diff --git a/.gitignore b/.gitignore index 07ff8e9..7b69018 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,229 @@ -wk7/.ipynb_checkpoints/ -wk8/.ipynb_checkpoints/ +.ipynb_checkpoints/ +*~ +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.toc +*.fmt +*.fot +*.cb +*.cb2 + +## Intermediate documents: +*.dvi +*-converted-to.* +# these rules might exclude image files for figures etc. +# *.ps +# *.eps +# *.pdf + +## Generated if empty string is given at "Please type another file name for output:" +wk7/week7.pdf +wk8/week8.pdf +wk9/week9.pdf +wk10/week10.pdf + +## Waldo data for the mini_proj +mini_proj/waldo_data/* + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync + +## Auxiliary and intermediate files from other packages: +# algorithms +*.alg +*.loa + +# achemso +acs-*.bib + +# amsthm +*.thm + +# beamer +*.nav +*.pre +*.snm +*.vrb + +# changes +*.soc + +# cprotect +*.cpt + +# elsarticle (documentclass of Elsevier journals) +*.spl + +# endnotes +*.ent + +# fixme +*.lox + +# feynmf/feynmp +*.mf +*.mp +*.t[1-9] +*.t[1-9][0-9] +*.tfm + +#(r)(e)ledmac/(r)(e)ledpar +*.end +*.?end +*.[1-9] +*.[1-9][0-9] +*.[1-9][0-9][0-9] +*.[1-9]R +*.[1-9][0-9]R +*.[1-9][0-9][0-9]R +*.eledsec[1-9] +*.eledsec[1-9]R +*.eledsec[1-9][0-9] +*.eledsec[1-9][0-9]R +*.eledsec[1-9][0-9][0-9] +*.eledsec[1-9][0-9][0-9]R + +# glossaries +*.acn +*.acr +*.glg +*.glo +*.gls +*.glsdefs + +# gnuplottex +*-gnuplottex-* + +# gregoriotex +*.gaux +*.gtex + +# hyperref +*.brf + +# knitr +*-concordance.tex +# TODO Comment the next line if you want to keep your tikz graphics files +*.tikz +*-tikzDictionary + +# listings +*.lol + +# makeidx +*.idx +*.ilg +*.ind +*.ist + +# minitoc +*.maf +*.mlf +*.mlt +*.mtc[0-9]* +*.slf[0-9]* +*.slt[0-9]* +*.stc[0-9]* + +# minted +_minted* +*.pyg + +# morewrites +*.mw + +# nomencl +*.nlo + +# pax +*.pax + +# pdfpcnotes +*.pdfpc + +# sagetex +*.sagetex.sage +*.sagetex.py +*.sagetex.scmd + +# scrwfile +*.wrt + +# sympy +*.sout +*.sympy +sympy-plots-for-*.tex/ + +# pdfcomment +*.upa +*.upb + +# pythontex +*.pytxcode +pythontex-files-*/ + +# thmtools +*.loe + +# TikZ & PGF +*.dpth +*.md5 +*.auxlock + +# todonotes +*.tdo + +# easy-todo +*.lod + +# xindy +*.xdy + +# xypic precompiled matrices +*.xyc + +# endfloat +*.ttt +*.fff + +# Latexian +TSWLatexianTemp* + +## Editors: +# WinEdt +*.bak +*.sav + +# Texpad +.texpadtmp + +# Kile +*.backup + +# KBibTeX +*~[0-9]* + +# auto folder when using emacs and auctex +/auto/* + +# expex forward references with \gathertags +*-tags.tex diff --git a/mini_proj/Load_Images.py b/mini_proj/Load_Images.py new file mode 100644 index 0000000..58148f9 --- /dev/null +++ b/mini_proj/Load_Images.py @@ -0,0 +1,66 @@ +''' +Created by Tony Silvestre to prepare images for use from a Kaggle Where's Waldo dataset +''' +import os +import numpy as np +from matplotlib import pyplot as plt +import math +import cv2 + +def gen_data(w_path, n_w_path): + waldo_file_list = os.listdir(os.path.join(w_path)) + total_w = len(waldo_file_list) + not_waldo_file_list = os.listdir(os.path.join(n_w_path)) + total_nw = len(not_waldo_file_list) + imgs_raw = [] # Images + imgs_lbl = [] # Image labels + + #imgs_raw = np.array([np.array(imread(wdir + "waldo/"+fname)) for fname in os.listdir(wdir + "waldo")]) + i = 0 + for image_name in waldo_file_list: + pic = cv2.imread(os.path.join(w_path, image_name)) # NOTE: cv2.imread() returns a numpy array in BGR not RGB + imgs_raw.append(pic) + imgs_lbl.append(1) # Value of 1 as Waldo is present in the image + + print('Completed: {0}/{1} Waldo images'.format(i+1, total_w)) + i += 1 + + i = 0 + for image_name in not_waldo_file_list: + pic = cv2.imread(os.path.join(n_w_path, image_name)) + imgs_raw.append(pic) + imgs_lbl.append(0) + + print('Completed: {0}/{1} non-Waldo images'.format(i+1, total_nw)) + i += 1 + + # Calculate what 30% of each set is + third_of_w = math.floor(0.3*total_w) + third_of_nw = math.floor(0.3*total_nw) + + # Split data into training and test data (60%/30%) + train_data = np.append(imgs_raw[(third_of_w+1):total_w], imgs_raw[(total_w + third_of_nw + 1):len(imgs_raw)-1], axis=0) + train_lbl = np.append(imgs_lbl[(third_of_w+1):total_w], imgs_lbl[(total_w + third_of_nw + 1):len(imgs_lbl)-1], axis=0) + # If axis not given, both arrays are flattened before being appended + test_data = np.append(imgs_raw[0:third_of_w], imgs_raw[total_w:(total_w + third_of_nw)], axis=0) + test_lbl = np.append(imgs_lbl[0:third_of_w], imgs_lbl[total_w:(total_w + third_of_nw)], axis=0) + + try: + # Save the data as numpy files + np.save('Waldo_train_data.npy', train_data) + np.save('Waldo_train_lbl.npy', train_lbl) + np.save('Waldo_test_data.npy', test_data) + np.save('Waldo_test_lbl.npy', test_lbl) + print("All data saved") + except: + print("ERROR: Data may not be completely saved") + + +def __main__(): + # Paths to the Waldo images + waldo_path = 'waldo_data/64/waldo' + n_waldo_path = 'waldo_data/64/notwaldo' + + gen_data(waldo_path, n_waldo_path) + +__main__() \ No newline at end of file diff --git a/mini_proj/Waldo_test_data.npy b/mini_proj/Waldo_test_data.npy new file mode 100644 index 0000000..6e74546 Binary files /dev/null and b/mini_proj/Waldo_test_data.npy differ diff --git a/mini_proj/Waldo_test_lbl.npy b/mini_proj/Waldo_test_lbl.npy new file mode 100644 index 0000000..451e839 Binary files /dev/null and b/mini_proj/Waldo_test_lbl.npy differ diff --git a/mini_proj/Waldo_train_data.npy b/mini_proj/Waldo_train_data.npy new file mode 100644 index 0000000..11ab5bc Binary files /dev/null and b/mini_proj/Waldo_train_data.npy differ diff --git a/mini_proj/Waldo_train_lbl.npy b/mini_proj/Waldo_train_lbl.npy new file mode 100644 index 0000000..e3e1583 Binary files /dev/null and b/mini_proj/Waldo_train_lbl.npy differ diff --git a/mini_proj/waldo_model.py b/mini_proj/waldo_model.py new file mode 100644 index 0000000..6a53da2 --- /dev/null +++ b/mini_proj/waldo_model.py @@ -0,0 +1,128 @@ +import numpy as np +import sys +import time as t +''' +from keras.models import Sequential +from keras.layers import Dense, Dropout, Activation, Flatten, Reshape, Merge, Permute +from keras.layers import Deconvolution2D, Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D +from keras.layers import Input +from keras.layers.normalization import BatchNormalization +from keras.utils import np_utils +''' +from keras import backend as K +K.set_image_dim_ordering('th') +np.random.seed(7) + +''' +Model definition +''' +def FCN(): + ## sample structure defined below + # inputs = Input((1, w, h)) + + # conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs) + # conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1) + # m_pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) + + # conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(m_pool1) + # drop1 = Dropout(0.2)(conv2) + # conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(drop1) + # m_pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) + + # conv7 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(m_pool6) + # conv7 = Convolution2D(1, 3, 3, activation='relu', border_mode='same')(conv7) + + # up8x = UpSampling2D(size=(2, 2))(conv16x) + # merge8x = merge([up8x, m_pool3], mode='concat', concat_axis=1) + # conv8x = Convolution2D(1, 1, 1, activation='relu', border_mode='same')(merge8x) + + # up4x = UpSampling2D(size=(2, 2))(conv8x) + # merge4x = merge([up4x, m_pool2], mode='concat', concat_axis=1) + # conv4x = Convolution2D(1, 1, 1, activation='relu', border_mode='same')(merge4x) + + # up4x = UpSampling2D(size=(4, 4))(conv4x) + # model = Model(input=inputs, output=up4x) + # # Optimizer uses recommended Adadelta values + # model.compile(optimizer=Adadelta(lr=0.01), loss='categorical_crossentropy', metrics=['accuracy']) + return model + + +## Open data +im_train = np.load('Waldo_train_data.npy') +lbl_train = np.load('Waldo_test_lbl.npy') +im_test = np.load('Waldo_test_data.npy') +lbl_test = np.load('Waldo_test_lbl.npy') + +## Define model +model = FCN() + +## Define training parameters +epochs = 40 # an epoch is one forward pass and back propogation of all training data +batch_size = 5 +#lrate = 0.01 +#decay = lrate/epochs +# epoch - one forward pass and one backward pass of all training data +# batch size - number of training example used in one forward/backward pass +# (higher batch size uses more memory) +# learning rate - controls magnitude of weight changes in training the NN + +## Train model +# Purely superficial output +sys.stdout.write("\nFitting model") +sys.stdout.flush() +for i in range(0, 3): + t.sleep(0.8) + sys.stdout.write('.') + sys.stdout.flush() +print() + +# Outputs the model structure +for i in range(0, len(model.layers)): + print("Layer {}: {}".format(i, model.layers[i].output)) +print('-'*30) + +filepath = "checkpoint.hdf5" # Defines the model checkpoint file +checkpoint = ModelCheckpoint(filepath, verbose=1, save_best_only=False) # Defines the checkpoint process +callbacks_list = [checkpoint] # Adds the checkpoint process to the list of action performed during training +start = t.time() # Records time before training + +# Fits model based on initial parameters +model.fit(im_train, lbl_train, nb_epoch=epochs, batch_size=batch_size, + verbose=2, shuffle=True, callbacks=callbacks_list) +# If getting a value error here, output of network and corresponding lbl_train +# data probably don't match +end = t.time() # Records time after tranining + +print('Training Duration: {}'.format(end-start)) +print('-'*30) +print("*** Saving FCN model and weights ***") + +''' +# *To save model and weights seperately: +# save model as json file +model_json = model.to_json() +with open("UNet_model.json", "w") as json_file: + json_file.write(model_json) +# save weights as h5 file +model.save_weights("UNet_weights.h5") +print("\nModel weights and structure have been saved.\n") +''' +# Save model as one file +model.save('Waldo.h5') +print("\nModel weights and structure have been saved.\n") + +## Testing the model +# Load test data +im_test, lbl_test = Load_Images() +# Show data stats +print('*'*30) +print(im_test.shape) +print(lbl_test.shape) +print('*'*30) +start = t.time() +# Passes the dataset through the model +pred_lbl = model.predict(im_test, verbose=1, batch_size=batch_size) +end = t.time() +print("Images generated in {} seconds".format(end - start)) +np.save('Test/predicted_results.npy', pred_lbl) + diff --git a/wk10/week10.tex b/wk10/week10.tex new file mode 100644 index 0000000..1740bdb --- /dev/null +++ b/wk10/week10.tex @@ -0,0 +1,25 @@ +\documentclass[a4paper]{article} +% To compile PDF run: latexmk -pdf {filename}.tex + +% Math package +\usepackage{amsmath} +%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link +\usepackage[capitalise,nameinlink]{cleveref} +% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document +\usepackage{hyperref} +% UTF-8 encoding +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} %support umlauts in the input +% Easier compilation +\usepackage{bookmark} +\usepackage{natbib} +\usepackage{graphicx} + +\begin{document} + \title{Week 8 - Quantitative data analysis} + \author{Kelvin Davis \and Jip J. Dekker\and Tony Silvestere} + \maketitle + + + +\end{document} diff --git a/wk7/correlation.png b/wk7/correlation.png new file mode 100644 index 0000000..127e27d Binary files /dev/null and b/wk7/correlation.png differ diff --git a/wk7/handdistr.png b/wk7/handdistr.png new file mode 100644 index 0000000..26c7455 Binary files /dev/null and b/wk7/handdistr.png differ diff --git a/wk7/handdistr_gender.png b/wk7/handdistr_gender.png new file mode 100644 index 0000000..b2e124d Binary files /dev/null and b/wk7/handdistr_gender.png differ diff --git a/wk7/heightrank.png b/wk7/heightrank.png new file mode 100644 index 0000000..d4fc9c9 Binary files /dev/null and b/wk7/heightrank.png differ diff --git a/wk7/outlier.png b/wk7/outlier.png new file mode 100644 index 0000000..78f5994 Binary files /dev/null and b/wk7/outlier.png differ diff --git a/wk7/pointheight.png b/wk7/pointheight.png new file mode 100644 index 0000000..652bd9e Binary files /dev/null and b/wk7/pointheight.png differ diff --git a/wk7/week7.tex b/wk7/week7.tex new file mode 100644 index 0000000..44cb641 --- /dev/null +++ b/wk7/week7.tex @@ -0,0 +1,205 @@ +\documentclass[a4paper]{article} +% To compile PDF run: latexmk -pdf {filename}.tex +% Math package +\usepackage{amsmath} +%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link +\usepackage[capitalise,nameinlink]{cleveref} +% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document +\usepackage{hyperref} +% UTF-8 encoding +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} %support umlauts in the input +% Easier compilation +\usepackage{bookmark} +\usepackage{graphicx} + +\begin{document} + \title{Week 7 - Evidence and experiments} + \author{ + Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony + Silvestere + } + \maketitle + + \section{Introduction} \label{sec:introduction} + + In this report we have documented a series of hypothesis tests regarding provided data in high-ranking + Tennis players. The focus of these hypotheses concerns a player's handedness with regards to overall + ranking. We first provide an overview of how we address these notions, with visualisations and + descriptions of our overall methodology. Following this, we then provide a brief discussion of what we + can infer given our statistical analysis techniques. + + \section{Method} \label{sec:method} + + We are testing two hypotheses. The first hypothesis that we test is that tall players have an advantage + over smaller players. The second hypothesis that we test is that left-handed players have an advantage + over right-handed players. To build an intuition of how the data behaves with respect to the hypotheses + we are testing, we created visual representations using tools from the Matplotlib, and Seaborn libraries + and then we perform statistical tests to measure these effects. + + \subsection{Visualisation} \label{subsec:visualisation} + + \subsubsection{Effect of Height} \label{subsubsec:vheight} + + We started by performing a scatter plot of points earned by players with respect to their heights, to which we were surprised to find a player recorded to approximately 18m tall. This, we found to be somewhat contradictory to the currently held record of 2.72m. Removing this outlier, we can see a sufficient spread in height, points and ranking. We can also see slight discrepancy in height between males and females, and because of this, we perform separate statistical tests on males and females as to remove the effect of the gender. We plot both points with respect to height and height with respect to ranking. + + The plot of height with respect to ranking does not show an explicit relationship between the two variables, however we aim to test this relation in the Results Section. + + \subsubsection{Effect of Handedness} \label{subsubsec:vhand} + + We use distribution plots from Seaborn to visualise the distribution of points earned by left-handed and right-handed players overlapped on the same plot. The visualisation uses a kernel density estimate of the probability density function derived from the sample provided. We also plot separate distributions for male and female players in case there are any noticeable differences between genders. + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{correlation.png} + \caption{Correlation matrix of the numerical values in the dataset} + \end{figure} + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{outlier.png} + \caption{Scatter plot of points against height with an outlier} + \end{figure} + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{pointheight.png} + \caption{Scatter plot of points against height with the outlier removed} + \end{figure} + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{heightrank.png} + \caption{Scatter plot of height against rank} + \end{figure} + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{handdistr.png} + \caption{Distribution plots of points separated by handedness} + \end{figure} + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{handdistr_gender.png} + \caption{Distribution plots of points separated by handedness for males and distribution plots of points separated by handedness for females} + \end{figure} + + \subsection{Statistical Tests} \label{subsec:stattests} + + In testing the first hypothesis, we perform T-tests to analyse the effect of height on the points earned by players. Two T-tests are performed; one for each gender. Each gender of players are separated into two groups; a group of players that scored above the mean number of points and a group of players that scored below the mean number of points and these groups are compared in the T-tests. Later we perform a $\chi^2$ test on the groups together. + + To test the second hypothesis, we use a T-test to measure the effect of handedness and a $\chi^2$ test to measure the difference between the expected values and the observed values and garner a probability that the sample belongs to the $\chi^2$ distribution. + + \section{Results} \label{sec:results} + + We investigate both the advantage of height and the advantage of being + left-handed using a $\chi^2$ test and a T-test. For every test we will state + the exact hypothesis and the null-hypothesis. + + \subsection{The advantage of height} + + \textbf{$\chi^2$-test:} To test if there is an advantage of being tall we ran + a $\chi^2$ with the following hypotheses:\\ + $H$: Players that are taller have a higher rank \\ + $H_0$: The rank of a player is independent of their height \\ +\\ + To perform the test the players are groups into groups dependant on their + rank and if they are taller than the mean height for their gender. The + expected data is computed using the chances of being taller than the mean, and + the chance of being in the group of rankings. The data used is found in table + 1. + + \begin{table}[ht] + \centering + \begin{tabular}{|l|r|r|r|r|} + \hline + & \textbf{M: 168 - 188} & \textbf{M: 189 - 210} & \textbf{F: 155 - 171} & \textbf{F: 172 - 189} \\ \hline + \textbf{1 - 99} & 67 / 73 & 32 / 26 & 38 / 42 & 60 / 55 \\ + \textbf{100 - 199} & 69 / 72 & 30 / 26 & 31 / 27 & 32 / 36 \\ + \textbf{200 - 299} & 75 / 68 & 17 / 25 & 18 / 17 & 22 / 23 \\ + \textbf{300 - 399} & 61 / 60 & 21 / 23 & 11 /12 & 17 / 16 \\ + \textbf{400 - 499} & 59 / 60 & 22 / 22 & 7 / 6 + & 7 / 8 \\ + \hline + \end{tabular} + \label{tab:chiheight} + \caption{Observed / Expected values used for the $\chi^2$-test. The groups are divided by their rank (vertical) and, per gender, their height (horizontal).} + \end{table} + + The $\chi^2$ value found is approximately $7.697606186049128$. With 12 degrees + of freedom our $p$-value will be $0.8082925814979871$ + + \textbf{T-test:} A slightly different hypothesis can be tested using a T-Test: + \\ + $H$: Players that are taller have significantly more point \\ + $H_0$: The points a player has is independent of their height \\ + + We ran this T-test twice, once for the women and once for the men, by + splitting the groups of players into two: one being taller than the mean + height, one being shorter than the mean height. Our T-test for the men + revealed a T-value of 1.711723, this has a p-value of 0.043815. For the women + the T-value found was 1.860241, which has a p-value of 0.032030. + + \subsection{The advantage of left-handedness} + + \textbf{$\chi^2$-test:} To test if there is an advantage of being left-handed + we ran a $\chi^2$ with the following hypotheses:\\ + $H$: Players that are left-handed have a higher rank \\ + $H_0$: The rank of a player is independent their preferred hand \\ +\\ + To perform the test the players are groups into groups dependant on their rank + and if they play with their left hand. The expected data is computed using the + chances of being left-handed. The data used is found in table + 2. + + \begin{table}[ht] + \centering + \label{tab:chihand} + \begin{tabular}{|l|l|l|l|l|l|} + \hline + & \textbf{1 - 99} & \textbf{100 - 199} & \textbf{200 - 299} & \textbf{300 - 399} & \textbf{400 - 499} \\ + \hline + \textbf{L} & 22 / 21 & 23 / 18 & 17 / 15 & 6 / 12 & 8 / 10 \\ + \textbf{R} & 174 / 177 & 139 / 144 & 117 / 119 & + 105 / 98 & 88 / 86 \\ + \hline + \end{tabular} + \caption{Observed / Expected values used for the $\chi^2$-test. The groups are divided by which hand they use (vertical) and their rank (horizontal).} + \end{table} + + The $\chi^2$ value found is approximately $6.467312944404331$. With 4 degrees + of freedom our $p$-value will be $0.1668616190847413$ + + \textbf{T-test:} A slightly different hypothesis can be tested using a T-Test: + \\ + $H$: Players that are left-handed have significantly more point \\ + $H_0$: The points a player has is independent of their preferred hand \\ + + We ran this T-test by splitting the groups of players into two depending on + their preferred hand. Our T-test revealed a T-value of 0.451694, + this has a p-value of 0.325815. + + + \section{Discussion} \label{sec:discussion} + + In our investigation we did not find any strong correlation between the + ranking of a player (or their number of points) and with which hand they + played or how tall they are. Most tests failed to pass the required p value of + $<0.05$. The only tests that did give us positive results are the T-test that + were conducted on the correlation between height and the number of points. + However, without the $\chi^2$-test confirming the correlation, the existence + of the correlation is questionable. + + These results might not be so surprising when the visual exploration is taken + into account. Only slight deviations are visible in our graphs, so the test + mainly confirmed our suspicion that no definitive correlation exists between + the different attributes. + +\end{document} diff --git a/wk7/wk7.ipynb b/wk7/wk7.ipynb index 69d7eea..554046f 100644 --- a/wk7/wk7.ipynb +++ b/wk7/wk7.ipynb @@ -27,6 +27,7 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "from scipy import stats\n", "from matplotlib import colors\n", "import seaborn as sns\n", @@ -43,106 +44,106 @@ "data": { "text/html": [ " \n", - " \n", + "
\n", " \n", " \n", " \n", @@ -154,73 +155,73 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", "
rankingheight
ranking1-0.165935-0.586707-0.2440730.17403-0.08260930.0196139ranking1-0.165935-0.586707-0.2440730.17403-0.08260930.0196139
age-0.16593510.121731-0.140033-0.9942960.157223-0.0282972age-0.16593510.121731-0.140033-0.9942960.157223-0.0282972
points-0.5867070.1217311-0.004905-0.1299710.159385-0.0153843points-0.5867070.1217311-0.004905-0.1299710.159385-0.0153843
tournplayed-0.244073-0.140033-0.00490510.13293-0.139194-0.0712482tournplayed-0.244073-0.140033-0.00490510.13293-0.139194-0.0712482
born0.17403-0.994296-0.1299710.132931-0.1636770.0333731born0.17403-0.994296-0.1299710.132931-0.1636770.0333731
weight-0.08260930.1572230.159385-0.139194-0.16367710.757689weight-0.08260930.1572230.159385-0.139194-0.16367710.757689
height0.0196139-0.0282972-0.0153843-0.07124820.03337310.7576891height0.0196139-0.0282972-0.0153843-0.07124820.03337310.7576891
" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -259,9 +260,9 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ]" ] }, "execution_count": 3, @@ -272,7 +273,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -303,7 +304,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -314,7 +315,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -346,7 +347,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -397,7 +398,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -413,15 +414,6 @@ "plt.title(\"Scatter plots of Points vs Height\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Oh man, thats some serious height.*\n", - "\n", - "Lets try again, but removing the outlier." - ] - }, { "cell_type": "code", "execution_count": 8, @@ -441,7 +433,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -468,7 +460,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Scatter plots of Ranking vs Height')" + "Text(0.5,1,'Scatter plots of Height vs Ranking')" ] }, "execution_count": 9, @@ -477,9 +469,9 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -487,12 +479,12 @@ } ], "source": [ - "plot(\"height\", \"ranking\", data=data_male, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Male\")\n", - "plot(\"height\", \"ranking\", data=data_female_clean, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Female\")\n", - "xlabel(\"height\")\n", - "ylabel(\"ranking\")\n", + "plot(\"ranking\", \"height\", data=data_male, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Male\")\n", + "plot(\"ranking\", \"height\", data=data_female_clean, linestyle='', marker='o', markersize=3, alpha=0.3, label=\"Female\")\n", + "xlabel(\"ranking\")\n", + "ylabel(\"height\")\n", "legend()\n", - "plt.title(\"Scatter plots of Ranking vs Height\")" + "plt.title(\"Scatter plots of Height vs Ranking\")" ] }, { @@ -574,7 +566,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -584,7 +576,7 @@ "source": [ "sns.distplot(data_right[\"points\"], kde=True, hist=False, label=\"Right\")\n", "sns.distplot(data_left[\"points\"], kde=True, hist=False, label=\"Left\")\n", - "title(\"P(points)\")\n" + "title(\"P(points)\")" ] }, { @@ -618,7 +610,7 @@ "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -642,7 +634,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Height is an advantage" + "### Handedness exploration" ] }, { @@ -756,6 +748,297 @@ "print(\"T score: %f, P score: %f\" % (t, p))" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
M: 168 - 188M: 189 - 210F: 155 - 171F: 172 - 189
1 - 9967323860
100 - 19969303132
200 - 29975171822
300 - 39961211117
400 - 499592277
\n", + "
" + ], + "text/plain": [ + " M: 168 - 188 M: 189 - 210 F: 155 - 171 F: 172 - 189\n", + "1 - 99 67 32 38 60\n", + "100 - 199 69 30 31 32\n", + "200 - 299 75 17 18 22\n", + "300 - 399 61 21 11 17\n", + "400 - 499 59 22 7 7" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate observations\n", + "minR = data[\"ranking\"].min()\n", + "maxR = data[\"ranking\"].max()\n", + "minH = {}\n", + "maxH = {}\n", + "minH['M'] = data_male[\"height\"].min()\n", + "maxH['M'] = data_male[\"height\"].max()\n", + "minH['F'] = data_female_clean[\"height\"].min()\n", + "maxH['F'] = data_female_clean[\"height\"].max()\n", + "rank_groups = 5\n", + "height_groups = 2\n", + "genders = [\"M\", \"F\"]\n", + "incr_rank = (maxR - minR)/rank_groups\n", + "incr_height = {'M': (maxH['M'] - minH['M'])/height_groups, 'F': (maxH['F'] - minH['F'])/height_groups }\n", + "\n", + "obs_data = []\n", + "for i in range(rank_groups):\n", + " row = []\n", + " ranklb = minR+i*incr_rank\n", + " rankub = minR+(i+1)*incr_rank\n", + " for g in genders:\n", + " if g == 'M':\n", + " cur = data_male\n", + " else:\n", + " cur = data_female_clean\n", + " for j in range(height_groups):\n", + " heightlb = minH[g]+j*incr_height[g]\n", + " heightub = minH[g]+(j+1)*incr_height[g]\n", + " row.append(\n", + " len( cur.loc[np.logical_and(\n", + " np.logical_and(cur[\"ranking\"] >= ranklb, cur[\"ranking\"] < rankub),\n", + " np.logical_and(cur[\"height\"] >= heightlb, cur[\"height\"] < heightub)\n", + " )] )\n", + " )\n", + " \n", + " obs_data.append(row)\n", + " \n", + " \n", + "observations = pd.DataFrame(\n", + " data = obs_data,\n", + " columns=[ \"M: %d - %d\" % (minH['M']+j*incr_height['M'], minH['M']+(j+1)*incr_height['M']-1) for j in range(height_groups)]\n", + " + [ \"F: %d - %d\" % (minH['F']+j*incr_height['F'], minH['F']+(j+1)*incr_height['F']-1) for j in range(height_groups)],\n", + " index=[ \"%d - %d\" % (minR + i*incr_rank, minR + (i+1)*incr_rank -1) for i in range(rank_groups)]\n", + ")\n", + "observations" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
M: 168 - 188M: 189 - 210F: 155 - 171F: 172 - 189
1 - 9972.80701826.75438642.17213155.426230
100 - 19972.07894726.48684227.11065635.631148
200 - 29967.71052624.88157917.21311522.622951
300 - 39959.70175421.93859612.04918015.836066
400 - 49958.97368421.6710536.4549188.483607
\n", + "
" + ], + "text/plain": [ + " M: 168 - 188 M: 189 - 210 F: 155 - 171 F: 172 - 189\n", + "1 - 99 72.807018 26.754386 42.172131 55.426230\n", + "100 - 199 72.078947 26.486842 27.110656 35.631148\n", + "200 - 299 67.710526 24.881579 17.213115 22.622951\n", + "300 - 399 59.701754 21.938596 12.049180 15.836066\n", + "400 - 499 58.973684 21.671053 6.454918 8.483607" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp_data = []\n", + "for i in range(rank_groups):\n", + " row = []\n", + " ranklb = minR+i*incr_rank\n", + " rankub = minR+(i+1)*incr_rank\n", + " for g in genders:\n", + " if g == 'M':\n", + " cur = data_male\n", + " else:\n", + " cur = data_female_clean\n", + " gnr = len ( cur )\n", + " for j in range(height_groups):\n", + " heightlb = minH[g]+j*incr_height[g]\n", + " heightub = minH[g]+(j+1)*incr_height[g]\n", + " rank_group = len( cur.loc[np.logical_and(cur[\"ranking\"] >= ranklb, cur[\"ranking\"] < rankub)] )\n", + " height_group = len( cur.loc[np.logical_and(cur[\"height\"] >= heightlb, cur[\"height\"] < heightub)] )\n", + " row.append( gnr * (rank_group/gnr) * (height_group/gnr) )\n", + " \n", + " exp_data.append(row)\n", + "\n", + "expected = pd.DataFrame(\n", + " data = exp_data,\n", + " columns=[ \"M: %d - %d\" % (minH['M']+j*incr_height['M'], minH['M']+(j+1)*incr_height['M']-1) for j in range(height_groups)]\n", + " + [ \"F: %d - %d\" % (minH['F']+j*incr_height['F'], minH['F']+(j+1)*incr_height['F']-1) for j in range(height_groups)],\n", + " index=[ \"%d - %d\" % (minR + i*incr_rank, minR + (i+1)*incr_rank -1) for i in range(rank_groups)]\n", + ")\n", + "expected" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.697606186049128" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stat_height = stats.chisquare([item for sublist in observations.values for item in sublist], [item for sublist in expected.values for item in sublist]).statistic\n", + "stat_height" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8082925814979871" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.distributions.chi2.sf(stat_height, 12)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -767,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -798,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -817,6 +1100,207 @@ "print(\"T score: %f, P score: %f\" % (t, p))" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1 - 99100 - 199200 - 299300 - 399400 - 499
L22231768
R17413911710588
\n", + "
" + ], + "text/plain": [ + " 1 - 99 100 - 199 200 - 299 300 - 399 400 - 499\n", + "L 22 23 17 6 8\n", + "R 174 139 117 105 88" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate observations\n", + "minR = data[\"ranking\"].min()\n", + "maxR = data[\"ranking\"].max()\n", + "groups = 5\n", + "incr = (maxR - minR)/groups\n", + "observations = pd.DataFrame(\n", + " data = [\n", + " [ len( data.loc[np.logical_and(np.logical_and(data[\"hand\"] == \"L\", data[\"ranking\"] >= minR+i*incr), data[\"ranking\"] < minR+(i+1)*incr)] ) for i in range(groups) ],\n", + " [ len( data.loc[np.logical_and(np.logical_and(data[\"hand\"] == \"R\", data[\"ranking\"] >= i*incr), data[\"ranking\"] < (i+1)*incr)] ) for i in range(groups)]\n", + " ],\n", + " columns=[ \"%d - %d\" % (minR + i*incr, minR + (i+1)*incr -1) for i in range(groups)],\n", + " index=[\"L\", \"R\"]\n", + ")\n", + "observations" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
1 - 99100 - 199200 - 299300 - 399400 - 499
L21.46647617.56348114.52781711.9258210.407989
R176.533524144.436519119.47218398.0741885.592011
\n", + "
" + ], + "text/plain": [ + " 1 - 99 100 - 199 200 - 299 300 - 399 400 - 499\n", + "L 21.466476 17.563481 14.527817 11.92582 10.407989\n", + "R 176.533524 144.436519 119.472183 98.07418 85.592011" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate expected values\n", + "lh_prod = float(len(data.loc[data[\"hand\"] == \"L\"])) / len(data)\n", + "group_nrs = [ len(data.loc[np.logical_and(data[\"ranking\"] >= minR + i*incr, data[\"ranking\"] < minR + (i+1)*incr)]) for i in range(groups)]\n", + "expected = pd.DataFrame(\n", + " data = [[ lh_prod*group_nrs[i] for i in range(groups) ], [ (1-lh_prod)*group_nrs[i] for i in range(groups) ]],\n", + " columns=[ \"%d - %d\" % (minR + i*incr, minR + (i+1)*incr -1) for i in range(groups)],\n", + " index=[\"L\", \"R\"]\n", + ")\n", + "expected" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.467312944404331" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Chi Square test\n", + "stat_hand = stats.chisquare([item for sublist in observations.values for item in sublist], [item for sublist in expected.values for item in sublist]).statistic\n", + "stat_hand" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1668616190847413" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.distributions.chi2.sf(stat_hand, 4)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/wk8/A1_data.xlsx b/wk8/A1_data.xlsx new file mode 100644 index 0000000..e7392ed Binary files /dev/null and b/wk8/A1_data.xlsx differ diff --git a/wk8/Dong_etal_2018_data.xlsx b/wk8/Dong_etal_2018_data.xlsx deleted file mode 100644 index 32aec57..0000000 Binary files a/wk8/Dong_etal_2018_data.xlsx and /dev/null differ diff --git a/wk8/distr.png b/wk8/distr.png new file mode 100644 index 0000000..3e5edf5 Binary files /dev/null and b/wk8/distr.png differ diff --git a/wk8/effect.png b/wk8/effect.png new file mode 100644 index 0000000..c9a5ff0 Binary files /dev/null and b/wk8/effect.png differ diff --git a/wk8/references.bib b/wk8/references.bib new file mode 100644 index 0000000..a429322 --- /dev/null +++ b/wk8/references.bib @@ -0,0 +1,10 @@ +@article{dong2018methods, + title={Methods for quantifying effects of social unrest using credit card transaction data}, + author={Dong, Xiaowen and Meyer, Joachim and Shmueli, Erez and Bozkaya, Bur{\c{c}}in and Pentland, Alex}, + journal={EPJ Data Science}, + volume={7}, + number={1}, + pages={8}, + year={2018}, + publisher={Springer} +} diff --git a/wk8/week8.tex b/wk8/week8.tex new file mode 100644 index 0000000..58350c9 --- /dev/null +++ b/wk8/week8.tex @@ -0,0 +1,187 @@ +\documentclass[a4paper]{article} +% To compile PDF run: latexmk -pdf {filename}.tex + +% Math package +\usepackage{amsmath} +%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link +\usepackage[capitalise,nameinlink]{cleveref} +% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document +\usepackage{hyperref} +% UTF-8 encoding +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} %support umlauts in the input +% Easier compilation +\usepackage{bookmark} +\usepackage{natbib} +\usepackage{graphicx} + +\begin{document} + \title{Week 8 - Quantitative data analysis} + \author{ + Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony + Silvestere + } + \maketitle + + \section{Introduction} \label{sec:introduction} + + The purpose of this report is to re-analyse the data presented in the paper by + \cite{dong2018methods}, which investigates the effect that protests (as an + example of disruptive social behaviours in general) have on consumer + behaviours. \cite{dong2018methods} hypothesise that protests decrease + consumer behaviour in the surrounding area of the event, and suggest that + consumer spending could be used as an additional non-traditional economic + indicator and as a gauge of consumer sentiment. Consumer spending was analysed + using credit card transaction data from a metropolitan area within a country + that is part of The Organisation for Economic Co-operation and Development + (OECD). Although \cite{dong2018methods} investigate temporal and spatial + effects on consumer spending, for the purposes of this analysis, only the + spatial effect of variables (with relation to the geographical distance from + the event) is considered. + + \section{Method} \label{sec:method} + + The dataset consists of variables measured as a function of the distance + from the event (in km), including: the number of customers, the median + spending amount, the number of transactions, and the total sales amount. + The re-analysis is conducted on the data provided in the + paper\cite{dong2018methods}, using Python in conjunction with packages such as + pandas, matplotlib, numpy and seaborn, to process and visualise the data. As + aforementioned, only spatial data and the variables mentioned above are + considered, for the reference days and the change occuring Day 62 (day of + first socially disruptive event). The distribution of the difference between + the reference period and Day 62 is visualised by plotting a histogram for each + variable. Since the decrease of each the variables from the reference period + to Day 62 is provided, the mean and the median of these distributions can be + used to perform a one-sample (as we have are given the difference) hypothesis + test to assess whether the protests on Day 62 had a discernable effect. + + Assuming the mean of each variable over the reference period is the midpoint + between their respective maximum and minimum values, we can reconstruct + approximate actual values for Day 62 (given the decrease in value on Day 62 + from the reference period). By comparing these value to the range over the + reference period, another assessment can be made to determine whether the data + presents a discernible effect on consumer spending as a result of social + discuption, scaling with distance. + + Although time series data was not explicitely provided, by extrapolating + information from a graph in \cite{dong2018methods} we can quantify the decrease + in number of customers and median spending on Day 62 using information about the + reference days (from 43 to 61). After collecting the values for each of the + reference days (43-61), the mean and standard deviation of this sample can be + calculated. Assuming a normal distribution of the data, we can calculate a + z-score for each observation on Day 62, and use this to assess the original + hypothesis. + + By performing each of the above test, a re-analysis will be conducted on + \cite{dong2018methods}'s paper hypothesising that consumer spending decreases + as a result of social events such as protests. In the Results section, we will + perform the statistical analyses described above. The results of these tests + will then be explored in the Discussion section, along with assumptions and + limitations of the tests and what can be conclused from them. + + \section{Results} \label{sec:results} + + For each of the variables in the given data (number of customers, median + spending amount, number of transactions, and sales totals) we construct a + histogram of the decrease of each (on Day 62). We then compute the mean and + median of the data so we can proceed to perform a one-sample hypothesis test. + + \begin{figure}[ht] + \centering + \label{fig:distr} + \includegraphics[width=\textwidth]{distr.png} + \caption{Distribution of each of the variables recorded in the data, as a function of the distance from an event} + \end{figure} + + Using a mean/median of the reference period, obtained by taking the midpoint of the minimum and maximum values over for each distance measure, a value can be reconstructed for the measurement on Day 62 (for each location) using: + + \begin{equation} + \textrm{value} = \frac{\textrm{min} + \text{max}}{2} - \textrm{decrease.} + \tag{1} + \end{equation} +\\ + We can then plot the maximum and minimum values for the reference period, as well as the reconstructed Day 62 variables to observe the behaviour of consumer spending after the event. + + \begin{figure}[ht] + \centering + \label{fig:effect} + \includegraphics[width=\textwidth]{effect.png} + \caption{The reconstructed values for Day 62 of each variable plotted against their respective minimums and maximums over the reference period} + \end{figure} + + Using the data recorded, for each of the three distance recorded, the mean and standard deviation of the reference period can be calculated. The z-score for each observed value on Day 62 can be computed using: + + \begin{equation} + \textrm{Z} = \frac{\textrm{X} - \mu}{\sigma}, + \tag{2} + \end{equation} +\\ + where X is the observed value, $\mu$ and $\sigma$ are the mean and standard deviation (respectively) of the reference period. + + \begin{table}[ht] + \centering + \label{my-label} + \begin{tabular}{|l|l|r|r|} + \hline + \textbf{Variable} & \textbf{Distance} & \textbf{X} & \textbf{Z} \\ + \hline + \textbf{Customers} & \textless 2km & -0.600 & 6.87798 \\ + \textbf{Customers} & 2km - 4km & -0.200 & -3.33253 \\ + \textbf{Customers} & \textgreater 4km & -0.100 & -3.70740 \\ + \textbf{Median Spending} & \textless 2km & -0.200 & -3.05849 \\ + \textbf{Median Spending} & 2km - 4km & -0.100 & -1.46508 \\ + \textbf{Median Spending} & \textgreater 4km & -0.035 & -1.99199 \\ + \hline + \end{tabular} + \caption{The $Z$ score computed using equation 2 and the temporal data} + \end{table} + + \section{Discussion} \label{sec:discussion} + + As shown in each of the subplots of Figure 1, the mean and median values of + the decrease in each of the distributions are greater than zero (note: higher + values of the decrease variable indicate a larger decrease/negative change). + These mean and median values can be used to perform a one-sample hypothesis + tests, which finds that since each of the mean/median values is greater than + zero, we can infer that the event had a net decreasing affect on the number of + customers, median spending amount, number of transactions, and total sales + amount. + + In Figure \ref{fig:effect} values were approximated for each variable on Day + 62, using Equation 1, and plotted against the minimum and maximum values of + the respective variables. This allows us to visually assess whether the + reconstructed value for Day 62 lies outside the range of recorded values for + the reference period, and presents uncharacteristic behaviour. A decrease is + evident in each of the variables after the event has occurred (on Day 62) + within a distance of approximately 2 km, and appears to stabilise thereafter. + This provides support to \cite{dong2018methods}'s hypothesis that consumer + spending is affected by socially disruptive events, and also provides evidence + to the notion of spatial scaling of this effect (based on the event location). + It is important to note that the approximation used in this technique is + subject to a level of error due to the ideal calculation of the mean/median of + the reference data as the midpoint between the minimum and maximum values + provided. + + Extrapolating data from a graph in \cite{dong2018methods} provided time series + data (divided into three radius') to analyse. This data was collected by + visually estimating the values from the graph which will inherently introduce + a source of error. However, by computing the z-score as described in Equation + 2, the table provided in Figure 3 was constructed. Each of the z-score values + in the table are negative, indicating a decrease in both the number of + customers and median spending on Day 62. The much larger magnitude of z-scores + for the <2km distance ring for both variables is in agreement with earlier + discussion, strengthening the hypothesis of the spatial correlation of + consumer spending. + + Each of the above tests have agreed on the spatial and temporal correlation of + consumer spending and socially disruptive events. With the limited data + available, we can therefore concur with the hypothesis of Dong et al. that + consumer spending decreases in the area around disruptive social behaviour, + after finding the temporal correlation on Day 62, as well as the spatially + decreasing effect further from the event. + + \bibliographystyle{humannat} + \bibliography{references} + +\end{document} diff --git a/wk8/wk8.ipynb b/wk8/wk8.ipynb index d539460..16578a3 100644 --- a/wk8/wk8.ipynb +++ b/wk8/wk8.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -27,371 +27,69 @@ "import pandas as pd\n", "from pandas import ExcelWriter\n", "from pandas import ExcelFile\n", + "import seaborn as sns\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from scipy import stats" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We start the analysis by importing the data. Note, we are only importing the data provided in Table 1 of the Appendix of Dong et al.1 as we are only analysing data from the references days and Day 62.\n", - "\n", - "**\\*\\* Double Check this with Murray \\*\\***\n", - "\n", - "TO DO:\n", - "\\*\\* Need to test the statistical significance of the data" + "from scipy import stats\n", + "from tabulate import tabulate" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Contains data about the number of customers during the recorded time period\n", - "cust_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='# of Customers')\n", - "# Contains data about the customers' median spending amounts during the recorded time period\n", - "spend_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='Median spending amount')\n", - "# Contains data about the number of transactions during the recorded time period\n", - "trans_data = pd.read_excel('edited_Dong_etal_2018_data.xlsx', sheetname='# of transactions')" + "# Contains the data from the paper being analysed\n", + "data = pd.read_excel('A1_data.xlsx')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Exploring the correlation between each variable regarding the number of customers:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - "\n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Distance from event center (km)\n", - " \n", - " \n", - " \n", - " \n", - " Min of ref. Days\n", - " \n", - " \n", - " \n", - " \n", - " Max of ref. Days\n", - " \n", - " \n", - " \n", - " \n", - " Decrease on Day 62\n", - " \n", - " \n", - "
\n", - " Distance from event center (km)\n", - " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", - " \n", - " \n", - " \n", - " -0.104681\n", - " \n", - " \n", - " \n", - " \n", - " -0.101477\n", - " \n", - " \n", - " \n", - " \n", - " -0.375261\n", - " \n", - " \n", - "
\n", - " Min of ref. Days\n", - " \n", - " \n", - " \n", - " \n", - " -0.104681\n", - " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", - " \n", - " \n", - " \n", - " 0.990716\n", - " \n", - " \n", - " \n", - " \n", - " 0.855398\n", - " \n", - " \n", - "
\n", - " Max of ref. Days\n", - " \n", - " \n", - " \n", - " \n", - " -0.101477\n", - " \n", - " \n", - " \n", - " \n", - " 0.990716\n", - " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", - " \n", - " \n", - " \n", - " 0.869914\n", - " \n", - " \n", - "
\n", - " Decrease on Day 62\n", - " \n", - " \n", - " \n", - " \n", - " -0.375261\n", - " \n", - " \n", - " \n", - " \n", - " 0.855398\n", - " \n", - " \n", - " \n", - " \n", - " 0.869914\n", - " \n", - " \n", - " \n", - " \n", - " 1\n", - " \n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cust_data.corr().style.background_gradient(cmap='Wistia')" + "## Introduction and Method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now to plot some data..." + "The purpose of this report is to re-analyse the data presented in the paper by Dong et al.1, which investigates the effect that protests (as an example of disruptive social behaviours in general) have on consumer behaviours. Dong et al.1 hypothesise that protests decrease consumer behaviour in the surrounding area of the event, and suggest that consumer spending could be used as an additional non-traditional economic indicator and as a gauge of consumer sentiment. Consumer spending was analysed using credit card transaction data from a metropolitan area within a country that is part of The Organisation for Economic Co-operation and Development (OECD). Although Dong et al.1 investigate temporal and spatial effects on consumer spending, for the purposes of this analysis, only the spatial effect of variables (with relation to the geographical distance from the event) is considered. The dataset consists of variables measured as a function of the distance from the event (in km), including: the number of customers, the median spending amount, the number of transactions, and the total sales amount.\n", + "\n", + "The re-analysis is conducted on the data provided in the paper1, using Python in conjunction with packages such as pandas, matplotlib, numpy and seaborn, to process and visualise the data. As aformentioned, only spatial data and the variables mentioned above are considered, for the reference days and the change occuring Day 62 (day of first socially disruptive event). The distribution of the difference between the reference period and Day 62 is visualised by plotting a histogram for each variable. Since the decrease of each the variables from the reference period to Day 62 is provided, the mean and the median of these distributions can be used to perform a one-sample (as we have are given the difference) hypothesis test to assess whether the protests on Day 62 had a discernable effect.\n", + "\n", + "Assuming the mean of each variable over the reference period is the midpoint between their respective maximum and minimum values, we can reconstruct approximate actual values for Day 62 (given the decrease in value on Day 62 from the reference period). By comparing these value to the range over the reference period, another assessment can be made to determine whether the data presents a discernible effect on consumer spending as a result of social discuption, scaling with distance.\n", + "\n", + "Although time series data was not explicitely provided, by extrapolating information from a graph in Dong et al.1 we can quantify the decrease in number of customers and median spending on Day 62 using information about the reference days (from 43 to 61). After collecting the values for each of the reference days (43-61), the mean and standard deviation of this sample can be calculated. Assuming a normal distribution of the data, we can calculate a z-score for each observation on Day 62, and use this to assess the original hypothesis.\n", + "\n", + "By performing each of the above test, a re-analysis will be conducted on Dong et al.1's paper hypothesising that consumer spending decreases as a result of social events such as protests. In the Results section, we will perform the statistical analyses described above. The results of these tests will then be explored in the Discussion section, along with assumptions and limitations of the tests and what can be conclused from them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each of the variables in the given data (number of customers, median spending amount, number of transactions, and sales totals) we construct a histogram of the decrease of each (on Day 62). We then compute the mean and median of the data so we can proceed to perform a one-sample hypothesis test." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAJqCAYAAAAPGAfIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4FFW6+PHvmwVC2JeAQFjCIpCwRAiLgAgioIjguCK4XNAZGERnRkfHcdyvXsf7u+OMK4gLKrijjDigCDosoiKrGJZhDZCwJpAAIYGEvL8/qtJ2QkI6oTud5f08Tz/prjp16q3urtMnVWcRVcUYY4wxxhhzfkKCHYAxxhhjjDFVgVWsjTHGGGOM8QOrWBtjjDHGGOMHVrE2xhhjjDHGD6xibYwxxhhjjB9YxdoYY4wxxhg/sIq1KZGITBeRR/yUV2sROSEioe7rJSJypz/ydvP7QkRu91d+pdjvUyKSKiIHynvfxpjKzcpYn/YbtDJWRJJE5HL3+UMi8np5x1BeqtOxBkpYsAMwwSUiSUAzIBc4A2wC3gFmqGoegKpOLkVed6rq4uLSqOoeoM75Re3Z3+NAB1W9xSv/K/2RdynjaA3cB7RR1UPltM8lwGxVtULPmArMyli/xFHuZWxxVPV/grn/8lSdjtWf7Iq1AbhaVesCbYC/An8C3vD3TkSkqv4j1xpIC3aBHyz5V8aMMcWyMvb8VOsy1lQuVrE2HqqaoarzgJuA20WkK4CIvCUiT7nPm4jIv0QkXUSOiMhyEQkRkVk4hd/n7m3IB0SkrYioiNwhInuAb7yWef8AtBeRH0XkmIh8JiKN3H0NFpFk7xjzb1OJyBXAQ8BN7v5+ctd7bnu6cT0sIrtF5JCIvCMi9d11+XHcLiJ73FuMfynuvRGR+u72h938HnbzvxxYBLRw43irmO3HiMh69xh3uPEXuO3mvn5cRGa7zyNEZLaIpLnv9yoRaSYiTwOXAC+5+3zJTd/fTZPh/u3vle8S91bqd+42n4tIYxF5141plYi09UrfWUQWuZ/xf0TkRq91b4nINBFZICKZwBARGSkim0TkuIikiMgfi3svjamurIwNTBmbfxzue3JIRPaLyDVuubTVfR8f8kofIiIPumVxmoh8lP+euOtvdWNIKxyzdxntvv5YRA645e4yEYnzWveWiLwsIvPdsnGliLQv5viLLO+93vNnivoM3fX93LI9XUR+EpHBXuuWiMh/i8gKN4avRKRJaY+1pM9TRGqJyNsiclRENrufRYHvVnVhFWtzFlX9EUjGqbwVdp+7Lgrn9uZDziZ6K7AH58pMHVX9X69tLgW6ACOK2eVtwESgOc7t0hd8iPFL4H+AD9399Sgi2X+5jyFAO5zboy8VSjMQ6AQMBR4VkS7F7PJFoL6bz6VuzBPcW7JXAvvcOP6r8IYi0gfn1u/9QANgEJBU0jECt7v7bAU0BiYDWar6F2A5MNXd51S3kJ2P8941Bp4D5otIY6/8xgK3Ai2B9sD3wEygEbAZeMyNtzbOD9l7QFN3u1dEJNYrr3HA00Bd4Fucq2+T3KtyXYFvfDg+Y6olK2OLVOYy1nUBEIFTvj0KvAbcAvTCeZ8fEZEYN+3dwDXufloAR4GXAdxybhpOWdkCpzyNLmafAF8AHXHKyrXAu4XWjwWeABoC23HKzaIUWd57rS/yMxSRljhl/1M4ZfkfgU9EJMpr23HABDfGGm6ashwrFP95Pga0xfn8huG899WSVaxNcfbhnKSF5eCc2G1UNUdVl6uqlpDX46qaqapZxayfpaqJqpoJPALcKP5pXjAeeE5Vd6rqCeDPwNhCV3KeUNUsVf0J+Ak468fDjWUs8GdVPa6qScDfcAojX9wBvKmqi1Q1T1VTVHWLD9vl4BR0HVT1jKquUdVjxaS9CtimqrNUNVdV3we2AFd7pZmpqjtUNQPnx2CHqi5W1VzgY+AiN90oIElVZ7p5rQM+AW7wyuszVV3hHk+2G2usiNRT1aOquta3t8aYasvKWJcfylhw3renVTUH+ABoAjzv5rcRp217/r4nA39R1WRVPQU8Dlzvxn098C9VXeauewTIK26nqvqmu4/8fHrkX7V3zVXVH91y9l0g/hzxn6u8L+4zvAVYoKoL3PJ4EbAaGOm17UxV3ep+Pz7yiqFUx+oq7vO8Efgft/xPxod/3qoqq1ib4rQEjhSx/P/h/Nf9lYjsFJEHfchrbynW7wbCcQrF89XCzc877zCcq0D5vHuYn6ToTj9N3JgK59XSxzhaATt8TOttFrAQ+EBE9onI/4pIeDFpCx9rUTEe9HqeVcTr/GNvA/R1byumi0g6zg/oBV7pC3+m1+EU5LtFZKmIXFzCsRlT3VkZ+4vzLWPBaYN9xn2e/w/Gucq4uV7l22acjqXNcI7J8365Fdm0onYoIqEi8le3SckxfrkT6f3e+nL8UHJ5X9xn2Aa4oVB5PRDnn7OSYvD5WEubFyV/J6ssq1ibs4hIb5wC7dvC69z/zO9T1XbAaOBeERmav7qYLEu62tLK63lrnP/cU4FMINIrrlCc26O+5rsPp9DxzjuXgoWtL1LdmArnleLj9ntxml4UpcAx4lV5da9WPaGqsUB/nCvJt+WvLpRP4WMtbYyF412qqg28HnVU9bdeaQrsX1VXqeoYnFuN/8S5KmKMKYKVsWc53zK2tPYCVxYq4yJUNQXYj9f7JSKROFeSizIOGANcjtOMo23+ZqUNqITyHor/DPfiXM32PpbaqvpXH3ZbmmP1JS/vZiStiktY1VnF2niISD0RGYVzG222qv5cRJpRItJBRATIwPkvP//W0UGc9lWldYuIxLon9ZPAHPfKw1YgQkSucv9zfxio6bXdQaCtiBT3PX4f+IOIxIhIHX5pL5hbmuDcWD4CnhaRuiLSBrgXmH3uLT3eACaIyFBxOs20FJHO7rr1OLdOw0UkAefWHAAiMkREurk/dsdwCtLi3usFwIUiMk5EwkTkJiAW+FdpjtX1LzevW924wkWkd3FtI0WkhoiMF5H67m3YY5R8O9GYasfK2KL5oYwtrenuvtoAiEiUiIxx180BRonIQBGpgfN+FXf8dYFTOFd5I3GOv0xKKO+h+M9wNnC1iIxwr6BHiNOZs6S20lC6Yy3JR8CfRaSh2+57ahnzqfSsYm3A6WV+HOc/37/gdHybUEzajsBi4ARO57dXVPXf7rpngIfd21GlGRViFvAWzi2mCOAecHrQA1OA13GuXGTidOrJ97H7N01EimrT+6ab9zJgF5CN02mlLO52978T5yrTe27+JVKno9IE4O84P5RL+eXKzCM4V7OP4nRwec9r0wtwCr5jOLcql7rHA/A8TpvAoyLygqqm4VzhuA+nkH8AGKWqqaU9UFU9DgzHafO4D+dzeZaCP7iF3QokubdDJ+M0HTHGOKyMLVmZy9gyeB6Yh9Pc5jjwA9AXwG2PfZe7//04ZXNxo1u8g9MsIwWnDfcP5xHTucp7KP4z3Itz1fwh4DDOd+x+fKjflfJYS/Kku+0unO/vHJx/OqodKblPhDHGGGOMCQaphBOCichvgbGqemmwYylvdsXaGGOMMcaUmYg0F5EBbnPHTjh3T+cGO65gqKqzNBljjDHGmPJRA3gViAHScfoRvBLUiILEmoIYY4wxxhjjB9YUxBhjjDHGGD+wirUxxhhjjDF+UC3aWDdp0kTbtm0b7DCMMQaANWvWpKpqVMkpqycrs40xFY2v5Xa1qFi3bduW1atXBzsMY4wBQEQKTz9vvFiZbYypaHwtt60piDHGGGOMMX5gFWtjjDHGGGP8wCrWxhhjABCRK0TkPyKyXUQeLGJ9TRH50F2/UkTausv7iMh69/GTiPzKa5skEfnZXWftO4wxVVq1aGNtjC9ycnJITk4mOzs72KGYKiIiIoLo6GjCw8ODHUqJRCQUeBkYBiQDq0Rknqpu8kp2B3BUVTuIyFjgWeAmIBFIUNVcEWkO/CQin6tqrrvdEFVNPZ/47Pw0/laZzk9TeVjF2hhXcnIydevWpW3btohIsMMxlZyqkpaWRnJyMjExMcEOxxd9gO2quhNARD4AxgDeFesxwOPu8znASyIiqnrSK00E4PeZx+z8NP5UCc9PU0lYUxBjXNnZ2TRu3Nh+tI1fiAiNGzeuTFdYWwJ7vV4nu8uKTONejc4AGgOISF8R2Qj8DEz2ulqtwFciskZEflPW4Oz8NP5UCc9PU0nYFWtjvNiPtvGn6vR9UtWVQJyIdAHeFpEvVDUbGKiqKSLSFFgkIltUdVnh7d1K928AWrduXeQ+qtP7aQLPvk8mEOyKtTEViIhwyy23eF7n5uYSFRXFqFGjyi2GpKQkunbtWm77C4TnnnuO2NhYunfvztChQ9m9+5fhR0NDQ4mPjyc+Pp7Ro0cXuf2yZcvo2bMnYWFhzJkzp8C6Bx54gLi4OLp06cI999yDqt9bPQRLCtDK63W0u6zINCISBtQH0rwTqOpm4ATQ1X2d4v49BMzFaXJyFlWdoaoJqpoQFVUx586x89M/pk+fTrdu3YiPj2fgwIFs2rTprDR79+5lyJAhxMbGEhcXx/PPP+9Zd9NNN3nO4bZt2xIfH1+e4RtzTnbF2pgKpHbt2iQmJpKVlUWtWrVYtGgRLVsWvhtfOeTm5hIWFpwi5qKLLmL16tVERkYybdo0HnjgAT788EMAatWqxfr168+5fevWrXnrrbf4v//7vwLLv/vuO1asWMGGDRsAGDhwIEuXLmXw4MEBOY5ytgroKCIxOBXoscC4QmnmAbcD3wPXA9+oqrrb7HU7L7YBOgNJIlIbCFHV4+7z4cCT5XQ8fmfnp3+MGzeOyZMnAzBv3jzuvfdevvzyywJpwsLC+Nvf/kbPnj05fvw4vXr1YtiwYcTGxnrOZYD77ruP+vXrl2v8xpyLVayrsfdW7vFbXuP6Fn3r1pTeyJEjmT9/Ptdffz3vv/8+N998M8uXLwcgMzOTu+++m8TERHJycnj88ccZM2YMSUlJ3HrrrWRmZgLw0ksv0b9/f5YsWcLjjz9OkyZNSExMpFevXsyePfusW6Br1qxh4sSJAAwfPtyz/MyZMzz44IMsWbKEU6dOcddddzFp0iQAnn32WWbPnk1ISAhXXnklf/3rXxk8eDDx8fF8++233Hzzzdx2221MnjyZPXuc79o//vEPBgwYwI8//sjvfvc7srOzqVWrFjNnzqRTp05s3LiRCRMmcPr0afLy8vjkk0/o2LEjs2fP5oUXXuD06dP07duXV155hdDQ0GLfwyFDhnie9+vXj9mzZ5fqM8ifTjskpOBNPREhOzub06dPo6rk5OTQrFmzUuVdUbmV4qnAQiAUeFNVN4rIk8BqVZ0HvAHMEpHtwBGcyjfAQOBBEckB8oApqpoqIu2Aue73LQx4T1UL1qAqGTs/z//8rFevnud5ZmZmkU0ymjdvTvPmzQGoW7cuXbp0ISUlhdjYWE8aVeWjjz7im2++8f0DDDB//a7ab2rlZRVrYyqYsWPH8uSTTzJq1Cg2bNjAxIkTPT/cTz/9NJdddhlvvvkm6enp9OnTh8svv5ymTZuyaNEiIiIi2LZtGzfffLNnSuh169axceNGWrRowYABA1ixYgUDBw4ssM8JEybw0ksvMWjQIO6//37P8jfeeIP69euzatUqTp06xYABAxg+fDhbtmzhs88+Y+XKlURGRnLkyBHPNqdPn/bse9y4cfzhD39g4MCB7NmzhxEjRrB582Y6d+7M8uXLCQsLY/HixTz00EN88sknTJ8+nd/97neMHz+e06dPc+bMGTZv3syHH37IihUrCA8PZ8qUKbz77rvcdttt3HnnnUyePJmEhIRi38833niDK6+80vM6OzubhIQEwsLCePDBB7nmmmt8/mwuvvhihgwZQvPmzVFVpk6dSpcuXXzevqJT1QXAgkLLHvV6ng3cUMR2s4BZRSzfCfTwf6TBY+enf87Pl19+meeee47Tp0+XWDFOSkpi3bp19O3bt8Dy5cuX06xZMzp27FiKT9CYwLKKtTEVTPfu3UlKSuL9999n5MiRBdZ99dVXzJs3z9NEITs7mz179tCiRQumTp3K+vXrCQ0NZevWrZ5t+vTpQ3R0NADx8fEkJSUV+OFOT08nPT2dQYMGAXDrrbfyxRdfePa3YcMGTzvjjIwMtm3bxuLFi5kwYQKRkZEANGrUyJPfTTfd5Hm+ePHiAu0njx07xokTJ8jIyOD2229n27ZtiAg5OTmAU3F9+umnSU5O5tprr6Vjx458/fXXrFmzht69ewOQlZVF06ZNAXj99dfP+V7Onj2b1atXs3TpUs+y3bt307JlS3bu3Mlll11Gt27daN++/Tnzybd9+3Y2b95McnIyAMOGDWP58uVccsklPm1vKj87P/1zft51113cddddvPfeezz11FO8/fbbRaY7ceIE1113Hf/4xz8KXOkGPHcMjKlIrGJtTAU0evRo/vjHP7JkyRLS0n7pG6aqfPLJJ3Tq1KlA+scff5xmzZrx008/kZeXR0REhGddzZo1Pc9DQ0PJzc3FV6rKiy++yIgRIwosX7hwYbHb1K5d2/M8Ly+PH374oUA8AFOnTmXIkCHMnTuXpKQkTxvlcePG0bdvX+bPn8/IkSN59dVXUVVuv/12nnnmGZ/jBqfS8PTTT7N06dIC70F+m9h27doxePBg1q1b53PFeu7cufTr1486deoAcOWVV/L9999bxbqasfPz/M/PfGPHjuW3v/1tketycnK47rrrGD9+PNdee22Bdbm5uXz66aesWbOmTPs1JlBsVBBjKqCJEyfy2GOP0a1btwLLR4wYwYsvvugZiWLdunWAc6WqefPmhISEMGvWLM6cOePzvho0aECDBg349ttvAXj33XcL7G/atGmeK1Zbt24lMzOTYcOGMXPmTE6edOYF8b7V7G348OG8+OKLntf5nQYzMjI8Fdy33nrLs37nzp20a9eOe+65hzFjxrBhwwaGDh3KnDlzOHTokGdf3qN8FGXdunVMmjSJefPmea6eARw9epRTp04BkJqayooVKwq02SxJ69atWbp0Kbm5ueTk5LB06dIq1RTE+MbOz/M7P7dt2+Z5Pn/+/CKbcqgqd9xxB126dOHee+89a/3ixYvp3Lmz52q/MRWFVayNqYCio6O55557zlr+yCOPkJOTQ/fu3YmLi+ORRx4BYMqUKbz99tv06NGDLVu2FLgq5YuZM2dy1113ER8fX2D4uDvvvJPY2Fh69uxJ165dmTRpErm5uVxxxRWMHj2ahIQE4uPjzxo9I98LL7zA6tWr6d69O7GxsUyfPh1whqz785//zEUXXVTgCt1HH31E165diY+PJzExkdtuu43Y2Fieeuophg8fTvfu3Rk2bBj79+/3xJffXtTb/fffz4kTJ7jhhhsKDKu3efNmEhIS6NGjB0OGDOHBBx/0VKwfffRR5s2bB8CqVauIjo7m448/ZtKkScTFxQFw/fXX0759e7p160aPHj3o0aMHV199danea1P52fl5fufnSy+9RFxcHPHx8Tz33HOeZiD79u3zNK9ZsWIFs2bN4ptvvvEMrbdgwS/N/z/44ANrBmIqJKlCY7AWKyEhQYs6uas7GxWkoM2bNxe4+ujP9weqxntkSq/w9wpARNaoavE9Lqu5ospsOz9NIBR1fp4PGxWk6vK13LYr1sYYY4wxxviBVayNMcYYY4zxA6tYG2OMMcYY4wdWsTamAhERbrnlFs/r3NxcoqKiGDVqVKnyGTx4sKfT0MiRI0lPT/drnODMTpiamur3fMvL6dOnmTBhgqcj4pIlS4pM9/HHHxMXF0dISMhZHbGeeeYZOnToQKdOnc45xJmpGuz8LH979uyhTp06xXbAHD9+PJ06daJr165MnDjRM0KKqnLPPffQoUMHunfvztq1a8szbFONWcXamAqkdu3aJCYmkpWVBcCiRYs8w16V1YIFC2jQoIE/wvO70ozZ62+vvfYaAD///DOLFi3ivvvuIy8v76x0Xbt25dNPP/VM0JFv06ZNfPDBB2zcuJEvv/ySKVOmlGoYNVP52PlZ/u69994CM6cWNn78eLZs2cLPP/9MVlaWZ1KaL774gm3btrFt2zZmzJhR7FjZxvibVayNqWBGjhzJ/PnzgbNnFsvMzGTixIn06dOHiy66iM8++wxwZjsbO3YsXbp04Ve/+pXnhx8KXrm65ppr6NWrF3FxccyYMcOTpk6dOvzlL3+hR48e9OvXj4MHD54VV1paGsOHDycuLo4777yzwLBfs2fPpk+fPsTHxzNp0iRPBfPLL7+kZ8+e9OjRg6FDhwLOZBm33norAwYM4NZbb+XMmTPcf//99O7dm+7du/Pqq68CzoxrQ4cOpWfPnnTr1s1zrJmZmVx11VX06NGDrl278uGHHwKwZs0aLr30Unr16sWIESM8Q34VZ9OmTVx22WUANG3alAYNGhQ5NFiXLl3OmvAD4LPPPmPs2LHUrFmTmJgYOnTowI8//njOfZrKz87P8jk/Af75z38SExPjGe6yuM9DRBAR+vTp45kV9bPPPuO2225DROjXrx/p6ek+7dOY82UzLxpTjGANdzR27FiefPJJRo0axYYNG5g4cSLLly8H4Omnn+ayyy7jzTffJD09nT59+nD55Zfz6quvEhkZyebNm9mwYQM9e/YsMu8333yTRo0akZWVRe/evbnuuuto3LgxmZmZ9OvXj6effpoHHniA1157jYcffrjAtk888QQDBw7k0UcfZf78+bzxxhuAM1zVhx9+yIoVKwgPD2fKlCm8++67XHnllfz6179m2bJlxMTEFJikYtOmTXz77bfUqlWLGTNmUL9+fVatWsWpU6cYMGAAw4cPp1WrVsydO5d69eqRmppKv379GD16NF9++SUtWrTwVG4yMjLIycnh7rvv5rPPPiMqKooPP/yQv/zlL7z55puesXknT55c4Hh69OjBvHnzuPnmm9m7dy9r1qxh79699OnTx6fPKSUlhX79+nleR0dHk5KS4tO25vzZ+Vm1z88TJ07w7LPPsmjRomKbgXjLyclh1qxZPP/884BzfrZq1cqzPv/8bN68eYl5GXM+rGJtTAXTvXt3kpKSeP/99z2TJeT76quvmDdvnueHJjs7mz179rBs2TLPhBXdu3ene/fuReb9wgsvMHfuXAD27t3Ltm3baNy4MTVq1PC0E+3VqxeLFi06a9tly5bx6aefAnDVVVfRsGFDAL7++mvWrFlD7969AefqXNOmTfnhhx8YNGgQMTExADRq1MiT1+jRo6lVq5bnmDZs2MCcOXMA54d427ZtREdH89BDD7Fs2TJCQkJISUnh4MGDdOvWjfvuu48//elPjBo1iksuuYTExEQSExMZNmwYAGfOnPH8gBb+wc43ceJEz4Qxbdq0oX///oSGhhbzqRjjsPOzfM7Pxx9/nD/84Q/UqVOnmE+ioClTpjBo0CAuueQSn9IbEyhWsTamAho9ejR//OMfWbJkCWlpaZ7lqsonn3xSZNOEkixZsoTFixfz/fffExkZyeDBg8nOzgYgPDwcEQEgNDS0VG0rVZXbb7+dZ555psDyzz//vNhtvGeeU1VefPFFRowYUSDNW2+9xeHDh1mzZg3h4eG0bduW7OxsLrzwQtauXcuCBQt4+OGHGTp0KL/61a+Ii4vj+++/9znusLAw/v73v3te9+/fnwsvvNDn7Vu2bMnevXs9r5OTk8+7va2pHOz8DPz5uXLlSubMmcMDDzxAeno6ISEhREREMHXq1LPSPvHEExw+fNjTTAXs/DTBY22sjamAJk6cyGOPPUa3bt0KLB8xYgQvvviip/3kunXrABg0aBDvvfceAImJiWzYsOGsPDMyMmjYsCGRkZFs2bKFH374oVQxee/jiy++4OjRowAMHTqUOXPmcOjQIQCOHDnC7t276devH8uWLWPXrl2e5UUZMWIE06ZN8/Tm37p1K5mZmWRkZNC0aVPCw8P597//ze7duwFn2uPIyEhuueUW7r//ftauXUunTp04fPiw54c7JyeHjRs3nvN4Tp48SWZmJuB0QgsLC/NMb+6L0aNH88EHH3Dq1Cl27drFtm3bfG5GYio3Oz8Df34uX76cpKQkkpKS+P3vf89DDz1UZKX69ddfZ+HChbz//vuEhPxSpRk9ejTvvPMOqsoPP/xA/fr1rRmIKRcBvWItIlcAzwOhwOuq+tdC62sC7wC9gDTgJlVNEpE+QH7PDQEeV9W5vuRpTFUQHR3tuXXs7ZFHHuH3v/893bt3Jy8vj5iYGP71r3/x29/+lgkTJtClSxe6dOlCr169ztr2iiuuYPr06Z7OeN7tg33x2GOPcfPNNxMXF0f//v1p3dpp4xobG8tTTz3F8OHDycvLIzw8nJdffpl+/foxY8YMrr32WvLy8mjatGmRt7DvvPNOkpKS6NmzJ6pKVFQU//znPxk/fjxXX3013bp1IyEhgc6dOwPOKB73338/ISEhhIeHM23aNGrUqMGcOXO45557yMjIIDc3l9///vfExcUV24bz0KFDjBgxgpCQEFq2bMmsWbMKxDR58mQSEhKYO3cud999N4cPH+aqq64iPj6ehQsXEhcXx4033khsbCxhYWG8/PLL1pSkmrDzM/Dn57mMHDmS119/nRYtWjB58mTatGnDxRdfDMC1117Lo48+ysiRI1mwYAEdOnQgMjKSmTNnlur9NKasxLvnsF8zFgkFtgLDgGRgFXCzqm7ySjMF6K6qk0VkLPArVb1JRCKB06qaKyLNgZ+AFoCWlGdREhIStKje/tXdeyv3+C2vYHUk8qfNmzfTpUuXYIdhqpiivlciskZVE4IUUoVXVJlt56cJBH9/r/z1u1oVflOrGl/L7UA2BekDbFfVnap6GvgAGFMozRjgbff5HGCoiIiqnlTV/EZkETgVal/zNMYYY4wxptwFsmLdEtjr9TrZXVZkGrcinQE0BhCRviKyEfgZmOyu9yVP3O1/IyKrRWT14cOH/XA4xhhjjDHGFK/Cdl5U1ZWqGgf0Bv4sIhGl3H6GqiaoakJUVFRggjTGGGOMMcYVyIp1CtDK63W0u6zINCISBtTH6cTooaqbgRNAVx/zNKbMAtXnwFRP9n3yL3svYtQrAAAgAElEQVQ/jT/Z98kEQiAr1quAjiISIyI1gLHAvEJp5gG3u8+vB75RVXW3CQMQkTZAZyDJxzyNKZOIiAjS0tKssDV+oaqkpaUREVGqm22mGHZ+Gn+y89MESsCG23NH9JgKLMQZGu9NVd0oIk8Cq1V1HvAGMEtEtgNHcCrKAAOBB0UkB8gDpqhqKkBReQbqGEz1Eh0dTXJyMtYm3/hLREQE0dHRwQ6jSrDz0/ibnZ8mEAI6jrWqLgAWFFr2qNfzbOCGIrabBcwqvLy4PI3xh/DwcM/0vsZURxV57gE7P40xlUGF7bxojDGm/LhzD7wMXAnEAjeLSOGpKO8AjqpqB+DvwLPu8kQgQVXjgSuAV0UkzMc8jTGmyrCKtTHGGLC5B4wx5rxZxdoYYwwEee4BY4ypCqxibYwx5ryd79wDNqmXMaYqsIq1McYYCPLcAzaplzGmKrCKtTHGGLC5B4wx5rwFdLg9Y4wxlYPNPWCMMefPKtbGGGMAm3vAGGPOlzUFMcYYY4wxxg+sYm2MMcYYY4wfWMXaGGOMMcYYP7CKtTHGGGOMMX5gFWtjjDHGGGP8wCrWxhhjjDHG+IFVrI0xxhhjjPEDq1gbY4wxxhjjB1axNsYYY4wxxg+sYm2MMcYYY4wfWMXaGGOMMcYYP7CKtTHGGGOMMX5gFWtjjDHGGGP8wCrWxhhjjDHG+IFVrI0xxhhjjPEDq1gbY4wxxhjjBwGtWIvIFSLyHxHZLiIPFrG+poh86K5fKSJt3eXDRGSNiPzs/r3Ma5slbp7r3UfTQB6DMcYYY4wxvggLVMYiEgq8DAwDkoFVIjJPVTd5JbsDOKqqHURkLPAscBOQClytqvtEpCuwEGjptd14VV0dqNiNMcYYY4wprUBese4DbFfVnap6GvgAGFMozRjgbff5HGCoiIiqrlPVfe7yjUAtEakZwFiNMcYYY4w5L4GsWLcE9nq9TqbgVecCaVQ1F8gAGhdKcx2wVlVPeS2b6TYDeURExL9hG2NM5SUijYp4hAc7LmOMqQ4qdOdFEYnDaR4yyWvxeFXtBlziPm4tZtvfiMhqEVl9+PDhwAdrjDEVw1rgMLAV2OY+TxKRtSLSK6iRGWNMFRewNtZACtDK63W0u6yoNMkiEgbUB9IARCQamAvcpqo78jdQ1RT373EReQ+nyck7hXeuqjOAGQAJCQnqp2MyxpiKbhEwR1UXAojIcJw7fzOBV4C+QYzNmArnvZV7gh2CqUICecV6FdBRRGJEpAYwFphXKM084Hb3+fXAN6qqItIAmA88qKor8hOLSJiINHGfhwOjgMQAHoMxxlQ2/fIr1QCq+hVwsar+AFhfFWOMCaCAVazdNtNTcUb02Ax8pKobReRJERntJnsDaCwi24F7gfwh+aYCHYBHCw2rVxNYKCIbgPU4V7xfC9QxGGNMJbRfRP4kIm3cxwPAQXekprxzbWhDpBpjzPkJZFMQVHUBsKDQske9nmcDNxSx3VPAU8Vka20EjTGmeOOAx4B/uq9XuMtCgRuL28iGSDWm9FSVvUdOsnH/MTbvP87J07nENKlNh6Z16NaiPpE1A1rNMhWQfeLGGFOFqGoqcHcxq7efY1PPEKkAIpI/RKp3xXoM8Lj7fA7wUv4QqV5pPEOkFhrNyZgqJU+Vz3/ax8pdRwgRaBdVh9aNItlx+AQb9x1jyX8OM75va6IbRgY7VFOOrGJtjDFViIhcCPwRaItXGa+qlxW3jauoIVILd3QsMESqiOQPkZrqlaa4IVLPAJ8AT6mqdSg3lVrOmTw+Wr2XjfuOMbBDEy7r3JSI8FDAuYqdfDSL91ftYcaynYzu0YKEto2CHLEpL1axNsaYquVjYDrwOnCmPHfsNUTqcK/F41U1RUTq4lSsb6WIkZxE5DfAbwBat25dDtEaUza5eXm89V0SSamZjOrenP7tmxRYLyK0ahTJXYM78MGqPXy6LoUzqvSNKTxNh6mKSuy8KCJf+7LMGGNMhZCrqtNU9UdVXZP/8GG70gyRSlmGSAXyh0g9i6rOUNUEVU2Iiory5TiNCYol/znMrtRMrusVfVal2lvtmmH8V/8YOjWry79+2s/utMxyjNIES7EVaxGJEJFGQBMRaeg1g1dbzp5B0RhjTMXwuYhMEZHm3rMv+rCdDZFqTAmSj55kyX8OcVGrBvRs3bDE9KEhwo0JrWgQGc67K/eQkZVTDlGaYDrXFetJwBqgs/s3//EZ8FLgQzPGGFMGtwP3A9/xS7ld4ogcNkSqMeeWcyaPj1cnUzcinFHdW/i8Xa0aoYzv14bTuXm8/+MezuRZF4OqrNg21qr6PPC8iNytqi+WY0zGGGPKSFVjzmNbGyLVmGJ8vfkgh0+cYsKAttSqEVqqbS+oF8E1F7Xgo9XJrNyVds4mJKZyK7Hzoqq+KCL9ObuH+VmdT4wxxgSHiFymqt+IyLVFrVfVT8s7JmOqioysHL7bkUbP1g3o2LRumfLoEd2AdXvSWbTpIHEt6lO/VrifozQVQYkVaxGZBbTHuY2X38NcKaJXtzHGmKC5FPgGuLqIdQpYxdqYMlq69TB5qlzWuVmZ8xARRvdowfNfb2P+z/sZ18dGv6mKfBluLwGItXFHjTGm4lLVx9y/E4IdizFVSUZWDquSjtCrTUMa1a5xXnk1rlOTwZ2asnjzQf5z4DidLijb1W9TcflSsU4ELgD2BzgWY4wxZSQi955rvao+V16xGFOVLPnPIVAY3KmpX/Ib1LEJ6/ceZf7P++nQtA6hIeKXfE3F4EvFugmwSUR+BDwzaanq6OI3McYYU87yL311Anrzy1B5VwM/BiUiYyq59JOnWZ10lF5tGtIw8vyuVucLCw1hRNwFvLtyD+v2HLVZGasYXyrWjwc6CGOMMedHVZ8AEJFlQE93QhZE5HGcMaaNMaX0/Y40FGVwJ/9OWhTbvB7RDWvx9ZZD9GjVgPDQEufrM5WEL6OCLC2PQIwxxvhFM+C01+vT7jJjTCnknMlj9e6jxLaoTwM/Xa3OJyKMiLuAN77dxcqdaQzsaLONVhW+jApyHKdHOUANIBzIVNV6gQzMGGNMmbwD/Cgic93X1wBvBzEeYyqln5MzyMo5Q9+YwDTVaB9Vhw5N67Bk62ES2jYiIrx0Y2ObiqnEew+qWldV67kV6VrAdcArAY/MGGNMqanq08BE4Kj7mKCq/xPcqIypfFbuSiOqTk3aNakdsH0Mj23GydNn+G5HasD2YcpXqRr1qOOfwIgAxWOMMeb8rQc+BuYCaSJiA+YaUwopR7PYezSLvu0aIRK4UTuiG0bS+YK6rNiexqmcMyVvYCo8X5qCeM/iFYIzrnV2wCIyxhhTZiJyN/AYcBBnUi/Bac7XPZhxGVOZrNyVRnio0LN1w4Dva0inpkxbuoOVu44w6EJra13Z+TIqiPcsXrlAEjAmINEYY4w5X78DOqlqWrADMaYyys45w0/J6cS3alAu7Z5bNYqkQ9M6LN+eSr92jakRZiOEVGa+jApis3gZY0zlsRfICHYQxlRWiSkZ5JxREtqU3/jSQzo15bXlO1m9+wj92zcpt/0a//OlKUg08CIwwF20HPidqiYHMjBjjDFlshNYIiLzKTipl828aIwP1u1Np0mdGkQ3rFVu+4xpUpuYJrVZtvUwfWzCmErNl/sNM3Fm8GrhPj53lxljjKl49gCLcIZHrev1MMaUIP3kaXalZhLfqkFAOy0WZXCnKI5l57J+b3q57tf4ly9trKNU1bsi/ZaI/D5QARljjCk7rxkYI1X1ZLDjMaYy+cmt1Ma3CnynxcI6RNWhRYMIlm07zJk8JTSkfCv2xj98uWKdJiK3iEio+7gFsE4xxhhTAYnIxSKyCdjivu4hIjb3gDElUFXW7U2nTaNIGtX270yLvhARLr2wKaknTrNo04Fy37/xD18q1hOBG4EDwH7gesA6NBpjTMX0D5y5BtIAVPUnYFBQIzKmEtifkc2h46eIb90gaDHEtahH49o1mLZkB6pa8gamwvFl5sXdqjpaVaNUtamqXqOqe3zJXESuEJH/iMh2EXmwiPU1ReRDd/1KEWnrLh8mImtE5Gf372Ve2/Ryl28XkRekvBtBGWNMBaeqewstspknjCnB+r3phIrQrUX9oMUQIsIlHaP4KTmD73dY44DKqMSKtYi8LSINvF43FJE3fdguFHgZuBKIBW4WkdhCye4AjqpqB+DvwLPu8lTgalXtBtwOzPLaZhrwa6Cj+7iipFiMMaYa2Ssi/QEVkXAR+SOwOdhBGVOR5amyITmdCy+oS2RNX7qfBc5FrRvQpE5Npi3dEdQ4TNn40hSku6p6uqiq6lHgIh+26wNsV9Wdqnoa+ICzJ5YZA7ztPp8DDBURUdV1qrrPXb4RqOVe3W4O1FPVH9S5R/IOcI0PsRhjTHUxGbgLaAnsA+Ld18aYYuw9cpJj2bl0axm8q9X5wkNDmDiwLcu3pZKYYkPSVza+VKxDRMTTPVZEGuHbaCItcSYqyJfsLisyjarm4kxq0LhQmuuAtap6yk3vPX52UXnmx/kbEVktIqsPHz7sQ7jGGFP5qWqqqo5X1WZuE75bbBZGY87t55QMwkKEzhdUjJEpx/dtQ52aYcxYtjPYoZhS8qVi/TfgexH5bxH5b+A74H8DG5ZDROJwmodMKu22qjpDVRNUNSEqKsr/wRljTAUkIu1E5HMROSwih0TkMxFpF+y4jKmo8lRJTMmgY9M65TKFuS/q1wpnXN/WzP95P3uP2KiZlYkvnRffAa4FDrqPa1V11rm3AiAFaOX1OtpdVmQaEQkD6uP2ZHdnfJwL3KaqO7zSR5eQpzHGVGfvAR8BzXEm9foYeN+XDa3DuamOkt1mIF0rQDMQbxMGtCVE4I1vdwU7FFMKvlyxRlU3qepL7mOTj3mvAjqKSIyI1ADG4szg6G0eTudEcIbx+0ZV1e0sOR94UFVXeMWxHzgmIv3cwvk24DMf4zHGmOogUlVnqWqu+5gNRJS0kXU4N9XVzykZhIYIXZrXC3YoBTSvX4sx8S35YNUejmSeDnY4xkc+VazLwm0zPRVYiNMj/SNV3SgiT4rIaDfZG0BjEdkO3AvkXyGZCnQAHhWR9e6jqbtuCvA6sB3YAXwRqGMwxphK6AsReVBE2opIGxF5AFggIo3cPjLFsQ7nptrJy1MS9x2rUM1AvE0a1I7snDze/i4p2KEYHwV0TBlVXQAsKLTsUa/n2cANRWz3FPBUMXmuBrr6N1JjjKkybnT/Fu6bMhZQoLj21kV1OO9bXBpVzRWR/A7nqV5pPB3ORcTnDufGBMP65HQysnIYFtss2KEUqWOzulzepSlvf5/EpEvbEVkjuEMBmpIF7Iq1McaY8qeqMed4BLQT4/l0OLeRnEwwfJl4gFARulxQsZqBePvt4Pakn8zhw1WF530yFVGxFWsROS4ix4p7lGeQxhhjfCMiN4hIXff5wyLyqYj4MvdAUDuc20hOprypKgs3HqBdVG1q1ah4zUDy9WrTiN5tG/L68l3knMkLdjimBMVWrFW1rqrWA57HafvcEqdQ/BPwj/IJzxhjTCk9oqrHRWQgcDlOX5bpPmxnHc5NtbL14Al2p50ktkXFvVqdb/Kl7UlJz+Lzn/aVnNgElS9NQUar6iuqelxVj6nqNM7u0GKMMaZiOOP+vQqYoarzgRolbWQdzk11s3DjAUSocKOBFGVIp6Z0alaX6Ut3kJenwQ7HnIMvreAzRWQ8Tg9xBW4GMgMalTHGmLJKEZFXgWHAsyJSE9+HVrUO56baWLjxABe1akC9iPBgh1KikBBh0qXtuPejn/hmyyEur6CdLY1vhe04nF7m+RPE3OAuM8YYU/HciHPVeYSqpgONgPuDG5IxFUvy0ZNs3HeMEXEXBDsUn13dowUtG9TilSXbcUavNBWRLzMvJqnqGFVtoqpRqnqNqiaVQ2zGGGNKSVVPquqnqrrNfb1fVb8KdlzGVCRfbTwIwPBKVLEODw1h0qXtWLsnnR93HQl2OKYYJVasReRCEflaRBLd191F5OHAh2aMMcYY438LNx7gwmZ1iGlSO9ihlMqNCa1oUqcGryzZUXJiExS+NAV5DfgzkAOgqhtweosbY4wxxlQqRzJPsyrpSKVqBpIvIjyUCQNiWLr1MIkpGcEOxxTBl4p1pKr+WGhZbiCCMcYYY4wJpK83HyRPqbCzLZbk1ovbULdmGNOW2lXrisiXinWqiLTHGREEEbke2B/QqIwxxpSKTepljG8WbTpI8/oRdGtZP9ihlEm9iHBuubgNC37ez87DJ4IdjinEl4r1XcCrQGcRSQF+D/w2oFEZY4wpFZvUy5iSZeecYfm2VC7v0gxnzqLKaeKAGGqEhjDdrlpXOL6MCrJTVS8HooDOqjrQRgUxxpgKyyb1MqYYK7ankpVzptI2A8kXVbcmN/dpzadrU0hJzwp2OMaLL6OC/E5E6gEngb+LyFoRGR740IwxxpRBpoiMF5FQEQlxJ/iySb2MwWkGUrdmGP3aNQ52KOft14PaAfDasp1BjsR486UpyERVPQYMBxoDtwJ/DWhUxhhjysom9TKmCHl5yuLNh7i0UxQ1wnyajLRCa9mgFr+6qCUfrNpD6olTwQ7HuHz5ZuU3QhoJvKOqG72WGWOMqUBsUi9jirZubzqpJ05V+mYg3iYPbs+p3Dze+HZXsEMxLl8q1mtE5CucivVCEakL5AU2LGOMMWVhk3oZU7RFmw4SFiIM7tQ02KH4TfuoOozs1pzZ3+8mIysn2OEYfKtY34HTw7y3qp4EagATAhqVMcaYsrJJvYwpwqJNB+jXrjH1a4UHOxS/mjK4PcdP5TL7h93BDsVwjoq1iHR2n8a7f9uJSE+gDRAW6MCMMcaUiU3qZUwhOw+fYMfhzCrVDCRfXIv6DOkUxRvf7iLr9Jlgh1PtnauCfB/wa+BvRaxT4LKARGSMMeZ82KRexhSyaNNBAC6vghVrgLuGdOD66d/zwao9TBgQE+xwqrViK9aq+mv375DyC8cYY8x5uguYwS+Teu0CbgluSMYE11ebDhLXoh4tG9QKdigBkdC2EX1iGjFj2U7G921TJUY9qayKrViLyLXn2lBVP/V/OMYYY86Hqu4ELheR2kCIqh4PdkzGBNPh46dYu+covxvaMdihBNRdQzpw+5s/8s91KdzYu1Www6m2ztUU5OpzrFPAKtbGGFPBiMjvgJnAceA1t2/Mg6r6VXAjMyY4vtlyEFWqZPtqb4M6NiGuRT2mL9vB9b2iCQmxkZGD4VxNQWzkD2OMqXwmqurzIjKCXyb1mgVYxdpUS4s2HaRlg1rENq8X7FACSkT47eD2TH1vHV9tOsAVXZsHO6RqyadGOCJylYg8ICKP5j983O4KEfmPiGwXkQeLWF9TRD50168Ukbbu8sYi8m8ROSEiLxXaZomb53r3UXUGpDTGmPNnk3oZ4zp5Opfl21IZFtsMkap/GlzZtTltGkcybckOVDXY4VRLJVasRWQ6cBNwN07hfAPOkHslbRcKvAxcCcQCN4tIbKFkdwBHVbUD8HfgWXd5NvAI8Mdish+vqvHu41BJsRj/OJ6dw8Fj2XayGlOx2aRexriWbU3lVG5elW8Gki80RJg0qD0/JWfw/Y60YIdTLfkyHnV/Ve0uIhtU9QkR+RvwhQ/b9QG2ux1pEJEPgDHAJq80Y4DH3edzgJdERFQ1E/hWRDr4eiAmME7lnGH59lS2HDjGvvRsAOpFhNHpgnr0a9eI5vWrZg9rYyqxO3DmH9ipqidFpDE2qZepphZtOki9iDD6xDQKdijl5tqeLfn74q28smQH/Ts0CXY41Y4vTUGy3L8nRaQFzmxevjTcaQns9Xqd7C4rMo2q5gIZOG0CSzLTbQbyiFSHeztBciwrhxnLd/LvLYcIDw1heGwzrr2oJa0bRbIhOZ3pS3ewcV9GsMM0xmCTehlTWO6ZPL7ecpChXZoRHlp9hp+LCA/ljoExfLs9lcQU+40ub7580/4lIg2A/wesBZKA9wMZVAnGq2o34BL3cWtRiUTkNyKyWkRWHz58uFwDrAoOHMtm2tIdpGWe5vb+bZk0qD2DOzUloW0jxvVtw73DLuSCehG8t3IPy7YetuYhxgTffe7fvxXx+D9fMrB+MaYq+THpCOkncxheTZqBeBvXtzV1aobx2vKdwQ6l2imxYq2q/62q6ar6Cc6Vj86q+ogPeacA3gMpRrvLikwjImFAfeCcjYJUNcX9exx4D6fJSVHpZqhqgqomREVF+RCuyZeRlcNry3aSp8pvLmnHhc3qnpWmbkQ4d17SjriW9fly4wHe+HZXECI1xuTzntSriEeJM+VavxhT1Xy18SA1w0K4tFP1qwPUiwhnbO9W/GvDflLSs0rewPiNL50XQ0VktIjcgzOj1x0icq8Pea8COopIjIjUAMYC8wqlmQfc7j6/HvhGz3HpU0TCRKSJ+zwcGAUk+hCL8VGeKp+sTSY3L487B7ajxTlmqQoPDWFs71Z0aV6PZ7/cwvq96eUYqTHGm4hce66HD1l4+sWo6mkgv1+MtzHA2+7zOcDQ/H4xqvotTgXbmKBTVb7aeIBLOkYRWaN6toSaODAGAd60C1/lypemIJ8D/4XT9rmu1+Oc3DbTU4GFwGbgI1XdKCJPishoN9kbQGMR2Q7cC3huPYpIEvAc8F8ikuxeOamJ08t9A7Ae54r3az4cg/HRDzvT2H7oBCO7NSeqbs0S04eIcF3PljStG8HU99aSkZVTDlEaY4pw9Tkeo3zY3vrFmCojMeUY+zKyGR5X/ZqB5GvRoBajujfngx/32G9zOfLl37hoVe1elsxVdQGwoNCyR72eZ+MM31fUtm2LybZXWWIxJTt4LJsvEw/QqVld+rT1vQd1ZI0wXhx3ETdO/54HP9nAK+N7VovxQo2pSCrwpF7jVTXFHfbvE5x+Me8UTiQivwF+A9C6devyjdBUOV9tOkCIwOVdqm/FGuDXg9rxz/X7eHflbqYMtoHWyoMvV6y/EJHhAY/EBJWq8tn6fdQIC+Hani1LXTHu2boh9w6/kC8SD/DNFmtCaUwwlXFSL+sXY6qMhRsP0CemEY1q1wh2KEEV16I+Azo05u3vksg5Y8PZlwdfKtY/AHNFJEtEjonIcRE5FujATPnafugESWmZDO3SjLoR4WXK49eXtKNdVG2enr/ZTmBjgqSsk3ph/WJMFbErNZOtB08wPPaCYIdSIdwxMIaDx06x4Of9wQ6lWvClYv0ccDEQqar1VLWuqtYLcFymHKkqizYfpEGtcHq3aVjmfMJDQ/jLyC7sTM1k1ve7/RihMaYU+qvqbTijdzyBU35fWNJG1i/GVBVfJh4AqNbtq70NvrApMU1qM3NFUrBDqRZ8aWO9F0g811UJU7lt3n+c5KNZXHtRS8LOcxD9yzo3ZWCHJjz/9TZ+dVFLGlbz23DGBEHhSb3S8G1SL+sXYyqV91buKXL5uyt307JBLZZtTS3niPynuGMrq64t6/P5T/v46xdbaN0oskx5jOtrfR984UstaiewRET+LCL35j8CHZgpH3mqLN58kMa1a3BR67Jfrc4nIjw8qgvHs3N4/uttfojQGFNKFW1SL2PKzdGTp0k+mkXXlvWDHUqF0rN1AyLCQ1ixvfL+s1FZ+FKx3gV8DdSgFMPtmcph075jHDiWzdAuzQgN8c9IHp0vqMf1vaJ578c9HDpmw9oaU57OY1IvYyq9jfucLmBdW1iLVW81w0Lp3bYRG/dlkH7ydLDDqdLO2RTEnYmrrqoWN5uWqeS+25FKw8hwukf797/7KYM7MGdNMq9/u4uHRnbxa97GmOK55fZVQFvcMl5EUNXnghmXMeVhY0oGF9SLoHGdkudhqG4ubteYb7elsnLXEUbEWcfOQDnnFWtVPQMMKKdYTDnbn5FFUtpJ+rVrTIifx51u26Q2o7q3YPYPu+2/Y2PKV5km9TKmsjuWlcOeIyeJa2lXq4vSILIGXZrXY1XSERu5K4B86by4XkTmAR8DmfkLVfXTgEVlysX3O9IIDxV6ncdIIOdy15AOzPtpHzNXJPGHYSUOSmCM8Y8yT+plTGW2cf8xFOjawtpXF6dfu8Zs2n+MxJQMv/SrMmfzpY11BE6v8sso3fS4pgI7mnma9XvTiW/VgMgavvx/VXqdLqjLsNhmvPVdEidO5QZkH8aYs9ikXqZa2piSQVSdmjSrFxHsUCqs9lG1aVKnJj/sPOe8TuY8lFixVtUJRTwmlkdwJnA+XL2X3Dzl4nZNArqfqUM6kJGVw3srbVxrY8qJTeplqp0Tp3LZlZppzUBKICL0a9eIvUezSD56MtjhVEklVqxFJFpE5orIIffxiYhEl0dwJjDO5Cmzvt9NTJPaXFA/sP/Z92jVgL4xjXj7u93kWpsuY8qDTeplqp3ElAwU6GbD7JWoZ+uG1AgL4YedR4IdSpXkS1OQmTjT2LZwH5+7y0wltWJ7KinpWfSNaVQu+5swIIaU9CwWbz5YLvszppqzSb1MtfOz2wzkAmsGUqKI8FAuatWADcnpnLRmmn7nS8U6SlVnqmqu+3gLiApwXCaA5qxJpn6tcLo0L5+LWMNimxHdsBZvfptULvszppqzSb1MtXIsK4ek1Ey6RddH/DzCVVXVN6YxuXnK2j1Hgx1KleNLxTpNRG4RkVD3cQtOZ0ZTCR3LzmHhxgOM7tGC8POcvtxXoSHC7Re35cekIySmZJTLPo2pxmxSL1OtJO6zZiCldUH9CFo3iuTHpCPYzS3/8qVmNRG4ETgA7AeuByYEMigTOPM37OdUbh7X9yrfZvI39m5FZI1QZq5IKtf9GlOdeE3q9UThR7BjMyZQfk7OoFk9Gw2ktPrENCL1xGl2pmaWnNj4zJdRQXar6mhVjVLVpqp6jaruKY/gjP/NWZNMh6Z1/D7TYknq1wrn+l7RfP7TPlJPnCrXfRtTXdikXqa6ST95mt1H/j979x1fVYUxMwIAACAASURBVHk/cPzzzR4kIQMCJCRhyd5DUVFcLe7aYsU9f47WqtW6+uugWn+1tnW02mqr1q24WnGhqIgDGQHCniKBMLIgZJFxc5/fH+cJXmJCArnJuffm+3697isnZzzne8Z97nOe85zzVDMyo7vboQSdkRlJxEaGs/gbfYjRn1p8gbGI/OYQyxljzL0dEI/qQFuKK1mav5e7Th/iSju0yybn8NxX+cxasp2fnjSw09evVBehnXqpLmP1TudNkp1dWRQKIsPDGJfVnYVb9lBZ66FbdMf0adHVHKrGuqqZD8DVwJ0dHJfqAG8u20GYwHljM1xZ/8Ce3ZjcP5WXFm2jwattupTqINqpl+oyVhaU0ScphrRu0W6HEpQm5qTQYAxL8/UhRn9p8fLEGPOXxmERSQBuxmlb/Qrwl5aWU4HJGMN/lu9gyqAerrZDu3RyNj95cRmfbijilKHprsWhVKgyxugzMKpLKKmspWDvfk4f0cvtUIJWz8QYclLjWbJ1D1MGpRGmb1Vpt0O2sRaRFBH5PbASpxA+zhhzpzGmqFOiU36zbNtedpTt59wxfVyN47Rh6fRMiOb5hdoTo1IdQTv1Ul1F3vYyBBiVqe2r22NiTjJ7qur4Rh9i9IsWC9Yi8idgCVABjDTGzDTG6L2CIDU7byfREWF8b7i7V/aR4WFcOCmL+RuL2Vaq3akq1QG0Uy8V8owxrNheRr+0eJJiI90OJ6iNyEgiJjKM3K36EKM/HKrG+jacTPlXwE4RKbefChEp75zwlD94Gry8u2oXpwztGRAPJ1w4KYswEV5crLXWSnUA7dRLhbyVBfsorapjTF+trW6vyPAwxvRNZs3OcqrrtCfG9mqxYG2MCTPGxBpjEowxiT6fBGNM53TZp/ziqy2llFTWcc5od5uBNOqVFMNpQ9N5dcl2aj0NboejVKjRTr1UyPtv3g7Cw4ThffRtIP4wMScZj9eQt73M7VCCXod2vSci00Rkg4hsFpG7mpkeLSKz7PRFIpJjx6eKyDwRqRSRR5ssM15EVtll/iraf2mrZuftJCE6gqmDe7odygEXH5PF3up63l+12+1QlAo12qmXCmmeBi9vr9jF4PQEYqPC3Q4nJPROiiWjeyxLtCfGduuwgrXtAewx4HRgGHChiAxrMtvVwF5jzEDgIeCPdnwN8GvgF80k/Q/gf4BB9jPN/9GHjlpPA3PW7OZ7w3sRExk4GdBxA9LISY3jxUXaHEQpf9JOvVSoW/B1KSWVtdoMxM8m5CRTWO68aUUduY5scDsJ2GyM2QIgIq8A5wJrfeY5F5hph18HHhURMcZUAV+IyEG9iIhIbyDRGLPQ/v8c8APg/Q7cjqA2f0MxFTUeznH5bSBNhYUJFx2dxf+9t571u8sZ0ktbFynVHtqpl+oq3lhWQGJMBIN7JbgdSkgZndmd91btIjd/D31T4twOJ2h1ZFOQDGC7z/8Fdlyz8xhjPMA+ILWVNAtaSVP5eGflLpLjIjluwKF2qzumj+9LVEQYLy3SyjSl/KDdnXpp8z0V6Mpr6vlgzW7OGdOHyPAObc3a5cREhjMyI4mVBfuo83jdDidohexZKSLXikiuiOQWFxe7HY4rauob+GhdIdNG9CYiADOglPgozhzZmzeX7aCqVp9EVqo9jDF/afwA/wRi+bZTr/6tLa/N91QweG/lLmrqvUwf39ftUELS+OwUaj1eVu/Y53YoQasjS1s7AN8zP9OOa3YeEYkAkjj00+s7bDqHShMAY8w/jTETjDETevTomm+a+nRDEdV1DZw1qrfbobTokmOyqKz18FbeTrdDUSrotbNTrwPN94wxdTgF8nObzHMu8Kwdfh04pbH5njHmC5wCtm88B5rvGeeJqMbme0odkdeXFjCwZzdGZ+rbQDpCTmocqfFR5ObrO62PVEcWrJcAg0Skn4hEATNwOi3wNRu43A5PBz4xh3gc1RizCygXkWPs7cTLgLf8H3poeGflLlLjozi6X4rbobRoXFYyQ3ol8PzCfH0SWal28EOnXtp8TwW0b0qqyM3fy4/GZaItijqGiDAhO5mtpdWUVNS6HU5Q6rCCtc10bwQ+ANYBrxpj1ojIPSJyjp3tKSBVRDYDtwIH2vSJyFbgQeAKESnwuSX5E+BJYDPwNfrgYrP21zXw8boipo3oFZDNQBqJCJdNzmHdrnKW5mvHnkq1Q1B36qXN91Rr3lxWQJjAeWP12qwjjc1OJkxg6Tb9TT4SHdoNnzHmPeC9JuN+4zNcA5zfwrI5LYzPBUb4L8rQNG9DEfvrGzgzgJuBNPrB2D784f11PPdVPhNyArd2XalAZoxp7xX04TTfK+iI5ns4bcOZMGGC3r5SB/F6DW8sLWDKoB70SopxO5yQlhgTyVHpCSzbtpdTh6YTHqZ3Bw5H4FZlqnZ5d+Uu0rpFcXS/wHsbSFNxURFMH5/J+6t3Uay3npRyizbfUwHr880l7NxXw/kTMlufWbXbhOxkKmo8bCqscDuUoKMF6xBUXefh4/WFnD6id9BcaV56TDb1DYZXFuur95RygzbfU4HslcXbSImP4rRh6W6H0iUM7pVIt+gIcrWJ5mHr0KYgyh0fryuipt7LGSMDvxlIo/49ujFlUBovLd7GDVMHBHS7cKVClTbfU4GouKKWuWsLufK4HKIjAqcH4VAWHiaMzerOl5tLqKipJyEm0u2QgoaWXkLQuyt30SMhmkkB/DaQ5lx6TDa79tXwwZpCt0NRSikVIF5fWoDHa5gxKcvtULqU8dnJeA0s31bmdihBRQvWIaay1sO8DUWcOTJ4moE0OmVoOlkpcTz95Tduh6KUUioAeL2GWUu2MalfCgN6dHM7nC6lZ0IMWSlx5Obv1dfhHgYtWIeYj9cVUuvxBsXbQJoKDxOuODaHpfl7yduuV8hKKdXVLdxSytbSai7S2mpXTMhOpqSylm17qt0OJWhowTrEvLNyF+mJ0YzPSnY7lCNy/oRMukVH8PQXWmutlFJd3UuLt5EUG8m0Eb3cDqVLGpmZRFREmD7EeBi0YB1CKmrqmb+hmDNG9iYsyJqBNEqIieSCiX15b9Uudu3b73Y4SimlXFJUXsOc1bv50bhMYiL1oUU3REeEMyojiVUF+6is9bgdTlDQgnUI+WhdIXUNXs4a1cftUNrlimNz8BrDc1/lux2KUkopl7y8eDser+Gyydluh9KlTchJoa7By9srdrodSlDQgnUIeXflLvokxTC2b3e3Q2mXvilxfG9YL15atE2vkJVSqguqb/Dy4qJ8pg7uQU5avNvhdGl9k2NJT4zWfibaSAvWIaKsuo75G4O7GYiv607sz7799by8SL/ISinV1XywZjdFFbVcPjnH7VC6PBFhQnYKKwr2sXZnudvhBDwtWIeIOat3U99gOHdMhtuh+MXYrGSOHZDKvz7fQq2nwe1wlFJKdaJnF2wlKyWOE4/q4XYoChib1Z2oiDBmLdHKrtZowTpEvJW3k35p8YzISHQ7FL/5ydSBFFXU8sbSHW6HopRSqpOs2bmPJVv3ctnk7JC4AxsK4qIimDa8F/9ZvoOaeq3sOhQtWIeAwvIaFn5Tytmj+yASOpnQcQNTGZ2ZxBOffY2nwet2OEoppTrBv7/cSmxkOOeP7+t2KMrHjEl9Ka/x8P7qXW6HEtC0YB0C3lm5C2PgnNHB/TaQpkSEn5w0kPzSat5dpV9kpZQKdYXlNbyVt4MfT8gkKS7S7XCUj8n9U+mXFs9L+uzTIWnBOgTMztvB8D6JDOwZet29njY0naPSu/HXjzdprbVSSoW4ZxZspcFruOr4fm6HopoQES6alMWSrXtZv1sfYmxJhNsBqPbZWlLFioJ93H36EFfj8OcV7EVHf9t1bViYcOtpg7n+haX8Z/kOzp+gtwaVUioUVdV6eHFhPtNG9CI7VV+xF4imj8/kTx9u4KVF27jn3BFuhxOQtMY6yM22L2w/O8Sagfj6/vB0RmUm8fBHm/QNIUopFaJezd1OeY2Ha6b0dzsU1YLk+CjOGtmbN5ftoEr7mWiWFqyDmDGGN5cVcHS/FPp0j3U7nA4jItz+/cHsKNuv77VWSqkQ5Gnw8tQX3zAhO5lxWcluh6MO4eJjsqms9fBWnvbE2BwtWAexpfl72VpazfTxmW6H0uGOH5jGMf1TeHTeZr1KVkqpEPP2yp0U7N3P/5ygtdWBblxWd4b0SuDFRfkYY9wOJ+BowTqIvbGsgLiocM4Y2dvtUDqcU2s9hJLKOh6f/7Xb4SillPKTBq/h0U82Mzg9gdOGprsdjmqFiHDJMdms2VnOsm1lbocTcLRgHaRq6ht4Z8Uupo3oRXx013gGdXx2MueO6cMTn20hv7TK7XCUUkr5wfurd/F1cRU3njxQO4QJEueNzSAhOoJnF2x1O5SAowXrIPXh2kIqaj1MHxf6zUB8/fKMoUSGCfe+s9btUJRSSrWT19ZW9+8R3yXuvoaK+OgIfjyxL++t2sXufTVuhxNQtGAdpN5YWkBG91iO6Z/qdiidKj0xhp+dMoiP1hUxb32R2+EopZRqh7nrClm/u4IbTxpIuNZWB5XLJ+fQYAwvLMx3O5SAogXrIFRYXsPnm4r54biMLnnb7Krj+tE/LZ6Zb69hf52+fk8ppYKR12v42yebyE6NC7meg7uCrNQ4Th2azkuLt1FTr7/FjTq0ca6ITAMeAcKBJ40x9zeZHg08B4wHSoELjDFb7bS7gauBBuAmY8wHdvxWoMKO9xhjJnTkNgSiWUu24zV0ibeBNCcqIox7fzCCi59cxAMfrOe3Zw93OySllPIbf3W45dvZVqDw3baVBWWs3lHO9PGZvJpb4GJU6khdeVwOc9cWMjtvJz+eqB24QQfWWItIOPAYcDowDLhQRIY1me1qYK8xZiDwEPBHu+wwYAYwHJgG/N2m1+gkY8yYrlio9jR4eXnxNqYMSuvSPVMdNzCNyyZn8+8vt7Lg6xK3w1EqJIjINBHZICKbReSuZqZHi8gsO32RiOT4TLvbjt8gIt/3Gb9VRFaJSJ6I5HbOlqhA1+A1zF1bSHpiNGP6dnc7HHWEJvdPZXB6Ak9/+Y2+es/qyKYgk4DNxpgtxpg64BXg3CbznAs8a4dfB04REbHjXzHG1BpjvgE22/S6vHkbitm1r4aLj852OxTX3XX6EHJS47j9tZVU1NS7HY5SQU0rQ1Rnys3fQ2lVHd8f1osw6XpNGkOFiHD1lH6s313BZ5u0kgs6tmCdAWz3+b/Ajmt2HmOMB9gHpLayrAE+FJGlInJtB8Qd0F5clE96YjSnDu3pdiiui4uK4C8/HsOuffv57ew1erWsVPtoZYjqFHUeL5+sKyI7NY7BvRLcDke10w/GZNArMYZ/fLrZ7VACQjA+vHi8MWYcTq3KT0XkhOZmEpFrRSRXRHKLi4s7N8IOsn1PNfM3FjNjYhYR4cF46PxvfHYyPzt5EG8u28Hz+mSyUu2hlSGqU3yxuYSKWg/ThvdCtLY66EVFhHHNlH4s3LKHZdv2uh2O6zqydLYD8G3JnmnHNTuPiEQASTgPMba4rDGm8W8R8B9aqBUxxvzTGDPBGDOhR48e7d6YQPDS4m2EiTBjkj4g4OvmUwZx6tCe3PP2WhZtKXU7HKXUwbpsZYj6rrLqOuZvLGJ4n8Qu/ZxQqLlwUhZJsZE8/qn2jNyRBeslwCAR6SciUTjt72Y3mWc2cLkdng58Ypz7+bOBGfZBmX7AIGCxiMSLSAKAiMQD3wNWd+A2BIya+gZeXbKdk4f0pHdSrNvhBJSwMOHBC8aQlRrHT15cRsHeardDUioYaWWI6nDvr96NMWhnMCEmPjqCy4/N4cO1hWwuqnA7HFd1WMHa3ia8EfgAWAe8aoxZIyL3iMg5drangFQR2QzcCtxll10DvAqsBeYAPzXGNADpwBcisgJYDLxrjJnTUdsQSN5YVkBpVR1XHdfP7VACUmJMJP+8dAJ1DV4ueXIRReXaE5RSh0krQ1SHWrC5hFU79nHi4B4kx0W5HY7ysyuOzSE2Mpy/z+vatdYd+h5rY8x7wHtNxv3GZ7gGOL+FZe8D7msybgsw2v+RBrYGr+Ffn21hdGYSx/RPcTucgDWwZzeeuXIilz61mIufXMQr1x5Dardot8NSKigYYzwi0lgZEg483VgZAuQaY2bjVIY8bytD9uAUvrHzNVaGeLCVISKSDvzHtqONAF7qKpUh6mD1DV5mvr2G5LhIThikdyRCUUp8FJdOzubJz7fwk5MGMLBn13wwtUML1so/5q7dzdbSah67aFyXeNCjvZ0jXDQpi2cWbOWsv33BWzceR8+EGD9F5r+OGyAwO29QXZtWhqiO8s/PtrCxsJJLjs4mUh++D1nXnziAFxfm89DcTTx28Ti3w3GFnt0BzhjD4/O3kJUSx7QRvdwOJyj079GNS47JpqSylvMeW8D63eVuh6SUUl3Wht0VPPLRJs4c1ZthfRLdDkd1oJT4KK4+vh/vrtrF6h373A7HFVqwDnBLtu4lb3sZ/zOlH+FhoV9b7S9HpSdw7QkD8Hi9TP/HV3y8rtDtkJRSqsvxNHi5/fUVJMREcM85w90OR3WCq6f0Jyk2kgfnbnQ7FFdowTrA/e2TTaTERzF9vL5i73BldI/lrZ8eT3ZqHFc/m8vdb66istbjdlhKKdVlPPHZFlYW7OPeH4zQZ166iKTYSK49oT+frC8id+set8PpdFqwDmALvi7h800l3HDiAGKjwltfQH1Hr6QY3rjhWK47oT+vLNnGtIc/Y+7aQu2lUSmlOtiybXt5+KONnDmqt75er4u58rgc0hOj+d3ba/F6u9bvrRasA5QxhgfmbKBXYgyXTs52O5ygFhMZzt1nDOX16ycTFRHG/zyXyw//sYAvN5doAVsppTrAnqo6bnxxGb2SYvi/H4x0OxzVyeKiIvjlGUNZtWMfry3d3voCIUQL1gFq7tpC8raXccupg4iJ1NpqfxifncIHt5zA/T8cye59NVz85CJOe+gznvx8CyWVtW6Hp5RSIaHBa7j5leWUVNXxj4vHkxQX6XZIygXnjO7DhOxkHpizgX37690Op9NowToANXgNf/pgA/3T4pk+PtPtcEJKZHgYMyZlMe8XU7n/hyNJiIng9++uY+J9H3H2377gTx+sZ976Inbvq9HabKWUOgIPzd3I55tKmHn2cEZkJLkdjnKJiDDznOHsqa7jrx9vcjucTqPvsQ5As5ZsZ1NRJY9dNI4Ifd9nh4iJDGfGpCxmTMpiY2EFc1bv5rONxTw+fwsNXqfXqO5xkWQmx9IrMZaeidEkxkSypbiSqIgwwsOEiDAhTOwnTAgXp3v1iLAwIsOFyPAwYiLDiY0MJzoyjLAu8A5ypVTX9vzCfB6dt5kLJvTlwkn60H1XNyIjiRkT+/LMgq2cNzajS1xoacE6wBRX1HL/++s4ul8KZ4zU91Z3hqPSEzgqPYGbThlERU0963ZVsG5XORsKK9hVtp+CvdUs37aXihoPdQ3eI1pHmEC36AiSYiPpHhdFWrcoYiLDDqw7KkIvoJRSwe29Vbv4zVurOWVIT+47b0SX6NBMte6uaUP5eF0Rt726gtk/O47oiNBu3qoF6wDz+3fXUlPv5b7zRmqm5IKEmEgm9UthUr/mu45/dsFW6hu8eLyGhgaD1xgajMFrwOs1NHgNHq/B0+ClvsFLTb2X6voGqms9lNd4KN9fz46y/azZuY95G4oBiAwXjkpPYFxWMuOzk5mQk0xmclxnbvZBtHdJpdThmrehiFteyWNcVjKP6t1W5SMpLpL7fzSSq57J5ZGPNnHHtCFuh9ShtGAdQD7bWMxbeTu5+ZRBDOzZze1wVDMiw8P80h2vx+vl2AGprNtVwZqd5azaUcabywp4fmE+AFkpcRw3MJXjB/bg+IFp+vCPUipgvb60gDvfWMmQXgk8dfkEfT2s+o6Th6Rz/vhMHp//Nd8b3osxfbu7HVKH0YJ1gKis9fCr/66mf1o8N0wd4HY4qoNFhIUxsGcCA3smcPboPoDz0OqG3RUs+qaULzeX8s6KXby8eDthAmOzkpl6VA9OGtKT4X0S9W6GUiGustbDzrL9FFfUUlHjobLWg8frJdw+09HYtCwlPoreSTEkxHT+xbcxhn/M/5oH5mzguIGpPHHpBLpFa7FCNe/XZw/ji80l3PzKcmb/9PiQrTDSb0AAMMZw95urKNhbzSvXTtbX63VR4WHCsD6JDOuTyJXH9cPT4GVFQRnzNxTz6cZi/jJ3I3+Zu5GeCdFMHdyDk4f05LiBaa78oCql/Ku6zsMXm0qYt6GI91fvpqz629eThYsQHx1OZHgYBqeb8MpaD779biTFRtI3OZYBPbsxsEc3UuKjOjTe4opa7nh9BfM2FHP26D78+fxRId92VrVPYkwkf7twLDP+uZBbZi3nqcsnEhYWepVEWrAOAC8u2sbbK3Zy+/cHt9i2V3U9EeFhjM9OYXx2Crd+bzDFFbXM31jMvPVFvL9qN6/mFhARJozLSubEwT04bmAaIzOSCA/BjEqpUOT1GhZuKeX1pQW8v3o3++sb6BYdQXZqHJP7p9KneyzpiTHER4V/5y6V1xgqazyUVNWys6yGHXur2Vpazeqd5QCkxkexpaSKU4emMzEn2W9tnr1ew7urdjFz9hoqaz387pzhXDY5W++iqTaZkJPCb88exq/fWsPDH2/i1tOOcjskv9OCtctW79jHPW+vZergHtxwojYBUS3rkRDN9PGZTB+fSX2Dl6X5e5m/sZjPNhbzpw828KcPNpAQE8HR/VKZmJPMhJwURmQkai2SUgGmstbD67nbeWbBVraWVpMQHcEPxmZw1qjeTMxJ4fWlBa2mESZCYmwkibGR9E9znskxxlBaWcemogo2FFbw/Ff5PPXFNyTFRnLS4B6cPDSd4wemHVFtttdr+GhdIQ99tIl1u8oZ3ieRhy8Yw6D0hMNOS3VtlxyTzcqCffz1400MTk/gzFGh1d29FqxdVLC3mv95LpfUblE8+OMxIXlLRHWMyPAwjumfyjH9U7lz2hCKK2pZuKWUBV+X8NXXpXy0rhCAqPAwBvdKYGRmEkN7JRx4vV9yB98mVkp9V1FFDU9/sZUXF+ZTUethbFZ3bjn1KKaN6OWXJoAiQlpCNGkJ0UwekMa5Y/rw+aYSPl5XyCfri/hv3k5EYGRGEpNyUhjdtzujM7vTp3tMszXae6rqWLernLlrC3l/9S4Ky2vplxbPwxeM4ezRffTumDoiIsK9PxjBlpIqbpm1nLiocE4a0tPtsPxGC9YuKa6o5ZInF1FV62HWdZM7vD2cCm09EqI5e3SfAw9CFlfUsjR/D8u3l7F6xz7eWbGTlxZ5DsyfFBtJdmocfVPi6JMUQ++kWHolxZDWLZqSylrioyKIiQzT27tK+UHB3moen/81r+YW4GnwcsbI3lwzpX+HvxkhPjqCaSN6MW1ELxq8hpUFZXy+qYTPNxXz3MJ86r74BnCe70hPiCalWxRer9PMpKiilj1VdQBER4Rx4lE9OGt0H84Y0UtfpafaLSYynH9fOZGL/rWQ619Yyr+vnMixA9LcDssvtGDtgrLqOi59ahGF5bW8cM3RDO2d6HZIKsT0SIhm2ojeTBvh3GIzxrC7vIaNhZVsKqwgv7SaraVVrN1ZzkdrC6n1fLfjm3AR4qLDiYsKJy4qgviocOKjI4iPjqBbdAQJMREkxESSFBtJQkyE9iypVBNbS6r4+6ebeXPZDkRg+vhMrjthADlp8Z0eS3iYMDYrmbFZydx0yiDqPF42Flawesc+dpTtZ0fZfsqq6wkTp0ZxTN/uDOzZjQE9uzEpJ4V4fduH8rPEmEieu+poZvzzK655NpdHLxrLyUPS3Q6r3fSb0sm2llRx1bNLKNizn6eumMD47GS3Qwpp/uzsxN/cii0uKoKhvRMPXNAZY6iua6CixkNFbT2VNR6q6hqoqvVQVeuhuq6BqjoPheW1VNVVsb+uAdMkzTCBRPvqr5S4KNK6RdMzIZr80ir6JsdpMyfVpWwqrOCxeZuZvWInEeFhXHx0FtedOIA+3WPdDu2AqIgwRmQkdYkuplXgSomP4oVrjuaqZ5ZwzbO5/OasYVxxXD+3w2oXLVh3okVbSrnuhaUI8MI1R+sbQFRAEJEDNdG9iGl1/gavobrOQ0WNh/Kaevbtr2dfdT1l++sPtMmsqmsA4LmF+cRFhTO4VwJDeycyKiOJkZlJHJWe4JeOdpQKJLlb9/DEZ1uYu7aQuKhwrpnSn2uO70fPxNa/V0p1VT0TYnj1usnc9HIeM99ey8aiSn595rCg7WhIC9adoM7j5dF5m/n7vM1kp8bx9BUTyU7t/FuBSvlDeJiQEBNJQkwkfWi+Bm5/XQPFFTXkpMWzfncF63aV83bezgO19NERYYzMSGJsVnfGZyczLitZCx8qKNV5vMxZs5tnvvyGZdvK6B4XyU2nDOLKY3P0IWGl2iguKoInLh3PA3PW88RnW/jq61L+fP7ooLyrrwXrDrY0fy93v7mSjYWVnDc2g5nnDCcpVjv0UKEtNiqcrNR4ZkzKOjDO6zXk76lmZUEZK7bvI2/7Xp79Kp9/fe48QNU3JZbxWcmMz0lhQnYyR6Un6FsHVMDKL63i9aUFvLJkO8UVtWSnxvG7c4Zz/oRM4qL0p1WpwxUeJtx9xlBOPKoHt7++kvMfX8BFR2dx08mDgqriRb/9HWTZtr088tEm5m8spldiDE9fMSEkGuUrdaTCwoR+afH0S4vn3DEZANR6Gli7s5yl+XtZmr+XL78u5b95OwHoFh3BmL7dGZfVnTFZ3RnTN1nfnqNcVVRewwdrdjN7xU6WbN1LmMCJR/XgsmNzOHFQD32WQCk/OHZgGnNumcKfP9jAi4u28frSAq44th+XH5tN76TAeU6hJR1asBaRacAjQDjwpDHm/ibTo4HngPFAKXCBMWarnXY3cDXQANxkjPmgLWm6Zf8pkQAAIABJREFUaW9VHe+s3Mmby3ewfFsZKfFR3DltCJdOzqabPlGt1HdER4QfeFPBNVOcBym379nP0m17WJZfRm7+Xh6dt/lA182ZybGMykxieJ8khtkHMNMTo/W1gH7S1fLs1ngavKwo2McXm0qYv7GI5dvLMAYG9IjnjmmD+eHYTHolBU9NmlLBIiEmkt+dO4Krju/Hg3M38sRnX/PPz77m5CHp/HhCJlMG9QjYNtgdVtoTkXDgMeA0oABYIiKzjTFrfWa7GthrjBkoIjOAPwIXiMgwYAYwHOgDfCQijf1etpZmp9m3v561O8sPdMyxfFsZHq9hcHoCvzpzKBdOytJXFCl1GESErNQ4slLjOG9sJgBVtR5W7dhH3vYyVu3Yx6qCfby3aveBZZJiIxnUsxsDe3YjJy2enNQ4slLiyUiO1WZXh6Er5NmH4mnwsrW0ig27K1mzcx/Lt5WxoqCM6roGRGBEnyR+bjtzGdSzm17MKdUJslPjeWTGWG47bTAvL9nGa7nb+WhdIdERYRw3MI1jB6QyNqs7w/sk+aWTJX/oyFLfJGCzMWYLgIi8ApwL+Gao5wIz7fDrwKPi5FbnAq8YY2qBb0Rks02PNqTZbl6voaLWQ0VNPRU1HvZU1VFSWUtReS3b91azbU81m4sqKdi7HycOGJWRxLUn9OfMUb0Z1jtRM12l/CQ+OuJAL5ONymvqWb/LeShyU1EFmwormbu2kFLboUWjhOgIeiXF0DMxmp4JMaTGR5EcH0VKfBSJ9h3c3WIi6BbtvKM7LjKC6MgwoiO6ZOc4QZtnt6TBa6iqc14bWVnjoWx/PXur6iitqqOovJaiihoK9u5n+55qCvbup67BeZ97RJgwrE8i54/PZGK/FI4bkKYPIirloqzUOO6cNoSfn3oUi74p5eN1RXyy3vmA853NSo2jf1o3+qXF0Sspll6JMaR1i6J7XBTd4yLpFh1BbGR4hzfZ6siCdQaw3ef/AuDoluYxxnhEZB+QascvbLJshh1uLc12u/KZJczfWNzstISYCLJS4hjdtzsXTspieJ9ExvTtTvc4zXSV6iyJMZFM6pfynVdWltfUk1/iXPzuKKtmx979FJbXUlhRw+Jv9rCnqo799Q2tpi/idBsfHR5GVEQYEeFCRJjzNzxMCBfnb5gImcmx/POyCR21qZ0paPPsWUu28di8r6lv8FLf4KW23kuNp4H6hqZvXD9YclwkGcmxDOmdwGnD0zmqZwKDeyUwsGe3gKn9Ukp9KyoijCmDejBlUA9mnjOcovIalm8vY2VBGV8XVbGlpJLPNxU32+lZo7iocD65bWqHNeMK2XYKInItcK39t1JENvgr7dXfHZUGlPgr/QCh2xQcAnqbLj6yxQJ6m5rzr8tbnaXpNmV3WDBBqiPz7JbkA3mHv1jQnJ8XB0+swRInBE+sfo/zCPPztuj0fdr73iNarE35dkcWrHcAfX3+z7TjmpunQEQigCScB2IOtWxraQJgjPkn8M8jDf5wiEiuMSYkqqwa6TYFB92m4BAk29Rl8uz2CJJjCQRPrMESJwRPrMESJwRXrG3RkV2fLQEGiUg/EYnCebBldpN5ZgONdT3TgU+MMcaOnyEi0SLSDxgELG5jmkoppQ6f5tlKKdVOHVZjbdvf3Qh8gPOapaeNMWtE5B4g1xgzG3gKeN4+6LIHJ9PFzvcqzgMuHuCnxpgGgObS7KhtUEqprkLzbKWUaj9xKhtUe4jItfY2ZsjQbQoOuk3BIRS3qasKpmMZLLEGS5wQPLEGS5wQXLG2hRaslVJKKaWU8oOObGOtlFJKKaVUl6EF63YQkZkiskNE8uznDJ9pd4vIZhHZICLfdzPOwyUi02zcm0XkLrfjOVIislVEVtljk2vHpYjIXBHZZP8mux3noYjI0yJSJCKrfcY1uw3i+Ks9bitFZJx7kbeshW0K2u+SiPQVkXkislZE1ojIzXZ8UB8n9V0i8jMRWW+P8wM+4wPuHBWR20TEiEia/T/gzjsR+ZPdnytF5D8i0t1nWkDt00D+XTzcPMhtIhIuIstF5B37fz8RWWT37SxxHnQOXsYY/RzhB6cHsl80M34YsAKIBvoBXwPhbsfbxm0Kt/H2B6LsdgxzO64j3JatQFqTcQ8Ad9nhu4A/uh1nK9twAjAOWN3aNgBnAO8DAhwDLHI7/sPYpqD9LgG9gXF2OAHYaOMO6uOkn+8c55OAj4Bo+39P+zfgzlGcVxx+gPOq7jQ7LuDOO+B7QIQd/qPPdySg9mmg/y4ebh7k9ge4FXgJeMf+/yowww4/Dtzgdozt+WiNdcc40L2vMeYbwLd730B3oFtjY0wd0NgFcag4F3jWDj8L/MDFWFpljPkM5+0LvlrahnOB54xjIdBdRHp3TqRt18I2tSTgv0vGmF3GmGV2uAJYh9PrYFAfJ/UdNwD3G6fbdowxRXZ8IJ6jDwF3AL4PUQXceWeM+dAY47H/LsR5zzkE3j4N6N/FI8iDXCMimcCZwJP2fwFOBl63swREnO2hBev2u9Hexnra5zZLc10DZ3x30YAUzLE3ZYAPRWSpOL26AaQbY3bZ4d1AujuhtUtL2xDsxy7ov0sikgOMBRYRusepqzoKmGJvWc8XkYl2fEAdTxE5F9hhjFnRZFJAxdmMq3Bq1CHwYg20eFrUxjzITQ/jXPQ19jmeCpT5XGAF7L5tq5Dt0txfROQjoFczk/4X+AdwL04B7l7gLziZgwoMxxtjdohIT2CuiKz3nWiMMSIS1K/FCYVtsIL+uyQi3YA3gFuMMeVORYwjhI5TSGslv48AUnCaUUwEXhWR/p0Y3gGtxPlLnCYWAeFQsRpj3rLz/C/O+89f7MzYQk2g50EichZQZIxZKiJT3YylI2nBuhXGmFPbMp+I/At4x/7blq6BA1Uwx34QY8wO+7dIRP6DczuvUER6G2N22dugRYdMJDC1tA1Be+yMMYWNw8H4XRKRSJwftBeNMW/a0SF3nELdofJ7EbkBeNM4DUEXi4gXSMOF49lSnCIyEqdN8gpbqMoElonIJDfihNZ/Q0XkCuAs4BS7byHwviOBFs93HGYe5JbjgHPEeTg9BkgEHsFplhRha60Dbt8eLm0K0g5N2qedBzS+5aCl7n2DQUh0QSwi8SKS0DiMU4OzmoO7ZL4ceMudCNulpW2YDVxmn/4/BtjncxswoAXzd8m2EXwKWGeMedBnUsgdpy7uvzgPMCIiR+E8xFZCAJ2jxphVxpiexpgcY0wOzm31ccaY3QTgeSci03CaBZxjjKn2mRQw+9QK6N/FI8iDXGGMudsYk2nPzRnAJ8aYi4F5wHQ7m+txtpfWWLfPAyIyBuf29VbgOjh0976BzrTQrbHLYR2JdOA/ttYmAnjJGDNHRJbg3MK9GueJ+R+7GGOrRORlYCqQJiIFwG+B+2l+G97DefJ/M1ANXNnpAbdBC9s0NYi/S8cBlwKrRCTPjvslQX6c1Hc8DTwtzmsi64DLbQ1rMJyjEJjn3aM4b/6Ya/PqhcaY6wPtex8Ev4uHmwcFmjuBV0Tk98BynIuEoKU9LyqllFJKKeUH2hREKaWUUkopP9CCtVJKKaWUUn6gBWullFJKKaX8QAvWSimllFJK+YEWrJVSSimllPIDLVgrvxGRBhHJE5E1IrJCRG4TkaA7x0TkSREZ1snrHGNfmq+UUq3S/NYv686xr09ERCaIyF/diKM9RGSqiLxjh88Rkbvcjqmr0/dYK3/ab4wZA2C7EX8Jp2el37YnUZ8emTqFMeaazlqXjzHABJx3zXaKzt6vSim/0vzWj4wxuUCu23G0hzFmNgHUcU1XFXRXtyo4GGOKgGuBG21PX+Ei8icRWSIiK0XkusZ5ReROEVlla13ut+M+FZGHRSQXuFlEeojIG3b5JSJynJ1vkoh8JSLLRWSBiAy244eLyGJbo7NSRAbZ8Zf4jH9CRMKbxm7XPcEOV4rIfTa2hSKS3sz83UTk33YbVorIjxqX9Zlnuog8Y4fPF5HVNs3PbE9e9wAX2LguEJEUEfmvTW+hiIyyy84UkWdF5HMRyReRH4rIA3bdc8Tp1hYRGS8i80VkqYh8ILZnw2b260GxtPOwK6Vc0MXy2/bmgeNt+iuAn/qk61vz29J2XiEib9r1bBKRB5o7HiJyv4istfviz3bcMyLyuIjkishGETnLjm/2WNl4PhWR10VkvYi8KOL0oiMi0+y4ZcAPfdZ7hYg86rO+v9r4t4jIdDs+TET+bpefKyLvNU5TfmKM0Y9+/PIBKpsZV4bTC+K1wK/suGicmoF+wOnAAiDOTkuxfz8F/u6TzkvA8XY4C6frVnBqaCLs8KnAG3b4b8DFdjgKiAWGAm8DkXb834HLmon5U2CCHTbA2Xb4gcZtaDL/H4GHff5Pbro/cLprfcYOrwIy7HB3+/cK4FGf+f8G/NYOnwzk2eGZwBdAJDAapwe10+20/wA/sNMWAD3s+Atwegprbr9+Jxb96Ec/gf/pwvlte/PAlcAJdvhPwGo7PBV4p5XtvALYAiQBMTi9GfZtEl8qsIFvO+BrzOOfAebgVGgOwuluPuYQx2oqsA/ItMt8BRxvl9lu0xDgVZ+4r8D+jtj1vWaXHQZstuOn49wZDQN6AXuB6W6fz6H00aYgqrN8Dxjlc2WchJMxnAr82xhTDWCM2eOzzCyf4VOBYfaCHSBRRLrZdJ61NSQGJ0MFJxP6XxHJBN40xmwSkVOA8cASm04sUNRK3HXAO3Z4KXBaM/OcCsxo/McYs7eVNL8EnhGny943W5jneOBHNr1PRCRVRBLttPeNMfUisgqne905dvwqIAcYDIzg226Cw4FdPmn77te2xKKUCi6hnN/CEeaBItIdp6DbeHfueZyLjaZa2k6Aj40x+wBEZC2QjVPQbbQPqAGesjXg7/hMe9UY4wU2icgWYAgtH6s6YLExpsCuK89uWyXwjTFmkx3/Ak7hvDn/tetb61P7fzzwmh2/W0TmtbCsOkJasFYdRkT6Aw04makAPzPGfNBknu8fIokqn+Ew4BhjTE2T5R8F5hljzhORHJzaD4wxL4nIIuBM4D17e02AZ40xdx/GZtQbe5lvt+VwvjPGZzjmwEhjrheRo21sS0Vk/GGkCVBr0/GKiG98XhufAGuMMZNbWP7Afm0uFmNM6WHGo5RyWRfLb48oD7QF67a4l2a203fdLcVojPGIyCTgFJza4Rtx7jrCwb8Jjf+3dKymtrauNvBdXlqcS/mVtrFWHUJEegCP49yWMsAHwA0+7d+OEpF4YC5wpYjE2fEpLST5IfAzn/TH2MEkYIcdvsJnen9gizHmr8BbwCjgY2C6OA/6IE475mw/bO5cDm6rl2wHC0VkqDhP6p/nM32AMWaRMeY3QDHQF6gAEnzS/By42M4/FSgxxpS3MZ4NQA8RmWyXjxSR4c3N2EIsSqkg0sXy27ZoNg80xpQBZSJyvJ3v4haWb3Y726KxZt8Y8x7wc5zmKo3Ot22cBwD9bZwtHauWrAdybBoAFx5OfDh3KX9k40jHaXKi/EgL1sqfYsW+/gn4CCdz/p2d9iSwFlgmzuuNnsBpwzYH5ynmXHur6xctpH0TMEGchzvWAtfb8Q8AfxCR5Rx8Nf9jYLVNcwTwnDFmLfAr4EMRWYnzI9PbD9v9eyBZ7EOAwEl2/F04twEXcHBTjD+J86DNajttBTAP59ZrnohcgNOOcLyN837g8rYGY4ypw6kp+aONJw84toXZm4tFKRX4ump+26pW8sArgcdsrC3V4ra0nW2RALxjt/kL4FafaduAxcD7wPX2jkCzx+oQ21aD0/TjXXEeXmyteU1Tb+C0714LvAAsw2m+ovxEvr2DopRSSiml/E2ct0K9Y4x5PQBi6WaMqRSRVJyC/nHGmN1uxxUqtI21UkoppVTX8Y5tbx4F3KuFav/SGmullFJKKaX8QNtYK6WUUkop5QdasFZKKaWUUsoPtGCtlFJKKaWUH2jBWimllFJKKT/QgrVSSimllFJ+oAVrpZRSSiml/EAL1koppZRSSvmBFqyVUkoppZTyAy1YK6WUUkop5QdasA4QIvK4iPzaT2lliUiliITb/z8VkWv8kbZN730Rudxf6R3Gen8vIiUi4mr3qyIyVUQKXFz/eSKy3R7jsW7F0dHs9vV3Ow6l/E3z+zat19X8XkSMiAz0c5pbReRUf6apAo8WrDuB/TLtF5EKESkTkQUicr2IHNj/xpjrjTH3tjGtQ34xjTHbjDHdjDENfoh9poi80CT9040xz7Y37cOMIwu4DRhmjOnVmesOQH8GbrTHeHnTiR3xg9DRmisM2O3b4lZMSh0Jze/br7X8/nArN9yuDOnqRCTH/i5FuB1LZ9CCdec52xiTAGQD9wN3Ak/5eyUhfOJmAaXGmCK3A/GnIzxe2cCaTl6nUqrtNL9vn5DM71UXYYzRTwd/gK3AqU3GTQK8wAj7/zPA7+1wGvAOUAbsAT7HuQh63i6zH6gE7gByAANcDWwDPvMZF2HT+xT4A7AYKAfeAlLstKlAQXPxAtOAOqDerm+FT3rX2OEw4FdAPlAEPAck2WmNcVxuYysB/vcQ+ynJLl9s0/uVTf9Uu81eG8czzSw7FSjAqeUoAnYBV/pMPxCz/f8K4Auf/w3wE2ATUAHcCwwAFth99ioQ1WRdv7TbtBW42CetaJxa5W1AIfA4ENtk2TuB3cDzzWxLs/vUpltpY60Cvm5m2c98plcCFzS3TiAZ5xwrBvba4cwm++te4Eu7Pz4E0uy0GOAFoBTnHF0CpNtpVwLr7DJbgOuaxHcukGf36dc459h9QANQY2N+1OeYDDzUueF7LO0+3wt8A5ze5FhvsTF943us9KMff3/Q/L5D83sgvsn0SqAPTv74MLDTfh6241qafxLwld3vu4BHsXm8Xc+B/KeZ2JvNU3B+Mz7ByRtLgBeB7s2dG3Zb78LJB0txfmMaj1OLeWwzsTSmUQGsBc5rEueXwEM2nS3AsXb8dnsML2/tmNhpM4EXfOZtPN6+511Lvxnb7LyN+3+y29/TDs0D3A6gK3xoJqO147cBN9jhZ/g2o/0DTmEs0n6mANJcWj4n93M2A4lt4YTfAYyw87zR+AXhEBmtHT7oy+STXmNGexWwGegPdAPexBYWfeL4l41rNFALDG1hPz2H8yOQYJfdCFzdUpxNlp0KeIB77D47A6gGkpvGbP+/gu8WrN8CEoHhNs6P7XYl4WRYlzdZ14M4GfeJOAXZwXb6Q8BsIMVuy9vAH5os+0e7bGwz29LiPvWJtdkMv7npza0TSAV+BMTZGF8D/tvkGH8NHGXn/xS43067zm5THBAOjAcS7bQzcX5cxO6XamCcnTYJ2AechvOjkgEMae74NN2OVs6NK3AKA/9j47kB54dVcM73cp9j0xsY7naeoJ/Q/aD5fWfl90234x5gIdAT6IFTKXLvIeYfDxwDRNj1rwNu8ZnebD57qDwFGIiTv0XbGD4DHm5hX99s48208z8BvGyntZjHNhPP+TgXCmE4FSlVQG877QqcvP9Km87vcc7Dx+w6v4dTCO7WhmNy0LlB8+ddS78ZB80b6h9tCuKunTiFr6bqcb6s2caYemPM58aenYcw0xhTZYzZ38L0540xq40xVcCvgR83PuzSThcDDxpjthhjKoG7gRlNblH+zhiz3xizAliBk+EexMYyA7jbGFNhjNkK/AW49DBiqQfusfvsPZwr48GHsfwDxphyY8waYDXwod2ufcD7QNMHBX9tjKk1xswH3sXZpwJcC/zcGLPHGFMB/J/dtkZe4Ld22eaOV1v26eE6aJ3GmFJjzBvGmGob4304BWFf/zbGbLQxvgqMsePrcQrmA40xDcaYpcaYcgBjzLvGmK+NYz5OrcUUu9zVwNPGmLnGGK8xZocxZn1rgbfx3Mg3xvzLOO1Mn8X5/qT7bPsIEYk1xuyyx1epzqb5veWn/L652O4xxhQZY4qB3x0qPZtvLTTGeOz6n+C7eWBLms1TjDGbbf5Wa2N48BBpXo9To19gjKnFKbhOt/uyxTy2me14zRiz0+aps3Duuk7ymeUbY8y/bd44C+iLs59qjTEf4tylGOinY9LSb0aXogVrd2Xg3Ppr6k84tQIfisgWEbmrDWltP4zp+Tg1I2ltivLQ+tj0fNOO4NtCDTjNDxpV49R0NJVmY2qaVsZhxFJqjPG0YV0tKfQZ3t/M/75p7bU/Wo3ycfZFD5xahqX2waUyYI4d36jYGFNziDjask8P10HrFJE4EXlCRPJFpBynZqV7kx/flo7b88AHwCsislNEHhCRSJvu6SKyUET22G0/g2/Ps744NRqHqy3nxoFYjTHVdrCbPUYX4PyI7RKRd0VkyBHEoFR7aX7/LX/k922JrU9LM4vIUSLyjojstnng/9GGfXSoPEVE0kXkFRHZYdN84RBpZgP/8fmdWIfTJC6dQ+SxzWzHZSKS55POiCbrbPo7hjGmud82fxyTthz7kKcFa5eIyEScE/aLptPs1eJtxpj+wDnArSJySuPkFpJsrYajr89wFs4VcQnObaM4n7jCObgQ2Fq6O3EyCN+0PRz8ZW6LEhtT07R2HGY6LTloO4H2vlkkWUTiff7PwtkXJTgZ1XBjTHf7STLG+GYwnbVPfTVd5204tflHG2MSgRPseGk1IadW7XfGmGE47fXOAi4TkWic285/xmkP2B14zyfN7TjNRNoSn692nRvGmA+MMafh1Aqux7lVrVSn0fz+O9qb3zcXZ3Ox7TzE/P/AyQ8G2Tzwl7Qh/4ND5in/Z9c10qZ5ySHS3I7zLEh3n0+MvZPXbB7bNAERybbrvhFItXnu6rZuRxOtHZP2/Ia2dl6FFC1YdzIRSRSRs4BXcNorrWpmnrNEZKBtVrAP5yrWaycX4rRvO1yXiMgwEYnDaYv2ur01tBGIEZEz7RXxr3DaXjUqBHJ8XxXVxMvAz0Wkn4h0w8lYZjWpOW6VjeVV4D4RSbAZxq04V/z+kAf80NbUDsRpltBevxORKBGZgpPxvWaM8eJkdA+JSE8AEckQke8fRrrt3adtOUcScC4AykQkBfhtW4MTkZNEZKT9US7HyYy9QBTOuVMMeETkdJw2fI2eAq4UkVNEJMzul8ba4xZjbs+5YWuQzrUXQbU4zYO8rSymlF9oft88P+T3hUCqiCQ1ie1XItJDRNKA3/ik19z8CTj5V6XNh25oy4pbyVMS7P/7RCQDuP0QST2Os/3ZNt0eInKuHW4pj20qHqfQWmyXuxKnxvqwteGY5AEniPPe9CScZkBtVWzj7xL9EmjBuvO8LSIVOFep/4vT9urKFuYdBHyE8wX9Cvi7MWaenfYHnMyjTER+cRjrfx7ngZndOE8c3wRgnPbDPwGexLkyrcJ5g0Sj1+zfUhFZ1ky6T9u0P8N5OroG+NlhxOXrZ3b9W3Bqdl6y6fvDQzhtyQpx2uC+2M70duO8gWKnTet6n/bCd+Lc2l1obwd+xOG19W7vPp0JPGvPkR+3MM/DOA+YlOA8QDPnMNLvBbyOk+GvA+bjtOmswDmvXsXZNxfhPMQJgDFmMc45/xBOAWI+39aOPILTvnCviPy1mXUe6bkRhvPjsBPnNvyJtPEHVKl20Py+dUec39u89mVgi903fXAezMsFVgKrgGV2XEvz/wInj6rAqQyZ1ca4D5Wn/A4Yh5O/vYvzcGdLHsHJHz+058pC4Gg7rdk8tpn9sBanHfRXOL9tI3HeynGkWjwmxpi5OPtoJbAU5002bWKb590HfGn3/zHtiDHgNT55rJRSSimllGoHrbFWSimllFLKD7RgrZRSSimllB9owVoppZRSSik/0IK1UkoppZRSfqAFa6WUUkoppfygPV0kB420tDSTk5PjdhhKKQXA0qVLS4wxPVqfs2vSPFspFWjamm93iYJ1Tk4Oubm5boehlFIAiEh+63N1XZpnK6UCTVvzbW0KopRSSimllB9owVoppZRSSik/0IK1UkoppZRSftAl2lgr1Rb19fUUFBRQU1PjdigqRMTExJCZmUlkZKTboSgV8DQPVoGgvfm2FqyVsgoKCkhISCAnJwcRcTscFeSMMZSWllJQUEC/fv3cDkepgKd5sHKbP/JtbQqilFVTU0Nqaqpm6MovRITU1FStfVOqjTQPVm7zR76tBWulfGiGrvxJzyelDo9+Z5Tb2nsOasFaqQAiIlxyySUH/vd4PPTo0YOzzjqr02LYunUrI0aM6LT1dYQHH3yQYcOGMWrUKE455RTy8799/eidd97JiBEjGDFiBLNmzTpkOm+88QYictA7lVeuXMnkyZMZPnw4I0eO1BpppUKI5sH+8fjjjzNy5EjGjBnD8ccfz9q1awFn22JjYxkzZgxjxozh+uuvP7DMyy+/zMiRIxk1ahTTpk2jpKQEgNdee43hw4cTFhbW4vvtN2zYcCDNMWPGkJiYyMMPPwzAzJkzycjIODDtvffe69Bt1zbWSgWQ+Ph4Vq9ezf79+4mNjWXu3LlkZGS4HdYR8Xg8RES4k8WMHTuW3Nxc4uLi+Mc//sEdd9zBrFmzePfdd1m2bBl5eXnU1tYydepUTj/9dBITE7+TRkVFBY888ghHH330gXEej4dLLrmE559/ntGjR1NaWqoPJioVQjQP9o+LLrroQKF59uzZ3HrrrcyZMweAAQMGkJeXd9D8Ho+Hm2++mbVr15KWlsYdd9zBo48+ysyZMxkxYgRvvvkm1113XYvrGzx48IE0GxoayMjI4Lzzzjsw/ec//zm/+MUv/L2ZzdIa6y7qpUXb2v1RHeOMM87g3XffBZwr+AsvvPDAtKqqKq666iomTZrE2LFjeeuttwCnFmDKlCmMGzeOcePGsWDBAgA+/fRTpk6dyvTp0xkyZAgXX3wxxpjvrHPp0qWMHj2a0aNH89hjjx0Y39DQwO23387EiRMZNWoUTzzxxIFpf/zjHxk5ciSjR4/mrrvuAmDq1KnSf++uAAAgAElEQVTccsstTJgwgUceeYTi4mJ+9KMfMXHiRCZOnMiXX34JwOLFi5k8eTJjx47l2GOPZcOGDQCsWbOGSZMmMWbMGEaNGsWmTZsAeOGFFw6Mv+6662hoaDjkPjzppJOIi4sD4JhjjqGgoACAtWvXcsIJJxAREUF8fDyjRo06kNk39etf/5o777yTmJiYA+M+/PBDRo0axejRowFITU0lPDz8kLEo1RrNZwOL5sHtz4N9KyuqqqpabV5hjMEYQ1VVFcYYysvL6dOnDwBDhw5l8ODBh1ze18cff8yAAQPIzs5u8zL+pAVrpQLMjBkzeOWVV6ipqWHlypUH1Zjed999nHzyySxevJh58+Zx++23U1VVRc+ePZk7dy7Lli1j1qxZ3HTTTQeWWb58OQ8//DBr165ly5YtBzJWX1deeSV/+9vfWLFixUHjn3rqKZKSkliyZAlLlizhX//6F9988w3vv/8+b731FosWLWLFihXccccdB5apq6sjNzeX2267jZtvvpmf//znLFmyhDfeeINrrrkGgCFDhvD555+zfPly7rnnHn75y18Czu3Dm2++mby8PHJzc8nMzGTdunXMmjWLL7/8kry8PMLDw3nxxRcBuOaaa1rt+vqpp57i9NNPB2D06NHMmTOH6upqSkpKmDdvHtu3b//OMsuWLWP79u3/z96dh1dVnX0f/94JhEFlDmOYByGBGCEM4iwKCApWUXEuSLUW9bFO1deittXHWlv71KGoddYqCopGhSIOOFVkEhkVEMKMhClAGJPc7x9nJ4aQ4QA5OUn4fa5rX9ln7b3Xvk/A7WLtte7F4MGDDyhfsmQJZsaAAQPo3r07f/nLX0q8t4hUPnoGl80z+Mknn6R9+/bceeedPPbYY/nlK1as4MQTT+T000/niy++AKB69eqMHTuWbt260bx5cxYtWsS11157SH9uecaNG3fAP4YAnnjiCZKTkxk5ciRbt249rHrDpaEgIhVMcnIy6enpvP766wwaNOiAYx9++CFpaWn89a9/BUKz6FetWkXz5s258cYb8x96S5Ysyb+mV69eJCQkAJCSkkJ6ejqnnHJK/vFt27axbds2TjvtNACuuuoqJk+enH+/efPmMWHCBAAyMzNZunQpH330ESNGjMjvFW7QoEF+fZdeemn+/kcffZQ/tg5g+/bt7Ny5k8zMTK655hqWLl2KmbF//34ATjrpJB588EHWrFnDhRdeSMeOHfn444+ZPXs2PXv2BGD37t00btwYgGeffbbE3+Wrr77KrFmz+OyzzwDo378/M2fOpG/fvsTHx3PSSScd1OOcm5vLrbfeyosvvnhQfdnZ2Xz55ZfMnDmT2rVr069fP3r06EG/fv1KjENEKg89g8vmGTx69GhGjx7Na6+9xgMPPMBLL71Es2bNWLVqFQ0bNmT27NlccMEFLFy4kFq1ajF27Fi+/fZb2rVrx0033cRDDz3E73//+9L/wArYt28faWlpPPTQQ/llN9xwA2PGjMHMGDNmDLfddhvPP//8IdV7KNSwFqmAhgwZwu233860adPYvHlzfrm789Zbbx30Wuz++++nSZMmfPfdd+Tm5h4wfKFGjRr5+7GxsWRnZ4cdh7vz+OOPM2DAgAPKp0yZUuw1xxxzTP5+bm4u06dPPyAegBtvvJEzzzyTiRMnkp6ezhlnnAGExuX17t2bDz74gEGDBvH000/j7lxzzTUHPCjD8dFHH/Hggw/y2WefHfA7uOeee7jnnnvy79epU6cDrtuxYwcLFizIj2nDhg0MGTKEtLQ0EhISOO2002jUqBEQemU8Z84cNaxFqhg9g4/8GZxn+PDh3HDDDUDod5H3++jRowft27dnyZIl+cNj2rdvD8All1zCn//850O+1+TJk+nevTtNmjTJLyu4/6tf/SriE1E1FESkAho5ciT33Xcf3bp1O6B8wIABPP744/kPoW+//RYI9WI0a9aMmJgYXnnllVLHvxVUr1496tWrx5dffgmQ/4ov735jx47N781YsmQJWVlZnHPOObzwwgvs2rULgC1bthRZd//+/Xn88cfzP+dNLsnMzMyfEFSwZ3j58uW0a9eOm2++maFDhzJv3jz69evHhAkT2LhxY/69Cmb5KMq3337L9ddfT1paWn7PCoTGK+b9T3LevHnMmzeP/v37H3Bt3bp12bRpE+np6aSnp9OnTx/S0tJITU1lwIABzJ8/n127dpGdnc1nn31GYmJiibGISOWjZ/CRPYPzxmYDfPDBB3Ts2BGAjIyM/N/N8uXLWbp0Ke3ataNFixYsWrSIjIwMAKZOnUqXLl1KvEdRCo+JB1i/fn3+/sSJEyOecUUNa5EKKCEh4YAxennGjBnD/v37SU5OJikpiTFjxgDwm9/8hpdeeokTTjiB77///oAei3C88MILjB49mpSUlAMm1owaNYrExES6d+9O165duf7668nOzmbgwIEMGTKE1NRUUlJS8l+LFvbYY48xa9YskpOTSUxM5KmnngLgzjvv5O677+bEE088oPfmzTffpGvXrqSkpLBgwQKuvvpqEhMTeeCBB+jfvz/Jycmcc845+Q/K4sb33XHHHezcuZOLL76YlJQUhgwZAoSWTD711FNJTEzkuuuu49VXX82fNX/vvfeSlpZW4u+pfv363HrrrfTs2ZOUlBS6d+9+0DhsEan89Aw+smfwE088QVJSEikpKTz66KO89NJLAHz++eckJyeTkpLCsGHDeOqpp2jQoAHNmzfnvvvu47TTTiM5OZm5c+fmj/ueOHEiCQkJfP311wwePDi/937dunUHDNXJyspi6tSpXHjhhQfEcuedd+an8fv000/5+9//fkh/NofKipqdWmaVmw0E/gHEAs+6+58LHa8BvAz0ADYDl7p7upn1Ap7JOw24390nhlNnUVJTU720CU5Hm7KYbX5571ZlEEnFsXjx4gP+hVzWM/Kr2u9LwlP47xWAmc1299QohVThHW3P7JKeNUfTc0PPYKkojuS5HbEeazOLBZ4EzgUSgcvMrPA702uBre7eAfg78HBQvgBIdfcUYCDwtJlVC7NOEREREZFyF8mhIL2AZe6+3N33AeOAoYXOGQq8FOxPAPqZmbn7LnfPezdRE8jrVg+nThERERGRchfJhnULoGCC2DVBWZHnBA3pTKAhgJn1NrOFwHzg18HxcOoUERERESl3FXbyort/4+5JQE/gbjOrWdo1BZnZdWY2y8xm5c0yFanozIwrr7wy/3N2djbx8fGHnB7ojDPOyJ9QMmjQILZt21amcQK0adOGTZs2lXm95WXfvn2MGDEif+WyadOmFXne+PHjSUpKIiYm5qBJOg899BAdOnTg+OOPLzH9lYhUDnoGl5/9+/dzzTXX0K1bN7p06XJAOr///Oc/HH/88XTo0OGAtHu//OUvadu2LSkpKaSkpBy0NHpB27dvJyEhgRtvvDG/bN++fVx33XV06tSJzp0789Zbb5X594pkHuu1QMsCnxOCsqLOWWNm1YC6hCYx5nP3xWa2E+gaZp151z1DMAEyNTU1cjM0RcrQMcccw4IFC9i9eze1atVi6tSp+SmRDtekSZPKKLqyl52dnZ+Vo7z961//AmD+/Pls3LiRc889l5kzZxITc2B/Q9euXXn77be5/vrrDyhftGgR48aNY+HChaxbt46zzz6bJUuWaIlzkUpMz+DyM378ePbu3ZufwjQxMZHLLruMli1bMnr0aKZOnUpCQgI9e/ZkyJAh+alNH3nkEYYNG1Zq/WPGjMlfdCfPgw8+SOPGjVmyZAm5ubnFpik8EpHssZ4JdDSztmYWBwwHCueySgOuCfaHAZ+4uwfXVAMws9ZAZyA9zDpFKrVBgwbxwQcfAAfn5MzKymLkyJH06tWLE088kXfffRcIrYQ1fPhwunTpwi9+8Qt2796df03BXo0LLriAHj16kJSUxDPPPJN/zrHHHss999zDCSecQJ8+ffjpp58Oimvz5s3079+fpKQkRo0adUBKqFdffZVevXqRkpLC9ddfn5+n9D//+Q/du3fnhBNOyF9E5f777+eqq67i5JNP5qqrriInJ4c77riDnj17kpyczNNPPw3Azp076devH927d6dbt2753zUrK4vBgwdzwgkn0LVrV9544w0AZs+ezemnn06PHj0YMGDAAblLi7Jo0SLOOussABo3bky9evWKTBvVpUuXgxaDAHj33XcZPnw4NWrUoG3btnTo0IEZM2aUeE8Rqfj0DC6fZ7CZkZWVRXZ2Nrt37yYuLo46deowY8YMOnToQLt27YiLi2P48OH59w7X7Nmz+emnnw5ap+D555/n7rvvBiAmJiZ/sa8y5e4R24BBwBLgR+CeoOyPwJBgvyYwHlgGzADaBeVXAQuBucAc4IKS6ixt69Gjh8uB/j195RFvVc2iRYuiHYIfc8wx/t133/lFF13ku3fv9hNOOME//fRTHzx4sLu733333f7KK6+4u/vWrVu9Y8eOvnPnTv/b3/7mI0aMcHf37777zmNjY33mzJnu7t66dWvPyMhwd/fNmze7u/uuXbs8KSnJN23a5O7ugKelpbm7+x133OF/+tOfDortpptu8j/84Q/u7v7+++874BkZGb5o0SI/77zzfN++fe7ufsMNN/hLL73kGzdu9ISEBF++fPkB977vvvu8e/fuvmvXLnd3f/rpp/Pvt2fPHu/Ro4cvX77c9+/f75mZme7unpGR4e3bt/fc3FyfMGGCjxo1Kj+ubdu2+b59+/ykk07yjRs3urv7uHHj8n8fY8eO9bFjxx70fZ5++mkfNmyY79+/35cvX+5169b1CRMmFPtnc/rpp+f/Tt3dR48enf9n4e4+cuRIHz9+/EHXFfX3CpjlEXz2VvbtaHtmH03P2ZLoGXx0PYP37dvnl156qTdq1Mhr167tTz/9tLu7jx8/3q+99tr8815++WUfPXq0u7tfc8013qlTJ+/WrZvfcsstvmfPnoPqzcnJ8dNPP91Xr17tL7zwQv61W7du9YSEBP/tb3/rJ554og8bNsw3bNhw0PXuR/bcjmj/v7tPAiYVKru3wP4e4OIirnsFeCXcOkWqkuTkZNLT03n99dcPSH4P8OGHH5KWlpa/GMCePXtYtWoVn3/+ef5iBsnJySQnJxdZ92OPPcbEiRMBWL16NUuXLqVhw4bExcXljyHs0aMHU6dOPejazz//nLfffhuAwYMHU79+fQA+/vhjZs+eTc+ePYFQz03jxo2ZPn06p512Gm3btgWgQYMG+XUNGTKEWrVq5X+nefPmMWHCBCC0ItjSpUtJSEjg//2//8fnn39OTEwMa9eu5aeffqJbt27cdttt/O53v+O8887j1FNPZcGCBSxYsIBzzjkHCK2w2KxZMwB+/etfF/m7GDlyJIsXLyY1NZXWrVvTt29fDeMQET2Dy+kZPGPGDGJjY1m3bh1bt27l1FNP5eyzzy7mTyXkoYceomnTpvljpR9++GHuvffeA8755z//yaBBg0hISDigPDs7mzVr1tC3b18effRRHn30UW6//XZeeaXI5uZhi87AGhEp0ZAhQ7j99tuZNm1a/hLcEHrD9NZbbxU5NKE006ZN46OPPuLrr7+mdu3anHHGGezZsweA6tWrY2YAxMbGHrASV2ncnWuuueaAiScA7733XrHXFFyVzN15/PHH81fTyvPiiy+SkZHB7NmzqV69Om3atGHPnj106tSJOXPmMGnSJH7/+9/Tr18/fvGLX5CUlMTXX38ddtzVqlU7YAWuvn370qlTp7Cvb9GiBatX/5ykaM2aNUc8FlNEKgY9gyP/DH7ttdcYOHAg1atXp3Hjxpx88snMmjWLli1bFvtszWus16hRgxEjRhS54uTXX3/NF198wT//+U927tzJvn37OPbYY3nooYeoXbt2/sqMF198Mc8991zY8YarwmYFETmajRw5kvvuu49u3bodUD5gwAAef/zx/LF13377LQCnnXYar732GgALFixg3rx5B9WZmZlJ/fr1qV27Nt9//z3Tp08/pJgK3mPy5Mls3boVgH79+jFhwgQ2btwIwJYtW1i5ciV9+vTh888/Z8WKFfnlRRkwYABjx45l//79ACxZsoSsrCwyMzNp3Lgx1atX59NPP2XlypVAaBnb2rVrc+WVV3LHHXcwZ84cjj/+eDIyMvIf6vv372fhwoUlfp9du3aRlZUFwNSpU6lWrVr+5JhwDBkyhHHjxrF3715WrFjB0qVL6dWrV9jXi0jFpWdw5J/BrVq14pNPPgFC47anT59O586d6dmzJ0uXLmXFihXs27ePcePGMWTIEID8cdvuzjvvvEPXrl0Pqvff//43q1atIj09nb/+9a9cffXV/PnPf8bMOP/88/MzQH388ceH9MwPl3qsRSqghISE/NeKBY0ZM4ZbbrmF5ORkcnNzadu2Le+//z433HADI0aMoEuXLnTp0oUePXocdO3AgQN56qmn8ifj9enT55Biuu+++7jssstISkqib9++tGoVWh44MTGRBx54gP79+5Obm0v16tV58skn6dOnD8888wwXXnghubm5NG7cuMjXm6NGjSI9PZ3u3bvj7sTHx/POO+9wxRVXcP7559OtWzdSU1Pp3LkzEMricccddxATE0P16tUZO3YscXFxTJgwgZtvvpnMzEyys7O55ZZbSEpK4qmnngIOfh25ceNGBgwYQExMDC1atDjgdeCoUaP49a9/TWpqKhMnTuSmm24iIyODwYMHk5KSwpQpU0hKSuKSSy4hMTGRatWq8eSTT2ooiUgVoWdw5J/Bo0ePZsSIESQlJeHujBgxIn8IzRNPPMGAAQPIyclh5MiRJCUlAXDFFVeQkZGBu5OSkpJf96xZs3jqqad49tlnS/wdPvzww1x11VXccsstxMfH88ILLxzSn0E4LO9fXcWeYPYX4AFgN/AfIBn4rbu/WubRREhqaqoXNdv/aPbaN6uOuI7Le7cqg0gqjsWLF9OlS5dohyFVTFF/r8xstrunRimkCu9oe2aX9Dyuas/ZkugZLBXFkTy3wxkK0t/dtwPnEUp51wG44zDiFBERERGpssJpWOcNFxkMjHf3zAjGIyIiIiJSKYUzxvp9M/ue0FCQG8wsHtgT2bBERERERCqXUnus3f0uoC+Q6u77gSxgaKQDE4mG0uYciBwK/X0SOTT6b0ai7Uj/Doabbq8zcKmZXU1o6fH+pZwvUunUrFmTzZs368EuZcLd2bx5MzVr1oxaDGY20Mx+MLNlZnZXEcdrmNkbwfFvzKxNgWN3B+U/mNmA0uo0s38H5QvM7Hkzqx6Um5k9Fpw/z8y6R/ZbS2WlZ7BEW1k8t0sdCmJmrwDtCS0vnpN3b+Dlw76rSAWUkJDAmjVryMjIiHYoUkXUrFnzoNW/youZxQJPAucAa4CZZpbm7osKnHYtsNXdO5jZcOBhQp0oicBwIAloDnxkZnmr5xRX57+BK4NzXgNGAWOBc4GOwdY7KOsdoa8tlZiewVIRHOlzO5wx1qlAouufkFLFVa9ePX/pV5EqoBewzN2XA5jZOELD+Ao2rIcC9wf7E4AnLLT821BgnLvvBVaY2bKgPoqr090n5VVqZjOAvP8zDQVeDv4fMt3M6plZM3dfX+bfWCo1PYOlKghnKMgCoGmkAxERkTLVAlhd4POaoKzIc9w9G8gEGpZwbal1BkNAriK07kG4cWBm15nZLDObpR5LEamswumxbgQsCnog9uYVuvuQiEUlIiKV1T+Bz939i0O5yN2fAZ6B0AIxkQhMRCTSwmlY3x/pIEREpMytBVoW+JwQlBV1zhozqwbUBTaXcm2xdZrZfUA8cP0hxiEiUiWEk27vM+B74LhgWxyUiYhIxTUT6Ghmbc0sjtBkxLRC56QB1wT7w4BPgrHQacDwIGtIW0ITD2eUVKeZjQIGAJe5e26he1wdZAfpA2RqfLWIVFXhZAW5BHgEmAYY8LiZ3eHuEyIcm4iIHCZ3zzazG4EpQCzwvLsvNLM/ArPcPQ14DnglmJy4hVBDmeC8NwlNdMwGRrt7DkBRdQa3fApYCXwdmv/I2+7+R2ASMAhYBuwCRkT+24uIREc4Q0HuAXq6+0aAYOXFjwjNIBcRkQoqyNQxqVDZvQX29wAXF3Ptg8CD4dQZlBf5/5OgB3z0IQUuIlJJhZMVJCavUR3YHOZ1IiIiIiJHjXB6rP9jZlOA14PPl1JEb4WIiIiIyNGs1Ia1u99hZhcBJwdFz7j7xMiGJSIiIiJSuYTTY427vwW8FeFYREREREQqrWIb1mb2pbufYmY7gILJ+o3QfJQ6EY9ORERERKSSKLZh7e6nBD+PK79wREREREQqp1Kze5jZK+GUiYiIiIgczcJJm5dU8EOw7G2PyIQjIiIiIlI5FduwNrO7g/HVyWa2Pdh2AD8B75ZbhCIiIiIilUCxDWt3fygYX/2Iu9cJtuPcvaG7312OMYqIiIiIVHjhDAWZYWZ18z6YWT0zuyCcys1soJn9YGbLzOyuIo7XMLM3guPfmFmboPwcM5ttZvODn2cVuGZaUOfcYGscTiwiIiIiIpEUTsP6PnfPzPvg7tuA+0q7yMxigSeBc4FE4DIzSyx02rXAVnfvAPwdeDgo3wSc7+7dgGuAwpMlr3D3lGDbiIiIiIhIlIXTsC7qnHAWlukFLHP35e6+DxgHDC10zlDgpWB/AtDPzMzdv3X3dUH5QqCWmdUI454iIiIiIlERTsN6lpk9ambtg+1RYHYY17UAVhf4vCYoK/Icd88GMoGGhc65CJjj7nsLlL0QDAMZY2ZW1M3N7Dozm2VmszIyMsIIV0RERETk8IXTsL4J2Ae8EWx7gdGRDCqPmSURGh5yfYHiK4IhIqcG21VFXevuz7h7qrunxsfHRz5YERERETmqlTqkw92zgIMmHoZhLdCywOeEoKyoc9YE+bHrApsBzCwBmAhc7e4/FohnbfBzh5m9RmjIycuHEZ+IiIiISJkptWFtZvHAnYQWiqmZV+7uZxV7UchMoKOZtSXUgB4OXF7onDRCkxO/BoYBn7i7m1k94APgLnf/qkAs1YB67r7JzKoD5wEflfYdREREREQiLZyhIP8GvgfaAn8A0gk1mksUjJm+EZgCLAbedPeFZvZHMxsSnPYc0NDMlgG38nPP+I1AB+DeQmn1agBTzGweMJdQg/1fYX1TEREREZEICie7R0N3f87M/sfdPwM+M7NSG9YA7j4JmFSo7N4C+3uAi4u47gHggWKq1XLqIiIiIlLhhNOw3h/8XG9mg4F1QIPIhSQiIiIiUvmE07B+IFh58TbgcaAO8NuIRiUiIiIiUsmEkxXk/WA3EzgzsuGIiIiIiFROpU5eNLO/mFkdM6tuZh+bWYaZXVkewYmIiIiIVBbhZAXp7+7bCaW2SyeUreOOSAYlIiIiIlLZhNOwzhsuMhgY7+6ZEYxHRERERKRSCmfy4vtm9j2wG7ghWDBmT2TDEhERERGpXErtsXb3u4C+QKq77weygKGRDkxEREREpDIJp8caoDPQJlhSPM/LEYhHRERERKRSKrVhbWavAO0JLSGeExQ7aliLiIiIiOQLp8c6FUh0d490MCIiIiIilVU4WUEWAE0jHYiIiIiISGUWTo91I2CRmc0A9uYVuvuQiEUlIiIHMbMY4NhgbQEREalgwmlY3x/pIEREpGhm9hrwa0JzXGYCdczsH+7+SHQjExGRwkptWLv7Z+URiIiIFCnR3beb2RXAZOAuYDaghrWISAVT6hhrM+tjZjPNbKeZ7TOzHDPTa0gRkfJR3cyqAxcAacF6AppMLiJSAYUzefEJ4DJgKVALGAU8GcmgREQk39NAOnAM8LmZtQbUuSEiUgGF07DG3ZcBse6e4+4vAAMjG5aIiAC4+2Pu3sLdB3nISuDMaMclIiIHC6dhvcvM4oC5ZvYXM/ttmNeJiMgRMrMmZvacmU0OPicC10Q5LBERKUI4DeSrgvNuBLKAlsBFkQxKRETyvQhMAZoHn5cAt0QtGhERKVaJDWsziwX+1933uPt2d/+Du98aDA0REZHIa+TubwK5AO6eTSj1XqnMbKCZ/WBmy8zsriKO1zCzN4Lj35hZmwLH7g7KfzCzAaXVaWY3BmVuZo0KlJ9hZplmNjfY7j2cX4KISGVQYro9d88xs9ZmFufu+8orKBERyZdlZg0JMoGYWR8gs7SLgo6RJ4FzgDXATDNLc/dFBU67Ftjq7h3MbDjwMHBpMNxkOJBEqKf8IzPrFFxTXJ1fAe8D04oI5wt3P+8Qv7eISKUTzgIxy4GvzCyN0FAQANz90YhFJSIieW4F0oD2ZvYVEA8MC+O6XsAyd18OYGbjgKFAwYb1UH5eBGwC8ISZWVA+zt33AivMbFlQH8XV6e7fBmWH+z1FRCq9cBrWPwZbDHBcUKYcqiIi5cDd55jZ6cDxgAE/BLmsS9MCWF3g8xqgd3HnuHu2mWUCDYPy6YWubRHsl1ZnUU4ys++AdcDt7r4wjGtERCqdcBrWi9x9fMECM7s4QvGIiAhgZhcWc6iTmeHub5drQIdvDtDa3Xea2SDgHaBj4ZPM7DrgOoBWrVqVb4QiImUknIb13cD4MMpERKTsnF/CMQdKa1ivJZTFKU9CUFbUOWvMrBpQF9hcyrWl1XlgoO7bC+xPMrN/mlkjd99U6LxngGcAUlNT9VZURCqlYhvWZnYuMAhoYWaPFThUB8gOp3IzGwj8A4gFnnX3Pxc6XgN4GehB6GF+qbunm9k5wJ+BOGAfcIe7fxJc04NQ+qlawCTgf9xdD2ERqVLcfcQRVjET6GhmbQk1focDlxc6J41QTuyvCY3b/sTdPZhT85qZPUpo8mJHYAahoSil1XkAM2sK/BTU24vQsMLNR/jdREQqpJJ6rNcBs4AhwOwC5TuA35ZW8ZHMSAc2Aee7+zoz60ooh2ve+L6xwK+Abwg1rAcCk0uLR0SksjKzwYQydNTMKxgqyskAACAASURBVHP3P5Z0TTBm+kZCz89Y4Hl3X2hmfwRmuXsa8BzwSjA5cQuhhjLBeW8SmuiYDYx295wgloPqDMpvBu4EmgLzzGySu48i1GC/wcyygd3AcHWGiEhVVWzD2t2/A74zs9fCnChT2GHPSM+bXR5YCNQKercbAHXcfXpQ58vABahhLSJVlJk9BdQmtIz5s4QaqjPCudbdJxHqgChYdm+B/T1AkXNm3P1B4MFw6gzKHwMeK6L8CeCJcOIVEansSl158TAb1VD0jPQWxZ0TLHqQNyO9oIuAOUHapxZBPSXVCYQmwpjZLDOblZGRcZhfQUQk6vq6+9WE3u79ATgJ6FTKNSIiEgXhLGkeNWaWRGh4yPWHeq27P+Puqe6eGh8fX/bBiYiUj93Bz11m1hzYDzSLYjwiIlKMYhvWZvZK8PN/DrPuQ5mRTqEZ6ZhZAjARuNrdfyxwfkIpdYqIVCXvm1k94BFCqevSgdejGpGIiBSppB7rHkHvyEgzq29mDQpuYdSdPyPdzOIITYpJK3RO3ox0OHBGej3gA+Aud/8q72R3Xw9sN7M+wepgVwPvhvVNRUQqIXf/k7tvc/e3gNZAZ3cfE+24RETkYCU1rJ8CPgY6E8oKUnCbVVrFwZjpvNnji4E382akm9mQ4LTngIbBjPRbgbuC8huBDsC9ZjY32BoHx35DaALPMkIrQmrioohUWWZ2sZnlrXp7B/CCmZ0YzZhERKRoJWUFeQx4zMzGuvsNh1P54c5Id/cHgAeKqXMW0PVw4hERqYTGuPt4MzsFOJvQkJCnCG8pcRERKUfhZAW5wcxOMLMbgy25PAITEREAcoKfg4Fn3P0DQotniYhIBVNqwzpI+v9voHGw/dvMbop0YCIiAsBaM3ua0OJZk4Kc/hU6o5OIyNGqpJUX84wCert7FoCZPUxo+dvHIxmYiIgAcAmhFWb/6u7bzKwZobHWIiJSwYTTsDZ+fhVJsG+RCUdERApy913A2wU+rwfWRy8iEREpTjgN6xeAb8xsYvD5AkLZPEREREREJFBqw9rdHzWzacApQdEId/82olGJiIiIiFQy4fRY4+5zCK34JSIiIiIiRQirYS0iIuXLzHYAXtxxd69TjuGIiEgY1LAWEamA3P04ADP7E6HJiq8Qmjh+BdAsiqGJiEgxSsyFamaxZvZpeQUjIiIHGeLu/3T3He6+3d3HAkOjHZSIiBysxIa1u+cAuWZWt5ziERGRA2WZ2RVBR0eMmV0BZEU7KBEROVg4Q0F2AvPNbCoFHubufnPEohIRkTyXA/8INge+CspERKSCCadh/TYFFicQEZHy4+7paOiHiEilEE4e65fMrBbQyt1/KIeYREQkYGadgLFAE3fvambJhMZdPxDl0EREpJASx1gDmNn5wFzgP8HnFDNLi3RgIiICwL+Au4H9AO4+Dxge1YhERKRIpTasgfuBXsA2AHefC7SLYEwiIvKz2u4+o1BZdlQiERGREoXTsN7v7pmFynIjEYyIiBxkk5m1J1gsxsyGEcprLSIiFUw4kxcXmtnlQKyZdQRuBv4b2bBERCQwGngG6Gxma4EVwJXRDUnKy2vfrCrx+OW9W5VTJCISjnB6rG8CkoC9wOvAduCWSAYlIiIh7r7c3c8G4oHO7n5KkClEREQqmFIb1u6+y93vAfoBZ7r7Pe6+J/KhiYiImf2PmdUBdgF/N7M5ZtY/2nGJiMjBwskK0tPM5gPzCC0U852Z9Yh8aCIiAox09+1Af6AhcBXw5+iGJCIiRQlnjPVzwG/c/QsAMzsFeAFIjmRgIiICgAU/BwEvu/tCM7OSLhARkegIZ4x1Tl6jGsDdv0SpnkREystsM/uQUMN6ipkdhzIziYhUSMX2WJtZ92D3MzN7mtDERQcuBaZFPjQREQGuBVKA5e6+y8waAiOiHJOIiBShpKEgfyv0+b4C+x6BWEREJGBmnd39e0KNaoB2GgEiIlKxFduwdvczyzMQERE5wG3Arzi4kwNCnRtnlW84IiJSmlInL5pZPeBqoE3B89395jCuHQj8A4gFnnX3Pxc6XgN4GegBbAYudff04FXnBKAn8KK731jgmmlAM2B3UNTf3TeWFouISGXi7r8KfqqTQ0SkkggnK8gkYDown0OYMGNmscCTwDnAGmCmmaW5+6ICp10LbHX3DmY2HHiY0BjuPcAYoGuwFXaFu88KNxYRkcrGzC4s6bi7v11esYiISHjCaVjXdPdbD6PuXsAyd18OYGbjgKFAwYb1UOD+YH8C8ISZmbtnAV+aWYfDuK+ISFVwfgnHHFDDWkSkggmnYf2Kmf0KeJ/QsuYAuPuWUq5rAawu8HkN0Lu4c9w928wyCS2AsKmUul8wsxzgLeABd9dkShGpUtxdmT9ERCqZcBrW+4BHgHv4ORuIA+0iFVQprnD3tUEu17cIrUL2cuGTzOw64DqAVq1alW+EIiJlyMwGA0lAzbwyd/9j9CISEZGihLNAzG1AB3dv4+5tgy2cRvVaoGWBzwlBWZHnmFk1oC6hSYzFcve1wc8dwGuEhpwUdd4z7p7q7qnx8fFhhCsiUvGY2VOE5p7cRGgVxouB1lENSkREihROj/UyYNdh1D0T6GhmbQk1oIcDlxc6Jw24BvgaGAZ8UtKwjqDxXc/dN5lZdeA84KPDiE1EpLLo6+7JZjbP3f9gZn8DJkc7KKkYXvtmVbHHLu+tt7Ui5S2chnUWMNfMPuXAMdYlptsLxkzfCEwhlG7veXdfaGZ/BGa5exrwHKEx3MuALYQa3wCYWTpQB4gzswuA/sBKQkv6Vg/q/Aj4V7hfVkSkEspLLbrLzJoTeqvXLIrxiIhIMcJpWL8TbIfM3ScRStdXsOzeAvt7CL3WLOraNsVU2+NwYhERqaTeD9YTeASYQ2iOy7PhXHi4awkEx+4mlBI1B7jZ3aeUVGfQkXIL0B6Id/dNQbkF5w8i9Pbzl+4+57B+EyIiFVypDWt3f6k8AhERkYO5+5+C3bfM7H1CKVAzS7vuSNYSMLNEQm8Qk4DmwEdm1im4prg6vyKUPWpaoVDOBToGW29gLAdniBIRqRLCWXlxBT9nA8kX5gRGERE5AkEDeTAFVr81M9z90VIuPey1BILyce6+F1gRDNfLmyheZJ3u/m1ebIUMBV4O5s9MN7N6ZtbM3deH/1sQEakcwhkKklpgvyahoRsNIhOOiIgU8h6h1WgPafVbjmwtgRaEVtwteG2LYL+0OsOJowWghrWIVDnhDAUpnP7u/8xsNnBvUeeLiEiZSnD35GgHEWlae0BEqoJwhoJ0L/AxhlAPdjg93SIicuQmm1l/d//wEK87lLUE1hRaS6Cka0ur83DiwN2fAZ4BSE1N1Wq6IlIphdNA/luB/WwgHbgkItGIiEhh04GJZhYD7Ce0SIy7e51SrjvstQTMLA14zcweJTR5sSMwI7h3aXUWlgbcGIzH7g1kany1iFRV4QwFObM8AhERkSI9CpwEzC9pAa3CjmQtgeC8NwlNdMwGRrt7DuSn1TugzqD8ZuBOoCkwz8wmufsoQilXB/HzYmMjjuzXISJScYUzFKQGcBEFZqQDuPsfIxeWiIgEVgMLDqVRnecI1xJ4EHgwnDqD8seAx4ood2D0ocYuIlIZhTMU5F0gE5hNgZUXRUSkXCwHppnZZA5c/ba0dHsiIlLOwmlYJ7j7wIhHIiIiRVkRbHHBJiIiFVQ4Dev/mlk3d58f8WhERCRfsDjMce5+e7RjERGR0oXTsD4F+GWwAuNefp6RXuXzqh6ttu3ax9zV2wBoeGwN4o+rQdM6NaMclcjRx91zzOzkaMchIiLhCadhfW7Eo5AKYe3W3Xzy/U98v2HHQWvYd2tRlyEnNOeYGkphLlLO5gbp78YDWXmF7v529EISEZGihJNub2V5BCLRtWzjTl6Znk5cbAynd4qnZ9sGHBNXjc1Ze1m0fjvTvs9g+aYsLjyxBV2alZY+V0TKUE1Ci7acVaDMATWsjyK79mYTE2PUrB4b7VBEpATqfhQWr9/O6zNW0ejYGow4uQ3H1ayef6xZ3Vo0q1uLxGZ1mDB7Da9OX8lVfVrTWY1rkXLh7sr7fJTKyXW+37Cd2Su3suSnHZgZic3q0L1VfTo1ORYzi3aIIlJITLQDkOhatnEn//5mJU3r1mTUqW0PaFQX1KxuLa4/rT3N69Vi3MzVrN22u5wjFTk6mVmCmU00s43B9paZJUQ7LomsXHfemLmKf3+zirXbdnNKh3h6tW3Ajxk7eenrdN6Zu5bDSG0uIhGmhvVRbPe+HCbMXk3DY2sw8uS21I4r+QVGXLUYrjqpNbXjYnn563Q1rkXKxwuElgVvHmzvBWVShU2ev54F67bTP7EJdw7ozMCuTTk/uTl3DezMqR0bMTN9K1MW/hTtMEWkkGIb1ma2w8y2F7eVZ5ASGe/NW8fOvdlc0qNl2OP26tSszjV927AvO5ebX/+W3Fz1mIhEWLy7v+Du2cH2IhAf7aAkcr5ctomvftzMye0bcsbxjYmN+XnIR7XYGAYmNaV32wZ8vjSDz5ZkRDFSESms2Ia1ux/n7nWAfwB3AS2ABOB3wP+VT3gSKfPXZjJ39TbO6tyYFvVrHdK1TerU5PwTmjN75Vb+/Y3mtopE2GYzu9LMYoPtSkKTGaUKWrk5i0nz19O1eR3O7dasyHPMjPNPaE5yQl2mLNzAik1ZRZ4nIuUvnKEgQ9z9n+6+w923u/tYYGikA5PIydy1n3fnriWhfi1O79T4sOo4sWU9Tu3YiIf/8wPrMzUkRCSCRgKXABuA9cAwQBMaqyB3Z9L89RxXsxrDerQkpoTJiTFmXHhiAvVqVee979aRo7eHIhVCOA3rLDO7IugpiTGzKyiQS1Uqn7Gf/cjufTn84sQWB7xiPBRmxv/+ohs5uc6YdxZoEo1IhLj7Sncf4u7x7t7Y3S9w91XRjkvK3oJ121m9dTfndGlCXLXS//ccVy2G85KbsWH7HqYv10sMkYognHR7lxMaDvIPQrlTvwrKpBLauH0PL/53BSe0rEezuoc2BKSwlg1qc1v/TjzwwWI+XPQTA5KallGUImJm95Zw2N39T+UWjERcdm4uHy7cQOPjatC9df2wr+vSrA6dmhzLR4t/oltCXeoUk9lJRMpHqf8kdvd0dx/q7o2CHpML3D29HGKTCHjsk6Vk5zj9Oh/eEJDCftm3De3jj+GRKT+QnZNbJnWKCBB6M1h4A7iW0FwXqUJmrNjC5qx9nNu1aYlDQAozM85Pbk52rjNlwYYIRigi4Si1YW1mnczsYzNbEHxONrPfRz40KWsrN2cxbsZqLuvViobH1iiTOqvFxnDHgONZtnEnb3+7tkzqFBFw97/lbcAzQC1CY6vHAe2iGpyUqeycXD79IYN28cfQqclxh3x9w2Nr0LddQ+au3sbmnXsjEKGIhCucMdb/Au4G9gO4+zxgeCSDksj4v4+WUi3WuOmsDmVa74CkppzQsh7/N3UJe/bnlGndIkczM2tgZg8A8wgN3evu7r9z941RDk3K0Py1mWTtzeb0TvGHvZriKR0bERtjfL5U6fdEoimchnVtd59RqCw7EsFI5Kzdtpu079ZxRe/WNK5Ts0zrNjN+N/B41mXu4dXpSr8nUhbM7BFgJrAD6Obu97v71iiHJREwfflmGh0bR/v4Yw+7juNqVqdH6/rMWbmNzN37yzA6ETkU4TSsN5lZe0ITFzGzYYRSPpXKzAaa2Q9mtszM7irieA0zeyM4/o2ZtQnKG5rZp2a208yeKHRNDzObH1zzmB3uP++PMi/9Nx2AESe3iUj9fds34tSOjXjy02Xs3Kt/d4mUgdsIrbT4e2BdgQW6dmiRrqpjwdpMVm/dTe+2DQ9pbHVRTusYj+N8qV5rkagJp2E9Gnga6Gxma4FbgBtKu8jMYoEngXOBROAyM0ssdNq1wFZ37wD8HXg4KN8DjAFuL6LqscCvgI7BNjCM73BU27k3m9e/WcW5XZuSUL92xO5z6zmd2LprP69/o0xgIkfK3WPcvVbeYl0FtrzFu6QKePnrdKrHGt1bhZ8JpDj1j4njhIR6zEjfwi51cIhERThZQZa7+9mEltDt7O6nhJkVpBewLLh+H6EJN4UXlhkKvBTsTwD6mZm5e5a7f0mogZ3PzJoBddx9uocSJ78MXBBGLEe1N2euZsfebEadGtn5Tie2qs/JHRryry+Wa6y1iEgptu3ax7tz15HSsj614mLLpM7TOsWzP8f5eoXyWotEQzhZQf7HzOoAu4C/m9kcM+sfRt0tgNUFPq8Jyoo8x92zgUygYSl1rimlTikgJ9d5/qsV9GxTn5SW9SJ+v9+c0YGNO/by1pw1pZ8sInIUGz9rDXuzc+nTrkGZ1dmkTk06NTmWGSu2sF8pUEXKXThDQUa6+3agP6FG71XAnyMaVRkws+vMbJaZzcrIOHrHm324cANrtu7m2lPKJztX3/YNOaFlPZ767EfltRYRKcFbc9aQUgaLdRXWp11DduzJZspC5bUWKW/hNKzzZlMMAl5294UFykqyFmhZ4HNCUFbkOWZWDagLlPT+am1QT0l1AuDuz7h7qrunxsfHhxFu1fTqNytpUa8W5yQ2KZf7mRmjz2jP6i27eX9eWHNcRUSOOovXb+f7DTu4sHvZv3Tt1OQ4GhwTx8v/VZYmkfIWTsN6tpl9SKhhPcXMjgPC6YqcCXQ0s7ZmFkco93VaoXPSgGuC/WHAJ8HY6SK5+3pgu5n1CbKBXA28G0YsR6X0TVl8tWwzl/VqSWxM+SVPObtLEzo1OZanPvuREv44RUSOWu/MXUtsjDG4W7MyrzvGjN5tGzAjfQuL1yuBjEh5CqdhfS1wF9DT3XcBcYRW/ypRMGb6RmAKsBh4090XmtkfzWxIcNpzQEMzWwbcGtwHADNLBx4FfmlmawpkFPkN8CywDPgRmBzGdzgqvT5jFdVijEtSW5Z+chmKiTF+dWo7vt+wgy+WbirXe4uIVHS5uc57c9dxWsdGZbYKbmE9WtenRrUYXv5avdYi5alacQfMrLO7fw+kBEXtDjVltLtPAiYVKru3wP4e4OJirm1TTPksoOshBXIU2pudw/jZazi7S5MyXxAmHENSmvPIlB/41xfLOa3T0TsUR0SksBnpW1iXuYffnds5YveoHVeNC1Ja8M63a7nr3M7UrVU9YvcSkZ+V1GN9W/Dzb0Vsf41wXHKEpiz8iS1Z+7i8d6uo3L9GtVh+eXIbvli6Sa8iRUQKeOfbtdSOi4343JerTmrN7v05TFSWJpFyU2zD2t1/Ffw8s4jtrPILUQ7Ha9+spGWDWpzSoVHUYriiV2tqx8Xyry+WRy0GEZGKZG92DpPmr2dAUlNqxxX70rhMdG1Rl+SEurw2Y5Xmu4iUk2Ib1mZ2YUlbeQYph2Z5xk6mL9/C8J6tiCnHSYuF1a1dnUtSW/Led+vYkLmn9AtERKq4aT9ksH1PNkNTmpfL/S7v1YolP+1k9sqt5XI/kaNdSUNBzi9hOy/yocnhGj97DbExxsU9Eko/OcKuPaUtObnOi/9Nj3YoIiJRN3n+eurVrs7J5fQ28fwTmnNsjWq89s2qcrmfyNGu2PdQ7l5q5g+peLJzcnl7zhrO6BQflUmLhbVsUJuBXZvy2jcruemsDhxTI7KvPkVEKqq92Tl8tHgjg7o1pXpsOEm5jtwxNapxwYnNeXPWGu49P5F6tePK5b4iR6uw/ss2s8FmdqeZ3Zu3RTowOTxfLN3ET9v3cnFq9Hur84w6tR3b92Qzftbq0k8WEamivly6iZ17szk3ArmrS3J5r9bsy87lrTlFrqcmImWo1O5DM3sKqA2cSSh/9DBgRoTjksM0fvZqGhwTx1mdy2elxXB0b1Wf7q3q8fxX6Vx1UptyXaxGRKSimDR/A3VqVuPk9uU7qTyxeR1SWtbjtW9WMvLkNhxq6tyilDa0JFoZqUSiLZwe677ufjWw1d3/AJwEdIpsWHI4tmTtY+qin7ggpQVx1crnNWO4fnVqO1Zt2cWHCzdEOxQRkXK3LzuXqYs2cE5i06g8n6/o3YofM7L4ZsWWcr+3yNEknP+6dwc/d5lZc2A/UL7vsSQs785dy/4cr1DDQPL0T2pKywa1ePbLFdEORUSk3P33x01s35PNoG5No3L/85KbU6dmNV6drpUYRSIpnIb1+2ZWD3gEmAOkA69HMig5PONnraFrizp0aVYn2qEcJDbGGHlyW2av3MrsleoxEZGjy+T5Gzi2RjVO6RidtQVqxcUyrEdLpizcQMaOvVGJQeRoUGrD2t3/5O7b3P0toDXQ2d3HRD40ORQL12WyaP12LkltGe1QinVJakvq1qrO059pwRgROXrsz8llyqINnN2lMTWqxUYtjiv6tGJ/jvOmJpKLREypDWszizWzIWZ2MzAauNbMbo18aHIo3pq9lrjYGM5PLp9FBw7HMTWqcfVJrZm6+CeWbdwZ7XBERMrFjBVb2LZrPwO7RncUZfv4YzmpXUNen7GKnFytxCgSCeEMBXkP+CXQEDiuwCYVxL7sXN6Zu5azExtT/5iKnaP0mr5tqB4bw7Na5lxEjhKTF6ynVvVYTu8UH+1QuLJPa9Zs3c3nSzKiHYpIlRROwzrB3S909/vc/Q95W8Qjk7BN+2EjW7L2MawCrLRYmkbH1uDiHgm8PWctG3domXORSDKzgWb2g5ktM7O7ijhew8zeCI5/Y2ZtChy7Oyj/wcwGlFanmbUN6lgW1BkXlP/SzDLMbG6wjYrst65YcnOdKQt/4szO8dSKi94wkDznJDYh/rgavPR1erRDEamSwmlYTzaz/hGPRA7bhNlraHRsDU7rGP3ekHD86tR27M/N5YWv0qMdikiVZWaxwJPAuUAicJmZJRY67VpCqVQ7AH8HHg6uTQSGA0nAQOCfwbDAkup8GPh7UNfWoO48b7h7SrA9G4GvW2HNWbWVjB17GZAUnWwghcVVi+HK3q2Z9kMGP2ZoSJ5IWQunYT0dmGhmu81su5ntMLPtkQ5MwrN5514++X4jF3ZvQbVyWiL3SLVpdAzndm3Kq1+vJHP3/miHI1JV9QKWuftyd98HjAOGFjpnKPBSsD8B6Geh1UOGAuPcfa+7rwCWBfUVWWdwzVlBHQR1XhDB71ZpTF6wgbjYGM7q3DjaoeS7vHcr4mJjeOm/6dEORaTKCacl9iihRWFqu3sddz/O3StePrej1Dtz15Gd61zUveIPAylo9Jkd2LE3mxfVay0SKS2Agukf1gRlRZ7j7tlAJqH5NMVdW1x5Q2BbUEdR97rIzOaZ2QQzKzJ1kZldZ2azzGxWRkbVGP/r7vxnwQZO6diI42pWj3Y4+eKPq8GQlOaMn7WGzF3q3BApS+E0rFcDC9xdU4grGHdn/KzVJCfU5fimlWs+aVLzupzdpQnPfbmcHXv0YBepwt4D2rh7MjCVn3vID+Duz7h7qrunxsdXjmFtpVmwdjtrt+1mYNeKMQykoBEnt2H3/hzemFXy0uQicmjCaVgvB6YFE1luzdsiHZiUbv7aTL7fsKNC564uyc39OrB9TzYvf62VwEQiYC1Q8OGQEJQVeY6ZVQPqAptLuLa48s1AvaCOA+7l7pvdPW9FkmeBHkf0rSqRyQvWExtjnNOlSbRDOUhS87r0btuAl/67kuyc3GiHI1JlhNOwXgF8DMShdHsVypuzVlOjWgxDUipu7uqSJCfU44zj43n2i+Vk7c0u/QIRORQzgY5Bto44QpMR0wqdkwZcE+wPAz4J3k6mAcODrCFtgY7AjOLqDK75NKiDoM53AcysYPLmIcDiMv6eFZK7M3nBBvq0a1Bh06COPKUta7ftZvKCDdEORaTKqFbSwWAG+HHufns5xSNh2rM/h3fnrmNQt2bUqUBj9w7VTWd15KKx/+WV6Sv59entox2OSJXh7tlmdiMwBYgFnnf3hWb2R2CWu6cBzwGvmNkyYAuhhjLBeW8Ci4BsYLS75wAUVWdwy98B48zsAeDboG6Am81sSFDPFkLrIlR5i9fvYMWmLEad2jbaoRTr7C5NaB9/DE9+uozzkpsRmoMqIkeixIa1u+eY2cnlFYyE7z8LNrBjTzYXp1auSYuF9Whdn9M7xTN22o9c1rMVdWtX3n8kiFQ07j4JmFSo7N4C+3uAi4u59kHgwXDqDMqXE8oaUrj8buDuQ429sps0fz0xRoVJs1eU2BjjN2d04Lbx3/Hx4o2cnVjxhqyIVDbhDAWZa2ZpZnaVmV2Yt0U8MinRm7NW06pBbfq0bRjtUI7Y7wZ2Zvue/Yz97MdohyIicsTcnUnz19OnXUMaHVsj2uGUaEhKc1o2qMXjny5DOQpEjlw4DeuahCamnAWcH2znRTIoKdnqLbv474+bubhHAjExlf/VXWLzOlyQ0oIXvlrB+szd0Q5HROSIfL9hB8s3ZTGoW7PST46y6rEx3HB6B75bvY0vl22KdjgilV6pDWt3H1HENrI8gpOivT5jFTEGwyr5MJCCbj2nE+7wf1OXRjsUEZEjkjcMpCKm2SvKRT1a0LROTR7/RL3WIkeq1Ia1mSWY2UQz2xhsb5lZ1WnRVTL7snN5c9Zq+nVpQrO6taIdTplp2aA2V/ZpzfjZq/lhw45ohyMicljcnQ/mr6d324o/DCRPjWqx3HBGe2as2MK0JVVjcR6RaAlnKMgLhFIvNQ+294KyUpnZQDP7wcyWmdldRRyvYWZvBMe/MbM2BY7dHZT/YGYDCpSnm9l8M5trZrPCiaMq+XDRBjbt3McVvVtFO5Qyd9NZHahTqzpj3lmgXhMRqZR++GkHyzOyGJRc8YeBFHRZr1a0aVib//1gsfJaixyBcBrW8e7+grtnB9uLQKnLYgWp+p4EzgUSgcvMLLHQadcCW93/f3vnHR5XcTXu92glrdqq2bLcu42xKcaFjkMoJhBCSSD0Gloaye+DJPDBR0iAEMgXEhISSiAQqgMO8BFCMQSMQ7GNDTa4NyEo2AAAIABJREFU925JbmpWl+b3x8zaV+vd1UpeaVfyeZ/nPnv33pm5Z2bOPffcuVPMSOB3wH0u7ljstE/jgK8Bf3bpBfmqMWa8MWZSDPL3KJ6bvYGBBZlMGdUzVibzUpCdzi1fG8PcdTt5+bPQdSwURVGSn38u3GK7gSTxbCDhSE9N4ZbTx7CyrJoX521KtDiK0m2JxbHeISKXiojPbZdiBzO2xZHAKmPMGmNMAzANODskzNnsXd52OnCy2Ik0zwamGWPqjTFrgVWEmcbpQGNVWTWfrNnBRUcO7hGDFsPx7UmDOGJwPve+uZSKGl3qXFGU7kNLi+HVz7dwwqgiigLdoxuIl9PG9WXy0AIeeGcF1bpol6J0iFgc66uBbwMlwFbsylpXxRBvALDR83+TOxY2jDGmCagAerUR1wAzRGS+iFwXgxw9hhfmbiA1RbrtEuaxkJIi3HX2Iezc3cBvZixLtDiKoigxM2/9LjaX13LuEaGPuu6BiPDfZxzM9up6Hp65KtHiKEq3JJZZQdYbY84yxhQZY/oYY84xxmzoCuEicLwxZgK2i8n3RWRKuEAicp2IzBORedu2df/BGDUNTUyfv4nTxvXtli0h7eGQAXlccexQnp29gY90+idFUboJr3y+max0H1PHdd+FVo4YXMA3jxjAY7PWsKykMtHiKEq3I+LKiyJyR6RzgDHG3NVG2psBb9PqQHcsXJhNIpIK5GG7mUSMa4wJ/paJyCvYLiKzwgj4GPAYwKRJk7r9SLh/fLaZitpGrjxuaKJF2cPzc/b//eriCIMwf3raGD5YsY2bXlzIWz8+gfys9P2+lqIoSmdR39TMv77Ywmnj+pKVHnVR46Tn9jPH8sGKbfxs+hf847vHkuqL5eO2oigQvcV6d5gN7IDDn8WQ9qfAKBEZJiLp2MGIr4WEeQ24wu2fB7xn7HQQrwEXullDhgGjgLkiki0iAQARyQamAotikKVb09JiePLDtRw2MI9JQwoSLU6XkJnu48ELjmB7dT3//cqXOkuIoihJzfvLtlFZ18Q53bQbiJfC7HTuPGscCzdV8NeP1iZaHEXpVkR0rI0xvw1u2JbfTGzf6mnA8LYSdn2mfwC8DSwFXjTGLBaRX4rIWS7YE0AvEVkF/Bdwi4u7GHgRWAK8BXzfGNMMFAMfishCYC7wL2PMWx3Id7di5ooy1mzfzXeOH4Yd23lgcOjAPP5r6mje+LKEl3SUuqIoScyrn2+md46f40b0SrQoceHMw/px6thifjtjBWu2VSdaHEXpNkT9XiUihViH9xLs7B0TjDG7Yk3cGPMG8EbIsTs8+3XA+RHi3gPcE3JsDXB4rNfvKTzx4Vr65mZ0i+Vx4831U0bw4crt3P7qIkb0yWbikMJEi6QoitKK8poG3ltWxqVHD+kx3SZEhLvPOYTTfj+L7z33Ga987zgy031tR1SUA5yIFkBEfoPtzlEFHGqMubM9TrUSH5ZureSjVTu4/NghpPUQg90efCnCny6eQP/8DK57ej4bd9YkWiRFUZRWTJ+/iYbmFs6b2LMWJS7OzeD3F4xneWkVt72qXfIUJRaieWo3YVdavB3YIiKVbqsSER0q3EU8/p+1ZKb5uPjInrfSYqwUZKfz+BWTaWhu4Zq/zaOyTue3VhQlOTDG8PzcDUwYnM/Y/rmJFifunHhQH3508ihe/mwzz89N5IRgitI9iNbHOsUYk2mMCRhjcj1bwBjT86xHErJhRw2vLtjMhUcOOuBnxRjZJ4eHL5nI6m3VXPb4HF08RlGUpGD2mp2s2babi48akmhROo0bTxrFV0YXcedri3UKVEVpgwOvb0E34s8zV+FLEW74yohEi5IUHD+qNw9fOpElWyu5+PHZ7NrdkGiRFEU5wHluznpyM1I587CeOwYmJUX4w4VHMLx3Dtc9PY8vNpUnWiRFSVrUsU5SNu6sYfr8TVw0eRDFuRmJFidpOHVsMY9dPomVZdVc8NgnrN+xu+1IiqIoncD26nreXlzCtyYOJCOtZw/sy8tK4+nvHElBdjpXPvkpZVV1iRZJUZKS7j2LfQ/m4Q9WkyLCDSdqa3UoXz2oD09eOZnvPjufb/zxQ35/4XhOGrP/K5115oI3iqL0PF6at4nGZsMlB8h9X5ybwbPfOYrzHvmYJz5cy1XHDaOvNvwoSiu0xToJ2VJey0vzNnL+pIH0y8tMtDhJyXEje/P6D09gYEEWVz81j3vfWEpNQ1OixVIU5QChsbmFZ2ev56hhhYzsE0i0OF3G0N7ZPH/t0Qjwl1lrdKYmRQlBHesk5H/fXo4gfFdbq6MyuFcWL3/vWC46chCPzlrDqQ/MYsbiEp0SSlGUTue1BVvYXF7LdVPaXC+txzG6OMB1U0aQme7jiQ/XsrykKtEiKUrSoI51krFwYzkvf76Zq48fxsCCrESLk/RkpPm495uH8dINx5DjT+W6Z+bzrYc/5u3FJbS0qIOtKEr8aWkxPPzBasb0DXDSmD6JFichFGanc/2U4fTKSefpT9Yxa8U2bdRQFNSxTiqMMdz1+hJ656Tz/a9qa3V7mDy0kNdvPJ67zjmEbdX1XP/MfE5+4AP+8O+VrNuuAxwVRYkfM5aUsqqsmu+eOAIRSbQ4CSOQkcb1U0YwbkAeby0u4aX5m2hoakm0WIqSUHTwYhLxxpclzFu/i3u/eSiBjLREi9PtSPOlcNnRQ7ho8iDeWFTCs5+s54F3VvDAOysY0zfAMSN6ceyI3owflE9RwJ9ocRVF6YYYY3h45ioGF2bx9UN77hR7sZKemsJFkwfxfm4G/15ayubyWi46gBc0UxR1rJOE2oZm7n1zKWP6Bvj2pEGJFqdbk+pL4azD+3PW4f3ZUl7Lv77YyswVZTw/ZwNPfrQOgD4BPwf3y2V4UTbDemczuDCLsso68rLS8Kf27GmzFEXpOB+v3sHCTRXcc+4hpPr0oy+AiHDSmD4MLszixXkb+fP7q+gT8HPJUYMP6BZ95cBEHesk4f63l7FpVy3TrjsaX4oaonjRPz+Ta6cM59opw6lvambBhnK+3FzBkq2VLN1axdy1O6ltbG4VJzPNR25mKnmZaZ4tnYLsNAqz0snNTCNFHxaKcsDR0mK4982l9MvL4FsTBiZanKRjZJ8cfnjSSKbP38Ttry7inSWl3Petw+ibp1PyKQcO6lgnAXPX7uSpj9dxxTFDOHp4r0SL0y1pzxzUWempTBpSyKQhhRhjqKprYufuBsprGymvaaCitpHKuiYqahvYvKuW3Q2tHe/UFKFXTjq9c/wU52bQNzeDfnkZFGb3vGXndW5vRdnL9PmbWLS5kgcvHN/jF4TpKIGMNK44digtxnDvG8uY+rsPuOMb4/jWhAHaeq0cEKhjnWBqGpr4yfSFDCrI4menj0m0OAccIkJuZhq5mZH7tDc2t1BR28iu3Q3srGlgZ3UD26vrKa2sY8mWSoLj4DPTfMxYUsqEwQUcNbyQ8YPy9eGrKD2EqrpG7n97GROHFHDW4f0TLU5SkyLCpUcP4YRRRfzkpYXc/NJCXvl8E78691CG9MpOtHiK0qmoY51gfvXGUtbvqGHadUeTla7VkYyk+VLoneOnd86+Ax4bmlooq6pjS3kdm3bVUFJRx+//vQLzLqT7Upg4pIATRvdmyqgixvXP1RYbRemmPPT+KrZXN/DEFZO7zX0cjy9O+8Ow3tm8eP0xPDd3A/e/uYypv5vF9786kuumDI/a6BBN7s78AtZWeR1oX9+0PDqGenIJ5KV5G3l29gauPWGYdgHppqSnpjCwIIuBBVkcOayQi48aTEVNI5+u28nsNTv4cNV27n9rOfe/tZziXD8njenDSWOKOWFUb23NVpRuwsrSKp78cB3fmjCQwwflJ1qcbkVKinDZ0UM49eBi7np9CQ+8s4Lp8zdxx5ljOfngPt3mJUVRYkUd6wSxYGM5t726iGNH9OJnX9MuID2JvKw0ThlbzCljiwEoq6pj1ortvL+sjNcXbuWFuRvJTPPxldFFnHZIMSeNKSYvSlcURVESR11jMz984XMCGan87PSDEi1Ot6VvXgZ/umQCF63czs9fW8Q1T8/jmOG9uO3rB3PIgLxEi6cocUMd6wRQVlnH9c/Mo0/Az0MXT9Apm3o4fQIZnDdxIOdNHEhDUwtz1u5gxuJSZiwp4a3FJaT5hGNH9OZrh/Rl6thieoXpcqIoSmK4942lLCup4smrJtMnoLNb7C/Hj+rNWz+ewvNzNvDgv1dy5h8/5KzD+3PjyaMY2Scn0eIpyn6jjnUXU1ZVx8WPz6GqronpNxzbI2eSUCKTnprCCaOKOGFUEb84axwLNpXz1qIS3ly0lVtf/pLbXvmSyUMLmTrOOtmDCnVZe0VJFO8uKeVvn6zn6uOG8dWDDsylyzuDNF8KVxw7lHMnDODRD1bz5EfreP2LLZx1eH++99WRiRZPUfYLday7kG1V9Vz8lzls3lXLU1dNZmz/3ESLpCSQlBRhwuACJgwu4NbTx7BkayVvLSphxuJS7np9CXe9voQxfQN8dUwfTh7Th/GD8vXrhqJ0EYu3VPBfLy5gbL9c7QLSSeRmpPGT08Zw9XHDeGzWGp7+ZD2vLtjC6OIcjhvZmxFFObpmgNLtUMe6i9i4s4arnvp0j1N9lA5WVDyICOP65zGufx43TT2Iddt3M2NJCf9eWsZjs9bw8MzVBPypHDW8F8eN7MXkoYUc3C9XFxNSlE5g9bZqLn9iLjn+VB67fKKuxtrJ9Mrxc+sZB3PDV0bw3Jz1PPLBGp78aB2F2elMGmIbH6JNiaooyYQ61l3AByu2ceMLn2OM4Ul1qluR6Omg4k0885PjT+Ps8QOYOrYvq7ZVs6qsms827OLdpaUA+FNTGFCQSf+8TPrnZ9AnkEHvHD/pqbZVW6dCUpT2s3FnDZc+PgcRePaaoxhYoN2xuoqC7HR+cNIocjPSWLSlgk/X7WLGklLeWVLKkF7ZHDoglymje2udKEmNOtadSENTC3+euYoH/72Sg4oDPHrZRJ0cX2k3mek+Dh2Qx6Fu5Hx5TQPrd9SwbsduNpfXMnvNDppazJ7wuRl2Ofb/rNxGUcC/Z1n2jDQf6b4U0lJtK7cx0NxiqG9qob6phbrGZnbXN1HT0Ex1fRO765tYVVZNfVMLDU0tNDS30NjcQlOzodkYjDG0GBDsghC+FCE9NYV0XwqZ6T6y0n1kp6eyubyGvrkZ9M/PZHBhFoMKs3SqQSUp+Xj1dn74/Oc0Nrcw7bpjGF6kg+kSQaovhfGDChg/qIDtVfUs3FzOl5sq+OcXW/nnF1sZXpTNlFFFTBpqW7P752cmWmRF2YM61p3Ex6u28z//t4jV23Zz7hEDuOfcQ3QBGCUu5Gelk5+Vvmc+3eYWw7bqerZV1bO9up4d1fVU1jaxsqyaT9bsoLK2EY/fHZXUFCEr3UeOP5Vsfyp1jc34U+3/9NQUUlOEVF8KPrF9xAXBYDAGmlpaaGgyNDQ1U9vYTEVtI1vKa1mwqZzmEAH65WUwoiiHEUXZjCoOMKZvgNF9A+Rm6OdepetpaTE8/uEafv3mMob1zubRyybpDBVJQu+An5PHFHPymGLKqurI8acya+V2pn26gac+XgdAUcDPmL4BDioOMLwox73AZ1Kcm6Ev8UqXo55eHDHGMHftTh6dtYb3lpUxuDCLJ6+arKPJlU7FlyL0zc2gb27rqcCCXUFaWgzVDU3UNTbT0NRCY7N1csXF9ael4Pf5yEz37elGEiQeXVsumDyI7dX1bC6vZePOGjbsqGHt9t2s3lbNPz7bTHV9056wA/IzObhfgDF9czm4Xy4H9wswpFe29iVXOo1563Zy1+tLWLipgtMP6ctvzj+cHL8+GpORPoEMLj5qMNecMJzG5haWbq1k/vpdLNpcybKSSp6evZ6GppZWcXIzUukd8JOfmUZ+VjqBjFSy0lPJ8fvITE8lM81HZpr9yrZwYzlpvhT35U3wp/nwp6aQ4X4VJRY61XqIyNeABwEf8Lgx5tch5/3A08BEYAdwgTFmnTt3K/AdoBm40RjzdixpJoLt1fW8+eVWpn+2mYUbyynMTufmqaO55oToy7YqSleQkiLkZqQlrDXYlyIU52ZQnJvBhMEFrc4ZY9hSUcfykkqWlVSxbGsVS7dW8v7ybXtauTPTfIzuG+DgvgFGFwc4qG+AUX1yKAr4ddW2NuhKGywiw4BpQC9gPnCZMaYh2jUSRUuL4ZM1O3j6k3W8vbiU4lw//3v+4XxrwgDVqW5Cmi+Fwwbmc9jAvSthNrcYSirr2LCjho27aiirrKOsqp4d1Q1U1DZSVlXH6m22m1t1fRN1jS1RrtCaFIHfvrOCgqw0euX46Z2TTp9ABn1y/RQHMuiXl0HfPNvlrTs/9+sam9mxu4HtVfUsL6mkpsF+gQx2CWxsbqG5xWCALzdXuK+Y4r5s+shKt10RC7PTKchOp3dOOr1z/N26TNpLpznWIuID/gScCmwCPhWR14wxSzzBvgPsMsaMFJELgfuAC0RkLHAhMA7oD7wrIqNdnLbS7HSq65v4YlM5c9bYZas/XbeTFgMj++Rw1zmHcN6EgWSmHzhKpCgdRUQYkJ/JgPxMThpTvOd4XWMzK0urWbrVOdwllcxYUsq0TzfuCRPISGVEUQ7DemczpFcWQ3tlM7AgkwEFmfQJZBzwrdwJsMH3Ab8zxkwTkUdc2g9Hukbn5n5f6hqb+WzDLj5cuZ3Xv9jKhp015GWmcePJo7jhK8O1q14PwJey154cQ9uTBLS4MSY1DU3UNbXw0ryNNDYbO6akqYX6puY9409qG5rpX5BJeU0D26sbWF5SxX9WbqeqrmmfdHtlp9MvP4P+eZl7Bpj3yw8635kUeQaZdxUtLYZdNQ2UVtZTWlnH1oo6SirrKKmoZWtFHWWV9ZRW1VFe0xgxjRSxLzS+FEFEWLNtN80tdtxNXVPznq+h4cjNSKVvnm1gCZZD8GWkX5794pqXmdYjXmw705IcCawyxqwBEJFpwNmA16ifDdzp9qcDD4kt1bOBacaYemCtiKxy6RFDmvvNjup6yqrq2bm7gR27GyipqGVLeR0bdtaworSKTbtqAatkY/vn8r0TR3Lm4f04qDjQI5RCURJNRpqPQwfmcejAvUsdG2PYXt3AitIqVpXZWVJWb6tm7tqdvLpgM8Zj030pQp+An+LcDIoCtnWpV7af/Cz7OTgvM40cf6r7LGxbWTLTfWSm+UjzSU+5j7vMBovIUuAk4GIX5m8u3YcjXcMYE2PP/9hoam6hsq6JitpGtlfXs7Wijq3ltawsq2ZFaRXLS6qob2rBlyIcNayQm6aO5rRxfQ+oljSlNSkpYu971xDW1sqa4WZaqm1o3uOobq2oZUt5LVsq6ti8q5Z1O3bz0art7G5o3ider+x0igJ+euWkU5gd7Kpivyxm+1PJ9lt7lJ6a4sa3pJAitjGixRiamg1NLS3UNdoXgJr6Zqrqm6iqa6SitpHymkZ21TS4cTcNbKuqbzXIHUAEinL89MvLYHCvLI4cVkifgJ8+uX56ZfuZv37XHrvoT7MyRCuP+qZmdtfb8TW7ahrYtbvBXrvaOvMlFXWUVtaxvKSKbdX1hFqA9NQUinP9FOX46Z3jp3fAT2GWbfkucGUTyEglkGHtd5bfDpLPSPWRkkQNKZ3pWA8ANnr+bwKOihTGGNMkIhXYz4gDgNkhcQe4/bbS3G9uemkhM5dva3Us4E9lQEEmRwwu4MLJgxjbP5dJQwt1sJWidBEiQlHAT1HAz3Eje7c6V9fYzKZdNWzaVcumXbVsrailpMIa8407a1iwsZyduxv2GUQZ/jp2KsM0n53hJNUnpKbYX1+K4HMzoKSIkJICA/OzeOSyiZ2V7f2hK21wL6DcGNMUJnyka2zvcM5C+MusNdzzxtKw54ID2y4/ZgjHjOjFkcN6aR9qJW5kpvsY2jubob3Dz/hljKGyromSijq2VNRS4lqHSyrr2FFdz47dDXy5qZzyWusQx+N1M+BPJS8rjYKsdIpy/Izpm0tRwE9xwE+f3L2txkUBP2lRFh0rq6pv13X9qT78qT4Ks9MZRvQZ0BqbWyitrNvbel5Rx7Yqa7O3VdezfkcN89fvYldNQ0yD79ODfeNTU0hzNjstaLNTBJ97MQm2tv/1ikn0yvG3K3+x0mOti4hcB1zn/laLyPL9TXMR8HbHovYmjg+RTqQ7yNkdZIQkkPOS2IJ1upwxytEWCS/PaDx6OdA+GYd0mjDdlM6w2QDrgXlJrj9R6LZyX9JJcsfJnkQianl38rX3h4ToSRzKI2H63fsHHYoWk93uTMd6MzDI83+gOxYuzCYRSQXysINbosVtK00AjDGPAY91VPh4IiLzjDGTEi1HW3QHObuDjKByxpvuIGcSytiVNngHkC8iqa7V2hs+0jVa0Zk2OwnrJiZU7q5F5e5auqvcbdGZvec/BUaJyDARSccOhHktJMxrwBVu/zzgPdfv7jXgQhHxu5Hmo4C5MaapKIqidKENdnHed2ng0vy/Nq6hKIrS4+i0FmvXl+4H2N4TPuCvxpjFIvJLYJ4x5jXgCeAZNzBmJ9ZI48K9iB1k0wR83xjTDBAuzc7Kg6IoSnclATb4Z8A0Ebkb+NylTaRrKIqi9EREGw46HxG5zn3mTGq6g5zdQUZQOeNNd5CzO8h4oNJd60bl7lpU7q6lu8rdFupYK4qiKIqiKEoc0DU6FUVRFEVRFCUOqGPdSYjInSKyWUQWuO0Mz7lbRWSViCwXkdMSKaeT52tOllUickui5fEiIutE5EtXhvPcsUIReUdEVrrfgrbS6QS5/ioiZSKyyHMsrFxi+YMr3y9EZEICZUw6vRSRQSLyvogsEZHFIvIjdzzZyjOSnElXpgcCInK+q4cWEZkUci5suUeydW4w5hx3/O9uYCZiB2/+3R2fIyJDuzB/CbfL8bJzInKFC79SRK7wHJ/o7PsqF3e/V/mIpz3pYrkzRGSuiCx0cv/CHW+3brZX/+OBiPhE5HMReb07yd0pGGN064QNu9LYzWGOjwUWAn5gGLAa8CVQTp+TYTiQ7mQbm+jy88i3Dugdcux+4Ba3fwtwXwLkmgJMABa1JRdwBvAmIMDRwJwEyph0egn0Aya4/QCwwsmTbOUZSc6kK9MDYQMOBg4CZgKT2ir3aLYOeBG40O0/AnzX7X8PeMTtXwj8vYvylhR2OR52DigE1rjfArdf4M7NdWHFxT09DjLHxZ4kQG4Bctx+GjDHXaNdutkR/Y+TrvwX8DzwekfuqUTJ3Rmbtlh3PXuWCjbGrAW8SwUngj3LHhtjGoDgssfJzNnYJZNxv+d0tQDGmFnYGQ68RJLrbOBpY5mNne+3X4JkjETC9NIYs9UY85nbrwKWYlfrS7byjCRnJJLtXu9RGGOWGmPCLSITqdzD2jrX2ngSdrl12FfXgjo4HTg5Hq2TMZAUdjlOdu404B1jzE5jzC7gHeBr7lyuMWa2sZ7V08TBlsfRnnS13MYYU+3+prnN0H7dbJf+76/cACIyEPg68Lj735F7qsvl7izUse5cfuA+Lf1V9nZXCLfMcLSHc2eTbPKEYoAZIjJf7MpsAMXGmK1uvwQoToxo+xBJrmQr46TVS/dZ8Ahsa03SlmeInJDEZXoAEqncIx2PeTl2ILgce2eTzLrT3vsy2vFNYY7Hjf20J10ut+tOsQAowzryq2m/brY3P/Hg98BPgRb3vyP3VCLk7hTUsd4PRORdEVkUZjsbeBgYAYwHtgK/Taiw3ZfjjTETgNOB74vIFO9J12KQdFPbJKtcJLFeikgO8A/gx8aYSu+5ZCrPMHImbZl2d9qwsUqCSab7MpTuYk+8GGOajTHjsSuXHgmMSbBIbSIiZwJlxpj5iZYlWejMJc17PMaYU2IJJyJ/AV53f2NZZrgrSTZ5WmGM2ex+y0TkFayxKRWRfsaYre6zXFlChdxLJLmSpoyNMaXB/WTSSxFJwz4EnzPGvOwOJ115hpMzWcu0JxCrjQ2hy5dj7wSSWXfae19uBk4MOT7THR8YJvx+Eyd70uVyBzHGlIvI+8AxtF8326v/+8txwFliB21nALnAg91A7k5DW6w7iZA+n+cCwVHVkZYKThRJu0y8iGSLSCC4D0zFlqN3iWTv0smJJpJcrwGXi+VooMLzSbJLSUa9dP3rngCWGmMe8JxKqvKMJGcylukBTk9Yjj1p7TLtvy/fBqaKSIHrJjUVeNudqxSRo929dTlxsOVxtCddLXeRiOS7/UzgVGz/8PbqZrv0f3/lNsbcaowZaIwZ6tJ8zxhzSbLL3amYJBhB2RM34BngS+ALrBL085y7Ddt3ajlxGE0cB1nPwI6cXg3clmh5PHINx44AXggsDsqG7Y/1b2Al8C5QmADZXsB+9m/E9vn6TiS5sKO9/+TK90s8MxgkQMak00vgeOxn2S+ABW47IwnLM5KcSVemB8KGfYnZBNQDpVinJ2q5R7J1ztbMxQ6Yegnwu+MZ7v8qd354F+Yv4XY5XnYOuNqV4SrgKs/xSdgX0dXAQ7hF6/ZT5rjZky6W+zDgcyf3IuCOjupme/U/jvpyIntnBek2csd705UXFUVRFEVRFCUOaFcQRVEURVEURYkD6lgriqIoiqIoShxQx1pRFEVRFEVR4oA61oqiKIqiKIoSB9SxVhRFURRFUZQ4oI51D0JEmkVkgYgsFpGFInKTiHS7OhaRx0VkbBLIMVREFrUdcr+v43crzC0QkQtCzl0pIv07W4b2ICLj3WIAwf9nicgtiZRJUQ4EDlQbLyInisixMYQ7J5Z0ReROEbk51utHSKNLng9dRahdVzqOrrzYs6g1djlURKQP8Dx2FaSf70+intWTugRjzDVdda3OpB3ldgRAsO5CuBI7p+mWMOn7jDHN+yVkxxiPncf1DQBjzGsk+4T9itIzOFBt/IlANfBxG+HOwa58uqQDYh3otLLryn6Q6Im0dYsv5spSAAAZWElEQVTfBlSH/B+OXSpUAB/wG+wqRl8A13vC/Qw7Mf5C4Nfu2Ezg98A84CagCLtE7KduO86FOxL4BDux/cfAQe74OOzk7wvc9Ua545d6jj8K+MLkYyZukn6sMb3HyTYbKA4T/k7gry7eGuBGd3wosMgT7mbgTs81fufytxSYDLyMXTzgbk/8ZcBzLsx0IMudmwh8AMzHrtDVL1y5hchZCLzqymM2dkGAPtiJ8itcmYzwhD/P5X+5O5cJrAPuAz7DrkB1rauPha5+gvI9BfzB1cka4Dx3vB8wy6W3CDjBHX/YybwY+IVHhskujYWu3vKADcA2l8YFWOf/IU+Zvefy+G9gcEfk0U033fbdOABtvLMpJdhlrBcAJ4SzM8CxwE5grQs3gsj28U7g5jByne/s0EJgluf6/8Ha3M+AYz3HF7n9sGUfi30D7nDxFgGPwZ71RWbSxjPKhfsvF3cR8ONQ2dz/0Gfffa6OVrjyTCfErida17vzlnABdItjZYYYXXesHCgGrgNud8f87mYdBpyONZZBgxNcjWom8GdPOs8Dx7v9wdjlYsG2lqS6/VOAf7j9PwKXuP10rFN4MPBPIM0d/zNweRiZZ7LX6BrgG27//mAeQsLf6fLgB3pjHzRpsRgXt/8jbItwP5fGJuwqXUPd9YMPmL+6NNLc9Yrc8QuAv4YrtxA5/wj83O2fBCxw+yfiVquKVhbu/zrgp57/vTz7dwM/dPtPYVe3SgHGAqvc8ZvYu4KlDwiE1LvPXfMwV29rgMneusbjSLvje/67+r3C7V8NvNoReXTTTbd9Nw5sG3+z5380O3OeJ1wk+9gqPU+YL4EBbj/f/WYBGW5/FDDP7Q9lr2MdqezbtG94Vg7GruIaLIuZtP2MmuhkzgZysA0jR9D2s++3bv8M4F23fyUeu65bxzftCnLgMBU4TETOc//zsEbiFOBJY0wNgDFmpyfO3z37pwBjRST4P1dEclw6fxORUVgDmebOfwLcJiIDgZeNMStF5GSsIfjUpZMJlLUhdwP20x7Y1uFTI4T7lzGmHqgXkTLsg6Ytgt0XvgQWG2O2AojIGmAQ9oG10RjzkQv3LHAj8BZwCPCOy4cPu+xvEG+5eTke+BaAMeY9EeklIrkxyBmKN/1DRORuIB9rWN/2nHvVGNMCLBGRYHl8CvxVRNLc+QXu+LdF5Dqs49wP6/waYKsx5lMncyWARwfCcQzwTbf/DPZB2RF5FEVpHz3dxnuJZme8RLOP4fgIeEpEXsS2DoPN70MiMh5oBkaHiRep7GOxb18VkZ9iHfhCrHP8T3eurWfU8cArxpjd7vjL2BbotrrmBfM2H+uEK3FEHesejIgMxxqCMuynwh8aY94OCXNalCR2e/ZTgKONMXUh8R8C3jfGnCsiQ7FvwxhjnheROcDXgTdE5Honw9+MMbe2IxuNxr1Ou7xE0tl6z34wXBOtB+hmRIjTEhK/xXMdQ2sMNh+LjTHHRJBld4Tj8cKb/lPAOcaYhSJyJbb1O4g3TwJgjJklIlOw9fKUiDyA/cx5M7ZlepeIPMW+ZRUPYpLHGPN0J1xbUXocB5iN7whPEdk+7oMx5gYROQqbp/kiMhH4IVAKHI4to7owUcOWPUA0+yYiGdhW/UnGmI0icietbW8sz6hwxPrsi3d5K+isID0WESkCHsF+2jHYN/XvujdnRGS0iGQD7wBXiUiWO14YIckZWAMTTD840C4P2/cN7Kek4PnhwBpjzB+A/8N2Lfg3cJ4bdIOIFIrIkDhkNxKlQB/XMuwHzuxAGoNFJOhAXwx8iO3zXBQ8LiJpIjIuhrT+A1zi4pwIbA+2AkehCghEOR8Atrp6vaQtAVx5lxpj/gI8DkzAfurdDVS4luTTXfDlQD8RmeziBkQktQ2ZPsb2/cbJ858OyKMoShscYDY+1OZEsjOh4dprH0cYY+YYY+7A9jcehM3/Vve17TLsF8pQwpZ9DPYt6PBud18HzqN9/Ac4R0SyXF2f64515NnX1rNGiRF1rHsWmeKmYgLexRrKX7hzj2NHSn/mpgh6FNtv7i3sZ6N5IrIA23IZjhuBSSLyhYgsAW5wx+8H7hWRz2n95vttYJFL8xDgaWPMEuB2YIaIfIE1+P3ikvMwGGMagV9iB2m8gx2I2F6WA98XkaVAAfCwMaYBawDvE5GF2MEebU4Fhe3XN9Hl/dfAFTHEeQp4xNVrZpjz/wPMwX7CjCV/JwILXX1dADxojFmIHZi0DNvP8iMAl88LgD+6fL6DfRC8j/1kvM/0gNgH81Uuj5dh+wa2S54Y8qAoByoHqo3/J3Cuy/sJRLYz04CfiMjnIjKC9tvH34jIl678goO2/wxc4WzgGMJ/kQxb9rRh34wx5cBfsAMP38Z2HYkZY8xn2GfEXJfPx40xn3fw2RfNrivtQPZ+gVEURVEURVEUpaNoi7WiKIqiKIqixAF1rBVFURRFURQlDqhjrSiKoiiKoihxQB1rRVEURVEURYkD6lgriqIoiqIoShxQx1pRFEVRFEVR4oA61oqiKIqiKIoSB9SxVhRFURRFUZQ4oI61oiiKoiiKosQBdawVRVEURVEUJQ6oY60oiqIoiqIocUAda0VRFEVRFEWJA+pYK4qiKIqiKEocUMdaURRFURRFUeKAOtaKoiiKoiiKEgfUsVYURVEURVGUOKCOtaIoiqIoiqLEAXWsFUVRFEVRFCUOqGOtKIqiKIqiKHFAHWtFURRFURRFiQPqWCuKoiiKoihKHFDHWlEURVEURVHigDrWiqIoiqIoihIH1LFWFEVRFEVRlDiQVI61iAwWkWoR8SValq5ARP5bRB6PY3rVIjLc7T8lInfHMe1HROR/4pVeO677XREpdXnr1UXXnCki18QpreNEZKWT/5wYwg8VESMiqfG4fjxpjw5EK8NkzmO82B8daqPs4m0z1onIKfFKLyTtuN1H3QEROUhEFohIlYjc2IXXTchzU0SKRWSWy+9vY4zTJfomIpeIyIzOuE5X0N7nhpJcJMSxdjdXrVOa4NbfGLPBGJNjjGlOhFwhMqaLyHQnqxGRE9sZf6aI1DmjUyki80XkFhHxB8MYY35ljGnzwRPrA8qV3Zr2yBnheleKyIchad9gjLlrf9NupxxpwAPAVJe3HV15/TjxS+AhJ/+roSc780ETbxKhA0prYrUZ4Yj3y3Y86U73QRR+CrxvjAkYY/7QWRcJLasEPjevA7YDucaYm0JPJlLfjDHPGWOmthUuie+JqM8NJTrhfJiuJJEt1t9wShPctnTmxTrYOvYhcClQ0sHL/sAYEwD6ATcBFwJviIh0ML2w9OCWv2IgA1icaEH2gyF0b/kB6G5fkbrqnujB957SfnrEvd4OhgBLjDEm0YL0QCLqkliSqreBEoIxpss3YB1wSpjjQwEDpLr/w4BZQBXwLvAn4Fl37kRgU6R0gTuB6cCzQCVwDfZF4hZgNbADeBEojEHeTcCJ7czjTOCakGODgRrgTI+MwfxkOFl3AOXAp1jH8h6gGagDqrFvsbhy+j6wEljrOTbS7T8FPAK848rvA2BIuHL2ygsc7K7V7K5X7knvbk/4a4FVwE7gNaC/55wBbnCylbt6kwjl5Ad+D2xx2+/dsdHAbpdWNfBehPhHAx+76yz01hNwFbDU5X8NcH1I3LOBBU4/VgNf85TFXcBHLu4MoHeUug5bFi7NFqDW5cEfEu+ZkPM/9dTNFcAGbIvQbZ44Meuwy/uZnv+pwDZggvv/EvalsQJ7n43zhH0KeBh4w9XDKV4dAAqA1116u9z+wBB9uheY68r3/4Jysu99ngc8AWwFNgN3Az53biRWdytcWfw9Ql6DaX7HldusGPSjEHgSq3e7gFfbod+h996pwDIn50NO5ms8ca529bELeBt3L8YSNySfd7LXZkTVlZB41wGNQANW1/7psZk3A1+46/8dyPDEOxN7j5S7cjwsyn0QMR/ACOA9rM5uB54D8iPdB23pZwz290FgI1b35gMneM4dCcxz50qBByKkEVXHQ8K+R2s7PZqQZwBwJfBhrHbS6WDQfi0BJoQrK/a9n/pjdXYnVoevDdGfF4GnXbqLgUlRyvFY7LOowv0e67EPXn06JSReovVtT1kDAvwOKHN1/iVwSBQZg/Y1WO7nhtYh8L9OJ9YCp8doU2LKG2GeG1hdugf7TKrF2sW26vklrE9R5fI8GrjVlcNG7JfgSGXb4TIIk1Z/4B/Y+2gtcKPneC2e5xdwBNY+pMVgN8PeP0TwYbpy69KLeQpkHbE51p+4yksHjnc3RXsc60bgHKwzkgn8CJgNDHTK+ijwQgzy7uNYAxcDX0SJM5MwD0fsA+I+j4zB/FwP/BPIAnzAROwntrBpuXJ6B3sjZ3qOeR3rKmCKy+uD7DU0rco59BqEPAA86QWdqpOc8k9waf8R58h45HgdyMe+TGzDOa1hyuOXrk76AEVYg3NXJDlD4g7APqjPcHV8qvtf5M5/HftAF+Ar2JeaoFN5JNYgn+riDgDGeMpiNdYQZbr/v44gQ1tlsY4wuh7pvCfPf3HXPhyoBw5252PWYeAO4DnP/68DSz3/rwYC7H25WRBS3xXAca58MkJ0oBfwLay+BrBG3PsQmYl1kg8BsrGGNdQhDN7nr7h8ZDs9mIt7CQJeAG7zyHB8hLwG03zapZMZg378C/tgLwDSgK+0Q7/33HtAb+y9dp5L5/8BTey9n87GPvgOxr7c3A587M5FjRsmn3eGKcewuhIm7p76C9G/udiHXCH2IXaDO3cE9iF8FNYmXeHC+8Ok3VYZjHTl78fe57OA30e7T4iinzHY7EuxOpqK/VpYgnPgsM+Vy9x+DnB0hDSi6niY8DNp7UiH/r+SfR3rsHYSOB97/0zG2q+R7G0YaVVW7Hs/zQL+jL1fxrt0T/LoTx32nvBhX35nR8hPIdahucyV40Xuf69I+pRE+ranrIHTsC9X+ex1vPpFkfF8J18KcAG2YaGfJ91G7EuPD/gu1omWNmxKzHmLUMczsS/P41xdpMVYz6e58E9jndrbXNxrcY0CEa7f4TIISSfFlf0dWD9uOLaR6zR3/j1avxD8BnikLbsZw/2zp/4TsSXmolZpqrFvGeU4Y4XHQLiCagKyPPGepX2O9ayQ80uBkz3/+zkFCeu4ecLFpcXaHZ8G/MUjYzA/VxPhLTZcWq6cTgpzzOtYT/Ocy8G+wQ1i/x3rJ4D7Q9JuBIZ65Djec/5F4JYI5bQaOMPz/zRgXag+RIj7M+CZkGNvA1dECP8q8CO3/yjwuyh1d7vn//eAtyKEbass9uhklHsh3EPS2/o7F7iwvTqMfRhX4e4hbCvhHRHkyHfXzfPU99ORdCBM/PHArpAy/LXn/1hsy5CP1vd5MdYZzPSEvQjbVxXsA+ExIrQUhim34bHohyu3FqCgA3Xa6t4DLsfjnGAf3pvYez+9CXzHcz4F+5I3pK24YWS7k30d67C6EibuPvXn9O9Sz//72ftgexj3kus5vxznLIQcb28+zgE+j3QftKWf7d2wDuHhbn8W8AuifIWKRcfDnJ9J+x3rsHbS6emPIlynVVnR+n4ahLXzAc/5e4GnPPrzbsh9WRvhOpcBc0OOfQJcGUmfkkXfaO1YnwSswH69SmlLxjDXWgCc7Ul3ledcliv7vkS3KTHnLUIdzwR+6fkfSz2/4zn3DazPFfwSGHBy58eo+zGVQZh4RwEbQo7dCjzp9q/BfY12dbgRmOL+R7SbMdw/e+o/EVsi++mcY4zJd1u4Ua/9gZ3GmBrPsY3tvEZo+CHAKyJSLiLlWCelGftw7yoGYD/dhPIM1phOE5EtInK/G7wXjbbKY895Y0y1u27/9ggbgf7A+pC0d2DzFsTbL70G65y0mZbbj1XGIcD5wfp0dXo81sAhIqeLyGwR2enOnYFt6QBrmFZHSbtD8kcoi44Q6fox67AxZpU7/w0RyQLOAp4H22daRH4tIqtFpBJryGFv+UAU/RKRLBF5VETWu/izgPyQvtje+OuxLSXe9IP5SQO2evL0KLblGuynbgHmishiEbk6kkxhrhlNPwZh7cuuMGnEUqcbQ8J77zUTRo4HPTLsdHkaEEPcWIhVV9sbfwhwU0j5DSL8/Rk1H24GiWkistnpy7Psqwt4wseinxERkZtFZKmIVDi58zxxv4P9GrVMRD4VkTMjpBGLju8vkcq+LfsUieBzs8pzbD3RbXNGhLECobY5XFododP1zYsx5j1sV5E/AWUi8piI5EYSTkQuFzu7S1CGQ2itd3vk9/gnOUS3Ke3JWyRCbU5b9Vzq2a8Ftpu9A1xrPXLvw36UQShDgP4h+f5v9j6v/gEcIyL9sF/XW4D/eOJGspv7yEHHbF+nkMwd4LcChc4hCDLIs78b+6YE7BlcVRSShgn5vxHbFyjfs2UYYzbHU/BIiMggbBeP/4SeM8Y0GmN+YYwZi+3Xdib2rRz2zQdtHA+yp7xEJAf76W0LtuzAU37YN+5Y092CVfpg2tnYz6YdKcdWaWG/VMQ6kHUjtkXSW5/Zxphfu9lX/oHtSlRsjMnH9hcWT9wRHZA3qvwdKIu2yjqU9urwC9gW4LOxA41WueMXu2OnYJ2OocEsxCjbTcBBwFHGmFysUQyN771fB2NbfbeHyU89tvUwmJ9cY8w4AGNMiTHmWmNMf2x3qT+LyMgocnlljqgf7lyhiOSHSSOWOvVeZyut7zUJyftGbNcWrxyZxpiPY4gbTzqia/eEyJ1ljHkhTNi28vErd/1Dnb5cSnRdi0U/wyIiJ2BfyL6NbT3Mx3ZrEgBjzEpjzEXYl7f7gOmujkOJRcej0eoZRWsb2xbR7FO0etyC1euA59hg4mOb25tWIvWttSDG/MEYMxHbQj8a+Ek4GUVkCLZr1Q+wXV7ygUXEVufRbEp78hYxG579eNZzK/azDELZiO1y4s13wBhzBoB7CZmB7W5yMfYru/HEjWQ326K9uhdXktaxNsasxw4wuVPs1HfHYD9nBFmBfdP+umvZvR3bFy8ajwD3OMVBRIpE5OxIgUXELyIZ7m+6iGR0ZEYP1/LxFewArrlYBy80zFdF5FD3glCJdUJa3OlSbN+k9nKGiBwvIunYwXizjTEbjTHbsDfgpa5l6GpaG/FSYKCLF44XgKtEZLxzYH8FzDHGrOuAjC8At7u66I3ti/VsjHGfxbbGnubykSEiJ4rIQGx/Lj+231WTiJwOeKdfesLl4WQRSRGRASIypoPy709ZtLdu26XD2K5HU7H94J73HA9gHdod2If/r9ohQzB+LVAuIoXAz8OEuVRExrqX418C003IlGDGmK1Yw/pbEcl1dTHC3S+IyPmuPsF+zjfsvS/aIqJ+uOu+iXXUC0QkTUSCjlN76/RfwDgR+aZr+buR1k7UI8CtIjLO5SlPRM6PMW48aa+u/QW4QUSOEku2s7eBMGHbykcA+ym6QkQGsNexiSRbVP0UO53WughyB7DdCLcBqSJyB5DriXupiBQZY1qwXREhvE7FouPRWAB809n/kdiW8lh5HLhZRCa6sh8ZvOeJUo/GmI3YLoX3On0/zF03Vpvq5Q1gtIhcLCKpInIB1jF9Pcb4idS3PYjIZJdmGvZlp47Iz9ZsrI3Z5uJehW2tbZM2bEp78hbLteJZz6F0uAzCMBeoEpGfiUims8OHiMhkT5jnsY2I59H6GRXNbrZFWz5Mp5K0jrXjEuAYrHG9GzsooB7AGFOB7fv6ONZJ3I3tYxWNB7GjaGeISBV2ENhRUcIvxxrWAdhuGrW4N3ixE9C3NbXSQ+46pdjBN//Adq4PZ8T7YmcxqcR+vv8A2z0kKPd5IrJLRNozP+rz2IfBTmxL+aWec9diH247sAMivG+B72FHi5eISGgLI8aYd4H/cfnZinXKL2yHXF7uxr5AfYEdufyZO9Ymzricjf20tA37hvsTbD+6KqyxfRHrkF2Mrftg3LnYWUN+h23N+oB9W2dikWF/y+Je7ItFuYjcHEP4dumwM/afYL+C/N1z6mnsp8PN2FHfs9shM1h9zsS2QM8G3goT5hlsH8YS7ACbSItmXI59EVqCravpuO482MFbc0SkGpvvH5kY52qPph8uyGXYF9hl2IFFP3bx2lWnxpjt2ME+v8beT6Owo/eD51/BtoxOE9ulYBFweixx48wTwFina23OjWuMmYe1Ew9h62UVtu9iuLBt5eMX2MGgFVin6OWQJELvg7b0cxCRy+ltrD6ucGnU0foz+teAxU6nHsT2Sa/dJ5XYdDwav8OOKygF/oYd4xATxpiXsLNAPI8dJ/Eq9osjtG0zLsK28G/BDgz+udPpdmHsugFnYlvud2C/Apzp6joWEqlvXnKxju0urD7swA6S20dGY8wS4LdYm1kKHBol3XBEsikx560dxKWeQ4lDGXjTasbq0Hjs4MntWJ8tzxPsNWz9lRhjFnriRrSbMRDVh+lsgiNZuwUi8ndgmTGmvS0HiqIoSg9B7Kp6PzLGLE20LIqiKF6S2rF2nwt2Yt90pmLf2o8xxnyeUMEURVEURVEUJYRkXzWsL/aTYS9sN4/vqlOtKIqiKIqiJCNJ3WKtKIqiKIqiKN2FZB+8qCiKoiiKoijdAnWsFUVRFEVRFCUOqGOtKIqiKIqiKHFAHWtFURRFURRFiQPqWCuKoiiKoihKHFDHWlEURVEURVHiwP8HtDaxbZTl0OsAAAAASUVORK5CYII=\n", "text/plain": [ - "[]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -399,119 +97,212 @@ } ], "source": [ - "x = cust_data[\"Distance from event center (km)\"]\n", - "y = cust_data[\"Decrease on Day 62\"]\n", - "plt.scatter(x, y)\n", - "plt.xlabel(\"Distance from event center (km)\")\n", - "plt.ylabel(\"Change on Day 62\")\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = spend_data[\"Distance from event center (km)\"]\n", - "y = spend_data[\"Decrease on Day 62\"]\n", - "plt.scatter(x, y)\n", - "plt.xlabel(\"Distance from event center (km)\")\n", - "plt.ylabel(\"Change on Day 62\")\n", - "plt.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = trans_data[\"Distance from event center (km)\"]\n", - "y = trans_data[\"Decrease on Day 62\"]\n", - "plt.scatter(x, y)\n", - "plt.xlabel(\"Distance from event center (km)\")\n", - "plt.ylabel(\"Change on Day 62\")\n", - "plt.plot()" + "cust_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"cust_dec_62\"].mean(), data[\"cust_dec_62\"].median())\n", + "spend_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"spend_dec_62\"].mean(), data[\"spend_dec_62\"].median())\n", + "trans_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"trans_dec_62\"].mean(), data[\"trans_dec_62\"].median())\n", + "sales_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"sales_dec_62\"].mean(), data[\"sales_dec_62\"].median())\n", + "\n", + "fig1 = plt.figure()\n", + "fig1.set_figheight(8)\n", + "fig1.set_figwidth(10)\n", + "\n", + "plt.subplot(221)\n", + "cust_dist = sns.distplot(data[\"cust_dec_62\"], kde = True, label=cust_mm)\n", + "cust_dist.set(xlabel='Decrease in customers', ylabel='Normalised count', title='Distribution of customers')\n", + "plt.legend()\n", + "\n", + "plt.subplot(222)\n", + "spend_dist = sns.distplot(data[\"spend_dec_62\"], kde = True, label=spend_mm)\n", + "spend_dist.set(xlabel='Decrease in median spending', ylabel='Normalised spending', title='Distribution of median spending')\n", + "plt.legend()\n", + "\n", + "plt.subplot(223)\n", + "trans_dist = sns.distplot(data[\"trans_dec_62\"], kde = True, label=trans_mm)\n", + "trans_dist.set(xlabel='Decrease in number of transactions', ylabel='Normalised number of transactions', title='Distribution of number of transactions')\n", + "plt.legend()\n", + "\n", + "plt.subplot(224)\n", + "sales_dist = sns.distplot(data[\"sales_dec_62\"], kde = True, label=sales_mm)\n", + "sales_dist.set(xlabel='Decrease in total sales amount', ylabel='Normalised sales', title='Distribution of total sales amount')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout(pad=0.4, w_pad=0.6, h_pad=1.0)\n", + "fig1.text(0.5,-0.05,\n", + " \"Figure 1: Distribution of each of the variables recorded in the data, as a function of the distance from an event\",\n", + " size=12, ha=\"center\")\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Computing the mean changes to consumers' behaviour:" + "Using a mean/median of the reference period, obtained by taking the midpoint of the minimum and maximum values over for each distance measure, a value can be reconstructed for the measurement on Day 62 (for each location) using:\n", + "\n", + "\\begin{equation}\n", + "\\textrm{value} = \\frac{\\textrm{min} + \\text{max}}{2} - \\textrm{decrease.}\n", + "\\tag{1}\n", + "\\end{equation}\n", + "\n", + "We can then plot the maximum and minimum values for the reference period, as well as the reconstructed Day 62 variables to observe the behaviour of consumer spending after the event." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist = data[\"distance\"]\n", + "\n", + "cust_min = data[\"cust_ref_min\"]\n", + "cust_max = data[\"cust_ref_max\"]\n", + "cust_recon = data[\"recon_cust_val\"]\n", + "\n", + "spend_min = data[\"spend_ref_min\"]\n", + "spend_max = data[\"spend_ref_max\"]\n", + "spend_recon = data[\"recon_spend_val\"]\n", + "\n", + "trans_min = data[\"trans_ref_min\"]\n", + "trans_max = data[\"trans_ref_max\"]\n", + "trans_recon = data[\"recon_trans_val\"]\n", + "\n", + "sales_min = data[\"sales_ref_min\"]\n", + "sales_max = data[\"sales_ref_max\"]\n", + "sales_recon = data[\"recon_sales_val\"]\n", + "\n", + "fig1 = plt.figure()\n", + "fig1.set_figheight(12)\n", + "fig1.set_figwidth(12)\n", + "\n", + "plt.subplot(411)\n", + "plt.plot(dist, cust_min, label='Min. # of Customers (Ref.)')\n", + "plt.plot(dist, cust_max, label='Max. # of Customers (Ref.)')\n", + "plt.plot(dist, cust_recon, label='Day 62 # of Customers')\n", + "plt.xlabel('Distance from event (km)')\n", + "plt.ylabel('Customers')\n", + "plt.title('Effect on Number of Customers on Day 62')\n", + "plt.legend()\n", + "\n", + "plt.subplot(412)\n", + "plt.plot(dist, spend_min, label='Min. Median Spending Amt.(Ref.)')\n", + "plt.plot(dist, spend_max, label='Max. Median Spending Amt. (Ref.)')\n", + "plt.plot(dist, spend_recon, label='Day 62 Median Spending Amt.')\n", + "plt.xlabel('Distance from event (km)')\n", + "plt.ylabel('Median Spending Amount')\n", + "plt.title('Effect on Median Spending on Day 62')\n", + "plt.legend()\n", + "\n", + "plt.subplot(413)\n", + "plt.plot(dist, trans_min, label='Min. # of Transactions (Ref.)')\n", + "plt.plot(dist, trans_max, label='Max. # of Transactions (Ref.)')\n", + "plt.plot(dist, trans_recon, label='Day 62 # of Transactions')\n", + "plt.xlabel('Distance from event (km)')\n", + "plt.ylabel('Transactions')\n", + "plt.title('Effect on Number of Transactions on Day 62')\n", + "plt.legend()\n", + "\n", + "plt.subplot(414)\n", + "plt.plot(dist, sales_min, label='Min. Sales Amt. (Ref.)')\n", + "plt.plot(dist, sales_max, label='Max. Sales Amt. (Ref.)')\n", + "plt.plot(dist, sales_recon, label='Day 62 Sales Amt.')\n", + "plt.xlabel('Distance from event (km)')\n", + "plt.ylabel('Total Sales Amt.')\n", + "plt.title('Effect on Total Sales Amount on Day 62')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout(pad=0.4, w_pad=0.6, h_pad=1.0)\n", + "fig1.text(0.5,-0.05,\n", + " \"Figure 2: The reconstructed values for Day 62 of each variable plotted against their respective minimums and\\n maximums over the reference period\",\n", + " size=12, ha=\"center\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the data recorded, for each of the three distance recorded, the mean and standard deviation of the reference period can be calculated. The z-score for each observed value on Day 62 can be computed using:\n", + "\n", + "\\begin{equation}\n", + "\\textrm{Z} = \\frac{\\textrm{X} - \\mu}{\\sigma},\n", + "\\tag{2}\n", + "\\end{equation}\n", + "\n", + "where X is the observed value, \\mu and \\sigma are the mean and standard deviation (respectively) of the reference period." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Average change in number of customers on Day 62: 26.095\n", - "Average change in spending on Day 62: 12.65825\n", - "Average change in number of transactions on Day 62: 30.645\n" + "Variable Distance X Z\n", + "--------------- ---------- ------ --------\n", + "Customers < 2km -0.6 -6.87798\n", + "Customers 2km - 4km -0.2 -3.33253\n", + "Customers > 4km -0.1 -3.7074\n", + "Median Spending < 2km -0.2 -3.05849\n", + "Median Spending 2km - 4km -0.1 -1.46508\n", + "Median Spending > 4km -0.035 -1.99199\n", + "\n", + "Figure 3: The z score computed using equation 2 and the temporal data\n" ] } ], "source": [ - "cust_change_mean = cust_data[\"Decrease on Day 62\"].mean()\n", - "print(\"Average change in number of customers on Day 62:\", cust_change_mean)\n", - "spend_change_mean = spend_data[\"Decrease on Day 62\"].mean()\n", - "print(\"Average change in spending on Day 62:\", spend_change_mean)\n", - "trans_change_mean = trans_data[\"Decrease on Day 62\"].mean()\n", - "print(\"Average change in number of transactions on Day 62:\", trans_change_mean)" + "# Computing all the z scores\n", + "z_cust_r1 = (data[\"r1_cust_62\"][0] - data[\"r1_cust_change\"].mean())/data[\"r1_cust_change\"].std()\n", + "z_cust_r2 = (data[\"r2_cust_62\"][0] - data[\"r2_cust_change\"].mean())/data[\"r2_cust_change\"].std()\n", + "z_cust_r3 = (data[\"r3_cust_62\"][0] - data[\"r3_cust_change\"].mean())/data[\"r3_cust_change\"].std()\n", + "z_spend_r1 = (data[\"r1_spend_62\"][0] - data[\"r1_spend_change\"].mean())/data[\"r1_spend_change\"].std()\n", + "z_spend_r2 = (data[\"r2_spend_62\"][0] - data[\"r2_spend_change\"].mean())/data[\"r2_spend_change\"].std()\n", + "z_spend_r3 = (data[\"r3_spend_62\"][0] - data[\"r3_spend_change\"].mean())/data[\"r3_spend_change\"].std()\n", + "\n", + "print(tabulate([['Customers', '< 2km', data[\"r1_cust_62\"][0], z_cust_r1],\n", + " ['Customers', '2km - 4km', data[\"r2_cust_62\"][0], z_cust_r2],\n", + " ['Customers', '> 4km', data[\"r3_cust_62\"][0], z_cust_r3],\n", + " ['Median Spending', '< 2km', data[\"r1_spend_62\"][0], z_spend_r1],\n", + " ['Median Spending', '2km - 4km', data[\"r2_spend_62\"][0], z_spend_r2],\n", + " ['Median Spending', '> 4km', data[\"r3_spend_62\"][0], z_spend_r3]],\n", + " headers=['Variable', 'Distance', 'X', 'Z']))\n", + "print(\"\\nFigure 3: The z score computed using equation 2 and the temporal data\")" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "## Discussion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As shown in each of the subplots of Figure 1, the mean and median values of the decrease in each of the distributions are greater than zero (note: higher values of the decrease variable indicate a larger decrease/negative change). These mean and median values can be used to perform a one-sample hypothesis tests, which finds that since each of the mean/median values is greater than zero, we can infer that the event had a net decreasing affect on the number of customers, median spending amount, number of transactions, and total sales amount.\n", + "\n", + "In Figure 2 values were approximated for each variable on Day 62, using Equation 1, and plotted against the minimum and maximum values of the respective variables. This allows us to visually assess whether the reconstructed value for Day 62 lies outside the range of recorded values for the reference period, and presents uncharacteristic behaviour. A decrease is evident in each of the variables after the event has occured (on Day 62) within a distance of approximately 2 km, and appears to stabilise thereafter. This provides support to the authors'1 hypothesis that consumer spending is affected by socially disruptive events, and also provides evidence to the notion of spatial scaling of this effect (based on the event location). It is important to note that the approximation used in this technique is subject to a level of error due to the ideal calculation of the mean/median of the reference data as the midpoint between the minimum and maximum values provided.\n", + "\n", + "Extrapolating data from a graph in Dong et al.1 provided time series data (divided into three radius') to analyse. This data was collected by visually estimating the values from the graph which will inherently introduce a source of error. However, by computing the z-score as described in Equation 2, the table provided in Figure 3 was constructed. Each of the z-score values in the table are negative, indicating a decrease in both the number of customers and median spending on Day 62. The much larger magnitude of z-scores for the <2km distance ring for both variables is in agreement with earlier discussion, strengthening the hypothesis of the spatial correlation of consumer spending.\n", + "\n", + "Each of the above tests have agreed on the spatial and temporal correlation of consumer spending and socially disruptive events. With the limited data available, we can therefore concur with the hypothesis of Dong et al. that consumer spending decreases in the area around disruptive social behaviour, after finding the temporal correlation on Day 62, as well as the spatially decreasing effect further from the event." + ] }, { "cell_type": "markdown", diff --git a/wk9/FIT4005_Wk9_Report b/wk9/FIT4005_Wk9_Report new file mode 100644 index 0000000..63d341f --- /dev/null +++ b/wk9/FIT4005_Wk9_Report @@ -0,0 +1,27 @@ +/* NOTE! This has not been proofread */ + +What we did (Method) +Provided with a set of 132 unique records of the top 200 male tennis players, we sought to investigate the relationship between the height of particular individuals with their respective weights. We conducted basic statistical correlation analyses of the two variables with both Pearson's and Spearman's correlation coefficients to achieve this. Further, to understand the correlations more deeply, we carried out these correlation tests on the full population of cleaned data (removed duplicates etc), alongside several random samples and samples of ranking ranges within the top 200. To this end, we made use of Microsoft Excel tools and functions of the Python library SciPy. + +What we got (Results) +We performed seperate statistical analyses on 10 different samples of the population, as well as the population itself. This included 5 separate subsets of the rankings (top 20 and 50, middle 20, bottom 20 and 50) and 5 seperate randomly chosen samples of 20 players. + +The results for the tests is as follows (all data is rounded to 5 decimal places): + +Test Set Pearson's Coefficient Spearman's Coefficient +Population 0.77953 0.73925 +Top 20 0.80743 0.80345 +Middle 20 0.54134 0.36565 +Bottom 20 0.84046 0.88172 +Top 50 0.80072 0.78979 +Bottom 50 0.84237 0.81355 +Random set #1 0.84243 0.80237 +Random set #2 0.56564 0.58714 +Random set #3 0.59223 0.63662 +Random set #4 0.65091 0.58471 +Random set #5 0.86203 0.77832 + + +What this says (Discussion) + +The results generally indicate that there is a fairly strong positive correlation between the weight and weight of an individual tennis player, within the top 200 male players. The population maintains a strong positive correlation with both Pearson's and Spearman's correlation coefficients, indicating that a relationship may exist. Our population samples show promising consistency with this, with 6 seperate samples having values above 0.6 with both techniques. The sample taken from the middle 20 players, however, shows a relatively weaker correlation compared with the top 20 and middle 20, which provides some insight into the distribution of the strongest correlated heights and weights amongst the rankings. All five random samples of 20 taken from the population indicate however that there does appear to be a consistent trend through the population, which corresponds accurately with the coefficients on the general population. diff --git a/wk9/Tennis players 2017-09 final.xlsx b/wk9/Tennis players 2017-09 final.xlsx new file mode 100644 index 0000000..eddef13 Binary files /dev/null and b/wk9/Tennis players 2017-09 final.xlsx differ diff --git a/wk9/pearson.png b/wk9/pearson.png new file mode 100644 index 0000000..3fc0e5e Binary files /dev/null and b/wk9/pearson.png differ diff --git a/wk9/spearman.png b/wk9/spearman.png new file mode 100644 index 0000000..73e3ed1 Binary files /dev/null and b/wk9/spearman.png differ diff --git a/wk9/week9.tex b/wk9/week9.tex new file mode 100644 index 0000000..4b5ce66 --- /dev/null +++ b/wk9/week9.tex @@ -0,0 +1,113 @@ +\documentclass[a4paper]{article} +% To compile PDF run: latexmk -pdf {filename}.tex + +% Math package +\usepackage{amsmath} +%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link +\usepackage[capitalise,nameinlink]{cleveref} +% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document +\usepackage{hyperref} +% UTF-8 encoding +\usepackage[T1]{fontenc} +\usepackage[utf8]{inputenc} %support umlauts in the input +% Easier compilation +\usepackage{bookmark} +\usepackage{graphicx} + +\begin{document} + \title{Week 9 - Correlation and Regression} + \author{ + Jai Bheeman \and Kelvin Davis \and Jip J. Dekker \and Nelson Frew \and Tony + Silvestere + } + \maketitle + + \section{Introduction} \label{sec:introduction} + We present a report on the relationship between the heights and weights of the + top tennis players as catalogued in provided data. We use statistical analysis + techniques to numerically describe the characteristics of the data, to see how + trends are exhibited within the data set. We conclude the report with a brief + discussion of the implications of the analysis and provide insights on + potential correlations that may exist. + + \section{Method} \label{sec:method} + Provided with a set of 132 unique records of the top 200 male tennis players, + we sought to investigate the relationship between the height of particular + individuals with their respective weights. We conducted basic statistical + correlation analyses of the two variables with both Pearson's and Spearman's + correlation coefficients to achieve this. Further, to understand the + correlations more deeply, we carried out these correlation tests on the full + population of cleaned data (removed duplicates etc), alongside several random + samples and samples of ranking ranges within the top 200. To this end, we made + use of Microsoft Excel tools and functions of the Python library SciPy. + + We specifically have made use of these separate statistical analysis tools in the + interest of sanity checking our findings. To do this, we simply replicated the + correlation tests within other software environments. + + \section{Results} \label{sec:results} + We performed separate statistical analyses on 10 different samples of the + population, as well as the population itself. This included 11 separate + subsets of the rankings: + \begin{itemize} + \item The top 20 entries + \item The middle 20 entries + \item The bottom 20 entries + \item The top 50 entries + \item The bottom 50 entries + \item 5 randomly chosen sets of 20 entries + \end{itemize} +\vspace{1em} + Table \ref{tab:excel_results} shows the the results for the conducted tests. + + \begin{table}[ht] + \centering + \label{tab:excel_results} + \begin{tabular}{|l|r|r|} + \hline + \textbf{Test Set} & \textbf{Pearson's Coefficient} & \textbf{Spearman's Coefficient} \\ + \hline + \textbf{Full Population} & 0.77953 & 0.73925 \\ + \textbf{Top 20} & 0.80743 & 0.80345 \\ + \textbf{Middle 20} & 0.54134 & 0.36565 \\ + \textbf{Bottom 20} & 0.84046 & 0.88172 \\ + \textbf{Top 50} & 0.80072 & 0.78979 \\ + \textbf{Bottom 50} & 0.84237 & 0.81355 \\ + \textbf{Random Set \#1} & 0.84243 & 0.80237 \\ + \textbf{Random Set \#2} & 0.56564 & 0.58714 \\ + \textbf{Random Set \#3} & 0.59223 & 0.63662 \\ + \textbf{Random Set \#4} & 0.65091 & 0.58471 \\ + \textbf{Random Set \#5} & 0.86203 & 0.77832 + \\ \hline + \end{tabular} + \caption{Table showing the correlation coefficients between height and + weight using different test sets. All data is rounded to 5 decimal + places} + \end{table} + + \begin{figure}[ht] + \centering + \label{fig:scipy} + \includegraphics[width=0.6\textwidth]{pearson.png} + \includegraphics[width=0.6\textwidth]{spearman.png} + \caption{The Pearsion (top) and Spearman (bottom) correlations coefficients + of the data set as computed by the Pandas Python library} + \end{figure} + + \section{Discussion} \label{sec:discussion} + The results generally indicate that there is a fairly strong positive + correlation between the weight and weight of an individual tennis player, + within the top 200 male players. The population maintains a strong positive + correlation with both Pearson's and Spearman's correlation coefficients, + indicating that a relationship may exist. Our population samples show + promising consistency with this, with 6 seperate samples having values above + 0.6 with both techniques. The sample taken from the middle 20 players, + however, shows a relatively weaker correlation compared with the top 20 and + middle 20, which provides some insight into the distribution of the strongest + correlated heights and weights amongst the rankings. All five random samples + of 20 taken from the population indicate however that there does appear to be + a consistent trend through the population, which corresponds accurately with + the coefficients on the general population. + + +\end{document} diff --git a/wk9/wk9.ipynb b/wk9/wk9.ipynb new file mode 100644 index 0000000..35b7f21 --- /dev/null +++ b/wk9/wk9.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using matplotlib backend: MacOSX\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab\n", + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "from matplotlib import colors\n", + "\n", + "data = pd.read_csv(\"Tennis players 2017-09.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DOBRANKHEIGHTWeight
DOB10.2777660.139684-0.030479
RANK0.2777661-0.16755-0.121946
HEIGHT0.139684-0.1675510.779526
Weight-0.030479-0.1219460.7795261
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def background_gradient(s, m, M, cmap='Wistia', low=0, high=0):\n", + " rng = M - m\n", + " norm = colors.Normalize(m - (rng * low),\n", + " M + (rng * high))\n", + " normed = norm(s.values)\n", + " c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)]\n", + " return ['background-color: %s' % color for color in c]\n", + "\n", + "data = data[[\"SEX\", \"DOB\", \"RANK\", \"HANDED\", \"Country\", \"HEIGHT\", \"Weight\"]]\n", + "data.drop_duplicates\n", + "\n", + "pearson = data.corr()\n", + "pearson.style.apply(background_gradient,\n", + " cmap='Wistia',\n", + " m=pearson.min().min(),\n", + " M=pearson.max().max()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DOBRANKHEIGHTWeight
DOB10.2803860.1224120.00769861
RANK0.2803861-0.160006-0.0908714
HEIGHT0.122412-0.16000610.739246
Weight0.00769861-0.09087140.7392461
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spearman = data.corr(method=\"spearman\")\n", + "spearman.style.apply(background_gradient,\n", + " cmap='Wistia',\n", + " m=spearman.min().min(),\n", + " M=spearman.max().max()\n", + ")" + ] + } + ], + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}