<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>Varsha_Project2_CreditDefault</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
<style type="text/css">
/*!
*
* Twitter Bootstrap
*
*/
/*!
* Bootstrap v3.3.7 (http://getbootstrap.com)
* Copyright 2011-2016 Twitter, Inc.
* Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
*/
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
font-family: sans-serif;
-ms-text-size-adjust: 100%;
-webkit-text-size-adjust: 100%;
}
body {
margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
display: block;
}
audio,
canvas,
progress,
video {
display: inline-block;
vertical-align: baseline;
}
audio:not([controls]) {
display: none;
height: 0;
}
[hidden],
template {
display: none;
}
a {
background-color: transparent;
}
a:active,
a:hover {
outline: 0;
}
abbr[title] {
border-bottom: 1px dotted;
}
b,
strong {
font-weight: bold;
}
dfn {
font-style: italic;
}
h1 {
font-size: 2em;
margin: 0.67em 0;
}
mark {
background: #ff0;
color: #000;
}
small {
font-size: 80%;
}
sub,
sup {
font-size: 75%;
line-height: 0;
position: relative;
vertical-align: baseline;
}
sup {
top: -0.5em;
}
sub {
bottom: -0.25em;
}
img {
border: 0;
}
svg:not(:root) {
overflow: hidden;
}
figure {
margin: 1em 40px;
}
hr {
box-sizing: content-box;
height: 0;
}
pre {
overflow: auto;
}
code,
kbd,
pre,
samp {
font-family: monospace, monospace;
font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
color: inherit;
font: inherit;
margin: 0;
}
button {
overflow: visible;
}
button,
select {
text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
-webkit-appearance: button;
cursor: pointer;
}
button[disabled],
html input[disabled] {
cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
border: 0;
padding: 0;
}
input {
line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
box-sizing: border-box;
padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
height: auto;
}
input[type="search"] {
-webkit-appearance: textfield;
box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
-webkit-appearance: none;
}
fieldset {
border: 1px solid #c0c0c0;
margin: 0 2px;
padding: 0.35em 0.625em 0.75em;
}
legend {
border: 0;
padding: 0;
}
textarea {
overflow: auto;
}
optgroup {
font-weight: bold;
}
table {
border-collapse: collapse;
border-spacing: 0;
}
td,
th {
padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
*,
*:before,
*:after {
background: transparent !important;
box-shadow: none !important;
text-shadow: none !important;
}
a,
a:visited {
text-decoration: underline;
}
a[href]:after {
content: " (" attr(href) ")";
}
abbr[title]:after {
content: " (" attr(title) ")";
}
a[href^="#"]:after,
a[href^="javascript:"]:after {
content: "";
}
pre,
blockquote {
border: 1px solid #999;
page-break-inside: avoid;
}
thead {
display: table-header-group;
}
tr,
img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
p,
h2,
h3 {
orphans: 3;
widows: 3;
}
h2,
h3 {
page-break-after: avoid;
}
.navbar {
display: none;
}
.btn > .caret,
.dropup > .btn > .caret {
border-top-color: #000 !important;
}
.label {
border: 1px solid #000;
}
.table {
border-collapse: collapse !important;
}
.table td,
.table th {
background-color: #fff !important;
}
.table-bordered th,
.table-bordered td {
border: 1px solid #ddd !important;
}
}
@font-face {
font-family: 'Glyphicons Halflings';
src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
position: relative;
top: 1px;
display: inline-block;
font-family: 'Glyphicons Halflings';
font-style: normal;
font-weight: normal;
line-height: 1;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
content: "\002a";
}
.glyphicon-plus:before {
content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
content: "\20ac";
}
.glyphicon-minus:before {
content: "\2212";
}
.glyphicon-cloud:before {
content: "\2601";
}
.glyphicon-envelope:before {
content: "\2709";
}
.glyphicon-pencil:before {
content: "\270f";
}
.glyphicon-glass:before {
content: "\e001";
}
.glyphicon-music:before {
content: "\e002";
}
.glyphicon-search:before {
content: "\e003";
}
.glyphicon-heart:before {
content: "\e005";
}
.glyphicon-star:before {
content: "\e006";
}
.glyphicon-star-empty:before {
content: "\e007";
}
.glyphicon-user:before {
content: "\e008";
}
.glyphicon-film:before {
content: "\e009";
}
.glyphicon-th-large:before {
content: "\e010";
}
.glyphicon-th:before {
content: "\e011";
}
.glyphicon-th-list:before {
content: "\e012";
}
.glyphicon-ok:before {
content: "\e013";
}
.glyphicon-remove:before {
content: "\e014";
}
.glyphicon-zoom-in:before {
content: "\e015";
}
.glyphicon-zoom-out:before {
content: "\e016";
}
.glyphicon-off:before {
content: "\e017";
}
.glyphicon-signal:before {
content: "\e018";
}
.glyphicon-cog:before {
content: "\e019";
}
.glyphicon-trash:before {
content: "\e020";
}
.glyphicon-home:before {
content: "\e021";
}
.glyphicon-file:before {
content: "\e022";
}
.glyphicon-time:before {
content: "\e023";
}
.glyphicon-road:before {
content: "\e024";
}
.glyphicon-download-alt:before {
content: "\e025";
}
.glyphicon-download:before {
content: "\e026";
}
.glyphicon-upload:before {
content: "\e027";
}
.glyphicon-inbox:before {
content: "\e028";
}
.glyphicon-play-circle:before {
content: "\e029";
}
.glyphicon-repeat:before {
content: "\e030";
}
.glyphicon-refresh:before {
content: "\e031";
}
.glyphicon-list-alt:before {
content: "\e032";
}
.glyphicon-lock:before {
content: "\e033";
}
.glyphicon-flag:before {
content: "\e034";
}
.glyphicon-headphones:before {
content: "\e035";
}
.glyphicon-volume-off:before {
content: "\e036";
}
.glyphicon-volume-down:before {
content: "\e037";
}
.glyphicon-volume-up:before {
content: "\e038";
}
.glyphicon-qrcode:before {
content: "\e039";
}
.glyphicon-barcode:before {
content: "\e040";
}
.glyphicon-tag:before {
content: "\e041";
}
.glyphicon-tags:before {
content: "\e042";
}
.glyphicon-book:before {
content: "\e043";
}
.glyphicon-bookmark:before {
content: "\e044";
}
.glyphicon-print:before {
content: "\e045";
}
.glyphicon-camera:before {
content: "\e046";
}
.glyphicon-font:before {
content: "\e047";
}
.glyphicon-bold:before {
content: "\e048";
}
.glyphicon-italic:before {
content: "\e049";
}
.glyphicon-text-height:before {
content: "\e050";
}
.glyphicon-text-width:before {
content: "\e051";
}
.glyphicon-align-left:before {
content: "\e052";
}
.glyphicon-align-center:before {
content: "\e053";
}
.glyphicon-align-right:before {
content: "\e054";
}
.glyphicon-align-justify:before {
content: "\e055";
}
.glyphicon-list:before {
content: "\e056";
}
.glyphicon-indent-left:before {
content: "\e057";
}
.glyphicon-indent-right:before {
content: "\e058";
}
.glyphicon-facetime-video:before {
content: "\e059";
}
.glyphicon-picture:before {
content: "\e060";
}
.glyphicon-map-marker:before {
content: "\e062";
}
.glyphicon-adjust:before {
content: "\e063";
}
.glyphicon-tint:before {
content: "\e064";
}
.glyphicon-edit:before {
content: "\e065";
}
.glyphicon-share:before {
content: "\e066";
}
.glyphicon-check:before {
content: "\e067";
}
.glyphicon-move:before {
content: "\e068";
}
.glyphicon-step-backward:before {
content: "\e069";
}
.glyphicon-fast-backward:before {
content: "\e070";
}
.glyphicon-backward:before {
content: "\e071";
}
.glyphicon-play:before {
content: "\e072";
}
.glyphicon-pause:before {
content: "\e073";
}
.glyphicon-stop:before {
content: "\e074";
}
.glyphicon-forward:before {
content: "\e075";
}
.glyphicon-fast-forward:before {
content: "\e076";
}
.glyphicon-step-forward:before {
content: "\e077";
}
.glyphicon-eject:before {
content: "\e078";
}
.glyphicon-chevron-left:before {
content: "\e079";
}
.glyphicon-chevron-right:before {
content: "\e080";
}
.glyphicon-plus-sign:before {
content: "\e081";
}
.glyphicon-minus-sign:before {
content: "\e082";
}
.glyphicon-remove-sign:before {
content: "\e083";
}
.glyphicon-ok-sign:before {
content: "\e084";
}
.glyphicon-question-sign:before {
content: "\e085";
}
.glyphicon-info-sign:before {
content: "\e086";
}
.glyphicon-screenshot:before {
content: "\e087";
}
.glyphicon-remove-circle:before {
content: "\e088";
}
.glyphicon-ok-circle:before {
content: "\e089";
}
.glyphicon-ban-circle:before {
content: "\e090";
}
.glyphicon-arrow-left:before {
content: "\e091";
}
.glyphicon-arrow-right:before {
content: "\e092";
}
.glyphicon-arrow-up:before {
content: "\e093";
}
.glyphicon-arrow-down:before {
content: "\e094";
}
.glyphicon-share-alt:before {
content: "\e095";
}
.glyphicon-resize-full:before {
content: "\e096";
}
.glyphicon-resize-small:before {
content: "\e097";
}
.glyphicon-exclamation-sign:before {
content: "\e101";
}
.glyphicon-gift:before {
content: "\e102";
}
.glyphicon-leaf:before {
content: "\e103";
}
.glyphicon-fire:before {
content: "\e104";
}
.glyphicon-eye-open:before {
content: "\e105";
}
.glyphicon-eye-close:before {
content: "\e106";
}
.glyphicon-warning-sign:before {
content: "\e107";
}
.glyphicon-plane:before {
content: "\e108";
}
.glyphicon-calendar:before {
content: "\e109";
}
.glyphicon-random:before {
content: "\e110";
}
.glyphicon-comment:before {
content: "\e111";
}
.glyphicon-magnet:before {
content: "\e112";
}
.glyphicon-chevron-up:before {
content: "\e113";
}
.glyphicon-chevron-down:before {
content: "\e114";
}
.glyphicon-retweet:before {
content: "\e115";
}
.glyphicon-shopping-cart:before {
content: "\e116";
}
.glyphicon-folder-close:before {
content: "\e117";
}
.glyphicon-folder-open:before {
content: "\e118";
}
.glyphicon-resize-vertical:before {
content: "\e119";
}
.glyphicon-resize-horizontal:before {
content: "\e120";
}
.glyphicon-hdd:before {
content: "\e121";
}
.glyphicon-bullhorn:before {
content: "\e122";
}
.glyphicon-bell:before {
content: "\e123";
}
.glyphicon-certificate:before {
content: "\e124";
}
.glyphicon-thumbs-up:before {
content: "\e125";
}
.glyphicon-thumbs-down:before {
content: "\e126";
}
.glyphicon-hand-right:before {
content: "\e127";
}
.glyphicon-hand-left:before {
content: "\e128";
}
.glyphicon-hand-up:before {
content: "\e129";
}
.glyphicon-hand-down:before {
content: "\e130";
}
.glyphicon-circle-arrow-right:before {
content: "\e131";
}
.glyphicon-circle-arrow-left:before {
content: "\e132";
}
.glyphicon-circle-arrow-up:before {
content: "\e133";
}
.glyphicon-circle-arrow-down:before {
content: "\e134";
}
.glyphicon-globe:before {
content: "\e135";
}
.glyphicon-wrench:before {
content: "\e136";
}
.glyphicon-tasks:before {
content: "\e137";
}
.glyphicon-filter:before {
content: "\e138";
}
.glyphicon-briefcase:before {
content: "\e139";
}
.glyphicon-fullscreen:before {
content: "\e140";
}
.glyphicon-dashboard:before {
content: "\e141";
}
.glyphicon-paperclip:before {
content: "\e142";
}
.glyphicon-heart-empty:before {
content: "\e143";
}
.glyphicon-link:before {
content: "\e144";
}
.glyphicon-phone:before {
content: "\e145";
}
.glyphicon-pushpin:before {
content: "\e146";
}
.glyphicon-usd:before {
content: "\e148";
}
.glyphicon-gbp:before {
content: "\e149";
}
.glyphicon-sort:before {
content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
content: "\e152";
}
.glyphicon-sort-by-order:before {
content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
content: "\e154";
}
.glyphicon-sort-by-attributes:before {
content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
content: "\e156";
}
.glyphicon-unchecked:before {
content: "\e157";
}
.glyphicon-expand:before {
content: "\e158";
}
.glyphicon-collapse-down:before {
content: "\e159";
}
.glyphicon-collapse-up:before {
content: "\e160";
}
.glyphicon-log-in:before {
content: "\e161";
}
.glyphicon-flash:before {
content: "\e162";
}
.glyphicon-log-out:before {
content: "\e163";
}
.glyphicon-new-window:before {
content: "\e164";
}
.glyphicon-record:before {
content: "\e165";
}
.glyphicon-save:before {
content: "\e166";
}
.glyphicon-open:before {
content: "\e167";
}
.glyphicon-saved:before {
content: "\e168";
}
.glyphicon-import:before {
content: "\e169";
}
.glyphicon-export:before {
content: "\e170";
}
.glyphicon-send:before {
content: "\e171";
}
.glyphicon-floppy-disk:before {
content: "\e172";
}
.glyphicon-floppy-saved:before {
content: "\e173";
}
.glyphicon-floppy-remove:before {
content: "\e174";
}
.glyphicon-floppy-save:before {
content: "\e175";
}
.glyphicon-floppy-open:before {
content: "\e176";
}
.glyphicon-credit-card:before {
content: "\e177";
}
.glyphicon-transfer:before {
content: "\e178";
}
.glyphicon-cutlery:before {
content: "\e179";
}
.glyphicon-header:before {
content: "\e180";
}
.glyphicon-compressed:before {
content: "\e181";
}
.glyphicon-earphone:before {
content: "\e182";
}
.glyphicon-phone-alt:before {
content: "\e183";
}
.glyphicon-tower:before {
content: "\e184";
}
.glyphicon-stats:before {
content: "\e185";
}
.glyphicon-sd-video:before {
content: "\e186";
}
.glyphicon-hd-video:before {
content: "\e187";
}
.glyphicon-subtitles:before {
content: "\e188";
}
.glyphicon-sound-stereo:before {
content: "\e189";
}
.glyphicon-sound-dolby:before {
content: "\e190";
}
.glyphicon-sound-5-1:before {
content: "\e191";
}
.glyphicon-sound-6-1:before {
content: "\e192";
}
.glyphicon-sound-7-1:before {
content: "\e193";
}
.glyphicon-copyright-mark:before {
content: "\e194";
}
.glyphicon-registration-mark:before {
content: "\e195";
}
.glyphicon-cloud-download:before {
content: "\e197";
}
.glyphicon-cloud-upload:before {
content: "\e198";
}
.glyphicon-tree-conifer:before {
content: "\e199";
}
.glyphicon-tree-deciduous:before {
content: "\e200";
}
.glyphicon-cd:before {
content: "\e201";
}
.glyphicon-save-file:before {
content: "\e202";
}
.glyphicon-open-file:before {
content: "\e203";
}
.glyphicon-level-up:before {
content: "\e204";
}
.glyphicon-copy:before {
content: "\e205";
}
.glyphicon-paste:before {
content: "\e206";
}
.glyphicon-alert:before {
content: "\e209";
}
.glyphicon-equalizer:before {
content: "\e210";
}
.glyphicon-king:before {
content: "\e211";
}
.glyphicon-queen:before {
content: "\e212";
}
.glyphicon-pawn:before {
content: "\e213";
}
.glyphicon-bishop:before {
content: "\e214";
}
.glyphicon-knight:before {
content: "\e215";
}
.glyphicon-baby-formula:before {
content: "\e216";
}
.glyphicon-tent:before {
content: "\26fa";
}
.glyphicon-blackboard:before {
content: "\e218";
}
.glyphicon-bed:before {
content: "\e219";
}
.glyphicon-apple:before {
content: "\f8ff";
}
.glyphicon-erase:before {
content: "\e221";
}
.glyphicon-hourglass:before {
content: "\231b";
}
.glyphicon-lamp:before {
content: "\e223";
}
.glyphicon-duplicate:before {
content: "\e224";
}
.glyphicon-piggy-bank:before {
content: "\e225";
}
.glyphicon-scissors:before {
content: "\e226";
}
.glyphicon-bitcoin:before {
content: "\e227";
}
.glyphicon-btc:before {
content: "\e227";
}
.glyphicon-xbt:before {
content: "\e227";
}
.glyphicon-yen:before {
content: "\00a5";
}
.glyphicon-jpy:before {
content: "\00a5";
}
.glyphicon-ruble:before {
content: "\20bd";
}
.glyphicon-rub:before {
content: "\20bd";
}
.glyphicon-scale:before {
content: "\e230";
}
.glyphicon-ice-lolly:before {
content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
content: "\e232";
}
.glyphicon-education:before {
content: "\e233";
}
.glyphicon-option-horizontal:before {
content: "\e234";
}
.glyphicon-option-vertical:before {
content: "\e235";
}
.glyphicon-menu-hamburger:before {
content: "\e236";
}
.glyphicon-modal-window:before {
content: "\e237";
}
.glyphicon-oil:before {
content: "\e238";
}
.glyphicon-grain:before {
content: "\e239";
}
.glyphicon-sunglasses:before {
content: "\e240";
}
.glyphicon-text-size:before {
content: "\e241";
}
.glyphicon-text-color:before {
content: "\e242";
}
.glyphicon-text-background:before {
content: "\e243";
}
.glyphicon-object-align-top:before {
content: "\e244";
}
.glyphicon-object-align-bottom:before {
content: "\e245";
}
.glyphicon-object-align-horizontal:before {
content: "\e246";
}
.glyphicon-object-align-left:before {
content: "\e247";
}
.glyphicon-object-align-vertical:before {
content: "\e248";
}
.glyphicon-object-align-right:before {
content: "\e249";
}
.glyphicon-triangle-right:before {
content: "\e250";
}
.glyphicon-triangle-left:before {
content: "\e251";
}
.glyphicon-triangle-bottom:before {
content: "\e252";
}
.glyphicon-triangle-top:before {
content: "\e253";
}
.glyphicon-console:before {
content: "\e254";
}
.glyphicon-superscript:before {
content: "\e255";
}
.glyphicon-subscript:before {
content: "\e256";
}
.glyphicon-menu-left:before {
content: "\e257";
}
.glyphicon-menu-right:before {
content: "\e258";
}
.glyphicon-menu-down:before {
content: "\e259";
}
.glyphicon-menu-up:before {
content: "\e260";
}
* {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
*:before,
*:after {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
html {
font-size: 10px;
-webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 13px;
line-height: 1.42857143;
color: #000;
background-color: #fff;
}
input,
button,
select,
textarea {
font-family: inherit;
font-size: inherit;
line-height: inherit;
}
a {
color: #337ab7;
text-decoration: none;
}
a:hover,
a:focus {
color: #23527c;
text-decoration: underline;
}
a:focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
figure {
margin: 0;
}
img {
vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
display: block;
max-width: 100%;
height: auto;
}
.img-rounded {
border-radius: 3px;
}
.img-thumbnail {
padding: 4px;
line-height: 1.42857143;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 2px;
-webkit-transition: all 0.2s ease-in-out;
-o-transition: all 0.2s ease-in-out;
transition: all 0.2s ease-in-out;
display: inline-block;
max-width: 100%;
height: auto;
}
.img-circle {
border-radius: 50%;
}
hr {
margin-top: 18px;
margin-bottom: 18px;
border: 0;
border-top: 1px solid #eeeeee;
}
.sr-only {
position: absolute;
width: 1px;
height: 1px;
margin: -1px;
padding: 0;
overflow: hidden;
clip: rect(0, 0, 0, 0);
border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
position: static;
width: auto;
height: auto;
margin: 0;
overflow: visible;
clip: auto;
}
[role="button"] {
cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
font-family: inherit;
font-weight: 500;
line-height: 1.1;
color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
font-weight: normal;
line-height: 1;
color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
margin-top: 18px;
margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
margin-top: 9px;
margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
font-size: 75%;
}
h1,
.h1 {
font-size: 33px;
}
h2,
.h2 {
font-size: 27px;
}
h3,
.h3 {
font-size: 23px;
}
h4,
.h4 {
font-size: 17px;
}
h5,
.h5 {
font-size: 13px;
}
h6,
.h6 {
font-size: 12px;
}
p {
margin: 0 0 9px;
}
.lead {
margin-bottom: 18px;
font-size: 14px;
font-weight: 300;
line-height: 1.4;
}
@media (min-width: 768px) {
.lead {
font-size: 19.5px;
}
}
small,
.small {
font-size: 92%;
}
mark,
.mark {
background-color: #fcf8e3;
padding: .2em;
}
.text-left {
text-align: left;
}
.text-right {
text-align: right;
}
.text-center {
text-align: center;
}
.text-justify {
text-align: justify;
}
.text-nowrap {
white-space: nowrap;
}
.text-lowercase {
text-transform: lowercase;
}
.text-uppercase {
text-transform: uppercase;
}
.text-capitalize {
text-transform: capitalize;
}
.text-muted {
color: #777777;
}
.text-primary {
color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
color: #286090;
}
.text-success {
color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
color: #2b542c;
}
.text-info {
color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
color: #245269;
}
.text-warning {
color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
color: #66512c;
}
.text-danger {
color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
color: #843534;
}
.bg-primary {
color: #fff;
background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
background-color: #286090;
}
.bg-success {
background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
background-color: #c1e2b3;
}
.bg-info {
background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
background-color: #afd9ee;
}
.bg-warning {
background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
background-color: #f7ecb5;
}
.bg-danger {
background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
background-color: #e4b9b9;
}
.page-header {
padding-bottom: 8px;
margin: 36px 0 18px;
border-bottom: 1px solid #eeeeee;
}
ul,
ol {
margin-top: 0;
margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
margin-bottom: 0;
}
.list-unstyled {
padding-left: 0;
list-style: none;
}
.list-inline {
padding-left: 0;
list-style: none;
margin-left: -5px;
}
.list-inline > li {
display: inline-block;
padding-left: 5px;
padding-right: 5px;
}
dl {
margin-top: 0;
margin-bottom: 18px;
}
dt,
dd {
line-height: 1.42857143;
}
dt {
font-weight: bold;
}
dd {
margin-left: 0;
}
@media (min-width: 541px) {
.dl-horizontal dt {
float: left;
width: 160px;
clear: left;
text-align: right;
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
.dl-horizontal dd {
margin-left: 180px;
}
}
abbr[title],
abbr[data-original-title] {
cursor: help;
border-bottom: 1px dotted #777777;
}
.initialism {
font-size: 90%;
text-transform: uppercase;
}
blockquote {
padding: 9px 18px;
margin: 0 0 18px;
font-size: inherit;
border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
display: block;
font-size: 80%;
line-height: 1.42857143;
color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
padding-right: 15px;
padding-left: 0;
border-right: 5px solid #eeeeee;
border-left: 0;
text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
content: '\00A0 \2014';
}
address {
margin-bottom: 18px;
font-style: normal;
line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
font-family: monospace;
}
code {
padding: 2px 4px;
font-size: 90%;
color: #c7254e;
background-color: #f9f2f4;
border-radius: 2px;
}
kbd {
padding: 2px 4px;
font-size: 90%;
color: #888;
background-color: transparent;
border-radius: 1px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
padding: 0;
font-size: 100%;
font-weight: bold;
box-shadow: none;
}
pre {
display: block;
padding: 8.5px;
margin: 0 0 9px;
font-size: 12px;
line-height: 1.42857143;
word-break: break-all;
word-wrap: break-word;
color: #333333;
background-color: #f5f5f5;
border: 1px solid #ccc;
border-radius: 2px;
}
pre code {
padding: 0;
font-size: inherit;
color: inherit;
white-space: pre-wrap;
background-color: transparent;
border-radius: 0;
}
.pre-scrollable {
max-height: 340px;
overflow-y: scroll;
}
.container {
margin-right: auto;
margin-left: auto;
padding-left: 0px;
padding-right: 0px;
}
@media (min-width: 768px) {
.container {
width: 768px;
}
}
@media (min-width: 992px) {
.container {
width: 940px;
}
}
@media (min-width: 1200px) {
.container {
width: 1140px;
}
}
.container-fluid {
margin-right: auto;
margin-left: auto;
padding-left: 0px;
padding-right: 0px;
}
.row {
margin-left: 0px;
margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
position: relative;
min-height: 1px;
padding-left: 0px;
padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
float: left;
}
.col-xs-12 {
width: 100%;
}
.col-xs-11 {
width: 91.66666667%;
}
.col-xs-10 {
width: 83.33333333%;
}
.col-xs-9 {
width: 75%;
}
.col-xs-8 {
width: 66.66666667%;
}
.col-xs-7 {
width: 58.33333333%;
}
.col-xs-6 {
width: 50%;
}
.col-xs-5 {
width: 41.66666667%;
}
.col-xs-4 {
width: 33.33333333%;
}
.col-xs-3 {
width: 25%;
}
.col-xs-2 {
width: 16.66666667%;
}
.col-xs-1 {
width: 8.33333333%;
}
.col-xs-pull-12 {
right: 100%;
}
.col-xs-pull-11 {
right: 91.66666667%;
}
.col-xs-pull-10 {
right: 83.33333333%;
}
.col-xs-pull-9 {
right: 75%;
}
.col-xs-pull-8 {
right: 66.66666667%;
}
.col-xs-pull-7 {
right: 58.33333333%;
}
.col-xs-pull-6 {
right: 50%;
}
.col-xs-pull-5 {
right: 41.66666667%;
}
.col-xs-pull-4 {
right: 33.33333333%;
}
.col-xs-pull-3 {
right: 25%;
}
.col-xs-pull-2 {
right: 16.66666667%;
}
.col-xs-pull-1 {
right: 8.33333333%;
}
.col-xs-pull-0 {
right: auto;
}
.col-xs-push-12 {
left: 100%;
}
.col-xs-push-11 {
left: 91.66666667%;
}
.col-xs-push-10 {
left: 83.33333333%;
}
.col-xs-push-9 {
left: 75%;
}
.col-xs-push-8 {
left: 66.66666667%;
}
.col-xs-push-7 {
left: 58.33333333%;
}
.col-xs-push-6 {
left: 50%;
}
.col-xs-push-5 {
left: 41.66666667%;
}
.col-xs-push-4 {
left: 33.33333333%;
}
.col-xs-push-3 {
left: 25%;
}
.col-xs-push-2 {
left: 16.66666667%;
}
.col-xs-push-1 {
left: 8.33333333%;
}
.col-xs-push-0 {
left: auto;
}
.col-xs-offset-12 {
margin-left: 100%;
}
.col-xs-offset-11 {
margin-left: 91.66666667%;
}
.col-xs-offset-10 {
margin-left: 83.33333333%;
}
.col-xs-offset-9 {
margin-left: 75%;
}
.col-xs-offset-8 {
margin-left: 66.66666667%;
}
.col-xs-offset-7 {
margin-left: 58.33333333%;
}
.col-xs-offset-6 {
margin-left: 50%;
}
.col-xs-offset-5 {
margin-left: 41.66666667%;
}
.col-xs-offset-4 {
margin-left: 33.33333333%;
}
.col-xs-offset-3 {
margin-left: 25%;
}
.col-xs-offset-2 {
margin-left: 16.66666667%;
}
.col-xs-offset-1 {
margin-left: 8.33333333%;
}
.col-xs-offset-0 {
margin-left: 0%;
}
@media (min-width: 768px) {
.col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
float: left;
}
.col-sm-12 {
width: 100%;
}
.col-sm-11 {
width: 91.66666667%;
}
.col-sm-10 {
width: 83.33333333%;
}
.col-sm-9 {
width: 75%;
}
.col-sm-8 {
width: 66.66666667%;
}
.col-sm-7 {
width: 58.33333333%;
}
.col-sm-6 {
width: 50%;
}
.col-sm-5 {
width: 41.66666667%;
}
.col-sm-4 {
width: 33.33333333%;
}
.col-sm-3 {
width: 25%;
}
.col-sm-2 {
width: 16.66666667%;
}
.col-sm-1 {
width: 8.33333333%;
}
.col-sm-pull-12 {
right: 100%;
}
.col-sm-pull-11 {
right: 91.66666667%;
}
.col-sm-pull-10 {
right: 83.33333333%;
}
.col-sm-pull-9 {
right: 75%;
}
.col-sm-pull-8 {
right: 66.66666667%;
}
.col-sm-pull-7 {
right: 58.33333333%;
}
.col-sm-pull-6 {
right: 50%;
}
.col-sm-pull-5 {
right: 41.66666667%;
}
.col-sm-pull-4 {
right: 33.33333333%;
}
.col-sm-pull-3 {
right: 25%;
}
.col-sm-pull-2 {
right: 16.66666667%;
}
.col-sm-pull-1 {
right: 8.33333333%;
}
.col-sm-pull-0 {
right: auto;
}
.col-sm-push-12 {
left: 100%;
}
.col-sm-push-11 {
left: 91.66666667%;
}
.col-sm-push-10 {
left: 83.33333333%;
}
.col-sm-push-9 {
left: 75%;
}
.col-sm-push-8 {
left: 66.66666667%;
}
.col-sm-push-7 {
left: 58.33333333%;
}
.col-sm-push-6 {
left: 50%;
}
.col-sm-push-5 {
left: 41.66666667%;
}
.col-sm-push-4 {
left: 33.33333333%;
}
.col-sm-push-3 {
left: 25%;
}
.col-sm-push-2 {
left: 16.66666667%;
}
.col-sm-push-1 {
left: 8.33333333%;
}
.col-sm-push-0 {
left: auto;
}
.col-sm-offset-12 {
margin-left: 100%;
}
.col-sm-offset-11 {
margin-left: 91.66666667%;
}
.col-sm-offset-10 {
margin-left: 83.33333333%;
}
.col-sm-offset-9 {
margin-left: 75%;
}
.col-sm-offset-8 {
margin-left: 66.66666667%;
}
.col-sm-offset-7 {
margin-left: 58.33333333%;
}
.col-sm-offset-6 {
margin-left: 50%;
}
.col-sm-offset-5 {
margin-left: 41.66666667%;
}
.col-sm-offset-4 {
margin-left: 33.33333333%;
}
.col-sm-offset-3 {
margin-left: 25%;
}
.col-sm-offset-2 {
margin-left: 16.66666667%;
}
.col-sm-offset-1 {
margin-left: 8.33333333%;
}
.col-sm-offset-0 {
margin-left: 0%;
}
}
@media (min-width: 992px) {
.col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
float: left;
}
.col-md-12 {
width: 100%;
}
.col-md-11 {
width: 91.66666667%;
}
.col-md-10 {
width: 83.33333333%;
}
.col-md-9 {
width: 75%;
}
.col-md-8 {
width: 66.66666667%;
}
.col-md-7 {
width: 58.33333333%;
}
.col-md-6 {
width: 50%;
}
.col-md-5 {
width: 41.66666667%;
}
.col-md-4 {
width: 33.33333333%;
}
.col-md-3 {
width: 25%;
}
.col-md-2 {
width: 16.66666667%;
}
.col-md-1 {
width: 8.33333333%;
}
.col-md-pull-12 {
right: 100%;
}
.col-md-pull-11 {
right: 91.66666667%;
}
.col-md-pull-10 {
right: 83.33333333%;
}
.col-md-pull-9 {
right: 75%;
}
.col-md-pull-8 {
right: 66.66666667%;
}
.col-md-pull-7 {
right: 58.33333333%;
}
.col-md-pull-6 {
right: 50%;
}
.col-md-pull-5 {
right: 41.66666667%;
}
.col-md-pull-4 {
right: 33.33333333%;
}
.col-md-pull-3 {
right: 25%;
}
.col-md-pull-2 {
right: 16.66666667%;
}
.col-md-pull-1 {
right: 8.33333333%;
}
.col-md-pull-0 {
right: auto;
}
.col-md-push-12 {
left: 100%;
}
.col-md-push-11 {
left: 91.66666667%;
}
.col-md-push-10 {
left: 83.33333333%;
}
.col-md-push-9 {
left: 75%;
}
.col-md-push-8 {
left: 66.66666667%;
}
.col-md-push-7 {
left: 58.33333333%;
}
.col-md-push-6 {
left: 50%;
}
.col-md-push-5 {
left: 41.66666667%;
}
.col-md-push-4 {
left: 33.33333333%;
}
.col-md-push-3 {
left: 25%;
}
.col-md-push-2 {
left: 16.66666667%;
}
.col-md-push-1 {
left: 8.33333333%;
}
.col-md-push-0 {
left: auto;
}
.col-md-offset-12 {
margin-left: 100%;
}
.col-md-offset-11 {
margin-left: 91.66666667%;
}
.col-md-offset-10 {
margin-left: 83.33333333%;
}
.col-md-offset-9 {
margin-left: 75%;
}
.col-md-offset-8 {
margin-left: 66.66666667%;
}
.col-md-offset-7 {
margin-left: 58.33333333%;
}
.col-md-offset-6 {
margin-left: 50%;
}
.col-md-offset-5 {
margin-left: 41.66666667%;
}
.col-md-offset-4 {
margin-left: 33.33333333%;
}
.col-md-offset-3 {
margin-left: 25%;
}
.col-md-offset-2 {
margin-left: 16.66666667%;
}
.col-md-offset-1 {
margin-left: 8.33333333%;
}
.col-md-offset-0 {
margin-left: 0%;
}
}
@media (min-width: 1200px) {
.col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
float: left;
}
.col-lg-12 {
width: 100%;
}
.col-lg-11 {
width: 91.66666667%;
}
.col-lg-10 {
width: 83.33333333%;
}
.col-lg-9 {
width: 75%;
}
.col-lg-8 {
width: 66.66666667%;
}
.col-lg-7 {
width: 58.33333333%;
}
.col-lg-6 {
width: 50%;
}
.col-lg-5 {
width: 41.66666667%;
}
.col-lg-4 {
width: 33.33333333%;
}
.col-lg-3 {
width: 25%;
}
.col-lg-2 {
width: 16.66666667%;
}
.col-lg-1 {
width: 8.33333333%;
}
.col-lg-pull-12 {
right: 100%;
}
.col-lg-pull-11 {
right: 91.66666667%;
}
.col-lg-pull-10 {
right: 83.33333333%;
}
.col-lg-pull-9 {
right: 75%;
}
.col-lg-pull-8 {
right: 66.66666667%;
}
.col-lg-pull-7 {
right: 58.33333333%;
}
.col-lg-pull-6 {
right: 50%;
}
.col-lg-pull-5 {
right: 41.66666667%;
}
.col-lg-pull-4 {
right: 33.33333333%;
}
.col-lg-pull-3 {
right: 25%;
}
.col-lg-pull-2 {
right: 16.66666667%;
}
.col-lg-pull-1 {
right: 8.33333333%;
}
.col-lg-pull-0 {
right: auto;
}
.col-lg-push-12 {
left: 100%;
}
.col-lg-push-11 {
left: 91.66666667%;
}
.col-lg-push-10 {
left: 83.33333333%;
}
.col-lg-push-9 {
left: 75%;
}
.col-lg-push-8 {
left: 66.66666667%;
}
.col-lg-push-7 {
left: 58.33333333%;
}
.col-lg-push-6 {
left: 50%;
}
.col-lg-push-5 {
left: 41.66666667%;
}
.col-lg-push-4 {
left: 33.33333333%;
}
.col-lg-push-3 {
left: 25%;
}
.col-lg-push-2 {
left: 16.66666667%;
}
.col-lg-push-1 {
left: 8.33333333%;
}
.col-lg-push-0 {
left: auto;
}
.col-lg-offset-12 {
margin-left: 100%;
}
.col-lg-offset-11 {
margin-left: 91.66666667%;
}
.col-lg-offset-10 {
margin-left: 83.33333333%;
}
.col-lg-offset-9 {
margin-left: 75%;
}
.col-lg-offset-8 {
margin-left: 66.66666667%;
}
.col-lg-offset-7 {
margin-left: 58.33333333%;
}
.col-lg-offset-6 {
margin-left: 50%;
}
.col-lg-offset-5 {
margin-left: 41.66666667%;
}
.col-lg-offset-4 {
margin-left: 33.33333333%;
}
.col-lg-offset-3 {
margin-left: 25%;
}
.col-lg-offset-2 {
margin-left: 16.66666667%;
}
.col-lg-offset-1 {
margin-left: 8.33333333%;
}
.col-lg-offset-0 {
margin-left: 0%;
}
}
table {
background-color: transparent;
}
caption {
padding-top: 8px;
padding-bottom: 8px;
color: #777777;
text-align: left;
}
th {
text-align: left;
}
.table {
width: 100%;
max-width: 100%;
margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
padding: 8px;
line-height: 1.42857143;
vertical-align: top;
border-top: 1px solid #ddd;
}
.table > thead > tr > th {
vertical-align: bottom;
border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
border-top: 0;
}
.table > tbody + tbody {
border-top: 2px solid #ddd;
}
.table .table {
background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
padding: 5px;
}
.table-bordered {
border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
background-color: #f5f5f5;
}
table col[class*="col-"] {
position: static;
float: none;
display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
position: static;
float: none;
display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
background-color: #ebcccc;
}
.table-responsive {
overflow-x: auto;
min-height: 0.01%;
}
@media screen and (max-width: 767px) {
.table-responsive {
width: 100%;
margin-bottom: 13.5px;
overflow-y: hidden;
-ms-overflow-style: -ms-autohiding-scrollbar;
border: 1px solid #ddd;
}
.table-responsive > .table {
margin-bottom: 0;
}
.table-responsive > .table > thead > tr > th,
.table-responsive > .table > tbody > tr > th,
.table-responsive > .table > tfoot > tr > th,
.table-responsive > .table > thead > tr > td,
.table-responsive > .table > tbody > tr > td,
.table-responsive > .table > tfoot > tr > td {
white-space: nowrap;
}
.table-responsive > .table-bordered {
border: 0;
}
.table-responsive > .table-bordered > thead > tr > th:first-child,
.table-responsive > .table-bordered > tbody > tr > th:first-child,
.table-responsive > .table-bordered > tfoot > tr > th:first-child,
.table-responsive > .table-bordered > thead > tr > td:first-child,
.table-responsive > .table-bordered > tbody > tr > td:first-child,
.table-responsive > .table-bordered > tfoot > tr > td:first-child {
border-left: 0;
}
.table-responsive > .table-bordered > thead > tr > th:last-child,
.table-responsive > .table-bordered > tbody > tr > th:last-child,
.table-responsive > .table-bordered > tfoot > tr > th:last-child,
.table-responsive > .table-bordered > thead > tr > td:last-child,
.table-responsive > .table-bordered > tbody > tr > td:last-child,
.table-responsive > .table-bordered > tfoot > tr > td:last-child {
border-right: 0;
}
.table-responsive > .table-bordered > tbody > tr:last-child > th,
.table-responsive > .table-bordered > tfoot > tr:last-child > th,
.table-responsive > .table-bordered > tbody > tr:last-child > td,
.table-responsive > .table-bordered > tfoot > tr:last-child > td {
border-bottom: 0;
}
}
fieldset {
padding: 0;
margin: 0;
border: 0;
min-width: 0;
}
legend {
display: block;
width: 100%;
padding: 0;
margin-bottom: 18px;
font-size: 19.5px;
line-height: inherit;
color: #333333;
border: 0;
border-bottom: 1px solid #e5e5e5;
}
label {
display: inline-block;
max-width: 100%;
margin-bottom: 5px;
font-weight: bold;
}
input[type="search"] {
-webkit-box-sizing: border-box;
-moz-box-sizing: border-box;
box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
margin: 4px 0 0;
margin-top: 1px \9;
line-height: normal;
}
input[type="file"] {
display: block;
}
input[type="range"] {
display: block;
width: 100%;
}
select[multiple],
select[size] {
height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
output {
display: block;
padding-top: 7px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
}
.form-control {
display: block;
width: 100%;
height: 32px;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
background-color: #fff;
background-image: none;
border: 1px solid #ccc;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
border-color: #66afe9;
outline: 0;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
color: #999;
opacity: 1;
}
.form-control:-ms-input-placeholder {
color: #999;
}
.form-control::-webkit-input-placeholder {
color: #999;
}
.form-control::-ms-expand {
border: 0;
background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
background-color: #eeeeee;
opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
cursor: not-allowed;
}
textarea.form-control {
height: auto;
}
input[type="search"] {
-webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
input[type="date"].form-control,
input[type="time"].form-control,
input[type="datetime-local"].form-control,
input[type="month"].form-control {
line-height: 32px;
}
input[type="date"].input-sm,
input[type="time"].input-sm,
input[type="datetime-local"].input-sm,
input[type="month"].input-sm,
.input-group-sm input[type="date"],
.input-group-sm input[type="time"],
.input-group-sm input[type="datetime-local"],
.input-group-sm input[type="month"] {
line-height: 30px;
}
input[type="date"].input-lg,
input[type="time"].input-lg,
input[type="datetime-local"].input-lg,
input[type="month"].input-lg,
.input-group-lg input[type="date"],
.input-group-lg input[type="time"],
.input-group-lg input[type="datetime-local"],
.input-group-lg input[type="month"] {
line-height: 45px;
}
}
.form-group {
margin-bottom: 15px;
}
.radio,
.checkbox {
position: relative;
display: block;
margin-top: 10px;
margin-bottom: 10px;
}
.radio label,
.checkbox label {
min-height: 18px;
padding-left: 20px;
margin-bottom: 0;
font-weight: normal;
cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
position: absolute;
margin-left: -20px;
margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
position: relative;
display: inline-block;
padding-left: 20px;
margin-bottom: 0;
vertical-align: middle;
font-weight: normal;
cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
margin-top: 0;
margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
cursor: not-allowed;
}
.form-control-static {
padding-top: 7px;
padding-bottom: 7px;
margin-bottom: 0;
min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
padding-left: 0;
padding-right: 0;
}
.input-sm {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
select.input-sm {
height: 30px;
line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
height: auto;
}
.form-group-sm .form-control {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.form-group-sm select.form-control {
height: 30px;
line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
height: auto;
}
.form-group-sm .form-control-static {
height: 30px;
min-height: 30px;
padding: 6px 10px;
font-size: 12px;
line-height: 1.5;
}
.input-lg {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
select.input-lg {
height: 45px;
line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
height: auto;
}
.form-group-lg .form-control {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
.form-group-lg select.form-control {
height: 45px;
line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
height: auto;
}
.form-group-lg .form-control-static {
height: 45px;
min-height: 35px;
padding: 11px 16px;
font-size: 17px;
line-height: 1.3333333;
}
.has-feedback {
position: relative;
}
.has-feedback .form-control {
padding-right: 40px;
}
.form-control-feedback {
position: absolute;
top: 0;
right: 0;
z-index: 2;
display: block;
width: 32px;
height: 32px;
line-height: 32px;
text-align: center;
pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
width: 45px;
height: 45px;
line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
width: 30px;
height: 30px;
line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
color: #3c763d;
}
.has-success .form-control {
border-color: #3c763d;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
border-color: #2b542c;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
color: #3c763d;
border-color: #3c763d;
background-color: #dff0d8;
}
.has-success .form-control-feedback {
color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
color: #8a6d3b;
}
.has-warning .form-control {
border-color: #8a6d3b;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
border-color: #66512c;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
color: #8a6d3b;
border-color: #8a6d3b;
background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
color: #a94442;
}
.has-error .form-control {
border-color: #a94442;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
border-color: #843534;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
color: #a94442;
border-color: #a94442;
background-color: #f2dede;
}
.has-error .form-control-feedback {
color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
top: 0;
}
.help-block {
display: block;
margin-top: 5px;
margin-bottom: 10px;
color: #404040;
}
@media (min-width: 768px) {
.form-inline .form-group {
display: inline-block;
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .form-control {
display: inline-block;
width: auto;
vertical-align: middle;
}
.form-inline .form-control-static {
display: inline-block;
}
.form-inline .input-group {
display: inline-table;
vertical-align: middle;
}
.form-inline .input-group .input-group-addon,
.form-inline .input-group .input-group-btn,
.form-inline .input-group .form-control {
width: auto;
}
.form-inline .input-group > .form-control {
width: 100%;
}
.form-inline .control-label {
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .radio,
.form-inline .checkbox {
display: inline-block;
margin-top: 0;
margin-bottom: 0;
vertical-align: middle;
}
.form-inline .radio label,
.form-inline .checkbox label {
padding-left: 0;
}
.form-inline .radio input[type="radio"],
.form-inline .checkbox input[type="checkbox"] {
position: relative;
margin-left: 0;
}
.form-inline .has-feedback .form-control-feedback {
top: 0;
}
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
margin-top: 0;
margin-bottom: 0;
padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
min-height: 25px;
}
.form-horizontal .form-group {
margin-left: 0px;
margin-right: 0px;
}
@media (min-width: 768px) {
.form-horizontal .control-label {
text-align: right;
margin-bottom: 0;
padding-top: 7px;
}
}
.form-horizontal .has-feedback .form-control-feedback {
right: 0px;
}
@media (min-width: 768px) {
.form-horizontal .form-group-lg .control-label {
padding-top: 11px;
font-size: 17px;
}
}
@media (min-width: 768px) {
.form-horizontal .form-group-sm .control-label {
padding-top: 6px;
font-size: 12px;
}
}
.btn {
display: inline-block;
margin-bottom: 0;
font-weight: normal;
text-align: center;
vertical-align: middle;
touch-action: manipulation;
cursor: pointer;
background-image: none;
border: 1px solid transparent;
white-space: nowrap;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
border-radius: 2px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
outline: 5px auto -webkit-focus-ring-color;
outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
color: #333;
text-decoration: none;
}
.btn:active,
.btn.active {
outline: 0;
background-image: none;
-webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
cursor: not-allowed;
opacity: 0.65;
filter: alpha(opacity=65);
-webkit-box-shadow: none;
box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
pointer-events: none;
}
.btn-default {
color: #333;
background-color: #fff;
border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
.btn-default:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
background-color: #fff;
border-color: #ccc;
}
.btn-default .badge {
color: #fff;
background-color: #333;
}
.btn-primary {
color: #fff;
background-color: #337ab7;
border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
color: #fff;
background-color: #286090;
border-color: #122b40;
}
.btn-primary:hover {
color: #fff;
background-color: #286090;
border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
color: #fff;
background-color: #286090;
border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
color: #fff;
background-color: #204d74;
border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
background-color: #337ab7;
border-color: #2e6da4;
}
.btn-primary .badge {
color: #337ab7;
background-color: #fff;
}
.btn-success {
color: #fff;
background-color: #5cb85c;
border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
color: #fff;
background-color: #449d44;
border-color: #255625;
}
.btn-success:hover {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
color: #fff;
background-color: #398439;
border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
background-color: #5cb85c;
border-color: #4cae4c;
}
.btn-success .badge {
color: #5cb85c;
background-color: #fff;
}
.btn-info {
color: #fff;
background-color: #5bc0de;
border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
color: #fff;
background-color: #31b0d5;
border-color: #1b6d85;
}
.btn-info:hover {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
color: #fff;
background-color: #269abc;
border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
background-color: #5bc0de;
border-color: #46b8da;
}
.btn-info .badge {
color: #5bc0de;
background-color: #fff;
}
.btn-warning {
color: #fff;
background-color: #f0ad4e;
border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
color: #fff;
background-color: #ec971f;
border-color: #985f0d;
}
.btn-warning:hover {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
color: #fff;
background-color: #d58512;
border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
background-color: #f0ad4e;
border-color: #eea236;
}
.btn-warning .badge {
color: #f0ad4e;
background-color: #fff;
}
.btn-danger {
color: #fff;
background-color: #d9534f;
border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
color: #fff;
background-color: #c9302c;
border-color: #761c19;
}
.btn-danger:hover {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
color: #fff;
background-color: #ac2925;
border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
background-color: #d9534f;
border-color: #d43f3a;
}
.btn-danger .badge {
color: #d9534f;
background-color: #fff;
}
.btn-link {
color: #337ab7;
font-weight: normal;
border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
background-color: transparent;
-webkit-box-shadow: none;
box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
color: #23527c;
text-decoration: underline;
background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
color: #777777;
text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
padding: 1px 5px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
.btn-block {
display: block;
width: 100%;
}
.btn-block + .btn-block {
margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
width: 100%;
}
.fade {
opacity: 0;
-webkit-transition: opacity 0.15s linear;
-o-transition: opacity 0.15s linear;
transition: opacity 0.15s linear;
}
.fade.in {
opacity: 1;
}
.collapse {
display: none;
}
.collapse.in {
display: block;
}
tr.collapse.in {
display: table-row;
}
tbody.collapse.in {
display: table-row-group;
}
.collapsing {
position: relative;
height: 0;
overflow: hidden;
-webkit-transition-property: height, visibility;
transition-property: height, visibility;
-webkit-transition-duration: 0.35s;
transition-duration: 0.35s;
-webkit-transition-timing-function: ease;
transition-timing-function: ease;
}
.caret {
display: inline-block;
width: 0;
height: 0;
margin-left: 2px;
vertical-align: middle;
border-top: 4px dashed;
border-top: 4px solid \9;
border-right: 4px solid transparent;
border-left: 4px solid transparent;
}
.dropup,
.dropdown {
position: relative;
}
.dropdown-toggle:focus {
outline: 0;
}
.dropdown-menu {
position: absolute;
top: 100%;
left: 0;
z-index: 1000;
display: none;
float: left;
min-width: 160px;
padding: 5px 0;
margin: 2px 0 0;
list-style: none;
font-size: 13px;
text-align: left;
background-color: #fff;
border: 1px solid #ccc;
border: 1px solid rgba(0, 0, 0, 0.15);
border-radius: 2px;
-webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
background-clip: padding-box;
}
.dropdown-menu.pull-right {
right: 0;
left: auto;
}
.dropdown-menu .divider {
height: 1px;
margin: 8px 0;
overflow: hidden;
background-color: #e5e5e5;
}
.dropdown-menu > li > a {
display: block;
padding: 3px 20px;
clear: both;
font-weight: normal;
line-height: 1.42857143;
color: #333333;
white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
text-decoration: none;
color: #262626;
background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
color: #fff;
text-decoration: none;
outline: 0;
background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
text-decoration: none;
background-color: transparent;
background-image: none;
filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
cursor: not-allowed;
}
.open > .dropdown-menu {
display: block;
}
.open > a {
outline: 0;
}
.dropdown-menu-right {
left: auto;
right: 0;
}
.dropdown-menu-left {
left: 0;
right: auto;
}
.dropdown-header {
display: block;
padding: 3px 20px;
font-size: 12px;
line-height: 1.42857143;
color: #777777;
white-space: nowrap;
}
.dropdown-backdrop {
position: fixed;
left: 0;
right: 0;
bottom: 0;
top: 0;
z-index: 990;
}
.pull-right > .dropdown-menu {
right: 0;
left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
border-top: 0;
border-bottom: 4px dashed;
border-bottom: 4px solid \9;
content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
top: auto;
bottom: 100%;
margin-bottom: 2px;
}
@media (min-width: 541px) {
.navbar-right .dropdown-menu {
left: auto;
right: 0;
}
.navbar-right .dropdown-menu-left {
left: 0;
right: auto;
}
}
.btn-group,
.btn-group-vertical {
position: relative;
display: inline-block;
vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
position: relative;
float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
margin-left: -1px;
}
.btn-toolbar {
margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
border-radius: 0;
}
.btn-group > .btn:first-child {
margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.btn-group > .btn-group {
float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
padding-left: 8px;
padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
padding-left: 12px;
padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
-webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
-webkit-box-shadow: none;
box-shadow: none;
}
.btn .caret {
margin-left: 0;
}
.btn-lg .caret {
border-width: 5px 5px 0;
border-bottom-width: 0;
}
.dropup .btn-lg .caret {
border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
display: block;
float: none;
width: 100%;
max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
margin-top: -1px;
margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
border-top-right-radius: 2px;
border-top-left-radius: 2px;
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
border-top-right-radius: 0;
border-top-left-radius: 0;
border-bottom-right-radius: 2px;
border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.btn-group-justified {
display: table;
width: 100%;
table-layout: fixed;
border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
float: none;
display: table-cell;
width: 1%;
}
.btn-group-justified > .btn-group .btn {
width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
position: absolute;
clip: rect(0, 0, 0, 0);
pointer-events: none;
}
.input-group {
position: relative;
display: table;
border-collapse: separate;
}
.input-group[class*="col-"] {
float: none;
padding-left: 0;
padding-right: 0;
}
.input-group .form-control {
position: relative;
z-index: 2;
float: left;
width: 100%;
margin-bottom: 0;
}
.input-group .form-control:focus {
z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
height: 45px;
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
height: 45px;
line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
height: 30px;
line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
border-radius: 0;
}
.input-group-addon,
.input-group-btn {
width: 1%;
white-space: nowrap;
vertical-align: middle;
}
.input-group-addon {
padding: 6px 12px;
font-size: 13px;
font-weight: normal;
line-height: 1;
color: #555555;
text-align: center;
background-color: #eeeeee;
border: 1px solid #ccc;
border-radius: 2px;
}
.input-group-addon.input-sm {
padding: 5px 10px;
font-size: 12px;
border-radius: 1px;
}
.input-group-addon.input-lg {
padding: 10px 16px;
font-size: 17px;
border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
border-bottom-right-radius: 0;
border-top-right-radius: 0;
}
.input-group-addon:first-child {
border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
border-bottom-left-radius: 0;
border-top-left-radius: 0;
}
.input-group-addon:last-child {
border-left: 0;
}
.input-group-btn {
position: relative;
font-size: 0;
white-space: nowrap;
}
.input-group-btn > .btn {
position: relative;
}
.input-group-btn > .btn + .btn {
margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
z-index: 2;
margin-left: -1px;
}
.nav {
margin-bottom: 0;
padding-left: 0;
list-style: none;
}
.nav > li {
position: relative;
display: block;
}
.nav > li > a {
position: relative;
display: block;
padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
text-decoration: none;
background-color: #eeeeee;
}
.nav > li.disabled > a {
color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
color: #777777;
text-decoration: none;
background-color: transparent;
cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
background-color: #eeeeee;
border-color: #337ab7;
}
.nav .nav-divider {
height: 1px;
margin: 8px 0;
overflow: hidden;
background-color: #e5e5e5;
}
.nav > li > a > img {
max-width: none;
}
.nav-tabs {
border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
float: left;
margin-bottom: -1px;
}
.nav-tabs > li > a {
margin-right: 2px;
line-height: 1.42857143;
border: 1px solid transparent;
border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
color: #555555;
background-color: #fff;
border: 1px solid #ddd;
border-bottom-color: transparent;
cursor: default;
}
.nav-tabs.nav-justified {
width: 100%;
border-bottom: 0;
}
.nav-tabs.nav-justified > li {
float: none;
}
.nav-tabs.nav-justified > li > a {
text-align: center;
margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
top: auto;
left: auto;
}
@media (min-width: 768px) {
.nav-tabs.nav-justified > li {
display: table-cell;
width: 1%;
}
.nav-tabs.nav-justified > li > a {
margin-bottom: 0;
}
}
.nav-tabs.nav-justified > li > a {
margin-right: 0;
border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
border: 1px solid #ddd;
}
@media (min-width: 768px) {
.nav-tabs.nav-justified > li > a {
border-bottom: 1px solid #ddd;
border-radius: 2px 2px 0 0;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
border-bottom-color: #fff;
}
}
.nav-pills > li {
float: left;
}
.nav-pills > li > a {
border-radius: 2px;
}
.nav-pills > li + li {
margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
color: #fff;
background-color: #337ab7;
}
.nav-stacked > li {
float: none;
}
.nav-stacked > li + li {
margin-top: 2px;
margin-left: 0;
}
.nav-justified {
width: 100%;
}
.nav-justified > li {
float: none;
}
.nav-justified > li > a {
text-align: center;
margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
top: auto;
left: auto;
}
@media (min-width: 768px) {
.nav-justified > li {
display: table-cell;
width: 1%;
}
.nav-justified > li > a {
margin-bottom: 0;
}
}
.nav-tabs-justified {
border-bottom: 0;
}
.nav-tabs-justified > li > a {
margin-right: 0;
border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
border: 1px solid #ddd;
}
@media (min-width: 768px) {
.nav-tabs-justified > li > a {
border-bottom: 1px solid #ddd;
border-radius: 2px 2px 0 0;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
border-bottom-color: #fff;
}
}
.tab-content > .tab-pane {
display: none;
}
.tab-content > .active {
display: block;
}
.nav-tabs .dropdown-menu {
margin-top: -1px;
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.navbar {
position: relative;
min-height: 30px;
margin-bottom: 18px;
border: 1px solid transparent;
}
@media (min-width: 541px) {
.navbar {
border-radius: 2px;
}
}
@media (min-width: 541px) {
.navbar-header {
float: left;
}
}
.navbar-collapse {
overflow-x: visible;
padding-right: 0px;
padding-left: 0px;
border-top: 1px solid transparent;
box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
-webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
overflow-y: auto;
}
@media (min-width: 541px) {
.navbar-collapse {
width: auto;
border-top: 0;
box-shadow: none;
}
.navbar-collapse.collapse {
display: block !important;
height: auto !important;
padding-bottom: 0;
overflow: visible !important;
}
.navbar-collapse.in {
overflow-y: visible;
}
.navbar-fixed-top .navbar-collapse,
.navbar-static-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
padding-left: 0;
padding-right: 0;
}
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
max-height: 200px;
}
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
margin-right: 0px;
margin-left: 0px;
}
@media (min-width: 541px) {
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
margin-right: 0;
margin-left: 0;
}
}
.navbar-static-top {
z-index: 1000;
border-width: 0 0 1px;
}
@media (min-width: 541px) {
.navbar-static-top {
border-radius: 0;
}
}
.navbar-fixed-top,
.navbar-fixed-bottom {
position: fixed;
right: 0;
left: 0;
z-index: 1030;
}
@media (min-width: 541px) {
.navbar-fixed-top,
.navbar-fixed-bottom {
border-radius: 0;
}
}
.navbar-fixed-top {
top: 0;
border-width: 0 0 1px;
}
.navbar-fixed-bottom {
bottom: 0;
margin-bottom: 0;
border-width: 1px 0 0;
}
.navbar-brand {
float: left;
padding: 6px 0px;
font-size: 17px;
line-height: 18px;
height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
text-decoration: none;
}
.navbar-brand > img {
display: block;
}
@media (min-width: 541px) {
.navbar > .container .navbar-brand,
.navbar > .container-fluid .navbar-brand {
margin-left: 0px;
}
}
.navbar-toggle {
position: relative;
float: right;
margin-right: 0px;
padding: 9px 10px;
margin-top: -2px;
margin-bottom: -2px;
background-color: transparent;
background-image: none;
border: 1px solid transparent;
border-radius: 2px;
}
.navbar-toggle:focus {
outline: 0;
}
.navbar-toggle .icon-bar {
display: block;
width: 22px;
height: 2px;
border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
margin-top: 4px;
}
@media (min-width: 541px) {
.navbar-toggle {
display: none;
}
}
.navbar-nav {
margin: 3px 0px;
}
.navbar-nav > li > a {
padding-top: 10px;
padding-bottom: 10px;
line-height: 18px;
}
@media (max-width: 540px) {
.navbar-nav .open .dropdown-menu {
position: static;
float: none;
width: auto;
margin-top: 0;
background-color: transparent;
border: 0;
box-shadow: none;
}
.navbar-nav .open .dropdown-menu > li > a,
.navbar-nav .open .dropdown-menu .dropdown-header {
padding: 5px 15px 5px 25px;
}
.navbar-nav .open .dropdown-menu > li > a {
line-height: 18px;
}
.navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-nav .open .dropdown-menu > li > a:focus {
background-image: none;
}
}
@media (min-width: 541px) {
.navbar-nav {
float: left;
margin: 0;
}
.navbar-nav > li {
float: left;
}
.navbar-nav > li > a {
padding-top: 6px;
padding-bottom: 6px;
}
}
.navbar-form {
margin-left: 0px;
margin-right: 0px;
padding: 10px 0px;
border-top: 1px solid transparent;
border-bottom: 1px solid transparent;
-webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
margin-top: -1px;
margin-bottom: -1px;
}
@media (min-width: 768px) {
.navbar-form .form-group {
display: inline-block;
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .form-control {
display: inline-block;
width: auto;
vertical-align: middle;
}
.navbar-form .form-control-static {
display: inline-block;
}
.navbar-form .input-group {
display: inline-table;
vertical-align: middle;
}
.navbar-form .input-group .input-group-addon,
.navbar-form .input-group .input-group-btn,
.navbar-form .input-group .form-control {
width: auto;
}
.navbar-form .input-group > .form-control {
width: 100%;
}
.navbar-form .control-label {
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .radio,
.navbar-form .checkbox {
display: inline-block;
margin-top: 0;
margin-bottom: 0;
vertical-align: middle;
}
.navbar-form .radio label,
.navbar-form .checkbox label {
padding-left: 0;
}
.navbar-form .radio input[type="radio"],
.navbar-form .checkbox input[type="checkbox"] {
position: relative;
margin-left: 0;
}
.navbar-form .has-feedback .form-control-feedback {
top: 0;
}
}
@media (max-width: 540px) {
.navbar-form .form-group {
margin-bottom: 5px;
}
.navbar-form .form-group:last-child {
margin-bottom: 0;
}
}
@media (min-width: 541px) {
.navbar-form {
width: auto;
border: 0;
margin-left: 0;
margin-right: 0;
padding-top: 0;
padding-bottom: 0;
-webkit-box-shadow: none;
box-shadow: none;
}
}
.navbar-nav > li > .dropdown-menu {
margin-top: 0;
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
margin-bottom: 0;
border-top-right-radius: 2px;
border-top-left-radius: 2px;
border-bottom-right-radius: 0;
border-bottom-left-radius: 0;
}
.navbar-btn {
margin-top: -1px;
margin-bottom: -1px;
}
.navbar-btn.btn-sm {
margin-top: 0px;
margin-bottom: 0px;
}
.navbar-btn.btn-xs {
margin-top: 4px;
margin-bottom: 4px;
}
.navbar-text {
margin-top: 6px;
margin-bottom: 6px;
}
@media (min-width: 541px) {
.navbar-text {
float: left;
margin-left: 0px;
margin-right: 0px;
}
}
@media (min-width: 541px) {
.navbar-left {
float: left !important;
float: left;
}
.navbar-right {
float: right !important;
float: right;
margin-right: 0px;
}
.navbar-right ~ .navbar-right {
margin-right: 0;
}
}
.navbar-default {
background-color: #f8f8f8;
border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
color: #5e5e5e;
background-color: transparent;
}
.navbar-default .navbar-text {
color: #777;
}
.navbar-default .navbar-nav > li > a {
color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
color: #333;
background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
color: #555;
background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
color: #ccc;
background-color: transparent;
}
.navbar-default .navbar-toggle {
border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
background-color: #e7e7e7;
color: #555;
}
@media (max-width: 540px) {
.navbar-default .navbar-nav .open .dropdown-menu > li > a {
color: #777;
}
.navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
color: #333;
background-color: transparent;
}
.navbar-default .navbar-nav .open .dropdown-menu > .active > a,
.navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
color: #555;
background-color: #e7e7e7;
}
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
.navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
color: #ccc;
background-color: transparent;
}
}
.navbar-default .navbar-link {
color: #777;
}
.navbar-default .navbar-link:hover {
color: #333;
}
.navbar-default .btn-link {
color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
color: #ccc;
}
.navbar-inverse {
background-color: #222;
border-color: #080808;
}
.navbar-inverse .navbar-brand {
color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-text {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
color: #fff;
background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
color: #444;
background-color: transparent;
}
.navbar-inverse .navbar-toggle {
border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
background-color: #080808;
color: #fff;
}
@media (max-width: 540px) {
.navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
border-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu .divider {
background-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
color: #9d9d9d;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
color: #fff;
background-color: transparent;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
color: #fff;
background-color: #080808;
}
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
.navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
color: #444;
background-color: transparent;
}
}
.navbar-inverse .navbar-link {
color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
color: #fff;
}
.navbar-inverse .btn-link {
color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
color: #444;
}
.breadcrumb {
padding: 8px 15px;
margin-bottom: 18px;
list-style: none;
background-color: #f5f5f5;
border-radius: 2px;
}
.breadcrumb > li {
display: inline-block;
}
.breadcrumb > li + li:before {
content: "/\00a0";
padding: 0 5px;
color: #5e5e5e;
}
.breadcrumb > .active {
color: #777777;
}
.pagination {
display: inline-block;
padding-left: 0;
margin: 18px 0;
border-radius: 2px;
}
.pagination > li {
display: inline;
}
.pagination > li > a,
.pagination > li > span {
position: relative;
float: left;
padding: 6px 12px;
line-height: 1.42857143;
text-decoration: none;
color: #337ab7;
background-color: #fff;
border: 1px solid #ddd;
margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
margin-left: 0;
border-bottom-left-radius: 2px;
border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
border-bottom-right-radius: 2px;
border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
z-index: 2;
color: #23527c;
background-color: #eeeeee;
border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
z-index: 3;
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
color: #777777;
background-color: #fff;
border-color: #ddd;
cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
padding: 10px 16px;
font-size: 17px;
line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
border-bottom-left-radius: 3px;
border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
border-bottom-right-radius: 3px;
border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
border-bottom-left-radius: 1px;
border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
border-bottom-right-radius: 1px;
border-top-right-radius: 1px;
}
.pager {
padding-left: 0;
margin: 18px 0;
list-style: none;
text-align: center;
}
.pager li {
display: inline;
}
.pager li > a,
.pager li > span {
display: inline-block;
padding: 5px 14px;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
text-decoration: none;
background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
float: right;
}
.pager .previous > a,
.pager .previous > span {
float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
color: #777777;
background-color: #fff;
cursor: not-allowed;
}
.label {
display: inline;
padding: .2em .6em .3em;
font-size: 75%;
font-weight: bold;
line-height: 1;
color: #fff;
text-align: center;
white-space: nowrap;
vertical-align: baseline;
border-radius: .25em;
}
a.label:hover,
a.label:focus {
color: #fff;
text-decoration: none;
cursor: pointer;
}
.label:empty {
display: none;
}
.btn .label {
position: relative;
top: -1px;
}
.label-default {
background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
background-color: #5e5e5e;
}
.label-primary {
background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
background-color: #286090;
}
.label-success {
background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
background-color: #449d44;
}
.label-info {
background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
background-color: #31b0d5;
}
.label-warning {
background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
background-color: #ec971f;
}
.label-danger {
background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
background-color: #c9302c;
}
.badge {
display: inline-block;
min-width: 10px;
padding: 3px 7px;
font-size: 12px;
font-weight: bold;
color: #fff;
line-height: 1;
vertical-align: middle;
white-space: nowrap;
text-align: center;
background-color: #777777;
border-radius: 10px;
}
.badge:empty {
display: none;
}
.btn .badge {
position: relative;
top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
top: 0;
padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
color: #fff;
text-decoration: none;
cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
color: #337ab7;
background-color: #fff;
}
.list-group-item > .badge {
float: right;
}
.list-group-item > .badge + .badge {
margin-right: 5px;
}
.nav-pills > li > a > .badge {
margin-left: 3px;
}
.jumbotron {
padding-top: 30px;
padding-bottom: 30px;
margin-bottom: 30px;
color: inherit;
background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
color: inherit;
}
.jumbotron p {
margin-bottom: 15px;
font-size: 20px;
font-weight: 200;
}
.jumbotron > hr {
border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
border-radius: 3px;
padding-left: 0px;
padding-right: 0px;
}
.jumbotron .container {
max-width: 100%;
}
@media screen and (min-width: 768px) {
.jumbotron {
padding-top: 48px;
padding-bottom: 48px;
}
.container .jumbotron,
.container-fluid .jumbotron {
padding-left: 60px;
padding-right: 60px;
}
.jumbotron h1,
.jumbotron .h1 {
font-size: 59px;
}
}
.thumbnail {
display: block;
padding: 4px;
margin-bottom: 18px;
line-height: 1.42857143;
background-color: #fff;
border: 1px solid #ddd;
border-radius: 2px;
-webkit-transition: border 0.2s ease-in-out;
-o-transition: border 0.2s ease-in-out;
transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
margin-left: auto;
margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
border-color: #337ab7;
}
.thumbnail .caption {
padding: 9px;
color: #000;
}
.alert {
padding: 15px;
margin-bottom: 18px;
border: 1px solid transparent;
border-radius: 2px;
}
.alert h4 {
margin-top: 0;
color: inherit;
}
.alert .alert-link {
font-weight: bold;
}
.alert > p,
.alert > ul {
margin-bottom: 0;
}
.alert > p + p {
margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
position: relative;
top: -2px;
right: -21px;
color: inherit;
}
.alert-success {
background-color: #dff0d8;
border-color: #d6e9c6;
color: #3c763d;
}
.alert-success hr {
border-top-color: #c9e2b3;
}
.alert-success .alert-link {
color: #2b542c;
}
.alert-info {
background-color: #d9edf7;
border-color: #bce8f1;
color: #31708f;
}
.alert-info hr {
border-top-color: #a6e1ec;
}
.alert-info .alert-link {
color: #245269;
}
.alert-warning {
background-color: #fcf8e3;
border-color: #faebcc;
color: #8a6d3b;
}
.alert-warning hr {
border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
color: #66512c;
}
.alert-danger {
background-color: #f2dede;
border-color: #ebccd1;
color: #a94442;
}
.alert-danger hr {
border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
from {
background-position: 40px 0;
}
to {
background-position: 0 0;
}
}
@keyframes progress-bar-stripes {
from {
background-position: 40px 0;
}
to {
background-position: 0 0;
}
}
.progress {
overflow: hidden;
height: 18px;
margin-bottom: 18px;
background-color: #f5f5f5;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 18px;
color: #fff;
text-align: center;
background-color: #337ab7;
-webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
-webkit-transition: width 0.6s ease;
-o-transition: width 0.6s ease;
transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
-webkit-animation: progress-bar-stripes 2s linear infinite;
-o-animation: progress-bar-stripes 2s linear infinite;
animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
margin-top: 15px;
}
.media:first-child {
margin-top: 0;
}
.media,
.media-body {
zoom: 1;
overflow: hidden;
}
.media-body {
width: 10000px;
}
.media-object {
display: block;
}
.media-object.img-thumbnail {
max-width: none;
}
.media-right,
.media > .pull-right {
padding-left: 10px;
}
.media-left,
.media > .pull-left {
padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
display: table-cell;
vertical-align: top;
}
.media-middle {
vertical-align: middle;
}
.media-bottom {
vertical-align: bottom;
}
.media-heading {
margin-top: 0;
margin-bottom: 5px;
}
.media-list {
padding-left: 0;
list-style: none;
}
.list-group {
margin-bottom: 20px;
padding-left: 0;
}
.list-group-item {
position: relative;
display: block;
padding: 10px 15px;
margin-bottom: -1px;
background-color: #fff;
border: 1px solid #ddd;
}
.list-group-item:first-child {
border-top-right-radius: 2px;
border-top-left-radius: 2px;
}
.list-group-item:last-child {
margin-bottom: 0;
border-bottom-right-radius: 2px;
border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
text-decoration: none;
color: #555;
background-color: #f5f5f5;
}
button.list-group-item {
width: 100%;
text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
background-color: #eeeeee;
color: #777777;
cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
z-index: 2;
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
color: #c7ddef;
}
.list-group-item-success {
color: #3c763d;
background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
color: #3c763d;
background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
color: #fff;
background-color: #3c763d;
border-color: #3c763d;
}
.list-group-item-info {
color: #31708f;
background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
color: #31708f;
background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
color: #fff;
background-color: #31708f;
border-color: #31708f;
}
.list-group-item-warning {
color: #8a6d3b;
background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
color: #8a6d3b;
background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
color: #fff;
background-color: #8a6d3b;
border-color: #8a6d3b;
}
.list-group-item-danger {
color: #a94442;
background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
color: #a94442;
background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
color: #fff;
background-color: #a94442;
border-color: #a94442;
}
.list-group-item-heading {
margin-top: 0;
margin-bottom: 5px;
}
.list-group-item-text {
margin-bottom: 0;
line-height: 1.3;
}
.panel {
margin-bottom: 18px;
background-color: #fff;
border: 1px solid transparent;
border-radius: 2px;
-webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
padding: 15px;
}
.panel-heading {
padding: 10px 15px;
border-bottom: 1px solid transparent;
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
color: inherit;
}
.panel-title {
margin-top: 0;
margin-bottom: 0;
font-size: 15px;
color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
color: inherit;
}
.panel-footer {
padding: 10px 15px;
background-color: #f5f5f5;
border-top: 1px solid #ddd;
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
border-width: 1px 0;
border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
border-top: 0;
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
border-bottom: 0;
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
border-top-right-radius: 0;
border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
border-top-width: 0;
}
.list-group + .panel-footer {
border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
padding-left: 15px;
padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
border-top-right-radius: 1px;
border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
border-top-left-radius: 1px;
border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
border-bottom-right-radius: 1px;
border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
border-bottom-left-radius: 1px;
border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
border-bottom: 0;
}
.panel > .table-responsive {
border: 0;
margin-bottom: 0;
}
.panel-group {
margin-bottom: 18px;
}
.panel-group .panel {
margin-bottom: 0;
border-radius: 2px;
}
.panel-group .panel + .panel {
margin-top: 5px;
}
.panel-group .panel-heading {
border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
border-bottom: 1px solid #ddd;
}
.panel-default {
border-color: #ddd;
}
.panel-default > .panel-heading {
color: #333333;
background-color: #f5f5f5;
border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
color: #f5f5f5;
background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #ddd;
}
.panel-primary {
border-color: #337ab7;
}
.panel-primary > .panel-heading {
color: #fff;
background-color: #337ab7;
border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
color: #337ab7;
background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #337ab7;
}
.panel-success {
border-color: #d6e9c6;
}
.panel-success > .panel-heading {
color: #3c763d;
background-color: #dff0d8;
border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
color: #dff0d8;
background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #d6e9c6;
}
.panel-info {
border-color: #bce8f1;
}
.panel-info > .panel-heading {
color: #31708f;
background-color: #d9edf7;
border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
color: #d9edf7;
background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #bce8f1;
}
.panel-warning {
border-color: #faebcc;
}
.panel-warning > .panel-heading {
color: #8a6d3b;
background-color: #fcf8e3;
border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
color: #fcf8e3;
background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #faebcc;
}
.panel-danger {
border-color: #ebccd1;
}
.panel-danger > .panel-heading {
color: #a94442;
background-color: #f2dede;
border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
color: #f2dede;
background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
border-bottom-color: #ebccd1;
}
.embed-responsive {
position: relative;
display: block;
height: 0;
padding: 0;
overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
position: absolute;
top: 0;
left: 0;
bottom: 0;
height: 100%;
width: 100%;
border: 0;
}
.embed-responsive-16by9 {
padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
padding-bottom: 75%;
}
.well {
min-height: 20px;
padding: 19px;
margin-bottom: 20px;
background-color: #f5f5f5;
border: 1px solid #e3e3e3;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
border-color: #ddd;
border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
padding: 24px;
border-radius: 3px;
}
.well-sm {
padding: 9px;
border-radius: 1px;
}
.close {
float: right;
font-size: 19.5px;
font-weight: bold;
line-height: 1;
color: #000;
text-shadow: 0 1px 0 #fff;
opacity: 0.2;
filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
color: #000;
text-decoration: none;
cursor: pointer;
opacity: 0.5;
filter: alpha(opacity=50);
}
button.close {
padding: 0;
cursor: pointer;
background: transparent;
border: 0;
-webkit-appearance: none;
}
.modal-open {
overflow: hidden;
}
.modal {
display: none;
overflow: hidden;
position: fixed;
top: 0;
right: 0;
bottom: 0;
left: 0;
z-index: 1050;
-webkit-overflow-scrolling: touch;
outline: 0;
}
.modal.fade .modal-dialog {
-webkit-transform: translate(0, -25%);
-ms-transform: translate(0, -25%);
-o-transform: translate(0, -25%);
transform: translate(0, -25%);
-webkit-transition: -webkit-transform 0.3s ease-out;
-moz-transition: -moz-transform 0.3s ease-out;
-o-transition: -o-transform 0.3s ease-out;
transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
-webkit-transform: translate(0, 0);
-ms-transform: translate(0, 0);
-o-transform: translate(0, 0);
transform: translate(0, 0);
}
.modal-open .modal {
overflow-x: hidden;
overflow-y: auto;
}
.modal-dialog {
position: relative;
width: auto;
margin: 10px;
}
.modal-content {
position: relative;
background-color: #fff;
border: 1px solid #999;
border: 1px solid rgba(0, 0, 0, 0.2);
border-radius: 3px;
-webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
background-clip: padding-box;
outline: 0;
}
.modal-backdrop {
position: fixed;
top: 0;
right: 0;
bottom: 0;
left: 0;
z-index: 1040;
background-color: #000;
}
.modal-backdrop.fade {
opacity: 0;
filter: alpha(opacity=0);
}
.modal-backdrop.in {
opacity: 0.5;
filter: alpha(opacity=50);
}
.modal-header {
padding: 15px;
border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
margin-top: -2px;
}
.modal-title {
margin: 0;
line-height: 1.42857143;
}
.modal-body {
position: relative;
padding: 15px;
}
.modal-footer {
padding: 15px;
text-align: right;
border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
margin-left: 5px;
margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
margin-left: 0;
}
.modal-scrollbar-measure {
position: absolute;
top: -9999px;
width: 50px;
height: 50px;
overflow: scroll;
}
@media (min-width: 768px) {
.modal-dialog {
width: 600px;
margin: 30px auto;
}
.modal-content {
-webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
}
.modal-sm {
width: 300px;
}
}
@media (min-width: 992px) {
.modal-lg {
width: 900px;
}
}
.tooltip {
position: absolute;
z-index: 1070;
display: block;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-style: normal;
font-weight: normal;
letter-spacing: normal;
line-break: auto;
line-height: 1.42857143;
text-align: left;
text-align: start;
text-decoration: none;
text-shadow: none;
text-transform: none;
white-space: normal;
word-break: normal;
word-spacing: normal;
word-wrap: normal;
font-size: 12px;
opacity: 0;
filter: alpha(opacity=0);
}
.tooltip.in {
opacity: 0.9;
filter: alpha(opacity=90);
}
.tooltip.top {
margin-top: -3px;
padding: 5px 0;
}
.tooltip.right {
margin-left: 3px;
padding: 0 5px;
}
.tooltip.bottom {
margin-top: 3px;
padding: 5px 0;
}
.tooltip.left {
margin-left: -3px;
padding: 0 5px;
}
.tooltip-inner {
max-width: 200px;
padding: 3px 8px;
color: #fff;
text-align: center;
background-color: #000;
border-radius: 2px;
}
.tooltip-arrow {
position: absolute;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
}
.tooltip.top .tooltip-arrow {
bottom: 0;
left: 50%;
margin-left: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
bottom: 0;
right: 5px;
margin-bottom: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
bottom: 0;
left: 5px;
margin-bottom: -5px;
border-width: 5px 5px 0;
border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
top: 50%;
left: 0;
margin-top: -5px;
border-width: 5px 5px 5px 0;
border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
top: 50%;
right: 0;
margin-top: -5px;
border-width: 5px 0 5px 5px;
border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
top: 0;
left: 50%;
margin-left: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
top: 0;
right: 5px;
margin-top: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
top: 0;
left: 5px;
margin-top: -5px;
border-width: 0 5px 5px;
border-bottom-color: #000;
}
.popover {
position: absolute;
top: 0;
left: 0;
z-index: 1060;
display: none;
max-width: 276px;
padding: 1px;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
font-style: normal;
font-weight: normal;
letter-spacing: normal;
line-break: auto;
line-height: 1.42857143;
text-align: left;
text-align: start;
text-decoration: none;
text-shadow: none;
text-transform: none;
white-space: normal;
word-break: normal;
word-spacing: normal;
word-wrap: normal;
font-size: 13px;
background-color: #fff;
background-clip: padding-box;
border: 1px solid #ccc;
border: 1px solid rgba(0, 0, 0, 0.2);
border-radius: 3px;
-webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
margin-top: -10px;
}
.popover.right {
margin-left: 10px;
}
.popover.bottom {
margin-top: 10px;
}
.popover.left {
margin-left: -10px;
}
.popover-title {
margin: 0;
padding: 8px 14px;
font-size: 13px;
background-color: #f7f7f7;
border-bottom: 1px solid #ebebeb;
border-radius: 2px 2px 0 0;
}
.popover-content {
padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
position: absolute;
display: block;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
}
.popover > .arrow {
border-width: 11px;
}
.popover > .arrow:after {
border-width: 10px;
content: "";
}
.popover.top > .arrow {
left: 50%;
margin-left: -11px;
border-bottom-width: 0;
border-top-color: #999999;
border-top-color: rgba(0, 0, 0, 0.25);
bottom: -11px;
}
.popover.top > .arrow:after {
content: " ";
bottom: 1px;
margin-left: -10px;
border-bottom-width: 0;
border-top-color: #fff;
}
.popover.right > .arrow {
top: 50%;
left: -11px;
margin-top: -11px;
border-left-width: 0;
border-right-color: #999999;
border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
content: " ";
left: 1px;
bottom: -10px;
border-left-width: 0;
border-right-color: #fff;
}
.popover.bottom > .arrow {
left: 50%;
margin-left: -11px;
border-top-width: 0;
border-bottom-color: #999999;
border-bottom-color: rgba(0, 0, 0, 0.25);
top: -11px;
}
.popover.bottom > .arrow:after {
content: " ";
top: 1px;
margin-left: -10px;
border-top-width: 0;
border-bottom-color: #fff;
}
.popover.left > .arrow {
top: 50%;
right: -11px;
margin-top: -11px;
border-right-width: 0;
border-left-color: #999999;
border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
content: " ";
right: 1px;
border-right-width: 0;
border-left-color: #fff;
bottom: -10px;
}
.carousel {
position: relative;
}
.carousel-inner {
position: relative;
overflow: hidden;
width: 100%;
}
.carousel-inner > .item {
display: none;
position: relative;
-webkit-transition: 0.6s ease-in-out left;
-o-transition: 0.6s ease-in-out left;
transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
.carousel-inner > .item {
-webkit-transition: -webkit-transform 0.6s ease-in-out;
-moz-transition: -moz-transform 0.6s ease-in-out;
-o-transition: -o-transform 0.6s ease-in-out;
transition: transform 0.6s ease-in-out;
-webkit-backface-visibility: hidden;
-moz-backface-visibility: hidden;
backface-visibility: hidden;
-webkit-perspective: 1000px;
-moz-perspective: 1000px;
perspective: 1000px;
}
.carousel-inner > .item.next,
.carousel-inner > .item.active.right {
-webkit-transform: translate3d(100%, 0, 0);
transform: translate3d(100%, 0, 0);
left: 0;
}
.carousel-inner > .item.prev,
.carousel-inner > .item.active.left {
-webkit-transform: translate3d(-100%, 0, 0);
transform: translate3d(-100%, 0, 0);
left: 0;
}
.carousel-inner > .item.next.left,
.carousel-inner > .item.prev.right,
.carousel-inner > .item.active {
-webkit-transform: translate3d(0, 0, 0);
transform: translate3d(0, 0, 0);
left: 0;
}
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
display: block;
}
.carousel-inner > .active {
left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
position: absolute;
top: 0;
width: 100%;
}
.carousel-inner > .next {
left: 100%;
}
.carousel-inner > .prev {
left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
left: 0;
}
.carousel-inner > .active.left {
left: -100%;
}
.carousel-inner > .active.right {
left: 100%;
}
.carousel-control {
position: absolute;
top: 0;
left: 0;
bottom: 0;
width: 15%;
opacity: 0.5;
filter: alpha(opacity=50);
font-size: 20px;
color: #fff;
text-align: center;
text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
background-repeat: repeat-x;
filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
left: auto;
right: 0;
background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
background-repeat: repeat-x;
filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
outline: 0;
color: #fff;
text-decoration: none;
opacity: 0.9;
filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
position: absolute;
top: 50%;
margin-top: -10px;
z-index: 5;
display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
left: 50%;
margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
right: 50%;
margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
width: 20px;
height: 20px;
line-height: 1;
font-family: serif;
}
.carousel-control .icon-prev:before {
content: '\2039';
}
.carousel-control .icon-next:before {
content: '\203a';
}
.carousel-indicators {
position: absolute;
bottom: 10px;
left: 50%;
z-index: 15;
width: 60%;
margin-left: -30%;
padding-left: 0;
list-style: none;
text-align: center;
}
.carousel-indicators li {
display: inline-block;
width: 10px;
height: 10px;
margin: 1px;
text-indent: -999px;
border: 1px solid #fff;
border-radius: 10px;
cursor: pointer;
background-color: #000 \9;
background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
margin: 0;
width: 12px;
height: 12px;
background-color: #fff;
}
.carousel-caption {
position: absolute;
left: 15%;
right: 15%;
bottom: 20px;
z-index: 10;
padding-top: 20px;
padding-bottom: 20px;
color: #fff;
text-align: center;
text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
text-shadow: none;
}
@media screen and (min-width: 768px) {
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right,
.carousel-control .icon-prev,
.carousel-control .icon-next {
width: 30px;
height: 30px;
margin-top: -10px;
font-size: 30px;
}
.carousel-control .glyphicon-chevron-left,
.carousel-control .icon-prev {
margin-left: -10px;
}
.carousel-control .glyphicon-chevron-right,
.carousel-control .icon-next {
margin-right: -10px;
}
.carousel-caption {
left: 20%;
right: 20%;
padding-bottom: 30px;
}
.carousel-indicators {
bottom: 20px;
}
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
content: " ";
display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
clear: both;
}
.center-block {
display: block;
margin-left: auto;
margin-right: auto;
}
.pull-right {
float: right !important;
}
.pull-left {
float: left !important;
}
.hide {
display: none !important;
}
.show {
display: block !important;
}
.invisible {
visibility: hidden;
}
.text-hide {
font: 0/0 a;
color: transparent;
text-shadow: none;
background-color: transparent;
border: 0;
}
.hidden {
display: none !important;
}
.affix {
position: fixed;
}
@-ms-viewport {
width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
display: none !important;
}
@media (max-width: 767px) {
.visible-xs {
display: block !important;
}
table.visible-xs {
display: table !important;
}
tr.visible-xs {
display: table-row !important;
}
th.visible-xs,
td.visible-xs {
display: table-cell !important;
}
}
@media (max-width: 767px) {
.visible-xs-block {
display: block !important;
}
}
@media (max-width: 767px) {
.visible-xs-inline {
display: inline !important;
}
}
@media (max-width: 767px) {
.visible-xs-inline-block {
display: inline-block !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm {
display: block !important;
}
table.visible-sm {
display: table !important;
}
tr.visible-sm {
display: table-row !important;
}
th.visible-sm,
td.visible-sm {
display: table-cell !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-block {
display: block !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-inline {
display: inline !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.visible-sm-inline-block {
display: inline-block !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md {
display: block !important;
}
table.visible-md {
display: table !important;
}
tr.visible-md {
display: table-row !important;
}
th.visible-md,
td.visible-md {
display: table-cell !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-block {
display: block !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-inline {
display: inline !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.visible-md-inline-block {
display: inline-block !important;
}
}
@media (min-width: 1200px) {
.visible-lg {
display: block !important;
}
table.visible-lg {
display: table !important;
}
tr.visible-lg {
display: table-row !important;
}
th.visible-lg,
td.visible-lg {
display: table-cell !important;
}
}
@media (min-width: 1200px) {
.visible-lg-block {
display: block !important;
}
}
@media (min-width: 1200px) {
.visible-lg-inline {
display: inline !important;
}
}
@media (min-width: 1200px) {
.visible-lg-inline-block {
display: inline-block !important;
}
}
@media (max-width: 767px) {
.hidden-xs {
display: none !important;
}
}
@media (min-width: 768px) and (max-width: 991px) {
.hidden-sm {
display: none !important;
}
}
@media (min-width: 992px) and (max-width: 1199px) {
.hidden-md {
display: none !important;
}
}
@media (min-width: 1200px) {
.hidden-lg {
display: none !important;
}
}
.visible-print {
display: none !important;
}
@media print {
.visible-print {
display: block !important;
}
table.visible-print {
display: table !important;
}
tr.visible-print {
display: table-row !important;
}
th.visible-print,
td.visible-print {
display: table-cell !important;
}
}
.visible-print-block {
display: none !important;
}
@media print {
.visible-print-block {
display: block !important;
}
}
.visible-print-inline {
display: none !important;
}
@media print {
.visible-print-inline {
display: inline !important;
}
}
.visible-print-inline-block {
display: none !important;
}
@media print {
.visible-print-inline-block {
display: inline-block !important;
}
}
@media print {
.hidden-print {
display: none !important;
}
}
/*!
*
* Font Awesome
*
*/
/*!
* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
*/
/* FONT PATH
* -------------------------- */
@font-face {
font-family: 'FontAwesome';
src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
font-weight: normal;
font-style: normal;
}
.fa {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
font-size: 1.33333333em;
line-height: 0.75em;
vertical-align: -15%;
}
.fa-2x {
font-size: 2em;
}
.fa-3x {
font-size: 3em;
}
.fa-4x {
font-size: 4em;
}
.fa-5x {
font-size: 5em;
}
.fa-fw {
width: 1.28571429em;
text-align: center;
}
.fa-ul {
padding-left: 0;
margin-left: 2.14285714em;
list-style-type: none;
}
.fa-ul > li {
position: relative;
}
.fa-li {
position: absolute;
left: -2.14285714em;
width: 2.14285714em;
top: 0.14285714em;
text-align: center;
}
.fa-li.fa-lg {
left: -1.85714286em;
}
.fa-border {
padding: .2em .25em .15em;
border: solid 0.08em #eee;
border-radius: .1em;
}
.fa-pull-left {
float: left;
}
.fa-pull-right {
float: right;
}
.fa.fa-pull-left {
margin-right: .3em;
}
.fa.fa-pull-right {
margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
float: right;
}
.pull-left {
float: left;
}
.fa.pull-left {
margin-right: .3em;
}
.fa.pull-right {
margin-left: .3em;
}
.fa-spin {
-webkit-animation: fa-spin 2s infinite linear;
animation: fa-spin 2s infinite linear;
}
.fa-pulse {
-webkit-animation: fa-spin 1s infinite steps(8);
animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(359deg);
transform: rotate(359deg);
}
}
@keyframes fa-spin {
0% {
-webkit-transform: rotate(0deg);
transform: rotate(0deg);
}
100% {
-webkit-transform: rotate(359deg);
transform: rotate(359deg);
}
}
.fa-rotate-90 {
-ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
-webkit-transform: rotate(90deg);
-ms-transform: rotate(90deg);
transform: rotate(90deg);
}
.fa-rotate-180 {
-ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
-webkit-transform: rotate(180deg);
-ms-transform: rotate(180deg);
transform: rotate(180deg);
}
.fa-rotate-270 {
-ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
-webkit-transform: rotate(270deg);
-ms-transform: rotate(270deg);
transform: rotate(270deg);
}
.fa-flip-horizontal {
-ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
-webkit-transform: scale(-1, 1);
-ms-transform: scale(-1, 1);
transform: scale(-1, 1);
}
.fa-flip-vertical {
-ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
-webkit-transform: scale(1, -1);
-ms-transform: scale(1, -1);
transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
filter: none;
}
.fa-stack {
position: relative;
display: inline-block;
width: 2em;
height: 2em;
line-height: 2em;
vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
position: absolute;
left: 0;
width: 100%;
text-align: center;
}
.fa-stack-1x {
line-height: inherit;
}
.fa-stack-2x {
font-size: 2em;
}
.fa-inverse {
color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
readers do not read off random characters that represent icons */
.fa-glass:before {
content: "\f000";
}
.fa-music:before {
content: "\f001";
}
.fa-search:before {
content: "\f002";
}
.fa-envelope-o:before {
content: "\f003";
}
.fa-heart:before {
content: "\f004";
}
.fa-star:before {
content: "\f005";
}
.fa-star-o:before {
content: "\f006";
}
.fa-user:before {
content: "\f007";
}
.fa-film:before {
content: "\f008";
}
.fa-th-large:before {
content: "\f009";
}
.fa-th:before {
content: "\f00a";
}
.fa-th-list:before {
content: "\f00b";
}
.fa-check:before {
content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
content: "\f00d";
}
.fa-search-plus:before {
content: "\f00e";
}
.fa-search-minus:before {
content: "\f010";
}
.fa-power-off:before {
content: "\f011";
}
.fa-signal:before {
content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
content: "\f013";
}
.fa-trash-o:before {
content: "\f014";
}
.fa-home:before {
content: "\f015";
}
.fa-file-o:before {
content: "\f016";
}
.fa-clock-o:before {
content: "\f017";
}
.fa-road:before {
content: "\f018";
}
.fa-download:before {
content: "\f019";
}
.fa-arrow-circle-o-down:before {
content: "\f01a";
}
.fa-arrow-circle-o-up:before {
content: "\f01b";
}
.fa-inbox:before {
content: "\f01c";
}
.fa-play-circle-o:before {
content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
content: "\f01e";
}
.fa-refresh:before {
content: "\f021";
}
.fa-list-alt:before {
content: "\f022";
}
.fa-lock:before {
content: "\f023";
}
.fa-flag:before {
content: "\f024";
}
.fa-headphones:before {
content: "\f025";
}
.fa-volume-off:before {
content: "\f026";
}
.fa-volume-down:before {
content: "\f027";
}
.fa-volume-up:before {
content: "\f028";
}
.fa-qrcode:before {
content: "\f029";
}
.fa-barcode:before {
content: "\f02a";
}
.fa-tag:before {
content: "\f02b";
}
.fa-tags:before {
content: "\f02c";
}
.fa-book:before {
content: "\f02d";
}
.fa-bookmark:before {
content: "\f02e";
}
.fa-print:before {
content: "\f02f";
}
.fa-camera:before {
content: "\f030";
}
.fa-font:before {
content: "\f031";
}
.fa-bold:before {
content: "\f032";
}
.fa-italic:before {
content: "\f033";
}
.fa-text-height:before {
content: "\f034";
}
.fa-text-width:before {
content: "\f035";
}
.fa-align-left:before {
content: "\f036";
}
.fa-align-center:before {
content: "\f037";
}
.fa-align-right:before {
content: "\f038";
}
.fa-align-justify:before {
content: "\f039";
}
.fa-list:before {
content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
content: "\f03b";
}
.fa-indent:before {
content: "\f03c";
}
.fa-video-camera:before {
content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
content: "\f03e";
}
.fa-pencil:before {
content: "\f040";
}
.fa-map-marker:before {
content: "\f041";
}
.fa-adjust:before {
content: "\f042";
}
.fa-tint:before {
content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
content: "\f044";
}
.fa-share-square-o:before {
content: "\f045";
}
.fa-check-square-o:before {
content: "\f046";
}
.fa-arrows:before {
content: "\f047";
}
.fa-step-backward:before {
content: "\f048";
}
.fa-fast-backward:before {
content: "\f049";
}
.fa-backward:before {
content: "\f04a";
}
.fa-play:before {
content: "\f04b";
}
.fa-pause:before {
content: "\f04c";
}
.fa-stop:before {
content: "\f04d";
}
.fa-forward:before {
content: "\f04e";
}
.fa-fast-forward:before {
content: "\f050";
}
.fa-step-forward:before {
content: "\f051";
}
.fa-eject:before {
content: "\f052";
}
.fa-chevron-left:before {
content: "\f053";
}
.fa-chevron-right:before {
content: "\f054";
}
.fa-plus-circle:before {
content: "\f055";
}
.fa-minus-circle:before {
content: "\f056";
}
.fa-times-circle:before {
content: "\f057";
}
.fa-check-circle:before {
content: "\f058";
}
.fa-question-circle:before {
content: "\f059";
}
.fa-info-circle:before {
content: "\f05a";
}
.fa-crosshairs:before {
content: "\f05b";
}
.fa-times-circle-o:before {
content: "\f05c";
}
.fa-check-circle-o:before {
content: "\f05d";
}
.fa-ban:before {
content: "\f05e";
}
.fa-arrow-left:before {
content: "\f060";
}
.fa-arrow-right:before {
content: "\f061";
}
.fa-arrow-up:before {
content: "\f062";
}
.fa-arrow-down:before {
content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
content: "\f064";
}
.fa-expand:before {
content: "\f065";
}
.fa-compress:before {
content: "\f066";
}
.fa-plus:before {
content: "\f067";
}
.fa-minus:before {
content: "\f068";
}
.fa-asterisk:before {
content: "\f069";
}
.fa-exclamation-circle:before {
content: "\f06a";
}
.fa-gift:before {
content: "\f06b";
}
.fa-leaf:before {
content: "\f06c";
}
.fa-fire:before {
content: "\f06d";
}
.fa-eye:before {
content: "\f06e";
}
.fa-eye-slash:before {
content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
content: "\f071";
}
.fa-plane:before {
content: "\f072";
}
.fa-calendar:before {
content: "\f073";
}
.fa-random:before {
content: "\f074";
}
.fa-comment:before {
content: "\f075";
}
.fa-magnet:before {
content: "\f076";
}
.fa-chevron-up:before {
content: "\f077";
}
.fa-chevron-down:before {
content: "\f078";
}
.fa-retweet:before {
content: "\f079";
}
.fa-shopping-cart:before {
content: "\f07a";
}
.fa-folder:before {
content: "\f07b";
}
.fa-folder-open:before {
content: "\f07c";
}
.fa-arrows-v:before {
content: "\f07d";
}
.fa-arrows-h:before {
content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
content: "\f080";
}
.fa-twitter-square:before {
content: "\f081";
}
.fa-facebook-square:before {
content: "\f082";
}
.fa-camera-retro:before {
content: "\f083";
}
.fa-key:before {
content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
content: "\f085";
}
.fa-comments:before {
content: "\f086";
}
.fa-thumbs-o-up:before {
content: "\f087";
}
.fa-thumbs-o-down:before {
content: "\f088";
}
.fa-star-half:before {
content: "\f089";
}
.fa-heart-o:before {
content: "\f08a";
}
.fa-sign-out:before {
content: "\f08b";
}
.fa-linkedin-square:before {
content: "\f08c";
}
.fa-thumb-tack:before {
content: "\f08d";
}
.fa-external-link:before {
content: "\f08e";
}
.fa-sign-in:before {
content: "\f090";
}
.fa-trophy:before {
content: "\f091";
}
.fa-github-square:before {
content: "\f092";
}
.fa-upload:before {
content: "\f093";
}
.fa-lemon-o:before {
content: "\f094";
}
.fa-phone:before {
content: "\f095";
}
.fa-square-o:before {
content: "\f096";
}
.fa-bookmark-o:before {
content: "\f097";
}
.fa-phone-square:before {
content: "\f098";
}
.fa-twitter:before {
content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
content: "\f09a";
}
.fa-github:before {
content: "\f09b";
}
.fa-unlock:before {
content: "\f09c";
}
.fa-credit-card:before {
content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
content: "\f09e";
}
.fa-hdd-o:before {
content: "\f0a0";
}
.fa-bullhorn:before {
content: "\f0a1";
}
.fa-bell:before {
content: "\f0f3";
}
.fa-certificate:before {
content: "\f0a3";
}
.fa-hand-o-right:before {
content: "\f0a4";
}
.fa-hand-o-left:before {
content: "\f0a5";
}
.fa-hand-o-up:before {
content: "\f0a6";
}
.fa-hand-o-down:before {
content: "\f0a7";
}
.fa-arrow-circle-left:before {
content: "\f0a8";
}
.fa-arrow-circle-right:before {
content: "\f0a9";
}
.fa-arrow-circle-up:before {
content: "\f0aa";
}
.fa-arrow-circle-down:before {
content: "\f0ab";
}
.fa-globe:before {
content: "\f0ac";
}
.fa-wrench:before {
content: "\f0ad";
}
.fa-tasks:before {
content: "\f0ae";
}
.fa-filter:before {
content: "\f0b0";
}
.fa-briefcase:before {
content: "\f0b1";
}
.fa-arrows-alt:before {
content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
content: "\f0c1";
}
.fa-cloud:before {
content: "\f0c2";
}
.fa-flask:before {
content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
content: "\f0c5";
}
.fa-paperclip:before {
content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
content: "\f0c7";
}
.fa-square:before {
content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
content: "\f0c9";
}
.fa-list-ul:before {
content: "\f0ca";
}
.fa-list-ol:before {
content: "\f0cb";
}
.fa-strikethrough:before {
content: "\f0cc";
}
.fa-underline:before {
content: "\f0cd";
}
.fa-table:before {
content: "\f0ce";
}
.fa-magic:before {
content: "\f0d0";
}
.fa-truck:before {
content: "\f0d1";
}
.fa-pinterest:before {
content: "\f0d2";
}
.fa-pinterest-square:before {
content: "\f0d3";
}
.fa-google-plus-square:before {
content: "\f0d4";
}
.fa-google-plus:before {
content: "\f0d5";
}
.fa-money:before {
content: "\f0d6";
}
.fa-caret-down:before {
content: "\f0d7";
}
.fa-caret-up:before {
content: "\f0d8";
}
.fa-caret-left:before {
content: "\f0d9";
}
.fa-caret-right:before {
content: "\f0da";
}
.fa-columns:before {
content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
content: "\f0de";
}
.fa-envelope:before {
content: "\f0e0";
}
.fa-linkedin:before {
content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
content: "\f0e4";
}
.fa-comment-o:before {
content: "\f0e5";
}
.fa-comments-o:before {
content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
content: "\f0e7";
}
.fa-sitemap:before {
content: "\f0e8";
}
.fa-umbrella:before {
content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
content: "\f0ea";
}
.fa-lightbulb-o:before {
content: "\f0eb";
}
.fa-exchange:before {
content: "\f0ec";
}
.fa-cloud-download:before {
content: "\f0ed";
}
.fa-cloud-upload:before {
content: "\f0ee";
}
.fa-user-md:before {
content: "\f0f0";
}
.fa-stethoscope:before {
content: "\f0f1";
}
.fa-suitcase:before {
content: "\f0f2";
}
.fa-bell-o:before {
content: "\f0a2";
}
.fa-coffee:before {
content: "\f0f4";
}
.fa-cutlery:before {
content: "\f0f5";
}
.fa-file-text-o:before {
content: "\f0f6";
}
.fa-building-o:before {
content: "\f0f7";
}
.fa-hospital-o:before {
content: "\f0f8";
}
.fa-ambulance:before {
content: "\f0f9";
}
.fa-medkit:before {
content: "\f0fa";
}
.fa-fighter-jet:before {
content: "\f0fb";
}
.fa-beer:before {
content: "\f0fc";
}
.fa-h-square:before {
content: "\f0fd";
}
.fa-plus-square:before {
content: "\f0fe";
}
.fa-angle-double-left:before {
content: "\f100";
}
.fa-angle-double-right:before {
content: "\f101";
}
.fa-angle-double-up:before {
content: "\f102";
}
.fa-angle-double-down:before {
content: "\f103";
}
.fa-angle-left:before {
content: "\f104";
}
.fa-angle-right:before {
content: "\f105";
}
.fa-angle-up:before {
content: "\f106";
}
.fa-angle-down:before {
content: "\f107";
}
.fa-desktop:before {
content: "\f108";
}
.fa-laptop:before {
content: "\f109";
}
.fa-tablet:before {
content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
content: "\f10b";
}
.fa-circle-o:before {
content: "\f10c";
}
.fa-quote-left:before {
content: "\f10d";
}
.fa-quote-right:before {
content: "\f10e";
}
.fa-spinner:before {
content: "\f110";
}
.fa-circle:before {
content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
content: "\f112";
}
.fa-github-alt:before {
content: "\f113";
}
.fa-folder-o:before {
content: "\f114";
}
.fa-folder-open-o:before {
content: "\f115";
}
.fa-smile-o:before {
content: "\f118";
}
.fa-frown-o:before {
content: "\f119";
}
.fa-meh-o:before {
content: "\f11a";
}
.fa-gamepad:before {
content: "\f11b";
}
.fa-keyboard-o:before {
content: "\f11c";
}
.fa-flag-o:before {
content: "\f11d";
}
.fa-flag-checkered:before {
content: "\f11e";
}
.fa-terminal:before {
content: "\f120";
}
.fa-code:before {
content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
content: "\f123";
}
.fa-location-arrow:before {
content: "\f124";
}
.fa-crop:before {
content: "\f125";
}
.fa-code-fork:before {
content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
content: "\f127";
}
.fa-question:before {
content: "\f128";
}
.fa-info:before {
content: "\f129";
}
.fa-exclamation:before {
content: "\f12a";
}
.fa-superscript:before {
content: "\f12b";
}
.fa-subscript:before {
content: "\f12c";
}
.fa-eraser:before {
content: "\f12d";
}
.fa-puzzle-piece:before {
content: "\f12e";
}
.fa-microphone:before {
content: "\f130";
}
.fa-microphone-slash:before {
content: "\f131";
}
.fa-shield:before {
content: "\f132";
}
.fa-calendar-o:before {
content: "\f133";
}
.fa-fire-extinguisher:before {
content: "\f134";
}
.fa-rocket:before {
content: "\f135";
}
.fa-maxcdn:before {
content: "\f136";
}
.fa-chevron-circle-left:before {
content: "\f137";
}
.fa-chevron-circle-right:before {
content: "\f138";
}
.fa-chevron-circle-up:before {
content: "\f139";
}
.fa-chevron-circle-down:before {
content: "\f13a";
}
.fa-html5:before {
content: "\f13b";
}
.fa-css3:before {
content: "\f13c";
}
.fa-anchor:before {
content: "\f13d";
}
.fa-unlock-alt:before {
content: "\f13e";
}
.fa-bullseye:before {
content: "\f140";
}
.fa-ellipsis-h:before {
content: "\f141";
}
.fa-ellipsis-v:before {
content: "\f142";
}
.fa-rss-square:before {
content: "\f143";
}
.fa-play-circle:before {
content: "\f144";
}
.fa-ticket:before {
content: "\f145";
}
.fa-minus-square:before {
content: "\f146";
}
.fa-minus-square-o:before {
content: "\f147";
}
.fa-level-up:before {
content: "\f148";
}
.fa-level-down:before {
content: "\f149";
}
.fa-check-square:before {
content: "\f14a";
}
.fa-pencil-square:before {
content: "\f14b";
}
.fa-external-link-square:before {
content: "\f14c";
}
.fa-share-square:before {
content: "\f14d";
}
.fa-compass:before {
content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
content: "\f153";
}
.fa-gbp:before {
content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
content: "\f158";
}
.fa-won:before,
.fa-krw:before {
content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
content: "\f15a";
}
.fa-file:before {
content: "\f15b";
}
.fa-file-text:before {
content: "\f15c";
}
.fa-sort-alpha-asc:before {
content: "\f15d";
}
.fa-sort-alpha-desc:before {
content: "\f15e";
}
.fa-sort-amount-asc:before {
content: "\f160";
}
.fa-sort-amount-desc:before {
content: "\f161";
}
.fa-sort-numeric-asc:before {
content: "\f162";
}
.fa-sort-numeric-desc:before {
content: "\f163";
}
.fa-thumbs-up:before {
content: "\f164";
}
.fa-thumbs-down:before {
content: "\f165";
}
.fa-youtube-square:before {
content: "\f166";
}
.fa-youtube:before {
content: "\f167";
}
.fa-xing:before {
content: "\f168";
}
.fa-xing-square:before {
content: "\f169";
}
.fa-youtube-play:before {
content: "\f16a";
}
.fa-dropbox:before {
content: "\f16b";
}
.fa-stack-overflow:before {
content: "\f16c";
}
.fa-instagram:before {
content: "\f16d";
}
.fa-flickr:before {
content: "\f16e";
}
.fa-adn:before {
content: "\f170";
}
.fa-bitbucket:before {
content: "\f171";
}
.fa-bitbucket-square:before {
content: "\f172";
}
.fa-tumblr:before {
content: "\f173";
}
.fa-tumblr-square:before {
content: "\f174";
}
.fa-long-arrow-down:before {
content: "\f175";
}
.fa-long-arrow-up:before {
content: "\f176";
}
.fa-long-arrow-left:before {
content: "\f177";
}
.fa-long-arrow-right:before {
content: "\f178";
}
.fa-apple:before {
content: "\f179";
}
.fa-windows:before {
content: "\f17a";
}
.fa-android:before {
content: "\f17b";
}
.fa-linux:before {
content: "\f17c";
}
.fa-dribbble:before {
content: "\f17d";
}
.fa-skype:before {
content: "\f17e";
}
.fa-foursquare:before {
content: "\f180";
}
.fa-trello:before {
content: "\f181";
}
.fa-female:before {
content: "\f182";
}
.fa-male:before {
content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
content: "\f184";
}
.fa-sun-o:before {
content: "\f185";
}
.fa-moon-o:before {
content: "\f186";
}
.fa-archive:before {
content: "\f187";
}
.fa-bug:before {
content: "\f188";
}
.fa-vk:before {
content: "\f189";
}
.fa-weibo:before {
content: "\f18a";
}
.fa-renren:before {
content: "\f18b";
}
.fa-pagelines:before {
content: "\f18c";
}
.fa-stack-exchange:before {
content: "\f18d";
}
.fa-arrow-circle-o-right:before {
content: "\f18e";
}
.fa-arrow-circle-o-left:before {
content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
content: "\f191";
}
.fa-dot-circle-o:before {
content: "\f192";
}
.fa-wheelchair:before {
content: "\f193";
}
.fa-vimeo-square:before {
content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
content: "\f195";
}
.fa-plus-square-o:before {
content: "\f196";
}
.fa-space-shuttle:before {
content: "\f197";
}
.fa-slack:before {
content: "\f198";
}
.fa-envelope-square:before {
content: "\f199";
}
.fa-wordpress:before {
content: "\f19a";
}
.fa-openid:before {
content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
content: "\f19d";
}
.fa-yahoo:before {
content: "\f19e";
}
.fa-google:before {
content: "\f1a0";
}
.fa-reddit:before {
content: "\f1a1";
}
.fa-reddit-square:before {
content: "\f1a2";
}
.fa-stumbleupon-circle:before {
content: "\f1a3";
}
.fa-stumbleupon:before {
content: "\f1a4";
}
.fa-delicious:before {
content: "\f1a5";
}
.fa-digg:before {
content: "\f1a6";
}
.fa-pied-piper-pp:before {
content: "\f1a7";
}
.fa-pied-piper-alt:before {
content: "\f1a8";
}
.fa-drupal:before {
content: "\f1a9";
}
.fa-joomla:before {
content: "\f1aa";
}
.fa-language:before {
content: "\f1ab";
}
.fa-fax:before {
content: "\f1ac";
}
.fa-building:before {
content: "\f1ad";
}
.fa-child:before {
content: "\f1ae";
}
.fa-paw:before {
content: "\f1b0";
}
.fa-spoon:before {
content: "\f1b1";
}
.fa-cube:before {
content: "\f1b2";
}
.fa-cubes:before {
content: "\f1b3";
}
.fa-behance:before {
content: "\f1b4";
}
.fa-behance-square:before {
content: "\f1b5";
}
.fa-steam:before {
content: "\f1b6";
}
.fa-steam-square:before {
content: "\f1b7";
}
.fa-recycle:before {
content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
content: "\f1ba";
}
.fa-tree:before {
content: "\f1bb";
}
.fa-spotify:before {
content: "\f1bc";
}
.fa-deviantart:before {
content: "\f1bd";
}
.fa-soundcloud:before {
content: "\f1be";
}
.fa-database:before {
content: "\f1c0";
}
.fa-file-pdf-o:before {
content: "\f1c1";
}
.fa-file-word-o:before {
content: "\f1c2";
}
.fa-file-excel-o:before {
content: "\f1c3";
}
.fa-file-powerpoint-o:before {
content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
content: "\f1c8";
}
.fa-file-code-o:before {
content: "\f1c9";
}
.fa-vine:before {
content: "\f1ca";
}
.fa-codepen:before {
content: "\f1cb";
}
.fa-jsfiddle:before {
content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
content: "\f1cd";
}
.fa-circle-o-notch:before {
content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
content: "\f1d1";
}
.fa-git-square:before {
content: "\f1d2";
}
.fa-git:before {
content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
content: "\f1d4";
}
.fa-tencent-weibo:before {
content: "\f1d5";
}
.fa-qq:before {
content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
content: "\f1d9";
}
.fa-history:before {
content: "\f1da";
}
.fa-circle-thin:before {
content: "\f1db";
}
.fa-header:before {
content: "\f1dc";
}
.fa-paragraph:before {
content: "\f1dd";
}
.fa-sliders:before {
content: "\f1de";
}
.fa-share-alt:before {
content: "\f1e0";
}
.fa-share-alt-square:before {
content: "\f1e1";
}
.fa-bomb:before {
content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
content: "\f1e3";
}
.fa-tty:before {
content: "\f1e4";
}
.fa-binoculars:before {
content: "\f1e5";
}
.fa-plug:before {
content: "\f1e6";
}
.fa-slideshare:before {
content: "\f1e7";
}
.fa-twitch:before {
content: "\f1e8";
}
.fa-yelp:before {
content: "\f1e9";
}
.fa-newspaper-o:before {
content: "\f1ea";
}
.fa-wifi:before {
content: "\f1eb";
}
.fa-calculator:before {
content: "\f1ec";
}
.fa-paypal:before {
content: "\f1ed";
}
.fa-google-wallet:before {
content: "\f1ee";
}
.fa-cc-visa:before {
content: "\f1f0";
}
.fa-cc-mastercard:before {
content: "\f1f1";
}
.fa-cc-discover:before {
content: "\f1f2";
}
.fa-cc-amex:before {
content: "\f1f3";
}
.fa-cc-paypal:before {
content: "\f1f4";
}
.fa-cc-stripe:before {
content: "\f1f5";
}
.fa-bell-slash:before {
content: "\f1f6";
}
.fa-bell-slash-o:before {
content: "\f1f7";
}
.fa-trash:before {
content: "\f1f8";
}
.fa-copyright:before {
content: "\f1f9";
}
.fa-at:before {
content: "\f1fa";
}
.fa-eyedropper:before {
content: "\f1fb";
}
.fa-paint-brush:before {
content: "\f1fc";
}
.fa-birthday-cake:before {
content: "\f1fd";
}
.fa-area-chart:before {
content: "\f1fe";
}
.fa-pie-chart:before {
content: "\f200";
}
.fa-line-chart:before {
content: "\f201";
}
.fa-lastfm:before {
content: "\f202";
}
.fa-lastfm-square:before {
content: "\f203";
}
.fa-toggle-off:before {
content: "\f204";
}
.fa-toggle-on:before {
content: "\f205";
}
.fa-bicycle:before {
content: "\f206";
}
.fa-bus:before {
content: "\f207";
}
.fa-ioxhost:before {
content: "\f208";
}
.fa-angellist:before {
content: "\f209";
}
.fa-cc:before {
content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
content: "\f20b";
}
.fa-meanpath:before {
content: "\f20c";
}
.fa-buysellads:before {
content: "\f20d";
}
.fa-connectdevelop:before {
content: "\f20e";
}
.fa-dashcube:before {
content: "\f210";
}
.fa-forumbee:before {
content: "\f211";
}
.fa-leanpub:before {
content: "\f212";
}
.fa-sellsy:before {
content: "\f213";
}
.fa-shirtsinbulk:before {
content: "\f214";
}
.fa-simplybuilt:before {
content: "\f215";
}
.fa-skyatlas:before {
content: "\f216";
}
.fa-cart-plus:before {
content: "\f217";
}
.fa-cart-arrow-down:before {
content: "\f218";
}
.fa-diamond:before {
content: "\f219";
}
.fa-ship:before {
content: "\f21a";
}
.fa-user-secret:before {
content: "\f21b";
}
.fa-motorcycle:before {
content: "\f21c";
}
.fa-street-view:before {
content: "\f21d";
}
.fa-heartbeat:before {
content: "\f21e";
}
.fa-venus:before {
content: "\f221";
}
.fa-mars:before {
content: "\f222";
}
.fa-mercury:before {
content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
content: "\f224";
}
.fa-transgender-alt:before {
content: "\f225";
}
.fa-venus-double:before {
content: "\f226";
}
.fa-mars-double:before {
content: "\f227";
}
.fa-venus-mars:before {
content: "\f228";
}
.fa-mars-stroke:before {
content: "\f229";
}
.fa-mars-stroke-v:before {
content: "\f22a";
}
.fa-mars-stroke-h:before {
content: "\f22b";
}
.fa-neuter:before {
content: "\f22c";
}
.fa-genderless:before {
content: "\f22d";
}
.fa-facebook-official:before {
content: "\f230";
}
.fa-pinterest-p:before {
content: "\f231";
}
.fa-whatsapp:before {
content: "\f232";
}
.fa-server:before {
content: "\f233";
}
.fa-user-plus:before {
content: "\f234";
}
.fa-user-times:before {
content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
content: "\f236";
}
.fa-viacoin:before {
content: "\f237";
}
.fa-train:before {
content: "\f238";
}
.fa-subway:before {
content: "\f239";
}
.fa-medium:before {
content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
content: "\f23b";
}
.fa-optin-monster:before {
content: "\f23c";
}
.fa-opencart:before {
content: "\f23d";
}
.fa-expeditedssl:before {
content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
content: "\f244";
}
.fa-mouse-pointer:before {
content: "\f245";
}
.fa-i-cursor:before {
content: "\f246";
}
.fa-object-group:before {
content: "\f247";
}
.fa-object-ungroup:before {
content: "\f248";
}
.fa-sticky-note:before {
content: "\f249";
}
.fa-sticky-note-o:before {
content: "\f24a";
}
.fa-cc-jcb:before {
content: "\f24b";
}
.fa-cc-diners-club:before {
content: "\f24c";
}
.fa-clone:before {
content: "\f24d";
}
.fa-balance-scale:before {
content: "\f24e";
}
.fa-hourglass-o:before {
content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
content: "\f253";
}
.fa-hourglass:before {
content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
content: "\f256";
}
.fa-hand-scissors-o:before {
content: "\f257";
}
.fa-hand-lizard-o:before {
content: "\f258";
}
.fa-hand-spock-o:before {
content: "\f259";
}
.fa-hand-pointer-o:before {
content: "\f25a";
}
.fa-hand-peace-o:before {
content: "\f25b";
}
.fa-trademark:before {
content: "\f25c";
}
.fa-registered:before {
content: "\f25d";
}
.fa-creative-commons:before {
content: "\f25e";
}
.fa-gg:before {
content: "\f260";
}
.fa-gg-circle:before {
content: "\f261";
}
.fa-tripadvisor:before {
content: "\f262";
}
.fa-odnoklassniki:before {
content: "\f263";
}
.fa-odnoklassniki-square:before {
content: "\f264";
}
.fa-get-pocket:before {
content: "\f265";
}
.fa-wikipedia-w:before {
content: "\f266";
}
.fa-safari:before {
content: "\f267";
}
.fa-chrome:before {
content: "\f268";
}
.fa-firefox:before {
content: "\f269";
}
.fa-opera:before {
content: "\f26a";
}
.fa-internet-explorer:before {
content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
content: "\f26c";
}
.fa-contao:before {
content: "\f26d";
}
.fa-500px:before {
content: "\f26e";
}
.fa-amazon:before {
content: "\f270";
}
.fa-calendar-plus-o:before {
content: "\f271";
}
.fa-calendar-minus-o:before {
content: "\f272";
}
.fa-calendar-times-o:before {
content: "\f273";
}
.fa-calendar-check-o:before {
content: "\f274";
}
.fa-industry:before {
content: "\f275";
}
.fa-map-pin:before {
content: "\f276";
}
.fa-map-signs:before {
content: "\f277";
}
.fa-map-o:before {
content: "\f278";
}
.fa-map:before {
content: "\f279";
}
.fa-commenting:before {
content: "\f27a";
}
.fa-commenting-o:before {
content: "\f27b";
}
.fa-houzz:before {
content: "\f27c";
}
.fa-vimeo:before {
content: "\f27d";
}
.fa-black-tie:before {
content: "\f27e";
}
.fa-fonticons:before {
content: "\f280";
}
.fa-reddit-alien:before {
content: "\f281";
}
.fa-edge:before {
content: "\f282";
}
.fa-credit-card-alt:before {
content: "\f283";
}
.fa-codiepie:before {
content: "\f284";
}
.fa-modx:before {
content: "\f285";
}
.fa-fort-awesome:before {
content: "\f286";
}
.fa-usb:before {
content: "\f287";
}
.fa-product-hunt:before {
content: "\f288";
}
.fa-mixcloud:before {
content: "\f289";
}
.fa-scribd:before {
content: "\f28a";
}
.fa-pause-circle:before {
content: "\f28b";
}
.fa-pause-circle-o:before {
content: "\f28c";
}
.fa-stop-circle:before {
content: "\f28d";
}
.fa-stop-circle-o:before {
content: "\f28e";
}
.fa-shopping-bag:before {
content: "\f290";
}
.fa-shopping-basket:before {
content: "\f291";
}
.fa-hashtag:before {
content: "\f292";
}
.fa-bluetooth:before {
content: "\f293";
}
.fa-bluetooth-b:before {
content: "\f294";
}
.fa-percent:before {
content: "\f295";
}
.fa-gitlab:before {
content: "\f296";
}
.fa-wpbeginner:before {
content: "\f297";
}
.fa-wpforms:before {
content: "\f298";
}
.fa-envira:before {
content: "\f299";
}
.fa-universal-access:before {
content: "\f29a";
}
.fa-wheelchair-alt:before {
content: "\f29b";
}
.fa-question-circle-o:before {
content: "\f29c";
}
.fa-blind:before {
content: "\f29d";
}
.fa-audio-description:before {
content: "\f29e";
}
.fa-volume-control-phone:before {
content: "\f2a0";
}
.fa-braille:before {
content: "\f2a1";
}
.fa-assistive-listening-systems:before {
content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
content: "\f2a4";
}
.fa-glide:before {
content: "\f2a5";
}
.fa-glide-g:before {
content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
content: "\f2a7";
}
.fa-low-vision:before {
content: "\f2a8";
}
.fa-viadeo:before {
content: "\f2a9";
}
.fa-viadeo-square:before {
content: "\f2aa";
}
.fa-snapchat:before {
content: "\f2ab";
}
.fa-snapchat-ghost:before {
content: "\f2ac";
}
.fa-snapchat-square:before {
content: "\f2ad";
}
.fa-pied-piper:before {
content: "\f2ae";
}
.fa-first-order:before {
content: "\f2b0";
}
.fa-yoast:before {
content: "\f2b1";
}
.fa-themeisle:before {
content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
content: "\f2b4";
}
.fa-handshake-o:before {
content: "\f2b5";
}
.fa-envelope-open:before {
content: "\f2b6";
}
.fa-envelope-open-o:before {
content: "\f2b7";
}
.fa-linode:before {
content: "\f2b8";
}
.fa-address-book:before {
content: "\f2b9";
}
.fa-address-book-o:before {
content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
content: "\f2bc";
}
.fa-user-circle:before {
content: "\f2bd";
}
.fa-user-circle-o:before {
content: "\f2be";
}
.fa-user-o:before {
content: "\f2c0";
}
.fa-id-badge:before {
content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
content: "\f2c3";
}
.fa-quora:before {
content: "\f2c4";
}
.fa-free-code-camp:before {
content: "\f2c5";
}
.fa-telegram:before {
content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
content: "\f2cb";
}
.fa-shower:before {
content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
content: "\f2cd";
}
.fa-podcast:before {
content: "\f2ce";
}
.fa-window-maximize:before {
content: "\f2d0";
}
.fa-window-minimize:before {
content: "\f2d1";
}
.fa-window-restore:before {
content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
content: "\f2d4";
}
.fa-bandcamp:before {
content: "\f2d5";
}
.fa-grav:before {
content: "\f2d6";
}
.fa-etsy:before {
content: "\f2d7";
}
.fa-imdb:before {
content: "\f2d8";
}
.fa-ravelry:before {
content: "\f2d9";
}
.fa-eercast:before {
content: "\f2da";
}
.fa-microchip:before {
content: "\f2db";
}
.fa-snowflake-o:before {
content: "\f2dc";
}
.fa-superpowers:before {
content: "\f2dd";
}
.fa-wpexplorer:before {
content: "\f2de";
}
.fa-meetup:before {
content: "\f2e0";
}
.sr-only {
position: absolute;
width: 1px;
height: 1px;
padding: 0;
margin: -1px;
overflow: hidden;
clip: rect(0, 0, 0, 0);
border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
position: static;
width: auto;
height: auto;
margin: 0;
overflow: visible;
clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
position: static;
width: auto;
height: auto;
margin: 0;
overflow: visible;
clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
-webkit-transform: translate(0, 0);
-ms-transform: translate(0, 0);
-o-transform: translate(0, 0);
transform: translate(0, 0);
}
code {
color: #000;
}
pre {
font-size: inherit;
line-height: inherit;
}
label {
font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
.corner-all {
border-radius: 2px;
}
.no-padding {
padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer. It allows the usage of flexible box
model layouts accross multiple browsers, including older browsers. The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below). Browsers that are known to implement this
new spec completely include:
Firefox 28.0+
Chrome 29.0+
Internet Explorer 11+
Opera 17.0+
Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
.hbox > * {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
}
.vbox {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
.vbox > * {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
/* Old browsers */
-webkit-box-direction: reverse;
-moz-box-direction: reverse;
box-direction: reverse;
/* Modern browsers */
flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
/* Old browsers */
-webkit-box-flex: 0;
-moz-box-flex: 0;
box-flex: 0;
/* Modern browsers */
flex: none;
width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
/* Old browsers */
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
/* Old browsers */
-webkit-box-flex: 2;
-moz-box-flex: 2;
box-flex: 2;
/* Modern browsers */
flex: 2;
}
.box-group1 {
/* Deprecated */
-webkit-box-flex-group: 1;
-moz-box-flex-group: 1;
box-flex-group: 1;
}
.box-group2 {
/* Deprecated */
-webkit-box-flex-group: 2;
-moz-box-flex-group: 2;
box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
/* Old browsers */
-webkit-box-pack: start;
-moz-box-pack: start;
box-pack: start;
/* Modern browsers */
justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
/* Old browsers */
-webkit-box-pack: end;
-moz-box-pack: end;
box-pack: end;
/* Modern browsers */
justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
/* Old browsers */
-webkit-box-pack: center;
-moz-box-pack: center;
box-pack: center;
/* Modern browsers */
justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
/* Old browsers */
-webkit-box-pack: baseline;
-moz-box-pack: baseline;
box-pack: baseline;
/* Modern browsers */
justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
/* Old browsers */
-webkit-box-pack: stretch;
-moz-box-pack: stretch;
box-pack: stretch;
/* Modern browsers */
justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
/* Old browsers */
-webkit-box-align: start;
-moz-box-align: start;
box-align: start;
/* Modern browsers */
align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
/* Old browsers */
-webkit-box-align: end;
-moz-box-align: end;
box-align: end;
/* Modern browsers */
align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
/* Old browsers */
-webkit-box-align: center;
-moz-box-align: center;
box-align: center;
/* Modern browsers */
align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
/* Old browsers */
-webkit-box-align: baseline;
-moz-box-align: baseline;
box-align: baseline;
/* Modern browsers */
align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
/* Old browsers */
-webkit-box-align: stretch;
-moz-box-align: stretch;
box-align: stretch;
/* Modern browsers */
align-items: stretch;
}
div.error {
margin: 2em;
text-align: center;
}
div.error > h1 {
font-size: 500%;
line-height: normal;
}
div.error > p {
font-size: 200%;
line-height: normal;
}
div.traceback-wrapper {
text-align: left;
max-width: 800px;
margin: auto;
}
div.traceback-wrapper pre.traceback {
max-height: 600px;
overflow: auto;
}
/**
* Primary styles
*
* Author: Jupyter Development Team
*/
body {
background-color: #fff;
/* This makes sure that the body covers the entire window and needs to
be in a different element than the display: box in wrapper below */
position: absolute;
left: 0px;
right: 0px;
top: 0px;
bottom: 0px;
overflow: visible;
}
body > #header {
/* Initially hidden to prevent FLOUC */
display: none;
background-color: #fff;
/* Display over codemirror */
position: relative;
z-index: 100;
}
body > #header #header-container {
display: flex;
flex-direction: row;
justify-content: space-between;
padding: 5px;
padding-bottom: 5px;
padding-top: 5px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
body > #header .header-bar {
width: 100%;
height: 1px;
background: #e7e7e7;
margin-bottom: -1px;
}
@media print {
body > #header {
display: none !important;
}
}
#header-spacer {
width: 100%;
visibility: hidden;
}
@media print {
#header-spacer {
display: none;
}
}
#ipython_notebook {
padding-left: 0px;
padding-top: 1px;
padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
margin-right: 10px;
margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
float: right !important;
float: right;
}
.flex-spacer {
flex: 1;
}
#noscript {
width: auto;
padding-top: 16px;
padding-bottom: 16px;
text-align: center;
font-size: 22px;
color: red;
font-weight: bold;
}
#ipython_notebook img {
height: 28px;
}
#site {
width: 100%;
display: none;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
overflow: auto;
}
@media print {
#site {
height: auto !important;
}
}
/* Smaller buttons */
.ui-button .ui-button-text {
padding: 0.2em 0.8em;
font-size: 77%;
}
input.ui-button {
padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
margin: 0 10px;
}
span#login_widget {
float: right;
}
[dir="rtl"] span#login_widget {
float: left;
}
span#login_widget > .button,
#logout {
color: #333;
background-color: #fff;
border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
background-color: #fff;
border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
color: #fff;
background-color: #333;
}
.nav-header {
text-transform: none;
}
#header > span {
margin-top: 10px;
}
.modal_stretch .modal-dialog {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
max-height: calc(100vh - 200px);
overflow: auto;
flex: 1;
}
.modal-header {
cursor: move;
}
@media (min-width: 768px) {
.modal .modal-dialog {
width: 700px;
}
}
@media (min-width: 768px) {
select.form-control {
margin-left: 12px;
margin-right: 12px;
}
}
/*!
*
* IPython auth
*
*/
.center-nav {
display: inline-block;
margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
float: right !important;
float: right;
}
[dir="rtl"] .center-nav .navbar-text {
float: right;
}
[dir="rtl"] .navbar-inner {
text-align: right;
}
[dir="rtl"] div.text-left {
text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
background-color: none;
display: inline;
}
.alternate_upload.form {
padding: 0;
margin: 0;
}
.alternate_upload input.fileinput {
position: absolute;
display: block;
width: 100%;
height: 100%;
overflow: hidden;
cursor: pointer;
opacity: 0;
z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
margin: -1px -5px;
}
.alternate_upload .btn-upload {
position: relative;
height: 22px;
}
::-webkit-file-upload-button {
cursor: pointer;
}
/**
* Primary styles
*
* Author: Jupyter Development Team
*/
ul#tabs {
margin-bottom: 4px;
}
ul#tabs a {
padding-top: 6px;
padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
text-decoration: none;
}
ul.breadcrumb i.icon-home {
font-size: 16px;
margin-right: 4px;
}
ul.breadcrumb span {
color: #5e5e5e;
}
.list_toolbar {
padding: 4px 0 4px 0;
vertical-align: middle;
}
.list_toolbar .tree-buttons {
padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
float: left !important;
float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
float: right;
}
.dynamic-buttons {
padding-top: 3px;
display: inline-block;
}
.list_toolbar [class*="span"] {
min-height: 24px;
}
.list_header {
font-weight: bold;
background-color: #EEE;
}
.list_placeholder {
font-weight: bold;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
}
.list_container {
margin-top: 4px;
margin-bottom: 20px;
border: 1px solid #ddd;
border-radius: 2px;
}
.list_container > div {
border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
background-color: red;
}
.list_container > div:last-child {
border: none;
}
.list_item:hover .list_item {
background-color: #ddd;
}
.list_item a {
text-decoration: none;
}
.list_item:hover {
background-color: #fafafa;
}
.list_header > div,
.list_item > div {
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
line-height: 22px;
}
.list_header > div input,
.list_item > div input {
margin-right: 7px;
margin-left: 14px;
vertical-align: text-bottom;
line-height: 22px;
position: relative;
top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
margin-left: -1px;
vertical-align: baseline;
line-height: 22px;
}
[dir="rtl"] .list_item > div input {
margin-right: 0;
}
.new-file input[type=checkbox] {
visibility: hidden;
}
.item_name {
line-height: 22px;
height: 24px;
}
.item_icon {
font-size: 14px;
color: #5e5e5e;
margin-right: 7px;
margin-left: 7px;
line-height: 22px;
vertical-align: baseline;
}
.item_modified {
margin-right: 7px;
margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
float: left !important;
float: left;
}
.item_buttons {
line-height: 1em;
margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
margin-left: 5px;
}
.item_buttons .btn {
min-width: 13ex;
}
.item_buttons .running-indicator {
padding-top: 4px;
color: #5cb85c;
}
.item_buttons .kernel-name {
padding-top: 4px;
color: #5bc0de;
margin-right: 7px;
float: left;
}
[dir="rtl"] .item_buttons.pull-right {
float: left !important;
float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
margin-left: 7px;
float: right;
}
.toolbar_info {
height: 24px;
line-height: 24px;
}
.list_item input:not([type=checkbox]) {
padding-top: 3px;
padding-bottom: 3px;
height: 22px;
line-height: 14px;
margin: 0px;
}
.highlight_text {
color: blue;
}
#project_name {
display: inline-block;
padding-left: 7px;
margin-left: -2px;
}
#project_name > .breadcrumb {
padding: 0px;
margin-bottom: 0px;
background-color: transparent;
font-weight: bold;
}
.sort_button {
display: inline-block;
padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
float: left !important;
float: left;
}
#tree-selector {
padding-right: 0px;
}
#button-select-all {
min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
float: right ;
}
#select-all {
margin-left: 7px;
margin-right: 2px;
margin-top: 2px;
height: 16px;
}
[dir="rtl"] #select-all.pull-left {
float: right !important;
float: right;
}
.menu_icon {
margin-right: 2px;
}
.tab-content .row {
margin-left: 0px;
margin-right: 0px;
}
.folder_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f114";
}
.folder_icon:before.fa-pull-left {
margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
margin-left: .3em;
}
.folder_icon:before.pull-left {
margin-right: .3em;
}
.folder_icon:before.pull-right {
margin-left: .3em;
}
.notebook_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f02d";
position: relative;
top: -1px;
}
.notebook_icon:before.fa-pull-left {
margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
margin-left: .3em;
}
.notebook_icon:before.pull-left {
margin-right: .3em;
}
.notebook_icon:before.pull-right {
margin-left: .3em;
}
.running_notebook_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f02d";
position: relative;
top: -1px;
color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
margin-left: .3em;
}
.file_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f016";
position: relative;
top: -2px;
}
.file_icon:before.fa-pull-left {
margin-right: .3em;
}
.file_icon:before.fa-pull-right {
margin-left: .3em;
}
.file_icon:before.pull-left {
margin-right: .3em;
}
.file_icon:before.pull-right {
margin-left: .3em;
}
#notebook_toolbar .pull-right {
padding-top: 0px;
margin-right: -1px;
}
ul#new-menu {
left: auto;
right: 0;
}
#new-menu .dropdown-header {
font-size: 10px;
border-bottom: 1px solid #e5e5e5;
padding: 0 0 3px;
margin: -3px 20px 0;
}
.kernel-menu-icon {
padding-right: 12px;
width: 24px;
content: "\f096";
}
.kernel-menu-icon:before {
content: "\f096";
}
.kernel-menu-icon-current:before {
content: "\f00c";
}
#tab_content {
padding-top: 20px;
}
#running .panel-group .panel {
margin-top: 3px;
margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
background-color: #EEE;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 7px;
padding-right: 7px;
line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
text-decoration: none;
}
#running .panel-group .panel .panel-body {
padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
margin-top: 0px;
margin-bottom: 0px;
border: 0px;
border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
border-bottom: 0px;
}
.delete-button {
display: none;
}
.duplicate-button {
display: none;
}
.rename-button {
display: none;
}
.move-button {
display: none;
}
.download-button {
display: none;
}
.shutdown-button {
display: none;
}
.dynamic-instructions {
display: inline-block;
padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
padding: 0px 5px;
}
.selected-keymap i.fa:before {
content: "\f00c";
}
#mode-menu {
overflow: auto;
max-height: 20em;
}
.edit_app #header {
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
/* Use a negative 1 bottom margin, so the border overlaps the border of the
header */
margin-bottom: -1px;
}
.dirty-indicator {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator.fa-pull-left {
margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
margin-left: .3em;
}
.dirty-indicator.pull-left {
margin-right: .3em;
}
.dirty-indicator.pull-right {
margin-left: .3em;
}
.dirty-indicator-dirty {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
margin-left: .3em;
}
.dirty-indicator-clean {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
margin-left: .3em;
}
.dirty-indicator-clean:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
margin-left: .3em;
}
#filename {
font-size: 16pt;
display: table;
padding: 0px 5px;
}
#current-mode {
padding-left: 5px;
padding-right: 5px;
}
#texteditor-backdrop {
padding-top: 20px;
padding-bottom: 20px;
}
@media not print {
#texteditor-backdrop {
background-color: #EEE;
}
}
@media print {
#texteditor-backdrop #texteditor-container .CodeMirror-gutter,
#texteditor-backdrop #texteditor-container .CodeMirror-gutters {
background-color: #fff;
}
}
@media not print {
#texteditor-backdrop #texteditor-container .CodeMirror-gutter,
#texteditor-backdrop #texteditor-container .CodeMirror-gutters {
background-color: #fff;
}
}
@media not print {
#texteditor-backdrop #texteditor-container {
padding: 0px;
background-color: #fff;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
}
.CodeMirror-dialog {
background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
http://www.xcolors.net/dl/baskerville-ivorylight and
http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
color: #3E424D;
}
.ansi-black-bg {
background-color: #3E424D;
}
.ansi-black-intense-fg {
color: #282C36;
}
.ansi-black-intense-bg {
background-color: #282C36;
}
.ansi-red-fg {
color: #E75C58;
}
.ansi-red-bg {
background-color: #E75C58;
}
.ansi-red-intense-fg {
color: #B22B31;
}
.ansi-red-intense-bg {
background-color: #B22B31;
}
.ansi-green-fg {
color: #00A250;
}
.ansi-green-bg {
background-color: #00A250;
}
.ansi-green-intense-fg {
color: #007427;
}
.ansi-green-intense-bg {
background-color: #007427;
}
.ansi-yellow-fg {
color: #DDB62B;
}
.ansi-yellow-bg {
background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
color: #B27D12;
}
.ansi-yellow-intense-bg {
background-color: #B27D12;
}
.ansi-blue-fg {
color: #208FFB;
}
.ansi-blue-bg {
background-color: #208FFB;
}
.ansi-blue-intense-fg {
color: #0065CA;
}
.ansi-blue-intense-bg {
background-color: #0065CA;
}
.ansi-magenta-fg {
color: #D160C4;
}
.ansi-magenta-bg {
background-color: #D160C4;
}
.ansi-magenta-intense-fg {
color: #A03196;
}
.ansi-magenta-intense-bg {
background-color: #A03196;
}
.ansi-cyan-fg {
color: #60C6C8;
}
.ansi-cyan-bg {
background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
color: #258F8F;
}
.ansi-cyan-intense-bg {
background-color: #258F8F;
}
.ansi-white-fg {
color: #C5C1B4;
}
.ansi-white-bg {
background-color: #C5C1B4;
}
.ansi-white-intense-fg {
color: #A1A6B2;
}
.ansi-white-intense-bg {
background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
color: #FFFFFF;
}
.ansi-default-inverse-bg {
background-color: #000000;
}
.ansi-bold {
font-weight: bold;
}
.ansi-underline {
text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
font-weight: bold;
}
.ansi-inverse {
outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
color: black;
}
.ansired {
color: darkred;
}
.ansigreen {
color: darkgreen;
}
.ansiyellow {
color: #c4a000;
}
.ansiblue {
color: darkblue;
}
.ansipurple {
color: darkviolet;
}
.ansicyan {
color: steelblue;
}
.ansigray {
color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
background-color: black;
}
.ansibgred {
background-color: red;
}
.ansibggreen {
background-color: green;
}
.ansibgyellow {
background-color: yellow;
}
.ansibgblue {
background-color: blue;
}
.ansibgpurple {
background-color: magenta;
}
.ansibgcyan {
background-color: cyan;
}
.ansibggray {
background-color: gray;
}
div.cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
border-radius: 2px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
border-width: 1px;
border-style: solid;
border-color: transparent;
width: 100%;
padding: 5px;
/* This acts as a spacer between cells, that is outside the border */
margin: 0px;
outline: none;
position: relative;
overflow: visible;
}
div.cell:before {
position: absolute;
display: block;
top: -1px;
left: -1px;
width: 5px;
height: calc(100% + 2px);
content: '';
background: transparent;
}
div.cell.jupyter-soft-selected {
border-left-color: #E3F2FD;
border-left-width: 1px;
padding-left: 5px;
border-right-color: #E3F2FD;
border-right-width: 1px;
background: #E3F2FD;
}
@media print {
div.cell.jupyter-soft-selected {
border-color: transparent;
}
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
position: absolute;
display: block;
top: -1px;
left: -1px;
width: 5px;
height: calc(100% + 2px);
content: '';
background: #42A5F5;
}
@media print {
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
border-color: transparent;
}
}
.edit_mode div.cell.selected {
border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
position: absolute;
display: block;
top: -1px;
left: -1px;
width: 5px;
height: calc(100% + 2px);
content: '';
background: #66BB6A;
}
@media print {
.edit_mode div.cell.selected {
border-color: transparent;
}
}
.prompt {
/* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
min-width: 14ex;
/* This padding is tuned to match the padding on the CodeMirror editor. */
padding: 0.4em;
margin: 0px;
font-family: monospace;
text-align: right;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
/* Don't highlight prompt number selection */
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
/* Use default cursor */
cursor: default;
}
@media (max-width: 540px) {
.prompt {
text-align: left;
}
}
div.inner_cell {
min-width: 0;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
border: 1px solid #cfcfcf;
border-radius: 2px;
background: #f7f7f7;
line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
is no content in the output_subarea and the prompt. The main purpose of this is
to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
padding-top: 0;
padding-bottom: 0;
}
div.unrecognized_cell {
padding: 5px 5px 5px 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.unrecognized_cell .inner_cell {
border-radius: 2px;
padding: 5px;
font-weight: bold;
color: red;
border: 1px solid #cfcfcf;
background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
color: inherit;
text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
color: inherit;
text-decoration: none;
}
@media (max-width: 540px) {
div.unrecognized_cell > div.prompt {
display: none;
}
}
div.code_cell {
/* avoid page breaking on code cells when printing */
}
@media print {
div.code_cell {
page-break-inside: avoid;
}
}
/* any special styling for code cells that are currently running goes here */
div.input {
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.input {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
color: #303F9F;
border-top: 1px solid transparent;
}
div.input_area > div.highlight {
margin: 0.4em;
border: none;
padding: 0px;
background-color: transparent;
}
div.input_area > div.highlight > pre {
margin: 0px;
border: none;
padding: 0px;
background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
* monospace font with inconsistent normal/bold/italic height. See
* notebookmain.js. Such fonts will have keywords vertically offset with
* respect to the rest of the text. The user should select a better font.
* See: https://github.com/ipython/ipython/issues/1503
*
* .CodeMirror span {
* vertical-align: bottom;
* }
*/
.CodeMirror {
line-height: 1.21429em;
/* Changed from 1em to our global default */
font-size: 14px;
height: auto;
/* Changed to auto to autogrow */
background: none;
/* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
/* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
/* We have found that if it is visible, vertical scrollbars appear with font size changes.*/
overflow-y: hidden;
overflow-x: auto;
}
.CodeMirror-lines {
/* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
/* we have set a different line-height and want this to scale with that. */
/* Note that this should set vertical padding only, since CodeMirror assumes
that horizontal padding will be set on CodeMirror pre */
padding: 0.4em 0;
}
.CodeMirror-linenumber {
padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
border-bottom-left-radius: 2px;
border-top-left-radius: 2px;
}
.CodeMirror pre {
/* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
use .CodeMirror-lines for vertical */
padding: 0 0.4em;
border: 0;
border-radius: 0;
}
.CodeMirror-cursor {
border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.CodeMirror-cursor {
border-left: 2px solid black;
}
}
@media screen and (min-width: 4320px) {
.CodeMirror-cursor {
border-left: 4px solid black;
}
}
/*
Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme
*/
.highlight-base {
color: #000;
}
.highlight-variable {
color: #000;
}
.highlight-variable-2 {
color: #1a1a1a;
}
.highlight-variable-3 {
color: #333333;
}
.highlight-string {
color: #BA2121;
}
.highlight-comment {
color: #408080;
font-style: italic;
}
.highlight-number {
color: #080;
}
.highlight-atom {
color: #88F;
}
.highlight-keyword {
color: #008000;
font-weight: bold;
}
.highlight-builtin {
color: #008000;
}
.highlight-error {
color: #f00;
}
.highlight-operator {
color: #AA22FF;
font-weight: bold;
}
.highlight-meta {
color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
color: #00f;
}
.highlight-string-2 {
color: #f50;
}
.highlight-qualifier {
color: #555;
}
.highlight-bracket {
color: #997;
}
.highlight-tag {
color: #170;
}
.highlight-attribute {
color: #00c;
}
.highlight-header {
color: blue;
}
.highlight-quote {
color: #090;
}
.highlight-link {
color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
color: #008000;
font-weight: bold;
}
.cm-s-ipython span.cm-atom {
color: #88F;
}
.cm-s-ipython span.cm-number {
color: #080;
}
.cm-s-ipython span.cm-def {
color: #00f;
}
.cm-s-ipython span.cm-variable {
color: #000;
}
.cm-s-ipython span.cm-operator {
color: #AA22FF;
font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
color: #333333;
}
.cm-s-ipython span.cm-comment {
color: #408080;
font-style: italic;
}
.cm-s-ipython span.cm-string {
color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
color: #f50;
}
.cm-s-ipython span.cm-meta {
color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
color: #555;
}
.cm-s-ipython span.cm-builtin {
color: #008000;
}
.cm-s-ipython span.cm-bracket {
color: #997;
}
.cm-s-ipython span.cm-tag {
color: #170;
}
.cm-s-ipython span.cm-attribute {
color: #00c;
}
.cm-s-ipython span.cm-header {
color: blue;
}
.cm-s-ipython span.cm-quote {
color: #090;
}
.cm-s-ipython span.cm-link {
color: #00c;
}
.cm-s-ipython span.cm-error {
color: #f00;
}
.cm-s-ipython span.cm-tab {
background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
background-position: right;
background-repeat: no-repeat;
}
div.output_wrapper {
/* this position must be relative to enable descendents to be absolute within it */
position: relative;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
/* ideally, this would be max-height, but FF barfs all over that */
height: 24em;
/* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
width: 100%;
overflow: auto;
border-radius: 2px;
-webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
margin: 0px;
padding: 0px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
div.out_prompt_overlay {
height: 100%;
padding: 0px 0.4em;
position: absolute;
border-radius: 2px;
}
div.out_prompt_overlay:hover {
/* use inner shadow to get border that is computed the same on WebKit/FF */
-webkit-box-shadow: inset 0 0 1px #000;
box-shadow: inset 0 0 1px #000;
background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
padding: 0px;
page-break-inside: avoid;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
div.output_area .MathJax_Display {
text-align: left !important;
}
div.output_area .rendered_html table {
margin-left: 0;
margin-right: 0;
}
div.output_area .rendered_html img {
margin-left: 0;
margin-right: 0;
}
div.output_area img,
div.output_area svg {
max-width: 100%;
height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
max-width: none;
}
div.output_area .mglyph > img {
max-width: none;
}
/* This is needed to protect the pre formating from global settings such
as that of bootstrap */
.output {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
@media (max-width: 540px) {
div.output_area {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: vertical;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: vertical;
-moz-box-align: stretch;
display: box;
box-orient: vertical;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: column;
align-items: stretch;
}
}
div.output_area pre {
margin: 0;
padding: 1px 0 1px 0;
border: 0;
vertical-align: baseline;
color: black;
background-color: transparent;
border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
the prompt div. */
div.output_subarea {
overflow-x: auto;
padding: 0.4em;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
output types */
/* all text output has this class: */
div.output_text {
text-align: left;
color: #000;
/* This has to match that of the the CodeMirror class line-height below */
line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
background: #fdd;
/* very light red background for stderr */
}
div.output_latex {
text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
padding: 0;
}
.js-error {
color: darkred;
}
/* raw_input styles */
div.raw_input_container {
line-height: 1.21429em;
padding-top: 5px;
}
pre.raw_input_prompt {
/* nothing needed here. */
}
input.raw_input {
font-family: monospace;
font-size: inherit;
color: inherit;
width: auto;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0em 0.25em;
margin: 0em 0.25em;
}
input.raw_input:focus {
box-shadow: none;
}
p.p-space {
margin-bottom: 10px;
}
div.output_unrecognized {
padding: 5px;
font-weight: bold;
color: red;
}
div.output_unrecognized a {
color: inherit;
text-decoration: none;
}
div.output_unrecognized a:hover {
color: inherit;
text-decoration: none;
}
.rendered_html {
color: #000;
/* any extras will just be numbers: */
}
.rendered_html em {
font-style: italic;
}
.rendered_html strong {
font-weight: bold;
}
.rendered_html u {
text-decoration: underline;
}
.rendered_html :link {
text-decoration: underline;
}
.rendered_html :visited {
text-decoration: underline;
}
.rendered_html h1 {
font-size: 185.7%;
margin: 1.08em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h2 {
font-size: 157.1%;
margin: 1.27em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h3 {
font-size: 128.6%;
margin: 1.55em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h4 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
}
.rendered_html h5 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
font-style: italic;
}
.rendered_html h6 {
font-size: 100%;
margin: 2em 0 0 0;
font-weight: bold;
line-height: 1.0;
font-style: italic;
}
.rendered_html h1:first-child {
margin-top: 0.538em;
}
.rendered_html h2:first-child {
margin-top: 0.636em;
}
.rendered_html h3:first-child {
margin-top: 0.777em;
}
.rendered_html h4:first-child {
margin-top: 1em;
}
.rendered_html h5:first-child {
margin-top: 1em;
}
.rendered_html h6:first-child {
margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
padding-left: 2em;
}
.rendered_html ul {
list-style: disc;
}
.rendered_html ul ul {
list-style: square;
margin-top: 0;
}
.rendered_html ul ul ul {
list-style: circle;
}
.rendered_html ol {
list-style: decimal;
}
.rendered_html ol ol {
list-style: upper-alpha;
margin-top: 0;
}
.rendered_html ol ol ol {
list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
list-style: decimal;
}
.rendered_html * + ul {
margin-top: 1em;
}
.rendered_html * + ol {
margin-top: 1em;
}
.rendered_html hr {
color: black;
background-color: black;
}
.rendered_html pre {
margin: 1em 2em;
padding: 0px;
background-color: #fff;
}
.rendered_html code {
background-color: #eff0f1;
}
.rendered_html p code {
padding: 1px 5px;
}
.rendered_html pre code {
background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
border: 0;
color: #000;
font-size: 100%;
}
.rendered_html blockquote {
margin: 1em 2em;
}
.rendered_html table {
margin-left: auto;
margin-right: auto;
border: none;
border-collapse: collapse;
border-spacing: 0;
color: black;
font-size: 12px;
table-layout: fixed;
}
.rendered_html thead {
border-bottom: 1px solid black;
vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
text-align: right;
vertical-align: middle;
padding: 0.5em 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.rendered_html th {
font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
background: #f5f5f5;
}
.rendered_html tbody tr:hover {
background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
margin-top: 1em;
}
.rendered_html p {
text-align: left;
}
.rendered_html * + p {
margin-top: 1em;
}
.rendered_html img {
display: block;
margin-left: auto;
margin-right: auto;
}
.rendered_html * + img {
margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
max-width: 100%;
height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
max-width: none;
}
.rendered_html .alert {
margin-bottom: initial;
}
.rendered_html * + .alert {
margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
text-align: right;
}
div.text_cell {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
}
@media (max-width: 540px) {
div.text_cell > div.prompt {
display: none;
}
}
div.text_cell_render {
/*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
outline: none;
resize: none;
width: inherit;
border-style: none;
padding: 0.5em 0.5em 0.5em 0.4em;
color: #000;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
a.anchor-link:link {
text-decoration: none;
padding: 0px 20px;
visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
visibility: visible;
}
.text_cell.rendered .input_area {
display: none;
}
.text_cell.rendered .rendered_html {
overflow-x: auto;
overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
max-width: none;
}
.text_cell.unrendered .text_cell_render {
display: none;
}
.text_cell .dropzone .input_area {
border: 2px dashed #bababa;
margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
font-weight: bold;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
font-size: 185.7%;
}
.cm-header-2 {
font-size: 157.1%;
}
.cm-header-3 {
font-size: 128.6%;
}
.cm-header-4 {
font-size: 110%;
}
.cm-header-5 {
font-size: 100%;
font-style: italic;
}
.cm-header-6 {
font-size: 100%;
font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
.notebook_app {
padding-left: 0px;
padding-right: 0px;
}
}
#ipython-main-app {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
height: 100%;
}
div#notebook_panel {
margin: 0px;
padding: 0px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
height: 100%;
}
div#notebook {
font-size: 14px;
line-height: 20px;
overflow-y: hidden;
overflow-x: auto;
width: 100%;
/* This spaces the page away from the edge of the notebook area */
padding-top: 20px;
margin: 0px;
outline: none;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
min-height: 100%;
}
@media not print {
#notebook-container {
padding: 15px;
background-color: #fff;
min-height: 0;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
}
@media print {
#notebook-container {
width: 100%;
}
}
div.ui-widget-content {
border: 1px solid #ababab;
outline: none;
}
pre.dialog {
background-color: #f7f7f7;
border: 1px solid #ddd;
border-radius: 2px;
padding: 0.4em;
padding-left: 2em;
}
p.dialog {
padding: 0.2em;
}
/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems
to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do.
*/
pre,
code,
kbd,
samp {
white-space: pre-wrap;
}
#fonttest {
font-family: monospace;
}
p {
margin-bottom: 0;
}
.end_space {
min-height: 100px;
transition: height .2s ease;
}
.notebook_app > #header {
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
.notebook_app {
background-color: #EEE;
}
}
kbd {
border-style: solid;
border-width: 1px;
box-shadow: none;
margin: 2px;
padding-left: 2px;
padding-right: 2px;
padding-top: 1px;
padding-bottom: 1px;
}
.jupyter-keybindings {
padding: 1px;
line-height: 24px;
border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
margin: 0;
padding: 0;
border: none;
}
.jupyter-keybindings i {
padding: 6px;
}
.well code {
background-color: #ffffff;
border-color: #ababab;
border-width: 1px;
border-style: solid;
padding: 2px;
padding-top: 1px;
padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
border: thin solid #CFCFCF;
border-bottom: none;
background: #EEE;
border-radius: 2px 2px 0px 0px;
width: 100%;
height: 29px;
padding-right: 4px;
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
/* Old browsers */
-webkit-box-pack: end;
-moz-box-pack: end;
box-pack: end;
/* Modern browsers */
justify-content: flex-end;
display: -webkit-flex;
}
@media print {
.celltoolbar {
display: none;
}
}
.ctb_hideshow {
display: none;
vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
border-top-right-radius: 0px;
border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
border: 1px solid #cfcfcf;
}
.celltoolbar {
font-size: 87%;
padding-top: 3px;
}
.celltoolbar select {
display: block;
width: 100%;
height: 32px;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
background-color: #fff;
background-image: none;
border: 1px solid #ccc;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
width: inherit;
font-size: inherit;
height: 22px;
padding: 0px;
display: inline-block;
}
.celltoolbar select:focus {
border-color: #66afe9;
outline: 0;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
color: #999;
opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
color: #999;
}
.celltoolbar select::-ms-expand {
border: 0;
background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
background-color: #eeeeee;
opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
cursor: not-allowed;
}
textarea.celltoolbar select {
height: auto;
}
select.celltoolbar select {
height: 30px;
line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
height: auto;
}
.celltoolbar label {
margin-left: 5px;
margin-right: 5px;
}
.tags_button_container {
width: 100%;
display: flex;
}
.tag-container {
display: flex;
flex-direction: row;
flex-grow: 1;
overflow: hidden;
position: relative;
}
.tag-container > * {
margin: 0 4px;
}
.remove-tag-btn {
margin-left: 4px;
}
.tags-input {
display: flex;
}
.cell-tag:last-child:after {
content: "";
position: absolute;
right: 0;
width: 40px;
height: 100%;
/* Fade to background color of cell toolbar */
background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
display: block;
width: 100%;
height: 32px;
padding: 6px 12px;
font-size: 13px;
line-height: 1.42857143;
color: #555555;
background-color: #fff;
background-image: none;
border: 1px solid #ccc;
border-radius: 2px;
-webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
-webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
-o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
height: 30px;
padding: 5px 10px;
font-size: 12px;
line-height: 1.5;
border-radius: 1px;
box-shadow: none;
width: inherit;
font-size: inherit;
height: 22px;
line-height: 22px;
padding: 0px 4px;
display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
border-color: #66afe9;
outline: 0;
-webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
color: #999;
opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
border: 0;
background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
background-color: #eeeeee;
opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
height: 30px;
line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
height: auto;
}
.cell-tag,
.tags-input button {
padding: 0px 4px;
}
.cell-tag {
background-color: #fff;
white-space: nowrap;
}
.tags-input input[type=text]:focus {
outline: none;
box-shadow: none;
border-color: #ccc;
}
.completions {
position: absolute;
z-index: 110;
overflow: hidden;
border: 1px solid #ababab;
border-radius: 2px;
-webkit-box-shadow: 0px 6px 10px -1px #adadad;
box-shadow: 0px 6px 10px -1px #adadad;
line-height: 1;
}
.completions select {
background: white;
outline: none;
border: none;
padding: 0px;
margin: 0px;
overflow: auto;
font-family: monospace;
font-size: 110%;
color: #000;
width: auto;
}
.completions select option.context {
color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
display: none;
margin-top: -1px;
margin-bottom: -1px;
width: 32px;
height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
float: left !important;
float: left;
}
.modal .modal-body .move-path {
display: flex;
flex-direction: row;
justify-content: space;
align-items: center;
}
.modal .modal-body .move-path .server-root {
padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
flex: 1;
}
#menubar {
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
margin-top: 1px;
}
#menubar .navbar {
border-top: 1px;
border-radius: 0px 0px 2px 2px;
margin-bottom: 0px;
}
#menubar .navbar-toggle {
float: left;
padding-top: 7px;
padding-bottom: 7px;
border: none;
}
#menubar .navbar-collapse {
clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
float: right;
}
[dir="rtl"] #menubar .nav {
padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
float: right;
}
[dir="rtl"] #menubar .navbar-right {
float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
text-align: right;
left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
right: auto;
left: 0;
}
.nav-wrapper {
border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
float: left !important;
float: left;
}
ul#help_menu li a {
overflow: hidden;
padding-right: 2.2em;
}
ul#help_menu li a i {
margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
margin-right: 0;
margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
float: left !important;
float: left;
}
.dropdown-submenu {
position: relative;
}
.dropdown-submenu > .dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
right: 100%;
margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
display: block;
}
.dropdown-submenu > a:after {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
display: block;
content: "\f0da";
float: right;
color: #333333;
margin-top: 2px;
margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
float: left;
content: "\f0d9";
margin-right: 0;
margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
color: #262626;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
left: -100%;
margin-left: 10px;
}
#notification_area {
float: right !important;
float: right;
z-index: 10;
}
[dir="rtl"] #notification_area {
float: left !important;
float: left;
}
.indicator_area {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
}
[dir="rtl"] .indicator_area {
float: left !important;
float: left;
}
#kernel_indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
padding-left: 5px;
padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
float: left !important;
float: left;
border-left: 0;
border-right: 1px solid;
}
#modal_indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
}
[dir="rtl"] #modal_indicator {
float: left !important;
float: left;
}
#readonly-indicator {
float: right !important;
float: right;
color: #777;
margin-left: 5px;
margin-right: 5px;
width: 11px;
z-index: 10;
text-align: center;
width: auto;
margin-top: 2px;
margin-bottom: 0px;
margin-left: 0px;
margin-right: 0px;
display: none;
}
.modal_indicator:before {
width: 1.28571429em;
text-align: center;
}
.edit_mode .modal_indicator:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
margin-left: .3em;
}
.command_mode .modal_indicator:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
margin-left: .3em;
}
.kernel_idle_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
margin-left: .3em;
}
.kernel_busy_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
margin-left: .3em;
}
.kernel_dead_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
margin-left: .3em;
}
.kernel_disconnected_icon:before {
display: inline-block;
font: normal normal normal 14px/1 FontAwesome;
font-size: inherit;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
margin-left: .3em;
}
.notification_widget {
color: #777;
z-index: 10;
background: rgba(240, 240, 240, 0.5);
margin-right: 4px;
color: #333;
background-color: #fff;
border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
color: #333;
background-color: #e6e6e6;
border-color: #8c8c8c;
}
.notification_widget:hover {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
color: #333;
background-color: #e6e6e6;
border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
color: #333;
background-color: #d4d4d4;
border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
background-color: #fff;
border-color: #ccc;
}
.notification_widget .badge {
color: #fff;
background-color: #333;
}
.notification_widget.warning {
color: #fff;
background-color: #f0ad4e;
border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
color: #fff;
background-color: #ec971f;
border-color: #985f0d;
}
.notification_widget.warning:hover {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
color: #fff;
background-color: #ec971f;
border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
color: #fff;
background-color: #d58512;
border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
background-color: #f0ad4e;
border-color: #eea236;
}
.notification_widget.warning .badge {
color: #f0ad4e;
background-color: #fff;
}
.notification_widget.success {
color: #fff;
background-color: #5cb85c;
border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
color: #fff;
background-color: #449d44;
border-color: #255625;
}
.notification_widget.success:hover {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
color: #fff;
background-color: #449d44;
border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
color: #fff;
background-color: #398439;
border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
background-color: #5cb85c;
border-color: #4cae4c;
}
.notification_widget.success .badge {
color: #5cb85c;
background-color: #fff;
}
.notification_widget.info {
color: #fff;
background-color: #5bc0de;
border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
color: #fff;
background-color: #31b0d5;
border-color: #1b6d85;
}
.notification_widget.info:hover {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
color: #fff;
background-color: #31b0d5;
border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
color: #fff;
background-color: #269abc;
border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
background-color: #5bc0de;
border-color: #46b8da;
}
.notification_widget.info .badge {
color: #5bc0de;
background-color: #fff;
}
.notification_widget.danger {
color: #fff;
background-color: #d9534f;
border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
color: #fff;
background-color: #c9302c;
border-color: #761c19;
}
.notification_widget.danger:hover {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
color: #fff;
background-color: #c9302c;
border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
color: #fff;
background-color: #ac2925;
border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
background-color: #d9534f;
border-color: #d43f3a;
}
.notification_widget.danger .badge {
color: #d9534f;
background-color: #fff;
}
div#pager {
background-color: #fff;
font-size: 14px;
line-height: 20px;
overflow: hidden;
display: none;
position: fixed;
bottom: 0px;
width: 100%;
max-height: 50%;
padding-top: 8px;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
/* Display over codemirror */
z-index: 100;
/* Hack which prevents jquery ui resizable from changing top. */
top: auto !important;
}
div#pager pre {
line-height: 1.21429em;
color: #000;
background-color: #f7f7f7;
padding: 0.4em;
}
div#pager #pager-button-area {
position: absolute;
top: 8px;
right: 20px;
}
div#pager #pager-contents {
position: relative;
overflow: auto;
width: 100%;
height: 100%;
}
div#pager #pager-contents #pager-container {
position: relative;
padding: 15px 0px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
top: 0px;
height: 8px;
background: #f7f7f7;
border-top: 1px solid #cfcfcf;
border-bottom: 1px solid #cfcfcf;
/* This injects handle bars (a short, wide = symbol) for
the resize handle. */
}
div#pager .ui-resizable-handle::after {
content: '';
top: 2px;
left: 50%;
height: 3px;
width: 30px;
margin-left: -15px;
position: absolute;
border-top: 1px solid #cfcfcf;
}
.quickhelp {
/* Old browsers */
display: -webkit-box;
-webkit-box-orient: horizontal;
-webkit-box-align: stretch;
display: -moz-box;
-moz-box-orient: horizontal;
-moz-box-align: stretch;
display: box;
box-orient: horizontal;
box-align: stretch;
/* Modern browsers */
display: flex;
flex-direction: row;
align-items: stretch;
line-height: 1.8em;
}
.shortcut_key {
display: inline-block;
width: 21ex;
text-align: right;
font-family: monospace;
}
.shortcut_descr {
display: inline-block;
/* Old browsers */
-webkit-box-flex: 1;
-moz-box-flex: 1;
box-flex: 1;
/* Modern browsers */
flex: 1;
}
span.save_widget {
height: 30px;
margin-top: 4px;
display: flex;
justify-content: flex-start;
align-items: baseline;
width: 50%;
flex: 1;
}
span.save_widget span.filename {
height: 100%;
line-height: 1em;
margin-left: 16px;
border: none;
font-size: 146.5%;
text-overflow: ellipsis;
overflow: hidden;
white-space: nowrap;
border-radius: 2px;
}
span.save_widget span.filename:hover {
background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
float: right !important;
float: right;
}
[dir="rtl"] span.save_widget span.filename {
margin-left: 0;
margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
font-size: small;
white-space: nowrap;
padding: 0 5px;
}
@media (max-width: 767px) {
span.save_widget {
font-size: small;
padding: 0 0 0 5px;
}
span.checkpoint_status,
span.autosave_status {
display: none;
}
}
@media (min-width: 768px) and (max-width: 991px) {
span.checkpoint_status {
display: none;
}
span.autosave_status {
font-size: x-small;
}
}
.toolbar {
padding: 0px;
margin-left: -5px;
margin-top: 2px;
margin-bottom: 5px;
box-sizing: border-box;
-moz-box-sizing: border-box;
-webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
width: auto;
vertical-align: middle;
margin-right: 2px;
margin-bottom: 0px;
display: inline;
font-size: 92%;
margin-left: 0.3em;
margin-right: 0.3em;
padding: 0px;
padding-top: 3px;
}
.toolbar .btn {
padding: 2px 8px;
}
.toolbar .btn-group {
margin-top: 0px;
margin-left: 5px;
}
.toolbar-btn-label {
margin-left: 6px;
}
#maintoolbar {
margin-bottom: -3px;
margin-top: -8px;
border: 0px;
min-height: 27px;
margin-left: 0px;
padding-top: 11px;
padding-bottom: 3px;
}
#maintoolbar .navbar-text {
float: none;
vertical-align: middle;
text-align: right;
margin-left: 5px;
margin-right: 0px;
margin-top: 0px;
}
.select-xs {
height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
background-color: #F37626;
color: white;
}
/**
* Primary styles
*
* Author: Jupyter Development Team
*/
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
* of chance of beeing generated from the ../less/[samename].less file, you can
* try to get back the less file by reverting somme commit in history
**/
/*
* We'll try to get something pretty, so we
* have some strange css to have the scroll bar on
* the left with fix button on the top right of the tooltip
*/
@-moz-keyframes fadeOut {
from {
opacity: 1;
}
to {
opacity: 0;
}
}
@-webkit-keyframes fadeOut {
from {
opacity: 1;
}
to {
opacity: 0;
}
}
@-moz-keyframes fadeIn {
from {
opacity: 0;
}
to {
opacity: 1;
}
}
@-webkit-keyframes fadeIn {
from {
opacity: 0;
}
to {
opacity: 1;
}
}
/*properties of tooltip after "expand"*/
.bigtooltip {
overflow: auto;
height: 200px;
-webkit-transition-property: height;
-webkit-transition-duration: 500ms;
-moz-transition-property: height;
-moz-transition-duration: 500ms;
transition-property: height;
transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
-webkit-transition-property: height;
-webkit-transition-duration: 500ms;
-moz-transition-property: height;
-moz-transition-duration: 500ms;
transition-property: height;
transition-duration: 500ms;
text-overflow: ellipsis;
overflow: hidden;
height: 80px;
}
.tooltipbuttons {
position: absolute;
padding-right: 15px;
top: 0px;
right: 0px;
}
.tooltiptext {
/*avoid the button to overlap on some docstring*/
padding-right: 30px;
}
.ipython_tooltip {
max-width: 700px;
/*fade-in animation when inserted*/
-webkit-animation: fadeOut 400ms;
-moz-animation: fadeOut 400ms;
animation: fadeOut 400ms;
-webkit-animation: fadeIn 400ms;
-moz-animation: fadeIn 400ms;
animation: fadeIn 400ms;
vertical-align: middle;
background-color: #f7f7f7;
overflow: visible;
border: #ababab 1px solid;
outline: none;
padding: 3px;
margin: 0px;
padding-left: 7px;
font-family: monospace;
min-height: 50px;
-moz-box-shadow: 0px 6px 10px -1px #adadad;
-webkit-box-shadow: 0px 6px 10px -1px #adadad;
box-shadow: 0px 6px 10px -1px #adadad;
border-radius: 2px;
position: absolute;
z-index: 1000;
}
.ipython_tooltip a {
float: right;
}
.ipython_tooltip .tooltiptext pre {
border: 0;
border-radius: 0;
font-size: 100%;
background-color: #f7f7f7;
}
.pretooltiparrow {
left: 0px;
margin: 0px;
top: -16px;
width: 40px;
height: 16px;
overflow: hidden;
position: absolute;
}
.pretooltiparrow:before {
background-color: #f7f7f7;
border: 1px #ababab solid;
z-index: 11;
content: "";
position: absolute;
left: 15px;
top: 10px;
width: 25px;
height: 25px;
-webkit-transform: rotate(45deg);
-moz-transform: rotate(45deg);
-ms-transform: rotate(45deg);
-o-transform: rotate(45deg);
}
ul.typeahead-list i {
margin-left: -10px;
width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
margin-left: 0;
margin-right: -10px;
}
ul.typeahead-list {
max-height: 80vh;
overflow: auto;
}
ul.typeahead-list > li > a {
/** Firefox bug **/
/* see https://github.com/jupyter/notebook/issues/559 */
white-space: normal;
}
ul.typeahead-list > li > a.pull-right {
float: left !important;
float: left;
}
[dir="rtl"] .typeahead-list {
text-align: right;
}
.cmd-palette .modal-body {
padding: 7px;
}
.cmd-palette form {
background: white;
}
.cmd-palette input {
outline: none;
}
.no-shortcut {
min-width: 20px;
color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
float: left !important;
float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
float: left !important;
float: left;
}
.command-shortcut:before {
content: "(command mode)";
padding-right: 3px;
color: #777777;
}
.edit-shortcut:before {
content: "(edit)";
padding-right: 3px;
color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
float: left !important;
float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
background-color: #BBDEFB;
border-color: #90CAF9;
border-style: solid;
border-width: 1px;
border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
border-right: none;
}
#find-and-replace #replace-preview .replace .match {
background-color: #FFCDD2;
border-color: #EF9A9A;
border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
background-color: #C8E6C9;
border-color: #A5D6A7;
border-radius: 0px;
}
#find-and-replace #replace-preview {
max-height: 60vh;
overflow: auto;
}
#find-and-replace #replace-preview pre {
padding: 5px 10px;
}
.terminal-app {
background: #EEE;
}
.terminal-app #header {
background: #fff;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
width: 100%;
float: left;
font-family: monospace;
color: white;
background: black;
padding: 0.4em;
border-radius: 2px;
-webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
line-height: 1em;
font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
padding: 10px;
}
.terminal-app .terminal-cursor {
color: black;
background: white;
}
.terminal-app #terminado-container {
margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
</style>
<style type="text/css">
.highlight .hll { background-color: #ffffcc }
.highlight { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
</style>
<style type="text/css">
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }
.ansi-bold { font-weight: bold; }
</style>
<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
overflow: visible;
padding: 8px;
}
div#notebook {
overflow: visible;
border-top: none;
}@media print {
div.cell {
display: block;
page-break-inside: avoid;
}
div.output_wrapper {
display: block;
page-break-inside: avoid;
}
div.output {
display: block;
page-break-inside: avoid;
}
}
</style>
<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">
<!-- Loading mathjax macro -->
<!-- Load mathjax -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
<!-- MathJax configuration -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ['$','$'], ["\\(","\\)"] ],
displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
processEscapes: true,
processEnvironments: true
},
// Center justify equations in code and markdown cells. Elsewhere
// we use CSS to left justify single line equations in code cells.
displayAlign: 'center',
"HTML-CSS": {
styles: {'.MathJax_Display': {"margin": 0}},
linebreaks: { automatic: true }
}
});
</script>
<!-- End of mathjax configuration --></head>
<body>
<div tabindex="-1" id="notebook" class="border-box-sizing">
<div class="container" id="notebook-container">
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Predicting-Credit--Default">Predicting Credit Default<a class="anchor-link" href="#Predicting-Credit--Default">¶</a></h1><p>Varsha Waingankar</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Overview">Overview<a class="anchor-link" href="#Overview">¶</a></h1><h1 id="Data-source">Data source<a class="anchor-link" href="#Data-source">¶</a></h1><h1 id="Data-cleaning-and-preprocessing">Data cleaning and preprocessing<a class="anchor-link" href="#Data-cleaning-and-preprocessing">¶</a></h1><h1 id="Visualization-of-Data-distribution">Visualization of Data distribution<a class="anchor-link" href="#Visualization-of-Data-distribution">¶</a></h1><h1 id="Scaling-normalizing-data">Scaling normalizing data<a class="anchor-link" href="#Scaling-normalizing-data">¶</a></h1><h1 id="Handling-imbalanced-data">Handling imbalanced data<a class="anchor-link" href="#Handling-imbalanced-data">¶</a></h1><h1 id="Feature-engineering">Feature engineering<a class="anchor-link" href="#Feature-engineering">¶</a></h1><h1 id="Predictive-modeling">Predictive modeling<a class="anchor-link" href="#Predictive-modeling">¶</a></h1><h1 id="Accuracy-and-best-model">Accuracy and best model<a class="anchor-link" href="#Accuracy-and-best-model">¶</a></h1><h1 id="Learning-Process">Learning Process<a class="anchor-link" href="#Learning-Process">¶</a></h1><h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Data-source-UCI-Machine-Learning-Repository">Data source UCI Machine Learning Repository<a class="anchor-link" href="#Data-source-UCI-Machine-Learning-Repository">¶</a></h1><p><a href="https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients">https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients</a></p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Abstract:">Abstract:<a class="anchor-link" href="#Abstract:">¶</a></h1><p>This research aimed at the case of customers default payments in Taiwan and compares the predictive accuracy of probability of default using various methods</p>
<p>There are 25 variables and each indicated below</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [162]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># There are 25 variables:</span>
<span class="c1">#ID: ID of each client</span>
<span class="c1">#LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit</span>
<span class="c1">#SEX: Gender (1=male, 2=female)</span>
<span class="c1">#EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)</span>
<span class="c1">#MARRIAGE: Marital status (1=married, 2=single, 3=others)</span>
<span class="c1">#AGE: Age in years</span>
<span class="c1">#PAY_0: Repayment status in September, 2005 </span>
<span class="c1">#(-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, ... 8=payment delay for eight months, 9=payment delay for nine months and above)</span>
<span class="c1">#PAY_2: Repayment status in August, 2005 (scale same as above)</span>
<span class="c1">#PAY_3: Repayment status in July, 2005 (scale same as above)</span>
<span class="c1">#PAY_4: Repayment status in June, 2005 (scale same as above)</span>
<span class="c1">#PAY_5: Repayment status in May, 2005 (scale same as above)</span>
<span class="c1">#PAY_6: Repayment status in April, 2005 (scale same as above)</span>
<span class="c1">#BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)</span>
<span class="c1">#BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)</span>
<span class="c1">#BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)</span>
<span class="c1">#BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)</span>
<span class="c1">#BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)</span>
<span class="c1">#BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)</span>
<span class="c1">#PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)</span>
<span class="c1">#default.payment.next.month: Default payment (1=yes, 0=no)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [210]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#importing all the necessary packages</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">'ignore'</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">imblearn.pipeline</span> <span class="k">import</span> <span class="n">make_pipeline</span> <span class="k">as</span> <span class="n">make_pipeline_imb</span> <span class="c1"># To do our transformation in a unique time</span>
<span class="kn">from</span> <span class="nn">imblearn.over_sampling</span> <span class="k">import</span> <span class="n">SMOTE</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="k">import</span> <span class="n">make_pipeline</span>
<span class="kn">from</span> <span class="nn">imblearn.metrics</span> <span class="k">import</span> <span class="n">classification_report_imbalanced</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">roc_curve</span><span class="p">,</span> <span class="n">auc</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">OneHotEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">LabelEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="k">import</span> <span class="n">DecisionTreeClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">fbeta_score</span><span class="p">,</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">precision_recall_curve</span><span class="p">,</span> <span class="n">accuracy_score</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [211]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Reading the data using pandas</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s2">"default of credit card clients.xls"</span><span class="p">)</span>
<span class="c1">#default of credit card clients.xls</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [212]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[212]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>ID</th>
<th>LIMIT_BAL</th>
<th>SEX</th>
<th>EDUCATION</th>
<th>MARRIAGE</th>
<th>AGE</th>
<th>PAY_0</th>
<th>PAY_2</th>
<th>PAY_3</th>
<th>PAY_4</th>
<th>...</th>
<th>BILL_AMT4</th>
<th>BILL_AMT5</th>
<th>BILL_AMT6</th>
<th>PAY_AMT1</th>
<th>PAY_AMT2</th>
<th>PAY_AMT3</th>
<th>PAY_AMT4</th>
<th>PAY_AMT5</th>
<th>PAY_AMT6</th>
<th>default payment next month</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>20000</td>
<td>2</td>
<td>2</td>
<td>1</td>
<td>24</td>
<td>2</td>
<td>2</td>
<td>-1</td>
<td>-1</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>689</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>120000</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>26</td>
<td>-1</td>
<td>2</td>
<td>0</td>
<td>0</td>
<td>...</td>
<td>3272</td>
<td>3455</td>
<td>3261</td>
<td>0</td>
<td>1000</td>
<td>1000</td>
<td>1000</td>
<td>0</td>
<td>2000</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>90000</td>
<td>2</td>
<td>2</td>
<td>2</td>
<td>34</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>...</td>
<td>14331</td>
<td>14948</td>
<td>15549</td>
<td>1518</td>
<td>1500</td>
<td>1000</td>
<td>1000</td>
<td>1000</td>
<td>5000</td>
<td>0</td>
</tr>
<tr>
<th>3</th>
<td>4</td>
<td>50000</td>
<td>2</td>
<td>2</td>
<td>1</td>
<td>37</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>...</td>
<td>28314</td>
<td>28959</td>
<td>29547</td>
<td>2000</td>
<td>2019</td>
<td>1200</td>
<td>1100</td>
<td>1069</td>
<td>1000</td>
<td>0</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>50000</td>
<td>1</td>
<td>2</td>
<td>1</td>
<td>57</td>
<td>-1</td>
<td>0</td>
<td>-1</td>
<td>0</td>
<td>...</td>
<td>20940</td>
<td>19146</td>
<td>19131</td>
<td>2000</td>
<td>36681</td>
<td>10000</td>
<td>9000</td>
<td>689</td>
<td>679</td>
<td>0</td>
</tr>
</tbody>
</table>
<p>5 rows × 25 columns</p>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [213]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Replacing the column name for convenience</span>
<span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"default payment next month"</span><span class="p">:</span> <span class="s2">"default"</span><span class="p">},</span> <span class="n">inplace</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [214]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking the columns</span>
<span class="n">df</span><span class="o">.</span><span class="n">columns</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[214]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0',
'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',
'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',
'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'default'],
dtype='object')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [215]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[215]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>0 23364
1 6636
Name: default, dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Distribution-of-Defaulted-Credit-cards-vs-Non--defaulted">Distribution of Defaulted Credit cards vs Non- defaulted<a class="anchor-link" href="#Distribution-of-Defaulted-Credit-cards-vs-Non--defaulted">¶</a></h1><p>Data is highly imbalanced with a ratio of about 78 :22 percent</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [216]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="s1">'No default- 0'</span><span class="p">,</span><span class="s1">'Default - 1'</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'gold'</span><span class="p">,</span> <span class="s1">'yellowgreen'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'default'</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">(),</span><span class="n">labels</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span><span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">shadow</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">140</span><span class="p">)</span>
<span class="c1">#plt.axis('equal')</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [217]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Checking for null values</span>
<span class="c1">#function to do it</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="p">:</span>
<span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"nan values present"</span> <span class="o">+</span><span class="n">c</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> No null values"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">c</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>ID No null values
LIMIT_BAL No null values
SEX No null values
EDUCATION No null values
MARRIAGE No null values
AGE No null values
PAY_0 No null values
PAY_2 No null values
PAY_3 No null values
PAY_4 No null values
PAY_5 No null values
PAY_6 No null values
BILL_AMT1 No null values
BILL_AMT2 No null values
BILL_AMT3 No null values
BILL_AMT4 No null values
BILL_AMT5 No null values
BILL_AMT6 No null values
PAY_AMT1 No null values
PAY_AMT2 No null values
PAY_AMT3 No null values
PAY_AMT4 No null values
PAY_AMT5 No null values
PAY_AMT6 No null values
default No null values
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Data-Manipulation:">Data Manipulation:<a class="anchor-link" href="#Data-Manipulation:">¶</a></h1><p>Reduced unknown values to category 4 (Education)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [218]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[218]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>array([2, 1, 3, 5, 4, 6, 0])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [219]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Change values for education (1 = graduate school; 2 = university; 3 = high school; 4 = others)</span>
<span class="c1">#Anything other than 4 will be changed to 4</span>
<span class="n">fil</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">5</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">6</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">]</span><span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">fil</span><span class="p">,</span> <span class="s1">'EDUCATION'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[219]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>2 14030
1 10585
3 4917
4 468
Name: EDUCATION, dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [220]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'MARRIAGE'</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[220]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>array([1, 2, 3, 0])</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Data-Manipulation:">Data Manipulation:<a class="anchor-link" href="#Data-Manipulation:">¶</a></h1><p>Reduced unknown values to category 3 (Marital Status)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [221]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'MARRIAGE'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'MARRIAGE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'MARRIAGE'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[221]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>2 15964
1 13659
3 377
Name: MARRIAGE, dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [222]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'LIMIT_BAL'</span><span class="p">],</span><span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[222]:</div>
<div class="output_text output_subarea output_execute_result">
<pre><matplotlib.axes._subplots.AxesSubplot at 0x1a25b4e198></pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [223]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'AGE'</span><span class="p">],</span><span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[223]:</div>
<div class="output_text output_subarea output_execute_result">
<pre><matplotlib.axes._subplots.AxesSubplot at 0x1a152f06a0></pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [176]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. </span>
<span class="c1">#X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. </span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Correlation-matrix-of-all-variables">Correlation matrix of all variables<a class="anchor-link" href="#Correlation-matrix-of-all-variables">¶</a></h1><p>The target variable "default" is correlated to "Repayment status"
Which indicates that Repayment status is the best feature interms of predicting default</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [224]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">corr</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">'ID'</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
<span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="c1"># Generate a custom diverging colormap</span>
<span class="n">cmap</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">diverging_palette</span><span class="p">(</span><span class="mi">220</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">as_cmap</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Draw the heatmap with the mask and correct aspect ratio</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">corr</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=.</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[224]:</div>
<div class="output_text output_subarea output_execute_result">
<pre><matplotlib.axes._subplots.AxesSubplot at 0x1a15679630></pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Distribution-of-all-variables">Distribution of all variables<a class="anchor-link" href="#Distribution-of-all-variables">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [228]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">continuous_var</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">columns</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="n">continuous_var</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Applying-the-MinMax-Scaler:">Applying the MinMax Scaler:<a class="anchor-link" href="#Applying-the-MinMax-Scaler:">¶</a></h1><p>Transformation to normalize values ( If the distribution is not Gaussian or the standard deviation is very small, the min-max scaler works better.)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [229]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">minmax_scale</span> <span class="o">=</span> <span class="n">preprocessing</span><span class="o">.</span><span class="n">MinMaxScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df_minmax</span> <span class="o">=</span> <span class="n">minmax_scale</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df_minmax</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df_minmax</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
<span class="n">df_minmax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [230]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Checking if gender has any association with the Limit Balance.</span>
<span class="c1">#Equally distributed , no such relationship</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'SEX'</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">'LIMIT_BAL'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[230]:</div>
<div class="output_text output_subarea output_execute_result">
<pre><matplotlib.axes._subplots.AxesSubplot at 0x1a28729780></pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [231]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Education also doesnt provide insights if a person will default or not</span>
<span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'EDUCATION'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'default'</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Frequency for educational qualification'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Education'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Frequency of default'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"1 : graduate school; 2 : university; 3 : high school; 4 : others"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>1 : graduate school; 2 : university; 3 : high school; 4 : others
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [182]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Marital status also doesn't provide insights if a person will default or not</span>
<span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'MARRIAGE'</span><span class="p">],</span><span class="n">df</span><span class="p">[</span><span class="s1">'default'</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Frequency of marital status'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Marital Status'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Frequency of default'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"1 : married; 2 : single; 3 : others "</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>1 : married; 2 : single; 3 : others
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [183]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#VALUES AFTER APPLYING MINMAX SCALER</span>
<span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'MARRIAGE'</span><span class="p">],</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'default'</span><span class="p">])</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">'bar'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Frequency of marital status'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Marital Status'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Frequency of default'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1">#print("1 : married; 2 : single; 3 : others ")</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Distribution-of-values-for-each-field">Distribution of values for each field<a class="anchor-link" href="#Distribution-of-values-for-each-field">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [184]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'EDUCATION'</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">'LIMIT_BAL'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Education level and amount of limit balance"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"1 : graduate school; 2 : university; 3 : high school; 4 : others"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>1 : graduate school; 2 : university; 3 : high school; 4 : others
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [185]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">temp</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'default'</span><span class="p">:</span> <span class="n">temp</span><span class="o">.</span><span class="n">index</span><span class="p">,</span><span class="s1">'values'</span><span class="p">:</span> <span class="n">temp</span><span class="o">.</span><span class="n">values</span><span class="p">})</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Default Credit Card Clients - target value - data unbalance</span><span class="se">\n</span><span class="s1"> (Default = 1, Not Default = 0)'</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="s1">'default'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">"values"</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df1</span><span class="p">)</span>
<span class="n">locs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Running-the-model-on-imbalanced-data">Running the model on imbalanced data<a class="anchor-link" href="#Running-the-model-on-imbalanced-data">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [240]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Perform oversampling to balance the data</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"default"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="c1">#Setting the X to do the split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># transforming the values in array</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
<span class="c1"># Separate majority and minority classes</span>
<span class="n">df_majority</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span>
<span class="n">df_minority</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_majority</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"-----------"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_minority</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"-----------"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>23364
-----------
6636
-----------
0 23364
1 6636
Name: default, dtype: int64
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [242]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">cross_val_score</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">linear_model</span>
<span class="n">logreg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">1e5</span><span class="p">)</span>
<span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"accuaracy of model"</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>accuaracy of model
81.01666666666667
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Conclusion-of-running-model-on-imbalanced-data:">Conclusion of running model on imbalanced data:<a class="anchor-link" href="#Conclusion-of-running-model-on-imbalanced-data:">¶</a></h1><p>Since distribution is 78:22 ratio, so running a model yeilds an 80 percent accuarcy. So it makes no sense to run a model on imbalanced data. Even random guess will give this result.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Since-Data-is-highly-imbalanced-:">Since Data is highly imbalanced :<a class="anchor-link" href="#Since-Data-is-highly-imbalanced-:">¶</a></h1><p>Random Oversampling of minority class is performed, to get equal proportion of both classes. Random oversampling just replicates the existing minority class data points.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [243]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">resample</span>
<span class="c1"># Upsample minority class</span>
<span class="n">df_minority_oversampling</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">df_minority</span><span class="p">,</span>
<span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># sample with replacement</span>
<span class="n">n_samples</span><span class="o">=</span><span class="mi">22677</span><span class="p">,</span> <span class="c1"># to match majority class</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">587</span><span class="p">)</span> <span class="c1"># reproducible results</span>
<span class="c1"># Combine majority class with upsampled minority class</span>
<span class="n">df_oversample</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_majority</span><span class="p">,</span> <span class="n">df_minority_oversampling</span><span class="p">])</span>
<span class="c1"># Display new class counts</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Now the distribution of non default and default are almost close"</span><span class="p">)</span>
<span class="n">df_oversample</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Now the distribution of non default and default are almost close
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt output_prompt">Out[243]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>0.0 23364
1.0 22677
Name: default, dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Splitting-into-train-and-test-80-percent-train,-20-percent-test">Splitting into train and test 80 percent train, 20 percent test<a class="anchor-link" href="#Splitting-into-train-and-test-80-percent-train,-20-percent-test">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [244]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#using the new data frame - oversampled dataframe --- oversampling of minority class</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_oversample</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"default"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="c1">#Setting the X to do the split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_oversample</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># transforming the values in array</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Using-logistic-regression">Using logistic regression<a class="anchor-link" href="#Using-logistic-regression">¶</a></h1><h1 id="Most-widely-used-for-Binary-classification-problem.The-sigmoid-function-snaps-values-to-0-and-1,-and-we-predict-a-class-value.">Most widely used for Binary classification problem.The sigmoid function snaps values to 0 and 1, and we predict a class value.<a class="anchor-link" href="#Most-widely-used-for-Binary-classification-problem.The-sigmoid-function-snaps-values-to-0-and-1,-and-we-predict-a-class-value.">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [245]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create dictionary for storing values of all models</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="c1">#Run the logistic Regression model</span>
<span class="c1">#import the linear_model class from sklearn package</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">linear_model</span>
<span class="c1">#create an object of the class, logreg is the object of class LogisticRegression</span>
<span class="n">logreg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">1e5</span><span class="p">)</span>
<span class="c1">#call object.fir on (X_train----Set of predictors, Y_train ------target variable. 80 percent is used for training)</span>
<span class="n">logreg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1">#Model learns from training process</span>
<span class="c1">#After training the model -- predict the the class for rest of 20 percent of data</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">]</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1">#after predicting we check for the accuracy</span>
<span class="c1">#Accuracy is defined as comparison between the actual class of target variable from the test data vs predicted</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"accuaracy of model"</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">])</span>
<span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="c1">#Print the confusion matrix</span>
<span class="c1">#Confusion matrix is classifying Actual and predicted</span>
<span class="c1">#False negative ---Predicted as negative but actually positive</span>
<span class="c1">#True Positive ----Predicted as positive and actually positive</span>
<span class="c1">#True Negative ---- Predicted as negative and actually negative</span>
<span class="c1">#False Positive----Predicted as positive but actually negative</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">])</span>
<span class="c1">#print(confusion_matrix) </span>
<span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">average_precision_score</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'Logistic'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>accuaracy of model
67.83581279183407
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.62
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Using-K-Nearest-Neighbors">Using K Nearest Neighbors<a class="anchor-link" href="#Using-K-Nearest-Neighbors">¶</a></h1><p>For a data point to be classified into two different categories, We find the k nearest neighbors (k is any odd value)
Then we use majority voting on the labels. The majority class label is assigned to the data point
If k is even then distance is calculated. The shorted distance is used</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [246]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KNeighborsClassifier</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">]</span><span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"accuaracy of model"</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="c1">#print(confusion_matrix) </span>
<span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>accuaracy of model
75.93658377674014
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.69
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Using-Decision-Tree-Classifier">Using Decision Tree Classifier<a class="anchor-link" href="#Using-Decision-Tree-Classifier">¶</a></h1><p>Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled
if it was randomly labeled according to the distribution of labels in the subset.</p>
<p>DTC will segregate the data points based on all values of variables and identify the variable,
which creates the best homogeneous sets of data points (which are heterogeneous to each other)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [247]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Calling the Decision TRee Classifier class</span>
<span class="n">clf_gini</span> <span class="o">=</span> <span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">criterion</span> <span class="o">=</span> <span class="s2">"gini"</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
<span class="n">max_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">min_samples_leaf</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">clf_gini</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf_gini</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"accuracy of the model"</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">])</span>
<span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">])</span>
<span class="c1">#print(confusion_matrix) </span>
<span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'DecisionTree'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>accuracy of the model
69.26919318058421
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.64
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Picture-of-the-tree">Picture of the tree<a class="anchor-link" href="#Picture-of-the-tree">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [248]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.externals.six</span> <span class="k">import</span> <span class="n">StringIO</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="k">import</span> <span class="n">export_graphviz</span>
<span class="kn">import</span> <span class="nn">pydotplus</span>
<span class="n">dot_data</span> <span class="o">=</span> <span class="n">StringIO</span><span class="p">()</span>
<span class="n">export_graphviz</span><span class="p">(</span><span class="n">clf_gini</span><span class="p">,</span> <span class="n">out_file</span><span class="o">=</span><span class="n">dot_data</span><span class="p">,</span>
<span class="n">filled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rounded</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">special_characters</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">graph</span> <span class="o">=</span> <span class="n">pydotplus</span><span class="o">.</span><span class="n">graph_from_dot_data</span><span class="p">(</span><span class="n">dot_data</span><span class="o">.</span><span class="n">getvalue</span><span class="p">())</span>
<span class="n">Image</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">create_png</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[248]:</div>
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABlYAAAHdCAYAAABv40QbAAAAAXNSR0IArs4c6QAAQABJREFUeAHs3QWcVUX/x/EvDQLSSEo30o10w9KSUgqoIAiIhLSkiC0q3RLSrbSC0iJIdypKLN3xvzM89/632YWtu/czr9flnjtnzpyZ99l9Xj73tzO/aI8dRRQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGnCRyI/rQWnEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHgiQGCFnwQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJgCBFaCCUUzBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAmBAgggAACCCCAAAIIIBA2Avv379fvv/8u837x4kXduXMnbG5ErwiEs0C0aNGUIEECpU6dWvny5VOFChWUIkWKcB4Ft0MAAQQQQAABBBBAIGIEopG8PmLguSsCCCCAAAIIIIBA1BS4dOmSxo0bp4kTJujY8eNKED++cuXOpZQpUipevHhRc9LMyuMEHP8/UtevX9eZs2d1+PBhPXr0SGXLltXbb7+txo0bK0aMGB5nwoQRQAABBBBAAAEEPEbgAIEVj3nWTBQBBBBAAAEEEEAgLAXu3bunUaNGaeTIkYoZM6Zat2qpxq+9pmLFiip6dHbgDUt7+o5YgZs3b2r1mjWaPmOmli5dqsxZsuizzz6Tl5dXxA6MuyOAAAIIIIAAAgggEDYCBFbCxpVeEUAAAQQQQAABBDxJYO/evWratKlOnDih3j17qFvXLorvWKlCQcDTBI4dO6Z+AwZqzo9z1bx5c40ZM0YJEyb0NAbmiwACCCCAAAIIIBC1BQisRO3ny+wQQAABBBBAAAEEwlpgwYIFatmihQoVLqSpkyYqU6ZMYX1L+kcg0gus/OkntXmznZIlS6YVK1bwexHpnxgDRAABBBBAAAEEEAiBwAH2JAiBFk0RQAABBBBAAAEEEPApMGXKFL3m2O6rdetWWrd6FV8e+8Th2KMFalSvrl07tjnyCsVV6VKldPDgQY/2YPIIIIAAAggggAACUUuAHCtR63kyGwQQQAABBBBAAIFwEli4cKENqvTu1VNDB38UTneNHLdZs3atTp06rVw5c6pUqZIBDurHufNscvNqVasoXbp0vtqYxOdXrlxRkiRJfNWH1weTaP1Z897cunVLceLEITl7MB+WSXBfvZaXzpw+o81btvj7WQhmNzRDAAEEEEAAAQQQQCAyCbBiJTI9DcaCAAIIIIAAAggg4B4C+/fvVwvH9l/t27X1uKCKeUIJEyTU2x06qmbtOjp37py/hzbfsT1a0+ava+z48UqVKpXrvLe3t9q//Y4SJEqiZClT6cUkydSkWXNdvHjR1SasDg4fPqwu3d5XxizZ7L296tTT2nXrgnU7E4jp06+/8uYvoISJk+qFhIlUoHAR/bxqla/rTbv8hQorT74C/l61atf11dZTPpj8KsuXLFbCF19Uw4YNde/ePU+ZOvNEAAEEEEAAAQQQiMICrFiJwg+XqSGAAAIIIIAAAgiEvsCDBw9UpEgRvfBCPG1Yu0axYsUK/Zu4QY/9Bw7SsOEjVL1aNa1YtsQ14kuXLin3K/ntapU/tm9VTseqFlPMF+rlKlbS1q3b9OYbbVSyRAlt275d4ydMVMmSJfTbr7+4+gjtg9u3b6tgkWI2CNS8WVOb92P+goU6c+aMflq+TGXLlgnyli1atdbMWbNVvlw5VapYQX/u3qOly5bZOS1eOF+1vbzs9adPn7aBm7x58yhp0qS++kzvWLUzfeoUX3We9MEEtooWL6l3O3XSiBEjPGnqzBUBBBBAAAEEEEAg6gmQvD7qPVNmhAACCCCAAAIIIBCWAqNGjdKAAQO0Z9dOZc2aNSxvFan7vn//voqXKq0//9ytiePH6Y02re14m7doqdlzftSXn3+m9zp3cs3BBFDMKpdRIz9W9/e7uerf6fiuxo2foG2bf3cErAq76kPz4P0PeujLr77W8qWLZXJ/mPLvv/8qX8HCih8/vo4fORTo7U6ePKnM2XKocaPXNOuHGYoWLZptu2nTbypboaJy5cqlfXv+tHXr1q9X5aomt8h25c+fL9A+PfXE92PG6r2u3bR7927lyZPHUxmYNwIIIIAAAggggID7CxBYcf9nyAwQQAABBBBAAAEEwkvg6tWrypQxo959t6MGDxoYXreNtPfZt2+/Chcrrrhx42rv7l3asXOnGrzWWBUrVNDqn1e6ghBmAma1ysGDh3Tm5HHFjh3bNSezyuOXXzeqbJlXlSFDBld9cA/M9lsbN27S1m3b1LPHBwFeljTFS0qfPp12/7HT1/nWb7yp6TN+0OZNG1W8eDFf55wfzEoVs2LFZ1DGeS5bztw6duyYrnlfUoIECTR23Hh1eLeTbl674kjaHs/ZjPf/CZjcOoWKFlOmTJllchRREEAAAQQQQAABBBBwUwFyrLjpg2PYCCCAAAIIIIAAAhEgMGnSJJmtwLp36xoBd498t8yTJ7eGDRmsa9euqfUbbR1Bhc560ZFLY/LE8b6CKmbkR44cdawWqWaDKsePH9eSpUv1xx+7lCZNGrVs8XqIgirmC/rffvvdrn5IlyGTKlSuohkzZwYIZPK3XLlyRZUrVfJ3Pnu2bLbOBIQCKwkSxFfHDu+oWNGivpqYJPZm2zMTVHIGUY46giwvv/yybty4oWXLl2vipMn6/ffNevjwoa9rPfWDWe0zaEB/LVq0SEePHvVUBuaNAAIIIIAAAgggEAUEYkaBOTAFBBBAAAEEEEAAAQTCRWDixIlq3ryZEiVKFC73c4ebdOvaRUsdQYT1GzbY4U6dPMmxOiS9r6GbQMP58+f10ksvqXbd+lq+YoXrfI4cOTR5wniVKFHcVRfYgcnPMmfuXM2dN9/mSzGJ0atXq6q6deqoZo0nW3z5vfbQocO2KnWqVH5PKXv27LbuvwsX/J1zVtSpXVvm5bd89fU3NmDTpHEjxYgRw54+etSxesURZMqYJZtMXhdnKVy4kKY5XMy2YZ5eTD6adI58M5MnT9awYcM8nYP5I4AAAggggAACCLipQHQ3HTfDRgABBBBAAAEEEEAgXAVMro19+/ap0WsNw/W+kf1m0aNHV24fAYM8uXP7G7IJOJhighEnTp7Q119+oR1bt+irLz7XqVOnVLdBQ/3333/+rnNWmC22TLCi5KtlbFClTm0v/bRimS6c/1tzZs2USUifOHFiZ3Nf72YViSl+k8mbuoz/23rMrGgJbvH29rZbg/XtP0AmKGTm4ixHjx3V9evXNbB/Px3av1ebftmgdm3f1K5dfzrm+Jpu3rzpbOqx7+bnpUH9elqyZInHGjBxBBBAAAEEEEAAAfcXYMWK+z9DZoAAAggggAACCCAQDgKbN29WzJgx9Wrp0uFwN/e5xc+rVmnM2HHKmTOnDh06pJZt3tDObVvsFlnOWVz2vmwP7927p3lzZtu2pqJQoYL61xFQGT7iY5vw3meye+e15n3V6jUyuVhy586l/n372tUpZrVKcEqcOHFss8uXn4zB5zU3bz0JdCRJksRndYDHZvsxE+DpP3CQTF9me7ARw4bK5zimOFY0mfvlzfskMXs2x1ZjpUqVtEGfTz/7XAsWLrLbngV4Aw+qrFC+nL75ZrTdMs3kpqEggAACCCCAAAIIIOBuAqxYcbcnxngRQAABBBBAAAEEIkTgwIEDMl+U+0y8HiEDiUQ3NTlG3mjb3uYYWbJwvkxgxDiZ1Rw+S1pHHhVTTIJ4E4DxWWrXqmU/Hjh40Ge1r+MvPhulkSOG22BNs9dbKEWqNKrpVUfjxk+wW4z5auznQ6pUL9ma4ydO+DkjmyPFVKZIntzfOZ8VFxxbhVWv6aWOnTrrlbx5HYGjrRr99Ve+giqmvdnyyxlU8Xm9c5uyvY4VTxQpb548euyAOBjEM8cJAQQQQAABBBBAAIHILEBgJTI/HcaGAAIIIIAAAgggEGkEzCqFFCmC/gI+0gw2nAbydod3bWDj4+HDlDVrVpvIPlOmTPryq6/1668bXaMwCd1NefDggavOeXD7zpNcJEHlrTHX9/igu90+7MjB/Xarrb//+VvvdHxXadJnUKkyZe2qGWefPt+dCeqPH/cfWNnz117btHixYj4v8XVsxmy2Klu7bp3GfPet1q1ZpQIF8vtqYz6cOXNG8xcssCtr/J503jtlihR+T3nk5xT/cwhoFZFHgjBpBBBAAAEEEEAAAbcTILDido+MASOAAAIIIIAAAghEhMCdO3cUJ/aTbaUi4v6R7Z7Tps9wbG21UOXKllWndzva4b3wwguaMG6MzLZZrd9sa/ONmBPx4sVThfLltXPnHzpy5IivqSxa/CTXRqmSJXzVB/YhS5Ys+rB3L/25c4cO7vtLHw0cYO/z7fffB3hJGsdqmbJly+jXjRt17H/5VkzD+/fva+as2TLnzUqTwMqSpUu1ZctWdX+/m95q3y6wZo7twbzVqEkzDXNsa+a3zPlxrq0q8+qrfk955Gfn9mzmd4qCAAIIIIAAAggggIA7CpBjxR2fGmNGAAEEEEAAAQQQQCACBU6ePKnOXboqfvz4mjRhnKJFi+YajQmgmACE2aarW/cPHIGWsfacWdVSvFRpNW7WXMOHDlH6dOm1bv16m7ekdOlSqlO7tqsPvwcLFy3S4cO+AzLONmZrthbNm+t2EF/S93EEYmrVrmvv3ffD3jI5VUZ+8qmOHz+uZUsW+Rp/ugyZ7CqcB3efrKTZuOk3e6sbN27og569nLf19W7mky/fKypRorjGT5ioZMmSqUG9enr06JFmzJyp1WvWOBK211exYkV9XccHBBBAAAEEEEAAAQQQcE+BaI6/JjPb21IQQAABBBBAAAEEEEAgCIF27drp9KlT+nnl8iBaRf1TJlhQvlJlbXIEHL4b/Y3eefstf5O+fv268uQroLNnz2rJogXy+l8elZU//WRzsvznSFjvLLW9vDR54nglTZrUWeXvvWGjJjLBlaBKnjy59defuwJtMnvOj2r31tu6deuWbWO2Hhs0oL+6vNfZ1zWp072sf//9V4/u37X1RYqX0B9/BN6vaXT9ymUbZDLzavfWO1q23PfPiDH6bNQnduWOr5t56Ie7d+8qXoIXtXjxYtWpU8dDFZg2AggggAACCCCAgBsLHCCw4sZPj6EjgAACCCCAAAIIhJ8AgZXQsTZbcP3lyG1y8dJFmwg+derUodNxMHox+VJ27NhpV5IUL15MMWLECMZVIW9yyhGAO3T4sBInSqxcuXL6S3If8h6j1hUEVqLW82Q2CCCAAAIIIICABwocYCswD3zqTBkBBBBAAAEEEEAAgYgSiBUrlgoVKhght48ZM6bdriusb54hQwaZFwUBBBBAAAEEEEAAAQSipgDJ66Pmc2VWCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEAYCBFbCAJUuEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIGoKEFiJms+VWSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAYCBBYCQNUukQAAQQQQAABBBBAAAHfAouXLNGPc+f5rgzGpzNnzmjipMk6evRoMFqHXpNHjx6FXmf0hAACCCCAAAIIIIAAAlFKgMBKlHqcTAYBBBBAAAEEEEAAgcgpMHT4CPXu0zfEg9vz119q//Y7+n3zlhBfG9ILDh8+rC7d3lfGLNmULGUqedWpp7Xr1gWrmzFjxylPvgIBvlasXBmsPmiEAAIIIIAAAggggAAC7iEQ0z2GySgRQAABBBBAAAEEEEDAnQU6deyo27dvh3gKOXPk0PChQ1SoYMEQXxuSC8zY6tRvqHPnzql5s6ZKliyZ5i9YqNp16+un5ctUtmyZILvbtn27zOqaQoX8jzNOnDhBXutpJ6NFi+ZpU2a+CCCAAAIIIIAAAlFMgMBKFHugTAcBBBBAAAEEEEAAgcgo0LpVy2caVpYsWdS7V89nujYkF/XtP0BmxcrypYtVo3p1e2mXzp2Ur2BhtWnbTsePHAqyu6PHjqlSxYpaOH9ukO04KT1+/BgGBBBAAAEEEEAAAQTcWoCtwNz68TF4BBBAAAEEEEAAAQQiVuDhw4caPuJjFStZSnnzF1D3Hj114sQJvfVOB30/ZqxrcO917aY3HAEKZzHbe3V6r4v+/vtvNW/RUhkyZ1WW7Dn1Zrv2unnzprOZtm3brhq1amvN2rWuurA4mDJ1ml55Ja8rqGLu8dJLL6l6tao6efKktm7dFuRtjxw5qhw5sgfZhpMIIIAAAggggAACCCAQNQRYsRI1niOzQAABBBBAAAEEEEAgQgTqOrbPMjlEihcvpooVKujXjRs158e5unbtmkzQxVk2b9miS5cuOz/qz927dfHiJS1avESZMmVU0yaNtXXbNpkAx9Wr1zR/7hzb9sLFC/p51So1a9rEdW1oH1y8eFFXrlzRG21a++s6e7Zstm7Hzp12jv4aOCpMIOjff/+Vabtjx079tXevEidOpOLFiilNmjQBXUIdAggggAACCCCAAAIIuLEAgRU3fngMHQEEEEAAAQQQQACBiBRYsHChDap07fKePv90lB3Ko0eP1KrNG5o5a/ZTh3bq1Cn17PGBRgwbKpN3w1xbtETJYCeMd95g/oIF2rdvv/NjgO/JkydXxw7vBHju0KHDtj51qlT+zmfP/mQVyn8XLvg756w4evSYPRz40WCbo8VZHzt2bPXr86H69e3jrOLdIUCOFX4MEEAAAQQQQAABBNxdgMCKuz9Bxo8AAggggAACCCCAQAQJTJ4yzX5JPnTwR64RRI8eXYMHDQxWYCVu3LgaNKC/64t2c23pUqW0a9efOnv2rNKlS+fqN6iDH+fO09x584NqIhMgCSywYvKjmJI0aVJ/fWTMkMHWmRUtgZWjx47aU6lTp9LUyRPtypUlS5fp2+/HaMCgj+yWYu3btQ3sco+rJ8eKxz1yJowAAggggAACCEQ5AQIrUe6RMiEEEEAAAQQQQAABBMJH4Njx40qfPr1eeOEFXzfMnDmzTNDkaSVlypT+2iVJksReduPGjadd7jo/feoUTZk00fU5oIOgVknEiRPHXnL58v9vVebs4+atJ/lenONy1vt8L1umjNavWa2iRYu4LEwQp0H9esqeK48+/mSUCKz4FOMYAQQQQAABBBBAAAH3FiCw4t7Pj9EjgAACCCCAAAIIIBBhAt7e3o4cIqn93d/kVgnOqoR48eL5u/ZZKsyWW89TUqV6yV5+/MQJf91cunTJ1qVwbCUWWEmRIoXKlUvh73Qqx9ZihQoV1K+/btStW7dcQRd/DT2sIqggl4dRMF0EEEAAAQQQQAABNxUgsOKmD45hI4AAAggggAACCCAQ0QIZM2awydqvX7+uhAkTuoZz8OAh3b171/U5rA8mTZ6inX/8EeRtUr30kvr36xtgG2eC+uPH/QdW9vy1115jEtEHVn5etcoGkqpXq+ariQkunThxUgkSJCCo4kMmOEE3H805RAABBBBAAAEEEEAg0gkQWIl0j4QBIYAAAggggAACCCDgHgIFCxTQ1q3btOGXX1Tby8s16HETJriOw+Ng7bp1mjd/QZC3yp49W6CBlTRp0qhs2TL6deNGHXPkW8mSJYvt6/79+zZXjDlfuHChQPsfM3a8lixdqtMnjilt2rSudlu2bNWZM2fkN+DiasABAggggAACCCCAAAIIuKVAdLccNYNGAAEEEEAAAQQQQACBCBfo37ePXY3xdod3ZVaNmODC2x06asrUJ0ntw2uAP0yfpru3bgT5+uvPXUEOp0/vXjKBlMbNmmvBwoVav2GDatetr+OOPDLjx34v5/ZV48ZPUMw48TR4yFBXf+3bvWlXrFSpXlOLlyzRgQMHNH/BAjVr0VLRo0fX8KFDXG05QAABBBBAAAEEEEAAAfcXYMWK+z9DZoAAAggggAACCCCAQIQIpE6dWpt+2aB333tPb73TwY4hb948WrvqZxUrWUqJEiWKkHE9y02rVqmi6VOnqN1bb+u1xk1tF2b8n386SjWqV3d1abaxevToka8cMjVr1NCkCePV68M+qt+wkatt+vTptW71KhUokN9VxwECCCCAAAIIIIAAAgi4v0A0x/8xeOz+02AGCCCAAAIIIIAAAgiErUC7du10+tQp/bxyedjeyE17v3Pnjg02mIT0JuF7ilRpNHniBLVu1dKtZvTgwQObN8YET4oXL6YYMWIEe/y3b9/W3r37dNn7skzelgwZMtgVK8HuwEMa3rt3T3HjJ9TixYtVp04dD5k100QAAQQQQAABBBCIQgIHWLEShZ4mU0EAAQQQQAABBBBAIDwFvv3ue02bMUOTHas1cufO7br1/AUL7XGB/O63UiNmzJgqUaK4ay4hOTBBpaJFi4TkEo9sy9/2eeRjZ9IIIIAAAggggECUEiCwEqUeJ5NBAAEEEEAAAQQQQCD8BAoVLKgu3d7Xa02aqY1jZUru3Lm0ddt2fTLqU9WpXVtmWzAKAggggAACCCCAAAIIIBDVBAisRLUnynwQQAABBBBAAAEEEAgngZIlS2jxwvmaPGWaRjqCKd7e3sqcObPatX1TX3z2aYi20QqnIXObSCAQLVq0SDAKhoAAAggggAACCCCAwLMLEFh5djuuRAABBBBAAAEEEEDA4wVq1awp8zI5Sa5fv+5WCes9/uFFEABbgUUQPLdFAAEEEEAAAQQQCDWB6KHWEx0hgAACCCCAAAIIIICAxwpEjx49WEGVFStXavacH93WySS3f/jwYbDGf/PmTd25c+epbU2fJvH900pI7n3r1q1gj/Np9w3t86xYCW1R+kMAAQQQQAABBBAIbwECK+Etzv0QQAABBBBAAAEEEPBggU8+/Uw9evV2O4EfZs5SqTJllTBxUsWNn1A587yib7/73q7U8TuZJUuXKk++ArZt/BcTK2uOXJo7b77fZlq1erUKFS2mFxImkmmXKWt2jf72O399BvfeZtVQn379lTf/k3ubfgsULqKfV63yd++IrGDFSkTqc28EEEAAAQQQQACB0BAgsBIaivSBAAIIIIAAAggggAACUVZg2vQZatm6jSOHzBV1ea+zOnZ4Rzdu3FDnLl01fMTHvua9eMkS1W/YyK4WGTliuM01Y1bzNGnWXKvXrHG1XbtunarX9NLJk6f0RpvWtk+zyuS9rt300ZChrnYhuXerNm/o45GfKGWKlBo8aKDq1K6tgwcPqUat2lq6bJmrTw4QQAABBBBAAAEEEEDg+QSiOf5a6PHzdcHVCCCAAAIIIIAAAghEfYF27drp9KlT+nnl8qg/2TCcYflKlXXs2HGdOXk8DO8Sul3nL1TYsaXXXW3f8rtefPFF2/nff/9tV5gkTZpU/5w9bevu37+vbDlzywRStm3+TcmTJ7f1586dU4bMWVW9WjUtW7LI1hmHX3/dqCMH9ytLliy2zuSoSZchkw3KXL18UTFixFBw733y5EllzpZDjRu9plk/zJBzu61Nm35T2QoVlStXLu3b86e9T0T/c+/ePbvqZ/HixapTp05ED4f7I4AAAggggAACCCAQUoEDrFgJKRntEUAAAQQQQAABBBCIJAJbt25T5arVlThZCvsqXbacVv70k6/RXb16VZ9/8aVdHZEkeUqZNr0+7KM9e/7y1a792++ozZttdfToUbV7622lz5hZFStX1YwfZtp2po/CxYorZeq0dgXEkSNHXNeb1RBmVYSpGzR4iG2XLGUq1apdV4cOHXK1C+zgypUr6tips93CKlXa9GrwWmOZXCx+S3Dm6/ea5/1s/Pbu3aca1au5giqmzzRp0qhihQq6fPmyTEDFlE2//abTp0+rS+dOrqCKqU+bNq0Wzp+rt9q3NR9tOXPmrK13BlVMZcKECVWsaFHbn8nNEpJ7/755i+23dauWrqCKqXj11dI2cHPgwAG7ysY2iuB/+Nu+CH4A3B4BBBBAAAEEEEDguQViPncPdIAAAggggAACCCCAAALhLmC+KK9YpaoyZcqobl3eU7x48bRg0SIbzFi5fKmqVa1qx2SCFOs3bFDp0qX0Ya+eOuIInIwbP0Fjx43X/r922wCBafjn7t06e/acY7uqtUqcOLEqlC+nOT/O1YZfftHMWbPtNlY1a9RQhpczaPmKFapcrYZOHD1sV2ecOnXa5vFo0KixXdlRr24d/ffff1q0eIkjyFJCO7ZuVs6cOe14/P5z9uxZlSlfURcuXFCrli2UKFEi21ftuvX12ahP1NUxN1OCO1+//T/v55gxY+rX9euUOXMmX12ZoMeev/5S1SpVFCtWLHvuyJGj9r1undoyiev/+GOXbt66qQL586u2l5ev6+vXq2sDXiYQVqN6dXvOBKHMs6pcqZLix49v+wjuvRMkiG+3EzOBGZ/FbC926dIlxY0b1/6M+DzHMQIIIIAAAggggAACCDybAIGVZ3PjKgQQQAABBBBAAAEEIlRg1pwfdfv2bU2fMkUFCxawY+nWtYvdSsrk5TCBFbNdlfmivmePD/Tx8GGu8ebNk0fdun+gjY5topo0buSq//fffzXko0Hq2+dDW9esaRMbqDHBFbONVPbs2W29Wdli7mFWtzjrzIlr165rz66dNjhiPq9Zu1bVatRSz959tGTRAlPlr/Tu01enHFusbd60UcWLF7PnPxo4wK6wMStrTLDFbLcVnPn669xRcfHiRX33/ZiATvmqa9iggfLkye2rznwwAQ4TlHKWL7/6Wqccq1KWr1hpt+wywSpnMUErs32XCbg0b9FKJqhhiql7t2MHffrJSJlAjSmd3+0ok2fFrOopVaqk4saJa5+VWQkzbMhg2yYk9zb5VMzLb/nq629kVgSZ52zGQUEAAQQQQAABBBBAAIHnFyCw8vyG9IAAAggggAACCCCAQLgLPHr0yN5zzLhxNkH6Cy+8YFdOnDp+VM6tlkw+kN83/qocOZ4ERJyDNG1NuXbtmrPKvpvcID0+6O6qy58vnz02W175DKCUL1fOBlb2O1bN+Kzv6kjsblacOItZeVGyZAm72sWMyZn3w3nebKNlVsMUKVLYFVQx52LHju3YNqudXS2zYOEitWv7poIzX2e/Pt/NShizPdnTiplHQIEVv9f17T/ABrRMfe7cueS0NJ+dK1aaNm+h5s2aqsXrzfXgwQNHQvlR+vqb0UriWAk0cEB/09SuCjKrf3bv3qPt23fYZ2eMTODl+o3rto3ff4K6t9+23t7e6tylq/XNkSOHvv7yC79N+IwAAggggAACCCCAAALPKECOlWeE4zIEEEAAAQQQQAABBCJS4G1H4CFTpkwaP2GiXkqTTjW96uiLL7/S+fPnXVs+JUiQQCVKFLf5VN7p+K7Nx5I1Ry693aFjgEM3qyVMUMNZzPZRpqRNm8ZZZd+dKx/u3XuSW8R50m8Ax9TnyZ1bd+/elUng7rccOnTYVt24cVNNmjX39Zo6bbo9d+z4kyT3wZmv3/7NZ7MF2c1rV576avRaw4Au91dn+jq0f68mjh/n2GLrsoqVLGXNTcPL3pftKhazFdiEcWNlAlAmuLRowTzr+pUjuOIsZcpX0JKlS/XtN1/r/LkzunD+b837cbYNdplVLCYZvd8S1L2dbU1wZszYccqeK49mzZ5jtwfbtvk3pUiRwtkkwt/9BtgifEAMAAEEEEAAAQQQQACBEAoQWAkhGM0RQAABBBBAAAEEEIgMAunTp9eBvXs0d84sVa9WTdt37FD3Hj1lAiejPv3MDvGff/7RKwUKqkLlKtq8ZYvNE9Lxnbc1fmzAW2OZracCKsH9Ijx1qtT+Lnf26QzS+Gxw6fIl+zFOnNh2xYbJVeJ8JUuW1K76MIEZU4IzX9vQzz9m7Cb/zNNezmCRn8vt6h/nahnnuWzZsumNNq3t9mpmRcqKlT/ZU2kdgSlT2rRuZd+d/5gAV4Xy5e2WXGa7NZMvZu/efSpXtqw6OJ5HkiRJ7Pga1K9vrzVbvJmVOiZIEtx7m3uZ1TnVa3qpY6fOeiVvXu3ctlWjv/5KCRMmdA4lUrybeVEQQAABBBBAAAEEEHBnAbYCc+enx9gRQAABBBBAAAEEPFbAbONlggEmN4h5mS/gf924UWYbqj79+quTI4fHiJGfaN++/TYAYPKsOMuy5cudh6H6fvTYMVe+F2fHJ0+esoGD5MmTO6tc75kdK25MyZY1m2ZMm+qqNwcPHz7U9evXXVttBWe+Jnjit5gVPEOGDfdb7e/zm23aqHDhQv7qP3YYmi24li1ZpJo1avg6nzx5Mvv5zJkz9j1Dhgz23QRb/Jbbd27brdBMkGPDL7/a0+XKlfXbTFUqV7Irj7wdeVFCcm9zz7oNGmrbtu0a8923dis1f51HkorgBuoiyXAZBgIIIIAAAggggAAC/gRYseKPhAoEEEAAAQQQQAABBCK/QNUaNZW/UBHXQE1+FLP1VK2aNVxBiePHT9jzrVu1dLUzB0uXhU1gZfmKFb7uY4IaK3/6SQXy5/dV7/yQNWtWmYDLz6tW6f5939uKjfh4pJKmeMkGCkz74MzX2a/Pd5O4fcLESU99Obcc83mtOX7llby2avWatX5PObZhm2Tr8ud/koum1v8CLyZvjM9y6dIlbdr0m0w7k5Mld65c9vS8+fN9NrPHP86dZ9/NipOQ3NtsK7Zly1Z1f79bpA6qmMmxYsU+Yv5BAAEEEEAAAQQQcGMBVqy48cNj6AgggAACCCCAAAKeK1C/bl192LefXZ1i8o+Y1RrrN/yiH2bOsisvUqZMad9XrFxp2/Xo/r7OO7ahMl/6z1+w0MKZFSYm8JDYkVQ9NMq06TOUOnVqNWrYUN5XvPX+Bz3sSprPPx0VYPcmn8uIYUPV/u131KJVa/Xq0UMvvphQi5cs1dDhI2x+ktKlS9lrgzPfgG5icqzcvXUjoFPBqjOrVPLmzaNvRn+rxIkSqVrVqjr39znNnTffEaBapqJFi8irVi3bV5kyr9pVLSY4YrYLq1enjq5evapBg4dYh5EjnqycyZMnt2NlSmWtXrPGbt1lktxndKx2Wbh4sc2Lkjt3LtWrW8cmsg/uvTc6Ajem3LhxQx/07GWP/f4zfOgQXzl0/J4Pr8+sWAkvae6DAAIIIIAAAgggEGYCjr8WoiCAAAIIIIAAAggggMBTBNq2bfvY8WX440f370aKlyNY8Lh5s6YmWYWvV6FCBR87kqvbMd646v3YkdvDdd7xhfbjqlWqPP7n7OnHJUuWsPVDPhpk2zq2wXqcI0cOX3O7+O8/ts1b7dv5qp86eZKtnz3zB1v/9Zdf2M+fjfrksSOgY4/NuBzbXj2eNGG8r2vLli3zOG3atL7qvvri88dx4sRxXRczZszHb7/V/rG5v9M7OPN1tg3t96OHDjwuUaK4a3xO8/r16j0+c/K4a4zmvlcuXXj8evNmvtomSpTo8Y+zZ/pqZ+bWrGkTX+1Mv47gzOMjB/e72gb33ua5O8cV2Pv1K5dd/Ya2UUj6u33jmh3r4sWLn/Jbx2kEEEAAAQQQQAABBCKlwP5oZliO//CmIIAAAggggAACCCCAQBAC7dq10+lTp/TzyrDZRiuIWwd56vjx4zp0+LBMwvNMGTOpQIH8NpeHz4t2796jCxcvqEjhwr5Wp+zfv18vv/yyTHL15ymjv/1O73Xtps2bNqpYsaLas+cvu2KlYIECcgQVgtW1yaeya9efunHzhk28bpLVB1SCM9+ArnveOpPD5sSJEzp46JBdHZQje3Y5AkSBdmu2Qdu9Z49SpkgpswLFETgKsO3Zs2e1z/EczPPLmSOHHMEtf88vpPcO8EaRqPLu3buKl+BFOQIrquNY1UNBAAEEEEAAAQQQQMDNBA6wFZibPTGGiwACCCCAAAIIIICAT4HMmTPLvIIqzhwgftvkzp3bb9VzfzbbPAV2v6A6N0ndHatZgmpizwVnvk/t5BkamBw2WbJksa/gXJ4qVSqZ19NKunTpZF5BlZDeO6i+OIcAAggggAACCCCAAALPL0Dy+uc3pAcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwEAECKx7yoJkmAggggAACCCCAAAJhJWC2EkuTJk2kSIweVnOk39ATIHl96FnSEwIIIIAAAggggEDECLAVWMS4c1cEEEAAAQQQQAABBEIksGLlSl27dl1NmzQO0XXh0bhN61Yyr4DKwYMHNX/BQtcpR1J6JU+e3PU5JAcPHjzQ/fv3bY6TkFwXVNtbt27Z/CcxYsQIqlmEnguLeZsJmdwtZpuxZyl37tzRZ59/4bq0WtWqKlKksOszBwgggAACCCCAAAIIRGWBZ/uv6KgswtwQQAABBBBAAAEEEIiEAp98+pl69OodCUcW9JD+2rtP/QcO0phx4zVl2nRduXLF3wUzfpip6LHi6Ny5c/7OmYpVq1erUNFieiFhIsV/MbEyZc2u0d9+ZwMD5vzadeuUJ1+BIF+t2rxhmtpiAgp9+vVX3vwFlDBxUttvgcJF9POqVc4mIe7TdWEIDp533gHd6ml9Hj58WF26va+MWbIpWcpU8qpTz87Vb19jxo4L1NME+UwCevM8x0+cZJ/v5i1b/HYR6OfHjx8Heo4TCCCAAAIIIIAAAgi4gwArVtzhKTFGBBBAAAEEEEAAAQTcXGDenNkqXryYv1lcu3ZNoz77zF+9s8IETarX9FLixIn1RpvWihUrlubOm6/3unbThYsX9dHAATJbS8WKFfD/tblz565MMCFb1qzOLmWCLDNnzVb5cuXUrEkT/bl7j5YuW6YatWpr8cL5qu3lFeI+XZ0H8yA05u33Vk/r8/bt26pTv6ENYDVv1lTJkiWzq4lq162vn5YvU9myZVxdbtu+XWfOnFGhQgVddc6DOHHiKFGiRDpycL9OnDihLNlzOk/xjgACCCCAAAIIIICARwgE/P8+PGLqTBIBBBBAAAEEEEAAAQQiSmD8hIlavmKl1q1frxs3bgQ6jCHDhttz27f8rixZstjjEcOGKl2GTHYrqgH9+qpihQr6c+eOAPvo3KWrYwu1axrz3Wh7/uTJkzao0rjRa5r1wwwbQDEnNm36TWUrVFTvPv1sYCUkfQZ440AqQ3Pezu3Lgttn3/4DbJBp+dLFqlG9uh1hl86dlK9gYbVp207HjxxyjfrosWOqVLGiFs6f66rjAAEEEEAAAQQQQAABBJ4IsBUYPwkIIIAAAggggAACCISywB9/7FKZ8hU0fMTH/nrevHmLPTfVsY2SKVevXtXnX3xpV2UkSZ5SpcuWU68P+2jPnr/8Xeuzwqy6aNGqtc8qe/zxyE9s/yYvh8+yeMkSValWQylTp1XhYsX1Qc9eNuDgs014Hh85elTeV7xVsGABpUiRItBbnzlzVmnTpnUFVUzDhAkTqljRojbfisn1EVj56eef9d33YzRj2hSlSpXKNvvd4W9K61YtXUEV8/nVV0vbexw4cCDIQE9AfZrrg1vCYt7B7XPK1Gl65ZW8rqCKGfNLL72k6tWqygSctm7d5prGkSNHlSNHdtdnDhBAAAEEEEAAAQQQQOD/BQis/L8FRwgggAACCCCAAAIIhIpAvnyv6NChw/p69LeuPCDOjqdOn67ffvtdxYsVtVUNXmtsgxw3b93Uh716KneuXBo3foJdPfH33387L/P3vtMRvDEvv8V8yW7695nHYqhj1Uf9ho1k7tHhnbeVJ3duG3AwQZyg7uG379D8/MnHI/TLurX2VaVypUC7rl+vrt26auVPP7naHDp0SOs3bFCF8uUVP358V73Pg0uXLunNdm+paZPGdkWL81yCBPHVscM7NjDjrDPvJom9uSZu3LiKFy+ez1Ou48D6dDUIxkFYzDs4fV50bJtm8ttUruTfOnu2bHbkO3butO83b97Uv//+K1O/Y8dOTZ4yVQsXLYqwn5VgsNIEAQQQQAABBBBAAIFwFWArsHDl5mYIIIAAAggggAACniAQM2ZMmRwWX38zWhs3blK5cmXttM0qkvkLFtpcIzlz5rRfVJsAQc8eH+jj4cNcNHnz5FG37h9oo2N7qiaNG7nqn+Xg4MGD+mjIULtKYdmSRa5VGq1atlDV6jX1xVdfa9RI/ytrzL3Ml/FmxcfTSsMGDZQnT+6nNXum853f7WiTq9eqXVelSpVU3DhxbVAlTZo0GjZkcKB9vtv5PRtIMNuG+Sx1ateWefktX339jW1vvJ1bbPltE1ifftuFxudnnXdg9zaBPlNS/2/ljs922bM/WZny34ULtvro0WP2feBHg21Qy9k2duzY6tfnQ/Xr28dZxTsCCCCAAAIIIIAAAh4pQGDFIx87k0YAAQQQQAABBBAIa4FWLVrYwMq8BQtcgZU1a9faVRFDB39kb//iiy/q942/+tty6YUXXrDnTW6Q5y3fjx2nhw8f6t2O77iCKqZPs3LBfKE+a/acQAMrFxxftA8aPOSpQzD9hFVgxSStz/ByBu12JJjfvn2HTV5vVuOY4NX1G9cDHNu+ffv149x56vNhb7388ssBtnFWent7y+RhMcnsc+TIoa+//MJ5ytd7SPr0deEzfniWeQd1K5MzxZSkSZP6a5YxQwZbZ1a0mHL02FH7njp1Kk2dPNGuXFmydJm+dQTZBgz6yG4f1r5dW9uGfxBAAAEEEEAAAQQQ8EQBAiue+NSZMwIIIIAAAggggECIBcwX+SZAEdxSqFBBG2xYsHCR/bI+WrRomvPjXLvVlNmeypQECRKoRIni+uWXXzVrzhyZlQInT53S8ePHg3ubp7Y7ePBJQvLJU6bJmdfFeZHZ/spsBWbylJgtsPwWs6rm5rUnX7b7Pefzs1nJEFbF5KrZu3efvv3ma7utlxmn2Ras/dsdZFax7NvzpzJmzOjr9p98+qnMmN7v2sVXvc8PJjgzdtx49R84SJcvX7bbg5nVLSZ/S0AlOH0GdN2z1j3LvIO6V5w4cexpM1e/xWwRZ0qSJEnse9kyZbR+zWoVLVpEziCf2T6tQf16yp4rjz7+ZJSeJ7Di/D0yv1MUBBBAAAEEEEAAAQTcUYAcK+741BgzAggggAACCCCAQLgLmNUlJtF8SIpZtfLPP//o99836+7du448FYtlcoYkSpTIdmPOvVKgoCpUrqLNW7Yoc+ZM6ujIgTJ+7NO33wpsHJcve/s6denyJUWPHl1x4sS2qz1ixYrlei9b5lW7ZdmjR498XeP8YIJBJt/I016BbZ3l7OdZ300ieRNUKVe2rM0NY774N2NpUL++2rRupdu3b8sErnyW06dP29Un9erWCXB1hmlrVuJUr+mljp0665W8ebVz21aN/vqrQIMqwenT5xie9/hZ5v20e6ZK9ZJtcvzECX9NTe4YU1IkT/7kPUUKu8rKGVSxlY5/Ujm2ETMBwxOOPkxQ7lmLc2WM+Z2iIIAAAggggAACCCDgjgL8iZA7PjXGjAACCCCAAAIIIBDuApkzZ9a4sWNDdN/XmzdT7z59NX/hQl28dFFmay8TEHCWESM/kdliyuRXMXlWnGXZ8uXOw0DfHTEPBRQQOXT4SS4NZ/L6zJky6w9Hkvs+vXsptyNpvc9ikpSb1QN+v0B3tjl//ryGOBLfP6282aaNChcu9LRmIT6/56+99hpnjhqfHZiE9198+ZW8/7d9lfPcuAkT7ZzavvmGs8rXu8lzU7dBQ23btl1jvvtWb7Vv5+t8QB+e1mdA1zxP3bPM+2n3cyaoP37cf2DFeb/ixYrZbn5etUrm56d6tWq+ujV1J06ctCutAvuZ8XVBIB+OHHmy1Zj5naIggAACCCCAAAIIIOCOAgRW3PGpMWYEEEAAAQQQQACBcBcoVKiQrjoCI/v37/cXoAhsMCbBepXKlW3CerPlVrp06VSpYkVXc+eX3K1btXTVmYOly54eWMmYIaNWr1mj+/fv2xUo5joTpDl69MmX1uazKSUdW43Nmz9fy5av8DVus/omS/acKpA/v9as+ulJYz//mpUFEyZO8lPr/6NZURIWgZXcuXLZm5nxDxrQ39eNTQ4VU8yKE59l1erVdksrn84+zy9ZulRbtmxVjw+6ByuoYq59Wp8++w+N42eZ99Pua34Wy5Yto183btQxR76VLFmy2EvMz4/JL2POO5/hmLHjZZxOnzimtGnTuro2bmfOnPEXcHE1COaBWZ31UsqU9p7BvIRmCCCAAAIIIIAAAghEKgECK5HqcTAYBBBAAAEEEEAAgcgqUKRIESVPlszxhfMyXwGKp423VcsWatGqtc6dO6fevXrabbmc15gvslesXKkP+/ZTj+7v6/y//9ovuecvWGibmITjJrhhEpn7LcWLFbXXtnmzrc13YfKzjBz1qd1mzLm1k7nG5Mb4bsxYmxfDBHZKlSzh+HL8rHr37Wv77t+3j9+uXZ9NjpW7t264Pof3QZ48uW1gygSQzNZdLV5vLpNofeHixZo1e47jOeSS2fLLWUwi+p07/1BtLy9fzs7z5n3jpt/sxxs3buiDnr18nnIdDx86xOZoMRXB6dO0S5wshUyfD+7eNh+fq4R03sG9mVm1ZPLSNG7WXH0/7G0DUCM/+dTm9Fm2ZJHM1m+mtG/3phYvWaIq1WtqxLAhNnn9fse2bN179LKuxud5ypJly1S9Ro3n6YJrEUAAAQQQQAABBBCIUAECKxHKz80RQAABBBBAAAEE3EXA5Clp2qyZJk2Zql49e7i+hH7a+E1OFZOk3nzp3sbPypReju2/Njm+6J8ydZp9mS+2zQqXA3v3qEGjxhr16Wd60ZFMvW+fD/3dpvv73Rx5WbbaAIMJMpgVBy1bvG7bjXQkF3cWk7R81crlatnmDRvgcdbnyJFDC+fPtbk0nHWR7d2Yz/phujp36WrnaVaOOEsZR36YSePHuQIgpn79hg12CyuzSiewsnHTJnvqe0ewKbDy0cABrn6D06fpx2ypFtDWbIHdI6j6kM47qL58nqtapYqmT52idm+9rdcaN7WnTL6fzz8dpRrVq7ua1nQEPSZNGK9eH/ZR/YaNXPXp06fXutWrVKBAflddSA/Mii+z8mXw4OcLzoT0vrRHAAEEEEAAAQQQQCA0BaI59sl9HJod0hcCCCCAAAIIIIAAAlFVwCQVz5Mnj2bP/EGNXmsYatPcvXuPLly8oCKFC/tanWK+hH755ZdtYCawm5lE7OfO/a38+fMFGewx/9lvtgk7cPCgkiVNphKO4ENYJZ33Oda58+ariWOFxOZNG1W8+JMcHj7PB/f47Nmz2ufwMAnrczqCQiYw5FxhEdw+wrKdCaoUKFxEe3b9Eaq3CYt5mzwzO3bstIEg80wC+zkw1nv37tNl78t21UoGx2ohE/TxWUwie7Ol3FdffK7Ond71eSrAY7N6a6cj54/5XYpMzy/AwVKJAAIIIIAAAggggEDAAgdYsRIwDLUIIIAAAggggAACCPgTyOXI+dG8uWMbpX79HdtN1VLcuHH9tXmWChMUCaj4TTYfUJsUKVLIvJ5WzJfY2bJls6+ntY2M5802ZuYVWUvvPn1V5tVXQ314YTHvmDFj2sDa0wYbL148FS1a5GnNgn1++/Ydmu1YXTVr9myCKsFWoyECCCCAAAIIIIBAZBTw/edGkXGEjAkBBBBAAAEEEEAAgUgkMHLkSP3733/qN2BgJBpV5B9KH0cwqnHT5jIrMKJiSedI8v71l19ExakFOCeztZ15np3e6xrgeb+Vd+7cUdu33lL5ChXUuHFjv6f5jAACCCCAAAIIIICAWwmwYsWtHheDRQABBBBAAAEEEIhogbSOL9C/+eYbtWnTRqVLlVT9evUiekiR+v4m4NCgfn07xtDKQRIZJ/xe506RcVhhOibzPM2qFvN8s2bNEuS9OnbqrLNnzmrJkqVBtuMkAggggAACCCCAAALuIECOFXd4SowRAQQQQAABBBBAINIJdOjQQVOmTNHqn1aqdOlSkW58DAiByCIwyJGofuiw4Vq8eLG8vLwiy7AYBwIIIIAAAggggAACzypwgK3AnpWO6xBAAAEEEEAAAQQ8WmD06NGqWbOmatTy0uo1azzagskjEJiA2QJuyJChGjt2LEGVwJCoRwABBBBAAAEEEHA7AQIrbvfIGDACCCCAAAIIIIBAZBCIESOG5syZo3qObZBqedXR5198qcePH0eGoTEGBCJc4OrVq2rYqIk+/fQzTZk6Ve3atYvwMTEABBBAAAEEEEAAAQRCS4DASmhJ0g8CCCCAAAIIIICAxwnEjBlT06ZN07Dhw9Wz94eqWLmqDhw44HEOTBgBnwILFi5UnnwFtHnzZq1dt06tWrXyeZpjBBBAAAEEEEAAAQTcXoDAits/QiaAAAIIIIAAAgggEJEC0aJFU8+ePbVt2zZdu35d+QoUUotWrbVly9aIHBb3RiBcBe7evavZc35U8VKl1ahxU1WuXFl79+1T2bJlw3Uc3AwBBBBAAAEEEEAAgfAQIHl9eChzDwQQQAABBBBAAAGPEHj06JFmz57t2P7oU+3atUvp0qVTxQrl9UrevEqRIoXixYsXqR3u37+vWLFiReoxesrgIvuzMNve3bhxQ2fOnNHOP3Zpw4YNun37turUrat+/fqpUKFCnvKomCcCCCCAAAIIIICA5wkcILDieQ+dGSOAAAIIIIAAAgiEg8Aff/yhpUuX6rffftN+x1/uX7p0SXccf9VPQSAqCERzTCJ+ggRKkzq18uXPr0qVKqlOnTpKkyZNVJgec0AAAQQQQAABBBBAICgBAitB6XAOAQQQQAABBBBAAAFPEBg8eLAGDRqk9957T59//rmiR2fH4Ih+7qNGjVLv3r3Vtm1bffvtt6wkiugHwv0RQAABBBBAAAEEEPh/AQIr/2/BEQIIIIAAAggggAACniVg8mK8+eab+vHHH/XNN9/onXfe8SyASD7bJUuW6PXXX1eRIkU0f/58JU2aNJKPmOEhgAACCCCAAAIIIOARAgRWPOIxM0kEEEAAAQQQQAABBPwIXLhwQfXq1dP+/fttYKVKlSp+WvAxMgjs2bNHtWvXVuzYsbVs2TLlyJEjMgyLMSCAAAIIIIAAAggg4MkCB1jj78mPn7kjgAACCCCAAAIIeKTAPkfOl2LFiun8+fPavHmzCKpE3h+DfPnyadu2bUqePLlKlCih1atXR97BMjIEEEAAAQQQQAABBDxEgMCKhzxopokAAggggAACCCCAgBH46aefVKpUKaVPn15bt25Vzpw5gYnkAi+99JLWr1+vWrVqqWbNmvruu+8i+YgZHgIIIIAAAggggAACUVuAwErUfr7MDgEEEEAAAQQQQAABl8Do0aPl5eVltwBbs2aNXQXhOslBpBaIGzeuZsyYoYEDB6pTp0729fDhw0g9ZgaHAAIIIIAAAggggEBUFYj22FGi6uSYFwIIIIAAAggggAACPgXMNkqnT5/2WRXoccOGDZU4ceJAz7vTCfMFfJcuXexKh6FDh6pPnz7uNHzG6kfAJLJv1aqVSpcurblz5ypRokR+WoTOx5kzZ+r27dtBdlamTBllz549yDY+T65YsULXrl1T06ZNfVZzjAACCCCAAAIIIICAOwmQvN6dnhZjRQABBBBAAAEEEHg+gTp16mjp0qXB6sTkIcmdO3ew2kbmRuZL7MaNG+vXX3/V9OnTZQJGFPcX2Llzp+rWrauECRPan+msWbOG+qRSp05t8/AE1fGkSZP0xhtvBNXE17ny5cvr2LFjOnPmjK96PiCAAAIIIIAAAggg4EYCBFbc6GExVAQQQAABBBBAAIHnFDh69KiuXLni6uXw4cN6/fXXVbVqVQ0bNsxVbw7y5s0rs/2SO5cTJ07Yrb+8vb21ZMkSFSlSxJ2nw9j9CPz99982uHL8+HGZVSwmaBGaxQRW7t69qwULFgTarcnRkypVqkDP+z1BYMWvCJ8RQAABBBBAAAEE3FDgQEw3HDRDRgABBBBAAAEEEEDgmQT8/lV/rFixbD9JkyaNckGH33I01sMAAEAASURBVH//3eZSSZs2rbZt26Z06dI9kxkXRV6BNGnS2JVIrVu3tsFBk9S+Xbt2oTrg2LFjh3rAJlQHSGcIIIAAAggggAACCESAAMnrIwCdWyKAAAIIIIAAAghEfoE///xTJn/Eb7/9JpNronjx4ho+fLgduMlv0aJFC3+T+Pjjj+01Dx488HVu8eLFqlKlilKmTKnChQvrgw8+sHkmfDUKxQ8//PCDKlasqJIlS2rTpk0EVULRNrJ1FS9ePM2ZM0e9e/dW+/bt1b17dz169Cjch3n16lV9/vnnql69upIkSWLzv/Tq1Ut79uwJcix37tzRwIEDlSVLFsWJE0fZsmXT22+/revXr/u7Lrx/j/wNgAoEEEAAAQQQQAABBP4nQGCFHwUEEEAAAQQQQAABBAIQMFuGmaDEV199ZbcLM9tqma2RTDH5LczLbzly5Ii95vHjx65TJll8vXr1dPPmTXXo0EF58uSxSeRN4nGzlVNoFnPfAQMG2KBP586dtXDhQsWPHz80b0FfkVAgWrRoGjx4sA0AmlUrJpdQQIGJsBx6gwYNbFDH/Jx/+OGHNj/RuHHjVLZs2SB/zjt27Gi34TPtRo0apZo1a2ratGmqVq2ar+GG5++RrxvzAQEEEEAAAQQQQACBAATYCiwAFKoQQAABBBBAAAEEEHAKmOCE+aLXJIA3f1EfknLw4EF99NFH9sviZcuWyXwBbopZ8WJWsHzxxRf2y+SQ9BlYW/OX/2ZLKDPe8ePHh/qWUIHdl/rII9CsWTNlzpzZ5l0pVaqUTWqfMWPG5xqgCTDWr18/wD5MfqLXXnvNBk7WrVsns0LFrNpyFpOnqGvXrtq4caOaNGnirHa9m/wtM2bMUK1atTR58mRXvVm90qVLF5kcSNmzZ1d4/h65BsEBAggggAACCCCAAAJBCLBiJQgcTiGAAAIIIIAAAgggYBLbt2zZMsRBFSP3/fffy2wL9u6777qCKqa+cuXKypEjh2bNmmU+Pnc5f/68ypUrp9WrV+vnn38mqPLcou7bgdmybvv27YoRI4aKFStmt7J7ntncv39fJl9PQK+zZ8/arl988UVt3rzZBlZ83uuFF16wH69du+az2nX88OFDe7xhwwbt2rXLVd+pUyfduHHDbg9mKsPr98g1AA4QQAABBBBAAAEEEHiKACtWngLEaQQQQAABBBBAAAHPFjB/Tf+sxfylvSnmr/GnTp3qq5tbt27p3LlzMitN4saN6+tcSD6YHBa1a9e2gZ8tW7bYv/APyfW0jXoC6dOntwEVs6KkUqVKMltymVVSz1JSpEghE7gLqiRIkEAlSpTQL7/8YoOFR48e1cmTJ3Xs2LGgLpMJvJj8Kv369VOhQoWUK1cuVahQwa7wMluBmeCQKeHxexTkQDmJAAIIIIAAAggggIAfAVas+AHhIwIIIIAAAggggAACPgVCsv3X5cuXfV6qS5cu2S+HTR+xYsXy9TI5JcwX38+TaNxsL2ZytZjtnwiq+KL3+A8mt86CBQvsllpmiziT98Rn7p/QBPrnn3/0yiuvqHz58nblivl5NLlTJkyY8NTb9O3bVyYQ079/fxtoGTNmjLy8vGwuImdAJ6x/j546SBoggAACCCCAAAIIIOBHgBUrfkD4iAACCCCAAAIIIIDA0wRMrpSAAiKHDh2ylzq/wDZfMJsk93369LHJvH32a5J8m62QnNsl+TwXnOMvv/zSJgtv06aNzJfRJnBDQcCnQPTo0TVy5Ej7s/fWW2/J/HxOnz5dJugSmmXEiBHau3evvVfPnj1dXZvAX1Dl3r17Miu3TB6YwYMH25cJpgwbNkyjR4/WN998Y4/D8vcoqPFxDgEEEEAAAQQQQACBwARYsRKYDPUIIIAAAggggAACCAQiYL4INlsdmfwTzrJv3z77l/fOz+a9ZMmS9qPfL5ivXr2qDBkyqEGDBj6bB+vY5Gx55513bFDFJAqfOHEiQZVgyXluI7NiZe3atTaJ/KuvvipnbpTQEjl+/LjtytzHZ1m6dKnPj/6OTcL7JEmS+Mo1lCpVKvXo0cO29fb2tu9h8XvkbzBUIIAAAggggAACCCAQAgECKyHAoikCCCCAAAIIIIAAAkbAJAg3f21vVouYxNtmy6N69eopUaJEvoDMdkhZs2aVCYDMnDnTBmM2btxoc0hcuXLFbn/k64KnfDDX1KhRQzNmzND8+fNdX0A/5TJOIyATUNm2bZsNBhYtWtQehxZL4cKFbVdmu7EDBw5o/fr1at++vebOnWvrzVZf5mfXbzHb2KVMmdKuVDG/RybgaFZ4de3a1TZ15jcK7d8jv+PgMwIIIIAAAggggAACIRUgsBJSMdojgAACCCCAAAIIeLxA9+7dbYDDBEtMsu1BgwbptddeU7t27XzZmNwqq1atslsxmXwqmTJlksmtYnKxLFy4UOXKlfPVPqgPJhG4+ct988W1Cc6YQA4FgZAImJ+/33//XQULFrQ/e7Nnzw7J5YG27dWrlypWrKjJkyfbn/XKlSvrzJkz9me1VKlS+uSTT/Ttt9/6uz5hwoT64YcfZLbFM79HiRMnVpEiRbRy5Uq7BZgzsBKav0f+BkEFAggggAACCCCAAALPIBDNsf/z42e4jksQQAABBBBAAAEEEPB4gQsXLujcuXPKnz+/TN6VwIr5T27zV/smKJIsWTKVKFHCJrUPrL3fehNIqV+/vs1FsWTJEqVJk8ZvEz4jEGwBk9vngw8+0FdffaUBAwZo4MCBQf78Brfj3bt3y/xOmOCICZI4y/79+/Xyyy8rQYIEzipf7ybPyp49e3T69GklT55cefPmtStZfDVyfHje3yO//fEZAQQQQAABBBBAAIFnFDhAYOUZ5bgMAQQQQAABBBBAAIHwEJg6dapM4nEvLy+bePxZk92Hx1i5h3sJjBs3Tp06dbJBuylTpihevHjuNQFGiwACCCCAAAIIIIBAxAgcYCuwiIHnrggggAACCCCAAAIIBClg/jq/T58+No/L+++/r3nz5omgSpBknAyhgAnY/fzzz1qzZo3dGuyff/4JYQ80RwABBBBAAAEEEEDAMwVYseKZz51ZI4AAAggggAACCERiAbM1UsuWLbVs2TKZVQWtW7eOxKNlaO4ucOTIEdWuXdvmOjFbzZkcLBQEEEAAAQQQQAABBBAIVIAVK4HScAIBBBBAAAEEEEAAgQgQ+Pvvv22C+19++UWrV68mqBIBz8DTbpktWzZt2bJFOXPm1KuvvqoFCxZ4GgHzRQABBBBAAAEEEEAgRAJsBRYiLhojgAACCCCAAAIIIBB2Art27VKxYsXsyoGtW7faAEvY3Y2eEfh/AZNsfuXKlTaQ99prr2n48OH/f5IjBBBAAAEEEEAAAQQQ8CVAYMUXBx8QQAABBBBAAAEEEIgYgcWLF6tMmTLKlSuXNm/erCxZskTMQLirxwrEjBlT3333nb7++msNGDDAbkd39+5dj/Vg4ggggAACCCCAAAIIBCZAYCUwGeoRQAABBBBAAAEEEAgngVGjRqlBgwZq0aKFXTVgVg9QEIgogU6dOmnFihVaunSpKlasqP/++y+ihsJ9EUAAAQQQQAABBBCIlAIkr4+Uj4VBIYAAAggggAACCHiCwP3799WhQwdNnjxZn376qbp16+YJ02aObiJw4MABm9T+wYMHNsjyyiuvuMnIGSYCCCCAAAIIIIAAAmEqQPL6MOWlcwQQQAABBBBAAAEEAhG4fPmyqlatqjlz5shsA0ZQJRAoqiNMwGxLZ3L9ZMyYUaVLl9ayZcsibCzcGAEEEEAAAQQQQACByCTAVmCR6WkwFgQQQAABBBBAAAGPEDh8+LBKlCih48eP67fffpOXl5dHzJtJup9AsmTJtHr1ajVu3Fh169bVZ5995n6TYMQIIIAAAggggAACCISyAIGVUAalOwQQQAABBBBAAAEEghJYv369DaokSZLErgbIly9fUM05h0CEC8SKFUsTJkzQJ598op49e6pdu3Yy29hREEAAAQQQQAABBBDwVAECK5765Jk3AggggAACCCCAQLgLTJw4UdWqVVOVKlW0YcMGpUqVKtzHwA0ReFaB7t27223rfvzxR/szfOnSpWftiusQQAABBBBAAAEEEHBrAQIrbv34GDwCCCCAAAIIIICAOwg8evRIPXr0sH/p37t3b82ePVvx4sVzh6EzRgR8CZht68z2dSdPnlTx4sVlEtxTEEAAAQQQQAABBBDwNAECK572xJkvAggggAACCCCAQKgLeHt7q3379jp//ry/vm/evKkGDRrom2++0YwZMzR48GBFixbNXzsqEHAXgVdeeUXbtm3TSy+9pJIlS+rnn38OcOhmZcukSZMCPEclAggggAACCCCAAALuLBDtsaO48wQYOwIIIIAAAggggAACES3w7rvv6rvvvlPBggXtX/M7V6OcPXtWtWvX1rlz57Ro0SKVKlUqoofK/REINYG7d+/agOLMmTP15ZdfqlOnTq6+TS4hs+WdKWZVS7Zs2VznOEAAAQQQQAABBBBAwM0FDrBixc2fIMNHAAEEEEAAAQQQiFiBPXv26Pvvv7eDMMctW7aU+dulHTt2qFixYrp3755NUk9QJWKfE3cPfYE4ceJo2rRpGjJkiN577z117NhRDx480JEjR1S3bl37e2BWZ5l6CgIIIIAAAggggAACUUmAFStR6WkyFwQQQAABBBBAAIFwFyhdurTdFsl8oWyK+SK5UaNGWrZsmcqUKSOzHdKLL74Y7uPihgiEp8DChQvVokULFS1aVKdOnZJZreX8nTDjMCu2TLCFggACCCCAAAIIIIBAFBA4QGAlCjxFpoAAAggggAACCCAQMQKzZs1S8+bNA7x59erVbXAlRowYAZ6nEoGoJrB9+3bVq1dP//33n6+gSvTo0ZU6dWodPXpUcePGjWrTZj4IIIAAAggggAACnifAVmCe98yZMQIIIIAAAggggEBoCJik9F27dg00Ef2aNWvsFmChcS/6QMAdBEyi+vPnz/sKqphxP3r0yNZ//PHH7jANxogAAggggAACCCCAwFMFyLHyVCIaIIAAAggggAACCCDgX2Do0KG6dOmSzSPh/+yTL5Nr1aqlEydOBHSaOgSilMDo0aM1ZswYG0QJaGIPHz7U8OHD+X0ICIc6BBBAAAEEEEAAAbcTYCswt3tkDBgBBBBAAAEEEEAgogXMlkY5c+aU+bI4qGK2AcucObN2796tePHiBdWUcwi4rcDatWtVpUqVQIOMzonFjBlT1apVs1vkOet4RwABBBBAAAEEEEDADQXYCswNHxpDRgABBBBAAAEEEIhggU6dOgW6BZhzaLFixbKBF/Nl8q1bt5zVvCMQ5QSOHTsmE0Q0r2jRogU6P5PMfvny5Vq5cmWgbTiBAAIIIIAAAggggIA7CLBixR2eEmNEAAEEEEAAAQQQiDQC5othLy+vAMdjknQ/fvxYL7zwglq0aKG2bduqaNGiAbalEoGoJODt7a1Zs2Zp3LhxdoWWCSzev3/f3xTN78jLL7+sQ4cOKXbs2P7OU4EAAggggAACCCCAgBsIHCCw4gZPiSEigAACCCCAAAIIRA6Be/fuKXv27Dpz5oyvXBJmVYr5a/xy5cqpffv2atCgAVt/RY5HxigiQGDfvn2aPHmyfV2+fFnO3w/nUExwZciQIerTp4+zincEEEAAAQQQQAABBNxJgMCKOz0txooAAggggAACCCAQsQIjRoxwfRns/LI4derUeuutt9SmTRtlzJgxYgfI3RGIRAIm2Gi2/Zo4caIrr4ozL1GcOHF05MgRpU+fPhKNmKEggAACCCCAAAIIIBAsAQIrwWKiEQIIIIAAAgh4hID5EtBsT3Pu3Dldv37d14oEjwBgkkEKmL+879y5s93eyOSSKF68uCpVqqS8efMGmVciyE6f82S8ePGUPHly5cqVS4kSJXrO3rjckwRu3Lih/fv368KFC+GSA+jatWvatGmT1qxZo7Nnz1rqEiVK6P333/ckdubqR8CsXkqYMKHSpUtnVwOagDUFAQQQQAABBBBwAwECK27wkBgiAggggAACCIShwM2bN21egHlzf9SGX37V3bt3w/BudI1A2AnkyZ1LdevV15tvvqksWbKE3Y3o2W0FzBZ2ZouuBQsXafefu9x2Hgw8agrEdqxiKl+hgpo0aqSmTZvaXFVRc6bMCgEEEEAAAQSigACBlSjwEJkCAggggAACCDyDgAmofPHFF/r001G6c/uOapQuqKol86tgzsx6OXVyJXwhXoStQniG6XCJhwrcvntPFy5f1b5jZ7Rhx14t/mWHzv17UQ0dOV6GDhumnDlzeqgM0/YpcOLECfUfMFCzZv6g+ImTK3MpL2UoVF7JM+dR/CQpFSvuCz6bc4xAuAk8fvxY925d19Xzp/XvkV06uX2NTmxdZYMqvXv1VJcuXQiwhNvT4EYIIIAAAgggEAIBAishwKIpAggggAACCEQRAbMVTbu2bXX50kV1blZDbzWsqmSJEkaR2TENTxZ4+PCRlm/aqeETF+jQyXM2H0z//v1t8nBPdvHUuT969Eiff/65+jl+BhIkT6Nir/dS9rJ1FSNmLE8lYd5uIHD76iXtWjxeuxZ+r1QpU2jypImq4FjJQkEAAQQQQAABBCKRAIGVSPQwGAoCCCCAAAIIhLGA+cvYjz76yL7qVSiuz7q3Ucqk5KUIY3a6jwAB84X62HmrNHDMHBUqXFjz5s1XqlSpImAk3DKiBLy9vdW0WXOtXbtWJV7voaJNuih6DPJXRNTz4L4hF7h5+V+tG91DR39focGDB6tfv34h74QrEEAAAQQQQACBsBEgsBI2rvSKAAIIIIAAApFN4OHDh2rryD3xg2MrnFHdWqtd/cqRbYiMB4FQFzhw/KyaffiFHsWIozWOL9gzZ84c6vegw8gn8M8//6hS5Sr6+4K3avabotQ5C0e+QTIiBIIpYFav/DK2r95o00bjxo2TSXhPQQABBBBAAAEEIliAwEoEPwBujwACCCCAAALhJPDmG29o9uxZ+mF4V1UpkT+c7sptEIh4gYtXrqn++5/o8s37+n3zZqVNmzbiB8UIwkzg8uXLKlX6VV269UD1h81XwpQ87zDDpuNwEzjuyLuybGgbvdG6lQ2uhNuNuRECCCCAAAIIIBCwwAH+1CNgGGoRQAABBBBAIAoJDB8+XNOmT9eMYV3cMqiyfvtfmrp0vbbsORzoU1mwdottc+6/S4G2Cc6J+w8eBKdZmLQx21c9Twnu9cFt9zxjiUzXJk/8ohZ/2VsvOHaB8vKqpTt37kSm4TGWUBQwK/Pq1W+gf71vqP6IBVEmqHLqjw3666fp+nvftkC1Dv2yyLa5fuGcvzZmG8g716/4qw+visfP8b9tD+7dDa9hRur7ZC5eVbX6TtLEiRM1atSoSD1WBocAAggggAACniEQzfEfmY89Y6rMEgEEEEAAAQQ8UWDjxo0qV66cRnZpqQ6Nq7slwfZ9R1X57YFKEC+uts8cpTQpkvqax6L1W9Wy71cqlCuz1o79yJGoPIav8+ZD/kbdVKZQbo3+sL2/c9dv3lavr6Zp9Zbd+vfSVRXMmUmVi+dTrzcaKHassM/JMHnxOpk5bNp1QFnSp1LFoq/oow5NFSf20xNsHzn9j8bNX6XlG3fq2o1bKpEvuzo1qanyRfP6m+ez3CcoN383iOQVJ879qzJv9lOz5i30/ZgxkXy0DO9ZBPo7ktSPHPWpmn65Siky53mWLiLlNf8c3KHZ3WooVtz4aj3+dyVMnsbXOA9vXOJYzfCGXspeUM2+/MmVS8YEU36dMFAH18/Xg7u3FSteAmUqWkmVOo1SvETJfPUR2h+8zx7Vn0sm6ujmFbp385rS5Cmuwg066OWC5Z56q9vXvLX+u146t3eLTKAoToJEerlAOZVu00dJ02dzXW8CNtM7ltMjR0DNb0n0UnrVHzrHb7Xbf94x71ttmjhImzZtUsmSJd1+PkwAAQQQQAABBNxW4ECMQY7itsNn4AgggAACCCCAQBACd+/eVfVq1VQ0V0Z90q1VEC0j96m0KZPqwYOHWrd9rw6dPKcm1V51DfjS1etq2P0TmVUYC7/orZRJE7nOOQ9mLP9F5lUgRybVLOM714JZoVK+3QCt3bpH1UoVVN0KxWTycixct1UXva/9H3tnARfVtoXxT5EQBEVEwACxwMbAwu7u7m7vVa/dHVe9V5/X7u5uMbCwOzABAUVFBEFCRX17bTzjDAw4YIJrv9/MOWefnf/Red7zzVofapYpogzzXY6r97ihz9TFsEhrijZ1KiBFihRYvec4bj30QYOKJeLNpR/x5i1q9p6A01c9ULe8M1ycHHHm+l0s3X4YpQs5wtbGUrXmxMwTHzfVwEnoxNwsDewEk5Ez5qFSpUqws7NLQqvnpX6JwPXr19G2bVuU6zYROUomTRE5rj2SkPJBfFf5XHHDS597yFO5qappRMhLbB/ZHPj4AY0nbYaxefTf+/fv3mLr0EbwPHsAeau0QKF6nWBkmg4ehzfh8a2zKFCjjWqMb33yTog4mwbVg98Nd+R0qY0sBUqLOc/h+t4VyFygFMyE6BFXeRseis2ir8/VE6Aojdzl6ou9v4P3xcO4LwSkvFWaSYGJ+pPocmrZBJiYZ0Rqs/Si3lj1orpcZerENU2Src+Utzj8PS5g96Y16NG9O/T0Yv+QIMlujhfOBJgAE2ACTIAJJCUCL77/TxCTEg5eKxNgAkyACTABJpCsCCwQv8r39fXBrukzk/y+hnZqhAPuV2RUCYkEbYUIQWXgPyukAPJ3v3ZwyPbZS4FSgk1Zug2XPB7i5gMf2Vbb2/IdR3Hb0xeD2jfA6O7NZJPhnRtj8KxVmL/pAKqWKoQ65Ypp6/rVdX7PAjFk1mqUKuiAvf+NgH6q6H+aOthtwZRl27Dx0Gm0rlUuznnGLdgIiljZOnMwqpVyku16Na+JEm2HoMfEBbi5dbasS8g8unKLc1G/+I3GVUphzb4TGNC/Py5euvSLr5aXlxACAwcOglWuQihUt3NCuiWZtiXbDILn+UNCYDiCmwfXIn/11nLtR+cOQcSrF6jQczLS2+ZW7eeW6wZQpEu5ruNRrElvWV+gRlukEP+7vm8lnt67AmsR4fI9yunlk0ARKxQxYu9cRU5RuGF3rOpeFgem90aXVVfinPbRleMI8LyJyn1noFCdjtHtWgG0z6u7luD+6b0oVLuDrA9+4imPNQYvQMYcsaP0ojsnv/dKfWdiZZcSWLx4MXr16pX8Nsg7YgJMgAkwASbABJIEAfZYSRIfEy+SCTABJsAEmAATSCiBd+/eYYrwVunWuCqyWH3flC8JXVti2pPosGR0b5maa+js1SABYPfxC9jiegbli+VDj6bVNYYNDY/EA19/pE1jLFOEadxUuzgq/FuoNKvuolYLtPgUFUPpuRJbnr98hUVbDuHS7Ydah6D1h4ZHoE/LWipRhRq2+iSmbHF119pPqVwrBIJ8ObKqRBWqp4idqiUK4ZF/ACiFGpWEzKMrNzlwEn0bL9KsXbp8GQcOHEiiO+BlxyRw4cIFuLoeQumOo2TUV8z7yeFaL5U+agoBQU/fAG4LRshojQdCZLjrtg1ZncqhcP1uGtv0OLJJpPvKIOo10x8WbzkANQbNg7G4l5hC6bd8r5/GhU3/i7P7Ldd1yGCfVyWqUEOKIKE0ZCHPfKTgE1dnSv9FxaF8Q40mSpROeNBzVX3Q42hhxTxLDlXd73CS1toWBWp3xMRJk0G+QlyYABNgAkyACTABJvAzCHDEys+gznMyASbABJgAE2AC353Arl27EPDiBXo00RQcvvvE33GCPNmzYEz35hjx31p0mzAfd7wew8wkNRaM6BHrYaqjiF45MG+0XM1Dv6dwajZA68r8XwQhjbERHOw0PQucHLKJNFwpZDSL1o5xVL4IDsFOt/PYduSs9Ez58OEj1kzuh6J5Yz/4uy+EHyoVi2n+0trWOoMUkK7c8YpjFoDmCQ4NQ5vasf0Kctpay35XPDzhnC8nEjKPrtziXFgSuFEglx3KFc2H+fPnoUaN5JUyKgng/y5LpOg8K+GpYisEhuRcMmRzFD4jI3Fi8WgZ+RHocxcGxqao8dd/sb4DKZqDokVIiAn290agtwfSiJRi5D2Tt4pIHZaAQrakT26fx93j22U6rrCXz5AhWx44N/sj1igRrwLx5vUr5KsmwkxiFPPM0d+Dz+5dhY2j9kjAAjXbSlGF0papF78bZ+QlpQdTCu3RNGMWvIsIg++VEwgLfg6LrA6wdiwqfGb0lGbJ8lhERAAt27kIe/fuRb169ZLlHnlTTIAJMAEmwASYwK9NgIWVX/vz4dUxASbABJgAE2ACiSSwbetWadaeVTykT06lT4ua2H/qMk5cui23tWhUz6+KyLHPbIXLQoCgl7r4QSm2SBTx8PL7Ir6gkNfYJaJPSEw5fumW+AXxB+TPaYvB7Ruidrmi0ttF2yD3H/nD2MgQpkIcUi8pU6ZEdrGuu4+eyLH09GIHWdP6qFhbaD58pLpctjZ0QIDwiKHyNfPIAZLhWwsRodR/5kqEh4fD2Ng4Ge7w99kS+Stt274DBRv/+VtsmgzgyTfF99pJuV+KPjHN+DkNIlW+jXgNEj/Ib2X76JbwOndIxcZcmL9X/2sOMuVxVtXFdUKpxO667cC9kzvx+sUTKeJkK1YJOUrVgn3xqlq7vRQpwKikSR8t8Ko3Ms+SU16GB79Qr9Y4t7B1UF0/EV4ivtdO4fn96yIF2G7hLdNMpHuLTntIjYKfeIE8WZa0c0KU8HVRCrWpMXge1MdS7iWXY1prO2TJXwJbt25jYSW5fKi8DybABJgAE2ACSYwACytJ7APj5TIBJsAEmAATYAK6EThy5Aj6NK2sW+Mk1IpEB0f7zDglDNupUBTL15SmVUtj6+EzmLRkC0Z1a4rCjtmlyELpxqiQSBJXoVRfPSctxNHzN/BR/I/M46f0bYPaZYtqGMfH1d9TRNKYm5lovU3G83e8HyMkLFy0SROrjaffM1mn7Z6tdbR5dfDrMNnma+aJNXEyqahashDevHkDd3d3VKlSJZns6vfcBpnWBwe9RDaRZup3KCnEd2B6OwdpDE/7tbBzjLVtEhyoXNmxEOky2aNir6kg0/MnwkD+5NJx2DmmDdovOgXjdNHfFTEHuLZ3Bc5v+Behz/1klEuOkjWQo3RNZC1YRkbAxGyvfq34nsSMOKE2imk9RbToUkhUcV85GZR+TITkiP62eC+M7CktGpVgkQrsbfhrlOk4EjldaiMiOBCUhow8aGiPbee7qYzudZkvqbWxLVpZpMBbmdSWzetlAkyACTABJsAEkgkBFlaSyQfJ22ACTIAJMAEmwAQ+EwgICMCz589RKHe2z5XJ5Ozw2WtYsv2wTN11T0RtdB03DyeFUbKRoUGidkgiSK9mNTBPGNW7irHTCxHjpYhAKV3IQUadpE8bW9RQJgoIeoVDZ66CIkq6N66GtnUqyD7K/S8dDQ308STgpdZm4RFvZGofMxPt0RSG+tH/jKVomZglPPKNrDI3jRZtvmaemGMnl2vrDOawsjDHjRs3WFhJ4h8qCSupDAyRPmvuJL4T3ZbvffEoru9ZLvf70u8+9v/dE23mHhUMjFQDRIYGyfP3796i7sgVKlN7q1yFRLqsAJxf/w/uCG+WIg26q/qonzy6dEyKKhZCwCnZapCITqkio1XU28R1rqdvKG9FhgbHavIuMlzWGcZI8xWr4aeKEi36izV2g7/HRdw+vAnn1s0QacaCUan3NNmixqC5QugxlGnJqIJSjWXKVxyGJmlxcct/uH9qT4LTnn2aOkkcMuYsiNMr/BAcHIx06WJHLyaJTfAimQATYAJMgAkwgSRLIHZehSS7FV44E2ACTIAJMAEmwASiCTx9+lSeZM6Y9E3r1T/TwFeh6CEiRFILEWXj9IHoKQzrKapj3MJN6s0SfD6tXzu4LhiLMT2ao02d8lg6tjd2zhoGv2eByGMfd0SMg11mbJkxCM2ruWDN3uMo1W4o8ot0RBTtQqb38UW70CKt0qdDmBBQAkTkS8wSGBIqRR5tacBk308pwLyffDZyVsYgTlQypDOTx6+ZRw6QTN8yib8f/v7+yXR3v8+26PvOzMI62Xtq0CcaEfISB2f2QSrD1Kg/bq0URl4Kn5VTyydqfOBpLKLTAZKPSXpbTcEpR8nqsu1Ln3safdQvKvSYhLJdxkJPiDV7p3TB/Ga5sW1EM1zft1KmGFNvG/OcTOqpBD/1lkf1N1o/FeO0cf9/E0WnkKeLUvSNTGBbuDyqD/xPRqw8PLNfuSXTgpHXS8yipCl7IXxlknMxtYxOAaf8f35y3ivvjQkwASbABJgAE/j1CHDEyq/3mfCKmAATYAJMgAkwga8k8Pp1dBQDmbInp/LntKV4FhiM6f3bI0cWaymE7BN+K3M37pfpt8oUjv2A7Uv79w8IQmh4BEoWzC1fSvt7wt+EzOHzZs+qVMU6pkqlh+qlC8vX23dRMuKFfFZW7j4m12SRVphKuxTGkI4NQV4uMUsuOxuZ0sxLiCOW6dOqbodFRML78XNhsJ5XVRfzJGfW6Aen1DdmufXAV1YVE8b1VL5mHjlAMn0zFX8/lL8ryXSLv8W2QkNDoZ9ae0q95Abg8OwBUtio2HOKiM7ILozsR4CEhsvbFyBn6VrIUqC03DIZulP58D5KHtXfot5EyktDk2jhVf2ecm4m+js37StflFbs7gnhsyKM62l+etnkKSYiQVqgUJ2OShfV0TxLDnn+yt9bVaecvPCK9sYic3lthUSV/9XLggz2edF6zmGNJilEKrDUZukR4HlTpgMLf/kc/ncvwdqhCGi96kWZO65UZ+ptk/I5iU5U+HssKX+KvHYmwASYABNgAkmXAEesJN3PjlfOBJgAE2ACTIAJxEFA+bVvCqSIo0XSq163/wR2up0HiSfdm1STGyDj97nDuspfN3efMB+hYREJ3ti/a3ejaMuBUMzglQHmCbHGSKTqquicX6mK92ggUnNRWjGKdvHauwBrJvdD+WL5sP3oOVy75621L/m7UFm9x00elTcSZyLevEWtMtofPlI7G0tzlHHKg9MiMkbxW6H6d1FR2HToNGxEqqvCjvZUha+ZRw6QjN+UvyvJeIu/xdbooXtyL7dcN4jUVruRpaALnOp3ldvVNzJG1f6zIb4EcWB6L2nkTjf0RURL1kJl8ez+VQQ9fqiB5oH7PnlNniu6FPJooZRcbeefQMel51BaROaRr8nVXUu0dqdoGRJ4Ht84I83llUbkjXLn2BbQfXUDeuU+Hck/xiKbI54/uCaic6LTmSn3nz+8IfdD6cnIYyVCpDvbM7Ejzq2fqTRRHe8e3yHPM+cvqapLjifKn3v+HkuOny7viQkwASbABJjAr0+AI1Z+/c+IV8gEmAATYAJMgAn85gQe+Qdg4MyVMEltiPkjukvvEQVJuaL50Kl+ZSzbeUSm4Jo7vJtyS6djvfLOmC/8Vf6YtgT/DuoEijIhoWP5rqOY3q+91kgTZeCAoJBYoohyj47kcZPL1kaKHOr1yjmJRPRaseuY8PtIJ6NbLnt4YsSctXBxchSeLeWVpli24wj6z1iGoZ0aYVinxrJ+YPv6aDzwb7QbORuDOjQAear8s2Y3KIqFUpQpD90SMo9qQj5hAkzglyHw6qkPjs0bKo3Yq/81R/V3mxZo61QWBWu1l2m63BaMRLUBQmgRpWzn0Vj3R1UhPnRCmU6jQGmjfK6ewHVhTJ8pXwnkKFVTttP2dv/0HgT5aQoySruUqQyQp3IzRL2NjnxR6tWPxVv2x/aRLbBnUieUaDkAhmnS4cKm2Qj290bDCRs01r+oVX6EBT1D//0Bcojizf6U/bYMbYhSbQYLIcYaXhcOC4+VjfJ+6XbD5NEyez4ZOXNj3yqkNk2PnGXqSJN7j6Ob8ejyMeQqUxc2cUTGyAH4jQkwASbABJgAE2ACTOCrCLCw8lX4uDMTYAJMgAkwASbABL4vgQ8iNUy38fNkui4SPrJlyhhrwol9WuHgmStYJQSROuWKoWaZIrHaxFVBosO0P9tizPwNcBYmzVTMTFKjc8Mq6Nq4alzdZP3zl8GyX7yNxM38OW21NiHhY9PfA9Fs0AxMW75dvqhh0Tw5sHrin9BP9fmfqh/xER8+iPfP1gOoXKIglozuhd5TFqPN8FlyjrRpjDH1jzaoVspJNWdC5lF14hMmwAR+CQKUHuvA9J4yGqVy3xlIa20Xa13luo6D5wVX3Dy4RqYEyy58VChFVsOJG3FwRh8hcjRX9clesgaq//Wf6lrbiceRzXggxJX4ioWdI0q3HaK1SbailVBzyAIc+udP7J7QQbah1GMVuk+EvXMVjT4fPryXgohSmbtcfZR/MRGnlo3HrnFtlWqkTptB+KzMVQlC9L1Wf+waOcf5jbNAL6UUFCnKyneboFzykQkwASbABJgAE2ACTOA7EEghwmbV/vP0O8zAQzIBJsAEmAATYAJM4AcTcHd3h4uLC+7tnCtTRv3g6ZPkdC+CQ3D7oa8UL1wKO2qIGj9iQ09fBMmUYYUdsyOjmt+KLnNHRb3H5Tuecu3OwlclLsN7Gutr5tFlLUmpTe2+k5HXuQzmz5+flJbNa41BYNSoUViyfjtazTsR4w5fEgFKwUXeJhEhgciQLa+MAPlRZMjj5em9K8Ls5SPIVyWlnp7OU78JC0Ggz12Ql4qZEJPSZ82JVAbafcNCnvnipd99GJqkhYVtbhgYm+o8T1JuSPte0s4J58+fh7Ozc1LeCq+dCTABJsAEmAATSHoEPD7/DDDpLZ5XzASYABNgAkyACTABJvCNCGRIZybM4vN9o9ESPoy18EShV2JKqlR6KJ4/l05dv2YenSbgRkyACfxSBMiPxCpXoZ+yppR6qZApT+Ie+FOEi659zayygl5cmAATYAJMgAkwASbABH4cATav/3GseSYmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACTCCJE2BhJYl/gLx8JsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkzgxxFgYeXHseaZmAATYAJMgAkwASbwwwjsOXER246cTfB8fs8CsXL3MTz0e5rgvont8EGYU39tIdvAgJev8Obtu68divszASbwixIIff4YNw6sRtBjzwSv8IH7Ptw9viPB/b6mw8ev+G4jf5YP79/rPD19B4YHByDq7Rud+3BDJsAEmAATYAJMgAkwgcQTYI+VxLPjnkyACTABJsAEmAAT+GUJ/L1iO16+eo1GlUsmaI03H/igz5TFWDiqB3JksU5Q34Q2Xr7zKHYcO4dTVzyQI6s1KjkXwLieLWBooJ/QofDP6l0Yu2AjVk74Q2PPS7YfxsLNB7WON7FPK1QvXRhuF25i0L8rtbZRKp0c7bF4dC/lko9MgAn8BAIBXrfg+m8/VB84F+aZsydoBefWzRQG9i/hUL5BgvolpvH1fatw/+Qu+N04jXSZssOuSEWU6TRKmM8bfnE4jyObcXX3Ejx/cEMIK1Givz0K1++KQnU6IUXKuH8XeWHjbJxaPgG1hy9V7XHXuHbC1P5BvHNW/+t/sHEsFm8bvskEmAATYAJMgAkwASYQmwALK7GZcA0TYAJMgAkwASbABJI8ge5NqiPizdsE7yO3XSaM7dEcTrntE9w3IR1W73HDH9OWoFjeHPirXX3c9X6MeZsOwPvJc6yZ1A9kSK9ruXj7ASYs3qy1+aXbD+H3PFDrfgz1owWcFClSxDkfRcDc9/GXwo/WCbiSCTCBH0YgfdZcKNNxVKLM6J3qd0HUm8jvvtabB9fi8Oz+sHYoiuLN+yHQ9x4u71iAV0+9UXfUCpChfVzllusGHJzRG+Zin0UadpfrvX9qN47OHYLI18Eo2Wqg1q7+dy7h9MrJse7RXHqptM/36qkP3oaHivUkXMiONRFXMAEmwASYABNgAkzgNySg/V9ZvyEI3jITYAJMgAkwASbABJITgda1yiVqO9mzWEmhI1GddexE6caGzFqNUgUdsPe/EdD/9ODPwW4Lpizbho2HTkPX9YeGRaDTmP9gaW6Gpy+CY63AU6Q0q1AsP9ZPHRDrnlJRvlg+nFk1VbnUOP41cwVCxByzB3fWqOcLJsAEfjwBit4o3qJfoibOV7VlovolpBOlKnNbMAKZ8pVA0793ClEjWrRwz5obZ9f8DY+jmxHfOi5tnYt0mXOg1exDMDQxk1M7N/8DS9oVxtVdS7UKKySO7JvaDcbpLBH2UjOFY52Ry7Qu/9XTR1jVoxxKtR2SKJFK66BcyQSYABNgAkyACTCB34xA3LHEvxkI3i4TYAJMgAkwASbABJICgffvP2D6yh0o33kknFsPwrD/rZFRHn2nLsbiba6qLQz8ZwV6TFyguqb0XgNmLod/QBA6jpmDPA37okCTfug5aSHCIj7/ipuiPxr2n4pjF26o+n7rk93HLyA0PAJ9WtZSiSo0R6tPYtAWV3edp6Q9RQkmI7s21drnge9T5LK10XrvS5WuZ69JpkvH9IaVRbovNef7TIAJfAWBAM9boNRVS9o5YceY1rjluh6PLrth94SOMoUXDU2RGdtGNJX1dP3k9gVsGFALz+5fxY39q7G+X3XMa5JD1pGnino5Om8oDszoo171zc/vu++RUSBFG/dSiSo0Sb6qLeRcd922xTnnm7AQvPD2gL1zFZWoQo3TWNjA1qksIkOD8D4qtofUkf8Gy5RhpdsPjXNs9Rvk+7J/Wg8pqMQVAaPens+ZABNgAkyACTABJsAEtBPgiBXtXLiWCTABJsAEmAATYAK/JIHmQ2bgoPtVOOfLifJF8+H0VQ9sPXxGChXv1YySz9+8Lz1WlE1cv++NwOBQ7Dl+EdkyZUSTKqVw8dZDrNl7HCGvw7F2Sn/Z9EVQKA6fu46m1UorXb/58b6vvxyzoogkUS+21hlgoJ8KV+54qVfHeb5JRLZsPHga++eOwnNhXB+zkGBE9Tmz2uCyhyduPfRBWlMTOOfNCRtL85jNNa4DX4VK0Yk4UUQLFybABL4fAb8b7kIwaYZUhqlhX6yy8BLRw9H/hiCNZSYE+d5Hhe4TARHAEfEqEN4Xj8KhQmO5GBIbntw6h2PzhoGEmbxVmiNjjoK447YVeyZ2REsR+WGVq5Bs6y9EGPJY+Z4lyO+hHN6ucHmNacwyZoWevgGe3ruqUa9+kVJPD81n7kVaazv1apDgEuB5G3ZFK2qINdTI4+gWGQXTbPpuYVz/XKNfXBfnN86SAlW7BSfi9WyJqz/XMwEmwASYABNgAkyACUQTYGGF/yQwASbABJgAE2ACTCCJENjpdl6KKr2b18TUP9vKVX8QYkrX8fNBIsOXis/TF+jfpq40iCdfEepbrtNIuF28+aWuGvfJcN7Dy0+jLuaFRVpTdGtcLWa1vL7/yB/GRoYwNUmtcT+lMGbOntkKdx89AUXm6OnFHVxNXiz9/l6Gv9rWg4uTI7YfPacxFl14+j2TdZOWbMGTgM8PVEm8GdyhIYZ0bBirj1IxYMZyvAoNk6yUOj4yASbw7QlQBMUxEU1CwkOb/47CzCqrnKRok95Y26eSThMGP/FCu4WnhChhK9vbFamAXePbCfN4d5WwostA94ThfOCjO/E2TZ3WAk51tacGDBJG8akMjWFgbKoxBpnOp7Wxx0vht/Lh/XvhaxLbQ0rfyASZRQoxpVzaNh+hz/3gee4QPn54jxLNo8Vv5T6l8zoyZ6D0cclSoBTundyp3IrzGOB1G2dWT0PBWu1hYecYZzu+wQSYABNgAkyACTABJvBlAiysfJkRt2ACTIAJMAEmwASYwC9BgKJLSBAZ3b2Zaj0kRozs2kQnYcXIQB/DOzeWY9AA1Ldkwdy4ds8bj4XBe+aMFqpx4zvZduSsViFDvQ+l34pLWCHfE3MzE/XmqnNbG0vcEUb2IWHhok0aVb36SVTUe+mrktPWGsO7RP9yXf2+cv5QzEPFOkM6LBrVE9R+78lLWLzVFROF2X3G9GnRsX7sB7cenn6gPQ5q3wBZRRQNFybABL4fgecPr8toE+fmf6pEFZrN0j4vHMo3hMeRTV+cvFCdjipRhRpnLhAdcRfoHb9IEnPgeyd24N6J+AUK8yw54xRWgp94wshMe9rAtEIweulzV6YKMzLV3kZ9PadXTBLm9RGyysLOAamMPgvRH95HYd+UbjAXfizkk6JrObduhowKKt1Ot7Rhuo7L7ZgAE2ACTIAJMAEm8DsSYGHld/zUec9MgAkwASbABJhAkiTgJSIwslhZyGgP9Q3YiygPEk2+VCyFkGBkaKDRzNw0Wrx4HR6pUR/fxRLhObJQCBXxlRTx3DQUa1WPIFFvGh7xRgo/ZibG6tUa55OXbsXNBz44vXKKhkeLRiNx4eKUB/vmjkTRPDlUzEjsqV+hOJyaD8A/q3dpFVb+XbNbpiQjDxguTIAJfF8CwU+85QTphWARs+gaVaFEuSj9FeHibeRrpUqnY83BC1B94Nx425K4HVfR0zdExIvoVIcx27yLDIf4cosVzRKznXL9xy4/BD1+iMc3z+LU8olY17cKuq65DpP0VnAXUScBXrfQdp5brPRgSv+Yx6DHniKqZRfI/4WibrgwASbABJgAE2ACTIAJfB0BFla+jh/3ZgJMgAkwASbABJjADyMQJFJT2WQwjzUfpc36GKs2dkXqGKJK7Ba61VAqra8pVunT4b6PPwKE/wmJPeolMCQU6UWkSlxpwN5FRWHGqp2wFz4xM8VRKZTmjMriba44dOYq+omUZ47ZMsPSPK/SRHUkI3qn3PY4JfxpwiPfqEQXauArxtnkelqKL7QOLkyACXxfApGvg+UERmbpY01EKbB0KakMPkdz6NI+rjaUjix2kq64WseuNzHPCEoHFh4cAON0lhoNyN/FyNRcaxowavjxo/gWFy9KG6YUikihF9UdnNEHXuddkUf4yJzf8C/S2WTDuQ2zlKYibZivPL+2Zxm8LhyGc7O+sLB1UN2/uGWOHL9A9TaqOj5hAkyACTABJsAEmAATSDyBr/uv4sTPyz2ZABNgAkyACTABJsAEEkjATqTJunzHE6FhERr+JPeEJ8mbt+8SOFrim6/a7Yard73iHYDSbA3t1Ehrm1x2NlLU8BI+KerCCpnNez9+jnJFY4sh6gPlzxnto3D9/iNVNTGh4uMfgFevw/E6PAKHz16TglPVktHm1UpjeoDp7f8caYyNNEQVur9851Hp79K+bkWlOR+ZABP4jgTSWkX/fSYT+hwla2jM9PzBDY3r731x88AaPLt/Ld5pTNJnRMnWg7S2Mc+aU/q6BPt7awgr7yLDQJ4oWQuV0dqPKslU/rSITGkwYQOyF6+q0S61WXSESWjAY1lvaZ9PHgMefubzNjxU1oU888Gb169EyrHP0To0/23XDbDJ64z0trk1xuYLJsAEmAATYAJMgAkwgcQRYGElcdy4FxNgAkyACTABJsAEfjiBQrmz4cKtBzh55TZqlSmqmn/ZziOq8x9xQmb3ZGAfX8kpPFbiElaaVi0tBYzVe9xQPH8u1TDkaxLx5q3G3lQ3P53op0oFd5ECLGbZd+oSmg+eiQm9W6FR5ZLydsuh/0hPlTs75iCT5edfw5+/eR9+zwIRU3ChTkfOX4e5qQkqFIt+cBlzHr5mAkzg2xKwyOYoIjL08OiyG8p2HqManMQJnytuqusfceJz9YRMlxXfXBRBEpew4lihMW7sW4VbB9ciUx5n1TB3j++Qfik5StVU1cU8scwWLSj7CA4xhZUb+1fJ5pbZ88vUX23nH4/ZHQ/PHsDOMa0Fw7HCm6aBxn3f6+54/+4tHMpp1ms04gsmwASYABNgAkyACTCBBBFgYSVBuLgxE2ACTIAJMAEmwAR+HoEhHRtiw8FT+GPaUrwICoVFOlMcdL+CLa5nVIb0P2J1y8b1Ab0SW8oUzgN6rdh1DJSWq4ZLYVz28MSIOWuFL4oj2tYprxp62Y4j6D9jmRRphnVqrKrX5aRDvYrYc+Ii6v0xGWN7thDm9Ta44+WH4WKelClTYGyP5hrDBIW8xpU7XkLYKSLuf07Ho9GIL5gAE/imBEwzZEKRht1xaes8HJjeGw4VGkpvkas7l3zTeXQZrNbQRaBXYkuWgi6g1439q2FsboXsJaqJCJirOLFoNDLnL4V81Vqphr6+byWOzBkoRZpSbQbDXkSpZMiWB1d2LoKhiRnsilXCa+HXcu/kTimaWDsUQfaS1VX9E3LiczlaiKG1cWECTIAJMAEmwASYABP4NgRYWPk2HHkUJsAEmAATYAJMgAl8dwLWwl/FdcFYDJixHH2nLZbz5c2eFXvmjED5ziMRn+H7d19cAiYg8+dNfw9Es0EzMG35dvmi7mQyv3rinxqG9B9FMq8PH8S7sB9IaKleujDmj+iOUfPWg6JXlJLFygJ754xEQREBpF5OXL4tfQ7Uo2jU7/M5E2AC34cARaoYpkmLy9sW4PbhDdKLJE+lprLu7NrpMDAx/T4Tf+NR6butwbi12DG6Fc6tmyFfNAWJInVHLdcwmqeUhB8/fJC+J9SGfFTqjV2D/dO648yav+WL6qnkdKmDir2mCH+WxP3nO0X+pDI0FsJN/GkWo2fjdybABJgAE2ACTIAJMAFdCKQQ/6BLxH+m6jI0t2ECTIAJMAEmwASYwM8h4O7uDhcXF9zbORc2lrHN3n/Oqr7trJEiZRb9I44M6QNfhSJbze5YMLIHWtcq920n+s6jPX0RhGv3vFHYMTvIl+V7FEovdvuhLygihaJWbK0zcESKAF2772TkdS6D+fPnfw/sPOYPIjBq1CgsWb8dread+EEzft9pIkODhbCSTk5ydO4QeJ47iC6rrn7fSb/D6K8Dn+K58ECxylUIZGqvayGxhfxYXvreF2KIEcyz5ARF9XCJTSDkmS+WtHPC+fPn4ez8OfVa7JZcwwSYABNgAkyACTCBb07AI3E/efnm6+ABmQATYAJMgAkwASbABL5EYNGWQ1i3/yTmj+yOPPZZVM13HjsvzwvmslPVJZUTisKh1/csJD4VzZvje07BYzMBJpBIAu/eRGDL4AawyVMMFXpMUokqZLjuffEoLHMUSOTIP7dbGgtr0CuhhSJX0mWyl6+E9uX2TIAJMAEmwASYABNgAj+OAAsrP441z8QEmAATYAJMgAkwga8iUMghGwbNWom2w2ehde3ycMyWGRdvP8C/a3ajdtmioLRgXJgAE2ACSYmAvmFqKaaQt8ibsBDpSxL5OlgYwK/D60B/VBswOylth9fKBJgAE2ACTIAJMAEm8JsQYGHlN/mgeZtMgAkwASbABJhA0idQokBubJw2EGv2Hse/q3chKDQM9pkzokO9Spj6Z1vo6bHhetL/lHkHTOD3I1Br2GKcW/8PHl12w61D66BvZIKMOQuiwfj1yFKg9O8HhHfMBJgAE2ACTIAJMAEm8MsTYGHll/+IeIFMgAkwASbABJgAE/hMoIZLYdDrg8jDHxoeibRpjD/f5DMmwASYQBIkYGhihnJdxsqVU9SKQeo00sw9CW6Fl8wEmAATYAJMgAkwASbwmxDgnzX+Jh80b5MJMAEmwASYABNIXgRSijz831JUOeh+BVtc3ZM0pPDIN3j//oPOewgNi0Dgq9A420dFvQcZ33NhAkzgxxEgkYV8Rr5V8Tzvijtu277VcL/0OO8iw/Hh/Xud1vhRiPNcmAATYAJMgAkwASbABBJPgCNWEs+OezIBJsAEmAATYAJMINkQmLVmDzwfP0OTqkkr7Q5F7oxfuAl7T13CXe8nSCXSoTkI75kJvVqiSslCcX4+JKiUbDsEaU2McXH9DI12R85dx+j563H7oR+ixENKW+sM+KNVbXRtVBUkaGkrGw6cQtcJzcyQAABAAElEQVTx83B353/IZJleowmtsXT7YVpFH1sbS2ydOVijPV8wASbw7Qhc3PQ/BPt7w7FCo2836A8eaVnHYshS0AXV+sf2myGB5PSKSXh4dj8Cfe4hpV4qWNjmRtnOY5GtWCWNlQb5PcDVXUvx4Mw+vBWRQZnylUDRRj1hW7i8Rjv1i7jm3jWuHV6K8eIr1f/6H2wci8XXhO8xASbABJgAE2ACTCDJEtD+X4ZJdju8cCbABJgAE2ACTIAJMIHfiUDX8fMxU/jNWJqnxciuTVC7bFHce/QEDQdMwz4htsRVek9ehKcvgmPddrtwEw36T4WP/wu0rVMeXRtXRbiIWhn4z0pMWar9V+8hYeGYtXZ3rLGUisfPX+LWQ18pymRIZwb1l7mZidKMj0yACTCBWATIcyb4iVeseqVi//SeOL9xFozTWcKl3TDkKFkTL33vY9uIpkJsOaA0w7s3EdgxpjVuHlwrBZdCdToh6LEndoxuBb8b2qMV45ubBBy9VNpfr188wUufu0Lk0VfNzydMgAkwASbABJgAE0huBDhiJbl9orwfJsAEmAATYAJMgAn8JgQe+Qdg06HTaFS5JFaM74sUKVLInbtfu4PqPcdjzLwNqFWmaCwai7e5wvXsNZibxhY1pi3fLtsfXzoR2bNYyfNxPVrAoX4f/G/9Xgzt1Ah6IiqGyvKdR0Ep1I5fuoXXwu8mruLp90zeWjKmFwrksourGdczASbABCSB0IDHOLPmbzy7dwUBnrfipPLqqQ/uHN2C3OUboPawJarvwMc3z2LjX7Vxcul4IbTUkP1PL58EilhpOHEj7J2ryLrCDbtjVfeyODC9N7qsupKgueuMXKZ1Xa+ePsKqHuVQSkQEWuWKO2pQa2euZAJMgAkwASbABJhAEiLAwkoS+rB4qUyACTABJsAEmEDSIHDh1gOMW7gRVzw85YLzZM+CwR0aolopJ40NnLx8G9uPnsPR8zekl0epQg4oUzgPOtarpHp4f+7GPYyaux6jujeVqa42CyHB5+kLVBdjDe7YEJFv3mHEf2tB7TKYm6FZNRcMbFdfNU+7kbPlw/yyhfNi3qb9OHHptojuMEPLmmXRr3WdOFNbKQPsOXERC7ccws0Hj5A5owXKFc0rxQUzkUJLKZEiomPmql3YcPAUHj8PRFarDChfLB8m9WkNU5PUSrNvfqQ9U2ldq5zqgSJdly7kiOyZrXDH+7EUPNIYG1G1LB6efhj+vzUyVdjyXUfx8cNH5ZY8+on1UyovRVShStpD0bw5cPqqByLfvoVJ6ujxHvo+RXBoGArlzoa7IkrmRVCIxljKxQM/f3ma09ZGqeIjE0iSBPzvXMQp8YCeHvhTscjmiJKtBqoe1FMdmc/fPLAG3peO4umdS0hv54DM+UoiT6WmsMyej5rIcujfP/EhKgolWv2FCxtny/bmmbMjX/XWyFu5GS5tnQePo5tBIgM9oK/YayrMM+eQfSkS49rupbLOQwgLnuI65JmPTDtVvvtEpM+a69Ms2g+Rr1+JfUzAYxGpEfHqJTLlLY78Ndsie/GqGh102a9Gh2908TbitRBBHsLA2AxWuQureMcc/snt87IqX9WWGt+BmfOXRLpM9jJqhMYySJ0Gt1zXIYN9Xo3PysQ8o7iujNuHN4L2Smm7dJ075lromtKS7Z/WQ35e9OeCCxNgAkyACTABJsAEkjMBTgWWnD9d3hsTYAJMgAkwASbwwwnQw/zafSbiWWAwereohYHtG0hvjcZ//Y3DIkpCKSdElEOdPyZJw/jKJQqifb2KePwsEP2nL8OY+RuUZggKCcOZ63cxbPYaDJ29GlmEaJFGPNhfuuMImg+eiSrdx+DaPW/UKSceiL2NwrgFG7Fy9zFV/+MXb2H1Hjc0/msa3gkz9o71KyG1kYGco+/UJap22k4oeqPl0H9ApvBdGlYFCUSLt7qKOcfCPyBI1aX/jOX4e+V2uDg5SjGlWmknrNt/UqbUUjX6DickcFCqLhI91Aut92XIaxgZ6CO1oYHqFglAHUbPQWmxzp7Non/Frbr56aRu+WJ4EvASh85cVd2i1GInLt8SolI+lahCNyf2aYUD80bLVyXnAqr2MU8oYiWr8GmhqJb9py7Lz+fs9XtaPVdi9uVrJvCrEAgUqZ02D26A8KBn0pejRMsB+Pj+A7aPbA7vi0dVyyTvjeOLRoGM1Iu36C/8PhxwY99KGUHxOjBaZKTGAQ9vSjFl08C6eOJxAVkLlcHjW+dw4O+eIo1VM5xYMhZpMmSSoozP1ZPYMqShfHBPfUOe+co5aa47QnzJ6lQW2UtUl/3X9K4kBIVo0ZXaxiwk1KzpVR63XTcic4HSQshphVdClNkxuiUubZuvaq7rflUdvuEJMWs+c4981Rq6KM6RDVKboFDdzrB2KKLRhthHhLxEKgMj8UotxKNAvBFikjYvFUWsenYv+jtP17k1Jvx0QSnJ/IWYVrnP30gRhx+Vtn5cxwSYABNgAkyACTCBpEiAI1aS4qfGa2YCTIAJMAEmwAR+WQJbXMUvoMUD/CWje6OQQza5zj4taiK3SCVFYoNiqL5ZtEulp4frW2Yh3aeUVAPa1EW+xn9i/+nL8qG9+iafCqHGY9v/YJk+LT5+/IjK3caAImM6CEFm9uDOMvLE+8lzFGjSD24Xb6J93Yqq7l6Pn2PKH23QRwg9VEZ1aypEnclYJQSXLo2qoLBjdlVb5eSuEIimLNsqo2y2zBik+jV0KxHpUu/PKfhv4z4porx5+w5k3F6jdGEsGNlD6S4jRgbPWoX7Pv7IFUekxo5j5+Dh5afqo+3EIq0pujWupu2W9FMhT5WYZd6mAzKSpHGVUqrIH2oz4r91wlclCDtnDVXtJ2bfHk2qC363hBD1N0oWyA1DQ30Z5WOTwRxjujeL2VynaxJWQsMikK/RH/LPhtKpsKM9Fo3uBcdsmZUqPjKBX5bAnWPbECV8OmoOXoCMOQvKdRYRxueLWuUXEQ8bpG8HCSe+107Cudkfwjx9jGovGbLlgduCESJC5CwcKjRU1YcHPYdL++EyaoUqHSs2lkKN3/XT6LDYHeZZcsq2lKqK5gh+4qmqoxtvw0PRbuEpGJqYyXaPLrth6/AmUpRpMH6drIv5dnLZeCnMtJx9UGWsXrrtUOlJcnLpOOSt0gKpzcyhy35jjk3XJGJcFdE0Xyq5ytRDBhHx8zUlR6maoFfMcnnHQimkOJRvKHxO9FQm82nSW8dsquIZHvwi1r2EVAR43caZ1dNQsFZ7WNh93b4SMi+3ZQJMgAkwASbABJjAzyLAwsrPIs/zMgEmwASYABNgAsmSwIdPqaWW7jiMqX+2hbGRIfSFwS+JIupJp0jk6NG0ukpUIRhvRVoc8v14JczQY5b2dStIUYXqyUskX46sUljpWL+yKp1XtkwZZWTEHa/HGt3TpjFG7+afH76lFL8kHtS+PigV2RGRhkybsLJk+2EZUdFNRIQo3iU0aEURmUFCyeZD7lJYeS9Sv1A5edkD1+56q8Sk7k2qoZ1Ys5HB54gR2VDtbduRszIVmlpVrFOaKy5hJWbjIBGlQibz5LuS2y4Tpvdvr2pCkSKLth7C2in9YS1EkrhKWsHfVkSX3Lj/CJc8HsrPjoSsVMJXJT4flbjGo/qHfk8RGh4hhJnmoIiYF0GhWLvvuBS2Woioo9MrJ2tEwsQ3Ft9jAj+LwMeP0X/Xr+1djgrdJ0HfyFiYl+uj65prUuyldRkYm6LFrANIn0UzFVcqw+jUgW+EEKJeKKqhWNO+qiolVRhFoCiiCt3MWshFCiuBj+5q1Bdp1EMlqlA7uyIVkCmPM0hgob+36t9ddD8iJEh6klB6LUp7pRQ9fQMUEIKA77VTeHB6DwqItGC67Ffpr34Mf/VCCgzqddrOaX9fK6zEHDcyNBhH5w2RezQX6dAofRoVEqSoGJmmk0f1NzOrrPKSIlq+ppxbNwOpDFOjdLuhXzMM92UCTIAJMAEmwASYQJIhwMJKkvmoeKFMgAkwASbABJhAUiDQqUFlUDQKGZvTA37y+6hUvIB4oO4MOxtL1RYcRJRC4KtQ/G/dXpy/eR9kxE6eHfQA3jpD7IdfdjYZVX3pxFCkuaKSyVJTJNATDyrfvYuS95S3HFmtYz1gzGOfRd72+mSsrrRVjpT+isqavceFCHBCqZZHSrXlLyI/KLUWCUfDOjfChEWbUabjcDgIQYNSZpGfTBWR4kwxetcY4NPFkjG9sXBUT223VHXRdvSqS60n9ACVUqNNFGugFGCUHowM5xV/F4pS6TlpoYziqSc+h/hKtR7jcNvTF/8M7IgmIuLFSKQSo7RgfacsllEsF9ZN1/gc4xtLubdQRPIY6usjrxDDqOTMaoOSBXMjbRoTzF63B7vcLkjPG6U9H5nAr0igUK0OuHtsq0jrtUo8uN8K8vGwK1oBOUvXQVprW7lk8vIgYcNXRJxQ26AnXtL75JW/t9YtmVhYg0QNpejpG8nTNBk0/YhSpNST9e+j3ilN5dE8hoBDleT7Qt4jr188gamlZjQYmbdTeRcRhj2TOslz5Y2iX6gE+3vJoy77lQ1jvKXPmht9d8UfiUdd9FJ93neMIRJ8Sd+B1/eukCJtZGiQTA9WttMoKXTJufQN5ZgkvMQslDaMiqEW0SVm27iugx574t7JXSjauBdSp7WIqxnXMwEmwASYABNgAkwgWRFgYSVZfZy8GSbABJgAE2ACTOBnE8hiZYFL62dg36lL2Hr4jIzkcBXeKmQwP65nC2EYX1cucdba3Zi4eIt44J5KGtZXdM6PQR0aYI4QWrz9n8fahknq6AdjMW/E/EV2zPt0bW0RW6gx/jQepbrSVl6+ei0iYVLAQAgCMYuLUx5Z9UE8zKMyuENDIUKUFqnOTuCg+1VQtMviba5CQLCW/iNWWuanfgZi719bAoRhfJdxc3FURN6ULZIXU0XKs4K5s2kMS+shEStERAL1mLhAdY98YuiBJNWRsTz51JCoUqZwHnRtVFXVrn6F4iBPlP827BMiyHn0bVlbdU+XE20RQdSvuvCiIWGF5uTCBH51AqYZM6PDkrMg4/i7btuFeHJK+JwcwYnFY1Cm02g4i8iT14FPsXVYYwQ+uiON0m0ci0pDeAMTU7j+2y/WFvWNTGLVUUUK8T9dSpr0VrGa6RtGj0n+IjFLpPAdoUJiTkoRbaNejMzSw7FSE1UaK132q95fOafvZH0RufGjCqXwIsP4R5ePSZ+a8t0nImMOTc8nMqmnEvzUWx7V38iLhYrxVwgiF7fMEc71H1Ggehv1ofmcCTABJsAEmAATYALJmsDX/9dsssbDm2MCTIAJMAEmwASYQMII0MN7ihppULGEfH0QqbJOX72D9qP+h7HCWL678PCglFKj521AhnSmuLbpX1VkBc00fcWOhE2oQ2vy+IhZfPyj8+nnts0U85a8ts+cEVfvemGgSBmmRLcoDcMiIkEpwCha5a2IjqEIForGGdm1qXw9E34wf4t9UOqtBZsPYkyP5kpXjeOq3W5yDo3KGBcZhafM0E6NYtRGX0ZFvUeLITNx8fYD6TND0ULaSoZ0ZiiQyw4PRESQeiF/GBKHrou0XyQi3XrgI2+XFcJKzEJRRySsBIeGxbwV77Xfs0C5vqJ5csg0beqNyfuGiqV5WvVqPmcCvySBN2Eh0q8jd9l6oNdH8R3gd8Mdeyd3wallE+BUrwvIvJxEFfJXIZ8VpXiePaicftNjsIiIUfxelIFDhBE9pbzSFjmR1sZONiPD9lpDFipd5PHD+/d4G/FaJYrosl9tAkrYy2c4u3aGxtjaLvLXaA2rXE7abulc9+F9FHaObY2ndy+jyp//SH8TbZ3Ns+SQ1doih14IbxQq1kIES0x5FxmG264bYJPXGeltcydmCO7DBJgAE2ACTIAJMIEkSYCFlST5sfGimQATYAJMgAkwgV+VQH1h7E7REdc3z5JLJD8TiqSo4VIYq/ccx2thYu4rHrZTpARFQijpqqgxPYS/ft8bJCZ8y3Lf11/6fOTI8tm4eLUwrqdCgoO2Ujx/Lul/cuD0FQ1h5dXrcBRs0g8Fctthz/9G4PilW2g0YBoWCxP2FjXKyKEoQqVf6zpSWIlPiHC7eBNkYB9foUiSuISVvSIqiNKo0VxxiSo0NnnZ0CtmKdNhuExn5r5yirx162G0sLJdrGl4lyYazckPhkq+HNEpjzRuxnNBvi9tR8xGx/qV8L8hXTRabj1yRl6XdnLQqOcLJvArEtg6rInwKAlE5xWX5PLIHyVroTKwL1ENtw6uFem1XkN5cJ+3aguNLTw8d0Dj+ltdeJ47hNzl6quGI1HD68JhZMpbXFWnfpIuU3YpuHhfOgpKK0YeMUo5v/Ff0HdB85l7ZZozXfarTVghr5IbB1Yrw8Z5zFLQ5auFlYdn9sPf46L0qSHT+LhKGgsbZClQGo9vnBF+K15Il8leNiUGd45tAd1PrMjje90d79+9hUO5BnFNz/VMgAkwASbABJgAE0iWBFhYSZYfK2+KCTABJsAEmAAT+FkEyEtlzPwNGCte9LA/tfDoOCFM4jcePC1M4u2lAT35dlBqL0oVVrVUIWm0TqmmyKfEzMQYYeFvQB4nZMD+Lcr79x9AJumjujdDLuHvsVOks5ovIkkaVioBFydHrVNQKqzFW13xz+pdyJwxPUoUyC2FnzHz1yP4dRiGdGwo+5UU9RnMzTB12TbZjtJweQqzdiXypnrpwlrHp8pl4/rIV5wNvnDDXUQCUQmLeIPhc9ZobT1WeK3omnKMInMoMoXSijXoPxUtqpeBrU0G7D5+UfrmONpnlunCtE4UR2X+nLYgkYo8d9KbpUE9IaZ9ECbgGw6ckvOQuFYsb844enM1E/h1COR0qS0iU8bL6JSCtTsIo3Ij+F49JfxWNsuH8sbpLOXR67yrbFOsaR+EvXwuH9zfP7VbboRM1COF8GCU5tuIx7cPb4CJhZWIoKmPN6+D4bZwpDSdp3RY2gqlACsjvEcoLdn+ad1FVM2f0oeEBIpz62bCtkgFZMpXQnbVZb/a5qCojX57NaPjtLX7FnWPb0YLvuQZc3zRKK1Dluk4SqY+K96yP7aPbCG9ZUq0HADDNOlwYdNs4SnjjYYTNsTy4dI6mJZKn8vHZS0JRVyYABNgAkyACTABJvA7EWBh5Xf6tHmvTIAJMAEmwASYwHcn0LdlLdx66IuZQpCgl1KcHOxVIgJFqcwf0V0aqjcXggcVc1MTTO3XVqbX6j5hAYq3GYzgk9rFAmVMXY8ViuWXJvdths+SkTLUj3xE/h2kad6sPp6hgT52zh6GruPmofPYuapbJPasnzoAZQvnlXW0l2Vje6PbhPmo1efzw0zqP1oIORSp872K+7W7cmjyc4mrjBCRJ7oKKxRdtHx8XwycuUIKKUfOXVcNSwLUvOHddR5L6Uh+CxsEr95TFsf6M9GlYRVMFp4wXJhAUiBAxuQvvG/LdF+U8kspGXMVQq1hi+Qlpf+ih/23Dq2TL/G0HnZCrCBvll3j2+Hi5jkwNDZFiVZ/Kd2/6li++wRc2Pg/8ZotxzEQY1cVKbEy5sgf57gFarRFVGQETiwZi3sndsp2KfVSIX+NNnDpMFIlMOiy3zgn+UE3/G5GR71d27MszhlLtxsqhZVsRSuh5pAFOPTPn9g9oYNsb2hihgpChLJ3rhJn/y/d8LniJkQ2Y2TIFv3/CV9qz/eZABNgAkyACTABJpBcCKQQaSiiXUeTy454H0yACTABJsAEmMBvT8Dd3R0uLi64t3MubCzNfwoPr8fPcN/HHxFv3iKbTUZhqG6nemCnLEimDLvnLczlzUHREIoRPdVTCi311F1Kn4Qe7Wp0Q5E82bH936GgtFRX7nhJJjF9U+Ial/6p+FBEoNz1foL0adOgeL5c0NNLGas5+azcFB4llM7MIq0p8mbPIqNzYjVMIhWPnwfCw9NPfn4kJtFL+XwSuwUf/wD5ZyKtqTEc7DJrpIFL7Jhf069238nI61wG8+fP/5phuO9PJjBq1CgsWb8dread+CEroQiHIL8HiHoTgbTWdrAURukx/248f3gTEa9ewCp3YY3oFPJfMc2YBQap03zVWq/sXIxj84ai5eyDsHYoigDPWzJihfxWSCzQpbwND8XzhzeEr0oYLIUoQGb12oou+9XW71etI1+Wp/euQITOSV+VlHp6v+pSv7iukGe+WNLOCefPn4ezs/MX23MDJsAEmAATYAJMgAl8QwIeHLHyDWnyUEyACTABJsAEmAATUAjYZ7YCveIrJEBUdC4QqwnV0+tbF3ORiopSXSWk0APTnCJ9GL3iK2RkTymv6JUcSuaMFiK1mcU33YqtjaVILWb5TcfkwZjAjyaQziYb6BVfiStixMLOMb5uibpH31FxzRffgBTdQr4jXyq67PdLY/xK9yk6J1MeFiF+pc+E18IEmAATYAJMgAkkTQKxf26YNPfBq2YCTIAJMAEmwASYABNgAkyACTABJsAEmAATYAJMgAkwASbABJjAdyfAwsp3R8wTMAEmwASYABNgAkzg5xGwzmAOi3TfPvrl5+2IZ2YCTOB3J2CQ2gRpLGygl8rgd0fB+2cCTIAJMAEmwASYABP4SQQ4FdhPAs/TMgEmwASYABNgAkzgRxA4t2baj5iG52ACTIAJ/DAC+aq1Ar24MAEmwASYABNgAkyACTCBn0WAhZWfRZ7nZQJMgAkwASbABJItgacvgnDwzFWULuSIXLbxe5P8ahCW7TiCF8EhclkO2TKjfoXiiV5iWEQk9FKmhJFh/L8q//DhA1KKdl8qUVHvpUm2nt6X274Oj0QaY6MvDZmg+99jzAQtQEvjI+eu45LHQ3knteDct2VtLa24igl8PwKvA5/C64IrsuQvBfMsOb/fRN945Jc+93D/1G7VqAVrd0DqtLF9ld5GvIZB6jSqdtpOPn78iDevX8HINJ222xp1uoxHHchkPkVKPfmdpzHAV1y8j3ononz0vzgCzQ2kgC7G9u8iw6Gnb/jFtjQmza9vmFpj/qi3kbi0Za6qzq5YJVjnLqy65hMmwASYABNgAkyACfyqBFhY+VU/GV4XE2ACTIAJMAEmkGQJ3PfxR58pi/HfsK5JTliZv+kAHvkHwEakEKtaqpBWYaVQ0/4oWySv3J+2D2nvyUsYO38D7ng/lg8Fs2WyxLieLdCwUkmN5st3HsWOY+dw6ooHcmS1RiXnArKdoYHmg7+NB09h0VZXXLvnjaj375E9sxV6NKmOLo2qaAgyr16HY9yCjdh25CwCX4XC2MgQ5YrmxeS+bRL9OegyZquh/4A+8/jKvBHdESbEnkH/royvGZwc7bF4dC/oOuaFWw+wfv9JPA96BX09PRZW4qXLN78HgSC/B3D9tx+q9p+VpISVF94eOL1yMtJkyIRUBobIXb6hSlh5dv8aTi2bgKf3LkvBxDidJXKUrolyXcbB0MRMhTEyNBgnlozBnWNbEfUmAvpCgLF3rozKfaarxqLGb8JC5Hh3T+xAZMhLpDI0RlanMqjQbUIsZp7nXeEu1hX46A4MjE1Fu7JwqtsZWQqUVs2bkJO34aE4tmAEvC8cQVjQM1jlckK2opVQsvVAIYhoit4eRzbj6u4leP7ghhR20mWyR+H6XVGoTich8nwWtD8KMfz0ikl4eHY/AoVAlVIvFSxsc6Ns57HIJoQR9eJ96ShOLh2PQMGbxBUzq6wo2ri33BON+f7dW9xyXS9Fl9DnfnLPLKyoE+RzJsAEmAATYAJM4Fcl8PlfR7/qCnldTIAJMAEmwASYABNgAj+UgIuTI65t/hczBnSINe+avcfh+fhZrHqlYs+Ji2gphIb34sHbhN4tMe3PtkiZIiXajfwfjp6/oTTD6j1u+GPaEoQIMeSvdvWRxz4L5glRp/2o/4EiU5Sybv8JdBk3D8GhYejVrAa6NqoKihz5658VmL5yp9IM9IvxFoNnYvE2V2TPYoVhnRqhiGN2HHS/iuo9x+FZYLCqra4nuo6ZKpUe4no9CXgpBSZ90SZFihRxtiNeJESFhkXI5cU1HtWrjzlU7JM+q7rlnHXdFrdjAkxAjUDdUcvRaflFmGfOLmuf3ruCzYPr49n9q3Cs0BglWg2EgRBTbuxbhS1DG4FEBSokCGwf2Rw3D6yR7UhYcqzYCPdO7MSOsa1lG3qj75GdY1rj2p5lMM+UHSXbDBYRGU7wEgLKxr/qIOzl5+9TEmh2jGoBEmyKNekL++LV4Hn2EHaMbgUSsBJaKEJkQ/+auHVwrRBmSqFU60H4EPUW59bPxLF5QzWGu+W6Afv/7oFIEXlTpGF3KXy8iwjD0blDcG7DPxpt90/vifMbZ4EEJ5d2w5CjZE289L2PbSOaCrHlgKqtz5Xj2Da8KUKe+SBf9VYoJASid5ERcu4za/6W7UioIv7Npu9S9eMTJsAEmAATYAJMgAkkBQIcsZIUPiVeIxNgAkyACTABJsAEfiKBx88DMWXpNply6uYDnzhX8i4qCoNnrYKtdQYcWjAGGdJF/7K7fsXiyNOwrxBO9qNS8QLwexaIIbNWo1RBB+z9bwT0U0X/k9TBbgumLNuGjYdOo3WtcnKe/63bi5wimuXYkvEwMzGWdQPa1EO+xn+IKJZDGNKxoayjqJdTVz3QokYZGfGhLHLKsq2YvGQrSBAiASchRdcxV038U+uw3k+eo1S7oRjepTGcHOxlmzOrpmpt+9fMFQgRosrswZ3l/YSMqXVArmQCTCBRBK7uXAJKT9Xqf67ImKOAHMOl/TBsHtIQvldPyPRhucvVF1EWG+B/5yLKdR0vRJDesl2BGm1FAq0UuL5vpYh2uSJTWvldPw2/G+7IU7kZag6eH72mtkNAwsKZ1dNw69B6FG/RTwo1JxaPgb6RCdrMc4NRmrSybbkuY7CoVX7smdQZbecfj+6v4/sNsQ6KzCnecgDKdBghe5UScx+bPxxXdixENucqyFm6lqy/tHUu0mXOgVazD6micpyb/4El7Qrj6q6lKCkEJiqvnvrgztEtIsKnAWoPW6JKVfb45lkhFNWW0Sk5StaQbc+unSGPreccAUW/UCnbaRQWtS6AiyL9V0kh9OiSbkx25DcmwASYABNgAkyACfxiBDhi5Rf7QHg5TIAJMAEmwASYwI8nQA+1q/UYB/JGiVn6Tl2Men9Oxtt3lHMeOHn5NgbMWA6nZgPgUL8POoyegyXbD+P9++hfMcfsr1x3HT8Pncd+ziOv1M9ctVPOrR6lQdEZ/acvg7N46JS9dg+ZFuqg+xWlyw8/hooIkQe+/kibxhhF8kT/qlvbIs5cuwvfpy9kZIkiqlC7TJbpsX7qAHSsX1l22338AkLDI9CnZS2VqEI3Wn0SU7a4ust2lIbrtqefSEnmpBJV6IaNpTnKF82HoJDXIDGHiu+zF/JYpnAeeVTeKhTLL08pyiWh5WvGJN+YriLShgSVIR2ixZ+45nc9e01G2iwd0xtWFnF7NCRkzLjm4nomQBEIGwbUAnmjxCyus/phy7DG8iE/3fO9dgpH5gzCso7O8mH43sldROTFcpHS6XNUWcwxqA+NT9EK6iU8OEDWk+igXihC4vCcgVjZrTQWNHfErnHtQOmwflZ5cvu8FFQUUUVZR/5qreSp/91L8uhxZJNI95VBpspS2tCRRIwag+bBWNyjEiLSW1HJUlAzlZdt4fKynjxXqAT63BWfib+IUqmiElWonqJC7IpWRIDnTZlSjOp0LY8uu8mmeSo10eiSp3JTee13Pfq7llKVkQBjL4QW9VRnaSxsYCtSkUWGBslUXdSJ+FDJV7WlSlSh68z5S0rx5KXYh7Kn0IDHMtWaIqpQO0pvZu1QRPwZeif+nCX8e5nG4MIEmAATYAJMgAkwgV+BAAsrv8KnwGtgAkyACTABJsAEfiqBHCJ11Jnrd7HL7YLGOvwDgrBytxvMzdLAQD8VTly6hTp/TAI9+K9coiDa16uIxyL6gkSQMcJTJL5y9Y4Xrt71itXkod9TOfdHfJT3KDqEfh29TvhmlHHKg7Z1KuDR0wA0HTQDczfuj9X/R1Q4ChP7A/NGy9eycX3inPKBb/SD2trlioGM609fvQMSDSgNV60yRVG7bFHZ974QaahU/CR6yAvxRpEuxPmKYEUllTCpPzh/NAa0qSuvlTcSXG4+9JGfgRLtUr10YSHS6GGV+LwUkYuOK4SPC5WaZYoo3XU+fs2YM1fvwoXbD/DPwI4aPjAxJycvmJ6TFqJJlVIoXyxfzNsa17qOqdGJL5hADAL0kPvJrXN4cHqPxh16qH9DpLUiA3by3vC5elKkvmqIu8e3Sd+MAjXagB6UHxEiyKll4zX6ql9EvAqU40e8eqlejfdv38r6kGe+qnoab02v8rjtuhGZhYcIpYt6JdJG7RjdEpe2fYruULX+/ieUOos8QpzqdYk1Ga2VipGpuTwGP/GUQgSxCvb3xsMz+0X6sGtIY2GNvFWaSy8Rapi9RDWkFIbxlDJMEaToSKnF5P2S1eVREbqsHaK/J2XlpzcSIqgEet/5VKPbgcYk35f0WXNrdLDKWUh6prx45CHrKWqk+cy9cG72h0Y7ElwCPG9LYUcxvTdIbSJTeilrUjqQiX0E+ccYGIlXtEF9TpfaeP3iCbwuHFaayZRhvtdOImuhMjI6R3WDT5gAE2ACTIAJMAEmkMQIcCqwJPaB8XKZABNgAkyACTCBb0+gaTUXDP9vLbYLI/VuTaqpJth29KzMj9+2dvQvizcLQSWVeAB1fcsspDM1ke3ooX++xn9i/+nLmNgn+hfNqgEScTJ63gb4iKiPo4vHwzlfTjnCiC5N0KD/VIyauw4ta5ZFeiH0aCtkBO/hFf3raG33qc4irSm6Nf68x7jaJaaeRCI9IYZQurBOY/5DeOQbOQzV0ZyT+7SWHiP3H/lLY3lTk+iHb8pcKYWRMRnT3330RIojJqmNZLow5T4JSz7+AdI3hUSTv9rWV27JfY3p3hzjFm5Ezro9QZErZ67fk6JO18ZVVSxVHXQ4IVaJGfOWEH0o/VgnEaFD3jHxFYp+eiUilMb1bBFfMyRkzHgH4pu/PQHHik1wfNFo3Du5S0NAuHt8BxmCIH+1aH+Qu25bpSl5pxWXVREUzs3/lKmhHp47KFJgjftqlieFQENCS8vZB2HjWEyOV7rtUOnVcXLpOCFQtEBqs2ghI+ZktH4yeI+vpE5rIb1C4mujfo/Eg0q9p6lXyXOKtrm6e6nkkb1EdRmRQd4oxuaW2C5EIK9zh1R9zLPmQvW/5iBTHmdZl9osvUjDNRKnVkzEwpZ5hNeJi4j6OCe9VchzRNl3Optssj2lG1NSiymDUjQLlRdiv5nyFVeqv3ikMZ+JlGT0UhdCyK+FvGIUoYbSj2XOV0I1HolaZCTvKfb18cN7lGjeX3UvR6maoFfMclmkFnsjoo8cyjdUpfcqXL+biFw6Ib1oMuUtDj0hupCoQpEwZTqOjDkEXzMBJsAEmAATYAJMIEkRYGElSX1cvFgmwASYABNgAkxAFwJkEk5FiQL5Uh9LczNUK+mEg2euIODlK1imj85tT5EpNhnMpS8IjdGnRS30aFpdJapQ3VuRispciCyvwsLp8qvKS5HaapPwF6F0W4qoQgNSFEen+pVkGrJdbufRoV4lrfNsO3IW24+e03pPqcxla/P9hJVPESsdhAF9MyFWkd9JlPhlNkVazBfG9CRGDe/cGJ5CgDE3ixamlHUpR1sbS2niHiJ4UqSQehm3YCMi3ryVVY72mYU4Y6B+GzmEF0tqQwO8CA6F28Vb0vCeGnz88FH2MzYy1Givy0Vixpy2fLtcG3mrxFc8RJoz+swGtW+ArCJaJ76i65jxjaHtnvJ3Rds9rks6BMggXddinC6DjLTwuuAKEgwo1RSVu27b5QNvuyIV5HXRRr3gJB6MK14fVEmG7UZp0iU4JZUcMMZbREiQ9Oqwyl1YJS5QE4oAKVCrvUxDRlE1BWq2jdEz+vLeiR3SKF7rzU+V5llyJkhY0TaW59mDOPjPH4h49QIVek6GpX1ePH94QzYlnxKKAKrYaypIOKBIIBKEdo5pg/aLTqnYpsucXUZyUDSPz9XjUoCgAT5+/ICoNxEicsMY5sLfxCqXk7h/Ajf2rxYCRQMp7FPKsXsndsr5SORISHGs2FhEHG2Hu/B2cukwXI5P3i9uC6L9VuIa7/SKSXJdNJeFnQNSGWmK4OpriAwNxtF5Q+RnSaISsVCKofCJMbPKKtOY0bwp9fSleEcRMm/DXyvNEn1U/tzz91iiEXJHJsAEmAATYAJM4CsIsLDyFfC4KxNgAkyACTABJvBrEkiTJvqBfEJ8NVrVKiujTnaduIDODargkYiMuHj7oYiKqKdK5eQgUmJR6iYyVD9/875s81CICeQXYp0hbm8MXSndF5EaVMKEH0i7kbM1uoUKY3MqXo+fa9SrXywRHh0LR/VUr4p1Hi05xar+JhXkeUKRJA0qlsDc4d1UYzrnywW7mt2kuELCiqGBPp4EaKYJUhqHR7yRefsVo3qlno7Pj62QXi/k5TJWiCzlO4/CnR1zpC8JReu0HTEbLk6OmNC7lRSm7no/xsxVu6QHDj14o7RcCSmJGZOidnYcO4++wj9G3WdG27z/rtktRTPymomvJGTM+MaJeY+8c5S/KzHv8XXSIWBqaop3EWEJWnDeqi1ENMJB3D+9F4Vqd5CG5E+Fd0jx5v1kiigaLL1tbpnaiUzG/T0uyBRdwY89xQPxUJikt07QfNoaU9QEFVr7nkmdNJrQHFSC/aPTAmrc/HRRc/ACVB84V9stVd3XPHAPfuIlBQjiROJJraELoYhO5DlChYSmuiNXSFZ0bZWrEMKEWHV+/T+447YNRRp0l5FBeyZ2FB4kpUSUz1gpIr30uYfzG2fhuvCrIbP7yn2nS+7VRKQLpUEjr5tj84fJqBISXwrWbAfyprGwc6RpdC4UWVKkYQ9c3r4A3hePyDRmtHbyQ7HMnk9cp9c61h+7/BD0+CHIkP7U8olY17cKuq65Lj53K1V7EjWu712B0ysnSw8Wir4hY3ryUFHKRuG1Q94tlftMh0OFRiJixRDeIi0Y7W/7qBZCfHJHWmtbpXmCj+8io//c8/dYgtFxBybABJgAE2ACTOAbEEj5DcbgIZgAE2ACTIAJMAEm8EsRsLaOfuhHfiW6lpouRWRExY6j0ca8Ww+fkV1bf0oDRhez1u6WhvXTlm+TpukVnfNjwageKFkgt67TxGoXFPL5gejLV9G/4DUQwgN5h6i/0ou0VBQFEl9qKYpsoYiN+F5G4v73KjbCpJ5Km08m9Mo8aYyNUK5IXhlB8lxEBFmlTyc8WN7I6CCljXIMDAmVqc4ofRg9uCPDdvWSM6uN9J0Z36uljIY56H5V3qbIDyojujZRRfuQEDb5j9bSe2XDgVPyfkLeEjPm7LV75LrbCW+c+IqvSPe2yfU06gg/mrhSuyn9dR1Taa/r8Yn4+2FjY6Nrc273ixKg77sQ4aWh+HfoskxKZ0XRBPdFOi0qFNVAJV+1lvJIbxc2z8GiVvlxdu10aVxuJ8zWqw/8T0ZmqBol4CTikxihdIkUfhxUKEKFPEjUX0YifZajMFyPT0igfvqGqeN9kd9HYsptESWyumd5+F4/jbJdxqLdwtMqUYXGo1RWVCiNFwlQ6iXHJ88UEk+oUGQNldLth6kic6hP+W4T5J49jm6W9+mNomHaLzyFqv1noaAQvFzaD0er/x1WzREfD9UgMU4q9JiEFv/sh4tIvUUeNjWHLETjKVsRIlJ9WWSLFmrou5ZSg6kXiqDJX701ynYeLf5sRcHrvKvqdnjwC2wb3lT67dCa28w9JsSTvzVEFUpfRqJKloIuwpOlk/Tuoc8rV5m6yCuM7ylSJ6bPj2oCHU8U3xvl//N17MbNmAATYAJMgAkwASbwTQhwxMo3wciDMAEmwASYABNgAr8SAUtLS1hlzIhr97xR0bmATkujKIrGlUtixe5jMiplixBWShTIBUqdRSUgKATkf5IhnSmubfoX6v4g01dEPziLdyIRKvJBRHPELEqUCmXyyZY5o7ydU6S0Wjq2t0ZTigShyJj40lmRcfvVu3H/wpsGzCjSnA3t1Ehj7G91QebzVKK07DPyzTsZiUIiSy47G5y66gGvJ89VadeoHxnee4uInHJF89KlTCFG6b+2zBgEMpJXL+R/QsXvk3h266GvFFCKi+gY9UJRI4VyZ8PlO+KX9u+iZISI+v34zhM6Jq3//+ydBXgUyRaFDxAIBEggaIJLcHcnuLu7LyywuC+6uMPibsHd3YO7u0vQYMHt1a3s5MUFIjOTU/s1011d+vekt6dv3XsWbzmgvzdi1PEvzVu/R3v3NKlUzL9imklg2/S3IW8nn7x4pfRnXiFz5sD9fXirzkMjIpAlSxZ8+/JZiYJfR9zk6QM1MgvlOSBaGBJySgTHrynvCrsMuSGhsyTJi3MJaWVlExfN553w8sL8mPLG8Depe50k8bTwnAweKuqEzraxS6Y/5QV+efWy33MSI9GXj27aaOI53/O+iMGLWLx/KbptfORr0N2/Ij7OSeivbaP+1Dwq9J4N6/g+dZJi/pcnBgfv6dvnTzrLMrq1/nx+57I2oBi0VAzlRf8lfqosag5ntOeL5L95cg+Sn7ms1/BnJ5R3i3iL+KU3Y2jT+6fbSxcdckt0WTxrs7g+uKHDkRm+L+I9c0h5plQdvAwp85Ty0kw06zj62GDEkDmvH9gAT66dRsmO45BFhW3zLb1QoveSxLDiPSXLUQynlY6LhBH7nfTs5nnYJUqMWLFi/U4zrEsCJEACJEACJEACv0SAHiu/hI2VSIAESIAESIAEjJ1AiRIlsPPo+SANs77ytBADxvhFG3Hhxj009OStIh4Gsqq3imMeL0aVh09f4vyNuwH2k0xph0h4sa9Kk8WQRGPj1sOnhkOkSpxQG252qXF7LicFxixcjyRlWqnwZDc9ynvf2XfyIuZv2OPvtmaPu2eH97rBcWwwfohOjOck4dMOn7+KzA5JtWGoVqkC+vSiTfs8F9N6I6KhUr5QTp2fMWUS/bnnuLuegefCMk9JmVMn1Z/plCHj67fv2OJ8Wh8b/hHmp67choPydBGPnqCkoLbpfOaKNt5UL54vwG52Hz+vtXkcc2X0t2xQ2vS3IW8ndx49B0tLSxQo4H4tvJ3moQkREMNKbNs4KsTS7iCNWsKBicbGieUTtQaGQbReGnn77IHWwkhdqKIXo8q7Z4/w/D99Eb86s07g/jfpYUj5r+CtI1u8VIlln1IbEe6eUkbGb1+9nDu+fDym1kipX957OeHpQGuRbFuEC/5s1/YHwujtqU3ZdZ43GGIUkRBfvhlVpIx4XiTJWlgZRc7qkFmSZ0g3D7vPUzRXJMVJmhY/1PxuH9tmKKI/3zy5jyfXTyttldTaa+er8uCY3zIf9kzp6aWcML/hvBGp8pX1kh+YgxMrJuk2vV8LCQ0m3jxJlReSpHjJ3Y3Z90/v08ee/7mwdaE+jJcyk/68dWSrCg13EjlrtPPTqCIFbZU2i6QbB931YfTBf/8YvHjiKm+X30n3VXizUur/9UwkQAIkQAIkQAIkEBYEgvbrMixGyD5JgARIgARIgARI4BcIVK9RA7VrL4cYRAISBjc0nyeTgzZuTFq2WYfTqq48WAxJPFeiR7OEhAgrlT8r0iSzx9Hz1zF45kqIHsj7D59xXWmkSL5vKVeG1JCwVa0HT0czJUQvuhliwLGJYaU9ZKSOvPgf+GddtB8+Cy0GTkEXpe8injGbD5zCqPlrlfdNJuTP4v6yyrc+5g5qD9nCKom+SZkC2SDGG/G6kTBXb5QI/bDZq1VIr58Y3La+Hlqh7Okh2/wNe7U+StmC2XFaGT/+nrRYa6Q0quj+sk/ayqCMK9NXbYeNEr4vmTeL1mZZu+eYNqDkTJ8KEsJNUpvaZbDp4El0HTtPtXULlYrmxpU7DzFfeYaIQaxHs2q6nPwzedkW/D15sfbc6d28hke+952gtCl19564qJuQufmXRIvmzNU7yoCUw0O/x6/ygW3Tr/p+5S/bfghlypSGlZWVX0WYbyIEIkaMiGpVq2DznhXIVSvwf//26XMr7ZCUOLVmKiyUoSBNkSoeM7ZVniuRo0bHdRUiLEXukrBVouQizH5owXBtaBFtC/F6kHzvKV6KjMpQYInTWtg9pRJwj4sbhzfj7qm9XopKKK9CSpNj5/hO2DqyNXLX7qjblhf3x5aMRdIcjsrLIq+XOp4PyveaqXRPZnrO+u198aCQ8FXxU2fBqdVTfG0vSZZCSKnCfUmIrCUdSmHTkOZ6HjHjJdLC86I7IuMWfRNJ2av+ATEq7ZncUxmKziB1wQp4ee8qLmxZpI1Xeet31eWiqtBsYqyR8GwXlcEjdcGKeP34ttIj6YwYce2VPssgXc7wz+RqySHXofPW54YsH58OyjB2Rl2HnRO7aB0X8T65uH2x8lRaiGJ/Dkcsu+S6TgrlpSLeK2fWz9RGpWS5isPthYvSh1mPW0e3IWHaHHrOUlh0VySJNs7+mf30vvd/CjXrh7hKD0Y8U+6d3ovVfWoifYnasFFGtxuHNuHq3tUqzFtapC5Q3nvVQB+/drmLh+o7WWNo70DXYUESIAESIAESIAESCE4CNKwEJ022RQIkQAIkQAIkYDQEKleujPgqJJi8lB/avkGgx1WvXGEMmbUSlR1za4OJoaIYOKb93Rp/Dp2BOj3G6uzY6mX/iE6NtBeGGEzyNOyB1wedDFW8fHaoX0EL3q/ceRiy2cWNDelL0rhFGzzKSmioj5++oO+UJRADgiSLSJHQpJIj+repo8NpeRQ2wh0x7HQZMw/D567RmwxRjEcLh3RE8TzuYadEUHrFqG6o3X0MRs5bqzcpJ4aSRaqcaMtIkhfGy0Z2QctBUzF8zmq96RPqn8rKcDK6SxNYWETSWYWzZ8D8fzqgzyQnpYWzSW9yQkKnjezYSOvT6ILqHzHyyPZfRCJDto/PoLQplcUIIv1lTOW+Yt9Hg/9lHDh9WRt7xJAXUApsmwG14/n8+et3ceDUJWwdMsZzNvdNmECbNm0wd+5c/WI/abYigZ5JhlJ1cFgZSxzUS3xD6CqpLALkZZSQ+vaxf2H9APf7Z9SYsVC09VBlcLHCttHtsOCPguql/jMffYnBpFLfedg0rCW2jmqjRdnFYFBloBNEzNxzkpBX3z59xIHZA5UWibtnQ8RIFshUtiEKNu0b6vc7MR5JkhBTsvma1P1LDCtibKg2ZDm2j2mPtX3reBRNqTxLynSd7HGcRIXCqtBnNvbP6IeTSrdGNkkWllYQ/ZP0SkvGkIT55uEtsWN8R71Jvhh5yvee6cVzSPJFE8W7Lorke06JMxfQfTjPHYwFrdy90+TaZqnQTOmetPAoGkHdayur6yMGriNOo/RmOCkGnmJth0Oui6SHF931x85tmmso4uOzQONe2gunfO9Z2Du1pzak3PNkWEuUKb/+fsl35VfTmXUzkdDOHhUqVPjVJliPBEiABEiABEiABH6LQAS1gu/nb7XAyiRAAiRAAiRAAiRgpAQmTpyIXj174MyysUicIE6wjFLCWsmL6YRxYiNdikQeL/4k//W799rjxb+ORKvF5bmrCouVzKOub+Xfvf+oNWJEt0Ne1AfX+H3ry3Ne7vrddV9rx/fynB3k/acvX+twavFi22hOomHjWxKtD9HCyZ4updZ/8a2MCNjfffxcewRFs4yiNVrs49n6VlSH4rp4874Ku/ZMe8OIx0ssZQDznsQDKIV9AtQq7f6y0ft5z8eizRKYNj3XCe39P/6Zhm2HTuP+9lkBdl2ty0i8+hIJJ0+dCrAsC5gOgdKly+DSgxeoM2GHv/eWoMxI9FfEwBDdNqH2MBCjqCTJF++O2IlS+tmcaHG8VALu0WPH114rfhZUJ758eIdnKsTYF+UFIWGpYsZP5F/xYDknhpxNQ5uj3sTtHqLyv9KwhDF7oXRUPr59qbw+Mihh+4S+NvP96xc8v3MJb1UIMNFLiaM8RMRLxXuSn+fS3huXu4jvkNXPcGRiVFn0ZxE0VmL3AaWPb16qvi+LNQaJlLElkoXv92NpU3RexBvJwjKq1tyJqbxlfjeJPsvLe9e0YL14OcVWm+G7ZGhb+p3TJIf2pBEvH/+ShFFb0DIvJk4Yj7Zt2/pXlOdIgARIgARIgARIIKQIXKFhJaTQsl0SIAESIAESIIEwJ/D582dkzpQJqRJYY+XobmE+HlMYQHAZVox5rhKGrWqnEdg6pV+oGaxCmkdgDSsSyq5p/0nYv38/ihQJvGdDSI+f7f8+gfPnzyNHjhwo2mYYslVu+fsNmnkLwWVYCStMB2YNUKHAPugQX2E1huDsNyiGlbV/10Lkd49x8cJ5RI7su5EoOMfGtkiABEiABEiABEjAFwJXKF7vCxVmkQAJkAAJkAAJmAcBEeeeo8LjbD98BtNWbDOPSYXCLM7fuIfGfSdqLZJQ6C7Uu7jz8ClWKENbaHkBheQEF23ap6+V89krAXZz59FTdBw9F21at6ZRJUBapldAROx79+6Ng7MHKDH6S6Y3gTAasfPcIVonRbwqTCnFiGevQnSNMKUh+zrWLx/dNP89k3v4et575slVU5Ruyz7MnzeXRhXvcHhMAiRAAiRAAiQQqgRoWAlV3OyMBEiABEiABEggNAi4ubnh/fv3uqvChZVmypAh6K2E0XccORsa3Zt0H6KDkj9L2v80SMwzYmzJfFmRPkVik75OhsFLUF/Ri8mhQqmVL5zTkO3j0/WtG2r3GIcUqVJj/IQJPs4zwzwIDBw4EPny5sXGgQ3w7sVj85hUCM1CBOEdClWCaMf8VCGyTC3lqNpa6Z64a0yZ2ti9j1f4W1hG09cjlj/h5W4d3QbnOQMxYsQI5M+fXzdz//59ODk54cOHD96b5TEJkAAJkAAJkAAJhCgBhgILUbxsnARIgARIgARIILQIfPv2DTt27NAvWNavX69fvPz1118e3Tdv1gzLli3F4mGdUEq9WGcigfBC4MXrt6jWZRRc33/F4SNHkChRyOtXhBe2xjhPV1dXFChYCC8/fEO1oatDRa/EGDlwTOZF4PaxHcqzpSmaNWmMmTNnekxuyZIlaNq0KaJGjYqaNWvqfVlQ4V3DxaMCd0iABEiABEiABEggeAgwFFjwcGQrJEACJEACJEACYUXg2LFjEAOKvb09KlasCFm9OnbsWDRo0MDLkGbNno3ateuoVftjMHvtLi/neEAC5krgyu2HKNl6EN58+oG9+/bRqGKuF9rTvGxtbbF3z27EixEFK7qUhcvVU57OcpcETI/AmfWzsGFQQzRp1BDTp0/3MoH69evj8ePH2jP17NmzKFq0KFKlSgXx3rpz546XsjwgARIgARIgARIggeAkQI+V4KTJtkiABEiABEiABEKFwI0bN7B48WK93bx5E+nTp9eGFDGmJE+e3M8x/FRxkwYNGqS3qsXyYmzXpohva+NneZ4gAVMl8OPHD8xYtQMDpi9Hjpw5sWrVaiRMmNBUp8Nx/wKBV69eoW69+ti9ezfyNeiO3HU6qtBRFr/QEquQQNgQeO/6FHsmd8fNw1vwzz//oG/fvgEO5MKFC1iwYIH2Xn327BnEe0U8WsSbJWbMmAHWZwESIAESIAESIAESCCSBKzSsBJIUi5EACZAACZAACYQtAXlBsmzZMm1MOX78OOzs7FC3bl00bNgQOXLkCNLgdu3ahZYtWsD15Qv8Va8c/qhRGnFs+MIlSBBZ2CgJfP/+A5udT2HYnDW4dvcR+vTpg379+sHCgi/UjfKChfCgxMA2btw49FXfAdEUydOgJ9IUqYJIFpFDuGc2TwK/TuDjm5c4s2E2zqyZioTx42He3DkoVqxYkBqU8KDbt2/H/PnzsXHjuMJIaQAAQABJREFURkSKFAk1atRAkyZNdFsRI1JuNkhAWZgESIAESIAESMA7ARpWvBPhMQmQAAmQAAmQgPEQEAH6devW6ZWnO3fuhJWVFapXr669U0qUKIHfeTEibY8fPx5jxozGp4+fUK5gdpTOnxXZlQh4Uru4iGkVjTHajeerwJH4QeDj5y947voGl249wL6TF7F+/0k8evoCNdTfyZChQ5EuXTo/ajI7PBGQkEj9+g/A0iWLET1WXKQsUBHJcjgibsqMiB47PiJHtQpPODhXIyIgnqRfPrzDmyf38ezGWdw5vlNtO/T/73v17IGOHTvq/d8ZsnhvycIMMbLIwowkSZKgcePGekuTJs3vNM26JEACJEACJEAC4ZcADSvh99pz5iRAAiRAAiRgnAS+f/8OMaI4OTlpo8qXL19QpkwZ7ZlSuXJlRIsWLVgHLgaWpUuXYtXKFdi3/wA+f/4crO2zMRIILQIZM6RHlarV0Lx5c60xEFr9sh/TIfDgwQPMnTsXa9etx7mzZ0xn4BxpuCAQxdISjsozpU6tWtojVRZTBHe6evWqNrDIM8ajR4+QP39+HSqsTp06sLFhaNDg5s32SIAESIAESMCMCdCwYsYXl1MjARIgARIgAZMiIKtIRTdFVpVK2C952SFhvmrXro24ceOGylwkdMi1a9f0y5Z3795BwugwBQ+B1atX48CBA5g4cWLwNMhWNAExNMrfh+gM8aUgvxRBIeDm5obLly/j+fPn+PDhQ1CqsqwnAt26dUOuXLm0IcBTNncDSUA8T0X7JHHixBDvkdAKWyj/f5ewoOLFIp6x4jlTtWpVHSqsVKlSOnRYIKfAYiRAAiRAAiRAAuGTAA0r4fO6c9YkQAIkQAIkYBwERHh+yZIl2jtFBOnTpk3rIUKfMmVK4xgkRxEsBIYMGYJFixZpw1WwNMhGSIAESMAICGTNmhXiTTl48GAjGA2H8CsE3r59i+XLl2vR+0OHDsHe3l4v7BA9lgwZMvxKk6xDAiRAAiRAAiRg/gSuULHN/C8yZ0gCJEACJEACRkVAVkdPnjxZe6Q4ODhg6tSpqFChAk6cOAEJ0SFC2zSqGNUl42BIgARIgARIwGwJWFtbo1WrVnB2doYs8pBwimJoyZgxI/LkyYMpU6bA1dXVbOfPiZEACZAACZAACfwaARpWfo0ba5EACZAACZAACQSBgISZER0TMaDIStDevXtDjCrbtm3TYbdERF5CqTCRAAmQAAmQAAmQQFgRSJ06tfY+unPnDvbs2aPDLPbs2RN2dnaoWbMmNm7cCAkbykQCJEACJEACJEACNKzwO0ACJEACJEACJBAiBESEfseOHWjcuDESJEigP6WjhQsX4unTp/pTROkjRYoUIv2zURIgARIgARIgARL4FQIRIkRAsWLFdHiwJ0+eYMaMGXj58iWqVKmCRIkSoUuXLjh37tyvNM06JEACJEACJEACZkKAhhUzuZCcBgmQAAmQAAkYC4GTJ0+ic+fOWohWDCfXr1/H8OHD8fjxY2zevBn16tWDlZWVsQyX4yABEiABEiABEiABPwnEiBEDTZs2xd69e3H79m20a9cOGzZsQLZs2ZA9e3ZMmDABEuaUiQRIgARIgARIIHwRoGElfF1vzpYESIAESIAEQoSAvGgQ4d506dIhd+7c2oDSpk0bHav86NGjaN++PeLFixcifbNREiABEiABEiABEggNAsmTJ0f//v1x8+ZNHDhwADlz5sSAAQN0mFPxZlmzZg2+fPkSGkNhHyRAAiRAAiRAAmFMgIaVML4A7J4ESIAESIAETJXAixcvtPB8wYIFkSpVKi1ILx4qx44d014q8qJBYpUzkQAJkAAJkAAJkIC5EShcuDBmz54NCRU2f/58fPz4EbVq1dJGlr/++gviwctEAiRAAiRAAiRgvgRoWDHfa8uZkQAJkAAJkECwE5CXBsuXL0elSpX0i4MePXogRYoU2LJlixahnzhxIvLkyRPs/bJBEiABEiABEiABEjBGAtGiRUODBg20rty9e/fQtWtX7Ny5U3vwZsqUCaNHj4aLi4sxDp1jIgESIAESIAES+A0CNKz8BjxWJQESIAESIIHwQODHjx/YtWuXji8uIvTy8uDbt2+YN2+eFqF3cnJCuXLlYGFhER5wcI4kQAIkQAIkQAIk4CuBxIkTo3fv3rh69SqOHDkC8WoZNmwYkiRJgvLly+vFKZ8+ffK1LjNJgARIgARIgARMiwANK6Z1vThaEiABEiABEgg1AqdPn9arLuUlQalSpXDlyhUMGTJEi9Bv3bpVG1iiR48eauNhRyRAAiRAAiRAAiRgKgTy5cuHadOm6VBhS5YsQYQIEfSzk52dHUSHTjTomEiABEiABEiABEyXAJeWmu6148hJgARIgARIINgJ3L17F4sXL9abGFJEO6VVq1Zo2LAhHBwcgr0/NkgCJEACJEACJEAC5kzA0tIStWvX1pvosYin74IFCzBjxgykSZMGTZo0QaNGjbRXizlz4NxIgARIgARIwNwI0GPF3K4o50MCJEACJEACQSTg6uqK6dOno1ChQkiZMiVEJ6VEiRI6hMXNmzcxaNAgGlWCyJTFSYAESIAESIAESMA7gYQJE6Jbt264cOGCFrcvU6YMxo0bh+TJk2vvYFnc8uHDB+/VeEwCJEACJEACJGCEBGhYMcKLwiGRAAmQAAmQQEgTkPjeK1euRJUqVSA/8kVoNWnSpNi0aZMO9TVp0iRICAsmEiABEiABEiABEiCB4CeQM2dO/Pvvv/q5a9WqVZDwqs2aNdPPZS1atMDBgwfx8+fP4O+YLZIACZAACZAACQQLARpWggUjGyEBEiABEiAB4ycgIvS7d+9G8+bNISL09erVgxhY5syZo0XoJf63CKtShN74ryVHSAIkQAIkQAIkYB4EokSJgmrVqmHdunV49OgRBg8eDNG5K1KkCFKnTq09h+/cuWMek+UsSIAESIAESMCMCNCwYkYXk1MhARIgARIgAd8InD17VoedSJIkCUqWLKnDT0h4r4cPH2L79u06rneMGDF8q8o8EiABEiABEiABEiCBUCIQL148dOzYEWfOnMG5c+dQtWpVTJs2TWveOTo6Yv78+XBzcwul0bAbEiABEiABEiAB/wjQsOIfHZ4jARIgARIgARMlcO/ePQwfPhwZM2ZE9uzZsXbtWkhYiatXr+LEiRPo1KmTDjVhotPjsEmABEiABEiABEjArAlkyZIFY8eO1QthNmzYgLhx46J169b6+a1x48bYs2cPQ4WZ9TeAkyMBEiABEjB2AhbGPkCOjwRIgARIgARIIHAEXr16pXVTnJyc4OzsjDhx4qB27dqYNWsWChQoELhGWIoESIAESIAESIAESMBoCEiI1ooVK+rN1dUVy5Yt054rJUqU0Pp4YmSRzcHBwWjGzIGQAAmQAAmQQHggQI+V8HCVOUcSIAESIAGzJfD582eI4KnE5hYRevFEsbe3h6xsfPz4MaZMmUKjitlefU6MBEiABEiABEggPBGwtbVF27Ztcfz4cVy+fBl169bF3LlzkSZNGhQsWFAvpnnz5k14QsK5kgAJkAAJkECYEaBhJczQs2MSIAESIAES+DUCIkK/d+9etGzZUovQ16lTB+/fv8fMmTO1CL2sZJSVjZEjR/61DliLBEiABEiABEiABEjAqAmkT58eI0eOxP3797Ft2zbtvdKhQwe90KZ+/fpaR0+eGZlIgARIgARIgARChgBDgYUMV7ZKAiRAAiRAAsFO4Pz585AwX0uXLtXxtnPkyIH+/fujXr16sLOzC/b+2CAJkAAJkAAJkAAJkIBxE4gUKRLKlCmjN/FWWbFihQ4VVrZsWe3F3KhRIzRp0gRiiGEiARIgARIgARIIPgL0WAk+lmyJBEiABEiABIKdwIMHD/RqxMyZMyNr1qxaQ0V+HEv4h1OnTqFLly40qgQ7dTZIAiRAAiRAAiRAAqZHwMbGBq1atcKhQ4dw/fp1NGvWTC/IyZAhA/LmzYupU6dCdFqYSIAESIAESIAEfp8ADSu/z5AtkAAJkAAJkECwEnj9+jVmz54NR0dHJEuWDKNGjUKhQoW0IP3t27cxZMgQrjoMVuJsjARIgARIgARIgATMi4CI2csz4927d7F7926kTZsWPXr00AtyatWqhU2bNuHbt2/mNWnOhgRIgARIgARCkQANK6EIm12RAAmQAAmQgF8ERIR+zZo1qFGjho6N/ddffyF+/PhYt24dnjx5gmnTpmlR0ggRIvjVBPNJgARIgARIgARIgARIwAsBeXYsXrw4Fi5cqJ8pp0+fjufPn6Ny5cpInDgxunbtCgk3y0QCJEACJEACJBA0AjSsBI0XS5MACZAACZBAsBH4+fMn9u/fr0M2JEyYELJ6UGJjixHl6dOnOka2/OilCH2wIWdDJEACJEACJEACJBBuCcSIEUOHB9u3bx9u3bqFP//8Uy/ikXCzot03ceJEbXQJt4A4cRIgARIgARIIAgEaVoIAi0VJgARIgARIIDgIXLx4Eb169dJhviTc18mTJ9G3b1+InsquXbv0D15ra+vg6IptkAAJkAAJkAAJkAAJkIAPAilSpMCAAQNw8+ZNvdAne/bs6NevHxIlSoSqVati7dq1+Pr1q496zCABEiABEiABEnAnQMMKvwkkQAIkQAIkEAoEHj58iNGjR2sBehGiX7ZsGRo2bAgxspw5c0aHYbC3tw+FkbALEiABEiABEiABEiABEnAnIKHCihQpgjlz5uhQYXPnzsX79+9Rs2ZNyLNphw4dcOrUKeIiARIgARIgARLwRoCGFW9AeEgCJEACJEACwUVAwnrJj1SJay0i9MOHD0e+fPlw4MAB3LlzB8OGDUPGjBmDqzu2QwIkQAIkQAIkQAIkQAK/TMDKykov/Nm5c6cWve/cuTO2b9+OXLlyQRYGjRkzRhtffrkDViQBEiABEiABMyJAw4oZXUxOhQRIgARIIOwJfPnyRceqFr2UBAkSoF27drC1tcXq1av1D9EZM2agcOHCoAh92F8rjoAESIAESIAESIAESMB3AkmSJEGfPn1w7do1HD58GAULFsTQoUO14H2FChW0FuDnz599r8xcEiABEiABEggHBGhYCQcXmVMkARIgARIIWQIiQn/w4EG0bt0aIkJfo0YNvHz5ElOmTNEi9KtWrdKxqqNEiRKyA2HrJEACJEACJEACJEACJBDMBPLnz4/p06fDxcUFixcvhjz71q9fH3Z2dvjzzz9x7NixYO6RzZEACZAACZCA8ROgYcX4rxFHSAIkQAIkYKQELl++rFfyifinxKY+evQoevfujXv37mHPnj1o0aIFbGxsjHT0HBYJkAAJkAAJkAAJkAAJBJ5A1KhRUadOHWzZsgUPHjzQz72yuEhC3aZLl06HvRVdQSYSIAESIAESCA8EaFgJD1eZcyQBEiABEgg2Ao8fP8bYsWORPXt2rY/i5OSEunXr4sKFCzh37hy6d++uQyQEW4dsiARIgARIgARIgARIgASMjIB4q8hz78WLF3HixAmUKlVKPyOLrmDp0qW1Z8vHjx+NbNQcDgmQAAmQAAkEHwEaVoKPJVsiARIgARIwUwJv377FvHnzULJkSUi86SFDhmgRz3379mnvlBEjRiBTpkxmOntOiwRIgARIgARIgARIgAT8JiDi9pMmTYIsQFq5ciWiRYuGpk2b6hC5LVu2hLOzs9+VeYYESIAESIAETJRABBUb86eJjp3DJgESIAESIIEQI/D161ds3bpVr7bbsGGDjiUtQp0NGjSAfFpaWoZY32yYBEydgPz9jB49Gm5ubh5TOXPmDG7cuKFDiHhkqh35m8qQIYPnLO6TAAmQgFESEBHvRYsW4cePHx7jW716NWSFfu7cuT3y5KWyrOTns4IHEu6EQwLPnj3DkiVLMH/+fO3VnSpVKjRu3FhvyZMnD4dEOGUSIAESIAEzI3CFhhUzu6KcDgmQAAmQwK8TkLUGhw8fhoT3ktV2rq6uWjtFXvzWqlULsWLF+vXGWZMEwhGBV69eIW7cuIgQIQIiRYrk68zl700MMFOnTtXCt74WYiYJkAAJGBEB8V5t3rw5IkeOrO9vvg3t+/fv2vDy5MkTxI8f37cizCOBcEdAwuUuWLBAL1h6/vw5ihYtqj1aatSogRgxYoQ7HpwwCZAACZCAWRCgYcUsLiMnQQIkQAIk8FsErly5on/oLV68GHfv3tVhvRo2bIj69evr0F+/1Tgrk0A4JSCx1vfs2eNlZbd3FGJ4cXFxQYIECbyf4jEJkAAJGB0BWXAhxhIxnviV5L5WsGBBiKA3EwmQgFcC37590x7h4sWyadMmbaQU44qEDXN0dPTTYOm1FR6RAAmQAAmQgFEQuEKNFaO4DhwECZAACZBAaBOQl7njx49Hzpw5dRgiWUUnXimyok6E6Hv27EmjSmhfFPZnVgQk3Id/KWLEiPolCo0q/lHiORIgAWMiYGtrq0W5/fLEk7GKYaVJkybGNGyOhQSMhoCFhQUqVaoECaEnz+IjR47E5cuXUbx4caRIkQL9+/fHzZs3jWa8HAgJkAAJkAAJ+EeAocD8o8NzJEACJEACZkXg3bt3WLNmjfZO2b17N2LGjAlZJSfeKRKSQF70MpEACQQPAfl7k3BgX7588bVB+XubM2eOXqXqawFmkgAJkIAREli6dKnWhvJLqlSMLhLqKHbs2EY4eg6JBIyTgBhXxItFwvGKwUW8vsSLpXbt2rC2tjbOQXNUJEACJEAC4Z0AQ4GF928A508CJEAC5k5ANBy2b9+uf6iJCL2E7yhfvrx+KVKxYkVEjRrV3BFwfiQQZgRq1qyJ9evXQ0J/eE+yavXFixewsbHxforHJEACJGC0BN6/f484ceLg8+fPPsYoRpWyZcvqEEc+TjKDBEggQALynL5z505tZJHnB/EAq1atmvYCK1myJBdBBUiQBUiABEiABEKRAEOBhSJsdkUCJEACJBCKBI4cOYJ27drB3t4elStXxuPHj3XoLxGTXbt2LeSFL40qoXhB2FW4JCDeYL4ZVeTlY4UKFWhUCZffCk6aBEybQPTo0VG1alWIcdh7+vHjh/aC9Z7PYxIggcARMBgnly1bpj1XJkyYgDt37qBMmTJImjQpevfujatXrwauMZYiARIgARIggRAmwFBgIQyYzZMACZAACYQegWvXrnmI0N++fVtrpxhE6JMlSxZ6A2FPJEACmoCs6JaV3bLC23OSFagrV67Uofg853OfBEiABEyBgIhui06E92RpaQkRuLeysvJ+isckQAK/QeD69esQPcRFixbhwYMHyJs3r/ZiqVu3LsPu/QZXViUBEiABEvgtAgwF9lv4WJkESIAESCDMCYgHiqxqW7x4MU6ePKk9VOrVq6dXjGbLli3Mx8cBkEB4J9C8eXMdik/C8hmSvHR8+fIlvcYMQPhJAiRgUgTkfiZGY9GSMiTxYBFvWNFgYSIBEggZAuIVtnfvXh0qTHQTJXSYeKY3adJEe7X45kkWMiNhqyRAAiRAAiQAhgLjl4AESIAESMD0CLi5uekVaxLHPHHixBgwYAAyZ86MXbt26VVsY8aMAY0qpnddOWLzJFC/fn14NqrIS48aNWrQqGKel5uzIoFwQSBy5MiQlfLyaUgS9lC8ZJlIgARCjkDEiBFRokQJ/TtAFldNnToVT58+1R5kSZIkQbdu3XDhwoWQGwBbJgESIAESIAFPBBgKzBMM7pIACZAACRgvAXlhsWPHDr3yXcQs5UVtuXLltAi9rFSjXorxXjuOLHwTkNWkCRIk0B4qBhLbtm3TK0sNx/wkARIgAVMjsH//fjg6OnoM29raGi9evPBibPE4yR0SIIEQJSAhgBcuXKg30WTJkSOH9mKRxR1x48YN0b7ZOAmQAAmQQLglwFBg4fbSc+IkQAIkYCIEjh07po0py5cv1y8sChQooFeE1q5dG7a2tiYyCw6TBMI3gY4dO2LatGnaIBo7dmw8f/4cIlDLRAIkQAKmSkBCEiVMmFDfz8RzpVmzZpgxY4apTofjJgGzIPDz508cOHBA67GsWrUKnz59QoUKFbSRRT49e5mZxYQ5CRIgARIggbAkwFBgYUmffZMACZBAeCDw7NkzlCxZEv/++2+gp3vjxg0MHDgQDg4OyJcvH3bv3g15MSur0ZydndGmTRsaVQJNkwVJIOwJGMKBSQgP2adRJeyvCUdAAiTwewTkfta4cWPIp3jRNmjQ4PcaZG0SIIHfJhAhQgQULVoUc+fOhYQKmzNnjtZCkhCk9vb2+vfE6dOng9SPtHH27Nkg1WFhEiABEiCB8EGAocDCx3XmLEmABEggTAicOnUKFStW1D9sRAvlwYMHfo5DDDAGEfrjx4/Dzs5Oxy+XeOXizs9EAiRg2gQk9vnDhw9x6NAhiOcZEwmQAAmYOgF5zsmVKxfix4+vn3XkpS4TCZCA8RG4f/++1mVZsGABZAGXaDOK4L38zpBwpX4lFxcXreconi5OTk6oWbOmX0WZTwIkQAIkEP4IMBRY+LvmnHFYErhy5QpEG+Kgck++cPGiDmv08ePHsBwS+w7nBGLGjKl/LIjQe6lSpbTwY3DFIV68eDGaN28O0VeQTdLhw4eRP39+D+rv37/HunXr9A+VnTt3wsrKCtWrV9erPosXL85V7R6kuBPSBCQ01YYNG7BLfQ/Pnj2DR48e452bW0h3y/ZJ4LcJWFpGQdw4cZAhQ0YUKlwYojkl93QmEvCLgOF5dL96Hr2onkdfKl0QPo/6RYv5oUEghnoeTZQoMbKre1fp0sH7PBoa42cfpkVAfo+IgUXCDMtvkTJlymgji/z/09LS0stkxowZg969e0O0HiUNGjQI/fv391LmVw8knOC+ffuwefNmHDl2HNeuXcOb16/w/b++frVd1iMB7wQiWVjA2iYW0qRJgwL58qJ8+fIoVqwYf2t7B8VjEgg6ARpWgs6MNUgg6AQ2btyIkSNG4JB6iIsXNw6KF8qPLBnTIUH8eLCKFi3oDbIGCQQDAXmYf6d+TNy7/xCnzl3EgSPHIXGJRbukb9++SJs27S/1IkaUnj17YuzYsV7qy0qvli1bYtKkSRAjiqz6EqPKly9f9A8aWTEmP2ii8W/CCzcehCyBy5cvY+iQIVip4nBHihgBhTKnRLbUiZEkfmxYW1mCq4+Dj7/ccz5//Y5olpGDr1G2hI+fv+L5GzdcvuuC/eduw+XFK+TIng09evbS93N+h/klMRCQ59Hh6nn0iHoejRUnHjLkcUSydJkRK24CWEbl86iBU1A/v3z6CIsoljokWFDrsjwg/2/49N4Nzx/fw+1LZ3Dl5EGoB1LUqlUL/fr1++XnUbIlgYAIiP6K/BYRI4v8NrG2ttbe8uLJkjdvXl1dfg9dv37doyn5f6qEFVu4cOEv/2aRfqdOnYpxEybi0YP7iJ0kDWKlywfrxGkQJUZsRLTgc5IHcO4EC4Ef37/hyztXvHt0A6+vHYfrvSuwU8bsTh3+Qvv27fXixmDpiI2QQPgjQMNK+LvmnHFoEpBVJ61atcLBgwdRqWxJtG/RGI6F8vFFXWheBPYVaALv33/AivWb8e/M+bhy/SbatWuHYcOGIUaMGIFu49WrV9pFXlZfyQ9l70k8ZKJGjaqFXsVzRYwpYsgJLi8Z7/3xmAT8IvDu3Tv0UgbAadOnI0MKe7StUgjVCmeFVdQoflVhPgmYBIGjl+5g1qbDWHPgrFr9nRWz58xF9uzZTWLsHGTIEJDn0ZbqedRZPY/mKlYBZRv+iYx5ivJ5NGRws9XfJPDpw3sc2bYaWxdNxsNbV3/pefQ3h8Dq4ZDA48eP9aIvMbLIopt06dJpjcjJkyf7oGGhVv9nzJgRW7Zs0botPgr4kyHRK9q0bYeXL12RrGQjpCzVBDHtU/lTg6dIIPgJuD25g9s7F+LeroWwsY6JaVMma4Nh8PfEFknA7AnQsGL2l5gTDDMCInInL6YzpnXA5JGDkCt7ljAbCzsmgaAQEIPIohVr0XPQCNjEio01a9YEKqzMpUuXtFux/DAxuMv71m/Tpk31CsSUKVP6dpp5JBDiBCQmfo3q1eD25hWGtKiIuiVy8gVjiFNnB6FNQDxYuk5dh+NX7mLkyJHo2rVraA+B/RkBAcPzaOLUGdC87wSkypzTCEbFIZBAwATkefTAhiVYMqYPbGPbYG0gn0cDbpklSMB/AidOnNBeLOKVIt4lX79+9VFBjCuxY8fWxhXRWQooSTtt27XHvLlzkLxoLWRs0A9RY8UPqBrPk0CIEvj89gUuLRmGO3uWoIFa8Dhzxgx6r4QocTZuhgRoWDHDi8ophTEBCaUkLy/Gjx+Pnh3aYGDPToxdGcbXhN3/GoGnz1+gYZvOOHbqLFapMEkVKlTwsyFxo69Xr542qPhnVJEfIaKhIjGNmUggLAjISsE6dWojf4bkmN2jPuLaBN4jKyzGyz5J4HcIyDPJxFX78M+CrWjcqJHyXpnDZ5LfAWpCdT0/j1Zt1Q212/dDxEiRTGgGHCoJuBN4/eIpJvdohpvnjwf4PEpmJBBcBCRUcbx48fD27Vs/m4yk7qmyiQGmTp06fpZzdXVF2XLlcf7SFeRoNwn2ucr4WZYnSCAsCDw5sxunJrdDOodU2Ll9G6NJhMVFYJ+mSoCGFVO9chy38RJo06YN5s6di3mTRqN2Vb9fRBvvDDgyEvg/AdFLade9HxYsX4PVq1ejSpUq/z+p9uTFjYg4yiYxh+U4oBQlShS8UEK5EhaMiQRCk4B8hyX0XJOyeTG2bTXGww9N+OwrTAnsOnkVjYYuREWlY7Vs2XJ+98P0aoRO561bq+fReXPRdtgsFChXM3Q6ZS8kEEIEfqjn0dn/dMD+dU6+Po+GULdsNhwTWLt2rV4MFlgEImg/cOBAHx7Qr1+/RsHCRXD/qSsK/L1chf1KHdgmWY4EQpWA25O7ODKsDhLGssJh54OIEydOqPbPzkjARAnQsGKiF47DNlIC//zzD2RbMXcKKpUpYaSj/P1hub56jXVbduiG6teoojQzLH00evPOPRw4fAzx4sbxlYWbEk2PET26j3qhkSGhBSJGjPhLXYk3hhgQZHVSeEptlXFFwoOJdkq+fPn01EWjokGDBti0aVOgDCoGXsJv3rx5EGFIJhIILQLOzs4oUaI4mpTJg9F/Vgutbo2in1fvPmDj4Qt6LLWL5UDUKD5FUW8/fgHnC7e0B0/5fBl9jPvrt++IbBE2973fumerl3ER1H+RIv3aPd8HCBPOkOtbo99s/PFHa/w7aZIJz4RDD4iA4Xm084QlWlMloPKmcP7CkT1K3PwBEqVMi7TZ3Z9DvI9bdDk+KhH0rAVLIk7CRN5Ph+nx79zHvn75jMhRfD5rh+mEwqhzMa4cXL/Yy/NoGA2F3Zo5gYoVK2Lbtm2QRWaBSfL7pmrVqlqnxcrKSleREGKOxYrj/LVbKPzPRljFTRyYpkK8zBe3V3h0fIvuJ2mhGogUJaqPPkWD4/nlw7C0jvvbHjbfPr2HRdSw+d3/U/3uj/CLv/t9QAkg4+tHN/z49gWWMW0DKGm8pz+6PsHB/pWQNrk9nA/sh6Ul/99jvFeLIzMSAjSsGMmF4DDMgIA8eJUrV07rqfzRpL4ZzMj/KVRt9Ae27NyLzn+2wMgBvbwUFuNDgbI1cPbiZayaPw2Vy5bU59+8fYe+w8Zi1YYteOn6SsXvjAbHgvkwamBvpEmVwksbIXEw22k51mzchgNHjsMhZTKUKFIIQ//uGqgHhiWr12PaXCc9p2/qBWOq5EnRrkVjtG5a/5eNNCExx5BqU14IVGvcGucvX8fZc+e0e7AYVZYsWfJLXTZSIWnEbZ6JBEKDwLNnz5A1S2bkTJUATn839rGaMDTGENZ91Bk4B9uPX8Ff1YticMtKXobzTb00KN5xIs7ffozF/ZqiQv5M+vy7D5/Qe8Z67Dp1DU9fvUO21IlRImda9KhXElEiW3hpIyQO5m89ivXO53FIGQRSJYqHYtnTYECz8rAMRN8r9p7GrI2HcP7WI3z7/gMp7OKgdeVCaFEhf7i4Z/t1PdYdPIemwxdh8eLFqF/f/J9V/OJgzvmG59EW/SagVJ2WZjPVG+dPoH/DEohqFQNj15+EbQJ7L3M7tmMdxndpiJSZcmCw0x5cPnkQ84d191LG+0HKjNnQbvhs79nBdvz47g3sWDoTJ/dswge3t9ogVL5xe2TOVyzAPtzeuGLe0G64evowXj55iOjWsZApryPqdOgP+xRpPOrL81mvmgXwXT17e0/xEiVFr2lrvGeb9LHMd0z72nC5eR7n/3seNekJcfBGSUC8TOLGjavHZliMJ1758v2TT/889DNnzoz9+/dr/ZVOnTtj2oxZKDpkM6yTpDOquR4a0QhPTu+EQ6U/kaXRAC9j+/H9G/b0Los3dy8if/f5sM9d1st5Obh/YBVOTG6P8tPPIJqtnY/zr26fx8UlQ/Hq1ll8ff8GljbKQKPaydxwACJbhWzUgnePb+HW9nlwObENXz+8RZx0eeBQoTXiZy7sY5z+ZQQ0R891P79zxa5uxdTcrFF6/EF96tmFgzg772/PxXzsx06ZBbnbT/aRH5YZwm9/n7Jo2qgBpk+fFpZDYd8kYAoEaFgxhavEMRo/AYm9mj59ehTJmxMLp40z/gEHwwifPHuO7I4VIN4r+zcuR75c2T1aHTpuMgaNmogmdWtg1oQROl8eQEtWb4iDyqiRJ0c2lC5WGPuVR4vz0ROIF8dW/ejciITx43m0Edw785euwh+deyN3jqwoW7wIrt64pQw8W1GxdHEsnzMZov3hVxJPjRYdeiBt6pSopIxEH5X44NpN2/H4yVOtodOnczu/qppV/qvXb5CzeCUULuqoDSq3b9+GCNbLCi350SFbYPalnIODA2xtTXc1j1ld2HAwmVq1auK48z44T+4Mayufq/LCAQI8dX2L/G3H4NW7j9g+ph3ypE/uMe1RS3ZimNN2NCiVG1M6u8cIFw+Voh3G4/LdJ6heJBvSJImPTcrr5eIdFzQtlw8T/grZ0EJOO46j/YQVyJk2KUrlSodrD55i3cHzKJc3AxYq45iFP16DS3edxJ/jlsEhcTxUyJcJH798xYZD5+Hy8i3+blQG3euV8ph7eNzpMW0tVhy4iCtXryJhwoThEYHZzlmeR9Op59FU2Quh/ci5ZjfP5ZP+wdoZo5CtUCn0mr7WY37vXr9E18q5lLfKO4xYeUh7tVw8tg8LR3ld+GOo8PXzZ7goo0euYhXQbVLIaL59+fQRPZXBw/XpYxSqUBsxYtni+M71eKGMJH1mrEP6XIUMw/HxKfMY2Lg07l+/iALlayORMqRcPumMS8f3w8Y2HkauPgKbOO6i1y9cHqB9qfRI4pARMWxie2krTsLEaD8i5AxHXjoLxQO3N6/Qq0Y+lCpWVD2PLg7FntlVeCEgiwRHjRqFV69eaeF68Tzxvn1W9xHRYTF8yr5s8ptSFp65uLigaNGi+qV50iIh+8z0K9fl0+tn2NnVEeK94jh4I+KkyeXRzJVV43B5xSgkc6yLXG0neOQbdr5+eId9/Svj7f0rvhpWxJhyYHAt9dvQAokLVEGUmLHx8PB6uLncRuxU2VBs6JYQ8yL5/uUjdvVQv9ddXZC0UHVEiWGLR8eUcfuluhf3WYp4GfIbpuHvZ0Bz9F758KgmcDm5HTETOfzfsHLxIM4v8Gq0MtT7/vUz3JQBw07p7RToscCQbTSfD49swLHxf2Dnzp0oWdJ9kazRDI4DIQHjIkDDinFdD47GVAl069YNC+bPw0Xn7bCNHctUpxHkca/ZtA11W/6lvU1O7t6oQ4JdvHIdeUtXhV2CeDi9dxOs/9PR2H/oGErVaIgGNatg3uQxHn0NGTsJ/4z+F0P6dEWPDm088oNz58EjF2QrWh6ZM6TFztWLEDmyexgc3a/qf/bEkWhcp7qfXeYsVhGf1MPzke1rPOYjRhWH3MVgG8sGDy4c8bOuuZ0QLyXxVpKVWEWKFDG36XE+Zkhg9+7d+gfBmiGtUDxHWjOcYeCnJN4fTYYtRGrl/eE8pYsOCXb5rvrh32ECEtpa49DUrh6GJ/H26K5ewHetUwL9mpTz6KSXeiE4fb2zF88Wj5PBtPPw+WvkbzMaGVPYYeOIPz1CkA1Xxp+Rygg0tUsd1C+Z28/eCrYdi0/qJcjeiZ085uPy8g2yNBuG2DGtcH2x7z9y/WzQzE58+PQF+RSj4mUqYt78+WY2u/A9HXkenT1vAcZuPK1espvf4oVv6u+6b72iuHv1PNoMngbHao30Bf+3ezMc3roSTXqNQrmGbQP8Eswb2hXHdq7DiFWHEStuggDL/0qBhSN7YcuiyeipPEayFy6tmxAR9h7V88EymhUmbb/kZ7PHd23AuE714d3rSMa9fekMtOw/ESVrt9D1xYA0pEVFjFx1BMnSZfazTXM7cXr/NoxqV5PPo+Z2Yc1kPuLZkjFzFryNaof8vZyMdlYPj27CsXEtEcM+FUqO2q1Dgr1RxpLdvUojWqwEKDlmrxfvkju7nOCivFyeX3SGhPeS5JvHyokpHfDg0FoUH7YVsZK7e0FL2QP/1NR183aeicT5K0tWsKdzC/rj5uaZKNh7MRJmL6Hb//T6ufYmiRTVCuUmH/e3z8DO0XMjt3bMx/n5/SHtR7WJ52FY8VzG+/7ZuX3wSPEvMWoXosZyN5R7LxPWx8fGtoCl6w1cu3I53IVBD2v27N+kCFxhwGmTul4crDESePr0KSZPnow+ndqGK6OKXIvqFctCNFau37qD/iPG6Ri0LTv20Ct65kwc5WGEkLL3Hz2WDxTOn0d/Gv4pXriA3n3n5v5wZsgPyufT5y90mK6TZ877Wm290oN55+aGTm2aexhVpGCj2u46CyvWbfK1nmRK+LKLV6+jTPGiXuZjnzABihXKB1flxSErmMJLKl+qGIqpa9avX7/wMmXO08QJ9Ov7N0ooj4fwblSRy1ilUBaIxsrNR88xZKHEDf+BtuOWQ7xTxFjh2Ztn75nr+spLec+pTrGc+vDQhdues4O0Ly8cRO9jwsq9vtYTz5h3Hz+jnQpb5lnXpV5J99WUq/ef9bWeZL55/xGX7z3RXi6e52MXxwZFsqZWHjsf9Hz9bCAcnLCKGgX9lOfOwkWLcOPGjXAw4/AxRcPzaNU/epilUUWuooVaGNNOeWBYRI6CBSN7qhBZj3BCLewRo4qEySrb4M8AL/ZZ553YsWym8uiZ88tGFbmHXT5xEBvmjPezv/3rnZA0TSYPo4oUFCNO1kIl8fzRPUhoM7/StdPuC3byq7C6nlOhSnX14euXzzyyn9y7pfftkqf2yAsPOzmKqpBC+RzRt1//8DBdztHECKxYsQLXrl5BRm8htoxtGonzVUSSwjW058SlZSPw88d3nJzaET+/fUWudhO9GFVk7G5PbuuwXrFSZNbaK37Nx/XaCW1Q8WxUkbLJi9XTVVxvnvGrqr/5opciui/X1k/2s9y9fcthnTS9h1FFCkaNFQ8JshXDh2f34XrjlJ915URg52ho5O2Dqzi/cCAyNeyn+gmcof7J2T06VFnuDlOM1qgi88vYoB/uqAgVTk7Gaxw0XAd+kkBYErAIy87ZNwmYA4HZs2cjutIKadnI/ceOOcwpKHOYMKy/Duk1ccY8HRrr9PlL6PBHMxQtmNdLM+VLOmqjxrwlakWhChEm4u8iBjhH6Z5IqlimuJfyAR28eOmKtZu3Y6XSazlw+LiOeSshvXJlz+Kj6vXbd3ReiSLuRhxDgWRJEiFKlCg4de6iIcvHp4Sb2bt+KVIkS+LlnBhcLly+hlKOhbwYa7wUMtODnh1ao2ytJrhw4QIkjjATCRgrgdOnT+PI0WPYPDLgl23GOofgHteoP6vB+fwtTFl7AI9fvMHZmw/RtmoRFM7i9aWchM2KEc1ShwDzPIasqROp0A4RlPHCxXN2gPsSDvLY5btYc+Cs1k0RzZYMyROiUy2fWgNi+JHkmM3BS7tJ48dGFItIOHPjgZd8zwcWSqR+66i2SK40VTwnMbhcUmHMiudI48VY47lMeNqvUTQbBi/ajmnTpmHcuPARwtTcr688j1pGi44StZqb9VSTpM6Auh0HwGnM35ja5w88vHUF0WJYo82Q6QHqZ0nIsOl926BAuVraEBMUUHIPu37mKA5vW41jO9ZCvE8k/FblFp19NPP21Qu8f/sajlXdPWo8F7BL5n5fu33pNByy5PZ8ymO/WI0myF+uho/QXldOHtJlchQp61H2yf1biGuXBJ8+uOHC0X14o4wuiVKm1W1H9CdkokcDJrxTuWVXDFWaYXweNeGLaKZDn/DvJCTKUx7WKiyUsadszYfhxaXDuLF5hgqf9QSvlTZK6gp/IF7Ggj6Gnrnh/w2Zx/9tiwfOa3yU+aGMMmLEiJ06u49zH14+0nlRYgQ+wofce18qQ42EEnt0dCMkhJl10nRIW6W9j/Y/v32pDT/Ji/l8LyNeOZJe3ToHWwf3RUI+GlAZgZmjod73L59wbGIbxE2XF6nLtYR4uwSURIvl1NROSFKwKuJnKhxQ8TA9HyNhciRRodzk+9ykSZMwHQs7JwFjJkDDijFfHY7NJAhoAVjltRE1qqVJjDe4BxnLxlqF0hqBcrWbYsW6zUifJrUO6+W9nzi2sTG4dxf0Gz4OSbIUQBHluXL4+CmIVkubZg207or3Ot6PReNDjCmijbLX+Yg2zGTJkA69lbeQaJ/kyJLRexV9fP3mHVgp41fMGDG8nBetDxGhF70VMfKIscd7ih7dCgXy/P/h69+Z83DvwWNs3bVX1+kZQuHLvI/DmI7FyyhFsqQ6fvDw4cONaWgcCwl4ISAxrlMnToCCmd1/THk5GU4PYsWIpr1Tqv49Uxs50iZJgP5N/x/qy4BFxN7FgCFbjjRJDdnK2+WFMmT/xNV7Tz3y/Ns5efWe6uec0kc5h8cqHFdMZawRD6IK+TKiVO70vla9+fA5rCyjIKY3PRy5Z6ewi4vrD5+p++8Pdc/26XgdXf2/OF/GFB7tTl13AA+evsL2E1fwXa107FK7hMe58LwjLBsqD6A5i50wduzYAF9Ih2dWpjJ3p8WLUbBiXUSxNH8dqfKN/8KpfVu15ohcn7bDZinjQuIAL9XcIV3w/t0b1Os8KMCyhgLiWXJk62oc3bFG66VEix5TeZ2UQq7iFZQ3ShlDMS+fLnfcPcFixUvoJV8O7P/zLHn70t2A7KOAykicKp1H9o1zx9U8D+DO5bM4vms9CiuvlZQZ///C8sn921pbpn3pDBBdF0OSMsLFc1uGc+byKV5KCZMk5/OouVxQM5nHvXv3cOzIYRRUeh6mkKJEt0HOthPhPKS2Ml6sQ8zEaZCpXp9fHnpEi8gQY4339OnNc9zeNg8RIlnALmdp76d9HItnyQMxphzZqPVSLKLFUAab4rDPXdaLN4rniiK6Lsk3z5GYdin1uc9vX+jP4PjngtM/+OT6FIX+Xh7o56izc3rjy4e3yFT/7+AYQoi3kax4fRwYVAPXr19HmjRpQrw/dkACpkiAhhVTvGocs9EQePDgAa5cuYIJ//Q2mjGFxUBSqpfs0aJFxcePn5A6RTI/jUypUyZHNPXSS7xN9hw8jNdv3urhyks6qSvGD9+ShPpq1akXdu0/BFm1Ujh/bowe2FsbU8TrJKB06+49rYXiWzmpf+X6Tbx954bYSi8loCSGIRmrpAxpU+t5B1THHM9XUCHBtm7dChpWzPHqms+ctm7ZjHJ5/v+Cynxm9nszEW+OaJaR8fHzV6RKFFdrrXhvsaZjdm14Gea0A30blUU2h8Q4c/0Bes9cr4uKkcK/NG/LEYxbsQcPnr2CvQrDVU4ZUirkz4hCysgVJbL/j5+3H79QWii+//8gaYLYWsj+3YdPiKX0UgJKgxds1fOUcumSJtDzDqhOeDkv12SY0q05e/Yssmf//4va8DJ/c5qnPI9eVc+jNbuNNqdp+TkXMQyKweCKEnSXlCS170Zazw08uHkZR5S3SbU/umsPD8/nfNvftWIO1s0aAxGHt01gj5xK6D6X0tzLmKewDkXmWx1D3pMHt/WudzF5yYxnn0yfEwNPYJIYVZZP+keF6PmhX9xJfdGakbBokgyGlbodBiJ3yUp46/oC+9c5Ye/ahRjzVx2tIxPVKnpgujK5MhEiREB2paG4hc+jJnftzHnA27ZtQxTlPRg/k0+PD2Odd4yEyZS+SjSI8HuMhCm01kpwjtXl1A6cmtYFYtTI2nQwbFSoLr/S7Z0LcW3tv/jw4iGi2dppcXcxpsTLWAARLaL4VU3nv3/iHqXCN48Yq3ju0Se+vHd//+BvQ4E4KXO6tW0u8nWbi2ixAxcCTMKGiedNuuodYRU34MUAgRhGiBeJmz4fosawgXyvaVgJcdzswEQJ+P/L1kQnxWGTQGgRkDAzknLnyBJaXRpdPxJnutlf3bWxQbxVNm7fDaeV69CwVlUvYzUI3RfKlxsj+vVAnpzZtKfIqH+nY+aCJfrH4qQRA73UMRw8e/ES23bv1x4lbZs3QpN6NSCeKoFNlirc1yOXp74Wf//ho+7bOqZXbxZfC6vMN3cu4Mbtuzh07CT6DRuLAir+9e3TB5Awfjy/qphlfl51/abNc8KXL190ODWznCQnZdIEPn78iCtXr6FnNa+6TiY9qWAYvNyz24xZqo0N4q2y5eglLNt9EnVLuOuXGLoor166/1mlMKatP4hdJ69q0XfRJ8mvvEEyKVF5W2v/X9TtOX1dG1XEmNGjXinlnZLOhweKoS/vn2J4EbF535IIr8vLNO/eLL6VlTyXtcNxS4UWO3LpDv5RRpbinSbi0oK+SGBr7VeVcJOfUYVis1KLHeRZhoYV077shufR1Jm9/h2b9qz8Hv05513YuXy2Dnn1+M51TO7VEsNXOvvrrbNh7nhtEKnQ5C+/G/Z05vzh3dqokjhVelRv0xPZi5SBeKsEJkVWGjCS3N688lH808f3Oi+6deBC4VRt1U1rx9w4fxwHNyzFmhkjdZixZn+P1e20HToDkdVzroQlk2SXLDXSZs+H6NY22DhvovJy2YAild11DXQBM/tHwqntWDqDz6Nmdl1NeTpyP46dMnOARgBjmaMYbU9M/ksbVcRbxeXkdtw7sBLJitT67SG6PbmL80pMXowQ0VVYqdwdpiJBliL+tvv03D5tVImZOC3S1+ysvFNKIrLyVglMivjfvfeL22sfxb9//qDzxEPnd9PHV0+VFk0nJFfeHBLyLbDp2vop+nvhULFNYKuEebkIESOpsG7Z9LNimA+GAyABIyXgM4aCkQ6UwyIBYyRwW4l52dslRIzo/r9gMsaxB9eYRk+aqUN6tWhQG5uWztUsOvUZhAePXLx0sUppoUga2KOjNqrIfjqHVBilPE8iq1V3S1a5r4KWfO8pvSq33mkW6teojAXLVqnwC5WQJk8xdOs/DAePHNchubzX8XycQBk93n/4gGfPX3rO1vuur17BNnYsX8OASQHxkJEXkZ6Tg/K8aVqvJob27Y5v376psGD7PJ8OF/sOqVJo7rJKlokEjJGAhGKQv9/UicKX0TOgayGC8Ucv30WTsnmxekgrraPSY9o6PHzu80fo8NZVsH1Me/RvUg4NS+XGrO71sXao0jRQZcVg4l8a9kdlDGpeQXvDNB/phFR1B6Bm/9mYv/Uonrr6v1owQeyYeK8MKM9fv/PRhasy7og3i29hwKSwb/fsVOo70LB0HgxsVh7fVAixHcpQxASllaNCq9nHgzzLMJk2AbmGcZRXRVSrwL18MuXZik7KtL6tESVqNHSfvAJlG7bFo9tXsXyi3+G9xOvk0OYVyF2iktItsQ3U9Bv3HIn6XYYgsqUl/u3eFK0KJcOINtWwa+Vcra/iXyM2SqRe0rOH7qunPZd1e+2qD61t43rO9rIvz51yLzMk8TjJnK8Y/lRGlHiJkuHk3s2GUzosmMGo4pGpdrL9F6bswc1LnrPNbt8uuQOfR83uqpr2hG7euo1oCdzDTpnCTK6tn4SXV48jeYkGKKTCl1lEjY6zc/so44a7HsqvzuH+gVXY1aM4nl86pIXdS43dH6BRRfrK2vQfXT5SFEscn9AGm1pkgPOw+ri9a5HWV/FvPFFjxden3z+756PY53fuhm5La68afD4KBiLj9o75+KK0Ur5+eKcMLB09tk+uLvj46ok+vrp2opeWxANHNGns85RDlBixvZwz9gP5Pl+/6R5mzdjHyvGRQFgQoGElLKizT7Mh8ObNG9jEDNzqNbOZtKeJnLlwCYNGT0TSxPYYNag3kiSyw8gBPXVYLQnd5flHoQi9iwFFPB08p7hxbJEtcwZt+BDvB9+ShYUFypV0xJx/R+HxpWNYvWA68ufKgbmLV6BEtQZInDk/Wnbsidv37vtWHWmVEUDSnftez79//0HVeaD0XbL6Wk8yR/07A1Ht0/pqPImrdGMkPXzs1YikM838Hxtr95c38jfARALGSMDw3bSOHtUYhxcmYzp365EO/ZREicAPUYK/iePFwuAWFfFWhdVqP365l3u2eIzcUFomeTMkR5c6JTBYla9VLIf2Qnnt9lEJz9v5Owfpo2PNYtj3byecmdMLvRqUxhPVZqdJq5C24T8o1WUS5m4+7GsbDondfxjffeL+AtJQ6P2nz7j75CVypU1myPLxOV6FH7Ot2AM7lKaK9xTnPy+bR74YkbyXDS/HNurv4+1b/w1d4YWFKc9T7ndWMcOHF9asQR20YaN+53+QMGkq1O0wAPETJ8eWRZM9QoN5v5a7lTHkh9LSK1a9sfdTfh6LIHzl5p0wfIUzJmw5j5pt+8D1uQtmq/7bOKZCvwbFtdeMbw3Iy35JT30xrNy/flGfS505t/70/o8YVZrkioe/6xX1fsrdWy+WrZr/Ex0O7IXLQxzbsU571ngv/OzhXZ1lY2veiwuixXD/3hv+n++dA49JILQJiCZo5GDwigiNcb9WkRgurxitw1JlaTxIfSZC5kYD8E0ZDE5N6+TluTAo4xEPlROT26uQXxlQatx+pK3cDpEiWwaqCQmRJeVLjNiBMv8eRfpaXfFJGSvOzOyOzX9kwd6+FXB7xwJf24phl0rnv3/q07Dy9v5lfc7WIYevdYOSKcYZm+QZ4fbkNl7fveixff/2BT++fNbH4q3jOd1RhqGfP75rLxfP+aawHyW6Nd78F8LdFMbLMZJAaBOgYSW0ibM/syIggucWFpHMak6Bncwn9YKrSduu2mNj1vgRHsLwLRvVRdGCebWGyrS5Th7NSZiwryom9KYdezzyZOfu/Yc4eeY80ijjRxQVyiCgJGUqlSmBBVPHaiPL8jmTUbxwfqzauBVnL7g/MHlvo061Sjpr/pJVXk6tVF40opci7fmVMqVPo0+Jvov3NMdpuc7KktHvOLHe65jLsYUSHpQkfwNMJGCMBAzfTQtfBM6NcbwhPaZPX76i1ajF2mNjcqfaHqG0mpbLh8JZUmHf2RuYtfH/97mJq/Yi9x+jIELyntPUdQeVF4oFHLO5vzj0fM6vfRGcF+OM85SuODmzJ/o0LAO3j58xa5PvhpUaju4GeKcdx700ufbAOR3CTMKU+ZUMBp+9KhSZ97Rg2zGdlSmFvfdT4fY4UsQI+v/j4RaAmUxc7neR/vv/splMyddpHFi/BMd3rkeG3IVRpr57KBXLaFZoPWiKfgE4tc8fWsjde+VzKqxXdOvYyKS8Pn4lJUyaEhKSa9Tqoxi36Qxqte+r+nHD9qUzfW3ONr4d0ucqpAw9h7QGiqGQaKM4K8+Z2Oq8ZwF6w3n51PoxSjNGxOrd3ng1Lt+9eh63L51BIqUvIxor71WosfFdGmLtjFGem9D7R7a5P/Omy2k6Og8+JhGIjEhq8ZUkw//zA1GFRUggRAlINNwRVnkAAEAASURBVAMJn2Ts6fuXTzj+b1v8/P5NideP9wi3laJkI6VnUhDPLhzEre1zf2kaF5cOQ2Qra+TrOue3tERiqPBh6ap1RMnRe1B6wiFkqN0D31Q4xVvb5/k6rmi2CRE3Q348v3JUGT3uepT58e0r7itvkajqfKyUfi+o9KgQwE7qci1RctRuH5t1IgdET5BM5+f6c7yXVp6e24/IMWIp7Z3CXvJN4UC+z/K9ZiIBEvCdADVWfOfCXBIggQAI9B4ySmuktGnWAMWUYcOQJPb9jHHDkN2xAqRMScdC2mjSvmVjbNi2Cx17D8LJsxdQtXwpXLp6A3OU14l4tvTu3NbQhI/P50pjZf5Sr0YRz4WyZcoAh5QpYJ/Q99A0RQrkgWzSl4QFE+H1U+cuoMegERDNlyZ1a3g0lzxbITx59hyfHl/TeeIpkyldGkyZsxCxbGKidLHCWq9ltTLkiJEoV/Ysuj2PBrhDAiRAAkZIYMDczbj+4BlaViyAop6MInLPntSxNvK3HYMB8zajeI60SJ04HioVyIzp6521h8nYdtURxyY6xNCxYNtRjGxdFcnt/A+lsPHwBa1t4huKKJEjoU7xHB6i8t7LiMC9bGIIia/CgpXNkwFnbjxA39kbUSBTSjRQYckMSUKLdZmyGj3rl1JbaZRWWi4ZlHbIjI3OsIkRDSVypsXjF2+wzvkcth67jBxpkqBs3vBnDDfw4icJmCqBZ4/uYd6wrrBUotBtBk/T3huGuWTMWxQlajWHeKYsHNkTrf+ZajildU7uKGNETsfy2mjhcSKAHdEmcbl309dSFiqOf+FKdfHl00dfz0tm1VbdMbJtdUzo2gjV/uiBGEpTZf3ccTo8WI+pq72M/8/iDtoLZ+l5d++xKi26qHqNMUR5FNZQnjK28exwxnmH0lhZovur3b6f/kyaNhMcsubB7lXzEEN5suQpWUU9U//AwY3LcP7wHuQpVQXhRXdHA+E/JEACgSZwYfEQvHt0AynLNPPysl+eC3O0GYddXR1x0WmICt/liJj27p4ggWlc9E3e3r+KWCky48bGab5WESF6u5ylfT336PgWuD2+5eu5iBaRkbRwDaUH88nX85IphphDwxvg2PhWSiS+k/Yeur5+MsSLpWAvJ497r4QWOzO7p9Jx6YoMagvJJExe3T6n5xxBhWFlIgESMC8CNKyY1/XkbEggVAjs2u+MKbMXImWypBjet4ePPiV/cO8uWgNFhO0PbFyuDBt5sXjGBPQYOBxjp8zSm1S0soqGsYP/Rr3qlX20Y8h48vwF/h46xnDo52eWjL4L2ssD4tqFM1C1UWsMGz9Fb9KIGEWWzZqkQ5QZGpUVb541VWTl4KoF07R3zuAxkyCbIVUtXxrjh/ZTXku8lRqY8JMESMD4CIj3xowNzkieMI7WPfE+QjGS9FM6Kn1mbkDrsUuxQ+mqFFSGjeF/VMGg+ZuRt81oXcXaKiqal1ehF5VxJqC0Ys9piHHFv5Q+WUL0Vt4r3pPcs5cOaIa6A+di9NJdepMyYhRZ0KcRInvyFHXXVPmpXia6tyL37CX9mqHV6CUYsXiH3gzti7FoZJuqsIhk/CtJDWPmJwmQAPRz2dTerbQ3Sot+E3ToL+9cGnYbirMHt2PvmoVaiy+nYzld5NLxA3oBj0O2vN6r+HvsvGmZFn73r1Di1BlQq93fvhbJWrAE2g+fjRkD2mF85wa6jFVMGzTqMQLZC3t9oShhykRA2pDylamOxk8fY8n4/hjboa4hG9YqrFfboTORq1gFnSf3ym7/LtN9rJ89FrIZUqk6LdGo+3DDIT9JgARIwIPA0/P7cWvrbO1dkblBX498w04M5XWRsV5vLTwvwvbFhmwMtBfOy2vu3sYSZkw2X5O6d/llWBFtlsfKuOJfsk6iFtHU7u5rkQRZHZH7r8k4Nb0rjo5tocuI90yWJip8ZPYS/68jD45y3zU8QP7/TLDvic6M9BMnTa5gb5sNkgAJhD2BCOoH6X8/RcN+MBwBCZgagX79+mH9mlU4tWejqQ09zMYrOiqit3JHhQCzSxAPGZU3SCyb0IsL7vL0mQ4ZliNrJiSI57dwqHdAYmy5c/8Brt1QgoRRoyJN6hRIZJfQe7Fwc3zvwSM45HbE8ePHkTv3/1ePhxsAnKjREzh8+DAKFiyIK4v6wS6OjdGP11gH+PLNe1y664If6nGxoPIW8WzUCI0xP1FC9+eVNky21Im190pg+5R79r2nrtpLJ2qUyBDdFvu4/B5451ep9wxkzF8C06b5vqrUe3keGycBeR51WrkOI1SoKibjIvBdhU+5fem0Ng45ZMmNiEEw7H5we4tHt65qb5b4iZLDLoUDolj6rhv2/PF9PL5zQ4U8s0GilGkRLXr40ICUef9VOgOfR43rax+uR5MlWw58Tl4QvhkswjWYUJ78DxXi7NWtc8qg8QOiq2IK4dlCGVGgu7u8YhS+X9yGa1cuBboOC5JAOCJwhcusw9HV5lRJwBgIiEZKzmyZ9RYW47FLEF8ZdOIHuWtZBZ0qeTK9BbkyK5AACZCAiRKQEGBFsqYOs9EntLWGbEFNcs8WbRfZmEiABEggrAiIBoiE6/qVZKWE2QNbN559UsjGRAIkQAIkoPSqlO5YnDQ5iYIESIAEQpwAA/yFOGJ2QAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkYC4EaFgxlyvJeZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACYQ4ARpWQhwxOyABEiABEiABEiABEiABEiABEiABEiABEjBnAhGUMDsTCZAACZBA+CFAw0r4udacKQn8FoEHj1wwd/EK3LxzL8jtbNi2CyvXbwlyvd+pIMLFv5veubnhpesrf5v5+vWrv+d5kgRIgATCisDmIxex9sDZIHf/8PlrLNx+DLcfvwhy3V+tEBz37J8/f+L563f4/PXbrw7Do5609frdB49j7pAACRgHgRcuD7Fn9QI8uX8ryAM6sWcTjmxbHeR6v1MhOO5tH9+/w7vXL/0dxqcP7/Hl8yd/y/AkCfyPvasAq2LroutXUboRUAlRELE7sLu7u/XZz+7u7u7u7i4MVDBQQkEEUTAACRGM9599rne8Fy5wUUDRs79vmJlz9qk1z3lzZ83eSyAgEPhRBF7ePoUX1w+nuPmHt0F4dmE7ooKfpbjtzzT47yfeBXyO/cA077+oNfzPjKPWAMJJICAQ+O0REMTKb3+JxAQFAr8HAg8fe6HP0LG4cdstxROasXA5xk6fm+J2P9Jg3bbdqNe6K/RtC6FYlXoYNmEGYmNjU9wVESoFnWujauO2CdoS4dLr39GwKeIMXesCKFenGSbOWoi4uDgl3zWbd6BIpboqt5PnLin5ihOBgEBAIJDaCMzddQ6TNqac1H707CUGLt6LW4/9U3tKCfrbdPImmo5dA4smo1Hun3kYs+bIDxMji/ZehH27yTjBCKXEbPeFuzCsNwwv375X6UJkysDFe5Cj2RjYtp6AXM3HosvMrXj3PlqlvygUCAgE0heBAB8PrJnYDz7ut1I88MHVs7Fj4YQUt/uRBuf3bcSMXo3QqYQphjUpjS2zR+FTXMqfR4lQGdKgGCZ1qqVyGncuHsfQRiXRpbQ5Opc0w6C6hXDz9AGVvlR49ehOtCmoi9CQl4n6iAqBgEDgxxGgDzP+RPPcvxAPd0xP8dLeBzyG26qheOd9O8VtU9og8qUv7m0ch5N9S+JoN0e4zOqA1w+vqtUNESQeO2bgzJDKONwpDw52sMW54dUQfO9igvY/Mk7AlX3Y38oCMaGvEvQnCgQCAoGMjUCWjD19MXuBgEAgvRDIZ2+HaWOGolihAikesm+3jvj4Me2/otu0cx/6DhuHUsWLYOTA3vB64oulazfh2fMA7F6/DFmyqH/LI+LkVchrGOjrKa2XIlQqN2gNDy8ftGpSH/ny2uHwibOYuWgF3rwLxYq5UyX/2+4PQJE+xQonxCxbtqySnzgQCAgEBAJpgUCvhs74GJvyqDoHq+yY2KUeiuTNmRbTkvrcdsYVg5fuQ4l81hjSujq8A0Ow8vBV+Ae/w5axnZAlc2bJN7mDu94BmLblVJJuER8+Ysm+S4n6xLFIlxYT1uEO66tjrdIold8Gbt6B2HTqJiNiwnFm/oBE24oKgYBAIH0QyJHbAW0GT0ZupyIpHrB2uz6I+xiT4nYpbXDp4FasnTQAeQuVRJOewxDk542T25bj9Ytn+HfhdmROwfPo6vF9EfYmGNp6BgmmQRE4Cwa1hYVNXrQbMg0aWbPi9I7VWDS0E8Yw/8Llqyu1+RAVgaMbFyuViROBgEBAIKAOAnnrdMOXuJT/ntfLYY8C7cbAMHchdYb5YZ8vcTG4PqcTJy6sKzRDVl1jBN06BpfZHVFhzE6YOZVLsu/by/oj8NoBmBVwhlWFpnjv74FXd87AZUZblBuxGTlK1ubtf2ScTx8i4X1keZLji0qBgEAg4yKg/lvGjLtGMXOBgEAgFRDIY2uDEQP7/FBPnVo3+6F2KWlEBMbQ8dNRvnQJnN2/FRoaGrx5vrx5MG3+UuzYfwTqzmP1pu04ffEqjAwT/ohdt3U3J1VGDfoHU0YP4WOMHzYQQ8ZNw7J1m1GnemU0qlODl1PatGoVy2HfppUpWYrwFQgIBAQCqYJAuxqlfqif3Jam+LdVtR9qq24jSjc2evVhlHWyxdFZ/0Aji4xEcch1GrN3nMWei25Qd/6RjDDpPns7zAx1ERwakWAKFBVz5rYnrtx/iqiYxL8Y33HuDidVpnZvgAHNq/B+OtUuA7B06dSHu08gijlYJehfFAgEBALph4CFtR2a9Bj6QwNWbtz+h9qlpBGlKts8awTyFSuH8RtOIMu351EihPavnIlrx3dD3Xmc2bUW966dhY6+UYIpfGYf+myeORymOawxeetZ6BuZcp8yNZugX01HRuSskIgVip5xv3wKHrcu4+OHqAR9iQKBgEBAIJAcAjZVWifnorJe18IWjk0GqqxLzUKPnTMRxSJWnEdvh0UxGamct15PnBtWFXdWDELdZa6JDhf9OoCTKrnKN0bpQasg18l563ULlyc0ZpEs0yViJSXjPDu3Da/czuKNxzV8/iginxO9AKJCIJDBERCpwDL4BRTTFwikBgIPWJqvll37wr5UFTTr1Btbdh/A+SsuaNNjgKQxctvtPhq07cbLacybd9xRpVEbuN33wHqmvVKxfkuYO5bkZaSpomiDx05Bj0EjFYtS/fjwiTOgFF2D+3STSBUapGOrpnysPYeOqTXmI68nGD5pJmaOHw5Lc7MEbc5dvsbL2jZvpFTXvkVjfn7l+veHtqd+/nBgES3CBAICAYFAaiLw5ctXzN91HlUHLUbZPnMxdu0RHuUxaMlerD9+XRpqxMqD6Ltgl3ROKa6GrTiAV+/eMyJiGwp2noai3Wag38LdiP74nXCg6I/m49fikruP1Da1D45df4hIRnL0a1ZZIlVojLY1SvKh9l9WXxtm2IqD+MJSOIzpKPuaMP5cSSsmPCoGhfPkhKmBbvxq6ZzIHKrv3aiCVEYHQ1k0zaqhbWBioKNULk4EAgKB1EXgufdDzGcRGANqOWFu/1a4fHg7Ht64gIX/dpA0Rp4+vIOZvZvwchrd594tTOxYE36P3HFh3yaMb18N3ctb8TKK6FC0jTOGYeXY3opFqX58+/wRkCZK/c4DJFKFBpGTKddP7FVrzMCnj7F17mgWiTIVRmYWCdp4u13H21eBqNuhr0SqkJOxeQ4MW7wL1Vt2k9oEP2ekckQ4bPMXgb5xwmdbyVEcCAQEAn8dAqQl4nVgMS6MrsPTYD3YMhHRr5/j7uqh8D2zScLj3oaxnKCQF7zzuYNLExohzO8+np3fjovj6uMIS79FZaTHIrfQp264xqI+Qh5ckRelyf75pd3Qt84vkSo0iKahGcyLVsUHRpyEPrmb6Li0FjKbyq0kUoXOTR3LQIcRQ5EvfCRiJCXjRAX74VP0ex6tk03flLoUJhAQCPyBCIiIlT/wooolCQRSgsDVG65o0K47tLW0ULtqJWTOnAmDRk9GrhwW8H7qh7mTx8CEdUhprs6wKI7WTRvy7kPDwnHd9S7+HTcVDx55oQMjMIqyNGG7Dx7lhMy1E/tQ/FsKLCJhyD8tzcdPJohXvVJ5pWFsrHIiK0uNcJcRQMnZR/ZisWOfwahQphT69+iM9UyvJb69ZOnBdHV04GifR6mK0n1lypQJj1iKMLLo6A8IefMW9na2uHvvIR56esPQQB+lWZqyHBbmSm3FiUBAICAQSAkCbads5BEYJVkKrUpF8sLFww8HmEh95IdYEOmC+rLeXL2eIyziuwD7A7+XCGVaISRqb2NujOaVi+KOVwC2n72NiOiP2DquM2/49n0Uzt/1RovKxVIyrRT5Pg16w/2rFLVXamed3QhZWfSK+5NApfLETvYyMoQIkeOz/8FrJlyvyqawCBS59ZyzHXsvuctPlfa+bE41Szoiq0YW+L96h8fPg5HD1AAFc1uiTXUZ4aPUQJwIBAQCqYaA551rmNmnKbJpaqNIhZr8mWrDtCEwsciJl8980HHELOgZAhGhb3Hf5Ryc67XiY0e9D4O3+w1smjUcz70eolLjdpxAIAJj0ZAOmLbjEksbVpT7Prl/ixE0oak2Z1UdvfJ/yosLlauqVE2RJVk0snICSKlCxQmJ0C8Z3hX5S5TnxAkRRvHtVYAvLypZrQGLQonGM897iI35AFvHwihRtZ6Se/uh33URlo7sDhcWNSNMICAQSBsE5NEOadN76vd6fU5nBLudg7F9cWQvWAFvHt9EIBOp/8zSVykKsxP5EBcVJk2Ajt95ueI+0zQJf/4INpVawZBpnAa6HMTNBT1QdfoJGNkVRmzEO4QwnRIrZ9nHjlIHqXhAYxCBYVu1TYJedXPIfrOH+d5nayyRoJ4KsmjqwK52VxjlVX7uJRH7uMgwZMqqicxsS+k4hTp81/RyXdKXR8WonIAoFAgIBDI0AoJYydCXT0xeIPBzCHxlX/j+O3YqsjHi4ebpgyASgmzwP91RtpZ6Dz++LN2V+6XjsLXOxdvWqOzMo1+u3XSViBVekcyfA8dO4bH3kyS9TI2N0KdrB5U+Pk+fQVtbC3q6yl8jE9mRx9aa6618+fKFEUeZVbanwpFTZoGIk+O7Nyp9raLYgPoiooS2ksUKS1U+vs9AeMrX8NT/Oa+bMncJgl4FS35E8oz5ty/b+kll4kAgIBAQCKiLwBGXB5xU6dukEhdGpnZ07+k9b2eihIFi3wGvwzC4RVVM7FqP3+eoLUW+XL6X9P1XsQ86PnztAbwCvt/b4tfTuYm+Dno0cFZVhacv3kCbaU3paWsq1dM9m1KR+bx4zUkiIvsTM9JiGbJsP0tbVhXlC9rh0LX7ibkmW04pwkLCIpHdSBetJ63HaVdPqY19LjOsGNIGpRxtpDJxIBAQCKQeAnQf2jhzGNMIyYYZe67CjJEQZA26DMToVhXVGigkgH0MdMgV2XPK/p0WLleNR78QYSMnVtTp6NaZQwj0/f7vX1UbfUMT1GrbS1UVXvo/QTYtbWjp6CnV073N3MqOkUTe+MqeRzMl8Ty6fd5YhL1+hTFrDif6PEpRKNRHgI8Hlo7oykkVGpDKarftjQ7DZqRIy0VpsuJEICAQ+GEEMpJ4fdCt45xUyVu/F4p0nsLXTGSKXG9EHRCigp+h5rxL0Mkuu29nL1wZN+d1xVvPm5xYUacPIiwUo2MSa5OrTH3oWzkmqCYxeTJNw4QfL+pZ2vG62Ii3fK/qD+mnyDVUFOufnljLCZtc5Zvgf5ky42fHUexbHAsEBAJ/DgKCWPlzrqVYiUAgxQjce/gYlAZs+IDeEqlCnRTKnw+tGtfD9n2Hk+2zdxf2ZeA3UoWcK5aV5fSnlFopsX1HTmDfkZNJNnHIkztRYsWXERnGKjRRqEMijDx9niIiMkqlbgr5HD9zASs3bMOeDctZCrDsVKTSKGJnz6HjmDx3MSaP/BfFixTkJMvQCbKvAYm8ISPCicyCpRPbsGQO7PPY4tjp81i5cTsmzV6E7Gam6NGhNfcRfwQCAgGBgLoIUHQJfQ05rlMdqQm9sBvbsY5axIpm1iwY1aGW9LKO2pZxssV93yAEMYH2nKbsk3A17ODV+zjEtqQsb06zRIkVSs9lpKelsrm1uREXsiftFEM9bZU+n9m9tgfTVcnDSI/R7VWnAFPZMJFCmg/ZykNXYZfDFHP6NEFphsutx/6YuOEY2k7eiBsrhzIdF+WXpYl0J4oFAgKBFCDg73kfAd4eaNx9iESqUHNrh4IoX7c5rh7dlWxvNVv3kEgVcs5fUkbqUkqtlNiN0wfYx0YHkmxiaWufKLESwiJJdA2MVLY3y2nNhOy9QCLyifncvXQSp3euxpBFO1SmAJN3HPxc9iJx8bDOqFC/FSo0aMMIm884vG4B11fR0TdEi75j5O5iLxAQCKQTAhkpYsX/Iru3smfKAm1GSej8jz0XOrUeqXZ0hV2tLhKpQp2YOZXlfUUEekl9JndAxIrnnrnJuYFIElXESjQjd8iy6iZ8htU2s+J1cdEJNfh4hYo/cVHhuLdhDMdAL0deljZ3GvdK7XFUDC2KBAICgQyIgCBWMuBFE1MWCKQWAr7+AbwrIizim1M+5fQs8evl59a5csoP+V4u+B4VnTKBtk3L5mH94jlKfcU/SepBlaJugl6FxG/Cz6M/xPCXiPp6ytEscudXLEqlx+BR6NauJZrUqyUvVrlvWLs6BvTsgqVrN+H0hSswNjLkac6cy5REYSdHGBvLHuiIYDp3YBtKFi3MI2moM4q2aVK/NpzK1cTcJasFsaISYVEoEBAIJIXAM5aiKpeZIbQ1syq52VqagEiT5IyIAc2sGkpuhroy8iI6Jk6pPKmTNcPaYiWL4kjK2G/1RI3SbZHWiyr78DGO37PjR7Mo+s7cdgaP/F/h6tIhShotij4pOQ6LlKVMi/v8BVvGdoaDlYxgL5o3F16zSJb5u8+DdF/6NK6Ykm6Fr0BAIKAGAiGBftzLkgm8x7dceZ3iF6k8p1RbiiYXfKc0WSmx/rPWoe/01Uk3SeLmloVF3YSGvFTZnlJ10bOstq6+yvqwN8FYNa4PqjbvjNI1Gqn0kRdSCjSKfCnDIsx7T1khL4Z9kdLo4WzNyRVBrEiwiAOBQLohkJEiVqJD/KFtkhNZsil/xKJrbsPTX6kDmraZLGuF3FdDR/ZbOCVi7Xo57dFkm4wckfejap8pi/Kzr9wnE0uzSEaESHz7wtJ5kWXVMYhfleCcrt2zs1vwaPdsnvaM0oMVbDcWGlqydwipNU6CgUWBQEAgkKERSP4XeIZenpi8QEAgkBQC4e9lL7VMGDkQ30gIWB3T0tRUxy1ZH0qR9TNmnt0MlI7r9Zt3LBqEVGG+W2hYGCdAEksDtnrTDrwLDcN7FtHSY9BIqSERNfSARWX2drkxclAfXjd/6li0aFQXJFQfGh7OtGWc0LRebVgXKY8K5WQRO2amJqAtvlmweZIeC2nbfGCED6UvEyYQEAgIBNRFgETYLY0TvpQjbRV2u0rW1CFfku2EORAx8jNmbqQH0ll5w3RR4keBhDKSg6JZEksD9omRHwv2XICthTHbn5emEcjSnJGtP34DZ+94YRBLeZbPOmFaCKmBwgFpqZCRbo2cVJFX1y3jxIkV78DX8iKxFwgIBFIRgWgmrE6mZ2CcoFciD9SxrNlS53mUdFCgzD2rM7zkY2hqjlcsHdj7d69hYCIjaOWVpO+iy9aYWBqws7vWMg2Yd4iJjMDKsb3lzRD6+iV/HqUyipZp0nMYjJhIPVmVxsopcjW1dVGgdCWuQxP+NgQ0H2ECAYFA+iGQ1IeA6TcL9UaKiw6HlpFFAmcStFfroZK1JO2RnzXCLHPWH/9NrGkou9dGv5ZljFCcTyzTSCHLpp/wd7mSH0sV5rqkH14/uAyzAs4o3Hky04wpqOjCUo39/DhKHYoTgYBA4I9A4Od+Ff8REIhFCAT+XgRsrGRfmFy/7YYGLBJD0ShNWHraxh174fbgUZJDWmQ3xdgh/VX65GNRN0RWPAsIUCJWSETe73kgqjjLwpJVNTYzMUaRAvnx1M9fqTo2Lo5pF/yH+x6eXESVKl8GhyAyKhrlShXnm7yB91M/hL+PQEFHB1505uJV/iO4drVKche+J6LGPyAQujo6glRRQkacCAQEAuogQOLu7k9eMKH6j0r6JKRJEvvpszpdpIrP1jOuuMfmkZQReTKiXU2VLva5ssPFww/+waFKxEr0x1hW9g4VC+dV2U5eSILyZA/9XsqLOCZ0EhASivfRMSDdFHUtl5ksdc9nFR8VxMR94t0YxNODUbdv4ScQEAgkjYBZDpkuConQxxdepzRh6WkXD2yB32P3JIcksqJ5n1EqfXIw4oN0XV6/8FciVihy5vWLZ5z0UNmQFeobm8ImXyG8YvopivYpLpaLSPt7PWB5/jPxKjNLK77/wtJ/xbe42I88Mia+zkt8P3EuEBAIpD4CGSliRYelySJR908xUVJUBiESGfQEXz+p/wz1syh+DH8Nz30Lku3GtlpbpttSJIGfrqVMoD46JCGxEhEge6dhbF88QTt5AaVRvD6nM0KfuqNYr7mwq9FRXqW0/9lxlDoTJwIBgcAfg4AgVv6YSykWIhBIOQIFHO25mPv5y9eAccOlDvyeB+D8FRfpPD0OLl69gf1MwD4pc2A6JYkRK6R9sm7bbmzasQ9lShSTutnLtFtiYj6CUnglZv16dAJt8a1MzcaIYS/5bp8/IlXNW7YWy9ZthofLGSimUFuyZhM0NbOhWqXy3HfN5h04yjRV/NyuIKfl9y+Bbt29h8CgV4hPuEgDiAOBgEBAIJAEAoXz5MQd7wBce+iLumUKSJ6bTt6UjtPjgMTuScA+KSONlcSIleZVimLTqZvYxggaRVH4g1fuIyb2E+qV/b62+GNoZMmMq8uGxC/GyVuPuBbK5G710bRS0QT1SRVoZdNApSJ5ceX+U/iySJo8bO5yO37Dgx+S5oowgYBAIPURsLJ34lEcD25cQDtMlQYICXyGhzcvSufpceBx8xJunjmY5FAUNZIYsVK+Xkuc37cRRNBQWi65kW5L3McYRhzVlxcl2Ndp/w9oi2+jWjrjEyNLZu+/IVUVq1wHRzYshMvxPSjOjuVGES9ebtc5QZNNSzm9j9xH7AUCAgGBACFgmLsQQp+44c0jFyXxdr9z29IVoLjo93h2YXuyY5o5lVNJrGgZW8CU1b3xvImoYH/oWtjyvr5+/oSAawegyeoNVRAy8gFf3TmNUJ+7cGjUL1FShXx/dhz5eGIvEBAI/FkICGLlz7qeYjUCgRQhQC/8B/TsjEWrNqD7wBFo2bg+j9pYsWFrivpJDectKxeAth+1SuVLg7b12/eA0oLVr1kVd+8/xIjJs1CB6Z10btNc6nrd1l3oP3Iixg3tz7YBUrk6B03r1+LESt/h47B01mSYGhth0859WM9InYXTxsPORpbjuzsTpj9y6hzqtuqCaWOHsVRitvD0eYoRk2by6JepY4aqM5zwEQgIBAQCSggQUbHnohsGL9mHt52jYaKvgzOunkz/w51/oazknIYn60a0B20/ahUK5QFtm0/dQnYW2VKntBOLxAnEuHVHUb6gHdrXLCV1TaTRkOX7MZKtfWS7WlJ5ah9M6loP1QYvQZeZWzGhc13kZFo2RLRsPHETZRmpkhTZk9pzEf0JBP4mBIxZWqu6Hfrh+OYlWDGmF8rVbYFgFrVxesfqdIdhwJwNoO1HzalURdB2Yf8mnoareJW68PNww9Z5Y+BYwhlVmnz/Evrc3g1YP3Uwmv8zGi3YlhLLz/oqVqk2bpzeDwubPChVvSE+RL7HvhUzeHRLuyHfCaqU9Ct8BQICgb8HAcfmQxBwZR/c1wxHHBOQz8rSZQW7nUWgyyEuap9eSOgzjZVmOwJ/ajjHpoPgMrM9bi3sCcdmg6HBNFV8Di8DRbE4j9qm9Ix8vE9RUJRM812yqOe3jJAhI12YB1sm8eP4fwq2GwPSeEnJOPH7EOcCAYHAn4mAIFb+zOsqViUQUBuBGSxSxdBAHxRxsXXPQa5F0rZ5Ixjq62P6gmVITPBd7QHSyZFysx7cshpNOvbGjIXL+UZDlyxWGLvWLoWGxveE2RSi/ZWle/mRUO2K5UqDNFbGTp+HIpXq8tURRr06t2Xi9N9fMtatUQVrF83CmGlz0KLL968PrXJa4uz+rShaUD0x1nSCTwwjEBAIZBAELJi+yql5/TBs+QEMWrKXz9rJxgJHZjLB48GLoa/z4zmq0xMCumfvnNgVbSZtwNyd5/hG4xd3sMLmMR2VBOll9+z/1E33/cPLKO5gjb2Tu6Pvwt1oOXG91A9prKwY0kY6FwcCAYFA6iPQ7t8p0NEzwImty3HlyA6uRVKhQWtetn/VLCb4rpf6g6ZBj3RvG75sD+b0a4kDq2fzjYbJU7AE/l2wFVkUnkfppvYfpR9URyBLxVwHzNmIDdOGYP/KmXwjF22G4WA2TuHy1VW0EEUCAYGAQOA7AlpG5qgy9Sjc14/C3TXDeIWBlSMqTdiHC6PrQEM7Y9x3aeLmRaqg1IBluLtqKG7O787XoqGtz7RSpsCimPL9kGvIKKR+fet1i/v7ndnE96r+OLUazomVlIyjqh9RJhAQCPx5CPyP/Vj9789blliRQCB9EBg/fjwOH9iHuxeOps+AaTxKWPh7GBka8FEGjZmME2cv4sntS2k8aup3/yrkNUgjpniRgjA3M039AViPb9+FwsPTB1//+4qKLCJGkbhRHJDSkD3y8mEi9+951IqNVU5Jr0XRL6MdPw8Mgn2pKnB1ZWl8Sn3/sjyjrUPM989F4Pr163B2dobn1vGwNJHd1/601X5k2h/0FEdprEIjomHXZiIjAFqjXY2M9W8yODQCD3yDUDRvLh698quv06fPX/DI/xXH1MnWEkRm/anWcPRqFChXHStXrvxTl/hXrIueR7ftPYRZ+9M3JWBagRv1PowRKzLdo43Th8Lt8kksPZO+2n+psbawN8EgjZjcTkXTVESeROqfez2EvokZcuVxhEbWbKkx/QzRx5uXARhQy0k8j2aIq/V3TLJIsRL4aFMehdqPy1AL/hL3kc33Py4iHxsZimPdnVCy72LYVGmdodZBeimkG8NYa5Cuyv8yZU6T+afXOGky+RR2+njPHHzxOAVvz0cpbCncBQJ/BQKeImLlr7jOYpECAdUI0Ev/ms07Mk2SojwKQ06qkOD7WSa+ToLuGdEszbODtrQ0UyZ4X6VC2WSH0NLS5FEzyToKB4GAQEAgoAYCa4+6YOf5O5xAcbT+rt90xOUhb13ILqcavfxeLkRc/E7kBWm4EMkjTCAgEEgfBEh7ZEq3elyTpPPI2RKpQoLv913OwcaxcPpMJJVHMTKzAG1pbYam5jCsYJ7Ww4j+BQICATUQyEjfLfue2oDnV/YyAmUR9HPlk1YXdOs4PzawLSiVZZSDTJmzwMShRJpPN73GSfOFiAEEAgKBn0ZAECs/DaHoQCCQcRGgl/7GRgZYvn4LIiIjUY/pklDUyuZd+xEUHILVC2Zk3MWJmQsEBAICgT8QgSJ5c2Lk6kPoNH0Lj0xxtDbnYvaL913kGiCUFkyYQEAgIBDISAhk1dTiZMqp7Su5TkjxynURHRGOS4e2IvT1S/SesjwjLUfMVSAgEBAIZAgEDO0K496mcSx1Vg8emaKfy4GL2XsfWQ7LkrVhYO2YIdYhJikQEAgIBH4lAoJY+ZXoi7EFAr8BAltXLsSsxStx/rILJ1R0tLVRrHABHN66FqQnIkwgIBAQCAgEfh8ESue3xc4JXbH97G0s2nsB4VExsLUwQafaZTCzVyNkzpzp95msmIlAQCAgEFATgYFML+Tg2rl4eP0CLh/ahmxaOjyF1sjl+5C/ZAU1exFuAgGBgEDg1yJAOksZxUwcSqL8iM14fnEXvJnQ+6eocOiY2yB3tfYo0mVKmqXRyij4iHkKBAQCAgF1EBDEijooCR+BwB+MgIG+HmaOHwGMB95HREJPVydNNUBOnrvEomOi0LppgwyJ6ufPn9mLy8xQ56E5NjYW2bKpl+c6Kjoaujo6GRITMWmBgEAgfRGoXdoJtH1lwpuRMbEwSCXB+jO3PRH54SOaVy6WvgvKAKMRLqS9Yqyf+H06il0LXa3k7/mfv3xhfX3l2jgZYOliigKBdEGARNfbD5kGDAGPWtHU0UvT51H3K6cRExWB8vVapsv6fuUgn+Ji1dJd+cKecTOp+Yyrbp+/ct1ibIHAr0AgI6UCI3wsi9fk23/smfLzxygmWJ+2unKv3M7hc0wkrJyb/orL89Nj0vX9FP0eWXUNk+3r88doTk5lzqqZpO/n2A/IrJFNbSLrU0wUvn6OQzY94yT7FZUCAYFA+iAgiJX0wVmMIhDIEAgQyZLWNm/5Wvj5B2Q4YoUIoUmzF+KR91Po6+miKtNX6dOlfYKontCwcAweMwUurncQGPQKhgb6qFaxPCaP+hf58topwev24BHGTZ+HO/ceIPx9BLKbmqBRnRqYNXEkG0N2LVp27Qsf32dK7eKfrF04E6WZTo4wgYBA4O9CIFOmTKlGqhByi/dexLNX7zI0sbL7wl30nrcTj7eMRw5TA6X/IIiIqth/IT5/+apUTifW5kbYO6VHgnIqCI2IRvm+86GvownX1exDBAV7H820ITadxMGr95jfB2hny4qKRfJgeo9GyJvLTMETuODmjUkbjuPx82A+B+vsRujfrDJ6NCifpi+QlSYhTgQCGQABIlnS2o5sWIiQQL8MRazQPWxUi/IgAiS+meW0xqiVB6TiqPeh2Dh9GLzcruNd8Avo6BuiYJkqaD1wAnLkdpD86IBIpt1Lp+DFU09o6eozv8qo1aZngkihlPSpNIA4EQj8RQio8/Hd7wjH/9gzZVqTKrRuH5ZmLDrYP8MRK3EsmufhtikIvHYQX+JikEVTB+bFqqNY95nIpm+idElf3jkNjx3TEfnCB+xrTOhkt0bBdmORq1wjyY+IrEe7ZoF8I4N88D+mDaOf0x4F24+HRdGqkl/8g9jIUJwbVpVfq1oLr8avFucCAYHAL0BAECu/AHQxpEBAIJCxENh18Cg6/TMEtta5MLRvDwS9Csa+oydx6vwV3Dh9AA55cvMFRUZFoXaLTnjw2AttmjZEPns7XLnuikMnzuC6613cOX8U2c1kD1537z1E7ZadkCVLFkYyNYSJkSH2Hj6Oddt2w93jMVxO7OMv2qheg22qzD/wBY/+yaKhoapalAkEBAICgb8KgQgWVbJk36VE1xz09j0e+b+Cky0TlNbVVvIz0lM+V6zsv2gPgkMjOLGiWE5fLbabvBEuHn4omc8aPRvkw7UHvjhz2wtuPoG4tmwIzI1lX35euvcEzcathaGuFjrULM3u/Zlw6OoDjFh1CG/fR2NMx9qKXYtjgYBAQCCQAIHQkCAE+HjAyr4A16RRdNA1+P7lckx0JKZ2q899y9drhZyMSHl85xpczx+Bt/sNzN5/AwYm2XlzlxN7sHREN5jltEHDboMRGhyEm2cO4t7VM5i++wpy2Npzv5T0qTgvcSwQ+NsQyGgRK3/b9fmR9VJ0iMvMdlx/xrZaOxg7lEDYU3c8O7cNMe9eouq0Y1K3L2+fwo15XaFraYeCHcYjc5as8D21AbcW9oKGjgHMC1fmvreX9WckzQGYFXCGVYWmeO/vgVd3zsBlRluUY+nZcjCNG1V2d+W/+BgWki4kmKrxRZlAQCCQEAHVb+sS+okSgYBAQCDwVyIQFxeHUZNng7RnXM8e5hEoBMSMcSNgW6wC2vcahNvshyrZ+SvXcf+RJ5bNnoxendvxsjH/9sOgMZOxcsM2HD55Bj07teXlKzZsRczHWLic3I6iBZ142aSRgzkxc/HaDRw4dhotGtXFzrVLeF38P8+eB6JEtYaYMHwgijNNHGECAYGAQOBvRWDTyZuMzPDElftPQem4EjO/l2951eph7VDILkdibkrl649fx7k7XpwQUapgJ9ce+nJSpXW1Elg9THZvJ5/ZO85g5rYz2HHuDv5tVY03m7vjLN9fXDwIuS1N+fHELvXg1HEqlh24jJHtagp9HI6K+CMQEAgkhkBwgC+v6j9zHWwcCyXmhoc3LuK590N0H78INVvLIvGaYSSLYBmK0ztXs+fWo6jRqjs+f4rDtnljuZ7NrL0uPKqFOm03ZCr6VnfA4qGdOAlDZer2Sb7CBAICAYHAn4TA80t7OKlSqONEODT8hy+NdGhYOAojV7YizPcejPIUZem5PuH+xnHQNrNClSlHpEiWnGUb4ETfEnh6Yi0nVqJfB3BSJVf5xig9aJWUYvyt1y1cntCYR7uoIlZ8z2xCyL2L0FAjDdmfhL9Yi0Dgd0dAECu/+xUS8xMIJIGA6917GD9zAe7ef8i9nPLZg17k16ku+xJC3vSyyy0eYXH+8jX+Mt+5TAlUYsL03Tu05noh5NdnyBiW8/0zRrP2c5euxpmLV5HXzgZd2rZA+xZNsGjVBuzcfxiBL4P5i/yF0yfA3s6WD3Hs9Hms2rQdVLZz/xEcP3Me/oFBKMPSU82dPCZBCizeSOEPpcEaN2M+rt10xdt3YShXqji6tW+FujWqKHgB6q5XqdFPnjz2eYqXwSGc5KC0XnKjyJOaVSqAUoSRNg2lUXO5dYdXt2xcX+7G9+2bN+bESsgb2Us9Krxx2w1FCuaXSBV5g85tm4OIldvu9/mY8nLFPaWC6NJ/GIoXKcCvt2KdOBYICAR+DwTueD3H1M0n4f7kBZ+Qo405hrepgZql8itN8OqDpzxy4aK7Dz7GfkLZArlRoZAdOtcpK71od/X0x4T1xzCuc134BIRg7yV3BL4O431Rn7GfPmHcuqNw9XwOUwNdtKxSDENaV5fG6TJzKwrlzoEKhfNg5eGrjIB4AjMDPbStUQIDm1dJNg3V8RseWHvUBQ/9XiKXmSFLdZUXIxgRoK/9PWf0x7hPWLD7AvZcvAuKDCG/ykXsMbVHA+gp+EmTSsUDIkzCo2JQOE9O+AS+ZhEgUSp7lxMreXMqp+hS6cwKPVnKrrFrj2BytwbYfOomvrIIFUV78Tqcnzqz66VotG4iViJjPkrFQW/DkcPEQCJVqIJwKcEiXSji5SO7hjqZk9dnkToUBwKB3wiBJw9uY/fiyfB95MZnZZU3P5r2HoliFWspzfKR6xUeDUEC9XGxMchXvDycmDB99RZdubYHOa+e2A9f2L+HZn1G4vC6+bjvcg4WNnlRtWlHVGzYFsc2L8G1Y7tZeqsg2DkVRZcx82DJ6snuXjyBM7vW8DLyuXvpBN4EBcC+SCl0GjErQQos3kjhT3REOHYtngTPOy6IDH8Hh6JlUK15FxSrpPzlsLrrVeg6VQ6Dn8uIFUtb2XoT69Tb7QavKlenuZJLhYZtOLES/u41L3/h64Ww169QtnYziVShCopmKVy+Ok8R9iHyPSg1m7p9Kg0oTgQCfyEC6ZUK7EvcR3gfWoKAK/vwIfQVtE1zInvBCijUcRI0tHQl5D99iMCzCzvw+v4lTg7o5XKAqWMZWFdsDgMb2cd95Hx31RB8/fIZ+Zv/y/pdyl/gU9SFbdW2sK7UAj7HViHw6n58YBEaRnaFUaTrdOixejJKZ+XHXvpTGfm8unsG0W8CYWJfAoU7T4ZejqTvWXFMs+TRzhl48/gm4iLfwSRfKdgy4sKyeA3ev/xP6JO78Ng5E2F+93mRfq58fL4WLCVXWlrA1X2MJDFF3rrdlYZxbDYIJo6lkfVbKjAiRj68fYHCXaZKpAo10DK2RLlhG6W273xk7wxsKreSSBWqpOuiY2HLU4iRPgulG5NbRKAXHmyZxKNgKFIG/yVMayv3FXuBgEAgfRHIlL7DidEEAgKB1ELAk73wr9miI0LevMGg3l0xatA/+MJeuDdq34OTIvJxLl27yVNO7Tl0DDWrVmSERUuu/dF/5ESMZfoecrvHIi2ITKnepB1u3nFHFecyLH2VG7r2H46G7bpj9NQ5yGlpAefSJdiL/5uow9JY0Qt+soAXL3nbVt36cvKlSoVyqF+zKiMa7qJMrSbweiL7ISgfS3H/4uUrlKrRCNv2HkSFsqXRmRE5zxkp06RjLyxZ8/0BRN31KvadGsevgmU/PksVK5Kgu1LFCvOyx95P+J7IIErhZWSonBv86s3bvL5eDVm+1E/shQFdi77dOibo8wXTZSEzNjRMUCcvmMOIL1e3+1gyc1KyL0TlbcReICAQSD8EvBn50XD0KoSERaJv00qc5Pjy9T+0nLge5+96SxOhCIvGY1Zj/2V3VC+RD53qlGGkRDiGLD+AyZtOSH5hkR9w87E/xq45gtFsI9JCRzMbNp64gXZTNqLW0GV44PsS9csWZCTLZ0xhhM6W07ek9pdZGqqtZ1zRcsI6TqB3qVsWWpoamLTxBAYt2Sf5qTqYu/Ms2k/dhOiPcehevzwcbSyw7pgLarMxX717LzUZyuY8b/c5lC9oh6ndG6AWI5B2nr/D019JTml0MIWNd2JOX75VLSZLW6NqKCJWrJiuSTSLFjx16zHH6BbD9YsKzRUiirrP3oZyBezQp3EFVd2hdun8LFVjZmw97Sr1QX1tPiXDvm6Z79GEDcoXwkuG11kWWSO3Jy9e4woj1ioywouupzCBQEZEgF7OT+1WD+FvQ1C/U3807TmcvZz7gtn/NMP9a+ekJT26dRnTGNF6nT0nFXGuwQkL0v5YP3Uwdi6aIPk993rAyZTJnWvD554rCpSuxF/qLx/dE7P6NMWOBeNhYp4TjsXLwYP6ZP/+5c+jb14F8LbzB7Xj5AtphRSvUoe3H92qAoL8vt9/pQG/HRBRQ/olV47sYNoizqjSpAMjZZ5jTr8WOLFlmeSu7nqlBql4QBErppZW+PghipFGJ3Fh/2aW2ouRvgxvRavavDOm7byUIF0YEUZkxSvV4XsiVcjyFirB94p/8hQqyU8Dme4Kmbp9cmfxRyDwFyOQXqnA3NeNgueBRTB1KofCHSbAolgNPL+8F9emt1ZC/8a8bnjIXsiTQHq+pgOhz4gVirK4NLExYkKDJd9w/0cIuX8Rlyc2Ab34NyvojLferqCUVddmtIPHtqnQMrFkL/9L47XHNVyd2pK925e9CyAygSIpbrKxAq7tZ20rMFKkJohoOD+yFiKCZL+VpcEUDoioOT9CNnczp7KcyIl+HYjrszviyfE1kmcE0yu5MrkFPoa/gX393nBsOoiP7zKzPYLZ2GlpUa/8mJ5KNWRiab2iQp5zIinM7wE0jSxgU6kldFiECllU8DO+p2gTIkbeet5kc7vA5vyap/aSR6EQYWJXuyuM8hbj/vI/dI3iIsOQiYndKwreE4l2a3EfTrzkrdtD7i72AgGBwG+CQJbfZB5iGgIBgUAKEdh98Bhi2NewG5fNQ7FCspc3RLBQeqptew6iFntxT7ab6YOQTof3rQtSGqvh/XvDvlQVFllyAbMmjJRGpogKElkfPbgvLyPtDyJqLl+/hftXTkpaIt0HjsBWNsbTZ8+lMmoQERkFt4vHefQGnZ+/4oJ6rbtyUubgltVUlMDGTpvHiZRrx/dKAuwTWXqr+m27sXZz0aFlUxgz/RF11xt/gLfvQnk0Tfzy+OdN69dBAceEL+TsbK25K0WR/PuP8lcqRPaQPWLECkXZ5HfIy8/pz6277rh07RbcHz7CweOnWdRPYxZhUpDXazBNlMUzJkq+8oPXb95h5cbt/HrVq6VatO6hpzemzF2CHh3bgCKUhAkEBAK/HwL7GFESw6JPVg9vhyIsioKsHyNY8necyskGIlHIiFDJwsRC720YI6WaGtyyKgp3nYGTtx6BCANFI50Pj81jYWaoB/rhXnPIUtzxDmDRLWWwsH9zTrT6B79D0W4zQWRKp9plpOZUPr1nQzaPyrxsXMc6nNQhwoUIk6L2uSRf+QFFf8zafhY1Szpiz+Tu0ld1bauXQJOxa7Di4BUWkdKQkzkkGl+bkSkrhrSRN2fRGSYYtfownr54k0DIXe50+NoDeAV8/2EvL1fcm+jrMIF3Z8WiHzr2e/UWkUyHpVCX6fz6yDuhta8e2hb5rM3lRRjPIoRevYvAgWm9pHVLld8OjNm8xrMoIopMcmg/mUUa5WEE2DNOqJEgPemuyK1XQ2d+TYhcK+NkC02NLIxU8YUl02CZ0Lme3E3sBQIZDoHrJ/Yi7mMM+s1ah9z5ZR+h1GMEC6WSIpKiSIUafE0uzC8zE+ddcuqhFB3RqPsQDKjlxEmC9kOnS2t/zyIqWg2YgGa9R/Ay0gkhoubR7auYd/iOpPuxYkwvPgYRDnItEGoQExWBuQdv8UgLOn944wJm9GqM7QvGYcSyvVSUwHYsnIA3LwMwdcdF2Bcuxetb9h+HGb2bsHbjUalxO0ZUGDNiSL31xh8gIuwti6ZZG784wXmZmo1hlff7V+SKDsEBfiCtk/4MM8JcbnYFiqHvjLXIlceRF8n3dPLkvisoUujZ43twPXeYRf20AfmTmVvJNAKJoGrQZRAvk/8JYoQZ2QtfT+QrVlbqm8qS6pPqhQkEBAJpi8CXT7GgKAqK6CjZd7E0mK65De5vGo/Il74sSiQPJ07eMBLEoXF/FGo/TvLTt86PB8zvrddNWJVvIpXHMtLCqc0o5G82mJdZOTdl2iLtWSTJddRccIX3SRW3lw9EwOU9nEigceT2KSYSNeexVFXasiwPIQ+ucKKHSJnyI7fI3ZT2Htun4QOLbqk6/TiMWYQLmVOr4axdWzzcPhU2lVsiq64RAl1kovGlByyDYW5ZKkQiWE70KcaidvYkKvgeG/EOlEIrOctVpj70rWT3UEVfIkiIGNE0MIPLrI4IdjsrVVMkTgmGvwnTXCEjAuZ/mTLjfYAnXBezj14ZUUJGZXnqdOXRRJnY/weJYJGTLNzh2x9KFfaJRe/kYteE2sjt4bYp+Bgaggpjdyf6TCr3FXuBgEAg/REQxEr6Yy5GFAikCgLyr/PWbt6JeVPGQltbC/TC3vfOFf7CTT7IoD7d0LdHJ4lUofI4llOZoioohZWiZWIv+EicXW6FC8geLqqyCBS5QDvVVSpfhhMrRCwolg/s1UUiVciveiVnlC1ZDOdYCjJ6CRg/NDo0LBw7DxxBiaKFJFKF2mXNmhU9OrQBpTA7yITfu7NIEHXXS+0V7Q0jVoiISM7s7XKrJFYo3RkRIheu3sD67XvQqnE9Npf/sGPfYew7cpJ3G/9LQSokUmXi7IV83rRuG6tcoEgVukaqjEiuXkNG483bUCyYOg6F8stevMb3nblwObS1NEHkkzCBgEDg90SA7hFkG4/fYC/zGkFbMyuPbCBSRDGbFJEcvRpWkEgVahP3+Qs/j4j+nkaKysk6MqKESBUyuq8UyG3JiZUuLG0Y3b/JbC1MeFQGRc0omoGOJvo2qSQV8fs9Sxd2lb3cv+DmrZJYIX0RioTsyUgBxft3lWIOoHRaRCARsSKP+CDh9vu+QRKZRGRCx9qlGYmg+r5Hkzl49T5LhXZfmpeqAxorVYiVl+8YsRLLyZCG5Qty0fgd527zaJ62LPLn6rJ/eeQIRbNQ6rOt4zrD4pv4vKp5UVmeHKbQyqqBdxHRuMxSrFFKMjL6b4DINbr2ZAZMtJ6iZSidGgnba2TOzP+/mCVzJqWUYdxZ/BEIZCAE/vuWjuTc7nXoNHI20+vQRhb2b37ZWU+WqkR2L6Tl1O88AHXa95FIFSojjQ9dAyNQuilF+x+7nzViQupys8kne4lGESiKBIoTi2Yh8oZIAMXyep36SaQK9VGoXDXYs7RelIJM1fNo1PtQuBzfDbuCxSVShdpl0ciKGixN2WNGTLjFDcENAABAAElEQVSePYJqLbqw9rIvtJNbL7VXtIjQt9i3/Dt5pFineJyDpTVLjlhpM3ASi/ZuCOrz8qFtuHhwC+YNaI1Z+65DU1tHsTtOquxeOoV/2U33cbMcNgz3T/waUYo1Ilk8bl7ChX2bUK5uc+539dgu3Dx9gPej6hmXiJrE+lQaXJwIBAQCaYLAf19lUWpvHl1H+LOHEtGQp053lkKrnRTtoKGthyrTjkuEiHwyWbJq8cPPLPpNydi9N18j2QeWVG5gI/t4k1KMKRIoZgXKc2KFokgUy+3r9ZJIFWpvXrgSIx1KIuTBZZX33rioMK41QvokclKF2lFkSO4aLGrwkQuCbp1A7urtpdRXfmc3s/RiU5Almzbz00DdFXdYi+//r6H2ikbEiueeuYpFKo8prZkqYkUehUKkh45FbhTpNp2tqRTesWieh4wUujGnM2rMv8iJFyJWyG4t6g3rCs14ujW6h1LKtqcn1jHxekM4tRyWYPy4qHD+kRWJ2RNZU7TbNMmH0qr5ntqAssM2QMvIXCoXBwIBgcDvg4AgVn6fayFmIhBIEQIkgk5RHOu27WbkxFGWRqskalR2RuO6tWBr/f3LY0f7PHgXGoaFK9fzFF/PA1/gid9zREZFwdI8u9KYOSyyc1JDXqiZTZaaJKeF8v/EM7MXQmRxLFWKojnkkeVZVSwrwKIqSE8k6FUwcuWwVKyCj+8zfh4d/QHteikTBRT9QubnH8D36q6XOyv8ofW/Zw+cyVlW9mJMldHLx7WLZqEpS032z9CxGDJuKidL6KUZadSs27pLZeTIyEF90J8RWrfc7mH73kOYwQiRsPfvE0Sq+Po/x7AJM3j0UB4WHbNlxQJOSKmaC0UI7T96CoMZWWZqYqzKRZQJBAQCvwECXeuV49Eom5gux95LbijH0mNRiipKB2Vj/v3froNVdoSyF/JL91/CbabJEhASBt+gN+xFe6zKF/o2Ft/b0jKzsagHMktT5fSDmTP9jxM0vPLbH7scZkrkCBVTWi+yZ6/e8X38PxSxQrb9zG0uxK5YHxMbxyM6KGUWkQej2tfCtC2nUHnAQtC6KhbOy9KBOfIUZ5kZeZCYrWGi7ysVolxU+bF3galiNE5WjcxwspX9vygPI2woesRARwtL2DU46vIQRBr1XbiLRfuURkN2vZIyirbpPGMLT382pVt9lHS04RovC/acxwaWpo1eYs7v14x3UXf4cjz2D+bnzSsVRTb2/5yzdzwxcPFetGJRLDdXDVf6byOpcUWdQOB3QqB6y+4sDepenN+3EdeO70H+EuVRqHw1lKreCNlz2khTzWmXj+uWHNu0GD4sioLSbJFmCEVgGJnJ7kVyZ+PslpzUkJ9n/fY8apw9h7yI7zN9+6KXCBpFy2HroHjKj63y5IcPS5sVGvISJhY5lepfPnvCz2M/RGMRE2xXNIp+IQsJlL0wU3e9in3QMa1/y5038YsTnBOZk5j1nb4aGuzjIyv7AtyFtGUomkRH3wBHNy5mESlHUKlRW6XmTXoOY4TWP3jywBVXj+zEgdWzQVoyXcfO54R8n6krWbqzllgzqT82zRrOiRX6mKkaI5TO792AXEwvJ74l1Wd8X3EuEBAIpD4CRCrkZy/oH++axVJt1YReTnuYFXCGRfHqMC9SVYp2oJRTFE1BESeB1w7yCBOKDolm6axUmRZLbUWkhtwyZ5W9C9AyVr5Hy6Mp/vusfO/VVYhekfehb5WPkRC3WfQM04ExUb6HU2QNGUWF3FzYS95EVsaiX8iiQ/z5PneNTnwNpC8SwAgIU8eyXAg+R+m60MluzX1U/SFsmmyTvXNQVS8vU1y3vIz2RP6QfWVrLTt0HfRZf2SkMxP7/g28DizGC5dDyFuvJ/MNZ/fQL8hZsiFK9FnA/egPXYOj3fJz8XpFYoWI/mdnt+DR7tl8HEoPVrDdWEkjJyYsBHdWDOZkWc7S9aT+xIFAQCDweyGQ+C/d32ueYjYCAYFAPASsclri4bVT2LVuKWpXq4Q79x5i+MSZcCxbHfOXr5W86ZjSg01fsIxHTFRjUSTrl8zmqaskp28HOtra8Ytk52q+1bIwTygIrP2tTzlJozgAET5k2diPRA32xYniZmJkhLbNGjHSIi/3UXe93FnhD73Y0mIRHsltcrJIoal0SNEj7peOY9X86ejFCK3JI//F9VP7WeovWeizPCUX/RClByS56ehoo1rF8li3eDYnu46dPi+v4vvt+w6hZLVGPDJn5vgRuHf5RKKkCjVYwK4l9d+1XUulfsSJQEAg8HshQBoorqtHYPOYTqhRwpFFKAQwEfSjKNZ9JhbvuyhNlo4pPdjcneeY9skXVClqjxVD2/CX/ZKTwoFOtu8/dhWKoQ7vYGEsi3RRbCePpsiWVfV3NqTtkomRNERGaDByRHEjLZWWVYrxyAzqc1ibGnBfPwrD29aANpsnEQutJ21AmT5zEcJSmCVmWRk5pJVNI8lNMxHiO7E+EyunlF9yUkXRpyYjgMhIrJ6idEIjPoAihvou2CVtpCcTzFKDUdmC3bJ7OUXbkI3pWJuTKnRMpNL0Ho14hNIelh6NjKKHiFShVGGUds1QT5uvt5FzYbSvWYpHthCpI0wgkBERMLXMhQVH3fDvgm0oWqEmfD3csHXOaAyqWwhHNiySlkTHfas5YP+q2VycvlDZqviHEQUOjBiIb9m0dOIXyc7VfB41jEfUUGN5nxrfSBrFASLDQ/kpkRqZ2fOo4qZraALn+q0lgkHd9Sr2T8f0PJpVUyvZLdO3j5fit6dzii6RkyqK9UUr1uangU8f8X3851GKYpHjbcbIrjssba/crB0KYu4hV/SavBw1W/XgKdhm7LrCiSDysfpGrKSkT3nfYi8Q+BsRoH/r6WGUrqv2kptwZGLzmbNpgSI5rrNUVWeHVOKpq2gO9GL+7NAquDKpGddN0WGpwujlffE+81VOkQgb1abemlRFVGT+1mdmDRlJo9h/XKTs3puJ3XspRZbiRum/rFjUBwnUk2mb5kStRddQZsg6lvarGsJ87zEx94k4NbAsvI8sV+xW6ZiuR2YWoZPcJieLlBqzExKeJzO2Ly6RKryA/bEsUYsfyjVk5L42VVrLXfieCC7SrKE0X6QRQxYb8ZalO2sD93UjYcBSs1WffZb/TtDQ0uX19MfvzCamuRKKTx8iGcEySNo+MpIqJiyYn3sdXCz5iwOBgEDg1yCg+pf0r5mLGFUgIBBIAQIRkZHIzL7Ua9agDt/oB8/VG7fRvvcgjJsxnwujR0VHY8y0uTBj0Q2eN89BT/f7/6hnLVqRgtHUc/V9FiDpvchbUIQMpR1TFWGR28aKu+Vl6bY2r1B+wPvCwmYjo6J52ityUme9RJ7Et+DXbxiplPjDlty/a9sWkgaKvIz2cXFxeBbwAqbGRiBxekWby0TkLbKbcQ0Ywt+A5XstyEiYG6cOKLrxH9OkE/PgkZeUDoxSf3XtP5ynStu2ahGscyl/waPUATuhqJ4tTNeGUqtRFI4wgYBA4PdFIILpeFDUSOMKhflG9wcXDz90m7UNUzad5Om/SECdxONNDXTgxgRI9bS/37/m71ImYVNjpX4sDVZ8CwiR/aC1z6UcvSj3s2URMveevsDQNtXhaK38tSLN/wuL3CNyJu7TZ3xgaa+ssxtjLNNuoY3IlHmMgKCUWquPXktUR4Q0Xu49eSEfUuXe3EgPI9rVVFmnbuGLN+G4y/RoijtY8ZRciu38g2U4mBroMhIpCwrZ5YAvE7pXtFi2RopUpFRemb69NHn07CUnUEqxSBVFM2HXtDDT1nF/EsixefTsFa92LpQwqrMqi5BZzrRq5CnEFPsRxwKBjIDABxbRQZEjZWo14Rvd7zzvXMOS4V2wa/FE1GnXm4ut71w4HvrGZlh04j60dPSkpR1cM0c6Tq2DEKa5Itd7kff55uVzFtlhBH0jU3mRtDe3suXHFAEyYPZ6qZwOKI0LRdVQijMyddZLBEp8C38bwkilWfGLE5xXbdpJ0kBRrHz76gV8H95BHiY0TwL2ivb6hT8/NWD4Ev6dS5rByqEAiCBRNHrBqGdojOfeD3k6MEqfQ231jExQjQneK9qh9fNhaGrOdWVS0ielgRMmEPibEVD8yC6tcKDoic+xMSxSwwoFWo/kG+mAeDExe0ob9fTkOhRsOwbeB5cgItALBZm+Sj6msyI3Si+VFkZps+T6J/L+KUJGQ9cQ2fRN5EXSnogeMl0LO5QeqPxugiI/PsVEsZRfsvspkQuUJjJX2QZ8+4/d69563uBptx7tmIG8dbpx8kTq/NsB4eK5b0H84gTnttXasiiUIgnKidAhU5UWkUTlyeSaMtpmsqwh/339zMsV/3Bfdg/Owj4c+PrlM67P6YzQp+4o1msu7Gp0VHSVjgkzA9sCLNJIFjEpr/hCkUJs/eH+HiTgIi8We4GAQOAXISCIlV8EvBhWIPCzCNRlovDv3oXB65bsBRylrKrsXAb1albFpp37OCkRGPSSRzg0qV9biVQJDHqFex6eMDdL+OPyZ+Z18txFtGhUV+qCSI1TFy6rjI4hp7y5bTjhcubiVYlwkDeevWQVJs1ehIuHd8K5TEmos15VxEr4+whsYNooyVmlcqVVEisfYpjQcYXaaN2kAbauWih1QxgeOHYaXRghQ0b4U9oz9wePQNoxRKTI7Z7HY7jd90BhJ0dJY2U8I78M9PWwe/2yBCnZ5O0U91duuHKSpyXTeBEmEBAI/N4INGXC7u/eR7N8yaP5ROn+QKmxSNx929nbiGL3lRevWboAFoFG6aYUSRUiAB74BSH7Ny2V1FrpU5ZizI+RBXZME0RulOKLjIgEVVYqvw0OsXRXp255KhEr76NjWP7nmSjM2h2e2QdX7j9FiwnrsJql9WpdrQTvytxYHwObV+HESnhkjKruednle09AKbWSMtJY+VlihaJvKG0X6dEsGii7b8vHPHD5Hj+kKJwSTHC+d6MK8ippTynOYljas6vLhkhlRDY9efEGJ289QpMK33+MP2eEFemo2Ocy40RNPmtZOk1a5+gOsi/L5Z3Io14K2CoTV/J6sRcI/O4IzOjZiKf4WnxSFnXFn4eY9kmxSrVx6eBWTkoQKUD3u9I1GimRKlTu7/UAhiaqyd0fXbvb5VMoW7uZ1JxIjXtXz8ChaMLoGHKysM7DyAVT3Hc5J+mPyBsfWjsPe5ZNxaQtZ+BYvDzUWa8qYoXSb5GOSXLmVLKCSmIl+j1L6zukA6qzFF09Jy1V6ubGqX383LGEM38epfRdJFZP2jG6BsaSL2Ht98gd1vkKco0VmtOQhsVRvm5LDJy7UfKj6+J69hCqMJKHjK6pun1KnYgDgYBAIM0QeM0E6V1mtEOp/stgXUn2TKNpmB0OTB+FiBWKjCCLfi1L+WVTWTmCIq2IlVdu55CrXCNp3URqBLtfgEm+UlKZ4oEu0yzJysiDkPsXWaqtT1wzRV7vxUihxyxFVuUph1narzK4Oq01i954hzpLb3EXIllk6c9q4vnFnZyEoaiU+BbHsHh2YXv84gTnZk7lVBIr1KcZ05h5wzCPZBoqpMUit5e3T/JD+fosi9eAz+FlXDfGsvj3D4JiWdTJW69bXLOGooKCbh1HqM9ddr36JUqqUMd56/bgm3w8+Z7SvxFRU2OO7D2QvFzsBQICgV+DgCBWfg3uYlSBwE8j0KRuTYydPo9Fp8xDz45toaWpiUsuN7Fj/xFOEGQ3M2Hpr7KB0nvtPXwcdVi6sHws0uG6611OWOjr6SKKRUF4P/VDvrzfHxB+ZmJbWUSFBdNtad6wLsKZnshwph1CX/jOnTxGZbckUj9tzFD0YdolnfsNxbD+vUHzOnrqHNMkWcHTYpUvLXtJp856VQ1C0R3RgY9VValVZmigjyoVymL/sVOotqM8mtSrBV+mdfLPsHFMM8YCsyeOlPqh+ZNWTJ2WnTFu2ADkYFgQsbRtzyHuM3HkYL4PC38PDy8fFt3jxLVvpA4UDiqXL4P6tapJJeevXOfHFRkBJEwgIBD4vREgYXSKRpm86QS61i3L0z5dve+LPRfduEg8CdBTeisdFu1x4Mo9UCoqBxY1cvOxP6YznRJ9Fr0SzXRWnrx4zV7Op84LRxKhbzt5I8Z1rsOF5yn11Koj19CkYhGuEaIKURKMX3fsOhbtvYCcTMeltJMtghjxM2njcRC5MvxbFAnplFC0x+wdZ5GD+VG0xjMWITNv1znebe3S+VV1z8vWjWgP2tLaCua2BEWWkO6Nkb42GjFC6yt70bubpeu66O6DRs6FOKmSknn0blwBx296YPiKg3BnRApp6FA6sc2nbvGXyMNZejSy/DbmTGPHgY/TbNwaTj5ZM62dY9cfYt8ld0ZamaN+uYIpGVr4CgR+GwRKMbJk58IJ2LloImq06o6s2TS5YPq1Y0wMnqWuMmCkCRENlIrr+sn9LF1YLeSwc4A30zvZw0TVtXX1WURLNF4+80GO3A6psi4StCfdFiJXoiLCeGoyirroNEJ1xAilAGs7eDLWTOyHZaO6o3H3IdDS1cOdC8e4JkmhctWYlkk5Pjd11qtqEaSxsv2eLAWuqvrkyogMsS9SmmvZ6LKok9I1GrP7DItWP7oLD65fQOmajZG3UEneDc2ftGKmdW+A5n3HwNjMEu7XzjCNlR28vlX/8Xyvo2+IAqUr49bZg7h4oCrTxWmIkAA/rrdibJ4THYZNl6albp9SA3EgEPhLEUiPVGAm+UqzCBBTFokxH1omljxKhKJFKGKFzIK94CcjHZBgRnZ47JjOSRciOkggnV7sk1EbIh6y6hjw85/9E3B5DxdYz1m2ISd37m+ewO9ThTtNVtk16ZoUbDcGbquGwnVpPx5Vo6Gth5e3T8Fr/0JkL1yJkTKy3745mZYKrcODRafkrtmRRadocnH7wKv7YcgiTTQNzFSOQZoozXYEqqxTt5B0Ty6OqYtbC3vySCAtphVDRIvf2a0wcSyNHCVlH82Y5i/LsX9x/QiPwiH9l08fIvB4zzweYVKog+ze+9bzJh+atGUebJmkchqES2K6LyobiEKBgEDglyEgiJVfBr0YWCDwcwiQgLmHpw/mLFnNN3lvxQsXwNaVssgKSv1Fwus9B49Cs859uAul5Zo3ZSwjXLTQbeAIFK1cDzFBXvLmP7WfO3k05i5dw7bVvB8af8XcqShSIPGXapReK4Z9vT1q6mzsOyL76iNLlixcR2Tq6CGS2LI66/2pySfReO3CWejQZzB6DxnDN3IlUmTrygVKkUAUrfMyeAxLvzYPLbr8I/VoZmrMdG3moGHt6ryMyC0y94eP+cZP4v2hh3JFYuXCFRdos2tGei/CBAICgd8bgX5NK4PSPy3cc4Fv8tkWzZsL67+RCBSlsvzf1ui3cDcnPMjHUFcLM3s15um1/pm/C2X7zMO7Y6mTJof0WyxNDNBp+hb+0p/GI82PBd/E1ek8vmVjabEOTu+F3vN2oudc2Qs58qFIjO3ju/D2dE5rWTeiHfqwOTcctYqKuFH78Z3qonZpJ3nRL9vTPXXHhC4YsGhvguvSrV45TGdf3afUCL8NozpgHNPPWbzvEt+oD9KYoevYsmpx3iV97U1+w1ce5ETKBTcfaSiKkln+byse2SIVigOBQAZCoH6nAQjweYTD6+bzTT713E5FMWCOLAqCUn/9M20lVo77B3MHtOIulJar88jZPMXWirG9MKxJKey4L/vKWt7Hj+47jpiJI+sX4vB6WfoXGp+iPGwcCyXaJaXCivv4Advnj8PN0we4X2b2PFq1WWe0GTRReh5VZ72JDvITFXQPG7ZkF1Yz8ic+1jVb90DH4TOl3olQ6hTyEjsY4TV/YBupnFKx9Z2+BiWr1pfK+rDrQmnbVk/oyzeqsM1fBAPZtVNM2ZaSPqXOxYFA4C9EID1SgZEORymWOuvO8gG4Mrm5hHImpmNSoM1oyKMlHFj6r7dernh+aRff2I2MC77XWngNN+Z1hQ/TJsnCiAzSa0kNIwLF+/BSeB+SRdVlYfMszlJdGbJ0VolZ7mrt8YWlNXu4bSqCbhzhbv9jeiu21doxEmO0dO+1b9AH7wM8Wd9L+Cbvz5CRR6UHrZSfpsneOG8xOI/ezjVNXGZ+/xjIsmQtlOy7WGnM0gNXcs1BIr1oI6NUYWWGrOXY0zlFr5CRhkpi5tRquCBWEgNHlAsEfjME/sdu/P/9ZnMS0xEIZBgExo8fj8MH9uHuhaO/bM5+zwPg8/QZYj5+5ALpRQs6SQ8g8kmRSDylo7LInp2Lwcu/pKHyMJYqi1Jy/YytWL8Vg8dOwbXje1GqeBE8eOyF8PAIFGXkA6W7Uscio6JwjxENFEVDOiUkVq/K1FmvqnY/W0a3yoee3nj2PJDryCSliUJ6MJ4+TxH8+i2/Jvny2EFTM9vPTuG3av88MAj2parA1dUVpUqpDu/+rSYsJvPXIXD9+nU4OzvDc+t4Tij8CgD8X73jUSeUQsrGwoSnzpLff+XzCY1gX6v5BoFSZ1Hkgryeykl3QzF1l7xNSve5W0/g2iL7p/ZEOEuJ5c40TSxNaTwLtbqi+x+lEfMOfA1jJrxOkR+ZmZh9fPvwMQ6P/F+xNGdhMNbXYULxFqDonN/NSFuGUqMZ6GhxsXnFVGw/MlfSmCEijVKA0XXMb2PBSTJVfQW9DYfX8xAuWE9C90RSya+5Kv+0Lms4ejUKlKuOlSvT9qVEWq/jb++fnke37T2EWftlX8H+CjxCAp/hpf8TRk7EIHsuW9g6Fk7w33Zk+Dv4e94HicvnypNfqqdySktFKbl+xk7tWIVNM4Zh6o6LPHojwNuDR6yQ3oq2nnpfZJOeCs2RomhIKJ7E6lWZOutV1S41yt68DGARPk+YZowBF5lXJEAU+yc9mCBf9kzOUqFlz2kLy9z2PKJI0YeO6R4f4OPB9VaIEIuv4aLor26fim3S8piwGFDLSTyPpiXIou8UIVC4aHHE2jqjENM1SWv7HPsB7597IubtC5ZSyxj6Vo4qIzfC/R8xsfR3MMpTRCk6JeKFNxOFzwUSV/8Ze3pqPe5vGIuq04/DKG9xNqfHLGIlnEfSyPVHkuuf9FTCnzH9JxbFQWLucm2T+O2iQp4j6uVTngpLJ7s10yApKP2/JL5vap9TujIid0hQXp/NUcvIPNEhKDqIcNc0MIVeLgdkZqRXRrXHe+bgi8cpeHs+yqhLEPMWCKQlAp4iYiUt4RV9CwTSAQE7G2vQlpSZMOH16pWcE7hQOW2pafSCKKkIlcTGougWddJcqbPexMb4mXJaF2mk0Jac6evpoUyJYsm5iXqBgEDgD0fA1tIEtCVlREBUYWmi4huV05baZsiIkarFE46X1Dh0/8vDdE5oS8pIyJ5Il/hi7km1+RV1lIaLttQyErwv5mDFt+T6zGlqyNKqGSbnJuoFAhkOAXOr3KAtKdMzNAGl1YpvVE5bahrdt5KKUElsLCIp8jOdk+RMnfUm18eP1pvlsAZtyRmlWaP0YckZxypfIdiwLTlTt8/k+hH1AgGBwM8jQHodJg4lANqSsMQiRvRzpX4mBLqfJDZeElMEReGQzklypssE72n7FZYpiwZPr6bO2KR5Y1E0uzquwkcgIBDI4Agk/Nwwgy9ITF8gIBAQCAgEBAICAYGAQEAgIBAQCAgEBAICAYGAQEAgkJ4IELEgTCAgEBAICAT+HgQEsfL3XGuxUoFAmiGgo6ONHBbm0GBizMIEAgIBgYBA4PdCwNJEHyZpEP3ye61SzEYgIBD42xHQ1NaFUXZLkBi9MIGAQEAg8CsQ+Bsz7VMqMU1jC6EJ8iv+gxNjCgQEAr8cAZEK7JdfAjEBgUDGR6Bzm+agTZhAQCAgEBAI/H4IXF8x7PeblJiRQEAgIBBIZQSqNOkA2oQJBAQCAgGBQPohYFulDWgTJhAQCAgE/kYEBLHyN151seY/BoFXIa9x8twlVChbCg55ks5r/Tst2uuJLw4ePy1NqWfHNjA1Sb1891LH8Q4+fIhBtmxZmehy5ng1qk8jo6IQx0SnU0uHhr5gevM2FAb6umweiQvYRUVHQ1cneW2Fr1+/IlOm5AMPP3/+zEX94q/748dYLFy1Xlp8rSoVUaJo8vm1pQbiQCAgEEgUgeDQCJy57YnyBeyQlwmUZyTbdPIm3r6P4lMmgfVGzoV/ePrq3qc+ff4CjSzq3ZtTMhm679Ja9JlQfTamhZIalhZ9qjsvVWNfcPOGm08g70KTRY72b1ZZ3e6En0AgVRAIexMM9yun4ViiPHLY2qdKn+nRSZCfN26dPSwNVaNVN+gbmUrnKTn4wp61MrHnS3XTANG/5YjQN9DWM2AR34k/E6p7D1V3rp/iYpMcL34/MdGR+PwpLlU0cAgjwodwUrS42I84tmmJVFTEuQbyFCwunYsDgYBAQH0EYsJCEOx+DqaOZaGXI4/6DX+xZ0TQEwTdOi7Nwq5GR2TT/zntrU8xUfj6OQ7Z9BJ/z0CC9KSd8ivs88doULRPWtuXuI/wObZKGsaiSBUY5SkqnYsDgYBA4OcQSJ1fmD83B9FaICAQ+EEEfJ4+Q5+hY7Fq/vQMRax4ePpg4qyFyGlpAU1GdLRsXJ8TK/TjsVT1Rvj85UsCRGyscuLI9nUJyqkgf9nqqFy+DFYtmJGgnvqcMGsBjp0+D08fX2TJkgX57fNgxvgRqFW1YgJ/ecG70DAUr9qAkSB6eHD1lLz4p/Zzl67GuBnzsX31Yrbmekp9uT14hHHT5+HOvQcIfx+B7KYmaFSnBmZNHAl9PT0l33XbduPA0VO4csMV9nY2qF6pAqaPHZqArNmx/zBWbtiGex6P8Zm9tMxja41+3Tuhd5d2nJCJjYvDll378Yn90A148RL6urqCWFFCWpwIBH4cgScvXuP/7F0FWFRbF10mooKAjYodKCoGiN3d+uzuwu7W3+7uwsDuZ3d3oKiYKIpigkrZ/mcf3owzMAwMEsPM3t83zJ3TZ93hzr1nnb12n3nbML9vk3hHrCzZfRbP3/oig5U5qhbPp5FYKdJpCsoWyinm11QjSETO7Dl3G+fdn8ig9xWL5MHYDrXUyA3/oC8YvmwPjl1/gDd+/rDPlRmVi+XFkBZVQAHhw7OI+latN3fbSYx3OYA1w1qjYbnoeYgMr83V+y9g2d7zqt0rjyd0roNqDrY45fYIQ5fsVqZrOrDPnRnLBrXQlAVNfV+7/xybjl/Du48BSJwoIRMrGpHjxJhE4NXTh1g+the6jl8Ur4iVF4/uYuuC/8EqvbUgG5KhVM3GGomVvjULIb9jWXQT8wttRChtEW14P/aAqQgWb1eiPKo17wLb4mVCF1X7vGfVbGyeOxZ9Z65FyRrqXt+vnj3CkU3Lce3EPgQFfEbeIk6o1dYZBZ0qqrUR2Q8Bn3yxZtIg3L9xAR9eeyOFuYUYZwU06zMG1tnzhNuM/8cPGNKwhCR/Zu29rlbu6JaVOOT6Z6FONbP1oEkoUq66Muncvs04LObzzOMWfv78gfRZcqBGq+6o2qyLvB/9Icie07s34KdY4Hzv8wLJU5oxsaJEjw8YAd0QCHj1GDeWDkTR7rPiF7Hy3AP3Nk+FqVVGJBRkc5ZS9XFj+SD4v3qiFYDiPebAKnexMGW++vvi2KCKSJLcHNXmnFXLJ8LllstovHE7gS8f34qA9IWRXpANtv8MiJKc2W+x3nBsSGX8/hV2DSNF2iwoPdxV2f/3oM+4s3EyvC/uxTcxxkQmyZG2QGkUajtOeb7eup+F25qRyjqaDixzFIKD80KZ5XlkLR4fWqWpGAq2HiNINkd4ndqM3+KZP+i9N5KYCtlMJlY04sWJjEBUEIh4q3NUWuU6jAAjwAhEAoEtKxfA49Jx5MqeVZb2fvUa7h4PkEh4YZAHi+rLysJCY4trBTHw5NlzjXmU2MF5MKbPX4a0gqgYN7Qf6lavjPuPPVGnRUdJtoRXsWv/4SCPoOiyqzduYey0uRqbu+7mjmqNW+PG7Tto1rAuRvTvJQkdIlCqN2kHIocU5rJpO3oOGoVP/v4Y2qcb8ufNjQUrXNCya19BnvxQFMP6rbvQvtcgSdL07tIe3Tu0QkBgEPqOGI+p85bIckQaEf5Hd2xQ1uMDRoARYAQIgVJ2OXBz1XBM79EwDCCuR6/iqc+HMOmKhA1HrqDfgu34LIiTAc0qI1/W9Fiy5yw6TFmvJM7JQ6X6oIXYINqivoa2rCpJ3pmbj2HI0vCJh4j6VoyB3q8/eI6J66KHGFe0q63N68Jr5OX7j0iTKkWYl4IoopC2SRIn1Pj6Ka71D168ARFOmiy8vocI7Ohc1Sllp6kapzECjEAECPSf44p5B28jg03Y3d2nxIL/mxeeGls4f2ArpvVsjMDPH1G3Yz8UFWTCjdMHMb1XExA5Ep49dr8mCR1N+d++BGOGc1Oc3LkO5LlRtVlnvPZ6Itv0uHZOUxWtaeRxMqFjbVw4uE14FJVGU+fRyJ6/CK4c34v/daiJTx/Cv9ddNronyBtJk9EciKQxt0oT5pU46Z8YN2f2bMTCYZ0lRjVb9xSkU1d8CQoQRM9A7F4+QzZNXjuE/5g1BzV1xWmMACNgRAg4DVyJGvMvIWWG7EiQMLHwcNP8Cvb1gb/3QyRIpNnb5PqS/vgivHdCG3monBpdB14nNyGNbUlBpgyUXi33d86F22rtZEbothSfg31f4bMghhKINQzyslF9JUn5Zw2DPBUvTG8HzyMuYn7ZZN+WOQtLD6PTY+tLkke2KW4Ww5s3kTc07+9B/oru4fv4JoLfv1TrVzGGRElMJLlEmJYbt0NZhw8YAUYg+hAIfztg9PXBLTECjAAjECkEnjz1kuXWLJqJwgVsw63j/coHE2cuEN4d7rh973645Z4998amnXuld8iGpXOV8gznL19DxfotMFJ4iNQRREtoW+biisMnz8LSIlXorCh9JkmxNj0GSC8UTWTN4tXrESxkuc4fdIW9XX7ZB5FA1f9pi5PnLmLnvsP4p15NvHjpg4GjJ6GUYzFBhqxHkiQhN5J5c+XExFkLsHHHXrRt1kjWn7tklfBmySba3K70eBns3BW5HSpKLxYib9gYAUaAEYgsAkQYTHM9IiWn7jz1Cbea97uP0gvFKX82/Du1h1LeK0/mw5i28Si2nryBllUcQB4t9569xkBBvIxuV1O2N6xVNQxbthtL95yTnjK1S4YQBZHtW3VQRE50muaKtBYpQbJs0WERten56j3K2+eC6+gO4XZX3j43zi0aqDF/8JJdklSZ46y+e50KR9S3xgY5kRFgBKKEwIfXL7F9yWQ8uXMdzx/c0dgGyWNtmDkSJqYpMHXbeekFQgVbDpiAnpXzYN7Atpi242KYukR0LBjSAams0mokLTbPGw8fQcoMXbITRcpWk/WJkBjSyAmLR3bDgsN3w7SpLcH94kl4PXBHp9FzJUlDZRthqCQ2Dm9ahqvH/0WVpp3CNHFk8wq4nTsq5mUZJo8S3jz3hJ1TBQyav1ljviJx39r5yJg1FyZuOiU8Ucxlcr1OA9C7Wn7hxbIMjboPVRTld0aAEWAE1BBwGrBC7bPiQ+BbL+GNUgm2TQcLb5OwcrVPBHHxxu0kVEkNRd2nx9YLEuQ+8jXqiwLNh8vk/E0GCQ+WUXh8YCUyFKkMa4caiuKReg94/VSWc3BeBItsBcKt8/7eBby/dxE25f5ReptQ4XvbZ8Fj6ww8E14l+Rr0QTq7sqgy44TGdtxWj8APQaoU7RpCTFMh6j9dwbIoOdhFYx1OZAQYgZhFgD1WYhZfbp0RUEOAvAUq1Guu0ROih5D0qtm0vYjp8U3WOX3+MnoPG4f8Jasge5GyaN29H5av3Shc6MO6mCo6oTrU/omzFxRJ8v3tuw8yfeV69YcfkpxyHjoW9uVrIrOdE5p06CljtqhVjsUPj58+k73lyZFda6/+AYF46PkM5sLjQltMkIvXbsh22jRtpCRVKKF0ieJSFsvj4WPhxRGo1tfd+48weNwUTBk9GBnTp1XLi+qHPuI8/hDSB2OH9NXYxMWrN1DYzlZJqigKtWsRsrh29eYtmbTnwBEQSdOve0clqUIZbZqG7CjfunufLPfpsz/u3H+I6pXKK0kVyrDOkB4VyzjB9+MnfP/+XZblP4wAIxCCAC1q1xAeFJoW4fvO34YGI5fh2/cQr7Cztx9j4KKdKNp5KvK3mYCOUzeAZKB+/vzjXaYJ124zN6HL9D9yAIoyc7aekH2ryiB+DAjGgIU74NR9BnK3HIfWE1xkzBZFndh+Dwj6iscv38tYJUVyZwm3+30X3OEf/BW9RJwP1ZgpLaoUl3V2nHaT7ydvPpTvTSuq6+g3q1hMpp93/7NDPLJ9y4r//Rm0eBfIA2REmz9yNKr5UTmOqM0ngljJnSldVJrGsWv3sXLfBSwf3BLphQRbaIuo79Dl+TMjoA0B8hYY26aqxoX95WOdMamLkGUVxAHZ3StnsGpif/SrVTiEMBjcHiQD9UvL/SjVofbdL51UGwZ5R1D6sW2r1dLJ42PVhH4YVN8B3crnwKy+LWTMFrVCsfiByA+fZ48FEZAKOcKJ9eH95D783vpIySuS1lJYqtTpUKhUZUlmBPl/UiQr31cLLCnWSBPhOaLJTu/ZAJs8dkpShcpYpEmPwmWq4N1LLzy6fVVTtXDTHtwIIXdCy42Vqdtc1vmowWPlxeN7WD9juCSJLNNm0Ni2j9fjCGXfaP4kt2YvCCIFqUKNWaXLKCXTAj75ie8Z349qBJgTjQIBP8/bODWmHu7vnBdmvh8eXpN5Xqe2KPPe3T2PmyuH4XCfkjjQvQguz+0uvB/WapSgUlSiOtQHyUup2pdP72S6pyAaVO1b4CfRx1AcGVAe+7rY4eLMDvC5cUy1SJwek+zW1QXOsBDyXbaN+ocZy+cX93F73TjYtR6NZBbpw+S/uX1apmUpo76JxabsPzL9nSA/dLUAnxBixcw6h9aqQcKrhCxN/pJq5YhIIfshJMq02WshXfbk8Bo49Fkk5vbnfjPgtSdSWufSVpXzGAFGIAYRYGIlBsHlphmB0AiQ5NWFK9exe/8RtaxXr99g9cZtsLJMhaTCff7UuUtCAqotaKG8qogD0rFVE+mtQCQIeVmEZ+8++Mr233/wUytCsTSoX68XIT/mlEleHw5V6mHDtl0o4+SIdi3+kfkN2nTF/OVr1OrH1ofHwmPFJrO1JDv2HzmB1a5bQaRDaDLJNk8unNi9Ub7WL5kT7vAoADxJYDkUUd/JQkHsP/h9RLJkJjBNlkxZn4K5txEEVpkSDnDu3E6Z/jcH5DFDniQuC2dKea/QbRHBQee4Z8c2obPgLTxUyBQyaA89Q27aKpcrpVaW4s/Q9+b6rTsyPbEICnpyzyYM7t1VrRwRLu73HqBqhTJqxIxaIf7ACBgpAjkypsGle8/w73l3NQR8PnzCusNXYGmWXMb9OHPrMeqPWIYdp2/KeCBta5SQ8k8DBNFCsTy0mdsjb7g99g5T5MnLd7JvoRAgjTw0yjkL/f3j16VMVquqDnj+xhfNxq3G4t1nwtSPjYS8NulxYHpP+Vo1tFW4XT4WcyGrIDwzVM0mnSWSiuD0Nx+9kMk+Hz4jpakJ8mT582BIGYVzZRKa+wlwzyvk+kdpke2bypJtE14x5BlDcUrMU/y5xofkRu1vRG0Git+PtyJOTM7MaXFTSIKRHNq/gmSi709E5vs5EL3mbEHj8vbC40UdN6obUd8Rtc/5jEBoBNLb5MCDmxdx5dgetSxfQRSc3LkWKVNZInGSpLh7+TQmivhAFw5sl7JUlRq3l/JPRIJsmjtGra7qB3+/97J9fz91ycDv4n6U+n3/6rmyOHmHDPunFM7s3SjikpRGhQatJYEwvdc/OLBuobJcbB5kzpkP49Yelq8+0zXfExOpQparYAgZrDq+nAWLy48vRNwVVTu3fwvO7duCXlNXirglYQnUzwI3Ipk0xVLJmDXk2uB5N2TTkGq72o4rNm4nvUXonKqax7Xz8mPRcuo7symQ/PzBHWBbrBTIU0aTfQkKlBJiGbPlFl49N3Bq13rxXdoL+v6oGknZjFt7BOShompEuDx/eEd8pyqL71kS1Sw+ZgSMCoFUWfPLGCKPD64Q5MgvtbkTofLhvrj/zB2yAeXtnXM4M0GsCVzYjfT2FZGtcisEf3gpSZA7Gyep1VX98PXzB9nOV3/16/EvQZ5T+0HvQu7LqE7Qh1c4PqQKvE5vQ9r8TshWsQUC377AhWlt8Gj/ctVm4+z4we4F8H10A0U6T5HSW6oDoSDtl+d1FzFFSiBXzc6qWcpjkgejgPFmmUKuqYoMi+xivUBIeRExo6uRx0jyNJlBweh9rh/B0xOu+PDgahjCK0PRKkiQOAmendiozCNpr6fHN8guMxarHm7XFDPm+uJ+yFK6gfRoURSkPr9+fAezjDng98QNz4TE2csrBxDs+1pRhN8ZAUYghhFIHMPtc/OMACOggkBzET9jyLip2LHvIHp0bK3M2b73AEhzs13zf2Tall3/yiDrDy6fgEWqkAevwc7dhIxTBRDhMHXM37vNj5w4UxIp5/Zvg2Mxe9nv2MF9UFvEHhk+YQZaN2koiJ4/O/CUgxUHO/cdwr0Hj1STwhynsbIUpMafOYYpoCGBpMA++wcgl5hncPAXZYmihe2wZsEMEKGii1E8FXqFtgUr1srYI00b1EYiQUIobOj/puKViKuyf8saNQ8XRb6u70+9XsB5yFgM6d1NkFcO2PHvwTBNkJzXvMljw6STl9GSNa7ye1CrWkWZ//CxuGlLbgozEWRe1RKKm0AKTH//0RNJQqVIkVzKhSnKEFHm9eKV8EY6KfOH9umuyOJ3RoAR+A+BJhWLYNTKf2XA9S51Sytx2XX2lrw+t67qKNOIUEks/ufIFd8ipalM69ekIgp1mIyDl+/if53qKOtG9WDc6v0ieLwfjs3ujeL5sspmRrSujsajV2CsyGtRubgkejS1TwHj7z/X/jCV2jwFOtf5M0dN7UQ17bH3OyQ3SQqz5MnUmqDrVHZBXj30fis9e7JnTC1JFiJaiuaxUZYlr5hfv37jvtcbZZouB89ef5CePv2bVpSk1O5zt3SprrFsZNr0fBWyYDFl/WG8UiFTiEwa3KKKeFXV2DYlDly8E58CgzG2fa0wZSLTd5hKnMAIRIBAmdpNhYzVCFw+shvVW3RTlr50aIe83lVoELLZ4/yBbeI+KTHmH3JXSl0pZJyunzqIVgPDX8xTNhrBwcY5Y/BOEC0TNp5E7kIOsnQT51GY3K0BXGePRrn6LQXRY6WxFRr/iyceGvMUieYWqVGthfpGE0Xe37ynzxLiXX1HkE912qt7JL8U3ixk3mJsFHie7K33M6z6Xz/U7zxQkBalcenILpmu+sfnaci9tYUGLxHrbCH3wJ8/hJDXqvW0HRNJpLBHt65ID6Sn99wkqVZWeK3kKFBEkS3fXYW8GZFGI5bvCfdemGTAyLYtmgjfN6/kMf0hMq5Rt6FKea9kyVMgb9E/O7OJKHsngtPfPH1Ixg+s33mQsi4fMALGiACRjzZlGkoJqvf3LwkyI2Tz3C+hdPDy8n4RlL0ozP8jAF6c3yXW/ROjxoLLSJoilYQrb31nHHR2gM+1IzJI+d9ieMd1oiRaKk6ivkNI4/xCbuvcpBZwd52ArOWbIGlKdZJW0af3pX347P1A8VHju4mZFXJW76AxLzKJn0Qck3vbZiB7lTYwz5w3TBX3Df/DF983KDNyS7jXrxTps0kCgkgIq1x/rn/+Pk8gLkyCWNE+hzCdioRAQax8D/bHwZ4O+PktWFmEvGocnBeIseaRaTR/OyE/dmfzFOzrWkic75KS3Pry8S1yCFzofIdnbiKW3jcR+N6u5Ui1IgGvn8nP94SUGMWdUVjCxEmRr3F/2IoXGyPACMQsAkysxCy+3DojoIYABVCvUbm8lNuihfN0aVPL/C2790uZpirlQxa6+gqpp56d2ypJFSr0TewqoZgf5HXwt+YrvDXIk4JktBSkCrVJXg+dWzcHSYrtErJTnVo11dgVEUHb94YlCVQL58mZXXdi5ZmXkLoKxIThA1C/VjW8Fx4460Rw+jUiYHvjdt1xVeyGI9IgquYnJLD6jfifnHveXDkwZ+JoZVNEWC1ZvQFbVy8SEmDqO6iVhXQ4oEDyFFcld85sGCMIK12MxtJ1wHC8e++L2RNGoaBtyI3jE4GPVThxX8hrhaTNiJgKHRtm9JTZSqIqf95cMDVVX/DUZWxclhEwVATSpEop43ocueaBdx/9RWwOMzlVkq7KmNocFYuE7Gzr1bA8utYtoyRVqNA3EYidSJbPgX8I4aji5OcfhG2nboLkthSkCrVFwc/b13TC2dtPsFd41bQTnjKajIig3eKlzXJlShtjxArFGbE0CyGcQo/BJr2lMjj7PxWKYOcZN0zecASj2tSAfe7M0tNjuFjMIyMZL12NpNQ6i7gq5DUyvFV1XatrLB/ZNmneZCTjtWRgc+QSYzh46S5W/HsBkwTZQt8nOn+hzcPrNXaduSVjzWQRXj2qFtm+VevwMSMQGQTMRXwPkme6eeaw9Dwg+SqyCyIum6WQaSpYqpL8XLtdb9Ro1V1JqlAiSYSR94MmmStZSYc/AZ98cV54cZDcloJUoeq0QF/lnw64JyTFrhzdi0r/tNfY6sXDO3FJvLQZeVXEBLGSQcQNIVLizqVTOLHdBSVrNpY7zs/u26wck0IujaS/5ou4KhkFOfJPT/VFMdWxv34RQliE9i6hMmmtQ0j2QA3yYqptaDsmibYtC/4nx5kgQQLZJklxKbxGiCyjuCcD5m5EeBJg1P7r52IBUhgRQD0nLxcxVHLj+sn9ou5ybF04AanSpENlcf5C2+b54/HtS8iiY+actiI+TdTv6UO3zZ8ZgfiKgE25ppJYeSmICQWx8tb9DL4JD4WszYcpp5W7TnfphaEgVSiDArEnTWEhApl/VpaL6sG3AD+8OLcTljntlaQKtUUL9NmrCE9CISn28vIBZBeeMprM++JevBQvbZbSOudfESseO+YgkYkpiOwJbeQp8uTQajgNWg1Ty/Shs5Wfswgiy1t4/RARUaD5UBGjpbAkWm6tHSPLkAeJrkbkBsl4FWgxHNaOtfBNeAlRvBTyHrkogtVXnn5MeslQuymFZ0mipMlkGZJn+y6k16T9/iVJmcQmYa+L5EXjfWGPjAtDnjGqpojvkkzMubjzfNF+TkG0HZaSYfe2TEOyVGnl+VOtw8eMACMQvQgwsRK9eHJrRoZAMiEj9eXrV51mTfEwaOF8z8Ej6NK2BSjA+tUbtzCkTzexCyWhbCtf7pz44OuHOSIA+aVrN4W3gTceeRLpEBAti/4PnzyV/QQGBqFlV/VFf1qYJ/N89kemQSao/CFZq1XzpqukhD2kBzZdjdokcsfONmRXR+4c2VDSoShSCa+d2YtXSrKndZMGujYrd1+uWLcJY6fNBZFKJA82aeQgpecHBZTv3G8YOrZsggaC0IkO+9+M+XD3uC/JIEWQ+YjaJeJk0JjJ8vtBHijrFs9G5XIhZBvVNRHYvPTRvIs7UMibEebmZureLFTv01N38f15hvOXr2H05FkoVaMxPG+cQYZ0UY8ho/je0/8AGyOgjwgovptfv/2I9PAoDsihK/ew78IddKhVEl5Cfuv6g+fo37SS8vpM0lUk3bRgxylcve8lJLr8QFJeFFckg1hU/1t7JDw6yEhaqv2U9WrNUQBzsqc+IYv4apn/fVgu5K+WDGiuKUuZFoXLs7JuRAdEAIUnfxX05Zu8TpE3Sy2nAuhRvyyW7Dkr44uQ1BqRSiULZIdd9oywEl41utoUQdLcfeaDswsGqMV30bUd1fKRbbN0wRzYN60HigpCLHmypLIJ8gqqW6oginWZhrnbTmgkVuZtPykl0pxFTJrQFtm+Q9fT5fNXQQoq/ld0qcdl9QsBOoffhYSTLla+XivcEAvpiuDlb0X8jsfu16RHheJ+NFOOvPD/+AH7XObhofB2oBgfr72egGKQaFt4j+w4Xv3nofFVSEvNFcHeVS04IGSh8M1/ZINqnuLYWUhq9Zy0TPFR83sMXfAIo+4TlmB6ryZYPs4ZLlMHS8LilyCFKwlS4biII5M5l60c0/bFk6T01dTtF5QkhqbBJhGEEhnFHgltX4IDZZJqPJfQZSL63KDLIEGU9RBxWq7g7N5N2LlsmpQe6zByloy3s3RUd5B0mKOQCtZmJNk2Zs1B5LQrpiRHiLxyrFpfxuLZs3KWRmJl3bV3oNgsFPdl01whb9y8HBYdeyBjyGjrT1ue4nvP1zFtKHFebCJAMtPB3yN/Pabg6+ZZ8kkPlcIdJsn7JFpETygW30n2SWHkuUJyUA//XQJfEX8lUEh4Bfh4ygV9WlT/W/N/FUKYkrTUpTld1Zr7IbwxyALfPJPvmv449l6I373macpSSdN9fUBRmQiEl5f+BRFMJuYhm1MVecFC3uuakMnKVqklMgliQ5tZF6+OXLW6CDJrhQhwf0J64BCplFrIh5E0W1LhVaKrFRfzJgIqlU3INR+CPEmd10F6Fj3cu1hKc2Ut1wTk1XN5dmeksXUSHkajJYH1+eUjPNg1X8bKARIIibOpYbp/sGeRbJ/mHtpIsq3cuBBCTEHKkFdQphK1ZSyeB7vn/zWx8lNsqOBrbGjk+TMj8AcBJlb+YMFHjIDOCFhZWUmvAl0q1q5aUXqi7Pj3kCRWtu3ZL6u3bdZY2cysRSswbvpcuZBerqQjKonF9WH9ekqihYgYXc3340e1KkTakNFCfRKh86lqqS0t0aJRPZBnQ3hG5EdMGEl+abKalStIYuWuCMiuq717/wHteg3CsdPnUL50CcwYPyJMkPhlLhslkfVJkEqd+w5VdkEkBkm0UVruHNkxtG/YmxllYZUDipsybf5S5MiaBdPFu8K8vF/Kw2VrXXH4xGkM7NVFKW/mun23lA0jcmTK6CEixktbmJiYKKrK9/SCCCFSTNXbSVHA189PSreRtBmNmV6KhREqQyQVvSiN5nPw2Cl0EERSVI28icjof4CNEdBHBBTfzfefApBNyE5FxmqUyC89T0hOi4iVXcKjgqxVFQdldVoInyw8EEwEgUCL6RRLZGDzyli48zS8Xof8XygLR/LALyBIWdL3c8gxtZ8kUQjZrsi0EuRDE+HpYZs1gyIpzDsRG3Fp6S3NRJD7d2peP4rx+ArihLxZEv03rynd6qNB2cI47/5EkiqFcmZC3dIFka/1/6SMl6JeZN6/C4Jg9tYTyJbBSrwfV1Z5ISTVyFbtv4ijIkB8338qypgtygJaDnRts0zBsMQ2ebAUFvM6f8cTRCwpSBfqlsa27eRN1CtTMIy0m659a5mG1qwPnwL5Oq4VofiRSde7T77hE66aZlG0Qk3piXJJyGlVadoJF4UMGFl5EeNEYXtXz8U24YGQOKkJ8hcvI2N/NOw6BPvWzsc772eKYpF+DwxFGPh/DLlmkodKolD3oymFhFfp2s2U5ISmTqge1G9jNRWLsTQKMj9j9xWB3U6Q/Bd5cBQqWQl3r56RfWYRxAp5hOxeMRPpMmfHHvGuMJLEIju6ZSXczh5B3Y79hadHyOLoW++QDVCKsvQe8B9W5lZpVJMjPCaih+4tFRueSJ6LYrjYlagAj+vncU14mhCxcnTzCkmiBft/xpKR3ZTt+r59Je8pKY28f4icIY+n/OIV2izE+LPnt4fHtXP4GhyEpMlMw9yPZhSePvRKIO5HqU3ymqrYSJ1UC92uts8Ul4ZM8ZuvrSznMQKxgUDatGngIwhpXcxGSGzd2TBBxuWwzFlYLsRncqyJJMnNlc082LsI97ZMR0Jx3SMJqXQFywkPhn54JIiWwLfhb4hUNhDq4FuA+voAeciQUfskUaZqJP+VpUwjjfJbinJELMSkPRTzgjhhYAAAQABJREFUFxcUSZ6E7sfziIv08Pke5C8Ilr7K7C9CGks8Fcs08hTJ1zAkr3D7Cchcsh4oUD3hYJG9oCRk9ncrLEiPksr6kT0grxdNlqFIZRCx8vn5fZlNXj1k+ZsNUXoFEWFWqO04GTvn+dntYYiVoPfeIE+iTE51NMqwmZinEd+HsL8LFNzeQpB27+9dxI+vQVCQLnIAOv6hOD2ZU/Mzv46wcXEjQkD9imlEE+epMgLRgYCtra2I1fEJFHzeOkPkdorQYnmT+rVlYHYiOLYKGTCn4kVA0llkRASMmDgDacWPl8elY0qvCsqbOncxvYVrik15v4QrqapRbA4yurEgyy4W/MlyiYX2tYtnyWPFHwoUT3JcybXIRa3ZuA03bt9VVNH4niFdGowc4KwxT1PiCxGo/erNWyhuX0gGsFct4+kVcrOYTkip6WIkx9WIJMRu3sbiGRPQuU1zjdUJ68IFbPFYeHWo2lcRZJW0/m/d8VAjKVTLhHdcKH8+mUV1FabwBvJ68RKfPvkjQOBMRh5MHZwHy+/BhqVzw8xfUT+v+I6cvXgFT58/V8rIUR55HnmKeC4VSjvJotPnL8PoKbOwZ8MK1KxSQaYp/lDsGzLvVz6KpCi9E8llZmaGTJkyRak+V2IEYhoBGxsbKXt3T0gtqUpqaeuXyIyG5eyx7tBl6ZVCMmCOtlmlrBPVI5Jm3JoDSJMqBW6sHKYWR2TW5j+L+eH1QddouqaEtkciLgkZEaLZMoY8uOS0ToMVQ9TlFn7+/CU9Y5KbhL+SuF4ETXd75B26C7XPRH4MaVlVLS26PuTOnE6SCM8EyaSQU6O2yQOH4oWULZRLdkVeLQHCy6dE/mzypeifPHY+BgQjf7aMiqRIv5OnC5m75ytlHYWXz3PhfURxTKhPXSyybR6//kCevyrFQ679ij7onJLnU0pTEzVShfJdDl6Skmdtq5VQFFd7j2zfapV0+BD89TuevnoHupdhi98I0DkMEEHPKXi4lZDyiowlEWRJSeHBemKHi1xQvyDiqeSxLwFrsXhO9tn3HTbNGS0X0eceuAXTFCHyiJS3a7l2r2Wxik/FxP+E+v3oq2chG2To/4IsfZZs8p0W2ntPWyWPFX9IRos8Y7TJRZ3cuQ6e924qqmh8p8X+xt2Hacz7m0SSRKO4KWaWqVFJeHmo2u5Vs6QXBsWGIWKFCBiyZ/dvK4vR3MjIC4gC1n8JEl7p/2H/RgOxQsHeyXIVdJDvkflDpEq74mmRJU8BTN4cQvYo6hHRYmZhBa8H7nKMRNhkzVtQepQoytD7929fpScOjZ3IELJb547JZwr7Muq/I3ReCZNkyVPK80aE0uZ54zB08Q4UKacuz2gmiDOyD6+1/17JQlr+vHh0Dyn5flQLQpwV2wgUyG+LS1v36dStTZnGoPgmLy/vkwTBD0EQZK3w57n16+f3Mp88NarPv4QkpimV7d/fOVd5rPHgzwKBWnbAq8chn/+7LU2RPqv8nDJDDjj2UV9zIHms70LqKrGQ4QrPKCC7n+efa5ymcrTYb/vPAE1ZWtPIi8br1FZY5SmujDmjWoFwSZWtAAJee6om4+ePbzJuysdn4vqZIOT6RUHdf3wJkB4l5FWiMH+BB8lymduo38cp8sN7D3r/Er6Pb4p4LfYygL1qucA3XvKjSaoQ4uOz1z0ZvF4Rv0ZRlsZvka2gwM9NyLt9k94pirynx9aLa/BPjYQSlXntdlL8/Y0M9pUUVeQ7XY+JcEucLMVfkSrUWJD3fdjVLKvWPn9gBBiBPwgwsfIHCz5iBHRGoFixYjK4+Knzl9Cycf1I12/brCFImmrGwuW4ddcDS2ZNUtZ97h2yM6xB7epqpAoRD25ikT692AUTnmXLkllmPfpP6ktR7t9DxxSH8j1X9qxII8iEIyeFrqd44FOVqiJPi3FCMuvknk0oXaK4Wj3Fh5NnL2KHCGCvzfLkzKYTsUJeNc079xYxXpph8cyJak1v23NAfi4TznjUCqt8+PfwcVy+7iY9Q8IjVah4L+EdQq/QVkJIGgSLxcCrx/eGztL6mfDUVGefGA8RPZNHDRHk2h83ZZLnSmVuhi2rFmqVemvWsC5WbtgCl43bUaJYEeUYtomYN8HBX1C3emWZppBSO3b6fBhiZZWoT1ZIEEl/Y/SdL+kUQuT8TTtclxGIKQTIO6uEYwkZk6Rtdc0L15r6binkwNYcuCikm07KBfp5fZooi70Qsl/0oELyTqrB2b3ffcRtz5dI919cFmWFUAc26a1w8uZDkDdCksSJZC7F2FDE56CEHCLAOwWXp4V61XKUR54YFK/j4IxeUjKL0kLbabdHII8bbUYxVmKKWGlcwR4uhy5hgyB4HPKFPKTTWCiOCC3kkwQYGXn+LN1zDteWD1USV5S+ePdZJEuaWHoC0efIGuF5dmHYh/WDl++ixfg1GN+xtiTNItseldOlzVYT1uDApXu4u3YUrNOkUnZDcnH0/QhNuFCBEzceSg+p8vYhZJOykjjQpW/Verock6cQLbyWLFlSl2pcVg8RUNyP3rtyGmXq/FmMi2io5eu3wrGtq7B31Wy5wN513EJllXevXsjrHclCqZIq7328JUFg8V9cFmUFlQNFPBCfZ/8t3P2Xd+3EfpVSQAabnIKYSINb54/JxX1FrA8qRIvyFK9j3LojyFe0lFo9xYc7l05pDAKvyKd3Iitiglghj4wBdYuiVM0m6DNjjbJLwufK0d2o0DDkvpLmNG3HRWW+4uD6yQOY0bspWg6YIAkuRbqt8AzyuHZexDHxFPjkkMlEzpzbv1XGvwkdbF5RT9M7/Q6SHBkFq6d4NkT0KIyIEs+7N2GT107Kk5FEGL1C27AmpaXMnOocjm5dKWOqkIyXVXprZZVHQi6OiBIF4ZIld8j1/vbFE2GIlRPbQzAjMudvjOLwODnxNexvMOS60YsA/abOmDEDtMs/tGRVeD2ZWmVA+kLlpRzYF783ME1tjXR2fxayg94JAlLcf5K8kyqpQov6RBpQHI3wLEXaLDJLBmdXKfTqmvqzfMoM2ZFULPC/uXVSxm5JqOJFeF9IVVG8jvL/24M0QjJLk729c1ZKXWnKU6SZifgfUSFW3gmvCyIcMpfSvN6Sq2ZnGX9G0Y/i/fjQqiJuyRdUmX5ckSQ8SBbKmDbV5p6HmYj5orBH+5dL+TXyBNLFyOOF5L0oDk3RrjPVqr4Qkm5kaWxDMDMTQeyJwKF4MJmd6irLEgHi9+SmGE8uNVKFCry5dRpJUlqofR+UFcXB06Nr8UrEVKm15AZMrf5srPB9dB3B4vuRPhTholo3MscUv+eDIMxKlRoameJchhEwSgSYWDHK086Tji4EaMd+xQoVsHPfYZ2IFVoUJ3Jj7lIRXE14hjSpV1M5pDy5siNF8uQgibAalcohb+6cuHDluiQ7KH5GgPBOePDYExR8PbQVzJ9XyEclxYIVa5FTtE8eHrsPHMVRIYOlaiTlNXHEQHQfOFLIZA3EIOduMjYHETCT5yyWcT1KORZTraJ2vG7JbNArOo08PEoUs5fEgZWVBRrWqi4XfFx37JEyXg0F0eRQtLBOXZ67dE2WJ8yGjJuisS7hoKu0WZrcReR5+PLqgcY2dUn0+/gJd4T3R5GC+aXUm6a65UuVQO1qlVCulKN8rXLdCpIFI1m567fcMWT8VJRxckC75o1ldfJSscuXB4tWrROyc2aoVrGsjM2y49+D2Ce8Y4oXKSTrauorMmnkIXPoxBlMmTI1MsW5DCMQZwjUq18f48eMkgv6plq8PFQHSGRADuEtsnDXaVCdhuX+XHcoIHkKET+Dgq5XdciHPMI749K9Z5i07hDMRdyQQOENQR4X5LWhyYrntcGRqx7oMXuzDD7v+eqDjL1hniKZ8JAJklVIymtsh1roM28buszYiH5NKsq291+8gxmbjqFCkdxwEl4e4dlK4eVCr7iyMgVzgl5rhddPOuEZU8MxP24+eoFRK/+V8l6tqobsDiRyioiVfgu2Y1avRkgtvICIjFkrSJlp3RpEWr4tqvNcJM7v6FX7MFR47gxtWS2qzSjrtavhhP0X76LByGUY274WiLy6//yNnHfChAkwpt2f33mq9FHIork99kZNIT+nKtuobDAWDvacvw37woXY8zAWsI7pLuh+tLy4H70sZL10IVZyF3aU5Ma+tQukbJNTjUbKoVpnzy28DlKIgPY7xEJ5NVjnyIMHNy9hqwh+njylufCwCMSrpw9hnT2Pso7iIKtYrCePmIMbFktygKSjKJbL7Qt/FreoLEl5teg3HsvH9sLCYZ1Qv9MAmKY0w7UT+2T8j4JCVitvkfAXzXtPXw16xYVRrJMCjuVx+egunNxZEQ6V6+KNIEMo3opV+kxoPejPpildxtegy2BM69lIxJxpA5JdSyn62bN6tvAEeYohwvNDIelFbXZwspaeLptufw63C8KU4tdM7FQHjXuOgFXajLh57oiIsbJR1mnqPDrcuuFlUGB6OkeTutRFc3H+yOOIpNDWzxguvVoojYy8VIhcOeS6BCnMUqFw6SrwffNKkmHXTx2QMVqKlle/NobXp6Z0+g66iblMn6r5Hl9THU5jBGIagSpVqojrn4iLeeUAclRpE+nubMo3xdX5PeEt5KvyNeit9BCjBlKKBfdEyZILuag9cqHcTMhHfXhwBfc2TxNEi5nwwAiUC/a0MB/aKG5IwiQmMqYIkSfkPfFKjI0W7FWNpLzsWo7AjaUDcWVBL+St7yykyMzw6uoh3BdB49MVKie8PBxVq6gdO/ZZIjxdlqilRdeHt+5nZFMkgfa3Zu1YWxIrN5YPlrJbJuZWeHZiE54e3wD79hOR8j/PHern0b5luL1hvCCDBiK/eGkywtcqTzE8PbZBSHVZCfKrlvTWfH5mB97ePi3JMKtcRWXVXLU6CxLkENxWDYffYzcZ6P6z8AZ5esxVEmf5GvdX64JIGz/PW8hYrJra90G1ULbKreU5OjuhqTh/I0Xw+hz47P0Q7uvGCV23hPKcqpbX9fjVlYPyd6datb+/V9a1by7PCMQXBJhYiS9niseptwh07NQJrVq1Anma2GT+s2srogG3FkHsyTOECARz8UCsMLOUKbFi7lR0EcHUybuBzNIiFWb+b6QgXEzRsc8Q2JevheCX9xVVlO9EEGxesQCtuvdDexFXhBZrSB5qh8sSVKinvoOxY6um0sth2IRp2L73oGwjceLEMu7GhOED1B7clB3E4AE9KO5wWYpuA0eIuCTL5EvRXdd2LTFj3HDFx0i/n798VZZd5iJuVsKxsYP76kyskBwP7fKNDiPSjOym+z350tQmYUPECr3vWrcMDdp0EwTYIvmi8kSU0HlXeB7Red++dgna9RyICTMXyJei3Qa1qmHOpNHS00qRpus7xYP5/v0HWrZsqWtVLs8IxCoCrVu3xtChQ7Ht1A3o4rXSonIx6RlCi/9EmCiMvFQW9W+GXnO2SC8ISrdIaYopXetLmaceszbDqftMfNinWSaHApRfER4M20/dlK+Mqc3RrFIIiU0eMgqjsZJ3xxix8L/77C2ZnFjEJWkj0mmBXnVhTVFHX95pbJvGdkDzcaslEURkEFnRPFmwdkQbpadOaUG+EG7jXfajRPcZsgxh3bFWSXSuo3l3uiwUTX9Iko1eYgNotFg1B1v53Ri7ej9aTXBRtpk5rQX+ndIdFD9G1c7cfhziDSCk5uLCiNjZLqTups+YFRfdc58xgEDn/+5H34vYHWkyZol0D+XqtZSeIY5VxHVMECYKIy+VHhOXYMmoHtKzgtJTmFui3dBpUuZp8ciuGNTAARtvfVJUUb4TYdJv9gbMH9xOECad5YIQkRCD5m/G2LZVleXogGS0vn0JguusUbh0eKfMSyTuRys2aofmfcfq9fWuu8Bn/uD2WDamp3zR4LPZFkaf6WvUvHzkpCL5p3DpynCeshLLBNk0p38ISZ5ckBJthkxFkbLqC1skl/Y7gvtRp+qN0FaQGRvnjMGsPn+eBYjs6jlpOYpXrB3Jkf0pRoRJ94lLsXH2aLU2U2fIjDGrDyBbvkKyMN2P0jkn0mz74snypWiFPKHaD58pYutEfTni7L+b5M56vh9VoMrv+oBASvEs37RpMxw44qITsUIxVW4K2SYiSVRlwGhO5KVSvMdcXFvSDxent5PTJC+Gwu3+h0QmyXFtUR8cHVAejTa/DAMBESZOA1bg8lwhjy0IE1psT1egNEoOdsHpMfXUymev1Ao/vwbDXcR7eflfPJAEIt4KBYW3ayGIU3GPFxdGxArNUxkc/i8GQeRMIRFj5c7GSQKzEO+UxIJAylG1HXKIoO+qJuUs6Rqr5WaRMCEsiZCiQPH0UliOau1k/BTF57T5S6FEP0HWCNKDYsbIuDEik+ZGY7IRcWxU7d3d87Lv1EICLTzLWLQKivWcK2L0TMTFGe2VxUzTZEK5MTuExFiIFKUyQ8eDZ0dd0KhRI1iKOLxsjAAjoBmBBELSIpoeKTV3wKmMgKEjQFJauXPnRuXSjlg6e3K0TZfir7jduYcM6dLJQPKKGxlK9/v0WXq8hNcZxRbxePQEGYRsWNoI4pL4BwTATSzqk1eHnW1eZMmUMbxmoy2diJyWXfsIWYNtcBReKqGNYpA8fOIJC3Nz5MsjZCLEDao+GZEqxSvVxY1T6pIWsTlGnzdv5XkrWtguXHk4GufT5y/w4JEnTJMlA3lDZcqYIcwwn4r4LHlLVMKciaM1SqKpVvj69StsS1ZFnXr1sWRJzOxKUu2PjxmBv0WAFhuPHdgrJKcGKxf1/7ZN38+BuP1EuNeLwOT5bNIrHzQpneKDkMeLNqNYLa/ef0LBHNbKuprKU3wQ6idQBD2nmCO0SB8bVqLbDGROZ4EdE7r8VXevfT/L8dvnyiy9VzQ1RsHT7z7zwS9xO1raLke0nSNNfYVOm7n5mAh2nxr/VCgSOivKn4kQ8/DygZ9/MHJmSgObdJZyk0OUG9ShYvdZm3Do8j082zohwlrj1uzH+uNuePrMS8bLirACF9B7BOh+NJe4H81VvAK6jl8UbeP1F0GYn3nckoHZM+e0VV6zKJ1ig5CcV3j2U9yPvvQUQd1FnBNayNdmFHOE+iEvBPJySJMxRN5WW52/zSMih7w5Jmw8idyFQrzpdG2THqUp/gnFFqHA7bqQWtr6Iuw8796QG3lobAkTJQpTnO7zhjYWskO7LofJC50QFPBZepV8fP8G6TJlQ0bhkZTU5M/GgdDlI/P525dgUIyTgE9+QnItF9JY22i83tE4CR/ycEoq7kets+VRkxBT9EVl+tSwE4TLDI2yZIpy9E6xX/rXKoR/GtTl+1FVYPhYLxC4desW7O3t4TRwlfRYiK5BfRUB5j8+vQNTy3Qwy5xXeT2mdIoPQh4p4dmvnz/g//IRklmkFRJl2u9TKZ7Kx6ci/pIgeYjMSC4W6WPaKLD75TldUXHSfmVg95jsk6TaPj33kMRFGlsnIcGVRGN3HiKGTcp0WZGlTEON+aqJge9eIODVEyRJYQ6zTHnUZNtUy5Gs2ScRb4UkwJJZit/HLPmQNMUfGVnVspE9/vktWMznPsjLxcw6hzhnWcJ4uQS+9cIh5xIo3GGiRum00H353DiKC1Pb4PLly3B0DN9bKXQ9/swIGBkCHlHfImJkSPF0GYHwECAvgYkTJ6Jt27boJOKD6CpXFV67qa0spSRX6HxKp5c2I8+TgoIkiYwRaVG2pH79UGbNkgn00lcbMXEGSgvprbi0jOnTaY3HQmOjnYI5s2WVr+ga69R5S+Er5MtGj9ZdOiK6xsDtMAK6IDB23Dhs3LRRSm4NblFVl6rhlrUSMVAqFMkTJp/S6RWRpUmVEvSKyMhDhjw74qtlEMQTvbQZSYCVK5xLW5EYyaO4NiQ9tm9az2htn+TjiuaxidY2o7sxkqujWDbTps9gUiW6wY3D9uh+dNJ/96OVhFRTroLh73DVZZgUZJwkuUIbpSsCkIfOU3wmbwRF4HZFWnjv5CFD8UXim9HGJ4oT8rexQkLPm7AjqTZttmnOaOQrFjkPP/JGiqg9bX1pykuazBQ5C4YvHayoQ/ejFC9GETNGkf4377uXzxDEnh/fj/4NiFw3xhAoXLgwmrdogf3rxyF94QoygHh0dGZiZiVisYR4Wai2R+n00mYJhedJZD0+yEMmOmS3tI0nrvMo/k06O+2/OQGvn8LrxEaUG7crUsOleDaKmDbaKpAXkWVOe/nSVk6XvERJTWGVK/o2ClFsmrvrxqJBw0ZMquhyIrisUSKQ0ChnzZNmBKIZAZKbqVSxopTpCggMjObWDbe5UVNmoUWXPvB+5ROvJplZeH3MFXJa8d3ou0r49x0xPlJTuXLdDdPmLxWxVabA2to6UnW4ECMQ1whkyZIFEyZMxIzNx+H2yDuuhxNv+nf3fIX2k9eBYpEYoj31+YDN4zrGmhdQTGJIBBGdq/PunhF2803IOHaftQV2dnbo3bt3hOW5QPxCgO5HK4j70SVCputLUED8Gnwcjnbz3HGYO6CNCLz+Mg5HoXvXFMulg5DTiu9G31XCf/WkAZGayqPbV7F75UxM5fvRSOHFheIGgblz5iDBV3/cXjsmbgYQT3u9s2kKLs3ugqAPr+J8BgGvn6HUsPWx4rETk5MlzyPC1G3ViEh3477hf/jx+R0WLvgjbRbpylyQETAyBJhYMbITztONOQTWrluHDx8/o2PvIdEWfyPmRhu3LWe2zgAKRm+ZKlW8xMq5Szsk0iDJELeoRq13kmcgmTA6Hzmzh6/1/9LnNZp17o1qVavyYlzUoOZacYhA//79Ua5cebSZvB5vhDwVm3YEKhXNA6f82aQ8l6EKxlYullfIuIWVRtSOjH7mkqYvSakVyZ0ZtZwKaB3kwMU78cD7vfDi2mwwv2NaJ2yEmevF/eiXz75YPKJrvLzHis1TRsSEY9X6SJHKQvwP/YrNrqOlr5qte2qUCIuWxmO5EcKfPGDofKTXIi/nK+LFzB3QStyPVuP70Vg+R9ydbgikT58ea13WiKDorvA8uk63ykZY2jS1tZRNk5JYenI9zmBfEeZCcs0gTGCaKGkyibE2yTia67NTm/Hk0GqsXrUSmTLpr4qIQZwXnoRBIMAxVgziNPIk9AWBs2fPoqpYeG7VuB4Wz5yoUWdYX8bK42AEIovA23cfUO2fNvidMDHOnz/PwesiCxyX0ysEPnz4gFIlnZDkZzD2TuoKkqBiYwSMCYGxq/dh4a6z2L17N+rUqWNMUze6uSruR0vXaY7OYxfw/ajRfQMMc8KfPrzFpE61kVyEQrjA96OGeZINcFb/+9//MG78eDj0WYwspRoY4Ax5SoaEwMsrB3BlThcMGzoUkyZNMqSp8VwYgZhCwIM9VmIKWm7XKBEoW7Ystm/fjg3b96B19/4IDv5ilDjwpA0HgUeez1C+XjN8/fELR48eZVLFcE6t0c0kderUOHrsOIJ+JUaNIYvx1Oe90WHAEzZOBL7/+Il+C7Zjwc4zcHFxYVLFCL4GivvRc/9uwoLB7UFBxtkYgfiMgI/XY4xrUwWJf3/DMb4fjc+n0ujGPmbMGPTt2xdX5/XA40OrjG7+POH4g4DnsfW4PLszunfrzqRK/DltPFI9QICJFT04CTwEw0KAdoEeOnQIx89eRNk6TXD3/iPDmiDPxmgQ2LzrXzhVbwir1Glx4cIFdgU2mjNvuBO1sbHBhYuXkDJ1BlToOx+7zrgZ7mR5ZoyAQIAIxNrDlmLbmdvYtWsXKAYHm3EgoLgf9bhyEmNaVcKLx/eMY+I8S4ND4PyBrRjZrCys01nhIt+PGtz5NYYJzZk9GxMnTsSt1SNxbaEzvgV+MoZp8xzjCQLfg/xxfUk/3Fw+GGNGj8aiRQvjych5mIyAfiDAxIp+nAcehYEhUFEEDr1x4waSp0wltYKHT5gOv498A2Vgp9lgp+Pu8QB1W3ZC2x4D0LZtO5w9dw7p0qUz2PnyxIwLgQwZMkhypUWrNugwdQOajV8ND6/XxgUCz9bgEfAP+oKprkdQsudsBCdMjqtXr6FevXoGP2+eoDoCdD96U9yPprNIjuFNSsN19igEfPJTL8SfGAE9ReD5wzuY1qMRFgzpiPZt2+I834/q6ZniYUUGgREjRuDAgQMI8DiDEwPL4dmJjfj962dkqnIZRiBGEPgtYq16nd6KE4PK4dPtY9i7dy/GjRsXI31xo4yAISPAMVYM+ezy3OIcAQoMvnjxYvkD9UXIMLRsXB9N69dG6RLFkDhx4jgfHw+AEVAg8PHTZxw5eRbrtuwU72dQpEgRzJ07VwT8Lqcowu+MgMEhcPLkSfTr2wfud+6iSnFbtKxcFJVEUPNUKUwNbq48IcNHgO45rt5/jl1n3bDp+A38TpAIw8VCzsCBA5EkiQhKwGa0CCjuR8eKBROSqaXYKyVrNEa+oqWQiO9HjfZ7oY8TD/z8EbfOH8OZPa5wO3cU9uJ+dB7fj+rjqeIxRREBPz8/+du8csUKJE+dEVkqtUYmx5qGEyQ9irhwtdhD4PPLR3glYqm8OOGKgPcv0aF9B0ybNhUkm8zGCDACOiPgwcSKzphxBUZAdwT8/f2xevVqrFi+HHfv3UOyZMmQP28upE+bBslNI17Ae/f+A1JbWXLwUd2h5xpaEKCFFv+AQHh5v8RjEUslYcKEqFqlCnr07Im6desiQYIEWmpzFiNgGAj8/v0be/bsweJFi3D8xAnQ51yZ0yNLOgukTJYECePo/+BjQBAsUiY3DJBjYBY/fv5CooQJ+DolsA3+9gPvPgXivvC8CvryFbly5kDnLl3RtWtXjosVA9+9+Nyk4n502bLl8PC4h6QmyZAlly1SpU6PpMkivh+Nz3PXNPafP34wsaQJmP/SAj77IaW5pZYS0ZP16/cvfA0KwPuXXnjl9UTej1auUhW9evbg+9HogZhb0UMEPD09heTSIqxZuw5+H97DVPyvmWfKg0QpLZEgEW+G0MNTptdD+vrpPUxSpQl3jL9/fsfPQD989n6E4M++sLC0Qru2beDs7IxcuXKFW48zGAFGIEIEmFiJECIuwAhEMwJPnjzB2bNncffuXbx79w5BQUHh9kB5bm5uePnyJUqWLInMmTOHW9YYM06dOgWS9cmXL58xTv+v50zEibm5uYydYm9vjwoVKsDCwuKv2+UGGIH4igDtIiQvllu3bsnrLi1CEtESm/ZDLPRdv34dL168QK1atZA8OZMrofH/9u0bDh48CAcHB1hbW4fONrrPpmKDBu0yzJ8/P8qUKcO/iUb3DYjahHW5H41aD/pd682bN7h48SJq1KghNzzp92hjf3TBwcFStoiuscWLF49Rrze+H43988s96g8C0tv06lVcuXIFDx48wMePH0H3OWzaEQgICMAJsSGKnl/pedaY7dOnTzhy5IiU7i5atCjMzMzCwEGey5aWlsidOzccHR1RokQJ3rQbBiVOYASihAATK1GCjSsxAjGMwNevXzFz5kxMnjxZLnrPnz9fPvjFcLfxrvnChQtLzfgJEybEu7HzgBkBRoARCI3Aw4cP0bhxY/j4+GDTpk2oWrVq6CL8+T8EaDGUSKhjx44xJowAI8AI6IxA7dq15eYmItPZNCNA2DRv3hxWVlbYuXMnbG1tNRfkVEaAEWAEYhkBIqFocyVthipUqFAs965/3REx161bN+GN6oHhw4dj2LBhMDEx0b+B8ogYAcNDwIOD1xveSeUZxXME9u/fjwIFCmDKlCkYPXo07ty5w6RKPD+nPHxGgBFgBCJCYNeuXdIDg7wPbohg00yqaEesf//+OH78ONzd3bUX5FxGgBFgBEIhQAty5PVG1xG28BGoWLGi/D2iXc60w3nr1q3hF+YcRoARYAQYgThDgK7R165dw6RJkzBjxgzQBlTeOBBnp4M7NjIEmFgxshPO09VfBEhnleJa1KlTB8WKFcP9+/flToOkSZPq76B5ZIwAI8AIMAJ/hcDPnz8xdOhQNGrUCC1atJBSkTY2Nn/VpjFUrlatmtw9PVcENWZjBBgBRkAXBObNm4ccOXLIe25d6hlj2UyZMuH06dNo3749mjVrJsko8hZkYwQYAUaAEdAvBBIlSoSBAwfinojpmydPHlSqVEleu9+/f69fA+XRMAIGhgATKwZ2Qnk68Q8B0jAeM2aM1EYncoV24G7ZsoXjqcS/U8kjZgQYAUZAJwTevn0rPVMWLFgAFxcXLF26lN32I4kgafL369cPrq6uIBzZGAFGgBGIDAIUS2vt2rXo27cv68tHBjBRhrT56XeKrrcrVqwAebKQZCUbI8AIMAKMgP4hQBu09u7dix07dkjJXJJMW7Nmjf4NlEfECBgIAkysGMiJ5GnETwQUesW0c47iqZBGKO0sYGMEGAFGgBEwbAQoaDIFmPTy8sKFCxfQrl07w55wDMyuTZs2SJkypSSkYqB5bpIRYAQMEIHly5dLoqBDhw4GOLuYnVLLli1x6dIlSWbT79eZM2ditkNunRFgBBgBRiDKCJA3PMVcadWqFTp37ozy5ctLVZQoN8gVGQFGQCMCTKxohIUTGYGYRYC0natXry6DFJcrVw70ecCAAUicOHHMdsytMwKMACPACMQ5ArTzlx5uihQpguvXr8Pe3j7OxxQfB0DxaChQ5+LFi/Ht27f4OAUeMyPACMQiAiRhtXDhQrnARKQsm+4I2NnZ4erVqyhZsiQqV66M2bNn694I12AEGAFGgBGIFQTMzMxAm3gpuH1AQICMvUJqKV++fImV/rkTRsAYEGBixRjOMs9RbxCgHzPS0i9YsCDevHkjtfTXrVuHDBky6M0YeSCMACPACDACMYNAYGCg3DVGElb0UENu+hYWFjHTmZG02qtXL/j6+mLTpk1GMmOeJiPACEQVge3bt0sJq969e0e1Ca4nEDA3Nwd53VOQ5CFDhqBJkybw9/dnbBgBRoARYAT0FAGK4UvkyvTp00HxCWk96tixY3o6Wh4WIxC/EGBiJX6dLx5tPEZg8+bNIH1L0iaeM2eO3KVcpkyZeDwjHjojwAgwAoxAZBF4+PAhnJyccPjwYRw6dAijRo0CxQlh+zsErK2t0bRpU/m7+nctcW1GgBEwdATo/rthw4bImjWroU81VuZHpMrRo0elJJijo6OUnImVjrkTRoARYAQYAZ0RoOD2FF+MgtsTsVK1alW0bt2aYxXqjCRXYATUEWBiRR0P/sQIRDsCd+7cQYUKFUC6xDVq1JCyX7TDln7Y2BgBRoARYAQMH4Fdu3bBwcEBJF1148YN+SBj+LOOvRmSBxDFKDt16lTsdco9MQKMQLxCgOJa0W5dul6wRR8CFMieftcsLS1B5MrWrVujr3FuiRFgBBgBRiDaEcicObP0OtyzZ48kxhWbf3///h3tfXGDjIAxIMDEijGcZZ5jnCDw6dMn+fBGGvpBQUEy2OPKlSuRNm3aOBkPd8oIMAKMACMQuwj8/PlTyj9S8MgWLVpI+UcbG5vYHYQR9Fa8eHGQByjtRmdjBBgBRkATAnR9IIK7dOnSmrI57S8QyJQpE06fPo327dujWbNm6N+/PyieDRsjwAgwAoyA/iJQr1496WnYoUMH9OjRA2XLlsXdu3f1d8A8MkZATxFgYkVPTwwPK/4iQEz/2rVrkTdvXri6usqgupcuXZK7uOLvrHjkjAAjwAgwArog8PbtW+mZQoHqXVxcsHTpUpiYmOjSBJfVAQFayNu3bx8eP36sQy0uyggwAsaAwPPnz+XuXPZWibmznSRJEtDvHT37LF++HOTJ4uPjE3MdcsuMACPACDACf41AihQpMGvWLFy7dg3fvn0DbQoeMWIEgoOD/7ptboARMBYEmFgxljPN84wVBG7evCl3wnXq1AmNGzcGaep36dIFCRPyv1qsnADuhBFgBBgBPUCAJGeKFi0KLy8vXLhwAe3atdODURn2EOrXrw/yBpo/f75hT5RnxwgwAjojQAv+GTJkkEHWda7MFXRCgKSPL1++LDX76XfwzJkzOtXnwowAI8AIMAKxj4C9vb1UWJk9ezYWLVoEOzs7GRcy9kfCPTIC8Q8BXu2Nf+eMR6yHCPj6+qJnz54gORIiUYjxpx8k0htmYwQYAUaAETAeBGgBr3z58nLH1/Xr10EPKmwxjwDFLevTpw/WrFkDkuJkYwQYAUaAEAgMDARJ8VJ8Q/KqYIt5BGhB7urVqyhZsiQqV64MWqhjYwQYAUaAEdBvBGgdy9nZWcqDETFO8YFJyvj169f6PXAeHSMQxwgwsRLHJ4C7j98I/Pr1S7q758mTBxScmBZ0zp49ywtp8fu08ugZAUaAEdAZAVq8a9WqlYytNWbMGOzduxcWFhY6t8MVoo4AeYsmSJBALqJGvRWuyQgwAoaEAN2bk7xJt27dDGlaej8Xc3NzKb82adIkDBkyRHoL+fv76/24eYCMACPACBg7AtbW1ti2bZuU2CVJe1tbWylpzMHtjf2bwfMPDwEmVsJDhtMZgQgQIDf3EiVKyB1wbdu2xYMHD0DvtKjDxggwAowAI2A8CJDso5OTk3SZP3ToEEaNGsW/BXFw+mkhr2PHjlLn/+fPn3EwAu6SEWAE9AkBWgQiecA2bdrAyspKn4ZmNGMhUuXo0aNSEszR0VHuhDaayfNEGQFGgBGIxwjUrl1bBrMnafvevXujVKlSuH37djyeEQ+dEYgZBJhYiRlcuVUDRuDdu3egXbHk3m5mZgY3Nzfp4k4LOmyMACPACDACxoUAeSs6ODjA1NQUN27ckAHrjQsB/ZotyYG9ePFCepHq18h4NIwAIxDbCOzbtw+PHz+WnoSx3Tf39wcBCmRPv48kkUzkytatW/9k8hEjwAgwAoyA3iKQPHlyTJ8+HSRvTFasWDEMHToUQUFBejtmHhgjENsIMLES24hzf/EWAdr9unDhQpDs1+HDh7Fp0yacOHECBQoUiLdz4oEzAowAI8AIRA0B+k2gB4tGjRpJ/WGSgaTg6Wxxi0COHDlQr149zJkzJ24Hwr0zAoxAnCMwd+5cqRGfL1++OB+LsQ8gU6ZMOH36NNq3b49mzZqhf//++PHjh7HDwvNnBBgBRiBeIFCoUCGcP39eeoUvW7ZMroEdOHAgXoydB8kIxDQCTKzENMLcvkEgQAtmFMBr4MCBUqOZZL/ooYCNEWAEGAFGwPgQePv2rfRMoUD1Li4uUnfYxMTE+IDQ0xn369cPFy5ckMGT9XSIPCxGgBGIYQRIroQ2QNH1gE0/EEiSJIlclHN1dcWKFStAniw+Pj76MTgeBSPACDACjIBWBCi4fffu3XH//n0pgUxSYU2aNMGrV6+01uNMRsDQEWBixdDPMM/vrxCgm/3WrVujXLlyyJgxI9zd3TF16lSkSJHir9rlyowAI8AIMALxE4GLFy9Kot3Ly0su3rdr1y5+TsSAR12+fHkUKVKEvVYM+Bzz1BiBiBAgb5X8+fOjWrVqERXl/FhGoGXLlqCAyLRJgTaunTlzJpZHwN0xAowAI8AIRBWBDBkySPUWiitJMo8U3J6UXX79+hXVJrkeIxCvEWBiJV6fPh58TCHw/ft3zJo1C3nz5pUuj6ShTz8cJAPGxggwAowAI2CcCJCHimLRnrSG7e3tjROIeDBr2qW+fft2vHz5Mh6MlofICDAC0YkALdhv3LiRvVWiE9RobsvOzk56FVLMysqVK8t4ldHcBTfHCDACjAAjEIMIVK9eHXfu3EGvXr0wYMAA6cVC8YfZGAFjQ4CJFWM74zzfCBE4fvw4ChcujFGjRkn933v37qFBgwYR1uMCjAAjwAgwAoaJQGBgIFq1aiUX6caMGYO9e/fCwsLCMCdrILNq3rw5UqdOLXfQGciUeBqMACMQSQQWL14MMzMz6XUeySpcLA4QMDc3x86dOzFp0iQMGTJESsr4+/vHwUi4S0aAEWAEGIGoIGBqaorJkyfj5s2bSJo0KYoXLy7l8+nZiY0RMBYEmFgxljPN84wQgRcvXqBp06aoUqUKcufOjbt372L8+PGgHws2RoARYAQYAeNE4OHDh3IH1uHDh6XnIpHuCRIkME4w4tGs6eGuZ8+eWL58OYKCguLRyHmojAAj8DcIfP36FUuWLJExEfke/m+QjL26RKocPXpUSoI5OjrCw8Mj9jrnnhgBRoARYAT+GoECBQqA4hLT7++aNWukFCdtRGNjBIwBASZWjOEs8xy1IvDt2zfJsufLl08y7fv378eePXuQI0cOrfU4kxFgBBgBRsCwESAZSAcHB0mwk4Zw1apVDXvCBjY7CrBJpMq6desMbGY8HUaAEQgPAZIA8/Pzk9Ik4ZXhdP1DgALZ0++spaUliFzZunWr/g2SR8QIMAKMACMQLgK08axLly4yuH3ZsmVRv359NGrUCN7e3uHW4QxGwBAQYGLFEM4izyHKCBw8eBCk8Usu6CNHjpQakbVq1Ypye1yREWAEGAFGIP4j8PPnTwwdOlQ+DLRo0ULuwLKxsYn/EzOyGaRNm1ZKuM2bNw+/f/82stnzdBkB40SAgtY3a9YMGTNmNE4A4vGsM2XKhNOnT6NDhw7yHPbv3x8/fvyIxzPioTMCjAAjYHwIpEuXDhs2bJCeiO7u7tJ7he7F6fmKjREwRASYWDHEs8pzihCBp0+fSgadSBSKp3L//n2MGDECJiYmEdblAowAI8AIMAKGiwAFPSbPFApU7+LigqVLl/JvQzw+3RTEnn7jDx06FI9nwUNnBBiByCBw4sQJ3L59W8ZIjEx5LqN/CCRJkgTz58+Hq6urlHIkTxYfHx/9GyiPiBFgBBgBRkArAiSxT8RK3759ZRytEiVK4Pr161rrcCYjEB8RYGIlPp41HnOUEQgODsa4ceMka/7o0SMcO3YM27ZtQ5YsWaLcJldkBBgBRoARMAwELl68iKJFi8LLywsXLlxAu3btDGNiRjwL8kqlBzvaxc7GCDACho0A/Z+T/Ahdx9niNwItW7bE5cuXQZsd6HyeOXMmfk+IR88IMAKMgBEikCxZMkyYMAFubm5IkSIFiFyhTU/+/v5GiAZP2VARYGLFUM8szysMArt375aEyuzZs+XF/datW6hcuXKYcpzACDACjAAjYHwILFy4EOXLl0eRIkXkbip7e3vjA8FAZ0xyMkeOHMHdu3cNdIY8LUaAEaANU/v27WNvFQP6KhAxfvXqVZQsWVI+s9EzHBsjwAgwAoxA/EPA1tYWp06dwooVK6RMGH2mWJZsjIAhIMDEiiGcRZ6DVgQePnyIGjVqoGHDhihdujQePHiAQYMGgVzN2RgBRoARYASMG4HAwEAZh4Pc1MeMGYO9e/fCwsLCuEExsNnXrFkTefPmBek7szECjIBhIkDyUdmyZZNSv4Y5Q+Oclbm5OXbu3CnjYQ4ZMgRNmjThnc7G+VXgWTMCjEA8R4CC21MMLZLoJW9yCmxfr149PH/+PJ7PjIdv7AgwsWLs3wADnj8tlg0fPhwFCxaU2rzkQk5BtDiYpQGfdJ4aI8AIMAI6IEDEu5OTEw4fPixjcIwaNQp0089mWAjQOSXibP369Xj//r1hTY5nwwgwAvj48aOMidWnTx8kTMiPt4b4lSBS5ejRo1ISzNHRER4eHoY4TZ4TI8AIMAIGj0CaNGnkbzbFRaNnsfz582PWrFkc3N7gz7zhTpDvPA333Br1zLZs2YJ8+fLJoMMzZ87EjRs3pOayUYPCk2cEGAFGgBFQIkDu5w4ODjA1NZW/ERSwns1wEaB4OXSuly5dariT5JkxAkaKwMqVKyUp3rFjRyNFwDimTYHs6ZnO0tISRK5s3brVOCbOs2QEGAFGwAARoGs6yfMPHjwYI0eORPHixXHlyhUDnClPydARYGLF0M+wkc2P9NMrVaqEFi1aoFq1apIB7927NxIlSmRkSPB0GQFGgBFgBDQh8PPnTwwdOlS6n9NvxdmzZ2FjY6OpKKcZEALJkydH165dsXjxYnz79s2AZsZTYQSMGwG6pi9YsACdOnUCyUaxGTYCmTJlwunTp9G+fXs0a9ZMxtT58eOHYU+aZ8cIMAKMgIEiYGJigrFjx+L27duSNKeYWs7Ozvj8+bOBzpinZYgIMLFiiGfVCOdEF94BAwaAgg3T8cWLF7Fq1SqkTZvWCNHgKTMCjAAjwAhoQuDt27cgzxRahHNxcZHeC3RDz2YcCNCD2rt370BerWyMACNgGAhQ/A1vb2+QDBibcSBAcTLpd9zV1VUGQqZdzz4+PsYxeZ4lI8AIMAIGiECePHlA0mBr1qyR3oikPrNt2zYDnClPyRARYGLFEM+qEc3p9+/fUjOdgtKuW7cOixYtku6DJUqUMCIUjGeqwcHBCAoKUr5+/fqF79+/Kz9THu9ENp7vA8+UEdAFASLcixYtCi8vL1y4cAEkDcVmXAhkzpwZ//zzD+bOnaucOMVcmThxovRgUibyASPACOglAuShMH78eBBJrrA5c+bIgPXZs2dXJPG7kSDQsmVLXLp0SX4f6Ped4mmyMQKMgGEiQM/9qusAtC5Apml9wDARMI5ZtW3bVga3r1mzpvRKrF27Np49e2Yck+dZxlsEEoiF6d/xdvQ8cKNGwM3NTboJ0mIZyXtMmjQJVlZWRo2JIU9+9erVUuYhojkmTpxYLpxaW1tHVJTzGQFGwEgQoJ2tAwcORPXq1SUZb2FhYSQz52mGRoC0m2nzBXksnTt3DmvXrgXJyNDt8KdPn1hKKDRg/JkR0BMEvn79imTJkslYKiTx26pVK+mB2Lp1aykNVa5cOT0ZKQ8jthEgtQKSBvv3338xbdo0qWIQ22Pg/hgBRiBmESCP82PHjkXYSdmyZZlkjRCl+FGAyPLu3bvLtR2SCyOFGlrrYWME9AwBD/ZY0bMzwsOJGAE/Pz/06tVLBreinQtXr17FkiVLmFSJGLp4XaJAgQKRGn+qVKmQIUOGSJXlQowAIxD/EaCHrN27d2ucSGBgoFx869evH8aMGYO9e/eCSRWNUBlFIpEnHz58kPcLtAhHpAp5PSr2GJH3ChsjwAjoJwKK/0/6fyUydOPGjSBSha7ptLCu+D/Wz9HzqGISAYqtQ5JwtMluyJAhaNKkCfz9/TV2uW/fPhw6dEhjHicyAoyA/iJQqFAhJEyoffmS8qkcm2EgQBsmaDP18OHDZRwW8kykTdVsjIC+IaD9yqRvo+XxGDQCtMDx8OHDcOdID0wrV64E6S/u2LFDxlA5f/68lHYJtxJnGAwCtMOYZFy0GWku00N2RDdd2trgPEaAEYg/CLx580ZKOJG8E8mBqBr9njg5OeHw4cNyEWXUqFFyp7NqGT42DgRIOmLp0qXIlSsXatWqJT1TaOZEqqiaYuFWNY2PGQFGQD8QCP3/qfj/pQX0unXrgqTASBKYCHU240SASJWjR4/K3eqOjo7w8PBQA+L69eto2LAhGjdujJcvX6rl8QdGgBHQbwRI+o821WozyqdybIaDQNKkSUHPcO7u7nLzbOnSpdGjRw98/Pgx3EnOmjVLKhSEW4AzGIFoRoCJlWgGlJuLGgIUaJ52j9IPoaYdZ+SVQgvrdBEl1/8HDx5IffwECRJErUOuFS8RoJgIRJ6EZ/SQzTdT4aHD6YyA4SFAvwmkrUy/G/Xr15eByWmWu3btgoODA0xNTXHjxg0pF2N4s+cZRRaBypUry/sHT09PWeXnz58aq4ZeuNVYiBMZAUYgThAI7/9T8f9M8bOcnZ1BMjBsxosABbKn331LS0sQubJ161YJhq+vL+rVqyfvFygeY5cuXYwXJJ45IxAPEShWrBiyZcumdeQZM2ZEyZIltZbhzPiJAG2OOnLkCDZs2CA9FCm4/ebNm8NMhiR/Bw8eLNcWT58+HSafExiBmECAiZWYQJXb1AkBuuB169ZN1qEbYdI9Vxg9RNGNL5EqyZMnx82bN2XQWZJ7YjM+BFq0aBFmh7EqClmyZJEPUappfMwIMAKGiQDJfxGBQpIwtEONFk3Ic4Vuphs1agS6Xpw9exY2NjaGCQDPKtII0Hcioo0Y5OkY3sJtpDvigowAIxBjCJCMX0RG/+fktcBm3AhkypRJxt2hTXvNmjUDyYHS+9u3b0FEHN03HDx4EFu2bDFuoHj2jEA8Q0DbJkvafEn5Ed3vxbMp83BDIUCbaO/fvy831NFxjRo1oLpxqmPHjkr1kgYNGuDFixehWuCPjED0I8DESvRjyi3qgMCTJ0+Uu4eoGu06pgDDtEBG7vwk+3XgwAG4urri1KlTsLOz06F1LmpoCFCcFdqdoMkUN1Oa8jiNEWAEDAsBCjLetWtXtYcnWiihYOTHjx+XBD1JP5mYmBjWxHk2UUKAiDbyjNVmFAwzMgu32trgPEaAEYg5BIj41Oa1TD1TzMXmzZvH3CC45XiDAH1XFixYIJ8haZPFiRMnJKGimAAtvpLXK1/3FYjwOyOg/who22RJyhWUz2b4CJBH4rJly+QGOpJ1pDXCyZMnY/bs2VICkgh02nQXEBCAOnXq4MuXL4YPCs8wThFgYiVO4TfuzmlhrHr16iDtc1W9TNJKpvT+/ftLbxWS/eIfSeP+rqjOnnaf0QJYaOObqdCI8GdGwHARIALez88vjHQk/ZaQZyN7NRruuY/qzDp06CAfuLTVZ48VbehwHiMQtwjQ/6e2nchTp05VesDH7Ui5d31CgBbgSBFB9VmTxkeb+eiZs2/fvvo0XB4LI8AIaEEgb9684W60zZ07Nweu14KdIWZRvBW6vo8ZMwb/+9//ZJB71Ws9bbq7d+8eOnXqZIjT5znpEQJMrOjRyTCmoRCLTDtISQ+ZLniqRp8puOD27dsxbdo0pEyZUjWbj40cAdqJGPo7Q5DY2toif/78Ro4OT58RMHwEyHuRvA80XQdo9rTwRrG4Hj16ZPhg8Ax1QoA2bIwePVrj4iw9iDGxohOcXJgRiFUE6P9TUxxGuuaT3N/QoUNjdTzcmf4j8OzZMykBFh4hR/cRpIpw+PBh/Z8Mj5ARYAQkApo2WdKmS5IBYzM+BMg7cdiwYShVqpTG+/v/s3cVcFE8X/z7s1AkJURFBEUUUQE7ALu7uwu7u7u7A0XFDhS7G1tU7CAVUBCkRNTf7/+fN3jnHRxwBxzc4T4+y+7OzryZebvvdnbevPel3/m9e/di5cqVf59whB5nmgQEw0qmiVqoSFICtDqIsFWSmxjLmTMnN6pIlhGOBQmQBIoVK8ZxVCQ/kmgwRYMsgQQJCBLI3hIgoHrSdcLDSI5o4o2AaQmkljwiBRIkICkBWtE2ZMiQJM8QjUdCQ0MlswrHggQECaiQBChkU+LvBnoXUDz1JUuWqFBLhaaoggQo9AuNA2jcIMsgJ2qj6BmikDECCRIQJKD6EiC8pMTvAjoXIpyo/r1TVgsJc/PKlStJngvJ+ijaAYWEFEiQgDIkkPzMhDJqE3gKEmAS2LBhA8dPIa+V5Ihejp6enty6nFweIf3vlUDPnj2lJsXoeRFiav+9z4PQ879HAjNnzsSHDx+ShPSQJQECNvTw8JB1SUj7yyVAcffpA5wWcUhScHCw5KlwLEhAkIAKSSAkJERqgpz0t02bNtiyZYsKtVJoiqpIgMDpvb29U20OeSsSqP2UKVNSzStkECQgSCDrJWBqasq9E0SLrGixZcWKFVG8ePGsb5zQgkyXAIV0JLwsyUW3shpB12nMQJ6MAgkSyGgJCIaVjJaowC9FCVy4cAHDhw9PMY/kxVGjRiE2NlYySTgWJICOHTuKP67pJVm1alWYmZkJkhEkIEggG0uAQkQuW7YMyRnlRaDGFE+9f//+fOUSrWoTSJBAYgnQe8PV1RWNGzeWwuwSQoEllpRwLkhAdSTw6dMncWPIU7lOnTrYt2+f1EIbcQbh4K+XAE2gXb9+Hc7OzjAwMODyEI0TEguHFmitW7cOt2/fTnxJOBckIEhABSVAi/qSbxIAAEAASURBVCxFRAYWyXNRurD/OyRA+Go0PkjJM5EkQUZ0imRAYPZCRIO/49nIzF4KhpXMlPZfXheB0BOuSko/evRilBz0EgDx169f/3LJCd1PLAEjIyP+QU3PizCYSiwd4VyQQPaTAE16JPZUo16K3hd6enogcPJLly7xcE4bN25E7dq1U129lP0kJfRIXgnQxCxhuVWvXl1sXKFQQwIJEhAkoJoSEBk+SXcrVaqE48ePi98BqtlioVVZLQFHR0eQhyJ5pBA+Gy26oMUXRKLxg6iN9D1BGA0USlQgQQKCBFRbAu3btxeP8WnCnBZdCvR3SqBevXpo164djI2NuQBo8VTi33eRZOh7kuYkKay0QIIEMlIC/7BJ7v9lJEOBlyABWRIIDw9HhQoVEBgYKA7hQgNYcuP/+fMnL0JundWqVeP4GfTBRC6dOjo6stgJaYIEsGvXLv4BRM8RhYcgY4tAggQECWRPCSxcuFAcpoMGy/TeoPcDfUiRVwqtXE4c1il7SkLoVUZLgEIIODk54fHjx9xQTx9d9FEmkCABQQKqJYG8efMiPj4eNjY2uHXrFmjxlUCCBBSVAHm9Es7ngQMHcPDgQb6ATzSuIF4zZszA7NmzFWUr5BckIEggkyXQtGlTUMi/2mwhFeFrCCRIgOYaCU6AvA/pd57CQdJvPv3G056McCIibLbx48eLToW9IIH0SOBlhhhW3r9/jxs3buD58+d8pajgWpWee5I9y9IPG60WEhF9HJFbdoECBfjKIVo9lCdPHtHlNO9pMoQm24oUKQI7Ozv+oqWVzOpIZPOk0Dd3797llnUyTgmrqP7cSZpYpdWKtDqBJsUE+iOB/Pnzc7mULVsWtWrVUuswaREREXyVIU16fvz4ETQJKqwH+HOv/4YjCgd59uxZPhimlcpkhC9atCh/xsmwqoqUL18+/o4rU6YMHBwcULp0aVVsplxtojEdvcO9vLzg7++PyMhIqQ8TuZioeCaarCVASwIvbt26dbIr3VS8GyrVPNJNGo8VK1aMj8foXaSlpaVSbZS3MfQxTmOx+/fv482bN3wiVrQoSF4eQr70SYDuwdGjR0Hjm7p164K+I/5Wogki+raxtLRE5cqV+aI0ejeqI9E7hd4vz54943MImR3+mcaT9H1Kk3G0iQzrFCZSXX+v1PE5UGab6V2kra3Nx42iuQF1Xrj5/ft3HuLu0aNH8PX1RVRUVLIhcpUpV1XgHRAQwN/NtCDXwsJCFZqU6W2gRWX0PJubm8Pe3p7PiWhqamZ6OzKqQhqHk3chfffT/aXnW9IYomg9NHaguQTySA8NDQV5vkqO32hhnqGhoaJshfxqLgHSEVqUTXNlNI+YAb8faTes0EPv4uLCAAM348WLl2yAqwHrksVhbKCP/GxCQSBBApISeP3ej0+G6uvqQI9tGnlyS17OsGP64Y35Fgf/D8F45xfAV582qF8fg4cMQYsWLdRiFSq9RDZs2IAdLP77ZxYvUkdXD+YlraGtVwB5NDQyTFbZgVFQgC/ya+tCV79AduhOhvSBPhK/f4tFeGgIfN++wk8W0sCWGRmdBw3iHj404avqRH0go9mG9etxiU120rmlqTFMDXWhlTc302thNbmq38OMbN/3H7/w7mMojPS0YKSrpRb3n9r8OfIbXgd8wrfv8bAsURz9BwzEwIEDxWFIMlJGyuBFk10Uc97Dw4Mb9U3NiqGYRXFo6+hmS++g73Fx8PN5j1JlbNRirKCMe56RPOljNiY6Cv6+Pgj09+OLZ5o1a46hQ4eAwjaoA5ERhXTAzc2Nf5gXYB/fllbW0GGT2nlyp38xkDrIQFXaSOOANy+fw8y8OPKp8aRRRsiTJoUiv0bg/ZtX+BIWyo0sXbt2xbBhw2BtbZ0RVSiVBxnrXdk3zqbNW+D99AlyMV0qVLw08hcoiFwa+bLs9/d/9A35NQwx4Z9hWLQEcrO2CKT+Evjf//7Dj2/RiAjyR+gHXx76sxEznA0bOpTjrKlLD2kV/rp163HU/SjimXHFwMQUhqbFkVdLF/+wyfW/kf5j44wQ31coaFGKjUvV07ic3vv2PyaD+NgohH3wQVhwIJ8ratWqFYaz9wGFQlQXIhzmdes34PTpU/jF3nG6JsWQz7gYcuTTZu+EjF1E9+t7LH7ERLDtK/KzOnJraquLmIR2ZpAE/ov/hp9Rn/E18A1+/YiHtU1ZDB40EH379uULeNJQjeKGFZq4pknfWbNmgT5Cu7RqhA7NG6BmJVtxjOo0NEQoIkggwyXwNSoaF67fwe4jp3D++m1uxV+1apXKejeQRZ7cz0m/9AyM0LxzHzg1bgVL63IZLhuB4d8hATKqPL57AxeO7cflk4f5hO78efP4S0NVJUCu3KNGjoD3s+eoX7EUutSxQ137ktDN//euTlXVeyW0K3UJ0Jjp/utAHLvljX1XnuB//+TE5ClTMHbsWJX1inj69ClGjhzFVoxdQcWq1dGpex/Ua9wMhkYJsYtT77WQQ5CAtARo8vfyudM46OaKe7dvMi8uR6xZs5qPy6RzqsYZrWqcNGkSn/wtWswcnXr2Q8PmrVHc0ko1Gii0QpDAbwn4+bzDhVPHsX+nCzMMv0OPHj1A4U0KFiyocjIi49i2bdswddp0RLJvnvL128G2fntY2NVAzlzKWXCnckIQGpSlEvgWFYHXdy7A6/RevH1wDZUqV8Fa9i6iUOSqSq9evcLIUaNw/tw5mJepgKote6KsYxPoGpqoapOFdmWRBKK+fMKzm2dx78Ru+HjfR122iGXN6tU8fGYWNSnVah88eIBhI0bi7m1PFLSpAdNanVHQvh40dAxSLStkECSQXgn89+snvry6iw+33PHR0x3aWvkxd/YsODs78wX6CvBXzLDi5+eHzp07g1wPh/fpjPHOPUEeCAIJElB1CTx7/Q5TFq/H+WuebLXkUCxfvhwaKuT9QZPJ3dnHUBxb2dxn9HS0YEaVXMzVXyBBAhklgfDQT9i5dhGO7d4Cp1q1sXePGwoVKpRR7NPNh1zbR7MPh02bN6NR5dKY2bMhrM1Ub2Ig3R0VGPy1Eoj+Fo/1x29ijftNWJYsiQMHD6nU6mIyAs2fPx9z5sxBObuKmDp3ESpXd/hr75fQceVI4NH9O1gwfRJoP4UZGWmhliqF9HN3d+cA13k182PstLlo1aGLSrVPOXdF4KruEiCjxcmjB7F0zlTEREViMxtLqRKYM4Vy7cK8aggbp1rbAajTcyy09IXwK+r+3Klz+z++foJzG2fg3cMbfLELYfmpWki9ZcuWYerUqTApbo2Ww+bCqnItdRa50PZMlMA7r1vwWDsdH9885ZhRtFhElfADyat5+vTpWLx4MQxLV4V1t+koYFkhEyUkVCVIQFoC8dHheHNsDXzObOOY3wf271MknL78hhWa+G3Xrh1MTYywc8VslLEqLt0S4UyQgBpI4IDHOQybvpjFu7fGyVOneIz+rG72araSYMyYMdw7ZdyCtSyslWChz+p7kp3rf/X0IeaO6M3ChUXj5IkTPD53Vvc3JCQETZs0hs+7t1g5uCXaOAheWll9T4T6lScB3+AvcF59FM/9P2Pvvn1o2bKl8iqTkzPFte/QsSMuMlf8KXMWofegYcJkspyyE7IpLgGaBN7tsgnzpo6HI4ttfPTIER4DX3FOGVti2rRp3LhIHirT5i9DfjXFhMlYqQjc1EkC39hv+aKZk+DG9ItAeWnSKqsn0whEuEXLVsilpY+OM7aisFV5dRKp0NZsLoEHp/bg5KoJqFSxAjxYGGLCf81qosVm3bp1wzHWnmaDpqFe95HI8ZeG+8rqe6HO9dOCqav7N+DE+plo0qQJDuzfD1XAXyGsxpatWsPz9h2U7TUXFvV7qLOYhbZnMwlE+r+A17oh+F/0Z/ZOOCZvSD35DCsnT57kRpUW9Z2wbel05PuLAQOz2XPzV3bnnW8AWvUbA+YSwgATr3Og+6wSxMyZM/nqYOdJ89Bt8NisaoZQ718mgVgW93728J54eu8mTjEDI4EKZxURplDtWk7I+SsOB6d1h0UhwbCYVfdCqDfzJPDz178Yv+UEdl94iJ07d6J79+6ZV3mimigMZYMGDfDexxfbDxyDXcUqiXIIp4IElCMB78eP0LdTKxQpXBiXLl3MMvwhMvSQ2z9hRy5YvQkduvVWTocFroIEMkkC7gf2YNLwAXxydvv27VlmXLl06RKaM4xLiwq10GnmVmhoamWSBIRqBAnIL4FPPi+xa0JHGOvr4BoLg2psnHWhTwmDqDGbBH/0+Cn6LdkLS/ua8ndEyClIQIYEfJ7ehcv4LrCxtsKF8+ehlYWLRghEvladuvAL+oyqE/ZA19xGRouFJEECWSuBXwyD5dH64Qh9fBHHmCc7GSZTodQNKzdu3OAf3F0Zlsr6+ZOFFYypSFS4rB4S+BwWjkbdh+KfXBq4ydzS9fX1M73hK1as4K7Hk5ZsQrNOvTK9fqHCv1sCv379wpwRvXD/2nlcv349S2Ld0+CqRvVqyP3rG47P6Q0Dnfx/900Rev/XSWCm61ms9/DEsWPH0Lx580zvf3x8PBo2bITXDKT7wKlLsChRMtPbIFT4d0sgwM8HnZrVQzEzM25cyZcv8wGjJ0yYgJUrV2LDrkOo3yTz9fDvfgKE3itLAtcunsOgbm250ZC84zOb7t+/j9p16sCqRhN0mLZJWHGf2TdAqE8hCUR+/giXES1R2FAXN65fyxIvSvo2I0Ok5537GLrhJAqXKKNQH4TMggSSk8AnvzdYN6QZKtiWxbmzZ7ME55GMhhSO/E1ACGrMOApNo6LJNVdIFySQ5RL4H/P48to8GiF3juMyWyRSo0aNlNqUsmGF4qHa2dmhZsWy2LduoWBUSUmUwjW1k8DHkM9watcftvYVeFiwzHSVv8DCrTRq1AhDpixA54Gj1E52QoOzhwR+/fyJ8b1bIcT/HR57ecHAIPO8Rcg9uVHDhnjt7YULSwaioL529hCq0AtBAgpKYPg6dxzzfIkHDx+iVKlSCpZOX/bBgwfDbc8eHD13HaXKlE0fM6G0IIE0SuDdm1do29ARbVq3xo4dO9LIJW3F9rDnnzzGVmzeidYdu6aNiVBKkICKSuD08SMY1rsz98bq27dvprUyNDQUtnb20ClaGj0W72fg9LkyrW6hIkECaZVAeJA/Ng2qjwZ1a+HI4cNpZZPmchS+b+269Ri+6TTMrAW8iTQLUigoUwIf33hj9aBG6N+3D9auXSszjzITu7Lwdu4nz8BxziloFRJgJZQpa4F3xkjgf//9i3vL++JHwGM8feyVEj7xyxwpVdmrZ08Y6GrBZenMbGlUCQz6hB0Hj+OdX2BKYpB57cSFazh86qLMa8pKpInI9FJ0TCy+RESmyCYm9luK10UX5c0nyq9q+yImxti3fgHOMyNHZr5cIiIi0I19xNdr0UGtjCqfggJxcr8rPvi9V/hW3jh/ApdPZu4ANSP05VtMNCIjvqTY32+xMSlel7xI4UYiwj7jB1slrgqUK3duzFq3G7/YT8ugQYMytUm0Ovg6WxG2a2Lnv8qocuruC7jf9FZY1h9Cv7KwUQ/gw/A5MosyQofomQ/9GoP4n7+SbXZG1JMsczW4sHxQC1gVMUDXLp1BYI6ZRcdZ7O5NmzZh2XoXtTKqnD/lgZPuhxQWU9CHQOzftR1+Pu8ULpueAul9vkmHwkI/g7yLkiNa5Ur51JUsrUpjJTNsuLq64uDBg5nWDQpFSe++voNHZlujygWmL6fSqC8Hdgv6Qpgl8QzvQF2paat2cB41AUOHDsX794qP39Pa7379++MHcrHwX9vU3qjy9dMH3D+xG2EffBQWx4sbp/D0krvC5dJTIL3vHKo7nuEwxkaGp9gMeev5EReLn/HqoUMFChdD59k74H70KCiEXmYShc0jsPoOE1aqrFHl6bVTeHTxqMJiiWA6dPv4LoQGZt5vEDVS3mc0uQ7RuCo6PBQ/fyQ//qKy8d/kmwuQN19y7UlvehGrcug8ZS3WrVuH06dPp5edQuVpEcu+vXtRYdjGbGFUCbp/Fh9uH1dIBpT5W9hH+F3eg5gQX4XLprUAeV5kBv3366dc1fz3L/tmYQYLdaB/cuREhaHr8DNXfvTq3SfFJidrWHFzc8NlBli/bekMaOXXTJGJul589votBk9egDuPnirchYXrt2PakvUKl0tLAZf9LERIrxHQs3FChSZdMH7eKvaB/UNhVmRQKd+gI+p2GpikbGR0DEbOWIIilRrCsHwdFChbC236j8FbhkciSfLmkyyjysdV7MpiwuBemDx5MoKCgjKlqZMmTcL/kANj5mW+W356Ovj+5TMsnjgYzx7eVpjNzjWLsGnRNIXLpaWAx97tGNO9OeqX0kfPBhWxds6ENBkyyKDStY4thravl6QZMVGRWDFtJJrbmaJRGSM0KG2AiX3bIsDnbZK8kgl7Ni5Dy4rFcPPCCcnkLD3W1TfAhMUbcYSBBxPeSmZQYGAgpk+fhnEdasPOskhmVKkydSw7eBWzdp5TuD3P/UIwgnk23H3pr3BZRQvsPHcfbWfuQKEOs1Bj+GpMcTmVomEkJf6rjlyHVa+FOH33ZZJsaamnwqDlXA5JmKlxQp7cubBpZFs8e/Ys04z85I4/ZMhQtO3cHU1atlEr6a1ZugCLZk1RuM0vn3tj4ohBeHhX8XeYopX5vHuDWRNHo0a5ErC1MEafji1x8+plRdnw/BtXLUHFkkVw4bRHkvKXz59Bs1pVYF1YFxUsC2Non664e+u6VL6B3dujftXyKW5eD+5KlcmKk3qNmqJLr34YPnw4oqOjM6UJQ4cNQ6EiRTF+5vxMqS8rKlm3bAEWp0FfXjF9mcz05VEm6cvsSaPhUL4E7Jm+9OvUEreupU1fNjF9qWJVBBdl6MsVpi8tmL6ULaKLyiULY3jfrriXSF9E9+ji6RNoWK08yprqoUxhHdS2L4XTxzJ3gZCoLendj54yC+YszOPgwUPSy0qu8hTa8oSHB9pOWod8OnpylVHlTCHvn+Po4hEI8Fb8d/Ky6zKc3TQrU7p3z2Mnto9uixn1CmFVjxo4uWYKfqUyISyrYWRQWd6lCjYPkR1TXt56Xtw8jZXdq2FmA1PMrF8YSzvaw/vyMVlVqlRa8QoOqNlxMEaPGQMKWZwZ9OPHD/QfOBC2dVqgSrMumVFlmuo4t30pPNbPUrhs0Nvn2LdgOHyf3lO4rKIFPge8w+HlEzCzVVlMbmCOTWM64PX9q4qy4fkv7lqJqU0s4X096fdx4KvH2DCiDSbWL4bxdYrwfPsXjkRcTJRUXXExkTi4ZCwmN7Tg+cbVKoTNYzqC2pkVVKFBO1Rq2J4tKnHG90xaNEBg9cNHjETxhn1Q0LZ2VnQ7w+t8fXQlnu9RfOwYFcCA0TePRfjr+xnepsQMfS+54db8TvDoYY6L42rj6c4Z+PdnykbCxDzoPODGYbh3MkFceHCSyz/jYvBo42iccbbF8e5muDKlMV4cWIT/fiWdqw68cQTXpjXDiV4lcLybGS6Mqgmfc9uZkSVzDD9JGi9nQm5Nbdg6r8bFC+dx4MCBZEvlnMUo8dWfLDxMmzat0aZhLQzs1i7x5WxzniNHDhQyNoRTlQowNiygUL/y5M6DyrY2qFjeWqFyimbeeegEBrNwUQb6eujVvjkHH6Q079fv0LZJXYU8iXqOmo5H3i9hWEAPg3t0EDeFrPEteo/E0bOXUa50SfRmEwD0DFz2vM+9crq0bsKNa/LmEzNWk4Nq9uWw48BxhHz6rPQY92/fvgW54o+aswI29lXUREIJzcyRMycMjE1gV80J+obGCrU9d548KGNXBaXLV1SonKKZTx3chSXM+KNXwBBNO/Rk+pIDpw7uxPtXz1C7aVuF9GX28F4sTNUjzqttL2dxU0gPxvVsiaun3VHCuhyad+4NCqn14OZl5pVzBA3bdIFmfi1xftHBi8f3MXdkH76quE6ztrCwKiO6lOX7IsWKw/f1cxw/epB9eA9WOsjpWPax8inQB9vHdUJO9jv8N1Ge3DlRycoU9iVNFep2zhz/cM+emmWLw0gv6fOlELMUMu+5+BAj1rszvBtNdKtXkT8Lbhcf4QUz7LSqaaOQDj18E4iBKw7xZ75VzbKwNisorjkt9ey99BB7Lj2CbYnCaFJFue9ecUMz6YDwhWLjfmAdw3gYPGQINDQ0lFozYXydZwCWrmw8oampXthGedj7xK5SFZS3r6SQjHLmzAFjExNUq+kEQyPF3mGKVPQ9Lg4dGWbIXc/raNy8NarUcML9O7fgtmMzqlR3gKlZMbnZPX54DyMH9OI61Kx1O1hZ24jLHj+8H849OkIjb1507zsIRgUL4typ4zjBfsfJWKZfwIDnPeNxFJGRXzm+Ab3HJbegj4EIDvrIyxc0KSTmnVUHlavXxOa1K5Djn39Qq1YtpTbD09MTkyZOxMotu1C8pJVS68pK5mnVlxykLwVNUDUT9KVz83q4x/SlYbPWvL4HTF/2/NaXIgrqy5iBCfpCnhqS+uLB9GVIT6YvGnnRlfTFuCDOM305yfSlUYs/+kL3irx8nHu0h66ePpxHjkPdhk3x1OsB9rpuRYWqNVDMonhW3lKF687J9J68wpbMm8n1ysLCQmEe8hagFeKt27SFqZ0THLsOl7eYSuejFavaBgVhwUDEtfSNFGprTjZfUNSmEkxL2ytUTtHMD06xkJ7M+JNfzwAVm3XjY7eHp9wQ/P4FytZupdDYbf+s/vj42ovzqt5ugFRT5K2HPHXcpvRAPm091Oo+EqVqNMSHV164d9wVxcpWhUER5T2DUg1O40mxclXheXQr80SI5VjDaWQjd7HNmzfj0KFDGLTyMPJp6chdLrMz5mLjL3Obigp71PzDvvV0DU1gyYxW2gUU0yFF+vjjexzWODfFOy9P2NZuDkumsz6Pb+OW+3aUsKuBAoXM5Gbn//wBds0cwMdfdvVao1DxP98dAS8fYd3Qloj68gkVmZHCunp95o3jg9f3ruDN/Wuo1pLmIP7hZTeyhVOPLx9HkZI2PP3fnz9YnqvwYp4/lZt0goam8r7pkuusefmqOOu6Ajo62qhevXpy2TIsff78+bjueZeB1e9CztzK/b7JsEanwigH+23XL1kB+iXsUsmZ6PI/OZFX3xhGZapDQ1d5uuB/ZR834OTRNkCx2p358+h/dR/IsFO4WguQTspDP5n3otfmMYiPDINlc2fkzqctLkYeKtemNsHnp1dRsEIDFKnaHFEfXiHozgnER4WhUMWG4rwB1w7iIQODz5k3P8xqdYKueTlE+j/HR+b180/OXDBk8lBl0jQsgm+fA3DJ3Q3Dhg6R9U4N+4dNEiaJHbB//350YzHwXl1zh1lhE1XuY7ZuG4UqIw+VcqUtcc5tAwOZSohPO3f1Vsxfsw1bl8xAj3bN5JLB5j1HmKfLSuRnoKBkRHpy/o+17dqdh2jUbQi6MgPK9uWzxPyoDqpr7vghGO/cC/LmEzNQo4Mte45i/PxV3GulQAHFjGyKdJNc8U+cOQe3y09lKaQirIS8iSRAocrIQ6VE6XJYs/8sKMwV0faV87Bj1XxMWb4FTdr3SFRK9qn77i3M02U88ubLjwJsAs7t0mNxRq/b1zGicyM0atsV01a6iNOpDqpr0MQ56D5kvDidDiikWJ8mVXk4iS+fgzF7/W7Ubd5eKk9Wn/gww0qvhpVwlgHaEf6PsojibhcpUgTLBzVHjwaKTYwqq00C3wQJULixGsPXwMbcBB7z+iF3rpz8wqJ9l7B4/2WsH9kOXetWkEtc0d/i4TR6Hb7/+ImQ8GhsH98ZbRzK8bKK1PMxLJLX7fX2A54x4w4RPTdrhqmXlwVveCr/vsbEwabfUixeugzD2Ep6ZRGFjSpatCjadO6BSbMWKKuav5bvnCnj4LJhNTda1WnQmMsh9PMnNKphz4zu+XHzScqejSLBxTCvjSaOldiKwjh8DgnG+h170bxNB36ZVrc62JZENPOevPPcj00AJ6wKp5BhVa2L8QnlMzceiFjJ3Af4+aKxQwUMHD4GoyZOl5knKxJXLpwDN5eNIJxHMgooi9qwyd/A4E84dPaasqoQ+MohgXlMX7ZvXI3tB0+gtoS+NK1pj3zM6HtdAX1p7vRHX9Zu34tmEvpSyy5BXzyZvujo/tGXGmWKoWRpG5z6rS+0sKxOxdLMuJcDxy7fRgEDQ96LEGaAdChXHLXqN4LLAQ85eqZ6Wbq1agA9LU2cVqJ38smTJ9GCgW+P2XsPRmYlVU8I2bBFFKqMPFRMLG0wYI0HC72W8P1z0WURLu1YjPZT1qNiU/nwo+64u3BPlzx5NaFVwBhj9twVS0zeev5lk23LOrGFOWxB0NCtl7mBhphEhgZhcbtysKpaH72X/pmHEFegYgfX967F9V1LEBIcDC0t5U1+01Rc8RKWKFqxHjqMX6ZiUlCv5hxdNRlX922AMzNQlanRgDc+6stnLOpWHXnYN/0s96dydeh7bDSW9HBkES/iEBUWgt7zd6BC/bbism6znfHw/GGM3XEZplblxelkbHnz4Br6LHCFfb02ePvwBtYOac4NKD1mbRHnO7NtEc5sXYgWQ2aiQa8x4vTMPHBfNQWvrx9HgL8fyPiuLCKvGFq4U6SxM0q3HaWsagS+EhKgcGOXxtWCrlkZOMw4ghy/3wkvDy3Fq8PLUWHIahRjxo2UiLxdPj26gNBnN/HreyzP2nijF/IV+LMIi7xNnmyfglJtRqJM58lidk9dp+H9mW2oOs4VhSsnfAddGl8H/zFvmdoLzoE8QIjiwkNwblhl5NHSQ9Mt3uLyqnoQ+9kfF0ZU40bwdu2SOJ/IxlhxcdmG5vUc1dqo4v3qLToOnoBSTq3RbuA47D5yCpdu3UOXoZPFGCP3nzxHyz4jeTrdwDuPvHmYLPLq2M48GGq174dCzPpGobMIU0WSxsxehgET5kgmZfixx/mrIEyUkcxVXWRUoUq6t23K6zp08rxcdb5444OJ81djwYRhMDFOWMEoWTAwKGGyyrGq9GqaOjUq82zRMd/4Xt58krzV5bhbmyZsEjEX9rLYj8oimgjZvXs3WnYboHJGlXcvvTFlYCd0qFkKk/q1x5nDu7kHxvTBXcUYI+RxQZ4a5JlB9OzhHQxpV497dZzYtwPObWqjafnCPI0wVSRp1cwxWDB2gGRShh9fP+vBDRidB4wQG1WoksbtuvO6LnkckqtO3zcvsG7uRBYmcD730Elc6FNQAE+yq+oodalizTr8/FtMjFQ6nayYPopjJ/QfOyPJNVVJKF7KBnZVHeCi5JjCFGZSUyM32jvZqkrXM6Qd//77H5Yfuoq6Yzeg+rDVmLr9NPw/hWMk8/5wOfPnw3TilhMYsvqIuE4K7zV+sweCv0Sh/7IDKNdvCewGLsPQNUcQ+/2PGy15f7Sf5Yqrj9+Jy2b0wck7LxAdF4+hrWqKjSpUR5e6Ce+Go9fl+yChMtQnksmUrvXpVIoUqSeGted9UBh08ueFfTYPG6enlQ9tHcvBZdtWKXll9AkZT0NCQriXQkbzTg8/wpdZt5x9aNapjvrVbDF36njQ5P+kkc7Y7bJJzHrmhFEYO6Sf+Pzhvdto17g2vB8/xL6dLmjTwBHlzY15GuGxiIi8P3qyxSg3r14SJSllf3jvTpS2KQeRUYUqodXxNCEbyD5g5Q27NX3ccKZDvzB2yqwk7Xz76gU+BQexOpqIjSqUiTxxnOo2wAvvJ4hioReSI1pVPnpQb5Szq4AR46cmly1L0rv27o/w8HB4sFBCyqKwsDCcOOGB7v2clVWF0vmSvqxn+tK6bnU0qm6L+UxfAv19MZnpi5uEvsyaOArjJfTlEdOXjk0S9GX/Lhe0a+jIw29RGnlqiIj0pXf7zNMXkVGF6id9cWL68iHAD4/lDFM3Y/xw/GL6QmGvEtO71wn6Upvpi8ioQnlIXxyZvrx89kdfyFsmKDAAfZyHi40qlNekcBFscjvCwtUpdyxLdSmLuvcbjLNnziCYTRQri1xctqNk5dpqY1QJfvcMu6d0x+L25RnmX1c8PL0X7+5fxZ5pvcQYI4EvHmLH2PY8neTm/+weNrEwWR9ZKKD7J3Zh46CGmNPEgqeRp4YkeayciEPzh0gmZfjx8+snOSaKY6ehYqMKVUKLM4meXpIPE+OTz0ucWjsNTQbPgjbzLkhM8tbj9/QOvn4KRM0OzmKjCvHSNSqMHgvcUKVVr8SsVfK8UvPu+MEWBx1WMoj9tWvX4OfrA4d2/bNMDv+x98n5HcuwrHcdLOhcFTTp/uWjHwvfNQI3jmwTt+vwsglwmzNYfO7rfQ+rBjZCwEsveB7fiRX96mNSA3OeRngsIiLvj41scRZ5dCiT7p7ci8LMwCgyqlBdOgbG3KMkPMgffs9SXnAiatuhpWP5+KvZQNnjI18WFrAIM6hIGlWobNUW3TgL/+cP+T4i5APfW9o78L3oXyn2G0n0XU5sFp45g/85tO+PoI8fcPHixQzmLM3O3d0dMWxuxKJegmykr6reGWF/vHZfzUNaXRxbC967ZoIm1Cl8l895V3GDn+yYiocbRorPHzGvjscuk7mx4P5qZ5wdUhHnhlfBw42jxIYJyhz+7hFuLejCvDyui8tm9EHw/dP4xUJ0kYeJyKhCdZjV6sir+nDrWKpVxgb74EdsJHQtyiGPTsICk8SFPj9NmB83dZA2MhR1bM+zhr3w5Puf36IQFfgKBe3qio0qdCFfARMYlXXAj5ivLHTYT55Xlf/lNy4GE/t62Lrtz8JqyfbmkDyhY4prfPXqNbRhYabUlW7c84JTu364df8xalSyhb6uNkbNWorRs5bBnYW7+haXAKAWFh6B89fvIPhTKO9qBPsI9XzwBGPnruDeHXZlSqFjc/aBygwTXYZNhtezV2KR3PHyxo27XuJzZRyI8E3q1kwwcIjqKFakEFvFlxsPmQEoNfrOwE57jJoGh8p2GNpbtmWySR0HbrhxPUgTYf9ylrTffiBB6ZrVS3gZyJsvtTap4vX8mvnQqFZ1HhNYWe27fv0616/aTVorq4o08X189wacW9fC03u3UL5yTWjr6mPl9NFYOWM0D3f1PS7BsPb1SxjuXruAsE9BvJ6orxHwfuCJ1bPGce8OKxtb1G/ZAX5vX4AMMq+9/+jHs4d3QfUokwJ933L2lRykf7tMTM1AochePX2UavUEUDprWE/YVqmJ9n2GysxfvW4Tbrg5ecBVSl/IuERUs36C4VNU+MKx/Tjvvg/TV7kgv7bqundTeylcGn1004p2ZZHH8WNoWNEK+ZhxJTtRV/axOM/tAg9t5lS+OGhVbJNJW3H0xlN4vfso7uq914G49cxXfO7tG4xzD16j3ji2Qpt5Z7R1ZAN1Q13sZSGvnFceEucLi4zFJa+3CA6PEqdl9AEZMIhq2VpKsS5qpIc8zHtFsh9SGRKdHLr2BAfZtml0B+ho5k10FdxQQony1FOqqDFOLRjAt20sdFx2p1Y1bPD4yVO+Wl9ZfT1x4gTsKlZWKCSVstoiybd/lzZYOncGXzlX06kOwwq5gfZsstfjyAEWhifhI5XyP7x3RwpH5GtEBGgydNbEMZgzZSxsytuhZbtOePvqOQb36sQMLgm//V/YZPq1S+e5QUKy3ow8DmfvycivX+FQu14StsUtS/I0yb4kyfQ74dihfXA/uBerNrtCW0c3SbZPzIOFiO5jYrL9nfaG9T852rByMTfwzF22VuUWehQsVBiVWLglWvmuLCLjIoUDrt+khbKqUDrfgV3bYPm8GTy0W3WmL/c8b3CDycmjB+AtoS9eyejLnEljMO+3vrRg+vLm5XP+nfDsSYK+hDN9uc705TMz4CmLSF+iWJi6mrWS6osFwwQhkkdfjjN9Oc70ZcWmZPTltyFBpBuS/bGtkKBD9HtB5Pc+YSzZoFlLEHA9yfXaxXMI/RTCnheG39dUfZ8ZCmmWl4UOVBaeXjz75jx3/hxsarWUFLHKHvs+vsWNIn5PbsO8fHWOB+OxcgJoe3bVAz+/J3z/xH4Nw5u7l9jK9YTf3bioCPgz48GJ1ZO4d0dhNsFqW78dPvu+ZAaZ3iyM1hNxnwOYEYbqUSaF/QYEt6xcS6oaPZOiLOxOHh6CS+qCjBMCl983qz8sbKujBjOIyCJ56/nyuz1lHJuBgOt9n3ji9Z2LiGZhk6wdmqCMg/R3kqy6VCFNU0cfJSo64bgSjfzUTxqTFS5emoWaKp1l3d7CvMpPbprL3ic5YFWpFg+ltWpQYzy6cASBzGgiIjKkvPP68zx/i4yAz5M7OLJiIo6unAzTUrYgDI9gn1fYPrknCIeEKCbiC16yZyAyNETEKsP3MV+/IC76K0RGC8kKjM0SvmsohFdq9ODcQTw4exA9mYdJXhlh2cgjq3S1+nDqMDAJq6+fPvI0enaIbBwacWPnbY9dIOMVEe09j7ny47JMH7KKjEyLw6xUef78KbMNxz1OwFjJYa8ysv23l/TCi/0LeagsmvQPe3kH12e0wgfPY/j6/s9ve/ibBwh7cVtcdaTfc3zyuoirDGMk7ksQTGu2hqZBEQRc3c9DYIky/oj6gs9PruB7hPJ0ITrIh1dnVM5JVC3faxoWZYaWPPjq86cfUhkkTsp2nwGnWcf4Zlxemo8oW1zEJ+Riob20iySM10TpehblwQTIjSmURuE0nWYdh1Wr4aIsfM8NLiw0GeHuSBqApDKp2EkhFu7syhVmT2BYpYkpV+KEhw8f8km1WtUqJr6kFue0Cm/M7OXQYBOpZDknIwTRqP7dUF3OFRLv/T7g4Zm9MDctzMvWc6jCvF8m4iYz2NiXlf+lR0YcMsqkRAYM78SZAYvKojc+/tDMlxfaWvmlLtPHYHEzU7x+78cndlNy35u8cC03HJ10XcNj60kx+n1ioK+LOWMHY8byjShWrSkcq1TA7YdPEBL6hbeNAN6J5M33m63a7eiZn7p0g9Laffv2bRQxs4CJaTGl1aEoY9KXVTPHIg+L57/t5C1x2zoPHIn+zWvIxe6D33vsPP8QhYom9KuSYz1MZd4vT+7dRKly9nLxoEyEWULeIimRLsNOadtzkMwsgT5vWOguTWhqaUtdJ30hDBH/d69T1ZcNCyYzw1Ewlu8+kay+ENj7wPGzsWXpTLSuZM4wZxyZgek2vnwOQRvWtjIS2DlBAX5YNnUEDw1mW8UBV04dlWqbqp1UqFELq5hx/fnz57C1tc3w5tHzdvfePSzul3UDyQzvFGN44vZznGfGkcEta2BBv2a8Cuqr86rDICNDahT4+StGtnXCzJ4N+XNHZeuM3YjrT9+nVlTquofnM7wM+CyVlviEsFP6N62WOJmfv/0Yxr2JtDU1pK6TDlkUKoA3H8KYDv3HJr6TrMkQ5ycvnbEbj2N0OyfUsDHH8VvPxNdEBxlRj4hXdtvXtLHgE770vmjfXvbYIL199vS8Dcf6jdPLJkPLn/FwBwGx9xsyEjMWLOO8SQ/Iq4KMDPKQn887nPd8jKLFzHl2xzr1QcDtNDFKnhnyEE307tq2MdWsTVu2lcJvEBV4//Y1PyRsisRU3NKKJ31h4RBTIvLSmTpmKIaMnsjwWRxx6tiRJNlFGA+e16/wUF6SGd6+Slh08+blC26gkLxGxwRKTuG2uvYeAKvSZRJfVonzGsxQcPLIfqW1hfBVaEI9vxLDuyit8Yzx2RPuICD2voNHYpqEvox17g0yMshD/kxfzjJ9MTUz59kdmL7Q9wjpS1lb+fXFTQ59aZyMvviI9IVhHyUmsb6Epawv5KUzfexQDP6tL6ePJ68vt5m+DBg2Rqqqt68T9IW8wCoyg57v+3fcuPvqmTfDN+qOuN8fz/St1WPAEEyZuwS5mIe7OhLhMdlVrgp6v/Tv3z/Du+Dt7c3lVbyCY4bzzmiG9H7xWDUJhBkxzOUK9E3MeBWOnYdiXb8ED/TU6vzywQejdnlCv1BCWcvKdRiuSHdmSPFEETbBLC+REecTM8qkRJq6BqjeVvY9Cwt4i9wsdJfG7/AqIj40diMsk1D/N3wyl/C1kqMzG2YgmhmO+q44kuz3j7z1hH14zw2+we+fYf+sAWIDFdVfre0ANB06l002q4cOWdg7wvOI8uYG6H7cvOWJElmoM0+ueODFrfOo3WUI2o5ayB8R0g+3WYNARgZ5KIzpwuS9t2FQOGEuoHSVOtg2sRs30BQtbScPC5Bh5MbhranmtavbSgrvRFTgs3+CUVxHhreVyLASE5GwgExUJvGevHQOLh6D+iw8Vwn7GvC6lLDIWDIfhdrrMG6pZBI/jg4P5e3PkTMXyjokjLHz6xZA88EzcHLjHExrWpJjzJAhirBZHNsPgHnZSkn4ZGYCPXf0/CmTiL9+jQTvOWXWkxG8P949xY0jJZoORPleczhLAlZ/sH4YPtxMfQ7nW2ggSrYcBpuuU/nvKJW9MqURPnsrtrj4452TDKsk4XsiuX5paBdA8UZ9ZF6OCWbjGA1NhoeiJXWdcFXyFzRHdNBbBhj/Lzd4SGVQ8IR4fX3/mG/6ln/m/GKC2fzF//5D9O8+kPHFoHQVMfd3p7bgW1ggQh5d5O2waj1CfE3VD4xsarIQgfHw8vJCzZo1pZqb5K328uVL6OnqoHBBI6mM6nLy+PlrUBiwcc49xUYVanvZUpbo0KwB9h47k2pXBnZrKzaqUGaHKgkPyvNUjCSJGR8+dRFHTl9KnCx1XtLCLFnDynv/DyigpyOVX3RSzLQQXr3zRVRMLPPIkZ3n9OWb2Lj7EA5sWIxCxoaiojL3luZFkS+vBsLCv+IKA63/GhXN8/3HlCLuezw38FCCvPlkVqLiiTalinOPEorrTRgQGU2kW+YqBFhO/Xv7/DHeszBg3QaPExtVKL1E6bKo16IDzh3dS6cpUuvuA8RGFcpI4aSICLNDESLg9yunkn4QS/IoWrxksoYVMvDo6BWQzC4+JmOW39tXLFRYFPfIEV+QOLh18TSO7tyE+Zv3w7BggkFW4rLUoamFJQMMzoev4WF4eOsKotmKSyJ6gcazePhk4CGvj9kjeqGoRUn0HT1NqryqnhQtbsUnFOhZVYZhJSAgAHHMY1ASxFxVZaFIuwiInUAKp3VvIC5GH7QUBksew0rePLkwqUtd8ccsla1mbYanPkHci6UI82CRh9xveuOYDEOGZFnLIobJGlZ8gr9AX1tTMrv42MxYH68DQ3moMApZJYt+sVVY/ZcdRAlWx6QuSVcgi8qktx4Rn+y4J08ui0KGIB1UBlEs71ds4r3f8LHKYJ9mnof2uPLnf/y0hA8ZYkR6MHbqLLkNK937DRIbVah81ZoJk3uv2Up8eekLm8Qlo0NqRJO+ksDYovz+PuxjgpGeftJ3kQi0nlboJ0f03hjRvwcsSlhh9OQZyWVj10syY1FF3Lp2mYc/a9G2I2gixP3gHmaIOczL/cc+mmTRmqULGHaFJsZMmSnrskqkkWzfswluwrvI/RsvLSMb9uLFC5RkdagrHd6ToC9jE+kLhcGS17DSre8gsVGF5EBGPKI3L+TXl3CmL6sWpa4vFqnoi64MfRGB1qemLyN/68vIScnri/lvffFk+kLhz5q3SdCXY0xfTv/WF5HHvp9PwuTc8H5d0ap9F7Tu1I2P5zYxLy/XTWuhy/BZUqqLC1GF/1lZl8WLJw+U0kJ6b+ViHhIGbCW0qlPwm6cIYWHAanUfJTaqUJtNStigfL228Dp3INUuVG3TV2xUocwWDByb6JPvC76X99/Ty+7wvpx0AleyvGFRy+QNK2xSW7RCXrIMHZPB6LPfa8QzzIh8OnqJL/Pzl7fO4vaRreg+fzdkTUqLCtHkuTz1hDEQb6J9M/rBjgF72zfsxAw7v3DVbSU8D21igPa6qN93koitSu8LMsDyz8xbLZJFNdHVlW8srmiHaEzWuHZHRYtlWP47J9z4+Ku583QxTxp/NR00RW7DikPbfmKjCjEhowRRsI/8ukBGD8IdSY3ISCIJJC/KH8qeTyJZz6gItP4b82hJjv5l46+d7Jk1YrrWdMDk5LLJTH928yz2zhvKPHPC0G7MYh6OTJTRqGgJ5NbIxw1HrxmwPXnVENGcwU+G4UJ4RllFhUqUgSfDVVIWxcXF4WOgP0yLyr8wXVltkYcveZcwZWB4IX9+n8gYUabjRLkMKzly54V1h3Fcn6g+KmtQqgoifdmiA+bFks+gsDzNYIDuHvh4xyPFvFqFSiRrWIkN8eO4JbIYaBoXRfTHNyBQesI2SQ8VrdkGH5knD2G3WHeaCP3itohghhbvnQnjMTLeyCLyCPr3Rxy/pG1aihmBZM8ryCqb1WmaRkUZXpMW/05P1bBCcY2NCuhndZvTXL9PwEde1soiwWIuyahMSfkGema/vVxEZUWGixgZLj+iPLL2O5bPxralyQ/0qQxNxiVHFO4r6JPsF8C3b3G8rE4ibxYRr+DPYRwDpk/HlmjVqLYoWeaePGsIe4bChS2YNBzkoULeMEs27gSBuv/D/lbPmcDDqMmTT2YlapAoeu5JB5RhWAn78gV6BilP2Ge2mD76JwxCzNgkTmKysLJOnCTzPLEHDoUSI4pjIRQUoemrtmPqiq2pFEleX3LnYYbBkCCZ5b8z3SVd05Th0ksFyEtl4biBaN65N5wat5LJQ5RInjUU6ow8UAZPmQ8b5qFC3jC71y/FMbetvJ4x81ZjBwOyJ6PVdoavkUsJE0Oi9mTknlZh6ujp8/j2GclXxIt0i8hQN78oKVvsfUPCefguTY08Uv0xNykAMpqkRka6WixfbqlsIuOFJM6KVAYZJ5tHd8CGkSl7OaTwyoFG7lwc60UGa3xjeC+kQ9r5NGRd5mmL9l3GcwYwf33VMCmMlsQF0ltPYn7Z7dyA6YdIVzK6bxTnmCbvDQyNMpp1uvj5+/qgsClb4MEm/CXJzLw4M2LnlUxK9tj0t9ekKIMu+y0jipWBeyXKk3hvaVUar4OjEicnOafwkrKIvD+JvkYk/NZJ5vkW+42fitoleU10TEadl8+fgoDnUzIo0KTHsvVb0bdza45BM2vSaP6RTsaVLr36Y6/rVpneKOTVQyv6BwwbLYUfIapfVfYEGE5GwK8srJqRUcY/q6RfZSulvOBIVWQhqx2kL4XSqS8iw4WIv+i5jI2NESWlui/B9OVFUPr1JVKGvog8RUTtktWY1cyo84rpy8nrqevL4nVb0b9La0xhGDRzmL6QrtDEVmemL/uYvpT87b0VyUILkpGlSat2WLR2i7ha+0pVUbFEQexgxhV1NqwYMN1S1vuF+GqxBU70+6Tq9OWjL2+ikVnJJE0taCHfJKDIy0XEIJ92wiTVj2+Kff90nL4ZHaZuELFJZp/890+u3BriMGWJC/9g4cxo7KaRX9qbX5SPgLkPLxiKSs17sBBuzUXJMvfy1kOh0ijcUdnardBu0loxr6I2lTC3WQluXFEXw4qWXsJ7gp5tZRhW6HeI8NBE9YiFlYkHYUwX9AqaJpngN2TeTrk15Bt/iQwXomaLjBvxCuhCQXMrLLsWImKR7J6Mt7JIlP6NPX+J6UdcwvhL1K7E1+mcjDof3z7HRLebUlhFsvKK0siY485CoJFhxZAZlHvN2YZSzFtHRI8vH+ch0Uowo2ur4XO5h8onvzc4v3M5bh5lBg2mmx0nLBdlz/S9lr4hWxD6HQQwT2EiM5oi2PuUSEPHIKNZK4Vf7Cc/ZvwoglzM20OS8hcsBjKapEYauobImUc6X578uryYCAA+NR50vdLwdag4dHUqWZN/J1C4r7jwYJnl/6UQl+y5EwHIy8wkZ2KhSo1QoukAvD+9FZ8eX2aGGn2GlxLBvFOqQqdYGXaedJEZsW652xcxDMPly6u7eL5vAQ+f1njDI+TVM5az5qzNlpd5on1h87qJKcmMDymWRqLJocSFVPk8IjJhgE9hqxLTv8lYzRLnI8+NjCAyjADSk2WK8DUxMgDhrHwOC4exofSD+eVrJPdmSS4M2JY9R/AlIhKR0bHcwCKqNygkFP9jfwMmzAF5y0wY3BvkWUM0Y/QgblSh41IlzLF4ykgcPHkee4+f5YYVefNReXUk0XNPOqAMIr5acg5QlFG/LJ4iTwtZqwVFq/dklZNMk3fSS7KMrOPkJqpk5ZWVZmBkgkC20jAi7DP0DaV/mCNZbFfyZklOX8ggQnlio6OwYOwAMXvCk6HJHUojb5keQyeAPGuI+o2dzo0qdFzMshSGTV+ESycO4hzDUxkxcxkztCxBYRb6zY3tRRTyMYAfuu/egjtXzqGr81iYlywtuqwSezJQKVMHqJM0sZ6d6GtMHEwK6CTpEoXNYo9PqiSP8SVVJixDnnTKtaCeFt6xcGChX2NgxI4lKTz6G/SZp0pyYcB+/voXKw5fg3lBfb4XlQ0MTRhUuzAD44WHb1jIM0ekpx4R3+y812B4NkrXwd8GAFWRYyTD7CJsjcTE30PyKBErmDdf+lc80QRUevgYGSeENKJwXokpIjxhEF7AUPaEPnlnrF+xCGRMWr9isbg4rfYj2s0Aya9cOAvnkeNQspQ1StuU46HPTrofAoUzovBjFP7szs1rPL8sj5rNa5bzd1qnHn14HlX9p/F7HK5MPdBQMR1Q5F5wfTGRrS80ZpGH8jKv2/RSuvXld8i8FPWFGQJkEenLht/6slGGvrgxfbnK9GUQ0xfL3/py9tZj5tF1CO/YCnFjFn7MoXZSfRH9DrXv2kuqWgobV82xNsedCf38CUbGBaWuq8sJGX+VqVe5E00qqapcRKvGZU20Juftl7gvuTLom040IZyYv7zn2gYFERb4jq2WD4WWvrQh+ltkOPNU0eehuWTxu3tsOyhPfGwUDs0fIs4SFUrfP+Bp5C1Tp+cYyFuPjlHCIsKKTbuK+dGBhqYWD3lFeDXR4Z+hXUD6W00qs4qc5GLfRETK0hnCJSIS1cNPMvnft6iv0P19zySrJuOYvO8TeQ0wkvwTH9P7JE863ks6TA+IKJxXYoplzziRlp7sCX7CTbnAjB0UOu+86x9DR0RIIC9384gLXnheQP0eo2BiUYqn3T9zAAcWj+aGy1bD5sCpkzPDdJWeQ/S66M7zNh00VRz2iwxIbUYuwKPzRxiWy4EsNayI2kvPtzIMKyK9yZGMMYwLR4X+EYh6XgaonpgSPC9SH1slNqok5iPvORlG0kNkoKBwXPGRodDQlX4nxDPDBxlACPckI6h8r7koUq0lw5vx5CD0egzsvnCVpjjjbAtD6+q8Cv47wl4o5MEjIq1CxUEbW4mBRxtGIoTh05jXkX5niPKq2j4nW8wgerYl25a9ZrdYz4r9xkXxZBghzeo5SvYVj5+/kTpX9onrIQ94eb9KsZqCzHgyZXg/mXmsihfDDYbr4hv4UcqwEsu8VXyZZ06t6pVklqNEQ+Z1VN66JN75JUzkijLG//gJCu/15MUb5GCgQkTer95x8Pqqv7FURHkNGf6LXZlSeOj9Ej9YOXnzJRiURFyEvSpLQORtQhghNes3k2rq2+dPpM6VfXLqwE4GeP8oxWoKGBVE75FTZOYxY6EeHt+9gaAAXynDShxbLUNphB+SHOkx7BbLMuUR6PtOKgvFUPwf05e3L56KXwbvX3lzDxQb+6pSeYmHlY0dXj19iJ8/fqCEdTl+ncqKiEKREYV88EdMVCQDRo0WXVKZPQ1sBVJMAhQmi4Ddo7/FQxKf5M3HUMT//KUYs3TkdrvwAI/fB6XIwVhfCxM61ZWZx7KIEW4994Mfw0mRNKyQ14xfSAQcy7MBUApkY54wGPX2DRbniv72nR8HfI5AZOx3xMTFI731iJkLB9lGAoTz8NTrAWIYxpOWtra4X+/fvIJo4kGcqMQDCvmxZsn8VGvo1KM3D8WVOKMIoD7AL8EbVPI6YZsQ2VeqIpksdWxdtjw/f+H95/0bwwwlzyKSAABAAElEQVT+RB8C/PnK1tiYaDYm+wHCliDPjs49+/Lron8bVi7hRpbE4cgIiPvw3l2oWKU6yDNHIPWVAOmLdzL6QuOWzCICdF+7NHV96dBdtr6IAOpT0he7FPSltE3q+hLzW18+MH3RZ/rSqYe0vmxctQRGzMAj0pcivz3fyLMvMdGHNI2RtBJh+SXOp8rnwhgv4e6IcFH8ve9yQHXJexbEwoRlJj046cYA7x+nWKUWM0LU6zNBZh4jFhrJ9/EthH/0kzKsEHB8eJAfUsK8yc8mmgtZlmWGmfdSvH+xdwx9/wS/9RZ//8hbj75JUc6Lwn8lpp/xCTpERhaBVEMCBoXNEMAA6r+zb9K8Ep5Nnxg2z68fmfc+IdyRsy5/FiMmJ51qLbrDzNo+yWURjkoY04PEFPTuOU9KCdOkMNMDoo9vEsZqdEwyIQoPZqGsoyMRz8InEZGHyu5ZA2FRrgp6zduOAr+feX5R4t9HFm6QMFnMy1aWSE0w8Jgy4HiS+6+fP3gIRakMwkmWSIDCZBFA/c+4GCl8kuiPb/Hfz8zTBb8rexnAfMrvITKelG43RqactAqXQNjL24j9FCBlWCGvmW+f/GHIcEIyguLCQ/DrewwLd1aZbyKe0UEslG9sJHR+h4B7c2wNKPxX9YluMKlQX5SN7wkrhiguLOX5C55Jxf9lO8OKjVVxvir90s17mC8x/qAQYZdv3cvU20FYJUfPXE6xTvIaSc6w0rFFQ7jsP4adh06gqn3CJC0xI88Rwj1pnshwJFnR0F4dQVtiqtayJy97jw3iRGRd0gJvfPxx6vINtG3yJy6+34cgPHj6AmTgIWOJvPlEfIW96kugeKkyXF/u37gE50nzxA0mQ8SDmyk/u+LMGXTwgGGVXD19NEVuhFeSnGGlfstO8Ni7HacO7oRNhT9GD/IwIdwThwbJu7i37zMEtCWmfs2q87I72Gp7EZmXtEbA+ze4dfEU6jRrK0pGMFtV/JLFraawavny54dkGVEmKjOpX3sMnjwfdZu3FyULezWXQPnihfDgTSBuPvNBkyrW4t7sPHdffJwZB9cY2P1xz4SPh+TqsyxskKxhpb1Teew8fx9uDDOmcikzMYtjDLsljhnXm1RJfjI2N/OyuMFCgCWmM/deout8N8zu1RhtHBLeY3HxP9NcT2L+wnn2kIBNeTt4PbjLvS3qN/nzW73XdVumdpDwHAiDITWq5uAk07BCq92r1nTCXQYA7u/7HsUsSnBWtLr+GAMVp+uEjSKLKPQXhQBLTBfPnES/Lm0wefZChg3RgV+OZCGy6lYui5btOmGty5/xXNCHQJzxOIqO3ZN6pNy5dZ0bZJq3Ed49iWWsbuekL49l6Mu+nZmvLwfk0BfSCVnPPelDFXbtngx98ZBDX04loy8Du7bBJKYvzX7rC+l1/Spl0YLpy+pt0vpylulLBwl9qdOwCTavXgqPw/tQt1FT8aNBHmcPbt+EdVnbJCELxZnU4EDeFehq0JV0NZGwMwhM/e39K2g8eJaYFxkn3j24Kj7PjIN3D6/h2ZXjKVZFXiPJGVZs67fHPY+deHDKDWYSE7hPGW4LYTiUcWiSLO8a7QeBtsS0tm8tVvY7RrjeEF+St55SNRri2p7VeHzhMErXaCQuT14Dfk9vw4RNYGclroS4QcIBl4CplS38nj3A20c3Uc7xz7Nyy31HpkqIDBe3j+9MtU7LCjVlGlbI68bSvibeMyMjhegy+o31RN4oD84d4l45RUsnNchQhWT8oBBgicn7xhlsHdcZLYfNRoX6f775T2yYjXxauui7aDd0DZN6OIj4mLCwgp/93+IZ42Nfr7UoGV+C/BHw4hGMi5UUjCpiqWT9gZ55OUS8fYSw57dAYa5E5Hvxz7hBlKbMfSgDuycA+5SIjCfJGVZMGfaJ3yU3+DMDTQGrP98chN1C2CaSfUupjtSuvfVYh/dntqH+ylvQZu0R0XsGTk+h04zLOfEkHbOEuZHP3teSGFaonUS6xdQX95B3gP3LJTrILvsiJsYY1psNnF32ov/42Ryw/p1fIDYwEPfMpp0r54K2tJJT1QqgbfuB4yDPlqZ1Hbj3yKQFazgeSq/2LcSst+1zx4gZSzCVeb9MHdFfnC7PwdBenXDiwnWMYuGLHjx9iVYNa+HFWx9s33+cu4BOHpawukvefPLUKeRRDQkYmRRB+77DcGDraswfM4AB1rfnXhtHd27M9AbOXOMK2tJKdtUcQduJfTtQgIUFq1GvCfMeeYT18yZxPJSmHXqKWXvsdcHyqSO4kabPqKnidHkOyABz8/wJrJwxmnunODVqCd83L1m927m+9Bo+SR42Qp5sJIHxnerg4LXHGL3hOL5ExqKATn6cf/AaR28wT6dM9ADaOrYTto5Nu2BrlrUAbbvOP2DhurTRqHIp7okzfccZ1LAxR7d6fwZnrufuYewmD26kmdi5rkKVKlKPQoyFzGorgRETpnLg9cmjBoMA5MkT4/L50/A4ciBTdYg8Od6FfkuXHIeNnYTeHVpgSO8uGDZ2MggjglbF06r8HQc9pPpTxboYaNW/b7hiK+F09fRQw6k2x0txqF0PjVu0BuGnTBo5GIWKmGLq3MVJ+nDz6iWeRpPcAqm3BIaPnwoCXp86ejDCvzB9YR6zpC8nj2auvhDGyuvP6dOXoWMmoW/HFuzbrQuGjkvQl02/9cXlgLS+VC+ToC/vviimLzoMcL66Y22cYfhCNZm+NGqeoC9TmL6YFDbF5Dl/9KVKDUeQceUUC7FHoPcNm7VENMNAWMXwXAgPYSIz2KgzZeaYRJXlpGtUGDU6OOPm/vU83FX5um0Q9uE9B3HP7HZ3nsnwJWlLI1mwyWTa7p/YxcN1kTHjwysvnF43Hea2NVCxWTcx53vHXXFs+VhupKnXZ6I4XZ4DeeuxYHWWqt4Q3pfdGe5ECZRxaobvMZG4uH0RxzVqMni2PNUJeTJJAo36TcB9FpLqwMKRHHydwmU9v3UOjy4ckRqvKLs5FCJr5a2wdFXToPdYbB7TATum9EajPuNAuEcXd61i4cF8MWjFIan+TG9eGuQls/p2QshieSsmDJfg9y9gWsoWl/eslVmsZEVHlHVojFosPJj39VM4vGwc8055hPK1WiDE5yU8mR6SkbtR3/EyywuJWSMBMlQE3jgMr63jER/9BRraBgh5dAEfGEA7e3gyrVGVR2wEbWklwzI1QJvf5T3QYJ4tJhUbcE8c792zYWBdDcVqdxaz9r24G4+3TUTp9mNhzTZFqHCVZtyw8pjJy7bfIoalU4AZc/bBlxlLbPvMA2HTEJnY1+feK+/PuDBsF10UtK3DMWA+3jmB4IfnoV/CnrdRkbpVMW+2M6yQkOdPGAo9HW2s3bEPbkdPcyySzq0a87QFa12go51fFe9FkjbR4PfIlmVoO2AsFq7bzjfKVKl8Gexdt5CH7xIVoh9nDsTI9ooSGW/c1szDxAWrsWLLbr4RD818ebFs2mh0bplgsZU3n6L1C/mzVgLkqaKto4uDLutw9ogbxyJp2KYztHT04Lp6AfInA/ieta1OWjvpy2KXI5jYtx12rlnIN8plbcvAEjfu4eG7RKXSoy/21Zwwa91urGMGm72bVvCN+ObNp8mwVZaiQes/LytRfcI+e0uA8FXOLBqE8czQMHIDG3wxKmNWEMfn9kXdcRuho6mhFgIgHdo3tQe6zNuFpQev8I0aXqGkKVwndJECpKdXzX//McSuNLxzFKlHLQQnNDLdEihoUghHzl7DtHEjOBg7MSxVpiz2eZxHizrV+Tsq3ZVkEgOnug2wastOTBg+EM49EzyHdXR1MWPBMtRp0FiqFRTDnMZuaaGl67ZheL/uvB6qi6isrT3WbNstFU5NxJsMK/k0NTk2iyhN2KunBIyZvhw8cw0zx4/gYOzUCyumL27Hz6N1XfXSF0emLys278SkEQMx5Le+0Jh02vxlqJ1IXwhzKa36soTpy4j+3TGJ6QptRDbl7ZkHS1J9WbXVjcl2ONYsnss3ykttWue6n+MY0blA6i+Bxs6z+KrzWwc34tGZfSC8FbuGHZGXrUS/7LpEKiySKveWxlS9Fu/DTjZOu+y6lG/UXlPrCug2z1UKiJvGbP9j75y0jt3krafzrK04vnw8Lu1YzDdqT172Pdl1ritKSoB7U7pAWSsB8rgYteUcDi0di/0LR/DGFCpRBsPWe2BZ7zr8vmVtC+Wv3bpaPfSYtQX75g+Hy6QevCB5lrQZtRBlajSQYsQxZNIw/vJ5epfz+fD6CWiTRaSTZFgpWcEBveftwLHVU3Fp92q+UX7y2Go7ehEqNUoaXUYWPyEtcySQV78gnOacwBOXSfDaPI5XqmNWGg7TD3OA9Vya2pnTkHTWQs9ftQm7cGdxD7w+upJvxJIMGFVHb0UO5qElJvqOZ2Ef2UtBnCTvgWGZ6ijHMFae752PS2MTFm3lyqcNiwa9YNGwj5gNYatUG8+8KtcOwavDy/gmukh4LOX7zGcepOpvlviHvVilpDh9+nQcO3yAu5OKOqzOewKz19fV4V0YPWsZTl++idfXEya+1KlfwZ/D8OT5a9iXK42ChgZKaboIR4VCgJkYG8DGqgQ3RiWuTN58icup+rn/x2CUcmqNe/fuoXJl6ViYGdF2p1q1YGhujTFzV2UEO6XwiI6MgLauPudNHhmel07j0K3XSqlLmUzDPgWDMGJKlbNn3isFlVIV4agQ3kpwoB8MGGCxhZUNk52eUurKTKbtq5fEhLGjMWaM7Lid6WmLp6cnatasiRfbJ6KQQcLvcnr4qWLZ7yxkFr1V82nkRnjUN5ToMR/rR7ZD17oVVLG5ybYpJDwKT32CYWdZBMaJgOyTLZSGC5lVTxqalmVFWk7fAZsa9bFxY9pXKyXX+NDQUBgbG+PAyYuo5lAruWxZms4BAZkSEYg8hd+xK26C5Rtc0L5rzyxtl6KVE0YD4cbQRLB9pao87KaiPFLLT0N4wm4h8G8yqhQpapZaEbW4/uj+HbRp4IiAgAAULZoQrz8jG25lZYUWHbphGPP8UHeKZ7gf9ByI9KViCRMsXe+CdmqoL4QbQ/pip2R9IXwiMqqkpi/kTfby2VMYGBnBkoXO1WDA7+pOW9cux55tG+Dv75/hXVm8eDGWr92EMQe8Mpy3shnGMQDvfGxBGZHHygl4eessJh5+quxqM5x/VFgIgt4+RZFSdkoFh5e3nmjmERDMcCa09I1gbF4qS0Ha0yLsT8y7YFXPGnjx4gWsrRPC2aSFT3Jl4uLioMkWPAxcfoBPxCeXL7PSKfwbvU8IRJ5Ct01uaIFuMzaiajP1AJUWyelfNv4i/BLCCTK3qcTD/omuZcWecFSCmB5QCDAdAxMUKmENTeZNk9X0+t4VrB/eGhEREdBjntAZTT4+PihRogRqLzwH/eK2Gc1eqfz+/fGd8f8fcubJx7xXwnG6fxlUGLIaxWp1Umq9Gc38e8QnfPX1hh6Tf149o4xmz/nFR31BVMBL/tthyDxipAw3EjWSUT/2cwBigt4yueaFVmFL5CtQSCKHehxeHeeIkf26YebMmZINfqn+piHJ7rDjOPaB0ajbUFSxL8u9LURGFQJ8P3/jDsqXsUpUQj1OCxkbgjZlEuGoVCxvzbeU6pE3X0o8hGuqIQHCHhnRuTFs7KtwbwuRUYUA3+9du4CSZdTrJSiSqmHBQqBNmZQ7Tx6ULl+Rb8qsR+Ct+hLYeuoO9l/xwvoR7VDazFjcYI/bz/hxOQvlPoviCjPwgLxwaFM2ZVY9yu6HwD99Eti5dQOO7HPDsg3bYFW6jJgZ4YUQlSmnfu+iXLlyoULlauK+KOOAVqUR4L0I9F4ZdQg8VU8Cu5i+uO93w5L121BSQl8IL4RIXfXFXsX0hUDtaRMo+0mAsEe2jmgJMzbp2nzEQrFRhQDf39y9hMIlEzDh1K3nOsz7gDZlk7z1aBsU5OHJlN0egX/aJXD90BbcO70f3aZvQKHif7AUH19OwP0xVUNdyMnGXxblMn6RbFqlnCt3HoYLU4FvaeUhlFO+BHzObUfAtUPMgLIKOqalxBUG3T3Fj/WKlRWnqcsBeeGYsE2ZpKFjAKOyDqlWQZ4rWibmfEs1sxpmyHaGlXx580JfTwcbdh5EVHQMmtZxQERUFHYdPomgkM/YtFD9V6ep4XMmNFlFJaDBVqTosPjvR1w3IJaBxtWo15TFkv6K04d2ITQkCBOXZPyKaRUVhdAsQQJploBticKYtO0kC8WwF13rVUCposZ4yMDsVx+9gaYMzJ7CggkkSECQQPISIG+LWRNHY3DPTtwzhSaLHz+4h00MRLpB0xY8LFjypYUrggT+LgmQvsyZNBpDGEYieXKRN8Xjh/ewhelLfaYvFBZMIEECggSSl0BujXxsxbg+PA9vYfgfUQxkvTHior/i4ek9iAoNRrtJa5IvLFwRJJCNJFC0tB2OrJiI7ZN7MM+UbjBhxhUCs7+0exXKOTVl3hV/Frtko24LXREkkEQCuhblEbFjGu6t6A8z5pmibWqFiHeP8Ob4eg74TmHBBBIkkJwEsp1hhTq6a9VcLN7giks373KDSn7NfLC3KQX3bSvgWMU+OVkI6YIE/koJzFyzE7vXL8H9G5eYQWU3i8GeH1Zl7bFkhzvsqjr+lTIROi1IQBEJVClthr1TumPPpUdYdeQ6vsbEwdykAHo2qIQF/ZqyEEA5FGEn5BUk8NdJoGKV6ti2zx2H9rgyoPeliPwaATPz4ujcsx9mLFyulDBaf52QhQ5nGwlUYPqyZa87DjN92SShL52YvkxbIOhLtrnRQkeUKgHCAbmyawXe3r/CDCp7kSdffhS2skWvpQdgYVdTqXULzAUJqIoELMpVwcCl+3HnpBsuMmMKgbMbFrFA9Va9GA7IwiwPo6UqchLakf0lYGBVCdUn7IT/1f3MmLIOP2O/cgB283rdUL7XHPyTI2f2F4LQwzRLIFsaVnS1tbBg4jCAbZHMa0U7vyZyMNcjZdHZK7cQFROLji0aKqsKleFLIdVokjBvKnGGKbZ4zpw5QWEqBFJtCWgxQM7Bk+fzLSYqEppa2krVl9uXzyKWrQ6r3zL7A7ZRSLUc7CWswTzpUiLSF9IV0pnU6HvcN+TOoyFX3tR4CdczTgKNKpcGbRQfPjruB3Tzp3zP5a35/IPXiP4Wj3ZO5eUtovb5qL8/f/2LAjqaKfZFnnzxP39BI3fqQ51fDBQ5JxsnCO+sFEWu1Iv1GjUFbaRDMdHRIMB3ZdLl82dYPVFo2U694iWnRSYkU3nHwRRf/UtYKAfrTg5TQhjjpeUuZGyZukxXaMssfbnyW19aqLG+xMbEIL+WVqo3Ij4+Xi48lZ8/fyJ3bgkQ2FQ5CxlUSQIEUt9kyGw0wWzmtRKJPAyYWN7fybT049Xt84iPjYZt/XZpKa4SZeK/xUBDM3Ud+vfXT+SUBEhOpvWEhZFDzvkCeetOpiohOQUJ2Dg0Am30Pon/Fg0CfM9sen7rPL7HRqFiw/aZXfX/2bsOsCiSrXv+XSNGJAlKToIBDJgQxKyYMGfMWUQxB8yYc1bMOWDOCirmHEFQEERFjIA5rLvvv7eGHmaGAcYVXfe9ud/X093VVdXdd+ZOz9Spc4/2fFoPyD1QuEwt8MJ6IF8/vUN2nfzyY1m9wfonT68HQ69YReQzsc7q7n9Kf18/vReAE2umaGJ/fHyHv75+Qc58hdKtrmmff9FzJj09l3Q7/4EHMh9t+IEn/xldM8jyo23W8vWIeRj/rwFWjp+9DL8JszJ0S5kSxbBq1jh5nf3BpzB6xmJERseKgSdLUxNMHNIXzTxryOvwBoNM4+Ysw52oGOTPmwcelcqhZ/vmaZhCyzfuxOJ125TaSjtTh/ugbjXtTCHJHz9zzSDLj7ZNS2cjPi7mXw2stHYvjtKV3DFsmvpUaWeO7ceyaf54EBUp4sXY1AK9hk9EtfrKf6aO7tqMneuWISr8BvjPhYm5FZp36gOvDj2U/tzxD93AGePA/cZFR9KflWwwtykmwLAKVWv96LdM2/83eID/lGcVqMKnnb/zFGKfJv6rgZWtJ2+g15ztCF81FCZ6GX/HJL75AFff+civkwsXFw1I1/MZ1Ut6+wFDl+/DhTtxePzytXg/qjpZY3S7WrAtqizcx8BVwMZgRD58hnw6OeFeyhpd61WAawnLdM+tPfBjPcAx9KNBFb6DpfNmIi72/r8aWHEvXQyV3Kpi2vxlat+UTWtW4OCeHbh49hQsrG3hVq0mho0NyHDQeMnc6Zg2fjQWrd6EBk1aKPXLYNSsgLG4FxGOvPnyo7J7NXh364UKru5K9bQ7P88DPytels2fiYcUL/82YCXs5jVMHz8Kt65dwRtKdatvYEjp0hphxIRpBB6mDpgkJyVi3FBfXLlwFk8eP6LvoIJwrVodg0ZPgJVtaq51Bn0njvBDaMgRsLh9SeeycKtRGz5DRiEHae9Jxr/bGriXA4OQqlbUzByrtu1TLdbu/4MeYJDlR9upjfPxKj72XwesxN+9iSNLx+Nx5DVKl/ZaCNE7unmiXt8JyJUnNYZ4QH7fvBG4dyEE7xKfoYi9M2wr1ED1TkPAGhOKxiDTscAAPIuNpD7ywbqMOyo27ZqGKcSA15FlE3Hr+C58IDH17Ll0qK4bPPtNgoGZjWKX2u0s8AA/T/4JUIUvnVOPvaT4+DcDKxOalYZtmSpoM2pBmnfjzM6VOLVN/W+1xj6TUNxVNkGbnx3TO1TBX3+mfXYUMjaj/1NBafrWFmS9B1gP5EeCKnzFb59E4/qyQSjdc9a/DlhJuHIE4ZsC8Db+HmiwC3kMzVC8zSgUqdQo3Tfj89tEHB9STfi15uzTaepp0icDM7fX+OPZzeP4lPwcBa2cYOTkgWLN/AhkUX7OpDnBDy74cTSOH3zh2u7/vgeYRJKdBmbVLX/SrF0GT5iBI9m+Y6Fo0Wso+BgzgWaOHkjI5G9o5zMSwZRuTbKte4/Aq5sfkl+/hV/39qhH4MjB42fQtPsgRMU+lKqJ9ZVb4Xic8AwGhQqmWRT/mCg10u5oPfALeIDTpTEwlJ6dProPI7u3FPHCTCCfMTMESDKmT3uRbk1qd3jHBkwc0IU0bZLQoks/NPHuiY/v32POmIFYv3CaVE2sJw3sig2LZ0BX3wBdB41BlVoN8PD+XQz2boSzwTJBNaUG2h2tB34RD7z58AkLCBzS1HwW7MTTxLeZVk+vHrNYGvmvwo7Tt1GpuAVGtq0JZ5si2H/hDhqOXokXye/kfQeduolWE9eJ1G39m7qhdrliOHI5Em0mrUd0/Et5Pe2G1gO/oge2b6R0BTTQnZ5t27AGIwb0xlvSGezjNwx2pFuzasl89O3cRu1gL/fDOh0zJ41V2+WeoC3o3LKRSNPWs/9gwZYIOXIAXVp7ISaa/lhpTeuBX8wDt65fQbtGtRB24xoaNW+NfgR+5KMJRFvWrkAHL9nsbL5kBkvaNaqNfTu2wqVSFfiNHEeASRkc2b8bbRrWxMsXz8WdMUOlRV13kbKwfGU39B86Gl/++IJFMydj/DDliQAJ8Y8RGX5bsIsL0W83xaWgbvozNX8xF2ov53/cA48jryOwfyPE371BgFBzAZLkypsfl/auxUpfL8FwYBcxQ2VJr7q4emAjgSOVUb3zUHrO/IETa2di39xhSl68cSwIa4e0Epo27m37w75SbUScO4K1Q9vgxcNoeV1mT64b3g4Xdq2EXlEr1Og8DEWLlcZdAmWW9yVN0FfP5HW1G1oP/NMeuLh/I14+Tn98IC78KpKexSNvQf00SzYFUD75eTyeRIcLFoBqXZ38uv/0bWrPr/UAnlw+jAszOxGr508Ub+cv0qTRBxaX5vbA81uh6Xro+tKBYJaOOtOkT2aonPJvQKnaNkPPoRKBKYME++Xuzrm4ufqf11H/r2esqHvj/tfLqlV2weUDG9S6YeC4mXhLoMrCScPF8T8ojcqgCbNhXsQYJ7YFQp+AEDavutVg69YYC9dsRc0qFfDlyx8YMXUBWM/mwr51KJg/n6gXMLQfrFwbCBDmEuXulOz+g8fg69i+dLpUpF1rPfDLeuB5wmOsnhuAiJtXcT/idrrX+ZX+dM8bNxiFi5ph8Y7jKFhIX9T18PRC80p22L5qIVzcZCyvLcvnwdTSBsv3nEYemvnL1q73ILRwLSZYLB37jxBlCY/icGz3FlRv0BzjFq6Tpyq6dfks+javiaVTR8O1Zn1RV/ui9cCv4oG1Ry6D2SCnbt/HO0qNpomtPHQRwdfuoWDe3BlWz6he6M1ohMUmYFavRuhCzBO2IaiGocv2IfDgBQGwdK5bngbDvmLM6sPIkysHTs3piwIp5xzfsQ4cu0xDZ8o3fXpuvwyvQ3tQ64Gf7QEerJ07bSJu0uz7iLBb6Z6eZ9yPp1n15Sq6Ysu+Y/J0RTZ2xUT73ds2CdFzxQ54cNmnawfo0Yz+508TFA/Rb7wvCPAfBp08eXAg9DIKFJT9FhwxfgoqOJgTWNMWh05fUWqj3dF64J/2wLrAxfj08SN2hZyDY0lncTkMmrRvXBvnTp3A4b074enVHGdDQyiebmLirIVo16WnqMcgDDNYuI+jBLC07dwDm9cE4u6dMPQdNEIwWbii7/AxmECxtmbpAnjUrIta9WWzNSXQc/bSNXAo6ST61L5oPfBv88D5HYH4+vkjegSGwMS2pLj8Wt1GYoVvY9y/egrhJ/eiZHUvXNqzBs9i7qCa9yDU7jFa1KvZZbhgsJzbvhT2FWvC0a0+vhIQeWjRGKFn47PqFHLnkzGFOCXbFC9HbB7TGf3XyGYyx14/i9gbZ1G6Tiu09F8qd13I6mkIXjlV6OJ4dBgoL9duaD3wsz3AQMnhFVMRF3ENT6LCMjz9CwJd7Fyqovv0TRnWe/lIBs54j1uOInaymMuwgfag1gM/0QMMbtxaMxo6+qZwn7AXOfPribObVGiAw33KIvpgIAxLVU1zRTFH6Rlx4wSy55H9f1CsoGmfD0LW482jSNg38YVja9k4mUOLweJ67h9aASPnGjBxqavY9U/d/uUYK5duhKFeh74wdK4uFo8W3XDk5Lk0Tgm9cBW+Y6ajeI3msHZtiA6+o8HppZhVIRnPFu82ZDyiHzyiNDwBol6ddn2wafchUWXeyk2o2MgbRV3qoFFnX0QrsCoOhJyWl02cFyjqGVO+vcZdB+JeTJx0inTXyW/eov+YaShdtzXMytdDy95DRZos1Qaa3q9qux+xfyT0PJZuCMLq2eNR2EAWJGev3MDDJ0/Rr1MrOajC5y5S2FCAIt1opiJbBLFcnjx7gboeleWgCpcb6hdCTbeKuBURJfRuuIwt+sFD2FmZyXa0r3/bA+HXL2FA23qoW8JILL2bVsOFE0fS9Hf9/CnMHu2LNlVLoGkFG4zr543dGwKV4mXasN4I8OuOxw/uY+rQXqJe/9Z1cGSn7AfAlsB56Fq/EhqWNhVMiUexqbOKmDXB7AkuWzVnkqjnWcoEQzp5EbMi85msbyk9w6xR/dGhZhk0KmuOkT1agbVYVE3T+1Vt9737Hygv96OYaEp9UgDFSpVNt7tbl8/hWfxDwUCRQBWubFC4CCYHbkOjtl1FW9ayibkbjgoedeSgCh/QNzJB2coeeJOcSH8+/hB1w66eF+t6zdvLQRUuKOXiiiKUOozTjX14nzoLX1TWvqj1wJW7j9DYfyXM20wQS51hy3Ds6t00dc/cjsHgpXtRttdsFKdB9q4zt2AVDfr/+edf8rqXIh+i3vDlOBMWK455jliOkl2nw2/JHjx59RqxCa/QbvIG2HlPRmWfeZgddFLeljc6T9+MWdtP4mJEnNi2bh+Aiv3mYu6OUPkMQKUGKjsHLt6B15hVsOkQgKoDF2H0qoNgdoiifSLAe8qmYDj3mAnDZmNQhqjGAxbtFpotivV+xPZ9uv/k9x9RysoE+gXyZHqKCErFxfcwrmNdFNaVAfPqGmVW7zz5k62pWyml5i09ZINqz1MYK3cfPUdC4hvUKmsnB1W4gUHBvKhe2laAM6/fK/tTqUPtjtwDnz59wuzJ4+HmbAcbAx1weipmSvBAvaK9ef0agQvnoENTT5Q010fT2u6YMnZEGoBgWP+e8OvdBQ/oO3eoTw9UcLRAa5o1vnPrRtEd91G/anmUtjaGd7P6iL0fJT9N8KH98rI5UyaIeqUsDNGpRUPcj0ob6/KGKRuvk5Mxyq8falZ0QlnbIuhBqUw5/ZWqXb9yEW1pcLaEmZ5Y+F5OHEv7zFJt97377969JXZIlJh1X6p0uXS7O0wDwez/7n0HyEEVrtysTQfRZi/NzFc1/8E+9B1Hk2lo4FnVoiLv4FnCE1SrVU8OqnAdTqvkXr0W7ty+SWmWXqs20+6r8cBnipc5U8ajKsWLvaEOqpUphpHpxMuKRXPQsZknnChemtdxx1Q18TKC4mVwH1m8DKd4qUxMvbYUL7tS4oX7aEjxUs7GGJ2ap40XqWzu1AmiXmlLQ3Rp2RAxGsQLp9byH9QPdSo5obxdEfSieGEtFlW7QfHCYIaTuZ5Y+F5O/oR4uXbxvABUJFBFuq7m7TqJzRtXL4v1lfNnxbpBU2XtP6+WbUX5y+eyGZZnTgaL/cYt2oi19CLV45R7kj1I+V6ytLGTirTrLPTAw/ArYnB/XB1z8LKkVx1iMhxLc4aYa2ewZ9ZgzGxdFlOaFMfmsV1xYfcqSrOTOl6wY2p/bA/oI2ab75jqI+oF+jTE9SOy78nTWxZhQZeqmNTABqsHNcfLR/fl54k4c0hexoP9XG9CPUusGdyS2BepzyZ5A5UNTq21e+YgzOlQCQEN7bB+ZHtwmixV0/R+Vdt9737c7YswJkBFAlWk/srWbyc2H0VcFeuoyyfF2rm2cvrI0nVkMRVDIAnb8wd38eZlArFUaslBFS7Pq2sA2/LVkRAdJvRuuCz52SNepUkPZl1WNmjHmitaU++BB2FXsLBvIwytbiqWOd3oOX1OOT4+Upq145sWYnH/phhWwwxcZ8/CMYhXAQg2B/hgw/heeEGf+00B/TCmoQMW9GmAy4dk8cF9TPd2x8g6Vlji2wzPH6bGx+3Th+RlBwOniHrDa1lg6cDmeBaXeXx8eJuMbdP8MLl1BYyqZ4MVQ9uBtVhUTZP7VW2TFfv8GXxOLKvclBLPzKFMhl2+IL8YmdlmWIcPsp/ZtKnuhBs0fvnzyydEbJuOIz7lsbutKY72r4jryweDU0cp2ovws7ixcjiO+lbCod6lcWleL8QcWyvYF1K9V/eu4NSYRnhx55w4dmpsYwEa3Agcio+JCXj39IFgbBzsUQLBgz1wd9c8qalYX5rTQ5S9unsZvH2gmyOC/dxxb88Cod2iVFnNDrM3zkxqQe2K4/jwWri9fhz+oFSLiqbp/Sq2yYrtV5EX8fHlY1h7dpeDKtxv7kLGqDh4NSxrdkhzGgZDbq8bJ9gtuXSN0hzXtE+JDVO0SjOlPkzdmov9l/R+/ZP2SzFWOAUVAx8WpN/Rn1IV5M6VE7uPnBRgxt7V81DbvaLw1cnzV+Dp7QPWT2ndqA70dAsg5OwlAWTEPorHFNLoYLsZcQ/xCc8RcuYSChCDomrFsth+4BgYlNlCaau4nIEAsyKFcYi0Qep598Pd0N0ibU9cfAKOnrqAln2G4fPnL2hYqypevErCXkqLxWDM+T1rYW9tIc6j+sIprmq06okXiUloTwMI+ek6j1FfTSgl1vSRvvDpIvtBrun9qvb/I/ZfJb1Gz2ETSSemlmCSSOdgUIqtQS13sHD99fBIWn+Ck6Md6tdwk6ohgUAVNhen4vIyacOllKMAle7ci0GlsqVEP89eJsLW0gxXb0Ug7G60AGNcnIvDxMhAaqZdZ+IBHlD3bV0XrN/RsqsPCaTnxqnDewSYMXPdXkjaG9fOhWJgO08awC+AWl6tUEBXD1fOHBdAxpOHsegzcrI4U1T4Lbx4Gi/SVeUj0eAylasiZN92MChzbM9WXDkdgorV6sKoiBmBHocI0PHE9rORIl6ePn6Ii6HHMIoAkS+fP8GtTiMkkfjt6aN7BciyYv850gSxV3tHzAbp27wGkl+9RN1m7cR1Xjp1DMO6NEU//2ni3rihpver9iTfWWhhWwwLt8t+kDLwxACVOnv8QAY2VanVECxcfy/sOq0/wNaxlBKrhDVSFm4PhomZpVI3DLhER4ShvHtNykecXRzLnSevSBPm4Kw8iMYi9gzA5MiZS7z3Sh1pd9J4gAfSG1EqKHMjXfRu5IrcObNj3/lwtJywDkFjO6JGGdngx+lbMfAau0pofLRwdxIC6idvRGMQAS0PniVhQifZTAjW8bhAg/ijVh6E6LtycSRS2erDl3At6jGekLZHrhzZ4VnBEacJqJm4/hgMCuRFh1qy9zH01n3cuP9EaKhUKWmFTnVccJzOM37dUdx/8goLfJqmuQepYMbWE5hMgImLvanQA4mj62IWx/HrUdgxrjOM9WQMKL7mLSeuo5VHaQI4jPGAtFrWEJPkTtxTHJ3eS+ruh6wlP3HnPWZvw/bQm+mehwGgbjO3ohINoPdqWAnrjsoGu1QbaFLPm/zbtErJNKyXc+Gxors65WTfQwkp6cbK2pmqngZlbIsKtg3rrvBsfK1l7IHRNLC6Y/N6NG3dHsVLOZMOQww20czuyPAw7Domm3nKPfTs0JxmiZ8ULIo+A4cJ4ITrbVy9HCEXb8PI2EScKPzWTTx98hinTwSjAOkcVHbzwL6d23D+dCj2bN8syqvVrocipmY4fuQgARx1cPZWtHgWPSaGX2jIUQJEWoAHsOs0aCzS+Bw9sEeALPtPXgSzNtQZs0Ga1/MQwu3NWncQ4MWp40fRpVVj+AfMQNc+vqJZ1N0IAnpqwdTcUpTlomfv4X27BXizbscBVCW9hR9ltvYO2H7wuOiegaeqZRzUniqWwBe2Kh41lI6ztgOnW711XTYYJh3cTX7dRSyWbQdC8OK5LO2RdIzXz1IYLM5lXRSLxbYTlTH4dC8yHOUqVE5zXFug7AEGInZuWY8mrWTxEvcgRs6E2HE0NV56U7ycP50SL5TK7cH9aFFv85rlOHohNV4Y1EqgeDlD8cK6IBUpXg5QvFw4E4q9QZtFuUdKvJw4elCkwDpFzD7OpR9P8XKK4qV3B1m81KrfmD7/z3GM4qWhR3nsPXER1hnES0uKl8RXLyj2U+KF+urWujFGUbx06S2Ll2iKl7aUjovjhcty5ZbFC4M3a4IOwP0HxQun7eK+ncqk/cwmxMv+2xTU1RVvTivvLqjftAWBhrJ96R27dE72flSr4ymKnickIE/evGl8UsKpjPDnPfr9JtmDmPswoe+o9zTx5Wzocfoeeia+e5zLVRDpwaR62vW3e4AH51dQeipdE3NUadUb2XPmRnjoPqwZ0hKdZwXBjnQ92O5fO42VA7yEDggP+OsUKIRoAgD2EJCRGP8AnqQRwpYQdRuvXzxB1KUTYrCfNTxuhexEzPUz4LRV0ZdPEOOiNgoWNsXdc0exgvocuv2meM+Tnj7CvYsh2DCyA77SwJ6je328S3qJO6cOEMjigX4rT8DQXPb7UpxM4eU1pfxZ2qce3ie/QhmaiMkptrivdUNbk4ZIgLg3rq7p/Sp0nSWbnN6LfVlUzYDxa5qpz5Y7nyxm3hJYkiN3Xhio3KuJnZNIH/4sNkLUZ1CFzdQh7SQ1U8cyIs0X666Yl6wA+8p1SFMyO67sX4+ynm2FyD0DYpcpMwabg+s/NzNZXMAv+vI09q4APvQoPqq16SPi4yYxixjM6D1vBxyIPcS2Ylh7RF05BSuniqjV0U8M6J/btQZnd67GqK00dmZgLOo9vncbnJ4qkuJDh8YSbMu64dqxnYi6ehpXjmzDXSp3rFwbhSg+ws8ewaJ+jTB2921ZfCQ8RMSFYKwc1g5/fPmMUlXr423SC9wOPYAZBMYMWRsKIwv18cFskHk964p4Ku/ZRsRHJOn3LB/UEl6+k8W98QVqer/iZrL4pbClPXyXySYTMCAysbl6cOXzx/d4m/hcgCUPmd0SfUd811gULyf3s3RpzGzRJV9ym3tXQinl3XMY0Xm47m+//y5V065VPMBgycPQbTBzb4GCliUI/IiDYDg8jEDVSQdE7RdhZwiwaCk0PkyrNEUOElB/fjsUN1cMw4dncSjRfoyo98e7JLy6e4nAgLF4+/geTCrUx5e3SYgNXoek+zcIXHmC37LngnG5umCg5s6WKchZQB8W1dulnOc0kmNvEpCyEAYlXGFRo71IkcWaJO8SYlGGJm6mZ5E75hBANA2FbMvCqnZHvH/+EDFHiO1x8yRcR24hAKOwaKrJ/aZ3ju8pf/dU9n/auFwdsMh8cuxt/Pn5AwpYlCB/1EnTNQNAlwm80neoAOt63eg92ZCmjqZ9fkx6hmy58iBfEWWAsqAlTaakVGQM4PyT9ksBK1v3HcXHT5+xetZ4OBeXDX7079JWpJLauOuAHFjZRvWyZfsdESd3ytkRg3t5w97dC8w0kYAVdiwP4I/z64XhfTsLP7dqWFsANacuXMONI1vE4D4fYGbLhp0HcT/usbyMyzkt1pWDmwSIw/sM4DTo2F+kvdoZOIuL0tjo6YvAwMypHStR3lk2ADvGtwcaEitm5HSimRPYUqhgAWh6v6oneJmYjGXELMnMmtStDkc7q8yqieO+Y6cj+c07IUiv2ICBld/pSzwsMhreA/zx4eMncZjLetGfvmkj+tN7kQ1W5kVF+QkCvQZ0k32pSP0wm4WNBe0ZWLkfJ/tDM3FuIOKfpv6Bz0GDkCP6dsGIfl2kptp1Bh4I3ruNBo4+wn/uKtiVkM3GbtWtP5pUsMbhHRvlwErw3q1C7Hzr6TvIR3+62dr1HoyWVYoJfQ4JWOHyRPrj123wWHT0Gc67QmCeWScMrqwLvg4zK9kXGTNbWCOEQQapjOu/p9m0nAIoL+WwZmMAx699AyyePBLTVu0QZaovS6f6g4GZpbtDUbx0eXG4q58/Bnk3xJIpowTYkr9gIWh6v6r9Jye+xC4SiM/MPDybwNLOMbNqGR5nxg7Hxv3I2xjv05FSUHwQ9bmsiXcv9B09VcRLbp08xDhJHYDatnKB8AEDVn9Rvsr2fYbIz8N6KryoGqcVY6ZPjYYttH/SVZ2jZn/HqVv4SAP4ywa2QClr2QBuHwJYOO0Tgw8SsBJ0+iay0YDTDRKTk9JDDWjmDqfuM3H4UoQcWJFO8SzpLW6vGCKYDpwPuvbQZbhy7xG8a5fDnN6NxR+LuGeJxBqZhdCb9+XACrdnoCOgiyf6NHYV3Y1qVxONiYWyIfiqAExYG0TV7j1+gWlbjgumxVZ/bzmLqXW10mgydjUW7z2LiZ3r4TOlutpGovEMJCymGWSSWRQuhBErDggNEZsi+lKx0nrvuTBEPEz9blY6mLKjl18H3Twrqjv0zWWckovZIzvGdZLfj7pONKlnb2oob3r57kMBat0kAGvf+TsEMDkLvRWuYEl+YDtFAFc/rypiW3phoIwtknygBVYkr6hff/78WcyMr04Dj7MWr5RXMrO0wvjhfkJ7w4pmbDPbgUGV3gOGYPi4yfJ69g7FKWXVIFw6fwYNFWaLv6AZ4oNHj4fP4JGiLusjMOvkPA0WB1+8Be6TjZktDOowyCCVcfm7t29w5Ox1GmiWPYvOnAxB+yb1MHnMcKzaspurpLGp40bi8cM47A4+g9I0+MnmN3IsMWA8BbOG2R6sjbA3aKtILzR32RqUcCot6nUjZgh/Vvha0gNWEmnywLoVS0T9jF48GzWFHfnleywm+i5y6+gQyzKfUjc8mG5uaY3oe5GCscrPpocPYoml0xcMdrFuxIHdaZ/V5vR+snH6pB4+fkp9RkVGiP17EXe0wIqSZ9LucLzs3rYRPFA/QyFezC2sRDop1qqR4oVBlZ6+QzBMIV74czFx5CBcpnhRZFcwo8Jv1Hj0k+KlWWvBOmFw5ciF1HhhZsvOdOLl4BnleOnYtB4xZIYjcLP6eJk+fqQAZnYeOwMGC9gGjBiLThQv04hZw2CLiJcdsniZTfFSvJQsXrr0GYDKjuYCYEoPWOF42aBBvNRNJ16y0+SUcdPnpXkTWC9lPfXL/12q16kvjtsQYCnZ9csXBKAVfvM6Aaa74NWqnRCo5+P8vca6LbwoAjax9L6x4PA9YnZJFkffSe9I38itlLX4vpDKWex+1tLVUDyndEy71swDN4N34A9KT9XKfxlM7GTs1Cqt+ohUUtcOb5EDKzcJFPnt92wYsu2GnB1Rtd0ATG/hhMizh+XACp/1HQ161uo+CtU7DhYX4VSzmQBqmPEyYP0F+exxZrZcO7QZr2jwU3FG+SeaTTxg7Rka/JU9cxjAWeXXFIcW03+qaZvV3tjhJeORTMBM72XHYEaDpmw1u47Aar9mOLxkLMrUaw3WVND0flVPwoDNhV0rVIvT7Jeo2ghGVqkxIFVgUKPRwOnSrnz9LukFzu9cIXzr4CobSCtUxJLE7a+LxVQBiHn5KFrM0H4WIxvw0qN6bJxGzK1NP3mfvPGcAAE2CVjJQ0BY7R7+OLp8IiY3thfMFWbQsLZKxabdYJriM9FI+yL3wNWjQSI+OoxfDlN7J1FerW1f+DcohksHtghg5fWLBAGq1OwwAI36jZe3NbZ2xM45w3H/xjmUqZX634FBgfq9RqNOZ9n/07IEVDJQE03xMXLLJRia2Yg+mNly6eBmwf6SyvjAp/dvMXzTOeROiQ8GYxb3b4I9C/zRY5aM+SK/iJSNfYvGIZGAGb+VIbAoIYsPzx4jiQHTFHuJWVPeszX4M6LJ/ar2zfvvKD5OBwWqO6RU5ly9MYzVxIdSpUx2pPRehwInE0j1RF47W/YcqNNlCC1D5WUMrLC/xjUuId5H6YAp6Qt1GLcMDOZoTdkDf/7xGY9OB6FwmZoo2yf1uZ+3sDmlifInkfj7Qhz+8dld4nur9oKLyJFH9l1t17gfjvRzQcLVo3JgReqd9UDqLrpCoIkB+D9+qH99JEVdEwCKc/cZAjR+/zwOR30q4MXt03Jghdu/J6CmpPd42NTvKbpzbDVcsFDiTmyCJQEmuiS6rmpv46MQGTQTRqVroNKwDfL/xGbuLXE2oCWiDyxDyQ5joen9qvbP+/EX9uPNY9l3rbrjXJaTACerOrKxc9U67xJi6L5/xxsCrC7P7y1AFa7DZdymRIdxwsdSu7ANE4SuiuuorfL7kY5Ja037zGNkgWQCtnjRtZH9nuQ+3iXcBz1oCATL+L6k8/2o9S8FrPCPUrbATTsxgwTSdXLnohQG2RB1eo/4MEtO8O3aFn06tpSDKlzOGh8FC+THm7fvpGpizX8iWUhdspIOsoFhj8rllAAU9wplBbASQYP/zKSQzKdzazmowmU1XMujYpmSgu3CAfZ/rASvYInJrwUbpmxJBzmowocZNOhKabOYLcMsHJ4Bqen9KnQvNl8SE4bTk2VmfB+aACvMJAk6EIxhfTrBzKSwUrcSY6V9/9GCHdTGqy6+0myRmUvXYtGardAlJtBo3+6wsTBFmZLFcOLcZazaugct6tfEX+QfTru242CI6FNKo8PgFRunG1sxY4zw934CxJauD8L4OctE+jD2ldYy9sB/UuJlz8ZAIZCeK7eOYDkEnbtHDf8jb9yqmy+adeojB1X4AOe4ZZCF//QpGsdL256pgyY2jiXF4bKuHkoASulK7gJYeRAVoVTesms/OajCDctVqY4SZSsSwBIiYlg1XphxwfohnF5LAlW4XXaaUctpsxjQCT1EMyfbdJZTJzO7X26vaMyE4fRkmZkpgUbfC6w8TkmPNrZvB8EOqtOkrUitwsLzQasXCZ93GTg6zaUsnz5WgGR8wMLWgWZ06qSpIxWw2P2cMX7Cb2bWdvAdP0s6pF1n4AH+PmJbRYySyd08oZMzB7ITQM+gSMohcbxv4yroQSnvJFCFC798/VMwIFRTbfGxDrXKClCFt/nz7WhhJICVTrVdBKjC5eZGhWBqUFAwW3hfsvx5chF7JhVg4/gb1NwDZ27HEnslSg4CSPV5LVKSUewzqKEYTx7ONmCghAEkBlb+TPl+4L5uEbAggUk96lcU4E4ueramZ7vO3Mbus2HpHRblfK6sAFYOk1g8a5+sH94WhQvJmDbqTqxpPcW2zBQK2BhMz1rZs9rMUBd/0HvJ77u1iZ7wL4NdzJBpUqWUeGYxGCXdu+RDxT6128oekNKp8ABuGA1ESkBDpx590bpDF2LT5RIN8pKOFLNXrG2V/xBK33UssK5oHAsskC6ZYwnZ4JmrezUlAKVSlaoCzODBfUVgpWvv/nJQhftg5kbZ8pXE7H11v92SkxLBrA1OryWBKtyO2R1tO3UXbJlDe3ehTceu8t9uzLQZM3mWADB4EPdcWAw9elOfvdxe0V4Ri5PTk2VmfB/fC6zwbPn0BLKZtcKsGwaf8uTNh/7dOsCSniUDR8hm6qm7PktrWzG4zDPvN69dKUAw/g27i0CCA7tlE314UoDWMvaAFC8XKV7Cb12XAw3eFC8tVeKF2StWKvHCYBkbv3eKxvHSQyFeHFLipbJKvFSkeGFghVkkivHSWU28lKF4YdZYevHC7DGOFwlU4evheGlD8cLfBwxKtPbuKv/ttnHVcvgrxAun3OS+07NEihdOT5aZcaotTeMl5PABDPfpLlhpY6bMRrHist+4iudgQGt2wFgR5/yMLWpmAWa/cIw3IsBqP7GB5lIqNwayGCRhkGXSSNl3lfT+cn+sscKTjQb7T0TtBl7g+wnatA7bN6xGj7ZNsf/UFaFZpHhu7bZmHpD+/1yklF71+09Gjlw6gtkwbMdt6iD1M1WldV9Ubt5DDqpw739+/UL7BSndlHIM/R/FEAupS2ZsW0JsWpd1VwJQrEpXEcAKs0gUgZUqLXvLQRVuaONC/5tKlBdsF3Ux9OFNErFhtgsxdglU4XY80Fq+cSfBlmEWjktDb3kMZXa/3F7R3ie/FFokimXqtvVNbdQCK+rqRhAgtWOKD7FsXqKB7xQUtpZNAnCiQXhm+QSvnILa3UahSDFnAbLsny+bHMEix2x6Ra1RxN4Z0VdDBfOkVI0m4v6uE/Ph9gkZiKv4LNE3tUI2YiQxSHSf2nDqNDb+DDC4xu+91pQ9IMXH2Z2r0HTgFHl8jN8TLv/OzZUnHwauCIaRuY1S4xy5cot9HthXNI6PGu195UVFUuLDtpy7HFThg8xmYWDlKQFpisCKR+s+clCF69mXrwaLkuUFC0ZdfLx/nSjYMJxeSwJVuB3HR2WvToItc/PkPlRu3FEeHxndL7dVNWaWHaL0ZJkZ38f3AisMlrDl1yuM9mOWiu+OsNMHBbBzYFkA8hUyFPfFdRiE+UxAbYNeY1DKoyEBQC9xcf9G0jBej8AhrTF0/RnkzJ2Hq2otxQPS9wungmIGRUFL2bPdqk5XmFdri99zyP6L2DToBStiTUigCjdnfY8ceQtSqi3lZwIfs6jeVoAqvM2/BwqYOsiAlZodBKjC5XkMzZFbvyjexPP4W6pl18lP6bJ6yAs4huy9fPGSGC7Pb55UC6zEHJWlJLOq3VnpP75hKXfkNbYGA0MMrGh6v/KTK2zEn99L4MpehZK0m3yu9ICV909ln+VLc3uCWT+mbs3wHxobvrdnPljnhDVUWPeEjcGqmCOrUGHQKqhLASadWdM+TV2bIP7cbkRsnwGHVsOED5lBdHut7P+L5Bep35+9Tn9k5WdfCZ2vW5umYDbKSppNyKm6XF2cSRi9PBrV9oBFURP5FXEKLk5dNXcFfclcuy3YIQwAMLvE2FBfXo83TIz0BaghFeaiwTS2Iiopp37//TdRzoK2imZnaa64K7Ydba1w/uotwbYoamykdPxezEOx/47SZrXzkf2YkCrw9bHFPHws1prer6is8ML3nxQeqlCifjNHMKT0QgAAQABJREFUSioh9UdTS2ctXy98xICVqiUlvxGzGpvWq04i2aPkhysQE8ekXG0hXs/ACv+pWz7NH027DRKppQZNmCUGs/4i9JBBkhWbdxHIYynaV3EpjaOblqAcpQhj8IyN8zJ71amGEqSZw6CNFlgRbsnwpVG7rsTi2Iq9m1bRIPtWlCrvKoTR3evQzArT1M8tp+B6nfQKW5bPRdi1i8SMiBNaKB/oD5+eobHSOVjjg0ENyTjNFJtB4dT4431+v9kYoFE0Mys7xV2xbWnngNtXzos0Y4bGRZWOS/orHylH6Zg+qQAoV/qQ8qcnPk72Ba7p/SqdgHb4/oPvJqoWp9nnH2vfa2+Sk0S8eHg2xfDpS+XdORITp4FzEQGuqANW+PqY7cIaLcunj0H3RlWw43wUvT+pQCf/8NyzcQVWzBwvUoA18e6JXpS+T4cGxrSWuQc41VbQqZtYS4PovOa0U9UIjGhQ0RFmlB5MMruiBkh88wELd58B66g8fJ6EGErN9fbjZxr4T+trcxqsVzQJsJDScUnHfvvt/wRAI+3z2tpYT+mHE5cVMzPkFWm0qP/MMmOFbWPIVWw+fk1sSy8fP/8hmB+cMouBo2GtqwtgoarfIvB9uVHKMdYUqVHGllhOshiW2iqumdWz2Le5YlGabfp9+d32lFgqfSktAadHa1BJ9udcXaea1lNt60cgVc8GlcHaOluJlTRj2wkkv/uI6T0biu+whT5N0WbSeviS7szwwAMCWGEAriOxjThlWjEz5ee7av/afUoDQgO9A4b7Y+aksSLVFqfZquTmAU7VxcwNZkSwcfqcMi4VxYDrHmJ8xNH33aO4OGJLyL7fVX3JacF4kFYyCaApbKLM4pLSIvyh8ixSHZDmfuwcHHHl4jlKMxYP4yLKzyJJf4X1qvp0aiOdVqylQey4WNm1tuvcHXt3bKF0ZysEGFO+UhW4VatJace8KN2RhVJbxR32zd2EtH/cFOvwtuIzWPWYpvs5cuTE04R4tdU/UHpK/oPIYBdr40RQGlAWnueB4/SMn/kzFwWiC/2eG+7bC+OGDxSDGQyutOnYTaR+syvmmF5zbXmKBzhe+g/zFwP3rHvCabY4XjxIu4aZG4rxUprihQEY1sNh9gOzqb41XoyMlePld5pRyPbHF+XfblZqUrXa0vt5NZ14kfRXOM1VP0rdrGhSvHBKQDYGWvYGbcGWtStEajIXipcqKfHCoEV6xr658yRr4oVBjknE9GFghRlbcwLXC7BV3bn7+A1Hp54+YB2lXVs3YOGMAPrNlSTYLzU9G6Jzr/5YvXQ+QoOPCPCSQVm+p2IEZukSo00yZiRxHNo7ygboGZwsS6nyOF1b4IJZOELAUxNKn6i1b/cAAw83g4Nwae9akarLwqkSbF2qobh7A+gam8k75BRcPEB7evNCxIVdInbIQ/mgZT4a4FS0/PrGYtBWKsuWMhAnpUOSynlwjO1PlWeOfsqMfaker40si4EZFm8ozVgBQ+VYlPRXON3PpjGdFZvhc8qg9qv4WFGu6f0qdUI7nJZrQsgT1eI0+79nS33WpjmYUsDXwiAJM32YddJ6bKAAj6T6jlU84dqyF85uW4p7lPqJmTYMHlmUqoTCNiXEPtflZ0nzkQuxdmgb7Jzmi31zh9Ng/1/ieeLSqCMu7Vkj/MZ1wyh91cbRHWHhVBksbs8A1PO4ezi5fg4YZOLnWONBM7mq1hQ84Nq0s2BxnNu9hsCJ7bB2roRipF9TyqMBOD0YW06dvLAs6YIoYpww44MH85kd8jLlM6fQndgskCY+coryggbK4wM8a53tKwGYimZoLpvYrFjGYEXsrYuCwaFrpBwfrFvC9pn0MVaP7CS2pRcJ9Hn5WBYfmtyv1FZxzSnIZoY+VSxSu50V4wPWpV3hs4SeP5TuTgID3Zp3h1O1RiJ92LG1c+TASrsxxKikZ4cJsYfYDM1okLtUBQEQH98wHzdP7BNsHbUX+z9amC2nDoo1H4yIrVNxgjRJOFWUfnFXFCbmh6FTNcGmYNdw+ee3iYjatwSJUVfw4fkjShkWg6/0OVM38K9jmPo84fa/pTwXWE9E0fi58B8CaBQtj7FVmv/4+U1lk8uYzaLO3j2JEsVxJ7eItGaKdf788pGYH0/BqbU0vV/F9tJ2OZ+FKNt3nrSbzjr9P/lf3iULYMekYkOllGaF7MriQFcHAlcCBbDCbJ9rSwbAnMApk/Ke6ZxHVqxpn5xqjLVd7h8MxLMbxwkQ08UXStumV6wC8ps70n7qb7AMT/iDDv5SwIqpiRFuHt2GA8dPE8ByDKcuXsVRElQfPmUBJg3pA78eHYQbZhMQMH7OcuSkHPlu5cuguqsLhvfpjLkrN+LBI+UfEDqUy1ed8cNYEyusAtRwG6nPXDllDxXFfpixwpaTGCrZiWauaJz+izVhGJhh0/R+Ffvgbb723CmzQFWPfes+C9MziNWkbjWRnky1vUlhA1HUoVkDpUN58+jAo2JZoUPz7OUrGOmTgKu9Da4e2oSgg8GIiIoVIFcNAsY47RqbdN8GerrgRdWYwVKaUsCdvnRdpByTQBfVetp9mQeMTEyxIeSmSOclaaFcPHkUiwNGCL2ctr38RMVNS2dj5ewJNFiTE84V3ASLxLvfMLAY/ZOHD5TcmStlNqRSodjRLF4UgQCpj1wpsyokkEYq5/Vr+kPKxtcmaYqIAnrJr6tHrI/WxCJxEEWa3q/UXlpzvLD+zM8wCYCq10L2XSWdU4d0UspUqip0aDjdmq6+oZg1JAFUXM/U0kYsPAA/eVAPXDhxBPVbdRRdJFEO84m+nYX+DbOFfPynw7Z4WgqpdD7tOq0HihJj5NKiAWDmw47TtwQrJPjaPfivOYSxHWqjf1N30Wj+zlOkXxKCnNl/h2sJS3g42WBwCw8BtMQRyKJqOrnU/yHV5BmjDqiR+suVQ/n5IZ2XdVz4M5JTDeOERYvZJHbO4JbV0IxE3Dcfv46jV+8Ktg5rsTBb48Dk7jBKRyQ+h5q+RcdZ/LLq0CWhS8NMoD4EsEj2hAAXChBRZlNED58+f9WoHgMpPNDLvpf8n4fen6pO1nAvZYWz4Q9wiNK5MbDCVtyiMM7O708MlduIpPRfhckfzPxhlg+bQwrIJXa0L+l6gNN1NWrWimZjrycB90PYsGoZ1q9cSiwIW2wjPRBDo8JCo4NTcd2LCBezxEuXKy+E0PNR2kgWq1c1HUqXqN40exbxOVWNUzCySSCN4vGkRNmzKAf9rlMFGHQL6cGrRRsBzHAbk6KmCLkUhuBD+0n7Zatgs5ykQdYA/2EYNjYAvXxlM7UU++dt/kyyvsTPMAO6f04rxWmPWGBe0ZISX4kBYY6VRbOnwozSUC2aPU1ehXU32Pg9PEEC43w/rO3Cs/uPnruB/bu2C8YL+5gBJWYnsGnKGhCV/4dfOF1XQ4oXTht38ughbKR42ZASL1sOHAe/d89J08abUnFJ8eJUtjxYJ4XjhcXqVU36bKuWS9+DquWq+4aF08aLFIMZxgv9dlMXLyzuzsAMG8fLMYqXkMP7sZ9AIv68MCgxheJlKMWLItNG8bqyKl52bd0I/0F9RfwNHz8FHQk0yany/031uaGTJw9cq1YHM34unT1Nsb5PnlbMf8oseHo1AwvVJyclCV2pug2boEIxU5FKT7oHZrOoMwadGVi5S9+FWvt7HihoVBQDN14Sg/ycJou1UHgw/+Aif9TtNVbOPDm1aT6OrZiM37PnhJWzK2zKEejvPRintyxE4hPZ95x0BdJgp7QvX9P3tibGM9FVLXsKm0ICaRSPfyDAh40HbX//XRnU1slfCM61WsgBBk3vV7F/3uYYYv2Z77XrR7ZiN+nSUI8C4KjcvKcY+FXtt0H/KShZzYvej7PELEkiwftSKF61IaVoKwZLAkckY5bLgPVnieGym/RjIsEgl62Lh3gfuY6RpYOoeuv4LrGu1W2EPFUag2X1+03CLXrfmeWiBVaEi5RedCk+Rm27jDASjr92bIdgd0ScD8buBaPRqM841Ojgi9cvn2KxjxcSYiJgYlMc5gRaObrWFiLsmyf7KPXHOznSy6SgaXzop52oJPWZPadsEqfiSRkQZctGscsp6RSN03+Vq9NSziLR5H4V20vbHB8SQ0cq+1HrfLr6yKdbJU33+fWMRLq2aIqZL58+CNDFzKF0mnpcUJx0bBhYSYi5o/b4/3phsaYDULSyFwESW/HseghiSZA+9ugaYnpYwW3cbuQqaIh7excJgfvfCEzWd6wEg5LusKd2UfuXEMjyMI0LGcBQaxp87vl8qvZ7Sn+/0edanbGOC6FApN+SdmxBz6GiaMJANJsm9ysqqrzwvX+P5UoBlcyrtlLqhrVPGMx6fvMEPiW/QAz5/guBWF+JfXV1sa+87qfEBDEGxmX83tg3Ie09DfvMVdAApTpORJGKjcDsJAZkmJ3EwM2hXk6k41JJfp5/YkP9yM0/cSV0Tk7jJbQISBuE9UH4hy4PsnegNFT+M5egt3cLvHv/EaNIw8SgkC7CjwchX97UP99TF6/O8ivntFWS3ovUeVz8E+hS2jH9QgWlIvna0lSG3NtYmGHNnAnyct74k2hSb99/gE4KKKLJ/aoDUJ6+eIUpC1Yq9a1up2MLQhIpJVlGtpKYJHxdnVo2UlvNrIjsh+Kff35Nc5z1cPihlI/+gHAqtgePn0BPtyA6t2ysVHfG0nUi7RcDS2xHT10QAVWnqvKHn2fkcx8M2mhBFSUXqt15T6kgeLYua4PwwvFy8+IZjO3XQbAemnXqDZ59u3TqaBTUM8CW0DAldsO6hakDKWpP8DcK4+Puy/VepOYJxJDJV0AXBQsps8n4uCTezqDCmHnK8cufS2bVSKliNLlfdQDKq+dPsXb+FOly0l0ziGFfsky6xzU5ULiobGbDn1/TxgsLKXO8MMOEU4Nx+q/pq3ehUvW6Sl0XoB9fbM+ePBLrr9TXiG4tEHHjMoZMWShSpIkD2pdv8gAP3v9OM0oaVS4hFo6XczTQ3mXGFkwgYfnulP7r/acvGEfi8fqkH3J16SDk00n94TNz+8lvOp8mlWMSXqWp9vB5siizKSIDtVUrsEYKa4YwiCCxW6Q6fP2cvorZKsy+ZAYLp78aSdotvLAezKxtJ0XqreX7z8OfACV1tuHYFdygc2Rkhrp5MbRV9YyqZHpMr0AelLA0xn1iBCkaXzun77odmyBApJJUR5N6/J6atBwPR3MjHJ/VR7FLEXuF6H0Noz45HRg/b+Ke0SyX/HmUdG+40dwdJKZJIItuvnR+UCv1/L+984Vmvn8kBgTPPB80apxYnj97ioUzp2Bt4GKsWbYIQ8dMxGIauOdBYtZXYZ0VyYJpBvmPMJ6hLqUlk/p/TBMJWJy6kF7aZ5GZhaWoZmltg3mB66QmYs3Pondv3wp2Dhdw2jL+rerZuKlY+HN3kQZf+5Em4PQJo8Fp0NQBKOyX+dMDlPpWt9OqQye5poO645qUWdvaiYFfZjgoAisf3r8XrIfKbh6iGyllFAugSyYxDpgh8eb1a5HOiN/nR3GxwnetSehb0RbPmS7As/RSjynW/V/fZj+y9hrHi9/IcWJ5QZ+LRbOmYB3Fy9rli0TqqMVzZPHC+iqssyIZMy5+hMVR6jhJ/0TqX9N4mbM8bbxwCizpt5sUL/VIC4UXjhcWhWcNzRkUL97d1ccL+2UBsUUysxbt048X9tegXp3Aac3mrdiAIqay32iKffL1OJrkRzFKfbv7+HnFQ+K5wcBqRNhNkQ6M03nxvTHzhBfJmMHzhjTvWDOK7cnjR7h57bJIlaZ6Tol1pGdgIDXXrr/RA5/e0/8fmhlfwqORWPg9fECaEJvHdsGRZRNQqVl3SqXznnRKxiFPQX0M3nqVZujnk5/lxLqsZzmw5oqk9yKdKIkYMpx2LE9BPalIvi5kYiG29U2t0Wrscnk5b3BKuc/E5M+eMilMk/tVB6CwFsnxNTOU+la3U65+e5G6S90xTv21bWIvkdaszbgVKEii2uqMRen5mll0nhfJmJnD6bsKEzuBjTMdJCXEQaeAHqU5U56EdnLDXAJZjOTslqfR4WJQ3ay4i9SdWLM/2des6cL9ZQWjQOkE//Kdj5TxgccHWBuEF46P+zRwv2Z0Z+wjXR+3Fj1wbM1sAao06jseNb0HyO847Mxh+XZWbryk+JD0XqR+E588FO91XjXxoZcSHwbE1vCeECg1EWuOD9Y0ksBQTe5XHYDyhuLj8MrpSn2r26nYsD3SAzvU1VdXFkHAL//vcKxUS+kwl716EicYRHw/Sc8eIy78KsyI2VJIJdZexj8QbfPpap8dSk6knb+IIfX180dKy2UKR0oRxcun5Oe4u3OuSEXFKaps6vdA+KZJyJmfJu7Ou4DsufPKu+F6WW3vnz5I0+WHF7KxnXwm1mmOcUEeI3NKZXZLgA35i9or1WGheE7zx2CPJvdbvM1IpfbSzoMTm5Acc0vaVbtmUKhYMz+1x3Qo7Rnbf/5KO9b1F7Fp6IcTstGkavZzAfPipH8So9SPYHsSOPT6QRhV/U0c07TPj4lP8fXTO+jZu4hF6vjtk2j88f418psWk4r+kfUvBazUJ1H4V0nJuHNip3AGz+auSqyIetVcsTZoH6X6+oBHT56JLyavuh5KoAqX37xzj5gThbLUkYdOnEEzzxryPhnUOHLyvBBhlxcqbFibmwrA5djpC/Qj/CvN5Ep18fQla4WGSMjWZXAt5wxN7lcdsPL6zVus2rZH4azqN90qlMkUWAk+fVGARNUrK/9okXpk389atl6wWupVqyIVi1RsZ6/cRCnSrGEQJJmuqVStlmjZsDbWzZ0or8fvy67Dx0l0NhW4YQ2d/cGnEH1mL4oUTkVzL14PE+9vbRXARd6ZdkPJAywKz8LsLErPxvHCbAYeqD+4bZ0AJXhwnh/aVes2VgJVuDwq/CYxJ9LOIFE6yTfunAs5jGr1m8lbMahx8eQRobMiL1TYKGphLQCXi6HH6MfxH0qslQ2LpmPFrAlYFBRMQu+u0OR+1QEr7968xr4tqxXOqn7TuaLbdwMr7PuNS2bh2J6tqFyjnvxEnIrt1uWzsHEsJQYbrOxLiGOXT4ekAVb2bV4ljnFdtrPH9iP82kUwA4l1Z7T29zzQlITdX1GKr+vLeNadLF6qUGqs2i7FsJHE4t9Rqq/HL4jeSvHSkNJSKYIqXM6D/IYFU3+E/b2rUG4VHf8KDK5YUUowyfha2BhMUGfl7c2wh/RPjlyJVAJWXr//hNI9Z4p2eyZ2xSnKX99i/FoSl2whRNu5LwYLfJq6CWCFU2KlZ6Ek6L7nXHh6h0W5DbFevhdY6dmgEqXqUgbYufOqAxeB05mdnttPfg2a1mOw6WbMEyQRs0cRGLlNZTei41GCWCqssfKa7r9837mC0bNicOqsG36v99K9t69ZVn5u7Ub6Hjh36gQ6Nm+AOcvWoCmJPLMxk6Fn/0ECWHlNKXTYpMHE5m29xb70wrPYf4SFHDmI+l7N5V0zqHGS2BdlK6T9vHElCysbARqEhhyV6ylIjZnVMStgHIIOnRApf5h5w8Lap2/cFVX42VvJrSoJYXti24Y1eCcGlXNLzeVrHnjdsi7zSTEVq7h/N7DCWhCcqoyvh1OwScZsk08fP6IWpTRipgGnAFM1ZuJ0bdMEI2h2f4MmLcTh18nJqO5CoDQxLRas3CBvwgPIh/buRMv2neVl2o30PXCe4qVziwYkXr4GTVLihRkq3X0GCWBFipdHKSnymrb5OfFy/OhBYmGkxguDGieDKV4IkFBn5inxcup42nhZQvEye/I4bD0oixdm3iRRvJy8nhovrPVSrbYntm/MOF62ahAvFVzTj5eZE0cLls/itVthWNhY3a2I3852xYqTRtQ1YqAkKmkT3blNubtvXBVpvjhels2fiTVLFyD4criSRs2qJfMFE87VQ/Z/kdnYfTu2QmtKkzd57hKl87JGCxunD9Pa3/PAqoFNhebGkG3XRQf8HWxVpgrsaUb31QMbxQB/Mg1Q8u85Zkwogipc/iTqNvLqpv7//HtXodwq8vxRlKzuJS9kUINZNKyzos70iloJwOXexeOk+/KH0qz8k+tnC6ZNz0UHKQ1WJWhyv+qAlY/vXgsdE3XnVyyzJDYPa6KosyPLJpJ2TH60m7QW+fVlky3V1QvdOB/nti+F36bLStozZ7dyWqNcgi3E7f749BGz25aHU81maE1AjWT8vnDqr3IN2ktFMCSRbgZmGNwpWa2xvDyJUlY9jrgGfTNbLagi90rqxuL+XhQfiRiz84Yo5Phg7ZPirnVIp2ODiI+XT2LFsfL126Y2pC1mufwICz9zBKVJT0cyBjXunD8GS0pxpc4MSFuHAZfICyFp4uPY2llgXRLfZYdFmjNN7lcdsMKA3/k9a9WdXqnMpozrdwMrZ0jvJuzUQYzfewcFDU3k/T8IuyzAFIdKNUXZB9JRXTXCW6QFaz1inrweb1wPlo2Pcmo3rSl74EXYGZyb0hZl+y2EmZvstwyDA7aN+ghghQfdP7x4LLIhmJSvrwSqfHgZj2Qa5Gc2RFYaC6q/exqLvIUt5d3GndwstgtYyMaB5AdSNgrZlSP9k314evUYFIEV1n856lMBBSyKo4p/EDS5X9W+pf0Xt08LAXtpX906LwE/6QErhcvURNTehXh0ZicKl0kFCjnF2svIiwJMYfDHmrRseFG145SqjQGY6tND5Ic07fPO5slCx6XmnLNQBKfuH1hOLJ9cMCQG0j9p2f7Jk6ueu3FtD4yesQj+MxajW9smyE1UbRZ737z3sBBGNyTQJHeunMijkxvb9wejTtXKsLcyx7mrtwRgkZ/YK6xtci8mDnZUnhW2YedBcDqwZvVqIOn1WwwNmEtpVv7C9FG+artnkfqJlLas94jJ6OQ3BoN7egsAiIGEKYtWoYZreVQu6yTaanK/6k7CGitvI8+qO/RNZUmv3+BaWCTq13ATfyzUNWY9lLoErgQdCCGBejM0qlUVr4lZNGleoJgBETBMNvBVkETsPSqVw85DISI1G98bs336jJyMooWNMGW4j7z7rq0aY9+xUHh6+4gUb7aWZoiIjsWwgHniOiYO7iOvq91I3wPudRsRG8Ufy6aNQWPSW2FQ4dr5UCFqzswLTjfFZZwiImRfECpWqwMza3uhd8I6HXkovzprm7DOCYugZ4Ud3rFB6IJUq98Ub2kQaeHEoeJzwqmr1Bnnku9JOiHThvXGBEp11a73IHFdZ47uw9oFU0XaspLlKoummtyvunOwxsqJ6DfqDmV5mVP5KgIoObE/SKT1cqtNonP0w23VnEnCD71HyGZfMgBjZV8cO9YsRl5K7VGhai3SoHmCEwd2itRuDk7lCJjxFNd385Is1j++f4eFk4arveaeQznV2/dRO9V2/F9UyFoq44mNMoGWTnVdkJu+q1ncfDuJlTvbFBEC9LmojFNH7STx9pqkRcK6JBcj4oROSb7cOfH+4xdEkcaJLZVnhTG7pG3AeoxuVwvWJAa/73w4lhGTxMu1BKS0Xqrn6epZAStI7H1OUChM9OizU8wMj1++pns7AgZXhqSwSCoUM4c+MUKmbzmOInr5UdLKhHRbXkFi3tQupzwTRvE8gYNaIVCGPykW/yu2BzRzR+fpW9B4zCqhMWNcKD+OXb2HLaSxwjaibU2xLpA3t9Cc2XMuDB7HrIXGC4NcrLfCfp3QKRUYFQ20L2o9UI5mbevpG2DetEkwNikq0uKwfsoCYqywMdjAxmlxjlPao2njRwnQ5cWzZ9gTtFkMyvNxZpjw4H2BggV597uN0ywZGhkTuNJM9Dtx1GDxHewfMFNt36znwmm8OC2Zb3dvwarJlz8/jh7YS7PmJws9hnIVXUXbug0bY+q4UXQvo8F6K8xOYcHr3ds2iftUZIgonow1VqJffFAs+mHbDM7wwkLzBqTVVaOuJ4lsX8Wk0UNFuqIW7Tp907n5fans7oGDe3YIX9Rt6IUHpPsx3Le30KsZNXHaN/X3v1qZWQ4cLwumU7yQzo9jSWehN7QoJV4YbGArQfFyguJlBsVLDwIpXzx/hr0kFs8gFtsDYpgwUMd6HVlhLGjP8cIprjgOAyheeGbk6AziZQjFC6clG9jDG72IhcaaPccO7iW2mnK81GnQGNPpPpid0pb0VnJSvFygeNmzPeN4YY2Vu8//frwwSHX3Tphg4qxYOEetmyoQwFOjbn1x/awV075xHfiSBo6hsTFCCYjduWWDaOc3YpxY1yUdJQZWRg3ojYmzFkKX2G8sRs/6MWOmzhFp9bgi662wRg6XM+OlDsULzxrn74gzJ4LBqcOcKb2b1v6eB1hL5fDS8YKdwvojDCrcv3YaN45uF8LoeWlGN6cXykGzZllQ3b5iTaE3wnonRwMDwMLdX0jbhAftDWhwPivs2qHNgm3BqbA+vk3GgQWjRAw18AlQ2z2zLOr0HEs6I/2xdUIPVG03ADnpuiJI0Pr42plCv8S8lAwU1+R+1Z2E02ZNOvlc3SGNyj6+ScYzSjvE7BBOn6bOrEpXgYNrXZSo2kAAK7tmDIDXoFmCkXL1wAZc2rcWDX2nolARC9E8d74CBIK5kVD9HgG28L29jI/BLtJbKUADzp59J8hPwynH7pA/9s4eIoAUrvssNpLAIhJ4JtCsesfB8rrajVQPsOD5vkXjsG/xeLg26SziI+rqKVw5vA2mxUqTULoBzGh95+xRqkOpwUiUnoGOq6THcuPEXtERi61/oM+xDjGussJY0J6BudI1vES/u+aOFPHR1Hey2u45Phr2GQdOS7ZuTHfBquG4vU3gxJFVM2BfvhqsnGTxocn9qjsJa6zMOftS3aEsL6tM31O3Qw9gkU9jcV+GpMn0lD7Lu+aNEiLoDfuMFec0sS0BixIuYH0cTnnmVK2h8NPlQ9sQSSAsa7Jw2jatKXugkH155Mivj8igWWD9E04PxaCGxEThgfu8JjbgVFyPz+2BkXN1obfy6u4l3Nk6DdmJ0ciMEGY+5KN6WWEspH5hekc4th4uUl49uXRQgAKcxko/Ja2X6nksa3ciIGg1CcEvQG49EzDQ8vHVE8G0+ULgkH0Ki0ST+1XtW9p36b8EvPxd42s3Kl0T8ef3ivsydqkHBn4it9P/KxojL9HO/5u71rRPBsWYfXQjcAicuk4lVkwhxJ3YjNiQDXDqPEkwfr755FnYIFsW9vXdXbF4etjdaMwg8XJeJCtdohjWzZGxIDj1F4uk96DB2GY9ZA9UTss1Y/QAAbh0HTwepeu2wft756Tm37VmAGXm0vW0rBP98PkXTRpBTI30B6I5FdYHmv08cuoC7DgoQ+Oy0QzZzpRua/yg3oJazp1pcr/fdfGZNA49f1X8MKlYumSGNddRSjPfsTMQMH+FWLhygXx5sWnhZNSskjrTYNk0ovT7jkav4ZRjnBY2TqO2du4EJXYRAzX8Ho6avhAteg0V9fiFNWeObFwMJ8f0fSuvrN1Aq26+uB8ZLtJKcWopyexLlsbYBbL44bRTI2Yuw5TBPTG8a3NRhdNy9R87nQaD8iDArxu8a5XByZh3UvPvWvfznyYYGxuX0JcrGZ9/yJRFgqmRXscNWncSqTGWTBlJwMIOUe130idq0KoTegwdL48XTe43vXP8zPKx89ditv8ArJ4bIBY+N4MnExZvhIubbDYjzx6asmK7AJMU63Fdd2IXDRg/C9lSNJqY6cK2a/1ysVb30tXPXwusqHOMQlnfxlVwJ+4Z5lCaJ14kc7I2wYpBLcUus1QW9m+KfvN3EuAhG1ApSAPwk7t6CsCl99wgVPKZj5e7ZM8jqY+/u2btDxMa+Peetll8F3M/rOsyq1fjdLtkbZVdEzqj5+zt6DF7m7yeLQEzG0a0QxVqz8b3EujXEr3nBaHh6JXyetx+dPtaqENMnf9G83ItiYSubzBu7RG0n7xRfosMMi32bQbPCg7yMn6vu83cCp+Fu8TCB0oRALVicEslxpK8gXYjjQfy5suH+SvWY2CvzmjdsKb8OGsYDPGfgBopwAqn/7p84QzNUF8rFk6LyPocrFXSs30LLJ03kwZmiVFF+hNZYf4BM7BkLi/TRXfc9xSaOe5YUjaxRd05OMXVx48fMGXMcBzYHSSq8Pdwqw5dRDozvma2bn0HIjI8DJyuiRfJSjqXITbHemn3H13zta7cvFuIzc+ndEq8sDmVccGStVvS6GJocrEzFq6AT9f2GOrTQyzchtOt8fvP/tVa5h5gP7Fw+uDendFWIV5Y22fQ6AlyILIXpf+6cv4M6RatFQu/nyz4zlolvTu0wHJiTnBfrNeSFTaK4mUZxctShXgJoHhxyCBeOC4+URrAKWOH46BCvLSk8sH+E+W/3bpSvDDAsYRihRfJSjiVwVz67Pwou3JB9j8w/NZ18KLWyK8MrDBbZ3RCPKaPG0nfR83kVRkEm7l4FVi0nq08sWP8J8+ilH+jULtiKVHGAGy7Lj3RoVtvsc8v/H4t27iDgKceae6b646alPqbXd5Iu6GxB6q07oun9+8IEXMWMpesiL2TnAXBLJXmIxYiaEo/rBveVlThtFwN+k8WgMu2Sb0xt0MlBIS+lJp/17o+ASihlMqKFzY+f5Ohc2Fsm/5/bE6F9cfnDzi0eCxuH98t2v32eza4NOiA2j395TGkyf2Kxln88uD2BdHjk3u3wIs64886AyvMemHfHl46AXPaywa8c+bJjwpeXVCxaTelpixev2VsN+yY6iMWPsjgTeuxK5TYRQzatBm/CgcWjgbr5fDCxto1fC7n2jJGpSjUvsg9UL1tPyRE38GxtcR8okUyU2IldZwo+z/A6b/u3zyPi/s3ioXfR3sSuB+19TJWDmuHkPXzBABZp/MQqfl3rZsQgBJMsRq8ThavDJIwI6OIXfrxUamxN75QfOxZMAbXQ3aJ83N8VGrkjQa9U+NDk/v9rovPgsbFSb+mnf9i7Fk4BiuGyr6PuFvWh/FZvB9F6fPPxu9D9xmbsTmgX5r3r0rTrvDylf2WE5W1L3IPcFovF5/FpOXhgzMTUp/hrGXi2HqEnFlRtvdcXCVB9QszOoq22fMUJM2OCQJwubq4P0IGVYXX5nh5v9+zYVDSDbl1jXFxdlfBlOG+9B0rw6nb1HS7ZT0w19FbcWVhP1xZ0Edej0GhioNXw4Das2l6v/IOsniDgZmbK4cLIIvBLLbsOvlRfmAgDEtV/Vtn06RP1sUpSRor4ZsC6L2SsVOy5c4Hy1odYVm78986b1Y2+j9C/P+j2KG/vz92B9EbSrMM/imLeRiPKGKdsIaHBWmW8EA7f9Eo2quk17hx5y6MDfThYGspP87lzMSwsTBVrP7N24vXbYMfDW6e2rESLk7FcTsySjBWGChgUEETe/vuPV3jPbwnXZXiJOzOwIE60+R+1bX72WWcBu1WRBQMSXjewcaSxB9zpLkE/jgxOBZL76EzAWJmJoXT1JEKPpLmRPi9GCQmv4GtpSnMixiny5yR2vzIdVx8AuzdvXDp0iW4uLhk+ancq1aFvoUD/CbKfnRn1QmePIwVrJPPRK82NrUQouaq8cKpqDj1F4vLW9g6yOOFy5lZwim5vsd2rFmCuWP9sHR3KBydXXA/4rbolwXWGVTQxFhP5V74DXykHPBWlJaBxerVmSb3q67dzy7jNGjR5AddPQPhcx44UTWewZjw6AHiou8KdpEZiT0bFC6iWu2n7jevZIuhgwbCz88vy8977tw5uLq64s6qYTAmFsU/YQ+eJiIq/gWlmvoKc9IfKWllLI8H6XoSKWXYLUodZVQoH4qZGsqPc3ky6Xwppu6S2nzr2qr9JJSxKYqgcZ3AabmuU5oqZleo6qak1y9/1zLD4u6jF2DtEBc7U9J9+C1N9Q+fvyD8wVNKc/aa9ER0SJDdSLBz0lT8LytgTZ27JEj/POkdzIx0wcATM5JUjf0YToBbHH0uShHIZmqQNbPzVM/zLfuN/FeTSGVNLFny92cUpXe+Fy9ewNDQEFuJ9cvpeLLKWGclIpwGXyg1lG4hfdg7FlfS9pDOw1oenEaLGSyK7JR7kXdQhDSq8uTV7PeV1J/qeg1pVIwdOgC7g8+IGeERYbfEDPzipZxpdr9mzyLWU+GB2Pf0LCrmWEKIb6ueh/c5vdn9qHsitZapuYVg66g+e9W1+9llz0gIPfzWDfJ5GWKvqP8dquk1cbxEht+me48VoIqqfoSm/WRW79rlC2hSyw0PHz6Eqan63wKZ9ZHRcTs7OzRs0Q79hozKqNoPO8bxEinFC7Ee7EibQx3TKYLi5RXFS6nSZZXYKVEULyZZEC+s6TJ+2ADsPHYGTsSgiOR4od+EzKT5lni5kxIv9pnESwzHC/1W5Xjhc/xq8cJ6MNF37wiGkKmZBaxs7UWKL9UPAn+HMVjEv+HKV3bLEKiMJ62imOh7pDVYADZ2Dv84CBm4YBY2rliMuLg41dv67v1p06Zh1gJKB7X1+nf3pUkHifEPBOvkj8+fUMjEXIAYqp8pFsFOIFCAtTsMLYvJP3NczswSfUrJ9T12Lmg59s0dht7LjsHUsSyeRocJTRETGjDOlVezZ87nD28FcPGZWDSFrRzBYvXqTJP7VdfuZ5a9T36Fp8RyYcabpXNlpRRnitfBz5Kn98OR+CSOBtdLpavdwm1YR4XrJlFdfh+NyEfMfPkZ9ozE3ed6V8adO3fg4JA6MSerzv2RUnPq6Oigx6ytKFGlblZ1K/p5GR+L53GkPUDaExwfPHivGh/x927jXfJLmDqUVmKnJMREksZHUaH98T0XdWrbMgTNGgq/lSHEtChLafjCBGOlqH0p5NYwPj69f4vHFMOsm2Ri4yjACHXXpMn9qmv3M8u+0POPxec/vEmCoakNdI3N0h37SqSUd8/iouh9KQAjC3sBdGX1td69dIJYNF5ISkpCwSxijCteY0xMDKytreEx5Qh0rZwUD/2Q7a8ExL2Ji8CHV4+RM18hobmRs4CB0rk4ZdXrWEr9pWuIfKRjIsUEl3PKMMXUXUoNv2HnQFcHFLR2huvIzUJgPTnmJgm0F1ZK75VRd/z9+J4YN2/jo5AjL2lD2pUlZtPvaZpocr9pGmVhAevYvH4QjpwF9MmXdmBg6HtNkz4/v3mFNw8jxKRUZrv8li3t//vvvY6M2p8c7EYEiXYYO3asYrWIbIp7v8q2lVkR8JKR6ekWEGm1VOtwOS9ZaRxwGTFU0jsXs1vcypdO77C8XJP7lVf+BzcKG+gJEfqMLoF9VbKYrVgyqsfHWD+mXCnHzKppj2fiARaAl0Tg06taQFdPpNVSPc7lvGSl8WdA0gf5ln6Z3eJcwS3TJprcb6ad/IQKDGLxkpExc6WIuZVYMqqnPZZ1HmDxd14yMgYqPJxt0lThcl6y2pgVU03N+TI6D8eZtYm+WDKqx0L2LqTL4pJ+5q+Mmv9rj+XXySXuO7MbYD+y7govWvv7HshNAwOs5aGo56Gut/QYI3bFsv63AL+36Z1P3bVJZcwEYN2GzMzMwkqe+iezuv/kcSPSluAlK4x9yoL3kuh9VvT5v9gHxwuniuIlI0uPMWL7g+IlvfNldI0cL8zkyMz+DfHC7JPM3hO+z0IEhlVy88jslsXxImbm4EVrWe8BTi8lpZhKr3dOp2Pj4pHmMJfzkpXG348ZMVTSOxezW5jxkZlpcr+Z9fGjj7OwvDWl+srMhK9sSsCYlsyM00IVpdRVvGhNcw/oF7EELxlZeowRY6usZ7Xze57e+TK6Rma32JTOPD40ud+MzvMzjrHWizkBsJpYIQJdeNGa5h5gbQ8GIQohfR8z4GJYKu1vFi7nJastR96C38zi4FjJa0w6Q7RkZJrcb0btv/cY69jkcjb83m6U2mvSZ878ejAoUUWp3a+wk3Z6669wVdpr0HpA6wGtB7Qe0HpA6wGtB7Qe0HpA6wGtB7Qe0HpA6wGtB7Qe0HpA6wGtB7Qe0HpA6wGtB35BD2iBlXTelLw0m8zEyAA5sv9calE6l6Mt1nrgl/ZA7jx5oW9kQukQ0qaH+6UvXHtxWg/8Qx7gtF+cmktrWg9oPZB1HshDzyIjY+2zKOs8qu3pv9kDnHpPxEsO7W+3/+b3WXtvP84DOXXykii3MZhVoTWtB7QeUPYAx0cBA2NKEaQdT1P2jHbvv9kDuXSNfgj75b/ZZ/8N9/ZLpgL7FRzr3bwBeNGa1gNaD2TuAc8WHcCL1rQe0HpAMw+cnd9fs4raWloPaD2gsQdatOsIXrSm9YDWA5l7oHnbjuBFa1oPaD3w9zxQ1rMteNGa1gNaD6T1QIUG7cCL1rQe+F/yQI2ZJ/+Xbld7ryke+OWAlYTnL3H45FlUcSlNgub/z951wDWRfd2z69oVFQSsqIANbGCvIKKIXbArdrH33lDsvfdesaIoVkAUFcWKvYsUQREUpVl397v3hYlJCJAg/Hfdz8svzMyb1zMvM/POu+f8PLyCj58H4/Cps/ILq2/ntiioq14El0Xtv3z9lqYWjKbx5IVqsJOReX4mUWR1AvbqqhGfkIg8uTVbnc2CTVFvY5Avbx6N85fK/PT5M5ZtcpMO0bh+LVStlPFic/ICfqKdy76nkBAfC9tWHX6iWgNH3TYj5m2UqHNJ03Kwsm+jVH++XuJj35MwaAGl8B89YFFS1kDJKPv29SutaEt7xY427fn0MRFZs2Un0XJlQbOr533w8PZ1UfXsxOfaqd/wjGrGT53P63ex8L7xBLXNSsKUxMx/Jtt2+iqiPySIKpctboCWtc3TXX1trm2+Hrlcndw5kD2r+keGb3/+id/oL0uWtMfLZ7r3pZRPuhtECTWppzbt1jRuwqcvyPL7b8iRTXls+wY+xc2nL0WTctK5wW3+fVywP9Lf6U3LQupnvU6S4HM9GJuWSW82//N0z548wsmjh+Tldu3lLHQW5AEKO/FxcfhKYrsFdFPWL0uIjwd7C/ybjcdULAmZ58uf+r3127dvJC78FTly5lRqzqdPn7Bx1RJ5mFWjJiTCXk1+/P95h8dAfFwsWjp2/Km64bnKOOiSyjjQpGGa/s5+pesrqwbPT5qUmVlxeBwwL7rq85i68jRtN6flcfg2Ogp5dfLRO5FMGPaCrzdu37wmsuZx13fwSHXF/L8Li41+jceXvVGycm3oG5n+VO2/emSbEBHnShuUKIsK1i2V6v8nXV9//UnjILvy76xSJIUDvm4SSJQ8R24d/EHvCeqM8/yd3h/4us0o+/Ipkd51sot8MypPzkez9nxFFjUixk+u+uLlw5uiOtx/9TsNzsiq/dR5faAx88DfCyYWdWDwk40Z/8Nb5WOmEIm7V27YSu138SkhDn9++5qqptLnxHiwd80/YdrcD9TV7+vnT/B1Wyk/Vb5WIxiVt5Qf/3/feX3TB98+xqFY3bY/VVe88NkJFmlny1u0NIrWbJ7u+v9Nc1q/ZeCcVrorkkEJv31KoPZkQZZsOVLN8a8/v+Fvum9myaZ83/zzyyc8PbZOntawsjUKmFSRH6d3R/0sSXpzy4B0T4JCMHDiHKydO+mnAlbuPX6O6UvWoWghEvEhseB2zW3VAitvYz6gGq1s0SHQ4LbXvhR7LLV4G3Yfwpod+9WmnTdhKJo2VC/wlVqeajNTE/ju/QeMdF2ES9dvIywiEvl18qJhnepwHT0AZYyVxRkD7z3C1IVrcP3OA7yPjYOBni5aNm6AuVRHbn9Ktmj9DpFu14rZoh9Timdu44gGNS3pWpksj/L5y1fsdD9GkxrfEBrxGnnz5P4FrCT1jtu6JQgPCfrpgJUDW1bhVVgIUY0VRq2GdnJgJe5DDNbMmQRvj334/OkjmI6slrUdRs1ahvy6sknzSc4dERb0RH59qNuZsHA9zC1qiFOhQU9xeMc6XPA6hoS4D6hYrTY69BmGavUaqkuaZlhifByWu47BlXPeeBf1GmUrWqKGVWP0HDaRABFl2gBN2sMF8gPYxoXTcdH7GEKePaKXiD9QggCngRNnoyblzfYg8CpOue/Gu+g3+INeMn4BK6Jb8Cw8GsNWHcaKIW1/OmBlneclhEa+RyHdvGhctYxaYMWy/2LUq2gs2idrsfL/7aev4cile/C/94JE7/VgXcUU07rbpQp0LHM/jxk7vbBlbCe0rVdRKcP9525h04kA3Al6hW9//oVShXXh3Lw2+tjXUAIlY+ISMW6DJwIehOBl9AfkI5DGqrIJpnRtjNLF9JXyTO9BavXUtN18fXB7Tlx5iA+Jn1CrfAkMalVX1FW1Xhxnxs7TeBwWJSYlShgWoL5sgjZ1ZX10/UkY9voGIupDPP6giYtfwIqsB4OePsH4Yf0xf8X6nwpYefzgHhbNmoZCRYrS5GYOtHTooBZYiXn3Fk3qWECHJkHPXL2rdNnEfviABTOm4NjhA+B4LGBep741psxemGJfHNq3GyP798SVB8GibKUMtTjYtWU9tq1frTbFpJnzYdPEXn7uw/sYzHGZAI8De/Dp40cBAFnbNsWsxSuV2nyeJnfnTZ8E7hueVC5GAt39hoxE974Dxfj/QgtdDuzeQee+IjwsFHny6vwCVpJ6ef2KRQh98fynA1b4u14y+/s4aKFmHByma3b0gJ64dF/9NRv07Al2bloL7xNHEUdjolqtOug9aATqWtnIr0HeYYBy5sRR8DtzGlGRr1GxSlXUJ3Bu6NjJyKby/CQlTKtsKZ4227Ty9NjvJtpz/04gTd59g1EpE/RwHoxufQYo3Qc1bbdq3dYtW4CF9LuxcosbmrdtL07fun4Fh/buQnRUpHjG+wWsyHotOvQZDs0fBofxK346YMX/wDrEvAolWrFCKFOrsRxYYVDg1NrpiAx6SMDKN+QvVJyAgSGo5dBX6fpSvW78di3D6fUz0Nl1Cyo1Up5QfHTZC94bZyPyxSMCXvKS2HwDyq8PSlVRP3+gmrfqMb+XeG+chQcXTyIq+DGBKn9Anya67QdOR5majVSjy48XdbREKYt6cJywQh6W0k5K7fmcGAfP5RPxJOAM4t9FomjZKihNZdr0HCunZwu7fx2Bp/Yi7l2UeGf6Bax87+U3IU+xZ85QdJ608qcDVvz2rcXbiFBBNWZWu7FaYCXhwzvM7VIbOfPoYPI+GRgttf5j/Ad4rpmBQJ9D4HjZcuRC6ar10XbEnAzpixmOtEjcsh46T/4Oekhl8/aSxzbc8j2CZzcvQr+4CcrWbIiWg6aLBZN8/vG1c3BfPI53U7Ti5aqg3ZiFuHLMTYBHMa/DxJj+Bax877KnR1cjPjL4pwNWnp/YiMQo+j6JVszQopEAVgIW9UJ8xPPvjVOzZzlwKXRLVxVnXpzZhYgAT0Q/uIzchY1hULEBzLtMJmo+9WA7J/IaVgsFzevAsv8SNblrFsRAju/4RgRq/JksQS6D4qgzYbcI17Y9r66fxn232YgLp7k9WhCQ28AI5p0no2htZVA18vY5ijcLsWGPqA7fkEu/OExbDIBxk14CYPrr2xeEntsr7qkfo18ia848GQKspL28NFl3/ApIrQf2rJ6L+77uMC1ZXG20/hNmgb1y0rLU4l2/cx8vX0VCnzxiVD8pvWxweanlmVZ9+Dx7uzTtNhj7Pb1Rt1oVTBvZH5YVyuGI1zk06TIQb6LfybO5cech7LoNws17D9GxZRNMHNKbwJTc2LzXA/ZOQ8TksDyyws612/cJoFqvEKJ+d8fBY3geIlsNrBiDvVy4/0+7rVEM/rX/k/dAlZr1sPf8fYxwlf3If/3yBWN6tMGxvdtg27oDxs9fi8a0PXvcHRP7yl48ucl/EOjAq5fUfaJeRyD46SPxUspxGZyZ0McRx/dtJ4DCFm26OePli2cY39sBt65c4ChaGa/gHejQECf27wDXv+fwSWIV846V87Bs2iilvDRtDyeaNbIPdq1ZiAIF9dFntAvqNW6B0OePMaZ7K/j7HBf5clncXw3sWiuV8+vg5+6BOuYlcXP9aMx3bpmsIW5nbuDF6++/waoRdvvcwIg1HoglwGBkOyuUMzLEOs/L6L1gL4EiyR98OP0NAgdm7/ZRzUoc7/G9if5LD+B9/EcMaFkHfZvVRMLHLwJAWXzAT54mLvEzWk3dAvcLd1Gb6j+piy2qmBbFsYAHaDllM6Lex8vjpncntXpq2u6Pn7+i8+yd2EX9ZGNRGn2a1sTziGh0mrVDAFGKdTt+5QG6zd2NP//6G6497DCnTzP8Tg94vagvz956JqKO62gjvqsWtcwVk/7a/8l7YN2OfTgf+AgljdWvhh47pB/ekFeOqvFq275dHLBz8zqRdsT4qQJk8CXPhfb2DfGGJo5VLS42FutXLFYNTtfxrRvXEBEeBl26b6h+simsZv5C99Ye7Vpi744taN2ukwDAeHvc4yD6dnaQl33xnC+cHJrhZWgwOnTrKcCUj4mJmDZuBJbNmyni6eTLJ/pqr6f63xB5Zr92froeWLN9H87eTD4O+JrduDLla5aBun7k0X9g11Y0sGkiwIfg58/Qt1NrXPU/L+8H9lBp37QBAXPbyLutPoaNm0Ke/l+wetEcuI4fIY+nuJNW2YpxNd1PK89De3ZiVP8eYDCy14Bh6EagYmJCPKaPG441i+fKi9G03fIESTu3blwVQJZq+FDqD+7/Ji2UPbhV4/06/rl6oFSVOhiz7yZajZwvKv7suh+2jnLE+9ehqNa8qwBTvtK7iuey8TizRRZHXQvDHtyA96bZ6k7hlvdBbB/bER/j3qNBl2EoW7sJHl46je3jOiOKgKn02IGZA3Bu51LkyV8Qtn0noXw9e0SFPMHW0e3wkMAWdXbjhBvehr9QdypZWErtYS+EtQOa4sbx3QQK1YFNr3ECyD+7fZHoIymjRhTO/Wpu1UIK+rX9j/SAqUVduLgHEriwQG2L3GYNBnuyqRo/k20c0xkX3TcRqGGMpn0nkJeHBR5c8sLy/k0R+zZSNYlWx1eO7Ub0y6AU0wR47sLeucPB4E7jHqNQyLg8/PauxbbJPQVAzwmZCeB3msNQ9/mL3ttev3gM9sbJmSef6INha46lWN6vEz9nD+iVr4UmKwJQuZfs95xB699obkvd5+O7VwJw+D1LVtHYkLN7cGvDGHwl8LlMm2HQKVYWz09uxLVlzgJQUNcjIQQ2JEQGqzulVdjHdxGIDX0oQIzsOnpQ/GTLnV+elzbtibh2CgGLeuLvv/6EeVd6h+oxgwbJ77hK7Xlz5/u8w5u7F3BpTicBSpWw7oRSBKZ8+/wRd7ZOxqODi0TZWXPpiH6tP81dXpeM2PnXeaxkRKP+rXms3+0Or/OXUSCfTqpVTCve8+CXwkvkwDr1NxF1maeVp7o0qmG+/tdw5+FTrJgxHs5dZS/YDJiMnL4Ia3ceEABLP5o4YFu7cz8+fvqMi4e2orKZjO6DgRh7p8E4e+m6oE1zbKa8ioWBmx4jppJnSwG14BODSbNXbCIPmIe4++ipavV+Hf8/6oGTB3cJr4zBk+eik7PsJbtFp57UA78J6rBHd26gXKWqmLFmt9peiQgNRs+m1dF75BTyIrEQcTYsmEbeLU+xcJuH8IzhwPa9B6OHXXXMGd0P+y8+UptXSoFMYRb0+D66DxmPfmOni2hc3vLpY3Bw62pRRv0msglyTdvDnjveHnth06Idpq/aIXffv3PNH4Pb2WLdvCmoa5t+V9GU2vIr/N/ZA+Hk/TF/ry8CiXLqXnDyFwep1i+j3mPipuPCA+PorD7I+kcWcapMsTMi/X6/2+hiYylFF1sGRPou3g/9/Lnx+l2c0jk+WO1xUXi9+CwaCJ1cMlfc4Q4NUNl5kfD6GNtR5uXld/sZ7r14hcUDWqG3fU2Rz1g0xLj1nthI3iEMsPRqKvMYS1aIBgGp1VObds/c5SU8mva7dCePoLKi5AGt6qDusBUYtNwdtzeOEWFfv/2JiRuPo7h+fpya5xekgUcAAEAASURBVAw9ndwivFUdc1Tsu5DAqktoSJ5Av+z/Xw8waOLnc1otdVbAxfO4QhPHDh27Yun6baJzRtL/ZfNnYuncGTjoth2DRo4X4W7bNsHX6wQunT9L9J0/DjxypsFBz4VHwEZ6Dk3NDrrtQCCthp9MXizOQ0eJqJ2696btb3DbthF3Aq8LQGjFQtlLnufZAJSgFfps46eR16RZCWwg+q/h46doRIkkEv7699P3wN7tsmv2chrX7KKZU/GCPFa27PeEdeOmot09BwxFs7oWGDOoD87flj3b76Frjb1jBo+eiNFT6OWZbPgEF8wgD5Zt61aCPagaN28lwjUtW0TW8J+meW5avRSlTErjsM8louqSvdsNGD4WDSqbYgd55Qwh7xo2TdutWD322BnR1wl6+gZqwVrFuL/2/5s94LttoWjY4E1noVe0lNhvOsAFc9uY48LeVWDAgKm8FI09OPZO74vc+fUR91b5ufAbgZMnV7sgW87cGLrlPHLmzSeS2g9ypTzNsMelF4Ztu6CYXZr77GVzy/sAKtm0RSfXzfL3kuDbl7F+cDOcWucqgBbO6MObcPgQIPTyUSBeP7uXZt4cIbX2MH1aZNADNOw+Gk2cp4j8bHtPEB4sl8gDqGwtW5jV//VOpFFH/wcjXSDQ5OFlH+TSKZCsdewl8izQH9XtO8Jp+gbZ+X4TcXLTPJzcOJc8QHYLwCNZwlQCYiLDcYrShxDtXMTTlK/vmMiXOLR0Aowr18JQAkMk+roTJcuI9NdP70fN5l1QproVJuzyV1vigUVjBajSccIyted/Bf43e6DGyI1qG5bwJgS+Y21Qrv1Y5DeuhMTocNzZPhV6ZWugnou7AOc44cMDpgQuLEbYRXeUsOoo8vr4NgIPCXCIeX4LsSEP1OavbWDCaxloXm3IauQraZ5ick3b8xeB6He2TUGugsXRYMZRAdRwpkVqtsCpQVXxjLx7DCpZiXIeuy8RW+s5p5GnUEmxz146pwZWEfRf5dqNFjRi4kQG//v9R/PjSXWbjs5qJ8IHTZqD5t2H4gvRM7H5BdzAcJcFMG/UDiZ1W8Jp+BQwrdWfKayWldJw/jypr2jsHcHhm/YcVgwWlFPDXObDomknGNWwR4eB43DqrPofJaWEmXzw4EkQxs9ejjnjhqCQgV6KpWkS71lwKNFuaa4/o0meKVZI4YT/9VviqH0LW4VQoHMb2cuRosfK5Zt3Ubl8GTmoIiXoTish2dgzRdWGT1tIK03+hMtIZ9VT4ph1Wp6+IJdP8nypWrG82jj/lcClLiMxyLERoiOTr3xdMGEQRnZtDvZyYIuP/YC9G5djtFNL2FcsJLwkmB7t2cO7qXYHez7MGN4rWRz2huCymdpD0S54eVK5zdDSojj6EN3PqlkTiC4rVjHK/2zf6/Ae5NfTh2PPQUpldh8yDpOXbJJTgSmdTDpgl/VZI3sLQKUHUXJJdvLgTpiUqyAHVThcV9+QvFeaCCqy+0SvpY1du3BGRG/cppNSMjuHzuL4VsD3lxdN23PvxmWR1r5dN/nLCwdUql4XRUsYCw8cXi35XzGefLefsIEm9pNfZ8NXH0Zbly1Cr4rbe/FuEMasO4qqA5bAvPd89Fm0F1tOXqH7y1+pdscA8rpwXrI/WZylB/1E2YreHB/IO2P02iOoPWQ5ynSfI7wWvK4/Tpb2fxUQ//Gz8Kpg/RML8gJJyRi8iKO4g1vXlYMqHLezjQxUPHT+TrKkY9cfFX3HHiaq9iHhEx6EvhGUZBKownEK6+mgAVGRxVA/MQDBdvlhiNg61K8kttK/DtZVxO6bH/RYSa2e2rTb7QytZixhKAdVuHIG+fPA1rIMQt/E4PrjMFFfpjMLI6CKvXQkUIVPFNHLh10Tu6JHk+oi3n/ln8vY4XBsag3WRlG1CcMHoCvd/9nLge3yBT9MGT0UVpblaYK9JIb07gqmoErtGY/TcP7sAaFo0VFvRDhP5ivah/fvMXnUENjWqoyqpYvCuVs7AiHUr4hVTJfZ+08e3sfMyWMx0XUuDAoVTlZcxMtQEVazbgOlcxL9UXzc99/t4KBntAL+PcwrWUCPPEwywoKfP4VJaRlgmFp+h4nWiMvs2X+IUrQhoydgybqtRAUmq88r8n5hWjQJVOHIefLmRZWq1YXeymfSV/mv2L3bN9HB3hqrFTwQpLbdvHpZnHMnQEqygIt+cBkzFDZVy4O9DIf16YrdaYwDTsNl+PslHwcczkCDonkfPwqnNnaoZloYLa1qYM6UcWDvin/KXpDXCV+zZmlcswwgljOvKAdVuL76BoZoYGsnvJ+Y4ort4jkfsW3dXva8JA7oX5sOXcQug5SSaVq2FF+TrSZ5MrUfgz9WBBBJoArnbVi4CGo3aIgPMe/IS1n2/qtpuxXr5jJ2KHmTfsPISdMVg/9T+0eXjsO6QfZqV5Qfmj8cm0e2pd8T2f3lE63svrB3NbaQB4dr0xLkqWCHk2um4VUaE/T7yZtin2vy90r2sOCymb5Nso9EAeyxaDSWOtXG7JZlsHNSNzBt1j9lH968hI5+ETmowvXInisviptZgieavhFPvKodWTyWViP/icbkOaJqb4imKzb6FXmpNJaDKhwnTwF9lK5hI/qS+1kbC7krG7OW9p2V3ktY54bBIC6T9SvYeBsd9lzovhQrJ3v+TKus1Nrz9No5kbxKk/ZK2VjYdRDHQTRx/l8znlBf5mwH1kZRtT1zhmH10DbyMcPeEL5uq7BmmAPGNzLC0r6NcWSVC8JTmfTnPHdOd8YOl36q2cN7+xJRtuKYSSTPp/3zR2FOp5qYbG+KTeO64j5ptvzT9oqo8zyWT0arITOgo2eYrDoxr1+KMFOiolO0stWtxeGnpGtW8Vxa+3x9vyGvr5yka5QaFdedc8cEKNKwyxA5qMJ512wmu9/d9Ep9AcwD0pG6eHAjurtuVNu2tOr5bz7/PugOzru0wuPDy5NV8+2T6+JciN8+ce5rYqyYKPcn74RjvcrAb2oL3Ns9Ex/SAAeurxqCaysHJcv/sccKkT9TLioae0pcnNUex/uaw3dCY9zdOV14gSjG+Sf3mXaL25TfuDLKOYwUVXl17QTpysQL+iv2eJLMyEr22/jS30MKwleKF/8qCOzFkT8DdEY44/hXMmAlTxFjeTma7qhrz9tHV8CUXSbN+slBFc4vpy7JBIzZilK2TvLsE9+GIweFS6AKnxBUX6YW4r7J+iqZZb//aMYmJYoJvQ2P02eVsoqIjMLW/UdRIL8OceFmxbnL1wUF1P5j3mhCguK9OrQSdFYMgkwhHY6ULPrde5H/25j3SlFYS4N1PkLDv99Y2KOhZgsn7Dp0AvVrWKB7+xYIefkKbfuNJm7aPUrp/5cHLKjuNGIK6lWvgsE9ZeiguvI1iZeQ+BGRBCqVLmUEptvafsATR06fA/e3OtMkT3Xp1IX17tgaFw5tSeZxc/FqoIhun6Ttwvom/B0P7K78oMOR+Dti06XrQtH2Hj2NPUdOYcuS6UK0XvGctF/OtBR89q4Xn+3LZkrB/8ltsZImuHv9Es6fOqLUvujICEF/pZNfV67RMbl/R6wmkINpOLoNGouSpcsJr40h7W0JmIlQSq948PjuTfBH1cKI/orLJqVA+antK+ZiUr8Ooow2Ts4oVcaMtEjWCxAntTLkGWTwzsvgZ0JPhXVKIkJfCL2Rx3cDoWdYBE0du6JQsRIplsjAEWuQjJq5XM5P/P5dNHF9vyctFZtk6YonUb48vpO8r5JFVghgUIx1X0qYKk9mlalgIcplbxbJNG0P59e2e3+Ur1JNSiq2LGIf+574YYn7n8Xqf8TYPfrfYqUK6yGAJuY9LyuvoHj1NhY7vW+gQN5cyEaC6hfuBKE1gSzuF+6gEdE4OTWuhvCoDxhNQIsr6YOkZreeh+PWs/BkUZ6/eivKlrqDvUPqj1yFvWcDxWRZV9uqYsK906ydWHv0n3l5YxH743P6ic+mMSnfW5jSis2KVtEqGntdZCPvlUCV9h8gDxb2Ylk3sr3cG0Ux3R9ZfseJuf3AHiqKxoDL/ZDXsCGPDckrpjt9Fz4LByB/HuXr8tL9FyKpXTXl8aGYX1r7adVT03a/jU0A191KjacJa9GwSX30/JWsL5vXLA8WrmcdAZ+bTxAZEwf7GuXRjML/S1bS2ATXA/xxylN5EUvkqwhBF5W/gK7QO7h0/hy6tG6Co+77wCLl7OXwKvylAEHmu05OsUvevY0S+cfQb7CisT4Hl/syNFQezPk1a1AN7nt3oibRA7Xv2pPOh4CfTTavSf4SJk+YyTss0j6kTzfUqF2P6ICGqi3Nxq6ZEN/et3OrHGhiwGnP9s0ivq19c3m6STPm4cAJX/Gp3zA5sCmPqOFOYkICot5EipX17HGyf9c2+j49wN+hqjEAw54ETCsbGhxEOhieuHvrppgsduzUTeiocBo7oiB6HRGOs96n5Fk8f/qYvGzOoXZ9a+TKnVse/rPvlDOvhCBq2/b1q5LR2LoTFRRfp1Wq1RDNvHzhHLrROPCkcdCAxkFHJ9k4mDp6CBakNg5InJzziXmrfhywRo1kq8hbqH83RyTSM19X0vEoXd6MKObWCuosdd+plC4ztxPpmt133Fd86qVwzb6jtsXSc1Zdq0bJqsJeH2x3Am+I7ZtXr4S2j0mZcuJY+lehsqV4fnry8J4UBE3KlkfWcEeTPJlqdt+Js2APFUVjwOXR/buob9NYjHlt2i3lc4T0jY4QyLlk3TYhWi+FZ8T23/SMxxPvIXcCcN/PU6lpPPl//dhO5MpbQK6TsXOSE06sIko4Eke3dhoFA9LwuHp0G3lFNBdggVIGCgfhj2+BP6oW/fK5KJtedsQp9qZY0as+bpIWR6nKdVCVqLfYG2PHuE64SFoO/4SZNWiB2KgIPKaJVMmiQp/i+c0LMLasLzxPpHDe3vI6QJ/96OCyDjlIU0LVuF/ZipevqnpKgDUcyLor2li2XLkFRVkxM+U8+XtKjH2HP7LlAAvGs/F31n/1cfHpOH1TmsWk1Z44ak+2nHmgX6KMUl5FylQWFDSRLx4qhf8XDvSLGSPodgBunz2q1JwPUa8QcHSH8M74I2s2cW7T+G4CXPhCQs+CbqpUOVw6vI2oruzB8VOysEe3EEpeRaoWRaAYly2NGfbQWOBUH1dP7AFTctVs0Q3vaMxsGE3U3HtSnttTzTejj1nMfduU3jAhejirjgPUZm9ez06AGpepzxiIZOMt656wVSA6O22tUKmyGL7+pPj0mJny9c3gC5sE4kjlFChsJH7vQsnjJSVjLRi3mYNh2bid8GhJKd7PGq5TwgxxpCHClFU8wa5ooQSovH18FbqmliI4YFFv3Ns5HX/Sbw1TXeUtVgbBJPh+flprfHz3fX5YMQ/eZ/CGP6qWQGAA5684//XIfSmuLCJ6NirDuEkPQacVdHob/FxaplqGat6ZefzkyErEPL2Jyn3mit89LisuIkgUqU+aKorGHh+//5GN2n9bHqxD/dZguof4VB+WMfe6ePJYyVmwGFhk/tUNLwT77qa+vSZovOQFp7Cjrj2cH1vhanYiz+iHAYi85YtP79+IMA6XrEiNZvhEtGivA89IQdQfzxB1zx/6FerijxyZ937yh7zEpJ3fiCdcm4eujq3sMH7uChw+6YuBTt8n0t2P+4h8JA+F/Z5epGWQBQ/PHRKC51zcmAHdUbZBGxw/c0EImqvWRdvjKQtWIyT8Fc67b0aNKhVEcpfhzmjZazgmLViFrsQHrZs/n9psD5/yBXt2pGZ6pGkygFZHamsT567EKwI+jm1bobSaQzUfTeI9D5Gtmp25bCPCX7+RZ8Hg1cTBvYWWiTyQdjTJUzF+avsMbEh2JfCu8EAKvPcYDKp1aWMPyyQvkqw00bmU6I5UjT1a1u08KK6DZjb15KdfhIVj6NT5GDugB4FPFjh08vtAkEf6H+xI1z2Pgcwwka80S5tGAbatZWDJ2ROH4NDj+0OB7zF3Ma6adegucmBQ4+YlP3QdOBoDSL9HMuOy5ljhOha3r/qjUcvv41I6r8025NljbF02W3hyLNh6WH4NM4DBnjP7Nq0EU3KpMwYsGIBJy6ybtRVgTVrx+Dx7ZLx985q8SQyE/smlMyflyYxMymDSog0wt6wpD1Pcef7oHrYsnYVWXUikscz3CdDQ509END2DQorRxb6RseyFP4YmALUx9iBhSjL+lK/8HQhhujH2mnnxRPbAr017WE+FP6p2YMsqAQzxd51FhRZANa4mx5k6BqgCfye9yKZVl/ZWlTF160khuN6veS15dA//u2IcdG0ke7g6eOE2/vj9d9wi/ZF8SRP4IxyJlqrfIpy6SiLjPZvK06Z3x3XHaYS9eQ/vBQNQrWxxkc3Ezo3g6LoN07afRqeGFgLoUZf/URKMf0geHqmZnk4u0in53sbU4mp77imJsufKnhV5c2VXSvo79RkLzj95GS28U7IQYBIS+U545Yyk/uPV1kf8v09gSYlz58gmaMWkYwaWQqlv2HuHPYRYw0UyBn8ku/Y4FBfIs+j28wgBlnUkrxXWW0mPaVJPTdvN8dgKFcibrCqmRQuKsGgSoWd7HvEWWajfmHqt3+J9SCRtFjYO69e8Jmb2shdi9SLwB/5l+hiUzSWlWUNeMT5ryjicOOKOHv0GyeOzADvfM1lfg+2o+166txPIeesJUWHlF2EDR4xDvUqm8Dl5DAwW/KixUDoDKR4+F2FRTfYbP2rSNHR3bIa50ybCsbMTGOhRZyeOHMKTR/fVnZKH6eoVFFoh8gANd+ZMHS9Aip20qCel762Arh7GTp2JhUSFVK1MMdSq14Am0i8JbRUWe5fao2GRWkVjDxi2JXNdBRgiJWbwZOjYSRiWRFfEtGOs9cIeBAxWnTl9QooqvF0WrdkEy+qy36ieJM7N3hU927dEtZp1CNDPLkAVXq0/zuXHF778L57HpDLkjUxhh6/rVjQOmILq2qULqFlP9vvGXr2njh4iUKUmJADg6EHZOPCjcaCTTzYOBtA4qE+g9plTxwQIkEIxGgU/f/IIy4k+jsGvzfuOyq83h05O6N6WwghgnEQ0buqMJ/h3ET1VWta0lQPKlDdPK5rW5xmcYjMolPw5y9hUNjH6lgAmNqNSxoJ2joHAypbVRRj/Yxoxfn568uiBPOyf2mHwsFqtuvLit6xdjnACgs96nRATdAOTqP20aTdnFhbyAlNHDwanZ30Z/u3NaEvpd+pHy+F8NR1XXFblxu1xYvVU3D1HnsCO/eTF3znjIfJhcIONAYEgAhOsug5H04HTRRj/Y12CYysm4QXRTlVu5CAPT8/OqbWupGUShoHrvWFkLntmt+0zUWicnFo7DZb2ndRSCnFZCe9pIc7hTWkWW8GqFQypzppanXbOeE46K9vGdkCJijUJpMgu+iFvwcKwc56qlM27CLo3kreNVbeRAhi6e1Z5YR5HlujEnt84j/qdhyilf0N6DWwMrHBZmppZvWbgj6r5719HOi4fUIm+F1W6MtW46o41aY8uAXNMK8af4uVl7wOcV3TYMzEpGxn0SF3WWoVJ7yuZOWa0qVBVu/bwWDFZiJ43aO8sT3rT57AYM7VaysYMAydPr5+HrdMI8tpwlccrbGImaKie37pEk/OO8vD07Hiuni6AlFGbz6BkBdmYaeY8CWuHO+AoecbUaNYJufOpfyaLpzFzgbwu0rIqNq1RWIsxw/l5rJwqgKNBK77PWaiWw/VqMdAFx9bOwJRmpWFKIvMMGrG2Sv12/eTtUU2XEceRIU+RLUcuITKvmB+/kxUsVgqRwXSfI5BH3bjZv2C00GVpOXiaYtJ070u/1/+W65s1N4rXa0vAyiZEPwqAvlkd0Tb2Iom4chwFSlsib9HSAtSIvn8RpVsNQYWuU+Tt1yleHneJAustpS1Wp408PD07ceFPhSYHC8fXHr9L/rxl1KAD/Gd3wLPj61HRSf338Dn2LYK8tqVZbNGazaFTvFya8VKK8IF0TB4eWIiS5LHBGiqSxb96hizZcwlPDSmMt7/RNZbbsCQBDU8FyPHb71kUT2fYfkLkC/KYicPpwdXx55eP8nzZq6bq4JVUV2UwXIqQcnuCBH0X67ZcWzEQf35OFEm4/sZ2vVDBaTqNFxmsYdK0D6JIZ+XyvK7QLVsdWbJmR9R9f+QoUAhmnSZKRf3Y9m/qSzXzxcmAlTx58oC9IjQ1fb0CsLOqjVPn6AWRJs4NCsp+QNkzpYihPmzr1RBZDe/TBYN6dJCDKhzIFGH5SW8kVoECQdNyVeO9e090SOT1wBRREqjCcRhw6NOpjQABPE6fEy+Lqmn5+CABQe4nUp/QZy8RbYGVE74Xhf7IvjXzUdhANkGjrnxN40mC7YX09bBpoYvwXDlGwBQDFq5L14v+5/ayaZqnuvqkFca0biwyzy84fGGVKFaYXN6/0eqsZJeUyIrr0p8m/qPexmDx1FGoUNZUhPOLaY8R3I7imDr8+0N1WuVnxvmEBNl1z2MgMyxvnrwCFNAk7wJEt1HT2g4BZ08hJvoNCZUbiGRnjh5AQfLKqF5ftuIvF61IWnf4HBhQULTsdMNmSyCO5h81j10bxApbh+4DlH5E2LujOIEOPkf2pwys0Is8AxlpGefDXjCaWHjwcxGNwYSiJUyEoH2FqrXIy+YymAJtQt/22OF1Xd5ninluXzEPOXLmQu9Ryi8k4SGyPNkTSNUk75f42Peqp1I9tm3dAWc8D2DzkpnoN2YaUY9ZCpBlhes4ke4vEt9i+5H2xH2IwVKXUUJ3ha+B4a6LRZ4/8i8xPg6ZNQakfFnkXBMrmC+3oJvyuvFYiJzrEzUTG3umFNbVkWtZDG5dD85ETyeBKhznC1FRsZcEi7X/qMXEJYK9I5huSwJVOE/2lunZpAbRkL0goID0dFKggTp88S481AAUivXiCfzMAlaCyPuGvXvUmZFBATwOixJUYXlyZkPfRfthQnWZQKCRpjZzpzc+JlF+liMghUEcdcagyuzdPnTf+Fv8lnDZTBkmebeoS6MujOnZNKmnpu3meGwFkkA5xTK5jmzs0cIWRMAKG1PNtWtQGQwOfSMwaam7H+mrXEa+3Dm16juRmcq/ePKEkcaKyqkfPpTyTdCQMpBpoXgS9yzRbTE9V0ECtNmOuu8XXgySR0XfwSPAk+0SqMJxmK4yX/4CiCW6yh+190Sr40GruCtZVFMCIRgc6NKzn6AhO3n0MDr36KO2KAaCWIA9NePJXQY5tDGfU8exfeMarCedOUM1FGCKefGq/Bw5c4InuBmUYOokNn6OYnHrnLnUj1HFPNKzz/oqbAaGhbFk7VYYm5aG90lP7Ni4FotnT6fv1JD6sC/psMgAmC1rV6CEsSlcFyxD1Rq1cf3KJQFcsXi91+Vb4hpg0KBocSM8uHsbt29eo1WWWcWkDoMQirRm6akvp2EvGzbpehUHGfiP801IKkOTbB3IW4eBlZMEpEjAiv+5M4h591auAcL58DjoQeNAAlU4jKnyeBzEZcA4kKj1nPoOUnoeq2fdCKXo+vUkgDNFYIVAi2XzZnCVUjXOJzOAlZCk6zCfGvCzqFEJUSf2aGFr5UjUHof2YxmBgaMmu6JilaoCaJk1aYw4L60wFgf/kn+so8LjmK10OTP5eNam3fxONLyvE3mX0fMcacpkhsUTgJqZ4+rzR9nY1aTueQoURJlajckjwwvxMVGCkorT3TnjDh0CD0yrNxTZMP3VwHVe0DeSLXSS8s6a9K7zmQScf8QSY2OETgjTU0mgCufHK/9rtO6JoMCLwqumekvZojbVshLeR8Nnc9qLBwoWN9UKWGFR6vyFjARF10taxc6ULjwRyhOuEr0W14Wpmfa69kXB4iaw7TNBtXryY71iJihatgqe3fDDNc8dBHq0FQBEIGk63D3rIeJJ7ybyRFrufKR3JaZ4Y90V/r5ajlAP9KaWrabtqUzAwJ0zh6jv56JJ38koWq6KAFkYbGNjkeMftS+JmXsvypEjh1gQp+k1nJfGjBlRud0nkfW4d1HIq6svmnjT+yDy6RdGWaJ0Y8uROy9GbvKBYQnZfIsIpH/ZklgNWPT8R4w9J1gLhCmvJFCF8+MxU6dNTzy9cQG3z3miTuseaouJj4kWWiZqTyoEGhiZagWs3Lt4ChcObECf+buQr2AhhZyS7+rTeGFvKgZ5Hl/zIyBQdv9hT4mvJHbN4EdmGIvaq9N94bJ0CxslidLHJovD9GaBPofQpOcY6BYqniFVk36vc9NCgcww6V7DtFSaGgMXDKxEBByTAytRd8/jS9w7mHWU/b5lpXuC1czjyFPERCnbLPR9sjG91Y9akNd28Rti3KSX0vOWQaUGyFPYBC/9D6cKrDwiwCMty1vY+IeAlcfkUZMlW06UJ20VRUt4HYxsefIrBsn3cxkUFyL3LGqfUhx55HTuxFP5/J0zkMEeJAw0hZ7bi+CzexCwsAds5vuo9RxJuT0y54ery/oT8OaA4vUd8TfNBTw5skJcK1lz56c+kD0fZs2dD7n0ixEl3H3EPLsl7pt04xTAizbXYWpNZ0+cvER/rGrJZsEL0UqiV2+ixESqpiufnRybi0n8I17nwOLlwS8jhIYGeyAw+spW1qQk3sZ8wLJNuxFA+hvsWfIsOAwsWJ4a4KBa4ZSOnwSFilPxBAp1HSq7oUpxuQy2oNCXUlCy7dbFrgKoSHZCIUAdMqVwOtnuqzfR6DduhqA9a21nney8FKBpPI7PHh1ebmtRrZIZcuXMIbJgsKeNXUNUIO2aReu2CyBJmzylemizHTewJwb36Igrgffg5nECc1dtwfsPcck8VRgIGjtrqbg+mDZu29IZaFRXBrZxeTOXbxRC9FeP7UoRlNGmXj8SV6Ip4zGQGVa4cCHcvP9U46xZR+PSmRM4f/ooWnftK3Q+Hty6Jui+pHGVi6ih2DsjMOC8ADgYIGCBc6bHyihjjxW2Ewd2gEXWFe0zvUSy1wzzqWenh0NVYxosn8fvVIOTHUtuy8lOqAmIfR8jQnnSbta6PXKqLRahZxBqx6r58CEAioXnFe0lATLnyAOoY7/hyTRYstIqMDam01K1T0S3wZY3n2yCVfV8SsfsWdK+9xAwAHTlnBcYtOH8WQ/FpHxFSJML6WkPv1gd2b0Jmxa5ijyZHmzA+JnIReDdjxiDKvGkm1O4cOEfySbFtNLYYlqt0sVkLwMpRk46wTogp649kouch0bG4MaTlxjpaCW/v5ShvN7FJmIVCapffRQqKLp4Apx1RQrp/lifcDUkjwamfuq1YI9SlVk8ne3F6+TXjhRx/cj2WDO8nXSodqtm4YPaeOkJzE4AENOnqbNEahPf2/LmzI65e87gPnlinF82RCuwI+LAdKH1wvojM4h6zWbMWtzbPA6GKh4go9pZo3+LOkKvZB9Rqi3cfxbvSY9lQf+W6qqWYti8Pb4a1VPTdmenyWA21oZRNe4fNonKLCY+EX/SS1frmuZYOdRBHp0BN5Nus4V4vTaglDwDhR0eH5k1BvklXodEll+FhymUmPpuu87dabX9cZw+5oGuvZxpRXUwbt24KsTWpXuRaZlyYpJ5w8oluHktQHiWvCBaKRZgVqc5knqJyc8yzRQbe/gN6tlZKQL/ZrGFvJA9eCudTDpYtmE7gQpb1J36HqblIGTdGRbcZtqzpi3bfM9HzR57zAykZyZegT5pxlwBDj0j74PVS+YLHRoeg7MWr1ST8seDatatj33HfMTKfwm8YQCpacu2sCYdkLXLFghg5X2M7N7KQMC6HfvA3ylbxSqWiH7zBqtIY4Sp3noT3Vk7e2uhL8F1buXYEdmz5xC0YOOH9UfPDi3hE3AHxUuUFOnT849p33ISCJUvX770JE8zTWHyrHkdkfI7gWoGTEHFYANT4k2bv0z8ZvLEPz/3tHDoKI/OnisMtmxctQSBV2XjIJi8VDNsHNA1w3Zw9za479kh9qV/TAnKVGApPY9x3R5EqL8PSHnwlilWM8OyZZc9Z7H2iKoxlS0bA1Bsts1aEq3eMGxdtwJ+PqeFJxqDq9WJbq9chUoooAacEQn/wX/ct/ybx5RuC2dMQRub2vC/94IoWjVv93ICvh7dv4Nj568LGrHMaM5rGlt8/WeG8TMeT1LypKRE/5RWOVVJm+OR/ykCLo6hZptegn4r7MENovsaKX/Gy54rD4wqVBcAx20fd7yliUmm6XoXEZxW9hqdZ3otNp5kdCMBd0WTJrzfhqf8XsVUVDPORCgmU7ufhShYtLF1g5oJcfbWoxeRR44j/qBr6TGJcR+aP0x4sYzcFQCmDzqzZR6Jwd/H0K3nlTQbVMvi+3W7SauwfVxnymM4PJdNoPmmvwS4Ur1VD7AYvCHRRaXH+L3kypGt8N44Gx8JqKrl0BdNB7gITRht89O0PewpU7fDALB3zJMAmUg5g2QlK9VGIdMKySamta0Hx2eKOO43fX3N3lnSU4aBYSG8f5P29SPlXYOEzRlAuEPARV2H3ngbEYKQ+zcE3Zf0TMZjplTF6nhKIu03vA6SF0+Q8C6JTuU6lvLXZCvRWX2mydOtk3oqJZFAm+iXKY8ZQxJqX+T3WimdugNt5gdYd2b3jIGo3ao7Klun/l5xy/cItkzsLujCWg+dKcAh9hTx2r4YFw9t5uXo6DBusboq/XAYt+n9+1dq8/lC93F+HsyRWyfZeZ+dywRw1bCL8vxGsohaBLwnOjf2qOZFMZlhurq6YuHNR6Jm0tRYfD0veXFEXD2OSr1mi/54eekIfs+aA8Xqyp61mc5Jt0xVRD24JAAOFktPjApDQmSIpsWkGS+evDrYQggQCPXbrxSfvTA+xbwmb4xPBGwkn/9ir5pWO1O+/qXMmJYrvcb0WOFXPGHafICS7gjnx/mm1OdMa8bXN4NTmWVVBy2ne1E26BiVF0XkIQBJj7xHGPR46rmGvtsTMGrQXqn41NrzJf69ALmK1GoJS9LSlYyvgeN9ygvqOAlYYY2e2LBHRI02T3gtZaE5vteBvggkVpPL87qh0WI/5DYwkrLQesv6ZgkxbyDNaSlmkGwUVapUCZ8/f8GjZ8EwL6uMAiomVNxv1rCe8ERhOjAGVg7SCxxb93Yt5NGWbNhJHhUb6OUrK+mfWMKmbnVMGNQLyzbvRnCY5jcTKcMY4rBVNPZYYctOHipZVX4cmP6rE1GWmZU2VkyitM+eLfQ6oRT2owcbdtPDH4FJH+ISBMAi5RfxOkpQ4TDowl4wHz991igegxnsIcQfVWMPFgvzsrhAeieJHz9B07I5T01N8k6RAKbcuXKK77FhnWq4ePUWjp05rwSsuHmcFBRfHH/O+CECiMme/fsPCHu4LFi7HcZGRWm7TV4NSTeH23Da7xJGOztBkYZMHjGDd249eIxixYoifxKNSQZnDx5b+w+6i9Wp0oNPamXUadSMJvPzCzCAgRXfYwdEdPv2TvJkrOMxqlsLopV6IITXzSyqE2VXUxKRzYf54wfK42mzE5cEXEhpGAzg+krggxTO28o164lDfjBXZ/zd/6jmh2q++oVkL4RmFjXkoIoUp45tMwGshDyTTT5I4bzds36JWOnVomNPxWCxr6cvA9PUAVIfYt6KOPl1CyZLl1bAsGkL0bC5A25duUAASAzKmFdGg6Zt0LZGKVRJ6jtt28OUZDOH98K1C2dgUbsBjbEFKE35ZoQ9uX9LZFOxYsWMyC5ZHvxiYmigjztBEbBWo2eRLAEF2FUvR14AOQQdWK+mNXDo4h0RrUsSDRgfrDh0HnPcziB71iyoW6EUrIl2ZUx7awG0hJDweHqMvVQke5e0zx4qqt4VukThxZRl5YwMpejJtpzunzRD8vR5RnRXUSQUL3n9SPXhtrGnxl/0UrzkoB9KGhYQW+l8WJSs/zafvALvG09IV6U+GMgSqybpd0EykyIFwZ/ffv8Ng5e7w5towbqRvkqy+wbRiFlVNkGDSsbwvx+Mk0TVpg2wwh4umtZTk3Yz/ZlhAZknVLAacEz67iWh+sJ6spedLjaWUtPFNg8BU/UrGuNM4FO8oX5m4fv02Ot3sXjz7gMyawxynfhedP+ObKxrUsdGTZsLTxSmpGFg5dhh2QtG+67d5cnXLV+EJXOmi0lEFmjnFfRDRk+kCealCCVqG22NJ1EVLead7JgnKbOSd4Si8UthG6JqKlPeTDFYaZ89WzLadm1eLybR2RNhNAEskkW+Chfjg8PYO2TwqAnUZ7L792iiLpNovxi4mDp7odDjOLx/d6YBK+x1pJdEXyXVkbc8mcOAAQuB88R2oSKyeyvXTwJVpPisAcPAyrPHj/D08UMBqjCdmRNpfEhm36qt8G7ZtHqZACD6DRkpndJ6e+92IMzNzcULtdaJNUhQqVJFeh47pEHM71HYa2UeUc7dIA+eihbV4HXcQ2jN6CiAPxtWyMZBdrpOa9A4qEvjYPCYidhE44ApnrQ1VRDiPY0Dfh6TJusV82PQjo1/c9WZmKwhsOqfMn263thCg5P3A4NRbEzHJ9nUuYvRrI2juD4Z9DOvVEWAgTXLFRcApRTvn9ryPVD1Psheafz5/bffMXZwHwIbT8KopOzdM612s9D9miXzRPy1BLhKFk6Lpdh2bV6Hc6Rp1H/4GJiWlU1WSHG02d6/Ewj7JrbaJNE4Lt9buE9ekTg2AyGaWLk6dkJI/R7RgTGwwh4IbJb2XeTJY2nCdMtIB6KpeohCJuakB1KVBNibiMlHBhnSYzz5L1kirb5n4wnPLFmU7y+5dHRRhSjLUgMceGxpCiRJZaa1ZdH3yKAHKEXaFbXafr+/VKAJ45C7AaT7sgb3SJuG6cLO7VwC3SIlxVbKl2nN2AI8NuNxgLegUWONE+6/ETv9qZ89SFj+EfLqFULp6tYCtOL4hqW0v7bY+2D/DGc8vXYWxiQG3nzYHBQpnb73iD9p0kqb9rQYNhcVG7ah+vuTx0EMlVsJ5lYtMbdNOUGJxm36EQt/cpuoMMvQ/JUMIP2RvFJKy/ejsMe3UzqdLNy8XlMaM/kRSOAAAys3yYuBrWaLrvK4DDKsGdoG7OVQxNQcJYjezqxuEyGsvmfOUHk8bXYSFMYMe6yw/UE0O1kUxLE5jGm2qtl1SNXThMeM5D3DaTLCLpIUANfrY0IsdhHAIhnTovHvEoexB0yTnqPJ8+OwON2s/2S5xw2DPW2HzwGLx18/tS/TgBUdPUMhcq/ocSTVlevP3iyqNGDvaDzfOH0AlRu2TpFeTcpDmy3r6VSuXEmbJFrF5UX6ZcmDkzVNjOq30zgtT7rfJyH6d6TLkd+kspiIL1LDXgiscyafYiJxcXZHxNEEOk/es+6KoYWtAAt4Aj099iXh+z2B03+h3xN6qSVAJ/n7g175WqKI1Oa/2JMkM+3p0dVCD6akzfd7pVRejvwGJCD/HJ8/RCF7PmVQ+HN8DHmqFBDUWlL8jN4WIMovdca0agysMPChaqm2h8To2UpYfV/IxMcMsBU0r4s3t8+S3koUvlDbOO+CRCFn3KQnRxHGlGvvSD+H6dsY1Cnd4vu7ixRH0y3nz9R0/LyjaslmeziSnp4uvM5f1hhY4cny9i1ssYX4fhlI2H/MC7UsKwrQgAtk+qfJpH+ir1sA930PIm+e3PJ6zFuzVb6vbod+d4WpvihIHir8Q8lWqrjshdC0pJHwihCBSf9YIDQuIRG5aGVZSrbtwFEE3k3+JSvGNyTwYtLQ7w83iufU7Rek9lYqX5o8c0KVTn8mypS/aCL69oMn4uG7klkZjeJxJl7nA8TNgenXFI37gT2F8uTOJTxZNC1bMY/U9rn/C5hbEYWXCfw9tilF5ZujbgEd3Hn4VE4HxtRfvUdPF9fBjuWzYFRE9kKllJAOKpYzFUHcF5JJHkYhL1/hfWyc+O6kc5m5Pe0XgEaNMudlg+ttY2OD2GHD8JC8TlLSAFFsH78427RoR2L1W8GT++yFwZRXkuYHx2UhdgZVWF+FdVYk8/c5Ie2msiUuZDUv4KFBsu9CGltFjEqRyH0gnAaPU9Il4Yw/kos0UzIwvZY6Yy0UFr5Py5p37CGostKKx+cNi8rcX9lVXNW+kOcMWx6dfEqnuJ7sbcP9pyomzxElgXp1wArrsrAxaKWNsSdPIlEuVKxWW3yktKznEkd0F6yDw6ZNe5gmYiJRnfE1NHbuKqEVI+WbEdsr57xRslQplChRIiOyU5sHjzGfwKsYpiJ8rjYyBbLXgUP9SoLejb1S3C/cRY1yRviufZGA6Tu8UJAAjhvrRivpiCw6cC6lbOXhv+E38XssD0jaYSCCjcdBSUNdsc9C5htGdRD70j/WFIknz5icKdBfcbxd3tdxi3RFUjMDmtwf19EmtSjpPmdaVF+AGMGkn6IIrLAHTvDrGNQnkIPNvKTsd/rui+8ri+KSqNRCCaBiOixuK9NeMf3Xvqnd0URFfF4viXLsJXld8H2jSAdXmJUwhO/iQUr1F/cN+s7uUVna0oFpWk9N282AEFsweUOpGnvwsLEuBltxfdnCBqb/UrVPdG/ndjGlWnrtzM2n9AJPVAp16qQ3izTTNWrUCBs30Yo8DY0nFFq07UBi9ZsFkMA0YEwRpaiLwPonPIHvd/MRAft55TnzZHzqJnvIU33GCyItBTbpPmRUspQ4LmViiuUbd4h96R8/47FHgOSNIYUrblk0nkXYUzN9Q0MMHzcltShK53QLEiVHxcq0Sv2ZUvjnz5/FvZVpsqRFFCxmzYCQRZJGiZSAJ5LNK1kImiP2FMkMAMjvjJfoR2tbO6lYseW+5cn+3ESLxX1XtJiRCOf7jKp9ogU7bAwicFvYatW1ElvFf0wNx8CKRHOmeE6bfb8zpFvVob02SbSKy89jc+fOJU2MEEg0VGll0Lp9F8yn6/zU0cMkMk8ekbGxcOzSQ56M9UH4PI8D3xvK42D1ojTGQdLLjibj4N7tmxhEYB3TTSka06fxWEhpHESRfs5KEr5Py9p36ymot9KKp+15BhzYQoODkiWVrqkq1WqIc+x5k0AetFVJv4c/krFeCdOFlSXvoX/a1i6dD6b/Yq2bhk3slapTIAkgevUyDFaNZONOk3aXM5e9qPNvh2SSRx7rS8XSosJ46pf02hvysnt47w4WzpuT3ixSTWdqaooiRYvRRL6PxsAK64ZUsnHAtWM7xKQoe6QYVahBNFKy90Mu8NzOpQJUYX0V1lmR7CF5uqRpNLbUTX5FJQlI8+8ggxJsTKXVcdoGsS/943ccpt3KmkShJIUrbuNIl8F320LFILX71Zp3E3RVak+qBL56fl+EGFepp3IGgiKNgRWJuojBErZXT2W/zbz/mWhe2Niz5xNpnXAbvn39QschyJVPD9Vbfl+kx/HO7VpGIIuh1l4e/C62c0IXhD28gbZjlwrqNM7vR0zT9rD2DreLNWEUdWHYA4n1XViD50ft+dUzaN240Y9mk2p6W3omc501N0VdDdXEvNDR0tYBl49uF2OGgYBSFWsI0ECK671tiQBVWg12hW33EVKw8HSRH6S4o35+4A1pg7DxmNFLGjP6RiboPmOjUk48Zj4xzVAO9XMDHJm1TE5tXqCUTt1BrZbdiG7MQt2pZGF5CuihKAF6UaHPlc59+yp7Jgt/clf+TBb+7J4AhEqqAMB58uuhWNlKCH0YKMaLNh4zSoWmcmBQguYGCQiMDg+WU7lxdPaY47DS1eonS33p8DZxfdRupTxuk0XUIoC/xydXfTFmRPqANk2LsqPxs+WAp6bRRbzi9Rxx320WeWQcw2eiAGO9jhLWneR5PPZYIUAV8y5TUKb1EHk4C6Wnafy8pWb+Kz5Cum7+FlnkNiyB9y/uoGzb4Ur6JXySqaB4Du0P0jFRZyyq/sh9ibpTSmElGnZGSiCEUkSVAy6fvWh0y1QTmjMqpwVFWvTDy+TBE6oErHC6RPLqYTAisywxOpwouAJRwLQKcpGAvaIlvAkRh9l1vi+i4YC02iPl8/dfyd9P/iKvIfbA+SNnbkQ/8Bf5FzRTnifnQINKVgJY+ZrwXsRJ77/IW77Q0zeAmZlZsiySASv8EtimTVvs9jiFkf26JUuQUkA3h+bY6HYYi9bvEBPsa+ZMkkdlDwQevG2aWiuBKmERkQJcMEzSZZEnUNgpUbSIOHr6IlQhFPD08VM6NilRHAVJXN77QoB8cl+KwF4RrD9yZt961K1WRQpW2p69dI1E032VwlQP2LtEG2BlMGnK8EfVapGLInupMP2VZJrG2+h2CMd8zuPZxaMoWshASi4oubg/myQBLtqULc8klR2+LszKGCPw/mOwdxB7AUnGoMhNAqUqlistp/OaumgN8uXNgz2r56VI9cZ6LIp9IOV3nDRjHJ3HYDZ5ubRrnnlAh1Qeb4NCw3H5xm2Mn+qqGJyh+7z60rR0aZw+5KYRsMKFMx0YUz7tXrsYzx7cwbh5a5TqJAEBHE/RLp05rniodr9w8RLC6+EbrZJjfnQ2BmmYMkvRGAQ6e/yQoCVTFHyPp1W6HeubkbdEJSxzO6mYRL7PcTwJGErLqtSqrzGwwh4wlnWscPOSH8JePEPxUt9fvpg2jY0BFEW7FXBB8P3btHBUDJbvs25NlZr1ybPkIsJDgki7xVic477x9tgrdG1YI0Ub2712CQ5uXY3dZ2mVBmnISLZ/80pabZoDrFHDpk17/L2P4f7NK+gyYFSGgyo8oeNzZB+6dU7+myXVPSO2Do6O6LBvH8Ki3tMkdX6NsmQ6sK2nrmIZeabwRPzywW3k6cJowp/vLy1rmyuBKi8pfwYI0vIcMCIPjbO3nilN7j8MjYSku8EFGZPAO4vL+9KktyoIwN4Tc9x8cGJuP9Q2Kymvl+KO353n5HEje0lWDFfcNyXQJrOAlXYNKmG71zXs8rmB6mVlk6dctgdpv7A2in2NcsIT5wJRgKkae5R0mb0Lrj2aom29iuJ01IcEseV+UwVWWN+IrWKpwuIFppyRAW6ThxJ7ACnqvNylsFvPwlGBwBxVLyCRQQr/OK6m9fxIwvJptZuLYS+UuuYlcen+C7wgvZVShfVE6fxds7YO6/lUIX0dNm7vcroOD56/LbypRCD9Y9Dv8sMQVChViDRm0g+s7KPy7OzskCuT9Da4vg4ODnB1dUXART8SUbeSmpDqtl0XJ+zeuoFooxYKXY15y9fJ4/OKah6DTC2lCKpE0KQie8YUJDH0lKx4EogrASlSPK/jst9y6bgkaX4wCMEgAa/sVvRaWU2rvFkr5ODJs4IqSEqjuGVNk7REoFlbQhtgpVf/IeCPqjW3qiG0Fk5ekI0FPl+6HLmq06Swz8ljaE6r8CULCwkWGiUmpctmCqjC5bAuh/cJTwTQ9V2oiOw65nCmbOPvSAJcWP+lTgNrIULPlEbSRDjHPX38CG8EoCZRfB0nD6aRE11EuPRP8swpZ15BCtJ6y/ViXRi+TjPLGjRoQIvI9HB43y4MGTtZo2IMiT6pHgFHpzwPIfJ1BArTBHJdK9l9nDOQxoGdmnHw4G7q46BYksYIi7MrmvcJ5XFgUaOWuI59Tx9XAlZ4wt3aoowA+nYdUT+xwIDEPgJH0zL2OGNNk4w27j/24rl66QLR9j1HiVImoggez0dJP4nPS+WuJ88f1rTxuXZfDuByZNb/Yfo19gT6p62smex+ePGsTzJgZe+OTaJ65Ql41bTdDMofV/jNkNrHvxnOXdpigutcNG/bXgpO19aDPOOYCpKBxcyy9u0csdv9gND64DZpYpZEB8Y0Un40uf+KJjwdxi9XSiZRfnE8RWMKsbSsQCEjPCNPCvaEkFbWR9IqfqYTk0yvmDFy04Tqkyu+SvH4PHtPeG+ag/6rT6Bk5eSTNRznY/wHoVnC+6lZqSp1NQZWDMm7hO0uefKo6qbc9T0szhUyNhNtGrbtgjhW/Pfw4knsIMDDfqCr0FLhcww2LOlSA5VtHdFpuuwa5fD3kS9x79xRVGuh/C7J59IyLif0/jU06DIsQ0AV/o40bY/n8om4dGAdRrldUwLi/PetxR9Ez2NazTqt6qd6/jV5DL2kyXiHtctSjfejJ9u2bYuxY8fiwWVvVCBvFE2M6cD8D2+B946lCCdArdOkFUrJoiNeiGOOp2j3Lqh/X1eMo1vECI+vKI8Z9nyJIjoxyfSLG4NBiEcBZ5KNGW+i0zq+fjaGrz9FVFspjBm6Fi8f2S5ll+LW1LKuxsCKFdHC8UfVFnRvIOgJx++6KD9ViCjvGCji/rBo9P2dkmnVQh/cBIMfmQGqcAWqNmmHSx7bEOC5U9C1SZViLxqmUaxYv5kUJN8+ot8m9mQpU91aHvajO4+vEh1zdGSmPmtxHflZbsmSJYgJuq0xiJBTt5CYCGfBevZOyalXBPoV6subLFF+Gal4MLzWAFjJpV8cb+74gSmdWLeKjb0QmIpK0Ri0CA/wxOsb3krAytfEWHgNrYl8Jc1Rb+pBxSTy/a8JHxB8Zrf8OKUdBgDSA6xEP7hM9f+CorVbq826WN22VP4uhJx1E5RpUqTwy0eFmHzhasoLraTzGbH9SrRdV5f2RclG3WDhvEgpy3CidGPTK19TKTyt9hSytMXTo6sQdvEQClk2lqdl0C360RXkK2EuQK68xWT3zXDS51HVnXlJbWeT6MnkmWi5E3HxIDo6Oqj1qP9DXV79+/fH5s2bwWBDwzrV1UVJFlbToiJMShbH8s1uyJkjO9o1+/7QW8bYCEwbdYAowuys6qCscQlcunFHgB065L3CuihPgkJQhsJVjT0ashOFw6pt+0T+TIN11OscfC5cUYrKVF4zxw7CwIlz0HOUC8b07y5AHAYh5q7eInQ96lStrJRG8WD70pngz7/d+nRsDU9vPzTrPhSzqL0M9jx89gLjZy8Xk1gzxwxKVxMMqtggnsTbE59eTjE99ynr19g7DcGUYX1R2FAfp89dwu7DJ0SaaSOdxTbmQyzuP36OKkRNxpo66syqVlU0s6mn7tQ/ErZ6+z4UKVIYzZs3z9TyBw4YgKku09BntAtpbMgm71IrkEGNYiVNsG/jcjEBrwoM8GT/Zd9TWD9/Kjr3H4V3UZHwPrIXfic9RLasucLeEUwppmpmVaqLtLNH90Orzr0FoLJ77SJBIybRX3Gatk79cXjHegJ3FsGgcFECLWrjzaswrJs3FSzo3mPYRNWs5cfsHXL2Waz8OKN22EPHuVV9uAzqiv5EqWdQpBhu+J/DUbdN5B1SB6xvomjXL/qKQwZPUjKnIeMwrmcbkWf3IeOFpgq3mcGrBVsPK/2ANq1gSK7G8fB7IZtgVpenlX1rAawsnDgYo2etQD6iqzmxfzs892zB8OmLwZ5AkmnanttX/UUSLnvVrAlScqUt90d6ONL5mnkdHgpnZ9k4Vso0Aw9atWoFA/2C2HDsMmb2Ul7lmVIxDAYY02T36iMXkZN+69skTfBzfPZKyE30UocIJLCtSqK7RFN1hSa4WSSddUMSPn7B05dRKWq6VCVPBC+irRq0/CB6kPg8AyrL3M9DJ1cOSDRQTOU1rbsdhq06DOcl+zHCsQGBODlw4spDLCKdEGuitqpVPvn9S2rPxtEdsXG0dPS/3zI9Gn8Y9DDMn5cAgbIIJFBj6taTqEOAQtdG2k2iNaF+Zi+UDcdZrD0HGlmWFhouHv73cJL0cCxLFxNlcEu5r3ot2IvWLlswvpONACmYUmwvaaywTezyHTxfc8QfU7edFAATx/1R06bdo9pbo8OMHehJdR3TwRr5SYSegTz2Ytk31Uk+/rm/GFw5fPGeoD5rXrM8PpDOzjzSp/nrr78FAJXeejPYdIFAuJPzV6U3C43SsWdyzZo1sXnNco2BFUvytGBwg6m9eAK+hcIEnzH9zuci4UtP0pxo2LgpTMqUJZ2BS1g0exry0iQe66IwqMDggarxCm32iNlKk6icf0FaBXTK0wPnfb2VorInx/hps8FSL3Q2AAAlTElEQVQaHsP7dcfAEWNF3gzArFw4R1CPVatVVymN4sGKTTvBn3/KGIDhurqMHSa8U+xatMaThw+wZ/tmAUoNHTtJ66qxZ8jsqeMxfPwUjBg/NcX0nXv0FWV3bdNU9GEp09J4+ughZk0ZK54bx7nMkqedMH0OWtnUETo2HM6rz/3Pn4UbgWrcv41J+4JBePZMuUATyk4OzdC2Y1cUJ2Dg1LEjOHpwr5jwb9Jc/cuevKBUdjatXo4qVSxQvbpm7x+pZJXiKb6eevfujZ3Urn5DR4vJ+hQjK5xw6OSEkc7dSZ8lHANHjpevfuUo0jhg7RUrW9k4YNqwJTQO8uSVjQP2uDBOYRywlzKPgxI0Dng1nBdpGl1QGQfd+gzErk3rsHbpAgLJipE3R20Bji1wnSw8OYamAhKxxsrjN4kKrfnf7zItXm/S4BlCOklMkcaaKutI44e9OdjzQ5qIb9qijQBWJo8YiJmLV4E9QA7s2oq92zfBZd5SOb2WNi3YvGYZ5tJ4GUZeacNSGS+a5sleKmXNKmD7hlXCk6tBoyZ4TZ42JzwO4gyBIZUtq8PGTvZOoWm7NS07PfHYI27nprXo2bOn0C9KTx6apOFnyOXLl+Oh/0mwBoYmxrRhDG5c3LdaUGpVtPk+0cnpWXCdBe5Pr3MVE/hx797gltcB0mWRTZQwSMKggTpj2jBOe2D2INRo2QNvw4MEgJMjD41Jot1h4wlUu/7ThHbJPqK0suo6AtlJ/PvhhRPw3b6IPESsUaKS8oItxbIMSGNl1rk3ikE/vG9AlFylqzcU9FpbRjnCgqiVGCS6f/4Y2KvHgCaGzRpo986ak2iijS3rk1D9EQE6mDdoQSvkg3CY9FbyGRRBs8Ez5PVmj5gTq6eiUa9x9BkvD1fdCb5zSQR9odX2x1dNUT0tju36u2TKJHUFqxYCWDm8cATajF4sPHFuHN+Fq57b0XL4POgWLam2PpoG+u9fCxO6XzZs2FDTJOmKZ2Jigka2tvDbs1pjYIX1UxjcOOu2SowZi0Ztlco2KmeBB/5e8FwzHY26DRceIkwldeusbMxE0ZhJTBJrV0pIByWJNozT7nIdSEL0PcBxfXYsQ04aM98pwLKh5aDpYFqxHS79hFdMDhozd8+fwOktC1G2RkMYV055zDDt1lL/aNWi/2fHVh0HUF2P4+CiMeSdchOViD7uNYFHl45sE89kdr3Hyuty1m01PFZOQdM+42HfV/27tzyyBjumlvXAHwaWmBasQj074SHjsWKK0HxhLx1FY92gsEeBqFDfXumZQzFOevb9CICsW68eypf/cc+u1MqvW7cuypmZI+jEBlQdsjq1qErnWMT++spBBG68Qtk2Q4m66nf5+QKkwxIZ6IP7e2ajdMtB+EweImH+h8BADBtrrnwhcCMbaXqomm5pS5H2xpphYvKfhd6fHFkpaMa+0ES9ZKWa9ETQ6a3iHAM7DLR8fBshPGk477KOo6SoybassdLGLSxZeEYFvLl7XmSlzjODTzAVFn+CfXcjO9GCFaraGO+f38bdna4EahB7i4L3jzZ18uxZWniXtN0bkWIynRJm0C1dVQA72fLqCvF69hoNu+AuAK0iRMvF1G2KlmZ7qM5M9cbAEOu1FK5uDwa4Hh0g4IbyrtBV9g6kQ8AKe6YwcOY/pxOJ3LcjIfvieEX0Xy/9D4OBlyKUNr0WeYvAyJdPMaD/frVZqAVW+GXGrkkTTF20Fhfcq8kfdNXmoBDYrW0zAZawkLoOeStIxtRfG2ji15nEldkTga1APh0snDJCAC59xrjComlnJDyR3ZyldLxlwGTP6rnoNmwyeo2aJn5QrGlS/sC6hbDpqDwB2KtDa9IX+YxJ81bC/cQZkc0ftLq1V4dWcB09UON2KJb/b9tv2rCu6MvJC1ah/YBx8uoVL2KI07vXoDLRiqXHmM5GlYJANR9HAssiXo/A5IWrlcpmsGvTQhe0sG0gklwm0IztFnm38Eed8cvTvwVYYQo19rZaunQpcetmUVfdDAtj0HLuvHnYsXI+hros0Cjfpo5dsWnxDFjZt0FuejlXNKb/unPNn4Tld4oP92v1+o2w68wtTO7fCW7rlghB8x5Dkz8IdHIegXvk/cBeCvxhrw07B9nKFgYUJOOX/SW7jmPWyN6YQdoekhmZlMGcjfthUUv2vUvh/4tt+crVsHCbB+aMccZYAkMkq2vbHJMWb5AO5VsGVpiuzLhcBXmY6k6NBraYsmwL5o8biCkDOovTTCnG31OthsrIPrs5pzVeGMRhjRUGoJxsLUR+/P217tYPLDavaJq2h79rtsM7k7dRyq/PqKlaAyvsmbN5sSs6duqE0uRVlZnGK80nTJyECePHkZB5bRTT0GulU0ML4RnSsraZAD2kOubNlR2rhjlgyIpDwrOCw1lofE6fZgJwGbjsIGoPXYHowzOlJErbIW3q4dqjMPJAuCM+7J3QsWEVEYcBFsmcSC8kkTwgpm07BQYQ2P4gfQ4On9qt8b/6/sK/C3smO6HzrB1CMJ5F49kYANlGIqbaeIxwOvZg3D2pmwCZ5u/1BX8ka1HLDPOdW1DfyH5L29StiFd9YjF9+2l0m7NbioaC+XJjzXBHNCNgQjLWeWFwgr0fMsK0abeNRWmsH9mewLND6D7PTRSvQ6DR7N7N0LiqMiCwcXQHjF13VLRbajvH5b5sqKF2kLr2uRK9WlVLCzRtqtmKRXV5aBrm4uIiFhLcuHpZeCFoks6xczfhGWJPK/IZMJGMvVQWrd6EMYP7ok9n2cs9T5hOI42EnLlyY9TAXmhcqzKC3n6Sksi3PMG9hhY2DOndBSOce4hri70mNrq5o11Ta3k83mGR+I8fEzHXZQKO0+QlGwtudnTqjXEuM//VY5A9g1ZtcRNgBuvR8IeNaZu4n1gjRlvjexB/0hovNjQBzN/PXNIH6dfVUV5MkWLFsdfTW2hXSIE8GbyNKBvGkD5Mz/YtpWDY2rfA4jWbxTGP/1VbdhNINBxHCEhRBMFY52PR6o3p9r5hujb+bt3d3eVlZ9bOqFGjsGoVCTnTxLjzMNm7SVpl2dGEP1OnJcTHEw1Yd6XoPA4WrNqEcUP6Cg8DPsnjYOocGgcEPI6hcWBXuzKeRqsfB6tpAdmwPl0wqr9sHNSub431pDXYwd5aXg6DkDsOn6Q4PQXAI51gsGbdLnfU1NADTUr3v97Wt2mMJeu3Y8IwZwzq3kEUn5ees6bMXgRrAmUlY88W7rcFMyajSa1KSfF00LV3fzj1HShF02qr6XjRNFMeB/z9jKTfreXzZ4qPlJavk2nzSWg4SfNT03ZL6TNj60aea0wHN2aMZtd6euvA9BgODo44QyLm5WrbJdMKSClfy6adhGcI62OoCjdb0cRw8J3LuHHCTXz43m5KoMPI3Vexa5ITzruRJxNN6pasWCtZ9vU7DxEeFbe9D4I/OgUL05xDRxGPPWQkY2qsr58T8X/tXQd8Ttcbflo1gsggkogMJCFmYsT4U7v2qGprtkZUraJVq1FB7L2LxkgFQVGV0lq11ShSsowESYhESGJF1fd/38unkXzLl2/cpOf9/a4vzj33jOfec8d5z/s8u5dNxF8HdkjJbxd4B3Xa98Z7AyeY/PnC11e3SUHYOX+01O7LRNmjNLcaDSQRen1W1LN4/aaJfvhxxjBp4zLLeFZHt4nfvyY0zzQ30qblfSzuwgmpWSe3v3g+KNuY9bdlf1qAR84rQxtHALUnPZc9303G/F4vzn1hEv2u27kf6nXxy1V1d64TGwddb2vWrDHJuf92wgQ0btwYHJlQqW4zndru27a7FBlSo2kHyemR9SCm/7pK5+aPXSHSxmOmom8zfBN6GkFjemL/DwtpnFmivAqHYbOewxD71ykSvd8ibVZ2jqjTpptU/D6KkFFa/U6f4CmNmZ8Wf4tz+7dLyTxmWDy+/SDTjxllu3T59SDHRp/ANdix8BsJC8aDjenLuoycIWnEKMvhSWGJOl3LWFDm1/bL5+KzOZuwkhbd/bp6lrTxMS7kBO43PfhVZJ2ynMtnj0jveW5E92YoYyqyS+Q8++233wxVpMZyAr6lBcDdu8O9wxCKLqisMa9yJ2uqsIYG00S5ZHMEeBD9V0rUKdxgYXnaaJBKE+ot5h3FH3P7UnTDUqKGskSlLiOUxb36dSd9jdSYM9JEO0+2F7FxeCWkzg4WpRUoWBj/8w/FmSVDJQePMr14GXfUG7UGduS4MJclk2OlANGQWb0Uh8/eDr7G6o0OxsmZvRG9bb60cR6bCj6oO3LVq0id7Mdp+7/i+T+SI0NTPqnur9fiT9K6iSHKNt6UVq7lp6j2SYDyv69+tfWHM9b5YjkuBI1F1Na50sZpBYuWgC/1h50pbOx8qzP8O1xYPV46v3cu/C6l8z/sUKr5+QLqu37PIr4HRIdOQ5u27Wjxl/ercrP+8RZ9kKmcwQgPD0fNmjUxZ8JIDOr9YdZj9P6b9VdYINyRVit7eZR79aDidI5ycKeIF3XGnM9RV+NgX6qkSvH2rMexRsd5oqd6SLoqVSq6g50OxjZ25HA0x2ESzvL1Vj95a6h2PCYtiUsx14iWK52iVpzh6vSCdkXf8vmDow6FAPNLqzZLz3ggRckkJd+FG2nbcKRREfrYk5PF3kyAV5MumPvtVyrp2LK2tUPf4WA6ofDwFxzoWfcZ4++VK1di8ODBWPHTEaK/ejHhntt6mCbsfmqKRKeVNTolNiZS0vAoWqy42ipYDD2FaC3cK1d/NSZVZeZbBdOEsTB8CWtbic7M2I4obgc7JezLOGNO8M4czWKHAGugcIQNa5aUsnfMkedNE/heEx1+Vpqwquzjq9LZxuOFhdTXvaQ90lQHn5drUZek8liwXkm7puoYQ/cncKSfROH2S3iiqupepbEOzvqls3DpEtFulC//Kt1Yf7AGQbWqVVDephA2+b++MkffOpmKKZxW/Nvb0ouUc+lX1zKn36doPI540WQpRG91624aUTk5vjpWVf4Mik7gelifhKM2dHUMqSrrTdLqDV2AsqWssTWgz5scliMvi6OHX7slUVtpo0nLcXC2BB4H1ymiI4YiglhjhnVvypTMuTqID0snrZbom3dw594DMP2aB+UtQgsnshtHALk52KLruzWy78rV/3Xt9zNymnI0Dzt4ans60/h/W229SfcyJGo6O6viqEiUZ6wHlN3YsbeHonhiQ1Sv5FTm33YkHP3nhOLQoUNgmiJTWAtaIRmfeBthh0+/Rq2lb90sQM3UXyyKzvoP/HLNxulp9+9JESnqyub77hUSRWeBa9ao0GSsp8ICzA9JU6ISrRhnB4GxjSf7B9Mq+x37jr4Sn9enTl41HnUpnLRN4iScPL2q0uS7tT5FSccsnjNNWr3fqeuLSQ9NBT15/BjRkRfBQuBM88X0Uzx5p8qYnol1L/jc8ap8ewfVz9ZbCfGIiYqQqM84UomjkpTnXVkui3Y38vZEwMz5KqnTlPn4GujUvAGsaWHWkSOHlclG/WVKvFmzZmH38fNwdv03ijQ3lZpiHPD7WNy1K7gaHQVrW1tJu8cU72McjTG0b3ds23sU3rXr6g0Tn+u/zp158V5E5ahre+rdFERHXJTysdMuKwWgPpUveTleOuowXnQtn5+DrFV0NSZaiuZj7amslHtZy9G131mP0fT3V+SsO7AnDOdi72jKBh6nrepXx7ChQzFt2jSNeQ2xMyYmBtUoMrJZ37Fo3CvnxJa+dbCGyMP7d4lOy0cSvFeWkxRL48C+LDkG1H/rsMA6a3I4ulfNcY9SlsO/rE2SGBMu6R0w1RaXawpjxwDX1Xfu1hzVpd1JII2ZKHL8PIEdURTZuXho7EOOArIl8P3jNmm4pCaSxhQ5VawdVD9DOVqH9We8W3bNVoK8/svXBNN28cRXOe8GOSamla3dHDgITB/37e5YZZLKXx7T3w9rB+sCT3Hm9Gm1z0mVB+cikan4T/xJtN/rjxtM2J11RR7cT4Ez6ZQUJcF7pd26FgVbB81jJoPGTFpyoqRbkv25riyHf588zCDKNBozpGdaxr0ybEw0ZqZ186W6nDFoof4LMVh3KJHoB5kCrERJBzhW8HoNJ2U/f10zG6VoLNRqZZg5UWW5aSm3ER8dDudK3lR/aWWyXr93E+IwqUsNfPDVTJWUaMpC/36aiTmfNEL1iuWwZ/duZbJRf/meU7d+A8SmZqLh5F00AW6YhcxpcZeQmX5XErjPGp2SHh8t6XuwY0adZaan4HHqbYlGStP1zW3nCJiMhMuS6LutZy2DtV9d2zh935fvwqKUE/43fqOmbFr3MZXa/di/YE2C8kWsNX9faSuM77EHRjdD8zm/a8sq7X+UfBMZiVdRkBzelk6eKGih/hmtU4GUifVr+LwXtipFESieYAeYKuPoovSb0UR99oTqdifdGfccz03WfGFat+p9AlGhjWZn/JVfViEiZBIunD8PlndQYZE5ZwFe5mKqiHHjxmHcjNloWMdb0s9QUcAbJZW0sZIoubIfxOm8aTJe9VOVnCS6GEfINPI1zIS1LvWZI48FcQzXrq6bx1eX9n0zayka1lbtfct+PEcjMfVbfjCmKtt/9BSOHj2a6w82XfEYMGAANm7chEnDemPVz8dzRKHoWk7WfOwUUWVZNVFU7ec0m5J20qZuvzKdHzqsZ5JV00S5z1y/7KQwlHNK2Qe+1zAFmyb7boY/aviqp53Jeqy1bSlJEyZrmrq/jdEfdXUp08NPH8fahdMwc+ZMkzhVuF5edRu0eo20Ous7GgOfd8j9qg9b0kBpoiJagNN502YcQcGbNuMIGaaYyqvmQBE5vBnCeFKW9UiUmiSaymRqtaz6LqryMg0b68Dsmqr55UbVsdrSdO03R9poa6eyLnsbS/CWW4u7nYovv/uZQosHmsypwm1mJz+/600ZPwqTZ79YrZebvtgQ3WFDFdoHnM6bJuP7bqUqur1XcGQA60DkReMIneo+taUtt+3nifXN69cidNd+nYpiCjeOSNHFeAK7mvfrofqqjmOdEd4MYTOJzupqTBTOnTtniOJ0KoO/c7Zu/RFf9OuJ0N2/6x1pk7UyU4wDfh9j51hWHZysbZD73zzefYheUJuxrlJ9itwxhPF42ULjZaOO40XXOvk5yHoxSs0YTcfp2m9NZbzpPnbmDPcj0fQyZcCRiqYwT09PTCanpT+twi/n0xAuRC1kCHMkYWpVZk+0WNqsuE0p8KbNChe1pMl53d7vtZVlqP1WpZ2IqsvJUMVJk0vsYOJNnaUQ/RPTag1YvEtdFtmksz5OBaI4M5QdWDsL8RFnse3UKZM5Vbjty5YtRWWasNsy+yv0nLDMIN1x8lQ9ZhzLax8zljReeNNmHPni7iOvMaOtzcr9HPXl4lVT2pRp2X+Tb17FyZ3r8cV3Ydl35fr/VqUcwJspbdu8MchIScSKg7+arFp+Z1m7Ogg+tGg/InQmqnQfb5C6WedElTEllDZjAfXsIuqqjuG2M/0Ub3nRitjYw4E2Q9ilDYFS5IeuZTENF2+GtCJEbVbEW7sTkunbeDOE3b8WjsiNgeDIQjVOFaka1UvVXrYgICBA4uH+4LOvkXBb82oYQzQ6P5TxLdGn9RhK4Ue3kvJUd8o6lMa8iV/lqTarauwDilJi/EcEzFG1+7W0sP1HMJ647GcQNVf9+vVf22fM//ANesOGEFrZ8YD0PHqAoxSEaUbgMkXksJ5K6Pf/hhNqPsK4e+0cnDB80jzjVpKL0sM2B0t4nf/jiMZSOAJpAtGetWnTFiNHjtSY19A7GzVqhMDAQPiTzsfes9GGLj5flvdX7C3SANkI1iLJj8bC8Zv8PzFZFJAxMQwhBxGfq2MXNa+MvJfxCN2mhtAEqTvmL1hgzCblKJuj01avXo11q5YhmLj3hWlHYNZkf4pc6SatANee27g5rseSLsWmHSaJ2NGnJ0yXxVixrow22xS8GisXz8OKFStQsaL2D2Jt5em6nx1dP/64lRw6kRhDFF68MlGYdgRm0zgYKpNxoL21L3LcoPHyvYzHi6794HxbQtZK+P9xTHtk1/jhnyOCIvyYXq8ILcozlY0ePRocFbnhm15SZISp6s3L9SRSRM6GCX1Ia8Ywk+q5wSI1IRafzNxksoid3LRV27FnwkIkXGOJ/kibsX7NgTWzJJ0gdXQv2srQd38Zcn6GrF8vUXexpokw7QhwpMzq8Z+S1sxS7Zn1zJFCY+GzuaEmi8R502byfBJjsIX0YrTZ75uW49j2NQhetw6urq7asht0P9NELl+2TKKGunFos0HLzq+FpcVdJCH4AbgStkIWXbQo6YgafafKoi25aQTTyzGuTBmmzR6lJOD03D5oRHpE/v6a2SfURqxwJRyavW3bdqkgFkvftXaRSWi1tHVQjvudyDHB2jJsHEKa12xIn4/zWpPVtpfxt6AV8Xw+3F1Vr6TcffAoehB1m5+fH77++mu1ZRlrh6OjI3b/8ou0Yp+dKwFL1oO1TITlRKBOoxa4cyteGldymfT4sN+QnA2VUwpNDvE4qFS9JolKqw67vBl7BV/2bIdybi6So48dfqa28ePH48rly/h05gYEj+2OFjU9Td2EPFNfU28PJKSk0Xk1nAaJ3DrfPB+df75X8bnydndCcQvV9/a76Q/RdVIwHivewf6wX0w66aU89x999BGuXLkCf9LLeOedgujRx/DRQsq68vIvU/u06fi+1AW5vOM1JrFsuRtjxZEyjJ1beXeVzd26IRjjSKScV9P37t1bZR5jJvLq+u3bt6Nt27YUTUm6SguWq6WmMmY78kLZPA5ak8YSm1zGga64sbh8frEXz5fnqO5dizR/VEdN8vmZSE7NHZtDsHPnTo2rLI2BC79Thm7ahMZNmmD1iE7oN39HrsXEjdFOuZTpQWLf95OYhlS7bpYp2uxZt7kpqjFNHS+/iZyIbkkTXRxr62wJ/FxaaDZokH5aTrntULt27bBo0SJ88cUXRHFTCE27D85tkfn2eNaiuUdjhumJ6MvIaP30qtfCaGUbqmDGoGBhikpu2hF2ZSuoLPbIj99j2/yxmD17Nt5//8VzXGVGIyb269dP+uaYOWsk3iI9HueGXYxYW94uunSNxmA6K0njh+5hcrAKbQbIoRkGaQPjWqBQEZSp2w7FHMqpLJOpzE5O+xjO9rbYRouw1NEnKw9Wq7GizMC/t27dQsuWLXD/Lq0mXTYddWqoDrvKeoz4WyAgVwSWBW/G14EL0LdvX2l1pLZBYsx+HD9+XBIQdnH3wqRlIQbRCDFme0XZ+QOBcycOY+KQnnCvUF7iV7UljnZz2T+kadGfXrRCKIprpl879GujmYbNXO0U9QoEDIlA5I0k9JqxEYqCRbFv/wGT0fCp68PkyZMxceJEDPlyLEb5T9L68qiuHJEuENAVAZ4cXkCi3wtmTAE72adONe8quLCwMHTt2hX1322K+SuDUcLqXz56Xfsk8gkE5IBARno6Rg3ui0N79yA0NBSdO3c2W7NSUlLwXqvWuBp3A92mrEU5El4XJhCQGwKHNyzCnuUBGDp0GEWrLMjBxW/q9s6dOxejRo1Ck26D0GlYIOnGaFwLbermifryEALP6Tv75+WTsP+HhdJ7Fr9vmds4opEdPF4fj0XF94ebfbyZGw9Rv/wQuBtzBmfn+8HNqTT27/0N9vZa6dQiCxDdV4C2rlgSn3X37j1Ih+IYAmYvxtu0CqVezWriw1sbcGK/rBC4nXwX/UdNwuI1pG9C3L9z5swx+43c2dkZHTp0wA/r1iA0aCkcnd3g5uElK9xEY/IPAplPniBo7mTMHjcEbdu0xo4dO2BlpVnfyti9Z8dmJ/ro59X94+euRNSNZDSo6oZiRQoZu2pRvkDA5AjwatQVu06QUP1meHpVw6+/7QU/B8xtjRs3houLC6YETMDR3/ejdr0GWnVRzN1mUX/eReBG3DUM+rQbrabfgKVLl2LMmDFm7wxHrjB10bIli7BxXRA8vKrAxS1vcmqbHUzRALMhcPLoIfh93BEJ12Oxa9cutGrVymxt4YqLFi2KHj264/yff2LjwgA8/+cZXKv64m1arSxMIGBuBO4nxSM0oD9O71wnTfTyIhNzRPBnx6FBgwaoVKkSls2ejEvHfoVbNV/SO7HLnk38XyCgEYGk65exekwPXDwchtVBQeQ4HKoxv6l2tmzZEqVKlULI/Im4F3UKtpXqIqv4vKnaIeoRCGRH4PmzvxG1bQHOLR+BZk0aYnfYLpQsqVkn9GUZKTpFrCgr5AmBefPmYQIJt7Amh/9wP3zQpjmJfouXIyVG4ld+CKSk3sfyH7ZgYdBG2JW2Q1DQajRt+oK2TS6tTafVZSNHfkl890GoWf9d9B3pD++6hhPik0s/RTvMg8DTzEzs3bFJEql/kJaKWbNmwVxh7poQ2LdvH/z690MqrXAc0qkBBrStp5PwvKYyxT6BgBwQ+Oef59h9KhLTNx1ETHyytEKf36VYzFhOdv78efTp0xcREZfQq99AfDbsS9lqeMgJN9EW3RC4nZiAVUsX4AfS9PHw8MDatWtRq1Yt3Q42Ua47d+7Q83EwUSH/iBZt2mPYaH9UI8olYQIBOSMQ8dd5LJ49Fb/+vIMWbHXEypUr4OBgWlFkbfgsWbIEY8aOhYVVKTTtMxo1WnyAdwqppsrUVpbYLxDIDQIZqXdwbPNynNi6Ai60uGXd2jVgZ4bcLCIiAn2IYePs2bOo3+lTtOg1HCWd3OTWTNEemSGQeusG9q9fhBM/rUW1atXo+n7xK7Nm4tSpU+j9aR9cuxaLcq36wb3dQLDYujCBgKkReP7sKeJP7MTlrXOQmZaE6RRFP2LEiDdxtEe+kWNF2cHY2FhMJC7kkA0bYFfSBp3fa4LmDX1RtZIHHOxKoqiF6cTxlG0SvwIBRoCpJdIfPMT1+ET8eTEKuw8eo+24tGKKww6HDx8u/S1XtI4dO4Zx48bhyJEjKOdRCY1ad4ZPvUZSFIuVTUkUJKFVYQIBbQg8fvQQd+/cxuVLF3D6yD4c3vMTHmakg7lNme6HBRLlag8fPsT8+fMxh0KEnzx5jFa1K+K9Wp6oQVoVLqWtYUl6FXJYTSZX/ES75IHA48y/kZz2ABHXk3DowlX8fDISCcn38EGXLgiklzVeiShXe/bsGVatWoUpU6YgKSkJjZq2QPPW7eBT2xeu5SrAsoSViFiW68mTUbt4MRZTEt2Iu4rzZ09j/54wHD6wV1qlyAKQAwcOpIVZBWXU4tebsmfPHskBeu7cOXKs1ETLth1Rp35DlPesBGsbW1m3/fWeiP/lNwT4Hn3/XiquxUTh9Mlj2Bu2E+HnzqB69RpE9RKI9u3by7bL8fHxmBgQIE30WVhao/K77eHh2wyOHtVgaVsahSyKybbtomF5EwGeG8h8mI7UxOuIj/wT0Sd+Q/TJvShB7zLfjB8nreIvLGOdU36W8iKEiQGTkBB/ExXrNEaVhm3gWqU27JzLw6I4vZORLrKw/yYCfH08eZCG5PhruH7xDC4d3YOoUwfhQN/6ATRf279/f1nrxj19+hTLly/H5MCp0nPN3rsZ7H1awqaCN4rau6KghSXeInYLYQIBQyLwLPMRMu8nI+36JdwJP4Sk02F4kp6Knr16Y8rkSXB1dX3T6iJ5Ilpvu3HjhoKYxBQ+Pt6sqCM2gYGsrgF6SVK0bt1aERQUpKDJWr2vc3MceObMGQWFaypcXF1lhakY53nrPkcOCEUdX1/F9OnTFaSVZY5LWe86Hzx4oKDJXcV7LVsqChcuJMaBeL7k2WugSmUvBXEaK0gkXu/xYI4D6WNHsXnzZgWJXCqKFy+eZ/EXzy3zP7eKFSum6NSps2Ljxo2KzMxMc1zOetd5+PBhhZ+fn8LR0VGMAfEckt01QLzfClo0ozh48KDe17g5DkxMTFSQrpKiZq3aCn5XFfdp89+n/wvnoIiFhaJtu3aK4OBgxePHj81x6etdJzlUFdu3b1d8+OFHihJWVmLMiOdRjmuguKWlokuXLoqtW7cq/v77b72vNXMc+OTJE0VISIiifYeOiiIWRXP07b9wfxJ9NM9zsIZPTQXRQCpu3ryZm0s/Qq+IFTrpOYwmwYg6IgLJycl49OhRjv0iQSBgCgRYr4E1gcqWLQvmy5YbzYo+GDAtRXR0NO7duweakNCnCHHMfwgBjuZgTuvSpUujcuXKso7Q0vW08OpMHgMJCQnIyMggPZbnuh4q8gkEzIKAhYWFtDLfy8vL7DpGhgCAx9zVq1dx/fp1MHXlPySGKUwgoAmBArSCtkSJEpJ2j7u7e76IcqLJYFy+fFl6H6NJC03dF/sEAkZDgCO9rK2tJTo9Jycno9VjqoI5UjkyMhL8vcN/CxMIGBIBnhvgZxHr2TEFJT+b8rrR7B/RJ11DXFwc0tLSxDtZXj+huWi/8l3Lzc0N5cuXzxfvWvyNQYvRQIv4pW8O8d2fiwtEHKoSAVrwBTs7O/B3Oi0eVJnnDRP1owJ7w0pEdoGAQEAgIBAQCAgEBAICAYGAQEAgIBAQCAgEBAICAYGAQEAgIBAQCAgEBAL5AYFIQViXH06j6INAQCAgEBAICAQEAgIBgYBAQCAgEBAICAQEAgIBgYBAQCAgEBAICAQEAiZBQDhWTAKzqEQgIBAQCAgEBAICAYGAQEAgIBAQCAgEBAICAYGAQEAgIBAQCAgEBAICgfyAgHCs5IezKPogEBAICAQEAgIBgYBAQCAgEBAICAQEAgIBgYBAQCAgEBAICAQEAgIBgYBJEPg/bOxPNWXCUgMAAAAASUVORK5CYII=
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Using-Random-Forest---Ensemble-learning-,-deeper-Decision-trees">Using Random Forest-- Ensemble learning , deeper Decision trees<a class="anchor-link" href="#Using-Random-Forest---Ensemble-learning-,-deeper-Decision-trees">¶</a></h1><p>Particularly, trees that are grown very deep tend to learn highly irregular patterns: they overfit their training sets, i.e. have low bias, but very high variance. Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same training set,
with the goal of reducing the variance</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [249]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#calling the RandomForest Classifier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="c1">#criterion=RFC_METRIC,</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">],</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">])</span>
<span class="c1">#print(confusion_matrix) </span>
<span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>91.91008795743295
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.87
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [250]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pydot</span>
<span class="c1"># Limit depth of tree to 3 levels</span>
<span class="n">rf_small</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">max_depth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">rf_small</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Extract the small tree</span>
<span class="n">tree_small</span> <span class="o">=</span> <span class="n">rf_small</span><span class="o">.</span><span class="n">estimators_</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
<span class="n">dfnew</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">feature_list</span> <span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">dfnew</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="c1"># Save the tree as a png image</span>
<span class="n">export_graphviz</span><span class="p">(</span><span class="n">tree_small</span><span class="p">,</span> <span class="n">out_file</span> <span class="o">=</span> <span class="s1">'small_tree.dot'</span><span class="p">,</span> <span class="n">feature_names</span> <span class="o">=</span> <span class="n">feature_list</span><span class="p">,</span> <span class="n">rounded</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">filled</span> <span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">precision</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="p">)</span> <span class="o">=</span> <span class="n">pydot</span><span class="o">.</span><span class="n">graph_from_dot_file</span><span class="p">(</span><span class="s1">'small_tree.dot'</span><span class="p">)</span>
<span class="n">graph</span><span class="o">.</span><span class="n">write_png</span><span class="p">(</span><span class="s1">'small_tree.png'</span><span class="p">);</span>
<span class="n">Image</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">create_png</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[250]:</div>
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">¶</a></h1><p>Check the best features to used for predictive Modeling</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [251]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Shows the plot of best features to use , while modeling, importance of features</span>
<span class="c1">#The esitmators can be assigned a value, change in the values will result in different values of accuarcy</span>
<span class="n">target</span> <span class="o">=</span> <span class="s1">'default'</span>
<span class="n">predictors</span> <span class="o">=</span> <span class="p">[</span> <span class="s1">'LIMIT_BAL'</span><span class="p">,</span> <span class="s1">'SEX'</span><span class="p">,</span> <span class="s1">'EDUCATION'</span><span class="p">,</span> <span class="s1">'MARRIAGE'</span><span class="p">,</span> <span class="s1">'AGE'</span><span class="p">,</span>
<span class="s1">'PAY_0'</span><span class="p">,</span> <span class="s1">'PAY_2'</span><span class="p">,</span> <span class="s1">'PAY_3'</span><span class="p">,</span> <span class="s1">'PAY_4'</span><span class="p">,</span> <span class="s1">'PAY_5'</span><span class="p">,</span> <span class="s1">'PAY_6'</span><span class="p">,</span>
<span class="s1">'BILL_AMT1'</span><span class="p">,</span><span class="s1">'BILL_AMT2'</span><span class="p">,</span> <span class="s1">'BILL_AMT3'</span><span class="p">,</span> <span class="s1">'BILL_AMT4'</span><span class="p">,</span> <span class="s1">'BILL_AMT5'</span><span class="p">,</span> <span class="s1">'BILL_AMT6'</span><span class="p">,</span>
<span class="s1">'PAY_AMT1'</span><span class="p">,</span> <span class="s1">'PAY_AMT2'</span><span class="p">,</span> <span class="s1">'PAY_AMT3'</span><span class="p">,</span> <span class="s1">'PAY_AMT4'</span><span class="p">,</span> <span class="s1">'PAY_AMT5'</span><span class="p">,</span> <span class="s1">'PAY_AMT6'</span><span class="p">]</span>
<span class="n">train_df</span><span class="p">,</span> <span class="n">val_df</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df_minmax</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span> <span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="c1">#criterion=RFC_METRIC,</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_df</span><span class="p">[</span><span class="n">predictors</span><span class="p">],</span> <span class="n">train_df</span><span class="p">[</span><span class="n">target</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">val_df</span><span class="p">[</span><span class="n">predictors</span><span class="p">])</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'Feature'</span><span class="p">:</span> <span class="n">predictors</span><span class="p">,</span> <span class="s1">'Feature importance'</span><span class="p">:</span> <span class="n">clf</span><span class="o">.</span><span class="n">feature_importances_</span><span class="p">})</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">'Feature importance'</span><span class="p">,</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Features importance'</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'Feature'</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">'Feature importance'</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="n">tmp</span><span class="p">)</span>
<span class="n">s</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">get_xticklabels</span><span class="p">(),</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">accuracy_score</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span><span class="n">val_df</span><span class="p">[</span><span class="n">target</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt output_prompt">Out[251]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>0.85</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [252]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="nb">cmp</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">predicted</span> <span class="ow">in</span> <span class="n">prediction</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="n">accuracy</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">+=</span> <span class="mi">1</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Logistic 67.83581279183407
KNN 75.93658377674014
DecisionTree 69.26919318058421
RandomForest 91.91008795743295
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Area-under-the-curve---Receiver-Operator-characteristic">Area under the curve - Receiver Operator characteristic<a class="anchor-link" href="#Area-under-the-curve---Receiver-Operator-characteristic">¶</a></h1><p>Shows how well the model performs
In regression problems , accuracy is used as a solid metric to identify model performance
Whereas in classification problems, Confusion matrix and AUC is used as solid metric to check models performance</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [253]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Plotting the ROC - Area Under the Curve for all the models</span>
<span class="k">def</span> <span class="nf">formatt</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="n">vfunc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">formatt</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'b'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">,</span> <span class="s1">'k'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">predicted</span> <span class="ow">in</span> <span class="n">prediction</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="n">roc_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">,</span> <span class="n">colors</span><span class="p">[</span><span class="nb">cmp</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: AUC </span><span class="si">%0.2f</span><span class="s1">'</span><span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">roc_auc</span><span class="p">))</span>
<span class="nb">cmp</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Classifiers comparison with ROC'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'lower right'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">'r--'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">1.2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">1.2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Using-SMOTE---Synthetic-Minority-Oversampling-Technique">Using SMOTE - Synthetic Minority Oversampling Technique<a class="anchor-link" href="#Using-SMOTE---Synthetic-Minority-Oversampling-Technique">¶</a></h1><p>over-sampling approach in which the minority class is over-sampled by creating ``synthetic'' examples rather than by over-sampling with replacement.</p>
<p>Reduces the chance of overfitting</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [254]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Perform oversampling to balance the data</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"default"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="c1">#Setting the X to do the split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># transforming the values in array</span>
<span class="c1"># splitting data into training and test set</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span>
<span class="c1"># build model with SMOTE imblearn</span>
<span class="n">smote_pipeline</span> <span class="o">=</span> <span class="n">make_pipeline_imb</span><span class="p">(</span><span class="n">SMOTE</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span> \
<span class="n">classifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">))</span>
<span class="n">smote_model</span> <span class="o">=</span> <span class="n">smote_pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">smote_prediction</span> <span class="o">=</span> <span class="n">smote_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1">#Showing the diference before and after the transformation used</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"normal data distribution: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">Counter</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span>
<span class="n">X_smote</span><span class="p">,</span> <span class="n">y_smote</span> <span class="o">=</span> <span class="n">SMOTE</span><span class="p">()</span><span class="o">.</span><span class="n">fit_sample</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"SMOTE data distribution: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">Counter</span><span class="p">(</span><span class="n">y_smote</span><span class="p">)))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>normal data distribution: Counter({0.0: 23364, 1.0: 6636})
SMOTE data distribution: Counter({1.0: 23364, 0.0: 23364})
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [255]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Train test split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X_smote</span><span class="p">,</span> <span class="n">y_smote</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [256]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prediction</span><span class="o">=</span><span class="nb">dict</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KNeighborsClassifier</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">]</span><span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"accuaracy of model"</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="n">a</span><span class="o">=</span><span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>accuaracy of model
79.02846137384978
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.72
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [257]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="c1">#criterion=RFC_METRIC,</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">],</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">a</span><span class="o">=</span> <span class="n">a</span><span class="o">*</span><span class="mi">100</span>
<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">],</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">average_precision</span> <span class="o">=</span> <span class="n">average_precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average precision-recall score: </span><span class="si">{0:0.2f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">average_precision</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>82.90177616092446
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Average precision-recall score: 0.78
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [258]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="nb">cmp</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">predicted</span> <span class="ow">in</span> <span class="n">prediction</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="n">accuracy</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">+=</span> <span class="mi">1</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>KNN 79.02846137384978
RandomForest 82.90177616092446
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [259]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">formatt</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span>
<span class="k">return</span> <span class="mi">1</span>
<span class="n">vfunc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="n">formatt</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'b'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">,</span> <span class="s1">'k'</span><span class="p">]</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">predicted</span> <span class="ow">in</span> <span class="n">prediction</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="n">roc_auc</span> <span class="o">=</span> <span class="n">auc</span><span class="p">(</span><span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">false_positive_rate</span><span class="p">,</span> <span class="n">true_positive_rate</span><span class="p">,</span> <span class="n">colors</span><span class="p">[</span><span class="nb">cmp</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'</span><span class="si">%s</span><span class="s1">: AUC </span><span class="si">%0.2f</span><span class="s1">'</span><span class="o">%</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">roc_auc</span><span class="p">))</span>
<span class="nb">cmp</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Classifiers comparison with ROC'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'lower right'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">'r--'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">1.2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">1.2</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'False Positive Rate'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Random-Undersampling-of-Majority-class">Random Undersampling of Majority class<a class="anchor-link" href="#Random-Undersampling-of-Majority-class">¶</a></h1><p>Not expected to produce good results cause lesser number of observations.
Good number of observations are need for model to perform well
Sample size has to be large</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [260]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Perform oversampling to balance the data</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"default"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="c1">#Setting the X to do the split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># transforming the values in array</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
<span class="c1"># Separate majority and minority classes</span>
<span class="n">df_majority</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span>
<span class="n">df_minority</span> <span class="o">=</span> <span class="n">df_minmax</span><span class="p">[</span><span class="n">df_minmax</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">==</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_majority</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"-----------"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_minority</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"-----------"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>23364
-----------
6636
-----------
0 23364
1 6636
Name: default, dtype: int64
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [261]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">resample</span>
<span class="c1"># Upsample minority class</span>
<span class="n">df_majority_undersampling</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">df_majority</span><span class="p">,</span>
<span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># sample with replacement</span>
<span class="n">n_samples</span><span class="o">=</span><span class="mi">6677</span><span class="p">,</span> <span class="c1"># to match majority class</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">587</span><span class="p">)</span> <span class="c1"># reproducible results</span>
<span class="c1"># Combine majority class with upsampled minority class</span>
<span class="n">df_undersample</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_minority</span><span class="p">,</span> <span class="n">df_majority_undersampling</span><span class="p">])</span>
<span class="c1"># Display new class counts</span>
<span class="n">df_undersample</span><span class="p">[</span><span class="s1">'default'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[261]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>0.0 6677
1.0 6636
Name: default, dtype: int64</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [262]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#using the new data frame - oversampled dataframe --- oversampling of minority class</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df_undersample</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"default"</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span> <span class="c1">#Setting the X to do the split</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df_undersample</span><span class="p">[</span><span class="s2">"default"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># transforming the values in array</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="o">=</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [263]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">classifier</span> <span class="o">=</span> <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'KNN'</span><span class="p">]</span><span class="o">=</span> <span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
<span class="c1">#criterion=RFC_METRIC,</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
<span class="n">prediction</span><span class="p">[</span><span class="s1">'RandomForest'</span><span class="p">]</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">predicted</span> <span class="ow">in</span> <span class="n">prediction</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
<span class="n">accuracy</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">,</span><span class="n">accuracy</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
<span class="nb">cmp</span> <span class="o">+=</span> <span class="mi">1</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>KNN 66.99211415696583
RandomForest 72.62485918137439
</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Learning-Process">Learning Process<a class="anchor-link" href="#Learning-Process">¶</a></h1><h1 id="Understanding-various-new-concepts">Understanding various new concepts<a class="anchor-link" href="#Understanding-various-new-concepts">¶</a></h1><h1 id="Using-MinMax-Scaler-to-Normalize-data.">Using MinMax Scaler to Normalize data.<a class="anchor-link" href="#Using-MinMax-Scaler-to-Normalize-data.">¶</a></h1><h1 id="Understanding-the-effect-of-unbalanced-data">Understanding the effect of unbalanced data<a class="anchor-link" href="#Understanding-the-effect-of-unbalanced-data">¶</a></h1><h1 id="Random-oversampling-of-minority-class,-under-sampling-of-majority-class,-SMOTE.">Random oversampling of minority class, under sampling of majority class, SMOTE.<a class="anchor-link" href="#Random-oversampling-of-minority-class,-under-sampling-of-majority-class,-SMOTE.">¶</a></h1><h1 id="Using-Sklearn-library-for-running-various-models.">Using Sklearn library for running various models.<a class="anchor-link" href="#Using-Sklearn-library-for-running-various-models.">¶</a></h1><h1 id="Using-feature-engineering.">Using feature engineering.<a class="anchor-link" href="#Using-feature-engineering.">¶</a></h1><h1 id="Understanding-confusion-matrix-,-accuracy-and-Reciever-Operator-characteristic-concepts.">Understanding confusion matrix , accuracy and Reciever Operator characteristic concepts.<a class="anchor-link" href="#Understanding-confusion-matrix-,-accuracy-and-Reciever-Operator-characteristic-concepts.">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">¶</a></h1><h1 id="The-most-important-parameters-in-determining-default-of-credit-cards-are-the-repayment-status-variable.">The most important parameters in determining default of credit cards are the repayment status variable.<a class="anchor-link" href="#The-most-important-parameters-in-determining-default-of-credit-cards-are-the-repayment-status-variable.">¶</a></h1><h1 id="With-Random-oversampling-of-data-and--Random-Forest-classifier,-achieves-the-best-accuracy-of-91-percent-,-with-precision-–-recall-score-of-0.87-and-area-under-curve-of-0.92">With Random oversampling of data and Random Forest classifier, achieves the best accuracy of 91 percent , with precision – recall score of 0.87 and area under curve of 0.92<a class="anchor-link" href="#With-Random-oversampling-of-data-and--Random-Forest-classifier,-achieves-the-best-accuracy-of-91-percent-,-with-precision-–-recall-score-of-0.87-and-area-under-curve-of-0.92">¶</a></h1><h1 id="With-,-SMOTE-Random-Forest-Classifier-,-achieves-the-best-accuracy-of-82-percent-,-with-precision-–-recall-score-of-0.79-and-area-under-curve-of-0.83">With , SMOTE Random Forest Classifier , achieves the best accuracy of 82 percent , with precision – recall score of 0.79 and area under curve of 0.83<a class="anchor-link" href="#With-,-SMOTE-Random-Forest-Classifier-,-achieves-the-best-accuracy-of-82-percent-,-with-precision-–-recall-score-of-0.79-and-area-under-curve-of-0.83">¶</a></h1><h1 id="KNN-is-the-next-best-model.">KNN is the next best model.<a class="anchor-link" href="#KNN-is-the-next-best-model.">¶</a></h1><h1 id="Random-Under-sampling-didn’t-yield-great-results-because-of-less-data-points.">Random Under sampling didn’t yield great results because of less data points.<a class="anchor-link" href="#Random-Under-sampling-didn’t-yield-great-results-because-of-less-data-points.">¶</a></h1><h1 id="Random-oversampling-can-lead-to-overfitting-as-it-duplicates-data-points-,-SMOTE-is-better.">Random oversampling can lead to overfitting as it duplicates data points , SMOTE is better.<a class="anchor-link" href="#Random-oversampling-can-lead-to-overfitting-as-it-duplicates-data-points-,-SMOTE-is-better.">¶</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [ ]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span>
</pre></div>
</div>
</div>
</div>
</div>
</div>
</div>
</body>
</html>