IGtoMexLR4ZqI+V+j0Kac5MSgROdsyu3IODzQJI5pDZ2m4yVPUg064+J1xBvZnSKoPo7MpZacSCqXDJ6JNpR3W0ounxfjKQD/0Z916uOemqrURdXYYrj5CJWYMRgVcirkXOez0Vk1JYGqh3X6cFjdIbulmzDqvuugN25G7r1f6dSJFsqutg7oeS75tCcGCLMpvdV1gyzg2rXmw+HafqzrBwqfS0dqZS4qhuQivm7qQp2qw2Lxl0Gp9uHF9fuwrYKJyYNinPJTigCEWMq1jhy19H3bbQDhUuI48ihZCDU5UvWjsCYtXDYjGj28XDHMjg2jCLbob5Wel/AlqZ5fT8zoM1FwVo9UY6lV0QcpxzaI3ekB1Cp8f5+EA+iNcwB0L+V7QMA9Az+jAPwumqbm3N+YfDn1+08yHKweVwF/fZ7SWHhljk0sQvVpVUPkTTgzW+Eqx3kzgAWj6F9/C00YRQtBxI6A9lTYNj6NnikGkfuDKVON/jevnqORH8d/jW2B569Pg0zN+zGQ2Vfw6iX8MKaXaisc2O/040/lX6Ng6bu2Ju7DBWjPkfTdVMglYZHvrDiXlrEHJn4ttpDEqqWjm1SaoiVaWCMtarWpJW1iOMUQPV39Dv5NM3NnHNUuziSTUBdxvXwWlKR+eVUcjqOAptRj3p33IGI42cicu79OdFQERhRQzgJm16hcqbIuTcpkxyMRSPA6ytQ19yCw7Id3rxo1SRp9aNIrP8euh2lwKXjQwpP0rzB+POFAfTOSAQAVLmCghp/fEpZMxbkAf0eBW6cHK7pv2sVNYQbXgYMnRUe7Rbzd+7raMwrxU8tFjy/+juML96KbRXOOJfsl0DkmNr0SrTCYu4M4Ps19H2/dz8wvS+JppQVhX+Xm2cB599CY+GtbEirJ8Ku8wN3fkQZigHTgICHFJUKSoCdy5VrpfclG2T901HKTc7cuXAZktv+mY6hAiGOXwfaIwPxFoAvGGPvAKgIvpcB4FYA/2ztQM75x4yx01vZJRfAvCCX4jPGmIMx1oVzfvD4b/skQChs+L3wMwPqkAiDXoLDXQlW9wMtKD4XeFovsKYqwNNAE0P3q4HL76PyJc6p3jHnJcBxGiK7h+Kz16nj9IL8MLJVwNYZewYuxZkOCfr6nyhFKlKejkzwEYvRrcUJ81xKxZ/nyMS7+QtQZT0DHAwvF1wIv8whMSDVbsLtc7fiyYG9MHPDXszP76Y9MdhSUTNoLuZ/2YgR5wPcbCJt6Ts/oq6qqhpINTkaoGuosw2VdW7oGGKqNcW7WZ/CqNkDMB1g76y5uckHtASAZDPAJQOqzhiG9P/ORNLB/6C+69Wtntpi1KG8Nl7CFMfPQKzmb7EauonabYOFykUDwTpuczKRmpsOhxTucO3jxIFoqgLWP0XGWWJXABxw11OkOLErkD0FzN+CyuoKDHr7e2T3SsWM0e9D17CfzhXM4rLDO6L5b85yGMoK8feBS9HsSUV6og68//Ngc3PCDUjRtThyjt61Crh8Av2tVvZz15HB6q6FLJsw/eM9eCb3DzhY34IUmxFpCSYkH4Pcchxtg3oNtBqT4Lh1IdhHU0IZJySfRmVJgaDBrTcBV/yJupRnTyFnosMZNNbEd1i5hY4XJXYqgnWYCMuWOQoZ+5Y5pBhWtJyI+oI/2XyYrmNLRbOlC+rQAadZTG1v2NrGCoQ4fj04bgeCc/53xtgykLF/OYgkUglgBOd853GevhsUpwTB83YDcBCAmTG2BYAfwPOc82WxTsIYGwfKYCAzMzPWbicWqsWKOcthcGRCN2gumiydkew5GN5Rcuhs+Ie/C31LHTkPfccqC4d44HcuBy64Lbp7qFbH6dIi6HNeQqrUEXvqk3H2528AVz1ETkjQaYHeBMviYeHKSqXDkTByLXY4jXjk3e0hw/ylvAuweGslsjol4MX8C+CDU3NiaDJ3xrTNtXjkIiiqTUHCNtJ6QdIpmYNI43/GiItQ8ulPKN1Kya30ZAskSYqp1lTd6NHMWiyZcAXSEswn9Ks9ETglxmx7oWYPpah12kZHtYuivsnBarOGzpfDt3shOu8qOaoDYTPq4ipMpxB+VeO2rSUVakdDq9wnv5ii+cIAKygBOmZRSWnRCgCcxC3WT6LzqUqJ6PgSpNlpjlq7sxpV/Rzo8nZ22K0S/0272+9ZDgapJLg+jFmruQ/nsmZ5FNx11C078jPlzgACXiQtvxMPDlwKP2N4buXO32Rw5lQYs1pr4KI7L0bXax4DWzRCc9zx/GIw9bjLmwese0p5LcrQ1A3hNLtLjyTHYNcquo7PpVxHPZ4qt5AjAsD8px04LckOAG0P3FlTyUGPdNjjvSJ+tWgX+jvn/FvO+fOc8/s45/cG/97JGLvyOE+tNTuJupRMznlfAMMBvMIY69HK/c3mnPflnPdNTT1JgzVysQqSltKt/uj08dJxgCkRgYSuwNUTo1OSpSODkYc6SlkXlCiEKcdpMetxU94bBYueQ+7/PEUY/B5a1ObngTUe1DxOJ3tDzgNAhvlDZV/jrmt6oPCfm3HdSxvx6OoD8OcviEpvjiyrQF4vuq76/qVFw9FSfzhUYqRVsjRh/pe465oe6J3hCE1KyRZDzEhHrL4PLk/gV1nKdEqM2fZCzV4gsUvMzdUu+n6EA8ElA5zdrkXy/n/B2Hyg1VNbTXq4vAH4A3Ei3qmAX9W4bWtJhXruvvKB6Pm6dCQFbsTrRYVEoG46FCyLClBWonILcO0TigEnOlb73eho1eHWPl2weGQPpNg1RCocmeSAa7wv1am6WsfoPi1LRpLd1CpjZVL0Z1o+ge7bWY7TkvS4q2Trb7Yp56kwZrXWwNrqQ+Q8xBh3LHLclRWFv15xb7B/VDfiNYxeBaSdQ06CGoJgDURfR4wnQcAfvQoYUQadkVS4jqlhqyQpvSIe+IZ+a2X74vjV4LgzEIwxHYB8UGZgDef8G8bYQACPA7AAOB59xUpQOZRAOkgiFpxz8fsHxtiG4HX2Rp7glIF6sVKRltiQ16OJb1yGzlVNk0fhEu1FTqcHyj8Hrn5EcTBE9Es0lhMQkSZnOTJsHJJIcaujFGKi0CBJRRrmqXYD0qR6LMzvhioXR9nOFnzt6Yo/jFoHPfeB64xoDiSgumkz0qxM8/5ZwIuGFi98AcDt9Wsa/7XNXkwf3htGvQ7JFgN2VzfFjHTE6vuw70gzbCZ9nEx9ssA5ZSDOvC7mLtVuciA6qBJFzi5XIXXfMnSoWI9DZxfFPNZmpCxWY4sfycfReDCO3yHaWlKhnruDSjZhUBtgABloTYfDZV3zi6mhXFK6Mt+rMhE6Ryam5BVD+mY+YP4jzft1+4gL11QFOb8E0vZFQH5JeA+gghLg89nKtTUazQUKFoAzBqx+RClTCvgAcPCb36R+QZprDDksTG/SnJ/jTTnbD1oBsBApHmjbuNN6nXYO0HAgrMIh1C9ClD2rBVUSOofbI0npwPBSoPlIeIbq1oWApUPMwJ0sy6hu9EQH++K9In5TaA/X758AxgJIAfAqY+xtAC8CeIFzfrzi7CsAFDHCZQDqOecHGWPJjDETADDGOgK4EsDxlkudWKhJUeo0osgiqIlv/hYl8iAHtKNRAHDpndHZiUWFQPbkKCIeNr0SHa1Sk53LP4ecXxJ2nG/4YkgSw6d398TikT3QOyMRvTMS8fZNNtiL+yNj7iXo80EenrtCwux/78XZU7/ENW/swbeNZgQAvJx/IRwJds3731PrQ0VdC/66dDs8flmTHC2iGKkJJtS5fa1GOlJsRswq7BMmPTt12Pl49cPd8YXuZKLpMKXEE7vF3CWyhAkAvLYuaLF1RXLFB62e3mqkGEhjXMo1jmOFKKk4iqhD2Nwt1HDUiFS06zcxWta1dCRw6ThyqB2ZmqUkUtlIoE+RQohd9RCQ87/A7Wvgt3cBthVT3XvhEiotyZ4CbJgKnH+rIrsabDQnj1oF+f6v4SpaiwkfuPF1ZQPxMTa9QveqMwBMgqwzg0k67c/kc8GfvwD1UpLm/EwiiXG0B0QATA2nVzq2caf12usGFo+Nzi71m6jsIyoXCkrIAVHbI7OvAVw10Rmqd24DXNWa931jrzQcafZi6IxNuHLqRxg6YxOJqPwKKwHiaB3t4UD0BXAD5/wvILnVPADXtMZJEGCMLQTwKYAsxlglY+wOxthdjLG7gru8D+AHAHsAvAEgyPjCOQC2MMa+BvARiANxajsQ6sXKkkxRqoKSoGZzhEKGWnP5039QbaN6kcsvJm19WdaOSgDAoFeBe7cQzyEox8eFwkLk/p3PAz/vFkgbp9KiNGYt+O2rEfC4YZuXjS5vX4w+H+Thzf42TMvJhGN5eEmSoWwExvUhJRBRfuT1c9S6vCha9ANqBoWnzmsGzcVT6w/h7pKtGNYnAy2+AF4bflGU8b94awUYY9hf54LXH0CqPTyLoI6CSRJDF4cZz+Wei0XjLsOTA3vhxbW7UN3kiSuGnEzU7KHfCV1j7lLt4tAzwB5BkWhK7YPEw5uh8zZoHwjAaqLvNs6DiOOY0daSCvXcraWGkzePSpTE6+Tu2vNywKuo2ajr0tX7uGqiA0IBDwzci0DRSio3KrmZmr1tegW45E6K6A57Cxj1Hhl/l90NafHtkOYNQsBdjwev6EAE61ErSXFHBKqW3Q1d7R6wjVOjPhMvKMFBey/cs94NGSyqL9DUYedDxxBXvWsPyDI6wol/je2B924/C70zEpGebIHO3lEpDW7LuMuPfF1MwRutcZbSE7jvSypJ2v4uMOsqGhc6I3DDM+H2iDgm4hzc60JHOFE85uKwsfFETi+ML/7tlrzFoaA9VJi8nJPeIue8hTH2Pef8UFsO5JzfdpTtHMA9Gu9/AuC8n3OzJw2qxYrLMpiaEHXnR+EPqFrzeVsJvTdicShqBJ+LVJZGr9JOwUt6Rb3pygeAG/8GNFeDbZgKXHY3yWqq05d+L1hpoVIbKfvBvC6Y/zM1bDFLeW8UHKNWaU4maVYlGlVZ54ZBR5Kr2yoaMHYNMK9wDZyNTahycUxeU41tFWQUOiwGHKhvwZc/1mDemEtQ2+xFTbMXcz/Zh/uvOwtPr/gG63ZWIT3Zgmm3nI8X1pAOOYAoOUGHxYjOSWZNlaY4ThKEA5HYigPh5kg2A5Gcu8aOvdHxx/eQdOgT1GZqqz2LDERDXMo1jp+DtpZUJHSm+bbhADkCg15VOAmb36Da88snUGkQi9Grp3YvlZY2HwYGv6a9j2i6KeAsB5qqwDwNkOydwc2JYKLE5PpnqWGdujTluieBT6fT/J7eFwneIzh7ZQQ52p5Gr0U0OnsKlbTkvAQ5pSfq/QbM+qIeV2WZUdXoA7iM0yxubBx3JhgPoFk24rXNh5GRfDqGv/npb5JY/YshSNBn79wGo5PUD0vz5+OItQcON3rxYyARZwqxEy7TuDNYqL/Hp/8A+o4Gsv8G1OwGvnhTGYcAOamyX3uc1ewmARZRodBSC5zVnwKTOr0yRoCYPSjY4W+AtY/j9FsX4r17r4DLK8Oo18Usa2rXSoB4R+tTAu3xHz+bMbY9+LND9XoHY2x7O5z/twNJAiwpYLKfFh/RsbmlvnXN530fUynIkrHA3IGAq5YecCZFaTNj8HRawEYuJXT6/vgAACAASURBVJUmEa1aVEiL1/IJpAleUEJp8MIlRKi2p4WXUc0fRprjIjUOAM5y+P1+zVRqlUuJPmX3SoVDrkNPkxPfPHwhstISsOWIAbeV7sew4r0h5yE92QKn24eZG/bij+d0xvOrv0VNsxcpNiP+mtMLW/YdwbA+GaGMwtub9uH+63qGjo10DuLdp09B1P1I8sO22OTEKpccVr4k4E7qAVkyIqFqS8xjBQcinoGI44RAKDCtuJ/EAKwp1Gvno8nAsrsBMOC8YTRvrp9EwZt1T0XPy/klxGcAyLhfcU/0PgUlSgRZQDgVjtPAfC6SQ778fnJAJJ2yhjjLScKb6YAr7qdsxJBZ4euMcBiufEA5v6ibr9wCzM/D/novev/v15j57x8x95N9ePW2C5HmO4B074/Qzc2B9OqFSCjpj0d6y1jw2Y/xKPPxQkMJzFA6Ag7egBkf7UG6nZOhPycHmDsIKB5C67nfDfTKJV6NpKexta2E1vn1k6j0+e3+1B9Kq3+EGIvOcuoT0fcOGsOvXQzMyyXFJ7H2a2U/RFm0sxzsnduQzOvRLcmMVOZEZxwJZVIEQsE+rb4rx9qLJd7R+pRBe2QgYnaajiMCYuALfoN4EE0J9ICKjERTFWC0AaPfpwhCze5w0tOyu6g0SfbTw6/W7948i6IQax8nJyIyW2BPAzdYwdSdKvPnkVOxZFw0PyJ7Ski6DY5MVDZxZObPh6FU+Qz+/AWYvZ6cguxeqZh+nRmG+QMAZzmMjkxMzl8AV3JiVA+HmYV98OqH32NbhRMSY7jtktNgNepQ0+xFkkWPnp2TMHGxIh87ddj5OKOjFR8/ei3MBgkdbaYo5yDeffoUQ91PgK0TGTsxUO3icGh8ZVwywJ10BhJbcSCUDEScAxHHCYCrGvhoCgVT1NKrBSUU+Gk8RBHbUatIM1BDMx8JnQGdSem7A4S4ChixmAJBehMZgtdMBA7vCF8fNs+iSPP8PLCsHPB+j4KpZb0HTwd2lALn5VPgR0h+lgyNFsuo3BJOtBV18+l9gX4TkWY3YPHIHijb2YJBvdPhrD6ADGmfQsIFiPRdOhw33FCGmf9WfaQ4sfrYEUMJzMB9GHVFdwR4c3j0P70vrdVMiiZGb3+Hsghp5wDFQ5UsUzC7BMdpNA8vHa/YEoC29PvyCXTM/Dwat7ZUYMjr4IndKPMgxlJwfy7LYEGpY10wk1KWV4InPrHhPz84Kdhn1Uf3XSlcSg55W3qxCMQ7Wp8yaI8+ED8BAGOsO4A/gGRWv+Wc/3C85/7NoblacR4AxUgfuTRcIcNdB2z5J/A/f6a0pd8Tfh5nOUUePv9n9MImFgpnOVD7Q3Tqsd9EsCiCX5F2oyFnuRI5dmTClzcfTq8Ddltn1A1cCpsugCoXR5KuCx4boMOY//HgLLsbhgUDws6vLx0O8+h1yOrUOdTDgTEGo47h6cHnYtIgjgDnyHvh09ClP3jw6pDzANDiNHHxdrw9+mLcPucLvFHUFx1tcUfhlEfdT9GygRGodnFkxEhQuBxZ6PjjSki+ZsgGW9R2W5wDEceJhN9LBlakdv6iQuDm2eQ8lAaFLMasjW7Itu6vVEKqN4U3gnNk0ty9/G4y0ISxdvn94U3pNs+istPt75DTknYOmDAOxb2suDf83NlTtDtLZ0+hwJLPRe+rDc9gSa3JWY4+jkyclzcf9334A564KglgVs21oYs93MCLd6j+GYhUAutdCFzxJ+gQwIVJzfAzg6KqJRxDT2N0sG/5BDLGS4ZSIzn19xXMLmH0Kvru1Y4sEJuPk9KTjHJ3HdBSB58xEXtrAzhbBB8FHJngcgAswqjXlxXi70Ur0JjTC3azCZKrKtrwr/shyjk9qjMQ72h9yqA9ZFwTAbwJIlN/BYrDXMAY2wrgDs55bAbk7wmyDO5zUf2qGs5ychKaqpRIv2gfXzwkPEvgaQS2L6LUJTiQeQlFnrKnAKnnAI0HKHV5/TP00O9cTseJBS4rhyYFLUJUjEZDTebOaBm7FR3sZkhMh/N89QgEPLhr7UFsq2hAerIFTw50wKiT8OTyb7B4eIbm+XWcDLwUmzHUeCbVbsLjN52DLklmcA68M+4yTF39HbZVONHk0ZZ21UkML+VdgHq3D0eaPPAF5Na7X8ZxcuH8EejWN+bmgMxR08I1S5gAwOU4G4wvh/3IV2joEt1WxmzQgQFoiKswxXEioDfGNrASulJJqdgW8MZsyIYld1KWYvQqIkrXV4ZHcQ1WmvfPugEAJ+MpsRs9O9uKqezI5wYkA3DLW5T5EJ2GneXhTeZiSX7aUoGhs6lkdcxacHsnBEyJ0PWbCCYyJ8F9jWUjMO6GMlS5ODKMLs0aeEeCPSSdHeeb/TzIlo7gBQugWzScOIsXjwXmDwNzlsMWJLNj93oKMkoGGm+RDgJArz0N9DsGZwGOTKqCuPkNGo/2NFJjsneiPhEbp4ZzI5kO+PfL4NdPAjdYoZf9yDQb0Ty0GLalI0NjvH5YKRJlv+Y9MXcdEiUd4JEBFsGtAMIFY9SfpTVnIN7R+pRBe5QwvQqSUL1VkKkZ6bs9CWA6gKJ2uMavHry5WpHLixz4xgSaIFoa6IHSmwDRah5QsgRDXqcJRjgEjkxgyEyABwBwWtA+eErpRJk7g2QDs6dQ+pJzKofSIkQxFl5G5cgEL5gPg8EEs8kAqfYHYPkE6ILb5gyZh0c/NqHoijMw95N9uOfaM/H26L5INDZrnr+F61HrdMFm0oech0mDe8HtDeDWNz4LLUIv51+AKe9/B6fLp9nX4acaF26f80UYqbq6yYM3ivqiZ6oddW6fZqO5OE4CPE1kLCXETivXtnDIHLEdiCTqD2mv2a7pQEiMwWrUxUnUcZwYWFMBr7YBjYAnPOOQ0EUJ+gBKZHjQq/R3fTnxJkQDN7WxJrLHzUeoBl3M78PLgJ43AB9MooyFuvw1by7x3ZqrqeRV3KPagBR6/rZUur+PXwgJczBHJnSjVsIfCMAQI8Nwz8rDmDM4A0mRa8OtC2BO6oSlE1Li8+3PhCxz7KpqxisfuPHgwKXI6miMcuTYokLwohVgNbsBx+k03qwdtcejKYG+7+/XELfRVUNj46uFlMV693YKVN6+GhizDmiuCu9VIvpDNFUFidVO8Jz/BZoPQ5p/C+Ash9WRCXl4GX4auhydbDrU+Y3obGwBC3i1nRBTglLWJwKhADnQm16hjIjWZ2GMnB2tMqZ4R+tTBjGKzI4JV3LOnxbOA0DqSZzzZwFc3g7n/01AlmWKIKnJSFk59DA3V1HN4hvXUho6hjcPW6riPIj3lt1FNYTT+1L6UhCfxeLVUk9pa4OFGhBtnBpN3subC6x5nCaP7CkUJct5CazFCdPcG6F3HSGCnuq6ScuK8OrgDHTvaMWj/c9BWqIJHj/HA+9VwJs3P0q29c8rK+Hxc7i9pNBw1zU9UNfsi+py/WDp15g+vDcuyEjCG0V9w+Thpt1yPlbvOIhZI/vgpbwL0OKT8Wj/rBCB70C9O649fSrB+RP9tneOuYtoIpds1t4uG+zwWtJgq42t0mwz6VEfdyDi+Dk4GoFTkoCkDKAgfE5DQQkFfq57WhGeaK7WnreFWlOwmWeUDn/uDCJo11dElx7VB4NHWmVUZaPIEFv1EO03vDRc8lOt5/9WNkWvz8tXyLHOcrCAD4a6PZrCGHabDdVNPoxecQR7WGaot4Rn9DocsfbAEZcPKTYjuiVbkZoQzUeLo3WILs5VjT7UwIGAP0YUn3Og03mA0U7j7cNntcVTPphEnc77jKEmhLKfgpHXTQLKPyXD3p5GDsLhHdG9SpZPAIYGOZWbZwHOn8Bkb1TJs7QgDxmJeuz3J6EzrwKbN5jI16seUsjXwq74YFJ0ILS+ksbkdU8DaeeR3GzkZ3n/0djE6Ej55TvWE8+oYX/bSNhxtBvaIwMRnzVaQ1BuTOI+YP4tSsQqKZ0yAlU7o2sA6/bFkGfVaS9QBqvyt5r47CynxS97CjkvznL6+dezStQsKYNSo82HaYIRZVQAORKi3jd7Snh3ayd1k+YSYNAx+AOA2xtAVaMPu3lPtNxQhjQro07VX7owrk8iTtfXwA8jPpl4DXSShMYYZUoA0MFmgsNixJIJV6DFF4DEGGqaPBjWJx0PlX0dyli8PuIi9M5wYFuFE1WNnihVkKUTroyTqk8W6oQDETsDUR1U74qVgQAAd8LpsNV8E3O73axHnSte/xrHMUKIWhyNwKnTA53OJUPF20RyrJ/PJmPN76bMcMAXOzLsc5ExteWf9J6zHEg+A3hgB8lmNuwHLA7A2xw9v4sSD9EhWA1nuVKutHgsULRcmdd1JmDA80r0V+yvXh8cmVSDLoJKKi6dP38Bvq6RsGjcZfAGZOgkhq1OD8wGCRajHjpJxuEGN5pa/Dg9xRZ3Hn4GhNzpkwN7YeLi7Vhz+xmwa40fnR74dAZw2XjFwVST9M0OUvWq3AL0n0LOcCTB+sLhwHnB4GTJzbHLoCQDkbCzJ1Nptc6ouZ8UaMEZZoDN0XBCRi4NfkAX3adoUOeuI+dWjNnlEyiAuvEFyrS1OMkJF6V9h3fE5kII+eW2PsNxnBC0x394E2PsKRbRlpIx9iSAz9rh/L9eqOTGmJBevfIBeoD0FoogadUA7lwe7ZXnzaMSJ41IEawdgXEbgAmbqT427RwlCiD7yVkxJyrHCkdh2d1EtH47O1y2TZxXdLUU2Y+I63q4HgWzP0O/aRtw2xufwS/LmDS4Fxo8Afxp5QFcNWsPJm+oxiMXAX0+yIP+1fNhnnsjrM5daPT4UNfs0+xwatArw7KmyYvhb2zG/0z9CLXNvpDzAJCTcPf8L3HXNT3COlcLxFVBTjJEBiKhlQyERhfqSLQknA5L008xG8rZTXrUxuUj4zhWxFJzcVVH7ytJFMBpPEg8sivup8zxqofISH/vfqpBHzIzOlORQtLTuPIB4Nq/UqmHpKN+PMvvpmPFUhw5v4sSD2OC9tyvnqM5yEBbPwkwJxH3Qki4CginQ5STiJITEVQavQqBUatwz3o3HHYr5n2yD4fqW7C/zo03/r0XDS1+jH77c1z74kY8XPY1Dje0wOmOP3s/B6KLs8NiQGWdG/O2t4BHrvv5JWSIn3czRe7FWBVr+FvZgOsIvXZkkjqTcDLS+9J3ak4iZ0D203Z7mnZn66wcKnsqHgq82pt+Nx4iYr8aweswOaDthDQfAcCofEmdoVv7ODUyTOxGz4U9jRzvXavoMwi5eZW601GJ0c3H8AzH0e5ojwzEfQD+CWAPY+wr0DTWG8A2AGPb4fy/XqgXKHNSOMFuzNrYhKdeueSVq9U8Pp5GDWLy5gFlKlJ09mRSaeI8XOEjby5g60gPYMBPC19EHStyZwDgSjRAKIGo1ZwAuk5iV7rnYE2l7+rH8PD7+8OM+Ufe3Y7ncs9FB6se79+RhebmZiRYjLAvGBT2gDuWj0LFgCXwSA5MHXZ+mFTry/kXQB+MZokUr7iG1ajTzFik2IyYVdgH//fh92HbIp2ROH5hOMsBvRkwJcbcJVTC1JoDkXg6AMBW9y0aOl0atT3BpEd5reu4bjWO3yGORc1FlqmJ3LK7aV7Wm6Izx2WjgNzXlMiwrSPNvYLPlpUD9HsUKFUIqCFxjI1TgZumRs/RjtOJ+BzwArfMAdw1FHTyuQBLCrDmUbq+IxOo3QP0f4H2nT9MOYdawtWRqWSlPY2KIo8wSB2Z2D1wKdburMZ/Dzbh7dEXQy8xjHzr81CkPHLOXzTuMiBaIC2Oo0CsW1WNHqQnW9CjiwMlPzRhxOj3wWQ/GJPI8K/ZTWMtewp9f+pApM9F42FEGdDhDBJRcZaTmlPfO4CPXwQuvyecOzN0NvBVSVTWCdmTFf4NECw5GkkKjbV7qYzOlkoBy4APMOg1M27c3gms+Qhx3yJL8spGKWpguTNofRCO8LESo2WZMoJaz7DPHZtDEUe7oT1kXBsA5DHGegDoBSppmsg533u85/7VQ71AMRb+MDVXK/WqkQ9ycnfyytUlQwB1nJR09EBLOooWzMtVHshYD2pBCcD1wLqHw52SD58mxSZxTEpP4P4vATBaLAFA6I6riFD+vBJUmU/HuD4H8MRVSdRdegN1l06zG3AWq4ChZAQS1Y6SGs5yOIwyXFzC86u/w5MDe8FhMaCrw4LKOhc8vgD8fjmso2XvDAc62Ix4967LUdPsxcwNe7Gtwon0ZAuSLAbYzTo8NuAc7DzYGHJGpt1yPppa/Oho4/EU+8mAszzY8DD2/76qmcOqB8ytzETuhO4AAFvNN5oOhN1sgDPOgYjjWNFWNRdZJoU7n0spP9UqN3KWU4RXlIE+sEPR4wfIABPOg9i/tIgCN5eOJ9EBo43mdx4g49GYCHzyf8A1wfldXZoydLZyz0NmAuufIm5FpGOjlnAtKKHM4KJCMjIjyLa+fo/hL0sPAlBU7wKcI9VuQo9Um2YAJyBzyLL2HCvLHDXN3jjROojI/0dnhwkubwBvj+4Lm1GHtOQqSHNU/TtyZyglP5teISfS5wp3MvOLgS1zyF4YUUaO6pUPUKnSoFeVbuUA/V46jkqY1k+isZfcnbIbbqf2mOYydTivr6AsRt0P5IT6PTSe1ETsghIwnYF4mQ0HWi+7Wz6BuEXDyygDUbiEyrc3TiXHVhCjY3WddlWTY6P1DB/5np6neCnTCUV7yLgWcs5LOOd7GWOdOeebVNvu5ZxPP95r/GohFih7WjQxWu04/OvZ8AcZTPuh4AHg4DcUBUjsqjy4sWT7xPuLCmlR6jdR6UB55QOkT252UKrT1omcCtFQRqRPrclRyhD6skJ0GbUS3T6gjEeGIxNvDpqLN79LRpatCfq5ql4XwlGK+CxOr4QODlqoxxdvDcrB9sJzK3dixoiL0OwNINFsQHqyBal2Ex7OzsLtc74Iayo395N9GHVFd0xb+x1uu+Q0pCWa8HL+hZA5h9PtCyk0tcaDiC9wJxD1la12oAaAard2F2o1AqYk+I2JsDq/19yeYNajscUPX0CGQRdfLOJoI9qi5qJVY503l7ZpzdGipMiRSQaWenusedpgpcxG0XKSY208qKjnXPMYRZJdNWT4RRqCRSuC0WALkDeHMh5a10g9hww1ow3Y/i7N+eflk5EZ/Fxyfgl2BdKxraICAGVwAzJHk8eHR/tnoaLWjexeqRjXJzHEb5u9tQEH6lvQ7A0gq1NC2Nwpyzwk2a2Weo3c7/cC9f8j1W7C/df1RPdUG9KTzah3e5EsO6EXDVqFepbOACRlAvd8QYEYvSma11I6UuEobpxKHchdR2hbYlft8ZDYjZwTSUeG/keT6XqaHAwDjWu185o/j8QxbKk0bpuqaMxumEpZts9eJ4e5tWfEWU5lTp4GGv9qhyihM2AL9g+KxXHwezX5O6GMW1NVvLncCUZ7rLZ/Vv39j4htY9rh/L9ayJaOkAsWkOEumroJiE6kw8uoLjDlTFIsKB5CEYZIlYUhM2lxEIoaau9eq54x8kGtDxKrbphMaW5xng+fpQknezJNOqLpl7OcVJt8LTFIVN6wfVPeG4VHLrdB37hf21FSfRZn7lzI1hRMW/sdHs7Owo290vDa8Iswc8NeVNa5MWH+l6iopUjXrJF9cP91PTWbyj2SfXbIiXj1w90YX7wVtS4vCmZ/hvHFW7GtwtkqD0JM6HHlphMELe5MBGJ1oY5Eiz0DVucuzW0JJoqDOF3xLMTvCkdTUDoaItVcxq6Pjlhq8STKRinRYfUcLeRZhREj3hdobZ52lgNg4dKbl44HNjxPcrEmu7YhCE7lSu+OBpyVikx35DWqvyWSavW3QJ8iWpMiVJ2k0kJY/LRmpCdb8HphH7y7pRzNngAeeXc71u44gOnXmdHngzxkzL0EfT7Iw/TrzFi2tQJ3ztsSxUGLLEEVwhaR+/1eIP4fqXYTHhtwNp5c/g0efOdLJPjr0NNwBBaJ05qa3ldRz9pWTNmv+cMokxBjPQ51F6/cArTUKYE7IR2vhiOTejzMvwX4Rx9g+T10vfLPKWgYycEAojtVlxYRybm+Alj7hMJf2LWKHJpL7qSy7fx50QpLm15RXtfsJm5nmN0xkpxikWWIxXHQG8lJ+NezRNwevYocKVGu1xYORRzHhfZWYYoMKxw1zMAYewvAQABVnPNzNbYzAP8H4CYALgCjOedfBreNAvBEcNfJnPO5x377JwayzPFTrQsdZT0SHKcTWU6rhlWoJ4z/N3nsl08g5YPNs8LLjUx2kurLn0cEbElSdJe1yqDUHAb1IrV0HGU7RJTj0vEk/6pVL+ssp+toRRHq9gGDXyOnI6iuIInuqer9g46Sf9T74JzDw3WodFlg8tTiyasS4fTW4cmcc+B0+7GtwgkASLWbcHqKFX5ZhsNigM2kj9lUblifDLy4dlfo2MhGRq11R421wMWVm9oBXhfgrlWiSDFQ7eLoaj366Tz2DDj2b6B0OguPe9jNwoHwxr+33wvaS31FkijjIEokXNVKiQSglKGKiLCYj5mOyjhGrVTGJJOoSZcsA5tepp496nn5q4XhHDb1fCuMKTUHbfMspe6c8xhZ6SCHLXsKze32NMqQqDPJ4lzZk8kg/Z8HgY5Z0U29nOXITNTjXw/1g0kvgXOOwsu7wx+QUVnnRl4vMwxleWHGnKFsBPJuKMM7W6MDNeoSVIHfs7CF+H88f/N5eKjsa6TaDXizvw32kv7h2S1bR2D1Y/Sdpv2BuC3dr6aMkfPH1qP6AP391cJgX5Fqbe6jOtAnytyKlpNtMvp9ajYo6WlMC/K2+hkI+ICUHhTIvO4pUlsClO1JGcCav9D7Q16nHlVcpj5VgoujzhQIZTBxPzw4RlrjKSV2UzKIVd+Gl3GL/0u8udwJRXs4EDzG31qvtTAH1HBuXoztAwD0DP5cCuB1AJcyxjoAmATqgM0BbGWMreCc18U4zy+KmmYvmmoPofvq/CCprhPVBUamAW9+kx4svYWiTbtWRZPtsnKAayZSneOl44EVReGTwYdP0wJRtJweeC5TVCDyQQWUlDlAD3uktnikzF/DgfBu1urziTrKpiql46qGM+O9+jH87ycNKLj0NPx50Ta82d+GlJWjQuVPvvz52O/pAoC4Do/2z8LfV3+LUVd0x8TF2/HkwF6aTeVkDjgsBtx1TQ/M3LAX1U0epCWY2twdNb7AnUDUV9LvNpQw/aHD0U/XYs+ALtACc2N5iFQtYA9mIOriGYjfD2JFJo+1ZKE1RwSgspHx/6YSVLVRXlBC5RrNh2nua6qKqEufRxljEQhK6EzlolvnUV16UjoFYYQBJeZx8VnEPJxyJklrajT6RO4MkoAFlPIoZznJxaq5DZtnUcbhi7eB82+lOX35Pco1VY2/th9y408rP8dzueeGGnYWj7kE6ckWpFmZpjF3bpoR2b1SowI1QmUoct6OFdD5rUP8PzonmVFZ58abwzKR8t7Q8DH88YvATS8C/R4JX3Pz5tK25sPRwcL8YhJdAei1JYWI05++Blx2FwVxcl5SApf2NBpHajjLyRk98j3ZKcndady5amkMZeUEbY97o22PpipyjPUmYGFBtJ3AZXKCbKlA/78DVz1I6k7qTuyWZHJQrptEjgHnQONhKs2LxVNSZxBlmfgUarJ4vLncCUd7OBBnM8a2g7INPYJ/I/j6jKMdzDn/mDF2eiu75AKYxznnAD5jjDkYY10AXAPgA855LQAwxj4A0B/Awp/7QdoTXn8ADqOMEPnp5je1u0sLctvQ2aTCkf038u4rNgOFSynCb02hWtXsKdEGv9BdrvqWnIb+fyfy0HVPAVc/TAo4m16mBzW9Ly0ktjRaAJMyYqdD1RPEdZOUCchdpyx6dT9S6vNfz1LN443PEik74CM1Ep0RTebOeGhNFYquOANmvYQ5+d2RNH9AeBSrdARSc5cBAO6/riceeXd7mOLHzA17o9SaZhb2wQtrvsW6nVUhwnSnRDO6JlmwdMKVbeI0GPSS5gIXV25qB9RTHTXssSfwFj9Hoxfo0IakgceeAQCwOndFORAJZgMAxKVcf084FgWl1uCupRKRIa8rOvXv3Abc+REZOe/cpi1SsaiQHAEAcP4UTVwuLQJGvUfzbWmhEgi64VlyBvwtQGovKh/VGYDFYxRjSpzDcRoFZRbkk9E3YFqEIdgJWP807R/wKYbWthKSiO03EUjNAm54hoy7rGw6zqwisd48m2Q3bamQ7Z3Qwd8B88akY+aGveid4cDE/mchTWrAhjt7wC9rc/NMzj147foukKzh5kSKzYg3ivpGcSBiBXR+6xD/D53EkN0rFT0dUP6X6X2pr0hSOikLRTaMLRtF4614SEQfp0zg8zcoU3X1w9RPZO0T5Gj0m0iRf4OFxozeRLwFV7WiviUguA6JXYF1T1EgUzgu5Z/TGBJ8GXFPyycowcayIrJXRFZLOMG5rxHPYfk94Y5HZCd2ABj0D3ouRDd3RyZw6wI6r7pKQu0YiH4QAP0euz6abB3HCUN7OBDntMM5WkM3ABWq15XB92K9HwXG2DgA4wAgMzNTa5f2hSwjjdVDl2QE7vtSiRLFMtbVpUUihV1QQum+XauUhm6xSHiNh2iByy+mtKF4+AdPp6hF39G0oKhlZB2ZlLHQ8u4TulAa0+eiiebDZ+hYNdFJnX7MfY0iBsXh6hH1LAmjyyqwraIB/z3YhHfGXQaXy4Ukjc9wWpIB/5l4LWSZo7LOHdLGBoBtFU68uHYXnhzYC2d3ToBBJ+HpFd9g3U6aBIWc4JIJV0Cvl9pcxqKXGKbdcn6oG7ZwRPSnAMHvFx+z7Q3hQLRSwtQWCVeBFns6OBiszl2ozcwO25ZkIQeiptnz8+41jnbDLzZu26qg1BoCfpqb1VlhMa/5WpSsRKx5NykjSELdr71dDgC2FGDEYjJkGg4Ay8bTnFm4BJgTDKSMKNM26EwJwNq/KgbZ6keCAaBUclo4ji0C6gAAIABJREFUB65/mmrRJR0FoQTRuqmKstqcU7M7FVkaBSXkIG35J6C3hpwjyZGJxEFz8fgmP+6+ticMDOjm2wdbMWVe9Fk54PnFYGoZ2uD/S69BWJUkhqxOCW0O6Jws/FJjVvw/jjR58OJN3aCr+68isvLHp5TgYAzlQiSlk6OhktxFzkvAp68Cnwb3y8qhMeFrBgw2cla5TOPDkkxrtD1NO4vx9TtU4jZ4utJYtmwUjVVPY2z7RfztOkJVDepSpMSu4UpkwvFQ2zpDZlJjRV+zksEQ+74znJrNjXqPygYNFsDSQdsxUDsTcfwiOG73jHP+U2s/7XCPWrMNb+V9rXuczTnvyznvm5p6glNawZS4Yc6NkJaMJZmxhC5UTxiLQCcaviR3VxqsLCqkqAKgkO9ikfASu9EDtvEFRfpVRAAuvI0iWUNnRWsyr32CIgxqklPeXFqEAl5g/TN0Xzf+jbaNXqUQlXaU0mQx5HU6v0Zm5KDHgG0V1Pyrss6NmiYvDjTJmp/h64Mu3Dqb+g6mJ1vgdIc3mdtW4cRzK3fCatSDcx5yHgQq69zw+eVj+qrc3gBeWEOOyaJxl+HJgb3wwppdcHtPfgnTLzpmTwTqK2nCt6bE3KUtTeQEuM4Mr7UTrHXRROpEC8VBqhvjDsTJxi82boWCknruOsaSBd50SFGyA5Q58/pniLgs3o817+pNZJiJZm+R2zkH3h4AvHYxGVGcK9dx1Sjn3zg1eh4ePJ2cB7EGAGTQbZxK1zRYgfqfKMOcPYXmYH8LULgY+NN2mqfBab/Iz7iokJ7La55QsiPBbSnvjcK4Pomoa/ZBbj4Cx3IVeXbXKrCNL0AetarNhFVJYkhNMKFbshWpCaZTznkAftm5VpIYOtoMsOkDlD3IL44mtAseoRqCd9hvovJaNAJUY9cqyiQARLz+x0XAnJvouxFOQETjQIxaCexYTA1oxTNw5QN0Dmc5lUVbO2jfk7UjnWNEGdkM6qahjkxyXjQcD57SE/xPX0MeuQLYtQaQvYpyVMS+qK8E/u8C+hyNh47tHx7HCUV7yLjeAaAD53xa8PV+AAkgA/9Rzvnrx3mJSgAZqtfpAA4E378m4v0Nx3mt44eozY2MKlx+P00WkdGbHaXh+4mJwdNINbOAwivYPCs6cpA7I3hhpjgParJTQhegpZ46UavT9JVbaP9+j4STtRmj6FXt3vBeFI5MmmiW3d3miIlNpxji6ckWHGpowcwN1Xhz0FykvKfUE9cMmovJa6pRWefG5FUk4zr9X7ujypZE+rum2dsutbVGvQ7VTR6ML956XOeJQwPOCjJSpNj/y2pXMANhbtspPbZ0WJ3fRb2vlyQkmvWoijsQvx+o659/RsmCLHOwgE/bYEnsCtTsUTIcWiIV+fMoKNTipKhnJD8hfx5lkCOdk+wpRHA1O8jwEvMxY+HzsDDMr344WG6aTt2owUnRb+NU4tVl/414Em4ndbRWry2bZwHXT4qRtT5I0VwNInWalcHPdXAwDeNv1yr4b/w7jCIbLRAnrLYNsgyp+lulNO6rhcCNk8P/l5teaZ13KMaNuhEgoJQoMym6BGrFvaT4KMa0OosxvIzK2xI6KxkO4Qg4MokjVP55NMcgfx6pOIqKh2FvAgnd6Jl015GDpNPOFLIWJ/VSSegKXDKWJI9jSL6juVr5HD+H5xTHCUN7lDDdBeIeCFRxzrsxxswA1oFIz8eDFQDuZYy9AyJR13PODzLG1gKYwhgL5tBwI4C/HOe1jh+iNlfwFexpysIATg9/wEcP+RdvAZffp3SQBpT62ZyXaP8xaygVLumA/s+TitPwMoo2Ge00act+Ovby+4GWWuCqhyg9L0lAcxXVvKrrGtXlR26ncu96E+BvAS8oAVsVVOdVOSNcZwTLnUEpca2IScSD7/TSYp6ebAl1it5W0YCxa4AnbihDF7sEi8WKsWX7QpmKdTur8MTAXrjtktOQaNbj7dEXo8njh9PlQ6dEimC1V21tvEb3BKK+4qgE6ipX20uYACJSJxz5EpK/BbI+3OtwWI3xDMTvDcdRslDT7IU1IMGmZbCAh+vLAzTPjlqpyEt+8TbQewQFZwJeoEN3YOSy4H0ZAIkRH+3qh6n0SETpHaeRQIaY87NyqIQDoPl3/SSlNjwrhxR5JD3N8aIOPCuHMsoA0HSIosCl+dEG46iVxPHQ+oy2juSI3DgZeKt/2LYqF4dLF4CTScjQONbLTHAPmYekZYqBKxcsgBQnrB4davJ/UjplmLifIvg7lwNn9SdbwZpCNoAtlUrRvI3kHIieIfY0ypSNXErf487lRJBfPoGcDC2n0eeKVujKi3ACBk8Hdq+n0tMxa+k+/rsU+MMQGoOCg5PYjTJk6oqHxWOjy7C3zafx/elr4Z2sJR2wcRrYrlUkUmCw0LNRtJwqI0IcjHnA6ofDP4c60xWryVwcvwjaw4GQOOc1qtdlAMA5b2GMWWIcEwJjbCEok9CRMVYJUlYyBM8xE8D7IAnXPSAZ19uD22oZY88B+CJ4qmcFofqkQtTmWpKjI/WOTPLirR1pkJ9/Cz3UsWoLFxUSyc1dRw8eA7BxGtUn9n+BokjqqFfBfPL4RTt6RybVxfpcpMawa5WyuOS8RBEsHqDIlT1NIVjrzbRN6FEH759l5RA5m0nREZOICJ2cXwIWSMG7d12O1AQTPvr2EEZd0R07DzZiW0UD/rTSh5mFffDqmu9DzgMQbF4U4Lh9zheIxKaJ1wK29qut/bXU6P4q4Swnmb9WUO2SwQAktdFf8yRkgHEZ5oa9cHX4Q9i2JIsh7kD8ltHOhoLXH8BLG2owJa8Y0scvKMaNPY2ioUJfXpBA1dHgobPJoBJOgCOTJLpNCVTHrd43fx6RQ9+7j85pTgLmDqTtIRltFT8h2LwTPjfN5Q0HKFux4fnwY9Y9RVr7SemKlKsaIstgTqZmc0Jiu/xz4OIxFP01WInHcedHxOP4aiFqLnkYszc14JH+3QBugjN3rlLG5MgEv3UhqgM2PP+xD+OCQSBHgh3mpE5xw60tUMsCc66Q80MlSdNonR6zhuwET1P4eCqYD9y+hr5LdTVDwXwav0JWePSqcGdUlBvpDLRN9gNMD6x5LNwJ2DyLFB/FGBVlzQEPsGSccu9DZirHCTjLFYVHUSqXPYWcg6sfjnBc5lL27LK7yAYJewbmAQOmgst+ME9j+DUcwawG0H5SznH8bLSHA5GkfsE5nwIAjDEJQOwCaGX/246ynQO4J8a2twC81eY7/SUganMbD2g268GiEWS8+z00eajTigLiYR/0Kk0KnkZg3V9pAcovpgyAuyZa+UOcW/2eaFuflK6c31kOdDgT8NTTQ939asoyuGqAqp1gXy2kScSUBNR8r6S5xYTRf0r4Paub4rmOAD4XJL8LZyZ4cCiQiAa3D0+v/A69Mxx4cmAvOCwGON0+6CWEnAo1iflQQ8tRS5REbe3xor3OE4cKcoCMl8zLWt2t2s2RZALa2jy6xRZUYqrbFeVAOCwG7K1u+lm3G8cpjhNgKBj1OvznByeWnnY6br5mIpjgCQgDpmgFqea11CsKMoAyp+a8FJ5ddtdQOcb8YdHZ5BGLaS3QmSgjLLZryWiXFgaDO2nEQVNHhpsP0zGbZ4VLao6IsYb43JQxEUEmIQ8u1AAjpDh5wXzUS6fh1ks8sJv0ePs/P6DwsrOgH7kGZhaAzmACs6XiNDBMHnoBvP4ADHodzPGgS9shAoxXPqDdnE10lJYDgK9eKUsT+ywaQU6CcB7U70eqFQ2dDXzwRNBumEdr88cqidghr0c7ARfeFs2ZKRtFmQ4x3jueRc5PVk748YKnKeAsJ/vFnBhNpC4bRefSm6Kfr2AFBhOZDLVcbO4Msodsqe0n5RzHz0Z7OBDrGGOTOedPRLz/LKiE6fcFUZub0JkWH63IkMFKP85ycgYi62tvmUPHRta0/utZmjiEKlOszEXke6IRkahvdGRSNsPbTJNC3zvCIwCDp5PG+YW3kZMjuBoivcplusd3RyvHXDpeaYoHAKNXgcleyJzjSBNxFrZVOEN8g/RkC54c2AszN+zFc7nnIqODBYfqW2Ax6jDjoz0x+Q+yzFHT7I1nDE5lNB6iCFcbmsi1tXwJALzWzpAlA2zOXTgSsc1hNaC6yQPOOaj3ZBy/GZwAQ0GUL2bq68DmF0YbMLevJiU6f4zOv7ZUjexyiSanAIxRecbyCeRICGM/lrqTwUrzvDAmRdZYOCsX3hbueGycSobVZ68rmRRbR+rwqzZSL7wt2vBUSXGyRSPQPGAJ7KYO8AZk5F9yGn6qdeP+dT+iuslDDTYlCZDb0t4pDk2IAKO3qfX1W1QdaO0Ti7ujJiELR3fUSnIkP3wWGPA8yQgLJ0OIA6jPFeuakj5axbGgBLjyPmCdykl5X1VuJARe5Bj3q1ZwitymzmQsn0DlgQEf2RhNVcAd66l0UOvYePfpXwzt4UA8AuBNxtgeAF8H37sAwBYAY9vh/L8+SBJF773N2pEh4aU7Mklh4KuF4QQ6v1uRTAXCF5BFhYpxpnVuc7LiKIj3JD2Roq98gByC3BmUAQFoQYuMhKgXK5HWjExBDpkJ3Dwb3N4ZrGZ3eFMYRybgc8HL9Vj19QEMOL9LlFzqy/kXYMr736G6yYNkmwGH6ltg0EnwBzj+mkNNnBaNuwx6iUGSpBAvYdfhxijOQlanhLgTcSqhrU3kXPIxORCQdPDYusHqjFZicliN8AU46t0+OKxxDstvCu3V80EFUb7InDWxjTSjFaj+VnueNdiiiaqLCpUacPW+okFXUxWQmE7GUHM1lSbFWh8ig0HCaWmujjbyKrcA29+hrHFkJkXt0MRyWFSG3JkdDKjkRkxeuTPUY2fqsPPx4tpd8PoDkGUeNQfPGtkHHW3G0Dwdn4tbgQgwNuzX/u65TIZ5QufYa7zsb51sLOAsJyPbdYQc0SvvJ06DOO77NeENB79aSONF69xMilZxFOP9phfJ0bZ0UEjdwsFYPTGoBHkUOyjWNnEtdy3ZLMLG8LupNCorh84vbKevFsbJ/L8gfl7+VwXOeXOwDOlGUFfpOQCyOee3cs5DNQWMsT9on+E3CFkGGiqpJ8Pg6eHyfKKJyqZXqA7wq4UUvV/7ODAnh34ndIk90TsygcP/pQZCBSXKubNygnrNDUDu6+REiOvpLcCBr4DO59M+CZ2VxkNp5ypybgUldJxYrMRDfOFt0U7Gsrvgt3bCXz6sQ70hNXziyJ2BRmsGfnRb0Ld7B7ywhpRziu+4BBsfuQYzRlwEgOF/Cy7EkruvgM8v47ElO1Aw+zM8sOgrHHC68cA7X6Fg9meodflCi1JNsze0cAEk3XrnvC2oiTcQO7UQaiJ3lAyE+9gyEADgsafDWhetxCQczAPOlmM7YRynPkTZhxo/V/VHlokP4KyA5KoC08U4ty7Is+h4NpWNqufwgvmATq89R3foEb5v/jxyRDqfR2VRAQ+w7kky2AJeei8rR9l/8HRaGyKNKEcmrQsdzqCsS+Q998qNLj0pLaIyFjGvx5KjVRlyksGEaWu+C+uxM3Hxdtx/XU8wpj0Hjy/eiq8q6zF0xibsOtwIOZ6haB2SBNneBTxiXPHhZUTAX/s48Ob1ZD9Ejr3B04FvVyiyv+l9qYRt5DKSYe1dqFzHkUnZL/G9N1eTFKw47rx8qjx4K5uuefXDwI53yWaIHMMx5FgpW1ZEz8rHL5BDcd+XwC1vE/dy1yqFIxn5OYQdFHk9YSOpP4cpKdzhYDrquN3v0XDbqd+j9H4cvwjaIwMBAOCc/wDgh1Z2KQZwUXtd75SGq5oWqV2riBAnUuFNh8mYbwrWwX6zFMieTESp4WX0wJkStFOLwah+qJSpcgsZ/tlTFCJdJBHJ3wJIRkor9nskusnc9jLgvGHhRC4h/2dNId4FEDOtGWBURzy6yoAXc5ehe5IOYBJ+qOdghmQk6XW4e8FmVNa5QwtSerIFxXdcAoeFoWuSBbVuLx4s/TpsQRKdqMcXb8Wd87ZQ6jzBBK8/EMaLEPt7/Se/b0McKoix0koGgnOOahfHFZ1j7qKJFnsGHAf/A73HCb/JEXq/Y5DHUlnnQq+uicd8y3GcwhBlH5EciGNV/dHiUoxYHN6ALRR0MVG0WG+iaKdQn3HXkXMh+vpERWoZBWkkHZFU9Uagdh+dX8h2qvkLglB6zWN0/k0vK+UgG6cp582bSwbavo+B4aVEti5VZRs69NA28Ny1Shnq7vWkalNWFM2BcGSCF8xHo5So2WMnM8UKHUPMOVg0/lTP13HERr3HjwSzA/qiFfSGpANrqlLGIaDwC0YsJu6MtSOpGvW8nrgMw8vIIY3MOgE0TsQ4/mqhYjcAtE9zdTT/pmwUlTw1HiD+pclO0sFMIvuitWyZTg9c+SBlR7bMAc67BVgzUVFxNCfS/epNxO/44CmlnNqcCIxeTb0guEz2kDogOWQmHScc69wZRAZ31USX5JWOjHMgfkEcdwbiGPD7yWv6vfSAZuUAfcYA4MAHkyjqb+1AD+m9W4E+o4B/vwQ4fwIM5qBkm5lqBofMjIh6ldA2dakQ99PiUF+pTchK6EoZiebD9Fo0JRJlSr1HRD+AK+4ldYSt85QHXCvi5cjEvjofnhzYC48N6IU9Lhv+63LgR38HVLRYkGA2gnNoLjY6SUJ6shV6vQSfX465IIm/hYNg1OvCmssB8b4NpyTqK6nBlSG2CFuDF/DJbZdwFfDYaRxGljGl2ulE+53uqGPi+JVD3fPhgW/o988hUGtxKeYPo/lWZGGzp5BB7QuOo4CXDJ75eRTlXFQI1Oym8tTIqGreXDJsGKNn4OOpVHbBGBlknc6N5i8Iw835E9Wm95tI64NkBAZMBe7dQs7L6kfIKLxlDuCqJd5D9hSS2ixaHuRZaGQXjAnKvH7JWHomRywG7t9GnzflTGDANCB7CtiG55Hc/APuuur0sNOkJ1tQ3eiBJEkx52Cn2wcgHtBpE2QZSb4j0DcdIkfT30JqW1p8m12rSNnLXUdSrpfeSU7p5RMos6WVdbr6UWUcB/zADc+QkX7lA6Su6G8BUnpqO5w8QLK+H00GvC5SGjuyiyRbY2URHJlBZ9lEP5eMJbWxAdPIjvlqITD7GjoX59SN/cLbaPwNepXucc4A4NXeRLaWA/T+mLU0Vk12Gt/XP6M48iv/TM+mVvVEnAPxi6HdMhBtwG8/rymkBnmA0s05LylkqaseCicdqzMJ+z5WOAeC+yC6Uyd2o2yAzhg+WQDkpAhvXGsyqK8gIra4VmRNraSLPYlkXgJkraKsh7suKkrnz1+Avyw9GJJgTU+24Lncc3GkyYvUBBM6WI2oC3aTjlRTshh0oTpZsSBF7iMWJLWDEO/b8CtBfQWROFtB1TF0oVajxa4oMTV0ujT0foJZD5NeinJG4/iN4Dh6PoQQi0sh5lYBRyYZ2vWVCt9MrTjz/RqazzfPojk6oTPxGT6YpNKvnwtcfGe0HGZit9jlqc7yYHno3TSvW5KBZXcB1z5BhljdPgoICXGNXauU5mFpvaLFOAZPp+yJuEZ9BTlB4jNmT1GyE8HmorrDO3Bf0Vr8UOMKcSCm3XI+0hJMoXk2cg4WHAkgHtA5KoJZMEmdBcudAYCTYR8Z5c/KiZZ7FVmjIbNi8He8SpM4WUMi1eyg6L0mvyKAkEqUGEuWZBprzYfJsE9Kp7Eoeknll1AGwtNEmTlXTThfUiiIAbRd9LNw15GjIewicf/vjqaxWTyEnINld5N0raQnh3zNo0Dq2Up2Tc3L/PS1OAfiF8Qv6UD8thHwU3pc3alRLT82ZCZpihvtgMVBzVIqt9AEccMz1NHUoiI4VW6hSf2PT9HDmPtatNKGPQ2ARBNGa+lF0fchsqZWTBZahKlQl8pSUoRK7ErRAE894K4DM5iRFkxTi0UmNcGEBJMeKTYT9HpJ0+CfNbIPZFlGdaMHKTaj5j7TbjkfL6zZFeUgxPs2/EpQ9+NRjb3qY2wiJ+A3JcNvsEdlIBgjOd79cQciDi3IshKlj5zvrCnK+6KsqWF/RGlIMe3ffJgadn3xNnD1I1QOlD0FeHdMuBHkOhItsy1KRGLN1eo5e/kEkuVsqiJDSpSCpJ6hHJvel/oBuWsoI6I3koGnM9B5Ns9Sss5axFThtKgFOpzlqG9swt+GnIt/DHZCJ3vBdX7o7JbQPKuegwMyx+RVO7GtwhkP6LQFWlmw5RPoe7OkRHc0v/E5+v4j98+eEptMLdb1/BJg69xwgZaN0yh7weXoa4lSocIl4b1FxNis3KKMxX4T6XjnT+Rsu52UYRBOaVRVwxRyFhr2K8+FFtFfHCP4nuJ5CASrOizJwHWTqH+JkCMWxyy7i56v421oGG9O12acUAeCMdaVc34g+PK3m1eSZYruCOcBCH/QFxXS4M6eAnz4DHDzG8R9GDCVFjWvi6ICuoi6WnUU4MNnqCHR1Q9T/aNYGOzBZnAFJeELnrrmUZD71j1Jr8X2bfPpAVY3qhEZjfH/pge+8SCw5M7w826cCl1TFaaNWINH+p8DncQgMQa9xJCaYA4tNJLE0DPVjtLxl8MXkKGTGIo/2YfPf3Ti/ut64qxOdjDG0MFqQOn4y8E5h0EvQS8xTB/eW9NBiPdtOMUhFp60Xq3u9nMdCDAGjz0DFuf3UZs62k2o+H/2zjw+yupc/N8zS5LJvgMSIiiK4IZCtcivVy0qKAouBVxY1LpVvb22trX1erXa1m5ytVZxrRXEKuKKolKwWr1KVdzqiqBICEsSQvaEZJbz++OZN7NnnclMwvl+PvlM8s67nMmc5XnOs9W19vKGhiGPFfvw6q2Ru/RWwOYZd8omic0h9Roi6jksCBTgsgp1Hnyy5NKPZlWw0nQHU18hFumIasBLJR5uzjJAy3Pa6uRZVkyatWlkdwasIdNuEgtxsEA2e4kUELNy5vvjGzp/twhWKCyBzX+80W3joJbN2FecHxpz4ncbC56DfT7Nb846gpvOMBs6PSKmFcwpO+vTbpKAaGUTYbttT8AjYeo1AUUgrwzW/zkypmXuI6IETL9V+stBJ0XG22T6g4xba8X9rbla+nBHi1io6iukkN0FK6Ufe91w3gpJDOPMlD6XN0pePe3w/iNw9AIZCznDpa6UlfK9rU7Gl6tA3guvB+Gv+RCRuczdKgrNuhsDQdOWYnLBytipbNH9E/ZNcbpekWgLxL+AcgCtdddVpQYzVtB0SGGhoIED0hmHHyF+gcGVouc+Av/8gywI42aGCvTBwcuVG+QZ1Rthxm9lAPk88O5DcNhZsujNXAz5+8uuQHha1ZYaWYT+4yfiXuLzwviZoiRYfoXuVnl98Tp5RsO2LtPJNjQ3c9J9HwLw5BVTKC/KDFk8fD7NpprmEOvC4jlHcuoR+7Hk1c0sOm5MRK2HzpSsWQP15RniSnO1mOK7s0C0+V2YMnr/iPasMnKr1ouyElTzYXheBv/8sgafTxshxhAgeNe3pSogXGUVQ1sDtNaJwPbo9/zC05rowonPA6jAe+k5UmuhdlPkTrC7Nbo7iiMD0rJESfB0yBz7+m2yo7t1PQw/NGC1nrdcrg9PoW1ZQ3L3ixTInrtSdmEbtknK2DPvAWzi+hQcmBq8wTRupgToXrwGX/YwsvamY18Rdt8YdTfMhk4vsTKKhVsN3K2yXj93lXw32cNEhigdL99PeOD9+U/AYd+T2MkL/YXnbHb4+p9yXn45LHo+eryNZSWYvUSsB44MUQSsyuplkwEVaSnY8HDARc8qUpc1TCxxlsI9bmagUF2wUuvMlAxTZ94TkI0qNwQ2N63/ybiZsrmKAjSMOk7km711gc/hzAS7PYb3RD/d50xxul6RaAVi31jFPR3SccMLrcxeIqZC8E8SbVGCnYMKBlk+totekJ3/7GGBXYC2OigYLQpJiF/tMvjkaZi8SDI8rbtZJpvgxWLucsDnzyxih6YqGZAFY2Th9LTLMyz/wsoN+KbfisodiYrlr5tfTrV/F7mswEVpTjrFWaELSbSUf9eu/IhfzT6McyaN6lQerPdMBo8hgNVfcrpOr7SrRZNuh6w+zEB7s0dRWLmWtJYddGSP7Dxelu+ircPLjoY2ygoye39jw9AkeNe3ckMg3uHC1bJbP+vugPAEstkSy7UTW+A9hwtWLZSNozPvFSuzNS/njQqNG7OqQFvuKONmSlGv/FFwzKUSFH3yzRJHMfUaaeOK+SIELj0jcs04f6W0J5YPfPYwEYaySiV7U80Xsq601ka60B7/s87Pb8svpyxGQTyvu51dda3G0tAfMkskDXCwq/NZ90sg/yXrxBPhvWUiNK+53t+37pOsRdbmpNctskRwodl5y6X4qyXgz14ibkWx1m9L2Zy5WOIJUIH7ZxaHjgfLUhBc2NDKKgYBCwj4ixUuDN1MtdnFshcst1gKbHO1yDbTb5XNT3ToBuu85fDvJ2HcjEB9q7Y6UY7DXbBmL5Hn1G/ru+tRAmrODGUSrUAM/cBpEC0+qyjQ8SHUt9EaMB1NoZ2z06/1EBkob94hA3TGrbIA7K0P3QWYt1ysFSE7CgsDi8krvwxNmdbRJAtIcPrWuY/IDthTFwfMgcE+iwD55Xxa46bD42VSjN0Sz5zl/PrZms64hv3yXBELSqyUf5lpdjKxd7531Kh8rjjhQPJdzs5iRWZxGqTUb5XXbqpQ72rxUZQRYkDoMXtz/IHU9V+EKBAj/dlhNlU1GwXCEKC7Xd/gCr4QyFu/6moRhI6/TnZJG/wuHJbgYs3n9RXiamG5QVnZx4ILcgYLZWWTZZPHqghsrQ8+T6AoFsh7wb7oFvUV8uym5uify+4MKB3BwaV7vhYFxoq7m3JlVGFRxSiI91l1O2f89dV+FfD0+TS1LR37dgybqyAQfJ89DFp2w8ogRXPGbwPCdn2FpGoNt0DMXhJQ8uorRNmc/wyccgs07YKCA0G7Y8dXrynlAAAgAElEQVTbgBx3Zsom596GgBwQywIXnoQlWiVpV4G0K7hK+wUr4enLQvpYZ1xmRp6ke63cIPJNePzEivkyriwX8BXzZXye85C0Pdx7oqUG7vtO312PYs0VJjA7Kv1WIJRSfya6oqCA/CjHhx4+b6C2QzD1FVCwv3T8f9wi2TSszlk2OXSQWYvI2/f58y57o1c6tXYBgp9hc4iG3FwdmU1k5uLA+dbu1YJn5EdredaiFyRV2z9/D83VNJ21jBtf2AXAk+c/ib1ha5DvYzm0VONxFfPfM0exX76L4bkZUReBWBmWWju8dHh9lBW4KMlO5yfTx8V2ZTIMLuq+kdduzL27WjRFfXBfAmjPCmRiqi+b1nm8LF+Uhk3VTZx4SNcKjGEfIlYdCUdGaABzsJXi7fukyNve+lB3jLMfgLyREmjtSA9cZwWY5peLm0b28MB8XDbZH3Dqv39wbBsEBKpFLwRcUEHuZYvhqpGWAy//PNLyMW85vPOX0Hs/e4VYMlr3BOIrOprF3/3C1dHXrWC3kvxy6mcv5cYXZU3oq7U4WhXrIT/XhwfkKrtUN19zvazlb9wuGb1mLha3OlehWCOCvxNlj+wvwfGV1jGl/LKDD+q/gbf+HD0zl+W6ZinRno7QDI+xLHDhSVi8bulHwdWgM4vhlF+HKgyx4oEKRouyY7lax6qUnlcmr1ZdoeZqPFrjeGOxPNfynvj0WUlNb/XxV2+FM27vnetRvGrO7CPEwwKxoY/vDR2sDAGxfPIsU2R6bmD3KtYisvA5MTN72qMPpvDiXJZ2/PbSyMli3nJY/ePIe4QXnZt1l/g3+kvS+1QGIEXEtXtvqBXkzHvhX/fy6RE38r1HPuTN606MOfnHysKU7rDxh5e/4PfnHEGHx2dcmYYS9VtlIXB2rR3savExto/13nzOTDoyiskKy8SUneGgINPJpqrmvt3YMDQJriMRnFkF5JjPF+lWcuzlogxbbiIgr09fKgqC9ombUvh1s5fIHO3MCMS3HXu5jAtrfYglKLXWSlzG6h8F3FOVipzX5ywVoWnqNbDzY1F0mqtkDXrt9/K8bW8FBLP6Cr+w1xKIrzj/CQnWzRkRQ0HJhkvWoT0ddODgokc3d6bshr7Ve4jm0jqk5/poAbnzHoXPnpPv1JEeSI867Sa5ZunpAa8F6zsJ91yASItAfjns+UrkBkc6rL5KzgmO+cndD176eaC+0+wl4o3Q3hDbAme1O7ywoZV6tuIdsdAFFzWctzw0kDqzOEa2KJ+4bFnEKqBrT4NxM2nOGE7doneobtWsfGUPvznhF4FA/3EzJRbDsqZZco3XI8/pqRUi1lxhAqij0m8FQmu9NNZ7SqnburteKTUD+BNgBx7UWv8u7P39gYeAEmAPMF9rXel/zwt87D+1Qms9q08for/Y06RYSvhu0Nxl8M6D/o7cLvmNLd/A0vHRJ4W9jTKoGyqjD6bs0pCdIeY+AhXvio/g+rsDk0X2MFFerFiI4HvUbYlUXKbf2unnmLfmMm44eSUAjpVhvpDPXoF7/nP8esX2bnN+x0q7CvCbs47A5/OxN0YhOVOMaJBSX9Htjo/WmqoWzbH9MBLszS4na8+nEcdH5rv4sqqp7zc2DE1sNnyZpeI+4/aS1uIW9xmrr2YPg++vFSF7z1eyS3vKb6LP0SAuQmWTJb4suEq1JaBf/HdRMk76pQSYBsdJxBKUGrdLodEz7xWr9vo/S1GwrupNzF0uRb6CrdJVH4fuTueXiyJi7Vq/eYe4zTz/Q2lXuC/5uY9BVgk+FBurmtjVsJeaZnfIv6Ev9R5iubQO2bk+WkDuigukv/zjFnHDyS8XgX5vgwRQ11eI1Sn4O4nVX9ytgd8ty8JJNweeBaExP1dvELe1U34l/cHhEsF/6jXRLXAXrpYCd1nF8H+3y27/lCsD/fzMe6UY7vKwgHvLnco6Hp4cxmrvK7fACdcFsop9+Fj0rJBNO/Cc/GsWrNgWUneq8bTjKLAEfYCHT4uUay54SpS4YFem7tK0xqPmzD5ComMg5gI/ifWmUsoO3A2cDFQC7yqlVmmtPws67TZgmdZ6qVLqu8BvgQX+99q01hMT0/ReYHfCtP+RwKaz7xcB3ueTXahtb8mPZcK2Bti85dEnhYZtkq2p4p3o6f4+fjKQwcORJilgn744NM2bt0MmoZbdktM5ZMFZBi+GfSXWbkZ2qSg2Z97D4Tku6lqjBxTtafNS0+zuUc7vWFk6rGM1Te1R3ZxMMaJBSt1WMTl3wZ69GrePPrswAbTlHUjuVyuxdzTiTQuYMkYWZPLGphq01qi+BFgYhiTdus/YbCKgN+6UXdyTbhZhvStXjsoN4sJhxQoEz8E+t8zdZ94TmPetOImC0dHTbr99H5x6ZGC+HjdTKlJPuxEaKsSCXbclUJSrvkIEwGhurZal2hLCGncE5vmp1wSE0/oKEQZnLobigyV+wy9Q1Ta1c+myDZRkp/P7c46IcDPtbb2HWC6tQ3aujxWQW3igKJmv/0FiFRsqJKGJpdwpmwT3W/Ez2hfdVc3dGnDXCQ5Ihuj9tm6LZGv8+//A5Asl9rK5Wp4Zfv8pV4kc8dB0edaW1+GD5aH3q/lC+ky0z+huDc1I+fHTcOGLoiS31ASyRFZ9LMrGlCvFQuJpj4xrePln2M9+kCfPHUmbdxR3v9vM3GP2J9/XIG10pMX22GhvkBotl6yTft22R8ZCsNXQpGntM8nOwnQMsFlr/TWAUupxYDYQrEBMAH7k//1V4Nl4N7JPWFqszycDQnvFyuBwBVLrBac72/1l6KD+8uXIRcTK1T13ORx+jpi/Oy0KpaI8fPAIHP49ScvnKJbBc/HL4N4riozXLYFJVj5na7KZ8VsZZG210a0SXrdkkfK3PS2/nJKFq6JORAU52Txz5VQKXM5+B8SZ6tJDCJ9XLGcjJ3V52q4WCZnqnwIxFoDs3R/RsN93Oo+PzJcYm50Ne9kv39X3BxiGFD12n7HZAoGcZZMDgpUVTF0wRvq4lRFG64BlODimzQpEDd49Do6TOPfxQA7+lhpRHo69XFxMTrgOTl8s7714rQhzwW6kc5bKDnJDpQh/0dxas4rhstfAmSWfqX6bKCRtdZEuVJUb4NE56P98n3qVRx4KGwGLQWVdG7et2cj/nD6BfJeTsgIXI6IkzeiOfW6ujxWQm5YNF70kMSmeMBfhWXfJWhwcz1g2WeIKFr0g79kdsO09KD5QYhqDvREc6bD+rkirUnDWo+m3ihLjyAiq3pwRKrg7MgB/4cVoLk1zlsHrf5Sg7fDPGK169qy7pM8+ND30f1RfIbLTsz+QuExHhvTn9BxZTxp3QNYwVO0m7I/OITu/nJ+dvxK836D+ElSnZOEqee4xl/rrudhlXLnb5BzLnaxpR2SBx8fPk0rXOSOMEtFL4hFEXRjrLbpXIEYC24L+rgSODTvnI+AcxM3pLCBHKVWkta4FMpRSGwAP8DutdVTlQil1GXAZQHl5eTdN6gHBvo3TbxXT2/E/lQ5vDWiQ12cuk12odTcFBnV2qVQzfe33oTnJ9zaK2Tz4PtbOkhUQPfYk2SVqbxIlIbtUBP/glG7RMjSceY9MDhkF0ScXR1pEpgTbmv9Gz3sUFaSt189eSp07m1HZzogaD30JiDPVpaMT9z47EDTukJ3XblK4VrX4gH4qELkHAJEKRJk/E9PGqiajQCSBVO23XbrPeD3QvMsvnDlh0WpYOlNOSs8Wq7IjI9IF4+375Py5j0BLdWhMmxUTF034mr0EXvih5Lg/9lI5f+J5obuy568MJM2wdoZBXsNz+eeODHVrnb1E3FeVLZCf37I+v/sgTJgdVbBVTTvZ0whNucOx2xRKKU6ZUMrfP6vmg231XP7Ie5QVuHjmyql9mp9Tda5PWJ+NFZBrKXxed2Q15VVXw+y7Q2WFab8MrM3W91t2tLiuhVSZ/oO4zB1xrsgTC5+TOdmyUFgxMVklImA3VIpVLDyNMchzFq0OuBT94xaRPywF+pOnJZOXIz1Snpj+m+if68IXoytUNoeMkWcuF6vIty4J3YS1+q3/XqqhIlIJePehyFiM2UvEajdupigpj58XsAgGU18B7pZIVydDt8TDAvEekoUp2izQXfLcaNeEZ3T6CXCXUupC4HVgO6IwAJRrrXcopQ4A/qGU+lhr/VXEDbW+H7gfYPLkyf1PLRvs2+gqCOQ+vuDJ6J0zd6QUkMsulV0EnzswQMIVBHu67EpEu0/RWLE01H4lGnt2qQz+vfUB82flhugZGrJL0RrcGUXU+3LIm/8CCh8ebHR4FXlOb2TNh42r8Z22mM9OfZr8NB/VrZpfv1hDTfO7PHH5lLgFxJliRJHEvc8OBFb/6UEGJuifAuFzZrE3ayTZuz8MOV5eKJmYPt/ZyInjTCamgSZV+20s95mcdBtUfRK5k3vZP8U99OFTA8J6uEC06HnZhZ38fSg6KHTODlYcgoUvKyf+2Q/KvO3zRt+VtdkD64tlDQmvRFxfIXP9wlWRcRjHXxcpZD2xUHZ5lT20RoUlbGkfI3PsnPTg252bQvfOF2vi3z+rjovFIBXn+oT12e4Ccm2O6Ot8zghJLbzoeVECLW8C632rWGBw7SiLk2/294nnAS3WBMtt7c07xAKRWQQvXy9uQ9HSGFvP8XbAprXSDu0D5RBF2l4F5ceIYgsw4w+B/pdVKn0w1v2i1W5wuEQZr9zgVwIWRvbb6bcGXKiiZXUqPyagPAT/n2YuFoXG54m0CFrkl8v/2RSM6zXxCKIe04/LK4FRQX+XATvC7r8DOBtAKZUNnKO1bgh6D63110qp14CjgAgFIu4E+za21flT71XETrlns4uvbDNivoypIBwkuwrRzIJW1oLn/1P8cy2TeXjWAWunITxDgz2drc125j/0Ef9z+gTufe1rbjihhNJMRXWr5vBRhaRFeaZHwxl//TLiX+D2muBnQxhW+sns7mtAKKCwHwoESBxETs37/sVNFuWsdAelOel8ur2xm6sN+xLh7jPTJ5Rw++lluDpqAsqDJaR79kowtfaGCvHB1FfITuwHy+GYy6X/hQeibloXiFfzeUSA+8Af+7boBclqM+PWGHO9NxBvEa0S8ZylATcqdGjNBoidOrNlt7Ql2MfeUjpO+Q2bat0hm0JXLH+PJy6fwk1n6JSxGAwqugrIDXdxKpssArTNLkqEe6/s8Ef7Hq3+Ea3fWDvqextD3aNmLxEB36o3dfItgT4W7V5OF5RPCdQUsapEW4oqSP97Y7FYPuwOsao0bo8hpCupURXsKpWWJda/U38H3/mRKDfRPm+wPBOtyntWSfTrnJmAht2burYIWjFCpmBcr+i3rUYpdXTYz1FKqVHdXwnAu8BBSqkxSqk04FxgVdj9i5VSVjt/gWRkQilVoJRKt84BphIaO5E4rIEPUP25+Ovll0snnL0k8J7VOVtrIWc/6eR2p1QCtc6xsBSNk24S83f4fWbdBc9eLgPW3SoTTbQ0sFZGhfAMDS//glH2WkqynZQXZPDgjCwmrZ3DqKXHMGntHBwtVRLMFPzMcx+j1VnQ6RZiUVbgwmm3RT0+ZAPiDN1TtxVQ3RaRq2rVFKSDo5+zT2vBeJwd9bjqN4UcH12cxcfbG/p3c8OQIth95p1fnMg9J7vIXDYdZW3mWHV51lwvFoGHTwvEN1gCVjDBx135km1vzrLAeeNmwuFny+7xXZNko+fwuXLcCoA99Xew5Y3IuX7OUhHG5i0X99iTb46c61cuCsz1ljAZjCVkhbfZVSivlo/9wzPltbkaX/Ywbly3K+SSyro2tNaMLMikJCfdKA/xxHJxyi+X/jftlyLw33mUCO2ttWKliNr39oT2NysuYf2f/XEWWQELEwR25O0OmHQhXPUuONMlycp+k6TPhffBpp0Ba4FV/HDZbHjgRBknp/4RpvwQvv0D2SCt2yqbSB8+JjJH+P2aq8TDouhgUYw87fDST8Uy4XXL35YiHv5580bBUfP9v5fDuX8LvX/2sOjXuVuhdrPUuJp1l/R7yyJ49YaAa/crN8v5pmBcr4iHC9PiKMcK/QrBeVrrD6O8D4DW2qOUuhpYg6RxfUhr/alS6hZgg9Z6FXAC8FullEZcmK7yXz4euE8p5UMUod+FZW9KHK6iQP7vQ88Sq8Gsu+DLtVLIJFjDziqRHdK1NwZ2keYui17gxQqgy8iXgTRzsWjeGQVyvZW/2VUkmnw0jTurRIodZQ+TQL6gjAf2qo+55dSnOcDVRvoTi0LjHVacD99fF2FuzUNFDXwrzU7ftwLiDN1TXyG+t3Znl6dZVaj7S0vBeAByq9+mrWBc5/ExRVm8s2UPjXvd5GZ03RZDCtNdusVe0uk+01wFVv54S/iOVpdnzX+L4PP6bVFq7Dwqc+0l60TwWX8n7N0j7h4+r4yBcNeTVVf7U2Nulxi2hm0w6lgR9i58USwDaFhzQyAL08k3y1oQa66fvUSEr/AsOq6iyGNn3S/3ytkvwjffM2c5X7TkUdO8JeQxZlMogQS5OGlPByo8DemzV0j8TXjfO+t+UW5zSvz9xi1xPG/9SbIlWVXNo1qgakRBDo7jmXKVWBsWPItWdkCj3n0IJgfJCNHGx8pFYklz+z0RXr9NiuJ9+wfwr3tC4zv/73Y48jxRpGculjGzQhQCnT0MZaUinvLD6BnKXv4FHP8zqZdhc0hsQ7Cs4iqKjDeZvUTkoOd+ILLTP24JqgDuj9Nzt4o1rrnaFIzrA/FwYTox2nGl1GTgTuA/urn+ReDFsGM3Bv3+JPBklOveAg7vQ5P7h88n6cte+50EO9nsgWIws+6W3MbB1RFfuUVSpk08LzAAGypFSz9/pfjBRktrZpkIPe2SOeDkm2HqD+Xcl38WWtXaIr8cnbsf6qWfi3/jwzND215fwbjiNJwqxuTi7YD8UOORDWIGvqViQJwhidR/0637EkgMRH/dlwDcGSV0ZBSTt+ttqsYt7Dw+uljiID7b0ci3Dyjq/4MMA0+0IlzxSrcY7IK6/s+yc+uNkgZy42o49fcyf2fkSk55u0PmdptDBCdbmuyq5peLe9KR58ncXTwu+hzraY8MiLU74KEZolw8+4PAdRtXBwKqo7mEZOTDqqtE+Jl9twhmRQdB7SZZIyCoiNhIERJdhRG++dqeRpMtjxKP5r4Fk7j8kffMptBA4XdxUvXbYrsqWYJvVokof09dFAiItqxUE8+TvnfwDFEKpt0Yvc+01ATubdV/evYKmLkYn83J7swDyXDayDn2MglQte4Ry43P2yEJBNJzpA02GxQeILUmfB7xynjmcumjB88IuBU5M0VembdclJWNq8XKcdBJklwmlmx0/kpRQqLNBaUTZBPU0yZxPk6/h4SVddKqiZFfHkjr6kiD7z1sCsb1kYSlcdVab/DHLAwtrADq7FIZzCi4YKWYyFp3Rw9smnJlqE/qm3eIuXxvffQAutbdgJYUfNYO0sVrQs999dcROxN63qPcs6GFY4+4kcNzXFFjGpxpGShF9MklhvkuVuBbKgbEGZLIni1SR6QbdjX7OHBEHJ6nFK0Fh5Bb9a+QOIjRRVkAfGoUiMFLtCJc8QpyDPY9twIz/+Nn0edEexqMODJ0p7Pmi1DFZv4z8lP3texu2hyx/cDDi3g+d6UENltEVTraolusV10VECTtaYASy0dWiQhN9RXiajLrLmlTVnHgvkG++QqwPMxLcjLMplAyiJXyta0utBjcDz8MTcP+5h3i+hQemLz+z7FTuVoEKwbFB2PLK6fUEqA7EDnE6nex4iTqtoiy3LQjNO1seJusZ1tuRUVjYfqtKJRY7yDUyhFjAxS7I/ZcYLNBTtjc4PWEVmr/8DE48fqAsmACpvtFwtQtpdQwIjMqDX48HVKm/bTbpNLinRPFb3HaL8WUHMsPL9iP1jKnWf6o4ee31ED2CNHkL3xRJo3c/ULPtapFnr9Sdq4WPEtH5nD+9t5OznnkK/5zdRWeuWF+guc+hsoqCfW9DHrPmO8Mfaa9SXxmuyki19iuaeiAYZnxeWxz0RE42+vIrv2481h+ZhqFWWl8auIgBi+xinDFI8gxfP7b8rrM3dHmxKwSv3/1KHltq41UbNbeKHP86mtltzWrGN55QFyIgu83b7lsNIV/Jp8/8USsWIuGSpnrF66StcCqZGwpD1ZsgzNT3Er++UdZF67eIDvMb9/X451Va1MoasyDzyeCWP02efX5evgPN3RLtDV59hJRECyseJfgmJnmasjIRS96QTYZz7hThOz/92Mp8nbRSyJoL3ohkOko+H5Wn1N2UQIad/pjFdIC9Umm3yrzulX81rp21l3Sn7VX4t4u9FsRmqvFLW/R89IHrf7aXC1tzyuHpy/1uyltDdwz2MoRayzY0yQWoidzgeUtsmyWbL6uuR5O+LmkijWWhrgQjzoQfyZSUSgEjgP+q7/3TzmcLpj6o8jy7c9dCRetifTfm/eoBAytuzl0F6m5WnaLrFiKYE397fvglPHw0BmB4997ODL13vE/DZiwF67CmV0Usntky3TETiHXVXo5g6G31G6W19yuFYiKJhE6RsRNgTgSjY387f+gufjIzuP7F2XyiVEgBi+xdmTjEeQYK70mdD8nRlNsJp4nLq1WVqPWOvHXDi4EmlksSka0Ip6N/sSDb94RuWs8b7kUBp1xuKyyzTvFZcq6j3VOWy38/QY5ftItAVfaNdfHZ3MokS5lhkCfvOglURjdbaIQBn/Ps+6CjibJmGX58qflgDMDtXujCPPBSuXMxeLr/8btkFkg8ZVVH4fKGpvW+Qsa7vIXGSySrEon3yKWheN/GkiretlroemCLaWgcYdYC/LLJb4TJDXr7i9hw8NS3G32kkBxt5bqQDuDsyIFWzmiZUua+4gEln/nWnHXcoYmcYkgmhVzxQUmVWsciYcL04awvzVQC/xYa10d5fzBjc8bO29y3ddimpv/tHT0lhpZWL5zrcQv5IwQP9r2Br/JGfETPH+l+BI6XfDeMhnof78xtOM/eaFUSzz7AbnG3Sq7vs3VooTkjcJmt1OSExbwFmugGPOdIZ7s9mdC6sYCUdEoCsTwrPg81puWQ2v+WAoqX6XyyB91Hh9TlMWzH26nrcOLK80EgQ46YhXhipeVNNb8192cGFWx2T8yzer3HpaUl83VMu8/dbGcGx7YPDfIKtFcLcLZwudknanbAu/cL4XfCsaIUJa3PzRslZ3mjDxJe+lzi1Ix43cihNlsstNqBXnHY3MokS5lBsFmE7fotnrZoc8ulSxBOSNgz1cisB9/nfQTyw36yYsj3YQqNwRiDVZcIJar2k3w7ydFNmlvkveUDY74nmRWqq/wB+3fIvGWdV/D8CPEmmUpxtnDRU556pLQgO61N0j76yv89bCekoDuI+aFKiDWhuqbvwt85mBPCk+bJC1YuShwfOFzEjCutWR6Cr8XBOJ6wkmkFdMAxEeBeFVrXdH9aUMEb0egymj47pi7VUzhy88Ofa/qY9HcbU7ZCbD5/+1WtUZr8L/2Oxm87rbIOIr6CsnaYWVQmLdczIbxWiAMhv6we5M/w0vXwQ2dCkScLBAAzcUTGbb5CZyt1bgzJYh7dFEWPg1f7GrkqPKCbu5gSDm6K8KVLKIpNq78gBUZAhs+FzwlQdN7vg7EJKy7MSiweT/4+p+hCTLeWCyKwPLZgVo/4bEPb98nm0xZpVI8CwJF5jztUHqojMN4/q+MMDYwROv3riKpiP69h8U1aOEqkTOspBX1FaFB0VagsFXQzeYQd6ZH58C2tySTUUaubGJalcqtNK2WZ4UlY7RUBWIbFj0vdawWPCP1RLKKxWUv2C2qvkIsDVtel/iicTMDSktWifTLE6+XcyeeF3ARfPchiYUYN1M+n80OKHjpOpGF5i2PLOa4wp/xMme/6JawRFoxDUB8YiCetX5RSj0Vh/ulNo40CcSZ+4gET1+4Wl7nLBUzdWtt9Im26CBZHKo+lSJFlpuT9f6qq2VAgdwjmv9f7n7iA3vRS7JI5I6QwZfsRdVg2P2lmNS7SeG6tdFHbhpkxTG7alPx0QAUbH+189jo4kAgtWGQYlkJrPiDVJjnggW8az6RV4g+59ud4iICIkRdsFJ+X3O9CIBaw+t/EItES43kup/+60BGqGipM611YsV8sVKc8muJx1tzvbiRrLle1o94E1z7yMIIY4khvN/bHfKaO1LchZbNgru/JTE3371RhH8IBEVbiuabdwS+I6cr8P1pDY+cFVA8IHpfWzFfLB4Wr9wsry01kgHM54NTfiPCvdWG/HJx2V74nMRkTDwP1t4kMo/TJZ+l5BApPOdIF2+K2k1w9AL48Rdwxu1i0csvl897ws/9SnqMLFDOTFHmW2si/48m1jPhxGNGDk7RcEAc7pe6+HwSbHTSL8VKsPpambRXXytavqtAzGzRJtq6b2Qw5ZXJwIg2GLJKxGztzIgs7DLrLn+Rlf3lHvaEJdAyGHrP7k3Sr7thS4OP/eLkvmTRnj2Kjowi8oMUiOLsNHLSHXy6w8RBGOJMuIDnSI8hXGeIi8jzP4S7Jss6MfN/4eK/Q+EYKT5nFbdaMV98w5fNFh/4roQm67i3XXZ2LbcO6/0VF0QXqPqDEcaSTzQ3Mqt4LMh3kjcqNGjZ+o6s7y+4AG1woHKsvlZ4YGjAttagHFKt/dFzAsHJ371RrAfnPiauTj4ftDfKfSdfKJYHV6HcZ2+9BGoHy0/NVTKOQDKYNVfJ76UTxCKRMyL6GLOsLNEsYdGUfROzE1fi8Z/UMX4fWlhBZA+cKB08vMrjigtEwy48MHoV6Vd/LYvEEwvFPBdtMGQWiT9sVil88oyYIy9cHZRJw2E6vyH18HokiLqbAGqAL/f4KM+J8/OVorloIvk7/w/lbfcfUuxfnGkqUhsSTyzhWqlIS/OK+QFf96lXBzLlXPOxWKnrK2StOPPe2NWkLcFvz1eh6cEtElnneSkAACAASURBVOFaZISx5BPLjcyyOsxZKi5sJePF3Sn4O7K+v+KDA/ewApWD+1Qw+eXiMnXRS/BfH4kg/8ZiCbh+YkGkInPaH+QZdodkkBxxpGx4jjhS/rb6irstkCjAuv5f94ji/OBJcMdh8lr9WWDTNCMvehaoYCtLNFLRijmEiMc29pFKqUbEEuHy/47/b621zo3DM5JPsPZvd0YfyF63/B6cJSGzSCqLWlWkZ90lJubwbBtzlspgaK6WwXzQSRGVT32ZJdQ2tZsc3YbUonaT7IYWjunytN1tPvbs1ewfbwUCaCo5msLtr5C3az31I08AJA5izae7cHt9OO1m4TAkiFjxGo3bYwv34UHczVWw8yOZ6ys3SKzEtJskq01w4KgVAzFvOaz+sew+D5Sft0m8kVxi+fTnjBB54/XbJL6gaGyIoOzzaWpbOujweCm1ZeC07mGlk5+5WFyiwzNCnvuYbGZa9/L5xMWoozV6v9Y6cG5XfUV7o2cyC48jCg7S114pMHfGneKBUbcl0spiGHDiUYl630hxEqz9xyqqYk3alkkaJPXZxPOkMIqV+gxg9j2SYSF7mAyGl34aGAyODHjjfwPBdtnD8OWOYmN1C5cu2xBSJXTcsByjRBiSS9Wn8lrQtQLx5R4JoE6EAtFSdBheRyaFFS+HKBBur2ZTVTMT9hsa+xiGFCWawNSbIE5Ph1ifrdSVlRvguavQFzyJOuNOCaJNy5FMNRPPEx90KxtPeLpLI1ANTWJlJsvIgxETYdQxEYkGfD7NxqqmTrlh+oQS7p77NxxPnB9IJ5+znyghOSO6Tlpg9fHmqv4prQ5X5PVZJV1b0jwdgSK9ZZNFcT7pZrFO5I40loUkYRzpe0rwYtDdpB08yFtqQrMHgN9dqRBceaLVl04ILacOoukHDeTaFnfnJABQWdfGpcs28MyVU001aENy2fWxuNd1k8L1iz1SMCsRCoS2OWkqnkjhtr/z9bG/BpuDA/yB1B9vrzcKhGHg6U0qWkeaCHP/uCWQNtPdSps9m8znfxi5flz0cuDe1i5y4YGSJScrBbJVGeJPHzKT1bZ0hMgNaz6r4SpKuOuitTi1O/IePbEw9TfFclaU67OHda2UBMtfVmXu/HL5X5i+njSMAtFTggdNZ47iVSI4dVWkzemKsWuQL8V/fJ7oE0HYQO7weDsnAYvKujY6PN5Ef3KDoWuqPvFXCe06tdIHVV6KXVCYkZhmNJYeQ/6ut8iteofGEccxPC+DrHQ7H25rYN63EvNMgyEm4QKfUpKEo7Umcr4PXl/8wlHtGUv54ytV/PSMpRQ9vyh0/ejJjrFh6NFLN7JocsOaz2qoPuMwRhb0MZd2bxQZn0/6e/h50VLVdqWUJLoujKFPGAWip/Rm0IQPcldh5GCp+aJXVT3THHbKClwhk0FZgYs0x77hQWZIYXZ9DMMO7fa096u8jE9gSYbm4iPx2tMpqniZxhHHoZTiwJJsPtxWl7iHGgxdYbPJOtFdFeew9cWtnFz/XAVrPqthY3UuN5y8khHZNkryc3HmlPZux9iwz5IwuaEnikx31cvDr+9KvkrVujD7OEn/7yulZiilNiqlNiulfh7l/f2VUq8opf6tlHpNKVUW9N4ipdQm/8+ihDe2rxH94de11Uav6tlF6r2irDQeWDiZsgIp327FQBRlmTzchiTStEt8YgsP7PK0qhYf25s1hyRQgdD2dJqLjqRw2xrQEm9xYEk2X+5qprXDk7gHGwxdEauKc/h8H7RO2HOGcc3Jh1BW4OKDbY381ws7aEgbjj3HZJIx9Jykyg097fcW3clXJqNSypFUC4RSyg7cDZwMVALvKqVWaa0/CzrtNmCZ1nqpUuq7wG+BBUqpQuAmYDKSPvY9/7Wpv93Yh6qeNpti3LAcnrlyqsnCZEgdKt+V15JxXZ72fpW42iXSAgHixpRX/Q45Ne/TVDqZsSXZeLXm0x2NfGt0YWIfbjBEw8z3hiSR1H5kqpcPeZKtwh0DbNZaf6217gAeB2aHnTMBeMX/+6tB708H1mqt9/iVhrXAjAFoc/8JrupZNllS8l28Rnxkfb6Yl9lsipKcdEYWZFKSk24WE0PyqXwXbM5uLRDvVXlJs8EBeYltTnPJRHw2B4UVLwNwQIkEUn9YUZ/YBxsMseiuirPPJ1a8+m3y6l8DzHxviAdJ60fh/b5sslRj196Qfm4YvCRbgRgJbAv6u9J/LJiPgHP8v58F5Cilinp4LQBKqcuUUhuUUhtqauJcobMvWAFB42ZKBcc110tFx7+eKj6DLbsjFhPDvkXK9dlYVG6Q+g/dBFC/X+VhbD44Ezzj+ByZNBceQWHFGtCa/Mw0SnLS+bDSKBADwaDptwNJV1WcLT/x8AJaqTDvx1BshhqDus+m8ncU3O/LJsO0X0rV6T8dmVr93NBnkq1ARFOFw6tZ/wQ4Xin1AXA8sB3w9PBaOaj1/VrryVrrySUlcYja78+gtbISZOTBqb8PpIKFgI/gjvdTbzExDChx77OJwOuRvtqN+1K7V/NxjS/h7ksWTcO+RUbLdrL2fAzAAcVZfLTNKBADwaDotwNNV1Wce+sn3hPiIVQGKzZPXihF7uq2QFOKCalxYND2WZ8P9nwt3039Vnmt3SzzcioQ3O+/93Bk9en+9nND0km2AlEJjAr6uwzYEXyC1nqH1vpsrfVRwH/7jzX05NqE0J8do/BrY1UqdWYGfjeDzJCq7PoI3G1QckiXp31S48XtI6EB1ME0lUxCKztFW8WNaWxpNpV1bexubh+YBhgM4cQKAI23n3i8LBqWYpNdKlby1dfCn4+Gv5hNrZShbQ807ZTv5uGZ8tpcBc27Uuf7Cc62ZOIhhhzJViDeBQ5SSo1RSqUB5wKrgk9QShUrpax2/gJ4yP/7GuAUpVSBUqoAOMV/LLH0Z8co/NqWmui+sW1BceBmkBlSlS1vyOvwI7o8zQqgHigFwuvMpqVwgsRBaM3YkmwAY4UwpB7dxUf0lnhZNCzFZuo10a3kZlMr+bjbInf1n7sSPO2p9/3Eu58bUoKkKhBaaw9wNSL4fw48obX+VCl1i1Jqlv+0E4CNSqkvgWHAb/zX7gF+hSgh7wK3+I8llv7sGIVfa1W0DvaNnb1EjluYQWZIVb55A/LKpWpuF2zY5WV4ZuIKyEWjsfQYXE3fkFm/kdHFWdiUUSAMKUhX8RF9IV4WDUvgcxWYneNURXujfzc+T+p9P/Hu54aUIOmF5LTWLwIvhh27Mej3J4EnY1z7EAGLxMAQXFLdoqdCfvi1VkXri14CrcGeBu1N0FwduK8ZZIZUxOuGrW/BASd0eZrWmrd3epg0wF24sWQyIz7/K0XfPE/rUT+lvDCTD4wCYUg14l0gqz/rUzCWwNe0Iz73M8Qfhyv6d9NSA8UHJa9d0TCF4IYk5tvrLf3RpKNde+L1kLOf+MbmDIPCA6IH2xkMqcT298Dd2q370qY6H/XtcHjRALXLjzc9j+biIyn96knweTigJJuPttWjddQ8CwZD8ohngax47fRaAt9+R8O8R83OcSqSVQLn/i30uznrfsgenprfjykEN+RIugVi0NEfTbon1/akRLzBkGw2rQVlgxETuzzt7Z0S/3DYACsQAHUjT6D8o9sp2P5PDiw5nH98Uc2W3S0c4I+JMBiGHPHc6bXZIKsYXIVm5zgVsdmg9FD4/jrwtIGyg9Ml35f5fgwDgFEg+kJ/hHyjIBiGApvXQsl4SO9aGP/XDg8lLhieOUDtCqKp+Cjc6QUM/+KvHDz5fgDe/WaPUSAMQ5t4rzFmzUpdbDbxXDAYkoBRUw0GQ+9oqpKc4yMndXma1pp3dno5tFCKrA84Nge15aeRv+stxnk2ku9y8tZXtUloiMFgMBgMQwujQBgMht7x5UvyWja5y9M21/uoadMDHv8QTF3ZNDzObPb/8DYmjMjhrc21Jg7CYDAYDIZ+YhSInpDK5eINhoHms1WQMwIKxnR52rqtUhF1culANCo6PkcG1QfOIW/XeuZmvE1NczubqpuT1yCDIZGYtcpgYfqCIcEYBaI74lXZ02AYCrTVw5Z/QvmUbv2S1n7j5qA8KHYNUNtiUFc2jda8scys+COj1U7e2rw7uQ0yGBKBWasMFqYvGAYAo0B0R7wqexoMQ4EvVkuhov2P6/K0r+u9vF/l47gRA9SurlA2Kg//T5RSLE1fzMdfbEx2iwyG+GPWKoOF6QuGAcAoEN0Rr8qeBsNQ4KPHxH2peFyXpz3+uRu7gpNGDVC7usHtKqFi4o8Zrmq5uuK/aNr+ebKbZDDEF7NWGSxMXzAMAEaB6A6rsmcwphKnYV+kvgK+eQMO/G6X7kv1ezXLP+tg6ggozBjA9nVDW/44/jXuF+TSQsZD0+CdB4xJ3zB0MGuVwcL0BcMAYBSI7ohXZU+DYbDz/iOAEgWiC5Z80E6bB+YdNDDN6g1FZQdzVdqv+bc+AF78CTx0CnzzZrKbZTD0H7NWGSxMXzAMAKaQXHfEs7KnwTBYcbfBhgdh1LFdFpXa0uDloU86OGkUjM4dwPb1EKXgO2NLOOeDn7Py0H/xrR3L4eHTYPR34ISfw+j/l+wmGgx9w6xVBgvTFwwDQNJ7k1JqhlJqo1Jqs1Lq51HeL1dKvaqU+kAp9W+l1Gn+46OVUm1KqQ/9P/cmrJFWJc78UfJqBqFhX+PDR6F1D0w4s8vTfrO+nTQbLDxkgNrVB/5jJIwvUFyyaQpfnXgffOtSqPoUHp4JTyyEZhNoaBikmLXKYGH6giHBJLVHKaXswN3AqcAE4Dyl1ISw024AntBaHwWcCywJeu8rrfVE/88VA9Jog2Ffo70JXvsdlI6HYYfGPO3/Kj2s2+ph7kGpFfsQjk3BtUeJNeKc1R5eyjgNfdb9cNQC+OJFuPsY+PhJMAXnDAaDwWCISrJV0mOAzVrrr7XWHcDjwOywczRgOUPkATsGsH0Gg+G130FLDUy+JGbwdKtbc/0bbYzIgjO7ri+XEozIgj8cB0UZ8IO1bVy41svW/efAGX+C7FJ46vvw+AXQUJnsphoMBoPBkHIkW4EYCWwL+rvSfyyYXwLzlVKVwIvAfwa9N8bv2vRPpdR3Yj1EKXWZUmqDUmpDTY1xTzCkPinTZz97DtbfBQfPgJLYqVt///ZeKho11xwJTvsAtq8fjMyG2/8fXHoovLPTyykrm7nzq2G0n/w7mHQxbF4LdxwOj5wtAeS7NxmrRDekTL81GHqI6bMGQ99QOokLolJqDjBda32J/+8FwDFa6/8MOufHSDsXK6WmAH8BDgOcQLbWulYpNQl4FjhUa93Y1TMnT56sN2zYkKBPZNjH6bo0cx9JeJ/1ukHZ/D/+j9DeBG/fB6/eCkVjYcZvwR49BeAzX3bwo1f3MmsMXH5Y4pqZSHa3wV8+g9d3wAF5Nn52TDrTinbj/Hod+qtXUS3VAOx15OEefhTZIw9BFR4IhQdAZgGk50J6DqRlgzOz5/7GWkN7I+xtkCrfe+slYN1mB5tDfuxp4sOcOxLsCcl7MTj7rWFfxvRZw2AkIf02WSQ7C1MlEFxqqoxIF6XvAzMAtNbrlVIZQLHWuhpo9x9/Tyn1FXAwYEa/Yd/F6wF3qwih1qv2iUCq7KIYNFRA7WbY+W/Y+RHUb/VfrMDpQjtd0FaH0j52l3ybjeP/i/w6OyUuHwUZCqddobVme7PmkU87uP+jDo4ogovHJ/WT94tiF1w3SQrf3fOJjyvWtpGblkWR60wqG89gf3ZwtG0TR3s2cUTFFkZXridTpp/o2NPBkY52pKMdGXht6XiVE5/Xg017cODB3tGEam+U76cnKBsU7A8l46HkEAmOzMgHVz5k5InykpYlP84sk/PdYDAYDAkj2QrEu8BBSqkxwHYkSPr8sHMqgGnAw0qp8UAGUKOUKgH2aK29SqkDgIOArweu6QZDkll9LXz6jFgQvB3y01NhFGRHu+hAGHM8a79x8/Wedhx728lo20sNebzmnciH28bCNg20dF6W7VS0ezVu/6Omj3Zw1dFpZDgG/+bKt/eHb43SbNjlZf0OLy1uzTEjHBxSdCDjCg5CKfhHlZf1lR6+qdrDcF8VeaqFXLWXQnsbebZW0nQHTrebNHc7Tu0mDTfpdJCOBzd2PNhxY6cFF222bFpVNhcfU0pebq4oAY4M+R61F3xe+V5bdkNzFTRsg+rPYdPfwefp+sPYnKJM2Bxw5Lkw/TcD8080GAwGw5AnqS5MAP60rHcAduAhrfVvlFK3ABu01qv8WZkeALKRgOqfaa3/rpQ6B7gF8ABe4Cat9fM9eF4NsBUoBnYn5EN1j3n20Hz2bq31jHjfNKjPDhTJ/J5ikWptSrX2QN/bNJj6bSr+36Nh2hk/orVxMPXZeJDK35NpW8/pVb9VSg1H5ONvIR433wDXAE9rrSMchpVSDmAX8IDW+hdBx08HfoXEPTuBP2mt71NKjQPuA/KBdOANrfVlPW5fshWIZKGU2qC1nmyebZ5tCCUV/1ep1qZUaw+kZpvizWD5jKad8WMwtDHRpPL/wLQtMSilFPAWsFRrfa//2EQgB7gnhgJxGvDfwHBgrNZaK6WciFJ8jNa6UimVDozWWm9USq0Blmitn/Nff7jW+uOetjHZWZgMBoPBYDAYDAZDgBMBt6U8AGitPyQ0c2k45wF/Qlz/v+0/loOEK9T679Gutd7of28EEots3b/HygMYBcJgMBgMBoPBYEglDgPe6+nJSikXEi/8AvAYokygtd4DrAK2KqUeU0pdoJSyZP/bgX8opV5SSv1IKZXfmwbuywrE/ebZ5tmGqKTi/yrV2pRq7YHUbFO8GSyf0bQzfgyGNiaaVP4fmLalBqcDr2qtW4GngLOUUnYAf6mEacA7wE+Ah/zH/wqMB1YCJwD/8rs49Yh9NgbCYDAYDAaDwWBINZRS05DkQP8Rdnw08EJ4DIRS6mlgKtDmP1QKzNJarws7rxjYorXOifLMT4BFWuseWT72ZQuEwWAwGAwGg8GQavwDSFdKXWodUEp9C9g//ESlVC7w/4ByrfVorfVo4CrgPKVUtlLqhKDTJ+LPNKaUmuEPsrYyPhUhJRV6hLFAGAwGg8FgMBgMKYRSaj8kjeskYC+BNK6fAVVBp/4JmKS1Pjfo2kJgIzAWiYk4ELFOtAD/pbXeoJT6X2Cm/94Af9RaL+9x+4wCYTAYDAaDwWAwGHqKcWEyGAwGg8FgMBgMPWafUyBmzJihkYrW5sf8xPsnIZg+a34S/JMQTL81Pwn8SQimz5qfBP8MKfY5BWL37lSqam4wdI/ps4bBiOm3hsGG6bMGQ8/Z5xQIg8FgMBgMBoPB0HeMAmEwGAwGg8FgMBh6jFEgDAaDwWAwGAwGQ48xCoTBYDAYDAaDwbAP4i8ot1EptVkp9fOeXmcUCIPBYDAYBjF73V68viGX5MVgMITR7vFO2V7X9tbW2pYt2+va3mr3eKf0535KKTtwN3AqMAGpXj2hJ9c6+vNggyEe+Hya2pYOOjxe0hx2irLSsNlUsptliAPmuzUYEsvKDdv45fOfctL4Yfzp3KOS3RxDEjHz7dCm3eOd8mVV86ofLH+vuLKujbIC1+h75k9adfCw7FnpDvv6Pt72GGCz1vprAKXU48BspNp1lxgFIlXx+aC1Bjwd4EiDzBKwDT2Dkc+n2VjVxKXLNuAfEDywcDLjhuWYiW+QY77bLthHxrchsWytbeH6Zz7G7dU89+EOTj9iP06eMCzZzTIkgZSYb828llB2N3UstpQHgMq6Nn6w/L3iFZdNWTyywHVcH287EtgW9HclcGxPLjTfbCri80H1Z/DgSXDHYfJa/ZkcH2LUtnR0TnggA+LSZRuobelIcssM/cV8tzHYh8a3IbHc9vcvsdsUfz7vKHJdDl76ZGeym2RIEkmfb828lnA8Pt8I6/u1qKxrw+PzjejHbaNplz3yhzQKRCrSWgOPnwf1FfJ3fYX83VqT3HYlgA6Pl2gDosPjTVKLDPHCfLcx2IfGtyFxVNS2svrfOzhp/DCKs9M5ZHgu73y9J9nNMiSJpM+3Zl5LOA6bbWdZgSvkWFmBC4fN1p+dg0pgVPAtgR09udAoEKmIpyMwCC3qK+T4ECPNYSfagEhz2JPUIkO8MN9tDPah8W1IHH99awtKKU49TDYfxw/PobK+je31bd1caRiKJH2+NfNawinOSbv2nvmTdlvfc1mBi3vmT9pdnJN2bT9u+y5wkFJqjFIqDTgXWNWTC40CkYo40iC/PPRYfrkcD8Ln09Q0tbO9rpWapnZ8gzALR1FWGg8snEzwgHhg4WSKstK6udKQ6sT7ux0K/R3o8fg2GGLR3O5h5YZKvj2mkEL/eBo3PBeA97fWJbNphiQRz/m2T3OtmdcSTrrDvv7gYdmzVlw2Zf0/f3rCNysum7K+nwHUaK09wNXAGuBz4Amt9ac9udYEUacimSVw7mMBc2B+ufydWdJ5SkoETMUBm00xblgOz1w51WSOGGLE87sdKv0d6NH4Nhi64pn3K2lu9zD90OGdx0bmu1DA1zUtyWuYIWnEa77t81xr5rUBId1hX9+PgOmoaK1fBF7s7XVGgUhFbDYonQCXrIuZzSBWwNQzV06lJCc9cK9+ZEUYqJRwNpsKbbNhyBCv77bH/T3OdDsG+jK+ejC+DYZYaK15+K1vOKAki7Gl2Z3H0xw2irLT2LK7OYmtMyST8PnWsiT0Zg3v9VwbPAfmDIfvrwOvmdf2BYwCkarYbJAdOx1feMDUUaNyueGEEgrcu6A5I6D1V38WuSNQOqHbQT2kdnwNg55kBAh2OwasrCN9GF9djm+TCtHQBf/6eg9f1bTwg+MPRKnQuXh4nouvdxsLhKHva3iv5tq+zIFmfhsymG9tkBIcMHXUqFwenJHFpLVzcNx5RCB9WtuePmdFSHpKOIMhiGQECHY7BhKRdcSkQjR0w3MfbsfltPHtA4oi3huRl8E3u1vQepDGBxniRl/X8F7Ntb2dA838NqQwCsQgJThg6oYTSih6flHkIHa39TkrQtJTwhkMQSQj2L7bMZCIrCMmFaKhCzo8Pl76ZBeT9i8kzRG5fA/PzaBxr4c9ZqNnn6eva3iv5trezoFmfhtSpKQLk1JqFLAMGA74gPu11n8KO+cE4Dlgi//Q01rrWwaynckkOGCqwL0r+iDWXjEpBr/Xw6wI1i5E8ARkUnAakkUygu27HQNW1pE+jK+YmFSIhi7Y8M0eGtrcHHtAYdT3h+dlAPBNbQtF2SaubF+mr2t4r+ba3s6BZn4bUqSqBcIDXKu1Hg98G7hKKTUhynlvaK0n+n/2GeXBwgqYcqRlxEif5hJ/ROu9XmRFMOlVDamG1d9HFmRSkpOe8FicbseAlXWkD+MrJiYVoqEL3vxqNzYFh47Ii/p+sV9p2FG/dyCbZUhB+rOG93iu7e0caOa3IUVKWiC01juBnf7fm5RSnwMjgc+S2rBk0V3QUaz0aVkl8tOHbC8mvaphX6fbMRCeTcnpAp8XGrf3PTjQpEI0dMGbm2sZW5qNKy36LrIlHO5qMArEvs6ArOG9nQPN/JaSKKUeAk4HqrXWh/X0upRUIIJRSo0GjgLejvL2FKXUR0jZ7Z/EKn6hlLoMuAygvLw82impS0+yHHSXFrKLbE5dYe1CWKksdza0GUVigBjUfbYXDFSq4L7SbRpaK5tSV+MUep51ZJCneN1X+m0yaGn38O/KemZPHBnznMw0Oy6njR0Nphp1TxkqfTbWXJrwFOldzYHzHpX5zO4InDuI57eUwNM+hebqxfg8I7A5dpJdei2O9D4XkvPzMHAXEjrQY1L6W1NKZQNPAddorRvD3n4f2F9rfSTwZ+DZWPfRWt+vtZ6stZ5cUjLINN2WGEFHLWFBRzabDERHmgzM1pq4ZDawUsGdteRNpv7+Vc5a8iYbq5oGbxXgQcKg7rM9ZEj1rVjBgU07e591xFqQ80fJ6yBaXPeFfpssPt/ZiE/D2JLsmOcopSjKTmencWHqMUOhzyZ9LvX5oGlH5By44gJo2BY63w3i+S3peNqnUP35Kh4+bQp3ThzNw6fJ3572Kf25rdb6dWBPb69L2W9OKeVElIdHtdZPh7+vtW7UWjf7f38RcCqlige4mYnHEyuTUtgOU6z0aF4PNFdB/TZ57Y1S4fPhbaoiv2MXfzp9P44alTug6VytIjjb61qpaWofnIKlISbdphn0+XrddxPeZ2K1KVZwoLvFZB0xxIVPtjcAMLo4q8vzCjPTjAViCNCbuaxPKVv7ML/GvE/1Z9BQGX0ObK4y8128aK5ezBMLikPWkycWFNNcvTgZzUlJFyYl1XH+Anyutf7fGOcMB6q01lopdQyiDNUOYDMHBmWPnuVA2UNjI5SCV28NFVRevRVO+LnsAvSgyEuwCTQzzUZ+02acj5/HiPoKRuSX8+AZS7nkZfhgW2PC07maQnZDny7TDEYxh+tzH6M+ZyytHb6o7k4J7zNduSnFykaibInNOmKKMu0zfLKjkXyXk4JMZ5fnFWal8emOcIO9YTDRm7nM59O0uT29S9nanyKY4bTWiKwx7cboc2BLDeTGdrsz9AKfZ0TU9cTnGZGM5qTqSjMVWAB8Vyn1of/nNKXUFUqpK/znfA/4xB8DcSdwrh6K1XOcLpi9JDTLwewl4MwItTj89VQ49nIomxy4duJ5AeUButz9DDeBVlZuQ4XtnBY9v4iH5ozhySumoJRKqEXAFLIb+qQ57JwyoZT7FkxixWXf5r4FkzhlQilpDjs6iuueevw8Kiu3xTTRJ7zPdJXDPFo2kll3QeOOxGUdMUWZ9ik+2d7A6OKsiOrT4RRlp7G7uZ0Oj+kHg5WezmXWuv1VdUtE8bdTJpQCsG1PC9vrWtnTEmTFiGc9Bp8PplwFr9wCc5dHzoEfPiYbnIb+Y3PsjLqe2Bw7k9GclLRAaK3/D+iyx2mt70KCPoY2rkLIGQEz8qZm8wAAIABJREFUF4MzE9ytkJErrkkdzTD9VnjzDqjcAKuulr9XzJdrs0p6vPsZPmHlp/miXtvW1spvVm/lh9MOosPjIzPdTnGWP81bP3ZDwwPAYu1O+3w+apraUzbo1tBzClxOfjjtYK5Y/l7nLtu98yeRn+HA3biXNKv/lU2GqdeAq4CDs9M4d9JITp84EqddgkUz0+zku9ISX/ywqxzmVnDgRS+JKb+lBv7hzyw9ewk8d2XPs470dBzFEgIuWdfnxAmG1MTr02yububUw4Z3e25hVjoaqG7aS1lBZuIbZ4g7PZ3LrHW7JDud359zBNc99W8q69o4ZUIpP5k+jh31eynOTsOrNW6vpqXdw8j8TGz9rcfg88kc52kTb4j1d8PG1WKFmP80tNbK+2/fJxubytSPigvZpdcy95FVnW5M+eUw95HdZJdem4zmpKQCYQjCZgNXPtT5/07PhYx8qN8qA/TDx+C7N4qwUrlBlAaQjpU9rMdFXsInrOpWzago1za6bfxk+rjOiarTtFqaha3m8z6ZRKOZa/92ybERRXBOmVDK7pYOLn/kPePWNASoa3N3Kg8gC+QVy9/jicunULu7g8PzyyG7VPr3qquhvoL0/HJ+NedRrn5lC2s+q6GswMUfv3cEw3IzyM90Jrb4oVKh46lsMhx/Hfj8cUaZJZCzH7TVw9OXBsZBzgj4/jrw9kCx7o1rgSnKtM+wva4Nj08zIt/V7bn5fhenmqZ2o0AMUnpaBM5atyvr2rhtzUb+5/QJ5LuclBdlUt24F4/Px4KH3ulcL++54Giy0jso7E8RTGuOevVW8XLIKoGTb4bMAqjdJDLJxPPAVSCvb98HZ9wer3/Nvo0jfT2l42dx4YtxzcKklHoMOAEoVkpVAjdprf/S3XWp6sI0OIlXUFI47jZ4dA6suwnsabD0dHhoOqy5XrT7t++THdr8csgrg2s+kV3IvFE9LvJiTVgWv36thvrZS0OurT1jKfUqr1N5gIBp1dvcd5NobUsHt6+VyW/FZd/mf06fwKP/+ob7FkwKKYJzw8wJncpD8LONW9MA08t+HisYMNYum8fr48Z1u6g9Y6kI6H7lAYD6CpwrL+CySbmd5//0yX+ztbYVj093Fk46alQ+f73wWyz//rFodHzc7ZRdTPL55aI8TPslrL4W7pwYcB+CQJpCaxwWHgA5Pcw60hvXAlOUaZ/h693NAIzwV5ruinxXQIEwDE56WgQueN3+YFs9lz/yHteu/AiPV7Onxc1Pnwys1SXZTmipIbd9F1oD85/pWxFMK+bh2MtFBnloOiw/GyZ/H758OXD84ZnyeuL1ps5DPHGkryd/1HEUjhlD/qjj4pDCFa31eVrrEVprp9a6rCfKAxgLRPzoxc5hr3PfW4LC1Gvg9dvETclVAG11ojxYuwDnPiY7oMHPK52A/v46fJ52PMpJq7OAPFSE5liUlcayiybTUldFfpqP+g4bjTnDsM9/mQzlwaOcPLC+jhPH26IKfcrb3ufdUJ/Px6LjxoSYX39+6ngcdsUTl0/BrsDpsNHW4WXxnCOpb3Nz72tf8cG2+vi6qBi6p5fBd10FA8baZXPYbdQ0u7nk5RZWzBsbcGWyqK+gNDMwXirr2mTX1efjkOw2/nnZWNq1gx+98A3VTW5+OO0gxhRnhbrb9QWbTcbb9FuhdDw8clZs96G+uhDFsipYSluwW5MpyrTPsGV3CwAj8npigRAhs6bZKBCDlZ4WgStwOXn0kmOpaWqntqWDp97bxkVTx4h7cZq9c249alQuD87Iouj5szvnCt+8v+G5+BXs3r3Y0jJRQQXgfK5ials90Z/t6RCZI2xjh9dvExcmdyssegFsDqn/YBI7DFmMAhEveuiP7PN62dtQhaepmV3NPu5/r5FrTj4k1A0n3Ac6oxAWrgLtE1Ph2pvE39AKUnIVQGZhqPLgv4f2dNDms/OjF3Z1unxEc/uxoRnjq0C9JJ9hVH457rmPcvW6vZ3X3TN/Ei3tnqhCn1el4eijSdSr6VQejhqVz6LjxrAwyOy67OJjaG91hwihvz/nCG5bs5Ga5vb4uajsi/Q2bqWXfvexggGfuXJq5y5buHJRmp3OAwsnc/vajewlnbQo/aq6NWBRKCtwMTwnjeLWr1ArzsdeX0Fmfjn/e9YjbLaN5aq/fRhy/2G56bR1hC2M4f8HVxG01UYK7CdeL5/3zHsS4z4UzbVg3ExxV4yWTc0UZdon2LK7hax0O7kZ3S/ZuS4HCmOBGOx0VwTO59Nsq2uhec8uhqX5SHfYuG7GOHxalMe2Dm/nWn3DCSUUPT8nZN62rTifj09eyf3vNXL3SS4cT5wfUC7m/o0b1rVFygxoceUMj68smyyWh7/NCZqj/galhyZmPjLZ51IC8x+PFz3xR/bv3mYum86Iv36LSWvncOtUB3es/SI0931wZpXnfwTVn8KyWfDno8VUaGVbqq+QXQBXfojy4PN68VV9Cg+ehLrjMDKXTefWqY6QOg67m9pwN+xCW24oLTURWZecT1zAbaeN5L4FkyjJTucHy99j/6JM7rng6BDT6n0LJnH7W3vE5STIJOqb9zfILOk2n7XWulPAvOKEAyNcpLbWtkYIodc99W9+OO0g7p0/iQJX12kNDTHoSxafHvrdW995a0fs9ILBu2xvXnciz1w5lXHDcnA4bIwrzeKuWWV0uN345j4a0q/ccx7l/vckTaUVA5GnG7CtOD+k/2Y9s4BsT12E8vLRtgam/v5V/vuZf1NZ10p1Q2vneOkcc1WfhPxfdPVn1LV1UO06APdFa9H5+yfGfShaNqeTb4mdTc0UZdon2LK7hRG5Gd1mYAJw2GzkuBxGgRjiNLS1U9C8icNfOptRS4/h8JfOpqhlM3kZDsaWZFFWKHNjWYFLLLZR5u3DStO487RhAeXBf9zxxPkha/+lyzbQ0NYu68OLP4Os4tD5b+o1kRaJx8+PLHgbD0z2uZTBrDbxoif+yK01EUJO0fOLuGxSbsANp22PVHQ88x6YtxyOuRSeWBA6MFddLQPW+lvrTsHB4/HhbqyO+pwbThDXhpJsJwXNm3H+9WSUNQA7mqNOMPVNzfzqhc/4yfRx/5+98w6Pqkzf/+dMSyYzSSaEhGKIBTHKWkAQC7uKixiRJiJEITQLWNDVXdvaKyuW36qrKNgIBBQiVVCiYP3q2hAXXRQVCwkthQzJTMq08/vjmZM5M3MGQltB574uriQzc855E973ed+n3PdDjjOFzXVNBFWVhy48gVevPJ0Fk0+nvcPGsx/8zOUrvawZUEbF+E9ZM6CMmrSuhFB22yFTX8fpslvjDpz6VKyGyrom8tul0eQLUtfkb+v/UhJ67I2UXxvmuV4S+NttDXHygnoyoBZlOywrjZz0iJqXqfobbLPOpf2MkzC99xD+4qVsv+xz1gwo486PQlzYK5+3/3YWdw7uzsMrN5CKcQldnjP6wFVZJ6pNWqZr9POfUFG5KXq99Lgkbs1pErJ9/vEOZz+7np/9LtQ28ov2CCYTpHcU1bUJK6RcqrkuSZb+neOX2kZyMnbPf9DgstuoSjoQv2mk+etwLR0fZadcS8eTFqhjW30Lr34mr798xWnkuDIM7XaK+wdSPBVt2vvT/HWyP2xYAf/3Txg1O3LPhIqPB6Ch4f6UoE1in5B0IPYXjCKHsQeKBNHbTk6THKhCIdGNX/G3CAEpM894YdqzIs9RQxAMoDZsJ7SzAiVB92qtbvy+czpiLYuJaO7YaGhgqhrVqIh/rdfHlHlrSbGYyU1PQVVVgqooJK2tqGfEnI1c93oN9eZ2NAdVttU388+3NhgSn7UodSgUaiVMu5v8rQfOnl1czBjbi2xnCi9NOIWeXVytQ8vLsvNDtYeQqiY5EHuLvVHxacM815ctPfvuRqaNODFMbM7gtYnH8PblXWmPO3HEKHaD2LACa+kwKuuDjJizkVfWbOb+5ev5vsrD5DlrpNZba7iohysfxRRd3qbNMX2mKy46Z88y/Lu4bDLeyromxr74Ge70o6PJ0nvThMkImmjCrEEiydziMXbazEmy9O8BoZDK1p1N5DgTl7PEItNuTWYgfqPQ9k1zyNh+W1RRtyvqczg3vbqOGk8z1WoGvpHRmVyGTYf3pgmXsg17v0X1R55X/a3Yby3QYW9nbKPaKt+6J8IcSfW5gwZJDsT+gqYDv6t65ATSaa50J6kOGzRWRUoVNO17VYUxZbLQKz9vvaZ10Q99Cspvh8IHUWYPFcLpmDLD51Q1quRl2emWbY1fgO9NQy0qRZlf3FrDWDukhAdWildfWddEfnYaNy74DznOFOw2M6Of/yQiD1fcC4DqBh83n1fQqv6g8RWqG3ysrXC33isUCkWRayf/6QhemXQaZgVmFPfiidXfcdkfj+JvZf9pvc8jF53IwyuF9/BscS+afOI4JDkQe4lYWVIIG/1dlEm0YZ7r1ZXWVrh5tHwDj150AqekbcM8P0Li4+KXCeUch7s5QLMviD+kYjEpdKQFs8EGcXimmCv9XMjLsvP06J60mPzYY/stDJtO0GJvrQPOy7IzfczJlP77F0b0ymsdY5xksba2YuRac51WFo7tygPvVrO2op5GX4isrAPQbyHWTpjM8b0khk2X15P4zaPa04I/qNLe2XaH0WW3sjGs3JTEbwOBQIidzT627WxhcukaVk48Cqeh/Rahk51Nfl654lTcTQHuX76e284vIGdcOTsbPGQ7UkhZcqmcKT58XM4RWglSeO8v+6KRhWO7kpumkOMKYrKlRexS3+vh1QnRNjLWRo0skUa4u8OedsXeFwnaJPYr9sqBUBRlqKqqy/b3YA55aPXIiWCgmhIqmkdqZgchJwV8UrqkhsRzX3Jl9GJs3ikL0pkrny2cGun/0Pe6yIJ6b1rcYg4WzeMwexceHZnDz+46jo1dgJ4qNra4yJ/wJmbVz/qqFu5auY21FZFa8xSLiW65Tgae0ClOv/+q0jW8NOEULGaFsS98GsdXeOjCEyh+4dPWe/lDaqvz0LOLizMLOnDxzI9bVZjuHvIHisI/a/e56dV1lF52Kj/VeGloFom6JAdiH6DJkuo2DoY+tfuo0W7meay60toKN46AG3NMWR2vXIJv/JtsdNu4YUHEUVw16TjMBhuEw+HgvZv6sXVnMykWhUdGnohZUUCB65d+z8NntydT13Ax6OzI1pZUXppwCs2BEKkWE75giElndWXbzqbWMT7wbjXPDykh+7VwOcCXL6OOmoOyYKystf73wNKrSXFvopcrn1dHlfKTL580235K4BoRuPV2wmyDN2+PVl9bfQ9cNGv/PD+JgxraOsrekwxEmpWaBh+qqraJN5HEwY1AIMTPO7yYFIXJ4b3X61dxGthvxWTm3O652MwmUJTWvXrc6UdQvOhbKuuaWDi2K708VXLzys/lHDHoMYLturG+JsDcLxq46WQixGuNFK01ynS0F9uoD3ZaU0V9qXmn2GCzTTITu8OeNsRMqs8dNNitA6EoyoWxLwFPK4piAVBVddGBGNhvEgbRW5M26fUe+JgyKWPSFpQzF3xeeO266IOe1oHalR9NVqr8HFbfQ2j8CgIhlQ01Pu5atI3c9P/y/wbn4W9RUMctQym/vVXNqXZICc9/Vsu953TCpCgcleMgN6wAIVHek9nZ5OeaPx9NikUx5CTsCBPBjd7r7BJt/tx0K4+cfxgpwWqeGNyZB96tjiNOv7m+ipvPO87wPsGQis1i4uGVG1objy2+ui856Sl7Lo/7e4dellQvC7yPTX+M1JUK2tsM085qoIUbFnwT5Sj+1GQnV3+gD8/Pbd5UmgItXDzz46jbzJ90GuXrq6lqaOGOfvnkWhSqfCqdLJ35fksDa3+u5drTs2hqbOSXnQGeXFPPHYOOo/yKY6lv8LDFE+L5bwNcVbySpqZGqhrhcFcXMrR1Ouv8qM3NvKCYowc9htrQAPZwlGxvVUESRd9yjo3YCQBPVaTDPCQjbr8jbHZrOv5tdyCy0mz4giHqmwNkJgMshzyqPC1U7GgiPdUSsZXuFjqsi7ffyrn/4KmheWzyKWzb2dz6eas5IsEeFTQ58kw44y+oZgtBzLiysxlzIhHJV4iQogc9JuWVWhZ03StwwqhoJ6ZoLmpaO9xqGo07m3e/F+9pSVJbqj2S+J+gLRmIBcBKoApxHgAcwBBABZIOxJ7AKHrr2R7tgVvTohdU3+sj2QSIEKkLpwpPomguvPtQ9D0dHQih0Ojzc3SOgxnFJ9PO+yOW2YWRrMSoUvyFj+APwdYmO7f3+YWUknNFwcaVz/Siefx47h8xm8089MY3vLm+SlSXintxbvdc3lxf1fq4vCw7HTNTCYZUQ5nXqoYWZhT3JLtxI+bS8yAczX1+SAl1TntrB02tx4NZIUEnThOPlm+IKofyBYK77DeQdCISQC9Luh8iOXoHLttpY9mUvq1yqVbchmnngBJPmt9U18zjawJMGlBGbppCVaPKzA/rubCX1HRP/tMRXNQ7H7NJIRhSW6WFhYMTyZi9NOEw1v5cy996BLHOLsTh3kR7Vz4PXzAbU3MljrJRONyb6OTK58TRC9ni9WEKhfAFVJr9ITIyO0hNrtHmZk0T1bLLV8nfa09S8HrsKvqm3fedqfGZomTE7XeDzeH10X4PORAgUq5JB+LQhz8ofR1qvb7ozOl5N0YFWhg2HdviS8FTRfthJdSEurR+XuMXVtY1sbainstXwpOjysmjCmXuCBT3JmyufPJGzaFzjjEPDGta5PulV8OYhTB3RLT9mj+GpnHlDHnuy7btxXtTkrS7ao8k/idoi8t2OmAHPgMuVVV1IlCjqupEVVUvPaCj+70g1gOPJTUlIHWScxxMfINQTndC/f4euaZgEOpZN2MpOR/XjJOxzy4kx/sDloBHshnh680Livm+upETHv0StbGW9MXjoiOt80fT3tTAuBc/bXUWKuuamFy6hr8PPC5KyvWl8b3oaK6ni6mWNy4roLB7Tut7z409meMzW3D5tmH2bI0aQ/Zr4+lk8XL/8vUUzfy4VfWhoSXQKkGn3eeRi05k287mVudBex1gW32zYb+BGu+uiYS7k5j9TUMfydlHIrBeeanvtHe4cPpHbK9voVOmnZz0FBRHPPk6VDSP7xpS41SaFq6p4Lr+Bfxl+Rb+NOMH/rJ8C5f+sSvPvruRrXVeBvfIY+Ksz/jzY+8xcdZnOFItPFsc3bV82ogTeaT8W649oz3Wxm1SGjh2CQx7mkxTC+k2c2QeOnOxerdz+OKhdCnpQ6+3RpLT+AMEA4lVp5rqIlGyfVEF2VX0TbvvhhVSYlA4FS4tlzKC/UXYTuKgx2Z3I84UC3Zb2zkvrjRxGqoamg/UsJL4XyBMLj5MqeXY9Ca21Xl4evTJrQGT2z4M4B1Xjn/KWskOrL5Hriucisvs48TMRp4e3YO8LDvPvruRf446qdVOVnv8dLIHpVRTrzi3YKwc9BPZPQ3uTdIoLoGCk5FwiiHaIkCTxEGJ3WYgVFX9TFGUAcC1wNuKotyCZB6SiMXeljHEeuAfPh7NYfA3Gnvo1d8Q7HgSv9Q1U/aFwrXFy0k1hVAVM+aSQTFGoVgMzJ/vivAmdMoyLlvI0BCkW0LkOFOiosSVdU2owP3DjifNZibNqnBE8Bess4QAbnXl86+Rc/n+z2fQ7A9xjFKBeU6kSQ1Dn4oagynk45kxJ5NqNeNpCeBu9NMxIwVPi6X1GY2+IB3CMoZ6UuwjF53IlHlruX2QcclTY0uQkEM1jHwksxbst0jOrhrG5Tissi5SM+Xwq5jlufb2ZO1o4pniXlwVrtPNy7Lzt3MLCKpq1P99itVEnyNcDPhDpzhuzMSXPqNs8qm8Nek4TMEWGkMWpr1XSXVDM/ambZFyQC3t/ubtUhKkzUODDJ8yf4w0b8zsEl9vq12nRcn2RRUkUfTNHHPfys8jJUzXf510Hn5H2OpuJnsPCNQgMq6QbCZ3KCGuBDfNgqn6G3jlEizuTWS68hkzah6Prt3MnYO7k+2w0d6ZwpP//pni7ha6zB0pfIQ/39WarUxz5XPEBbN59KITsJjNdMmyt9pVd5MfJdgYzWMIl0IpvkZj4QbNQYFWwraR/drqiVZR0ioFDKEPZIVCoAZFPKaxOlmadJCjTSRqVVVDwBOKorwK7FuB9G8Ve6okoEcsKchTBemd4LJVEPQJcXpUKSyIKCQxcjZ8vYifU7uTmqnw154hrKWD5f1LyxOnH5dcJZHM+cVRHX3jlGgAXPl8U93CvcP+gAJs2dnMs+9upNrTgtVsYuKszwBY+9eTsM6LloW1lY2heUAZAOalMeRZrfwqPIYtniD1wQBXzf2i9RA5o7gXx+Q6SU+1RnEaABZf3Zcmf5CNVR4eXinlTPrUroa8LHu4g6vFsKPnLg+9u+gAmkQ89MpLGirrmlBDQaj6PmpdqBe/jDv9aBrrW3CmWEizmXjowhPIa5fGVrfU+Y6a8XHc/+Xcy0+lJRCKe06O00pu44+Yw82QUlz53DSkBI8lD9OCodFzb+nVkbmnzcNEGT7PdrClyRq+bJX0StmxUZwHT1UkStZYvfeqIEaEwGHToaVBGkQm1UZ+99hW30xW2p79n2emRUqYkjj4YRTMevOKY0mLyWxaFoxmwIAyRsxZA4hdvHNwd6oa3bJ/GzR0y1wyDsfARSipudQ3B1r3bYBvbzoBc8EgaU6rL5EcOVv4DYMeI5R9DEFMWBurxe5BJJAS8KEWzZWAi04YZuZb9VG/n773jyFMpn0rBU3iV8Ee/a+oqrpZVdVRqqoedaAGdMhiX8oYNA/8slXwl//AhNfl8ODIgYzDINAs0pqjy8RLL5wK7z9CoPdEPJYsdlRvwbpAd4D3Vu+67MKeBa583MNKeOBdGd8D71bjHhbdSTo4qhSrxcLTb3/Hlp3N3L98PfcNPY63rjiWTlSzevKx9OySgd0UMDyAHZebyvEdUo0PZ7oxVIcy4rpPTy5dw9b6ZrIdtqgmY1rjsRSzgi8Y4taBxzJjbC9Wr9/e2m8AImUsT67+PmHkI9GhN9lXYs+hKIphwziXujNuXSivXMKOqs1s27KJYN0m0oNuNm7fiYI0DWwJhMgJS6Z+MPloFo7tSo5TdO01jg3Axb0OY+1fT2Lh6MNbnQftGdmvjSfPSeK5p/9ey/C1Dry3CBnYsyJZhPQOkHUkdDpJ1I/05V77koI3ahy3+h4oHQ6hYDK1nwTb98KBcNjMWM2K9EhJ4qCHUTBrZ4Nxc1etn5P2uWyHjQferaZ2SEnChm5/yLFwjKORzNRI09ZRvfLAkiqd7mO7SJeNg2POg7kj8YVg3dZG+OAxsU+anfpkBgHFQkPmMXFlsNcPODZqL35uXO/WAGAU9P0fGrYmG8QdYmiLClMm8HfgAkDbuaqApcBDqqq6E137u8L+aG7irY72vosXi/MQWz4RVl8KnfsPpr/zA0+e30FqvJvq5D0DXWd92UUoM5+mseVU+x1UeySScdoRWaRnuVDHLQPFjNKwGfPrf+VYTxX/GjmXTRY7OU4rXQI/Y589Hpy5dD3rFhZd3BXVbDGMlG7c4ccXCNLL4L1QZj5N48qZ+PJGbh1oMjzIVzW0YLfFZw9CIZUar4/7l6+P6jXx/obtzJrYh1pPC+4mP0vXbua6/t0IqsJziFWCiJUbhTZESpIwhFmBaSNO5JaF68hxWrnvnI50y7ZiDRl3iD4qA5SlEYnA4jGLCJrryU1tIYiV2Re0J71M3u/iyuelYSV8Gwzy3PsVvDihNz5/kONMlZjnDZS5b/AMk9lsHMHX6nhd+aiZ+QQVE+YxC1HmjoiSbTWMghmVe+2LKkgoJI3jrGmR9av1ewn6kmojv3P4gyFqPT7aOfaMCK0oCq40WzIDcYjAKJi1xROiU4J+Thrysuy0c9io9vh55At48NzDMY8pi7YnnipM27/GVn4b2UXzmH/Fqaxav5WiPziw+D2JO93bs6BgEBazmW7tbIQKH8SkU24MFc1l5pc+hvUKkaHrh2MCuuU4WTD5dALBEBaziVxnSnxZsL5qw5kLFzyb+Azl2Z60gQch2qrC9DbQT1XVbQCKonQExgNlwIADN7xDCG1VEkjEkzDKYNT9GC3nqi//Kb8Nq8XM9HNSMGulS1q/CEUR2dcJKyDQAnU/tZZd7LxgNje/to1L/9iVdk4Tdw7uTrf2do4M/oQya2C0wxF+pq1sDJ3GlTO76CjSS8+TxR6us1Tcm1AKBkV088PXu4eVcNfr2wCiNfZd+fhGzuWuVTs4u3tHqj1+/MGQ4UG+1uujU2Zq3J+61utj8pzoPhS3LFzHvMtPxWpWcJsUMlItjOydF9VfIJbfYCQ3mjBSksQuYTKZKPnoJx696AR6pW7F+v7d0OMSOQAbrAtlx8bIa85czJ5tmGPrbTWdcfcmXEvHkzfiNa7pk0EXay3YzJhW/kPej238pj2j7ufEdbzh75WFE7F4qvAOn0PK+NexhHww54L4KFgiTfLIH2DPuSRGZY+ao++pEvuQVBv5XaO6oQUV9jgDAclu1IcSjIJZM9fU86+Rc7GVRcqD/CPnMnN1RGnu6dEnEwyFePnyPnQObcXs/jme82VNg5U3owmjNA9bwpijmrCUDJezhCUlYj81LoQjB5wdUc+6BUvJ+WS0Pr8Ud7+ppFitTP/Ezald28UF3ELBIL767SgNHqo9IWauqef6AcfGcwv1Z57CqXJOMTpDhQLw/PnJsqaDEG35HzhCVdVpmvMAoKrqNlVVpwH5u7hun6AoynmKomxQFOUHRVFuNXg/RVGU+eH3P1EU5YgDNZY2YXdlDKEQNGyXRbL1P9LF8flzoGo9oWCQoN8gUhsr5wrysyNHGsbU/YxZ40Vo75WNl0YvS66C+i3Q5AbX4TB8JoHLVuNWXNzxp0zS/DvISDFz//L15Cp1cUoMLJsihiT8884GT6SVfWyd5YYVKO+WdF9dAAAgAElEQVQ9THDCG7RMWUfz+DeZ+LqXtRX1Ybk4L2sGlOG/bh11o9/g2tXNvLJmM8++u5GnRvckPdUSp7j02MiTWLimApvFbKCUFF8HrxG7R874mIue/TfN/lCr86C9H6sEYTIpFHRIZ/HVffnwlrNZfHXf3xeBej8i22HjhgEFtDc3Yg164aybRGJ48STZxPTrYlSpNDvUYCRTvPTqyPwLv3ZYWpDDFw/F9GQPTCXhut283pGMm/4ZQ5+C1feKszDoMfxT1tJYvAKfqyuh4c9HFEvCRH7H4rH8UNuCL6TsNpO435S7jIIGy6bAWbckS5WSAKR8CSBrL4IaLruVqvqkA3EoQAtm6ffA684p4P99aWbNgDIqxn/KmgFlPPalmRsLj2Px1Wcwf9JpPP3O9wz45wdUbavE7P5J7G2sHW0J8xGKSuGCZzjSZcXyXjj48uHj0rhy2HQoGCSBwfLb4MVCqP1OxFd097OWFVPrDXD+CxvoeUQ2R7Z3RAfcwkGRtNmFdHrpFHq9NZKpfS08/ta38SpM+qoNe5bsCbF2vKgUym9PljUdpGhLBuIXRVFuBkpUVd0OoChKB2ACUHEgBqUoihl4GsluVAKfKYqyTFXV9bqPXQbUqap6tKIoFwPTgKIDMZ42YVdlDLuKNL5yCc3jytlSr3J0bOoxkfpSqguWXQPn3Js49ejeBIuukIPSC+eAKx/zqDkc/tHD4N1Ol7NuQQ2YKL+8gFQSlF9pteJhVYV0h1Web0Q63bACtfAhqkztMaFQ7fG3vrW2op6/LPfz0IUn8Nib33FjYQH/3ephbYUbk6JQ6/HR3mnjpQmntKowmRSFGwYUkGW3xpHLZow17kPxY7W31WFIs5nbxG/QOBVJ7BtMJoWCXAdK1U/gaYBFk1qzB9ohnqwjoHqDdJM565bIXE/vuOv5B/FZC302bn5xaydVNbsbyvavIypfAHNHYrn2C6xzwspkl5ZLM6SY5znMQb6qChiW3GmZxD1W7tqVMluissf2x0BmfjLClkTEgdjLDMRPNd79PaQkDgD0wSxNNMSVamFIz7zWbLtWqnvzq+tYW+Hmrb+eyXX9j2H91gbhRSgJAo6ZeXDewxK0dG/C5MqXciHvdrGRK2+G/nfDwIdglk69MUEAs2uWlX9d0gObxUyKJcZGNVZjmh/PR5s0oCyeW6iv2miqk6yrJletcdMcuVIyFfs77UlpeBIHDG3ZoYqAbOA9RVF2KIqyA3gXaAeMOkDj6gP8oKrqj6qq+oBXgGExnxkGlIS/fxXoryjKrxs61soNXF3kq3YASBRp7Hu9aNAT4Chn+MC96m6JAJz/KIEOJxAaVRofWV12jSz8WPKn9hmtxtu9Karxi7JgLJx2pUQZVvwN5V8n45hzHiYF4/sE/TCmjFDxEnoc5sRus6IWzU343F92+qlvDuBMMTFjbLQu/zNjTmbZl1tYW+Hm0fIN3D/seN6/uR8mReHOpV8z5KkPmTjrMxp9QZ5c/T0dMlMp6JBOXZM/jlw2ec4a7hjUPer+M4p78eTq71uHozXN0SPJbziwMDXVoMwvjt94Kj+XA7unShzjFo+k2WcNkrmeKrW2FJVK2V1RqfyswZUPo2ZHZy0gko0D8FQRcnYkYHHAly/L2tLdK8r5SCAyUNWoMnNNPb6RcxNmEhMpdxlqnGuBg+fPgcePb804EgpLHCbqMWG1J52HJADYtlMciHZ7kYHITLOyw+sjEAzt/sNJ/OrQglmHZaWR7bDxQ42XJ1Z9x6MXncC3t5zM25d3pXd7PzOKe/LeTf1wWM3kZth45KITyXFlJD4P1P0k7+n6L7HkykiGt/JzWHoN+Juj7XZsP6rw/RQ1SCioggqzPvyRDdsbIlnYBEGRTk4T1hhnI2Rvj1oUPt98+DgMnxnZI/yN0K4rqKHovUD7nZJKdAcF2tIHog64Jfzvf4XDiM5uVAKnJvqMqqoBRVF2Io5OTezNFEWZBEwCyM8/YFVXiZEo0piZB/3vwTp7UHxmYsE4LIMeE0dkyJPikadmQPkdsuBPvw46nCAHpPnF8dfn9ZYoryNHPqORMzM6w5zhUc6MUn47alGpHP7C91GL5qKaUzDNuwiT/t7fryJ02mQYVYpJJyvrGzmXoLk9T7z5HXcP7k4OO3nniq60qBa+dtv419vfM7HvkXxf5aHa04LNYmJTbSO3Lvoqjstw/7DjsVvNmExKQqUks0mJitaYTUQpjjz77kYeuehEbnp13SHJb/jV5+zeQJvnCTgJNNXJprXkymhnek0JnHUz6Dg0jJotmbYbvhFOT6BZ5vN70yKZBVe+rI9r14BixtRYS0j1EzrrZky6e6mj5qB89lykvjczT7hCZRFeTu2QEmZ+WM+4M47i/325nevGlWM3BVFisgZ7pNxlFDh4ZyqhgQ+joKJY7fESrod46dIhOW8PYmxvaMFsUkhPbZPiehQy7VZUYEejj9z0eC5ZEoKDcc5qgYocp5VuVJBaErFV9uGz2RFwYbP4caY7yXNl8JfXfuSxc/JxxnK+9JwqLVsL8n7GYXI2cOSAoz1YHaI+p2WGv1sZzyEb+hSWt+7gmAGPcOPrm3lySBdMwRqUerdcZ7WH7XJupK+Ev5HMjEy2NwdonxbE1FSDGvDRHDJjS8uRc44jRxShLpwpXxeM0+0Fc2TMreTteWBvL9Hvve29lcR+wZ5bJR0URZmoqupL+2sw+lsbvBZbaNyWz8iLqjoTmAnQu3fv/30TPC3SqC2q9I6QkglW3UKB+LIMa5p8Hf8aNO4Q56HHJXDOPeKhr/gb9L0Oxi2V6xUzrPw75BwLfW+Axhqo+U4isn++Cz6ZIdKQBuVHdWf/A9vo10g1hcBspbpZodO8gYZjM700kJaJq/EVr6TB62WrJ8TM1fVM6Ovn2rO70rEloslvdeXTbUgJ1Q3N3PTqOl6+4jT8wRAPvfENl/3xKMPD2BHtHWTZRXVkV0pJ+tKjUEiNIkRXe1rokJHKoqvPwB8ItfaRiC0ziWveY/CZXwO/+pzdG2jz3EgFbGQJfP4CnHRJNFnPngVp7WHeyOi5tmCclD0BODrAZzPhp/cjJGhPlcip1m+JOCSufCxjF0cckfC9lAVjRakpFIyMqWAQoXHLcDeFsKak4rRaeXpwEyGTn+6dupJqT0ExmAd7pNwVGzjI6w2nTsakFysoXhzp9/Ib2AAPyXl7EKOmoYVMuxXTXiTXM8M2tKYh6UDsCgfjnNUCFU8M7kz2a9G2MX3xOI4d9Bi8NFJs3si5ZNktBIOqdIYevxy8VWBLh0CT2NkPH48rCcXuigRRTr8OTrgonoDtaC8BTKsd0rLlPhtW4Cx8kGeHdERp+BnevEPs8bDp0r9q/Apw/xLteAyfQ9nXFm46WYX5o1Hc0uQuMGGl2H+rA5p2SDY6bi8YS3DC62w/4x45a7xVz/UDvBTkOlob7Wk2ncIHwWT5TdjSQwH75EAA9wIHwoGoBLrofs4DtiT4TKWiKBYgE9hxAMay77Bnw5iFIkWmX1TFixLXfuv7NoRCstC1FKRW4jDgXklPap11L3gWzrwRTGbRkddHIT6ZgVr4IEr9ZsPo8E+1TYyYsxGA+ZNOo7NSs0t+hTnYzMAXfog6SP13q4dVk47DXBJfA/mPwYs57/kNhFSVYCjErQOPw2YxGR7Gttc34ww3f2urUpJRDenunIFkJ+r9DH1TtK8WyOG4sUZKht5/VOamvZ0Yen3joiveMZ5rWUfK93U/Qb9bYW2prJ/xy6Fmg5ADX7suerPxJpi3GZ1h9rAo4r9p+1dUDV5MR992UpZGIny2i18Ge3eMYhR7pNwVq8xm0OSJ0uHCm3J1ib8+id89ar2+VkdgT9HajTrZC+KQgRbQCqoqZZNP5w/OemN7pitNtpWN4Y7Rr+GcN0TeG/8aoMCrl0Y7A6ZwkEPL8JbfEQnm9LkMGrZJ8FKrVlh6tTgPcy6IXFc4FX56X3hm5bfJfc97WHgUS68WHlrOcSgxohiOxWO5ZvRrmFbfFuE4NNWJjKxigrkjIvw0g9+3ztPCGc9ESpT/u9XDO1d2x6RJwA57GlLSYfbQ6GxuUq3pgKItfSDWJXoLOFD6gp8B3RRFORLYDFwMjI75zDJESvbfwEXA26qqHhTRAyA6tRYKyGEnVmkmkWyZv1GcATUoC8psgSPPhF6XSkQh0CzXvjdNHActTbnkSjEeJUOMsxpAVeoRZI8qjag3hcuPHlgiqgZ5WXYafUGqgsadqbXylIBiNcweWFTjcq0jXBbO7Z6LP6jS6AuSnmqiY3oqM4p7Mbk0miQ27Y1veWp0T6obWvAFgnTISInKJGTZrYZZA5NJIdtha32v1uvbpROR7ER9AJDeUbgHQV90qRzA9q/E0A+4L+LggmyGRnOt7ifhTmhqHHm9ZWPzVok8cc6R8XNN4zfE3gtjdaWCdmaUcF+T1o2tYQsBRwdMjvZxc2ePHNXYLtMJmjwlCYFJJEJ1QwsZe1G+BPoMRNKBOBSgD2jlOFO4+bwCvt/h54Rd9bIBcG8i1WqO2C9nx2j76t4kZ48JK+CaT6G5Xj53zt1Q+IDYUu3MoC97qvwczNao52gKkLx9X+S+gx6T4Ei4akIJ+eOzzE112FNT4jtej1sKcy+K56fF/L6ZqSZ6dslgbYUoSlXWNaEEWyKS8oFm4XHof+e2yG8nsU9oi2XqABQCdTGvK8BH+31EtHIapgDlgBl4UVXV/yqKch/wuaqqy4AXgDmKovyAZB4uPhBj2SsYqS7FZhvyeku6LpbDUFQqKTh/c6tqAq58GL1AJFmXXBW90L9aAMEWiQTUfAeYjA8pjhwUICcVKhtzyC1ejokgJouNxpYQd/SzMXNNCpf+sSupVhPT36lnakz/Bi2T4R85lx+8qXHZg8LuOZgJGRqA72v93D6oO+98s41eh2fRxebB4qnjGKeVJ0adREAFd5OfR8s3UO2RjsPDp38YlxkAEmYNdvWe0QEv2Yl6P0I/5wunJj4sKybjxkUjZ0v3Uz0H4rPnI9fNL5Y11FgDKRlCxLamRdfsfvg4bPoUddwyFM922Yy+fFk2LTDuFaEGo/qaaM9XRpWyzW+nk9WLElNe1GblrrAym3/iW1S768lIteHchcJTEknEosbTQrdc515d2+pAJDMQhwT0Aa07B3dn1oc/ckPfHIIxAb/WMk4NBYMwN++QjMAuovgEA7B+qXSYrvlO7Ka/UUqIdD139L2mohwVV76UKLl/ib6vNS0SBPI3ynNis8yufExFc6WMWn/I91RFj9Wo/PWCZ7G+dQd39LuLEXMiPTBUc4ooUS65KmEz0WRw5sCiLQ7EcsCpquqXsW8oivLufh9RGKqqvg68HvPaXbrvm4GRsdcdFDBsCveTLKoel0iU1pELOyvAliYlGWpIMhVrZkOfy+Hli6Ov31kR31TukxlSFqIjg7Yq2OilzzT+ReMOlA8eo8tpV8Hr97TWLbpW30OvcMfpuz6qYEOVl6cv6YHd5CU0YQVqMIhithJQTXj7P8y092rYULU+jqT8yPmHoZTfFGcA/CNLuWvJNv5fUWf+fFwu7b0bsb40tpUjcdKoeVyzqony9dWtMq0PrFhvmBkAEmYNdvWe0YEv2Yl6P0Kb885csDnl64QVMq8Vk2xEX74c1QU6yuC//0hUapv3HpG1srZU3ndvgsZaMKcI7yG9ozgT+prd4TNR7VkoujS2WlSKsu5VOOFC4+7s9VuEnB1TWmReUExu8VKUF4a1fj5UNI+atK4opj3gyphMmNM7sLMxjXtXfhvvlB/ipOkkDhxUVaXW46PX4XtXwpRqNZFiMSWbyR0i0Ae08jJTmNrXQvZrQ8WWDnqMYFZXFGsqJs922btBbMiA+1D0GYdEWdgdP0CPMbDjx3iuQ/+7JQsBkUzDqDnw3sOR64dNh4WXRlc9eKrEaYBI07qPnojPMrs3obz7kJRdn351JOATO9bKz+VcM2GF8D69VVJ+6t1OJ6eUImmBQbMjDQJeubapLnK+0vaQL19OBmcOMNqiwnTZLt6LLStKAoxVl9YvjSjNOHOh/z3xagmfzBCv3WSQRTDSZO5xScR5AF2kdrGUi2j3HlkiUdv6LXDuA6JqM3I2NGyRhRY2HrayMdwy+g22BY4gt/lHLAtGy1jPfQA1/TAsqp/6JthQ1cDainoeXrmB+ZNOY+vOZmq9PpqaGsjYsEL0pXWHwR1kUO2p5ecaL31yA6Qtiia5WhaM5uExb3DpH7vS6AvislujejxAdGZgV1mDPckoJDtR70cEfDJXzrkvitTMsOkiTeypgtFlUoKX6oIxr0oky5omRt67PaIQouH0qyPfu/KlLMpsg5BfUuvzRkXP/cWTUMYuiarjVeYX4x2zHLtVwfRe2EnJORaqv5UNEKRc0CB6ZWmsirq/af5oKgaU8ZflW/aIK6OVPT0w/CSCoSD+iW9hUf1xCk9JJKFHQ0sAXzC01xwIRVFwpVmTGYhDBPqAVmebl6xXw3u7MxcCLZgbq1EzOsMPq8SWmsxiDxVFIvDaofy7lfL+zk2RLENmPvz7X9C+wLhp59jFkYG48oWr1rwTek+Ac++Hhq1ixzUFvGVTRDHJ2QEVUEwW8HmFR3HSJWBSIlkNaBWQoPTC6DPP96viqzBOnQxv3Cpf35sG5z4I/e8ht10WH95ydqRstKkWUOGaz4T/kNklRslvDqS2+x/+D/7+0KbiynB/hT6IdKqKEJo/Pag4B/9L7E46LJY8CdB9WGRyF06NX8TLpsiib3aDqsZnEYyayunLRPT1hiYzjHhRVBbMNvlXvyW6/EmvZlNUCj2LYW0pTU2NNHo8WN4KOw/nPQz+RpSSQSjuTRzuyqd0+ByKlws5L6gK6ctlt2JLCUu4VX4eOQy68qkauIhpI07k0fINTB+cg8PgsNbc3ETRzP8A8NKEU3aZGcjLspPjTOHKfl1x2a00+oLYbWaCIfYoo7A3xOvfFPanBJ7ZFk4nXxm/ORUvlg0FRbIGnz0PPYrjo2BaZ2iIpMO170eWgDMHPOEOpKoaTfjTntewVVLvujreHZ5mWhydOKrfrZjmj4mk57Vx7qw0jth5Y7qdujeRm6bsFVcmuuwpbU/+skn8TlHrkfKLvXUgADJSrdR4kmUchwKyHbZWTqCvpSnCI9CVVyoFg+Csm0SpKFEgMiVdnAnNvhYMgnPvgz/dKM6G/mAP8r0a7hWi3WPpVXDh83KGAHn/7DskcNNUB1XfCNfCsx3FWw2bPoXjL4yIWsTadCMBiWVTZG+wOUS5qWGL2FyNf7H9K+FXeKuh/DYsl63isKyw7QyF5Ewzf4zca0xZfIXGgrEw8Q2R7U7igGC3pwVFUc4FvgfuAc4HBiHqS9+H3/t9wag51Pavpe5Pg0ae1Dejatc1MrmNOjm7N4nz8GKhNNg68yYonAZXfwJT1kDu8VAU0+DKmStfNSNTfptcO3uoEK/fuhueOFEWorbQtGctvVoWtZa1+OMNUDAIuz2NP3RIldf7Xg9NtXHOjmPxWGaNOpI5l/WhvinA/cvXUzTzY25duQV/TBOu4Kh5eC0uHi3fwNoKN1s8IcPmNHZ7Gj27iLF6cvX3zCiObkSnZQayHTZmX9qHm88raH3unUu/Znt9C1l2K8+N6214XSLom/fkpKf8vpyHXTU525P7NGyXDSbjMON5jQqzL4Ane4oT+8e/wuJJ8fPxrHCrGa0Uz9kBrvtSomPvPypOg88rG8VTvWW+//kumf/adZpymdao0ZWP22ciPcVC0J6DOm4ZdDwRRr8amYefPkdo1JyoeRsYWSopcD1c0mwOklyZJA48tMzBvjgQmXYrVQ3N+2tISRxAmEwKHTJSKL3sVFzpTrFHsQfvHpdEpN/7Xm8ciHS0j9hXLfI/Zzg82UPOB/3vidhMCPOwUoVgPXaxOBmODiJ7XXqh3AtFnINZg8QuHlMo54wXC+XnPpdHOGzaWPQ2PdHeEGgW+XkFKeGOfT/rSAkSuTdJBlpDY3X0mSZB12yC/sR/8FBIlDHdFfJ1T/e+JNqUgXgCOEdV1Z/1L4YVkl4HjjsA4zq4oI/UKgq8MzWmbGiMeLoma0TPPedYUQDQoruqGolyJmq0leqKKM18vQhOGBHRRHblS4biopcAVTgUP70vpSAooqGsj8guGCc/b1iReHFputDuTeCtQR34EDvqWshLt4TTmLr3Y671NjaCw8WVYQWlnl0ymNQrg52qhXYT3sDdHGTzTh++lnZc/NzHrZfOXFPP9FHzWvtEaE28bnt9M1f268rkOWuo9rTQyZWaMDPgTLUw7sV1hlyH33VGQY/dZReMeDp7qlqhOSHvTIXTrwG/N7Gakv453uroz2jZs+xucO0XUL8Z3p0m/B6zBbxu6HOFfNZow9QyClrWQXvPkUPD8Nm4HB1I3/kd1kW69PbwmaIIZbahOjvwky8L/+DFZFhDZGem4zVnktnv75h1pYC1Q0p4YGVErSzJlUlirxAMyLzeDWrDDkTGPjoQP9Z49/r6JP53CIVU/EGVkKpSp2TQftRcLIHGaFupDz4mCkSGApHXjSL/mnKSXuFOMUXUkLTynzUl8vOQJ6Pt7mlXyoH7gmfkgJ6SIZlfo7FkHSF8hrR2Cc48GRDyiWOiz6Jo/Ar9Z/V8Bq1kViuVTmtvfH9zgrVjJHSjyb5CsjldG9EWB8KC9FyIxWZg7y3boQKjiTb0KanZ1konnLlyKNJ3T4zVIPbWyKFl8SQ55Md2eBxZAmvnSkT17fug55j4hipr58KpV0Q89a79wF0ZiTbESrBpDsCuOgNr33urwdmBrksvCOsqh3Wj7dmG19b7TXg8PnKcVp4fkU83F5jrfoSV08DRAVfhg2Tm2PArPpZPOYM7l66n2tPC5X/qSo0jlcoBZeSmKVQ1qjywspq1FfVc2a9ba9bAZU988PcHQgm5Dm1WyPktY1fGUZuPibqj74lqheaEFE6V0iVnbjxRedRseP3G6Os04pwzV/g3KenRa0dbX2XjYdwysNghVB29MerH3OF42RC1eQ+tdbypKihNO+J4Nyye1NqwUXHl4x+yDG9LgKwUM4GQyt+XfU1VQwv3DVxEQXsbWO2k+Py8XOSnRbXgMWcluTJJ7Bk8VXJI2/Y1nHI5DJwmAakEqN4PJUyZaVbqvD4CwRAWc/IQdDAiFFKp8bbgD4QIqSq5pnrsQT+qPQtCMRLX2l7uzJVDsxFxOBiIXJPIycjuBlM+E8fBWw2eYLQS04KxEmA5+s+QdXgkOAlirzXJVK1UCdX4jIEJ/j0dBj4ke0GsnX/jVuGGHnmmCGZoQSGtO/Vbd0vGJL2TnEU822WPMlujy7cKBsXff9QcKbMyQqIA2hXvCI9jV3tnEq1oiwPxIvCZoiivABXh17ogsqkvHKiBHTQwmmj6btEgaTpt4mrRVJ9HavrSO8vE8zcJ+XPQY5IRMKeIBrKnSrIW/kboNU4WSP+7RcpVvxh7FkuNod5TH7c0vhTESILNyGHR6hN1BG7l3PsjBmTdK7LJrbrH8FDYzW6jGT+zBztJX6xrWnfRLAg0Ywor4Zhd+eQNK+GRkSdQ5w2Qbjfj9amojlzWe308++5G1lbUk5dlp53DxpKrz9htxiCpnrQbtCW7YMTT2VNJUS0K1P4YuY8zVw5EYxdL+ttql2yCPpIEssmNXiBOdaApXr9b4wM11oQljcP1vEOeTBxlcuRGK5OMnA0fPYH1p/fJGbN01xk4Zy7d7PWYX4sQ+aYOKeHylS0Meek7CrvnMH2AHed8yZqluvJxjJoHandEZTqJJNqA12+C7esh/1T4dAZkHgZ9/5Lw41r/hozUvXcgXHYrKrDD6yM3I9mN+mCB1jAuFApR4/XxxKrvuPHcYzgi+Au2sjGRQ/F5/4g+GH/5sthGb7UEFM+8KUb+eg5UfBK5JlHwMBSE0hHR5wG9EpMz1ziwo4Yir0Eko1G8SPZ+vfT8qNnw30WSSZ41qFVNiqwjhXem5zqMLoso7jlzpYIjFJTf/9s3oH03Eb7Q9rUJK6KzIhpfVFP+M1nEeUiU6UsUQPM37Xtm/neEtqgw/UNRlCXAMOB0pFqtEhijqur6Azy+Xx+JJpojLL3oypcFoe+GqFt0oaJ5BBwdsIaCKM6O8PQpcl1RqUiiaRr0S6+KXngpGdEa9znHRhQMtDHEaijrxzZqjhzOJqwIaz1nywHMbA3XBaqSEQn6YfV90O8WiUZoJVTHnBf5Pbzb5drMw6RWsn4z5oUTcZx1Szxxqak27jXX0vFUDFxEi8lFwBviplejG8eVfPQT15zdjXkf/0xRn8N56I1vuGFAQZTKjWZwfQEhTCfVk3aBtmQXYpuc7U5S1KgkymqXKJD7l4jutz4yNboMnJ1kc9E3PjztKlH4WHp1Yv3uZrdsOtrm5syFdx4QxSSdypNaVIqyswI+/Fe0DOz7YRnY6m+xWiy7zsCddUtEZz38/OzXxnPHgDJGzKlnUq8MzPOjs4GWBaPxT3wLU2aCCFcSSehR8SmsXyLiAScWyeHpg8eg1wRIzTS8pNbbQnqqBfM+lGBm6rpRJx2IgwP6hnF3Du7O/ctFEr2TxYPtlTGRQOSpk6FkcOTg3a4rqtkmfWk+fgb63xVfpbBgLIxZKPZ17GJoaZDqhlip97fuincC9EpM+qCo9pllU8LqeQb2urFWDvzhslAy8yHYDH8YLrbfmSvnCq1sqnBqtPiF1iW7ZzH0viw6UDpqjgRcg77oTEjsODasgPOmCqfCbJXzTCIkCqCpwX3PzP+O0CYVJlVVvwG+OcBjOTiRaKJldJaGLd5qyTb0v8cwmmqaPxqbVm84piw+tVg4Nb5GccE4yS6s+JssvLNukQWmz3DYs4QzYTS2tGwhmy7SlTYNnykHMH2Jx7ilMubeE0S2zd8k6k2eKnET9fdVTNERi6FPSQ+L2MWWgG/hsoVIT0+jxtPCnTXscxkAACAASURBVIO7hzMPbm5ZuI6XJpzCI+XfMv6MI2nyBxnRq0uUyo3e4GoOw+xL+0R1p/7dch2M0JbsQrjJWRRPJ1GtZ6KSqPSOspGddiUUPiikOu2ZWlmfPus1ukx6RAQaZUNw5iaOkGkKSNrmpmX8Vt0l33f4A5gsKJ88BwWFsnnoVcsA/nQD9L8H08pb47Nougyc2q4rir6eNiyHmJsm86mz02Q4p5VgclNJoo1YN1/mfPehkqXrMQaWXw+fzBRVHQPUNPj2qXwJ9M3kknP1YIG+YVznzFTuHNydvKw0PA07SNfsjJ674N7UevBWxiyUIN6pkyXIYnTYNVvkvOCthjfvkMzC2MVhpSWTROdjbaV7U7QSU7ujjO9tSU1sr8tvC1dYOCSoZFRarf1uOceKI/Ph43LeUNXw2eaG+C7aC8ZKIPSNmyI9KIJ+43HUfKfjdsyVc5q9Xfy+liiAZrHve2b+d4RduGgCRVHO032fqSjK84qirFMUZZ6iKL/9nI6RotLFL0tpUrsjoVMPKZ9YenVisrI1LD323jQ5yGvRz4JBkfKP2Gs8VZHsxIq/wfb/yuf1akur7xPvXD+24TOF0LTAoOZbU0QoGCTOg69RFnv7Aimv+ugJiXgEmsUJ0e6bSILNlh6vqKTJzeoRVsL5qcbLRc/+m/uXr+fGwgJ6dnFRWdfEDq+PN9dXccvCddjMJlx2a5TKjd7ggvAdxr34KQrK7089qS1INGdjswsmk6RlXV3ka6Iaz0QlUUG/ZK4+fjbyuoZYhRDNWZg1EJ46ReZZ/3tEs3zoU9FjHfpUJMqk3VcrN6r8XOZ//RZJaXc7J7KZ6OHKl9/puzdls3z7PnEQLi0nNOENvJnHUHPeDBrHlUsEuP89kXVVfhv0vwdvyEZelp1MTREl5v6qObmpJNEGBP1SytGlT2QvyD4aOveENbMSqr/UeFr2qXwJwJUm1yebyR080BrG9eziIqTC/cvX4wuEohUKE3EXWnZKcG/ZlAiXTA9XvkTggz5wHQEDHpCg5r96SQDQ5wFLivF1llS4bq00tm1yG3+mYUv8mUOz1+5N4DocWuqNS6v73x05v2gqev3vEZ6bLU1kYzWZ2djfOxSIqEYumyI2e2RJ9DiGTZczlnbN/DGw5Yt4dUEtm56aKeI3f/1WAmm53aV6oy17ZxJA2zIQU4GV4e8fA7YBQ4ALgRnABQdmaAcJdhWpdXaQyaipzOyOrFz5Obx1hywYc4o0PnH/ktij1x/cP3xcyjf0ZUz6ur9QUNKVagjUFGPjk3UE/GWdpDdnRzrstjaxOzNMdg00ASYpD5lfnNiYBZriuBWq63AomouiSay58nEPK8Fsa8+TS6TirbKuiVsWrmtN37qb/K2vN/qCuJv8UZwGfYdODUkZzV1gT7ILbUHCkqgmUUs6dbJwHfTzOHbO9L3eWLpVIz+HU/Sgwpt3RTJlIPfV94QYNUeM/6LLxdGesCI+Ta8R9M68CZp3SH1tmLO0efynXLJgMzOKe3FMuhOlucawudJRE97ktSlnYMcTWQvh+wdGzcPsTG4qSbQBP70vXXWP7Bf9etdz4INH4OcP4Kiz4i6r9rTQOdO+T4+OZCCSDsTBAo3Dd2W/rlwz7wtynCmYTQoz19RHOtUbnSUKBknQLhSKnAliM6tFpWIrvWHRCa33E0Si+WOXRARd9GVCb9wqZwrt5wuehX8/LaWgjhw52IdCct8xC0X5Ud+3QcsAJDovZHQWOVn9eD5+BvrdGpFk1WeH9dUSJgvkHheRn1VM0iW7cKqMLb0zLJwYvW9owVs9h6EtAiP7c+/8jWNP/yq9VVW9Q1XVX1RV/SdwxAEY08GHXUVqG6thx0aZiNqCjvWK9dFUTxXsrBDJywVjxWO+4Nnoa4rCGvT6hVj5uXHKckPYedhQDjP+BN+/KTrQl5bLffQa+dUboGZDfE+IZVMiXa3/9DfJeDx5Esq706TRS0Yn42iEt5qgI5dvBy+mYvynfDVwEd8HOvL3/wvRULwS/3XraB7/Jr52x3L7kvWsrXC3Xl5Z10S2w8a0ESfy7LsbASFCZ9qtZNqtlF52KioqoZDaanD1SJKmd4O2Zhd2B23DMPr/V5F588kMSf0Omx75XGwmKtGm4jocLnxOsmANW8UJPvPG6PUwqlRUQy5fJRvGew/LhpJzrPwcaJHnXfqmzPvCqbKpbVghBMPTr40ad1WjSmVdE5NL17C1vhk16IuUBxaVikNSOBWbCbIafkB57myUFX+FQY+hXvuFcB86dMdkTs6/JNqAn96T+dr5pOjX80+TDPB/Xja8bIfHt08SrgCpVjMpFlMrITuJXxehkIrZBDOKe5HtsFFZ18SV/boy9fX1XPrHrtz2YYA1A8po7nAyalFpxA4WhHtDzRsppGNXvpwJtMzq5A8igcTaH+T84OxgbHMVBaypUlJ6+SrhqL33cCQgqTkazg5Q+IAc0L3VUH6H2OfV9wlnM9AiWQTNeRhZIhnlBFUIKAaloD0uMe5RFdsT6L1p4nz8+S449wEZ34YVEhR6sRBqvo0X69CCt3oOQ6JseqOuaej+2jt/B2hLBiJXUZS/IomzDEVRFF0H6uRfNuCTya1FArRoataRQiwyWaPVYUYvkBSiRoB25krqUFNn0iKtf/qbEJL1UYiGbcbZitrv4YjTRYLMZI7XVNayC2/cJN2CjYyKdsDTE7M3rBBjNexp40hHWjtagib+Xv4jayvqAZg/qR2vrNnMK2s2t97+/245m+qYCFhelp0OGancv/y/rK1wk5dlZ8bYXgRCIW4s+08UObpbjjNJmv610FgN5bfH//+PLAFFFQeg/13i3KIK2T7FKY2I9FEuo07qrnzJwKWky9zXomUFg2RTa3ZL/Wr9Zph5ZvS4+t0KvS+PViApKpW1pYd7U0QL3BXdy6GyromqhhY6ZVmxaiTwqDk+F959KKoOWXHlY718FSSdhyTaip8+EAfZEkNitqTAEX+E9Uvh/Edl3YTR7A/S0BLYZw4EQFaaLc7+JvG/h57Ld8ZR2VzbX2TLXXYrb66vorrBx5X9uhKwW9kcSMEcCHJE0Vyxj4oZSgbFZx4qPxdn4ayb4/d9z3Zjm6uYJeCzdq6UgKZmxnMinLkS5NRlXRn6VEScYn4xfLVA7HRjrTgY7z8qJa3pnYwlWxt3xI/HkZM4sHRpuRzgv9IpNC2bIiVWWsBH44MG/ZIVmRvD03z7vmgOw/6QL0+iFW1xIJ4D0sPflwDtgWpFUToCXx6ogR0ysNjk0P32feLRN7tlMS25UhZ3wSCZ8DsrpLyopUFKiLT6xb7XR6TPNLjy5SCW1j4i9aq1izdamFrTlfHLpbY8NrswuixcloTwNYy0o7WUqbc6+vdzb5LIweq7I+nCjM6t6c40Vz6zRixga0snMq0h0tKkqZzmUORl2Um1wBuXFdDS3ITZlsq2gJNUq4UlX1RySZ/DuWNwd1KtZoLBECNnfJxsEHcwIeCTzcW7PZpkrCjgawbU6GaHQ58SMv7KW6HvdTB2qURfLVYY8TwsvDw6VW1Nk5rZRZPiHddBj0GLR9Se9HDli9JHjDIS84vjm8p5qsSBmPwBAauTR95xR83NWq+PhuwM2g18SHgV+maM88dEmjFqSG42SewJmnfC1i/hhFHG7x/dH74vh2+WQY/RrS/XemWOZdjbpHOyS2TYLckSpoMAei7f8JMPY7O7iZJL+6CqcG73XN5cX8XkOWvo2cXF4xf34P1ffBx+VAhl9lC46MXoagQt89DhD3KuiC0NWjZFAn+x54Vh00X90VMlUtdfLxJVPL3i44ePSwZgfnH8PbUzAIhSo76kGsRuFy+CXz4JZ0QC4rCE/GBJi5eTT9gALkX2haAP3nkw8p57k7yWKOBzxTuy5nZsjNh/PYdhf8iXJ9GKtsi43pvg9W3AuP0+okMNejb/smtg4CNygNEm9Zk3ygJfclX4cHONfP3yZVncgRZjjzjFKc6Gvq57ZEkkW5F1pKQrA03ihHz4uHjhRvdqdsuhbuAjElXod0t0ZEEzJCNLJIqgh1Z/rpFXi0ojtZIAzlwyAzVkfvaMOCVKDmVFh3HHOw7+70c3z4zugavhBywLxojChCuf0JASblsVYELfo+iQkUqeK41ar4/N7uZkg7iDDZrBrfw80vfElQ8XzgRnZoRLA/L1kxnSNGjAvcINevN2MeLDpktvkSFPSmMiNSQH9pU3J86KZR0pvSAyDpMNQ6vPHfqUEP13lUnTmhGl5cAnz8ExA7C01DO1/2Hce3Y2FR7wmtN5Y91WBrSrhvmjo50gra5X2yw1JDebJPYEmz6Wud7xROP3c46TgMza0mgHInzg3x8ZiIxUa5JEfRBAT552pli4au4XrRn1kom9eXzwYRD0YbLYwOSl+Hgbyqyxkg2IVVzUMg/n3CMBEiNbmN5ZnILxr0kp6o4forkFZePCGYSaiPS65mRkdDK+Z06BBHRc+YnLUv1NUHCu2HdvtYzz1MnSdXr1PTFqd/80dnIsKZK1+/T56Pu78qWHxID74tWa5o+RkqysI+X8dNGseA7DnsqXJ7FL7NaBUBSlHTAF2II0jrsN6QfxDTBVVdW6AzrCgx0aYfWyVZEO0eOWSdTeZBWSUMVHsihsjkgK8ryHJY1odRh7xLZ0ePXS6AVSNl68elNY47i5BvzNcsgZPiOxxJqzgzx7Z6Us6NjeDWXjJEvxzTI5+PW9LrLwT79GtJG1fhIpGXKQ09KH7Y8RMtXp17Tq81tc+UwtmsunJ5+AtWUHluXRNY7Zr41n0oAy/vLqOhaFG8f5AkFqvb64BnHnds9FURQ21zUmMw+/BowM7vCZkJEnpUX6uaZpl8em0t++LyLFOucCcUI1JxsSiw/srJSv9ZXi9BY+gFRSKkBIombrl0okzJ4l81OTInRvgnZHw/89JmpNJ1wIC8Zhcm8ixZXP0cOm47XkcP0ZWZhKCo0jbeW3hWth85ObTRJ7h4pPJQKbc4zx+4oiZOq1s6F2I2R3BSKkZ9d+cCBcaVY2Vnv2+T5J7Bv05GnNeQDIcVrJ9v5A2tLx0YfoFKf8XDhVOjLre+AUDBL537kjZO82sp/V34gNG7NQDv3WtEiwsfLzSPm0qorMa/0WWH2v2OoJK4zvWfezVDEMe1pkvI0+k+I0Fmnpf5cEk7RAlPb5E4uinYrV90hQaclVQubesTE6ePTJDNkLEpUixfYr0nMYTCbhzk18QwKuZqs0nEvyHPYKbfmrlQIOoBfwDtARmAY0AbMO2MgONbQ0yAR+6Tx4soeUEu3YCD3HwAUzJIrqCLee73s9pKbDzk0inWpEvA40xS8QZy401sFr10ltuTlFUoOhAOz4Uco9LpoVT8jeUC7ybWXjE0vN+huhy6mSknyxUAzPWbfAf+ZJd8pVd0u2BFWMy3kPy2ee7iO/u2bYwvczzR+DtbkWh9m4MUtumkJlXRP+gBz4bBYzC9dUMG3Eia2E6XO753Jt/2MYNePf9J32DsOnf8iG7Q2EQipJ/I9gMslGMegxuOojiWY5c+Sg7m+KzLW83jD0aYkcFU6Vn7XDeN/rZe7mHidzx+aMluD78mVjacBPnwNU2eTenSYGf2clzB4CT/YUR/iUy+X6WYPkZ8Usz3blQ9V/JbJr1BRp6dU4PJtIYReNIovmilLa5avg+q8jUn/JzSaJtmLLF5Jxi+U/6HF0fwkIrZ3T+pLWt2FfZVxBshjuJj/+YGif75XE3iPbYWvl7umDZHf0y8GlOQ8QIRLbs4QH0OF46HMFpHeQg/71X0mHZs2mBZqMpbC/WykZX0URZ2LV3bJn//kuadg24AGYdb40t50zHFBkX3fmSpnQuKURMZaCQXLP96YJgTnQDIsnx0upjiwRsnVsQKbHJeBrkGxD7Oc/flacilmD5KunKkJ+XjBWsixTPpO9x54F5z8sJaxGRO1QAJ4/Bx4/Xr4aSbhWfwsvDZRz2ksD5ecEUspJ7BptKbDsrKrq+YqiKEClqqr9wq9/oCjKfudAKIryCCIT6wM2AhNVVXUbfO5noAEIAgFVVXvv77G0GY3VUPdjfGT/42ekhEkrQzr9OiE7aT0atIX+1YKIB56ZL+RpR45xXaLWMXfY09IsLjb1mN0NJrwuCxxFDnpd+8kCKZwqPxtFDRztI+PUxr+gWKIbIDW8sY24nLnycwKnJDdNoapRpYvB86oa1SglpWyHjRsGFPDPtzZw5+DuZDtsdMxM5eKZxpyIZDnT/xBBP6RmiXEuGRJdUqdJ/Z06OZoLMWp2WLHjXsjME71vrU5XU1Ya8aKktd2/iHrYuGVC/PNWS5Tp1Mki6dr/LtmA3L/Er7EF4yI8BfcmcWQHPSa1tZ+/IM/KOtLYSbCmoSgm4/WQ3kmydmaLZCGSSGJPoaqw+QsJzOwKadni9H45F86+HczW1gzEvqowgTgQqgo7vD46JLtR/2owmRQKOqSzrb45KtOel2E2tk8N28SpPGWSBA2juGOpkWt2VkoQpXCqRNerv5UzReyerWWDl02JJhxrz9NktS98AZrrosuKRpaIPdXKn7KPFvU8X5PIwnqr5Z/WpE5PcG6qE1K0Z5uUJeklYD9/Qez89q/ix6mNy1sl9ticIuV+DdvgnakGwi5zRfBD/zu9M1UcDlWVjISKsQqTJvOaxB6hLaE0k6IoWUAXwKkoyhEAiqJkAweiGPgt4HhVVU8EvgP+vovPnq2qao9f1XkASZcZHaI1aVTt9fw+8Q3elk2REoz5xZKy27kJXr9RjMKKv0U1tiLnuMi1GZ0Ntevxe2Vh+pvEyPgb5WD3VC+5T0p6fLR3zEJxLC54Jlr6VeNPGDWSW3q1vA6REhQ9wk7CzDX1+EfNjXpe7ZASZq6pj1JS0ozrg8NP5PjOGRyV4yCkqsn+D782QiGZT8118aS6svESje9/l3E39UCzzNvUrPi5uqBY5qLJLFGt3OMi3VPtWbJ2NClWs1U2nkTZM63JnPZzdjd4/1HUPpNg3GtyX6Nolb9RygGNoneKSRSgkkhib7HjR7Gf7bvt/rNHF0rk9fs3Aaj1+Ei1mEi17rval8suNjbJg/j1YTIpdMxI5blxvVsz7amp9gSyp2Y482bJMGhZ3SPPlINuWjZc85lkEj58XA7h5beJ81B+m5wpjJq/as3YEvEmrGlSshybsS0bL/fUxrb9vyIX+86D0nSupV7ssCU1vuFt+W2AKk7H+w/LnvFioewna0sjhPDr1kb6Aul7QPgb5b4ZnWRs/5+9K49vqkrbz7lp2iZN2nRnKUVQqLuouOKICopaWZVWBApuMDLqOIvDfOogMsgnKqOfIrK4QCkioGxaBAUVHUbBKowoCoJgKUv30qZN2yT3fH+8Ob03yQ2kG1Q5z+/HL03uzb235Szv9jzv26NI1IOrwOh3gIe+Ae79iIKuesELUVL75i1aRqLRGbr0SaLZCCcD8b8AfvT9fA+A1xhjHMC5AAwJ1q0B5/xD3dsvAdzR1vdoc0REajKVthTN87Z3pgkvarRDSZZZ4rVyo/w/0/dX3h9ssI9ZqUVLVePSIKgemuyDZtCkDmokk0OpyWGv+rx6My0ARvJnQv1JfNfouQFawALUFbzZb6F7TDruNDnxjy2HMfLGFUiLNSE62gK3OR7Th0cE8RkUhSExJhJltQ2odnngUXmTOoWA7P9wcqCqHOW1jYj1ViBq2WgaL7YU/1rVPeuJqMfV0JvR6gc02b3A4/VVlCofMJXGzuCX/LkRgG68u/3f64+7Kv3fMwXYnQ9WvBN83Ptg65/wrx/2RfFUWycADMrWef6/19Z5wK3PyVIlidbh8HZ6TQrBf9AjrS8Zhd/kAmdnoszZgDhr67MPAJquI5WYOgZEsGzVpH7weL2oA4dHNJAT69MdC2n9EaqKjnQK/AHaXp2RSVyA+moyjMflAwrzNZMzKIHW2xoiqBK4lrrrqOTJ6Lv2TlQVEd+DMsWxXYBBTwO5Q7Tz0/pSZiJQXGPZGCpfvWgUlR8JUQxAE2i5ex3xQvWy91m5FMRivrXY00j70A1TgrMPsV38fyejwKfo2RX4e0thjBYhHBWmpYyx5QAY59zDGFsDoA+AQ5zzI+38fPcAWBbq0QB86HNm5nHO54e6CGNsAoAJAJCenh7qtJbDmgzE96SJW1/tL1OWtVhr0jI6BNnJ3pkatpmjafKEUjeor9LSdtWHja9VfVib7NEO4+s4S6i2+7NngWv+ZNxYLnMWpQw/nkYTMdRiAwDOEtRHJwPjNqCi2okjThXzP6rGQwPceGnTT9h+sApvf6199bNHr0O6I7gESa+TLdQp5oy+BADw4a6S06r/Q7uP2eNA//+wNKsrulUVkpMgDP2mRTuPsgyiHtXIsK8qpHOMjteWEh/ivYdpU4iKDZb5Gz6fnIdPppOKWOBxMb/ENYfM9idSqx54Br9M6ibjPwBUN7xQoJosONxogb22HEmBcoAiAyHRbJzKcdvhcGSHca22ERQTcOYA0rwv24uymoY24T8AmpKTzEAY41SMWREsO1BWg0RWDbc1Ac673kOj24t4qxnsWGFw8K+2RCvhFNF1PVlZlJPeOpNKdkLt2VmLqTzTIKiCyBig8hfj71oTNWEXsfZGx/qfV1QQOqDkaaSMhHAMAI0cPXSOT+47mngeqpdsmWgH8NUbwJUTgaqD5NwIgrWfgzKahGz0gh9GAdvNM2nf0qtQSmGMFiMcFaZLdD+LH1UAnRljnTnn3zT3poyxjSAydiAe55yv8Z3zOAAPgCUhLtOPc36YMZYC4CPG2I+c88+MTvQ5F/MBoG/fvm3PwFUUIKEnddINLPNYPlar0d48M9gAGjKbCNExqcAtMynLEGry1xylWsdx71GEIXAiDJsLbJyiqThV7g9tuCkmImSzEPWXju70nD65OPfIPJhXaPfyZr+F/Q2xiBq3DVWNChKiuiB7/la/kqPvjzjxz6Hn4+6FXzV9lhZvQYTJ2DjT62QDVK40ack3eHP8Zbj3mp5Ii7egc5zltFBhavcxexzo/x+aOCyqV6vDBXRRpXXAB5OD61H1TXxqS4OPZ+UB6/6sSbgOfYW4P5Z4rW9JbSk5D0yhcfjBoxSZG/c+ZS4YAwoWUbnTVZO07MHl99MzOtLBKvbBlNgLi3d5MexsCxrrvbBao+A2x2Ls3P/g/27rgqRvDTIQg184mX/y3wxO5bjtcDj6nS/aG2Yvh3MGA7tWA/9+AaXOrDaRcAU0B0IQsyX8carG7DFXAxJq9yJOp77kHrMGbNsCoO+44H1ZX8JpFF3/4hUqJ3U3ADWHg+VRs/OAqDgK6NSW0/kjFlBJFFPoOx88StczahwaSI5ePpbW/0AbQw3BsxQqlaISYvS7tG6763yN6xqIJ7Hh71p36xHzwS+4HezNW7RnGbva2GbxNpLIxX0byVlhLPg5nCV0v7s/0HgRgUpNEmEjnJWtAMD3AESHMb31xgHc0Nybcs4HHu84Y2wcgNsADNB1vQ68xmHfawljbBWAywEYOhBtDlUNlgoDaAAfr9SnqIAkysb7CJ+uSo0sdMVEUkQQqcmsxcFk663zSM5y2+vUQXLrPCr7iEsjZ2HjFJogQ2aTAabvkN1kuOUCm5+jidv/UepibTTZq35pmsSe/v+Df21nGHjjCnS2KYiz2/DOrjqcmWpFp7gE2GwMjaoxX+GMpJgmwlhavAWvjrkUySEyCEInO/AaFbWN+MuK/2LVpH6nhfNwqqH/f5j+aSleG7wIieYQY1t1a43mctaQo1G5XyuBGzaXIlebpmlNiGyppAxWVEBzICOT+BBr/uA/3jfPBG56Wiujc5aQ47zmAfr5joXA2bdq3a5FxmJHHnCPr7zK2wDGFIzNcIPl3dw0v/igp/HuXd1Q2cBw7Jp/IO7dLBmRkmg7cA4c/RbocnH437HEA70GAd++DRsuQ2z8eW3yKNFmE6LNiixh6mCwuisRJZwHH+nYDJW4kjVHg/dlUSYtbAr9MZGR8BOyWOzfiPbTmdQ0LsanojfgH0BtGRGPb3wKeONm7Xr6RnVKRFNZqB/E/QODoooSbL+MzAW+eNn/u0oEZTxclfQ8L13kZ+vw6yaDWxKhLLylZWVIitm4O/WHT/jUKru1+P9OghCOA/EXALeDZFvfBrCKc95uotKMsZsBTAbQn3NeF+KcGAAK57zG9/NNAKa11zP5QVVJGkyviz9mFXn1NSHKivQ12s4SoLHOPwWXnecfTRATdVw+kYYUE7lqmbNIe//SHKpBvPL3lCI3W8jzHuFrurLhcYrKig7ZIrrqrqPnvG4yGUjr/kqGX6CTMWIB4G0Ef3g7dleoWLjFievP7QSPxYydTjd6WmJwdW87ymoaMP7Nr5Bsi8KsrIuCejikxVtwrK4R/7jtXDh8UoIvb9qD6cMvAKv3BHWVFjrZgdeoa/SeNqVLHQH6/4ftB6tx33rgrTu7w2I0trmqqYV5GslR6DOKMguuSopyZc6i5nKqbyz/sA5IPYe+v+VFkjkObArU1AguiZrRiTrXSCuNc1MkKJbBSb0JoDlhigSufpjGvq5+mA2fT1EuWwpwxUSw3CFIrSpEqiMdx4bl4sfBaxEb4UayIxZme4qMSEm0Ds5ioK6c6sWbgwuzwPd9jIcb3sCa6P9rs8eJs0TKEqYOhgju1pwHUdMvgiwfPk5Grquc1lZ3HZDYWzPMA3vnGGUklo+lvdzehYI2tzxDx1QP8Pks4iMIw//y+/2vJ3gJoh+Onn8p4EgnedjNMzUbAwAiYqgcVXSiViKomef2PP/vVuylMukdS+n+2XlNdgrPnAVXZDwsrqPhlyFZEoNts+wl1PRU9WoBW2eJ5Dy0EU64S3LOX+CcXwNqJtcNwCbG2HLGWJ92eqbZAOygsqQdjLG5AMAY68IYW+c7JxXAvxlj/wWwDUA+53x9Oz2PBlUlJ6HR6a91X/kzDVoR8dcrwNLtIwAAIABJREFUuozMpQki3g+ZTZ64Xg/ZqFZvdz7d66U+1PkZnGog37kbmN2XHJAoOzVBWTGOlAYAAIx0k1PPA+5arjVuWf0ALSIR0UTUfnMQRSwAzckYn0/pQWYC1vwBrHwvesabMf5iG+KiibgcaVJgMSuIMZvw6DvfoqjShd9fdyae+eAHvx4OTfwFBsz9dB+y53+JiYu/xoe7SlDX4MXwOVuCejsInWz9NeaNuRQXdYtDRqpdZh9OEgL/H648Ix5Ran2whvewudR7RKiFbZpGWS29+kb/RykbtjATOHaQVEI6n0+Ox9BXKMOghCijSziTSP4j5pOTYI0Hyn4iB7mhhpxf0Xcldwg5D4qJsmeBqk+rJtAma7DRxq3OgdvjwbHITjDZU6XzINF6HP2OXhOa6UBY4lF77p24Tvkv+rs+PPH5YcJhMcsMRAeDEhFF66h+TdryImVsu11Na5lYW/P/AtQfA+K6EkegSx8yoo9nQwgewPq/088LM4EXLyDexFUPEQ9SKC9+946xGt2WF+m7Hz0Z3PMhO49snqICsjE2PkmG+ps3Ac/38q35h+icXgODr715Jv3eN00jh0PsG/l/AastgdVZBFa2J5hD5CyhUqy71/v353GVB0u0LhtNNtLqBzQp/LGrKSArez+0GmEWZwKc8/0+ArUFwFgAvQG0eR8IzvlZIT4/DOBW388/A7iore99XBhlHkSdt6hNrCr0j/jbOwP//hfQdzylCMGA1RNpwl2YTRFWszW4TT2g1Y5fPIYWGE8DRbVE7wVRRzh2FZ1/+xuUZvQ0UJZi2wLqKD3O54iIFKZwGkSUd+grtDCJDEX1IXruOxYC6/+GyIFP4ez3H0DV0EW4e10tSp1uzB1zKVJitWY4DosZH+4qQWmNf7aBAXjwre34x23nYuJiYlGnxVuwv6w2ZG8HoU4RmJ2QOHnQq4Rw1YtkbzFY7hAaK2LMuip9cn86WWKRORuzksaqq5LK5fqOJzWyuDStDMkcTdmFY0VUq5qRSZkLwUPYsZReXxtAx26cBtSVUcnU7/5CsrIrJ/hvFivGkaNxolJCg2PnpkSBxUknVaKNULyTXuN7NvurRWmZqPjmC2QenIUfSy6GM+XSVj9OrCUCZTID0WGgqhy/1FuRNDwX9kiTPwer8Evgsnv81Y2qCkn6evS7tF5yDhz4gtbaunKyIYzW0Ip99Jk+aGJLIXsgkIu5czmt74m9gOLv/OVUd+dTMEiUNZXvJbvBWaL1fEjqTcEbvY0iekt8PI0M95ojWiZAXJsDeGd8ML8uc5ZxGfaQ2eRsXPd3arInAj6eEOu+EgHc/wmRsoVgjMhayMagrUI4JOqeAO4EMBTAQVAZ09Oc8/p2fraOhbrSYO9WpBz1tYnCG3ek0+S++mGKJAhFATFpNj0FZP6LzrWlGJOrdy4H+t5L3aEDnZaiAvqMc+oe6a7TSjYyMsmrr6+mkqUPH9fuW7yTnlncN7D2fPh8MtTc9X4dIR1rxuGJG1fg9sX78Pu8r7FswpVNZS5VLjfS4i3YfrDKz1H4x23noqjS1VR6JDIKT6z+zu9Pq+/toChMNonrAGj6f3AWA8eKaXxsegoYOE1T7rhnQ/CCXVtMG5z+fUwyjWO9YsjQOXQdZwlpePd/1J/wl5VLY1vU9ooSJ/Hd+O7Gm4WzmPo3hFIg8TQYHjOZo0gCUUKiLVC6myLJUbZmf7W8XsEf3Q/hY+s0nLspBz9fOQNlZwzxn1fNRJzFjJ9K2q3yWKKZKK9txNg3vsI1PR2Y0Y9DEfLVwm5wFhuvb64K6qHgSKeKgdIfKaDTUBPcpDZrMfDVAipV0l+r3yPBGVpRMhppp6yvkZz2sSL6PHMWsGQkrc1Zi+negbaL3kZxdKdMs8nsX7otrnu8nhRFBQFB2U7UAbuogGwZfQO4iBBqgBGRFOwKVJuUDeRajXBcr70AsgCsB/AFgHQAkxhjf2aM/bk9H65DIZR3G+OTcL1zqX+Kbvh8MuCX3AG8fCkZ6eBk3AOkumRJIBL0sLlAwhmUThyfT1GGrfOof0Rgd2jRDEbcB6DUnZjAwuBaPBxYcD0RiG6Y4t8cTkRi+08ObhizagI5D9FxWgrTdyzFShtYUaULHpU3lSzN/XQfnrvDv3xp5u0XYu6n+5AWb0FavAX/mXw9ltx3BeKsZjx26zm4uJuj6c8oezucYqiqb8M6SK/61K6nkaJVjnRatDdOobrah7dTba0+vZzWV+s4rW+AGB0fPI5FI8KqQuqoHjgOl+eQRGCoJoZCqUwPkbUzW8jJ0M/HEQuAiGh4O/eBmu3f2FCSpiXaHGV7gNiuLfuqi6MMcSg47wk0xKSh17//hIxPJiCy7miLHyfOEomqOjfcXlm20REghCpGnmuFslyn3GhLoYi5vXPo9U2cV1eulTjVVwY3qV0+Fjh3aHCj11Ay8YlnAR89QRzNwHKlIbMpoyHKlgSiY42dEb2NUraHnJ4f1lGG+J4NdJ2MTLqPs8T4dxUy8SIou/oBoOQHLRhaVejfAM6aHGyHibU9lP0mG8i1CuGUME0DJZkAoPnhlN8KQnm3cWlkSAEkaeaqpEkFBViUqRn1/R6h+sBbntF0mquLaLHIG0ELQv/JRLqLtFH5EWA86JN60+QbMIW+H9+DPPQtLxobXCJTIjIjYkGJ7xHC87cAFod/63oHdZYGfFKsCsOi/+xvKllSOcfsURcjzhqJA2W1eH7DbpQ6G7Dw7stQXtuIiYu/blJieu6OCzF1yLmYunYXSp0NkiB9KmFUmqdP7UZE0sYh0shFBdSgcOX9NGb1WuL9Jxt3Rx+fH9yEbsuLmiOrevzniTiHmUJvdlwNlikUSmWWeHLIc9YAXjdl/z58HHCWYMeNKzD/63q8NO5DRDIPmJTxk2hrcE5cnTN+16Kvl7nIyLfa47H/sieRWLgeKXuX46K1g7C33/Oo7HZjs68ppFwrahuRGhvdoueSaDsIoYoUq65pW1pfyvDmDqGSTyMlRqHaOOBJciaGvUprZSgORHwP4i/oy4D0FRMCjnQypp0lQNluWp8HzaCIf6SdMmk3/y/xzESjt36P0M+B97WlACnnkKNgSwUO/JtKsdOv0MqyBIfCmkhzZcxKUu/btYacnoQzybYRDedE5nnTVP9n1pOhFcVfxlW/th8vOyHRYoTTSG7qSXiOjg/h3QYaWvYumvFh70xRgW2vUw2jMIoCuyYOmU2lQ+56TVu/qpDSgo50WhQq9gM9+4eWWB04lcqT/GTb8k7c7XpkLtWf56wlvoNR3aQ1kTSf+44n5QRHOqqGLsL0daVNUqwmBXg881w8nb+rqcnb3DGXomtsFEwMeDzzHNS7vYhQFIxf/JUf5+HRd77FP4eej9l3XSx5DqcaRqV5+tSuNRm4/jHgkxnUn6G+SjPqbSlEyhe8iNguxpsJQBkAzmnM7VhKTeGEUodiAq56mIh2gZLDXDWeA85i4Id8f5nCrfOAa/9KWuYxPtURZwmJHsSkovz65zB9fSm2H6xG2eDz0dVhPTl/Y4nTC84ScrLjWp6BMDEgxgyAKSjvfitqki9B152voPdnD+KHAQtR3emqZl3ToWsmJx2IUw8hVFFVXUx9dqoKySAXwZgLs2ndGrOKjHauUka3qIBsisDS4yyfdPWHT/gF/aCYiD8m9nyA1uqsPOJUiO+PzAX2fUrrqauKnmXLi3StjEySfD12kBySnDW0/lqTKCiqX5/1WWh9KVWP/lpAFdB4DjlrNRtInCsa7woVpev+TnaV2erfpdooc6woxiVJoew3mXluFcLhQEQDyAZQCeA9AI8CuBbAPgD/5JyXtesTdhTovVtVpZo6zskAE16uKYJIPTFJmuETKiNw14rQZM/EXpSF+ODvxgSincuBq/9IkQSReSgqoAUhZw0ZY+mX+zsFsV0pQ8JMgLeevPvEXsZ1k9+vAnbno3HgdPAHvwU3mVHD4/CvLI4Ik4K8L/Zj3ucHmjgNTw05DxwM1kgFpbWNuOs1aig3b+ylKHM2GPZ2sEZSyVJz+A6qylFe2ygJ1m2JE6V2xbgf/AKltt8YpKl/9HvEn/w2drXxZrIw0z/DNmgG0HDMn9eQs0bjSIhnWJ5DG4xesi8jExj0NJ1z+X20ydpSKYvRZ5RWJ37FRLqv7/rukXl47j9ebD9YHVwyZ9TXRWYkJFqK8p/oNTatZV93cTgCKDmN1k745ZLJ6PHVUzjr33/GjqEfQTWHXxAQZ/U5EFKJqUNAURgyUmLgtTnAs/PAlo3RAjPCQdBnV0e/A9w8gyoTrImUVfBbK32k4wFTKUrvLKGI/cr7tZ/F59l51KPkrhVksygRZMuccZU/T01kdG/+X/pMdMHWc9hiUv0zJUZZ6OVjtd5XegjOWuC5ovFuVSHxFga/ROt8lE1rMmpNIpsm3HX6eNkJiRYjnBKmXABuADGgnhDfgaRWrwGwENTw7fSAotCgO17JhymCyppUlbxnd10IA81FC0GgwdV/MuBtoIlkS/Hp7L/ja/NuoU6NF90V3BxFkJacJcBld/svBFmLqXHXGVdp5On+k4GkDOO6yUEzAEc6dpY04o/vH8YzIy6A21uLXik23LngS79swsS8r7Fy0tUodzYia14BZo28yE+dqby2MWRvh+bwHlSVY3dxTVOH5LR4Cxbk9JXyrq1FOKldEdVxFqOpd8OQ2UBElP/3PpluXNLU41pyNurKifS3Yyk1NNKrdRilwsV7TyNtIhYHbXT6NPjQOUCUmyQEY1KprG/4PC0C5ruOecUYPD0uH3+9JgHWqEhEW31L34lKuCQkmouyPfQa1zIHosznQARCNdtw+LyJ6LHtSXT79iX8culjYV8z3udAlFSfXtonHRaqCqX0Byhvj6J1cMR88NiuYCIwo+eE2VKIJ1b1C0XhK/eTEl1tsT8fwGwlnoAgV2+aqh1fM0krY142htQba45q5aT9J2sOgrieCHRyblyaOnYVcRJ2vqsZ9lFxIeyd+tBKk4HnitJW8T4uzV9IZugckqBt7vocKjsh0WKE8z9wLud8NIA7AGRwzv/AOV/POX8C1Bfi9EKoko+6gImgKEDyOVTWIUg9aX3J+79nAzkDP23SorkiWismsS2FSp/eexh45XJg8TBaMGypgLtW02+2pdBEH/AkNfSyxNOEF6Ujwik451bNebhhCt3n2EHjyR6TDG9WHqZ/WoqiShfSE6w4v2scGINhNqHerTYZ9kKRCQCqXG68+/VBzBp5kR/B+rk7LkT3RGuzeA/ltY1N9xD3vT+3AOW1kgTVKhyPeGZ0bvYSrUGhJYEyAtl5tGn1ewTYvZ42k/s2UobLlgJcdh9tAG8MojT8VX8A9nwIDHmFvpedR9k4IyKd6gH+/QJJFLuqjMnYtSXUuK7/o1TSV2PQfKiqEKaaw0jilbA6f4FSuV/LPIQznyUkwkWZr0FWTFKLvl7qUhEXYml0xZ2Fqi6/Q6fdixHhCj/5H2+lCx45Jh2IDoHAdcfTCPbB3ykAE1iGPOBJciD0PSG8jdRQc8KnwKStJFNqTaL1VvVQObRwHgB/w7yqkDpQ64UukjKMbQGmAODGx2qO0vd7DaTP6qupOZzROl5XESxskZ2n9cjSn6tvvOtIJ4cpcM1XvSf+G0u0O8JxIBoBgHPuAXA44Njp97/YHDZ/fQVN/GFzydC6YQpNuDcGkaHT/QrA1pnKN25/Q/PyXZUUEQjqLJlD2QmxkGx4jK7Z41pKeeb/RXM2ApWXBFF1wJN0jWGv0oJjNNktCWiITmoq97BERiDZHtVE/NIjLd4Ck86xmPvpPj91prv79cDr//4Zz4y4AJv+3B9v338leney4YzEmGZlDoRqhR56+VeJFkKf2tU35TGK7igKbW6DZpDBbo2ncqKYZK1U7oLbSdbPmkT1twOfClZY+uIV4PwRNAfEOFbMlGkLVP746ElNxzy2K22QwmERDrTZSsfEfbzu0Aomy3Po+WqOkCSiVOeQaGuU7Sb+A2tZBqvcxRF/nMrOsjMGg3kb0fnHRWFfM8KkwGEx46h0IDoG9OuOKHPenQ9U/OwfdATovVFjTOdRMtrfGkmKi2+NJGcAzHj9E4a5PvIvDHKAApDCZgDIZvHUU0leqOuJTIU5ml6NmukOnUMOz6apwLj3gYe+Bsa9R5zRKx/wPzcr17/xbnYe8ez0z1VVSNeTOOUIZ4VLY4y9xBh7WfezeN8yltivGaLkQw8jNr+q+mrtoigqe/Mzxg6B6qVujTVHtGNbXgytkOSq0rpGD5pBNYpX/zHYSAuUUvM00oIgHA3ROXhkrv8EHrkIcNcignsw6NxkLL7nMiShCqg6iCRUIfdu/07RC3L6whKpORbbD1bh+Q278c+h5+O5kRcCAO65pifSE2MQazGji8OCxJjoZpcdhXJepPxrG0Ckdh3d6PV4qWFFoQXebCV1o9whWmbhiolEgPM2UL+FrQvIsA8cx31GAStygjfEhhrKXoix/fE02lRFbXCUnTZIfafrAVPJUWGKrn44Njjape+qarbSpul2EUHRaD4zBkNZWwmJE6HspxZLuHLOUebiiDuOA9EY0xU1yZcidc8SsGYYUom2SJmB6CjQ2xFifbv+caDzhcR9HDZXO66qISsFDCWuncX+3xdG/JYX/ddC/bWOHSS7YMBUWkMd6cS5WDba2CkIkHhvClDq+zaMz6cyp01Tae3uP5m4o1UHgXfvpVK/TVP97ZmvXgNueRZ46BtgxHzKXJgi6fe5eIx2f6me1CEQDgfiUd3PBQHHAt//9iFKPj6ZQYZQTDIZXZZE7RxRV/3JDOCGJygqW3PYeBHgbq0+UdQIFhVQ0xajmsEou9bkRUzkUI1YhPJSdh7ww1oiQ4lmc4DWOXj0u3QN7gU+nALszkekIx2vjl4J5v0F7HVKtTJHOs7IysPG35+Pcm8MOGNItUXBZFKwIKdvU4lRqbMBkREKHl3xLbYfrAIAbJl8fasaxAnVikAOhJR/PcmwJBLxPsoWxDNokguO7UoOxBcvkRpZ4DgOpRTmqaeNaGGm9rledthTH1omVkTdBLHblqKpRtWWahwhRzrVz45cRE6Cu5aUTj6aoil/ZC0G1v1Ney85ERLhwl1P47L71S36ek0j0OiFIQdCj8q0AYgtLUB80SZUdL8lrGvHWyNxuMp14hMl2h96VSCu0hoW143250B1O0UxtgVUr/E6qnromuL7XjcATs3cYruQg6Ivb9JnE9ZMAsblU4RfOAVVhbR+Dn2Fvs8UoPIX/+8rZn/7RUjGD5pB3DTO/UnYQ2bTMzpL6Fz9ta77H+DDf1BASq/QNHIRUFdJqoBSPalD4IQ7Iud8kfgH4F0A7wR8dnpBUYDks0laTJQj5Q4h0pKIVIr6xj6jqNfD8rEU7TSs8fYtAoKYKs7ZtiA4O5CVF6y+sPZBmtBG147rRhGAmBTgnCH+WQ6B3T4ZzYhoch76jGqKBii1xWAB9eHK8jGILtmB6Mrd+Od732FPKXU3zUi1Y9Wkfvjsb9fjn0PPx/Mbdjc5D22RKVAU1nSPLZOvx6pJ/SSBur1wvMZyrnLKMgCho2LOo0CklcbgsYPBTYmEeIAejnQSHLClBke6RB8K1R1iw/RSCj1rseacFBUAa/9AjoyQPxSb0LbXaGMt2UVZlMqfiXz9yE4qJ2x0Et8orS9tgI1OCgDITITEiVCxDwBvsQKT6AERf4K4iDPxArijEpCyd0XY106IicRRSaLuGBCloxM/B3d0pz0aIOdBBEGW+Eo8V00Ebn8tOKNQfTh0aRFTtO8vHgYsGky2iquKAkDHzSa4SSFPOAUA2Tz2zsSdKP6e7JMbpmgcOHddcNZDrN2Dphs3xGUKrdmB/DvOtbJV/XdWjANufVYGczoQwslAgDH2AID/ASkxgTHmBDCTcz6nHZ+t48JVbtwWfXw+yaQK0pEgLdlStLIKfcv37CUUBXWk+6f+ks8BSn8ACl4j9Zm4NErjKSYtayBQVUgGTqDca3YesP5//JuwgBtHMmqOUOOWKyb6X2PMSmODzWxF4upxmHDjCkxc/DVWTeqHZHsUku1RUFWO2gZPk1xgW2YKFIW1KoshEQZOpErkaaTFveJn47Fk70xOh6sKuGMhoDZqvAl9E7nA8Tp0DjVgbKzTMmJKBMBAG5CzhN4b3ZOZ6H6/fAmcfbN/JOzjaRSJiz8DAKN7X5hNWYlAWcL6COAN3/eHzSWnWsjUykyERDgo80m4tkKBCThxBgJMQVWXa5G0fy0i646i0drphNdOtEWhpt4DZ4MHtqiwtn6J9gSn0iQmbAmxDpmjg/fdSDvxB2qOUDDSbAU+n2W8jm6aSuVCRmtldJyWnUjsRdmJhmNa3wdnCTkOVYV03rC5xFnre6+/EpJe4hWMssP2TpT1BQciY6gk9fL7Q2dK4tLI0Q6UVq0rDZ2l5lyuvx0IJ/yfYIw9AWAwgOs454mc80QA1wO4xXfs9IOn0ZjMWVUIvHkz4Cwlz9xVSZ55/8lkiOjr/TJnEdF0/WOa515U4COUKvRa+iM5DnkjgP+7kAw7o4hD9SHNUHqwgJwOMM3Z0CsXBNYyDptLEpjexmCPv3J/6AiHLQXnp1qwNKsrYr0VTdFZmSn4lSOUKpGrghwD7iUjetea4LGUnUc/v3svpb8VBVg5ASjfq/EWlo2h5oRb55GD+odtlCWLP4McDE89SRSvvA+oPAC8eSvw0sXAlpeoN0pWYFYuF6groznS/Spg/+f+z+UsoaxGzVGSly39EXB0Ny6FiknW3q/+PQUKAv8OUp1J4ngQDkQLORDCgYgPo9dbVZdrwaAied+7YV1bBHGOHpNlTB0CzuLgQOSaSSSFqt93+z1ClQw1RyiLsHgYsP5vFMiJjqWSo/s2+vZ9X6lS6gXB0f2hcygj+9UbxGuoPkxr7WsDNT5ZzhoK2tyzgdbwL14BbnzKOIMwYAoARvLcS24HXr6EiNzeRnKCXJXkJHBubEeYLSR7H8i/syZTIMroOyZZstyREE4YYiyAizjnTblPzvnPjLEsAP8FML29Hq7DwmyhyabPJgydQ1GBQTOIRHrz/1KpxHnDSL9e1BLq6/0e3k5Gfm2xf4TW0+gjM9dpmvoATfrALEZWLhldA5+iaELNESoRiY6lRaXmqNZozmQmZ2HQDDKiLA6a7P0nw1CqbfNMur6+oY1oZDdgKqLybkM3g+iszBT8imGkSmRLoc0mMFL27ds0lhJ6knFfV07p7x7X0vgTHIlPpgdHyq59lDJnx4oozd1nFKXJBX9hyCvEXxg0A9izHrggi0h3X8zxnyubn6PmSlWFWiPFA1soEhYRSdmJunLq2iokjD0NoUuh9O/N1uBzpDqTxPFQvoccUXPLuj2X1oWZgQA1l6uNPwcpe1fg0PmTtCaKIZBoI+OrqNKFs1LsLXo+iTZEYCPZtL7kLJjM1EBzw+O+CoLuVBKqekkpafNMf55BzlrgnXv81+ZPphPpeNz7dJ9jRVojuZw1wFk3+NsWPueF56wFEw04MzJJGruunNbOwCxyfRUpNAlOprjOMt99Vz+gXWdkriacIeyFUDwGRaEMRqCtM3QOVWFIdBiElcfUOw+6z1yMsdOzKFj1hm6sIjx1EZG1dyaDxSidKEoyxGIgPh80g9KL0Q7/7xQV0CIwdrUvlVlHeuPuejKsrpgIfDmXXvXGnkg3uuvoGjuWkgGn69SLkbk00fUlUs4SSkOKhcPemdRyjLpNvj2KHBbZqOXXDaPGcv0nG0fKMmdRJuKyCRo5X/AM6qu18/XleSnnUkRKT1oemQt8txJIOUcz8t8a6X+9z54HrppE3wks47vxKdose99MmbCe/UkBKv1yMuZikmiTLf+JnJjBLxnPx+rD/u/ddf73keofEidC6e4W8x8A4kAoAGLDHGZVnX+Hrrvmw1b2XziT+xz33BQ7OTUHK2UG4pRDVckYFutQWl9a9wLLkG+aRlLwIhijL1NyltDa+dUblM0FqGrg27cp4LJygr8NAGiBkvgzDIMoTN8Zenc+lTZfNck4YOpt1BSkAq4Db4P/dQAK6tSV0fdj045fiuR2aRUbwmnZNJXKYiU6DMIpJitijA0I/JAxdgOAI23/SL8CBEYOAHpfWxbsiddX0wIQWO4xchEtIoEE05G5QNUhn6FikPpzllAp08YnyTFpqKYIxU3/pMXHiHy09kFaiCKiKTV584xgGc0VOWSIBZKgNj1Fv8fqByjDcdPT4Im9DH9/LqOzv34YNZZLONN4vCf2An73V4r8B5LdzFb/sSvK87w+gp6+vG5FDqk1eT3A0FdJ+njQDNpUxfX6jNLUmPRwpFMpQN976fqzLyWVkQtu10QOFg8H6o8BSb21jEgg4S8rj4iB4v2dS4H4nsEEP6n+IREKnAPl+6gHRAtRVkcSrqYwKz6rUy6DqkQg6cDaE57rsJphNjEUVdad8FyJdkZdKUX2BTla9IIItB8UM70GBm+Gzyf1OHBSu8sbQZyFJSMpkGJkA/R7hO5VuV9zXvRwpBM3TDS8vW8jcGkOfWYUMFW9odfkwB4ou/PJeVj9AAU9T8RjiIjUFJpE6auzRAZwOhjCcSAeBjCPMbaQMfYQY+xBxtgiAPMBPNi+j9dBEaoXhFFb9jpft9CfNlKU4J4NZBx99jzVlZutJI/28A6KklocwHlDgQ1PUIbCSM9+z3r/pnRvDmoiZMHeydjYc1VSGYengZ7T6JyGGvCctcDEz+lZt87T1GuylxABK6EHWKBx6Hs2l2qCqvLW/W0lTi2MGstF2ozHO1d9ak1GEahGY+3wxprQ5LgIX9mHNZE6ow5+WXMiYpKDlcrENblKTobgJd00jZTP9Bve8rGUqRMZv41TaB7es4EiYzvfoaZ4+mZ6CT3Da7AnIQFQuWijs8UEagAodfGwypcEVHMMnIl9kHjg/RN251UYQ7ItCkUVMgNxyuFo5FDVAAAgAElEQVRpBNb9lbL6mbOofNNwXVRDlFt6gNUTtSxpVSGtn4700FmBmGRaLzfPBDiC+GQ8ewlQuE2zLV4bSI5JpNW4rNXRjeyNMSupesF3HQydE2wLiYzunUs1rtnxYBTIkgGcDocTljBxzr9njJ0P4C4A54EoNp8BmGhU2tRaMMamArgfgBiBj3HO1xmcdzOA/wNgAvAa5/yZtn6WkLAmk0GtLxMSZRZ6CKdCMQGX3Q3kDtXSlf0eISPLlkq1hLlDdLyGPOryW3OEdJdHv0t69eYYYONU4yxD5X6axNEO4/IMawJNdNHZ1+AcHpMM1RQF00t9tGe8ahI5HzHJQGxnOldVoWa/BWXZXU3PXD54ER57vwjThydK/sOvHaKxnICqaprlYowOn08Obm2p8XiLsgOfPuNTFcsgQvTH07QoWOD5jAH1lf58m6xc4JbnfM5BKkWghFhAwpmUffvqNeCaPwN3vEFj/6MnacwabaDuOiIWLh+rZUSGzCa516IC4IfVwWV4siRPIlyU7aHXVpQwlbpUOJoZZD3W6WrElhYgtmQbqjtdddxzk2xRKKyQGYhTDhFhr6ugrEF2Hq15QsZVKDiazMbrJfeRpa2J1NXZFEnrcdZioLYkhA2QSLwHZwmVGG1+Dnx8PlQVMJkjwQB/OwWg10DFvbS+VNKkL6vKzgNueJwEXaLs5PiI74jjsV0BS0J4QRh9IEuv0CQDOB0KYXMgGGOfACgB+a4/tIfzoMMLnPPnQx1kjJkAvALgRgBFAL5ijK3lnO9qx2fSIAZ3zlqK+pT+ABS8Dlz7V6B4ZzD3oM8oSkXaUijbEGXXDKXRKzQ5SYBeN88ErpuspS4d6bQwWByk4eypNyY83/46qToFElZHLqJ6yJhUesbPng8+Z9hcsA8mw3Td3zUuhJ6Xcd9Gv9+/zHomDt64AilWhpI6junrS7H9YDWmDD5+FEziV4jAxZyr9FnJD1qPhsDaXbMFuOUZkletOUoyhM4SyiIMn09cGr3DHBEd3E19eQ4pjIxcBOzeQPPNVUmb1If/IBWQy+4nVRL9nNNvXgJic7Z1Au7dSM5E8XdagzlxT7fLV58sNyqJZqK8dRKuAJUw9Y5r3ndqki+G1xSNpP3vndCBSLZH4evCyhY/n0QbwOuhUs6ctRRczMikdfGOhbQu6bkGw+fT53o56ew8YNM0f4n2Dx+n9fWOhWSoZ+f52w9D51BA0lniywRTrxvOQSFht4tI27/7k7FtoRdTMeJALhtDHFBPA7DuL/T52FVU1m3vQlmMmKTm/Z0CA1kSHQ4ndCAYY7EAXgNwKYAdoLKnixhjXwO4l3Ne3b6PaIjLAezlnP/se8a3AQwFcHIcCIDkxxzdqcGUXoVgzEqqbawtJefh2r+Sc3HZfeS1e1xUSiTONxukB/uMCq57XD4WGPYq8F8fAfqeDXQPobAUk0ppTSNVJ8Zo4cj8F13v8vtJJeeuFbRgxSQB79xN1yneSQub3hEySB0yxYQ/vn8YRTpCXls0jJPooBCLuapShMvt8jVUnAx8OlNT9oqy69RDfI5vXDeaL3e8CdhTgQan1iXVXUfSsEZOcVUh3dddB/QaQI4DV2nMC7USQbYW5wuSdKBTc+dS2sgUhaJg3gbKqgn9c1GqV7aHnk+WK0k0F2U/kSNsTWzR1znnKHNxXNFMm4mbolCTfCkSC9dh/+VTwY8jdZlij0JVnRs19W7Yo80tek6JVsDrocCFKLEUvMfPniMFu0DDfNUEWs/0+7mRRPugGWQzvDOeVJacxWTAc5UyE5/PorVu0AyyS678PTBgKpRFmf7BlwZncPDFWQJYEoER82nNZApdR6yb4jlqjvoHHSt+pj2BMXJYVC99vznrqqoSX8TTSEEp1UuVGzIj0SEQTgbiJZBhfifnXAUAxhgD8A8AswHktMNzPcgYywFQAOAvnPPAkElXAAd174sAXBHqYoyxCQAmAEB6enqo05oPRSGjRJR3bM8DohOIEAqQI/DZ88DAJ7UI67BX/SenICHpPwvVRMWWSo7IosHBWY4bn9JSjYGqTmNXATmrASUSePH84N/jwa9JBlbIsykRWrRZTNrqQ36TNjEmEgty+uL+3AIUVbratGGcRDuO2dbAqMlcdh6R8muOklMqxiagOb7j3gdc1WTwe93AW1nB2YHx+cHzICOTnHEhByiyFTc/Sxtl4FwS9xRyxZmziDjNFPpXc4Q6vpb+YKxS1n8ysO7PtGFKRbEWoUOO25OFsj0U/T2BnGooON1AgxfN4kAIHOt0NRxHtyDuyOeoSgvSPGlCpzgLAOBAWR0uSGtmquM3ipM6Zp1Hg/lZK3Jo/fO6Q69ngSqNgeeIkqeqQtqv9QpMQ+eQSl3pj1rZJlO0dVV8b+2DVCExbK4m8SrW+G/fBs4a6F+2NGS2lsHVq9aJwJE9lc5dcod/ICfc4Ix+v7GlBCtBycaepxzh/OX7cc6nCucBADhhGoDj50tDgDG2kTH2ncG/oQBeBXAmgD4gladZRpcw+Cwke5dzPp9z3pdz3jc5uY1JOKK84+4PKCtw+X3EZ3hjEE363flA1S9kvAgys56Qume9Vv8IaLWKhqRVHlzmsfZBKhVpqKFUo1Fzr0P/BbxerbRDIK0vlVBxlZ5rx1KapGYLGU+xXckwfH0gOR6vDaQJraqyYVw7o13HbEth1GRu2RjSGLckhCb8cS9tgt++A4AZn8OU4CZxg6YbZOLGUBbPaC6J77kqyQmISweqj5CKxwvnUZPH0h+ImxE4hwZMoRLBogL6TCqKtQgdctyeLJTuaR2Buo622PgWxGBqEy+Ax2xD0v73jnteVwc5EPtKnc2/yW8U7TpmVSE0cZBevZ7QpOiKfcbrmd4wz86jfTrwHFel9nPlfv/1bc0kwN4VSDyLgjnxPSiQYvQcZgsFOzNnURBl9LvEnzx3mHEzOcFpGzZXU3kcl09VDccOAe/e5/+d5jTj1O83/R4xlo6XjT1PKcLJQLS5Rcg5HxjOeYyxBQDeNzhUBKCb7n0agMMG550ciEyEq8o4imC2amRToSSz9kHyqi+8k4wvUdJhTQK+zqVaRle5VuZhTSIitdGkr6ug0hFBMhXpTncd1Z4nn0WdIm0pWnMWI49+yGzgy1eB216ga4fqSuyLzsqGcacZjJrMife1JTRGjbgHxd9rvUeqDxlnGtwuavo2Pt+X8naFdkiEiod+LjVFy5bQ2B+7ytd9OiDKtmw0zQ99L4mqQhIy0D+zlAuUaA4a62hs9+zf4ks0pwt1ILgSgeqUy5BQ9BEUjwtqhMXwvNTYKCgM2FsiHYh2h1HGNmet8RqpRGgBwABuIriXDHNLAgU5rnzAv8RY9IUQGdp1f/Z/DhHEiYiiQA33AmW7jZ8j0k5loaIfj2hQd88G42ZyyWdTX6pVEzR+xbt308+j3zFev8MNzuj3m1DKUjLQc0oRjgOxhTE2BcA/OedNUX7G2D8AfNnWD8QY68w5F/0lhgP4zuC0rwD0Yoz1AHAIwJ0glahTB0UhSbPG2uCJ6a7zJ5sKJZmks4FFmZQ2jLCQsWNLIeUZ1a2Rqx3pwIgFVPtnNOkjoulYVh5FaEWHyqxcMsZEFLeqkBaazFkkkymafwFaRGHQDLoWENpglJP29IRRkzl9hOyLl4M7joo0d79H6HNbin+KPCOThAHyRujKlHIBMHKKje5niqRInCWenIxhr/rK/n4hEt/CW7XGSsdzQPTXtKVQnbCUC5RoCSr2AeCtI1C7wu9CbYTqTlcj4dAncBz6FBXdbzE8J8KkoFNctMxAnAwYBeA2PB6C4FxMRvfO5Vr3aNVLa+r2PPr+Q98AW14GzhumBRwBIL47MOJ14pFFxdB19HCkk3MbfwbZCm6XsfhFVh4QFUs2QFJvWk9tvkyFt9G4mVxdGVUqDJtLtoZoxllVqDXKDbJXwgzO6Pcbo1JvGeg55QjHgXgIwOsA9jLGdoBKhS4GsB3Ave3wTM8yxvr47nMAwEQAYIx1Acm13so59zDGHgSwASTj+gbn/Pt2eJbmwe0CPns22IiyJFJL+M3P0eSMSaZorerLVmx6iuq6xaJgiddkXQF6XXk/RS+EFGsTSTVXU2TIyKTIK0ApU4AWIf2kKyog2bgHfaUaQq5VRBUc3bVJGcpglJP29ITQ5tZH1IbOIS1zANj/Gb2OfpdKlvQqRyKCVFVIPRgGv0TGFueUHfMrU8qhJkmmSC1jJu43fD5tZkK4QJAQ//MSbbTj8+nzyv10vYxM4iKJ8b1jKZXn+UkMLqHoXv+/UymTJOdJNBdNEq6tayIHtNyBqHWcDY/Zhviij0M6EADQOc6CfTID0f4wCsDtzgdufY72ekd3MtI3TaVjY1aRNLUI7IkATOmPviZqUUD65QBUKs/0uKh8NC6NjHeROQjkMAybS2vu8PnAm7do1QdfvqrZI7ZUckCcxf5rqwgAqV7gvYeDS6Ny1lLQVPDa9JyyiKjg9bs5wRn9frPlxdZdS6JdEE4fiGoAIxljZwI4F1TSNJlzvk9/HmPsvLYw4jnnY0N8fhjArbr36wAE9Yc4pYiI1Iyoce8T76G2FFj/N0r1DZpOtY5Moc/tnTXS8/q/kSEfFUvfN4qcVh+iLMXIRcAtMwFw6roryjF251Nqc/BLNNkzZ5FhZOQEqF4yrq6YGCzBafGpiBgZjHLSnr4QfJ97N9LmxRSSJzaZqQdD0+cmOqZXJ9NHkIoKSJUjb0RoInRdGV07Os5fscnRjTZB/Ua2Ioc2wv2fabXAm2cCI14jx92vt8RiMvKM9MUtjpP3t5T4baFsLwBGfXtaiFKXCgVAbEvjM4oJzqQ+iC/aROu7YqyI1yUuGuuLquDxqogwSUe53RAqACc6Ro/Pp1eAAnmKYswzyJxFDkNdRbBxv2MpcPOF9H8tMgobp9B34nvQ5yvvJwekYp9/JcLAp8gGqdhHMrD9JwdLyouqBJM5NHdj0z/9S5u2zqOmnLZOlPEYv85XQmVpngpToHy42UJ7jFRh6jAI+6/POd/HOX+Pc7420HnwYXEbPtevE8Lg3v8Z8O49FF3d8BgZTPs/A+oqiVhUc5T0kZVIip4KJ2LDY8ShQADZGdCIUlWFtMhUH6Kf9bXcgEaEGjoHiOsO/PhBMLE6y5cSHTQ9uCHdsjHEvQCMuxJL1YPTG4pCm0CDk8jJ/zobWHA9Rclikmmjqz8GrHuUIl9i3O1YSsa7eC+UxkIRoWtLSWnJXQsk9iIHIr4HZflClSUNmU2RKoA2TMVs0FtiLDWss6WSM2JLleNZovUo20OR3YgWEBjEJeo44qIAUytYhzXJl8DcWAV76dchz+kab4Hby/0kuCXaAaG6KZst2n4ujvV7hNYso7XN0Z2CkUI5Tny+9kHaw7lKtsbWeWTID3yKSjk/epL4Z84SyrJunqldt6iA7r94GDkxRQXGkvJVhRqf0midVkwUhNzwGO0HGx6j9xHRJN0dk0TrbPwZpMrU3LVWyIc7utG17HLd7kgIq5FcmJDyO6E8Zo+LNpj1f9N0k4Uc246lpNusemnSVRcZl26IVCKgef6h6gLtXcBNZniZCaYzrgYTHYFjkmmT++pN4IuXgPs/OTHHQTZzkQjE8cj1gHasSx/qkso5NdlSzBpfISI6WFQgcKxXFZIimSmKHIcPHgWGzwsx5jsD6/9HkxQcmUvEbsnhkTgZEBKurUCpi7e4fEnAmXAhOBQ4jmxBTerlhud08Um57i1x4oykmNbdUCI0QnVT5ioZ9J8+o619lnhNaCVwbSvbE1ranXMy1D+YHFxNkJUHHpMI3PsRGWeB3IjAa4a0JzoRtyGwNGrIbAAsOAi59kFSpZT4zaMtHYiQMqqnFYwMblUlmVUxgZuIoqD6bJFizFkDvHEzpTNvneVfJ6nvmCuiFyKqu/lZuk5MMnnpn80Ev3YylLhOYDFJwOAXNKPpg8la1sJIEUdyHCROhFDkelUlx3bYq0Sgi3ZQXwhbCnDr80BjDUWpABrjgaIC8T2oplevLV66G0g9XyP215YGO9dZuZQeH/Q0dVKNSSFnos8oOb4l2h+cA+V7SSe/FSitU+Fo5dBUzVa4Ynsg9sgWoM+fDM/prJNyHQgZHGpXBNoDqkrZ2k+f0bhZ4/N9hOk5wcGUkYuA71aRPHwo9SYOah6rV2B0VQLWBLB1fyORip3vBl9bzwUDjHkGQ2YDqybSepzWl66fej6t8x89ady5uqqQnCSJ3zza0oGQOB5MUVonR1UlI2bb68DB/1DtYUJP2ohEOdO6v1Ba09NAE13vfIxcBNiSgeRzKIV47V+12kkf0VSJiATAtS6OEZFUHqUveTKK/kqOg8SJYFTbm5FJxr2+QdvQOeQ8FBWQA23r5M+D0DsOSgQ5CYKoJzavnzb6SpgswLB5gDmaSHt6XkSEFXj/EeC2f5HzwFXanPesD46ayfEt0daoPkzjMLblCkwAcLSW48KWNbH2Q23CeUj8ZR0UtxOq2RZ03BYVAYfFLJWYTgX02VuxF0/8nPbmKx8IJjabIoAL76CAiJFz8dmzVB4tpFz1DefG5/vzIoWDEZNMxGtbJ3+Oo7OE7jliATkhZgsFGQVEmfWYVWQ7DJxK3IjRK6g8Sh/gZLK86HRAqxwIxlgXH7kZAGRdQCjUlQIbp1KKUd/JMTsPuGSM1qkxI5MyCsvHapN1+HxKIeoNJrMVWDeZjKSIqGDi06oJRFyq3E8Eqc0ztTrIjExt4SoqoLrJ8T4uuiQmSYQDI3L9oKeDlcPWTKINa9kYTWlMH+FylpBjvfr3NBYHzaRNz9NIEa59nwLnjyB1Ef2ciY7Xas09DeRU1BaTgoheHvGOheRg+zkbLa9Rl5AwhFBgaoWEq0flKHVxJLbB8KxNOB/JB9YitngbqtJuMDynsyNa9oI4FdBnb4UCYqQNqNgL7FqjZSUaqgFwCoiINa22WHMAoh2kvnjFRMporJlE69ySkVrwRqCqkMZm8tl0LyF/XV9Fn+lLrBQzraP67tHCOXGWkH3y/UrqXyXU8wLPGTKbhDQkfvNobQbiSwDpAMA5v7L1j/MbhaeRFobAWsFPZ5IXP+xVrTHL5mfBx68DEyo31YeBnSuoE6QpAvB64LUm4ug1T6NLDAerPhxasemNQf415ctGk+yavglN/79R/bhJJqMkwoRRbW+osqa4blrPBo8LSOjhS9l7iOAnypXS+gKp51CJkxibOWuA3KHBJP/MWeR4bHqSNqy7VlAWL7Brtavc37kG6Lq+RogSEm2C8r302soeECoHktrAgahz9IaqmBF39D8hHYhu8VZs2VcGVeVQFElfPGkQ2VvRqM2I++WL5POHd4DVlWnrV1GBlmEYn08OhbeBsqyql3pAPPQ1rY21pUDlATrXkU6qdpfd569IJyS4E3pqQcOa4uCOz2sm0f04J/GM828n8nXgOWNXASU/UFBy8Asn5+8pcUrRWqtRrjzhICIymLCU1peiB3ov3reAMHDqbLp8rKbZrDtPuXMp7IlnAY0lmjpCoJFU62vxrpdiWzaGIrJ3f0ApU5OZ0pjSeZBoLgJre2uKjcdhdCywXCc9OGwuEGUDImP8ZV77PRLsYIdSJTFbSc44cxZlQ7YvAfqODz43lKqIJFFLtCXK9mj9e1qII06qGU80biDdLHBTJOocvRF3ZEvIc3okxeDDXcXYX16LM5ODy5wk2gkie1tz2Jh8LPZpRzrtz6GIzV63sQOydR4FU8wWEp0Qn7vrgZUT/LtJu+uoOiLarq3l3hCBoKpCjb8meu0EnlNzlNZ0WSZ62qC1tSqSOB0OrMkaYUnAyGBa+yBN/ogoivCOXwfc8WZQRIC9PQqx7jKwrQtINnPoHH+pOL2cpbi2Jd5HII2iSFlCD3qVzoNEW0AxUU2ufhyOXARseMJ/jK/+PfV2YAHnG6mMCFUSPfRyxom9gLiuQL8HNVUnPUJJD0oStURbomyP1syrhThaS1tpW5QwAcSDiKnajQhXmeHxHj71pe8OHWubG0qEB5G9TewVWjLVx9Vitk5AfE/j/V0xGdsPfUYBy8dQgOZmn/rix9PIoRBZDyG5mv8XWjdVHeFZZEj0EGuuQKh11dFdSr2fZjih9cgYexnGjgIDIDsvhYLXAziPapF+awKpxYgUYihZtoQzNe+9/hg13DI6z+smKdb6CuB3f6XGdaqX7vXBZI3QBNDkdtfJyIBE+8HtIgNKrwLCmHGfEq+b6m9tqeQku+uoXC8w0rZjKTkZeoGAkYu0yFqklTZFgOab4A+Jc+PSgTvfAt6+S5KoJdoPZT8BSb1bdYmjtWTEtUUJE0A8CGA54oq/RPkZtwUd7xpvgdnE8N2hYxjap3XysxLNhKLQPm2UWRAlloKLmNCTGlyOy6fsAANQsIi4k6EckKpCKn0WZttNT1ODw/6Tg52O5Tn+kqtG/Dah9CieMb6ncYPZ2K7ScTjNEE74uaCFx05feD1A8Xf+xkzWYiCmk6Y6o3qNF5DIGJqEzmKaoINmhJZvc6QD2/Pon/j8ng+J16DnOWTn0eS2JMgJLtE+iIikjIG+LCk7z3jsVu7XyH5ZucDm56ieN1BC8IqJQMHrwU6JsyTYETBFkLxgYHkeU4y7TktItAUaa4lvdqYx1yBcHK3lMCut6EIdAJe9B7wRFsSGcCAiFAXpCVZ8d6i6bW4o0TzYOgUHPLIWA/Yu/lUBikJdnBf5uGEXjwEum0DS7kZrqyh5crsoaCnW44xM4KZ/hpBc5f73C+S3WRKJ03DLTG0NBeS6KnFiB4JzvijUMcbY8237OL8ROI9qCwPg8/THUrQ1+RwAHPjg78GybNlLSHUB0Eipe9YHR2HvXEqORqDBNXQOGU5GhpQsVZJoT1iTtXS7GJM7lvpn3fRqHYAWARu/jjbJmiMkNxjbBQAj5bLd+f4O8vh1/hE6PUwRxkRWSZiWaC8IAnUrm8gdqVWRZGlVFZQ/FBNccb1gLwndkbpHUgy27q8A5xyszW4sERZCBTyM9mm9QEXvm6lEyZZi3IBz6zx6jYj0F5XYnQ9cNSm8vjhGvayM1lC5rp72aK1VmQXgr23xIL8peN0hyJsNwOxLgaseBq6bTCpMes3nuG6aUcQYRQ0uyAI+ez74PKaQgoJeotLeWcsytEIRREKiWVBVIuNFRAFJvYDxHwCqmyKzX71GTkFcGo3ZVRP8y+uqCsl5iEsnhY+VE0iVbOOTVK/rl0lbItPkEh0LZT/RayvX26O1bSPhqketIwMp+96BqeEYvFFxQcfPSIrBxh9KUFhRh+6JsiP1SUeogEcg9H137J00UrPo62DvRHaBq4o4EFvnGWcbNj5Ja6i+V48s6ZRoBaQKU3sgVH2j6iH1pV4DyXkQ3aNtqUQsrTmipQOZCbjxKSBvBF1H1JLrZSgTepKCgkwjSpwqqCpQsiu4HtaaqKl29L6ZupYOmKI1RBRwpFMX6cDUubPEv7Oqu44yE3J8S3QklP1EjnFsl1Zd5qhTRc/YNnomH+ocGWDgsJd9g6qu1wcd75lE6ks7Dx2TDkRHggjI6EuIhOEfHe/fjFMoNt29HrCn0lrZ7XKiPwTaIM4SGqey9EiijXDCkcMYSwjxLxHSgTCGqG/0U6TJBb54WVNf2p1Pk/+NQdSA61AB8OL5wGsDiT/BGDkcx5OhFKlGRzd6lQuBxMmGvrMqQK9vj6Kxm+FzIOLSiM+waRql1/Xz4s6l5EQD2ni2d6YN01lCc2T1AzSnAH/FEAmJU42yPb6OwS0nL3DOcbSu7TMQrrgzwZkJ9hJjqmK3eAsiFIadUonp1EJVifNYdRCoLaOAzGsDNXugZBetkWNWaqXL+jV06BxyYvW2QIyPDB241loSpM0g0WYIJwPxNcifNXIWpKC6EQLrG5UIYOt8quW+dLyxU5CUAUzaCjTWEIfCXUulSfrO0YCUoZToWAjVQM7joowDQBmGtT4ehL6bamwadZGuPkQyg54GUhpRTMCB/2ilT5X7gfw/aeRpKRMo0VFQtqfV/IfKeo5Gb9spMAlwUxRcsT0QG8KBiDApOCMpBl//Uml4XOIkIDCDO3oFULDQXzji2xXAJWOB2hIghhOHTBz3ugFwwFNPTojIKBiRofXZhsAsh8xESLQA4ZCoe5yMB/nNQV/fqKrARdlAxT5qQR+YWszIBOrK/YnSWbk+jsTT/nXgY1aRakLlASpzMpnpvaJoi4BcHCROFvT1uQKOdBqfERbgtn9RE6PAbqppfYHMfwELx2jNEvWCAFmLKVpWc5gcC0DLbggSdbhjXM4HifaAqhKJuvegVl3miOgB0QZN5AJR58hAwsGPwLwN4KaooONnd7Jjw/dHUe/2ItpsavsHkDg+AjO4McmUrRXk6IxM4NpHtUayo1domdm0vsQTW/MHf55YyrmUkQi15nk9wLGD5HDUlpLYxfWPycCMRLMRTgnTJQH/LmaMdWuvB2KMLWOM7fD9O8AY2xHivAOMsZ2+8zq2nKwvGsBvfZZqwQPLOAZN15wHQFOn6TOKshf3bQQe+Q64/xOK7L5+I/B/FwELb6Ua3OrDwHt/okiG12OcApWlHxLtAatBqnzIbGDzTHIIvG4g0hLceKj/ZE0lpN8jQc0SsXwsUPYjlfhteIw2yrS+WglfuGNcRPjkfJBoaxwrpMhvXOu2Q9EDoq1LmACgztEbitqImPLvDI9ndLLD7eX478Gqtr+5xIkRmMGNsPj3augzCliRo73fPFMrYTJqRrtsNGV0A9Y8tfh7lByrQ2VtPXjJLiqbFmvrFROBT2aQwyEh0QyEU8I0y+CzBMZYJIBRnHNDA7+l4Jxni58ZY7MAHK9A83rOuXGrzQ4GFQwejxeRu/O1Mg6RouSqcRlITDJFD4RcmrNYa4olzlkziZSY+oyiSMbdHxjXpAvitYREW0Kkysfn01hzVRL5WSgtca9xc6L4HtoYFc2P9KgqpHkNAYYAACAASURBVBI+8fPaB2nObHiM+EHhjvFQHA05HyRai5If6dXRvVWXEV2o27qECaAMBADElhbAmXJp0PGMVDsA4KsDFbiiZ2LbP4DE8RGYwW2s8V8LA9fGogIqYRr3PpV7GjaZbQha85Rld+HgjSsQGWFC/AejgztYD5qhcSslJMJEOCVMwfINABhjfQG8BODatn4o3/UZSCb2hva4/slGeW0jjpY14gJHulbGAdDicdcK4zIQW6q/xFqoenOzlf6JLr/HI15LSLQHmInIzkEa4z6FJXsnTXLY3okIg2LMi+ZHRk2RBIRDfedSule4YzwkR0POB4lWomQXvQZm15qJI04VCoD44AqjVsMbGYv6mK5EpD5vYtBxe7QZ3eIt2HZA8iBOCQKDK4FrodHa6CwBKn4GGp3G6yZTDNe8FCsDcIJgpYREM9DigjfOeQEAWxs+SyB+B6CYc/5TqEcA8CFj7GvG2ITjXYgxNoExVsAYKygtPTVpukaPF1M2HkX54EV+5R48azGwfUlwWVN2HkUf9DWJIlqhhyOdJC7FQiMkZAPPkYvDrwodYcyGBVEitO5RYNjc0ApLbhd1n16YCZT8AGxboI35LS8aK4tseVG7jyOdyKqiTjfcMR5qzsj50C741YzbtkDpj9T4M7J1EqhFNRzJVsDUTuXnTQ3luHHZXkYnO77+pQJelRse/63jlI5ZPdn5ke+ALpf4l4TuWBqs6DhsLvDJdON1c8hs4jUYrHkldRwlddx4PQwMVkpIhAHGecsWDcZYKoB1nPPgvOiJv7sRQCeDQ49zztf4znkVwF7OuVEJFRhjXTjnhxljKQA+AvAQ5/yzE927b9++vKDg5FMmSmsaMHzOFiTbzHjiumSkWBkSbNGI+eY14KwbgMQziQytekmFRlEAJZIMML1yQqDm/tA5FNX9fBYRoZLPpo0tUJdfEqROBtpF1vhUjdmw4CymWtuqQuIo9HuExmxcGmDX9W0IPO/mZ4nPE5NMPJ9IO6C6wb2NaEQEzJ46KG/d0TSGedZisNTzSZwgVO8JozHenHNPX5x+47YtMPcakm8d+FSrLjN8lROqqmLGVW30XAFwHN6Mrt/Pw47B6+Fy9A46vmVvGWZ/shfvP3QNzu8a3HCug+K3O2aF6IOqUgmo103/VDdgigI+mkLKjCLQGB0H1BzVCNE3TiNujm7NKx+8CPetrwUAvHlrDBxrdIItgnht1AVboq3xm2p9cMIRwxh7GRTt1yMBwNUA/tiSm3LOB57gnhEARgAI6Zxwzg/7XksYY6sAXA7ghA7EqUJiTCQW5PTF/bkFuH3xPqTFW/DxfWcCX7xE/9L6AgOeBKLsRKD2TW5v9luoju2FOEsUFBGtuHcjGV96FabBL2hKC8eTb5OQaEvoS4R0pXn8ke9QVutGo8eLyAgTEq1JUESqHqD6XVHypDPqORh+Lq7B2m+O4Q93vYdoRQWLMIPZOoOJDa45Y1zOB4n2gOoFSvcAZ2e2+lKF1Rx9U9rgmUJA8CDsJQWGDsTZnYgHsW1/xa/JgfjtQigqBgY+svNIke7y+4GrJlHVQf6fgVufBxqqaZ277QUt63vfRnBPI1yqCY+9X4TtB6uRFm9Bpa0X4u7dCOaV66FE6xCOyxnojnMA5QD+zDkvMTi/LTAQwI+c8yKjg4yxGAAK57zG9/NNAKa107O0CRSFISPVjlWT+jUZVWZU+XeVrD+mSbIBQFUhTMvuQtEtK3E0NhUZqXZyIuwG5E8pVSlxKhBCxtWlmjB83hYUVbqQFm/Bgpy+yEg+G8rdH1ApxcJMQ2KzYqNxfs+1vXDM44UrwoTEmEgoSkDgRjSdCwfNOVdCIhxU7CeyaisJ1LVujvJ6jk7WNnouAzRaUuGOciC2pAAlve8KOp5oi0KKPQpfHajAPddI1fYOASPxh2VjgGGvAouHaec50qFaEsHsXcBiAvZ8WyoYgGiVY/rwREwZ7AvmxESCKfaT+utI/DYRjoX5Ced8ke5fLuc8vx2dBwC4E8BS/QeMsS6MsXW+t6kA/s0Y+y+AbQDyOefr2/F52gSKwpBsj0LXeCuS7VE04fX1jjHJhgQnR6SK+3MLUF4bgvgppSolThUMZFzV7Lfwp/eLUFTpAgAUVbrw4kc/AqU/AG/eQmP8OMTmwHkS5DxISJxqtBGBurCa1ujOraNRHB+Moc6REbIjNUBqTNv2V6ClJc0SbYwQ4g/c3pl6QwBNpUkPvHcUZXCEDBjK9VSivRBOBmI1gEsAgDH2Luf89vZ9JIBzPt7gs8MAbvX9/DOAi9r7OdodgeUVjBlGc0vqOIoqXWj0eI2vI6UqJU4VDEqEyrx2bNj1id9pEy6NhbJs5PFVlySxWeLXgtIfAbBW94AQDkR7ZiAA6gcRV7wVkbVH0BjTOeh4Rmc7Pt9bhp/LanFmcntqo0iEhRCZXVaxD84BM1B55ZMoqeOYvr4U2w9WY8rgELaBhEQ7IpwMhN5d7dleD3LaQpRXOLoR6TQgmls+eBGmf1qKtHgLIiNCdAqVUpUSpxL6MWxLBVNMSIv3b6vbxaaTFtzyYrDq2J1LpQqIxK8HJbtIjtjcuuYNB46dhAwEdDyI0q8Nj5/TKRYA8NX+ivZ9EInwYE0mcrNBg85qVyN+N28vbl+8r4nXENI2kJBoR4STgeAhfpZoa4iO1fduhLuxHrvLGjFl/VGUOt1YkNMXiTEhIrQhohUyoitxKqAXDBAciDi7zZ/v8/E06guR1BswWyRnR+LXhZIfWl2+BAA/VapIiAZs5jZ4puOg3tYdXlM07CUFKD/jtqDjneOiEWcxY9uBCtx5eet/L4lWQlGA2C5a7xzRoNNZAofdhrR4ix+/LKRtICHRjgjHgbiIMVYNykRYfD/D955zzmPb7elORygKmD0VESpHp8hGzL6raxPxKWTtolGnXxnRlThFMBIMiLZG+I9RZwll3OLSpeMg8euCpxEo3wucN7zVl9pb5UX6yagYUkyoc/RGbAgeBGMMGZ3s2PazzEB0GFgSaI0M2Nej41KxalKipnB3PNtAQqIdEU4napkbOwUQxKcwT5ZSlRIdCobjV45Rid8CyvcCqqfVGQjOOX6qVHF91zZ6rhPAFdcLyftXwdRYDW9kcNzvnE5EpD5U5UJXh8XgChInFSH2dUVRkGyXZpnEqYfsHPJbgZSqlOjokGNU4reAo9/Sa8KZrbpMcR1HrRtIP0mKmnWODDCuwla2A8e6XBt0/OzOGg+i68UnyauROD7kminRgSHDfxISEhISEuHi8HYgIhqIbZ2RvaeCCNTdTpLokSvuLHCmhCxjSo+3IibShK2SSC0hIREGpAMhISEhISERLg59Q9kHpXVlJDvLSHqz50lq/qxGRMMV2zNkPwhFYeidase2/eUn54EkJCR+1ZAOhISEhISERDjweoDinUDiWa2+1M5SL7rEtL8Ckx6uuF6wle0A8xpLfJ/dyY59pbUoczacvIeSkJD4VUI6EBISEhISEuGgbA/gdrWJA/FtqRdnnaTsg0CtIwMmbz1iKnYZHhc8iIID/8/emcdHWZ2L/3tmyb6HhC1EXFGr1oW61F9F6wKKiloBlUXqftXb2lr11nrVeu/l1rZWr1XqLqsI2CJYVBS12NJWxaXVorigLAJJyALZyDJzfn888/JOJu8kIZmQ7fl+PvlM8r5n3jkDz1me82zqxqQoStuoAqEoiqIoHeHriPvPoIO79Jjy+jBbaywH5SSgT3uBW1DO243pgEHpJAd8GgehKEq7qAKhKIqiKB1h098hJbvLAdTvbJP4h0P3sQIRSs6mIW1o3EDqgN/HQYUZvKX1IBRFaQdVIHoz4TDUlEDVZnkNh3u6R4qy79FxoPQWNv0NCg4D07XCXX/fFiLZD4fkJqhfe0FdziFklr4D1nscfWNYNuu27aJc4yC6H53blD6MKhC9lXAYStfBE2fAA0fIa+k6nWCUgYWOA6W3UFMKFRug8LAuP+pvW5s5LBeCPbAC1+Z9g2BDJRnlH3reP3K4BGb85fMd+7JbAw+d25Q+jioQvZW6MreEPcjrs5fKdUUZKOg4UHoLm/4ur4WHd+kxpXVh1leEOWpQAvrUCWoGHY01PnI3v+p5/4BB6WQkB3jzU1UguhWd25Q+jioQvZXmRndicajaJNcVZaCg40DpLWz4EwRTu5yB6fWNzQAc30MFhkPBDOpyDiVv8yrP+z6f4ciibN5YX0oobPdx7wYQOrcpfRxVIHorgSTIKW55LadYrivKQEHHgdJb+Pw1GHwk+LtWuGHVxmYGp8LIzAT1qxPsKjyOtJ2fkrJrg+f9b+2XR0Vto6Zz7U50blP6OD2mQBhjJhpj/mWMCRtjRsfc+6kx5nNjzHpjzNg479/fGPOWMeYzY8wiY0z/GnVpBXDJQneCySmWv9MKerZfirIv0XGg9AYqNkDVVzDsmC49ZmeD5c0tzZw4pMtx2F1iV+GJWHwUbFjmef/oETkE/YaXPtq+j3s2gNC5TenjBHrwsz8CLgIejb5ojDkcuAT4BjAMWGWMOcRaG4p5/73A/dbaZ40xjwBXAr/r/m7vI3w+8bW9apWYNANJMrH4YnS+cFh8Jttqoyi9nXhy3NFxoCjdyeevyevwY7v0mOWfN9EYgu8WJaBPXaA5JZfa/G8waMNSNn/zplbaTGqSn2OLc1n6/tf89JxDSQ74e6in/ZhEzG26/is9SI9JmrX2Y2vteo9bE4BnrbUN1tovgc+B46MbGGMM8F3gucilOcAF3dnfHsHng4zBkDNCXr2UB83ioPR12pPj9saBonQ3656H7BGQOaxLj1myvpH9s+DAfVyB2ouqISeTUruFrJK/e94/dVQhO+ubeOVfJfu4ZwOIrsxtuv4rPUxvXImHA5uj/t4SuRZNPlBlrW1uo03/R7M4KP0BlWOlN1O9Hb5aAyP/X5f8jj6tCPHPsjBnjOhZ9yWHXYNPpDmYydBP5njeP2p4NkOyknnszQ1Yq8HUvQ6dN5UeplsVCGPMKmPMRx4/E9p6m8e12NmrI22i+3GNMWatMWZtWVk/GlyaxaHf0m9l1guV435Dv5TbdcsACyO/06XHLF7fhN/Aab3kqMv6k6gs+i65m18luXpjq/s+n+Hcbw7jw6938sb60h7o4b6hz8qszptKD9OtCoS19gxr7REeP96RW8IWYETU30XA1pg2O4AcY0ygjTbR/XjMWjvaWju6oKCHA5QSWXlSszj0W3qVzMYjUbKsctxv6BNyuzdYC+88KalbY2V0L6jabXnm40ZOHgrZyQnsXxepGHEW1hdkxD8e8Lw/5uAChmWncPfydexuig1D7B/0epmNN8/qvKn0ML3RhWk5cIkxJtkYsz9wMPB2dAMr9tQ3gIsjly4H2lJKegeJ9lnULA5KT5FIWVY5VnorG/4EO9bDoed16TGzP2qkrgkmHZyYbiWK5uRcyovHUfDlMtI9KlMH/D5mnLw/myrqeGT1Fz3QwwFOW/OszptKD9OTaVwvNMZsAU4CVhhjVgJYa/8FLAbWAS8DNzgZmIwxLxpjnCi224AfG2M+R2IintzX32GvSbTPYnQWh5s+ktfCwzXIVOl+EinLKsdKb8RaWP1LSMmB/U/p9GNqGi1Pf9TACYNh/6wE9i9B7Bh5Hk3JORz411sxoYZW948cns23D8xn1htf8FlJdQ/0cADT1jyr86bSw/RkFqal1toia22ytXawtXZs1L3/sdYeaK0dZa19Ker6OdbarZHfN1hrj7fWHmStnWitbT3z9Ta6w2dRM9QoPUGiZVnlWOltfPR72PRXOHpKl4rHPf7PBnY29D7rg0M4mM7Ww64mvWo9+799tyhOMUw7cT9Sknz88NkPaGzWLD/7jPbmWZ03lR5EpW1foj6LSn9BZVnpz5R+An/8EeQfDAef1enHbKgKMev9RsYMh0NzE9i/BFNTcAxl+1/A4M8XMXLtf0G4ZbxDTloSV3/nANZt28X9qz7toV4OQHSeVXoxqkDsS9RnUekvqCwr/ZFwGD5+AWafA74AnPpT8HWuiJq1ljv+vJskP1x1eIL72Q2UHjiR8uJxDP1kNt949TLSyz9qcX/0fnmcNqqQR/70BW9tKO+hXg4wdJ5VejE9WYl64KFVdZX+gsqy0p+or4L350nGpcovIXckjLkNMgo7/cgnP2zkr1tD3HAk5KUkrqvdhjFsHzWd+syRDPl0Pke9eD41+UdROfxUqoaNoSb/SKaftB8fb9vFjxf/g5du+g5ZKZ137VI6gM6zSi9GFYh9jeOzqCh9HZVlpa9TXwmrfwXvzYbGWhj8DfjOLbDft7sU9/DihiZm/r2Bbw+Fs/dLXHf3BTuHnUJ1wXHkfv0nskrfpuifv2XEPx+kOZjBriHf5heHnsF17xTyH7//Jw9fdiymN1TF68/oPKv0UlSBUBRFUQYen78Gy26AmlLY/ztw+AVS76GLvPpVEz9YVc+hufDjo3tH1em9JRxMp3zkeMpHjsffuIv0inWkV3xEZulaTt38Cu+nprPg41N4esW1XHHuaT3dXUVRegBVIBRFUZSBQ2MtvHonvPMEZI+Ac34Ng7qeIilsLb97v5H71jZwYDbcfTyk9oMVNpSUxa4hJ7JryIlss2HSK/5Fztd/YkbJK5h3Xmbz5tMYcfbNsN/JfVNbUhSlU/SD6U1RFEVR2sFa+GQFvHwb7NwCh0+AY6ZDoPOloWsaLRt3hfm4PMSCdY28XxpmzHD4wVGQ0h9XV+OjNv9IavOPhLoK1r/zKqdufw1mj8cOPgJz4vVwxPcg2BeCPhRF6Qr9cYpTFEVRFKG2HL54Hd59GjaukQDpcfdKvMNesqM+zJotIf68pZm/bm1ma41bM2FwGtx0NJxRNEAO4tPyOOA7k7nlvQsZVLqGf9/xMkXLrseu+DFNQ0dTX3g01alF1KQVERw0kvS84eRkZ5MS7FxWK0VReheqQCiKoij7hnAIPlgA4WYINUO4CUJN8trcKO5FTbXy2lgLDdXQVBe5HnnFQFIaBCM/e35PpTFs2LarkeYwJDVUkLZ7O3n1GzFYdvjyeS5pOnOrzqTqj34Cvl0EfZCeZMhPMeSn+shNMeRFflIDht0hS10TbKoO868dIdZXSBG1jCB8cxCcNQKGpsGwdBiZBb6BoDhEEfTBTccl8cKXp3H2p6dyZOhfnNH8Ht/a+AmHbvob2aZlPYkam8JGstnpz6UumMfupDxIySaYnEZSahq+YCrD8rMZmpMGxgeHnA0ZmrJUUXojxnpUnezPGGPKgI3AIGBHD3VDP7t/fvYOa+24RD80Smb3FT35/xSP3tan3tYf6Hyf+pLc9sZ/dy+0n4nDq499SWYTQW/+f9K+dZxukdueYsApEA7GmLXW2tH62frZSkt6479Vb+tTb+sP9M4+JZq+8h21n4mjL/Sxu+nN/wbat4GLViNRFEVRFEVRFKXDqAKhKIqiKIqiKEqHGcgKxGP62frZiie98d+qt/Wpt/UHemefEk1f+Y7az8TRF/rY3fTmfwPt2wBlwMZAKIqiKIqiKIqy9wxkC4SiKIqiKIqiKHuJKhCKoiiKoiiKonQYVSAURVEURVEURekwqkAoiqIoiqIoitJhBpwCMW7cOAvoj/50x0+3oDKrP9380y2o3OpPN/50Cyqz+tPNP/2KAadA7NjRm6qaK0r7qMwqfRGVW6WvoTKrKB2nRxUIY8wIY8wbxpiPjTH/Msb80KPNFGPMPyM/fzXGfDPq3lfGmA+NMR8YY9bu294riqIoiqIoysAj0MOf3wzcbK19zxiTCbxrjHnVWrsuqs2XwBhrbaUx5mykMMgJUfdPs9bqsYGiKIqiKIqi7AN6VIGw1m4DtkV+rzbGfAwMB9ZFtflr1Fv+DhTt004qiqIoiqIoirKHXhMDYYwZCRwDvNVGsyuBl6L+tsArxph3jTHXtPHsa4wxa40xa8vKyhLRXUXpVlRmlb6Iyq3S11CZVZTO0SsUCGNMBvB74CZr7a44bU5DFIjboi6fbK09FjgbuMEYc4rXe621j1lrR1trRxcUFCS494qSeFRmlb6Iyq3S11CZVZTO0eMKhDEmiCgPC6y1f4jT5ijgCWCCtbbcuW6t3Rp5LQWWAsd3f48VRVEURVF6F9ZaXvjHVp78y5dY2++yhiq9jB6NgTDGGOBJ4GNr7W/itCkG/gBMs9Z+GnU9HfBFYifSgbOAe/ZBtxNLOAx1ZdDcCIEkSCsAX4/rdYqSeFTWFaX70PE14Hnu3S3c8tw/AUjyG6adNLJnO6T0a3o6C9PJwDTgQ2PMB5FrtwPFANbaR4A7gXxglugbNFtrRwODgaWRawHgGWvty/u2+10kHIbSdfDspVC1CXKK4ZKFUHi4TvxK/0JlXVG6Dx1fA57mUJj7XvmUAwal4/cbnlrzlSoQSrfS01mY/gKYdtpcBVzlcX0D8M3W7+hD1JW5Ez7I67OXwlWrIGNwz/ZNURKJyrqidB86vgY8b31ZwfZdu7npjIPZWdfE03/9ii/KajiwIKOnu6b0U/RooidpbnQnfIeqTXJdUfoTKuuK0n3o+Brw/PGf20gN+jhmRC7HFOcA8OdPNauU0n2oAtGTBJLE1BxNTrFcV5T+hMq6onQfOr4GNNZaVq8v5cjhOSQFfAzKSCYrJcDH26p7umtKP0YViJ4krUD8VJ2J3/FbTdNUcko/Q2VdUboPHV8Dmk0VdWzduZtvDM8CwBhDcX4a67Z5ZsVXlITQ00HUAxufT4LcrlqlmTOU/o3KuqJ0Hzq+BjR/+0Ky239jWPaea8V56bz2cQnNoTABv8qBknhUgehpfD4NclMGBirritJ96PgasLy/qYrMlADDslP2XCvOS6OhOcymijoO0EBqpRtQBaKzaM5tRWmJjglF6Vl0DA5I3t9cyYEFGUTS2gMwJEuUCVUglO5CFYjOoDm3FaUlOiYUpWfRMTggqWlo5rOSGi46tqjF9cKsZAA2V9T1RLeUAYAqEJ0hNud2RiFUb4XUXMCCtXr6owwsnDGRUQgTHoasYbKhqd4GmUN1HChKV/GyLoB7zRh4Y6bWghhgfLJtFxY4YFB6i+s5qUGS/D42lqsCoXQPqkB0huic20Wj4bt3wluPwgnXwvIb9fRHGXg0N4ryMO6X0FQH8y7UcaAoiSKedSGQAvOjxtr5D0FtCWxZK+/TWhD9nvUlkqp1RF5ai+vGGAqzktmkFgilm9AVvTNE59w++SZRGo6+1FUewD39qdNCLsoAIJAEY26D+nJYdr2OA0VJJPEqTVduaHlt+Y2yJjloLYh+z6fbq0kJ+hiU0fr/uTAzRRUIpdtQBaIzROfcTs2Vidt5jUZPf5SBQloB5B2o40BROks4DDUlULVZXsNh9168StPBtNbX0iOuTVoLYkDwyfZqRuSmtQigdhiUkcTWqvoe6JUyEFAXps4Qm3M7pxjqK+U1epLPKYZwsywE6r6h9Gd8PkjJlvgfr3Ggp6CKEp/2AqAdq3fsuGqKOV3OKYbsIrjpI43DGwBYa1lfUs2xxbme9/PTk9i1u5nahmbSk3W7pyQWnVk6i5NzO2u4TPQfLISJc1pWAj3/IVj5s/bdN9o6eVKUvkA4DM0N8OqdIvfR42DyAj0FVZS2iOeiVL1V1gXj9640nXtA62uZwyBnhKxPqjz0a8pqGqiqa2JEbqrn/bwMycS0befufdktZYCgKmlXcawR590PoSYYO1PcOOor4fV7JJjt7Hvjv19T7yl9HUeGG2tg/QoJ4oweB+l6CqoobRLPRWnnFnhqrKwLU5fClasgFJOFSatPD1g+3V4DQFFumuf9/HSx/G7bWc9BhVoLQkksqkAkAscaUVMCK29vbWb2J8k9r0k+3smTpt5Teiux6SSNX2R27EyR9y1rYdFUaZtTLLKsKEp84rko1ZZJpr+Tb4K6HRBMEat3tJKg68SA5ZPtu4DWGZgc9igQVWqBUBKPHlUkkujganCtCQ3V8MQZ8MAR8lq6znVTinfypEGnSm/EsTZEy/OurZLCdc0Drd2XNIhTUdrHa+04/yH49GVJE77ydrFEPH12y/VDGdB8UVZDVkqA7NSg5/3cPRYIVSCUxNOjFghjzAhgLjAECAOPWWv/L6aNAf4POAeoA2ZYa9+L3LscuCPS9L+ttXP2Vd89iQ2udk5nHz8tvoUh3smTBp0qvREvi9miKTD+PlgwUdz2xs4Ut6XsIvHHVpcKRWmb2LXDGHjx1vjpwdVCrQCbyusozEqJez/o95GTGmTbTs3EpCSenl7Zm4GbrbWHAScCNxhjDo9pczZwcOTnGuB3AMaYPOAu4ATgeOAuY4x3KoJ9iePO5ASxNdW3bWGIZ7XQU1ulNxLPYpZ3oOu+tPJ2SMpQ5UFR9obotSNzGJx2uyjiaqFW4rCxoo7CzOQ22+RnJLFVLRBKN9CjFghr7TZgW+T3amPMx8BwYF1UswnAXGutBf5ujMkxxgwFTgVetdZWABhjXgXGAQv34Vdon/YsDF5Wi64EwsX6p2tQnZJI4slzUkZLGU7NVzlUlM7irAvVW9teP3S+H7A0h8Jsq9rN6P3aPjfNTUtim9aCULqBXjPTGGNGAscAb8XcGg5sjvp7S+RavOtez77GGLPWGLO2rKybKuKGmiVjRsWX8hpqlusdsTDEWi18vs6ldvXyT1d/2T7JPpHZzhBPntML3LTGFqjaCNv+Ac/NSKwcasrjXk2vldu+hCPju74GX1CyL8WmRTZ+WWPamu91rHSIviqz23buJmQthZnxXZgA8jOSNQZC6RZ6RRYmY0wG8HvgJmvtrtjbHm+xbVxvfdHax4DHAEaPHu3ZpkuEmqHkI1g8zU3FOmkeDD4C/AFvCwPEz8zU2dSumtGp39DtMttZ2rKYhcNQsQEqN7gVcsf9El6+VeTwylWQ2QU51JTHvZ5eK7d9hXgyfvUbsHsnVHwBK34ENaUwfXn8+T6toOVYoWGneQAAIABJREFUbKqTmhF5B+hYiaGvyuzmCikiWJjVtgtTXnoSNQ3NVO9uIjPFO9haUTpDjysQxpggojwssNb+waPJFmBE1N9FwNbI9VNjrv+pe3rZDjXbXeUB5HXxNPj+SxJI6lgYHHNzTYmk51s0xXsj1FlFQDM6KfsCR55jqa+A6m2w4mZXrifMgomzoaEWwk0i+511s1AFWenvxJPx778E8y5oOb/XlMjfTppXp+5KOOw9FifOkWv+gLo69QM2OQpEezEQkUxM23fuVgVCSSg9OoNEMiw9CXxsrf1NnGbLgelGOBHYGYmdWAmcZYzJjQRPnxW5tu8JNXtv3MPN7t/R7kVfr3WVB6fts5e6FasdRaBoNEyeDzNWSGab9kzQjn96NJrRSdlXNNXDsutbyvWy66VC9TMTofwLeOFHnXdnilaQnbFxwe/kurpnKP2BeIdAoabW12vLYNR4N83r7PHyWlsGTbtbj8Ull8PW99S1tZ+wubIOv8+Ql95+EDWggdRKwunpI4iTgWnAd40xH0R+zjHGXGeMuS7S5kVgA/A58DhwPUAkePq/gHciP/c4AdX7HJ/fe+Nu/O7f0SdLqbltWwoCSfEXhrYmfc3opPQkNhRHkY5cX36jpKWMVpb3BkdBLhrdcmzMPkc3REr/IN4hkD/Y+voHC2Hs/7RO87poilj7vMais/Y8e6msJ0qfZVNFPQUZSfh9Xt7cLq4FQgOplcTS01mY/oJ3LEN0GwvcEOfeU8BT3dC1vcMXkKI/zkTuFAHyRf3zRp8s1Vd6Z9YAqC6RxWLs/8Dc81svDI67RrzsG4nM6KQoe0Mg1Vuud22V36M3MI6yHC3H/iRRxpvqvWXXUZCrt2pufKV/4sh4bAxExpDW10+7XcaLl6LgC3qPxfpKt02zbij7MpvKaxnUTgA1SDE5A2zVatRKgunxGIh+gc8Hbz0qbkaOH+pbj8J597ttotNfOhV7oxWOCbMkY01NqfyelhffShEbaDdqPIz7X8CK1SOYKtlwQFP8KfuOdI/NzwWPwKo75X5OMWQNgx/8A7BQ+RVg4OWfwvoV7jh47W4ZB14B0oEUyBmpsT5K/6StQyDnejgs1j5rpeDcqPEyfkCsc2f8HIyFSfNh8dSWh1qv3yPtcoqBSOIDXRP6JJsr6zlmRE677QI+H7npSVpMTkk4qkAkgrQCOQ2KPTWKdh2KPlnaslYUjOnLZQHY8alsmraslbbLrofLlsTP/x3tDlU0Gk66Aeac21IZydkPGnZpxhpl3xG7+Qk3w8qfiVznFMOFj4lvdl2565/tKBm1JdJu2fWiiC+a2tqqUFcG8y+U+1q9XemvxEtS4PPJOhKbpWnSPLlfWwKn3w3P/5t7sDT1D2LRS86AlXe4Y3HCLKj+GoLJarXrg9Q3hqiobWRQOwHUDnnpSZrKVUk4qkAkgvZSWzpWgMwhksoyFNVm19ewYGLL51VtEjemqX+AV++ShWHMbVLt1wI27G6eTr4Jnr+udeDqlN9rxholsext0arkTDj3fhg3U5TkcBPs2uJmhgF5ff46V2lw3Jyce9FWBccN0MuCd8lCKV4XLzWyovRFosecMWJhjp3XF0+DGS8CVmKCnHvrV0DJhzK2PlgI434BJ10vKV2DaZJe+eLZe9cHHVe9gpJdogzkpXXs0CQvTRUIJfGoApEovE6NOpK3Pl5l39J1EiR62WLJwBGd8nXyAtdsHS8g2xh181ASR0dkOV6btHxRkmeskI1LvOBOaOmnHWtVcMbKlrXiijF2prhNZReJj3jZJ2pxU/oPXuNp0lzIKGw5hqo2SeFGXyD+2Fq/As76b2mTXQzLbxA3wfasdlp7pVdSWt0ASHxDR8hLT+JfW3dirUWSXypK19EZoDuJl9M7OgNNWoEoBNGZk85/SE5ZqzbBzs2tU74umiJB1s5myytrh7Wa0lVJHB2R5XhtbMiV1aY6b7l05HjCLJH9ttwAHSVi5e2QlAGZw6C+vP3+KUpfwms8LZ4u1uhonPFTW9b22KreKodRjvLQkQx9HRn3yj7HsUDkpHasrkN+RhK1jSGqG5rbb6woHUQtEHtDR025TrvGOjklXfOAG98QawXw+eQUdexMKDhUTlFfv8dtH+/E1hdwA+omL2hpoZgwS4JNvbJ5aEpXZW8Jh8WPOl7RKod4OeytFdl7YyZ852aRz+gYiMkLZAxcuUqyylw823t8teUqqEUUlZ4kEW4+sc+IJ9O5+7tW69jgaK9sgG89KmMsa5i0iTe+vNBx1StxFIiOWiCcVK7bqnaTNUSLySmJQRWIjtJRU65XO2eCdwLYYq0APp+cpo6dKa/RE7ZzYhvr4mSMuwBkDJbNV3O9m4UpNU/aakpXpSs48ly9VdzmTri25QZl8gKRP58vvjteIEnGyTm/hLoKSCuC6c9LAcbaMnHJSC/smGzGCzBt67MVpTtJhJuP1zOmL/eW6Z1bYPx9kH8wlH/W8sDprUclHsKG5JDJ+CUbYGfnfh1XvZLS6gaS/D7Sk/ztN4Y9xea27qxn1JDM7uyaMoDQ3WRHic18NHYmNNZIEHSo2bsduAW0Tr4pvhXAcc34YKEoG9HuTLkHYGOLw53/ELx4q1s8y+eDzMGQOxJyRkD6ILnmbLZyRribPEXZGxx5Xn0vnPlz76JV1dugarNsVjwKGYZTB1FW20SFLw8LUt/kwWNhwcVSZO6PP+56ITgtoqj0FJ1x8wmHJeC/arO81le0fsbKn3m7t779uLjtpWRL7E9NqXv/xH+TdOBzzoP6Kpn3I3N/OGwpq27g68o6yqobCIdt+99Nx1WvpGTXbnLTgx2OZ3CqUW/XQGolgXSLBcIY86m19pDueHaP4ZhynSq40aewk+bB4CPAH4hv8h18hFgD4p0EBVJg9AyZ7C//o2Ra8gcJpw+msbaSpMtX4As1AlYUl6MvFZeQ8+7XrEpK9+HIc9Um2F3lLdtNdZJJqaYULl8B339JfK19AWxSOhsr6vjFSx8z8+QA5u1fu8HPafni3ufksD/nl+Lu1BlrmRZRVHqKvXXz8bI2TJ7fMji6aLTM8Sk5knwAwPhEST/3fmiohsfGyHvG3ycZ+qq3waq7XGtEVNa9cNiyvqSaq+euZUtlPUW5qcy74luMTKnDhNoYLzqueiUlO3eTk9pxK1BOWhCfgW1VWgtCSRxdViCMMdVIclFwq0qnOdettVld/YxegWPKPfmm1qewi6fJpim7KL7JNynNe6MfDkNtKVRukE1VUz08M3HPwmImzSdl9b1uju9o3/HzH+raqa2itEe0PFdv95btyi9lXKx5QCxyS69x5XfCLAYlDeJHJxeQ/0KksrqjMOQUizIBMPpKePpsV7anLpU0sM7mJjVfAqXb2sTEc29SlO5kb918vCwWi6aKIrBgovch1eQFcgjl80F1idRDySh045HCISke6ozDLWtbKDHltY17lAeAgowguTWfYRZc3r7blY6rXsf2XbsZnNV+FWqHgM9HbloSW9UCoSSQRBwjzAaeBw621mZaazOBTZHf+4fyAK4pN73A+7Qp1NSyXUdMvuGw+LM21UFyFgRS4c1ftVhYzOKpcPzVsjA4yoPzmctvFF9XRekuouV5zQOSRjJatifNhZRcSQBw/sPw19+2qkmSWbeZ/bN93uMmc4jI9pLL3fsZhXKa+uQZ8MAR8MQZUPIRvPAj9++uujwpSqJob86PdVcKh73HghMc7XVItWiKKB7hsFigMwpFyVh5u4yHBd+Tdh8slOtFo1soMY3NoT3KA8AdpxaQs+zylp+h2ZX6DKXVDR0OoHbIz0hSFyYloXTZAmGt/XdjzHHAQmPM88BDuBaJ/oNjyt31tfdpkz8qs0EgRU6TgmmuclBb1vo0NdQsmWwWT21pVXCq8oJczxom74+X4UZRuotYF4ZgilgH6naItSyQArWbIdQgsv6dm1vLbzDNTeXayjKXKZWpo697KcuLp4m1Yv0Kd7OjRRGV3kLsnB+InA5HuytlFLoFQWesaOlu5ARHT3hYLNnxXKLqyqDiC3lOrJKx/EYZI8tvlL5kDtujxCQF/BTlpu5RIgrTtE5QX6WmoZm6xhC5HSwi5yAWCHVhUhJHQmIgrLXvGmPOAG4EVgMdt631JXw+yBwqMQ+Lp7WMgcgYIm3qysS8HO3LGut6NHk+YMCf5CoP4C4Cly2RDVp9pZwoBVLkpFazYSg9QbQLg+M+UbUJLn8Bane4laWdFMKn3yVBnCDXmurAFyA0dSn+yg0SkF1TKm4ZyRmSSSZatuMVR3SKzTl/62ZH6Q3Ezvkg8nzVKvndUR5i3ZImzoG1T8KXb8rB0YeL4chJUP55/Lm+uRHWLRNF3WuMDDpEPmvQIVIwLuKOlJ+exOPTR+9xY6pq9DFC15M+SamTwjVt79Kx5qUn8eHXWkxOSRwJC6K21oaBB40xS4BjEvXcXoc/IL6oTqCoPyjKgz/yTxkbUOd1mrpoqhtI6rUI7K6C2eNd5cSfJEHVk+ZKIaFov9jU/H3zvRUFxIrmyGzWMJh3YUvZXnY9THte/nYUivQCkl/5D7Ee5BRjJy/AZA2Fpt0yhgZ/Q05km3ZDUy0E0703UE6Faudv3ewovYH2gqirNrmWgeixsuRySdV66k8lq97Rl0qbjMLW9RwmLxBrRjAF/t+P3EKhsWPEF4ALHhVLSDi0p66Ez5/EoVl+Vl9zEM0mSENSLvaShRitE9TnKNkVqUK9lxaI/PRk6hpD7KpvJnsvlQ9F8SIhCoQx5lBgAjAccV/aaoz50lr7cSKe3+vwB8TMHEs4LJkybnhHTn52bZVA0HgnRTUlklv/6EvdwlwfLBR3Jafd4mlijl59L5z3oGy0wiEpuPXxi/J5e5NvXFG6QnTAaDjUUradInPGwA//KTE9oQYIN8G4mXDWPRBqxnzxJyg+3o17cBTlxhqxdOzeKVa41+6RsZFeINffeUo+Rzc7Sm+ivSDqnOL4VrX6SlEYxs2UuTyjUNyaXr9HlI7MIZA2SFK6puVGXAQj7oMT58gYyiiEc34tWc3CzUimvlqpufLaPXsUdzNhFv5/Pov/8Akk5x0o6b6vfkOUeBsSpaSuTLMs9XJKqx0LxN4pEHmRmImtO+tVgVASQiKyMN0GXAo8C7wduVyExEQ8a639RVc/o0/glZrPqQg9arybeQYii81GKZ415taW7lAT58Lnr4qbk6NUZBfDOffJgrDwkpabrn8sgpNvVF9wZd/gBIw+e6nIprNx8kxvPBc2vgXFJ8CS6S3l9sPftwyabqhu6eb3vSfgjLvd4FDnFPak62Vzo5scpbcQPSa8TvMvWSiFGL2UjOTMltnHJsyC1+4WJWLRVJiyBJ67QsbIKT+BuRPcthc8IutEIEVcAqOt05Pmi7Xi+KvdAyqfH751Vct2l0RcZB0XrM4UwVP2KU4V6py9VAKia0EcNrT/5LdReo5EzBBXAt+y1v7CWjs/8vML4PjIvYGBV2q+ZdfDzs0w9r9bFwNafa+kb3WUB+c9S6bDUZMlJV/aIBg0SlLA1pZ6B5YeM8XTF7xTRYMUxYvoLDJ1ZZJx6fsviYxOmNVGeuPpcOg4V3nYc30aHDfdfb6Xm9/vr4Kqr1pnojG+7i+K6JU1R1HiEZ1o4KaP5NXZgDv3hh0rm/3odWDiHHj1rtZrxpjb9rSxeQdGXJoebpmprGqT1F4JpslhVKu1Yarcyz9QLHj1lWIxXBwzFp+9VFKIazamPkN5bSMBnyGtg1WoHfKjLBCKkggS4cIUBoYBG2OuD43ci4sx5ingXKDUWnuEx/1bgCmRPwPAYUCBtbbCGPMVUA2EgGZr7eiufIkuE88PNpgGGPELr94mE/nr98gJUzAtTkrYSEabpjpRInZtjd/WFxBTeTi8x9/V+pPYuDuNaU+9s6do0OPTRzNqcCY+XweCp6KepYWDBjhelrVLFooSUbVRTkvHzoz87SGfNk7KSp9fxoQ/GD8WKJjW+lpz1OLXHXIa7/vqiazSFm3VSvD5xF0oJUdiHmpKxE3VF2hpmQbXvfWmjyCQRMj6CJx+d/wijj5/G+tII8y7wJXjaUv3Ypw1Sj91Deh1VNQ0kpXa8SrUDrlpSST5fXxZVttNPVMGGolQIG4CXjPGfAZsjlwrBg5CsjK1xWwk7etcr5vW2l8BvwIwxpwH/MhaWxHV5DRr7Y7Odz2BxPODbaoT/9LSjyVnd/R9n19M1ME0USzWPCCm6MqvpKCQY9ION4sfeezzR42PZOZokM3cyp/B+hWYnGJyJ8yhICPIlsp6tlTWc/Xctbxw47fJtTsj6ThT5UQqtgqpbqCUaLwsa04K1aQMkddFU93T1Vj5N744wZ5BkWl/EDByLbowVlOdjI9onOdVfCmJBZrqE+960db3VTdBpSv4A5Czn1iUs4ZLrFC8QOg96bmtWBfOezB+2/RCuGKlKCVOEbmcYkn3Gu0m6AvGX6OiySmWNeeJc3QN6IVU1DaSmbL3WzefzzAsJ4XPSmu6oVfKQKTLs4G19mXgEODnwErgFeBuYFTkXlvvfROoaKtNFJcCCzvf024mrUB8tKNN1BNmiQUh1AyfvgwXPS6b/snz4do/S7aZFTdLxqWVt0u614lzxL0JXJO2zy/Zlhx3EZDnjLlV3vvAkTD3fBhzi6TWzCgkZ9nl3HGqG2RakBEka9dnUnTouRmi0EQX6nIKc8XbQKlJe2DSVoYZn9+V+TUPtJRPJ9bhk5c9is/Nh/oKV/ZX3S1B06ffLeNg9ni5F0gVOd/zvrlSh+XBo+HpcWLRyyiU+xmF4me+c1PX3I7ay6ijKF3BsVTkjJA6DbEF6CbNk4xMkXnZX7NNakMkZbjjq2i0HDxd/keor5I4oafGytj57p2RNWaBxDYUjXbjk17+qbjPRn/eBY/I2hJ9bfJ8OYzSNaBXIgpE54Kgh+em8WlJdYJ7pAxUElUHIgz8Pfa6MSbDWttlddcYkwaMo6VFwwKvGGMs8Ki19rE23n8NcA1AcXFxV7vjjc8naS2nLRW3DeMT16OXboH0wXDmPRBIFv/WxZE0rtEWCUdZOO9Bt7iQc9344OVbJb/+tOflb+OHOeNb+5yPv08Wi9fvkWJBEe45Ywj+RRe5KQVjfWadU1bdQPUK9onMdoR4lrVwMyz/gWSFcQpo+fyiwO7eJRaud+fCIWeK68a0pa7c+pPgqbPcZ65fIcHRXjE+l78g95rqZKPz+++795ddL7K85oHWAdydPTFtL6OO0ia9Rm57M9Gud+mDImuGlXHx9hOuW1PVJsyiqZGicEMl5mHCwxJ4vXi69xqy/EZ53tJrxTp4/kOyHjljo7bETSGeOQRevt295lj+0gu9Xav66RrQ12S2vLaR4bmpnXpvUU4qaz7fQfXupk4rIYri0N32yHUJes55wJoY96WTrbXHAmcDNxhjTon3ZmvtY9ba0dba0QUF3Zj6MTUPMPDQt+C3x7nFtE64Vlwttn3gFo6Ll9YvGDMxOCbmLWth2Q1SrXTlz8QXPJ4/6/IbYcxtVDXKf29RbiqHFiS57eN9tuPvmhMzieoGap+zz2S2PZwMMy1OKBeIDB59qVizFkwUq8FT40Tm/Ukw/yL424OSknXlz2Dn15JB5v+OEne7WPkDb5msLYPn/01Obd+8t7VynZrrHcDd2RNTr++rKWM7TK+R296K4yLqWIJ3fCa1VB4aDXPOhYPPEIuBgzOnV34pCsHunW4gdLx5vHq7jBNHocga5rZzsjs9NVay+o251XVDfP7fRHnwBwbUGtDXZLaitpGsTm7+iyKKh7oxKYkgEWlcfxzvFpDR1edHuIQY9yVr7dbIa6kxZimS9enNBH1e5/D5xNQcfYJ52h3u5iZ6wo9OgemQUywbJac2hJP/3h8U60D1djcA+6x7vN8faoKqTdi8AylKGc6a24aTmuSnqaGcoNM+3mc7sRBtpSRUBhbRGWYcBTMcdq0G8QI7qzbJRqjwMJHV6IJztWWt5a+pzlsmU3PlBDY5Syr2RuMo120pxIn4vhpAqiSKaBdRL0vw8hvl+qKpcs0poFhfBVOeA0z7a0h0wUXnUMqrXVq+WDwc60N9Jaz+JZz7G10DeimNzWFqGprJ6kQMBEBRrgTMf1ZSzbHFuYnsmjIAScSqOBPIBTJjfjIS8XxjTDYwBlgWdS3dGJPp/A6cBXzU1c9KCOkFcMkzrq9qdlHrCR/E7SLWH3XCLJnsT/mJmKafGiuxDTWlEiznMGo8BNJapwWcMEs2WqPGY5LSyU1PYXhuGqEw/OiPWyg/b058f3VngWgrJaEycLFIMoDmRgn+POkHEt8TfVLp+Gb7k0VGv3unKA7V21tuXrxkP7tYaj/E+me/cqfECv35163fc+Fj4uedOSSxJ6bRfurdnTJWGVhEu4jGU3zTIxv1UeNh6h8g/2AYcbxYDKq+an8NWfOA+7ycYgiHsZPmtR47IFbCRVPFgrhoqhwMhJp0DeilVNbJoUhngqgBCjOTSfL7+KxELRBK10lEDMR7wPPW2ndjbxhjrmrrjcaYhcCpwCBjzBbgLiAIYK19JNLsQuAVa2107rHBwNJIGrMA8Ex7Adv7lEAqXPA72RTt+MQ9/VnzAFw8G+rLW/qMN9SI20cwRSqIxub7XnK56+86cY74kj89VgJHx98HuftDXblkVAo3wbj/ledFaGwOsXJdGaXVWdxx5hIK0wy14SQOmPEKSTS3PmVtKyWhMrCIVyDxmCnw/gLZwCy/UWTx9LvlRDWjEC541M2QFHtSumUtvPUoTF0qSonPL3KfvZ8EhtqQtNu9E0bPgOQMOGScZK+5bAmEGrGpuZjm3eIqlVEofYouRNeXT0w1jXL/JTrGJp4FIS1fKrnXlYt8j50p8XMrbo7UhIiMOWccTV8ma0hKDgSCohyEm8XSlzYIgmmYr/4s460uUsXagDU+TDxLdFfWAJXfbqO8RhSIzrow+XyG4bmpfKouTEoCSIQC8X3iZ1JqszaDtfbS9h5urZ2NpHuNvrYB+GbHurePqSuTjdPYmWI1WH2vO+ED2GZZCJyNzoWPyUnUe/Og+HgoOEze++nLsmnKLhKFxB+Q68ZIcKmT8tLxjx00SnxoI8+1kxdgk7LwBQIkBfwU5aby/uZdfG/eLkB8IZdefzIFmck9+I+l9HriFUgcf5/I6+v3iFwWHg6v/Kf8nl0kcjltqaQKrq8Uxfm5GSK3Z/xc0lhiYeWdEsR5+t1yAuqMi/MfkmcDdvxvME6waE4xTJyLeesxOO5yuHKVKM7BVPf3eJuWvrCx0TTK/ZtoF1HHEhyt+J7/kFw/+SaRZWc8NdZKm6pNMi4mPCyxDYEUaWfDsrbs2uEeQOUUw8VPw2v3SEXq6q2QNZwwPj6tDLFszU5umfwMvkWXJU7xVvntVvZYIFI7HwA9PCeV9dt3JapLygCmywqEtXZ9G/dKuvr8Podjok7NlROgmlJ3kzX0m1D2iVgnnLoPS6+Rjdboy6FiAzx/rWRtOuUWePNXEoC9fLo7GU+eL6ewTXUtF4pJc2VzFllkzJ9+AWf/AgsM8icz74pvtSgs9+i048jtwiSk9CPa2li3VSAxmOYGZV7zJ5HVtx6VzEwVX7TcGE2aC99/WU5VF01puWEyJr4veCBZMtFE57OvK4PjpkkF3WHHQuYI9zu09R37wsZG61D0fwIpooDn7CfpjL/3lMi1DYl1+aQbxfKwR2GeI1a6aGuBteLed8K1LbOPTZjlrgMZhfL8sf8j72/aDaEmfKFGDsrJYNpJIyE7NbHxPiq/3Up5rWOB6PzWbURuKn/5fAc765vI1j2A0gUSEqNgjPmFMeYTY0x55OfjyLWcRHSyTxFMFT/w9AJJ0XfZEpjwOwkmDYdg7Wy37sN375RJvnaHZG1acbNcO/5qWDJdAqljs8s4wXWxbk6Lp8upFYgv+gnXYmaPxzxwJObJMxgZ2shz157Ac9edxH+eezj/t+pTPiurIRy2rb6CMoCIzgoTWxME4mflaqprGawZTBdZPfpScdFrlZJ1OjRUu8qDcz06S0zRaFGQZ6wQ5SFnP3HPc9o7+exX3CyZzlbcLKmSd22TzE4v/Mj7O0DfqW+iaZT7N46FesFEOWB6f75YquecCw8eI1nManfIulA0WsZBqFEsdpctkbHnZB3zWh+WXS/3o8fKg0fLmlO9TawR9ZUEXr2DoQ0b8BmT2Hgfld9upaKmAaBLKViL8iSQWutBKF0lEUdvi4FK4FRrbb61Nh84LXJtSQKe33cIhyVgdMXN8PDx8PGL0LxbCv389jgJiD7hWpncnc3TmNtkIYHWGyqvILuMQlFSLvidbLaclH9Oe/BMa2mevZSykq1c/MjfuHbeu7yyrpSr567dc6LRua9rKatu4OvKOsqqG1QZ6Yu0t7H2Sms6YZbE93ywcM+1sC/gymAwbe/kNhxyg66dQnIrbwes+Gs7n+2VrnXRFPh6beuxFasc9JWNjaZR7r+EwyJvzhgIpsHJP5LDolgl4PS73PHw1FgZE6FGuOgxKDi07TSu8VIbRysdR1+KWTQl8Qq0ym+3UlHXhAEyk7tigRAFYv12VSCUrpEIBWKktfZea+1254K1dru19l6guI339T9iN2PHTJF4hdhJ3LEUVG2C/IMki4yzqXLSYDrp+GKz3Jx+t5xWRVsxika7p8Ig1g+PhSUnKdzi0pbKehqbQ536quGwZX1JNRfOWsPJ977BhbPWsL6kWpWIvkZ7G2snK9eVq+CH/4AZL4o1Lf8gOO9+Qj/8kC0TX8T4g3DFSgnadFKyOrQnt7u2SqFFL2tbao4UT5yyRMZJvA2T19iKVg76ysZG61D0TxxL3+xzohRkI2PFS6azhnkry2mDJObNa30A+TtzKAw6pO2x4rwmWoFW+e1WKmobyEgJ4POZ9hvHYVBGEqlBv1oglC6TCAViozHmVmPMHgdHY8xgY8xtwOac+E5GAAAgAElEQVQEPL/vELsZc/LhRxNtKcgphvLPxeXC2VSNGi8WiUnz5IQ3Ok3fmNu8fcXH3CYpLwsOgx+8LwuIx8LiFJZzKMpNJSng79RXLa9t5Oq5a9lSWQ+IMtJVi4bSA8TbWBsDVZuhJhLGlDkYckeKq0P6IAnqzxiMzRzGEFOFmX2OnJS+v0Bcj6LTBLcltxPniDLiWDCiqdokysVvjxWrXlp+nA1TRAHPKGw5tqKVg76ysdE0yv2PcFgCmGMtfUumS7Y8L5kOh73Hw+4qSMkVeY9dHxzr4NJrxKXP67mO0uG8JlqBVvntVipqGzudwtXBGENRXiqfbFMFQukaicjCNBn4D2C1MaYwcq0EWA5MSsDz+w7OZiyjUEzQgeT4hX6cyf61u+V6RiGEGiIBbwEpw3f2LyW7xrSl0ibeopKzH6z6ORx9KbbwcELJGfgnzcM41o+cYuykeeSnDKUod/OeQOrHp48mP71zC0hjc2iP8uDQFYuG0kN4FQ6cNA9evFVywrcVbBwO46vZjm/1/4qvduYQSSX57lw45EyxHJjIe7zkNv8gqaz+5q9F7r3GSrR738o7ROF489duocW0QfCX+6XI3IRZUoPCSznoSwXiNI1y3yNeIgLH8tBY4z0GfAFJMOBUl3YSZcQr/tZQI2PqL/fLGMjdX1Ifh5okqcBrd0tig9X3ts7wdMEj8LeH3dfuUqBVfruNippGMpO7Hvg8IjeNdzdWYq0lkg5fUfaaRGRhqgRui/wMbFLz3QxJjbXw0n+4KVyjFwd/kigFS6+Vyd4JeItud/5DshD94Rq3BsTYmXEWlWo48+fQUI0JJBPASkVRJwVgIBXTVMdQfxUv3Pht6hrDpCX5yAnvxOza2WozFQ5bymsbaWwOkRTwk5+e1Mpk6qSGjVYiumLRUHqI2I21Ma7yAPGzqEQ2Rj4bbp0J5vyH4LWfS7sxt4llzEtuS/7lJgUAGRtxUrkC0qfT75RCi9EZyM5/SLKbLbteNlNXvwGpea2VA93YKN1BWxm+HLfWeHN35QbZ7Dv1fHx+eOdpcX91FIuMQhlHeQeCPwhvPS5jYf0KUdJf+IHEVSyY6D57y1pRJi7/oxxYBdOkztBZ92CTs2g8+z7qgrlkYxLihqDsG8prG8lJS4wC8fonpZRVN1CYlZKAnikDkURYIDDGHAoMB/4eXfDNGDOuVxV4627qyiOFeurcWg+1JbJ4pBeIX6s/CPcdKpulmlJ5n1fA21uPwjm/hunPAz4pFlRfJadHz1/nLlQXPgbJmS3T/k1eIJ+75oGIYiKnWyanmOzJz5BZcCi+sk8wsfm/Cw8njGF9SfUe96Si3FTmXnE8GckBdjeH8BtDapKf3NQgj08f3aJdVywaSg8SvbGu2uwqDw6xvtKhZsno8uylojA7heTGzhQXoqa6iAKcLAHSBpHTpde0PA1ddaf7zPUrYMwtspHKO1Asb6/eJRshh5xiOX2NzUDmpHxdNBWqt2Fz92dHbVObCrCiJIzaOIkIrnxVLAMX/E7kOXbudizQW9bK5j+nGM57UKpDH/Adydj3vafkgMdRrEeNh7H/DUd+D3ZulhihjEKxxF2xUvqy5gF5Zk2ppAY3PjcWL3KI9WVdFs9/8gWXnTgSv8/sGSdAu4dHSs9RUdvIiEgWpa4wIi8VgE+2V6sCoXSaLisQxpgfADcAHwNPGmN+aK1dFrk9ExgYCkQ4LGZqaJmFxsmTD3DjWjkpzSmWSX7iHNkMxWbTKBoN371DNmJ1O2RR+GAhfPvfIWsoXPS4tEvJgdpSWHBx62A7p9ZEjGLiW3QZdsaLbvEg5z2RU+Zym9MitqEgI5nymgamP/X2HkXhVxcfxeCsFA4uyGDp9SfrYtOfiK6U6xDtKx1qhpKPJLtY1SZorJYNjGNBcwrF+ZMkvmf1vbKRuXi2bI78QZFrn6+1crBzi4yVnGJJ5XrKT6DkwyjXqrmR2Iw4cUURl6em9KFc+MSaFortqMGZiZHNzhSj6wsF7JTO01zvLZONtTDvAld+nTEQTJU4td9f0XIMVG2Squsga4hjcXvtnpYugivvcN0LJ82Fs38Fz0xsaZF761GxDBoDy25omZFv0VQOHX8fNx89hBtX/IuV68r2HBQ1NIdbHQrFHTvhsNSZaKqXGhaBVDkoU9nuFsJhS1VdU5dqQDgMyxEFYkNZDacc0sviwJQ+QyJG+tXAcdbaC4BTgf80xvwwcm/g7CbryqR4VlNd6yw0EDFXfykbqolzpFhc+iBxZcoe0bL96XfJBm3+hRKYuvJ2WQz++lswfrFkpBfIKXDWMFlcnLSY4GbxiJONiVBT3Mw7sbEN1516ID9a/I8WwdK3PPdPNpbXUVnfREFmMsNz0yjITFbloT/QXrBxzXY5zXTSq1ZvF/eKPcrDPfD8v7mBz2fcI9efmyEK9uzxctqaMUROTCfPl1NVpwIvuMHTL0UsEjeulY3Xiz9xFfBonAxkk+bDprdZv6Oxe4L726uZEQ5L0LkTfB4Ox39PqLl1W6VvYvzeMlnxRctDmudmQLgJfEGZg8/6H7j8BXFDmrEiUj8oUv/BWUOCqTL3r7xdZGf+RaJYX/MnmfdX/0oOmWItcuP+V5QI4/Oe64NpBJdM4cFzBvP7aQdSkBFkY3ldxxNjhMNi3Sj9GF66DbZ/KOtb1UaRbS+8xofSYXbtbiJkbZdqQDjkpAZJDfr4qrwuAT1TBiqJUCD81toaAGvtV4gScbYx5jcMJAWiuVGUg9R8MSdHZ6FxTopW3ysnTp+vglNvk83Ub4+Dl38KE+e67bOGe7tpHH2pmK1f+g+Je3jhB/L+6LSYzuf5ApA5zHth8wdlsZqxwk0fGzlldmIb9jRPDXoGS6cl+TVguj/SXhYVR/n0+UWmP1joFns77Q7XRQPk9fnr5Hps9rFwCF75mcjuKT+BDxe7p7E5xaIAX/A7GHykbDb8QXH1+/Rl77GVORyS07Hf+j4HD0rmkuOG7vlKCQvub6tmRjxFob7C+z07N8dXRJS+RTC1tUxOni/zfTRVm2SsOIdDr/wMMDKPzx4vCndtGVz8NOQdANOXy/ti3VuXXC7WOudgKb2g9efUV0pq5PRCmeujD5icRB5Vm0iu2cxxr07kiXHpFGZ4z/WeY6euTOI3/v47V8F5aqzUY/GS5faUb6VdHEWuq1mYQDIxDc5K4avy2vYbK0ocEhEDsd0Yc7S19gMAa22NMeZc4CngyAQ8v28QSBJXjZdvFQtC7kgJYLNhcefYucmNeSg8zPVpBdfnfPoLcmJkQ96nRukFssAcfal3fQkn2HryfFEyaks8M3GYxqgYjYgvbjhjCL60AnLD8MxVJ1Ba3UB5bSMWPIOl6xpDGjDdX2kr2NgfdGMRGqph9Ay3bkn28Pg57aOzj53/ELx6pygEi6bKhmj8fVKVd9R4ychUVyEWi7SwWDSi/cbT8sUikT0cKr+Cz16Fg86EZy/BVG0iJaeYmRPnAUN59t1tHQvu74ibUVs1M+IpFzNe9H5PTYmnC2Hcf3d1g+q9pOaJS9L4+8T1KHOIzNPOfO+QUwzWSgaksTNlHZh3YYyV4vvyHCcmYvqy+C57zrw/5fetPyclWzbzsfEWNaVucoIoRSL/hcsJTHnJc65PS/JJdexoVyUblu/qVQ170ZTWshxvfLQl80oLKiMKRFYCLBAAg7NS+HKHKhBK50nECjQd2B59wVrbbK2dDpySgOf3DRzXj5pSmHOebOB9QdlchRrFqnDZYjkNKji0tdtRbQk07IQ548XH3MtykJYvbh7xKpA6z/UniVLiZOIYfx/c8Lbc8/nwPRMTM7HsekxKFmEMn5XVcNkTb3HxI3/jv/64jrz0IA9ecsweq4QTA7FffpoGTA9EMoZImtemOvj7IxBMl6wxk+e7KVSjcaxhE+dIRrCxM2Xzsn6Fa5Go2gT5B8OPPhZ3qLnnw2NjRHGoLRMXKKfdsutl8/3CD0SJWTARDju/VTVf35Jp3DEmr2PB/R09HW2rGF085cKGvN/jpKeNbhsdqB7t7lG7I37/1C2kd5CcKcXb8g6Uv9+fLzIfa5VIznRP7Ku3x3Uv2vN7+efx6zk4bXz+1p+z8met5ni+97SsBa/f4yoSUW6Daf4Qj08f3WKun3fFt8hpLJVnzD4H/u+b8OQZrvzGc5ONLVDXVyrB92IcC0RWamIUiCHZKXxdWU9zSOcMpXN0WYGw1m4BkowxOQDGmJHGmIuNMUdYa9d0uYd9hWjXjx9/AmfcLT6ws8fDg0eLQtHcICf/D41u7XY05jbXKrHmgdYFgibNlyxMEL8Cadkn8lx/0H2uk+GjtgxW3o5NL/ScyE2o0bM43BWz19LQHOIXFx3Jaz8ew7PXnMioIZmMzE/XmIeBiD8Ag48Q16LT74S0PDjiQvjTvaIwx7pyTJwj2Zia6iSGYdFUkcnoTdCo8fJ79VZYPLX1xsepLu1cyynGTl+ODaREFBTvgo3pAcvS609uP4C6LdekaFLzRXlqMS7nyfV4yoUv2DqmZPICcf1yKBotBws2JEpAqLmlwrD1Pe/+1ZapW8i+JhwWha5qs1i/qrfLJv/JM2RzXfKhWOaOuiRS32QmXPtnsUanZEsMxFuPum5GbSkHIG5Qk+e3lJ/ojb+jjM5YITFF5z0IydnemdTqdohr1EVPuIpElNtgIJjCqMJ03rjucD6/9QjeuO5wRqY3YUKNrV1qF02B7GJx142nVEfTVyrB92IqEujCBDAkK4XmsOXrqvr2GyuKB11WIIwx/wGsBv5ujLkKybp0NrDIGPPjrj6/T+G4fvh8UPVVy+q78dyOTr5JJtK8A1pmbnr9HlkM/v09mexf/LFk7fjunbDp7dYbmYsel6DsqX+QRe30u9x+5RTLpmvGCsLB9LgTebzicD5jmPrk25z+m9UYIC9dA6YHNP6AyHjdDvHlX3K5bFhCDZCULvJ61SqYulQ2Ufd/Q6wJp9/txttc+JhsgnKKJS3lR0slJWU8dw2HSAyP+XAJ5sVbsZctiVvN1/iD5KcnUV7byNeVdZRVNxAO29bfp6Ono/Xlbn2VGSsiQay/lMDycFgUg9iN3ou3QCAFroyJKTntdmlTNFr+XVbcLBtQRwl4Y6bbp+isbi36V98xxUdJDNGBw3tO48+EplqY8LD8XwbTIClT5v7aElEabAjmnCvtZ48XC0TRaO+DIsdS58Sm1ZRKLNuMFbIWTF8mCoijhE+YJfKPkZgK44PyT73n+F1fi8vU7ipsxmDXxSqSKMGk5eMr+5jg02cSePAogk+fidm5RZR/L/mrLYGGXa0VnEsWilIdbRlLze8bleB7MRUxLkyDNjzPqDeuYcgnc8U1bi8ZEknfqm5MSmdJhCo7DTgcSAO+Ag6w1pYZY9KBt4DfJOAz+hbNja0XfS+3o4xCGHKUBMv5/K3zeIeb3DSADstvFCXh3bmSg79uh/t8J52rE1jqLEATZkFdBWb1L6k75T8JXzCX7Oenu20jE3lSbZOnD2xVfdOe3zXuQSEcBovENtiwyHHVJthdJQtZME2CN+ec29qaMG2pbMJSc6TOyc4tgJHCWRUbvFPINtW5v5//kFjzTvkJZA7FBJKk/srUP0jdiD3pLecRTh/SqqaJZ1rK9lLXOjQ3ugW8ojn5BxJAOmq8bPR2bZVx7JzwlnwoikPOCPc90cX7Zp/T+nR37Ez3c5yT6tj+GW/Li7qFdBNO4LATPwbyuni6KM3fvVNk3+dzUxs3726VRrVF3ZLX73GLyBkDr9zpyvCEWaJMVG8TmajaBFe8LHFHY26RWITGalkD0ga59YQyClsXML3gEfjHM/K5KdlYX5CmK14jKdzgxtR4WeIWTZF1xkv+qrfLdygaLc8tOEyK1WUMEWt4bGG9gkP7RiX4XkpFbSMpQR9JAR+DNizl4DU30xzMJG/LKrBhth82Y6+eNyRbFIiNmolJ6SSJGL0ha209UAXUA+UA0QXl4mGMecoYU2qM+SjO/VONMTuNMR9Efu6MujfOGLPeGPN5xArSewgktU7lGmuudk4eX/5pxL90vJuy9bt3ymYk2irhULVJNm1/e1AWjtkR94/YWhCLp0sRovMelDiIxdPg+KvJXDKJbaEc3j1zCVu//w6NM16hNPUAymqbyEkJ8MjU41r4wM6aciyvrSvpuC+5+mP3b5x4gSfPgAePkRNNx7Kwcwv8+T7ZzFRv85bd6u3iUvfMJDeTDFbiJFbf2/pEdvJ8GHq0KNfR8RNv/hqGHgVzJ8Djp7npLa/9swQuDz6C8vpQx9JStpe61iHWDcNxPUrNlX7Wlojy8NRY11XL+d6xm/oWxfs8/p2iM+useaC1a9glCyX7j7qF7Du8DobAjVt461HAiEuTk9o4Xnvn/7emVGKHqrfJWIquAL/segg3u8oDSPay1ffC7l1S++GJM0Shqa8QZaNqk2vBdixl058X5eHISTLeHjwG3+xzCNSWSGyeYzWPZ4lzCkPGutR+GinxtGWtPLfsYzlAqC/3tozVl8tn5YxwP1PpMJW1jWSmBDHhJorf/xV12Qfz6Xd+y66CY9nvvZkkV2/cq+dlR1K5qgVC6SyJsEC8Z4x5BkgHXgPmGGNeBk4H1rXz3tnAQ8DcNtr82Vp7bvQFY4wfeBg4E9gCvGOMWW6tbe/zEkeoWVwXQk2y+QkkA1Y2HWkFkHtAy+q7HyyUk5ydm9wT2lV3i2vTW4+6VXzrK+Xvs38hi4XXyY8TNOf4ysYLZAs3y6JScKhM8rn7QUYh6b5Gxs37AoDnrjuJix95g6LcVB6ddhwvfLCF/zz3cHJSg1TVN/HQ659x13nf4JbAoW0XinM2lrGnTtEpQJW+i5MFqKleYhUcq4Oz0bngd7DqLjjzv2XDUVvmLbvRwZ/pBbIxDqZJfFBNqWx8Jjws1o1wGJLSZByk5spnOxx9actMZlWbxJXKGUf+AI3NjR1LSxkdv9TW6aijaDx7qXz/0+9umeHs/IckYUJHrBkO8awfWcNci+QHCyFnP/j+SzLf+INyymt8bn9irIlKggmHxULgpEV1UnKDK9fHXy3B/BmFcuJftUn+v7z+fzOHuv+/r98jxRe95vDoV5DPcZST6Purfym1H6Kt2E5RxmlL4ajJkvQgap3x/el/4bz7XUU2niymDZI1Z9pSif+oLZPvf8pPpM2Xb8r3/dvDMOJ4DZjuJsprG8lKCZD/1QqS67az8ehpWH8S2w67koP//EOGffwUXx7/8w4/T1O5Kl0lEQrEVcBExKnhOeAE4FLgE2STHxdr7ZvGmJGd+Mzjgc+ttRsAjDHPAhNoX2FJDE41XiemIadY6jh89Af45mTZjOSOFEvB+Ptk8a+vEBeP6PSp5z8kcQsn3eDmz3fMzcYHfl/rNKwTZskE7qTlyymWBctr4i9dJydDE+dAXaW4PJz9K2p3y2amKDd1z2nslsp6rp33Lv957uFcO+/dFl/3rvO+QUFmctv/Jpqmr//ipRw6qSC3rJVrmUNh8jMRdyMrFoJYN4qJc6Q4HLib5KZ6qdi78a+iYO+ulJgBJ71lbArKCx6BVXfGV5rTC/Zs1p2aJrEueZ5ueG2lro1u05br0fIbRZGavMA9NW5vUx+tlOxxP5wnblqOK8vkBaKYzLmw5TMLD++Y4qN0DS/5j02L+uFicSHKKIwE/Vs5/U/Naz2HT5onG/LUXIkhOOPnoph4zeGxh0hrHnCVE4ei0RJX4bgMOuPzrUfhxH8Ta0XGEFEwqre5SukJ17a0EnvJ4oRZ8Pvvi9IS7boF4po3fTkcOwP+cr/E9TiuUHujRCsdoqK2kYzkAIWfLaIhbSg1g74JQHNyLjuHnkzB50vY/M2baE7ObedJLoOzUtSFSek0XVYgrLXNwJ6UIsaYt4E64OuOuDF1gJOMMf8AtgI/sdb+CxgObI5qswVRXDwxxlwDXANQXFwcr1nHcarxtjj9nC4LhkVMteEwLPie3CsaLZP+/ItabzhmrICFl7S8/vx14obx8Qo46DQ3v3hTnSxQzQ0QqpKFx4bFAuL4lq++Vxa1SXMlG8j+p4ir09j/lpiKUAPYEGMPL+A35xZRU1vNy1cfDjZEuj9EblaIY0Zk8f7mXUDbcQ/hsKWqvpH6xhCD7W4CeuqUMBIus13BSzmM9uPOKZaMYwWHyonre/PkdNLJQpNeIHL7ztNu8OcFj7ixDEnpcMAYeW9qvljmYmMnnM96/joZDxmDPTcpNmMwIevDX7WZQf4k5l3xLaY99U6LGIhWbniOdSUcloBXa+Nvxh1Fo2qztwKTOVQODDq6qY9VSoyBF29t6cqyaIp853jKeS9S0HuV3CYKL/lfdj1cvgJ2fCKK9JjbZJ6NtUpNmAX/fNY9+W+qk+D35t2iRIBY7tIHy5y9eHpLJeBvv22piNeUuhZopz/RsQ/RnzP2f+APV8l7xt8nbZ36EhEFI3z2Lwk3h6msb6KxOURa5kHkXLkK01wPOz4VJWnL2viuWDUlchBw3v2unHspIr3YMtZXZLaitoEj8iBr21p27DdeDhkjlBePI3fravK/eoGSUdM7/MzCzGTe21RJOGw1MYqy13RZgTDGPAL81lr7L2NMNvA3IATkGWN+Yq1d2PYT2uQ9YL9IcbpzgOeBg/GucB03DYG19jHgMYDRo0fvfbqCWJxqvNFUbZKN/fyLZCFIH+S22bJWrA+ebkZxisaFm2HlbVA6FU76d1k0wiFIyoIda900fmf+f/beOzyqcmsfvp+pmckkmSSkACGIKFFsIFg5igoIGqQoCVKkHQXE/lo4h6OI9TtYfnoQEcRXIASQXqMGwcJ57SAKiHQkCSUZkkySKcm05/tjzTN775k9IUBUytzXxZVkz24Ja+9nlXvd62XqgwhltwrICds0lUrLeQXAptdlzaUFaJ9oxjs9XDAU9Ea8JR0ZYYve4rwFmPxNAv7vQFXUvodAgOP3SifKa+vx9LJt+E/fVugSyzo1G5rdZk8H0SgJpmTq1en1Atm3PxgsXtyTgofbX6SeB68b8NYDF94E5PQm50YXRz0Dm96gqexyAYB+0+kzeQ+BfGZE8oUUdIQ5XHxwIdw+wFxwW2jbBfcuwtqHb4TLE4BBp42k4QllHZeN7knuwA1eQMGPcIrkQUA0uofBQipVJ+PUK/ohStUlOMVsAPm2MzA4P6PstrkQzf5dx+md3/MF6iUI+CKTRPLgV2BUEVGBPuytrOZ99ToNhRN2JhqqXdXSVGrGyMbyCyXJYxGg3zZZWfHLL5DuQ2+m/UYVSTTZTkPgbPDgkLMO4wu3oKzajd4d0/BW3yyYwMEW5En3HK2R32mjwXlye28qJfAMwdlis1UuL65J+hWM+1GX1lnxWUNCW9RbspF2YPXJBRCJcfD6Ocrr6tEyydTctxzDOY7meKJvClYFAGA0gD2c8ysAdAHwzOmcmHNeyzl3BL//GICeMdYCVHGQSZogC1Sh+HMgpvHKIcrNooE5EFDuE4gyUEo+BEi+XWskru1VQ4DKvZR5XZgHVO0lZ+ru2cCgOcA37ygXrCUjKCvWoQ8tKi4b0PN5avK0pANLRkDDfTAsDVIsuj2ulJu1l8CwdBim9MzAogeuQ6pFPQCodHpwqNKFp5dtQ1m1Gy9/aUPlXWGDk87grFMMJ4FoGu5JWVTZ8rpJurK+lp6NvRvI8bGXknyrxwEsGkzZz7m59HXZKLK9TkNohoRcGvX7WdLsB9GoHJ9GNpyTS+kDvwc8sTX4qE/AH/0Z9cOLcMiXDPPCfgpbZh8NQTKvQetkM9ISSH44EOCw1TXgcLULPmcl0Toa6qTgIXgsFg8DDm+WpFX9PkkkgGnVG6/jT9Peo/2tva7IbbHg/M9BtP8TjyP4uZEce11ckMJWKM3hkQe/4jjRIyQ+F3Leu4sAcGDlOPrX7RHgse1ArylUZdYHJ0B7XYDPRVWFUUVAnFW9L2LJCEkm3OsC7Ifo+SueRPQla1sYjCa4PX68mXcVPnrgWrx2sx7m4qfAfA3UTyF+l6/fjhyM1286UaF0Ko6nCIpjDdPNgnqvH26PH50bfoBPb4E78aKIfWoyb0TC8a0w1pWonEEd6UFqckmMxhTDKaA5eiDkabBeAJYCAOf8GGOnVxJjjGUCKOecc8bYtaCApxKAHcDFjLF2AA4DuBfA0NO62MlATOMN74H49h36XFQQ5BlSS6Z6P4PWELk9bx7grFD2S/SfASS1pnL0igcaz9aKbJM8I5WTS7SRejs0DFITbJSp1lW1dbjpvZ9CtI+L0yyhMrdBp0UgEIDZoA3xy7eW1uL+T4Fney3FVS3N0BmMZ3TWKYaTgCpPv5ACh40vkTMS3utQXwUYE4M2ro9ewTAmRh7fbzp9JpTKFPzxAmDzPODa+8H8QQfObYePmZFpipOuk9WVnCdTMrjPAxYIABoNAgGO3eV1ePuzXRjbJRGtWhqJUpLYOvo92ktoLsMt/1D2NgxfSfMd/M2YZVX7Ww+YSc6pyADHgvM/F2r/J8OWk3xw8aTofRFi4rNchji/gPoiNr0mnV/YmTWbaENlm8l+fR5gXq5yjdn0OlX8An4KxAGJIhutJ6j/DPrqrKB9yzYDax4GH1WER9eU4uHbOsDR4EObuHok+etJInZhXmQ/hS4OuG8VBT9OG227ddLpB80xnBBiBsRFzp/gTLlMor/JUJN5AzL2fYSUkmIcveyBJp03IzgLoqTKhesuTG2+G47hvEBzBBB2xlhfkCPfDcDfAYAxpgPQaE2MMbYIwC0AWjDGygA8D0APAJzzmQAGAXiQMeYDScTeyznnAHyMsYcBFAPQAvhQVgX546HVSYuIVk+Upm/+A2wtpM9FNaKhjjKqia0BcFpcer9KJV9DAjkunANxico+B1MyUKDMpGL1BMo2ieBBbJdz0cW1E1rSffnqKSPm95KjFiyvMyGPaU6hSklOrpI2Yc3GUQc11wnpy901qSkAACAASURBVAX3X4dhH3wf4pLPuq8LOKBoUt1aWovH1nmxckI3pFlO0HQdw9kDQUkY/QlJr8ZZAaMFmHMH2V545nPpSGqIXphHPTg3P03yqloD6dbXHaNhiPHppBsvl6kUNj1yHckQh8+SWDKC+n0CXqJ43DIR0GhhcZTAqc+RRAVkwTOzZoPfuwgsvSMqnV68/dkuvNpNh9S1MidJVDfCnoOQalSnIZH3WTgwcr5DOIL9FdzngY/pUY1EMI0KlSr8b33/BsDjIrGGDZPpM8Fvt7ald0osOP9zEE7JCfgoeBAKe0AkXWnNw/RON6cRnfXx7WQL1QeJqnTdODquQx9ywE0pSpEB0dcQ3mfX+1VaW4RMuL2EAoKasuhqXkxDz6S84btDHyDgx9guiZj++R6Mvak9WmuqAFcdsGJs5PM4dCmw5iHp3jIuV/Y9xPCHosrpQQaqkOStwNGk3qr7eE1pcCe0RUrZhiYHEKkWAzQMKA1Tq4shhqagOZ78cQAeBjAHwOOc82PB7T0AFEU9CgDnfAjnvCXnXM85z+Kc/y/nfGYweADnfDrn/DLO+VWc8+s559/Ijv2Yc96Bc96ec/5KM/weJwePE3j3GuC/b9Ak0oObaLvIMgV8wMYXaDGZfSuVoB0VVAr2uMi5ev8W4MPbiRqhoemS8DXQsdEakqNlmcS1+8+ghW1uLp13w/PA2kelBmxxzOLhwJGfyUHr/gw5T8FzOAfOx8tfStNsy6rdsNU1KPT0x83fgrYpJrw+6ErF3IgTzoqI4eyERkPB7oe9ifvtdTdawYJGR/Z2RT7w6SRyxIVuffEk4Ip7qFnaUa5+vN9DGVO1z+qOkUrTdeOI/mRKBpLbwWQwwDlwviqdg300BKg7gmTvMbzZJx2pa0eG0ZWGU2ZXTtHImyf1GkVRffJ7o0y3BiT1ng96gr19OfRzekF7fBeeXfkLdpfXRT9O0D8MZvpblW2mf4uHEyVMZ4g5bX8lCvpRciZaxUp8n3oRVQzevjzYp3YI+GE22a2g6RVPomdqwT3k6McHewmiqYwlZFK/UcpFVAET7+0fZlNVPJxi9Mk/pInT9hK6bte/A8WTwKZ1QpfP8vBOjzh0TPGDLR4evVnadVyyw+JJZJsxatKfhmqXB1dr9gIA3NaLo+5X1+JqJNi2QNdQ3aTz6jQapFqMKK2KUZhiOHk0hwrTHgB9VLYXgyoE5yYEL1ZUHYavoLKiRkfO0tpHlDrhhnh6wTsrIjNLy0YpqwiP/BRF0s+nvj2+BfDoz9QrIVQzAGVGLLyZTyxG9hKiYo1cB9wwAfC6oElIx9bSw6FLZCWbkGTSo3MbK7aW2gFQEBHgQE5mAhaPvR5+DsTpNWgRb4ypOZyrEDbv91IwITL0ajap1VNz6aoH1asUS+6j7XXH1I+37ZK+D//MXa2svgWHt2ms2TANXQpf/EXqimA1ZdB92BuWMcXqThJjlGn1uSmjq9FJqlFRVJ92VjRAV18XOd0aUFXvSV07EmN7LcUDBZupUhevp/3Umk3PMjWbcxZyGdcB71FVLSkrum2K7yv3KdW0hL2Kr67KyGdi5DpSTwpXWhLnjLNKjdqicpb7JgUnx/dHn4Qu3v2dhlA1wl4SovkZ/C5wXk8Bf7TnWU7Ditngn44qpwddNHvg1xhQn3BB1P3q0q5G+sGVsB7+EscvHNikc6cnGFESCyBiOAWcdvqAMfYaY2y8yvYnGGNTT/f8Zyzk02u3FtJLveYw8MtHgD4euP0ViRZx7yJ68SdfAKRerO68JLam763ZVI0Inzwrl/STb+8/gyoN9TXEiRXBgzivPCMW3swXZ6VFRGR8gw2ucWjAnFHXYPHY6zFn1DWYPrQzXi/ehad656BzGysASd41Jd6I1slmZKeYkZ5AfErRoGqrayQ7G8PZB3MaZT3jkgBwqrT9vCjSJvPmUTY1oWXjVQpTMmX41Wz667fp3PkFkc+BqAqI6pusIVWzMA9MUAzlEIoxgDTkLvzzit+oSlJfS9c2pwCP7yDqSlKbiKbpyrvmYfKGY+rTrYGoFcN0M0NZtRs84A9VKPD25VKzttDml1NnxH3EBjP++ZAHgnHJwDX3U1Utmt2KKvRXYcuf/FmQ263885pSqkK7KsEHFyrPP7gQ+Oz5yMqZ30MVwYS06JPQEzLpXZ/ekYKgkWuBni+GKiBsbi71G+35NPL3uncR0OrqmA3+hahyUgXCaWkHrome961PbAevMRnJZRubfO70BGOsiTqGU0Jz9ED0BXC5yvb/ANgGYGIzXOPMQzgvVmugrGtcEnGjQxmiBaSPr9FQTwSgzPBkdSXKhSmZXtBaI+B1ULAxbDnQUAMYk4jusbuIMrNCWz+xFZ3jq6nEaT1RRkyeReo3nRYjUUYP+EOf+ZkOz63eEep3eDPvKtjqPJi4fBue69sRL63biVn3dSGqktDQ93nAtQZU+C0oszeg0unB8i2leKJXjnp2NoazDxoNKcEUDgxO251F2VK/FxixmqgSTltQwvUlmg3RWJXCXU1OzucvUhY19SJygHiAqhfuamD7Csqqet2UZRVZVXEOcyqw/l/See0lANMgMHgBNPKGZ9HUCkhBS3jj9ucvSpniEWuAhFb0OwcC9HsZLeCjiuDlOhyoasA/Py0LzUuJmG4NRJV6rXBxZCWbkIzaEw9fbMqAuxj+WMgDQaNF6stxlgN3TQOS25Iql9YA3PE6JXNMKRJ1SED+LFjSga+nRX4OUACxZASYJZ2ei+R2UkVsd5FCIADuahpSuvZRCjCMieqTsk3JdG/zB9C9D1uqHAwnejhy36Tn4O73gYTWAAKksmRKAeJjQcNfBbvDhY7sEOqsPRvfkWlQ16Izkg9/BeZvANeeuBcxPSEONocNbo8fJoP6zKcYYlBDcwQQnHMeUNkYYKcrw3SmI3xxd5RHNlouHiY5BNxPL/wRa2hfr5sCDlFSzsmlfgS5ulN+AWBMoGnV5dslDmq/6YDbTk7V1kIKLMKdIvm06vz5NPVU6IALR6zbo/TZt+8A1mzw/PlY+GsDnuvbEVaTHna3F//7fwcw/pb2GDd/Cy7JTMDScdcjzRIHDbhiQiuzZiOu/zws26xB7ytaYeIdl8JW1wC724OU+FhT9TkBf9CZspcAtt/IFnu/KqnRCHR7lJwYoeAyaC4NWBRCAUnZwMagQ++okPp4DPGRw7TsJcC3M4g7LpwykZHdUqCsulmzoQl4wbYtJTqSRgeuM4B98g9pv7LNdE/DlhNtybZLGZiIHg6A9Pprj4Sea2bNhqH/DLQ0tAhdMuqwRRUKUuVd8/D+17WYPaIrdLwmeq9TDGcO5IEgD5vbwzRAQX/Z+3o+9SOYk6kSJ97tcjWjvHnA9mVkz+Xbpc/z5pFaX8UOqhK4q+kZis8Aek5BaLq1zkS0V8Vx6VR1EM+iXBEqbx4JDnQdJd17lF4Hj/UisEHzoXMfB5MpQPF7F8GecFH0eSpREAhwVDo9IfW+ph4XgxKaqn2IY14cb4S+JFCX1gUphz9HYvkPqGl10wn3T0+ktbm02oUOGQmne6sxnEdojgDCxRi7mHO+V76RMXYxSDnp/EG0JmdfAwUMGj1xW4XCknDyhaRqpyGRE66XjCBH6Nt3JRUWdzVp7XceBqRdSo7U12/LMrkXkwOk0QF3fxDszdArzw3QwpDYGg3MCONNT8F/8z9Qq0tB+0w3Ji7fFqpAvDv0apgNGmQlm3DA5kRmYhwyEhngqojIoFpXj8SUkeuxo0aHfRUOLN9Sisd6dgAAuD2xReSsh5iBIqdjqFGUnDZyXj5/EejxPNmgXJb4ng+A68dT3427mpRp7pxKFYVhy2j/2iMkRAAAd75B+8gnW29fRkPrflslOTqDF4A11AJX3B2SomQiMJc7a9c/CKx+UKrARfRwGCg4rjuimqlNyn0Tz96SjcfWeaMLB8iqlEKFyY9EvDyQngHmcqtXZmLzHc4sCOpe9QEATMrwqyklLbkPGLOehDWYlnrjPE6SQDXGkxP/ydOSVKuovDEN7eMoVz4ng+ZSw/aCe9TXDHsJBSmix0E8i6snkOQqD9DasLuInjWBKFXBAzUBeP1OXPGJMhHGPhoCzbBP8PCSQ7A5GjB7RNcTVpaFZPIDBZsVk+BjFemTR6KdesLqEy844b7OlMsQ0BiQXLaxaQFEkHpcUhkLIGI4OTRHTXIygE8YY6MYY1cE/40GKTBNbobznz1gTJ1bHfATv/nIT/SSDy8bi6FZckcsqysFBgPeA3R64KYnydGZm0v87MuDDtL0LrT9tsmUqdLHA8X/ooWjch8FK29fQYtSmEoHH1yIehiwv06PMmRgwtpjqHQFQsEDQM3SDy38CQadFu8Nuxpxeg2cHh+O1daDRwmYqmrrMGjmt3hp3U6MvLEd/rNhD34prUG3qV9g4IyvG1egieHMhpiBIqdjiK9y/LyIMp+OCqJ0hAfGy++n7XNz6ZlwlgOuKvr53WtJZYkHbcRRQU5UpyEUPCS0om1tbyQ7v/t94OHN4KM+pqFejnLlULjdRcBXr1HF4ZGfiG4lxAbUejDuXUQBzEdDoqvS6M24qqUZKyd0a9whClYpmbUN9EmZSE+SBtop+qjk1441qJ558NXTO3RaJ/raYwqQcqE0AFEMXLOkk1DG/IG0b+HdZOcbXyQaktYoVdEcFfQ8+TyAsxI4ti1yfXBX0hDRaGuG2CbmSAjKqr2ERDdWjacKtZzCCpDdh/XZBfILkZDSEpe0MKjavNPlwlO9c5BmMUbv+5Gh0ukJBQ+AJAl+ouNiiEQLx254oEeDudUJ9+VaAxypVyCl9DPpHdoIRAUi1kgdw8miOVSYPmGMDQDwNIBHgpt/BXAP53z76Z7/rALTRtKIBheSQ99YM2mLDrT4yPmx8iFw1mxg4PvA6GKgpoSan8WgH3EOoZ1fdwS4/UXSyZdPmBZqIPetBMCAqv1gRf8Dk6MCOfkL8Ma3Wjxy28XgnIde+AJCxrWFxYgHF/wUyiatf+ASmFWyWPIZEhOXb8Mbg67AZUke/HfcRahwcbz92S68PPAqpMYbYuXtswmBADk05hRg1MdUjcibRz0P4XbffaI03yS5XfQmaoHuEyOdpzUPSzQjnZGEBurK6TmRD1McvCA4pZeDbXiBMq3h1xMZ2FUP0jmFEyfrwQikdgDTaMCYlqYM20saVaXRGYynN+8kvI+quYbRxdC8UFHTwnfv0QwS+SC5ftPp/1DNjkesoUr0xhelKlp8C8DbQPstG0XJonC7bUyAQEAEB/I+H2s20VuFitjA96mqJmw5PgNIuQAYsYpEOBrqEIhPh8Z+GA0mA/RR3usT11Ef3Lj5W9T7fmTw+Pyqa8mJjoshEln1e1GqbaM6QE4NdWldkGjbArN9F1zJlza6b4JRB5NeGwsgYjhpNAeFCZzzHQBGyrcxxuIYY3mc86XNcY2zAhoNcVzvmkYyf9UHKUMqnPdozoj9EAUMezfQtFGXLbI0vnIsZU69bql8LYfIOIkhQOP+q6Q8iTJ2rxekJu+srkDvV6HzufB0t9ao9LiRZvBiz5OX4kBNAP8sPoqtpbXISjaFskbybNIT68rw3uCF0CwequB4v/yppC6SZtGji+ko9IXDkGAvQRtrNl69ax4YArHy9tkEuZSlcJjuXQSYWxAtI74FZWJdVYDBQlzx4n9R1YBpTiwNGS3IcFeRqowIxhMygpr6smdj8TDgrmlgP8ym4JlDvZFUyL9qtMrp744KBJLaQlN3VBoO9tCPdMzXb9OUX5EFFhSShJanVimQiQ6EAoZYk/SZDbVKa6ch6oHCyHXqduw6Dvz3LaDXi0B9NdmiqFYkZdG72u+NFNiISz7xsyOGgn48UQoYRM/bmGKy1eVjSMxj6FIahMo0tP/uolDvm+7jJ9FydxGQk4tA3nxolkq9eDUDChDvN2JRfmtYE/zo3TFNve9HBoNOqxg0CjTSLxRDdHCOdv4D2BF3DZJPvDcAoK5FZ3AwJJduOGEAwRhDemJsFkQMJ49mCSAEGGNaALcDGAKgN4D/Ajh/AghzGnDrJKoCCK1uIcVnLyFnRK2x7vMXKSM6bDlNtL7xMfVFqPYIqW0MXaq+qFTtlwKDgC8yO/b9LHKeLOkRVQ5tTi7Suz8NLBwBg70El1izMXdAAZ7ZZMSIGy/EvG8O4p4uyom7xTttKO93C1JHfQY9vPAyPSatLgkp0wDAiz0zoV9yt2KhTV07Ep5R69Gv4LeI8vbKCd2QlhBruD7joJaF/WgI8PcNQMurSIULAPvqNaDHZBp21WkI2XzaJZF2n19Ak6jv30CzIFyVURwlt3S9xcMpk6v2bCRlUVPq/IFKR180kornzJoNrtGDp14M/4giBPw++JkOZkcJVSfEub1OqaqyYTJlhxNbUV+R3kyqNCdbKYgWhMVkMc9sqKlpRRv05veo23HdMXLWy7dTb0J8Bq0Tax9V9jvIg9XuE4HPJqtUtRfQ8zX2KxLhKP4XUQC7T6QEERigN1KQsm0+rSe5b9GaICrXYk1wlgNlm8HEXJbdRcDuImgAeEd+DFutG7VeDVpqvbhkldS7927+QmjMjbsPqfEGzB7RNSJJFBs0enII1JXDCgeOx2U3OYDwG5PgTroIKaXrcfjKR064f5olNgsihpNHswQQjLGbAQwFkAvgBwDdALTjnJ9fFikoCUaLtIDIJSPLNgM7VlKWqqZUqYYEECWk2+PSkC61rFO/6cBva8gBk6vV5M8HfpxN+3Z7XHLWAFkZfTXw4xxaaHwNyipHpyFK3ri9BEmrRuDfQz/BxE8P4pEeHbDu5zLFr5uVbMK2w3V4ad1OzB7RFRenWfB4LzN+PeoILRg5Ufi0moA3Vt4+mxBNIMDvAaxtEAhw1DpdSOr+DJjcSRFNoJvekKgb5lR6Lg5uIorT4uHAmE+VVQERAIArrxdtwJbOGFm1Wz2BnrXju6UgPb8AbNNUsIOb4M9bgEc31uPZm5JgZlCes6aM+jjkVbxvZwB3vUXVlnCoVRbCg4JoQdj9G2j/Ex0fw18DtYF+5lR1O3SUUxV5aZiSmKAW2UuoKg0u0fDE9mWjgP7vSsGq30sOvbNcaYemZOCHWUC3JyRBDoDmAFmz6ZkS3+cVkAR4pyFKsQCxJojma3sJkH4Z8MgWCpI9TmjB4bdkwnu8HEnL8xXH6pYMVcoNq0CjYcjJSMDKCd1iNNXTgOvwr7AAcJpan9RxtenXIHPvQhgch+GxNH5semIcdhypAecc57p4ZgzNh9MOIBhjZQBKALwH4GnOeR1j7OB5FzwICK18sbjIde6T21FwwDTKbCdA+wuFJku6ei+FOR348mWg83A6x8h1RBUJ+EnOsmN/apiLxpt124Hsa4GU9qSSI98nyjHxWj/+ccelWPzDIdx3Yztc3z4NZoMWLo8fLSwGTF79q6J6IF8wAOBA5RFcorLQcq0BWckmpFmMGH9Le1hNerhiOtRnLqLMNAA44Peh0uWHob6aMplyJ8VdKanKCCqfNZscl62FpBj28GZ6LlY/CNw9myoTYqo790sqY6JvISJ4LqDnQZXW5wVa5JBTVnOYlJyuGwfYdkG/dBj+PfQT6LUMqDqk/P2+fpsaZeUBTbQG56ZWFqKqtHlilYkzGSIx9PcN1BtTtZ/sQ80OG+qAzR+QfbfoANXZJYxFysEC9HNiKwCMbL2hlvYXzf7dHqcA3OsGbn2WEkrhdFZ7CVXIxPdLR1BQkthKkob9+m06p7yXwpoNVO2TAo9+06H5/BVkdX8GmS2irCdNkBvWaFisonyaaDiyAxYA9ZaskzpOBBAppcU4dumYRvfNSDCi3huAzdEQUmWKIYYToTkqEMsBDAAwGICfMbYairTheYjwjJWjgtQ3Vo2nF3dOLjlFIvMjAoSAj362l9CiI++lKPqfIM1pGV3DUaE8fsBM+ipXxQl39moPSxNQ46zKfaIc4/FzjJjzA57r2xH+AFcMmJs5vAsm33UpPD4Ou9uLQCCgWDBsdQ1465sqvHrXPKSulegrgcELobWkoWDMtSivrcfTy7YpStxWUyxLdcbBnEbUCflwtrx5wPezgSvzwM0Xwe9piHQ0oqkYCdWYyr20j7saaHMjPSfzZbr6/aZTJaDHFNqvrhz4v7eUGdmvXqfeBzWb1+iAT/8pBS8A0UiCmVeH04lHP67Asvx20Ib1RSChJTmN/hNUBRqrLMgztNGCMMaadnwMfx00Guq/CaRRhTnjCtp+3yp6bzMNDeYUdra1kN7zNz+lnF2SX0jVX52RPpfbpTUbCATAtXowvYmc/sGFwJdTKeiVJ5TyC8jue0yRFMXEOYQSE0ABhjFBSe2T02bFe1/Q/QBFdYItuQ+akUUxueG/EIHy31DNLdCZkk7qOK85A/WWbKSWrD9hABGaBVHligUQMTQZzaHC9Bhj7HEAt4J6H14HkMgYywfwMefccbrXOOsgV1fxuIDyHcoslLOcJoYOX0nNdU4bLRI3P0WLirOcsk2JrSh4kDeDuqsAUyrRne6aRplapiHONjjpjuviaKFaMly5aHw/iziyq8YTB1deav95kWrp3aDTYlF+a6QkBuDQMbyZdxXsbi9mfrkf4wu34I28q3Dv+98hK9mEWfd1QVpCXMj5T4034PFel2DSZ7swttdStLRoYE2wIC4pAxqtFpY4HUZ8uC3WB3E2QKMBEjIjJ05fNw748t9IvvP/wW/URzoaXld0Ot7QpUDdUWmI3A0TgA9vV6dZrJ5A9m5OJTqGCB72fEo/A2T7wokTNv/pJLrHINc7dN5gAJMcb0R6ghG15jZINieTuhT307Cu+CbSiBqrLMihRoW5dxGpt51ihjeGPxkaDf0/hleMBs0Fbv0nCQqIYYnmFoClJTD6E8DvBddowb6fDXw7TQoCAMle8+cDG6aAOcspMFg8nKoHPafQHAhLuhQ4O200R0VMjw5RlubRnAmB7hMjqKlY8zCQ+ya4JQMMjCrZy8coBzKKZ8ReAo1GA39+IbTy9SRkt6Uxyt0fDF3VHuzlrZFkPPmkWm16V6QdWAmd+zh8JhXqZRChWRBVLnRpm3LK9xrD+YXmUmHiAD4H8DljTA+gDyiYmAEgutWeiwjnQuvNkYOqbn+Z+K1iOJBA+XZJxUNOnRAZo7RL6HzhQ4U2PB/kdxdSU13AR5NQxcRrp42Ch+5PS1SQO14HwIPUqgso6Ph+tjKz+/0sGLqOQpvg4uTtPw///sQJm8OLqfdciTeKd6OFhbJQZdVujJu/BSsm3Ij0hDj4fAFUOBpgNmgx+a7LodcyeP0cLp0GTpcPqfEaeH2BJvdBxCaangHwe6SpuwLBbL6Oe6HTsUjqnSk1UsVocCHp3zvKlU2kgwvVKRkpF1KwoQsGlT8vIqcrJ5dsWk4jGVwI3PIPJXVEVnEAoOgnsmychHd7/hNOBhxuMMOgSzh524pWWQjP0EaTbXXZYhnevxJN6V+RQ63i9Osq4MpByiFww5YD7uPAR6RSx8S7vPQbssslI0i5rNcLVIXaPI+C4fRLpYrBxhfo+VGT9s4voO3WtqS2lJRFVMD+79H9O21AYpZ6cJp8AZi3HjAmgjMGJqokArJKNvO4oP34fyjoSGkPn84MndcJNvvWGOXujwbnMNv3Ym/gGrQ6hddBbfo1SD+wAillG1Fx8eCo+7WwGMEAlFSeX7N/Yzg9NPvTzjn3cs7Xcs6HAri7uc9/RkNwoT/oCbx9OX1tqFMOi8rJpSBA6MzLYS8BuE85v0FkjHo8T1WJaIPo7CVUceA+Uujw1tNiktiaHLCuo0g55qoh5EwxRg6h1gh8/R/AeRzocLs0rK54EnDjI1T9CF7Lunok5ue1QZpFj4nLt+HRHhdDK2u4Kqt2w9ngR427AbvK65A/61t0f/1LDH7/O5TXNqDgm4M4bK9HRV09yqpd0GtpurUcajJ/YqLpwBlfx4bR/VUIBCSKnRz2EiAtByzgA/O6SYp46FJyaIYuBf77JqkYiYFbuW/SYC1PnUSHEudZPJwypnLk5ErqMdM6UY/QdeNIaUyl8R+Lh1MPkimZngsx3Cu9I11/2NJgMBJHAcbuIuiWDIWtohzHjpTAV3UI9fajCPhPopn/ZAbCBQfLwdqGvoqMdmyg3F8Dv4+CzaqDwNGfgbVP0Ds8EIh+THjFKasr0GVE5LvZ/nsoeAhtW/OwZJe9X6V3sKOcpI8vG0BBMuf0WVZXCjRqyui5iJh6PYK26+IAcNpv7p3Au9dQAOL3UQU8J1d5/9ZswLabnr/KPWBfTaVgRG5/gjqYX0DKgGWbgQV5YPMHwOPxgAkpcHEvHw2hwCqG5oWjHEZfLfbwLCSeQgDRYMlGgykDqYc+bnQ/g06DlHgDDlU5T/FGYzgf0RxN1FoA+QBaA/iUc76DMdYXwCQAJgCdT/caZw3UMlOFA4EHvpCyjowBHz9DWSe1rKM/ipOW2IqcfLXPWnQg58hdDWgMQH2tMis7YCYFAnd/QPvEp9HE6jgrsOJ+SbVJXiL3uqgBNaysbak/hg/6xOP+T524MC0eHMBnT9yM2ZsO4JsDlThe1wCDlmF84RYFNenBBT+hYMy1GPEh9VO8tG4n5o6+pkkyf9EmmsaoTn8SRGBcd0TdZqt/V6q++D00uyGrK1W6lo4k50o4Jt/NJD18NVtOaS9dw5oN9H5ZWfWQ05oSMtXP4aqkIFhQS/weYP4A5fOw8QXJti3paGewQ7tKomgEBi8EMi5rWkb1dAfCxQbK/TUQdi3v6+k3HfjiVVLbitZ/El5x6vY42VxT+38SMiOrCUOXkZ3KKxii8vzFy2Sz0Z6XT/+prrK0egJNab/j3/S8BXzA1gXApbnA+uBwU72Z+jUAYNhycK0+SIvVgvf5NzSbpkqfB89rZFEawGOUu+ZHxW8AgN9ZFuJOxVtjDLWZ16PF72uhd1XAa06PumtmUhwO2mIBRAxNR3NQQI0OfgAAIABJREFUmP4XQBuQfOs0xtghADcA+AfnfFVjBzLGPgTQF0AF5/xylc+HARApSQeABznnvwQ/+x1AHQA/AB/nvGsz/C6nh2hcaK+bMo6BAE2S7jSEVJOGr6CFx2mjbM8tE+mFrtpoqaX91D6zH5IcuMGFRFeSLySrxhO3212tnA0xYCbtIxSYRAZXYEyx8nexZgNOG1KLJ+HFO1bggM2J0XN/RFayCe8N74Kn+nRASaUbdrcXz/XtCKtJH+qX2Fpqh1bDUFbthtWkR1m1G6Pm/IgVD96IxWOvh58DcXoNWsQbI+gjsYmmfzFEYKymDhbefLl0BNHwhHrM5v+V7NzvoX6d219UTsUVED8LxTK3XTqvHPYSomsYLOrncNqk/eQqUGLbqvFKSlP3iRK/O7iPZvGJZSoVEJWFU8XpHh/DycNli6yCieDU51FSmxijd7BGQ7Q8eS+LEKZoav+PIQFYNkZ53ZpDkXYq7qV4UnT5Yq2e+nvUVPQs6VSdEIMXRZ9FQqY0cE40XG8tBLYW4ujoH1GpTcY7n+/FW7mtEX9wk/Kc1mzUBzSwxCh3fw5suwEA5bqTk3CVw97yJqQdXI0WB1fj6GUPRN2vldWE7w9UxqRcY2gymiPF1RVAL875PwHcCSAPwC0nCh6CmAvql4iGgwC6c86vBPASgPfDPr+Vc97pjAgeAMkpkkO8WEW26/ge4qxe3JOGzX3YmxaI7hOpQdRdRY1w8nJyfgGg0VOQ0W+68rP+MxQ0IyweLjWVCthLAJ87crFcNV6SA1S5b27JiCxrf/02YC/Bxal6TNu4F0CwwlC4BQ1ejvc37YfPz/HSup0Y/P53eGndTjzVOwe3d0yHhjFkJZtgd3tDx5VUudBt6hcYOvs7VDrUM1hioqkcWckmMMZiNKY/AyIwFpLEgo40qgjY9hFlYEcVST0Mzgpg4PtkM1sLqbE5qS05/RodZdW+n03OTLh9ffEyBcOfPU9OU+W+KM+UifYJfx7y5pGNCkTLAovGbWs2/MkXxjKq5yOiJXzi04gGJ6ejzrmD3t1rnwBsu6gf7e8bgMd+IZqoyruZW9uCD14QaZ96U9OrFRmXU0D93zcjbX1wIa0r/aZLU6zlUGugXnKfNOyu/wzls2LNRrzZDEeDD7Y6Dw644mDvL1uLcnLhu281PF4ffMNXS9SoGOXuj4PtNziYBT7DySkwyeGJbwVX0kVIP7Cc6HFR0CrJhNp6H2yOhlO+VgznF5qjAuHhnAcAgHNezxjbwzk/1pQDOeebGGMXNPL5N7IfvwNwckLIfyYCAep3CB+GJV6s8izuoDmRzvyS4bRQxKcB5b9KajdeNz30nzxD3O/vZ0kDuRJaActHR6pnxIe9yK3ZUXXyuTUbDboE+AfOR/zK+0L37ctfiDe/deKhoWthqT9GGTbRlGrNxu92H7aW2kOnKqt2w8857unSBg8t/ElBN5q4fBsKxlwLxjimD+2MF9bsBEBBQKXTE9ovGi1JbaLp1HuuxJQ1O/BErxzkZCTEGqr/SMgpG2WbJTrSiLXAlfdGDH/zWlrjcJ0PbUavhzbgIV63o1wpO9xvOrB9OTWaanVk83Klsk5DJFqd2kwUrV59yJZGp3weomSBA4lZ8D2yDT6mh1bDoI1lVM8/RGt+t2TQbJ1wOuqah6l/ZuOLQN+36J0o3uk9pgDfvaeQ3marJ5Da3fCVAANgLyWFpO4Tm16tcJRTsHxwEwUuI9bQILqq/ZK0d35BcM5DmFhBcjv1oCTgp4Df41JIzNr7z8OhejNe+/Q3jL+lPR5c+DPSLHo822spsqxGpKIWuvn9kRI8Px+8ACz3TamPJ0a5a35U/IZDmiwknoICkxz2ljeh1a45MFfvhCvlMtV9WllJiWl/hTMm5RpDk9AcAcQljLFtwe8ZgPbBnxlIoOnKZrgGAPwdwCeynzmA9YwxDmAW5zy8OhECY2wsgLEAkJ2dHW2304PLRv0O4X0ECZn0Yg0EpIE+gSgcUr2ZMkZDl0q878GFklKNkHeNT6P+BU8dLUZCR18oLMmnpFqzSZ2JaamBVC4Ja80GqzsKXZIBGksyvCM/hssLGPU6PLa2FMU7bfju90TMzbUgSUZ9CgxbAb2D4b/jLkKFi+PlL22wObzQMIbUeIMq3Ygx4MW1O/HATRcCQCgIeKOYSrT5XbLwwM0XwuXx4Yg9gHSLETqdtCBlJBrx0djr0eAN4GiNG28U78bWUjt2Hq07J3sh/hSbbSrU5EfzCwDHsciG/9UToBv1CbITdWDcC3icRP8IbzAV9AxnBT0n4Upl8WkSrU5UPUzJQPKFRAPUxUn2LFdXum+l0vaT2kTOXOk/Ax63Az+7UvHUsu349NEbYcifLw3Bs2aD588Hj0sNlWhjKmBNwxlltyeCml0PXkA2U3dU/R1db6dETsAnHWcvIRpf94lAanuJMiRQvp2SQ74Gej8nZUcOobNkUEVOZoNUYfBT8DBKNi+itpzOJe5JqDkltaEAA5ySTloVWWVrdrDHQQfozeAj1sAPLQK6OBypM2DKql+xtdQeopmWVbtxz/xaLL+vPTI+Uz7DbPGwc2JWyRlrs5wDtl3YG+iK5NNc3mozb0DmnvlI378cv0cJIFpbqcq/3+bADe1TG7+vuqMkUxxLspzXaI4A4tJmOEejYIzdCgog/ibb3I1zfoQxlg7gM8bYLs75JrXjg8HF+wDQtWvXP4bzIsrhYX0E/se2o9LnQpqnDsxgAaoOkCqS2ovdXU3btAbpMzm3VWR/AWDcf+lBFrzZnFya4gsQ3WnMelrsNDqiejjLaYEbMJPUOn6YDdzwEKCLg27enaFzJPR+Bf5AAGO7JKKirgGABr9r20JzxwpYDQE0sDi089ai/WpSF2ljzcac/vNw3Nweq38qQ+5VrZCVbFIEEVnJJhyudmP9zgrsPFqHxWOvB2MMU9bswNZSO/K7ZGH4DW0xeu6PiiF1lwQrC7vL6yKqDwLnai/En2KzjSDCYU67FBr5XJOGOqWdCthLwJwVYA11UtVgTLG6M5Z2KTliv62JdPLj0yKrHjm5QM/naUJvwEfnueN1yuo6Kugch76lnguvm+7P66Jz3f0+OWPuamDjFMQ5KtBy4BqkWYzQ1VeBffWaopLBvnoN7t5vIM7aEgAibHD2iK6xypcK/mq7PRkEwFCTcBHMo9ZDx73Q6IxgYvZHtOqE00bB7qgi5WdlmymYHfi+lERKuRAwxJOtavR0zsELKKD+6vVgQqk1VYcZA3Z9qpyz8uVUUsK7tB9R/q64Wxl0iCbrss1k274Gquatn0wVvKQ2dL7iZ5WzJvxeoPYQsHoCmL0EumDF2aBvi7QEcghdHr/iPZ5uZucsze+MtVlHOVBfg+3+LFhPM4Dw6y2oa3E1WhxcjUNXTwTXRp4wJd6AOL0G+22NjO7atxH4+ElSLdMZgevGA7f+S5LYjuG8wmnXHDnnhxr7J/ZjjH17KudnjF0J4AMA/TnnlbLrHgl+rQCwEsC1p/ebnCZ06v0P3O9Dle0ImLuSsrFFTwIrxxHVSa2/QGSIBL9UTAoNOy9MVmlQXFZXyooV9AemdSYpP8cxwGilPgtnOal+FD0JTO9KFY3uE4mLu2yU4hyagn7Qv3MlunyWh7m5Fkzs0wEPLfwZd83Zg5tm7YPPH4DWeRQY8F6I825dPRKZOge2H6mBx+/HjGFXh3oWhMP/5vo9ABBakDIT4/BErxxkJZsw/pb2mLBASXsaX7gFFY4GVQWmicu3Yfwt7UPnD5d9jeH0oCqbW+FEwJwOGIJzTTa+IFW65LBmU0VMLjkpGkzD97P9RqowVwwCwKjy9sAXlEWNs0Y+I3dOJeeq6EnK8hY9Sc9U3nyijnw5Fci+gQJmdzVJv86+lZ4Hn4fmpSweTg6XvQTZCcCCIe1gCLjJwVo8nM67eDiwuwj2OgeOOxtwrLZeVQVM0O9iOPsgbPyu6d8g598/ofv7+7DLYUIAwYBQTVpX1gMGnyfSpnu+QBW1HlOoJ8J5HJh3F/BOF2BeX5qintiSnoc7X6OZPfMHkDzxvLuAC24gZ//D3iEbxMqxQH01cM1o9YFw3R6X1oyFeRREXDeOntH3u9Oa0H0i8MSvFPTEJRL9KaxyqFsyFC1ZNV7qfxk+f7I7LmlpwczhXULvcbtHo/oMe5letQ8tEOCw1TXgcLULtrqGWK/aqSCowLTTn3XaFQgAqM7qCX1DNVr8vk71c8YYWllN2FcRJYD4eSFQeA+xKa55AMi+kSTgF99H6pExnHf4M0mLJ02qY4xlA1gB4D7O+R7Z9njGWIL4HsDtAHY0142eEsxplF0KW3B0nz2LC61ayoKKl3bZZip5D3gPeOQnKm9//iI5R/1nkFxm75cpiIhLIkdd1siG4SsoiyS0wrs9rq4R7nMFOeTvUoZA7C96LuKSpGNUzpG0agSuSPaGHKfObRJxsalWcuCKJ1FgYklHrcOJR3t0wGc7yvH86l/xXN+OWDz2esz/+7UhuhEgNT8frSE1plUTbgRjUKU9+fyBCAWmzm2seK5vR1ycbsGcUdegYMy1EbKvMZweosnmVjo9kmPlqCBnKrzhf8BMsjVLOtntqCKyswEzyXYHF1JFYsRqGph1zWhymmbdRA5QQx1Q/C+qnm2cIjVsD11KNq9CmYLPTXNVdhfRZPdOQyKfB+FsCVizwcp3wFzQGxrOVZ2jo44AXA1+HLG7Yypg5xgatXFAktYd/QnZa+9XFT1gqClVBrg5uUBCSwCMbFLYoHgOBrxHwUUgaDMBfyStL5oARlKW9H34Z9a2FHAH/HSPujiVtWA4VeTm5pLEd3y66rni3UeR4twHV4MXjno/pm3cg+f6dsTKCTeiRXor+PIXKp516pkw4bDdpQgQYnN7mgm2XQCAvYEsJDdDS4Iz5TLUx2ch87c5UZupWyaZsF8tgPj9/4A1jwAtryJ/pWN/4KYngesnAHuLgc8mn/4NxnDWoVkmUTcRERbLGFsE4BYALRhjZQCeB6AHAM75TACTAaQCmBGUFRNyrRkAVga36QAs5Jx/+ofdeVMmlWo0FCQIGgQP0Ev9hgnQ67SA1xtZ8p6bC4z9krJGPV8I0SvQ8wXadss/qNnakk4PbYscUmkqvFuiLQ2YSeXv3q+SQyf6G+wldA89ppBjplb2ZhpaHJ22qJr6RviwbPwNqHR60NbogHbJwEjHLPdNHHUE8Ni6LVj0wPUoDU6lzko2YdED1+HRHhfDbNDC5fGjTYoJU9bswPqdFchKNqFgzLU4VOlSpT3ptJrQsLmyajc6t7Hiqd45mLh8m4JKEv2/7TzmrZ/sdF0ZGpXN1RiDQ9k+BuyHyHEfuY7s0pRCs0O4X5r/IOxu6BKSKQ5vpP5+FmVMneVkk2seJmqHRgfc/grZ5obn6ZkIn1INSHaekEk/O21S/0T4fjLlJeTNk56bLfPoZ9n9+vML8f6GWtx7rRN6LVO1T72uaX/PGM48nFAaWjw/TEsVNbn8tXiHAvQcAGRLThtV5ewltAa0u1maEeG0ASU/kPO+6kEKKBqzUYGcXMAVfLaGLVX2u8VnAOA0XFHc232r1M8rvi65T5JZVqFn6Ysn4eJR63HYm4D1Oyuwfic1WXduY8W0e6+CPUhlrXBxvPyxDTbHFhT+/TrY3R6kxFOaPDa3p5lQ8Ru8+gQcr09slgoEGENl9h1o/dtsJB39P9S0uilil1ZJcfh633G4PD6YDUH3sL4WWDGOel1u+WdwcGEQOXcC1YeA72YAl/YF2t7YDDcaw9mCPzOAiADnfMgJPr8fwP0q2w8AuOqPui8FhPyqvNHu3kXkRKkFEcWTyNG5bXKo+Znl5AJ9XlV/afvq6XtDPDUl3fE6EN+C+NvHttFC464mfm3PF2jxkdOWRDARHhxYs+kcPrfkeMmbV4snAZV7pfkR8uZT2f39ZmvAKxsO4Y07W+HCJK1qoOJPbo+XFx9BWbUb5bX1GHHjBcjr2gYZSUZU1DXgudU7Qg7/rPu6wFYnKS+5vX7otQzzxlwLW10DOOfQazVISzAiLd4ArVYTUmAaf0v7UPAgjpcvTPKAwWTQory24fzkrZ+MzapAyOaGO8whqpjgiAtb/PsGsmM5P7v/DLI7Szo5UVoDsDBfvZFaBA31NZRtZRqifMjtWqOl7apqSUZ6drK6kuOW0FJ9vzgrVfyYhqocghfebzo5ZjLxg4MeKx7tYcWzq3bgmT45eCv/Kjyx5JeQLb2VfxV057odncMQNp5m0ePZW9KQbmawezQwGzSRz09OrqR+5PcC374jvWP1JgqYPS4KHmoPk6Of2Bq4Zqzy/Tx8hfSzu5rO22mIpCD286JIAYxeLxAlr/vTyiFz/WdQ9WFervKZqtof5RkxUXAhBsWNLKJKt5hBdN04WjvsJaisqcNehzJo3lpqR3mdB4Pm7In4W/oDHG6PH4inn2Nze5oJtl2oNbUB6ljzBBAAalr9DWkHVyBr2zuoafk3CnxlEI3UB2xOXN46KB1bPIlspc9U8lPC0WUUcHgzsO5/gAe/JvuK4bzAn5lCOztXW7Xp0h8Noe3hEPSO7hOVZeTdRcAP/6ukIgkZTEsmLTYGC8kDfvI0LUKOchWqUEajlCMFJ7b/DGD5GDrHbZPJuRL7xadFzo9YPzni/vz5hViwrQ5zcy1ov3oA2LRO0r2I81mzsddOxaW1ozvg8vhacEcFNOCodfsw66v9Cod/3Pwtof6Fzm2s8Pk5/rFiO3q8+RWeWvoLAOCVot8w7IPvse84TcXMyUjAygndcElmQtSFKbxs/ktpzfnLWz8Zm1WBkM2V97FETAiXc8Q9DnVq0a3Pkq0UT4quamNKpiDDmED71ZSpKzbFpwFaI1XcwilTorbZ62UKapaPUe8x2rqAHChHOTlugs635mEKKhYPB1Y9CL+lJVLTMtDSGgebowEWow56nQYv9b8ci8dej5f6Xw69TgOvL3CS/zExnClIjTdg/phrMOfOeHT5LA9t5l2LKz65G9a6fVRNkz8/u4soy1+xk97R3R6nXp3Rn5Bdf9ATWHE/VZzNLeiY6t+lHjWAvsqnVe/5FLj5abJ58Y7v/gywd6NE2+v9KlH6Og2J7H9YPSEoDRv2TH01lRSe5LbffwY9oz/MpmvNuYOqh+v/Rde9+Smg9Hv6vcYUI9FkwBc7j2HqPVcq3gFpCUbVeTwaBvhl/IJoc3tivWongaACU4WB6GvNFUBwjR6Vbfsi0bYZ1iNfRnzeSqbEBADYUwxsnQ9cdjdRTtWgNwFdRlNP2y8fNc+NxnBW4LQrEIyx9Zzz25uw632ne62/BNGGDampTwjerNESecy304Drx9PC4PNQlF5vV8r2DZhJ5UF3JbBibKQTNbJIyi6pTR61l9D1hy0HVj8oVQlEplfo9ye2pgFdgjb19dvSNNMRa+jeAgGUOnXI6+hA0qo89cxx8STY+8/D3M21mHNnPKyrKbt2hTUb3rwFeHhjPf55R0fc06VNaBp1WbUb7dPi0bmNFeNvaR8xM+LpZdvwXN+OGDd/i6K6kJZghK0OUTPj4WVzs0F7/mbBTsZmVaDRsFDQFpX+JWxdKDOpXS+xFbBgkJRxjaY81n0iqdL0fpUGdKmdi2kAcODbd5VzH759F+g5hTjeuz6WKm0bp0gTrW27gL0bgCvuUU7llVXsPEyP8pE/oMLF0drUBukmWrFnj+gKvVaDUXN+iLC7JeNuaNLfM4YzDxoNwwVxLrAFIxXvNvbRkCA9T8UGEzKVVd9hSylBIyrOCirRyshzyKdVd+hDU9vD6UXyKoU1m97H/ijPc8Af+Uw5KqjSlvumRHfaOIW2936VgiERgIiqH0BCBkG1Jos1G0/fNQ+v/1QW6jcLcMDu8uD1QVfi6WUShfT1QVfiuMODtqnm0C2oze0RCYjzmlZ6Mqg7BtTXoCwxC3oNYNE336mrs3ogpXQ92m5+BTUt/waukU6emRQHDQP1QbiqgNUPA8kXAJ2GNX7Stt2AFh2AL/8NXHVvrApxnqA5KhBNGj/JOf9rm5xPFVHUlaLqH2s0FJGHH5OTC+46Tg7M9C5EHxLBA0BfV42n4CHaVFKNRsqsRlNnYkwZPIhjTclSkOJxEL1KZL76vEbZ27m5pAgyNxeoKUUboxOdMo3q95J2CfioIuxHW/zr1gxYVysXYv3SYSEpWDGNunMbK7KSTSitcuOp3jloExYMAOTkW0360Pdyh7+xzHh42dzu9p6/WbCTtVkVaDQMaQlGtE42Iy3BKC3ygQBl8O2lVNEwp5G9ql1Po5Ns4uu3Iyfp9ptO9IkWOZJyjG2X+rlqj5DDJPYTtnvdOMDrpGxvlxFSZaxsM9HzeIBsv/OwyOdNVrHbXt6Am2btw2PrjuC3Y07sLq8DQNUvo46p2ilvZKprDGc+WDTHnPvVbdCQoKz6ive0WjW46kDkOX5eJFV5oyWA6u1SBSL3TZIsjqZ2Vns4sqqdX0DSxQvyJEWxoOoYTMnS8fKq3+xbSa3punGhqlzq2pHI6xiHl9bthFbD4Gjw4t0v9iHVYlBU4swGLQw6Bp1WCgLkCYivJ96KlRO6IScjAQBizdVNhY0UmPaBJFxZM8ZYXKPDsQ7DYa49gNbbpys+02s1SE+MowBi7WPkj/ztf6hy2xgYAy4fRDN6flvbfDcbwxmN5gggkhhjd0f71wzn/2uhJucnpks3cgwPO8bX62UwOTUjWpCQ3I7K4OLYrK6Sco3fA6S0Jy5rxuWRpeq8eVJD9kM/AI9sAUauDSqEZNLCxP2RXHR3JckFhpXItTWHoPU3qC9ejnIEwJCO4zDwBtXfpWN6HFItBjzXtyM27S7H63lX4aMHrkOXVC9y4uxob3ahd0fl3zEr2QS72xv6Xu7wR1uYNBoWUTaf+eV+vD7oysZpOOcqTsVmmwLBDf+gJ/D25fS1Yidlm8IpQwPfp2qa2Fa2mbL9uW8CD28mVSVLBtBjMtlkfAtymjIuo683PBp2LhMF3WISu6B4fD+LaE+CIhKmtARHeXBCtVZSIRMI0vn44EK0Sk1C745pmHrPlZi2cW+I7qbRMMTpdedvMHouI2qgbVKXcPW5le85v5ferWpVs6+m0swF+Tmuf5CCgVFFNExOfu2srlTRMAZ557uLgdSL6J3vtkcGCgNmUnBsTqGm6Ic3k5TxV6/TzAm138tdLf3cfWJ0Wdjgz1ekGzB3dFfotQxaxvBMn0uJzqdlSLUYcEGqGQHO4fL4UVfvU8i1qiUgTqh8FYOEoITrDl+bZqMvyeFI6wx7y5uQtX0Gko78V/FZq6Q4dCoroPk8nUfQPJOmoM111H/2zbSoKk8xnFtojibqJAB9od7jwEEyrGcv5FSNpiraaDRgaZeAjw4OzuYc2oBPuchEo3TYdlGmKq8A2PQ6ZYVEdkvwWUVJeugyYNAcmgkBRguf67jU3Cr27zEZ2LGCOIwJLSMXu2jBjN5Mi+aAmVQdEefMLwDirNDOvRNtRClf5XfZV+XFXXM2ISvZhBnDrsayHw/hgUsakLhWUrt5N38hHkIainfaQiXx1z7dHdXh12ho2rUog1c6PUiNN0SUzW2OBmQkxmHFhBvh9QXOr3L5qdhsUyDvrRDywR4H8bSF5KqgFn32LJD7lnK6rqOClGO2FAAdelEGdMkIyob2mAIsuldmY/OBGyZQc7bzOM0rEVQR+fMgaEhBFRmF0tKwZZSNXf2QKm1JNFazjS+i5fUP4t0+GXj2q7KQ5LCofjVGyYjhLIbaJOp7F5ENxacFn5/gxGe3nYQAcnKJipqURe/bHpMBsChUoiQllWjbR8CV9xJ9yJJO72bxfY8pUh+RsH/5ALiR66Rz+b0UnIsZPuI9b0wkKqrBHDmYMb+AgguAfk5uF70nKbiPwb4P7SxO7Hdnw+Xx47jDg0syLfjHiu2NquJFE6qINVefBCp2AnHJONCQAKvxj+m1OnrJSMTVHULOV+Ox56Z3YM+6DQj4MTKwCv3r5yLQ9m/QXHYSOWCNluRdv59JPTXZ1/8h9x3DmQN2umV4xthPnPOrm+l+/nB07dqVb968+cQ7ng5EpvaLV6UA4O7ZwIoHpJd2VtfIRUPu3OTkAn3+P0mNRsCarexnGPUxZVmXjZbUlcL3z30TSL4QKBxIWaofZivVP0zJUtARfpyvgRr+/vYEOXJCteP6B8lpLNus+rvY+8/D6I+d2FpaS79usglrR3dA8sI7Iq5TM+wTHIcV1U4PvP4A9FoNMpPikJkQB12YVKbPF8CRGjeqXV6YDVp4/AEkGHVolWQKZbn+Qn7tH3KxP8VmmwJ7KVUesroqHXnBBZcHFvFp1AOh0VMW1d8A1BwGKnYDba8nTXxxzOBCdbsdVUQ0jGHLgXevoe1ZXak5OykLqD5ImV5HhSQJ22MyBdFel2Tzas9P8aTIYCL3TexiF6LPBxTAymUnz3Hu9rltt43B76Ohm34v0TQsmTTNGaD3eNUBoPoAOe7GRAp6a8roXdf7VVIA2zyXGpHlssX9ptO+s2+VrhVu51ldqRKQdonUmyMgf88DtB6IazT2ngfI7r9+i5y5lPYU+LiriNOuN1HAotFFv6b82XBUYH//Vegxa1dIfSzJrMeYuZvxXN+OeGndzojeoGhyrba6Bgyc8XWT9z8Bzm2bff82gPvQ9dg/0CWN49E/SHNSV1+N7K1TYXKUwGnNgc5bB6PzCD72X4sO/Z/BRaknOYDCWw8sHw206w7cu+CPuemzG+fMogE0TwUihzHWjXP+tXwjY+wmAEc45/ub4RpnFk6ksS8ytUKist3N0mRd4WQ7KojCMepj0s4v/1VyZgDKPPV+pfEskb2EKgTLRjfeWK03k1NlL6GsWPenlZKbQ5fSELzFw5QZrfgWUll7fpgjVr5dWuCCg/ECo4pQ5fAgzmTG6I/2h4IHgDJNJo1P9f7q690ocRrQJoX6IyxGHV5c+ytCV7NoAAAgAElEQVReGXilYmHx+QLYXV6HcYVbQhmvqfdcif9s2IPHenbApZmJqtWJc8jRa16c7KwIQfkI53x/NZXoc/4GyoyGy7mKilm/6UB6DlUk5Dr40exWVO10RmWG1+Ogpum0S0j2uPawNE9izUPSM/Tw5ij9O5dSs2r43BS9GR0StRh/0wXof3UbRYVBUDJi+ItwGnNNGj2nbVd0uWN3FaksyeVT8+YB372nfA/vLgLMyTQc0VFBSZbvZ5EEq9xuw2ftiD6dR7c2/p4X17hjKl0jEIi+f3w6NUbf/DQlh6r2UyDzYR9pXxGc95uurOblzSOKlXxoHoDWCVp0bmPF1lI7nljyCxY+cB1eH3QlWlnVe9iiVRRilbwmIhAAbL8h0L4Hqn7nfwiFScAXl4yD172ElJL1sFRuQ0N8a/ySOQgTfr0O79VocVHqSZ5QH0cCATuWU6AthiDGcE6iOQKI7wHUqWx3A3gbwF3NcI0zB03R2BcqOMIxuuERGuZmSVdozcMQT44QY+oZJb83unKN+F4XJ2V+zS2kwXDCObJm07V0cZQBM1lJgUnMhrCkU+NTagdSDmFa4tbqDMB379M5og3mki9wjgocrPZin8uCDnEW2Bxexe5ZySa4AzrEqfw+JpMZzy2UZkVMu7czRndrB5fHB1sdkGzSo8rtgdvjR0VdA9IsRpRV03Tgicsl1aYVE25EpcNzfs5+OFkIO/5lMTUY+xron6UloIvSMGdKJRvyuiPtwe8hKlO4epjI1C4eTs7KsGXB3oN04KEfyfa1eup5+HaadD5rNtmiNZueq8GFwJdTIyl9gwuBzCuArkZlAG7NVlepsWZTg2LxJLqvrYXSdq8Lmsq9ePrqlmDp8TGbOVNwmnNNokIkeuTv5boj5OjHtyA7D5cnFhWA3UX0HhbB7dZCes/HWckJ7zEZ+HWl5KRb0oG4oJCFmI2SkEk9D1oDVfG+mqq0X3nPgjUbNDSuP11fza7jkpXzU/LmUUXFaAE6D5ds3V5Cv5voJxKV6E1vAF1HSVWPYIVEp9Vi6dBs+Pyt4YUebi2DxajDAZuz8XkxYWiSulsMtB57XXDEZyPA0SxTqBsD1+hReUEuKi/IBQBofQD7FdhTHcAdp3LCDn2A7cuAnwqAWyc1673GcGahOQKIdM75tvCNnPPNjLELmuH8Zxaiaezfv4EqCoEAOUVjimkxycklbqC9hP6JlzMAPLadHPVAgDJLcs5r/xnAN/+JzBKJjK74HqBrXDdOOXE6bx6VqfVmKlvbSyKnqW5fAlyRH+mQxacD+zYA2dcCOUX0e6gtWF5X6Hv/4IUwGjNxmRXQahjeG3Y1HlzwU8iRnzm8C456OSx5C6BfKlU6PHkL8PTHhxWNdY9+tBUv9b8c3V//MnTstI17QtOrp95zJd4o3h2ShbWa9CirdqPeG4hNQG0qXDYKHi6/W2k3+fOpQV8b9moQ2VqviwIJuT10e5zoeQPeo2rbDY8EpYD91IiXfillPN3VpGTTeyqdRy5hnD+fzvXtNOlnXz3Z+NJR1DsRTukTz9OoIiD14uBkXkj2/e07wKC5krKZuPdPnwk1UAOg5+f2FylgQQBaRwVQX01OZAx/PU70zj1V+DzqfTWDF9DkZ+5XBhehCdBBu/n6baDfDLLR794j+wqnMe3dQBVerY5m7QyaG+zLCaOubp5LNFBRrRM9cID0PvfW0zFfvx3Zl5Y3D/hssnqwUzyJPrftkpJKThsCNz8DzVLpGfTlL4Cb65EggpweU4Dv3oMuGLTr7CWIs2bD338eoLsAn2w/iqn3XBnRA9FYRSFWyWsCyncCAI4bqRH+j6xAqCFOB2SagT1Vp9ibYskAWncBfpoH3PxM5FoSwzmD5vifbSw+NjXy2dmJxjT21TJleQWSKob8uJxcclLkjW5588hJ4n5yyEQ2avgKAAyoKQXApfkNG6cAA2cBvV5Ucr3DF4/BC4Bv3lF+vuZhWtiE8yi2Lx5OjmDG5cC+z4CsawBdHPjgQklFKrjY/O5JQvYj21Ba68NTK47A5vges4Z3QaJJh3c+34vn+naE1aSH1x8AA8fYwp+QZtHjxTtWIKeFAaW1PtTzFBTv/Ebx5yyrdsNs0Ia+H1+4Bc/17Yj1Oysiqg5CtSkr2QQtQ6xJr6nweajyEP7/v+Q+GpAVXnoWTly7m4GbniSbFD0IokKlNwPX3C+dMyeXhmMJ+pto5rz0TiX/Wlx3VBFwzRgKwLctAS4bIPXZAEC3R9WfPXsJ9fDkzQNu+xdldD97nhpK/R4lBWXATDrOmg2YUsAf3wHmrlbeY/8ZxBf3e2n+hEZLGdvmos7EcHI4zbkmqggEiCIXPvTTXkJUzvs30HDP8D61/jNIQSknl5I9CADgwB3/jrTpNQ+TTTuPAyye9u/2aGRVQ8zVWT2BFPZ4ACj5jihQ3R6livKmN4iWJK67YTL1syW1pmZuhuD9hP2NRBVcth748wux152IuT9WY9gdK3BRih6HanywxLXEI4t+wrO9luLyDBOMhX0lGq7sfq2rR6L0jhXo0TEDbxTvDr3ns5JNaBnsRYvhNFBBAUSJht7BJ9uG0Bxok0AViFNGhz7AFy9T/+SlfZvvxmI4o9AcK+GPjLEHwjcyxv4OYEsznP/MQmMa+06VTNnSEUTRCJf06/1K5MTdpSOB47vJaXFU0PYOfWiwkO03mt2w8QWp8bnnC4A+nhwuVa73JbQAfPlvapoO/1xnUD8uoSWpNnXoTdecdhXYl1OB4StJGnbAe4DOhHhrC7z5nQM9Zu3C1tJamjRduAVxWuD/9UlDjwwXLrPUwRqnxbhCqkZsLa3FXXP24LYP9v//7b13fFRV+vj/fmYmPZCEhNADilhQERWx7bq6FlAUVAREEXDt3f2qq+vatvmzftx1sbuuIKCAiqKoKNZdOwhYQBQRIYIkQAKklzm/P557M3cmMyHAJJPAeb9e85rJbefM5Lnnnuc8jWB6PukpSVFTZLqpXCG8NoT37545aTx0ziG8sHANT4wfRFry9ldADQYNxVur+bmkIiwN4S5PIDm8ToNL6WqdOEfirtYeOFrdKCYN0on5sP/DZPVUmXazKrnXHDhWFYPMfLVsnf6I3iPB+tjt1lWr72z3gVpMyVvPpLxYJ1BjpurEbMxU/buyRNuo2KDfaXOhZso54/HG6YlfulQnjcMnwcuXIcXLQrE/7jEvXw6lq+DnBfDvEzSl4vMTQ2lrgy2TFcUSgzjUNYHwe712axHM+1PsbER1NSqn0Sqsl6yEY2+EqxerG+onj6qi6sq5K5uZ+Vq/5InjoPQn7XN6btNjdfUWHe97HqbvTw3R58TyufosOfEvep3CBTo2B4Maq1G0rOnUraWrMV0OoG7Ca5RIFuVV1ZRU1kFmPufM/JmfqjP5vqic4rJaRj7zA0Vbq8IVkIj+ZicHyU5LYtGaUi55ZiHXzVpCcsBvlYd4ULQMMruwpkqfZfkJWIYt6ACrNgeprd/B52HPw9SleuF/4tsxS5siHgrEtcD5IvKeiNzvvN4HLgSuicP12xaxcuyn5WpwZ9SJUQ189UJ4/vrKkujHJqVrDuXREQWHPvyHmr+PvyNUSOulyzSewV0d8+KmhHWLbWX3bry/vi76ecF6GHxhuIKzfK5aOTathMoSAtPPZMP6tRzSR6OsDu7VkRfO68vc8/cir2IFmdNPIzDpYDKnn8b+/tX8as/ssGYKSyrZVF7D/5u5pFG9hnvPGsCj7/3A6EN78tbvj+Gd635Dt+w0Rh/as+GY/I4pTLvwcHpkp/L3MwawT5cOZKfFLjQXjWDQsGpjOV//vJnCkkq+/nkzqzaW7x5KRHrn2BOzaEWDAskxVmvHIRUbMWOmNVZk03JCLiKuzM69LtROZLviU+uFe5wvKbxuw+rP1KLhLSR3zA36wP3tbXrOQ4N1YoXEVqyze4fiJZpKYezeey9frm5arutMRXGzf2ZLHIhDXZNg0IQVMdtcUaUKrs8fXRZ9AXU1iiYboGPjxu9Vxk6+S61e3rF53s36tziLF+/fDWdPj92eO1YbE8qSFK3tqpLwQnPJGdqX9++OXqjxw3+E/hYfgcmnkPfYQRz61ij+dXwqeRkBisuqyU5L4sG3v+fukToWF1WYkAISpb+lNT4qatSya4Oh40zRUsguYG1ZkIBAdgI8vnpnQm0Qftqyg4slPj/0OxFWvA0lq+LaN0vbYaddmIwx64GjROQ44ABn81xjzDs7e+02Sawc+xXFmvEiWqzAxu+h3wnhQZ5uYaDIYzPydSU2taM+IDp0Da04VW/RyVHkitiIhxrHSrhp+FwT+YRXQ+25+z/6Z3hmqAZf23KdzMWaWDmTrvx0yEzLZO6VR7G3rCFp1qjGKQZLVyMzz+PWc1/luYXrGi7lWhmKy6rJzUzmvlEHIWjhkNzMZAb3yWbYQT04/+nPG/xrHz73EHLSAwwf2JOstCSy08ID8IJBQ25mMtMvOhy/CGnJ/kbHhH2dyhrWb6ni1pdDAdz3njWA7PQkOmXs4n66Pp8GTHvrNLixB5ldGx+flgt5+6oVwfUFL1ygkx0TRN6/VydSXpmuLImudMz7U+M89WOm6nbvcbMv1ntg2ig95rDfwZThjS18E15tHBvx8uWa4SzaPbbhu9B9WFsRO77HrQHgrsS6n3fGdcay/cShrom3iNnBvTqSFdys49Qex+iY541dOOtpnfTUbI0uG+l5Kvcde+gijPj0Fc1aMW526NzULHj9pujZj16/IWSFHna/+o1Ha7u6LDyOzs3gVLpax/shd+ozIz1XY+rcmIcRDyNb14b1L3nWueROfJNHxx1KZU09Vx/fj/RkP/+ZeBipAagbPZ3A+/9fo/6WjphMZmZXeqQn8+GNx9lg6HhSXwsbvof+I1hbGiQvDRLxsxZo4XC+KwmyV84OFszsNwS+nAELJ8MJt8evc5Y2Q9yiW4wx7wLvxut6bRqfr3HwXl1NaBUo2kS+rCg8r/fiZ6MX+5l/h672/26eTpx6DgoFgmb11GtEpp8UH7x9u+7rsn/jlLClqzW134RXNY6isiS0v/hb3W7qddWreqsqKoG0pidW+wwjr0MqRZvX0iUjhaS37mrS5J3uDzZk7OiZk8Zj4w4lOcnHraf258kPfuSaE/oBkBzwk+SHcUfuwdgnPgkLiL582hfMuPiIqH627gpjZAam7LTYq2KVNfXc8PyXYW3c8PyXzLj4CMhotjS0XwJJGuty/uvR8+C7REt36cq1t6LtKfdozM8s5+/Fz8JJf20sD8vnwvG3agyOL6ATwrqa6D7cOX3g4vfUVQ8T28IXbXtNWeMg6qwCePsvekx2gf4dLYVxUroGW7vHhWU+syutrU60MXc78BYxu+XYzrrY4brkfXCfymJVqbrJuQs1mfnRx/NF0+D0x/Q+MUF4448arxDVWmFCdVO2rFUZL18fHpgtEj5Wd+qrMUBeFzxX0Qik6vVcxcAEQ+N04YJQfaARD6mF5cQ/67379h3q8hrZv/oaemTn8HNpNb+fubhh7Jzyu8FcMb+Siw+9jZ4pKWSc8wpVNXV0yMzAJOXQO02rS+8W42RrsvEHTeue3Zt1hYbOCYoi7ZmpYTXfbQpyyp47eJGMPHVlWjRVszFFs2xb2jU2PD5eBJI1C4yITsjrqvSB4Z3Ie6vkHn4JfDdPTdF1VeBP0QeRO4ly/b0HX6TWiMqN6r/t5tP3FsCqLNHP827WB2G0lLDJ6YBpXDCurEgnj1M9QaRjpmp6wsiVObc2xKLpmN/ciP/pU+jmfbCWr49ZYTsoAab8bjBbq+rolJHML5ur6JAWYM+8DLoe0RsRoWvHVHw+IRg0bK6siBoQXR80DcqDt7iXiPDAW8u3KwNTvTHR29gNPJga8Ae2nas7WhacOVc6BQo9PuQbvocV7ziKgZOFqaZc5dhbuHDxs+p65yrT2QV6TjSFdfPPqiDPHB87fWWsdK2BVA2G9gZRj56qiQpOvkuz2lRvVoX4/Df0wS1+vYdf+0PY6m1D5rPtdJ2xtA2SA/6GBYz8dMe1zRsgfOTl6noEOiZHruqn5ejK/v8eUKXDO14On6SKcDQZ9CdrEVFv+tXCBboI5BZcDKSFKwVb12nsW8ceasGo2BAKpD7iMjj+dq2uPvLfULWl8ULU8EkaK+ddtPJmzfP0b/mGGjrlB7nUqa0DIffSeUuLmbc03F3vwxuPo0dGAqJ6dxeKvtH37AJ+LguyT3bTh7cUqQHomgHfl+xkApK9h6osfjtXk2JYdimsAhEv0nLVP9vrDjJ8Umi/k/WFKz7TgLp3/qIPkC1rNVBu4tzwFdjv3lD/bnc117vq62btcNPzLfh3yIJRvRXGzoAthaFV1+ze+uA77pbGSkG09H8zxun1Fz/rFC6qB/FhAinUSTL+wy/B9/TJjSeU583W7xORYtCMfoafajoy/unPuPXU/nRMC/DEf3/ggl/tyXWzloSsEucdSl5GMj6fjyS/L2qO8YBf3RaiWRzuHjmA4q01LFpTCmw7A1Nqkj9qG6lJzXeN2C2IlQUnp48GLLsTp6+fb1yxfMjfGxcuHP2MKqhuatcP/6FWBK/1wpV3EZ0suXFA0VaEP/5X9O2YkBy6fZ7pZBlLzgjv0xmPY3L6IME6jb0Y/iDU3q0TQJ9fLRk2C1O7xVvErKjC0MsbXwbhCx/ez+6q/j7DtLbDr34fXlTTHfvOfKKx/J4zS++D9E46nn73RqhiemQ9k9MfhY8f0u3zb9fJ/8S5jSupr/9KF6jOm60LP9NHq6Xk9Ec0+cWmH0IWb6/ie/qjqlB73Fg3njaZ2974hX+c3b3RQsrG8prtqvFgiRPrvgRfgPqOvfilvIqjo3iTtha9MncyExNA90PUcrjgKatA7IJYBSJeVG4MKQ8QerC4E/3RU+Dly3Tfb2/TAT4tRx8w3mA19/y9h4YeRpHXmzFOq+kOuVNXpY69UQsFvXa9WkF+c0P4quuYaTD4Eq1a3bF7yOrh8+sKVqz0f852c/JdID6CQUN51Vay0pJChei852z9RSd7oybDhFcgWE/Ql8S0b2o4op+P+0cdRNesVETgxqH7sXpTRVhRuEueWchdZx7ITS9+xbMXHc6j4w5tWBlz60HkZ6o1obSyhl82V3H/qIMorazl0fd+CEvvCtt+4OVlpEStjJq3q8c/bC9usHXk6mpSOuTtHSryduDo8HSooyarAjAzQo5nnqcrq+5EacTDmsHpv/eFVnuzeqlF7kiPX3nhgtCKcJf91YrhdcUbdr8qyxu+c4pk/S264pPRGaad1SjeQjzxFsEx09mQ3hfBT25aMr4M69/d6sSx+rS3iJkJ1hMcMx1f2TqV0z2OUVc+dyX/w3+Ex4btM0zH1Omjwiuou5Su1pSwi56Bc19QV41AqsZReF2Q3No7J/5ZMyx55e+lS/WeeOmSkMXarcQe2ZYJ6rjtLgSVrlbriVvP5IQ/a+rtDj3gzCeQ+jqNd6sooW7CawSDQUywnuLNQfI71OH3SSNl4YWFa3jonEO4YvoXYW6nNlC6hVm3GLJ7U1wToN4kJgOTS+8OsGilZmJK8u/g+Ofzw14nwuKp6p6V2ze+nbQklIQrECLyFHAqUGSMOSDKfgH+CZwCVAATjTFfOPsmALc4h/7NGDO5dXodhVirtO5Ev65KJ0thwW7d4OOHQ6tS3lXUpqo/ZxdoWlfXBWT9V3q9wgX6EIycsM04N6TIjHxSA7UnHRZyVYoVKHjhfEjNRj59AvqdgH/OlWR73Zm8OfpdJcgJBAxOfI3vSup44KNfuGHIPkhFEd2ljtINPkjP5bY5yyguq25UFK5rViqFJZWMfeJTXr3qaGZeciR19UECfh/5mSkEAqrIrCsND352r+M+4Jpb1MhWRm0GbhacyErAtVW6QpqZD2f9p3Ee/FkTYNwL0eW4YoNa4GaM04naaQ+qErD3UHUHQdSiV14ULp+uq97oKer256Y7LitSV5CXLw8pJSU/xXAriZHhJim94bNvxjmsOXEW17y61lYzTwTxrj4dDOKrKKZzvaOM5O+nNRTGz9HYhynDVY6H3a8xCIFUndBXlWghTXfCH8NFk8oS+PEDOOwi8GepRS0yhbC7AGRixPKY+vDxNJZb1Jaf1YIWuYizfC6ceAcmuzfBik34/3NSw29XP3oqNRk9SCpfS/JMjffZN7uASaOmUeKDh84ZiK9iI9nJQUprfKRm5YMI/5l4GGXVdWSnJ5GVlmTvgZbEGFi3BHoexs9bdeU/UTEQoIHUtUFYtSVIvx0NpAbNxrTkWVj4tMbEWXYZ2oIt/mlgaBP7Twb6Oa+LgUcARKQTcDtwODAYuF1Eclq0p00RKyVmUqqubmX30UmP6wM772aoLIWjrlLl4YhLdd/4OZpfPKNz9OvVVoSn54OQYgExg5gbtr9woa5sZRfoNdJydbLlTf83+hkNNH3yBH1wHjq+cSadly/XAFr3HG+fSldTVx8ko1N37jhtf3rX/UTfl0+n1+TBHPj6mfQJ/sTksX15dnQPOtaXcOPQvQGcYnD6gCosqaS6NkiS34ffJwT8wpbqWn4uqeCXLVVcEuGze+MLX3L18f3onp3Ghzcex+zLj27WpM+tjNojJ53OHVLsAzIa3iw4136t7x26htwrChfEru0gvuhyXF4cntkoNSuU7vWpITB5mFov8vdXN7p9hoXOHfGwTupcRfx382Di69Cpj7qSDLtflduvn1e3kjDZngKSFL1PbqC006f8dGmIpdlYbjMvtSqxqk/vSApdVxl58gT4xwH6XvytruSb+tCCS+ECtUA9c7rG7kw6VI+tKg31w3Wj88rUmKlq+XItblOGa7KKqAtK+4YWbby4CoP7efgk+PSJxvWDTn9ULXczx4fGX+81xIfUVuKfGV5jyD9zHKmmksDM8JonSbPOxVdVSn9/IQe+fmbDGJ1ftZI/PL+EEx/4gKueXYRfpFFCit22hk5L4SY4yd2LtWX6W3ZOT1x3CjL1/btNO+nGlJ4LBUfA4mmhzHaWXYKEKxDGmA+ATU0cMgKYYpRPgGwR6QYMAd4yxmwyxpQAb9G0ItKyxMpV3qG7PlzcAE1vLYg3/gBv3QKn3K8TqCnD4cGB+h6sC6V6da83egp0OVAVDm+BLe/kJ0bebtLzQu3W1+iDqKxI++APwIS5cOVCPeb9e0JuTaWroWJjbB/4q75Qs743WDy7gGoTYOwTn7B27RqSZoU/tDrMHk/HjV82PKwOTV3HkP6dufesAfyypQqAk/rns6G8piFv+5kPf8TyX7Zy5fRFrC2tjBr8vEdeBl07plploCVws+Bk99L32kr9n/YcpHIaK799UkZjOR4+SeMjvJmN0jtFrTHB2i80APWY6zUb05A7VTkI1ocU8WC9ZlyaNVH9wqeN0n17D4UP7g2/596/F3w+TGSfRjwcrpRnF2gufGw184QQz+rTXmWk5yCVg5oylWFfUvR23DSqAClZoc+uG92w+zWebcid8Nnjqoik5YSCmWONwyU/AtJ40WbEw6psX/yexk4kp0PB4JDL6ZULdPuS6dqH0tWawCDyGrMv0cJyUb6T1FVH3d4puY6kCMUi66Xx3HJs54bYtO4Rme8i62qc8fCHLF+/1SoRO8PaxfreqS/ryh0LRALj1RsyMe1sIDVoSteKjbDslZ2/lqXNkHAXpmbQA1jj+bvQ2RZreyNE5GLUekFBQUG0Q3aebeUq9yeri0S0DEm1ZY2rUk87C858XFdfxeesThlduT3mBnVbaghKnaITI9CJWWQw35ipalFYPjfkm75keiiLTtl6p4Lvap0cemMieg7S1d7fzdO23RSy2QVQvBzm3Uxw/Bx8ritJdgE1o6Zx/Ws/h2c88RLhLpI061zuOfd1VlX5uWPOUnrmpHHLsP6c8+SnjVKs3npq/5gBfukpu04l1FaR2Z0hkKxWATcYNDO/cU2RMx7XFaeUjmpZK1uvMvTpY5pNxg3wHD4pdmHFDl1DSm9qtspfWZG2f+F8Pc/Uq+yWFWn8g+v24cbxRMT4mJP+qpXVh9wJGZ0xHbphaqvCZHjjaZP52xu62m2DR5tP3OQ2VtzNjqTQdZURN52qN3jZrWbulZHsAlUsJr7mpLdOCncvLStSN9DKTSpjgy/Wd6/SEy3gf/QUdWU19TrODrs/lOgiMx+Wz4Mu+8GciIQD7oKOe68Uf6t92Fyo18jdS+OBXJdSN64u8rdzLc8R2+vq6kiOcu8d1C2d2ZcfHdWt01tXA5qX9a6t0mbG2nVLdJGx0x6s/S5IRhKkJzDzqZuJaactEADdB6rL9oL/wIFn7fz1LG2C9qBARJsRmia2N95ozOPA4wCDBg1quSWSpnKVp+fpildkFqQzn4hddTRYr6uv41/RB4Z73j7DYNyLmnEpOVNXy4b8HU68QwP3FjwZCkatrdCX16IwawKc95IqJhhd5arerClex78cesi4D9zpo8IDAd0sIk6huuLyOgpPnMX+XdL4Zn0lqaYL85Z+BBDKeBLNZ9jzXTP8QfI7pjDpnINJS/ZTUVMf1crQt3MGlTX1jQL8drXg51aT2R0lvbPKnFvYrXS1TmDcQGbXRcmthP7pY5rKNaOzBjdXl6kVrOTHUD2JaHKSmg3P/y4kfyMe1mt8+oSu0M4Yp8ptRr4qLB95MjLF8FeXTT+EKRaSXYCMeAiG3Y/p1JdqXxo3v1LIojVbbJXd7SRuchsr7mZHUui6ysjR10a3co2fE74gM2aqBvWnOTk0y9Y7QfnOmFpfq65KL16sE/+T79X4H2+aYa+lIrefWugQjV178xbodZQWRixbr0r2/D9rMoz37g7v38zz9LrL54biKIbdr8kyUjvC+q91ZXfaqND33Y5sZbWjprFyc5B9o9wngeQUOmdGH1O9dTVc2qulrs2MtU4ANf5kCrdWJDT+wWXPjvBVcRz+p+KDfifBFwCOj38AACAASURBVJOh+DvovPfOX9OScNqDAlEI9PL83RNY62w/NmL7e63Wq+3FH9DMMknp6i5k6jTIMzVbK1XHmmSXrtaHlas8gD5M3MDp7gerL++WtbpKm5ShwXyLpoZWvV67Prwvpas11/hLl3myRD2jys3yNzFjpiIzxkV/4M65Uk3pc65osEQUbqnnmlfXOhmQvuax87IbLAR/e6+YJ0+bTO4rEfUk3r4j7LsuK67mtP+821DEKOBkBumcmcKlx/YlOy2Jipp6SsprGfXYx5zUP59pFx5OwCdhwc/e2hA2KLoF8fl0UuSVWdeH/OrF+n92J1WuDLlKbHZBKK3lUdfAmU+CP7VxPvtRk+Gt2xvH3pz5BHz8IOw/X49L60QwuQOVSTlsOOrvpAYg89xXCQQCBEZPDfmDZxeobM/9f+HfxY3VmDYKyS4g5YL5/O2Mg7jtNCtDCSMO1acbcJWRmrIYwcvBcGtAsF4XbFI6anvpnbUQlqvMTJyrcpqZD8Mf0hgJb5pWr6Uis4tet74anj4l1P7R14ZXVYfQmO61hnjj29y/c/eC/94PB43VfpwbUUOlcIEqPBNe0cx45cUhF9OKEsrPfRXBsKyoitqqXP7z0Y/cGTFGB8dMx9eEsuatq+FiLXU7gTGwdpE+z4EfNwfpnsD4B5d9suHDdYaNlUFy03bS432vEzSY+qMHYcSkbR9vafO0BwViDnCliDyHBkxvNsasE5F5wJ2ewOmTgD8mqpPNwh/Q1dNNK/WhFazV1f3M/Ea1Ezjraa2I+rt5gERPm5rdW1efvJOus57WjDb+JHX/KC8OZapx8SonbnD1+/fAyXdh9hmqQX6nPQg5vaM/cKtKG5SHjadN5vEPt3DvWQO4543lADz63g/ce9YAbnj+Sxat2cLNH6bwwPh5VFRUsKEKugVqyfK4i5SOmMxtr/0C6CrWTxsrePazn5h0zsFh1aJ75qRx/6iDOLhXNm8uLWLpuq1hJvNY1ahtBp0Wwg2QjlR866pD1aFjBfVn9YSB42DayJDsnvu8xtMEUlSxrq2MnmLYdcVIzYFzZhIMpCL11SQjVNbBne8XM/6oPZn80Y9ccVxfUk6dTcekIBnp6aSnppAU635wri/1NXTO3nWsWe2Wnaw+HXad/P6wdW10ed30Q/gKfnaBKhRpWdq+c765YD519fUE6iuRzPzo1tk1n+oCSyBZLcurP9drRiYZiHVfZERM2iOttdkFuvD04wca45NdQFWH3iSPmYbPW039mOvh2zcwvY9AXLfZ7AJKj7ieDcFsHv9gFTMXFnJwr438Yeg+3PzhSi4+cRbdMn1kd8gkNatLk8qat66Gd6y1lrodZMtafZZ36kt90LB6S5CBuYnuFOztGOG+LK7nuIKdVCDScmDvIbB4utZTsSld2z0JVyBE5FnUkpAnIoVoZqUkAGPMo8BraArXFWga1/OdfZtE5K+AM0LzF2NMU8HYbQOfDzrtqf6z7gpuZr6mDTz9EX1gVWxQX9lXrm68au9N85eW3Th15vMTQ1aFiXM1+9OZT8CLF4Wv7CalwxWfa3+uXKAxGsXfIu/fHUqDWVYU/YGbkYe5ahHGn0yW+HhwuFDuT+O+U7qSTJ6mAcxIaqg8nZ2exD8/XsUx+3Thxhe+pHNmEn85+UX2yUsGfzLnT1sB+Jh6wWC6ZqWSEvBTvLWGsqo6bnrxqzA/2+tmLWmo9RBpMt+V/HLbBeIPubS57knpuTpxct1GYqW9TM9tLLvTzlIXuikjnJiKR6Kf62YiWzgZM2AUvul6naTsAvYd8TAPn9yVjclpjB3cm2mfrOHkA7uRlJXOytJK9u2aSV6ka4xboNG9/o742VvaNj6fJrSI/N+PmQZzfx9+rBuj5Q3Y9vmQDl0IbF2PFP+gtRZeuixcfj99TCfuXqVizFRVdIXwWItY90Vml9D2yPg2d+z2BfQ+qashOGEuY6f/xBF9cvjD+DmIG2f0wX2YY2+iPKsfFaPnkpkUpF6S+HZzMk/N+46xg3szc2EhxWXVdOmYyp0jB1BVE6TOGMqS/KRK05NFmwI7zqxbou+5e7G23FAbhB4Zie0SQN9sFd0lRUGOi0d4yIGj4fu3tDr16ClxuKAlkSRcgTDGjN3GfgNcEWPfU8BTLdGvFsXng4w8XZVyfXOfnxiKOxj+UOghBCHXDU+hK8ZM1TSwsVax3P1VpRoncfojapEwwMLJ0O+Exj6yC57WVbV3/uK4ijze2DJy+qPwvweQAWcjL1+Or3Q1gX2GkfSbP9DpZS2k1yu7gM2nT6Eie28yklNYum4rv92vK0FjuOvMA0lN8pObnUagYyoby2vo3CG1UVXqe88aQGZKIKqfbXaaRpZFmsx3Jb/cdoHPB9/P10mTN65n9FT46WMN5v/g3uj+2DEywlBZ2hDcTCC1sfI7ZqpO7ObdBEdfi8w4t9F94h92P1mdM8lMSeG8I3tz2bTwYliduu6Hz3WNCdbBvD+FEgPsqJ+9pe0TzS1K/NEttLUV4YqkU9ROais0ni01q7H8Dhwb7mrqxlh43URBlYhoyS6GT4LPn4JzX8D4k5CKjfD5kzBool6DIMy7JRRQPeJhqjvtwwNnH0JX3xZk8klhfZL1X1F/7ussKkluKK7pcsup+/PhjceRHPCTk5bE98Vl2225dVNgW+LAusVq0c3Zg1XrNWi5extQINIDWg9iSTziIECtEAeM1JSuK9+DPY+Nz3UtCWEnbVKWnSI9V1fAvEXjCheoBSLa5Cq3H1z0rlOMKAilP0VPFZiRp5Ogub/X4loVG3SfMZq7v2Bw9NiGgWP1/ehroXQ1NeldqM7qQ3DCXH3oDrkT5t+mpnM32w7AwLH4IqpwZ700njzZyoayGm59+WvGPP4JNzz/JSLC3+cuwxiDzyfkZiRzy7D+DcoDhDIupSUH6JkTHknWMyeN0sraqCZz1y838njrl9tCpHfWQNDISdPMcdD3WFUeBo7VYM9zZqnsTnhVV2p9vuiyu+VnnXS5tSDqa0K1Hs59QSd2Zb/ofRKr2GJSOoH6Sg7MrmbSO9+FydUlUxeysaIulJI2uzec9kCovsWOFiqztA8i0xGndWqcfnvEw5CzZ0iR9NaReHCgjqebfmgsv00V/3QDooc6KYUHjoWvX9T7wk0x/M5f4OMHMT4/UviFKikHn6fKdsUGtcx5k2G8fDnU11G4qYKSLVujtp0i9Y3cinrmpJGW5G9Id11SWRvVcmtrn7Qiaz7TtOhJqawsdRSIzMR2yWXvbFhSVI+u5caBA0ZqRqY5V2tFdUu7xT4pE0UwqOn43rtLFQnvwyhWDvGgswow72ZN4bf42cZFjUZNhvo6XQEGjbuYe526i7j1HLZVbM6pdh30p3H848tZtLZCM+HMGKcTt8jzY1zPF6yJWfDNndT7fILfJ1EtB0l+4enzD2tQCtyc5AN7ZkUtFOf65XqPt365LYjPFzuDmC+gk50Z4+DxY+Hhw+GJ43T/MddrMbfIXPjRCiSKL6RQiGgGG0c+jevu4cVZPfYXfUPq5JO48+gAB/fq2LC7kUUqckJplYfdC59Pi7tNeBWu/BzOm61jppfIonZJ6fD+3Y3H3sz86PLoia+hvk7H4nk3w4Fnanrtp4eFxtbsAmTrOsjM03uhvBgyOhNMz416n0mwhoyUAGvLglHbrjZ+cjOTmxwTreU2wQTrofBz6LwfoHUXMpOgUxsx7uydDZuqDKu3xEmB8CdrDMTmNfD6H+JzTUtCSLgL026L96FUvj7czWPxs2rudlf1XcXg+/mwzxB92FVu0onYB/eFXD7Sc/Wh8+MHmsO812G6ius+eNz84LH8b93tHbphxs8hEPDzz1O78+k6w4BxL5NUUaTXcI93z49xvVqSYhZ8i2Y5iMzosbK4nPyOKcy58mgqa7btZ2v9chNAjHz9JpCMRKsdsmG55tX/4D749XWh7DcZ+TD/jtgFErMLNGboNzeq1WDci2whjcwx0/HPOCc8VigpXQsklq4m95UJ3HLiLEY+oytd1iK1C+G4Fe10lqbKjTD51Mbj4YXzVamMLGpXWaJuT24ldDdddvXW6O56kfE1F7/nKAZ58JuImj7DJ2ml6bIivfaMcQDINV9Fvc++31hLZnaA+97b0iiTUv2Y6VQl55CTFGDGxUdQbyA1yUdeRniRTZtRKcGs/0YzhOXvC8DyTUF6d9D1krbAgDx9/+/PdfTOitNiXH5/GDBGszLtdYKtDdFOsctticL7UHJzhg+5U1NgnnKPTpLGz1HXjSF36oSrYDB8NUv9tmeOh9dvgJP+og+w8mKNVVg0VVfCjNEsT0Pu1LgKCOUHj2a5cLePeBhmX4xMGU6geBmHFk7j0v2qSZo6QleB591MMD2fypGeSr6LnyU4+pmw6208bTLFpkNUl6LIgm/RLAd3jxzAg29/zyXPLKQ+SLOrS7t+ubYadSsRpQJ7cNQzyOs3NcgLv71NA0hHT9GVW3+SWife+IO6Z4C6JZ1wh6aknDhX30dNVpl1FYMXL1JrWsUGWDiF1cVb+byiK9+eOpvqKxZhJswFjF7XVURKV9MtU4c5a5HahfC6Ff3jAH0vWqrbt5dtVb1OSgvJ5Zipmq7VTTIxY5wGU2d0ViU4t68ed/H7Wqvn08dCyvPwSfD6Teq28f7dsPlntUh4q1q76Va96VuzC5CtP1M7Krx6es2oadw2/xeqausZf9Se3PxhHQtPnMXa8z+n/Lx5TP0hDfH5yU7X8bCgUzr5HVIbjYnWcptg1nyq7533wxjDd5vqKeiQ2C556ZEBXdLhvdV18b3wgLPV6vLK1VD0bXyvbWkVrAUiUUSu3BYu0MmWu+pVtj56nvBh98Psi0NVf4uWhVe37jkIjr8jZHnwroK5+cGPvw3qKp10gym6smuCGqznzfTk1HzwRQR0+6aP5KfT5sCpsynICuALpLCqIhVOnU2Gv57yej/lvhxy6mHqBYdz52tLeXNpUcyCb67lYMbFR1BYUklpZS33zVvOojWlANaU3pbxBKaauhoMgu/1P4T7as+5Uq1mpT/ppMu1WBUuaFhhZZ9hWkhr7nXhAdMjn1KrhVcuZ46nbtzLpNXn8a95yxtk67UL9qHjy1c0WqXtnN2xIWDUWqR2ESLdikpX69/u+Lk9NFX1ur5O43K8cjniYcjqoYkpQOX5tetVtk97UFeT590cqhNx9NXhtRjccdxxTyI9TwvJRVbDdu+T4ZPgzVsoHfZvVjupVrM6ZLKyPJXOHapZu7mKFxau4aaT+7O1qo7yZD9XPPcli9aUcsL+3bf59a3lNsGs/kS9BzK78Eu5YUsN9G5DCoQIDMqHt9fUUV5ryEiKk1y4FuVXr9V796J3Q8UbLe0Ca4FIFFFWbsMywMRaFcvbG856mrq8fflhxEtsyhmAGeNZmfrNjeEBzu4E7uhr9ZjDL9EicI8fq5meMFrV1wQ1w5PXhaR0deNiYc72DF8NQ59czklPrWQjWWytCXLhC6u5+rUNlEg218xcwm/vf59x//6UK47rx9yrf8X0Cw+nX+fMqA8mn08QEa6btYRLnlnYoDz0zElD2oot1xIdJ45Asnvhw0Sv3VBXBZ88GtsCNuRvoXom7jmuchFVLgMkBXyMPLQXMy4+gltP7c+d7xaz8bTJje6ppA751iK1q7Etq8H20NRYXPZLY7l8+XL9/PSw8PiF0tVa38QtKFdWpNayp4aEjnGvkbOH3gep2bDg33DEZY1j2bJ6hqwSZUUkBwLkde3F8spsxj37I5dNX8xVv92bPfPSGXloL66buYQRD33IpvIaFq0p3S43JGu5TRDGqMtxlwNAhK826GJZ36wE9yuCY7pDVT28tSrOVoiMPDj2j7q49MKFoThPS7vAWiASxbYqrcZcFUuDDl1YX1LB8Y99BsAbF/Vn32H362pWWiddGassCfmel66G/P3UpP7W7SGT+qjJap4vGKzFiaK156aajdheVGEa0q2uLa0iNzOFFy87ipq6IGc/8UlY4PQV07/g1lP789dXlzZZk8EvcPfIAdz4Qqhw3N0jB+C3z7L2Qyy53bJWlddPH4MjLtWq7OPnqILqS9JKvVEnhFVRr7esuJq0HPjrq0vDfLdLKjsz6fy3SDK1O+cXb2nbNGU12F6aGovra6PLJRLefs9BunhjDBx5FSyapq5M9bXR+1m2PrSYU7gAir+letyrFG2torzeT0+pI3NmqJ5E6YjJbAh24J7X1Zrrctm0hQ11cSA8S91j5x1q3ZDaOhu+g/Ii6DoGgC+L6vEJ7NlxG+e1Mv07qRvT1KU1nN4vKb4X77I/DL4EPnkY3r0Tjr81vte3tBj2yZpImsoAE21VbMTDGqgXDIalLP3jG4VsTu0F1WUaDOhm+fjtbfpgyy5QV6e3bocT/xxKyfr6Dao4zBgH7/4telzEommYiPiGmlHTSM3qwl9HHEDnDin0zc+gT24G+R1TESFm7YZtZfbw+XxM/uhHbj21f8Oq8uSPfsRnJ4Dth2hyO/oZ+OwJXUk9+ipNTzlluKbEfHqYU5ndxEjrurZRtqaNp03mtvm/kOwXHht3aJjv9rUn7ou/g82qtMuzLQvu9hJrLPYnRZfLklUa05NdEHIbnXsdPHSYWnYPOAOWvaYKRGS2sTFTITkj5NIEUFbEdxtr+PVjKxj65HLOm13MDyNeou7qr6ie+CbB/P5kpCaFKQ+gY6urJPTMSeOhcw6he1Yqt57anzzrhtT2+fEDfe92EKAVn3t3gNQ2trTrEzh9T1jwSz2fr4uzFQJg75Oh30nw3/tg6cvxv76lRWhjYmppwOfTwm9ulprKEvUDLyuCC+eTm5HPE+MHcdGUBSxas4UNdd3Jiua6NOx+9bFNy9EYh5cuDa9m7aaQdeIj6sa9zJZqQ3KSn/JaQ1Gf8fTO7kVHZ3XO+JMp92WRXRMkPyu9ka9srIwe7qpYUyb13Ixkfn/iPo0KGtlVtHaEu5p7wXz1BQeorVLZS0rXqryRRRJfuhTO+g9m9DNIZOYxEbVCnDebeiN8X2r44xvrKC6r5Yficg7qlWV9t3dHtmXBjReZXRtnxHNjyo6/XcfXTnvCM2eEy/SsCbpIU1+jBREnvKpuov5kDbbesDxUwC67AHP2s2T6u9IzZw2FJZUUl9VSk9oZX3YHAj4hBSjeWh11bO3SMZXnLz2SjeU13DHnmwb3pdmXHx3f38ISf1a+pwprh67UBw2Liuo5qmuiOxWdk3rBc9/BI4urOaxbnKeOInC4U9l99qVa86pL//i2YYk7VoFoy9RWqv93JHU1jQLfuhKj+Fx275DP7vF3hD20GDNNV4PPfx2MoZoAZ09bwaI14cVdPryxBx1zNDBRgBwgJ0aVTDejh1cJuHvkACZ/9OM2lQEbzLeL4PNBhy4Q7Axb18Kbt6q7xkuXqXtdFDk16XlUpHUlfeJcpHS1Ksyv39Cg7AavXszlc4uZt7SYnjlp3D/qIO56/VsmnXMwPXLSE/AlLQnHtRq0JP6A+qef/7paE9Z/HbIcvP1ntfKWxxh7MzpDeie1rkUqOBHKj6R3pjfS5NgXbWx95NxD2FReTX3QNLjz2YWXdkJdNax8F/r8GoBlG4NsrYEDcxPcrxikBmD4HvDM8no+XVvH4d3jPH30J2k8xKu/h2fHwiXvhTKRWdokVoFoy2zDz9cNfAOgLCX6saU/hWIeOnTTleH66Ct2W7ZWU1xWG9aF7c0HHqkEiAh+gb+fMaBZykDYd7K0b3w+6NAdjrtZfVuH3KkyGEVOJTmNjLRUqE9RRSNif50vjZtO6c8NQw2/bK7irte/pbis2uaqt7Q8/oAGNJetD89452a1O/nu6GNvZheV/2hWkSjKjw+aHPvcsXXmJUeytrSSjeU13PayWhxO6p/PzEuOxBhjF17aCz99CDXl0HMwAJ84rkFtVYEAdWN6czXc8r8qXhuZQVK8AxTTc1WJmPdHeOlyOHt62ymIYWmEdRBuy2yPn2+sY7sfAtd+ratdnfbUleEY/uHxygfuzejRPTuNLllpNrPH7orranLaA9BtoFq8mpLpGHIc6NCZqtogE//zOeP+/RnFZdV2ldXSukSTzeNuVqU4cvuYaZooIM4uVT6f0LVjKhkpAf766tIGd6Xfn7gPXTum2ixK7Ynv3lSXtm4DAPhgTR09MiAvbRvnJZDUAFx0AHxfEuSxJTuQ8aw55O8Hh54Py1+Djx9qmTYscUGMiVN58nbCoEGDzIIFC7Z9YFthe6qtbm9l1ijHBxE2ltdYF6Ido0V+qHYnsy6x5HFbchpjfzBorGy2DFZum0s02QSo3KQup6ZeM+VltGz2L3svtHOZDQbhnwM0zvH42ymvNQx8eivD+sBF+7d88zuDMXDPF/DhOpgxPJ1BXVvAkcUYeO9OKPxc3Qd7DY5/G4lhl7pJrQtTW2d7/Hy351i3kqtbjMlZ6fXl97cuRJadJ4Z8kd9/23IaY791b7MknEjZjCXnGTuYDarZ3bD3Qrum8DPYvAYOHA2o9aE2CINbOKQnHojAVQPg+81wxVuVzD4jg+6ZcVaWReDoazQeYuYEuPR/kNGGfbt2U6wL0+5KrEquFcWJ7Zdl18DKl2V3wMq5ZUf46nl1Xyo4HIA5K2rJToEDOiW4X80kPQluGQRlNYYJr1WwuboFPFmSM7W2SnmxZkGrq45/G5adwioQuyvxrORqsURi5cuyO2Dl3LK91FbB1y9Ar8MhKZ2SqiBvr67j193B345mZH06wi2HwarNQS54o4KquhZQInL3gqOv1YDzl69Q1yZLmyHh4ioiQ0VkuYisEJGboux/QEQWO6/vRKTUs6/es29O6/a8neNmePKyo5VcLZZIrHxZdgesnFu2l29e1JiZvYcC8Ny3tdTUw9CCbZzXBhmQB9cdDAt/qefK+ZXU1rfABH/P38Ah4+GrWfDmLVaJaEMkVIEQET/wEHAy0B8YKyJh1UOMMb83xgw0xgwE/gW86Nld6e4zxgxvtY7vCsS7kqvF4sXKl2V3wMq5ZXswRlP/ZhdA1wHUBQ1Tvq7hoDxd0W+P/Lo7XHogzP+pjmvfqaQu2AIT/ANGwb6nwseT4JVrrIWvjZDoIOrBwApjzEoAEXkOGAEsjXH8WOD2Vurbrk1rVXK17J5Y+bLsDlg5t2wP378J6xbDkVeCCPNW1rKu3HDBfonu2M5xah+orYcnl9bhk0ruPTaN1EAcEw6JwOBLICkdvpgM67+B4Q9ClzaesmoXJ9EKRA9gjefvQuDwaAeKSG9gD+Adz+ZUEVkA1AF3GWNeinHuxcDFAAUF7dBO2FK0RiVXyw6xS8isla/djl1CbrcXK+ftmlaT2WA9vP0XrRuy1wnU1hvu+7yKgkwY3LXlmm0tzugLQQNPLavjpy3lTDohnYKOcVSkRdSVqdMe8Omj8NgxcPilcNRVmg7X0uokepkkmooay/51NvC8Mabes63AGDMIOAf4h4j0jXaiMeZxY8wgY8ygzp2tadnS9rEya2mPWLm1tDdaTWY/ngTrv9ZJsC/A9GW1/LjZcH5/iHdB50Qxci/NzrSiJMhJM8t4fEl1/F2a+vwaRjwCfX8LnzwMDxwAsy+FVf/TtMqWViPRCkQh0Mvzd09gbYxjzwae9W4wxqx13lcC7wEHx7+LFovFYrFYLDtI4UJ45+9QcCT0/hVrtga5//MqDsqDw/IT3bn4cmQ3eORYGNgZ7vykmqGzynl5RS0VtXFUJFI7wlFXw+mPwd5DYOlL8PQw+McBMP8OKFoWv7YsMUm0C9PnQD8R2QP4GVUSzok8SET2AXKAjz3bcoAKY0y1iOQBRwP3tEqvLRaLxWKxWLbFui9h+ihI7wRHXEFNEK58q4L6oBZkk13E+uAlL00tEZ/8AlO+DXLN25Wk+GG/XB/7dvKzb66fo7r76ZfjQ3bmB+jYTd2YDpkIaz6FH9+FDx+E/z2gKWD3Hgr9TlLFzWZGizsJVSCMMXUiciUwD/ADTxljvhGRvwALjDFuataxwHPGhOXv2g94TESCqCXlLmNMrOBri8VisVgsltahvhYW/Afm3w7JGXD8HVQGsrhmfiVLioPcfCh0y0h0J1sOEbVGDO4KX2+Ez9bDD5uDvLYyyHPf1gLQI1M4riDAcQUB9sz2keQTkn2QEhCyUrZDsUhK1XSve/4GKkvUnanwc8149fEkLdqX3x+6D4QuB2gcRc4ekNXLKhY7gZjdLKfuoEGDzIIFCxLdDcuuSYusJVmZtbQwVm4t7Y22JbPBIFRvhs2FULwcChdovYey9dD9YNYPvIb/burIpC+q+WmL4eIDYPge8e9/e8AYKK6EL4phYREs2gCVdY2Py0sT9sv1sV8nP72zfGSlqFLRMdl9h44pQsDXhCjUVsK6JVC8DDb+ABtXQE2Z5wCBjt0hpzd06K7JEDLzNSg7Mx/Sc7UidnKmKoHJGTtrMtql7E2JdmGyWCwWi8Viabt89bxmUDL1mk0pWA/BOufvOqipwJv/pU6S+YQDeM5MYN6qg6hdKUAVBR2EO3+dxMH5iQ4/TSzdUmBYNgzrBzX1hqUbDZuqDHVBqA1CRa1h9VbDqs1BPl1bT00TsdGZSULHFOHig9OZOCAtfGcgJWSZANVeKjbA1l+c1zrn9QuU/AQVG6GuqomeCySlgS8APj+I33n3OZ990KkvjI+aEHSXY7ezQIhIMfATkAdsSFA3bNu7ZtsbjDFD431Rj8y2Fon8P8WirfWprfUHdrxP7Ulu2+LvHg3bz/gRrY/tSWbjQVv+P9m+NZ8WkdtEsdspEC4issBJAWvbtm1bPLTF36qt9amt9QfaZp/iTXv5jraf8aM99LGlacu/ge3b7svubUezWCwWi8VisVgs24VVICwWi8VisVgsFkuz2Z0ViMdt27ZtS1Ta4m/V1vrU1voDbbNP8aa9fEfbz/jRHvrY0rTl38D2bTdlt42BsFgsFovFDIYrSwAAEqJJREFUYrFYLNvP7myBsFgsFovFYrFYLNuJVSAsFovFYrFYLBZLs9nlFQgRGSoiy0VkhYjcFOOY0SKyVES+EZHprdW2iBSIyLsiskhEvhSRU+LU7lMiUiQiX8fYLyLyoNOvL0XkkHi028y2z3Xa/FJEPhKRg1qrbc9xh4lIvYicFa+22yPNkM/eIvK28796T0R6tnB/Eia3O9iffUXkYxGpFpHrW7Iv29GnFru/EomIjHLG56CIDIrY90dHJpaLyJBE9TESEblDRH4WkcXOKy7jezxoznOxLSAiq0TkK+f32+XLmre1MXA7+nWsiGz2yPptrdEvp+1ezjxqmTNGXBPlmIT8brs8xphd9gX4gR+APYFkYAnQP+KYfsAiIMf5O78V234cuMz53B9YFae2jwEOAb6Osf8U4HW0rPoRwKdx/M231fZRnt/65NZs2/N/eQd4DTgr0TKaqFcz5XMWMMH5/FvgmRbuU8Lkdgf7kw8cBvwduL6V/m8Ju78S+QL2A/YB3gMGebb3d2Q3BdjDkWl/ovvr9O2O1pKL7ezXNu/9tvICVgF5ie5HK37fNjUGbke/jgVeTdBv1g04xPncAfguyrMsIb/brv7a1S0Qg4EVxpiVxpga4DlgRMQxFwEPGWNKAIwxRa3YtgE6Op+zgLXxaNgY8wGwqYlDRgBTjPIJkC0i3VqjbWPMR+5vDXwCxG1VuxnfG+Aq4AUgXv/n9kpz5LM/8Lbz+d0o++NKIuV2R/pjjCkyxnwO1LZUH3agTy12fyUSY8wyY8zyKLtGAM8ZY6qNMT8CK1DZtsSmOfe+JQG0tTFwO/qVMIwx64wxXziftwLLgB4RhyXkd9vV2dUViB7AGs/fhTQWrL2BvUXkQxH5RETiVWa8OW3fAYwTkUJ0RfyqOLW9LZrTt9bgAnRVoFUQkR7AGcCjrdVmG6Y5MrAEGOl8PgPoICK5rdC3WLQVuW0vtOr9lSDaukxc6bhMPCUiOYnujENb/828GOBNEVkoIhcnujNtgLb8vztSRJaIyOsisn8iOiAifYCDgU8jdrXl363dEkh0B1oYibItMm9tAHVjOhZdrfuviBxgjClthbbHAk8bY+4XkSOBZ5y2gzvZdjz61rIdEDkOneD8qhWb/QdwozGmXiTaT7Bb0RwZuB6YJCITgQ+An4G6Fu5XUyRcbtsLCbq/dgoRmQ90jbLrT8aYl2OdFmVbq8lEU30GHgH+6vTnr8D9wO9aq29N0J7uo6ONMWtFJB94S0S+dVbDd1fa6v/uC6C3MabMifV5CZ1XtRoikol6F1xrjNkSuTvKKW3hd2vX7OoKRCHQy/N3Txq7CRUCnxhjaoEfRWQ5Kvift0LbFwBDAYwxH4tIKpBHy7vXNKdvLYaIDACeBE42xmxsrXaBQcBzjvKQB5wiInXGmJdasQ9thW3KgDFmLXAmNAzOI40xm1uth41JqNy2FxJ4f+0UxpgTduC0hMpEc/ssIk8Ar7Zwd5pLu7mPnDEIY0yRiMxG3a92ZwWiTf7vvBN2Y8xrIvKwiOQZYza0RvsikoQqD9OMMS9GOaRN/m7tnV3dhelzoJ+I7CEiycDZwJyIY14CjgMQkTzUpWllK7W9GjjeaXs/IBUojkPb22IOMN7JTHAEsNkYs64V2kVECoAXgfOMMd+1Rpsuxpg9jDF9jDF9gOeBy3dT5QGaIZ8ikici7hjxR+CpVu5jJAmT2/ZCIu+vBDEHOFtEUkRkD3Tx57ME9wmACB/rM4Ams8O1Is15NiUcEckQkQ7uZ+Ak2s5vmCja5BgoIl3FWZkTkcHo3LJVFi+cdv8NLDPG/F+Mw9rk79be2aUtEMaYOhG5EpiHZp54yhjzjYj8BVhgjJnj7DtJRJYC9cAN8Vi1a2bb1wFPiMjvUXPaRGPMTpvVRORZ1CUrz4mvuB1Icvr1KBpvcQoacFgBnL+zbW5H27cBucDDznhTZ4wZFP1qcW/b4tBM+TwW+P9ExKCrfle0ZJ8SKbc70h8R6QosQBMhBEXkWjT7R6T5vNX6RAveX4lERM4A/gV0BuaKyGJjzBBHZmcCS1H3uiuMMfWJ7KuHe0RkIDq2rwIuSWx3lFj3foK7FY0uwGxHjgPAdGPMG4ntUsvS1sbA7ejXWcBlIlIHVAJnx2Mu00yOBs4DvhKRxc62m4ECT/8S8rvt6kjr/Y8tFovFYrFYLBZLe2dXd2GyWCwWi8VisVgsccQqEBaLxWKxWCwWi6XZWAXCYrFYLBaLxWKxNBurQFgsFovFYrFYLJZmYxUIi8VisVgsFovF0mysAmGxWCwWi8VisViajVUgWgERqReRxZ5XHxGZKCKTIo57T0QGOZ9XOYXtvPsbnbONdg8WESMiQzzb+ojI1xHH3SEi1zufnxaRH51+LhGR4yP6t9zZ/rmT49zdN0ZEvhSRb0TkHs/2AhF5V0QWOftP8ez7o4iscK7p7eNTIlIUpZ+dROQtEfneec9xtt/g+W2/dn7vTs39nSyNSYTMikimiDwiIj848rJQRC5y9vURkcqIPo139mWJyBTnvB+cz1lRzlvq7EvytNlIBkUkVUQ+c+T8GxH5s+f4PUTkU0cGZ4gW4kJEjhGRL0SkTkTOivheE5zjvxeRCZ7tfxeRNSJS1pzfx9I0CZLZmLLn7N9fRN4Rke+c//+tIg0FtyaKSLHT129E5HkRSXf23SE6du/ludbvnW1u38eKyFfOuPqG+z2cc3/2/A5xH3M9+w9zfvcwmbfEB9ECbc85srVU9Fla4fxfN0noWT0/xvlNjn/OMf905MUXsf1kEVkgIstE5FsRuc/Zvo9zDy129j3ubE8SkcmOTC4TkT8623s5/V7myPk1njZiPdP3FZGPRaRanLmJ55yhjvyuEJGbonznf9kxtXWwCkTrUGmMGeh5rWqldscC/3Pet4cbjDEDgWuByAJs5xpjDgIeBu4FEJFc5/Pxxpj9gS4SUjxuAWYaYw5GK54+7JzT3/l7f2AoWvjK75zztLMtkpuAt40x/YC3nb8xxtzr/rZoxeT3jTGbtvM7W8JJhMw+CZQA/Rx5GQp4FcEfIvo0xdn+b2ClMaavMaYv8KNzrbDzgAOBnsBoaFIGq4HfOnI+EBgqWr0U4G7gAUcGS4ALnO2rgYnAdO8XElVkbwcOBwYDt3smYa842yzxIREyG1P2RCQNrYB7lzFmb+Ag4Cjgcs/5M5y+7g/UAGM8+75C5dPlLLRgHiISAP4JHGeMGQB8CVzpOfYBz+/wmnNO3MZc53p+9H6Yt81fybLdOIrmbOA9R776A78Hhjjj2RycZ7Ux5oQmLhV1/HPa8KFV0tcAx3i2HwBMAsYZY/YDDgBWOrsfJCRf+6EFHgFGASnGmAOBQ4FLRKQPWuDxOufYI4ArHFmE2PK1CbgauC/iN/EDDwEnA/2BsZ5r4SjX2U38FpY4YhWIXRRn8DkLndScJCKpO3CZj4Eezdi3J/CdMabY+Xs+MNL5bNBKvQBZwFrn8wjgOWNMtTHmR7RC5GAAY8wH6AASyQhgsvN5MnB6lGPGAs82/bUsbQ0R6Yv+/28xxgQBjDHFxpi7t3HeXujD6q+ezX8BBjnXbMCpUPwZIbmNKoNGcVewkpyXce6p3wLPO/saZNAYs8oY8yUQjOjiEOAtY8wmY0wJ8BbORM0Y84kxZt22fhtL26QZsncO8KEx5k0AY0wFOsmPtmoaADJQpdTlJVRGEZE9gc2AO8aK88pw5LIjobE1FvEec68CXgCKttGuZcc4Dqh1KikDYIxZbIz5745cLMr457bxNfAI4QuNfwD+boz51jm3zhjzsLOvG1Doue5X7kdUHgNAGqoQbzHGrDPGfOEcuxVYRvgY3Ei+jDFFxpjPgdqIrzEYWGGMWWmMqQGeI3SP+NGFzD9sz+9i2XGsAtE6pHnMybNbqc2jgR+NMT8A76Fl3LeXoehDbFv7VgD7OubSADoI9HL23QGME5FCtJz8Vc72Huiqh0shsZUVly7uhMt5z/fuFDX/D0Ufapado7Vldn9gias8xKCvhLuo/BpdhVrsPByBhgflYueaDThK9OHAG86mmDIoIn4RWYxOjt4yxnwK5AKlxpi6yOObYEfk3LJjtLbMbkv29gcWek9wxuNMEXEXVcY4cvYzam17xXP4FmCNsxo8FpjhuU4tcBlqpVjr9OXfnnOvFHVtespj8YrbmCsiPdCV60gLtSV+HECE/OwMUcY/CC24zQZO9bg3NdX2A8A7IvK6qFudu+L/PFAOrEMtsvdFegI4FomDgU+dTU0+06PQlAxfCcyxizKth1UgWgevaf0MZ5uJcWys7dvLWFQ7x3l3Vxea0+69IrISmArcGXHcNEcZuBHHdOmsrF6GPuD+C6xCzZZuP542xvRElZhnHLOpbKMPO8Jp6IqfdV/aeRIhsw2IyJ+ciaB3VTXShem/qBxFa9+7va8zSdsIrHYsBe4xkRjQiaBj9u8JDHYmcTsisy0h55botLbMbkv2Yu33tj/DkbOuqDJwQ8Rxz6FuR6ejkzxtQCd6l6GTse6oC9Mfnd2PAH1R97t1wP2efsXqx/byD+BGr/JkabNEHf9E47dOAV4yxmxBJ/Unbetixpj/APsBs4BjgU9EJAW1DtSj8rgHcJ1jOcNpLxNd3LvWaW9HiCrDItIddaH6V5T9lhbCKhCJYyOQE7GtE7BhZy/smPJGAreJyCr0pjpZRDo0s90bgL3Q+IXJEceeiw4O01FfRACMMa8YYw43xhwJLAe+d3ZdAMx0jvkYSAXy0JWDXp7r9mTbJvj1ItLN+Y7daGw6PxvrvtSStJjMor7dBznKJcaYvzsTq45Nn8Y3wMHiCQB0Ph+Emsoh5AO8F3CEiAx3tm9TBo0xpagFbyj6PbMdK1vU46OwI3JuiR8tKbPbkr1vgEHeE5wJVZnjytGAMcag1odjCOcV4Dx04ueddA10zvvBOXcmGl+BMWa9owAHgScIxdnEc8wdBDznPF/OQuMpormUWnacb1AXuZ0l1vg3FHUr/sr5P/6K0EJjk20bY9YaY54yxoxAFwsPQF323jDG1BpjioAPceTfUXhfAKYZY170XGpbz/RIYsnwwc73W+F8l3QRWbGNa1l2EqtAJI7PgaNFpCs0BP+kEG6e21FOQN1Behlj+hhjeqM37+mOb/c6N8jZCfIcigZbN+A8fP4J+MSTrcPZV4sqF0eIyH7OdVzTdg4aJOgGsa4G3Lb2QxWIYjQA7GwRSRGRPYB+qH9mU8wB3Cw2E4CX3R2imU9+491miTstJrPGmBXAAuBvbmCnY3KPtuIUed4iVB5dbgG+cPZ5j12H+p+7K7VRZVBEOrtmeScQ9gTgW2ei9i46YYIIGYzBPDQGKce5N07CBp22Ji0ts03J3jTgVyJygtN2GhqAek/ktRx+BfwQ0UYlau39e8SxPwP9RaSz8/eJOAqzOyFzOAP1cYc4jrnGmD2cZ0sf1HXlcmNMLHdXy47xDpAiTiY6aMh69ZsduViU8W8scKHn/7gHOlalo7EEN4vI3k67PhH5f87noa6rk3Nf5aLyuBr4rSgZaMD0t06Mzr+BZcaY/4voVsxnegw+B/qJZsNLRhcN5xhj5hpjunq+S4UxZq8mr2TZeYwx9tXCL3TFKdr2EcAXqM/s/4BDPPtWoZp1ofP6PzQgusyzrRDoGeW6TwOXRmwbDrzufO6PToQWO69zI849y/P3SDRLAuhK7CDPvuuAfzufn0VXkZcCZ3uO6Y+uRCxx2jrJs+9P6ANzOXCyZ/uzqOm91vmOFzjbc9FMDd87750850xEAwQT/v/eFV6tLbPO+R2Bx9BMNgud61/p7OsDVHpkdjFwtbMvB3W3W+HI01Qg23Pe1542xJHFX8eSQWAAOjH8Ep183eY5f0900rUCNeGnONsPc75bObrq/Y3nnN85x68Azvdsv8c5J+i835Ho/3t7fiVIZmPKnrP/QHTcXO4cczsgzr6J6GLKYkfWXgPynX13ANdHae89nDEYuBRVGr5ELRW5zvZnUHeoL9EJWjfP+XEbcz3nPo3nmWFfcZXp7qh16QfUKjAXzVLXrN+d2OPfb9Cg+Y4Rx78IjHE+n4qOw8vQ5/q9zvb/c+RnifMa52zPRMfEb5zjb3C2/wp1lfuS0Nh9SlPyhbr0FaJxQKXO547OvlOA75zf5E8xvnfUscC+4vtyBzKLxWKxWCwWi8Vi2SbWhclisVgsFovFYrE0m8C2D7G0ZUTkU9Sn18t5JpSb2WJpU1iZtbQ3rMxa2jIiciDquual2hhzeCL6Y9k9sC5MFovFYrFYLBaLpdlYFyaLxWKxWCwWi8XSbKwCYbFYLBaLxWKxWJqNVSAsFovFYrFYLBZLs7EKhMVisVgsFovFYmk2/z8m4hOM+fkRIwAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"From the plots, the selected features don't distinctively classify the data. I therefore chose to use all the features in the data."},{"metadata":{"trusted":true},"cell_type":"code","source":"# To compare with the recursive feature elimination, i do the feature importance as well\nfrom sklearn.ensemble import ExtraTreesClassifier\n\narray = Train.values\nX = array[:,0:11]\nY = array[:,11]\n# feature extraction\nmodel = ExtraTreesClassifier()\nmodel.fit(X, Y)\nprint(model.feature_importances_)","execution_count":107,"outputs":[{"output_type":"stream","text":"[0.087 0.134 0.101 0.078 0.051 0.065 0.037 0.312 0.057 0.033 0.046]\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Algorithm performance and Evaluation\nThis is so we can evaluate the performance of the machine learning algorithm.\n\nFirst we going to split our data into train and test sets.\nThen train the models and finally predict the required solution.\n\nEvaluation of the models will be done by using the Matthews correlation coefficient(MCC) which will measure the quality of the classifications."},{"metadata":{},"cell_type":"markdown","source":"## Logistic regression algorithm\nLogistic regression is a predictive analysis algorithm of machine learning that is based on the concept of probability and is used to handle classification problems."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Splitting the data\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import matthews_corrcoef\n\narray = Train.values\nX = array[:,0:11]\nY = array[:,11]\ntest_size = 0.33\nseed = 42\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed)\n\n#using logistic regression\nmodel = LogisticRegression()\nmodel.fit(X_train, Y_train)\nresult = model.score(X_test, Y_test)\nprint(\"Accuracy: \", (result*100.0))\n\ntest_set = Test.values\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\n#Applying matthews correlation coefficient\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\n#Reporting the results\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_logistic1.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":108,"outputs":[{"output_type":"stream","text":"Accuracy: 91.62512462612163\nMCC: 0.8342865299822478\n[False True]\nFalse: 383\nTrue: 375\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Logistic regression with K- fold cross validation\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import matthews_corrcoef\n\nnum_folds = 10 \nseed =42\n\nkfold = KFold(n_splits=num_folds, random_state=seed)\nmodel = LogisticRegression()\nresults = cross_val_score(model, X, Y, cv=kfold)\n\nprint(f\"Accuracy:\", (results.mean()*100.0, results.std()*100.0))\n\ntest_set = Test.values\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_nb.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())","execution_count":109,"outputs":[{"output_type":"stream","text":"Accuracy: (83.5359128018065, 27.08521320979506)\nMCC: 0.8342865299822478\n[False True]\nFalse: 383\nTrue: 375\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## The Guassian Naive Bayes Algorithm\nNaive bayes is also a classification algorithm and can be used on binary and multi-class classification problems. It is good to use on categorical or two-class input values.\nThe Guassian naive bayes is an extension of naive bayes and only requires to estimate the mean and standard deviation from the training data which makes it easy to work with."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Naive Bayes\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import matthews_corrcoef\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel = GaussianNB()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\ntest_set = Test.values\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_nb.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())","execution_count":110,"outputs":[{"output_type":"stream","text":"0.880815746048289\nMCC: 0.8407203694376205\n[ True False]\nFalse: 370\nTrue: 388\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Support Vector Machine(SVM)\nThis is a supervised machine learning algorithm mostly used for classification problems although it can also be used for regression problems. It performs classification by finding a hyper-plane that differentiates the data classes.\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"#Support Vector Machines model with default\nfrom sklearn.svm import SVC\n\nnum_fold = 10\nseed = 42\nkfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\nmodel = SVC()\nscoring = 'accuracy'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\nprint(results.mean())\n\ntest_set = Test.values\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_svm.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":111,"outputs":[{"output_type":"stream","text":"0.9081618030224075\nMCC: 0.8187103751493263\n[False True]\nFalse: 397\nTrue: 361\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Support Vector Machines model with kernel set to linear\nfrom sklearn.svm import SVC\n\nnum_fold = 10\nseed = 42\nkfold = KFold(n_splits=num_folds,random_state = seed, shuffle = True)\nmodel = SVC(kernel = 'linear')\nscoring = 'accuracy'\nresults = cross_val_score(model, X, Y, cv=kfold, scoring = scoring)\nprint(results.mean())\n\ntest_set = Test.values\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_svm_linear.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":112,"outputs":[{"output_type":"stream","text":"0.9193579555323954\nMCC: 0.8414394511707155\n[False True]\nFalse: 385\nTrue: 373\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## K- Nearest Neighbors\nThis is a supervised machine learning algorithm that assumes close proximity of similar data points. It is used to solve classification and regression problems. K-nearest neighbor works by finding the distance between a query and all examples in the data, selecting the specified number examples(K) closest to the query and then votes dor the most frequent label when dealing with a classification problem. In case of regression, it averages the labels. "},{"metadata":{"trusted":true},"cell_type":"code","source":"#K-Nearest Neighbors\nfrom sklearn.neighbors import KNeighborsClassifier\nkfold = KFold(n_splits=10, random_state=42)\nmodel = KNeighborsClassifier()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_kNN.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":113,"outputs":[{"output_type":"stream","text":"0.8027933385443807\nMCC: 0.8690586462107053\n[False True]\nFalse: 402\nTrue: 356\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Classification and Regression trees(CART)\nClassification and and regression trees are types of decision trees and are both used for constructing prediction models from data.\nClassification trees are used where the target variable is categorical and the tree is used to identify the category within which the target variable would likely fall into where as regression trees are used ehre the target variable is continuous and the tree is used to predict its value.\nCART incorporates both testing with with a test data set and cross validation to assess the goodness of fit more accurately."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Classification and Regression trees\nfrom sklearn.tree import DecisionTreeClassifier\n\nkfold = KFold(n_splits=10, random_state=42)\nmodel = DecisionTreeClassifier()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_CRT.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n\n","execution_count":114,"outputs":[{"output_type":"stream","text":"0.7371981935035609\nMCC: 1.0\n[ True False]\nFalse: 387\nTrue: 371\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Linear Discriminant Analysis\nThis is a classifier that uses dimensionality reduction technique. It has a linear decision boundary that is generated by fitting class conditional densities to the data using Bayes' rule."},{"metadata":{"trusted":true},"cell_type":"code","source":"#Linear Disriminant analysis\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nkfold = KFold(n_splits=10, random_state=42)\nmodel = LinearDiscriminantAnalysis()\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_LDA.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n\n","execution_count":115,"outputs":[{"output_type":"stream","text":"0.8535044293903076\nMCC: 0.8377162908048379\n[False True]\nFalse: 401\nTrue: 357\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Boosting Algorithms\n\nBoosting algorithms improve boosting accuracy and often outperform simpler models.\n\nSome of them include Adaboost,Stochastic gradient boosting,Stochastic X gradient boosting(XGB) etc\n\nAdaBoost is a short form for 'Adaptive boosting'. It is a boosting algorithm that focuses on classification problems and aims to convert a set of weak classifiers into a strong one.\n\nGradient boosting combines predictions from multiple decision trees to generate final predictions.\n\nXGB is an improved version of the gradient boosting and here the trees are built sequentially trying to correct errors of the previous trees.\n\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# AdaBoost Classification\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.ensemble import AdaBoostClassifier\n\nX = array[:,0:11]\nY = array[:,11]\n\nnum_trees = 30\nseed=42\n\nkfold = KFold(n_splits=10, random_state=seed)\n\nmodel = AdaBoostClassifier(n_estimators=num_trees, random_state=seed)\nresults = cross_val_score(model, X, Y, cv=kfold)\n\nprint(results.mean())\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_ADA.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n\n","execution_count":116,"outputs":[{"output_type":"stream","text":"0.7701895518499218\nMCC: 0.8730261590913062\n[ True False]\nFalse: 386\nTrue: 372\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Stochastic Gradient Boosting Classification\n\nfrom sklearn.ensemble import GradientBoostingClassifier\n\nnum_trees = 100\nseed = 42\n\n\nkfold = KFold(n_splits=10, random_state=seed)\nmodel = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed)\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_SGBC.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n\n","execution_count":117,"outputs":[{"output_type":"stream","text":"0.788623632100052\nMCC: 0.9355236060852458\n[ True False]\nFalse: 385\nTrue: 373\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# XGB\n# Stochastic X Gradient Boosting Classification\nfrom xgboost import XGBClassifier\nnum_trees = 100\nseed = 42\n\nkfold = KFold(n_splits=10, random_state=seed)\nmodel = XGBClassifier(n_estimators=num_trees, random_state=seed)\nresults = cross_val_score(model, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_XGB.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":118,"outputs":[{"output_type":"stream","text":"0.787307842626368\nMCC: 0.9269836637327226\n[ True False]\nFalse: 383\nTrue: 375\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"## Voting Ensemble\nEnsemble methods create multiple models and combine them to produce improved results. They are also good at improving accuracy.\nVoting ensemble is one of the ensemble methods used for classification."},{"metadata":{"trusted":true},"cell_type":"code","source":"# Voting Ensemble for Classification\nfrom sklearn.ensemble import VotingClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nkfold = KFold(n_splits=10, random_state=42)\n\n# create the sub models\nestimators = []\nmodel1 = LogisticRegression()\nestimators.append(('logistic', model1))\n\nmodel2 = DecisionTreeClassifier()\nestimators.append(('cart', model2))\n\nmodel3 = SVC()\nestimators.append(('svm', model3))\n\nmodel4 = XGBClassifier()\nestimators.append(('xgb', model4))\n\nmodel5 = RandomForestClassifier()\nestimators.append(('rfc', model5))\n\n# create the ensemble model\nensemble = VotingClassifier(estimators)\nresults = cross_val_score(ensemble, X, Y, cv=kfold)\nprint(results.mean())\n\nmodel.fit(X, Y)\noutput = model.predict(test_set)\n\nmcc = matthews_corrcoef(model.predict(X), Y)\nprint('MCC: ',mcc)\n\nmyreport = pd.DataFrame(output)\nmyreport.columns = ['CLASS']\nmyreport.index.name = 'Index'\nmyreport['CLASS'] = myreport['CLASS'].map({0.0:False,1.0:True})\n\nmyreport.to_csv(\"myreport_VE.csv\")\n\nprint(myreport['CLASS'].unique())\nprint('False: ',myreport.groupby('CLASS').size()[0].sum())\nprint('True: ',myreport.groupby('CLASS').size()[1].sum())\n","execution_count":119,"outputs":[{"output_type":"stream","text":"0.8146278443633838\nMCC: 0.9269836637327226\n[ True False]\nFalse: 383\nTrue: 375\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"# Comparing Machine Learning algorithms\n\nThis is done to evaluate each algorithm the same way on the data. This is done by forcing each alogorithm to be evaluated on a consistent test harness.\nThe following classification algorithms used above are each compared to the dataset:\n\n- Logistic Regression.\n- Linear Discriminant Analysis.\n- k-Nearest Neighbors.\n- Classiffication and Regression Trees.\n- Guassian Naive Bayes.\n- Support Vector Machines.\n- Adaboost classifier\n- Gradient boosting classifier\n- XGB classifier\n- Voting classifier"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Compare Algorithms\nfrom matplotlib import pyplot\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\n\n#adding models to list\nmodels = []\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('LDA', LinearDiscriminantAnalysis()))\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('CART', DecisionTreeClassifier()))\nmodels.append(('NB', GaussianNB()))\nmodels.append(('SVM', SVC()))\nmodels.append(('ABC', AdaBoostClassifier()))\nmodels.append(('GBC', GradientBoostingClassifier()))\nmodels.append(('XGB', XGBClassifier()))\nmodels.append(('VC', VotingClassifier(estimators)))\n\n# evaluation of each model\nresults = []\nnames = []\nscoring = 'accuracy'\n\nfor name, model in models:\n kfold = KFold(n_splits=10, random_state=7)\n cv_results = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n results.append(cv_results)\n names.append(name)\n msg = (name, cv_results.mean(), cv_results.std())\n print(msg)\n\n# boxplot algorithm comparison\nfig = pyplot.figure()\nfig.suptitle('Algorithm Comparison')\nax = fig.add_subplot(111)\npyplot.boxplot(results)\nax.set_xticklabels(names)\npyplot.show()","execution_count":120,"outputs":[{"output_type":"stream","text":"('LR', 0.835359128018065, 0.2708521320979506)\n('LDA', 0.8535044293903076, 0.2571395669719574)\n('KNN', 0.8027933385443807, 0.2521136100771112)\n('CART', 0.7355556279312141, 0.28547348968489494)\n('NB', 0.880815746048289, 0.11642272449162755)\n('SVM', 0.8350280093798853, 0.25836507020625044)\n('ABC', 0.7747861299287824, 0.29219846937739774)\n('GBC', 0.7902737971165538, 0.28849018693832995)\n('XGB', 0.787307842626368, 0.2908368966924707)\n('VC', 0.8146278443633836, 0.28724005616684495)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"From the above accuracy scores and the graph, it is observed that the Guassian Naive Bayes algorithm provided the highest accuracy mean score and the lowest standard deviation value compared to the rest of the algorithms. The Guassian naive bayes algorithm is therefore most likely to give us the best prediction score of the outcome for the test data set."},{"metadata":{},"cell_type":"markdown","source":"# References\n- https://medium.com/@swethalakshmanan14/how-when-and-why-should-you-normalize-standardize-rescale-your-data-3f083def38ff\n- https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51\n- https://datavizcatalogue.com/methods/density_plot.html\n- https://towardsdatascience.com/data-preprocessing-concepts-fa946d11c825\n- https://www.kaggle.com/startupsci/titanic-data-science-solutions\n- https://towardsdatascience.com/feature-selection-techniques-in-machine-learn\n- https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html\n- https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761\n- https://www.datasciencecentral.com/profiles/blogs/introduction-to-classification-regression-trees-cart\n\n- https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\n- https://towardsdatascience.com/boosting-algorithm-adaboost-b6737a9ee60c\n- https://www.analyticsvidhya.com/blog/2020/02/4-boosting-algorithms-machine-learning/\n- https://www.toptal.com/machine-learning/ensemble-methods-machine-learning\n- https://machinelearningmastery.com/data-visualization-methods-in-python/"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/Reading Material/Pandas Cookbook/Pandas-Cookbook b/Reading Material/Pandas Cookbook/Pandas-Cookbook new file mode 160000 index 0000000..b45d95c --- /dev/null +++ b/Reading Material/Pandas Cookbook/Pandas-Cookbook @@ -0,0 +1 @@ +Subproject commit b45d95ccef24b020fdf28ecb5a07e3348bee62f1