forked from rdpeng/RepData_PeerAssessment1
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathPA1_template.html
376 lines (278 loc) · 180 KB
/
PA1_template.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<title>Reproducible Research: Peer Assessment 1</title>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: #990073
}
pre .number {
color: #099;
}
pre .comment {
color: #998;
font-style: italic
}
pre .keyword {
color: #900;
font-weight: bold
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: #d14;
}
</style>
<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>
<!-- MathJax scripts -->
<script type="text/javascript" src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
</script>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 13px;
}
body {
max-width: 800px;
margin: auto;
padding: 1em;
line-height: 20px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre, img {
max-width: 100%;
}
pre code {
display: block; padding: 0.5em;
}
code {
font-size: 92%;
border: 1px solid #ccc;
}
code[class] {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
</head>
<body>
<h1>Reproducible Research: Peer Assessment 1</h1>
<h2>Loading and preprocessing the data</h2>
<p>This assignment utilizes the source data file from:</p>
<p><a href="https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip">https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip</a></p>
<p>Although the file is already available within the repository, first check whether <code>activity.csv</code> file exists. If not, the code:</p>
<ul>
<li>Downloads a new zip file into a local copy with <strong>today's date</strong></li>
<li>Unzips the file and reads it into a dataframe.</li>
</ul>
<p>Given the appropriate csv, use <code>read.csv()</code> to read file. Finally, convert date string into a date format for future manipulation.</p>
<pre><code class="r">options(scipen=999) # Disable scientific notation in output
if (!file.exists("activity.csv")) {
zipfileurl <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip"
zipfilename <- paste0("rep-data-activity-",strftime(Sys.time(),"%Y%m%d"),".zip")
download.file(url=zipfileurl,destfile=zipfilename,method="curl")
unzip(zipfile=zipfilename)
}
activity <- read.csv("activity.csv",stringsAsFactors=FALSE)
activity$date <- as.Date(activity$date)
</code></pre>
<h2>What is mean total number of steps taken per day?</h2>
<p>Create a new dataframe for the steps per day with the <code>ddply</code> from the <code>plyr</code> package. The results are a summarized dataframe with the total steps per day.</p>
<pre><code class="r">library(plyr)
stepsperday <- ddply(activity, .(date),summarise,steps=sum(steps))
</code></pre>
<p>Plot the histogram of the number of steps per day. A bin width of (max steps)/20 was selected after various exploratory graphs.</p>
<pre><code class="r">library(ggplot2)
ggplot(stepsperday,aes(steps)) +
geom_histogram(binwidth=max(stepsperday$steps,na.rm=TRUE)/20) +
labs(title="Histogram of Steps Per Day",x="Steps",y="Frequency")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-3"/> </p>
<p>Find the mean and median. Since the data includes a number of missing observations, utilize <code>na.rm=TRUE</code></p>
<pre><code class="r">meanperday <- mean(stepsperday$steps,na.rm=TRUE)
medianperday <- median(stepsperday$steps,na.rm=TRUE)
meanperday
</code></pre>
<pre><code>## [1] 10766
</code></pre>
<pre><code class="r">medianperday
</code></pre>
<pre><code>## [1] 10765
</code></pre>
<p>The mean is <strong>10766.1887</strong> and the median is <strong>10765</strong>.</p>
<h2>What is the average daily activity pattern?</h2>
<p>Leverage <code>ddply</code> again, this time to average the steps per interval. Next <code>which.max</code> simplifies finding the interval with the highest average. For the plot, the blue lines highlight the location of the max and its interval.</p>
<pre><code class="r">stepsperinterval <- ddply(activity,.(interval),summarise,steps=mean(steps,na.rm=TRUE))
indexofmax <- which.max(stepsperinterval$steps)
maxinterval <- stepsperinterval[indexofmax,"interval"]
maxsteps <- stepsperinterval[indexofmax,"steps"]
ggplot(stepsperinterval,aes(interval,steps)) +
geom_line() +
geom_hline(aes(yintercept=maxsteps),color="blue") +
geom_vline(xintercept=maxinterval,color="blue") +
labs(title="Average Steps Per Interval",x="Interval",y="Steps")
</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAYAAACmKP9/AAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGFVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8AUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWaGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJPwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzYZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0HPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgjONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyoBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrYBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiEhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrBDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfSPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1cAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8ek6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWWing6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8OokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/wjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83Gv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAQABJREFUeAHsnQnYHEW1/os9EAKEAAHCGtYE0ECCsgoqgiKLirhcVFDBi1y4Iqh49Y/CVe5FEFdQ5ApGFAXckEWUTUBAkVUISxK2hC0QCFvYl+8/v4Iz6a/TM9P7dPe89TzfNz3dtZx6q6beOqdOVS8y1ApOQQgIASEgBISAEGgUAos2qjaqjBAQAkJACAgBIeAREMGrIwgBISAEhIAQaCACIvgGNqqqJASEgBAQAkJABK8+IASEgBAQAkKggQiI4BvYqKqSEBACQkAICAERvPqAEBACQkAICIEGIiCCb2CjqkpCQAgIASEgBETw6gNCQAgIASEgBBqIgAi+gY2qKgkBISAEhIAQEMGrDwgBISAEhIAQaCACIvgGNqqqJASEgBAQAkJABK8+IASEgBAQAkKggQiI4BvYqKqSEBACQkAICAERvPqAEBACQkAICIEGIiCCb2CjqkpCQAgIASEgBETw6gNCQAgIASEgBBqIgAi+gY2qKgkBISAEhIAQWFwQCIGmI3D33Xe72267ze20005umWWWaWR1b7jhBnfzzTe7oaEh9+Y3v9lNnjzZLbro8Pk7zxZZZJHS63/RRRe5F154oV3uiBEj3NixY72c7Zs5XZx77rlu8803d2uuuWbsHPuFy3333efom+985ztjy6qIQiARAq3OrSAEGo3Ae9/73qHFF1986OSTT25cPZ9//vmhf/u3fxtq/eiH1l9//aGNN97YX++yyy5DTzzxRLu+3/rWt4Z+9KMftb+XebHGGmsMLbvsskMtUvd/K6ywwlBrouFlnTVrVq6itCY1Q1OnTo2V51NPPTW06667Dj3wwAOx4ucdifbYYIMN8s5W+QmBNgLDp/iJpgaKLASqj8BDDz3k/vKXv7jPfe5z7sc//nH1BU4o4W9+8xv3u9/9zlsoZs6c6e644w531113ub///e/u2GOPbed2xhlnuBdffLH9veyLr3zlK27OnDn+rzXx8DK+/PLL7uCDDy5blHZ5yPOnP/2p/V0XQqBpCMhE37QWVX2GIfDzn//cTZw40R166KHuO9/5jie+rbfe2sf517/+5S6//HJP/pbolVde8fE+9KEPuXXWWce1NEwHOT744INu0qRJbr/99nNLLLGEj37WWWe5DTfc0F122WWupUm7Aw880K200koOcznE0dIM3VprreX22Wcfn5eVceuttzpMyc8884x/dtNNN3mz8mabbeajdCvT8rBP6tDSAn0d7d56663nWhq7e/TRR/2tX//61/760ksvdePGjXN77723v4/cf/7zn71Z/z3veY97xzve4e+/9NJL7rvf/a772Mc+5n71q1+5Rx55xO28887+z8qYPXu2a2nKjglUy3Lg9t13X7fyyivb456f48ePdx/5yEfcT37yk3ZcCJ88WWpYffXV3cc//nGPHxGoJ5MXygBb0m655ZbttFEX4MzfW97yFl8PJha77babN4nTXqeccopPdtJJJ7mPfvSjDvyTyLDFFlu4xx57rGv/efzxx90vf/lLN2PGDLfccst5jN/1rndFiat7QiB3BKTB5w6pMqwSAj/72c9cy4TtWmZi9/a3v921zPRt8VqmYvf5z3/e3Xjjje17F198sfvGN77hieSf//ynXyfm3vLLL+++9rWvube97W2eBEjw05/+1JPQaaed5n7729/6PBjMKQfiW2211fx91sTnzZvnn1999dVuypQp7pprrnEQzo477uhlYFJA6FWmjxT411p+cLfffrv75Cc/6SBw09KZbCAvATJ77bXX/DOuCdRxjz32cBDQ3LlzHQRvGj8E/+Uvf9ntsMMO7m9/+5tft//ABz7gJw2kJQ2kCekyCbrgggvcm970Jjd//nwexw5YGVZZZRUfH7m32WYb9z//8z+Odjn//PN9nkx+COCCFYAJ1nXXXefJ3j/o8o94//Vf/+Xe/e53+wkaGjvkyuSqZcN0zz77rE/NJ8SeVAYmPt36D5PCTTbZxE+imFhNnz7dy8KES0EIlIJA21ivCyHQMASuuOKKIdZkbY319NNPH2o5eA21CKpd05aD01BrkG5/b2mGQy2y9N+32mqrofe///3tZ88999zQiiuuONQy9ft7Lac9v47cIod2nP/4j/8YOuGEE9rfH374YS9DiwT9vY022miIOBb++Mc/+jXz1kTE3+pVpqULfp5zzjl+Lbc1YPj6UaeW6T4YZahFwEMtrdzfazl2eZ+EFtG047QIdWippZYaapHSUMuy4GX61Kc+1X5+9tlnDy222GJDrYnLUEvrH1p66aWHwIPQIsihloVkiHyjAmvwe+65p8cN7L7+9a8Pbbfddn4d/tRTT/VJwGzkyJFDTz75ZDuLT3ziE0OtyZL/3tK2vUytiVD7edRFcA2evMHklltuaUcFm5a1wX9vEa5/bv0jjQzd+k9r6cTXu2UVapffmjS1y9cafBsWXRSEgEz0pUyjVEg/EECz3nTTTR3eyvzhuY0JHq3+8MMP9yK1SMxfH3/88a5FWK5FuH7NHm0OrRFTcGvgb4uP6RjtGw2ZgLm/5cDXfn7iiSe6lvOWN/3feeedDg0Uz3XybpGX1+K+973vteOjUbbI0n+PW2Y78RsXLfJ0/KEhosWfd9553gx/xBFHtLXyYJp//OMfXqO/995723VDg+UPSwIWCALWAQvsQCDwnDqPHj3aTZgwwZfbclRzxx13XHvpwtIEP8GSpQfCqFGjvOaPhcHM1VgKsAZgFbHw6quvOmRtjX3+VstRz2EWTxKQ05Y+SMdyAhaLqJBGhk79h/yxetAuWDqwsvAH5vRDBSFQBgILRqYySlMZQqAkBFjfxgGNdU/WwC1ALqz7HnbYYZ54Wxq6O+iggzwxYlLFlLr99tu7lubtSRDzLU5rFlqapyc2+24mZvvOui55s01r22239X/4ARAw6RKCa9WQOyZpAmZ8TOm9yvSR3/iHuRdzOevuLeuA/6M+3//+9x0Ef+SRR7qWZhxM4teNW9q6Yx09GD7zmc944rZ7+BdYYImCfFhWGDNmjLv++uv9Gjbm+R/+8IeenFnPD6axtHwecsgh3lwevBe8Zi2biVAQa9qKtX1bVgjiFkzb7Zo8goHJGBhHhTQydOo/5I/PAJMkli5YfqDv4C+hIATKQkAEXxbSKqdUBHCAM8IIEhzrvgy2aLpopRAsWnrLBO01TAiFsOqqq/o986yfH3300W3Z2dPNs6gAEUFkOPO1zPA+CuvVaHmQChoqe8BZ02efOmHatGl+MsF1mjKxGCBjy9xLFu2Apou1AqtAsP5EwMENWfFgZ42YwD71lqnftbbZ+e/8ay1xeAsI1xD6008/7Z0B2buNNtoytfu/+++/3/smMLn59re/TfTEAZlYMw/udIAgKausswvSyNCp/wDAV7/6Vbfuuus6+kxrecNjgoNgpwlGYtCUQAj0QEBOdj0A0uN6ItBaf3VoV2Fyw7yMaTlIJBDw73//e3fllVd6jZEaMzn49Kc/7TDz422OmZjBGY0M60BU4GAZNF1zNsMsz/Y8AkSL1vzZz37WO/qdeeaZ3vyMk5aFNGUecMAB3iLBkgMkjCMZhEI5mM5bPgM+ez5ZMmDCwcQG4oGAMBlD9l/4whe8Ux5mcAtgCJHjMIgDHl7rYEd8zM+UAy78MUFYe+21LWniz/33399rvHjvY8HAmkL7gX1RwbDBa58y08oQ1X+QmfzBhYkWGLEjgT4WPPSnqLopXyHgEWh1PAUh0CgEWqTknada+98j69Vab/dOZjiUWWhtpRtqrQfbV/+Js1lr8PZxWxOFoZb5eai1ft6Og5Ndywze/s5Fa0Iw1CI6f6BLa/13qOWtPtTylB9qme19vJaHunfqay0FDLXW84e++c1vDrVM3kOtwd8/71WmjxT6hwMcsuFg1vpRD7UmGV7uFhG3Y+Jg19reN9TS9v09HM9aWr5P0zJjexlb1g3/DBnIp7W04Z32Wlqqd4rDYdACB+dwsE5rCWRoySWX9OW1iMseD/vEya7lHT/sXtQXsGuR4lBLY/eflN+aLPmoONm1JiVRyYbdCzvZtbYpDnuOg+P73vc+f6+lSft6U1fagZBWhqj+07JADLUmlEPg29o+6R3uWksmQ8hE2XKy85DrX4EILELenun1TwgIgUgE2DbG+iwOdnEDe+DZJmemWUvHOjVr5qY9ouXjCHbhhRe296ETN02ZaIas87P3HmtAOGBFwDyMWdkCa+pmebB7WCBYu7722mv9VjVkNHktjn3iq8CafIvk7VamT4YjtPco7DJl3CUxFhksPeBAyFsGnPrIv6ylhi5V1aMBQ0AEP2ANrur2FwEc+HCqY60aQmlpjn6vOWvxVSGAIMEzGVEQAkKgnghoDb6e7Sapc0YgiyEriQ2MLXpsk2IdHC97ts799a9/rQy5AyvaP175+AxkDVlwzVq20guBJAg0sa/WVoPHc5d9snkEPJsxk2JurHrA5Ivmx57lqge2NeGQZU5nVZaXPpDG+enPf17GnX76qJYD1etb4IquI23PFjzbU150eVnzT4tr1nKTpsc5kt+WnTiYNH2Z8VkOwXGvDt74bDtliYIlp6qHqvZVHF85AjtNkAafBjWlEQJCQAgIASFQcQRE8BVvIIknBISAEBACQiANAiL4NKgpjRAQAkJACAiBiiMggq94A0k8ISAEhIAQEAJpEBDBp0FNaYSAEBACQkAIVBwBEXzFG0jiCQEhIASEgBBIg4AIPg1qSiMEhIAQEAJCoOIIiOAr3kASTwgIASEgBIRAGgRE8GlQUxohIASEgBAQAhVHQARf8QaSeEJACAgBISAE0iAggk+DmtIIASEgBISAEKg4AiL4ijeQxBMCQkAICAEhkAYBEXwa1JRGCAgBISAEhEDFERDBV7yBJJ4QEAJCQAgIgTQIiODToKY0QkAICAEhIAQqjoAIvuINJPGEgBAQAkJACKRBQASfBjWlEQJCQAgIASFQcQRE8BVvIIknBISAEBACQiANAosMtUKahP1Os/POz7u77148FzEWWWQRt+iii7pXX301l/yUyesILLbYYu61115zdehi9IE0cj755CvuqacWdWuvXd5cefHFF3evvPJKLbpZWlzLrhy/fwL9teqhLpiCI32VcTXNb6vsdqgqrv/938+7ffZZLhUctSX4iy+e4+bPz+fHuNRSS7lRo0a5xx57LBWIZSaCNOmIdRjgR48e7V588UX33HPPlQlRqrLoA8iaNEydep+7+OLl3RlnjE6aNFV8iGjs2LHu4YcfTpW+7ERpcS1bzmWXXdbx23rqqafKLjpxeUsssYQnzTpMRlZZZRX3xBNPuJdffjlxPctOUNW+uskmS7kNNxyTCo58VOBURWdLtPHGL+emcY8YsagbPXqoNWi+lE2oElIzCDHI1+EHs/LKr7nnn3+lNRGrPq70gRdeSC7neefNa024FnWbbz6yhNZ3vu3XXNO5WbOSy1qKgKFC0uIayqbwr8sv/2qL4J2bN6/6uC65pPMT/DoQ/LhxQ27u3JfdSy9VH9eq9tVll201eMpQnl0xpYBKJgSqjADmRy3tVLmFJJsQGFwERPCD2/aqeQ4IoEXVQZPKoarKQggIgZohIIKvWYNJ3GohALlLg69Wm0gaISAEXkdABK+eIAQyICANPgN4SioEhEChCIjgC4VXmTcdAdPeX3jhhaZXVfUTAkKgZgiI4GvWYBK3WgjY+vuzzz5bLcEkjRAQAgOPQKHb5J555hl3wQUXuCeffNKtvvrqbtddd/UHH9xxxx3u8ssv91s99t57b7fqqqv6PejnnHOOY6DcYYcd3KRJkwa+cQRA9REwDf7555+vvrCSUAgIgYFCoFAN/rLLLnMbbLCBO/DAAz2oN910U2uv8QvuvPPOc/vuu697//vf784880z/7Oyzz3a77LKL23///d1f/vKXWhyOMlA9RZWNRMA0+Doc5hNZAd0UAkKgsQgUqsHvuOOOjhOiCJy+hCb/yCOPuDVbJ3Uss8wy/g/Nh1PZnn76aX+fuOPHj28d5DHLTZgwga8+1OGoQ5NVn4ODgGnwIvjBaXPVVAjUBYFCCX755Zf3OMycOdPdfPPN7tBDD3UzZszwxG4ALb300t48z5nFFiD/4JrmnDlz3DHHHGOP/edRRx3lxoxJd3zfsIwCX9ZZZ53AN13mgcDIkSPdSiutlEdWlcyD/kugr5fZf8osq5LAFyTUcsulO/O7IHEakS3LswrpEWCpO21YwKppc+iR7vbbb3fnn3++++xnP+sYDEeMGDHszG+OXOXM8uBRhlyb5k/2nGd83HHHDSuJc+Pnz58/7F7aL8i0wgorOCYSVQ/1Oqp25dZRtc/n1k5Ftg19II0n/Os/vrHu3nvv9ZanImUkb44pXmONNdzs2bOLLiqX/NPimkvhCTKB2FEy5s2blyBVf6Iu2TqrFqunLQ/1R4p4pULuc+fOrcXR2lXtqyhJvCslTSiU4O+8887WizgudgcffHBba19ttdXcn/70J/92IQZUTO8c8k+nRWtHe2fwevvb396uD4OaaUrtm62LvMz2lo99Bsuo2jUy2l/VZAvLY3LWCddwHXp9t0GWvltGPa0M++wlX7+fI6dkzbcVDNO64Ert6yCr4Zpva/U3t0IJ/re//a2faf7gBz/wtdxiiy3czjvv7KZMmeJOPvlkh/az5557+md8Tp061cefOHGi16j7C41KFwK9ETCClxd9b6wUQwgIgXIRKJTg/9//+3+Rtdl+++3d1ltv7c2NaOcEvO35w2SPQ56CEKgDAkbwQZ+ROsgtGYWAEGg+AoUSfDf4gk51wXgi9yAauq46AuZFLw2+6i0l+YTA4CFQ6D74wYNTNR40BEyD1za5QWt51VcIVB8BEXz120gSVhgBNHiWmUTwFW4kiSYEBhQBEfyANryqnQ8CaPBsXZSJPh88lYsQEAL5ISCCzw9L5TSACKDBQ/AvvvjiANZeVRYCQqDKCIjgq9w6kq3yCJgGHzyoqfJCS0AhIAQGAgER/EA0sypZFAIQPGvwIviiEFa+QkAIpEVABJ8WOaUTAi0EpMGrGwgBIVBVBETwVW0ZyVULBMyLXhp8LZpLQgqBgUJABD9Qza3K5o2ATPR5I6r8hIAQyAsBEXxeSCqfgUTAvOilwQ9k86vSQqDSCIjgK908Eq7qCEiDr3oLST4hMLgIiOAHt+1V8xwQkAafA4jKQggIgUIQEMEXAqsyHRQEeIe0tskNSmurnkKgXgiI4OvVXpK2YgiYBq+T7CrWMBJHCAgBJ4JXJxACGRDQGnwG8JRUCAiBQhEQwRcKrzJvOgI66KbpLaz6CYH6IiCCr2/bSfIKICANvgKNIBGEgBCIREAEHwmLbgqBeAjYGjxEz5+CEBACQqAqCIjgq9ISkqOWCJgGj/BytKtlE0poIdBYBETwjW1aVawMBNDg2SZH0Gl2ZSCuMoSAEIiLgAg+LlKKJwQiEECDX2SRRdySSy4pgo/AR7eEgBDoHwIi+P5hr5IbgAAavBG8TPQNaFBVQQg0CAERfIMaU1UpH4GgBv/yyy+XL4BKFAJCQAh0QEAE3wEY3RYCcRAIErzW4OMgpjhCQAiUhYAIviykVU4jERDBN7JZVSkh0AgERPCNaEZVol8IBNfg582b53j5jIIQEAJCoAoIiOCr0AqSobYIQOjmZPeZz3zGXXHFFbWtiwQXAkKgWQiI4JvVnqpNiQi88sor7dJsm9wzzzzTvqcLISAEhEA/ERDB9xN9lV1rBDDPE9Dgl1pqKX+trXIeBv0TAkKgAggsXgEZJIIQqCUCQYJfYoklfR3kSV/LppTQQqCRCEiDb2SzqlJlIGAmeluDp0wRfBnIqwwhIATiICCCj4OS4giBCATMg55HrMETRPAeBv0TAkKgAgiI4CvQCBKhngigwS+22GJeeCN4nWZXz7aU1EKgiQiI4JvYqqpTKQigwdub5JZeeml/LQ2+FOhViBAQAjEQkJNdDJAURQhEIYAGbwR/2GGHeW1eBB+FlO4JASHQDwSkwfcDdZXZCASCGvzKK6/sVlppJadtco1oWlVCCDQCARF8I5pRlegHAsE1eMq3w276IYvKFAJCQAiEERDBhxHRdyEQE4GgBk8SEXxM4BRNCAiBUhAQwZcCswppIgJ4zJsXPfVbYokltE2uiQ2tOgmBmiIggq9pw0ns/iMAwS+++AI/VWnw/W8TSSAEhMACBETwC7DQlRBIhEDUGrz2wSeCUJGFgBAoEAERfIHgKutmIxDcJkdNpcE3u71VOyFQNwQW2BdrJjnrnfzlERiYOU98xIgReWRXaB7IyV9w7bfQAjNkjoy0UR1wxdSeVE7agfqxF560yy67rEODT5pPEogpk1BkGUnk6RU3Da698iziOXLSX+uAK3LyNzQ0VAQUueZJf2V8tfMics0858zq0leTVLu2BM9AihdzXoEfywsvvJBXdoXlww+bH0sdTMG0D3LWAVcG9qRyPvfcc36y9dprr/m0DGbkkTSfJJ3FBsoiy0giT6+4aXDtlWcRz3ndb13GAAgT6xH9ruoBTDn8qQ4HQFW1r6I4pA0y0adFTukGHgEmL8z6LchEb0joUwgIgSogIIKvQitIhloiAMFjUbEggjck9CkEhEAVEBDBV6EVJEMtEYjyoq+DKbKWYEtoISAEEiMggk8MmRIIgdcRkAavniAEhECVERDBV7l1JFulEYjS4CF9BSEgBIRAFRBY4CFUBWkkgxCoCQInnXTSG9sVR7clZg1eb5Nrw6ELISAE+oyANPg+N4CKrycCP/vZz9zdd989zIuerVZag69ne0pqIdBEBETwTWxV1alwBNDU58+fLy/6wpFWAUJACKRFQASfFjmlG2gEOGiGg27s4BnAwETPwR6szSsIASEgBPqNgAi+3y2g8muHACeIQeLPPvvsMBM9BE+Qmb52TSqBhUAjERDBN7JZVakiEbBjYiH4oAZvxwiL4ItEX3kLASEQFwERfFykFE8IvIGAecqHNXge8/IZEby6ihAQAlVAQARfhVaQDLVCIEjwwaNqqYSOq61VU0pYIdBoBETwjW5eVa4IBMxEj5NdmODZKmcTgCLKVp5CQAgIgbgIiODjIqV4QuANBIzAMcWHCX6NNdZws2bNElZCQAgIgb4jIILvexNIgLohYASP3GGC32ijjdyMGTPqViXJKwSEQAMREME3sFFVpWIRMBM9pYQJfsMNNxTBFwu/chcCQiAmAiL4mEApmhAwBLpp8BD89OnTLao+hYAQEAJ9Q0AE3zfoVXBdEeilwXNGPYfhKAgBISAE+omACL6f6KvsWiLQTYNfZZVV3IgRI+RoV8uWldBCoFkIiOCb1Z6qTQkIBAl+8cUXfuMyjnYzZ84sQRIVIQSEgBDojIAIvjM2eiIEIhEImuiDR9Va5A022EDr8AaGPoWAEOgbAiL4vkGvguuKQBwNXlvl6tq6klsINAcBEXxz2lI1KQkBCN62x9lnsGg0eBF8EBFdCwEh0A8ERPD9QF1l1hoBCH7UqFG+DlEEz1a5e++9V++Fr3UrS3ghUH8ERPD1b0PVoGQEIPjlllvOlxq1Br/iiiu65Zdf3pN8yaKpOCEgBIRAGwERfBsKXQiBeAjgZGcafJQXPbnowJt4WCqWEBACxSEggi8OW+XcUASCGnyUiZ5qa6tcQxtf1RICNUJABF+jxpKo1UCg1xo8UmqrXDXaSlIIgUFGQAQ/yK2vuqdCABO9rcFLg08FoRIJASFQAgIi+BJAVhHNQiCOiX799df3x9USV0EICAEh0A8ERPD9QF1l1hqBOCZ6NHzOpefFMwpCQAgIgX4gIILvB+oqs9YIvPzyy27llVf2dehkouehDrypdTNLeCFQewRE8LVvQlWgbAQg+KWXXtoX22mbHA/xpNe74ctuHZUnBISAISCCNyT0KQRiIvDqq6+6pZZayseOOujGsmEvvN4qZ2joUwgIgbIREMGXjbjKqz0Cr7zyin/nOxXppsHrsJvaN7UqIARqjYAIvtbNJ+H7gQAa/MiRI93BBx/sllhiiY4i4En/0EMPueeee65jHD0QAkJACBSFgAi+KGSVb2MRQIOH2A8//HC3yCKLdKznMsss48aNGyczfUeE9EAICIEiERDBF4mu8m4kAmjw3bzng5XWkbVBNHQtBIRAmQiI4MtEW2U1AgE0+G5r78FK6sjaIBq6FgJCoEwERPBloq2yGoFAUg1+xowZjai3KiEEhEC9EBDB16u9JG0FEEiqwYvgK9BoEkEIDCACIvgBbHRVORsCSTT49dZbz82dO9c9/fTT2QpVaiEgBIRAQgQWTxg/VfRzzz3XjR8/3m266aY+/UUXXeRuvfVWf82Z3QcccIB77LHH3DnnnOOeffZZt8MOO7hJkyalKkuJhEDRCCTR4DkQZ+211/ae9JMnTy5aNOUvBISAEGgjUCjB81KOs846yx/XudZaa7ULve2229yBBx7ollxyyfY2o7PPPtvtvvvubsUVV3Qnnnii45AQthkpCIGqIZBEg0d2O/BGBF+1lpQ8QqDZCBRK8GjjU6ZMcaNHj26j+Nprr3ktfdq0aW5oaMhtscUW/hkmzDXXXNNfo+3PmjXLTZgwwX8nDe/gDodux4SG43b7bvnYZ7e4/X7Gvmv+JGu+LZEEU86iZ3JKG8Rpj4033tjdddddubSZtbt95otC/rklwTX/0uPnGKcd4+dWbMy6YAoKwrXYvtAr90IJHm2cv+ArM5955hm37LLL+r+HH37YnXzyyW7//fcftu0IzZ3JgQXWML/5zW/aV/951FFH+byH3cz4xSYYGbNR8gACvJSFPtCkgAaPRYrXwa60kmudS+/ak9Ooem699da+n+fZv/LMK0rmQb3H2KSQLwKrrrpqvhkOWG7z589PXeNCCT5KquWXX94deuih/tEmm2zibrzxRq+dv/TSS+3oXAd/aGPHjnU//OEP28+5uP/++x2ThTzCiBEjvJWBCUfVAwesoL2hRVY98ErV559/3mXpoGXVkT4QZSWKKp81+Dlz5vi6zZ27TCvdqJbF6ZGoqP4eExx8TrBKZQ20PeSeR15ZZYmTPgmucfIrKg7jEr+tefPmFVVEbvliPaIPYtmseuAkRxS04PheVZmr2lfhwlGjRqWCrXQvepzpTj/9dC8snZSGX2GFFbzJE60ds/3s2bOdZn2p2lOJCkbABtW4J9khzjrrrOOeeuqpWpBHwfApeyEgBEpEoHQNfqWWTZMXdfz0pz/1M7uddtrJa6R77rmnmzp1qp+ZTpw40ZN+iTioKCEQCwEmpYS4J9lZXPxK2A+/1VZb+fT6JwSEgBAoGoFSCB7v+GDYa6+9vIkZc6NpQhzpyR+m525v6Armo2shUDYCrL8TrN/GLZ++LYKPi5biCQEhkAcCpRB8lKCdSLzT/ag8dE8IlI1AGg0eGXnpzPTp08sWV+UJASEwwAiUvgY/wFir6g1AAA3etv4kqQ574WfOnJkkieIKASEgBDIhIILPBJ8SDxoCaPBJ1t8NHzvsxr7rUwgIASFQNAIi+KIRVv6NQiDpKXZWefbNc7Ljo48+arf0KQSEgBAoFAERfKHwKvOmIZBWg8ehdP3119c6fNM6hOojBCqMgAi+wo0j0aqHQFoNnprgaKd1+Oq1qSQSAk1FQATf1JZVvQpBIK0GjzBslZMnfSHNokyFgBCIQEAEHwGKbgmBTghk1eDZC68gBISAECgDARF8GSirjMYgkEWDX3311eVk15ieoIoIgeojIIKvfhtJwgohkEWD5xCnOrx0o0JwSxQhIAQyICCCzwCekg4eAhylnGYfPEjxFrA6vAVw8FpVNRYCzURABN/MdlWtCkIgqwYvgi+oYZStEBACCyEggl8IEt0QAp0RYA0+7fsS0PxF8J2x1RMhIATyRUAEny+eyq3hCEiDb3gDq3pCoEEIiOAb1JiqSvEIZPGiZw2eQB4KQkAICIGiERDBF42w8m8UAlk1eMCQmb5RXUKVEQKVRUAEX9mmkWBVRCCLBr/YYov5Kongq9iykkkINA8BEXzz2lQ1KhCBLBo8YmGm1174AhtIWQsBIdBGQATfhkIXQqA3Alk0eHLHA18afG+cFUMICIHsCIjgs2OoHAYIgawavAh+gDqLqioE+oyACL7PDaDi64WANPh6tZekFQKDjIAIfpBbX3VPjEBWDV7H1SaGXAmEgBBIiYAIPiVwSjaYCEiDH8x2V62FQB0REMHXsdUkc98QyKrB67javjWdChYCA4eACH7gmlwVzoJAHhq8tsllaQGlFQJCIC4CIvi4SCmeEGghkFWD1xq8upEQEAJlISCCLwtpldMIBPLQ4LUPvhFdQZUQApVHQARf+SaSgFVCAIK3I2fTyKV98GlQUxohIATSICCCT4Oa0gwsApjocZRLGyB4rcGnRU/phIAQSIKACD4JWoo78AjkYaInDwUhIASEQNEIiOCLRlj5NwqBrE52MtE3qjuoMkKg0giI4CvdPBKuagjkocHLya5qrSp5hEAzERDBN7NdVauCEMiqwet1sQU1jLIVAkJgIQRE8AtBohtCoDMCWTV4nWTXGVs9EQJCIF8ERPD54qncGo4ABK9tcg1vZFVPCDQEARF8QxpS1SgHgRdffNGNGDEidWFysksNnRIKASGQEAERfELAFH2wEXjttdfcooum/9loDX6w+49qLwTKRCD9SFWmlCpLCFQEgawEjwavffAVaUyJIQQajoAIvuENrOrli8DQ0JBbZJFFUmcqE31q6JRQCAiBhAiI4BMCpuiDjUAeBK+jage7D6n2QqAsBETwZSGtchqBQB4meh1004iuoEoIgcojIIKvfBNJwCohIIKvUmtIFiEgBLohIILvho6eCYEQAiL4ECD6KgSEQGURSP/eyz5XCWcl/vIIbF3CcSrL/uY85IiTB3Lyl+WwlTjl5BEHGWmjOuDKCXNx5AR7+ovFpX5sm7PvvXAbOXKk47jbuPHD+VE+IW36cH5Ff4+La9Fy9MofOemvdcAVOfnDH6TqwX4vWbaWllXHuvTVJHjUluBZx2SgzCvwY3nhhRfyyq6wfPhh82Opwzou7YOcdcCVgT2OnNSJbW4W9+WXF3Vo9fa9V8NbP4sbP5yfDZRp04fzK/p7XFyLlqNX/ksttZQnzDrgygSTPki/q3qgv+NUWgfH0qr21WWXXTZ1M8tEnxo6JRxEBLKa6Bmc6zA5G8S2VZ2FQNMQEME3rUVVn0IRyErwaIocd6sgBISAECgaARF80Qgr/0YhIIJvVHOqMkKg0QiI4BvdvKpc3ghkdWzCRC8NPu9WUX5CQAhEISCCj0JF94RABwQgeHN06xCl621M9HVwOOpaCT0UAkKgFgiI4GvRTBKyKghkNdFLg69KS0oOIdB8BETwzW9j1TBHBLISvDT4HBtDWQkBIdAVARF8V3j0UAgMRwATvR02M/xJvG/yoo+Hk2IJASGQHQERfHYMlcMAIZBVg5eJfoA6i6oqBPqMgAi+zw2g4uuFQB4aPAfdZPXGrxdqklYICIF+ICCC7wfqKrO2CGTV4DHRE+RJX9suIMGFQG0QEMHXpqkkaBUQyErwmOgJ2gtfhdaUDEKg2QiI4JvdvqpdzghkNdHb27VE8Dk3jLITAkJgIQRE8AtBohtCoDMCWTV4cpajXWd89UQICIH8EBDB54elchoABCD4LNvkgEh74Qego6iKQqACCIjgK9AIEqE+CGCiz3JULTXVXvj6tLckFQJ1RkAEX+fWk+ylI5B1DR6BZaIvvdlUoBAYSARE8APZ7Kp0WgTyWIOXiT4t+konBIRAEgRE8EnQUtyBRyAvgpcX/cB3JQEgBApHQARfOMQqoEkI5OFkJxN9k3qE6iIEqouACL66bSPJKohAXk52Osmugo0rkYRAwxAQwTesQVWdYhGQk12x+Cp3ISAE8kNABJ8flsppABDIaw1eGvwAdBZVUQj0GQERfJ8bQMXXC4G8CF5OdvVqd0krBOqIgAi+jq0mmfuGQB4ELye7vjWfChYCA4WACH6gmluVrQIC2gdfhVaQDEKg+QikJvj77rvPo3P77be74447zk2bNq35aKmGA49AHhq8jqod+G4kAIRAKQikIvgvfOELbr/99vPvtH7HO97hrr76avfe977X3X///aUIrUKEQL8QyIPgZaLvV+upXCEwWAikIvizzjrLnXfeee7SSy91o0ePdn/84x/dPvvs4y655JLBQk+1HTgE8iB4NPgXXnhh4LBThYWAECgXgcQEzz7gV155xS277LLu3HPPdXvttZeXmG0/o0aNKld6lSYESkYgj33wkydP9pPjV199tWTpVZwQEAKDhEBigudd2FtttZXbfffd3Zlnnuk++tGPulNPPdWdfvrp7p3vfOcgYae6DiACaPBZ3we/3Xbb+QnyxRdfPIAIqspCQAiUhUBigkewM844w33wgx90v//97/1Atfrqq7sbb7zRm+vLElzlCIF+IJDHUbXIvc022zgcVBWEgBAQAkUhkIrgH3nkEffjH//Yvetd73ITJ050H//4x72jXVFCKl8hUBUE8liDpy5jx451/I4UhIAQEAJFIZCK4D/5yU+6D3zgA+7RRx91Tz75pPvd737nDj/8cGkkRbWS8q0MAnkSPL8fBSEgBIRAUQikIvh7773XfelLX3JjxoxxSyyxhNthhx3cvvvu66688sqi5FS+QqASCOThZEdFpMFXojklhBBoNAKpCH7SpEnutNNO8970oIOp8U9/+pNfV2w0WqrcwCOQh5MdIK6yyireAjbwgAoAISAECkMgFcGzJW7//ff3Gvymm27qVlttNTd9+nT3oQ99yG288cbugAMOKExgZSwE+olAXk52aPCPP/64e/nll/tZHZUtBIRAgxFYPE3djj32WPff//3fHZMuv/zyHZ/pgRCoMwJ5rcFzjsQyyyzj5s6d69iFoiAEhIAQyBuBVAT/5je/2cvBgTc42a2wwgpu8cVTZZV3fZSfECgUgbzW4BHS1uFF8IU2mTIXAgOLQCoTPS+awUTPwMR2uUMPPdSh1SsIgaYjkJcGD04rrbSSe+yxx5oOmeonBIRAnxBIRfCf+tSn3Pjx49tmejzqf/WrX7kZM2b0qRoqVgiUg0CeBL/ccsu5Z555phzBVYoQEAIDh0BigsdEedttt7kvfvGLbsSIER6wtdZay334wx92l1122cABqAoPFgJ5muhZhxfB16f/4C+hIATqhEBiguccbgamW2+9tV1PtJrzzz/fe9O3b+pCCDQQgby86IGGlzOJ4OvTSXbZZRc3a9as+ggsSQcegVSecccdd5x729ve5tZdd11/0M1PfvITf2TtbrvtFgkob53DpM+WOsIdd9zhLr/8cr+Pfu+993arrrqqX4s855xz3LPPPusPzmGvvYIQqBICkDsh68tmrE4Q/Pz58+2rPiuMwNNPP+2eeOIJPz5VWEyJJgSGIZCK4HfccUd3ww03uAsuuMDv4+XYWrbG4VHP6XYWXnzxRce749kjjxmfwHuweZf8wQcf7ObNm+ffSIeT3tlnn+3fULfiiiu6E0880W244YZ+G5HlpU8h0G8EsFQRFl00seErUnQI/oEHHoh8ppvVQuD+++/3AnEGiIIQqAsCiQgeciZA8Nddd5076KCD/HcGPhzv3v3ud7v99tvP3+Mf2viUKVOGvWWOU+/WXHNNT97sA37++ee9Js8MmfsEtH1MYRMmTPDfeW82s+dgYJDNS5NabLHFfNZ12OpHvfkzbTKISdWuaR9krQOu9IFechrmSy65ZDsu6ahnr7RRbcOk+M4770yU1vp8mvKiZCj6Xhxci5YhTv72u+qE60MPPeSzYazrFCdOOXnEAdM8l4rykKlTHvTXuvSBusjZCeuo+4kI/r3vfW/bkW7ppZdu50cjjhs3ru1Vbw/Qxvm7++677ZbX8iF2C+TDVqHgj4bnTA4scOLXt7/9bfvqP4844gi//37YzQxf+IFzIl/Vgw3wRjZVlhdMccSsw8FH4NoLUyxSBPqJ9f/WERBuqaXS9R2sWldddVWqfleHvgpWcXAlXr+D/a6sXcPyYJ0kYHXpN/Yma6/+Gq5D8DtnmATH3OCzPK8ZA9gOWodQ1b4a5MKkOCYi+EsuucShTX/iE59wp59+erssm/22b3S5YMC3gZJoHNU5evRoFzR9cY0jnwXO7WbdPxgwmeXloIRMyPDwww8Hi6jkNbNM8K7DEacrr7yyt9DUYZ2ZPmAWqk4Nb/32wQcfbJH6Uj7a448v00o3yt1/f/JXv9LPeaOcmX87lRu8T9tj6UqSJpi+7Os4uJYtU1R5TEL5bbFsGBXMqRhNvt/YY0GCoG3JKErebvd4++c//vEPd/zxx3eLlsszFD92HwTH91wyLiCTqvbVIBcmrXaixURmOPwIIHdmf8wg//a3v7mbb745drnMfhkgSYt5nk8GSzotMxW+z5492zvexc5UEYVACQjYgArJ5hG0TS4PFMvJw0jdJnnllFpMKVgjzCJRTAnKtSoIJNLgf/Ob37j//M//dHjFT5482e2xxx5+TzzEzxaSU045pWe9ONyDdfmTTz7Za+B77rmnT8Pn1KlT/cx04sSJuZrfewqlCEIgBgJ5E7y86GOAXpEoRoh1sJz1goxJSi9rVa889LweCMQmeJzjOJ721FNP9eT++9//3t1yyy1tJyG2wOF4t+WWWy5U8913333Yve23395tvfXW3tRs2tAGG2zg+OMHxDvmFYRA1RAoguDzWmaqGlZNkwdSxDdIBN+0lm12fWLbGtkWx970D37wg56YL7zwQscedrQQHFPe+c53uuuvvz42Wpj4jdyDiUTuQTR0XSUEWD4imJNTVtlMg7d8s+an9MUhwBryyJEjRfDFQaycC0AgNsGzXh5c7L/00ku9Wd5kwoSF+V1BCDQVgbyJmN8TedbBCbGpbRq3XhA8E7I6OIv1qpNM9L0Qas7z2ATPmvs111zjpk2b5vDChNDR2gnsWcfDfvPNN28OMqqJEAghgIk+yuoUihb7Kw6rmH1F8LEh61tESFEafN/gV8EpEYi9Br/OOuu4o446ym211Va+KBzi8Hw/88wz/YE3n/3sZ/1xtSnlUDIhUHkE8iZ4KgzBP/fcc5Wv+6ALaCZ6afCD3hPqVf/YBE+1Pve5z7l///d/91oM5E7YZJNN3BVXXOE222wz/13/hEBTESiC4PFFYU9znQMWvLXXXrvOVegpu5nom+Jkh0VCofkIJCJ44OAwgGAQsQfR0HWTEWC9PC8HO8MJMz2HR9U1cJb+zjvv7HfU2OE/da1LN7mbZqIXwXdr7eY8i70G35wqqyZCIB0CRRB83TV43hGBdnv77benA7UGqbDcMAnDKbIpJnrqQb0Umo2ACL7Z7ava5YhAESb6umvwto8/yWmWOTZJKVkZqUPwTTHRA5y0+FK6T18LEcH3FX4VXicEiiL4Oq/B8xZIwiAQPA6RRvZ16rdhWY3YdZpdGJnmfRfBN69NVaOCECjKRF/nNfinnnrKbx+75557CkK9/9lCiDgVcwhXkzR4EXz/+1bREojgi0ZY+TcGgaI0+DoTPCb6MWPG1H4nQLdOCsHbC7GaoMEbsdtnt7rrWb0REMHXu/0kfYkIQPB5e9HX3ckOE/2KK67YaIKH1I3gpcGX+INTUZkREMFnhlAZDAoCmOjzPMkO3OruZAfBjx49utZb/Xr1Xwi+aSZ6Jqq2Ft+r/npeXwRE8PVtO0leMgJFmejr7mQ3CBq8EXwTTPQQO+fqy0Rf8gDSh+JE8H0AXUXWE4EinOzQ4Jk41DUMggbfRCe75ZdfXhp8XX90CeQWwScAS1EHG4EiNHitwVe/T9kaPOvwddfg6cNYjCB4afDV73tZJRTBZ0VQ6QcGAQbHvJ3smrAGv8IKK2gNvia/Alt3F8HXpMEyiimCzwigkg8OAkU42UmDr37/aZKTHQTPJJVT+aTBV7/vZZVQBJ8VQaUfGASKWIPHK7/O++BtDb7OfgS9OnCT1uCpC0sNvDRMBN+r5ev/XARf/zZUDUpCoKg1+DoTPO+yX2655Rq/Dx4vev7MxF1Sl8u9GBF87pBWOkMRfKWbR8JVCYEiCL7Oa/BMTLBqoA3WeZLSq48ZKULwdT/oxupCm9V9stKr3fTcORG8eoEQiIlAEQRf5zV4278PWdh1TChrFc286JtwFr0R/Pjx4911111Xq3aQsMkREMEnx0wpBhSBItbg66zBG6mzpqs1+Hr8KIzg99prL3fLLbe40047zb8J8Pnnn69HBSRlIgQWTxRbkYXAACNQBME3QYPHdE2A8KlP04J50VPPuu2D/7//+z//Frz99tvPNwtEjsVl5MiR7n//93/dOeec46ZOneoefPBBt/baa7sJEya0/zbbbDO3yiqrNK05B6o+zfs1DlTzqbJlIlCEib7OXvSsR0PoRuqsw9t1me1SdFl1JvjZs2c73mNvAYJfeuml/dddd93V8UfgrYDTp093d9xxh/+79NJL3bRp09wNN9zgnSh9JP2rHQIi+No1mQTuFwJFEDyEWFcHNSN0lhkIaPCY65sWjODruAYPoZuFhXZha5wRfLCdOJt+ypQp/s/ub7HFFm7evHkieAOkhp9ag69ho0nk/iBQBMHXeQ0eDR7SM629rhOVXr3J1q05HIb2gvTqEiD4YLsENfhedWD741NPPdUrmp5XGAERfIUbR6JVCwHW4PMOkKM5q+Wdd9H5Bdfc67zU0Asn0+Bpqw022MCbsHulqcpzzikIbu1LSvAcZKRQXwRE8PVtO0leMgIQvN4HvwB0CN7M83WeqCyoUfQVZm1besAJjXXquoSsGrwIvi4tHS2nCD4aF90VAgshUJSJvs4aPCZ6Qp2XGhZq6NANNGBbx544caK7/fbbQzGq+xWCD/YvvuNFHyfwQhoRfBykqhtHBF/dtpFkFUOgCIJH8w2ukVasyl3FCZrom6zBm4keMOqmwWOiDxN80Ku+WwOzBi+C74ZQ9Z+J4KvfRpKwIggUsQ++zpqvOdnRPHWuR6/uFST4jTfe2N19993D1rV7pe/nc5no+4l+/8sWwfe/DSRBTRBAg8/7ffB11nzRDG0Nns+gpliTJo0lJgRvSxGjR492Y8aMcTNnzoyVtt+RIPgsTnbyou93C2YrXwSfDT+lHiAE5GQ3vLFtHzx3m6zBB9fgqWudzPSY6INLQEnW4GWip7XrHUTw9W4/SV8iAkWtwddV8w2a6OvsS9CrCwU1eOLWheCZkLIDINi/IHitwfdq8eY8F8E3py1Vk4IRKILg2XZHvnUMEAfETpAGX1wLHnnkkQ5iThosTZjgo06yi8pbGnwUKvW6J4KvV3tJ2j4iUISTXZ3X4NHgjeDrXI9eXSroZEfcMjV4Jn9nnHGGmzNnTi8xF3oeRfBo9NomtxBUjb0hgm9s06pieSNQhAZfZ813UNbgwwS/7rrrOta2H3nkkby72EL54eTGxJLykgYjeFuDv+mmm3w+STR4OdklRb1a8fWymWq1h6SpMALS4Ic3TtBE32QNPuhrAAIsq7BdjhPtxo4dOxyUnL89+eSTPsc0BG9pkJ+w9957e9njrsEzEUDjV6gvAtLg69t2krxkBKTBDwc8SHx1tkQMr9XC38JOdsQoy0yfB8GjwfPCHD5ps7gaPKf3UXeF+iIggq9v20nykhEoQoOHGINOUCVXKVNxyI38hDrXoxcIkKIdVWtxIfgyjqzNQvCmfdNOzz77rIkeew2+jq/HbVdSFx4BEbw6ghCIiUBRGjz51jFAHHYATJM1eLTfKIIv46UzWQjeTPS0k13Tz+Jq8LQtk9q69s86/qbyllkEnzeiyq+xCBRB8HVeu4Y4gl705szVtA5APcMEzxr8fffd503fRdY3SPCcC88xuXEDpM477GmXIMHHXYO3trU1/LjlKl51EKitkx2zS9MessLJj5cjSONuH8laXpb0yMmfmUaz5FV0WmSkjeqAK4NZLzmpD3/BeNQPp6vgvSS4ok2hJcVNb0flxo2fRJY0cXmNKrJ0wiEOrmnKzTsNcobbljIgdyZ2o0aNGtZG1Hn11Vd39957r5s0adIwcR544AHHHnL+sob58+f7LCDZSy65xF188cXu9NNP97LSb7oFZEcGPoOTL94SZ/2oW3p7RW6W/k05jK/kUfVQl76aBMfaEjwdPthpk1Q6Ki4/FluzinpelXsMQvxY6jCrpn2Qsw64MmD3ktMcjoLxXn759YNqgveS9BUwIt+46W2gjBs/iSxJ47INiwEcWZCL72G54uCatNwi4kNmUWOAbTWD5MN1Q4v/17/+5T3qgzJ985vf9KS/3377BW+nun7sscd8OrR3fvto4vymbOLRLdNnnnnGjRw50q+/P/HEE36CQjux5BA3UCb5pFWmwJT+bb+duOX2I15V+ypWmLSh+tOqtDVTOiGQMwKQcRzNJ0mxDKB5TlSTlJ01LnKj9RDqXI9uONhEOorgOr0bHq076NTWLf9ezzDRYz0gP0jS5OmVjufEh+CZDDAx2HTTTd0vf/nLOEnbcag36RXqiYAIvp7tJqlLRgDT6Fe+8pXcTY0QZF0JHrIx4qMeTSSCKAc763qdtsqh9Zvmb3HTfkLw48aN8wSNLEkJniUgI3jW3jfffPNEotC+ddC+E1VqgCKL4AeosVXV9AhcffXVfi3WTOTpcxqess6aL8QxCBq8TWKGt9zre+HvvPPO8G1PxnkRPCfJrbbaan7CkFSDZ0IAqRvBo80nDdQ9yaQiaf6KXywCIvhi8VXuDUHguuuu8zXJ20RfZ803TPB8b1qAVDsR/JprruknfTjVBQPkHl6vDz5Pco1pfuWVV26b6JNo08QNa/BJyiauCD4pYtWKL4KvVntImgoiwCA7ffp0L1neBC8NvoINHhAJ7TW8Rc4e0xdwtAsfeMN6d14aPPmsuOKKPr+kJnpkD2rwcbfHWf34hOCbOHEL1rHJ1yL4Jreu6pYLAjfeeKNbf/31C9nu0xQNnnrU1ZegWyfppsGTLmodHlLOi+CxBKy00kptDT6JuRzZMcvTLkxS0xJ8EqtBNyz1rHwERPDlY64Sa4bA9ddf76ZMmeLGjBmTu5NdnTV4yMbM13WuR7fuCLnZfvCoeFEEX6QGn4RsiYuJHoJHprQEn2RSEYWR7vUPARF8/7BXyTVBAILfcsstvSYlJ7sFjYbpFmIn1NkSsaBGC18FJzELP+2swee1Bm8megg6qYkegjdSZy+7XUfVo9M9rcF3QqYe90Xw9WgnSVkgAlOnTnUzZsyILAES4z3akydP9muhea/B15kYwWYQNPhOa/B0GNbgcbLDBE6A2DncJQ8TPYROXkETfRoNHrk4KEcEDxKDFUTwg9Xeqm0EAhdeeKG79dZbI544/85vjvtcY401/ECbN8HX2bQNwTNBIdR5ohLZ8G/c7KXBYwJfe+21nW2XQ9Mm5EHwZgVYYYUVUjnZBTV4EfwbDTpgHyL4AWtwVXdhBBgIOw3IbI/DPE9Ak8rbRF9nYgwSfJ0nKgv3iAV36BtmpVhwd/hVcB0+T4KnTzKB4KjStCZ60hNE8MPbbFC+ieAHpaVVz44IdCN4c7AjMeZY9j7nGZgw1NX7HO3WNHgIHsJvWqBvdHOyo75BgreJomnfWfAwgse0jqmeQ2/AmOs4AdlZXqCNRPBxEGtenNfta82rl2okBGIjAFHZwBxOBMEffPDB/vb73ve+8OPM3xl860rwQQ2+Uz0ef/xxN2fOHLfeeutlxqofGcTV4C+99FIvHpo2pNqpPyWpgxE8FgTynDdvnk9Of41jSQoSPJODNCfZUS7lKdQTAWnw9Ww3SZ0jAgyEURrX7NmzvWl0o402yrG04VnV2bQNwZv5ulM9zj33XMfb1eoaILpVe6UAAEAASURBVDdIrltAg+cgJN44BymznTKqP3XLI+oZefCGMwJaPG+EI8QlXJucMPli4pHmrWSkjVueF07/KoWACL5SzSFh+oEAA1iUxsX6+xZbbNHeClaEbAygdTVthzX4qHrgCX7XXXcVAV0pedI3bBLTqUDeC0+cWbNmeSLl5DmsMhBslkCfNIJH+7b8wDROQHYmJ0y+CGkInnqJ4OOgXc04IvhqtoukKhEBBs4ogr/hhhv8ATdFimKDL9pf3QKEbvJ30uCJwzayPDTafuBD3+ilwSOXrcOjKY8ePdqLmrXO9Elzkgua1+MSLhMB/AdsghLMIy6WIvi4SFUzngi+mu0iqUpEgIEwiuDR4DnBrshgg2/cQbtIWZLmDXmb/J0sEVavu+++O2n2lYgfl+Dt3fD0I4gUYo3qU0kqFST44B52w7RXXia7TcLSELzW4HuhXO3nIvhqt4+kKwEBBszwYIxT0n333ecmTZpUqARGkHEH7UKFSZg5BA+xE7pp8Dyvq5meyV8SDZ5+BBmjeYf7FDgkCcE1+CA5Q9xxghE8bYQ8RvRx0loc0taxb5r8g/4pgh/0HqD6+7XN8GCM9zxaWVBzKgIqvKH5q+Mgisw2QemlwdeV4KljEoLHRA+Z5kHw9EnyIVg/TNJXIHjah7YJThCS9GPS17FvJqljk+OK4JvcuqpbTwRY+zbv52BkCJ7jacsIncixjLLTloFmy7Yt2yMO8URt90PLZ026rgRvWnAvnDbYYAP36KOP+iOP8aKHmLOuwZPeCN4c5CDqpBo8mvuoUaN6VSHyOZObuOVFZqCbfUVABN9X+FV4EgS222679pnfSdJ1i2uDV5QGbyfYdUufxzO0JJMjj/yKzoODVg4//HDH9sE3velNvjjqAJmHA9ofBwTNnDkz/KgW32kX6tYrMNEZP368u+CCC/yyDt7v4T7VK4/w8ygNHqKOq1Gb9QH5s2jwUe0allXfq4mACL6a7SKpQgigzTz44IP+RK7Qo0xfjViD2hb3brnllsId7EzwTuRoz6v2efzxx3uN/KSTTmqv60JwaPXhADlA8GwhqyNRGEmG6xX1HU96+s7mm2/uNeb58+dHRYt9jz4Z3CZHQog6DsEzCTPZ0eDTErzW4GM3VyUjiuAr2SwSKoxA8G1d4WdZvhvBB7UtyH3VVVd1K6+8cpasY6et0yB69tlnu9/+9rfutNNOG7avuhPBg+/YsWM94UHydQvIH2cNnnpB8Ouuu65fklh++eXdk08+mam6YQ2eftIJ53BB1q+JT7q0Jnomn3EmFOHy9b0aCOio2mq0g6TogYARcJSW2CNp18c2eFn+RGb9vSzzPOVBICYH36sarrnmGn8q3RlnnOE43CUYOhEPWjsksf7663utv25H1iYh+Pe85z1u3LhxHhbeAJcnwbMGTz+JS7hG8MSH4NNq8FqDD/by+l1Lg69fmw2kxGVq8GU62NGYDMBVN1/jJHfQQQe5b3/7226zzTZbqA9C8MFlDovAxAWSwQmtjo52SQie18buvvvuvuo4FtrRsoZF0s+wBg/GcSeDyI1pHufHLARP21W9bybFdZDii+AHqbVrXFe2HxHy1uAZCHnHu5ETa5ecYFemBs8AXGUN/rHHHnOf/OQn3SGHHOJ23nnnyF7EWnFU20AO1A/NPS3B33TTTe4jH/lIu40iBSjoJv0DkksaMNFzlkKWQJ+37XFo4JB7XI2a/kRcAvinNdFXvW9mwXcQ0orgB6GVG1BH0+CjSCRL9RgIGfzIl+1yduJamabkuFpZlnqmTQsuBxxwgNthhx3cpz/96Y7ZQPA2SQpGMg0eE31aT/qHH37YXXvtte4rX/lKMOtSroNEmaTAPEz0tqeeciF6NHgmG8jUKwQtD5C0TPS9EGvmcxF8M9u1cbUyDT6KRLJUFgJD2yKQN8fTsv8drb6sUFUtCWvGYYcd5vE5+uiju8LRzURP/SD4e+65J/a7zIOF0S60CZaVn/3sZ8FHhV8HiTJJYRA8Z/CzLp82YKI3DZ41eCZREDwy9QpBuTHV2z76XunCz+NOKMLp9L0aCIjgq9EOkqIHAkbwRWjwDKIMggyoZbxgJlxVBtEqrnP+4he/8BaNE088sb0dLiy7fe9loscpj0kTWx2TBtp8lVVWcT/5yU/cd7/7Xa/NJ80jbfwgUSbJA4K/7bbb3J133hmLkKPyDq7BM8Gh7nEJNyg3FqqVVlopqoie95icVbFv9hRcETwCInh1hFogUJSJ3gZCO3msjBfMhAGPq5WF0xX9HY0bDTSO9tfLRI+saPG2BJJEdgie/NlPf8wxx7iDDz7YzZkzJ0kWqeNa/0iaAQRvbwgkjzQhuAZPH+Ho5LjLOWBma/A4RprzX1I54k4okuar+OUgIIIvB2eVkhGBIjV4BjEI/v777/fEEeUlnlH8rskpv4pakhFrV+HfeIiJnvjhwHoxWiDBtsqF4/T6jome/AkQ1fvf/3732c9+NrK8XnklfQ450z5JAwRvIS3BBzV4ywtZonC25/YZnJikkd/yYZKQVn7LQ5/9Q0AE3z/sVXICBIrW4DFhnnfeef7oVSOTBOJlilpVM2iQWHtVEA0bjTXsAMZ3I5i0jnbhicYRRxzh16a//vWv9xIr83PkN004SWZ5EDyTWjuL3soGyzDG9iz4CSnn0Y/z2A0QlEvX5SIggi8Xb5WWEgG0GQKkk2cwTWfrrbd2v/nNb0o7njZYh6pqSWFiDcocvjYyCbcPlgkj+LRb5ZDD8qdc/CV++MMfuquuusr96le/CouS63frH0kzhZhtYpBWA6bPm5OdlU+ecQg+OLGytGk+Oc1x7ty5aZIqTQUQEMFXoBEkQm8Eitbgt99+ez9wTpkypbcwOceosgaPZh4ncKAKRB42H0M0SUz0rNFzYl4wMGkIy7Hiiiu6k08+2R177LGOffJFBcjZJihJyzj00EP9DoSkBI/mzpkB7GIIa/BxJ4NpJybhOkLwzzzzTO4T63A5+l4MAiL4YnBVrjkjYObKMIFkKYZB0AbwbbbZxi233HKlvSI2KDcEGEcrC6Yp4zqJiR55IOEoDd4InpPeaEcOzukULr30UnfmmWcOexzW4O3hpptu6jDTsx5flJZJ/whaD6zsOJ/IhYMieSQJvJHui1/8oj+FLlx23L5i/TpJuVFxKR8v/KLwjSpT9/JDQASfH5bKqUAE0ODR2sIEkrbIK6+80n3qU5/yxIpWBLnfeOONLrh2mjbvpOniml2T5ps1PsQa1py75QkZhCdgQVMxpvV11lmn64l2TADCbdyJ4JFlr7328qcO/vrXv+4mWupnECXtkzZEYdIrLzRmHD7D5nnSlb0GT5ky04NCPYMIvp7tNnBSsx4JwYcJJC0QHEIyb948r13ZAA4B9SPE1crKli2pBh9FZqzBmwaP/L3OpIfgw23cjeDJk3fS055FhOAEJU3+UZj0ygcMHn/88cjJFQQfZ8dF1olJUEbOIJAGH0SkPtci+Pq01UBLigbPCzzCg39SUD73uc+5hx56yD366KNeU8zLlJlUjmD8uIN2ME0Z1xB8Ug3+e9/7nrv55pvb4oXx7bVVLorge000VltttVQH6LSF7HDx6quv+p0BNgHsEK3rbdKCQZJg/iZRGnzcyWDWiUlQXjR4fi8K9UNABF+/NhtIiRn48zDRX3TRRf5MdDQSrAIMvmhZ/Qxxza5ly8hkKgnBE/fiiy9206dPb4sa1uB7ETxtEp7E9ZKDV7Qyacs7GDHTPmlDGoIHA0IWgkf2LBOTYH1log+iUa9rEXy92mtgpeXd2pgKw4N/EkB4fSfaIOQeJPgsA3iS8jvFrTLBJ5n82GQgaEIOa5K9tsoFNXj21W+33Xb+tavd5Cia4LMQZRqCNw0+7EFP/yG/IL6d+lQY907x4tyXBh8HpWrGEcFXs10kVQABtgvheb3WWmst5IAViNbzkreSETA3GsEzEGYZwHsWGiMCZtc4g3Y4K9ZpizSdMpnqRqxheSwumFqgXsEJFAQP9jiSRYUgwTMZ4+x6/mzyEJUGArI+EvU87b08tOA0BA8GhKpo8HjR26QjLZZK1x8ERPD9wV2lJkAAZzjCqquu6jV43rM9e/bsBDm8HjVM8AzgkEiQgBJnmkOCtBr8z3/+c/8ClhxEiMwCbLoRaziRxQ0SPNdBJzsmAWussUbHM+mDBG/WGiwvNnkIl8l3XmJD38jbTE//yNo30hC8kWkngo8zGQS7vCauYG9tEYW/7lUXARF8ddtGkr2BwCOPPOK36mCyZKD57W9/63iBRtJgLygxDZ70kEfUQJo07yzx0xL8/PnzvR9BlrI7pbVjZ7sRazitEbwREFo1+QQJnjTd1uEheCYWhCCp9JKDt9VZ+/rEOfzLw7qThuBtDd7wDFaFvsLEo1fIQ3YrI00dLK0++4vA62+BKFkGHJ1uvfVWXyr7jw844ABvgj3nnHO8KWiHHXZwkyZNKlkqFVdVBDDpsv7OIM/gb+b1pPKi4bEPe+bMmX7/O9viyCvO29KSlpUkflzP6HCekGFQWw4/z/LdyDWKZDrlayRsMtlnWAvuRvBBJzsjesrrJQdnpj/99NOdREt1Pw8TPZgYlnGFQIOnv0dNPMHSJlDd8kN2MMkjxJ1U5FGW8sgXgb4QPO9JPvDAA70JCfMa4eyzz/ZvisJTmvdPb7jhhpEdPN/qK7c6IIDGbQTPYMl6fHDwj1sHNDz2TJ9//vkOjQ9TP3mzxtjPEHfQDssIwbOV66STTnJ77LGHW3PNNcNRUn8HXzt+Nm4mRsJGQPYZpcH/5S9/icw2ykRPRJs8RCZq3WSS1mldv1OaXvf7ZaIHA5Yxogg+7mQwbw3eJmu9MNPzaiFQOsFjsmOGOm3aNO8Ys8UWW3hEmH3bADV+/Hg3a9YsN2HCBP+MzhU2vzH45HUwCQMsE4281qyKbGLqzZ9NjIosK2veyMmAlBVXnMnY6wwRM+iGD6iJKyfa+jve8Q537rnnur333tu/qIR77K9PKyfpqGeWOkKMkGGcPKzdictkBzM4ExbelY4DW9bAbgVO82PiAKnGkcnKNK9vfuOkg6gIYaJC1h/96EeReZOG3zu4IoMF2r6bLFgCmZR0i2N5RX0ylvAXTA+2STEI552kbS0tGHzhC1/wSxlBeXgOxuDCmIV8nQL9CdzD6TvF73affNJOGOivtGUdQtoxoOi62W8+TTmlI88sm9k2fzg98cKI/ffff1gnoEOZowmVQtM69dRTh9Xv4IMPzs0EBYAM0njjVj1YY3f7cVelDgyYDEQjR47MJBLtz6QPL3r6DyQEDknbi0nkW97yFq+9c+DNH//4R6/BM7GE1NJgutxyi7TqmK3vYLUCqyT1IS6THdIx+DKgJ0nfqUEmTpzosLDRZvwOk+TJ0b/8pmlz0oEng2Y4D97ch5MkpBzWzG1SAKHbhAFZ2Qo3ZsyYTmJ7Cw/EFy6rY4LQA37/hCAhUv+kGISy9f0qCoNwvOB3lil23nlnx9n94cBrjZlAGb7h5/ad+tCv0uJh+fCJ9czKDN6Pc03/ZAJdh8CYkmYMKLpu5pORppzSCZ51Id6yRNhkk038+d/MvBmsLHAdXBelUx911FH22H9yVjNORnkEZtl0QvOyziPPovLgB8OPl0G96oHBhc6ZtZ3uvfdet05r7ZzAQAMBMfixfcrucY58rzfBoa3zI7766qt9OoiIQP9La/afN2+ZliY9qiXLIz6vNP/Ah4mL1adbHrQ9ExLiMtGhDqTF4hUnfbe8ecZk6vbbb29riUnyxKyMdo5cpMPqhnxRefCb/tvf/ta20lE2v3vT2mnz4PGzdoYB8ToFfr9RZXWKH7zPuMRvy3Zs8AyfDfpL2jzJg98pmCbJo1t/IC9+U8jGb6FToA2YLCUpt1NeTIxRuNLkxcSM311wfO9UTr/vwwOMBVULQS5MKlvpXvQMpKeffrqXE62Dhkd7YuZMJ2IGxeyebS8KQgAEGKjQKBls1113Xd9ngj/E++67z5vccd7sFiCJ4MtkTEPM8gPqVl7cZ/T9NAMguIADfwzoWYNNGiEYzP8MeEkDhG758Pu2SVQ4nyhHO4iLNkart3pZurCmb/ftk/7BRAdFoBvxWfw4n7RJUKOPkyYcJ2nbgjvyYzmICkF8o57bPdqgE/YWJ+5n0jrEzVfxikegdA2emTs/xp/+9Kd+ZrfTTjt5jXTPPfd0U6dO9aZGzITBgbh4GFRClREIDrSsM99xxx3DZtoQA4PQl770JbfRRhtFmjbJg3hBc2FVCB4TLmSYNJg5GzJEs8sabJKBxga5pyF46mIE341kIHje/R4M1Ic2gZggOv4IfDcTejB+8JpJGla98847zx122GHe/B98nuYaZaRsgkfJoa6dJjRx+0rwN5Om7sE04G99I3hf19VHoHSCBxJe8ciPn46MWYzAW6b46zYo+Ij6N3AIBAcr1uIJkJoFiJv1+d12282/G/wPf/jDQgMkGi6DdZC0qkTwRopWpzif1JsAPnkSvC2pdCKZbrJBBjZZ4RNCigoQ/LXXXjvsEQSP5sqYALnTxqwjxyEXCB5LDsEmBv5Lyn8seRxzzDHuuOOOS5nD68mSar9ggALUKYBnHDyIQ1vkEahDmv6ZR9nKIxsCpZvoTVw6n5G73eMzr04ZzFPX9UYgOOlDg8e6ExzEIQLI+pBDDvFORV/72tcWqjDm+aD2TgTSQAyYhfsZ0g6gkIERu31mqYcRB6Zu8A1OhuLmy+/XyCDYbuH0Ua+NNQ3eTPTIsE7L94K1/V6BdrRliuDkr1e6Ts8vv/xyt+2227p3v/vdnaLEuk/bBvtqr0Q2yekUj/xsAtUpDvfBnrh5BPKxvpFHfsqjPAT6RvDlVVEl1R2BoDbCIUhHH320H3DM4xVNFrLGIsTrSnHe+s1vfjOs2gz+4WUfCKzfe+ARMmjWHiZ0ly/UnXpDxgQjty5Jej6yQdzW4NNo8EETcjcNnokajnTmVIdwRm60C6TIHz4XF154YU/ZIXgLeRA81gW8/bMGMDRc4+SFg2MvDd4mUN3yA7s07ReVJ5O2JHWIykP3+oOACL4/uKvUBAgEtRG8nU2rsoGcT9M20dI5+OUb3/iG9wa3YqII3jR4i9OvTwbQOFpZUD6ru93LU4OH4Mk/DUEECb6bBs8OC9os+E4B1p8hN8o1go8rQ5AUSZslMHmC4Lfaaqss2fi0Qe0X57lPfOIT7pZbblkoX9qf5YCjWk6CRxxxxELP7UYQX7sX9RmcFEc9T3KPNogzqUiSp+KWg4AIvhycVUoGBBisguZGrjGr20COJmsETzGbb765O/zww91BBx3UPr4Ugu9kos8gWi5J02hIaLvBkKcGj5PdzTff7E+TDJYR5zpsooeQOoWwJz1b1Fhzh1CYYCTRQoOWmPDkp1P5ne5Tf/BkGSFrsMkK+Zx55pneunT99dcPy5btgB/+8IfdP/7xD39oEXvgO4Ugvp3icD84Ke4WL84zyiSEtfhLLrnEH1gUJw/F6Q8CIvj+4K5SEyAQJniSQug2kPOJNh4M++67r9tss83cF7/4RX87vEWOm6QJEkMwfZnXcQftoExMaow8OTAGU33W7WE2gENwF198sT9sJVhmnOugholWGpyYhdN3I/h+avDUn36Rh2+GafC0zwknnOB23HFHd+edd7ahYPkB51AOYOK47l7+BnH7Cm1pxNwuLOWFtaH1D8vmnnvu8SeS2nd9Vg8BEXz12mSgJOIseDsXoVPFo0y9puWRxtbgw+mPPfZYd9ddd7lTTjnFa2RVXYOPO2gH64cGb/XB3A0ZQUxZgg3gaJL4MzBBShqCdcHk3mk/N/miIQe3ynEkMafVMXmD4Jm40c5xAuUYIdvEL066qDjgyKQpj2AEj/bOC7Qw0U+fPt3X76tf/arDIZR3b2CWtwlbt3KJg9+C+Z90ipunBo8zNP0hbKbne1asO8mv+/kgIILPB0flkhKBm266yW9HCpucg9lFaSOQwA9+8AP/8ph//etfw0z0lpZ1WY5CZgA966yzvJZkz/h885vf7D2lg/f6cQ0pJl2DhzzRMhnwqSeElHUd3gieE8vAxggzCSbIY0SA1trtjWZhDR6C55wMM2tD8sGll25yICs48BlFOtwLrvd3y6sIggdTXnTESX8zZsxwnPuBaR4NfrvttusmzrBnppX36i+0pWnewzJI+cUmKsHkIvggGtW87rxAVk15JVXDEOAwEQajK6+8su08F64iA0l4sGLg51ATBrr7Wvufo87tJh+0RJyXiPP2t799WNZsg+Kv34FB28g1rixmtYAMwSIPgodQLUBEaQIEb+TTiyjxpMfCQkCTpy8E1+Ah5bgaPHlg0WBCEawH9+k/OMwhF0cah/sScYKBiVJeGjyWBZwWWSLiDAdemjR27Fj3/ve/333mM59JPIkCXwJ1itpmbPXIm+Cj+igy0A8VqouANPjqts1ASMagzuDBmm+n0IngGbBxnHvkkUcWWoMP5oXXPa8nrmqg/kaKcWUMbimD4CG2rCb6IM68rjlNoC7kQ+hF8Kw3Y25mLfdd73qXf8cAJvqgBp+E4Hl5EJaHsAbPd0gbYv3nP//Zs1q95O6ZQSACFgksE0Enz8tbe+z//d//PTG5ky34Egxj/yXiHwRvcSMeJ75FO4QnocgQnkwlzlgJCkVABF8ovMq8FwIMfngNX3bZZcP2RFs6G1TCg5WZbjHz4n0ddrKz9HX4DJJiXHnRnNAOGXiN4PMw0aNBE9Jq8NTFJiu9iBJzOlothMeaMpM9W4OHlJOY6JEZ2cEiTPD0IbTdd77znb6fEbdbQO5uSwvd0oafIRPl08/NZyIcJ+n3oJUkKq2t0feyVESl7XQvykRPO0uD74RYNe6L4KvRDgMrBYM6ZnLWk6+77rqFcDBNJTxYQWxGECQywl8ogxrcSEvw1BkcmNxASFkJHkLlJU+sZa/zxtv7ksIXJJ9eBE/evDsg+JIgCN7IBKKmfkkCmFAPPNWvueYan5Q+RJ4s0fz1r3/tmV0cuXtm8kYE8MDczyS0LIK3SXH4NxNX5qh41ibBZ5QTnkwFn+u6/wiI4PvfBgMtgWltmGijzPQMzhB5eL2RgRztyDTOumvwdALTfON0CAZX096pOyQCMWUJ5Ln66qt7YgzjHTff4GQFJ7tea9lYCpjYGRnRnlyb+TfpxA1MIPgLLrjA/f73v/diUy/k4nXC9DdO0OsW8iR4ysFMz+QrfA5DNxm6PaMu1KlTsEkx8fIKUWVSjgg+L4SLyUcEXwyuyjUmApgu2ebVieAZyGzwD2bJwL/KKqu0TalJiSCYV7+vg45TcWQBE8PFSD4PDZ4BG6x7kXI3GZNq8BMmTPD799/xjnd48zxEYmQCUSfV4I3geRc96QlWL/Ldfvvte2rxkHEWDML4QOzBbY3h50m/U49uk0HqHfWbSVpOML5NuoL3ZKIPolHNaxF8NdtlYKSaO3eu13C23HJLf1gLe4SDwYgseI9rCJ2JgQ3ETdDgTfMK1zX4HYdCnMWM/IzgwSEPDT4rMQQJPq4GT/1YHzdTvZFJGhM9eJCO8xWM4IOEF8dMn7cGT9uwjzx4Xn6wTZNeQ/Cd+gqyc7hTmjMMuslBm/BbJNgefL4bxt3S6ln/EBDB9w/7gS+ZAQISYN0VkzBaXNhMTxwGtHAwgjdnqDpr8LYE0WnQDtadI2TRMMENMuPP1uCrQPC0lRFBHKJkkoYJm22OttxiZAJ5JG1X4kPwURo8OO64447ek77buQtx5A62Sa9rnCGT1qNbnsFJVDDezJkz/f561vp/+ctfBh9lvrY2gdw5dQ/8zIogks8Mb2EZiOALg1YZ90IAxyMGPgZAQpSZ3syr4bwgtaCJvs4aPHXrZXa1+tuLSiAhBt2qafDBejAJsQmYyR/1ySFEkydPbj8yMoE4qF+SQH8iHZYOI57gJJEJBY59V111Vcds8yZ4+iZ1yiuAcXgyiPVjr732cvvss49/o2KeEwrkDrYJfgxMMk0GedLn1bL55yOCzx9T5RgTATSt4ED0tre9zR/jyeBsgUGEAS0cOCTkk5/8ZNtEH8wnHLcO36kjR8ReccUVXcXl1D6CETzmXzQ2Phl0swSIMCsRBbVLZIxz1j9b5TBhWzACS0PwTAjYcw4WRvDhSWIvMz3pwTOvACbB+mXN1/AhHzTq7373u+7LX/6y+/GPf+z233//rNlHpjeC5zdLQIM3grd7kQl1s68IvH4sUl9FUOGDigAmPgY/C2jy22yzjeMtVWgihKD2ZfH45EQwgmkPTdDgWZ5gqYJ33ncKt956q39kJvpjjjnGT4DQ7LMSPIQYh5A7ycZ9Ix8IgJffQJTBd753S2vPyIN2R56kEw4menYkrRF8uA9B8J0OPoK08Aux/mUyZfn82Mc+lsurZ00G8OG3Qx/4/Oc/7x566CF37rnn9nxRjaVP82ltYmTOUclG8PYbTJOv0hSLwIJpc7HlKHchsBACYc2KCBx6Y85WfGdw7mamNRNwEzR4BmwG604B4gIzzMymwYMN2iE4cC9LAOukhBoujwkbWiVHs9ImEEPSgAzUs1fbR+VLmffff79/FCT4YB/iTHgI8vbbb18oC86MZ1KSpwa/ySab+LXxhQpLeQOM2ef/vve9z/tf/O53vyuU3BHT2sQIXhp8ysYrOZkIvmTAVdwCBBjEgxo8T/CmxlTN+d2EsPblbwb+oXGi+WfVPANZ9uUSHKhzN4LHPA85UV/IPEhaOKihwZvjU5pKRE24kuZjhI4sab3GIRO0QjT/YB3jyEJ8+gwH9gQJ3uQiDyZEWEmiDr1hErXWWmvFKapvcajLUUcd5XbffXf3wx/+sJRTHGkTcBXB963ZUxUsgk8FmxLlgQBkFBx4yRPtFI3H1qJ7kQ6D9fXXX5+aTPKoRx55MIBi9sT7u5NJG/M8BM9yBNo+aSygwaN1zpo1y24l/mQAD+aZOINWApuwIV9aqwoy2AQvqTxWJp7eRvBRfQgzPccjhwPafx0IHivJvvvuGxa/sO+0Q5DggyZ6I/3CClfGqREQwaeGTgmzIsDAGyZ48gx603eKEyy77uvv1MU0eNatIfmowDo7L1OBxMxEH4zHm/Ps7WzB+3Gv8yB4OwEP+dK2C32CCQLbB6P6R7f6GMG/9a1vbRM89Qrng0PntGnTvEOe5ceJekws11xzTbtVyU/qwtbCvE7Gi1NJCJ4Jk5E5FhZ+mwS7FycfxSkXARF8uXirtAACncjbXj6Dhh81OAeyaMylkRoVijLTQ/wQEho8JMYAGzZf8+Id9kKnDXkQPGVbXdISPGQCwYfrF6delqaXBo+1Y9KkSW1LEXnjhY6jY9U1eCaDad/2FwfDqDi29GFWETR4fp+8t0AEH4VYNe6J4KvRDgMpBQOEmXSDAPCecDSUa6+91hO8DdrBOE27hhQZNAlRBM/70iE+tEvTUvkeDFUheNoUgk5L8GCBiT5cv2BdO12PGzfOm65Z6mHCQug0cQmb6TmXgYkBf1UO4IO1psxAW4CjkTkTTL7j+8K1QjUREMFXs10GQioGCAarqGBm+k5aflSaOt8DB9be0Yg4nz8cMM/b8aNGnGECzGqiRzvLYzJFXbIQPPXCYpFGFs4EwAHNJkHUqRPBc3LilVde2fZ5wPP/kEMOcUwwqxywPmy66aalikibBE30tgYPwRvplyqQCouFgAg+FkyKVAQCUU52Vg4Ez374bpMAi9uET5voQFBRA6Y52FFXI68wAXJGPaeMpQ15YU1dsqzB28QlXL8k9bI8IKVOk0TM3EyoOP6XgAZf5rp2kvoE455wwgnuQx/6UPBW4de0Bf3D+qYdVSuCLxz6TAWI4DPBp8RZEOhkoifPLbbYwg8mbA2zwTpLWVVPawSPdgYphYNtkeO+afBhAmRywClsaQNEmAfWWU30JoN9pqkPDnqk76bBk++OO+7Y9qbP853taWSOmyZqWStu2rTxwDJM8PSXuCZ6fuvBEyrTyqF0yRAQwSfDS7FzRIABo9MgzvY3e/mMkV+ORVcuK6tjFMEzOHIoCw52hE4aPGmDg3DSSqKV2XsBkqYNxjeCNzmDz+JcGxbhCUyctME4pO+mwROXPsZ+eMiKdX8mSQoLIxBF8PQ1XvdrpysunGrBHZZCDj744AU3dFUKAiL4UmBWIVEIdNPgiY+ZnkE360AfVXbV7plWFkXwM2bM8MSDCZ5gGnx4cgROPEurxWNWp/ysAYLOYqLPm+C7TSQ5Gvmee+5xt912m/cHwWSvsDAC9DUmS5joOXPBvOjZ8XL11Ve3t8wtnPL1O8SP8i1hYtVpW2invHQ/PgIi+PhYKWbOCEDwNphHZb399tt7bbVbnKh0dbxndYRgbZ3T6oF5nv3vFkwzDhM8zxl8IXj2c5988snuL3/5i3+BT5TZ3/Kzz7wInkkGvgBprQFWr6wTOyMl6m55Wl3tEyy32morxxvt7HW19kyfCxCgLcw6BE4QNk6h7NxgxwuHTXULtEHUuxLop4ceemi3pHqWAYEFb/rIkImSCoE0CDBgGLFFpWfwheQ7Dc5Raep6z3CA4MMaOB70Zp6nfkbwUQRo6/C/+MUv/IDKUse9997rCZcXqKyzzjr+Dwezj3/84+23nDGpoD3y0OAhAPbsm6UhaZtYe0fVL0lepIdYevkWYKbnyFe21ilEI0Cb0D/A045FJib91t7Ot/XWW0cnbt3tRPDstrBTCzsm1oPUCEiDTw2dEmZFoJeJnvy/+tWvug9/+MNZi6p8+iDBMxgGQ3CLHPeN4I0Ig3HR4NGU0MZ5pS6a6T//+U+/TnrKKae4j370o/5NaSeeeKK78cYb20mJT35ZSZUM8URHBpOzXUjMC8MiqyxG8L0mkhAUFgdp8J0byLBkIhgmeBwVo871D+ZGn0bjD5M5e+jDFqtgOl1nQ0AafDb8lDoDAmhWNph3ymbttdfu9KhR9w0HNOggwXPNGnxQgzfNOIrgTYOHYCF7C6wtc8Y/fwQO07nqqqvclClT/HcIPhjf30z5z7aamZxJs+G4WywPeRF8Lw2ew4M22mgjEXyXhupE8LQVxwLzFj7+OGgoKlifpl8GX0IEuYvgoxDL5540+HxwVC4pEIhD8CmyrWWSTgSP9zymdSNNKmeacRQBQvCmwXcj7G233dY7RxlYpMnDPE9+JmtagicPJi9RExiexQ1GSr00ePLbZZddZKLvAixtAY6QMUsZaN7WPuCMs+Lll1/eMYcgwQcjsXNDBB9EJN9rEXy+eCq3BAj0crJLkFXtowa96IMDXnj9nYpCnOzztjTBykPqrOH3IuzJkyc78raQl4Md+dlWsywEz4QnagJj8sb5DBJ8r7y+9KUvua9//etxsh3IOJA5JE3fXGONNTwGNinli63DdwKnE8HLRN8JsXzui+DzwVG5pEAADT6KpFJkVfskDJZggee5DYZUKorg0eBNewpXHHJlPZmBs5sGj5ZtTlPkwYSgW/xwOd2+21p2FoKHkHuRcjcZeEZ66hjHUgTuNjHple8gPjcsIXj6Cdae4G93hx12cNdcc82wvhvEyfp02IFUBB9EKf9rEXz+mCrHmAjEGXhjZlX7aKaxMpDaYEilgifYWSUh+E7kx+A7e/bs9mTB0oQ/Sc/6KU5PvEHtd7/7XW4meiPKLAQPHp0mMeG6dPpuWELyWfPqVMag3Ac/M9HT/zDTBzV4tHr+eEFUVLA+zUQyGCB4nO8YCxTyR0AEnz+myjEmAjLRLwDKCJ7B0wZD9hrfd999C71YBOLsRFis1995552xyBpnJwj+ggsu8A53eWnweazBGx4LEEp+ZQSviWRy7MIp6G/0SzR4+ugqq6yyUB/ETH95h3V469NRGjxlBZelwmXre3oERPDpsVPKjAho4F0AIIRmmrkNhhwByr71oNcxKSzegtQLrniXeVxzO/kyibjrrrt8Bnk52ZmJHjnTBgilk5Uibp6UD3FIg4+LWOd4QQ2edkGDD5roSdltuxztQH+jb7J1k4krASc7ggjew5D7PxF87pAqw7gIQPDhQSJu2qbFM43VtE7qF3yDXLC+DK78RQW2fOGAF4esGXBxruOoVgbwOGmiygzfq4qJHksHJmARfLiFkn+nX2JKRwOnn4RN9OTIlsu5c+e6WbNmLVQAk1a0fgj+zDPPdNddd52PY8Runwsl1I1MCIjgM8GnxFkQkAa/AD0jeIjWBrsoBztSsJf9D3/4w4LEgSvSY6aPY26H4GfOnOlTv+c972l7RweyS3VJvrwN0Ez1aTKhHlk1eBzn0BBF8GlaYHga2oPwxBNP+L4FWdNng4Hv2223XeShNxA8/ZL0TBL4JEiDDyKY/7UIPn9MlWNMBLQGvwAoLBkQGmZlM9GHz6BfELv7FYcDxdHGIWLKGD9+vPve977ndt111+4ZJ3iK014WJ7s8CN40eCZMWZYLElS7sVGN4KlgJw2eZ522yxnB80peCN7W4rGwEOzTf9G/3BAQwecGpTJKioBM9AsQMw0ekn/ttdf8VreHH37Yv45zQax4V0kInreorbvuuvEyLjEWhBIklTRFmwaPnwHXCtkQoD3MwXOzzTbz74kI58g6PJ70YcI2gmcLJ2b6MMGb1Sqcn75nQ6C2R9UyIIZNRGmhoOOyblmHWT5y8scWp6oHZKSNOuEKkXGEaqfnZdYPDTqNHNSPY1XTpA3WD5P6qFGj2qb1m2++2R+fauvZFpe2J3Qr7wMf+IDfdtQtDnmgibH+zqDcKy7x04S0uO67775u0qRJmeSifhANf2PGjOmaF3LSX4vCIQ12ndIgJ39DQ0OdohRyn8knVh8w2nTTTRfa3UGhTC55w9wNN9zgdtppJz9WMb6yTIJ/yAMPPODlxveDfMy6wljQb+zT9tVCwM4p09oSPNofTh95BX4sdZhF8sOGUKh/1YPtb+2EK7P6quBug01STF9+eVGvcXeqY9z8OCiEV8LSrrQxh4YwiIbzpe0J4fvBcrbccsuecYiAVsvAy+s+u+UXzDvpdVpc3/Wud/missjFgP3II4/4fMC0W16QV1X6Yi+MIUyWtyDFMgMYMWnqhiPy0Jd5TTHr8WBKHyMNjnlcE9DkuccaPL4aEH6vfH3CAv+l7asFiuSzZlKVNshEnxY5pcuMAGSWlxUmszB9zoBBG8clAgMNXsbBF8wUIZ4NHGPHji0i+77niXUIIoGYIHiFbAjQR+M4b2IRCr9djsk8/dssUEETPVaqOOTOa48VkiEggk+Gl2LniIAIPhpMCImXzKDRFxmM4FddddUii+lb3qwXs23L6tk3QRpScFyCZwcF73m38xWoPgTOxBUyZykKL3osEGj0nJsQXrMPQ4YfBSb/8Otmw/H0fTgCIvjheOhbiQhgZsSMqjAcAQgeXDbccMPhD3L+ZsTXZIJHc0STV8iOgJnoe+WEteRtb3vbMC2ediA9ZL5O6/AmNHgjdUz0vTR4mxDMmTOnV/F6HkBABB8AQ5flIiANPhpvBsKJEycWvnxhxGdLA9HS1Peuec5bPetbk2pIHleDR1rM9JcHjq0NEzya++OPP+77OBPNXgRvJn0RfLK+IIJPhpdi54iACD4aTAiebUhFB0ylaFQM3E0MIvh8WzUJweNod/3113uTOqZ4rHX0a7R1nDrpew899JA322O6N22+k8R2MI4IvhNC0fdF8NG46G4JCOigm2iQGfCKdrCjZLYtbbvtttFCNOCuHbQjDT6fxkxC8JD4xhtv7C699NL2wU30a6xFeNOzLHTjjTd6wuc+Gn63YBq87YroFlfPFiCgBdAFWOiqZATQ4LUGvzDoRx55pB8cF36S75311lvP/eAHP8g30wrlJg0+38bgLAGOm40bMNP/+c9/9oc14T3PjpkjjjjCf7If/tRTT/WmfAjeNPROeRvBS4PvhFD0fWnw0bjobgkIQPBNNQ9ngW/y5MlyDMsC4BtpIQ6CNPg3AMn48f3vf9/tvPPOsXMxgmd9HfM8gfV2rt/61rc6jq3deuut/d56do1g0esUIHgsMtLgOyEUfV8EH42L7paAgLzoSwB5gItAa4QUbLfAAEORS9XtkKW4mbHNE2e6iy66qE3wlhaCJ2y11VbuIx/5iD8Q5/Of/3zHw3sg+I022shJgzcE432K4OPhpFgFICAnuwJAVZbDEMBMLw1+GCSlfWFCcMIJJ7ivfOUrbpttthlWLocr/elPf/JvMGQSdtppp/nXzH75y1/2ZD8scusLBM+20UcffTT8SN+7ICCC7wKOHhWLgAi+WHyVu/MavK3FC4/yEUA7v/DCC91JJ520UOETJkxo38Or/vTTT3e8Ivnoo49u37cLCN72z9s9ffZGQATfGyPFKAABzp6G4DnbWkEIFIUA5C4TfVHoxsuXHSF2RG23FJxy94tf/MJdeeWV7lvf+tawqBA8L7LB297Osx8WQV8iERDBR8Kim0UjgFMNXtzmfFN0ecp/MBGQib5e7c4WujPOOMOdd9557sQTT2wLD8GztQ6nXF43qxAPARF8PJwUK2cEeA/5JptsknOuyk4IDEcAs+64ceOG39S3SiPAVjxIHm2etXnOn3/wwQf9uQ1Y/LD+KcRDQPvg4+GkWDkjMG3atFJOa8tZbGVXMwS++93v1kxiiQsCmOMhedbwr776av/iJbR7CN40eNb2p0yZ4g/OEWrRCEiDj8ZFdwtGYPr06aUc5lJwNZS9EBACBSGw/vrre8c7Xp28xx57+FJ4XS0Ez3vmv/a1r7l//OMfBZXejGylwTejHWtXC14niVONghAQAkKgEwK8dOmSSy7x70wgDgSPif6OO+5wjz32mF4f2wm4N+6L4HsApMfFIPDcc8/5LUzF5K5chYAQaAoCwbcdmgZ/1VVX+eqhKCh0RkAE3xkbPSkQAQheB5AUCLCyFgINRMDW4O+55x7vUS+C797IWoPvjo+eFoAA62ecT21v+yqgCGUpBIRAAxEwDZ51+NVXX92J4Ls3sgi+Oz56WgACaO8EnTBWALjKUgg0GAFbg4fged2xCL57Y4vgu+OjpwUgAMFzYMViiy1WQO7KUggIgaYiENTg11hjDRF8j4YWwfcASI/zR0Dr7/ljqhyFwCAgAMFzqh0aPASvQ2+6t7oIvjs+eloAAs8++6zM8wXgqiyFQNMRwKOeN8oZwctE373F5UXfHR89LQCB559/XgRfAK7KUgg0HQFeM/vAAw+4V155xWvwIvjuLS4Nvjs+eloAAtLgCwBVWQqBAUCA42oh9xEjRvjDb0Tw3RtdGnx3fPS0AATQ4LVFrgBglaUQaDgCiy++uBszZoxbYoklHO+QF8F3b3Bp8N3x0dMCEECD1yE3BQCrLIXAACDAOjzOdhD8q6++6lAY0oQf/ehH7gc/+EGapLVJI4KvTVM1R1Bp8M1pS9VECJSNAOvwEDxbbflLo8W/9NJL/lW0s2bNKlv8UsuTib5UuFUYCEiDVz8QAkIgLQJo8E888YRPPnr0aP+u+OB59d3yfe2119xPfvITt9Zaa7nHH3881eSgW/5VeyaCr1qLDIA87IPXKXYD0NCqohAoAAE0eIiasNdee7mvfvWr/v3xP/7xj3uWhgf+cccd5yZMmOA22WSTxu+jl4m+Z5dQhLwREMHnjajyEwKDg8CHPvQht//++/sKH3DAAW6ppZZyf/vb32IB8NBDD/l4vG72ve99b+M1eBF8rG6hSHkiIILPE03lJQQGCwFOsNtoo418pVdYYQX3q1/9yi/7vfjiiz2BQIPHOQ/T/jbbbCOC74mYIgiBhAhA8NomlxA0RRcCQiASAcYS9sXbunxkpDduPvjgg27PPfd05513nnfUS+Og1y3/qj2TBl+1FhkAeXCyW3bZZQegpqqiEBACZSDA3vh58+b1LAqCxwIwbtw4x7vl58+f3zNNnSOI4OvcejWVnbOkOZFKQQgIASGQBwKY3OMQPGvwkDsBUz2n4r3wwgt5iFDJPETwlWyWZgvFj2y11VZrdiVVOyEgBEpDYMUVV4xF8KzBG8FzGl54H30asmdpgC13VQwi+Cq2SkVlwrTOyVFZAjPmuXPniuCzgKi0QkAIDEMAgu9Fsvfdd5977LHH3IYbbthOi5neXjl78803u0033dTts88+7pe//KUfp9oRu1x873vfc0cffXSXGP17JILvH/a1K/nLX/6yO/PMMzPJPWfOHL8Hnh+WghAQAkIgDwQg+F5OdmeddZbbbbfdhh2TzThkjnbTp093b3nLW9z73vc+d9lll7ntt9/e7b333v7EO9buOwUmBn/9618dp+NVLYjgq9YiFZaHGfC9997rdt11Vz+77dbpw9XALM9Md/bs2W7VVVcNP9Z3ISAEhEBqBHCy66XBX3LJJZ68g4UEX1hz9913+8NvjNSvu+4697GPfcz985//dDvttJP3vr/ooouCyR1b89hTz0Th73//+7BnVfiik+yq0AoVlgGT1korreQlhNCvv/5636E5NYrOftVVV3WVnjWtrbbayp8cde2117pf/OIX/kfUNZEeCgEhIAQSIICTHYTMCXeLLrqw3srWXJQTTPDBECT4e+65x++Nt+c8Y0sdf7w/4+KLL3Zf/OIXPZkzphFuv/12x8l6TABuvPFGt8MOO1jySnwujEQlxOq/EDNnzuy/EH2WAI1922239bNUOjgmsFtuucVLde655/ozoDG5dwtXXHGF1/Yvv/xyb+piDV4Odt0Q0zMhIASSIvCmN73JzZgxw02ePNkdeuih7o9//KN78skn29lMmzbNH2cb3p4bJHg0+PHjx7fTBC/Ya7/HHnt4kz1jmgWsApD9Bhts4Mu3+1X5FMG/0RJPPfVUu03oGLvssoujwQk4ltnZx+1IMS5w3jjkkEM8QcaIHisK+zZ7rTVFZXTTTTc5ZrFJAh2ZdSVwwMS+yCKLuKGhIZ+FmcOYtXYLF154ofvMZz7jjj/+eP9DYB1/u+2265ZEz4SAEBACiRDgXPmrr77a+whtvPHG/nS7t771re6DH/ygO+mkk9yf//xnt9lmmy2U59prr+1Q5hjXUGg6EbwlXH/99du8gHXy17/+tfv4xz8ugjeAOn1iCv7pT3/qvv/97zucFsoMkBcNZ0cdQoY0uM3UjjzySE9QSWU64YQT3Pnnn+9uuOGGREnZz4k5Oyp85zvf8Z02yXYOvN/33XdfN3Xq1Kgsh92j3sx2eePSOeec40kd5xPM8xwPifmLHxOBHxBmK3NSGZZR6wt58Zw1e35ohI9+9KNu991399f6JwSEgBDIEwHGqAMPPNDhUIfJfr/99nOY3rE4Ml6Fw5QpU/yy45133umd73q9lW699dZrEzxj8cSJEx3WAzzz/397Zx4jRfHF8QeLF4fgkYggmhUvlAgSIUYFOYJGCPEgJoQ/vBDRiEYQ/5CQrBE0IZL4h4hE4gGKckNUjEdUwGgwngiI94UCqyIeQdD1+O3n/dJDM/Ts9MzOjD3T30pmp7anurrr09X9Xr16/YqlZ5PmaJeYOfglS5b4gx9vyNmzZzuwSq041q1bNzffrFu3zoYPH+5zKczpMM/MHDNmaUavt912m7VrlxsZghDhyApHKCy0ifpYCIG4x3HTvHnzbOnSpe60wbua4fTyyy/7v3h7Yo7q37+/0Un79Onjiy6Eywb5hQsXeudl/nvYsGGZOM7B74zsaSeeo3iDYq3gZuCdUSwZmL5YXhFtF6sEizSg9TIfNX36dD8Pjj9o0CD/oCmjCGzYsMGFfPa8V3BcfYuACIhAuQjg+IbXPJ9ciWco8+gMxHg25ksIeByFmZdnYMhUAIn16ZEZ999/vw+GEP5JSLmlVYXPDsHRo0cPPypmErQhlvQjoRXhIBFOzJ1kC7/w74XkCXaAUJ41a5YLJcw5EydOdO9JBDa/c7zJkycbygAj4uwPQhKBSFlGypzz0KFDfeSKBQDnNDoEKyExr91SWrZsmQtZ6iHGMooO+9MZGxsbXeBiTsK7k5E+5Rlho0ki+BH4nC8JLXbu3LlusmJRBo7PCkwIcIQ0I2y8P9F8UUY4Zt++fV2hYX9MUAhxrBtow3RsHEnGjBnjc+mwwuKAEoNygPZMWYQ9I3icT6oh7jzXrZhEH0SZ4TpVIqFokip1vNa2qViurT1uofujuNfV1VUF1+Bcub+Snuiv9IEox7cknDtv9CBvHn30UTfl57uveP7zbOWZzPRkWAZdeeWVhmBHyOerp5C2B/d8IfsEZds0d5L/vJcgHOfMmWNTpkzx81q9erV7JiKoSMz3Yr4Pp3HjxmWEWHh7MXkAggHhhtBjjpvABYRTXbFihSsbKACMhBFeOGp06NDBv7nYQZ6Lz1w+Jn5G2gi7fv36uSUAT3QcMegAaIAtJToc72JStr6+3utE2Vm/fr0NGTLE7r77bj/fcB0IfgQ1I3GUgcCEj9I0bdo0H+FTfvPmzdbQ0OAKATGZL774Yv/kcnz77LPPbOrUqYaSwtxWuEOHjx/OcwwUB6Y4Jk2a5MI+/HsS8zyAivGzWLWqbbMCVdc8x9dUsWbxwEyaKTBX44vlmqu+cm1HuPMcwAk06Sl44Cfg0Z0XFX21qanpgOdV3h0rWIDBI892LJIsPZsvMdDj9Tme7dkJK+jgwYNLqtDwLMdCUExKhIDHiW3mzJkuSGjEypUrffSOQMmVtm7d2uqoakHdaFuYV7Zv3x5sSuw3DyIemtw0SU8oSHjfV8OCDvSBQCkqhOvzz7e3BQs6NVtIGgvZreiyXHuUNixc1ZCK5VrptvEA5d6KE8+80ueWfTyEJopIMQppdl3l/p+wsESurAaFNKl9lQFl8KpyodcrEV703Fh0WszeaKUKhlLoZVR5ERABERABEdifQGLm4JnbxUSOZopnYpcuXfY/U/0nAiIgAiIgAiIQm0BiBDzz03wwPceZ543dQhUUAREQAREQgRQSSISJPsxdwj1MQ3kREAEREAERKI5A4gR8cc3QXiIgAiIgAiIgAmECEvBhGsqLgAiIgAiIQI0QkICvkQupZoiACIiACIhAmIAEfJiG8iIgAiIgAiJQIwQk4GvkQqoZIiACIiACIhAmIAEfpqG8CIiACIiACNQIAQn4GrmQaoYIiIAIiIAIhAlIwIdpKC8CIiACIiACNUJAAr5GLqSaIQIiIAIiIAJhAhLwYRrKi4AIiIAIiECNEJCAr5ELqWaIgAiIgAiIQJiABHyYhvIiIAIiIAIiUCMEJOBr5EKqGSIgAiIgAiIQJiABH6ahvAiIgAiIgAjUCIE2/zanGmlL0c34+OOP7cUXX7Sbb7656Dq044EE5s+fb7169bIBAwYc+KO2FEXg999/t4aGBrv33nuL2l87RRN45ZVXbNeuXTZ69OjoAtpaFIF77rnHrrrqKuvevXtR+2un1hHQCL6Z3z///GN//PFH60hq7wMINDU12V9//XXAdm0ongD6uPpq8fxy7Uk/pb8qlZbAn3/+6c/X0taq2uISkICPS0rlREAEREAERKCKCEjAV9HF0qmKgAiIgAiIQFwCmoNvJvXbb7/Z999/bz179ozLTeViEPjmm2+sU6dOdsQRR8QorSJxCPz999+2ZcsW6927d5ziKhOTwA8//OAm+m7dusXcQ8XiEMC/6fjjj7fDDjssTnGVKTEBCfgSA1V1IiACIiACIpAEAjLRJ+Eq6BxEQAREQAREoMQEJOBLDFTViYAIiIAIiEASCLRLwkn8l+fw448/2qpVq2z37t12wQUXWN++ff/L06m6Y+O/8NBDD2XO+6KLLvL54VdffdU2btxohx9+uI0ZM8YOPfRQE+sMphYzTz/9tJ144omZefYolszDr1mzxl9DvOKKK6xr166eX7p0qTU2Ntopp5xiI0aMaPE4afpx586dtmzZMpswYYI3G37PPfdcBsF1112loKanAAAHiklEQVRnnTt3trisMzumNMN9v3r1avv5558NvwX6Wrt27dw/JLtf5rrvo1inFGfZmp36EfySJUsMocQN/sILLxiBRJTiE/j222+tvr7ebrnlFv+cfvrp9uWXX9oXX3xhN910k//20ksveYVi3TJX3m9fsGCBvfnmm5n4AVEs9+7da88884wHELnsssts0aJFXjHBWhD0BGxCyH/00UctHzAlv3744Yf28MMPG0I+SJ9++qkNHz48028R7oWwDupJ6zd97eSTT7YbbrjBEbz33nuWq19G3fdRrNPKspztTr2A//XXX61Hjx7WoUMHHzV9/fXX5eRdc3Uj4NHc0drxRG7btq19/vnn1qdPH6urq7Ozzz7bPvnkE2+3WLd8+bEiweucc87JFIxiifCmz7Zv396OO+4427NnjysElO3Xr59zP+usszLcM5WlNIPiFAiiAAH9FmX+tddec35sL4R1UE9avwcPHpyxdh500EE+ks/VL6Pu+yjWaWVZznanWsBzgyOcgsQDk4esUnwCRKpq06aNm+meeuopY2SE2Q6WJF6PgalY52d65JFHGhaQcIpiGd5G2YBxeLv68j6KKDso8OGEIkpiCum+++7z6IBhflFMKR9sJ5/mhMUDBZ77/f3337fzzz9/v/seNrDCPB/1jI1inWae5Wr7PulWriMkuN5DDjnEEFBBIt+xY8fgX33HIDBy5MhMKcKovv322/7QDMKpEv6Td+HFOoOpoAwPyWyW+DME26gMxgiwoCwjKvXlljGHR/Rbt251f5GAX8CUfpuLdcu1p+NXpj6effZZu/HGG73vRbEiBkbUMzaKdTqoVbaVqR7Bo4EefPDBPsJEOBGYhTlMpfgEVqxYYcG0BiY6HG4wG3/11VdeCXNtbBPr+EzDJaNYHnvssfbdd98ZfRbzPN+MkrLLaoGPMMl9edaeePDBBzOCp6V+m4v1vtrSmcO/A9+aiRMnWpcuXRxCFCsU+6hnbHZfVYCh8vSj1Ae6wcTESnIsNoF5FMcbpfgEGP3g9Y2Awclm/PjxPurBmxszHPNveC5jChXreFxxoGOOnTc6EEZRLJk73rRpk0dhvOSSS+zUU0/11dCWL1/uI3pG8ePGjfPpk3hHre1SRACcOXOmTZ061RuKI+M777zjrI455hh/06MQ1rVNK3/rZsyY4c9MRu0kfD8uvPBC92nI7pdR930u1vmPrBKFEEi9gA9gYebkoahUHAGEe3CzBzVgmkN7z05inU0k//9RLFFKmUsO5pODWqLKBr/pex8BhAx9kVFmOEXxy8U6vJ/y/yeQi1XUfR/FWhxLR0ACvnQsVZMIiIAIiIAIJIZAqufgE3MVdCIiIAIiIAIiUGICEvAlBqrqRCBpBE444QSP0JbvvBoaGjKOZ/nKtub3W2+91aZPn96aKrSvCIhADAIS8DEgqYgI1DoBnNDuuusud+qr9baqfSKQFgIS8Gm50mqnCDQTGDZsmD322GN20kknGa81zZkzx7mwXgCJCIQEJyEq4eWXX+6vQLFt3bp1/vuGDRvs6quv9tjjp512mod4JhRpkHij4vrrr/d/58+fb7169fLYEnhZv/XWW0ExfYuACFSAgAR8BSDrECKQFAKECH3yySc9QMm8efNs0qRJ9ssvvxh50tq1a+2oo46ya6+91hdf4X1nTOrXXHON/85790888YQNGDDAZs2aZeedd549/vjj/ht/+K1///7+SiRrEXAsXqUkBO+0adMy5ZQRAREoP4FUR7IrP14dQQSSR+D22283Rt98CDm6bds2X32OMyVoya5du3ylNd5nJuTt6NGj7ZFHHrEPPvjAG8PrkHfeeafniXWAAsDqYsRCIPjJ3LlzPc+75meccYYrEMSYWL9+ve+jPyIgApUhIAFfGc46iggkhgCBXYJEiFveTw4nFmJhfYGhQ4eGN9sbb7zhwXeIQhYkFIIhQ4b40qHEPDj33HONmPpE11u8eLF/CHbElADvnSuJgAhUjoAEfOVY60gikAgCCO+WUjCy37hxox199NFelDl5Rvvvvvuuhx0O78/8/cqVK33UHszls+wt668TWa93794e7fCOO+4I76a8CIhAmQloDr7MgFW9CFQDAdYKIKIb8/GMxHHGe+CBB3zUvWPHDg/jnGt9+VGjRtnrr79ua5qXDL700ku9uT/99JOvF45wZzSPY1+2paAauOgcRaCaCUjAV/PV07mLQAkJYGrH/L5582ZjtL1w4UKrr693s/uUKVPszDPPjDwaZv6BAwf6OvaswEYaO3asYerHA595eJzstm/f7ssGR1aijSIgAiUnoFC1JUeqCkWgegns3r17v7XTMc1jps9n1s/V4p07dxpLhmbHy89VXttFQARKR0ACvnQsVZMIiIAIiIAIJIaATPSJuRQ6EREQAREQAREoHQEJ+NKxVE0iIAIiIAIikBgCEvCJuRQ6EREQAREQAREoHQEJ+NKxVE0iIAIiIAIikBgCEvCJuRQ6EREQAREQAREoHQEJ+NKxVE0iIAIiIAIikBgCEvCJuRQ6EREQAREQAREoHQEJ+NKxVE0iIAIiIAIikBgC/wMecrbLl5E4kQAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>The interval with the greatest average number of steps was <strong>835</strong> with <strong>206.1698</strong> steps.</p>
<h2>Imputing missing values</h2>
<p>First, find the number of observations with missing values (<strong><code>NA</code></strong>)</p>
<pre><code class="r">missingcount <- sum(!complete.cases(activity))
</code></pre>
<p>With 2304 missing observations, let's see if there is a pattern. Summarising the missing values by date provides:</p>
<pre><code class="r">missingintervalcount <- ddply(activity,.(date),summarise,missing=sum(is.na(steps)))
missingintervalcount[missingintervalcount$missing>0,]
</code></pre>
<pre><code>## date missing
## 1 2012-10-01 288
## 8 2012-10-08 288
## 32 2012-11-01 288
## 35 2012-11-04 288
## 40 2012-11-09 288
## 41 2012-11-10 288
## 45 2012-11-14 288
## 61 2012-11-30 288
</code></pre>
<p>Given that there are 288 five minute intervals in a day - the pattern of missing data is all the data on a given day. Perhaps imputing the missing intervals with the average intervals for the same day of the week for the same interval is a good approach since individual movement patterns tend to be cyclical by week. First, assign the week day to date column and summarize the missing steps by the week day. </p>
<pre><code class="r">activity$day <- weekdays.Date(activity$date)
ddply(activity,.(day),summarise,missing=sum(is.na(steps)))
</code></pre>
<pre><code>## day missing
## 1 Friday 576
## 2 Monday 576
## 3 Saturday 288
## 4 Sunday 288
## 5 Thursday 288
## 6 Tuesday 0
## 7 Wednesday 288
</code></pre>
<p>This shows us that at most, 2 days are missing for any given week day with Tuesday not having any missing data. Next, create a new data frame with means for each interval and each week day. This is used to merge into the new <code>imputed</code> data frame.</p>
<pre><code class="r">weekdaymeans <- ddply(activity,.(day,interval),summarise,meanstepweekday=mean(steps,na.rm=TRUE))
imputed <- merge(activity,weekdaymeans,by=c("day","interval"))
imputed$steps[is.na(imputed$steps)] <- imputed$meanstepweekday[is.na(imputed$steps)]
imputed$meanstepweekday <- NULL
</code></pre>
<p>Now, a new histogram, mean and median with the <code>imputed</code> activity data.</p>
<pre><code class="r">imputedstepsperday <- ddply(imputed, .(date),summarise,steps=sum(steps))
library(ggplot2)
ggplot(imputedstepsperday,aes(steps)) +
geom_histogram(binwidth=max(imputedstepsperday$steps,na.rm=TRUE)/20) +
labs(title="Histogram of Steps Per Day (imputed data)",x="Steps",y="Frequency")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p>
<pre><code class="r">imputedmeanperday <- mean(imputedstepsperday$steps)
imputedmedianperday <- median(imputedstepsperday$steps)
imputedmeanperday
</code></pre>
<pre><code>## [1] 10821
</code></pre>
<pre><code class="r">imputedmedianperday
</code></pre>
<pre><code>## [1] 11015
</code></pre>
<p>The mean of the imputed data is <strong>10821.2096</strong> and the median is <strong>11015</strong>. Compared to the results from the original dataset, removing the <code>NA</code> had a mean of <strong>10766.1887</strong> and a median of <strong>10765</strong> - so both the mean and median increased by a small amount after imputing the data with this method.</p>
<h2>Are there differences in activity patterns between weekdays and weekends?</h2>
<p>Create a new <code>daytype</code> variable to categorize weekday versus weekend and use this to facet a plot to find patterns among intervals. Lines are added to represent the mean and max of the respective sets of weekend versus weekday data to highlight differences.</p>
<pre><code class="r">imputed$daytype <- as.factor(ifelse(weekdays(imputed$date) %in% c("Saturday","Sunday"), "weekend", "weekday"))
stepsperimpinterval <- ddply(imputed,.(interval,daytype),summarise,steps=mean(steps,na.rm=TRUE))
ggplot(stepsperimpinterval,aes(interval,steps)) +
geom_line() +
facet_wrap(~ daytype,nrow=2) +
geom_line(stat="hline", linetype="dotted", yintercept="mean") +
geom_line(stat="hline", linetype="dotted", yintercept="max") +
labs(title="Average Steps Per Interval",x="Interval",y="Steps")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p>
<p>The plot shows distinct differences between weekdays and weekends. More steps are taken in the earlier part of the day during the weekdays but lower numbers of steps in the later portion of the day compared to weekend trends. Weekdays result in a higher max steps per interval but lower averages across the entire day.</p>
</body>
</html>