Identify_Customer_Segments.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Identify_Customer_Segments</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="Project:-Identify-Customer-Segments">Project: Identify Customer Segments<a class="anchor-link" href="#Project:-Identify-Customer-Segments">&#182;</a></h1><p>In this project, I apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that I use has been provided by Bertelsmann Arvato Analytics.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import libraries here; add more as necessary</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="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">collections</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">Imputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>

<span class="c1"># magic word for producing visualizations in notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
</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">
<h3 id="Step-0:-Load-the-Data">Step 0: Load the Data<a class="anchor-link" href="#Step-0:-Load-the-Data">&#182;</a></h3><p>There are four files associated with this project (not including this one):</p>
<ul>
<li><code>Udacity_AZDIAS_Subset.csv</code>: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).</li>
<li><code>Udacity_CUSTOMERS_Subset.csv</code>: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).</li>
<li><code>Data_Dictionary.md</code>: Detailed information file about the features in the provided datasets.</li>
<li><code>AZDIAS_Feature_Summary.csv</code>: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns</li>
</ul>
<p>Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.</p>
<p>Note for all of the <code>.csv</code> data files in this project: they're semicolon (<code>;</code>) delimited, so you'll need an additional argument in your <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html"><code>read_csv()</code></a> call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the general demographics data.</span>
<span class="n">azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_AZDIAS_Subset.csv&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>

<span class="c1"># Load in the feature summary file.</span>
<span class="n">feat_info</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;AZDIAS_Feature_Summary.csv&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;;&#39;</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&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check the structure of the data after it&#39;s loaded (e.g. print the number of</span>
<span class="c1"># rows and columns, print the first few rows).</span>
<span class="n">azdias</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[3]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-1</td>
      <td>2</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1</td>
      <td>1</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-1</td>
      <td>3</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-1</td>
      <td>3</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 85 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&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">describe</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[4]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>886367.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>...</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>794005.000000</td>
      <td>794005.000000</td>
      <td>794005.00000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>-0.358435</td>
      <td>2.777398</td>
      <td>1.522098</td>
      <td>3.632838</td>
      <td>3.074528</td>
      <td>2.821039</td>
      <td>3.401106</td>
      <td>3.033328</td>
      <td>2.874167</td>
      <td>3.075121</td>
      <td>...</td>
      <td>2.253330</td>
      <td>2.801858</td>
      <td>1.595426</td>
      <td>0.699166</td>
      <td>1.943913</td>
      <td>3.612821</td>
      <td>3.381087</td>
      <td>3.167854</td>
      <td>5.293002</td>
      <td>3.07222</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.198724</td>
      <td>1.068775</td>
      <td>0.499512</td>
      <td>1.595021</td>
      <td>1.321055</td>
      <td>1.464749</td>
      <td>1.322134</td>
      <td>1.529603</td>
      <td>1.486731</td>
      <td>1.353248</td>
      <td>...</td>
      <td>0.972008</td>
      <td>0.920309</td>
      <td>0.986736</td>
      <td>0.727137</td>
      <td>1.459654</td>
      <td>0.973967</td>
      <td>1.111598</td>
      <td>1.002376</td>
      <td>2.303739</td>
      <td>1.36298</td>
    </tr>
    <tr>
      <th>min</th>
      <td>-1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>...</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>1.00000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>-1.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>...</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>4.000000</td>
      <td>2.00000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>-1.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>...</td>
      <td>2.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>5.000000</td>
      <td>3.00000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>-1.000000</td>
      <td>4.000000</td>
      <td>2.000000</td>
      <td>5.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>...</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>7.000000</td>
      <td>4.00000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>3.000000</td>
      <td>9.000000</td>
      <td>2.000000</td>
      <td>6.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>...</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>9.000000</td>
      <td>9.000000</td>
      <td>9.00000</td>
    </tr>
  </tbody>
</table>
<p>8 rows × 81 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&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[5]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(891221, 85)</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">
<h2 id="Step-1:-Preprocessing">Step 1: Preprocessing<a class="anchor-link" href="#Step-1:-Preprocessing">&#182;</a></h2><h3 id="Step-1.1:-Assess-Missing-Data">Step 1.1: Assess Missing Data<a class="anchor-link" href="#Step-1.1:-Assess-Missing-Data">&#182;</a></h3><h4 id="Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">Step 1.1.1: Convert Missing Value Codes to NaNs<a class="anchor-link" href="#Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">&#182;</a></h4><p>The fourth column of the feature attributes summary (loaded in above as <code>feat_info</code>) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. <code>[-1,0]</code>), this will get read in as a string object. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Identify missing or unknown data values and convert them to NaNs.</span>
<span class="k">def</span> <span class="nf">define_missing</span><span class="p">(</span><span class="n">string</span><span class="p">):</span>
    <span class="n">def_missing</span> <span class="o">=</span> <span class="n">string</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;[&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;]&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>

    <span class="k">if</span><span class="p">(</span><span class="n">def_missing</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">def_missing</span><span class="p">)):</span>
            <span class="k">try</span><span class="p">:</span> <span class="n">def_missing</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">def_missing</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span> <span class="k">continue</span> <span class="c1">#we are trying to convert string to int ie int(&#39;XX&#39;) -&gt; error  </span>

    <span class="k">return</span> <span class="n">def_missing</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&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">AGER_TYP</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[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>-1    677503
 2     98472
 1     79802
 3     27104
 0      8340
Name: AGER_TYP, 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&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</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[8]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute</th>
      <th>information_level</th>
      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1,0,9]</td>
    </tr>
    <tr>
      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[0]</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#num previously missing values from whole df</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;total NaN: </span><span class="si">%s</span><span class="s1"> </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">%</span><span class="k">sum</span>(azdias.isnull().sum()))
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;NaN per column</span><span class="se">\n</span><span class="s1"> </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="k">azdias</span>.isnull().sum().describe())
</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>total NaN: 4896838 

NaN per column
 count        85.000000
mean      57609.858824
std       78323.462873
min           0.000000
25%           0.000000
50%       18348.000000
75%       98979.000000
max      584612.000000
dtype: float64
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Another way to do the above to replace all the missing or incomplete </span>
<span class="c1">#in azdias with NaN based on the info in feat_info</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">azdias</span><span class="p">:</span>
    <span class="n">missing_rep</span> <span class="o">=</span> <span class="n">define_missing</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">==</span><span class="n">col</span><span class="p">][</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    
    <span class="k">if</span><span class="p">(</span><span class="n">missing_rep</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">):</span> 
        <span class="n">azdias</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
        <span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
        <span class="k">continue</span>
    <span class="n">i</span><span class="o">=</span><span class="mi">0</span>
    <span class="n">replace_with</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">missing_rep</span><span class="p">)</span> <span class="c1">#create list of None same length as missing_rep</span>
    <span class="n">azdias</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">missing_rep</span><span class="p">,</span> <span class="n">replace_with</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">
<p>Another way to clean missing data</p>
<div class="highlight"><pre><span></span><span class="c1"># turn missing_or_unknown to list </span>
<span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">))</span>

<span class="c1"># Identify missing or unknown data values and convert them to NaNs.</span>
<span class="k">for</span> <span class="n">attrib</span><span class="p">,</span> <span class="n">missing_values</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">],</span> <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]):</span>
    <span class="k">if</span> <span class="n">missing_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">missing_values</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">isnumeric</span><span class="p">()</span> <span class="ow">or</span> <span class="n">value</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">isnumeric</span><span class="p">():</span>
                <span class="n">value</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
            <span class="n">azdias</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="n">attrib</span><span class="p">]</span> <span class="o">==</span> <span class="n">value</span><span class="p">,</span> <span class="n">attrib</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
</pre></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">
<h4 id="Step-1.1.2:-Assess-Missing-Data-in-Each-Column">Step 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Step-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html"><code>hist()</code></a> function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)</p>
<p>For the remaining features, are there any patterns in which columns have, or share, missing data?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Perform an assessment of how much missing data there is in each column of the</span>
<span class="c1"># dataset.</span>
<span class="n">null_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">null_count</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>[685843   2881      0   4854      0      0      0      0      0      0
      0 392318   4854      0 111196  97632  94572  77792  77792   4854
   4854 108315 108164   4854      0      0      0      0      0      0
      0      0      0      0      0      0      0      0 111196  73499
 889061 111196      0 310267  73499  73499  18348 584612 147988  73499
  99611  97008  93148  73969  93148  93148  93148  99352  99352  99352
 133324 133324 133324 133324 476524 133324  93740  93740  93740  93155
 158064 133324   4854 158064 105800 116515 116515 116515 116515 116515
 116515 116515  97375  97274  97375]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate patterns in the amount of missing data in each column.</span>
<span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">describe</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[12]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>count        85.000000
mean      98516.811765
std      146604.203317
min           0.000000
25%           0.000000
50%       93148.000000
75%      116515.000000
max      889061.000000
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#most common # of NaN: from `https://docs.python.org/2/library/collections.html#collections.Counter`</span>
<span class="n">collections</span><span class="o">.</span><span class="n">Counter</span><span class="p">(</span><span class="n">null_count</span><span class="p">)</span><span class="o">.</span><span class="n">most_common</span><span class="p">(</span><span class="mi">12</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[13]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[(0, 24),
 (116515, 7),
 (4854, 6),
 (133324, 6),
 (73499, 4),
 (93148, 4),
 (111196, 3),
 (99352, 3),
 (93740, 3),
 (77792, 2),
 (158064, 2),
 (97375, 2)]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</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">hist</span><span class="p">(</span><span class="n">null_count</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</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="s2">&quot;# of NaN&quot;</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">&#39;# of features&#39;</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&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">i_5</span><span class="p">,</span> <span class="n">i_10</span><span class="p">,</span> <span class="n">i_15</span><span class="p">,</span> <span class="n">i_20</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">null_count</span><span class="p">:</span>
    <span class="k">if</span><span class="p">(</span><span class="n">value</span> <span class="o">&gt;</span> <span class="mi">200000</span><span class="p">):</span>
        <span class="n">i_20</span><span class="o">+=</span><span class="mi">1</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">value</span> <span class="o">&gt;</span> <span class="mi">150000</span><span class="p">):</span>
        <span class="n">i_15</span> <span class="o">+=</span><span class="mi">1</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">value</span><span class="o">&gt;</span><span class="mi">100000</span><span class="p">):</span>
        <span class="n">i_10</span><span class="o">+=</span><span class="mi">1</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">value</span><span class="o">&gt;</span><span class="mi">50000</span><span class="p">):</span>
        <span class="n">i_5</span><span class="o">+=</span><span class="mi">1</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;num null &gt; 200,000: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">i_20</span><span class="p">,</span> <span class="s1">&#39; 200k-150k: </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="k">i_15</span>,&#39; 150k-100k: %s&#39;%i_10, &#39; 100k-50k: %s&#39;%i_5)
</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>num null &gt; 200,000: 6  200k-150k: 2  150k-100k: 20  100k-50k: 25
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove the outlier columns from the dataset. (You&#39;ll perform other data</span>
<span class="c1"># engineering tasks such as re-encoding and imputation later.)</span>
<span class="n">labels_to_clean</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">azdias</span><span class="p">:</span>
    <span class="k">if</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">col</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">sum</span><span class="p">()</span><span class="o">&gt;</span><span class="mi">200000</span><span class="p">):</span>
        <span class="n">labels_to_clean</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
<span class="n">azdias_cleaned</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">labels_to_clean</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</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&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">azdias_cleaned</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>

<span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">labels_to_clean</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">label</span><span class="p">][[</span><span class="s1">&#39;attribute&#39;</span><span class="p">,</span> <span class="s1">&#39;information_level&#39;</span><span class="p">,</span> <span class="s1">&#39;type&#39;</span><span class="p">]])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</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>(891221, 79)
  attribute information_level         type
0  AGER_TYP            person  categorical


      attribute information_level     type
11  GEBURTSJAHR            person  numeric


   attribute information_level         type
40  TITEL_KZ            person  categorical


   attribute information_level      type
43  ALTER_HH         household  interval


       attribute information_level         type
47  KK_KUNDENTYP         household  categorical


       attribute information_level   type
64  KBA05_BAUMAX     microcell_rr3  mixed


</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">
<h4 id="Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">Discussion 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>There were some columns with significant amounts of missing data. Out of the total of 891,221 observations, 6 of the features had more than 200,000 missing values. I decided that these were the outliers since close to 25% of the data was missing. Most of these were supposed to hold information on a personal or household level. Possibly people were uncomfortable disclosing this kind of information. The columns excluded from the dataset are shown above.</p>
<p>It is also interesting to note that many columns had identical numbers of missing values. The most common was 116515 missing values which occurred 7 time, including the following key/value paris of (#NaN,#occurences): <code>(4854, 6), (133324, 6), (73499, 4), (93148, 4), (111196, 3), (99352, 3), (93740, 3), (77792, 2), (158064, 2), (97375, 2)</code>. Since so many features had identical numbers of missing values it is most likely that it was the same entries (rows) that didn't fill in particular information (perhaps because it was sensitive). This might also mean that these columns contain similar data and it might not be necessary to keep all the features in this case.</p>
<p>For example, the 7 features that all have 116515 are all from the <code>PLZ8 macro_cell</code> features. This is all to do with information regarding the building types and family houses of the region a person lives in. Maybe all the people with mssing data live in the same region for which there is no data.</p>
<p>The features that each have 4845 missing values mostly come from different feature groupings/categories: <code>CJT_GESAMTTYP</code>, <code>GFK_URLAUBERTYP</code>, <code>RETOURTYP_BK_S</code>, <code>ONLINE_AFFINITAET</code>, apart from <code>LP_STATUS_FEIN</code> and <code>LP_STATUS_GROB</code> which are the same feature on a fine and rough scale. Since they are mostly different features describing different things maybe certain people here didn't feel comfortable disclosing this information.</p>
<p>This means that there are feasibly two reasons columns would have identical numer of NaN's:</p>
<ol>
<li><p>they have similar meaning and if it is hard to get data on one then it is hard to get data for all features in that category (like <code>RR3 micro-cell</code>)</p>
</li>
<li><p>certain people didn't feel comfortable disclosing certain information and these people are likely to be more withdrawn in answering among various features.</p>
</li>
</ol>

</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">
<h4 id="Step-1.1.3:-Assess-Missing-Data-in-Each-Row">Step 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Step-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How much data is missing in each row of the dataset?</span>
<span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</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">describe</span><span class="p">()</span> <span class="c1">#count isnull along axis=1 --&gt; along each row</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[18]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>count    891221.000000
mean          9.396018
std          13.971829
min           0.000000
25%           3.000000
50%           4.000000
75%           6.000000
max          54.000000
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span> <span class="c1"># this creates a figure 8 inch wide, 4 inch high</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</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="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;# missing values&#39;</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">##number of rows/data points that has x missing values</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&nbsp;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Write code to divide the data into two subsets based on the number of missing</span>
<span class="c1"># values in each row.</span>
<span class="n">azdias_cleaned</span><span class="p">[</span><span class="s1">&#39;num_null&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_cleaned</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</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&nbsp;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#split into datum that have less than and more than 20 missing values</span>
<span class="n">azdias_low</span> <span class="o">=</span> <span class="n">azdias_cleaned</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;num_null &lt; 21&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;num_null&#39;</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="n">azdias_high</span> <span class="o">=</span> <span class="n">azdias_cleaned</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;num_null &gt; 20&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;num_null&#39;</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="nb">print</span><span class="p">(</span><span class="n">azdias_low</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias_high</span><span class="o">.</span><span class="n">shape</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>(797426, 79)
(93795, 79)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</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="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="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">azdias_low</span><span class="o">.</span><span class="n">columns</span><span class="p">[:</span><span class="mi">10</span><span class="p">]):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">[</span><span class="n">col</span><span class="p">][</span><span class="n">azdias_low</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;low_NaN&#39;</span><span class="p">)</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">azdias_high</span><span class="p">[</span><span class="n">col</span><span class="p">][</span><span class="n">azdias_high</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;high_NaN&#39;</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">&#39;Distribution for column: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">col</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</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,iVBORw0KGgoAAAANSUhEUgAABZgAAAWYCAYAAAA7raPEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl4VfW1//HPykAYkjCEMMg8I6CCRqkVEMWxtVV61aq9zi16i1orrZ28VttqW8fe/u71qi1Wb9XWoTjQVlFRVKygQYPM8xQMIQOEEAhkWL8/9gkeQgLhZCcnkPfrec6TZA9rr31yIN+zznevbe4uAAAAAAAAAAAOV0K8EwAAAAAAAAAAHJkoMAMAAAAAAAAAYkKBGQAAAAAAAAAQEwrMAAAAAAAAAICYUGAGAAAAAAAAAMSEAjMAAAAAAAAAICYUmAEAAAAAAAAAMaHADLQwZvaomf1nSLH6mtlOM0uM/DzHzL4dRuxIvNfM7Oqw4kXFbWdmM82sxMxeCDv+YeRxl5k9Ha/jAwAA4MjCWJ6xPAC0RhSYgWZkZuvNbLeZlZrZdjP7l5ndaGb7/i26+43u/ssGxjrrYNu4+0Z3T3X3qhByP2CA5u7nu/tTjY1dh4sldZeU4e6XNEH8o4KZDTCzajN7pI51bmaDay37z8iblJ1mVm5mVVE/LzSzpMh+ZVHLd5rZbZH9f2VmFZFl283sAzM7JSq+mdkdkdfmTjPLNbNnotbPNbNron6eZGbbzOziWjE2mNlntXJfEZVPVST/mp9vN7Nv1zqfmke3qBjfMrOPIueXb2bzzOzGWscZF3nztjPypugVMxsetf6syHO+M/LveLmZXRW1vuY57F/Hc1bzKGzA79bM7BYzW2Rmu8wsz8zeMbNLoraZG/U8FJjZi2bWvVackWb298i5lJrZbDMbG7V+cCTfmty2mNl/m1nSoXIEAKC1McbyDcVYvgHs0GP5RdGvrci48snI9/1rjeHWm9mPa8Woeb1Gj0P/O7Lumlpj53Vm9iczGxq1f+1j1Dy+eYjzetLMfhX188jIWHZaZDxeO97OyHHujPnJBBB3FJiB5vc1d0+T1E/SbyT9SNL0sA9yhBeI+kla6e6Vh7vjEX7eh+sqSdskXWZmKYfa2N1/GXmTkirpJknv1/zs7idEbToyanmquz8Ute6ZyP6Zkt6XFD0r5TpJl0k6M7LNyZLm1JWLmZ0v6W+Srnb3F6NWnSmpi6ThZjYmKvdhUbl/KOnGqPzui2z2fq28U919a+R4P5L0oIJ/c90l9ZD0XUmn2xezgsZLel3Si5H1AyUtlfRBTcE4YmMkj3RJP5T0hNUq5tfyTK2cuh5k2xqPKPgd3SopQ1IfST+XdH6t7W6M5DJUUmdJNc+FzGyIpA8kfSKpv6RekmZKmm1RHwxIUtRzO1rSBEn7Fd4BAMA+jOUPjbF8wxxqLH+MgrH1wXSKjOEulvSfZnZ2rfVfqzUOvSlq3YeRfTtKOkvSbkkLzGxUXceIejzX0BM0s9GS3pF0j7s/6O61x8WpCsa7+ZL+0NC4AFoeCsxAnLh7ibu/Kumbkq6u+UMe/YmvmXWNzD7cbmbFZva+mSWY2Z8l9ZU0076YwVnzCfP1ZrZR0ttRy6IHaoMsmMVZMzuzS+RYE80sNzrHmpkVZnaepJ9K+mbkeAsj6/ddphfJ6w4LZp9uNbP/M7OOkXU1eVxtZhvNrNDMflbX82Jmd0u6M+pY1zcw9r7zrifuhWaWY2Y7zGxN5JxkZseY2auR53e1mX2nnv3rfX4i399lZi+Y2dMWzGpZZGZDzewnkZw3mdk5UfvOMbNfWjALuNTM3jCzhhQeo10l6Q5JFZK+dpj7Noq7V0h6VlJfM+scWXyypNfdfW1kmzx3P2CgaGYXSvqLpG9G/g1Eu1rSDAWF3lAu2Yzk93NJN7j7DHff6YFP3P3yqFlB90l6wt3/O7JNkbv/REGB9oAZFZEYMyXtkHRcGLlG8j1W0hRJl7r7bHff7e6V7v6eu19X1z7uvk3SKwoKxDV+Iek9d7/T3be5+w53/52C5/439cTZIuktSSPCOh8AAI5GjOUZyzfDWP4+SXdbA4ru7p4taYn2Hws2iLtXufsad/+upHcl3XW4MepiwYSGtyT91N3/u55txkh6WNJl7p4XxnEBxAcFZiDO3P0jSbmSxtexelpkXaaCWZc/DXbxKyVt1BefSN8Xtc/pko6VdG49h7xKwUzTYyRVSvp9A3J8XdK9kp6rY7ZrjWsijzMUzPxMlVR7IDFO0jBJkyTdGSmk1T7Wz2sda3oDY9d73pHBzf8pmG3aScEMzfWR1X9R8Bwfo+CT/3vNbFJdz0MDfE3SnxXMJP1U0iwF/8/2UlDse6zW9ldIulZSN0ltJP0gKufPzOyK+g5kwWzb3pL+Kul5Bb/XZmPBLIurJBUoKLBK0jxJ15rZD8zsJIvMDK7lIklPSprs7rNqxUyV9A1Jz0QelzdkQN0Ap0lKUjB7t05mliZprPafkV3jeUm1Z4PUvBGbrOD3vTqEPGtMkrTO3XMaukPkDc3kWnmcrfrPZ7zVMVPGzHpJOkfB7xIAABwCY/kDjsVYXqGN5WcoGGdfc6jEzexLkkap8WPSGar7tXy4TlEwYeT77v7HujYws04Krhz8lbvPCeGYAOKIAjPQMnyuoC1AbRWSekrq5+4V7v6+u/shYt3l7mXuvrue9X9298XuXibpPyVdWk8h8HB9S9JD7r7W3XdK+omCy72iC4R3R2ZjLpS0UFJdg9tYYx/svK9XMDP1TXevdvfN7r7czPooGCj/yN3LIwW9P0q68rDO/Avvu/usyOWALyh4M/GbyGzfv0rqHxlI1fiTu6+M5Py8omYcuPvx7v7sQY51taTXIjNXn5V0vkX1G26kzyIzbWoe0YP0K8xsu6RdkRwurpkB7O5PKrjE7XxJ70naamY/qBX7TEnLFbS5qO1iSTslzZb0qqT2OrAlxMGMq5X3isjyrpK2unt1zYaRmT/bLehL92UFbShMUl0zJ/IiMWr0jTwHuxUMim9290UHyeuKWnm9eYjz6CppS/QCC3ojb7eg53KvqFWPmFmJgkJ/uqTvRa3rcpDzSVLwBq0m/vbIOeUquFRzxiFyBAAAX2As3/jYjOUPHMu7gt/xnXVNDIgoNLPdCsbWj0h6udb6l2uNQ+uc4R2lrtdyYa0YB3ywUIcvSSqR9FpdK83MJD0labGiWrwBOHJRYAZahl6SiutYfr+CT6HfMLO1VuvGDfXYdBjrN0hK1v7Fs1gdE4kXHTtJwWyNGtFFs10KZi+EFftg591H0pp64ha7e2mt2L3q2LYh8qO+3y2pMKr9Qs1gOfqcY3o+zKydpEsUzPKVu3+oYBZMvbMkDtPx7t4p6jE7at2z7t5JQY/iFZLGRO/o7n9290kKipdTJf26VoH6p5KqJc0wsza1jnu1gtkuVZGB+ks6vDYZc2vlPSyyvEhSN9v/BjynRM6jRMHfwmIFg/iedcTtKSn6xnwbI/umS/ofBbN4DubZWnkdMBu6lqLaebh7DwXPeYqCQniN77p7RwVvaDK1/2u3+CDnUyVpe1T8TpFz6iDpY9XzZgAAANSJsXzjYzOWr2Ms7+7/jKybUk+4rpHj/kDSRAWvh2gX1RqHHqrPcV2v5a61Yiw7RAwpGCN/LOlN+6KdXrQfKZhxfXUDPnQBcASgwAzEmZmdrOAP+dza69y91N2nuftABZds3RZVrKvvD/Gh/kD3ifq+r4KZFYWSyhTMGK3JK1FBwaqhcT9XcEOP6NiV2n+gFquGxD5YfpskDaonbpdIe4To2Jvr2PZQz09zmqyguPlIZGbrFgWvoWZrk+HuBZJukPQrM+tex/oKd/+rgl5w0TcK2algVnKmpOdqZq6YWT8Fl0ZeE3VOF0m6oJ5B6eH4QEFBtd4+1e6+Q9JHCgb7tV2qYFZ17X32KLhU80Qzu6CROUabrWCGzJhDbvlFLgsl/Vr7X276luo/n7mR/GvH2aVgNsm4WjN0AABAHRjLNwhj+f0d7lj+Dkk/U1T+0SKTMx6UVK7gJtaNze39RsaQgrH3txQUx2eZWXrNCjObqOB8Lnb37XXvDuBIQ4EZiBMzS48Upf4q6em6LrE3swvMbHDkEqIdCv5Q13yKnq+gh9nh+nczG2Fm7RX0Ensx8sn8SkltzeyrZpasYCATfSlWvoKiV33/b/xF0vfNbECkl25N77XDvnt0E8SerqA38KRI39xeZjbc3TdJ+peCWbZtzex4BZfgPVNHjEM9P83paklPKLix3OjI4zRJo80s+mZzbSLnVfMI4/LJfdx9iYJi6A8kycyuM7OvmFla5Hn+qoI+fR/V2m+Hgj6//SU9HcnrKklLI9vXnNMwBa+7Q909+1B5Fkv6paRHzewbZpYayW+MpHZRm/5I0vVmNjWyTRcz+7WkLAX/VuqKvUfBjUkOuAlgI/JdquA1+1zkNdsu8hx9+RC7PiGpT+R5l4IbtJxuZr8ws86R38utCmbH1DmDKnL55ZWSNjPgBwCgfozlDwtj+f01dCwvSfKgP/EiHfrKvt9Iut3M2h5OMmaWGPnd/D8Fs6DvPpz96xNpLXKJgg9A/mlmHcysp4J/M7e6+6dhHAdAy0CBGWh+M82sVMEn8T+T9JCCm0PUZYiCWYg7Femr5V/cAOHXku6I9MGq3ef2YP6s4CZrWyS1lXSLFNwJW8En3n9U8Kl/mYJ+rDVqbhZWZGaf1BH3iUjs9yStU/AJ+s2HkdfBNCq2BzdfuVZBIbBEwd2Ra2ZRXK6g0Pm5gpYMP3f3A3rkNuD5CZWZLTGzb9WxvJeClgy/c/ctUY8FCm6kET3wXKLgcr6aR32vs9qWWHDX75rHgwfZ9n5J/2HBTeZ2KBisb1LQx/deSVMil/3tJ9Jv7mwFs5ufUFBg/p9a55Sn4GYqDW2TMb5W3jtrZgG7+70KCsg/lbRVwev/fxXcfGd+ZJt3FcyuvjSyfn0kv9Pcfe1BjvtHSYPNrL5+0d+qI6+MQ5zLjZH8/kvBZYq5CorYl6juWTk1xe7/p6BXn9x9uYKbtGQpuFw0T9KFks529/1u4leTV+S8T4psBwAADsRY/vAxlv9i+eGM5aPdobr7fEf7h4IxeHSf5Zm1xqAvRa07NTL+2yFpjoJZ1SfX8WHJ9loxbjtEHvu4+14FN/EuV3DD7SkKWqP8Vx3j40cbGhdAy2O0uwEAAAAAAAAAxIIZzAAAAAAAAACAmFBgBgCgmZnZxDouC9xpZvQ9BgAAQIsWaQFS11j2gLYgAFoHWmQAAAAAAAAAAGKSFK8Dd+3a1fv37x+vwwMAUL9dhQ3ftn3XpssDQLNZsGBBobtnxjsPxsgAAABoKRo6Ro5bgbl///7Kzs6O1+EBAKhf9p8avm1WfTeOB3AkMbMN8c5BYowMAACAlqOhY2R6MAMAAAAAAAAAYkKBGQAAAAAAAAAQEwrMAAAAQCOYWR8ze8fMlpnZEjP7XmT5XWa22cxyIo+vxDtXAAAAIGxx68EMAACAhqmoqFBubq7Ky8vjncoRr23bturdu7eSk5PDDFspaZq7f2JmaZIWmNmbkXUPu/sDYR4MAAAAjJHD1NgxMgVmAACAFi43N1dpaWnq37+/zCze6Ryx3F1FRUXKzc3VgAEDwoybJykv8n2pmS2T1Cu0AwAAAOAAjJHDEcYYmRYZAAAALVx5ebkyMjIYODeSmSkjI6NJZ7mYWX9JYyTNjyy6ycw+M7MnzKxzPftMMbNsM8suKChostwAAACOJoyRwxHGGJkCMwAAwBGAgXM4mvJ5NLNUSX+TdKu775D0v5IGSRqtYIbzg3Xt5+6Pu3uWu2dlZmY2WX4AAABHG8bI4Wjs80iBGQAAAGgkM0tWUFx+xt1nSJK757t7lbtXS/qDpFPimSMAAADQFOjBDAAAcIR5dv7GUONdMbZvqPFaGwumfEyXtMzdH4pa3jPSn1mSJktaHI/8AAAAWgPGyPFDgRkAgFrmrytu8LZjs5owEaAFSU1N1c6dO0OPa2a67bbb9OCDQfeIBx54QDt37tRdd91V7z5PPvmkrrvuOuXk5Oj444+XJI0aNUp///vf1b9//9BzbIDTJF0paZGZ5USW/VTS5WY2WpJLWi/phngkBwAA0FwaWuQ9Woq3jJEDtMgAAABA3KSkpGjGjBkqLCw8rP169+6te+65p4myOjzuPtfdzd2Pd/fRkcc/3f1Kdz8usvzrUbOZAQAAgHodaWNkCswAAABoMHfXD3/4Q40aNUrHHXecnnvuOUnSd7/7Xb366quSpMmTJ+u6666TJE2fPl133HFHvfGSkpI0ZcoUPfzwwwesmzlzpsaOHasxY8borLPOUn5+/r51F1xwgZYsWaIVK1aEeXoAAADAYWvtY2QKzAAAAGiwGTNmKCcnRwsXLtRbb72lH/7wh8rLy9OECRP0/vvvS5I2b96spUuXSpLmzp2r8ePHHzTm1KlT9cwzz6ikpGS/5ePGjdO8efP06aef6rLLLtN99923b11CQoJuv/123XvvvSGfIQAAAHB4WvsYmQIzAAAAGmzu3Lm6/PLLlZiYqO7du+v000/Xxx9/rPHjx+v999/X0qVLNWLECHXv3l15eXn68MMP9eUvf/mgMdPT03XVVVfp97///X7Lc3Nzde655+q4447T/fffryVLluy3/oorrtC8efO0bt260M8TAAAAaKjWPkamwAwAAIAGc/c6l/fq1Uvbtm3T66+/rgkTJmj8+PF6/vnnlZqaqrS0tEPGvfXWWzV9+nSVlZXtW3bzzTfrpptu0qJFi/TYY4+pvLx8v32SkpI0bdo0/fa3v23cSQEAAACN0NrHyEnNdiQAAACEIp533Z4wYYIee+wxXX311SouLtZ7772n+++/X5J06qmn6ne/+53efvttFRUV6eKLL9bFF1/coLhdunTRpZdequnTp+/rTVdSUqJevXpJkp566qk697vmmmt03333qbS0NISzAwAAwJGKMfIXmnuMzAxmAAAANNjkyZN1/PHH64QTTtCZZ56p++67Tz169JAkjR8/XpWVlRo8eLBOPPFEFRcXH7K3XLRp06btd6fsu+66S5dcconGjx+vrl271rlPmzZtdMstt2jr1q2NOzEAAAAgRq19jGz1TeFuallZWZ6dnR2XYwMAcDDzX3iwwduOvWRaE2YCBJYtW6Zjjz023mkcNep6Ps1sgbtnxSmlfRgjAwCAI9mz8zc2aLswZhszRg5XY8bIzGAGAAAAAAAAAMSEHswAAABoUkVFRZo0adIBy2fPnq2MjIw4ZAQAAADE19E0RqbADAAAgCaVkZGhnJyceKcBAAAAtBhH0xiZFhkAAAAAAAAAgJhQYAYAAAAAAAAAxCTUArOZdTKzF81suZktM7NTw4wPAAAAAAAAAGg5wu7B/F+SXnf3i82sjaT2IccHAABA9p/CjZd1bbjxAAAAgObGGDluQpvBbGbpkiZImi5J7r7X3beHFR8AAADxs379eo0aNeqA5Xfeeafeeuutg+5711136YEHHmjwscxM06ZN2/fzAw88oLvuuuug+zz55JNKSEjQZ599tm/ZqFGjtH79+gYfFwAAADgcjJEDYbbIGCipQNKfzOxTM/ujmXWI3sDMpphZtpllFxQUhHhoAAAAxMMvfvELnXXWWaHGTElJ0YwZM1RYWHhY+/Xu3Vv33HNPqLkAAAAAh6u1jZHDLDAnSTpR0v+6+xhJZZJ+HL2Buz/u7lnunpWZmRnioQEAANDUqqqq9J3vfEcjR47UOeeco927d+uaa67Riy++KEn65z//qeHDh2vcuHG65ZZbdMEFF+zbd+nSpZo4caIGDhyo3//+9wc9TlJSkqZMmaKHH374gHUzZ87U2LFjNWbMGJ111lnKz8/ft+6CCy7QkiVLtGLFipDOGKjbs/M3NugBAACOfoyRwy0w50rKdff5kZ9fVFBwBgAAwFFg1apVmjp1qpYsWaJOnTrpb3/727515eXluuGGG/Taa69p7ty5qn212vLlyzVr1ix99NFHuvvuu1VRUXHQY02dOlXPPPOMSkpK9ls+btw4zZs3T59++qkuu+wy3XffffvWJSQk6Pbbb9e9994bwtkCAAAAh8YYOcQCs7tvkbTJzIZFFk2StDSs+AAAAIivAQMGaPTo0ZKkk046ab/ebcuXL9fAgQM1YMAASdLll1++375f/epXlZKSoq5du6pbt277zaqoS3p6uq666qoDZnLk5ubq3HPP1XHHHaf7779fS5Ys2W/9FVdcoXnz5mndunWxniYAAADQYIyRw53BLEk3S3rGzD6TNFoS00cAAACOEikpKfu+T0xMVGVl5b6f3T3mfetz6623avr06SorK9u37Oabb9ZNN92kRYsW6bHHHlN5efl++yQlJWnatGn67W9/e8j4AAAAQGMxRg76JofG3XMkZYUZEwAAALVkXRvvDA4wfPhwrV27VuvXr1f//v313HPPNTpmly5ddOmll2r69Om67rrrJEklJSXq1auXJOmpp56qc79rrrlG9913n0pLSxudAwAAAI4QjJElxWeMHPYMZgAAALRC7dq10yOPPKLzzjtP48aNU/fu3dWxY8dGx502bdp+d8q+6667dMkll2j8+PHq2rVrnfu0adNGt9xyi7Zu3dro4wMAAACxai1jZDvUVO2mkpWV5dnZ2XE5NgAABzP/hQcbvO3YS6Y1YSZAYNmyZTr22GPjncYh7dy5U6mpqXJ3TZ06VUOGDNH3v//9eKd1gLqeTzNb4O5xvxKPMfKR4dn5Gxu03RVj+zZxJgAAtCzN+TeSMXK4GjNGZgYzAAAAQvGHP/xBo0eP1siRI1VSUqIbbrgh3ikBAAAAcdUaxsih9mAGAABA6/X973+/wbMxioqKNGnSpAOWz549WxkZGWGnBgAAAMRFaxgjU2AGAAA4Ari7zCzeaYQmIyNDOTk5zX7ceLWHAwAAQPgYI4ejsWNkWmQAAAC0cG3btlVRURHF0UZydxUVFalt27bxTgUAAACNxBg5HGGMkZnBDAAA0ML17t1bubm5KigoiHcqR7y2bduqd+/e8U4DAAAAjcQYOTyNHSNTYAYAAGjhkpOTNWDAgHinAQAAALQYjJFbDlpkAAAAAAAAAABiQoEZAAAAAAAAABATCswAAAAAAAAAgJhQYAYAAAAAAAAAxIQCMwAAAAAAAAAgJhSYAQAAAAAAAAAxocAMAAAANIKZ9TGzd8xsmZktMbPvRZZ3MbM3zWxV5GvneOcKAAAAhI0CMwAAANA4lZKmufuxkr4kaaqZjZD0Y0mz3X2IpNmRnwEAAICjCgVmAAAAoBHcPc/dP4l8XyppmaReki6U9FRks6ckXRSfDAEAAICmQ4EZAAAACImZ9Zc0RtJ8Sd3dPU8KitCSutWzzxQzyzaz7IKCguZKFQAAAAgFBWYAAAAgBGaWKulvkm519x0N3c/dH3f3LHfPyszMbLoEAQAAgCZAgRkAAABoJDNLVlBcfsbdZ0QW55tZz8j6npK2xis/AAAAoKlQYAYAAAAawcxM0nRJy9z9oahVr0q6OvL91ZJeae7cAAAAgKaWFO8EAAAAgCPcaZKulLTIzHIiy34q6TeSnjez6yVtlHRJnPIDAAAAmgwFZgAAAKAR3H2uJKtn9aTmzAUAAABobrTIAAAAAAAAAADEJNQZzGa2XlKppCpJle6eFWZ8AAAAAAAAAEDL0RQtMs5w98ImiAsAAAAAAAAAaEFokQEAAAAAAAAAiEnYBWaX9IaZLTCzKbVXmtkUM8s2s+yCgoKQDw0AAAAAAAAAaE5hF5hPc/cTJZ0vaaqZTYhe6e6Pu3uWu2dlZmaGfGgAAAAAAAAAQHMKtcDs7p9Hvm6V9JKkU8KMDwAAAAAAAABoOUIrMJtZBzNLq/le0jmSFocVHwAAAAAAAADQsiSFGKu7pJfMrCbus+7+eojxAQAAAAAAAAAtSGgFZndfK+mEsOIBAAAAAAAAAFq2sG/yBwAAAAAAAABoJSgwAwAAAAAAAABiQoEZAAAAAAAAABATCswAAAAAAAAAgJhQYAYAAAAAAAAAxCQp3gkAAADg8D07f2ODt71ibN8mzAQAAABAa8YMZgAAAAAAAABATCgwAwAAAAAAAABiQoEZAAAAAAAAABATCswAAAAAAAAAgJhQYAYAAAAAAAAAxIQCMwAAAAAAAAAgJhSYAQAAAAAAAAAxocAMAAAAAAAAAIgJBWYAAAAAAAAAQEwoMAMAAAAAAAAAYkKBGQAAAAAAAAAQEwrMAAAAAAAAAICYUGAGAAAAAAAAAMSEAjMAAAAAAAAAICYUmAEAAAAAAAAAMaHADAAAAAAAAACICQVmAAAAAAAAAEBMKDADAAAAAAAAAGISaoHZzBLN7FMz+3uYcQEAAAAAAAAALU/YM5i/J2lZyDEBAACAFs3MnjCzrWa2OGrZXWa22cxyIo+vxDNHAAAAoCmEVmA2s96Svirpj2HFBAAAAI4QT0o6r47lD7v76Mjjn82cEwAAANDkwpzB/DtJt0uqrm8DM5tiZtlmll1QUBDioQEAAID4cff3JBXHOw8AAACguYVSYDazCyRtdfcFB9vO3R939yx3z8rMzAzj0AAAAEBLdpOZfRZpodG5rg2YhAEAAIAjWVgzmE+T9HUzWy/pr5LONLOnQ4oNAAAAHIn+V9IgSaMl5Ul6sK6NmIQBAACAI1koBWZ3/4m793b3/pIuk/S2u/97GLEBAACAI5G757t7lbtXS/qDpFPinRMAAAAQtjB7MAMAAACIMLOeUT9OlrQ4XrkAAAAATSUp7IDuPkfSnLDjAgAAAC2Vmf1F0kRJXc0sV9LPJU00s9GSXNJ6STfELUEAAACgiYReYAYAAABaG3e/vI7F05s9EQAAAKCZUWAG0HjZf2rYdlnXNm0eAAAAAAAAaFb0YAYAAAAAAAAAxIQCMwAAAAAAAAAgJhSYAQAAAAAAAAAxocAMAAAAAAAAAIgJBWYAAAAAAAAAQEwoMAMAAAAAAAAAYkKBGQAAAAAAAAAQEwrMAAAAAAAAAICYUGAGAAAAAAAAAMSEAjMAAAAAAAAAICYUmAEAAAAAAAAAMaHADAAAAAB+o+ZKAAAgAElEQVQAAACICQVmAAAAAAAAAEBMKDADAAAAAAAAAGJCgRkAAAAAAAAAEBMKzAAAAAAAAACAmCTFOwEcRPafGr5t1rVNlwcAAAAAAAAA1IEZzAAAAAAAAACAmDCDGQAAAAAAAED9GniV/aCNxQ2LN3ZaI5JBS8MMZgAAAAAAAABATCgwAwAAAAAAAABiQoEZAAAAAAAAABATCswAAAAAAAAAgJiEVmA2s7Zm9pGZLTSzJWZ2d1ixAQAAAAAAAAAtT1KIsfZIOtPdd5pZsqS5Zvaau88L8RgAAAAAAAAAgBYitAKzu7uknZEfkyMPDys+AAAAAAAAAKBlCbUHs5klmlmOpK2S3nT3+bXWTzGzbDPLLigoCPPQAAAAAAAAAIBmFmqB2d2r3H20pN6STjGzUbXWP+7uWe6elZmZGeahAQAAAAAAAADNLNQCcw133y5pjqTzmiI+AAAAAAAAACD+Qiswm1mmmXWKfN9O0lmSlocVHwAAAAAAAADQsoR2kz9JPSU9ZWaJCgrXz7v730OMD6CFmr+uuEHbjc1q4kQAAACawaCNLzRsw8QuDdsu69rYkwEAAIiz0ArM7v6ZpDFhxQMAAAAAAAAAtGxN0oMZAAAAAAAAAHD0o8AMAAAAAAAAAIgJBWYAAACgkczsCTPbamaLo5Z1MbM3zWxV5GvneOYIAAAANAUKzAAAAEDjPSnpvFrLfixptrsPkTQ78jMAAABwVKHADAAAADSSu78nqbjW4gslPRX5/ilJFzVrUgAAAEAzoMAMAAAANI3u7p4nSZGv3erayMymmFm2mWUXFBQ0a4IAAABAY1FgBgAAAOLI3R939yx3z8rMzIx3OgAAAMBhSYp3Aqjf/HW1r7Ks39isJkwEAFqxxMrd6lK6XEXpI1SdmBLvdAAcWfLNrKe755lZT0lb450QAAAAEDYKzAAA1MGqK9Sj+CMdU/CBkqrLlV62Xmt6T453WgCOLK9KulrSbyJfX4lvOgAAAED4KDADABCtqlKZ2z5V761z1KayVNtSB2tvckd137ZA29KGqrjjyHhnCKAFMrO/SJooqauZ5Ur6uYLC8vNmdr2kjZIuiV+GAAAAQNOgwAwAQI3KPdL0czQwL0c72x2j1b0nq7RDf5lXqUN5ngbk/UOl7fuoIjk93pkCaGHc/fJ6Vk1q1kQAAACAZsZN/gAAqLF4hpSXo3U9v6olA65XaYf+kiS3RK3pNVlWXaVBm1+V3OObJwAAAAAALQQFZgAApKBoPP9Rqeswbe18omS23+rylAxt7HG2OpatVffij+OUJAAAAAAALQsFZgAAJGnTR1JejjR2ygHF5RpbO5+k7amD1Tf/LbXdU9DMCQIAAAAA0PJQYAYAQJI+ekxK6Sgdf1n925hp7TFfU1VCsgbnvizzqubLDwAAAACAFogCMwAAOz6Xlr4inXillJJ60E0rktO0oef56lCep46lq5spQQAAAAAAWiYKzAAAZD8hVVdJJ3+7QZsXpx+risR2ytixpIkTAwAAAACgZaPADABo3SrKpew/SUPPk7oMaNAubonaljZcnUtXShW7mzhBAAAAAABaLgrMAIDWbclL0q5CaewNh7VbUceRSqzeK616o4kSAwAAAACg5aPADABovdyl+Y9KXYdJAyfWuUm1173rjg79VZHYPihQAwAAAADQSlFgBgC0Xps+kvJypLFTJLP9VrlLT+dmaspng7WqrO2B+1qCitNHSCtnSXvLmilhAAAAAABaFgrMAIDWa/6jUkpH6fjLDlg1M7+LZuZnaG91gu5Z2Uer6ygyF3UcIVXskla+3hzZAgAAAADQ4lBgBgC0TnvLpOX/kE64TEpJ3W/Ve0XpemZzN53aeYceHLFW6UlVumdVH62pVWQubd9XSu0hLZ7RnJkDAAAAANBiUGAGALROa9+VqvZIw7+y3+I5K7bq0fU9NSqtTFP75ykzpVJ3Dtuo1MSgyLy2LOWLjS1BGnmRtOpNqXxHM58AAAAAAADxF1qB2cz6mNk7ZrbMzJaY2ffCig0AQOhWzZLapEl9v7xvUc6m7fqPpz9Rn3Z7NG3QZiUnBHf469qmUncO3aj2iVX61aq+Wrsrqsg88htBoXrFa819BgAAAAAAxF2YM5grJU1z92MlfUnSVDMbEWJ8AADC4S6tfEMadIaU1EaSlL+jXNc9+bG6prXRT4ZsUvvE6v12yUyp1J1DN6ldYrV+t7aX3CMrep8spfeSltAmAwAAAADQ+oRWYHb3PHf/JPJ9qaRlknqFFR8AgNBsWSSVfi4NPXffouc/3qTisr164uqT1Sm5qs7duqVU6JvHFCh/TxutKGsXLExIkEZOllbPlnZva47sAQAAAABoMZqkB7OZ9Zc0RtL8WsunmFm2mWUXFBQ0xaEBADi0VbOCr4PPliS5u17K2axTBnTRkO5pB931lE6lSkmo1rtFHb9YOPIbUnWFtPyfTZUxAAAAAAAtUugFZjNLlfQ3Sbe6+353PHL3x909y92zMjMzwz40AAANs/IN6ZgTpbTukqRFm0u0tqBMk8cc+sKbtomusZ1K9WFxmvZWW7Cw14lSp360yQAAAAAAtDqhFpjNLFlBcfkZd+ddNgCg5SkrlHI/3q89xkufblabxAR9ZVTPBoWYkFGi3dWJyt6eGiwwC9pkrJ0j7d7eBEkDAAAAANAyhVZgNjOTNF3SMnd/KKy4AACEavVbklwaco4kqbKqWjMXfq4zh3dTx/bJDQoxMm2XMpIr9F50m4yh50nVldK6d5sgaQAAAAAAWqYwZzCfJulKSWeaWU7k8ZUQ4wMA0HgrZ0mp3aWeoyVJH6wpUuHOvbqoAe0xaiSYND6jRAt3dNDW0vJgYe8sKSU9uNkfAAAAAACtRGgFZnef6+7m7se7++jIg7sdAQBajqqKoAA85GwpIfgT+PKnm5XeNklnDD+8ewOM77JD1TK9mvN5sCAxWRowIYjvHnbmAAAAAAC0SKHf5A8AgBZr03xpT4k0JOi/XLanUq8v3qKvHn+MUpISDytU73Z7Naj9bs34ZPMXCwefJe3IlQpXhpk1AAAAAAAtFgVmAEDrsXKWlJAsDTpDkvTm0nztrqjS5MNojxFtQkaJlubt0LK8HcGCwZOCr7TJAAAAAAC0EhSYAQCtx8pZUv/TpJQ0SdJLn25Wr07tlNWvc0zhvtylVEkJppc+jcxi7tRXyhgiraHADAAAAABoHSgwAwBah23rpcIV+9pjFJTu0furCnTRmGOUkGAxhUxPqtIZw7vppU83q7KqOlg4eJK0/gOpYndIiQMAAAAA0HJRYAYAtA4r3wi+Dg0KzDMXfq5qly4aHVt7jBr/dmIvFZTu0dzVhcGCQZOkyt3Shn81Ki4AAAAAAEcCCswAgNZh1SypyyApY5Ak6eWczRrVK11Duqc1KuwZw7upY7tkvZrzebCg/2lSYoq05u3GZgwAAAAAQItHgRkAcPSr2C2tnysNOUeStKl4lz7LLdGFJzRu9rIkpSQl6oxhmZqzskBV1S616SD1O5Ub/QEAAAAAWgUKzACAo9+GD6TKcmnwWZKkOSu2SpLOGtE9lPBnDO+m4rK9+ix3e7Bg0CSpYJlUsjmU+ECsKqqqlbNpm0p2V8Q7FQAAAEA7KhP18pYu+tlLi5SzaXu800FIKDADAI5+q2dLSW2D9hWS3llRoP4Z7TWga4dQwk8YkqkEC+JKCm70J0lrmMWM+CnbU6knPlin57NzddZD7+r1xXnxTgkAAACt1PpdKXp0fQ9997NB+svmbvrbJ7m66H8+0EX/84FeydmsvZXV8U4RjUCBGQBw9Fv9ltTvNCm5ncorqvSvNYWaOKxbaOE7d2ijMX07653lwcxodRshpfWkTQbipnDnHj367hpt3rZb54/qoczUFN349Cf6j6cXaGtpebzTAwAAQCtRXmX69are+tGyAfrXtnRNzCjRAyPWKvuOs3X310dqx+4Kfe+vORr327f14ZqieKeLGFFgBgAc3bZtkApX7muPMW9tkcorqnXG8PAKzJJ05vBuWrS5JCjemQVtMtbOkaqrQj0OcCjrCsv0v3PWaHdFla4fN0Djh2TqlZtO0w/PHabZy7fqrAff1YsLcuOdJgAAAI5y1S79z/pjtHBHB13ea6seOW61vt0vX33a7VVqSpKu/nJ/vXXb6Xry2pOV1jZJN/w5W2sLdsY7bcSAAjMA4OhW06ZiX//lArVNTtDYAV1CPczEYZmSpHf3tck4UyrfLm3+JNTjAAezcNN2PfHBOnVISdR/nD5I/TKCNjDJiQmaesZgvf698RreI10/eGGhstcXxzlb4Mjj7vpk4zYt3tFe1R7vbAAAaNme/7yrPtqepit7b9VFPYqVmnRgG4yEBNPEYd305LWnKCkxQd9+Klslu7h/yJGGAjMA4Oi2erbUsa/UdYjcXW8v36rTBnVV2+TEUA8zome6uqenaE5NgXngGZKMPsxoNkU79+iFBZvUp3M73ThhkDJSUw7YZmBmqp687mR1TU3Rfa+vkDsVMqAhtpXt1fS563TOw+/pG4/8S79c1Vc3Lx6kFz7PUOHepHinBwBAi/N+Ubpe2tJVk7pu11e6bTvk9n26tNdjV56kTdt26bvPLlBFFT2ZjyQUmAEAR6+qCmntu8FN98y0rrBMG4t3aWLI7TEkycx0xrBuem9lQTAYat9F6nVi0P8ZaAZzVhQowUyXndJX7VPqL3i1b5Ok700arI/WF2vOyoJmzLD1MrP1ZrbIzHLMLDve+aDhyvZU6rbnczT23tn65d+Xqn1Kkn7zjeN064DNOqbtHv0tr6tuWjRIv13dW8UUmgEAkCSt3NlWj27ooRGpZbquzxaZNWy/k/t30b2Tj9MHq4v0i5lLmzZJhIoCMwDg6LXpI2lvaVBglvR25CZ8E4dmNsnhJg7rptI9lVqwIfIJ/eCzpdxsqYybVaBpFZft1aebtunkAV2U3jb5kNt/8+S+6tOlne5/fYWquc6/uZzh7qPdPSveiaBh9lZW68anF+iVnM91+Sl99Nr3xuuVqafpslP66tQupfrZkFz9ftRaTe5RpKWl7XTPqj7aWcnbKwBA61a4N0kPrOmtjDaVum3QZiUd5p/GS7L66IbTB+rP8zbo/z5c3xQpogkwAgIAHL1WvyUlJEkDJkgKZngO6ZaqPl3aN8nhxg3pquRE0zsrgkK2hp4ryZnFjCb37sqtMjNNGNKwD0/aJCXotrOHamneDv1jUV4TZwcceaqrXT94YaHeX1WoX3/jON194Sgd2zP9gO26pVTom70K9cPBm7VlT7J+u7q39lQ3cJoWAABHoekbe2hPten2QblKq6PnckPcfu5wnTm8m371j2XaVLwr5AzRFCgwAwCOXqvfkvqMldp2VNmeSs1fV6QzmqA9Ro3UlCSdMqCL3onMlFbP0VKHbtLK15vsmMC2XXv1yYbtyurXWR3bHXr2co2vn9BLw3uk6cE3VtDjrum5pDfMbIGZTam90symmFm2mWUXFNC2JN7cXb/4+1K9uvBz/ei84bo0q88h9xmVtks3D8jTqrJ2+t3aXqrkwgAAQCs0Z0sbfVKSqm/0LFLvdntjjpOYYLpn8iglmunefy4LMUM0FQrMAICjU2m+tOWzfe0xPlhdqIoq18RhTdMeo8YZw7ppZf5O5W7bJSUkSEPPCW40WMWdkNE03ov0UT79MFu/JCaYfnDOMK0v2qUXsnObIjV84TR3P1HS+ZKmmtmE6JXu/ri7Z7l7VmZm0/4fhUN7ZM4aPfmv9bp+3ADdePrABu/3pc6lur5vvj4pSdXjG3qKe2gCAFqTimrpFwtT1SNlb4Nu6ncoPTu203cnDtJri7foX2sKQ8gQTYkCMwDg6LTm7eDr4LMkSe+sKFBqSpJO7t+lSQ87cVgwQ3rOisgsxKHnSXtKpI3zmvS4aJ1Kdlcoe8M2ndSvszq1b3PY+086tptO7NtJ/zV7pcorqpogQ0iSu38e+bpV0kuSTolvRqjPS5/m6v5ZKzR5TC/97CvHyhp6V6KIszO365KeBXq3qKOe/7xrE2UJAEDL89TqdlpbmqSreucrOSGcT1m/M2Ggendup7tfXapKrrhr0SgwAwCOTmtmB+0puh8nd9ecFVs1fkhXJSc27Z++QZkd1LdL+y/aZAycKCW2oU0GmsS7Kwvk7oc9e7mGmelH5w1X/o493ESliZhZBzNLq/le0jmSFsc3K9Rl645y3fnKEp3Sv4vuu/h4JSTE1kv533oW6fSMEr20JUOLtiWFnCUAAC1PYbnpv5Z20ITue3Rix7LQ4rZNTtTPvnKsVuSX6i8fbQwtLsJHgRkAcPSprgraUgyeJCUkaEV+qfJKynXGsKbrv1zDzHTGsEx9sKYwmBGakib1O01a9UaTHxuty47dFcpeX6wT+3ZW5w6HP3u5xtiBGTp9aKYee3et9lQyi7kJdJc018wWSvpI0j/cnU+cWqC7Zy7Vnspq/ebfjmvUh5Fm0tW989UxqUo/XpCmSiZcAQCOcg8sSdXuKtOdJ+zUYV78c0jnjeqhUwdm6ME3V2r7rtj7OqNpUWAGABx98nKk3cVftMdYHulR28T9l2tMHN5N5RXVmre2KFgw9DypcKVUtKZZjo/W4f1VBapuxOzlaNePG6Cisr16ffGWEDJDNHdf6+4nRB4j3f2eeOeEA721NF//WJSnW84crIGZqY2O1yGpWtf0zdeS7cn60+p2IWQIAEDLtHhbkp5b11ZXD96twenhT1YwM/386yO0Y3eFHnpzZejxEQ4KzABC1658q4ZsfF6DcmeoW3G22pXnizvdoFmtni3JpIFnSJLeWb5VI49JV/f0ts1y+FMHZqh9m0S9tSw/WDD0nOArs5gRkp17KvXxhm06vncnZaSmNDreuMFd1S+jvZ6Zx6WHaH127qnUna8s1tDuqZoyYVBocb/UqVRn9dyjh5akalMZb7sAAEcf9+DGfl1SXLccG15rjNqG90jXt8b209PzNmjFltImOw5ix0gHQGisulK989/RqLWPK23XBqWXbdCAvH/q+DWP6aQV90t/uVwqWBHvNNEaLJsp9c6SOmSocOceZW8o1qThTd8eo0bb5ESdPjRTbyzJV3W1S10GSl2H0ocZoXk153PtrazWlwaEc9PKhATTFaf01Ufri7Uyn0E7WpcH31ihvB3l+vU3jlebpPDeHplJvxhTqgRz3fFJGp+1AwCOOnO2tNFHhW1064gydWzTtH/objt7qDq0SdJDb1JTaIlCG0GZ2RNmttXMuGkJ0Aqll63TcWseVa/C91WUPkqfDZ6qT4feqpwhN2tNrwtVnHastHGe9NTXpMLV8U4XR7PitdKWz6QRF0kKLnuudum8UT2bNY1zR/bQ1tI9ysndHiwYeq60/gNpD8U7NI6765n5G9Qjva36dGkfWtyLT+qtNokJenY+s5jReuRs2q4n/7Ve/z62n07q1zn0+Me0r9YPRpbp3fwUvbqp8VcbAADQUrhLDy7poD4dqvTNAbub/HidO7TRt8cP1Kwl+fqs5j0WWowwZzA/Kem8EOMBOBK4q1/e6zp2/Z9lci3r9+9a2/siVSa1l8y0p01nFXY6Qet6fU269rXg5mtPfU0qXhfvzHG0WvJy8HXEhZKkWUu2qE+Xdjq2Z1qzpnHG8G5KSjDNWhLpaTv0PKm6QlrzTrPmgaPPZ7klWvL5Dp0yoIssxLuoZKSm6CvH9dDfFuRq197K0OICLVVlVbV+MmORuqWl6IfnDWuy41w1eLdO6FyhXyxMU8nekO98BABAnLy+OUWLtyfre8eWqc2hqovual++Rd2KF6hH0Tz1KPxQmvuw9N790ge/lzb8S6ooP+QxrxvXX53bJ+vBN+jF3NIkhRXI3d8zs/5hxQNwhPjoD+pR/JHyO2dpQ4+z5QnJ9W/bbbh01SvSUxdIT31duvYfUqe+zZcrWoelL0u9TpI69dGO8gp9sLpIV3+5X6iFuIbo2C5Zpw7K0BtL8vXj84bL+oyV2naUVs6SRny9WXPB0eWZ+RvULjlRo/t0Cj32t77UTy/nfK6ZCz/XN0/m/2cc3Z7L3qRleTv0yLdOVHrbg4xfGinRpHtPKtUFb3XW/1vWQXecsLPJjgUAQHOocumhpR00KK1Sk/vVXRhOrihVx7K16rhzrdLL1qpNZa0ezflv7v9zYkrQ5rDfl6VBk6S+Xwr6TUVJa5usG08fpF+/tlwfry/Wyf3DaReHxmvWHsxmNsXMss0su6CgoDkPfeSo3CNtWSwtelG989/W4E0vqnvRfJmHfydOoNE2fSzN+qm2pQ7R+p7n71dcdpeW72ynpaXt9u852GOUdOXL0p6SYCbzjs+bP28cvYrXSXkL97XHeGf5Vu2tqtZ5o3rEJZ1zR/bQusIyrdq6U0pMDgZKq2ZJ1dVxyQdHvpLdFZq5ME8Xjj5GbZMTQ4+f1a+zhnVP0zO0ycBRbkd5hR56Y6VOGdBF5zfD34iRnSp1af9yPbW6ndaVhv9vFwCA5vTqxhSt2pGk20aWKbHWPJ62ewo1KPcljVn5Ow3a/Io67lyrHR0GaM0xX1fOkJuVPfyH+nj4j6SfbZH+s1C6fZ102V+kU74jVeyW3n9I+tN50h8nScv+fsB7p6tO7a/MtBTdP2uFnBsctBihzWBuCHd/XNLjkpSVlcWrINrubdKMG6TVb0mRYvIxMu1NTlPGjqXqvm2BNvQ4VyWp4d3ZGmiUsiLphWuk9J5a0+OifZ8sVlZLH25L1z+2dtG6XW0lSYM77Na/9SzUKe7BLNJjRkv//pL0fxcGRebr35Ta88kjQrD0wPYYmWkpGtMn/L6aDXH2iO664+XFmrV4i4Z2TwvaZCyZIeV9GsyyBg7Ty59u1u6KKl0xtq8Wb94Renwz07e+1Fd3vrJEn+Vu1/G9w58lDbQEj7yzRkVle/XkV0c02xUu00aVaeam/8/efYdHVbQNHP7NZje9VwiplNB7E5DeLaBgoaioYC/Yvtf2vvbeCxYEEQtFEUREAUWkI70TQkKAJLT0Xnd3vj9mkYABQkh2kzD3dW3K7tlznp09u2fOnJlnXHhjtwdTe1b/51fTNE3T7KHMCh/s86ClTxnDG5WcfiD/JMT/Truj27AKJ04EdCfdpx2FriH/6okMgMlN/Xb3hxZXqRuoOWt2z4N1H8L34yGoBfR6BNreAE4m3JydeLB/U55ftJe1Cen0bhZU8y9auyC7NjBr55B5CGbdCNlHoMcD0LA9BLdk88rFSOGEb/4BIk/8Tosjs8jyiuFIyBBKXHRjnOZAVgssmAQFqTDxdyzrVlBsESxL82Npqh+ZZSZCXUu4K+I4EsHPJwJ4MyGcX6es5aEBzRjcMgRDWGcYP0+ly1jyJIye5uhXpdUHexdCaCfwi6S4zMLKuDSu79gIg8ExOS9DvF3pGOHLsn0neGhgM2g6CIRBpcnQDczaRZJSMntjEm0b+dAuzJeiDV9W/sndH6/0otd1bMTrv+1n1t9JtLtBNzBr9U9yZiEz1h5iVKdGtA3zOfeCUkLWIUjZom5Ht0JRJhiMtM3LRgoDEgPFLv7kuUeS6xFBsXNgxSfRQLCrlftbFPLOXk82pBbRI7ishl6hpmmaptWcHw+7cqTAyJc9szEIoCRfdfQ5uhWcTBwPuILjgT0xGz2qtgEXL+hyJ3S8Ta137fuw8F6Vr3nkFIjsyZhu4XyxOpF3lsVxZdNAu6dD1P5NNzA7WvImmDMGpFWlDYjqBYDFKokrXEmwSxl4NSfHowkNMjfSKG0N7Q5+SmLoSDJ82zo4eO2yteotOLgCrvkAQjtilit4+2AYe/I8aONVwN2RJ2jvXcCpNr3+gdmsyfBhSb4793y7les7NuK9m9ojIntAn/+Dla9D6+ugxdWOfV1a3ZZ5CI7vgMEvAbAmPp3CUovD0mOcMrR1A95Ysp+UrELC/AIgrBvsWwT9nj5nI4SmVWTrkSziTubx+qiaPf57u5q4rmMoP20/yjNXt8THreZy02qaI7yxdD8GA/xnaIuKF0jZCpu+gIQ/oDBD3Wdyh9CO6ma1UFyyH4EVIS14FxwmMGcPAGVO7uS5R5Du254sr2b/WvWkmEJmJ7rxyi5PfhmYhYOuf2qapmlalRRb4KNYDzr6lzGgYalKT7h7nkpt0bg/NBlA8rHS6tmYk1H1Wm4zGg4sVR3TvhoO3e7GZeDzPDywKU/O383y2FQGtwqpnm1qVVZtDcxCiDlAPyBQCJECPC+lvIiuNZehvT+ptBg+jWDcPGRAE/YezWHh9qMs2nmM1LwoTMJKv8AcRoRkIgN7ke7TjqYp82l87GfKTJ7kekQ7+lVol5uE5bDqTWg/FjrfDsDM5BD25HlwX+Rx+gXm/OspRgH9A3N44u6+fLA8nil/JdCigRf39G0CvR+H/Yvhl0cgoodOlaFVXewi9duWHmPpnhN4uxq5onGAA4M63cD8+96T3HllNLS7CX59DI5th0adHBqbVrfM3piEp4uREe1Da3xb47pFMmdTMgu2pXBHL13X0OqPrUcy+XXXcSYPbEYDH9fTD5QVq7r5pi/g2DZw9oSW10J4NwjrCkEt1YmuTfy8d08/V0pcSjPxLkzCq+AIPgWH8M/bT7GzP4j+ah1GFwBcneDJtvlM3uTD/COu3BhV8cRImqZpmlYbzUp043iREx90OIbY/r06ZvqEwRUPgHdD21KZ1btRIaD5cIjuA3++DBs/h7il3HDNB3we6ME7y+IY0CIYJ33V1qGqbZI/KeVYKWVDKaVJShmmG5cvYNM0lb82tCNMXM7CZDeGvL+aaz5ey9cbDtMh3JcHo47RJyCXFem+PLKnMZ8ebsgRSwAHIsZQ7BxAs6QfcCtOdfQr0S4nxTmw8AEIbglXvwdC8M2Gw/yR5seIkIwKG5fLMzoZeHxIDFe3bcgbS/ezMi5VTXx23WdqyOmSJ+3zOrT6ae9C9Z3qFw+IYEwAACAASURBVEWZxcry2JMMahmCycmu89n+S3SgBzEhnizbe0Ld0WY0GF1h+3cOjUurW7IKSlm8+zjXdQzFw6XmB6C1DfOhfbgv3244gtWqp83Q6gerVfLS4lhCvF24p29jdae5ROV4fL+1Gn5bmg9XvQOPxcL1n6shug3antG4/C9CUOISQJpfRxLDrmN7zGTiw0ZjdnKDvfPhzxcg9hcoLQBgRHgJHfzLeHuPB4Xmmn/dmqZpmlYdcssEU2I9eMhvE933vaJ6Lze/Cno9Wq5xuQY5e8DwN+DOpWB0xmnWKL4KmsPhkxn8tP1ozW9fOy/HnnVfrhJXwZL/qA/ibT/zQ2whj3y/AxeTgVevb8PmZwfxxW1d6B2Qy92RJ/i4zUGGBGexIdOLx/ZG82dWCHER47AaTDRPmo2pLM/Rr0i7XCx/UeVdHvkJOLuzJj6NF3/ZR2efPMY2SqvUKoQQvH1jO1o08OahOdtJTMtXJ259/g92/wD7f63hF6HVS1lH1NXzVtcBsOlQJjlFZQx1cHqMU4a2bsDmw5lkFpSCm6/qFbfnRzWUTNMqYf62FErNVsZ1i7TbNu/sFUViegGrDlTu+13TartFO4+xMzmb/xvaAndnIxz4HT7tAX88pyYgvnUhPLBJzWLv6l31DQkDmT6t2dt4IvSaDIExKrXYX69B0t8IrPyvXR6pxU58HlfF/JSapmmaZmdT97tyh/VHHi/6QOVJ7v0YNBsCBif7BhJxBdy7Fno8SNShuSz2fJXZy9ZQXGaxbxzaGXQDs73lHIUf74SApjDqC36NzeKp+bvo3SyQ+ff1ZHz3SHzdnc94SoCzmdvDU5nS9iBtvAqZeqQBm4pCiYsYi9FSRPOkOWqWTU2rSUkbYcuX0P1eaNSJxLR8Hpi1jaZBnjwUffyicgi6Oxv54tbOmJwM3PXNFnKLy1SqjAZtVaqMwmoeUqPVf/t+Vr9bqwbmpXtO4GZyok8tmVF4aOsGWCUsjz2p7ugwXo0I0BdUtEowW6zMXH+YzpF+tAq9hEavizS8TUNCvF2Yse6Q3bapaTWloMTM60tiaRfmw6iIYph1E8y+UU28On4+3DIfmvSv/tz4ftHQ+Q7o8wR4hcCuubDuQzqbDjEivJipce4kF+hTMk3TNK12S01Lpcuhz3nYuBDCu0Ovx8C7keMCMrnB0FdhzGyixQm+LHmCFYtnOS4eTTcw25W5FOZNAHMx3Pwdfx0qYvLc7XSO9GPqrZ1xMZ7/qo+PycJjTY4S4VbCe4mN2COjiA+7AffikyrdhkWPsdNqiLkUfpkMPuHQ/1lyisqY9PUWjE4Gpk/ogpuT9aJXGe7vzqfjO3Eko5BH5u7AIow6VYZWdfsWQsMO4BeF1SpZtvcEfWOCcHO289X0c2gd6k0jXzd+P5UmI7qv+jzpNBlaJSzbe5KUrCLu6m3fXMjORgO39YhiTXw6B07qC9la3fbxigTScov4LHIlhs+ugCPrYcgrcN96aDao5gPwbgQ9HlIXGIsyYc17vO48A09RxCs7PWt++5qmaZpWVWlxiGkDuFLsIivmRmg35vypo+ypxdUY71tNnnMww3Y+TPHvL4NV92R2BN3AbE+/Pwspm2HkJ2zIDeTe77bSsqE3X97eVQ3TqwR3JytPNk3By2jhjfhw4p1bcajh1WritZWv1fAL0C5b6z+EtFiVk9DFk1cW7+NIZiGf39KZcH/3Kq/2isYBPH9tK1bsT+WL1Ylnpso4sKwaX4BWr2UnwdGt//Re3p6cTWpeCcNqSXoMUKlhhrQOYXV8OgUlZjAYoMM4SFwJ2cmODk+rxaSUTFuTSGSAO4Nb2X+fHtstAhejga/WHbb7tjWtuiSm5fPr2s384f82jba+DS2vgYe2Qs+HwOh84RVUFyHUhIH9noGo3ngcXccKt6dIO57EmnidikbTNE2rhfb/hvWLAYiSHL4Lfhy/mF7VP9rnUvk3pvC2pfxo6YPr+ndg9s1QlO3oqC47uoHZXnb9oGal7vEgO737MenrzUT4u/P1nd3wdjVd1Kr8nc083TQZsxS8Hh9GoncX6HgrrHlP5XfWtOqUngCr3la5bZsPY9WBNOZtTeGePo3pFu1/yau/5YpIhrYO4f3lBziYlg9XPgbBrWDxYzr1i1Y5u+ep361GArBk93FMToL+LYIdGNS/Xd22IaVmK4t2HlN3dBgHSNg5x6FxabXb1iNZ7EjOZuKV0Q6ZGdvfw5lRnRqxYFsKWQWldt++pl0qKSVLvv+MxcYniS5LgOs+hxu+UukqHMXkBm1GQc+H8DZJ5rm8yJF5T1NaUuK4mDRN0zStPKsV/nod5o4lydCIMbzBiA4OTIlxAS3CQ/i77Uv8zzIJmfgXTB8E6fGODuuyohuY7eHkXlj0MET2Iqfns9w/axt+Hs7MmtQdf4+q9ZoIcyvl/5qkkFZq4u2EMEoGvwaBzWDB3VCQXs0vQLtsSQmLHwGjKwx/k7ziMp6ev4umwZ48PLBZtWxCCMHLI9vgZnLiyR93YTWY4NqPIPcorHilWrah1WPmEtj4BTTup65cl5qZtzWFAS2C8XG7uIt3Na1zpB+tGnozc91hpJTgFwVRvWHHLFWB07QKfLE6ER83Ezd0DnNYDHf0iqbEbGX2piSHxaBpVVKSz/GvJ/JA+iuU+ERjuHcNdBhbe3pe+TdG9Pk/TgZcwS2l88iZ0hfS4hwdlaZpmna5K86F78fDqjdIbzqaoTlPM7JPNwJcpKMjO6/HBsfwvXUQn0W8D0VZMG2AmtBXswvdwFzTSgtUfmRXb+QNM3jq51hO5hYzZVwngr1dL2nVLb2KeCDqOHEF7ry9IgVumKE+RAvvVw2DmnapdsyCw2tg8Avg1YA3luzneG4xb93QDldT9eW2DfZ25blrWrHlSBbfbDgM4V3VDO4bp0LKlmrbjlYP7ZwL+Seg1yMAzN92VOUI793YwYH9mxCCO3pFEXcyjw0HM9SdHW+FrMNwZJ1DY9Nqp0PpBfwRe5JbroiodCqtmhAT4kXvZoF8s+EwZRZ9MUSrI45uxfp5bxocXsAs55vwe3AFBDRxdFT/ZnIltMfNTAl6DmNuCnJqX9jyla7La5qmaY6RHg/TB8KBZViHvcldOXfi7eXFRDvPBVIVYX7uTOgZyTtxAcSN/EV16Jl9E6z9QB9X7UA3MNe0Jf9RH9BR05i1t4Qle07wn2HN6RDuWy2r7+Gfx9CgLKavPcTKnBA1WUn8MtUwp2mXIvcYLH0GInpAp9tZfzCdWRuTmNgrmk4RftW+uVGdGtGveRBvLo0jObMQBj4H3qGw6CE1yaCmnc1qgfUfQcP20LgfVqvkq7WHaBfmQ5fI6t9Hq8O17UMJ8HBmxql8ti2vBRdvdTFH084yY+0hTAYDE3pEOToU7uwVzcncEn7bfdzRoWja+VktKm3cl0MoKChgbMl/ibjxNUzOLo6O7LyuvvlerjG/RbxLazV6bN4E1XFE0zRN0+wlbqnq9VuYAbf9zHdyGNuTc3hqWAuHdna4GA/2b4a/hwtP/J6J+fYl0Pp6WP48LLgLyoocHV69phuYa9KuebD9O+j9OPvdO/Ly4n30iQli0pXV27PulrBUWjTw4ol5O0lteSvEDIc//gfHd1brdrTLiJQqrYulFEZ+QqHZylPzdxMV4M7jQ5rXyCaFELx2fVucDIKnFuxCOnvC1e9C6j41yaCmnW3/r5CRAFc+CkKw8kAqiekFTLwyGlFbhj+fxdXkxLjuEfy5/yRJGYXg7K7ycO5dqIaiaZpNVkEp87YmM7JD6CWPeKoOfWOCaBzowYy1h1SKF02rjXKOwjcj4c8XyYsexsCiV/Ft1Y/ezYIcHdkFRQd6MKJ3J4ZmPMKhDv9Rx7jPe0PS344OTdM0TavvrFY179KcMeAfDXev4phfF95csp/ezQIZ1an25l4+m4+7iRdHtGb30Ry+2pSqRvoPfA52/wgzhkFOiqNDrLd0A3NNyUyExY9C+BUU9foPD87ejperiXdvbI+hmifpcTZIPh7bkbxiM4/P24V1xBRwD4Af79STpGlVs/07SPgDBr8IAU14e1kcSZmFvDm6HW7O1Zca42yhvm48c1VL1iVk8P3mZGg+XE0uuOptNdmgpp0iJax9H/wbQ8sRAHy59hANvF25qm1DBwd3frdcEYmTEHy94bC6o8MtYC6CvQscGZZWy8zaeITiMmutSfdiMKgULztTctiWpHtVarXQvp/hs55wdBvma6cwPvteSk0+vDSyjaMjq7TJA5vRJNibMfu6kzf+VzA4wVfDYdVbqme2pmmaplW3kjz44Vb46xVodxPcuQzpE8b/Fu7BKuG169vW2s4753JV2wYMahnCu3/EkZRZBL0fh7FzIOMgfNFfX7ytIbqBuSaYS1XjrsEAo6fz0m9xHEzL54ObOxDkVTPD85qFePHcta1YE5/O9G05MHq6auT++UGda0a7ODkpsOwZiLwSut7F+oPpzFx/mFuviKR744Aa3/zYbuH0aBzAq7/GcjS7CIa/BSZXlSpDn1xppxxeA8e2Qc+HwOBE7PFc1iVkcFvPSExOtfvQFmJrBP9hczL5JWYI6wIhbVRuMHOJo8PTaoHiMgsz1x+hT0wQzRt4OTqcf4zqFIa/hzNvLonTvZi12qO0QNURfrhN9bq6dw0fZXRj19FcXr++LSG1YARAZbmanPjg5g5k5Jfy1EZn5D2roc1o+OtV+HqE6qGtaZqmadUl4yBMHwRxS2Do63D9VDC58evu4/y5P5XHh8QQ7u/u6CgvmhCCl69rjdFg4NmFu1W9tflwmLQcXDxh5jWw9WtHh1nv1O6z8Lrqzxfh2HYY+Qk/HzYwZ1My9/ZtwpXNAmt0s+O6RTCsdQPeWhrHLmMbGPQC7FsIGz6p0e1q9YiUpxtyR04hq8jMY9/vJDrAg6evamGXEIQQvHVDOyTwyNztmN2DYNgbkLQeVr9tlxi0OmDtB+ARDO3HASpXrZvJiXHdIhwcWOXc0SuKvBIzC7algBBqtEDWIfj7M0eHptUCczclkZ5fwl21bDIVDxcj/ze0OZsOZ7Jo5zFHh6Npqr49tQ9s+1alS7rzd7YV+DPlrwRGdWrE8Fo+oqUibRr58OjgGH7ddZxF+/Nh1DS47nP1Wj/vBbGLHR2ipmmaVh/s/0315s1PhVt/gh73gxBkF5bywqK9tAvz4faeUY6Ossoa+rjx5PAWrIlPZ8E22wXa4BZw1wqI7g2/PAw/PwClhY4NtB7RDczVbf+vsGEKdJ3Efr++PDV/N12j/HhscEyNb1oIwRuj2xLk5cKDs7eT0+E+NYHUH8/B4bU1vn2tHtj2NRxcAYNfRPpF8fSC3WQUlPDhmI52Teof7u/OK9e1YfPhLKb8lQDtx0K7MbDyDUhcabc4tFrq+C44+CdccS+YXEnLK+HnHccY3bkRvu7Ojo6uUjpG+NEh3JeZ6w5jtUpoOghihsHqd1QlT7tsncwt5p3fD9C7WSBXNq3ZC9NVcVOXcNo28uG132IpKDE7OhztcmW1qAuN0werE8MJi2DQCxRYDDz6/Q4a+rjxwojWjo6yyu7p05jOkX78d+EejuYUQ4excO8a8I2A78fDr4/riYo0TdO0qjGXwJInYe5Y8I+Cu1dC477/PPzqr7FkFZbxxqh2GGv5yNALGd8tgi6Rfrz86z7S820jRd38YNw86P0EbJ+lJjVM3e/YQOuJur231DbHtsP8SRDakZzez3Pvt1vxdDXyybhOdhuy7evuzJRxHTmeU8SDc7djGfGJylE673bI1b2NtPPIOgLLnoXoPtBlIj9sSWbp3hM8PqQ5bcN87B7OdR0bMapjIz76M57NR7LgmvcgMEZ9xvJO2D0erRZZ9yE4e0GXiQB89/cRSi1W7uhVu3p7XsgdvaJITC9gVXyaumPIq2Auhj9fcmxgmkO9sGgvZRYrr1zXplbmu3MyCF4c2ZqTuSV8vELnxtccID1eTdKz/HloPgzuW6fqLsArv8aSlFnIeze1x9vV5OBAq87oZOD9mzpgtUqe+GGnuhAZ0AQmLoceD8Lm6TC1LyRvdnSomqZpWl2SnqBSYmz8HK64Hyb+AX6R/zy8bO8J5m1N4e4+jWkV6u3AQKuHwSB4fVRbCkssPDV/lzqeAjgZYeD/4NYFUJgOX/RT81DpFHCXRDcwV5ecFJg9BtwDsI6Zy+M/xZGSVcSn4zvZffb3zpH+vDSyDWvi03lr5TG4+TvVu+OHCSo/tKadrTgX5o4DYYARU0jMKOSFRfvo2SSAux04wdRL17UhzM+dR+buIMfiDDfOhJJ81cis8zFfnjIOqsnwutwBbr4Ul1mYtfEIA1oE0yTI09HRXZThbRoS7OXCV+sOqzsCm0L3e1Tl5tgOh8amOcbyfSdZsucEDw9sRmSAh6PDOadOEX6M7hTGl2sTSUzLd3Q42uXCaoF1H8HnV0L6AZU64qZvwd0fgF92HmPOpiTu7tPYLnNG1LSIAHeeu7YVGxIz1GguAKMzDH0Vblmgck9/OVh1DtDDezVN07QL2TlXpZXKSYaxc2HY62A8PUfYnqM5PDJ3B+3DfZk8sJkDA61ezUK8ePbqliyPTeXNZWf1VG4yAO5dq+bE+fkB+OkeKMp2TKD1gG5grg4leTD7ZigrhHE/8OmWPJbHpvLs1S3pGuXvkJDGdotgfPcIpq5K5OdjXjByCqRsgmVP66sy2pksZvjxDkiNhRtnUuoVzuS5O3AxGXjvpg4YDI7rQefpYuSjsR05mVvMMz/tRga3hKvfVRO8rXzDYXFpDmIuVQd9k4e64o7qvZyeX8rEK+tW72UAZ6OB23tFsfpAGkv3HFd39v0PuAfA0qf0d/VlpqDEzPOL9hIT4sldDrywV1lPDm+Oi9GJlxfvc3Qo2uUg7QDMGAp//A+aDIQHNqmZ7m29/NfGp/PYDzvslpbOXm7qEs71HRvx3h8HmLMp6fQDTQfC/Rugy50qNd9nPXU6PE3TNK1iOUdh7nh1HtWwPdy7Tk14V87J3GImfb0FP3cT027rjKvJyUHB1ozbekRy6xWRTF2VyA+bk8980KsB3PYz9Hsads+DT7rr+Q6qSDcwXyqLGeadbpxblRPEu38cYGSHUIcnRH/+2tZ0jfLjyfm72OM38PSQuhUv64YLTZESfnsCEpbDNe9D04G8+3scu4/m8MaodjTwcfzM6x3CfXlsiJrsZt6WFOg4HjqMVxP+Jfzp6PA0e1r+PKRsVhfMvBuy52gOby2NY0CLYHo2qZu91SZd2Zh2YT48OX83x3OKwNUHBvwXkjbA3p8cHZ5mR+//cYCj2UW8dn1bnI21v3oW7OXK5IHN+CsujT9jTzo6HK2+Ks6B3/+rGlAzEmD0lzBmFniF/LPIzuRs7v52C02CPJk+oSsuxvpzUiyE4M3R7egbE8SzP+0+fTESwNVbpQ+bsBiQMPNq1ftKpxHTNE3TQI382TRNNZgmLIdBL8CEX8Cn0RmLFZVauOubLeQWlzF9QleCvRzfBlDdhBA8f20rejcL5JmfdrPhYMaZCxicoN9TagJAjyA138EPE/TcOBep9p/B1GZSwtInIeEPuPpd9rl3ZfLc7TQP8eL1UW0dnjvR2Wjg0/Gd8XN35p5vt5Le87/QaQKseReWv6AbmTVY/xFs/UrNvN55AtNWJzJ1dSLju0cwrE0DR0f3j3v7NKFnkwD++/Me1sSnwVVvQ1ALlVv8yAZHh6fZw76f4e9Pofu90Po68kvMPDh7G/4ezrxzY3uHf99WlbPRwIdjOlJmsfLo9zuwWCV0ug1C2qoJWvUkTpeFPUdzmLHuEOO6R9DlUkc+SSuuJen45cYSmrqaxkd/JvrYYlj6DPz1msphvu1b1SP0EusBE3pG0STIg5cW76O4TKct0qqR1QLbvoGPO8P6KdB+jOq13PaGf3otAySk5nP7V5sI8HTmmzu74eNWd/Mun4uz0cBnt3SiQ7gvD8/ZwfqD6WcuEN0b7lsPPR+Cnd/DR53URXh9/NA0Tbt8ndynRv789oRK/3D/BnXO72Q8YzGrVfL4vB3sPprDR2M61ou8y+didDIwZVwnIgPcuW/WVg6lF/x7odCOcPdfMOB/ELcEpnRV9WadnrNSdANzVVnK1Id183To+TDbgq9jzBcbcDM5MfXWzrg7Gy+8DjsI8nJh6q2dSc8v4Y6ZW0nv/6YaTrfuAzXMUDcyX772LlQNWK1HwYDn+GrdIV79LZar2zXkxVo287rBIJgyrhONAz2Y9PUW1hwphFvmg2cIfHu97slc32UchJ8fhEadYfDLSCl59qfdJGUW8uGYDvh7ODs6wksSHejBiyNa83diJlNXH1RX0Ie/ofKj/fYEWK2ODlGrQWUWK08v2I2/hwtPDm1RpXW4lqQTmrqa1gen0TX2ddonfEpM8jzC01binX8I37wDsO1rWPWm+t5f9CB80hXeiVGjsDZ/qT5nF8nZaOClkW04klHI/bO2UWrW+6p2iaSExFUwrT8segj8m6gTvZFTwDP4jEWPZRdx25cbcTIY+PbO7naf88Se3J2NzLi9K5EB7tz9zVb2HM05cwFnDxjyCjywEZr0hxWvqJPi3T/qur6madrlJCcFfpkMU3tDZiJc/wXc+hP4/zv9mtUqeeXXWH7bfYJnhrdkUKuQClZYv/i4mZhxe1cEcOfMzaRkVTCHgZMJ+jyhcjMHtVD15s97w4Fl+ph6AbqBuSqKsmDWDapxuddk1kc/xC3TN+Ln4cwP9/SodRPztAvz5dPxnYhPzWP0539zuPvL0HUSrP9YTQyiPySXn/2/qhxM4d3hus/4dlMyL/6yj6GtQ/jg5g4YnWrfV4O/hzOz77qC6FONzKnOcMcSCGgKc8ZA7C+ODlGrCWVFMG+CmoDyxplgdGbelhR+3nGMRwbF1IuJnABu6BzG1e0a8t7vB9iRnA1RV0Kf/1MT/v3ysG5krqeKyyzc991Wdh/N4YURrfBxv4jel+nxNEpdRduEz2mf8CnhaSuRwokT/t042GgkexpPYnPLp9jR/BG2N38MnjkKz2XB00fhwS1w7UfQuJ9Kx/LrY/BxJ5g2QA2lLMysdBi9mgby6vVtWLE/lclzt2O26H1VqwIpVU+hLwfDNyOgIF2lw7hzqepNdJZdKdnc/MUG8orNfH1nV6ICa1fduyb4ujvzzUTVS/uWLzfy1/4Khu0GNFEpRCb8Am6+MH+iSi+yc67qHKNpmqbVT/mpsOQpNYpl+yzVqfCBzdD+5jNG/pySV1zGXd9sYca6Q9zeM4pJvevefDZVFRngwbTbupCeX8LIKevYdOgc9d6gGNXecMMMMBfB7Jvgq+GQ9Ld9A65Dal8rUm2XcRCmD4LD62Dkp/wZ9gC3f72VMD835t3Tg3B/d0dHWKGBLUOYfdcV5BaVMfrzDexo+1811PzvT2DxI3r26cuFpQx+/x/MHQfBLWHMHOZuT+V/C/cwqGUwH4/thKkWNi6f8q9G5uPA7b9Ag3YqR9LO7x0dolbdlj4FJ3bDqC/AN4L4k3k8t2gPPZsE8ED/po6OrtoIIXjturaEeLsyee528kvM0P9Z6PMf2P6t6smnG5nrlYISM3fO3Mzy2FRevq4N17QLvfCTyorU99yM4TClC43SVmF2cuFwg6Fsi3mEfY3vJLnBYNJ921PgForVcFbvfoMBXDwhsBl0ngCjp8FjsfDQNtX7saxY9Zp/Jwa+v0VdjDSXXjCs8d0j+d81rViy5wSPz9upUr1oWmVYzKqX7We91MXi/JNw9XtqnzwrHQaAlJKZ6w4x+rP1WCySbyd1p3Woj4OCt7+GPm7MuesKGvq4ccfMzbyxZH/FF3Wi+8Ddq1TPNSlVp4IPO8CGT6Ek3/6Ba5qmaTUjOxn+eB4+bA+bpkK7G+HhbSqlpEfFHXEOpxdw/afrWXkgjZdHtub5a1vV2XSDVdUlyp+FD/TCx83EuGl/M3tjUsULGgzQZrRK03X1e6pX+Iyh8N1oOPC7Pj87S+3I41BXJK6CH25Tw5cn/MLCzAie+HYrrUO9mXlHN/xq+TDtThF+zL+vJxO+2sTYaRv5ZNyjDDC6qnQZh1ar3kzRvR0dplZTco/Dj3eo3mpdJ2Ed/CpfbTrOK7/uo29MEJ+M71QnJpY61cg8btrfTPp6C5/d0okBty2EOWPhp7shJwl6TgZj7f48ahdQnAO//R/s+l7lC4sZSmZBKQ/M3oaHs5EPbu6Ak6F+VYR83E28f3MHxnyxgUfm7uD9m9vjNeBZ1Xt71RsgrWqYuKH+TGB1ucopLOP2mZvYmZzNeze1Z1SnsPM/4eQ+lY925xwozlbDHAe9yPakbMpMXpcWjBCq12PPh9Tt+C7V23H3D2pkiHsAtLlB5cAN7VhhLxiAiVdGU1xm4e1lcbgYDbwxqh2GevYZ1apRaizsmK2+4/NPQmBzuH6qOolzqrgnf05RGU/+uIule08wsEUw79zYvtbXvWtCRIA7P93fk5cW7+PzVQfZeiSTj8Z2pKGP25kLGpxUz7W2N0L876q+v+xplSqn7Y3qM92o8zk/05qmaVotZbVA/B9qLqX439WFxDajoN8zEHj+Djhr49N5YPY2DAK+ndiNnk0C7RR07dMkyJOfHujFw3O288xPu4k9nstz17aquMOdkwm6TlTHzo2fw8apMPtG8IuCLhOh4y3gfonzqNQDuoG5MjIPqcrYru8hMIbDQ2bwwooCVsbtoFu0P19O6IKXa92YVKRxkCfz7+vJnTM3c9e327i/3zjuHdsXj6WPwdfXQOc7YPCL4Hr59Aa5LCSuUsMkSwth1HT2Bg7h2Wlb2ZGczYAWwXw6vlOdmnX9VCPz+OkbuXPmFq5tH8qz135Hgz8nq7yDO+bA0NcgZqg+caqLDq9Tva1yj0G/p6HP/7Fs7wme/Wk3OUVlfDmha73Ntdkt2p//XdOKlxfvY/iHa3j/5g507f+02o9Xc6rhZQAAIABJREFUvn66kfkcDTBa7ZeeX8KtX24iITWPT8d3YlibhhUvWJABu+fBztlwfCcYTNDyWuh8O0T1BoOBsnnvVn+ADdup2+CX4OAKtf2tM1WvmKAWqmLd9qZ/zUAO8ED/ppSUWfhoRQJOBgPPXdMKN+e6c2zRaljuMYhdrPapY9vBYIRmQ9RJWcxw1UuoAmaLlV93H+ed3+M4nl3Ms1e1ZFLv6Muut1V5riYnXru+Ld2j/XlmwW6u/mgtjw+JYXSnMFxNZ33mDAZoPkzdkjbCxs/UBavN01SasXY3q97iFeTn1DRN02oJKeH4DpVOasdsNVeLZwj0flxNEO4bcd6nH0ov4IPlB1i08xgxwV5Mn9Cl1o6+t6dTOZnfWrqfqasTWXcwnckDm3FNu9CKOzM5e6gy7/EQ7P8FNk1Xc5v99Sq0uFrdmg4G1/o7WeL5COmg/LtdunSRW7Zscci2Ky3nqJqFefu3YDBS0nEi75ddz/RNabg5OzF5YDNu6xFVY70+N17EiWP3Gx+/qHXnl5h5ZsFuFu08hq+7iYd7N+K24lkYN30Gng1g4HPQaiQ46y+dOktK1Tjw92eQ8AcENqfg+hm8u83AzPWH8HN35r/XtOS6Do0u+SStsvvqxe6nF1JcZuHTlQf5fNVBTAbBwwObcWeDg5h+fwYy4qHpIBj6usqfpNV+5lJ1cF73IfhHw6hp5Pi358Vf9rJg+1FaNfTm3Zva07JhzR+wa/L7tzK2Hsni0e93kJJVyP39mjJ5UDNMa99R5eMbqSo27cfqnvp1SInZwk/bjvLxigQyCkqYemsX+sYEnblQYab63t6zAOKXgdWsUgB1GKd6HHqc2cvEbvtpUTbs/Un1bE7+GxDQuC+0Hwctr1GVbRspJW8s3c/UVYkEe7nw8MBm3Nw1vFanXzpFCLFVStnF0XHUiTpyZZQVQ9J6NRFvwp+QFqvub9BW7TttbwTPoHM+vbjMwrwtyXyxJpHkzCKaBnvy5uh2dI70s9MLOL9K132iK9mjqcsdVYrjYFo+T8zbyfakbIK9XJh4ZTTjr4jE0+U8/YiKc2Dfz7DrBzi8Rt0X0BQa91eTBEb1vmxPjjVN02qN0kL1HR23RE0wl3eMf+pgXe6E5lddsNNJcmYhH6+IZ/62o5icBBN6RPHQwGbnP0acz5avKrXYxnPlNT5LTZxHVdWfsSd5e1kc+0/kERPiySODYhjWusGFR+Sd3KvmZ9u3CArTVaeQxn3V+9O4n7qAW8cvile2jlytDcxCiGHAh4ATMF1K+ca5lq21lefSQjiyDuJ+g+2zkNJKZotxLPQayydb8skqLGVstwgeHxxDgKdLjYZijxPH3Sk5vLVsP2vi02ng7coLnYsYnPAqTmn7wNkLWo9UjRgRPc/Zs0SrZcqKVG/7vz+DtP3gEUxGq9uY7zKSGZvSOJlXzLhuEfxnaIuLm1DqPBzVwHzKkYwCXl68j+WxqTQJ8uDuXuFcVfwLXhvehbJCdSWx+dUQMwTcaseJqVZO1hGIXaQmpEiLhU4TKB30CisSC3l+0R7S80t5oH9THuzf1G5pXBzdwAzqQuCLi/Yyb2sK7cJ8eHp4S7qVbcJp9Zuq959PBPR+FDqMB2PNHo+0qisoMTNnUxLT1iRyMreENo28eXFEazpH+qshjid2QfxydSEwZbPqpe4RDO1uUg3LIa3PuW6H7KcZB9UxZuccyE4CZ09VeY7sBZE9VeOhwYnNhzN5a+l+Nh/OIjLAnccGx3Btu9BanTajJhuY60Ud+XwsZSrtxfGdqofV8Z0qf765GJycIaIHNB2oevWEtDrnagpLzWw+nMW6hHTmb00ho6CUjhG+3Nu3CYNbhtSq/ae2NDCDurCz4WAGn648yNqEdLxdjdzUJZx+zYPpEuX3717N5WUnq2Pwwb/UOVBZIQgnlRIntIO6yNWwHQS30scaTdO0mmK1QHo8HN0CKVvU75P7QFrA5AFNB6gGy2ZD/tXh4Gzp+SWsjEtjxf6T/LHvJALB+CsiuK9fE4K9LnEUaD1uYAawWiW/7TnOB8vjSUjNp0mQB4NbNaBvTBCdI/3Ofy5qtUDyJoj7VY3Yyjqk7ncPgLCup2/BrdR7WIcane3ewCyEcAIOAIOBFGAzMFZKua+i5WtF5dlqhYJU1biR/Dck/IlM2oCwlGIxuLDNexCvFFzDzjyVLqJX0wCeuaql3SYTseeJ44aDGby1bD/bk7JxElbGBCVzs8s6Wmf9hZO5QA25aDIAQtrYbq11z4baIj9VHYRSNqvbse1Qmk+Bf2tW+I5mysl2xGWoSZq6RPrx7NUt6RhRvY2sjm5gPmXF/pO8sjiWxPQCAPqESh51XUyb7D8xFaaqE6bInqrBuWF78G8CnsF16su9XigtUBMkxP+hTmqPbQegLKgNGyLuZlZ2a9YlZJBfYiYmxJN3b+xA2zD7pu2pDQ3Mp/y2+zjP/LSb7MIyfNxM9I8JZKz/ATof+QLjsa1q1El0HwjrAo26QIM2uhHAgcwWKwlp+exOyWFXSg6Ldh4jp6iUIZFG7mtvpINzMuLEbtX4dnKPaswB1ZjTbIhqgGvUqVK5th26n1qtqu6063tIXAlZh9X9Lt4Q3g1C2iADmrC9IJA3N5vZmGqgoY8bnSL96BjuS8cIX1qH+py/4cvOaqqBuU7WkcsrLYSiTCjMULeCDDU0N/uIet+zjkBOCljL1PLOXqpBMrQjRPeFqF7/6uWeV2LmRE4xx3OKOZFTRFJmIZsOZbIjOZsyi8TkJOjdLIh7+jSmW7R/rUyHUZsamMvbmZzNZysP8uf+k5RZJC5GA90bB3Bl0wCaN/AmOsCDUF9XjBWNKjCXqJPjxL/gyAb1PVWapx4zGFUvZ78oNZLGL1L97R0KHkHgHgim+pm+StM07ZJZLVCQDvkn1Pl7TgpkHlQX7jMOqsZIi21iZRdvdQwN66LOXaN6n7NuX2K2cDi9kINp+cSdyGN1fBo7krOREoK9XLiqbUPu7tOYUF+3Cp9/0ep5A/MpFqvkl53HmLs5ia1HsiizSDxdjPRoEkDLht6E+bkR5udGuJ87DXxc/z1ST0pIi1PzX51qp0k/cPpxFx81B0pAE9Um4RWiOph4BqtjqkegurBQSzp5OqKBuQfwgpRyqO3/pwGklK9XtLzDKs/rPlKVpuwkdcXeUvLPQ/EynL8s7VhjbcsmawucXd3p0yyI/i2C6RsTRJCXfU/Y7X3iKKVk65Es1sSns/5gOtuTsjFaixnutJkbnTfQlni85OmZp/PcGlHkGoLZ1R+LawBmVz/MLr5Yja4gjOBkQhqMaoiAkwlp+43BiDQYMQgBAmw/TkVR7k9R0b22v8U/MVe8TPnnll/nqT8E59vzpbSW/6fc71PPkv8sI+Spe+U/ywrbsqe2LaTtr1PrkKeCKb9u9Xr+iUxacLIUY7AUYzAX42Qpxliah0txKqaiNExF6biWpOFemgGABScSnRqz1dKYBSXd2CRbYDQY6NEkgCGtQhjUKuTfE8BUk9rSwAyqDGOP57Fi/0mWx6ayMyUbpJV2IpHr3HYwSGwh3HJ6ltgyJ3fyPSIp8ozA4uqLxeSF1dkLi7MXVpMnwsmINDghDEZ1c3JCCtvNYARhQAoDZ+7H9mL/bQppAawIq1X9lhawWhFYVcVJSrCUIsoKMJQVYCgtQJQVYCw8iWteEm4FybiVpP+zvkMuLVht7Mnisq5szlWNyKE+rvRtHky/5kH0ax7kkPzgtamBGVRv5jUH0vgj9iR/7U8lq7AMJwOM8IjjZsNyWpjj8LWocrUIE7leTSlzC8TsGoDFLYAyV38szt7q+9ig9mlp+/vUTTqZ1EUYgxMgznvdpeKqg4DzfLNW/Ig4z2MVP1GeCuw89ZeKH7F9R0t55v+oI4L853/1+4yjhJRYrFYsFqv6bS7DUlZCcXExJSXqZinOo7Qwl6K8LFytRXiLQhoasokw5RBgzcBgLT29Phdv1dO3ga0Rrkl/VaG8SLVqP805qirRR9aphqmMhNMNjkCZ0YMs4Uua2YOTZney8CQfT5qF+tOzWQO1H9rqCPSa7JDJLGuwgblu1JEXPwaH14K5SKW3MBerUVHl3sczuAeUa2yMUp0PGnZQQ0HPc0L0xLyd/Lg15Yz7DALahvnSs0kAPZsE0CXSv9bn766tDcynFJSY2XQok9XxaayJTych9XQd3uQkCPdzJ9TXDR93E75uJnzdTXi7mnA2GjA5GXB2MmA0SHyKj+Kftx+/nFg88xJxK0jBJT8ZY1n+v7ZpNnlidg3A7OyF1ehuu7lhNbpjMblhdXJDnvrb6A4GVZ9COKm61KnfhtP3qccNpx83GABDhZ0DTh8Wzn0Ak+Ic5x3nrVNVfE5Smedd7Jl2ZZYXZ5z/XOQW5EU/44zDbWVe0cVvgcq98Kqs99QzJeetN1S8tdPLV+p1X0xlpsJ7KlZ+2xfbdHP6/LNqRCWee/73u4LXXclwTn2yqhK+LPezck84e1mJkOo8R0gLwmq2/W1GWE/dV4aQFgyWEpwsxbbz9hKcLEUYy/Jtt1yMZXmYSvNwLslU50vlWAzOFHpGUugZSYFnJAXeTcn2b0eBV2MkgmKzheIyK8Vl6nducRnZhaVkFpSSVVjGydxikjMLsZYLv32YDwNahDCwZTCtQ72r/wLtZdLAXF5+iZn1CemsPJDGuoT0f5V5oKczW/47+MIrKsyEY9sgPUHVkzMS1MWFnGTOub8a3VTaWpOHulh/33qHNDpXto5cnZP8NQKSy/2fAnSvxvVXj+wjUJSlKsHNh5Mig/g6VpLv1wIXv3BCvF253tuFyf7utA/3rRM5A6uLEIIuUf50ifLn0cEx5JeY2Xwok+3JbViYM4bPsoswZ6fglxtHtOUwLSxJBBXk4Md+/EUefuRhFNYLb0irsjLpRBo+nJC+pEkf0mQ7EmQo+wwxZHq3JCTAjwh/Nwb7ezApwJ3ujQPwcbu8JgMTQtAq1JtWod48OKAZaXklrD+YzpGMFuzL7MfvWUWUZhzGJz+RcE4QZT5BVOkJwrN34yMK8KIQV3GOk2mtSkqkiTR82G8NJkm2IUkGkyyD2W9qidU5jBBvV8K9XRnUwIt+zYOJCfGslb3VHMnTxcjwtg0Z3rYhFqtke1IWq+PTSclsxIc5PTmeU4Q15yitrPF0MCTQIisZ/+wU/EUsgeTiJkovvBGtWpQaXbGaPDG4eWPyCUV4twPvhuAVCj5hqoe5b2T9Gznh00hNFNb2BvW/xQw5SZCRCBkJmDITCS5MJ7gwk5j8DMz5hzCUZOOUboE0s8o3fUqvRxzzGmpO3agjezWA4BbqZMbkqn4bXdTEz+7+qkHZPQDc/NX77eJVpc0Ma92A5iFeNPBxpaGPKw18XAn2crVbCqTLhYeLkf4tgunfQl28SssrITEtnyMZhRzKKOBwegEnc4s5llNETmEZ2UVlWKznapAJtt362v6X+JJPhEglRGQRIHIJIJcAcy6BxTl4UoS7yMONdNwpwU2U4EaJ7W99PNI0rf4pkSaKMVGMM3nSnTzcyZNu5BJCrowmjVPn776kSl9OSj+O448sNEBq+TXlANsr3IbRIPDzcMbP3YSfuzNtGvkwskMjmgR50CTIk8ZBHrg7V2cTnwbqPGxI6wYMad0AgFKzlRM5xaRkFZKSVUSJ2VK5Fbn7qzmimg46835zqcrdnJ8KBWnqd2G6GkFWVqB+lxaoHu61pEfzuVRnD+YbgaFSykm2/28FukkpHyq3zN3A3bZ/mwNx1bLx+isQSL/gUtql0GVc83QZ1zxdxjVLl2/N02Vc83QZX1iklPLcs85VkZ3qyPr9tT9d5o6hy93+dJnbny5zx9Dlbn+6zB3jYsu9UnXk6ry8kQKEl/s/DDhWfgEp5RfAF9W4zXpNCLGlNsxmXp/pMq55uoxrni7jmqXLt+bpMq55uowdqsbryPr9tT9d5o6hy93+dJnbny5zx9Dlbn+6zB2jpsq9OvtXbwaaCSGihRDOwBhgUTWuX9M0TdM0TdPqGl1H1jRN0zRN0+q1auvBLKU0CyEeBJYBTsAMKeXe6lq/pmmapmmaptU1uo6saZqmaZqm1XfVmgFcSvkb8Ft1rvMyp9OJ1DxdxjVPl3HN02Vcs3T51jxdxjVPl7ED2aGOrN9f+9Nl7hi63O1Pl7n96TJ3DF3u9qfL3DFqpNyrbZI/TdM0TdM0TdM0TdM0TdM07fJSnTmYNU3TNE3TNE3TNE3TNE3TtMuIbmCuBYQQM4QQqUKIPed4XAghPhJCJAghdgkhOtk7xrqsEuXbTwiRI4TYYbs9Z+8Y6zohRLgQ4i8hRKwQYq8QYnIFy+j9uIoqWb56P74EQghXIcQmIcROWxm/WMEyLkKI72378EYhRJT9I627KlnGtwsh0srtx5McEWtdJoRwEkJsF0IsruAxvQ/XYUKIYUKIONv791QFj0fYjhXbbcfZqxwRZ32i6+iOUYlyH28r711CiPVCiPb2jrG+uVCZl1uuqxDCIoS4wV6x1VeVKXNb/X6Hrd60yp7x1VeV+H7xEUL8Uq6+eoe9Y6xvdFuB/VWyzKv9WKobmGuHmcCw8zw+HGhmu90NfGaHmOqTmZy/fAHWSCk72G4v2SGm+sYMPC6lbAlcATwghGh11jJ6P666ypQv6P34UpQAA6SU7YEOwDAhxBVnLTMRyJJSNgXeB960c4x1XWXKGOD7cvvxdPuGWC9MBmLP8Zjeh+soIYQT8AnqWNoKGFvBceC/wA9Syo7AGOBT+0ZZL81E19EdYSbnL/dDQF8pZTvgZXQOz+owkwucL9m+h95ETViqXbqZnKfMhRC+qO/xEVLK1sCNdoqrvpvJ+ff1B4B9tvpqP+BdIYSzHeKqz3Rbgf1Vpsyr/ViqG5hrASnlaiDzPIuMBL6Ryt+ArxCioX2iq/sqUb7aJZJSHpdSbrP9nYdq3Gh01mJ6P66iSpavdgls+2W+7V+T7Xb2JAUjga9tf/8IDBRCCDuFWOdVsoy1SyCECAOuBs7VMK/34bqrG5AgpUyUUpYCc1HvZ3kS8Lb97QMcs2N89ZKuozvGhcpdSrleSpll+/dvIMwugdVjlTxfegiYD6TWfET1XyXKfBywQEqZZFtel3s1qES5S8DLVj/ytC1rtkds9ZVuK7C/ypR5TRxLdQNz3dAISC73fwq6cam69bANg1kihGjt6GDqMtuQ647AxrMe0vtxNThP+YLejy+JLbXADtSJ0x9SynPuw1JKM5ADBNg3yrqtEmUMMNo2VOtHIUS4nUOs6z4A/gNYz/G43ofrrsocQ18AbhFCpAC/oRqDtJql6zaONxFY4ugg6jshRCPgeuBzR8dyGYkB/IQQK4UQW4UQtzk6oMvEFKAl6iLtbmCylPJc9SrtIum2Avu7QPvBKdVyLNUNzHVDRb2LdK+v6rMNiLQNg/kYWOjgeOosIYQnqmfDI1LK3LMfruApej++CBcoX70fXyIppUVK2QF19babEKLNWYvoffgSVaKMfwGibEO1lnO6t612AUKIa4BUKeXW8y1WwX16H64bKvPejQVmSinDgKuAb4UQuq5fs/RnyoGEEP1RJ8VPOjqWy8AHwJNSSoujA7mMGIHOqJFJQ4H/CSFiHBvSZWEosAMIRaV0myKE8D7/U7TK0G0F9neBMj+1TLUdS3Wls25IAcr34gpDD3usNlLK3FPDtqWUvwEmIUSgg8Oqc4QQJtSX1ywp5YIKFtH78SW4UPnq/bj6SCmzgZX8Oz/bP/uwEMKIGoKu0+9UwbnKWEqZIaUssf07DXVipVVOL2CEEOIwKn3CACHEd2cto/fhuqsyx9CJwA8AUsoNgCugjwM1S9dtHEQI0Q6VDmiklDLD0fFcBroAc23HmBuAT4UQ1zk2pHovBVgqpSyQUqYDqwE9oWXNuwOVmkRKKRNQeWpbODimOk+3FdhfJcq82o+luoG5blgE3GabWfMKIEdKedzRQdUXQogGp3JQCiG6oT4XuqJ6EWzl9yUQK6V87xyL6f24iipTvno/vjRCiCDbZCoIIdyAQcD+sxZbBEyw/X0DsEJKqa+sV1JlyvisXGsjOPdkddpZpJRPSynDpJRRqAneVkgpbzlrMb0P112bgWZCiGjbZENjUO9neUnAQAAhREtUA3OaXaO8/Oi6jQMIISKABcCtUsoDjo7nciCljJZSRtmOMT8C90sp9Wi5mvUz0FsIYRRCuAPd0fUieyh/LA0BmgOJDo2ojtNtBfZXyfaDaj+WGqtjJdqlEULMQc1QGmjLm/c8avIjpJSfo/LoXQUkAIWoq2paJVWifG8A7hNCmIEiYIw+4b5ovYBbgd22/KoAzwARoPfjalCZ8tX78aVpCHxtmyHdAPwgpVwshHgJ2CKlXIQ6SH8rhEhA9foc47hw66TKlPHDQogRqMlUMoHbHRZtPaH34fpBSmkWQjwILAOcgBlSyr1nvb+PA9OEEI+ihpXero8Dl0bX0R2jEuX+HCp//Ke2a+tmKWUXx0RbP1SizLVqdqEyl1LGCiGWArtQcytMl1LucVS89UUl9vWXgZlCiN2otA1P2nqQa1Wn2wrsrzJlXu3HUqHrnZqmaZqmaZqmaZqmaZqmaVpV6BQZmqZpmqZpmqZpmqZpmqZpWpXoBmZN0zRN0zRN0zRN0zRN0zStSnQDs6ZpmqZpmqZpmqZpmqZpmlYluoFZ0zRN0zRN0zRN0zRN0zRNqxLdwKxpmqZpmqZpmqZpmqZpmqZViW5g1jRN0zRN0zRN0zRN0zRN06pENzBrmlYvCCGuF0JIIUQL2/9RQog9Zy3zuRBihxBinxCiyPb3DttzvxNCHCp33xrbcyYJIdJs9+0XQjxcbn0thRCrbI/FCiE+s90/SAixsNxyrwshfhVCONv+DxFCmIUQE8sts8W2nqRy29shhAgXQqQIIXaXu+9923OEEOIJW1y7hBA7hRDvCCGcbI/72V5XghDioBBiphDC2/ZY03JlEGt7zHh2/Ge9/lO35ud5H5rbXutBIcRWIcQKIcSVFypL2+P32e7fL4TYKIToWe6xtUKIuHLv38Szt61pmqZpmqbVXueor0shxEPllpkihLjd9vfMcvXznUKIgeWWW1mubrhDCPGj7f4XhBBHbffFCyEWCCFaXeh554j3BSHEE7a/XYUQfwghnre9jh1n3axCiOHVXmiapml1hNHRAWiaplWTscBaYAzwQkULSCnvBdW4Cv/P3p3HyVVWif//nO5sQBKWJGwJIUFRloCAUQQBURhBRR1nFAEVcBl0RByYDDg6jqKOjAJft/HnqDNRXMBRAUdRwAVHASXBIGsAFU0IYctGEkIIdLrP7497O6l0qrsr3VVdvXzer1e/qure5z73VHUnffvUqfNwZWYe0rkvIv4WOC8z/7fKoZdn5rkRMQX4Q0R8PzMfBb4IXJyZP4mIAGZ1PTAiLgReBJyUmc+Wm98M3FLGPLeMbXY5/l3ArMw8t2IOgKMzc3WX6c8GXg4cnplrImIsMAcYC6wHvg4syMy3lvP8G/CV8rwAf8jMQ8rE8g3A3wLf7e75V9ne9bluD/wYODczf1JuOxg4hOJ7s2murq9lRPw18HbgyMxcFRGzgasj4oWZubzzdcvMOyJiMvCniPhGZm7sLS5JkiQNCtWu15cB/xARX6m4Vq50fmZeGREvB74K7Fux7y2ZuaDKMZ/NzEsBIuLNwC8j4qCKa8rujqsqiiKRq4DbMvNj5eYfVOw/C3gL8NNa55Sk4cYKZklDXkSMB14KvJPigrUhyovSvwB7lJv2AJaW+zIz7+4S1weAVwCvy8wNFbtOBc4F9omI3fsR0oeA92TmmjKGZzLzosxcX1YZzwIuqhh/IfDSiJjR5XltBH4HTO1HLABvA27sTC6Xc9+Vmd/sOrDKa/kB4J8yc1W5fwFwOfDeKucZDzwFtPczXkmSJA2AHq7Xl1MUOpzRyxS30Idr1cz8LvAz4LRtPbY0Cvgf4E+Z+c9dd0bE84CPAG/LzI4+nkOShjwTzJKGg78Grs/MPwKrIuKwPs7z2YqPuW2VFC0Ts61AZ+uNzwA3RsS1EXFuROxYMfwY4B3AazJzfZc5ds7M24ArgZNrjO2mitjeHxE7A6Mz86Fuxh8I3F55oVsmku8EDqgcGBHbUVRZd1d18ZYuHwEc08M5f1/Lk6nyWh4A3NZl2IJyzk7fjYi7gPuACzMzazmXJEmSmq6n6/VPAXOibPPWjROBrp80vLzi+vSSHo79PbBfH44DuADYWO3TfBExGriCokhiSS/zSNKwZoJZ0nBwKkVlAeXtqT2M7cl5mXlI+XV6xfa3RMRC4AHgM50f38vM/6ZIjF4JHAfcUpF8/RMwutzeNdbONhTbEuvRFbF9AYjKnRHx6vIi+cGIeHG5v1oCtnL78yPiDmAl8EBmLuzm3JdXnPuQbj6+uPWJIn4UEQsj4nsVm6u+lt1N0eU5vDkzDwb2Bv45IqbVEockSZKartvr9cxcBNxK9SrjSyLiL8C32fKTeVC0uui8Pj2/h3NHl8e1HgdFS48jykrlrj4BLMzM/6myT5JGFBPMkoa0iJhE0YbivyNiMXA+RY/jrheS/XF5Zh4IHAt8PiJ27dyRmQ9n5tcy87UU/6fuX+56FHg18MWIOKZirlOBd5WxXg28MCJmbmtAZSuJjRExvXx8bdlT+j5gDLAQOCwiNv0/X1aFHFSOgbIHM/Bc4GUR8eptjaOLhcCmapTMfB3FxyB3qRjT3Wt5H/DCLvMdBtzb9SSZuYyiEvvF/YxXkiRJDVbj9fpFFC3TuuYozqe4Vv0w8I0+hnAom69/t9WNFK3trouIPTs3RsSxFOuXvK+P80rSsGKCWdJQ90bgm5m5d2bOyMy9gEVA3atbM/Nm4DvAOQARcWK5QB7lBefOwCMV4++nuPD8TkQcXK5g3ZqZU8tYZwCX0Pe+0f8O/Gdna45TXNjIAAAgAElEQVRyocFxFee+F6jsFfdRYF5mLu7yvB4BPlh+9ce3gGMj4jUV27avNrDrawlcDFxctv6g/NjkW4H/7HpsROwAvAD4cz/jlSRJUuP1er1ece16UteDy5ZvnwdaIuKEbTlxuZD3KymuO/skM6+iuGa/PiJ2Kq9Xvw6cnplP9nVeSRpORjU7AEnqp1Mp+rZVuopiAbznR8TSiu3nZeb3e5jrsxFxYcXjrhW1lOe6NSI+BbyKogp3A0Urh3Mzc3mR5y1k5vyIeBdwDXATFStOV8T6DYpkcU9uiojORe1uz8y3A/8BbAf8roxhXXmOO8txZ1JUUHcmYn8DnNXN/FcCF0bEEVX2vaWs0uj07syc33VQubjga4H/FxH/ATwOrGXrjzN22vRaZubVEbEHMC8isjzutLJaudN3I+JpYCzwX5l5Z5U5JUmSNLj0dL1e6ZPA7dUmyMyMiH+j6IncuW7I5eW1IcCKzDy+vH9eRLwV2IFivY9XlAtM08tx3crML5eLc/8IuA7YlaLQo3LYv5eLCkrSiBOukSRJkiRJkiRJ6gtbZEiSJEmSJEmS+sQWGZKkbRIRhwCXddm8PjOPbEI4kiRJUp9FxL8Ab+qy+fuZ+clmxCNJQ5EtMiRJkiRJkiRJfWKLDEmSJEmSJElSn5hgliRJkiRJkiT1iQlmSZIkSZIkSVKfmGCWJEmSJEmSJPWJCWZJkiRJkiRJUp+YYJYkSZIkSZIk9YkJZkmSJEmSJElSn5hgliRJkiRJkiT1iQlmSZIkSZIkSVKfmGCWRER8OSL+tU5zTY+IdRHRWj7+VUS8qx5zl/NdFxFn1Gu+inm3i4hrImJNRHy/3vNvQxwXRsS3m3V+SZIkSZKkbWGCWRrmImJxRDwdEU9GxOqI+G1EvCciNv37z8z3ZOYnapzr+J7GZOaSzByfme11iH2rZGtmviozv9Hfuat4I7AbMCkz39SA+Ye8iJgYEZ+LiCXlmwgPlI8nl/t/FRHvioi3lPvXlT97HRWP19VwnlMiYn5EPBURy8r7742IKPdfFhHPVs4ZEXdWHP/OiLi//Jl/PCJ+EhETupzjwojIiHhxl+1nlts/02X7X5fbL4uIoyvO+1S5vTKWRRX32yNiQ8XjfynfxHhel/lviIh/L+9nOe+6iHg4Ij7T+YaNJEnqO4sqLKqQJDWGCWZpZHhtZk4A9gY+BXwAmFvvk0TEqHrPOYD2Bv6YmRu39cAh/rxrEhFjgBuAA4ETgYnAkcBKYIskbWZeXr7JMB54FfBI5+NyW0/nmQN8HrgE2J0i6f8e4KXAmIqhF1fOmZkvKI9/GXARcGr5M78/8L0u5wjgbcAqoNofbn8G3tzl+3o68Mfy+d1U8VwOLPfvVBHLzIr9NwHvq9j3SeBSYG5FwvydwFTgYxXne0F5/HHAacDf9fS6SZI00llUUTOLKnoQEcd2LY6IiGvKfVt8n8qigLsrf8Yi4t8i4rIuc+5QznNtlfMtLgsidqjY9q6I+FV5/0NdYqkscDi9l+eyuCzKmNxl+x3l8TPKx5dFxL+V92eU+37S5ZhvR8SFFa/R0op9vyqPeUGXY/633H5sl+2dBR0nd9m+xbxd9m2KsXxctaCkfGOm83Vqiy2LUr7c0+slqX9MMEsjSGauycwfAW8GzoiIWbDVRcXkiPhxeWG+KiJuioiWiPgWMB24pvwFfUHFBcg7I2IJ8MuKbZXJuedExK1lpcQPI2KX8lxbXUR0XtBHxInAhygSfZsqVKOiOqSM68MR8WAUla7fjIgdy32dcZwRRcXtioj4l2qvS0R8DPhIxbneWePcm553N/O+vryAWxsRfy6fExGxZ0T8qHx9H4iIqsnDnl6f8v6FEfH98oLvySgucJ8XER8sY34oIl5ZceyvIuITEfGbcvzPul5w9uB0iu//GzLz3szsyMxlmfmJzNzqYrkvytf348B7M/PKzHwyC7dn5lsy85kapnkRcEtm3g6Qmasy8xuZ+WTFmKOBPYF/AE6JInle6THgbuCEMq5dKJLpP+rXE9zs34HxwHsjYjfg08A7MnND14GZeT9FknpWnc4tSdJwZlFF7yyq6N0WxRGZ+doexu4JnNLLfG8EngFeGRF7VNk/iuK6dCuZeVGXWMYDnwXuBa7q/amwCDi180FEHARsV8NxL4mIl9YwrtMfKf5e6DzPJOAlwPIqY8+g+0KPXkUPBSXlGzOdr9PlbFmU8p6+nE9SbUwwSyNQZt4KLKVItHU1p9w3haK64UPFIfk2YAnFhfv4zLy44piXUfxiP6GbU54OvIPiAmwj8IUaYrye4sLhu5UVql2cWX69HNiHImn3xS5jjgKeT1EJ+pGI2L/KuT7a5Vxza5y72+cdReuFbwLnAzsBxwCLy93foXiN96S44LwoIo6r9jrU4LXAt4CdgduBn1L83z6VIln7lS7jTwPeDuxKURH8TxUx3xURp3VznuOB6zOz1xYX/XAEMBb4YT/mmA+cEBEfi4iXRsTYKmPOAK4Bvls+PqnKmG+y+SL5lDKmWhLcvSr/oHsH8Ang28C3M/O31cZGxAEU/05vr8e5JUkaCSyqsKiiH0UV2+pi4GO9JN/PAL4M3AW8pcr+S4B/ioidejtZRLwaeD/wxsx8qob4vkVF4reM5Zs1HHcx8G+9jtrscoqfq862bqcCPwCerRwUEXtT/A11FsU1+27bcI5OtRSUSBpgJpilkesRYJcq29uAPYC9M7OtbAeQvcx1YWY+lZlPd7P/W5l5T3kR9K/AyVGfnrJvAT6TmX8pE58fpKhIrbzA+1hmPp2ZdwJ3AtUS1X2du6fn/U7ga5n587La9+HMvD8i9qJIen8gMzdk5h3Af1O0bOiLmzLzp2XS8vsUbwx8KjPbgP8BZnS5WP16Zv6xjPl7wCGdOzLz4My8opvzTAIe7WOMtZoMrKisqIni462ro/jI6zEVY/+p3N759Q0o2lcAfwMcBvwEWBkVPYwjYnvgTcAV5Wt0JdWrJ34AHFv+gXU6tV2I16y8IJ5L8QbFh6oM+X1EPEGRCP9v4Ov1PL8kSSOBRRVbncuiCnotqthWVwNrKV7DrUTEdOBYigTs5WyZ7O20APhVZYzdzDWD4jU4KzPvqzG+ecDEiNi/vB5+M0WBQ2/+P+B50UurmAqPUFRVdyb6u7t+Ph1YkJlXAfdRPeHem1oKSiQNMBPM0sg1leKjSV1dAjwA/Cwi/hIR/1zDXA9tw/4HgdEUycT+2rOcr3LuURR/JHR6rOL+eoqL5nrN3dPz3ouil2+1eVd1eYf9QYrvR188XnH/aYoEbXvFY9jyOff19VhJ8cZDI60EJlcm8TPzyMzcqdxX+Tvr0szcqeLrjIpjris/yrgL8HqKC/7ORXfeQPEHX2dbj8uBV0XElMpAygT8T4APA5Mz8zd1fJ6dFgKLM3N9lX2HZebOmfmczPxwZnY04PySJI0EFlX0f+6RVFQBsGeXQoaTexibFN/rj3ST6DwduCsz76VIuB8YEYdWGfcR4Jyu16Sdyrm/D1yemf/TQzzVdFYx/xVwP/BwDcdsAD7JtlUxfxM4PSKeT7E+yS1VxpwOdL72V9CHNhm9FZRIag4TzNIIFBEvokho3tx1XxZ9b+dk5j4UlQL/WFFp0N1Fd28X43tV3J9OcUG/AngK2L4irlaKi8Va532Eoo9c5dwb2TLp2le1zN1TfA8Bz+lm3l0iYkKXuatd6PX2+gykX1BUCuzQ68i+u4WiDcXr6zFZ+UfODRQf5+zsYXwGRVJ9SUQ8RnGhPpqK3nQVvklR3fStesQjSZKawqKK/s89kooqoOjBXFnI8L2eBmexHskSirYPXZ1OUdBAZj4C/JoqSdXMvAf4MdDdz+HnKf4WmVPzs9jsWxQV3WeybZ/K+y9gt4joqQd1pauBVwDnUOX6OYqezjMp3hCAIsF8UEQc0nVsb3opKJHUBCaYpREkIiZGxEkUv9S/nZl3VxlzUkQ8NyKC4uNe7eUXFBd2+/Th1G+NiAPK9gQfB64sLwj/CIyLiNdExGiKatHKd/4fp6hG6O7/qu8A50XEzIgYz+aP/G3zoiUNmHsu8PaIOC6K3nZTI2K/zHwI+C3w7xExLiIOpqj8uLzKHL29PgPpWxR/XFwVEfuVz2lSFCtbv7oeJ8jM1cDHgC9FxBsjYnx5nkOAmhLbUfQAPCUido7Ciyk+1jkvIqZSfGz0JIoqlkMoqns+TfXqiV9TVHr8R7+fnCRJGnAWVdTEoor6+DDwL2z5PI4E9gU+GBGPlcUNhwOndtOz+aPA39ElCR8RbwP+Fji5rNjeJpn5IMVif6+mSALXelwbxbX5J4CoYfx64Drg76leoHFGOc8d5Wsxv9xerW1IrTFWKyiR1AQmmKWR4ZqIeJLiAvBfgM9Q9CSrZl+KatV1FBWlX8rMX5X7/h34cPlRsR57hHXxLeAyikqCcRQLU5CZa4D3Unxc7mGKi8vKBT6+X96ujIjfV5n3a+XcN1JcNG2geMe8Hvo1d9nz7+0UqzyvoUhWdl68nwrMoLjw/gHw0cz8eZU5ent96ioiFkZE1T5omfkMxUJ/9wM/p3jz4VaKqpz5lUP7E0PZ5/AfgQuAZRR/3HyFYhX4yoXwLohicZrOrxXl9icoLsz/VMb4beCSzLyc4iOZd2TmzzLzsc4viv6IB0e5AFBFLJmZN2RmtaonSZI0SFlUsU0sqqiD8u+lu9myaOEMiuvmA9hc3DCLIgn9qipzPECxCPX7O7eV16dfAt5SvqZ99U7gFVnbwoCVvkXxvTixxvEfAl6WmYsrN0bEOOBkiirvQyq+zgHeUplwL39eKr+iy1zdFpRs43OTVEc9rXQqaRjIzBk1jDmz4v5nKZKi1cb9EPhhl83RZcziym2ZeWwv576MIvnc6dKKfSsperdVjj+24n4HxcX7x6vMu0UcvcWSmRd2ebxNc3cz5w8oEshdty+lqKKtJY7L6P716Tr2FxSJ687HG+nhe9F17sw8sOoT2bx/DXBu+VXNRIpeyZXH/AqY1tO8Vc7TuQhKd/vPpJuFVDLzRooq5Wr7PgV8qsr2Ryg+wgpwD1u+3pXjPlxl22J6+Fno5Wfusmrnysxef7YkSVJV10TERqCDYsGxzwBf7mbsvhSL2U2heIO6a1HFf0TExRQ9aK+s8fydRRX7URQX/D0U11AR0Vk00ApczNZFFW+lKKpYlJmHdZn3axQtJ26kKNb4KfUtqujz3Jl5a0R0FlXMpEiWn01RlHAqxev/CMVr3G1RRS+vT11FxELgovKas54+TJnkrEionl4WNFSe/1sUyedrqszxcbbsU/2PFJ/ku7pLnhWK53BRLYFlZrU2JrUc1x4RH6VIfNcy/hGK73dXf03RyuSblVXYETGXokr6RIoCp6lsbnnSad8uj5+gSMJ/kSL5/SibC0okNUlkr+sYSJLUs4g4kGIF7P3Kj+FJkiRJkqQRwBYZkqR+iYhPAz+jWCW8x+RyREzv0tqi8mv6wEQsSZIkSZLqxQpmSZIkSZKkIaQszri3m90HZOaSgYxH0shmglmSJEmSJEmS1CdNW+Rv8uTJOWPGjGadXpIkafBbv6L2sdtPblwcI8Btt922IjOnNDsOr5ElSZI0WNR6jdy0BPOMGTNYsGBBs04vSZI0+C34eu1jZ7+9cXGMABExKBYo9RpZkiRJg0Wt18gu8idJkiRJkiRJ6hMTzJIkSZIkSZKkPjHBLEmSJEmSJEnqk6b1YK6mra2NpUuXsmHDhmaHMuSNGzeOadOmMXr06GaHIkmSJEmSJNWVecT66W8ecVAlmJcuXcqECROYMWMGEdHscIaszGTlypUsXbqUmTNnNjscSZIkSZIkqa7MI9ZHPfKIg6pFxoYNG5g0aZI/FP0UEUyaNMl3cCRJkiRJkjQsmUesj3rkEXtNMEfEXhHxfxFxX0QsjIh/qDLm2IhYExF3lF8f6WtA/lDUh6+jJEmSJEmShjPzX/XR39exlhYZG4E5mfn7iJgA3BYRP8/Me7uMuykzT+pXNJIkSZIkSZKkIaPXBHNmPgo8Wt5/MiLuA6YCXRPMdXfF/CV1ne+0w6fXdT5JkiRJkjQIPPMkXHs+tI6BCXvAxD1gwp4w8xgYPa7Z0UkaAOYRm2ebejBHxAzgUGB+ld1HRMSdEXFdRBzYzfFnRcSCiFiwfPnybQ52IIwfP74h80YEc+bM2fT40ksv5cILL+zxmMsuu4yWlhbuuuuuTdtmzZrF4sWLGxKjJDXbFfOX1PwlSZIkbfLonXDnd2DhD+DXn4Zr/gGueBPM+1KzI5M0jJlHLNScYI6I8cBVwLmZubbL7t8De2fmC4D/AP632hyZ+dXMnJ2Zs6dMmdLXmIeksWPHcvXVV7NixYptOm7atGl88pOfbFBUkiRJkiQNA+1txe1p34V/XQ7nLYSW0bBhTXPjkqQ+GGp5xJoSzBExmiK5fHlmXt11f2auzcx15f1rgdERMbmukQ6wzOT8889n1qxZHHTQQXz3u98F4L3vfS8/+tGPAHjDG97AO97xDgDmzp3Lhz/84W7nGzVqFGeddRaf/exnt9p3zTXXcPjhh3PooYdy/PHH8/jjj2/ad9JJJ7Fw4UL+8Ic/1PPpSZIkSZI0fHRsLG5bRkPraNhxWtEuo3O7JDXQSM8j9ppgjmIZwbnAfZn5mW7G7F6OIyJeXM67sp6BDrSrr76aO+64gzvvvJNf/OIXnH/++Tz66KMcc8wx3HTTTQA8/PDD3Htv0Yr65ptv5uijj+5xzrPPPpvLL7+cNWu2fAf1qKOOYt68edx+++2ccsopXHzxxZv2tbS0cMEFF3DRRRfV+RlKkiRJkjRMdFYwt1YsNdU6avN2SWqgkZ5HrKWC+aXA24BXRMQd5derI+I9EfGecswbgXsi4k7gC8ApmZkNinlA3HzzzZx66qm0tray22678bKXvYzf/e53HH300dx0003ce++9HHDAAey22248+uij3HLLLRx55JE9zjlx4kROP/10vvCFL2yxfenSpZxwwgkcdNBBXHLJJSxcuHCL/aeddhrz5s1j0aJFdX+ekiRJkiQNeR1lIrll9OZtLaM3b5ekBhrpecReE8yZeXNmRmYenJmHlF/XZuaXM/PL5ZgvZuaBmfmCzHxJZv628aE3Vnf58alTp/LEE09w/fXXc8wxx3D00Ufzve99j/HjxzNhwoRe5z333HOZO3cuTz311KZt55xzDu973/u4++67+cpXvsKGDRu2OGbUqFHMmTOHT3/60/17UpIkSZIkDUebKpgrEsyto61gljQgRnoecVTvQ5rntMOnN+3cxxxzDF/5ylc444wzWLVqFTfeeCOXXHIJAEcccQSf+9zn+OUvf8nKlSt54xvfyBvf+Maa5t1ll104+eSTmTt37qa+K2vWrGHq1KkAfOMb36h63JlnnsnFF1/Mk08+WYdnJ0mSJEnSMLKpB3NFmqNltD2YpRHEPOJmA51HrGmRv5HoDW94AwcffDAveMELeMUrXsHFF1/M7rvvDsDRRx/Nxo0bee5zn8thhx3GqlWreu2bUmnOnDlbrAJ54YUX8qY3vYmjjz6ayZOrr404ZswY3v/+97Ns2bL+PTFJkiRJkoabqhXM9mCWNDBGeh4xmtUqefbs2blgwYIttt13333sv//+TYlnOPL1lDTUXDF/Sc1jm/nutDRgFny99rGz3964OEaAiLgtM2c3O45q18iSpBr8bi785B/hH++HiXsU2774Yth1Pzj5m82NTVJDmPeqr2qvZ63XyFYwS5IkSf0UEV+LiGURcU+Vff8UERkR1UtMJEn919kKY6sezLbIkKRGG9Q9mIealStXctxxx221/YYbbmDSpElNiEiSJEkD5DLgi8AWZXIRsRfwV0DtH9GQJG27zlYYW/RgHgUdtsiQNDgNpzyiCeY6mjRpEnfccUezw5AkSdIAy8wbI2JGlV2fBS4AfjigAUnSSNNRrQfzaGh/tjnxSFIvhlMe0RYZkiRJUgNExOuAhzPzzl7GnRURCyJiwfLlywcoOkkaZjpbYbRUJJhbbJEhSQPBBLMkSZJUZxGxPfAvwEd6G5uZX83M2Zk5e8qUKY0PTpKGo6oVzLbIkKSBYIJZkiRJqr/nADOBOyNiMTAN+H1E7N7UqCRpuGpvK3ouR2ze1jJ6c29mSVLDDO4ezAu+Xt/5Zr+9vvNJkiRJVWTm3cCunY/LJPPszFzRtKAkaTjraNuyPQYU1cxWMEsjh3nEprGCuYvFixcza9asrbZ/5CMf4Re/+EWPx1544YVceumlNZ8rIpgzZ86mx5deeikXXnhhj8dcdtlltLS0cNddd23aNmvWLBYvXlzzeSVJklRfEfEd4Bbg+RGxNCLe2eyYJGlEad+4ZXsMKCqa7cEsqYHMIxZMMNfo4x//OMcff3xd5xw7dixXX301K1ZsWyHLtGnT+OQnP1nXWCRJktR3mXlqZu6RmaMzc1pmzu2yf4bVy5LUQB1li4xKVjBLapKRlkc0wVxFe3s7f/d3f8eBBx7IK1/5Sp5++mnOPPNMrrzySgCuvfZa9ttvP4466ije//73c9JJJ2069t577+XYY49ln3324Qtf+EKP5xk1ahRnnXUWn/3sZ7fad80113D44Ydz6KGHcvzxx/P4449v2nfSSSexcOFC/vCHP9TpGUuSJEmSNIS1t1WpYLYHs6TGM49ogrmqP/3pT5x99tksXLiQnXbaiauuumrTvg0bNvDud7+b6667jptvvpnly5dvcez999/PT3/6U2699VY+9rGP0dbW8y+zs88+m8svv5w1a9Zssf2oo45i3rx53H777ZxyyilcfPHFm/a1tLRwwQUXcNFFF9Xh2UqSJEmSNMR1bOymB7MtMiQ1lnlEE8xVzZw5k0MOOQSAF77whVv0Jbn//vvZZ599mDlzJgCnnnrqFse+5jWvYezYsUyePJldd911i3cMqpk4cSKnn376Vu9SLF26lBNOOIGDDjqISy65hIULF26x/7TTTmPevHksWrSor09TkiRJkqThob0NWru0yGgZZQWzpIYzj2iCuaqxY8duut/a2srGjZvf8czMPh/bnXPPPZe5c+fy1FNPbdp2zjnn8L73vY+7776br3zlK2zYsGGLY0aNGsWcOXP49Kc/3ev8kiRJkiQNax1t3VQwm2CW1FjmEWFU70OaaPbbmx3BVvbbbz/+8pe/sHjxYmbMmMF3v/vdfs+5yy67cPLJJzN37lze8Y53ALBmzRqmTp0KwDe+8Y2qx5155plcfPHFPPnkk/2OQZIkSZKkIavbHsy2yJBGDPOIQHPyiFYwb6PtttuOL33pS5x44okcddRR7Lbbbuy44479nnfOnDlbrAJ54YUX8qY3vYmjjz6ayZMnVz1mzJgxvP/972fZsmX9Pr8kSZIkSUNWx8aiJUal1lFWMEtqqpGSR4zeSrUbZfbs2blgwYIttt13333sv//+TYlnW6xbt47x48eTmZx99tnsu+++nHfeec0OaytD5fWUpE5XzF9S89jTDp/ewEikQWLB12sfOwgrNoaSiLgtM2c3O45q18iSpBp8629gw2r4u19u3nbDx+Hmz8FHVzUvLkkNM1TyXkM5j1jrNbIVzH3wX//1XxxyyCEceOCBrFmzhne/+93NDkmSJEmSpJGrWg/mltGQ7dCkwjpJgpGRRxzcPZgHqfPOO6/mdxpWrlzJcccdt9X2G264gUmTJtU7NEmSJEmSRp72jVv3YG4tUx7tbTBqzMDHJEmMjDzioEswZyYR0eww6mbSpEnccccdA37eZrU+kSRJkiRpwHW0waixW27rrGjuaANMMEvDkXnE+uhvHnFQtcgYN24cK1euNDnaT5nJypUrGTduXLNDkSRJkiSp8drbqlQwj968T9KwYx6xPuqRRxxUFczTpk1j6dKlLF++vNmhDHnjxo1j2rRpzQ5DkiRJkqTG69hYvQdz5z5Jw455xPrpbx5xUCWYR48ezcyZM5sdhiRJkiRJGkra2zb3XO5U2YNZ0rBjHnHwGFQtMiRJkiRJkrZZR1sPFcwmmCWpkUwwS5IkSZKkoa19oz2YJalJTDBLkiRJkqShraMNWrq0yOh8bA9mSWooE8ySJEmSJGloa2+zglmSmsQEsyRJkiRJGtrswSxJTWOCWZIkSZIkDW099mC2RYYkNZIJZkmSJEmSNLT12IPZCmZJaiQTzJIkSVI/RcTXImJZRNxTse2SiLg/Iu6KiB9ExE7NjFGShjV7MEtS05hgliRJkvrvMuDELtt+DszKzIOBPwIfHOigJGlE6GgH0h7MktQkvSaYI2KviPi/iLgvIhZGxD9UGRMR8YWIeKCs0DisMeFKkiRJg09m3gis6rLtZ5nZ2fhzHjBtwAOTpJGgs0K5tUuLjM7HVjBLUkPVUsG8EZiTmfsDLwHOjogDuox5FbBv+XUW8J91jVKSJEka2t4BXFdtR0ScFRELImLB8uXLBzgsSRoGOiuUu6tgNsEsSQ3Va4I5Mx/NzN+X958E7gOmdhn2euCbWZgH7BQRe9Q9WkmSJGmIiYh/oSjauLza/sz8ambOzszZU6ZMGdjgJGk42FTB3E0PZltkSFJDbVMP5oiYARwKzO+yayrwUMXjpWydhLY6Q5IkSSNKRJwBnAS8JTOz2fFI0rDUUXYjaunSImNTBfNGJEmNU3OCOSLGA1cB52bm2q67qxyy1QW01RmSJEkaKSLiROADwOsyc32z45GkYav92eJ2qwrmMuFsBbMkNVRNCeaIGE2RXL48M6+uMmQpsFfF42nAI/0PT5IkSRr8IuI7wC3A8yNiaUS8E/giMAH4eUTcERFfbmqQkjRctduDWZKaaVRvAyIigLnAfZn5mW6G/Qh4X0T8D3A4sCYzH61fmJIkSdLglZmnVtk8d8ADkaSRqLNFRrc9mG2RIUmN1GuCGXgp8Dbg7oi4o9z2IWA6QGZ+GZ58wE4AACAASURBVLgWeDXwALAeeHv9Q5UkSZIkSepiUwVz1x7Mo7bcL0lqiF4TzJl5M9V7LFeOSeDsegUlSZIkSZJUk84ey61jtty+qYLZBLMkNVLNi/xJkiRJkiQNOu3dtMiwB7MkDQgTzJIkSZIkaejq6KZFhj2YJWlAmGCWJEmSJElDV2eF8lYVzK0QLVYwS1KDmWCWJEmSJElD16YK5tFb72sZbQ9mSWowE8ySJEmSJGno6q4Hc+e2dltkSFIjmWCWJEmSJElDV3c9mDu3WcEsSQ1lglmSJEmSJA1d3fVg7txmD2ZJaqgqb+9J0vB2xfwlNY077fDpDY5EkiRJUr91lC0w7MEsSU1hBbMkSZIkSRq6NlUwV6mhax1lD2ZJajATzJIkSZIkaeja1IPZCmZJagYTzJIkSZIkaeiyB7MkNZUJZkmSJEmSNHT12oPZFhmS1EgmmCVJkiRJ0tDVaw9mK5glqZGq/O8rqb+umL+k5rGnHT69gZFIkiRJ0jBnD2ZJaiormCVJkiRJ0tDVXrbA6LYHsy0yJKmRTDBLkiRJkqSha1MFc5UPabeMsoJZkhrMBLMkSZIkSRq62tuKRHLE1vtaR9uDWZIazASzJEmSJEkaujraqvdfBnswS9IAMMEsSZIk9VNEfC0ilkXEPRXbdomIn0fEn8rbnZsZoyQNW+0bq/dfBmgdZQ9mSWowE8ySJElS/10GnNhl2z8DN2TmvsAN5WNJUr11tFXvvwxWMEvSAOjmf2BJkiRJtcrMGyNiRpfNrweOLe9/A/gV8IEBC0qSRor2tq0rmBd8vbhdvQTWr9r8uKvZb29sbJI0AljBLEmSJDXGbpn5KEB5u2u1QRFxVkQsiIgFy5cvH9AAJWlY6NjYQw/mFsj2gY1HkkYYE8ySJElSE2XmVzNzdmbOnjJlSrPDkaShp72t6LVcTbRChwlmSWokE8ySJElSYzweEXsAlLfLmhyPJA1PHW3dVzBHK2THwMYjSSOMCWZJkiSpMX4EnFHePwP4YRNjkaThq1oP5k4tVjBLUqOZYJYkSZL6KSK+A9wCPD8ilkbEO4FPAX8VEX8C/qp8LEmqt46N0NJDiwx7MEtSQ3XzP7AkSZKkWmXmqd3sOm5AA5GkkcgKZklqKiuYJUmSJEnS0NVjD+YWIO3DLEkNZIJZkiRJkiQNXe0be65gBugwwSxJjWKCWZIkSZIkDV0dbd33YO7cbh9mSWoYE8ySJEmSJGno6qkHc5RpD/swS1LDmGCWJEmSJElDV3sPPZg7W2RYwSxJDWOCWZIkSZIkDV0dPVUwd/ZgNsEsSY1iglmSJEmSJA1dPbXIsIJZkhqu1wRzRHwtIpZFxD3d7D82ItZExB3l10fqH6YkSZIkSVIVHRu7b5GxqYK5Y+DikaQRpptlVrdwGfBF4Js9jLkpM0+qS0SSJEmSJEm1am+D1m7SGy1lXZ0VzJLUML1WMGfmjcCqAYhFkiRJkiRp23T0sMhf2CJDkhqtXj2Yj4iIOyPiuog4sE5zSpIkSZIk9ax9Y+89mF3kT5IappYWGb35PbB3Zq6LiFcD/wvsW21gRJwFnAUwffr0OpxakiRJkiSNaB1t0NJNesMKZklquH5XMGfm2sxcV96/FhgdEZO7GfvVzJydmbOnTJnS31NLkiRJkqSRrr2thgpmF/mTpEbpd4I5InaPiCjvv7icc2V/55UkSZIkSepRpj2YJanJem2RERHfAY4FJkfEUuCjwGiAzPwy8Ebg7yNiI/A0cEpmZsMiliRJkiRJgs29lbutYG7Zcpwkqe56TTBn5qm97P8i8MW6RSRJkiRJklSLjrbi1h7MktQ0/W6RIUmSJEmS1BTtZYK51x7MJpglqVFMMEuSJEmSpKGpY2Nxaw9mSWoaE8ySJEmSJGlo2lTB3EuLjI6OgYlHkkagXnswS5IkAVwxf0nNY087fHoDI5EkSSpt6sHcyyJ/VjBLUsNYwSxJkjTUrfgTzH0l/OH6ZkeiKiLivIhYGBH3RMR3ImJcs2OSpGGjtx7MYQ9mSWo0E8ySJElD1cZn4J6rYN7/Bw/dCj94N6xZ2uyoVCEipgLvB2Zn5iygFTiluVFJ0jDSWw9mF/mTpIazRYYkadB4zpLv1z748DmNC0QaClb+Ge68AtavhJnHwImfKqqYrz4Lzrhm8x/UGgxGAdtFRBuwPfBIk+ORpOGj1h7MtsiQpIaxglmSJGmo+cO1cMsXi/tHvA8O/BvY7UB4zf+DB38DN/2/5sanTTLzYeBSYAnwKLAmM3/W3KgkaRjptQezFcyS1GgmmCVJkoaSdcvgTz+DPQ+FYy6ASc/dvO/gN8NBb4JffQqWzG9ejNokInYGXg/MBPYEdoiIt3YZc1ZELIiIBcuXL29GmJI0dLWXLTJ668FsBbMkNYwJZkmSpKHkofkQLXDA62HU2C33RcBrPgM7ToOr3gVPr25OjKp0PLAoM5dnZhtwNXBk5YDM/Gpmzs7M2VOmTGlKkJI0ZG2qYO6mRUZLmfawglmSGsYEsyRJ0lDR0Q5Lb4VdD4BxO1YfM24i/O1cWPsw/Pg8yBzYGNXVEuAlEbF9RARwHHBfk2OSpOFjUw/m7iqYW4CA7BiwkCRppDHBLEmSNFQsWwjPPAnTX9LzuL1eBC//ECy8Gv7yqwEJTdVl5nzgSuD3wN0U199fbWpQkjSc9NaDGYo+zFYwS1LDmGCWJEkaKpbMg7ETYcr+vY894n0wZjws/EHj41KPMvOjmblfZs7KzLdl5jPNjkmSho3eejBD0YfZHsyS1DDdNCmSJElSs81ftGrT/TFtazlk2X08MvmlLH1wzVZjD5/dZcPocfC8E+D+n8BJny2qtyRJGm5668EMVjBLUoNZwSxJkjQETF59B0GyfOdDaj9o/9fC+hWw5JbGBSZJUjP11oMZigSzFcyS1DAmmCVJkga7TKY8cQdrdpjBM2N2qf245/4VjBoH913TuNgkSWqmjrJFRk89mG2RIUkNZYJZkiRpkJv41CLGta1m+c6HbduBY8fDc44rEswdHY0JTpKkZtpUwdxbiwx/D0pSo5hgliRJGuSmPHE7G1vHsWrCftt+8AGvg7UPwyO31z8wSZKabVMP5p4qmFusYJakBjLBLEmSNIiN2rieXZ68nxU7Hkz2tIBRd553QrHw0X0/rH9wkiQ1W609mF3kT5IaxgSzJEnSIDZpzd20ZDvLdj60bxNstzPMfFnRJiOzvsFJktRs9mCWpKYzwSxJkjSITV59F+vG7cnT43br+yT7vxZW/QUeX1i/wCRJGgxq6cEcVjBLUiOZYJYkSRqkRm1czw4bHuWJic/r30T7vQaIoopZkqThpJYezC2tkC7yJ0mNYoJZkiRpkJqw/kECWLvDzP5NNH5X2PtIuO9HdYlLkqRBo71skdFTD+ZosYJZkhqoDyvFaLC4Yv6Smsaddvj0BkciSZIaYcd1i2hvGcNT2+3Z/8n2fy1c/8+w4gGY/Nz+zydJ0mCwqYK5h/RGSyu0Pzsw8UjSCGQFsyRJ0iA18alFrN1+Ohmt/Z9s/9cWt1YxS5KGk/a2oj1GRPdj7MEsSQ1lglmSJGkwWvsI2z27sv/tMTrtOA32PMw+zJKk4aX92Z7bY0DZg9kEsyQ1iglmSZKkwWjRjUAd+i9XOuB18MjvYe0j9ZtTkqRm6tjY8wJ/UFYwu8ifJDWKCWZJkqTBaNGNtLVux/pxu9Vvzue8orhd/Jv6zSlJUjO1t0FrL8tLtbRYwSxJDWSCWZIkabDJhL/8mrU7zOi5p+S22m0WjJ0ID95cvzklSWqmjrYaK5hNMEtSo5hgliRJGmxW/QXWLq1vewwoelBOfwk8+Nv6zitJUrO0b7QHsyQ1mQlmSZKkwWbRr4E691/utPeRsOKPsG55/eeWJGmgdbRBSy8tMqxglqSGMsEsSZI02Cy6ESbsyYYxu9R/7r1fWtwusYpZkjQMtLfVWMHsIn+S1CgmmCVJkgaTjo4iwTzzmPr2X+60xyEwenvbZEiShoeOjTX0YG6xglmSGsgEsyRJ0mCy7F5YvxL2eVlj5h81Bqa9CB78TWPmlyRpILW3QWsvLTLswSxJDWWCWZIkaTBZdGNxO/OYxp1j75fCY/fA06sbdw5tEhE7RcSVEXF/RNwXEUc0OyZJGjY62mqoYLYHsyQ1Uq8J5oj4WkQsi4h7utkfEfGFiHggIu6KiMPqH6YkSdIIsejXsMtzYMdpjTvH3kcCCQ/Nb9w5VOnzwPWZuR/wAuC+JscjScNHrT2YSfswS1KD1FLBfBlwYg/7XwXsW36dBfxn/8OSJEkagdo3wuLfNLZ6GWDa7KLaa/HNjT2PiIiJwDHAXIDMfDYzLR2XpHrp2AgtvbTIiNZyrFXMktQIvSaYM/NGYFUPQ14PfDML84CdImKPegUoSZI0YjxyOzz7ZOP6L3cavR1MfaEL/Q2MfYDlwNcj4vaI+O+I2KFyQEScFRELImLB8uXLmxOlJA1VNVcwYx9mSWqQXt7mq8lU4KGKx0vLbY92HRgRZ1FUOTN9+vQ6nFqStt1zlny/toGHz2lsIJLU1aJfF7czjm78ufY+En77BXhmHYwd3/jzjVyjgMOAczJzfkR8Hvhn4F87B2TmV4GvAsyePTubEqUkDVU19WAua+usYJakhqjHIn9RZVvVC+PM/Gpmzs7M2VOmTKnDqSVJkoaRxTfDrgfCDpMbf669X1p8rHjp7xp/rpFtKbA0MzsbXl9JkXCWJNVD+8ZtqGC2B7MkNUI9EsxLgb0qHk8DHqnDvJIkSSNHRzssXQDTXzIw55t+eFHRZZuMhsrMx4CHIuL55abjgHubGJIkDS8dbfZglqQmq0eC+UfA6VF4CbAmM7dqjyFJkqQePL6w6L88UAnmsRNgjxeYYB4Y5wCXR8RdwCHARU2OR5KGD3swS1LT9dqDOSK+AxwLTI6IpcBHgdEAmfll4Frg1cADwHrg7Y0KVpIkadhaMq+4HagEMxRtMm79L9j4DIwaO3DnHWEy8w5gdrPjkKRhqWNjDT2YrWCWpEbqNcGcmaf2sj+Bs+sWkSRJ0kj00DyYsCfsuFfvY+tl7yPhli/Cw7cV9yVJGmra26C1l9SGFcyS1FD1aJEhSVJddCQsWD2eDe3V1o+Vhrkl88u+yAP48z/9iOL2wd8M3DklSaqnjjYrmCWpyUwwS5IGhUdWP83H/zidS/48jSse3rXZ4UgDa81SWLsU9hrA9hgA2+8Cux5gH2ZJ0tDVvnEbejB3ND4eSRqBTDBLkpru2rsf5cTP3cii9WPZd4en+eWKHVn1bK9dnKThY1P/5cMH/tx7H1lUT7dvHPhzS5LUXx1t0NLLdWOUqQ8rmCWpIUwwS5KaZv2zG/nAlXfx3st/z8wp4/n0AYs5Z+YjtGfwo8d3aXZ40sB5aD6M3gF2O2jgzz39CGh7Ch6/e+DPLUlSf7W3bUMFswlmSWoEE8ySpKY5//t38b3bHuLslz+HK99zBLuPbWO3sW0cM2kNv1i+E0+0tTY7RGlgLJkH017Y+yJFjTC9bMuxZP7An1uSpP7ItAezJA0CJpglSU3x5IY2fn7v45xxxAzOP2E/Rrdu/pX0hj1WFlXMj01qYoTSAHnmSXj8noHvv9xpx2kwcRo8NK8555ckqa86E8ZWMEtSU5lgliQ1xf/9YTnPtnfwmoP32Grf7mPbOHrSWn6+fCdWW8Ws4W7p74pFh5rRf7nT9MOLCubM5sUgSdK26mgrbnvtwdxZwewif5LUCCaYJUlN8dN7HmPy+LEcNn3nqvv/ZvcVVjFrZFgyv1h8aNqLmxfDXi+BJx+BNQ81LwZJkrZVe5lgtoJZkprKBLMkacBtaGvn//6wjFceuButLVF1zO7j2jhqF6uYNQI8NA92PRDGTWxeDJv6MNsmQ5I0hHRsLG577cFcpj7swSxJDWGCWZI04G760wrWP9vOq2bt3uO4v9ljBW0Z/PjxXQYoMmmAtW+EpQua2x4DYLcDYcwEE8ySpKFlUwVzLy0yrGCWpIZqwlLl0vD3nCXfr33w4XMaF4g0SF13z6NMHDeKl+zTc/uLPcoq5p8t35mT91zBmBb7w2qYWbYQnl3XvAX+OrW0wrTZ8ND85sYhSdK22NSDubcK5s4ezCaYJakRrGCWJA2otvYOfnHv4xx/wG6Mbu3919BLd1nLMx0t3L9uuwGIThpgS8qEbrMrmKFok/H4QtiwptmRSJJUG3swS9KgYIJZkjSg5v1lJWs3bOTEA3tuj9Fp//HraSVZ+OT2DY5MaoKH5sGEPWHHvZodSdmHOWHp75odiSRJtenswdw6pudxmyqYOxobjySNUCaYJUkD6vp7HmP7Ma0c87wpNY0f15o8d4enuWftDg2OTGqCJfOK6uWovtjlgJo6u/gD3D7MkqShorOCuaW3Hsxl6sMKZklqCBPMkqQB096R/HTh47z8+bsybnRrzcfNmrieP68fx/p2f21pGFn9EKx9uPn9lzuNHQ+7zzLBLEkaOjpqbJFhD2ZJaij/UpckDZjfL3mCFeue4YRZtbXH6DRrwlMkwb22ydBw8tAg6r/cafoR8PBtmyvCJEkazNrLFhm9LfJnD2ZJPUkXk+8vE8ySpAFz/T2PMaa1hZc/v7b2GJ323WEDY6KDe0wwazhZcguM3gF2O6jZkWy21+HQth4eu7vZkUiS1LtNFcy9tMiwgllST275Ilw0DZ5Z1+xIhiwTzJKkAZGZXH/PYxy972QmjOulyqSL0S3JfuOf5p61Jpg1jCz+TVG93NsfxQNpetmuo7O6WpKkwaz92eK2twrmCIgWSBf5k1TF008URRZjXPenr0wwS5IGxMJH1vLw6qe3uT1Gp1kTn+KhDeNY3VZ772Zp0HpqJSy/D/Z+abMj2dLEPWHH6UV1tSRJg117jT2YoahitoJZUjVPr4ZxOw6OhbeHKBPMkqQBcfMDKwB4+fN37dPxsyasB2ChbTI0HDz4m+J2xlHNjaOa6S+BJfPtRVdHEdEaEbdHxI+bHYskDSsdNfZgBmhpgdzY2HgkDU0bVsN2OzU7iiHNBLOkEaOjw2RJM/1u0Sr2mbIDUyaM7dPxM7ffwA6t7dzzpB9b0jDw4G9h1Haw52HNjmRr0w+HdY/B6gebHclw8g/Afc0OQpKGnfYaezBDWcFsiwxJVTy9GsaZYO4PE8ySRoRFK57i0E/8nLfNnc+i9X1LcKrvOjqSBQ8+wYtn7NLnOVoCDpiw3j7MGh4evBn2ehGMGtPsSLa2V9mHeYl9mOshIqYBrwH+u9mxSNKw07nIX00VzK2QtsiQVIUVzP02iFaVkaTGaO9Izv/+nXR0JHc/vIab1s/kyJ3X8uY9l7P7uLZmhzci/HHZk6x5uo0X9SPBDHDghPX8bvUElj2zbYsEqj6es+T7tQ8+fE7jAhnqnn4CHrsHjv1gsyOpbtf9YexEeGgevODNzY5mOPgccAEwobsBEXEWcBbA9OnTBygsSRoG2suWF/ZgltQfT6+GnfZudhRDmhXMUp09u7GDny7bif9cvDvPdtggfjD4+m8WseDBJ/jY6w/kxgtezht2X8Fta8bzjwv34VtLp9hmdADcumgVAC+e2b8E80ETngLgHvswayhbMg9ImDHIFvjr1NIKe70YHnShv/6KiJOAZZl5W0/jMvOrmTk7M2dPmTJlgKKTpGFgUwVzDbVzVjBL6s6G1bDdzs2OYkgzwSzVSUdH8r+3P8xxn/kVX3tod361cicuX+ofic325+XruOSnf+D4/XflDYdOZeK40ZwydQWfn/VnjtxlLT9+fBL3rjNZ2Wi3LlrFHjuOY9rO2/VrnqnjnmWnURtNMGtoW3wztI6BqbObHUn3ZhwFy++DdcubHclQ91LgdRGxGPgf4BUR8e3mhiRJw8imHsy1VDC3WMEsaWuZRQWzLTL6xQSzVAc3/Wk5r/7CTZz73TuYMHY0H3zuQ7xq11Vcv3wX7ljjgmTN0tkaY9zoVi56w0FEbK4o33l0O2ft/RgTWjdy3TLfqWykzOR3i1fxohm7bPE96IsIOHDiUyxcuwNp6bmGqgd/UySXR49rdiTdm3lMcbv4xubGMcRl5gczc1pmzgBOAX6ZmW9tcliSNHxsUw/mUZAu8iepi2fXFZ9ucJG/fjHBLPXTA8vWccbXbuXptna+cOqh/Picozhkx6c4bepypo17hv9cvAdr21qbHeaI9LWbF/H7Jav52OsOZNeJWydyxrQkx01ZzYLV4+3p20APrXqax9c+w4v62R6j00ET1rN64yj+tGxdXeaTBtSGtfDonYO3PUanPQ6BsTvCX37d7EgkSeqePZgl9dfTTxS3VjD3iwlmqZ++9ptFjGpt4aq/P5LXvWBPWlqKCs0xLck5Mx9hXXsLX12yu31+B9gDy9Zxyc/+wF8dsBuvP2TPbse9cspqAvjpcn+ZNMr8RSsBeHE/F/jrdOCE9QD89oEVdZlPGlAP3VpUT+09yBPMLa1Fm4xFVjDXS2b+KjNPanYckjSsbFMP5hZ7MEva2tOri1srmPvFBLPUDyvXPcNVty3lbw+byuTxY7faP2P7Zzhl6gp+t3oC/7dyxyZEOHJ96rr7+P/Zu+/4qKr8/+OvOy2994T0Qgi99ypVEFSsKIJr77rWn+5+t+i6umvvBQtgQ0UhSFeq1NACBAjpvfc2mXZ/f9wEgQ2QMunn+XjkEZnMnHtikpm5n/s572OnVfOv6wZcNpbBQ2ditFsV24pd0ZvFpoztIS69FFd7LZHejlYZz9vGiLfOwL7UEquMJwgdKuN35SQ4cFRnz+TKQidBWRqUZ3b2TARBEAShaS3KYBYdzIIgNEHfUGAWHcxt0ozLfEJXFZ75Q/PuOPrJ9p1IL/bV/kzqTRbumhB6yfvM9S7lWIUDX2b50M+xFj9bYwfOsHcqrTGwI7GIuyaG4u105YzTq71L2VfmzM4SF2Z5l3fADHuXuPQyRgS7n+vut4Z+TnXEpZchy3Kbc50FoUOl7wH/YaDrBvn8YZOVz2m7YKiIDRYEQRC6IEtDREazMpjVooNZEIT/JTqYrUJ0MAtCK+mNZlbuT2dqXy8ivJ0ueT+VBA+G5KGRZL7K9u7AGfZeG07kYbLILBgc0Kz7RzroCbevY1OhGxYRZWJVhVV60oprGBVq3Y0U+znWUlpjIKVI5DAL3YihBnKPdP385UZe0eDgJWIyBEEQhK6rsYNZ1Yw9b0QHsyAITREdzFYhCsyC0Eprj+VQXG3gnolhV7yvh87ENM9yjlY4ig3/OkBsfC4R3o7087t04f98kgRzvMvIrbfheGU36CrsRuLSlA0TRoV6WHXcaEclh/lgw/iC0C1kHVQ6rYIndPZMmkeSlJiM1J2IjQQEQRCELsliVLqXm7OiTaVW9kEQBEE4n+hgtgpRYBaEVpBlmWW70+jn58zY8OYVziZ5VGBGYk+ZczvPrnfLLa/jYFopCwb7tyg6YaxbJa4aExsLrdtp29vFpZdip1XT39+6v/e+NkY8HW2ISy+16riC0K4y9ijdU0GjO3smzRc6GarzoTips2ciCIIgCP/LbGxe/jKApBIdzIIg/C99ufIe3aZ5DWpC00SBWRBaYefZIpIKq7lnYmizi5hBdgZC7PTsKhEF5va0Lj4XgPlD/Fv0OI0KZniVcazSkVy9rj2m1isdTCtlWLArWrV1X24kCUaFunEwTRSYhW4kfQ/4De5eb15DJymf03Z27jwEQRAEoSkWU/Pyl0FkMAuC0LS6crB1ad5KCOGSmnXGL0nSbEmSEiVJSpYk6bkmvr5UkqQiSZKONXzcbf2pCkLXsWx3Gj7ONswb1LIi5iSPClJr7ciuEwXM9hIbn8uQQFeCPVoedTHdqxyNZGFLkVgaYw2VeiOn8ysZGeLeLuOPCnEnp7yOnPK6dhlfEKzKqIecQ90nf7mReyi4BokCsyAIgtA1mY2g1jTvviKDWRCEpujLwU6sZG6rKxaYJUlSA+8Dc4AY4FZJkmKauOsqWZaHNHwss/I8BaHLOJ1Xye/JxSwZF4JO07KuzPHulaiQ2V0qupjbQ3JhFQm5lcwf3LLCfyNXrZnhLtXsL3MSm/1ZweH0MmQZRoW2T4F5ZMO4caKLud0t35vOP9edEhfH2iLnEJgNENzNCsygdDGn7RYn5YIgCELX05jB3Byig1kQhKbUlYsN/qygOdWxUUCyLMupsiwbgO+ABe07LUHour7ck46dVs2iUUEtfqyr1sxg5xp2l7iIAmY7iD2Wi0qCeYP8Wj3GKLdqyoxakmtsrTiz3ulgeilatcTQwPa5Ghzt64yTjYYDosDcrr4+kMHfYhP4fE8aT54K48WzgRwsc8QsnsNaJvm3hvzlsZ09k5YLnaJ0duSf6OyZCIIgCMKFzKYWZDCrwSI2+RME4SJ1ZWKDPytoToE5AMg679/ZDbddbKEkScclSfpRkqTApgaSJOleSZIOSZJ0qKioqBXTFYTOZTBZ2HAyjzkDfHG1b10n30SPCkqMWk5V21t5dr2bLMusjc9lXLgn3s6tLw4Pc6lGI1k4UN6NMlK7qLi0UgYEuGCnU7fL+GqVxIgQN7HRXzvanJDPX9ecZFq0Nwefv4pb/AvJ0+t4PbUPj5wIJ7XWprOn2H2c3QTB47pnd0ToROWziMkQBEEQuhqLEVTNjMhQqUQHc2+WeQD+Gwk1JZ09E6Gr0YsOZmtoToG5qZTri/uW1gEhsiwPAn4Fljc1kCzLn8iyPEKW5RFeXl4tm6kgdAG/JxdRpTcxb3DrO2RHulZjpzKLzf6s7Hh2BRklta2Ox2hkr7Yw0KmWg2VOyLJo0WytWoOJ+OzydovHaDQy1J3kwmpKquvb9Ti90aH0Uh799iiD+rjy3qKheDvbcp1fKe8OTOGp8GzMssSyDF+xGqM5yjKg8BREze7smbSOky949oW0XZ09E0EQBEG4kNnYsg5mUWDuvXIOQ00hlCR1mHxJkAAAIABJREFU9kyErqauXHQwW0FzCszZwPkdyX2A3PPvIMtyiSzLjWf3nwLDrTM9Qehafjmeh5OthgkRrb9AolPJjHGr4kCZE/UWsUuptaw9lotOrWLWAN82jzXKrYpCg46E3EorzKx3iksvw2iWGR/u2a7HGdWwgWBcelm7Hqe3SSqo4q7lhwhwtePzpSOx1/3RGaSWlAtli/oUklJrxx6RKX9lZzcpn/vOaZfhM+t0fJvjycT/bOPWT/ZTazBZ/yBhkyFjL5gM1h9bEARBEFrLYrpkBvOBtNILPvKqjJjNpv+5XeglqvOVz1V5nTsPoWuRZdBXiA5mK2hOgTkOiJQkKVSSJB1wCxB7/h0kSTq/nXM+cNp6UxSErqHeZGZrQgGz+vu2eHO/i03yqEBvURMnYhiswmyRWXc8l6nRXrjYNbOD4TJGuFYjIbM5Id8Ks+ud9iYXo1UrERbtaWAfF3QalYjJsKKiqnqWfH4QnUbF8j+Nwt2h6Tigie6VhNnX8U2OF3qzuFh2WYkbwSMSPMKtNqTBIrEu352nT4Xw9KkwYvM98Hex40BaCfeuOIzeaOUOrdBJYKxVun8EQRAEoaswG0HdvIgMWVIhySKDudeqyr/wsyAA1FcpKxtEB3ObXbFKJsuyCXgY2IxSOP5eluUESZL+KUnS/Ia7PSpJUoIkSfHAo8DS9pqwIHSW35OKqao3MbcNG8g1inasw0tnFDEZVnIgtYSiqnrmD24qHr7lnDVmYpxq2XhSvPlorT0pxQwNcrug87U92GjUDA10FQVmK/pgRzKFVfV8sXQkge6XzopXSbAksJBSo5Z1BR4dOMNuRl8J6b9DX+vGY3yV7c1XOd7oVDJLAwv4cFAyq+4by39uGMzvycU8/M0RjGYrnkSHTAAkEZMhCIIgdC0W4yU7mC8mo0YSERm9V2NhuTL38vcTehd9ufJZdDC3WbPaMGVZ3iDLcpQsy+GyLP+r4bb/k2U5tuG//58sy/1lWR4sy/JUWZbPtOeke7PMklqW7U4lPqscEQ/bsdYfz8PFTmuVJf8qCSa4V3C80oHCSr0VZte7bTyZj51WzbRob6uNOdq1iuTCapILq6w2Zm9RXmsgIbey3eMxGo0KdedkTgXV9e0QC9DLlNUY+O5gFvOH+DMgwOWK9492rGOsWyWx+e4UG9r3YkK3lbJNOfmNsl48RlKNLVuKXJnlVca/ojOY412Gq1Y5Yb5heB9evHYAv54u5IlVxzBbKyTbzg38h0DyVuuMJwiCIAjW0IIMZllSKRtMiS7m3kl0MAtNqWsoMIsO5jZr2zp/oUOdLahi4Ud7eWn9aRa8v4dHToazIsubM9V2YpOldqY3mtl6qoCZMT5tjsdoNNGjEhmJ9SdEBlRbWCxKlMWUvl7Y6dRWG3ekazUAm0QXc4vtTy1BlmF8RMd0tY4Mccciw5EMkcPcViv2ZVBnNHP/5OZHOSwKKEIGvs0Rm/c26ewm5Q1r4GirDGeS4ZMMX9y0Jm4JKGryPovHBPP81dH8cjyPZ1cfx2KtNwnR8yA7DiqyrTOeIAiCILSVxQSq5kZkKOcKIiajlxIZzEJTRAez1Yh2o27iZE4Fiz87gFatYvUDY0ktquHbrXvZXOTK+kJ3RrpW8WRYDpKIwWwXu60Yj9EowNZAkJ2e9cfzuHN8qNXG7W2OZpVRWFXPbCts7nc+d52JYUGubErI5+FpkVYdu6fbk1yCg07N4MCOeZEeFuyGWiURl17KpChR5GytWoOJL/emcVW0N1E+zc+H97YxMs+nlJ/zPZntXUakg1iVcY7FDElbIHJms/Mhr2R9gTuZdbY8GZaNvfrSJ8j3Tgqn1mDmrV+TGBzoyuIxwW0/eP/rYNuLcCoWxj7Y9vEEQRBa4JsDmc2+76LRQe04E6FLMRtBa9esu1okpVFIks3IohTSKzQ+b6jNem7WVwBQUZTF+iaeT8TzRs90pdeOPvnJTAI2JOu5WpRl2kR0MHcDhzPKuPXT/djrNHx/31iGB7tz44hAno3I5tPByVzvW0xcuRPbSq68nFlonQ0nGuIxIqy75H+sWxWHMsrIq6iz6ri9ycYT+ejUKqvGYzSaPcCXkzmVZJXWWn3snmxPSjGjQt3RqjvmJcbRRkN/f2exC3gbfR+XRVmtkfuntHwjugW+pbhqTKzI8hbxTefLjoPaEqvlLxfWa/kx15MRLlWMcqu+4v0fuyqSkSFuvL8t2Tqb/nmEg89ASPi57WMJgiAIgjW0JINZdDD3Wrb1yqovg8YJe33TK8CE3snGqFx4MGjF/lhtJQrMXdzelGIWf3YADwcd398/lhBPhwu+bq+2cKN/MQOcaliR5U1BffNeXIXma4zHmN3f1+oFszFulQBsOCFiGFpDlmU2nsxnQqQnTrbW/92f3V/pWN+cIH4+zZVfoSe1qMbqF2OuZGSIO8eyyqk3iY1bWsNotvDp7jSGB7sxMsS9xY+3U1tY6F/M2Rp7Emua10XUKyRuVJbtRkxv81CyDMsyfVBJMncGFTTrMZIk8fj0KPIr9ayKy2rzHADovwCyD4qYDEEQBKFrMJtalMEMICHeL/Y2jUXlUpcYtOYaNKaaTp6R0FXojEpNxqAVDZttJQrMXVhueR13fXmIAFc7vr9vLAGuTZ+0qyR4ICQPCfgg3U/kMVvZrrNFVFs5HqORv62RGD9nNogc5lZJyK0kp7yO2f2tG4/RKMjDnhg/ZzaKHOZm25NcDMC4Dtrgr9GoUHcMJgtHM8s79Lg9xfrjeeSU17Uoe/lik9wrcFCb2VToZsWZdXNnN0PwOLBt+xvWvWVOxFc6cktAMZ665m9oOS7cg1Eh7nyww0pdzDHXKZ9PxbZ9LEEQBEFoK0sLNvlD6WBWyaLA3Ns0djCXOscAYFcvupgFhc5YiUVSY1Lbd/ZUuj1RYO7CXt9yFrMs8/nSkXg72172vp46E3cGFXCm2p71BS3vPhMubf2JPNzstYwNb58Ny+YO8uNwRhm55SImo6U2nsxDrZKYHuPTbseYPcCXwxllFFSKXNnm2JNSjLuDjmjf5mf4WsO4cA+0aontZwo79Lg9gSzLfLQzhUhvR65qQ9SMrVpmqmc5B8qcKDGIXEPK0qHoNETNafNQ9RaJ5Vk+hNvXMcurZZtZSpLEEzOiKKisb1F+6SV5RoDPADi1pu1j9RKSJAVKkrRdkqTTkiQlSJL0WGfPSRAEoccwtyQiozGDWURk9DZ29UoTTIlLf+XfIiZDaKAzVmLQOCM2NGs7UWDuohJyK/jpaDZ3jgsh0L15V1ImuVcy0rWK73I9yazTtfMMewe90cyvpwqY1Q7xGI3mDlQ6o0UXc8ttOpnP6FB33B3a7/f96oFKd/T64+LncyWyLLM3uYSxYR6oVB37Au1kq2V0qAe/nm5edIDwhx1niziTX8W9k8La/HOb5VWODGwtErswk7hJ+WyF/OUdxS5UmDTc3qeQ1vyIxoZ7MCbMnQ93plipi/layDoAFTltH6t3MAFPyrLcDxgDPCRJUkwnz6nb+eZAZrM/BEHoRSwtichozGAWHcy9jV19IWZJQ4WTsnm7vV6cMwgKnalS5C9biSgwd0GyLPPyhtO42Gl5cGpEsx8nSXBvUD4Oagvvp/ljEhdm22xHYiE1BnO7xGM0CvF0oL+/M+tFgblFkgqqSCmqYc6A9onHaBTh7USMnzNr43Pb9Tg9QWpxDfmVesZFtLDbvzQNfn8TPp9DVOZ39CnYhkfFSez0hUiW5p8AXNXPm5SiGtKLRaZaS3y0IwU/F1sWDAlo81jeNkaGu1TzW7GrdQqZ3dnZjeAZBe5hbRrGLMO6AneiHGrp59j6lS5PTI+iqKqer/ZntGk+APS/Vvl8WsRkNIcsy3myLB9p+O8q4DTQ9j84QRAEoaGDuXkrp0QHc+9lV1+M3saLWlufhn+LDmZBoTOKArO1iAJzF7TjbBF7kkt4dFokLnYt27jMWWvmnuB80uts2VoscjDbavWRHLydbBgb1j7xGI3mDvLjaGY52WW17XqcnmTjyXwkCWa1U/7y+RYM8Sc+q1wULq9gb0P+8vjm5C+XZylF5Y8nwTtD4Ne/g7EGG0MZfsV7icj+iUEpHzHizKsEFG5Hslw5c3Z6P+UNo+hibr6E3AoOpJVy5/gQdBrrvCWY5V1GpUnTu7v+9ZWQvgei2t69vL/MiSKDjvm+pW1auTc6zINx4R58tDOFWkPzM5yb5BmpxGQkiJiMlpIkKQQYChy46PZ7JUk6JEnSoaIicdIrCILQbC3JYBYdzL2Wnb6QOhsvTBoHjGp77OpFrJ6gEAVm6xEF5i7GbJF5ZcMZgj3suX1McKvGGOlaTYxjDWvyPKi3iByZ1iqtMbD9TCHXDg1A007xGI0aYzI2nhCbyTXXppP5DAtyu2I+uTXMH+KPJEGs6GK+rD3JJQS42hHscYVYnyMr4b0RSlFZpYWZL8HjJ+C+XZyIeIBD/Z7jePh9JAdcR6lTX/oU7WZgysc41Vy+8zLQ3Z4oH0d+Oy3eMDbX8r3p2GnV3DwiyGpjDnSqJcC2ni/3piPLvXTX2VNrlRPe6HltGkaWZdbmexBgW89wl+o2T+uJGVEUVxus08Uccy1k7YdK8bzYXJIkOQKrgcdlWa48/2uyLH8iy/IIWZZHeHl5dc4EBUEQuiOzSWQwC1dkV19Mna3y+lpn6y0ymIVzRIHZekSBuYv58XAWiQVVPDMruk3dZDf5F1Nu0rClSHQxt9a6+FxMFpnrh7X/KtZgDwcGBrjwi4jJaJbMklpO5VW2ezxGIz8XO0aFuLPmWE7vLZhdgcUisy+1hHHhHkiXarM01MKaByH2YQgcDY/Fwz2/wbhHwPWPAqes0lBn60OJ60BSAhdyJvg2JNlMTPpyQnJ/QW2+9IaLV/XzIS69lIo6o7W/xR6nrMbA2mO5XDcsABf7lq2WuRxJglleZZzIqeBIZrnVxu02ZBniPgWvfhA4qk1D7UoqJqPOlmt8SluVvXyxkSHuTIjw5OOdqdQZ2ti91RiTcWpt2yfWC0iSpEUpLn8ty/JPnT0fQRCEHsNsAHVzIzIaOpgRHcy9jV19IXU2yirLOhsvEZEhnKMVBWarEQXmLqTWYOL1LWcZGuR6bmOx1urnVMcgpxpi892pqW/jUthe6qcj2cT4ORPt2zFPNnMH+RGfVU5WqYjJuJJNCUohviPiMRotGBJAalENCbmVV75zL3Qqr5KKOiPjIy4Rj1F0FpZdBce+gcnPwuKfwS2kWWNXOIZzIuJ+8jzG4F12lEHJH2Jf13S3//R+3pgsMjvPijeNV/JdXBb1JgtLxoZYfezJHhU42WhYvjfd6mN3eTlHIC8eRt7V5t2oP9yRjLvWyET3CitNDh6ZFkFJjYEfDme1bSDPSPDuL2IymkFSrrp9BpyWZfmNzp6PIAhCj2Ixig5m4bJU5npsjJXU2XgDUGvrjZ1erHgUANkiOpitSBSYu5Blu9MorKrnhav7XboDsAVu9C+i0qRh+b70No/V2yQXVhGfXdEh3cuNGmMyNogu5ivaeDKfAQHOBLpfIYrBiuYM8EWrlkRMxiXsTlLyl8eGN5FXnrAGPp0K1QVw+2qY+jyo1C0a36LSkek7k4Swu5AliX7pKyAr7n/uNyTQDXcHHb+JHObLMpktrNyXztgwD/r6Oll9fFu1zA0j+rDhRB6FlZfuOO+R4paBzhEG39KmYY5llbM/tZS5PqVYKR4bgFGh7gwLcuWTXamYzG08we4vYjKaaTywGJgmSdKxho+rO3tSgiAI3Z4sg8XU7AxmCyKDuTdq7Faus2mIyLDxVm4TK1N7Pa2pBhUWDBpRYLYGUWDuIsprDXy6K5UZMT6MCHG3yphRjnqGOFfzya5UqvRiuXhLrD6Sg1olMX+If4cdM9DdnsF9XFh3XJyoX05SQRVHM8u5ZlDH/WwA3Bx0TI7yIvZYLhaLeDNysdj4XAb1ccHn4kzs49/DD0vBOwbu2w0RV7XpODV2/pwKWYpJYwcrFkDa7gu+rlZJTO3rzY7EorYXz3qwX08XkFuhZ+n4kHY7xh1jQzBZZL46kNlux+hyakrg5GqluGzTtsL9RztScLbVcJWn9bqXASRJ4v7J4WSX1bG+rRc0Y0RMRnPIsvy7LMuSLMuDZFke0vCxobPnJQiC0O01bgItOpiFy7CrVxph6mz/iMjQWOrRGcXK1N6u8XdAdDBbhygwdxHLdqdRVW/izzOirDruTf7FlNca+XJPulXH7cnMFpk1R3OYFOmJt1P7byB3vuuGBnAyp5Lj2b0wt7SZvj6QiU6t4obhfTr82POHBJBfqedgemmHH7srO5Vbyem8ShYOu+hncuJH+Pk+CJkAd6wFF+usCDDoXDkVshRcA+HrG+Dslgu+flU/byrqjBzKKLPK8XqiL/emE+Bqx/R+Pu12jFBPB6b382bFvnRqDb0kqunYV2CuhxF3tWmYlKJqNp/K546xIdiprX8SPL2fDxHejny4I6VtufJeUeA/FA5+omyyJAiCIAgdydzQRNXCDGaVKDD3Ko1xGI0RGY2b/dnVi5iM3u6PArNLJ8+kZxAF5i6gtMbAF3vSmDvQj35+1r1yEu6gZ3o/Hz7dnSo2vWqm/akl5FXouf7iYlkHWDi8Dw46NSv2ZXT4sbuDOoOZn45kM3uALx6ONs17UFkGJG6CAx/D5hfgu9von7qMsOw1eJUdxaa+pNnLo6b388Zep2btMdFlfr7VR7LRqiXmDz6vqzzhZ/jpXggcA4tWgc66cSZGrRMs3QCeUfDdogs6KCdGeqJVSyIm4xLO5FeyP7WUxWODUVtj57jLeGBKOOW1RlbFtTHvtzuwWCDuMwgeDz4xbRrqox0p6NSqduswV6kk7psUxpn8Kna0Na98wp+hNBVOiSxmQRAEoYNZGs5vW9zBLCIyepM/IjIaO5iVQrOdXuzZ0tvpTKKD2ZpEgbkL+HhXCrVGM49Pj2yX8Z+YEUml3sRnv6e1y/g9zeoj2TjZaJgR036dfZfiZKvlumEBxMbnUlpj6PDjd3W/HM+lUm9i0eigK9856yB8uwjeHgTf3gwbn1GKP8VJmFU6XKuTCctdx5Dk9xl69k3Cs3/Cvu7yy8XtdRpmxviw4UQeBpPofAAwmi2sPZbDtGhv3Bx0yo2nYuHHu6DPSLjtB9A5tM/BHTxgyToIGAY/3Aln1gPK39GYMA9+Oy26EpqyfG8GNhoVN48IbPdjDQ92Z2SIG8t2p2Hs6ZElyb9CeQaMvLtNw6QX1/DT0RxuGx2MZ3MvpLXCgiEB+LnY8tGOlLYNFD1PudCz+w2RZSgIgiB0rMbVM83MYG7sYJbo4e9JhAvY1RdhkTTU69wAqBUdzEIDEZFhXaLA3MmKqupZsTeD+YP9ifSx/kZLAP39XZgzwJfPdqdSVFXfLsfoKWrqTWw6mc/cQX7Yalu2CZm13DE2BIPJ0js6/lrom4OZhHs5MDr0EjnlsgxnN8Pnc+CzGZCxByY9A3f/Bk8lwQt58PBBzoTcwZG+TxIf8QBpfnOpdAjBpTqFAamfEpa9Bp3x0pmnC4YEUFFnZGdbu/56iN1JRRRXG/6IxzizAX68EwKGw+0/go1j+07AzlXZONB/iJL1nLoTgKuivUktriG1qLp9j9/NVNQa+floNtcNDfjjgkA7e2BKODnldazr6Rtkxi0DRx+l4NoG72xLQquWuH9KmJUm1jSdRsVdE0I5kFbKkcw2xMmoVEoXc2ECnN1kvQkKgiAIwpWc62BubkSG6GDujezqi6iz8YCGn7/exuvc7ULvJgrM1iUKzJ3so50p1JvMPHZV+3QvN3pqVl/0Jgtv/3a2XY/T3W1OyKfWYO6UeIxGUT5OjAlz56v9GZjFZnLnnMqt5GhmOYtGByNJTSzrL0qET6fBNzdBeSbMfgWeSIBpL0CfEeDoDec/TpLQ23hR6D6clD7XEx/5CHme4/GoTGBw0vsEFvwG+v8tNE+I9MTdQceaYznt+N12H6sP5+DuoGNKX29I3QE/LAG/wUrRt42bnDWbjRPc9iO4h8O3t0L2Ia5qyBbedkZ0Jpzv27hM9EYLS8aFdNgxp/b1pq+PEx/tTOm5G2SWpUPSFhi2BDStL9ynFFWz5mgOi8cEd8geALeOCsLFTtv2LuaBN4BrEOx6TXQxC4IgCO1n9+uw/eU//n0ug7mZHcw0dDCfl8GsMdWea1AQeiY7fdG5WAwAs9qWeq0z9npxntDbiQKzdYkCcycqqNTz1f4MrhvahzCv9u3yC/dy5LbRQXx7MIvkQtHRdyk/Hs4m0N2OEcFuzX+Q2Qhpu+C3f8KGp2H9U4TkbiAkdwPBeZvwKD+hvHFpgSVjQ8gprxPFsfN8c1BZ1r9w2EUbxVkssP8j+HiSUuRZ8D48dgzGPNCi7lmz2pYsn6uIj3iIUud++BfvgXeHQ9LWC+6nVauYP9ifLQn5ZJe17Ofa01TUGtl6qoD5g/3R5R1SIkk8IpVir20Hv0jbu8Pin8HRC75aSKAxnb4+TmxOyO/YeXRhlXojH+1MYWKkp9Xz/i9HkpRu3LMF1T33Oe3QF0pXzPClbRrmnd+SsNGouW9yuHXmdQUONhqWjA1m6+mCtr03UGth/GOQc0h5PRQEQRAEa5NlOPipsq+KpaFAbIUMZv/i3bDyWqgVm3j3VEoHs+cFt9XZeIsOZgGdsRKLpMaktu5+Qb2VKDB3og93pGC2yO3evdzosasisdOqeWXjmQ45XndzJLOMvSkl3DIyCNWVNr6qLoIjK2HVYng1FJZfA3vehhM/wMkfca9MwL3yFF7lR4nI+Zlhia/RP/UzAgp3XjHnF2BGjA++zras2Jdule+tu6uuN/HzkRzmDvLD1f687sCKHOUN4aZnIXQyPLgfht7e7C6Gphh0rqT0uY4TYXcry92/vgG2/AVMf2Ri3zspDAmJD9ra9dfNrTuei8Fs4baQKuX/k6O3UuS1v0SESXtz9oM71oLWDlZey5J+FuLSy4jPKu+c+XQxn+5KpbzWyLOzozv82PMG+RPgasdHO3vg34yhBo6uhOirwSXgyve/hOTCKmLjc1kyLqRds5cvtmRcCDYaFW//ltS2gYbcrjxn7n7dOhMTBEEQhPOVZ0JVHujLobhhVW5rM5jP62B2qskE2QJZB6w6XaHrsKsvos7W+4Lb6my9xSZ/AjpjJQaN84UrnYVWEwXmTpJdVss3BzK5cUQfgjw65mqJh6MND04N59fTBexLKemQY3Ynr21OxNNRx9LLLR2vKoCNz8Kb/SH2Ycg+BAOuh5u/hmfTz30ciX6aI9FPcSj6WU6G3kWO12RAJqBoJwNTP6Vf2nKcajIueRiNWsVto4PYnVQsMmSB2GO51BjM3DY6+I8bT/4EH46F7DiY9xYsWgVO1tuYsdbOX8luHnk37H0XvpitdEgD/q523DwykB8OZfXqLubVR7KZ4llJxObFoHVQirtW/Bm0iluIUuQ2G7jl1MNE25by/vbkzp1TF1BYpWfZ7jTmDfJjQIBLhx9fq1Zxz8RQDmWUEZfewzqEtr8MtSUw9pE2DfPWr0nYa9XcO6l9s5cv5uFow70Tw1gXn8vBtDb8bLS2MPZhSNupvDYKgiAIgjWdXwBu/O82ZjCrzPU46BtWu2Xus8o0ha5FZTZgY6xAf1EHc62NF3b1BZ00K6Gr0JkqRTyGFTXvmViwKlmW+euak2jUEg9P65ju5UZ/Gh/KV/syeHnDadY+NP7Knbq9xJ7kYvamlPC3a2JwsGniz6K6CPa8BXGfgdkAQxbB6PvAZ8Dlr3ZJKmrsA6ixDyDHezIaUw0eFSfxL95DTPpyKhxCIGMcBI/9n4feMiqId7YlsXJ/Bn+7pr/1vtluRpZlvj6QQbSvE8OCXJVO4q3/Bwc+hIARcP0n4NFOy8m1tjD3dQidBGsfgY8mwvx3oP91PDAlnFVxWXywI4WXrxvYPsfvwlKKqsnPTGGFy7+Q1Ga44xdwC77yAzuCdz9Y/DOqFQtYZfMyc08/y9mCvkS100aq3cG7vyVjNFt4ambf5j3AbIL848rJVnYc1CsXuvrmpyEDSCpqbXyodAii2i4Qi/rKucM3jQzk7d+S+GhHCiOXdlKXu7XlHIH9H8DwOyFodKuHOZNfyfoTeTw4JRz3Dtp88XwPTIngx8PZ/C02gV8emYC6te8NRtypdDDvfh1u/da6kxQEQRB6t6wDoHNSupWzDsDwJS3PYL6og9mxLgcJGTS2kLm/XaYtdK6+acsBcKzJJDzzh3O329aXYK8vJDzj+z/O50c/2RlTFNrZ+T/3i7lWnkUlG/+4j/gdaBNRYO4E647nsT2xiP+bF0OAq13zHyjLyrKgsnRQ22BjKMWktsOssm12S7+tVs3Ts/vyxKp4YuNzuXZo65fz9hSyLPOfzYn4u9iyaHTQhV801MLvb8C+98Gkh0E3w+RnwL11HWYmjQMFHqMpdBuGT+lhJef3i9kQfpWyKZ1X1Ln7ejnZcPVAP348lM1TM/s2XfjuBY5nV5CQW8mLC/ojVeUrm8hlHYDR98OMF9u0oVazxSwAvyGw+i74YSlkHsB/5ovcPDKQ7+IyeXBKOH3celdu07a9+/ne5p84yHq4/RfwambhsqP4D4XFa3BacS3f6V7i883e/N8dV3f2rDpFRkkN3x7M5JZRgYR4Olz6jrWlcOwbSP5VKSobGlZPuAaBvdL10Zgnr5JNuFYlEVAsIyNRY+dHpX0Ixa6DqbP1anJ4e52GpeNCefPXsxzLKmdIoKtVv88OZzZC7KPg4A0z/tGmod7+NQkHnYZ7JnZs93IjO52a5+f24+FvjvLtwUxuH9PKi0U2Tkr+/Y5/Q/5J8B1g3YkKgiAIvVfmAWXjbq3dH8VgS0NEhkpLXkUdv8TncTL3j026izP9APDUmhjjXkmYrdK5LKF8dqrNREZCGrJIiT801injCz2GzlQnJqvaAAAgAElEQVQFgEFz4d48Ro0TEjIacw0mTfvuhyV0XWqzHpNG/M1bS++sWHWishoD/4hNYHCgK0suF8UAUJwEp9dB/gkoSYaSFDDWnPvykIbPMhJGjT3VdoGUO4ZT4RiOQXfpE/cFgwP47Pc0/rs5kdkDfLHVqtv+jXVjW08VEJ9VzqsLB2KjOe//xdktsOEpKM+A/tfD1OfB0zod57JKS77nGArdhzGyjz3seh0+HKdskjTpqXNvbO4YG8zaY7l8F5fFXRNCrXLs7kSWZV7fehYHnZrrPdLh43uUvNOFn8HAGzp2Mm7BcOdG2PJXpXs69ygPzfqI7+LodV3MloIzXHv0HuzURlRLYsF/yJUf1BkChqG6Yw0en8/nzpRHyU4Lo09ox+cPd7bXt5xFq1bx6KVWzOQeUzbNOfmjciHNuz8MvgWCxkLwOHD2P3fXhB/+yNdVmetxqsvGqSYDp9pMfEv341+ylwqHUArcR4HFDKoLX1/unBDCtwczefqHeH55dMKFz7ndzd53oeCEEpFk2/rYkbj0UjaezOfRqyIvzJjvYHMH+vFVWAavbUlk3sV59y0x6l7Y/yGsuR/u2ipO1AVBaDeyLKM3WqipN2GrU+PYS5sxegV9JRQmwKRnlBWGiRuguojKmlqcgRc3JfF5nhFZhgBXO7RqpflKX2OHDOyv17K2wAM/Gz37JKg2Kl93qs2k1tYHh8iZcOhzZWVSyPjO+z4Fq9OalIYJg8aJHL2OgnotjmozKlwJAXTGKlFg7sXU5jpK1R7sK3Miu86GtT+fIMzTgWhfZ/r6OuHl1HH7ovQE4lW4g724/hQVdUa+un5g00tQi5Ph1M+QsAYKTiq3uYWARyQEj1eiANxDwWwiZdc3aEx1aMx16EyVOFen4V6lbOBXp/Og3DGCIreh/3MIlUri+av7sejTA7y/PZknm7tkugcyW2Re33KWUE8HFg7ro9xYkaNsGnd6HXhGwZJ1SkRCO7CodEpRefCtykZyu19TijxXvwaRMxgW5MbESE9e35LIzBgfAt17V5fsz0dz2H22gB8HxuHw7XtK5/iSWCUCoTOotTDnFaV7IvZRfL+dwXPRf+WVQ/DQ1IiWrUjorvKOY/h8PsgWjk77mokBwzp7RpcXMIy6m3/E+evrMX9zLTy4uetEeXSAkzkVxMbn8tDUcLydbf/4gsUCp2OV1RnZB0FrrzwPjboHfJoXyWNR21DRcFETQGOqwbvsCN6lh4nKWgVv74NRd8OIu8BGeePubKvl3wsHcucXcbzzWxJPz+qmBf+SFNj5KvSbD/3mtXqYijojj393jCB3+w7PXr6YJEn8fX5/rn57N29sPcs/F7Sy+9jeXYku+uYm2PgMzH/XuhMVBMCt4hR+xXs5FX53Z09F6CCyLJNdVsuRzDIyS2up1puoqTdjluVz9/Fw0BHi4UCwhz2jw9wJ83RAEhs39QzZccpGfEGjoaHb8Ksff2BTUg1faaGszsLjV0VxzWA/wrz+KBYeaLgwXmVScaDMiT2lzhiNarYVObGlyo/V5hyK3YbiENgQc5W5TxSYewhZljmeXUFWqZko4NGzA0k3eZz7+hDJxBobeDfRmWSbYCa6VxJZY+iUqDKhY/gX7abSIYRCm2B+K3ZlV4kzay1GdtT58lZJABIyTmW5VOpN5x7j4aBjaJDSHDohwlO8plyBKDB3oF1ni/jpSA4PT42gn995QeJmU8OJ/nuQc1i5LXC0EpnQb/4ld6UvPp5w4Q2yjK2hGNfqFFyqU/ApO4Rf6QH44oSyUVn0vHNxAuPCPVk4rA/vbU9mWJAbU6O9mzhCz7cuPpfEgirevXUoGiyw9wNlaa/FBNP+CuMe7ZgIBkdv5YR8yG2w/kn4+gboNx9p9iu8snAQs97cxTM/Hufru0f3mtzs4up6Pli3hzXOHzI46SjEXKsUKmy7QAj/wBuU/O3vF/On1CcoVd3Ih9v8een6LtrJay1ZcVi+WkiZQcPrfm/w3/Htc+HF2tyjxvBB37e5LfExzMtmoL71W+gzvLOn1SH+szkRV3st901uyCmXZaXrZ/vLykVMt1CY9W8lV96ubZEVJo0DuV4TyfUcj1tVIlHqfCUvfc/bMOEJpdCss2dqX29uHN6Hj3amMqu/L4P6dLOoDFmGdY+B2gau/m8bhpF54ecTFFTq+fGBcV2i8y7a15nFY4JZuT+DW0cFXfhepSWiZsHEJ5Us5sAxMPQ2605U6NGq9EZO5FSQWlRDWnENqUXVpBXXUGMw4+Ggw9PRhhfKXqNfTRyZ7uOodovp7CkL7aigUs/PR3NYfTibpMJqNCqJUE8HfJ3tcLRR42CjwdFGQ3W9ifSSWk7nV3I4s4yfjuYQ7evE/ZPDmTfID41a7G3frWUdBElFve8wVh7I5Q5ZQ23KHqb3nQap8PotI5CCL73S1EljYbpXBdO9KlCdUjHIrord1QVoNEZ+qhnIDWZ7vL2iRQ5zD2C2yGw4kceHO1I4lVfJUxozJrWKEGcVs5zyCLA1UGNWYdEboBgmO+WSbIhhebYP37z8K1dF+3DjiD5MjvISzxs9iJ2+kMDC7ZxVR/BA3TT0FjXR9tW4SDVEulh4xTuNAFsDE2/+M8XV9STmV3Emv4rE/Eq2Jxax+LODRPs6cffEMOYP9kenEb8bTen8s5leotZg4vmGdvuHp0UoNxpq4OhXSgdZeQa4h8Osl5W8V5c+LT+IJKG38SLfxot8jzFoTLV4lR8jqCIJfrwTHH1g+FJl+aqDJy9dO4BTeZU89t1R1j0ygWCPy2Rz9kBGs4U3tp6ln58zc12z4JMblYJL5EylaOAW0vGTCpsMD+yBve/ArtcgZRsBU5/nL3Pm8Nya03x9IIPFYzthXp3g+28/Z5XlZdwsBrjmbRi2pNlZ4x3COxru2YYU+whPJ3xH3LFj5A9fjm9wN+3IvJIzG5BX302h7MJi+Xm+vHl2t7qCe83sudyaUMzXxrdx+2KOslnj4Fs6e1rt6vu4LHadLeL5q6NxttFA0lbY/i/IPaqsBrjuE+ViicrKMRWSijLnfnDjMsiKU4655S9KpMTEJ2H4Uv4yL4ZdSUU8/cNxYh8Z372iMo6uhPTdyvOSk2+rh1l9JIdfjufx9Ky+XSqP+okZUcTG5/L32AS+u3dM6//OpzyvFATWPwl+g0Ues3BZsixzMK2UVYey2HAiD71R2YDLVqsi1NOR/v4upBXXUFJTT1VhJlHaQyBB6d4v+cXvUYYHuxHkbt+tXpeEy0svruGtX88SG5+LRYbhwW5cNySAgX1cLhnvNzFS+V0qqq7Hw9GGlfvSeXzVMV7bksg9E8O4aUQgdrpu9HojnCNn7afKpS/z3j9KZmktU1yiWOyWj92YPpAKUjM2Gz43lqQiwr6WfzrtgyJYWTmU9/+7g1UB/emftRWpiYgvoeurN5lZfTiHj3elkFFSS7iXA68uHMj4Xf/FUufAA6EFF9xfcpbPFZgjvDPIrLPhjPs01hzNYVNCPoHudvx5RhTzBwe0fvNjoUvI1evQZiQzCIgyJzPHOY1RvmoibStQnZHxd1Shsq8/d39PRxs8I2wYH6HsQVNvMrP2WC7Ldqfy1A/x/HfzGe6eEMYd44K71zlMBxBl9w7yn02JZJfV8e/rB2JbXwK/vQhvxCjLR518lQzHh+Ng7EOtKy43waSxJ89zHDx6FBb9oJzg7XwV3hwAG57Griabj28fjiRJ3P/VEeoMZqsct7v4dHcqFaWFLHNbgeqLmVBXBjethEXfd05xuZHGBiY9DQ/uVzJQNz/PzUcXszSoiH9vPENWaW3nza0jmOrJ+OZxHsx5DtnBG9W9O5ULI13xpNHGCW74grKZb9NPysDlyykY45YrHY49hckAm/4ffHcrZfbBXFP9AkvmTOx2mxoGutsTPXgsV9f9E6P/CPj5PqXoaemZz3t7U4p5/ucTTIz05E/+WfDZTGVlRE0JzH8PHoqDwTe3/wlU4Ei4Yw0s3QAeEcpr3jtDcUlYyasLokksqOK9bcntOwdrStul/D0ET4Chd7R6mPTiGv5v7UlGh7pzf2N3eRfhaq/j6VnRHEgr5fUtZ1s/kFqj5OXbusD3d4C+4sqPEXqdkup6PtyRwlWv7+TmT/azJaGA64f1YcWfRrH3uWmc+sdsNj42kfdvG8btY4J57Koo3uufiFqSybbvz3XqPZzOLuHjXam8+WsSO88WUVtvuvKBhS4rv0LP8z+fYPobO9mUkM9dE0LZ9uRkVj8wjpGh7k0Wl4ee/i8hOesAJe7H28mWxWOC2fTYJJbdMQIfZ1v+FpvA+Fe38cmuFPTGnvna31OlFpSjTzvAmuI+2GhULP/TKCKGX4Vd0fE/NiRWN79vTpZUqGQznvoM9Fo3no0pY1yEJ8syfJDqK4k7+Hs7fSdCezCZLXx7MJNJ/9nO8z+fwMVOy0e3D2PrE5O5eWQQjnIlBq3T/zxOltQY1Q7nNgEMsqvnr/Ni2P/8VXx0+3CcbbU8sSqeq9/eza+nCpB70vldL1Fea+DvsQk8lxDIaGMcaeoQZCSedPqVMId61GY9AGa17WXHsdGouWlEIJsfn6Q8/3g78q8Np5n+xk42nMgTvxvnER3M7UyWZd75LZkv96bz5FAYffLvEL8KzAaInqtEMASNbt9JqNQQNVP5KEqEPe/AoS8g7jOCBizk01mLuXltJS/8fILXbxrcK7o/fopLJWfr+/xuvxrHtGoY+zBMeU4pGHYV7qFw2w9wOhZp43P8repxBklTeGWVgXfvu6ZnRmUkbcW84RmCy1KJ1V3N7EeWgV0X76yXJNzGLWWr1B+njY8wZv2jyMmbkK55W4k+6c5K05TVD7lHqR1yF9OPTSMi1IPbRnfPDOMHp0Sw5mgOD6v/ygcjfkC9910oOAU3fAZ2bp09PatJKarm/pWHmeeawWvSe2i+2gNO/jD3DRi6uGNify4WMh6WrofUHUpH8y9PMMU1iFfDFvHXHRZm9fdlQEDrN8rrEKdiYfVdSvf3wk9B1bpr9Eazhce+O4pWreLNm4d0ya6YW0cFciKnnPe2J+Nkq/kjYqWlnHzgxi/gy3mw9mG4aUXXvFgodLjCSj0f70rlmwOZ1BnNjApx58GpEVw90Bd73WVOT2SZ8NxYCtyGkxz2J6Ycfoh3RhSywTicQxllbE7IZ/uZQoqq67lrYijeTpc/aRS6jtIaAx/uSGb5vgxkWWbR6CAenhpx4f4BTfAt2ku/9BXUa53J9p6KSftH/q5KJTE9xofpMT7EpZfyzm9JvLzhDMv3ZvDnGVFcO1R0JnZleqOZD7Yns3Pnb6zV1hE8ZCobr5uoxBaYxygro7IPKXdWaZs9riypkWQzTrWZlDtG4Gdr5NMbRxB3VANrPyB23U98nuzIX+fF4N8b9lfppmRZZtuZQl7ZeIakwmqGB7vx+o1DGB/hcUE9Q2esol7X9Eoxg9YJrbHqgtu0ahWzB/gyM8aH9SfyeGPrWe5ecYhhQa48Ozua0WEeTY4ldB0Gk4WV+zN457ckqvRGHnfdh5e+gjL/2VSWqvAqjyfHazIacx0AJnXz/s4lSWJylBeTo7zYnVTES7+c5sGvjzAqxJ2/zOvX/WL/2oEoMLcjWZZ5fXMih3fF8ovXDgac3gMaWyWLcMxD4BnR8ZPy6gvXvg9Tn4f9H8ChLxh14nt2eY/kb/GTWRnozB3jOnejoXZlMXNy0zKGH3iN67WFWALGIM19vesu3ZUkJTIlfBrSjldYcOBj5ub/zpnlNxNz0z/AwbOzZ2gdpWmw+XlI3ECZrg9PGp7libseRNfVi8vnmTF2JJ8YVvLrljd57uz3aN4ZBhMegzEPgq77fB/nnFqrFIQkCfmmlTwa50+NqYhXFw7qthc3Irwd+cf8/vx1bQJPDF7MW3MHotr4FLw3Cqb/XdnkrpVFw66itLqetz/7go/5gbG18VDiDbNfVVYBaDu50CJJED4VwqZA8q+w7SVuzn2FsTo/Viy/Eds7HyfCr4u+aT/0Baz/MwSMgEWrlI3sWsFikfnX+tPEZ1fw4W3DuuyJqyRJvHTtQKr0Jv698QzOdlpuHRXUusGCxyl/X1v/Cj8sgWveaXPet9B95ZTX8dGOFFYdysJskVkwxJ8HJocT6dO8C/ye5fE416RzKuxP5HmOo9bGi755sRQMn8HwYHfyK/XsTCzk092pfLk3nVtHBXHf5DD8XLrm35qgZG4v253Gst2p1BnNXDe0D49Pj2zextayhSGJb6LXuWNrKCU64ytORtzf5F1Hhriz8q7R7E0u5t8bz/DkD/F8ujuV5+ZEMznKq1c02HR5ucfgp3uRr3mL7XXh/C02gazSOl4LzoMCmDz9GmjMxG3clC+9odtY3ZICswq7+iK05lqq7P94bRs5ZDDyNj8W2+ZxzZlCdp4t4vHpkSwdG4Lu9GoIGgOurXwtFKwqPqucf288zf7UUkI9Hfjo9mHMivZA+voGSIqGq/9z7r5aUzVV9oFNjmPQOJ3rYL6YSiVxzWB/Zg/w5YdD2bz921lu/mQ/k6O8eHpW367fGNELWSwyv5zI440tiaSX1DIx0pMX5vbD+5sXMRrtKXeKQCUbicj+CefadGhoPDZdoYO5KRMjvVj/qAffH8rmja2JzH9vD9cO8efx6VGEeHbDc38rEQXmdiLXlbPlu3dYkPYdT+lykE3uMPk5ZbM9R6/Onp6yceCsfyl5mEeW0+fAJ3yue420zV+xO20J4xc+gsq2C3XztpXFAonrqd30DwZUJJGiCadu4bvY9ZvVPbqpbJxg1r9QjbqX/cueYUL615je/AnNuIdh7IPdt/uyrly50PH7W8gqDTsCH+K+pFEsnhDVpTJJm+ueSRH8o+IhZu0bwuee6wne9hIcXAZT/x8Mub1Fy/c6TfZh2P4SpGyDgOFwwxesSVfz6+l4Xri6H6Hd/AVz8dgQquvNvLrpDA42I3j57l+RNjwNax+Ew1/AnP9AwLDOnmbLmeoxxv9AxcY3eMeUgtHGHSa9qLzm6LpYnIkkQeQMiJgOiRvw2vIifyl9h+KPl5M56E6CZj7Udbr/ZVnJw9/+kpLPf+PyVv//rNQbefL7eLaeKmDpuBDmDPSz8mStS62SeOOmIVTXK3tIONpouGawf+sGG/cIyGbY9hLkHFVWDQSOsu6EhS4tIbeCz35PI/ZYLpIENwzvwwOTIwjyaNnfU1jOGkxqOzJ9ZyKrNKT7zyM6fQW29cXobTzxdbbl5pFBvHWdlprv7uH9AyOZdGAk8wcHcM+kUKJ9u8BGwQKgdKeu2JfOBztSKK81MmeAL3+eEdXsiw0AIbnrca86w57BrxCUt5notBUkBi/CqL30z3lchCdrHxrPuuO5vLYlkaVfxDEsyJWHpkYwLdpbFJo7i9kIax+C4kTyVtzNAzX/oo+XG9/cM5pxR3+CWn9wOa9I6OilrCjKP6H8W9WCiAzUONTlAlDlcN6YkoQUNJaozP38+sQk/r7uFC9vOEPe7hX8zfgWsncM0r07lDhDoVMczijjvW1JbE8swsNBxz8X9OfWUUFo1Sr4/U1I26l8RM6EyOlgMqA112LUODY5nlHrhIM+97LH1KpVLBodxPXDAli+V3nOmvfu78wb5MeTM/t2+3OjnkCWZU5sWYE57jP+Vv0APr4BfLF0JFP6eiHVlmKpSqTAfSSypKbUqS8mlQ2eZfGUOfcFrhyRcSmaht+Nawb78cGOFL7Yk8a643ncNKIPj0yL7LKNJO2pG1Q7uhGLBbIOIB9fhfHod8yy1JHtGINlxguoBlwP2i74C2bvDhOeQBr7MHXxP8HmN5iY9Cr6V9+CvvOwHb5I6TbrDoWxplTlKxspHl0JZenky/58bf8MDz/0JHaO3W/ZpOQWTMz9K3jsi5+ZXfQ583b9B3nvu0j9r4PhS5Sr+d3hjXFRIhz8BI59C8Ya6qOv46nyhaxLUnHrqCCemd23s2fYKpIk8dd5MTxUoWfKqQC+mXkPY1PfgXWPKZt5jn8MYq4Fm6bf5HSq/JNKdEHiBrD3gBkvYhhxL29tT+fDnSkMC3LlTxNCO3uWVvHAlHCq6428vz0FR5tQnv/TZqTjq2Dr3+DTaTD0dpj8TPfoUilNhfhVmOOWoa0txmDpQ/zwfzL46nu75mvO+SQJoudi1/dqCo9tJH39a4w6/hbmk++jHnij0nXdZ2TndZWXZ8H2lyH+Gxh0Cyx4r0UdUudLKqjivpWHySyt5e/XxLBkXIh159pOdBoVH942nCWfH+SJVcfQqiVmD2hFYVySYMITSnb16j/B57Nh2gsw/oluv2pAuDSLRWbH2UKW7U5jb0oJ9jo1t48J5t5JYa066VKb6wjO3USm7wxMGuWEPrXPtcSkfUFI7nrOhC5R7ijLhO5+Cqp2875dHB9FfMy7J/JYfSSbiZGe3DMxjImRnqKQ2EkKKvV8dzCLrw9kUFhVz6QoL56aGdXipcUqcz2Dkt6j1LkfGX5zqHAMJ3DPjfRN/5qTkQ9c/rEqiQVDApg9wJfv47L4aGcqdy0/RLSvEw9OjWDuQD8RndHBsn55hcCCk3xmmsNdbOTHfrvoe9vr6DQqiD2gREpe/DcbOEZ5HwQt7mBWYcGotkOvu2g1aPA4SPiJQHUJny0dyb4j8Qxcdw8ZFm+CC09x5tvniFj0uhLTIXQIWZbZl1rCe9uS2ZtSgpu9lqdmRrFkXAhOtg0/95IU2PEK9L0aSpLhlyfgwX3KHkvQZAYzgEHjiNZUgySbkaWGfHezSYkYvej3zVar5r7J4dwyKohPd6Xy2e9pbDyZz9yBftw+JpiRIW7idaWDWSwyu5KK+HnzFl4peQo7ycDmoJV43huLStNQvzr5IyrZQpHrEABklZYSl/54lp+g1tYHAJOqbedMTrZanp0dzZ3jQ/hgewrfHMhk9eEcbhsTxD0TW/eep7vqplXDLsRsUnaTP70O+cwvSNUFGNGxxjSWyoFLuOum67vHE41ai92wmwkZehNbt/xCyZ4vmXNmM7ZnVoODFwxYqGRG9xnV+cusr8RQq1y5PLICzm4G2Uyu20je5lr22U7g+/sn4tYNi8uNvJxseOPBm3h10yDe37ODx2x3MfPUWlTx34BXNAxbosRquAR09lQvZKyDlO0Q96nSHavWwcAbORt8K0s3GSiuMfCfhQO4aWTTS5j+P3t3Hh9VefZ//HNlT0hYw75vArILiihYd22LtbYuoK1aa21/bq3F2qettdhWW9HW5Wl9alusWq37UpdWVNxAQdlBQGSHsIclkJB15vr9cU5gErIRAhPg+3695pWZs9znnnNmJte5zn3u+0iRmGA8MHYIV/z9E7719k4uHXYfPxmynJYz7glaZvznNuj/dRhyOXQ5Jb7JldLC4FgseDboEiO1GZxxO5z8A5bnGbf8dRYL1+dx2fDO3HHB8UfVydat5/Yhv6iMv01dRVpyIjefNZbkvl+FDybCJ38JLkx1Py3os7jfmMaVrN22Aha/AotegU0LAJjqJ/BY9Pucft7FXD3qCOvmyIw2Q79Cer9z+Onjr3L8uqcZt/BlUub/K+g7ut8FwW9al5MPz6juucuDVjALnglej/oxnPnLen9X/7twI7c+P5/0lCSeunbEEdd3X3pKIn+/ejhX/O0TfvDkHE47rjU/Pb8P/TvU49bQzifC96fC6z+CKb8O/ieM+hH0OOPwHFs5LJZvyee/Czfy8rz1rNxaQLumafzPl/sy7qQuNEuv30UagM6bppAcKWBlx6/vnbYrswe5zQbRI+cVPu92JZjRb9U/YOnrMOrHJMz7F9dvuZMrbnmLJ+fn8fjHq7ny0U85rm0mN3ZexejUFbQ472eN706Po0w06ny0IpenZqzl7SWbiUSd0b2zeWjcUE6u52/icWufIbNwA1MG3AmWwM6mfVnb9mz6rn6Cpd0ur1MZqUmJfHtkN8ae1IVX523g4feXc/PTc/nDW0v5+pCOXDC4Pb3aHEV3dMaTOyx8AUr3wAnBdzWvsJTJizYx/dPp/H7zg7yTMBI7/3eUbWnBwIWPw9arISMb8tbByBv2L7PLiOAiMBxwH8xA0D1G5fP0LicHf9dMh6adGLnwl3gSLPjKv1j8zn2ct/wf/PDenvQdcT4XDOqw7y6MaCQYcDBN3SY0lE15RbyxcCP/nreeBTl59Mks5InBqxl2wXU0yYy5S8E9aMyTmBqMN7JzDTx6XtBwpv83AKptwVySlIUByWUFlCRlBZ/RyT+Hlj1h7FNVdonWLD2ZW8/rw5WndOWRD1by3Kx1vDp/A33aZvGtkV25aGhHMlOVZmtQRbuCu+A6DoPBl7Fu+x5emJ3DC7Nz2LVzG2+k/xbSmhE55fu0ee+3MHVi0CUswLynKEhrR2GYTAbIbT6Ytjvm0GbnXKB+XWRUpU1WGhO+1p9rR3fnf6cs54npa3js49WM7t2aS4d34ux+bascqPZoUqdPvpmdDzwIJAJ/d/ffV5qfCjwBDAO2AZe5++qGrWojUZwPG+ZAzkxYNxPWToeinUQS05mROIxnSy7hsyYnc835g/juiC5HRnI5hplxznkXsHzo6Xz7XzNpt+VD/l/mbAbPnETCJ38J+pDuPAJ6fAm6fwnaDoh/wjkaCfrsWvleMIDUuk8gUoJntGZBl28zIWcYcze2YmSPVjzxjYG0a3bkJpfLpSQl8Msxx/Nmt5b85Pme/MrG8cCAlQzPfZXkyT+DyT+D7OOC1uc9Toduow5/wOMOWz+H5VNgxRRY8zGUFQUJozNvZ223S3llWQl/emE5rbNSeeEHI4+ajvHTkhN59OoT+eNbS/nXp2t5cW46V538KDedvZ2mS5+Dz16GeU9B865BVwFdRgbfq+aHIbletAuWvx0MWLbsbSgtCD4bo34Mp9xEWWpznpm5jt++sZi05ET+8q1hnD+g3aGv12FmZvzqgv4UlET433eX88LsHA4L8PoAACAASURBVK4c2Y1xo39F8xHfh3n/Co7RS9cGifcB3wj6D+50IjStZzcB9eEOO1YF/29yZgbfoy2LANiVPZSnUq/hybzB9OvXn1+P6X/At5s3Jllpydx17UX85vU+nDB9Cd9sspBvJc+n5+zHSfj0EWjSJkj6dxoeHId2AxvuNtWiPFg/O7gwueiVoNzh3w26dqjH97KguIw3P9vES3Nz+Gj5NoZ2ac7/XTHsiP3/0zQtmed/MJInpq/mz++t4KsPTePCIR0Yf06fA//MpTeHi/8RJJXfvgOe/CZktoNBlwT9oLftf0jeQ7zVFksfySJRZ+mm3UxetIn/fraRLzbnAzCsawseHNubrwxsH9y+fJB6rH+F/PSObGk5rML0lZ0u5KRFv6HFrsUklxUweOmDwUWps+4IbpN+fAzNJv+QGy57kmtHd+e1eTnw7l187bNnAVg1999MHfpHThx+Ese1zdp3MTV/a3Chqf3g4LdHDsjOPSVMX7GNj1bk8sEXW1m3vZAWGclcO6o7407qUq8+KnuufR4IWrMPWvYndmb2JHPPOjLXrgu2mdmTLpvfYeT8n8Go1+tcbnJiAt8c1omLhnZk8qJNPPbxah56dxkPTllG33ZZjBnUnvMHtKdn6yZH3LndYRONwrZlQWKu8p2vJQXw+o/3XrjdOOtV7k6+ickrCimNlPFKxv14cjqnXv8oZ7fsAIV3w4p34NWbgoHYYV+fy7E6n7zv+QG2YAaq7pe3zfGQ2jQ4xy/cAas+wC54kFOGnYgP+juFD53C7UX/y5mT23Hv5KUM7tycq3rkM2bVb0nZtjToFm/kTUfu3b/xtGc7mwuivL2igNfmb+DT1dtxh/4dmvL3U/M46/NfYUu3wPYX4OJHoe3xwXpzngga/F3wIDRtHzxOvBZm/F/QoIkgkVyV0rBlc1bBGlrvnA+LV0Kb/rB+Fkw6B654PuiKpVw0EpwfrHiXNsO+wy/HfInx5x7Ha/M38MT0Nfzylc/4/X+WcGqvbEb1zubUXtn0yNbvBtEIWELVd1q7Bw2d1s+CYd+BVpUGlt66FH/mCmzbMgDeeetVbth+CSUkM6pnKya2fJh2G7dgl78enFPvWBU0GOp0EmS1g43z2dru/ApF5qd3ojClFenFuThGNKFhB0Dv1CKDey4exI1n9uL5Wet4YXYON/5rLs3Sk7lwSAfO6NOGE7q0oFlG/S+6N1bm7jUvYJYIfAGcA+QAM4Fx7r44ZpnrgUHu/gMzGwtc5O6X1VTu8OHDfdasWQdb/wO3Z3vwIU5OC5KlsS1mImVQsjtIIhfvhl0bgg/ozjWwY3UwENmWJUE/gkBek24sSzmeN4oH8/T242jVvDnXn9GTi4d1IjXp0F+Z+OT5P9RpuRGXjK9X+UWlER54ZxnPzFxL2Z48Tk/9gktareKEyAKy8pYGC1lC8KPbuu++R9MOwZc5s03Qd3BDiEaCY1ewNbgVKncp5C4LulrI/SK4YgwUtzqedS1OYmbCIB5c0Z5NBc6pvVrxw7OO46Tu9RuUqT7qemyg/sen3JptBdz09FwW5OSRYHBhx92MbfEFg0rmkLbhE6x0T3CcWnSD7D7Q+rjgb3bv4BhlZAeD0NXnH5978F3Zsy34juR+se+YbP08OF4A2X2I9jyTjdmn8MrO3ryxOJfFG3cBcGbfNtx3yWBaNmnYH/aaHOrvTqx12/fw4JRlvDQnh4yUJL46sD1D2ydzaskMOua8RsK6T/Z+fmnaKeiXtFWvoHuG8kezTgd2a35ZcXBMCnKD78vmRbBlcfB3x6pgmSatoe8YOP5rrMk6gQ9X5jFt2VY+XrGN3UVljO6dzX2XDKZtLaO3N7TD+d2BoFXVe0u38OhHq/ho+TbSkxP55rCOfGVAe3pkZ9B2+0xs3lNBQr4sGGmYpp2CJGfHE4KLBM06Bb97mW3r1wozGoXiXeH/nNUxj1Wwfg7syQ0WS27C1qYDmJ50Io/vHMzcvCZ0aZnBhK8dz5l929a0hQZzuI7P24s389Qna/jgi61kUMQPOqzg66lzaJc3j+SCjcFCiSlBkrllzyAJ3Kz80RGSM4JW50mpwf96Swz2ceEOKNoZ9P++c20QzObMCn63cEjJgpOuDQbiPYDxEkojUdbvKGTZlnz+s3Ajb362icLSCF1aZnDp8E5877Qe9Y4LDvd3ojZ5haU88sEKHv1oFWURZ3Dn5gzs2IxBnZoxqFNzemQ3qftAoGXF8MWbMP8ZWPYWRMuCY/rtV+IyiK2ZzXb34Yeg3Fpj6Vhxi5FrEY06O/aUsDGviKWbdrNwfR6frc9j8cZd7CmJYBYMpPaVAe04f0D7hr2gsnMt/sBA1rf+EuvbfKnCrMRIEScs/QM7s44jq2AtZYnppI+fty8Gnf5wcOH9rF8FXSC9+F1Y9SF7BlzBR4knMmLBHVi0lFtLv8/UpJGMaGdck/AGI7c+t3eEee9+GnbG7UGLSQjOFZa8BvOfhl3rg4T24HFBbFWurATWzQhaQrboCj3P3L9/+Wg0iJkKdwSf/aq60HIP5qc2bZRJq2jU2ZBXyOrcPazaVsCqrQXMXL2dzzbk4Q5NUhIZ0aMVXwsHyzqY1lvlv4edN71D+20f81nP69iTVvEieK91z9M8fwWJty4NWh9GymDzwuD/avvB0KJ7nWLezbuK+M/Cjby+YCOz1wS32TdNS2Jgp2YM6NiMQR2b06ddFm2bppKZmnRkJ5BKC4P906zz/p/B2AYjkZLgc9xu0L67eorzg+/BJ38JuiZo1gVGXEdkyLfYHslgw7J5dJ7y/2hesJJ/po5lXUECP018hs2WzX/73cOXW6yn08e/gAv/HHw/yy16JRgYtkmbIE7+n7X7x8LRKEzsHvxf/591kLZ/39tV/Q/tv/LvZBZuYFH3a8jP6ARU+v/55DeDmLlwR9BQZ9wz+z4zaz+Bf5xPwfFjeSr7ZrI+eYCLC59nJ01YntCTkT6XzU36svrUe+jY7yTaZKWRUrQNPn89GOC4RTfo97WquwCLRiFaeuT18ewedEuZmAJNqrgjoXAHrPoQNi8O4ueup1JIKlt2F7FySz5bl0yl24p/ckLBVIo9iZcio3mv6YUMPmEkYwZk03Ph/fDRg0GeYeQNMOU3QUx33t1Blxh/HhH8fl712r59WrQrmL57I+DMOe7HlCbv//uaUbiRgSv/BkAkIYXE8+6CE78L6z6FZ8YF8ePlzwb1XvkBTP5F8HuSnBG0xu/zFTjnN5DdC3fn8wUzyZ32d47fOpkd0QyejZzOx03Opk+vnhzXNoturZrQO2U7XXI/IHn7F9D1VOh1VsXxlNyD7+Oaj4PPdNdT929JHY0En9Gda4LftWad9/9dK9wRNJ5IbQbtB+3/uYqUBo30dq6BDkODnE5sGdEobP4sGEgzvXnQMKBpTDdp7kEf6J+/Hvwf63E69PnqvvjZPWiYOfsxWPgiZLUN7rQecsW+ZVZ9CO9MCOoJkJBE6eBvsW7gTawpbUrZwlcYvegOCjyFm0pu4LSEhfwg6TU2Zg3EL32cDuv+A2/dDufeBaeEF6NK9gQXB3atDxpEfv4Gs3v/kLKkig0i2m+dRpct71KamMGcvrfunX4oYulI1Pl4RS7Pzcph8qJNlJRFMYPj2mQxrFsLhnVpQbfsDDo0T6dNVlqjvGu4rjFyXRLMI4EJ7n5e+PpnAO7+u5hlJofLTDezJGAT0NprKDxuwfPTl8PSN/a9TkiCpPTgx7ysqOp1ktLCRE9X3tzRjmc2tmNetBc7yaJZejLHt2/KRUM7ctEJHRukhUZdHa4kWWkkyscrtvHGgg1MXrSZvMJSWpHHKUlLGZ6+gX5JG+gaWUt2yXoSiVRcNzGdopSWlCY2oSwxnbKkdCKJ6ZQlpMZcxbJgUAWPkhgtISFSTGK0iIRICSmlu0gt2UFa6U6Mih+nvOTWbEruQk5iJ+bSh5d29mRDaXAykZKYwMk9W3HTmb04sdvhSyyXO9wJAXdn0YZdvLV4M28t2sTnm4LRcDOTIpyZuYbRSUvobTl0KF1Lq+J1JHpZhfXLElIpSmlBSXIzPDGVSEIy0YQUIpaMWwLmZSREy0iIlmJeRnLZHlJLd5JasmO/sooTM8lN78aW1K4sSerHh5GBLMxvyuZdRZRFg2M4rGsLvjygHef1b1e3UcIb2OFMMJdbvmU3D05ZzodfbCWvsBSA5ESjT+sMhqZtYFB0CX1KFtF1z2KySjaTQLTC+mUJaZQlpQffpaQmRBNTwSOYR8NHhKRIIaklO0mOFFRYN0oCeemd2ZrRm41p3VmQNJBZ0ePYml/Glt3F5OYXA9CxeTqjemVzRt/WnHt8u7oniRpQPJNpSzbu4tFpq/j3vA2URIL93yQlke6tm9CrRTK9WUOv4sV0LVxMh90LaVq8scL6UUukKKVl8FsXHq9IYhpuSRhREsLjhEdJjBSSUroreJTl7/f7VpyQwdbk9qxM7ME8783HJT34tKAdURJokZHMiO6tOLV3NpcM63RYb7U63Mdn/c5Cnp+1judn5bB+Z5Doact2zshcy6lpq+gbXUZ26SaalW4lodL/n7ooTm7OthaD2N58EFubDWRr88GUJGbg7kQdouFfdycSdQpLI+wpibCnpIw9xRFyC0pYs62AnB2FRMLft6y0JMYM6sA3T+jIsK4H3x9fY0swl9u8q4hHP1rFnDU7+Gz9LgpLg/2fmZrEjJ+fdeC3hxbkwmcvwdqPgxbOcUjWHMIEc62xdKx4xcjPzVrHF5t2U1gaoag0SlFp8FnfvqeUrbuK2LK7eO//cYD05ET6d2jKgI5Bwu2047Jpk3WILkp+cC+891vm9b6J4pT9Bzbuue5FsnctIpKQzGc9rmXwt2IaiLvDC9cEXQtlZAeJia/+YV8ya+c6Sp65kpRNc1jS7DS67JpFE9/Da5GTebjsQk5OWMwNSa+SbXnMSRlOUUoLhhVMJdWL2J7SkV3pHemSN4sEomzKGkBOq1Npk7+E9jtmkhwmqMvtaNqXjdmn4JZIq50LaJm3iJSy4CJzlATysnqxvflA8jJ7kLknh2a7l9MsfzlpJTuIJKSwK7MHeZm92JnVC4DMPevIKlhL5p51JEUKyc/oRH5GZ3ZndKYotTVpJdvIKNxERtEm0otzKUnOYk9au+CR3g7cSSvZRlrxNlKLc7FICXuSW1KQ3JKCpBbsScgkuSyf5NLdpJbuIqlsNwXRFHZGM9gWyWBrWTp5RWWkRItIp5gMKyYlwWma1Yz2rVvSuW02Hdu0JOlAL76WFQYtyAvCx55tULyLom05JEaLSYrsIbfZIFZ2+vp+q6YXbWHgir9gPc8Mjn3OzH0X8iG4Y6LLyfsGGi3cue/CY0JSkOhJbw5pzcOuU4wde0pYunk3OTsKydm+h415RURiTnNTEhNomp5M07QkmqQkkZKUQHJSAqlJCSQnJpCUYJhBghmJREiNFJBatpu0yG6Sy/YQSUqnLDmL0uQsSpMySYwWk1q8g9SS7aSWbMc8SnFKC4pTWlCU0opIYhppxVvJKNpMetFm0oq3UZLSnD1pbSlMbcOetNYkl+aTtWctWQVrydqzFvMy8jO6sDujM7uadKE0KYvmu7+g5a4lNMtfSYJHcIxdTbqyvWk/dmYdR+aedXTI/YgmRZsr7OM9yS1Z2+Jk8hOa0n/rG6RHdrMmrS8fp5/OgN3TGFj2GQWeypvREzk/YSaFpHJn8o/Y0+k0BnRsxjlZq+n/8Y+wgq1B1xadhsOV/674u+8Oz34rSF51Gw1XV9Mi/alLYdlk+MWmKrszq+p/aL9V/yCzcCOz+v4UDz+bFf5/fnhvcDt+eku4fkaQGIv1zp0w7Y9BfmDnWnYd9w3+3e4mZm0xWqz+LzcU/oXm5PNc5HR62EZOSlxCIk5uYmuaR3aQRBm7krP5ouXpFKe1JbtoNS32rKRFwSqSosXkp7VnZ2YPdmX2ID+jMynRYlLK8kgp3UVyaT7RxFRKkpsGj6SmRBIrJg6rS8CUf2TNneSyfFJLd5JSupOU0qDRT3Fyc4pTmlGS3BzHaFK4gSaFG8gs3EhqyXYKU7MpSO9Ifnp7ClPbkFG0kZa7v6DFrqWklQYXYfJT25Db5Di2ZPSmOJpA152f0GnP4grnNcUk82mkD3P8OM5ImMughFXk04TZLb9Ch7Riem6eTEKkOEhYFu8Oko/Drobzfhd8J/O3wMs/CO6QzcgOlvl/H0N2r4pveOl/4emxOManx/8iyD1UkhgpYtDyP7M7oytr2p3HCZdP2Dczdxk8dXGQPO88IuiCs1kXOGdCkFie8X8w9Y/B79XgcUGSdd0nkJCEH3c+xXmbSNs4iwiJfGDDWVrahjMS5tE3IbjjopBU0ikmQgLL0gayqumJtC/bQPf8OTQr2RRzPI1tmb3Z2OJEylKa0nbnfFrnzSe5bN/5XkFaW3JbDGV7s+OD1tg75tEsf8Xe+ZGEFLY368/WFkOIJKTResccsnfMJym6L/9VmJrN1pbD2N60Hy12LaXttk9IK9leYX/lZfZkU/ZI3BLotGkKmYXriZJAcWor0ou3EiWB3BZD2NRiGB23TqXV7s8pTUhjRetzyCpaT8e8OUQsiWUtvkRiaQG9d89ge2Jrnm96FR8xkK/s+Bff9HcoJYmPogM4J3E2ixL68HKvuziud1+Gd2tB9y3vYP++IUiYF+4MunG99ImKvx/bVsBfTw/+3/e7gE8Shu537JNLdzH0iwcpSmnBgt437p1+qGPpPSVlzFu7k1lrdjBrzQ7mrtnB7uJ9+ZTEBKNd07Tg4mVaMk1SEslISSIjJZGM1ER+el7fuJyjN2SC+WLgfHe/Nnz9bWCEu98Ys8xn4TI54esV4TK5lcq6DrgufNkHWFr3tyRVyAZya11K4kHHpnHT8Wm8dGwaNx2fxk3H5+B0dfe6N12vozrG0oqRa6fPt1RHnw2pjj4bUhV9LqQ6+mxUrU4xcl2al1SVHq+cla7LMrj7X4G/1mGbUgdmNutQtLSRg6dj07jp+DReOjaNm45P46bj02jVGicrRq6dPt9SHX02pDr6bEhV9LmQ6uizcXDq0p9DDhDbA34nYEN1y4RdZDQDtiMiIiIicmyrSywtIiIiInLEqkuCeSbQ28y6m1kKMBZ4tdIyrwJXhc8vBt6tqf9lEREREZFjRF1iaRERERGRI1atXWS4e5mZ3QhMBhKBR919kZn9Gpjl7q8Ck4B/mtlygpbLYw9lpWUv3UrZeOnYNG46Po2Xjk3jpuPTuOn4NELVxdJxrtaRSJ9vqY4+G1IdfTakKvpcSHX02TgItQ7yJyIiIiIiIiIiIiJSlbp0kSEiIiIiIiIiIiIish8lmEVERERERERERESkXpRgPsKY2aNmtsXMPot3XWR/ZtbZzN4zsyVmtsjMfhjvOknAzNLM7FMzmx8emzvjXSfZn5klmtlcM3s93nWRisxstZktNLN5ZjYr3vWRfcysuZm9YGafh/9/Rsa7TiINRbGvVEUxt1RHMb/URucbUhWd6xw89cF8hDGz04B84Al3HxDv+khFZtYeaO/uc8wsC5gNfN3dF8e5asc8MzOgibvnm1kyMA34obvPiHPVJIaZ/RgYDjR19zHxro/sY2argeHunhvvukhFZvY4MNXd/25mKUCGu++Md71EGoJiX6mKYm6pjmJ+qY3ON6QqOtc5eGrBfIRx9w+B7fGuh1TN3Te6+5zw+W5gCdAxvrUSAA/khy+Tw4eusDUiZtYJ+Crw93jXReRIYWZNgdOASQDuXqLkshxNFPtKVRRzS3UU80tNdL4hcugowSxyiJhZN2Ao8El8ayLlwtuh5gFbgLfdXcemcXkAuA2IxrsiUiUH3jKz2WZ2XbwrI3v1ALYC/whv9/y7mTWJd6VERA4XxdxSmWJ+qYHON6Q6Otc5SEowixwCZpYJvAj8yN13xbs+EnD3iLsPAToBJ5mZbrVtJMxsDLDF3WfHuy5SrVPd/QTgy8AN4W3rEn9JwAnA/7n7UKAA+J/4VklE5PBQzC1VUcwvVdH5htRC5zoHSQlmkQYW9vX1IvCUu78U7/rI/sLbx98Hzo9zVWSfU4GvhX1fPQOcaWZPxrdKEsvdN4R/twAvAyfFt0YSygFyYlpnvUCQcBYROaop5pbaKOaXSnS+IdXSuc7BU4JZpAGFg0pMApa4+x/jXR/Zx8xam1nz8Hk6cDbweXxrJeXc/Wfu3snduwFjgXfd/VtxrpaEzKxJOIgSYfcL5wKfxbdWAuDum4B1ZtYnnHQWoEGuROSopphbqqOYX6qj8w2pjs51GoYSzEcYM3samA70MbMcM/tuvOskFZwKfJvgaui88PGVeFdKAGgPvGdmC4CZBP2xvR7nOokcKdoC08xsPvAp8Ia7vxnnOsk+NwFPhb9vQ4C741wfkQaj2FeqoZhbqqOYX0QOlM51GoC5a0BVERERERERERERETlwasEsIiIiIiIiIiIiIvWiBLOIiIiIiIiIiIiI1IsSzCIiIiIiIiIiIiJSL0owi4iIiIiIiIiIiEi9KMEsIiIiIiIiIiIiIvWiBLOIiIiIiIiIiIiI1IsSzCIidWBm7czsGTNbYWaLzew/ZnacmX1mZueZ2bzwkW9mS8PnT9RQ3klm9r6ZLTOzOWb2hpkNDOdNMLP1MWXOM7PmZpZhZk+Z2cJwu9PMLDOmzIvMzM2sb8y0buG038RMyzazUjP7k5n9ImYbkZjnv6pm+s1mNt3MLCwrMZx+SqV6f2ZmXzs0R0NEREREDpVKsd+8MJ483cxeD+dfbWZRMxsUs85nZtYt5vXQMAY9r1LZbmZ/iHl9q5lNCJ9PrrTdDWb2SQ31fMzM9phZVsy0B8NtZIev88O/5THxTTHL/snMro4p6+Lw+ftmtrY83g2nvVJeVsy0W8ysyMyaxUzbu58qLfu+mQ0Pn18TxvMLwv12oZn9OXzPi82sMGYfXFzd+xcRaUyS4l0BEZHGLgwuXwYed/ex4bQhQFsAd58MTA6nvw/c6u6zaiivLfAccLm7fxxOGwX0BBaGi93v7vdVWu9nwGZ3L09E9wFKYxYZB0wDxgITYqavBMYAvwxfXwIsCut+F3BXWF6+uw+JWe/Oqqab2SnAd4G/AzcBM939YzM7t7zeZtYPmGpmbdw9Wt2+EBEREZFGp7BSTEhs8jiUA/wCuKyaMsrj0nGEcXKoGPiGmf3O3XNjV3D3vcloM2sCzAZur6Wuy4ELgSfNLAE4A1hfzbJbgB+a2SPuXlJLuTuBU4FpZtYcaF/FMuOAmcBFwGO1lAeAmXUi2G8nuHte2Fiktbv/O5zfDXi98v4XEWns1IJZRKR2ZwCl7v6X8gnuPg9YV8/ybiRIVn8cU940d3+llvXaExMwu/tSdy8GCIPTUwkSv2MrrVcILClvNUFwIvBcPesOcAvwMzPrH76Xn1ZewN2XAGVA9kFsR0REREQap9eB/mGDhwrCxhkXA1cD55pZWszsMuCvBPFkTR4E/uPub9ey3NPsS3KfDnwUbqMqW4EpwFW1lAnwDPti6m8AL8XONLOeQCZBAnxcHcor1wbYDeQDuHu+u686gPVFRBolJZhFRGo3gKAFRUPpD8ypZZlbYm6Ney+c9ijw07CLit+aWe+Y5b8OvOnuXwDbzeyESuU9A4wNW01EgA31rby7bwQeAKYDv3X37ZWXMbMRQJQgkBcRERGRI0d6TBz6cjXLRIGJwM+rmHcqsMrdVwDvA1+pNP/PwBWxXUvEMrOLgOHAz+pQ12VAazNrQZDofaaW5X8PjDezxFqWmwKcFi43Fni20vxxBMntqUAfM2tTh7oCzAc2A6vM7B9mdkEd1xMRadSUYBYRiTMz+8TMlpjZgzGT73f3IeHjDNjbaroHcC/QEpgZdkUBFQPqZ9i/JcWbwDnh9MoBcn38GUh098cqTb/FzOYB9wGXubs3wLZERERE5PApjIlDL6phuX8BJ5tZ90rTa4xL3X0X8ARwc+UCzawj8BBBV3LFdazvSwRJ4BEECd9qha2FPwUur6XMCEEXH5cB6e6+utL8scAzYVdwLxF0QVcrd48A5xO08P4CuL+8D2oRkSOZ+mAWEandIoIgsCHLOwH4N4C7jwgH8BhT24runk8QxL5kZlHgK2a2BTgTGGBmDiQCbma3xaxXYmazgfEELagPqrWEu0fDbVW2X9/RIiIiInL0cfeycMC+vd2lhS1+vwl8zcx+ARjQysyy3H13zOoPENzR94+YdQ14HPi9uy8+gKo8E5b1eBij1rb83cALwId1KPdlKo5tQji4YW/g7XBbKQRjnvy5LpUNG2B8CnxqZm8T7IMJNa4kItLIqQWziEjt3gVSzex75RPM7ESgaz3L+zNwdThYXrmM2lYys1PD2/8wsxTgeGANQfL7CXfv6u7d3L0zsAoYVamIPwA/dfdt9ay3iIiIiEisx4Czgdbh67OB+e7eOYxLuwIvEnTntlfYxdpzBOOHlLsVKHL3OiVqY8paSzBw3sN1XP5zYDG1N+6YCvyOoCuMWOOACeH76+buHYCOZlbruYGZdajUld0QgnheROSIpgSziEgtwlYGFwHnmNkKM1tE0MpgA8FI2Ada3iaC2+1+Z2bLzexjgiTxn2IWi+2DeV44onRP4AMzWwjMBWYRBOzjCFpXxHqRSrf+ufsid3/8QOsrIiIiIlIVdy8h6NKivA/iOsWloT9QcUDo3wL9KsXA71WxXlX1eCTs87mu7gI61VKmu/t97p5badZY9n+PL7NvUMCzzCwn5jEyZrlk4D4z+zzsVu4y4IcHUG8RkUbJ1D2miEj9mNmFwBXufmm86yIiIiIiIiIiEg/qg1lEpB7M7NfAhcDVca6KiIiIiIiIiEjcqAWziMghYmbnAfdUmryqltG4RUREREQkZGZ/Bk6tNPlBd/9HVcuLiMjhpwSziIiIiIiIiIiIiNSLBvkTERERERERERER1mF8MQAAIABJREFUkXpRgllERERERERERERE6kUJZhERERERERERERGpFyWYRURERERERERERKRelGAWERERERERERERkXpRgllERERERERERERE6kUJZhERERERERERERGpFyWYRURERERERERERKRelGAWERERERERERERkXpRgllERERERERERERE6kUJZhHBzP5iZr9soLK6mFm+mSWGr983s2sbouywvP+a2VUNVV5Muelm9pqZ5ZnZ8w1d/gHUY4KZPRmv7YuIiIgcTRTnKs4VEZFDTwlmkaOcma02s0Iz221mO83sYzP7gZnt/f67+w/c/Td1LOvsmpZx97XununukQao+35BqLt/2d0fP9iyq3Ax0BZo5e6XHILyj2hmdrqZRcOTqvLHa+G8CsfJzNzMFsZ+xszst2b2WKUym4Tl/KeK7a02s81m1iRm2rVm9n74/OeV6pJvZgXhtq+s5b2MCr8HeWa23cw+MrMTw3lXm1kkLG+Xmc0zszGV1u8e7ouHqyjbw3rkm9l6M/tj+UloOP99MyuqZj/G7uPdZrbUzL5T03sRERE5linOrTPFuTU4WuJcM5tsZr+uYvqFZrbJzJLC16eY2bvh9yYvvPhwfDX7o8qYNCxzXhgv55rZFDPrFjP/eDN7NSx/t5m9Z2anxMzvFr6f8ve32sz+p4q6jzWzT8L3vyV8fr2ZWTj/MTMrqbSv5h/INkSkYSjBLHJsuMDds4CuwO+BnwKTGnoj5UHLEaor8IW7lx3oikf4+z4QG8KTqvLHBTUs2wEYW0t5FwPFwLlm1r6K+UnAD6ta0d3vrlSXTOB+YDHwYnUbNLOmwOvA/wItgY7AnWE9yk0Py2tO8D15zsxaxsy/EtgBjDWz1Co2Mzhc/0vAZcA1lebfWMN+3BCu2xS4BfibmfWp7v2IiIiI4tw6UJxbuyM+zgUeA75dnnyN8W3gKXcvM7ORwFvAv8P30R2YD3xkZj1i1qk2JjWzXsATwHigWVjGw0A0nN8T+AhYGM7rALwMvBVuP1bzcDsXA780s3PKZ5jZeOBB4F6gHcFFkh8ApwIpMWVMrLS/Btd1GyLScJRgFjmGuHueu79KkPS6yswGwN4rv78Nn2eb2ethK5DtZjbVzBLM7J9AF+C18ArwbTFXhb9rZmuBd2OmxQajPc3s0/AK9r/Lk3Xh1fGc2DqGV5bPNrPzgZ8Dl1W6Er33VsSwXreb2ZrwivYTZtYsnFdej6vMbG14Zf0XVe0XM7sTuCNmW9+tY9l733c15cZe2V8RvifMrEN4RX+7mS03s+9Vs361+yd8PsHMnjezJ8OWAQvN7Dgz+1lY53Vmdm7Muu+b2W8saLG728zeMrPsqrbdACYCd9ZyUnIV8BdgAXBFFfPvBW41s+a1bczMvgLcDFzs7gU1LHocgLs/7e4Rdy9097fcfUHlBd09CjwKpAOxAfeVwO1AKVDtyYe7LycIrofUVv8q1nV3/w+wHRh0oOuLiIgcaxTnKs5VnMsrBA0oRses2wIYQ5AQLq/7E+7+oLvvdvft7n47MAOYULnAamLSIcAqd58Szt/t7i+6+9pw/gSCBhu/CMvf7e4PAf8E7qmq4u4+C1gUlk34efw1cL27vxCW4e4+192vcPfiqsqpSeVtiEjDUoJZ5Bjk7p8COcQEHzHGh/NaE1wl/nmwin8bWEvQSiTT3SfGrPMloB9wXjWbvJKgFWcHoAx4qA51fBO4G3i2mivRAFeHjzMIEoCZwJ8qLTMK6AOcBdxhZv2q2NavKm1rUh3LrvZ9m9lJBIHcTwhawp4GrA5nP02wjzsQXEm/28zOqmo/1MEFBMFaC2AuMJngt70jQVD2SKXlLwe+A7QhuPJ/a0ydF5jZ5fWsR2UvAbsI9uF+zKwLcDrwVPio6na/WcD7sXWspqxuBPvgOndfUku9vgAiZva4mX05DLqrKzcJuBbIB5aF00YDnYBngOeqqXf5+n0JvmPLa6lTVesmmNnXgOz6rC8iInKsUpy737YU53JsxLnuXsj+8emlwOfuPt/MMoBTgKr64X4O2K9lbzUx6Rygr5ndb2ZnmFlmpdXOqWEbp4b1qLydk4EBMdsYCaQStLRuEFVsQ0QakBLMIseuDQRXuCsrBdoDXd291N2nurvXUtYEdy8Ig5qq/NPdPwuvuP8SuNRi+qU9CFcAf3T3le6eD/yMoNuC2NYEd4atVOcT3P5VVQBf37Jret/fBR5197fdPeru6939czPrTHAy8FN3L3L3ecDfCW5dq4+p7j45vOXxeYITpt+7eylBErRbpZYR/3D3L2IC0L1X8N19kLv/q4ZtdQhb/JQ/Lq1hWSc41ndY1d1IXAkscPfFBCci/c1saBXL3QHcZGatq9pIWPbzBLf9PVNDfYJKue8i2P8O/A3YGrayaRuz2MlmthPYBIwDLnL3vHDeVcB/3X0H8C/gy2bWptJm5phZAbCE4MShcl/ND1Xaj7H9QnYIt11IcCvhj919bm3vS0RERCpQnHvwZSvOrV6jjHNDjwOXmFl6TF3K+/VuSZAD2ljFehsJksjlqo1J3X0lQQK9I8F+zrXgToHyRHN2DdtIILhgUC7XzAqB6QQx8ysxZeR6TLcuFvSxvtOCftdPiynj1krHrnI/5tVtQ0QakBLMIseujgS3OlV2L8FV3bfMbKXVbSCEdQcwfw2QTMUApr46hOXFlp1E0CKl3KaY53sIWmg0VNk1ve/OwIpqyt3u7rsrld2xjvWqbHPM80KCQCwS8xoqvuf67g8I+mJrHvN4rqaFPbidbi1wXRWzryRo0YG7bwA+IEjeVi7jM4I+k6v7HD5I0FpofF3fhLsvcfer3b0TQSuGDsADMYvMCN9ftruf7O7vQDACO3BJTL2nh++vcmuYEwj262XACKBJpfk3V9qPsSPbb3D35gT93T0EnFnX9yUiIiJ7Kc49+LIV59agEce504CtwIUW9Kl8IkGjCAjGEIkSXGSprD2QG/O6xpjU3We4+6Xu3prgboHTgPJuWnJr2EY0rEe5bILjdCtB0jo5nL4NyI696OHup4R12kbFXNZ9lY5d5X1d3TZEpAEpwSxyDDKzEwkCvWmV53nQv9V4d+9BcFvaj2Nua6uuhUdtLT86xzzvQtB6JBcoAPbeIhW29oi9gl9buRsIBi2JLbuMisFofdWl7Jrqtw7oWU25Lc0sq1LZ66tYtrb9cyS4nSDYjH0fpwC9gZ9ZMKL1JoJE7Lhq+rL7FfA9Kp2cmNm3gW8Cl4YtWQ6Yu39OMCDKgDosfhFBkP1wTL07UsVtjx54jqClxB31qFcxwSBFA83s6we6voiIyLFKcW6dKM5tGI01zn2CID79NvCWu28GCFvZTydoMFHZpcCUyhPrEpO6+0yCbkPK4+l3atjGdHffU2n9iLv/ASgCrg8nTycYJPHC6t9m3VWzDRFpQEowixxDzKypmY0huKXsSXdfWMUyY8ysl5kZQd9ikfABQdDZo/I6dfAtMzs+7G/r18ALYeuDL4A0M/uqmSUTBGmxt5ltJrj1rbrfqqeBW8yse3hLVnn/cgc8QvYhKHsS8B0zOyvsu6yjmfV193XAx8DvzCzNzAYR3Gb4VBVl1LZ/Gj13f59gBOnYlgRXAW8DxxPcujiEICDNAL5cRRnLgWcJBjcBwIKBex4Grgj3aZ2YWV8zG29mncLXnQm6wZhRh9WvIhj0b2BMvU8FhpjZwGrW+T1wnZm1q2sdy7l7CfAH6pGgFhEROdYozj0ginMbQGOLc2M8AZxNkLiu3F3E/xAMgnmzmWWZWQsLBsEcCdxZVWGVY1IzG2Vm37OwmzgLxh35Gvvi6TuBU8zsLjNrGW7nJoKk909rqPfvgdvMLM3dd4blPGxmF5tZZvhZG8L+dwceiL3bOIgyRKQKSjCLHBteM7PdBK0NfgH8kWAAjKr0JrjqnE/YT1UYPAH8Drg97NuqxgEpKvknQSvRTUAaYQAV9mt7PUHfbOsJWjLEjiZdPjjENjObU0W5j4ZlfwisIrgifdMB1KsmB1W2BwPMfAe4H8gjuDWuvKXIOKAbQSuPl4FfufvbVZRR2/5pUGa2yMyqGuX6YN1O2A9iGMxdCvyvu2+Keawi2N/73T4Y+jUVg8kfh69fsmBE9NjHz2uoy26CViSfWNBP8gzgM2q59dDMOhIMoPNApXrPBt6srt7hye0HBIPglPtTpfrOrmHTjwJdzOyCmuonIiJyDFOce+AU5zacxhTnAuDuqwkS/U2AVyvNm0YwcOM3CPpEXgMMBUa5+7Iaio2NSXcSJJQXmlk+QSz8MjAx3MYygr64BxMM/riRoDX2ee7+UQ3beIOg+4zvheVMDPfFbcAWgosyjxAkqT+OWe+2Svsol+pV2IaINBzzWsc0EBERERERERERERHZn1owi4iIiIiIiIiIiEi9VNXJvIiIyBHLzLoAi6uZfby7rz2c9RERERERaQiKc0WksVIXGSIiIiIiIiIiIiJSL3FrwZydne3dunWL1+ZFRERERPaaPXt2rru3jnc9FCOLiIiISGNR1xg5bgnmbt26MWvWrHhtXkRERERkLzNbE+86gGJkEREREWk86hoja5A/EREREREREREREakXJZhFREREREREREREpF6UYBYRERERERERERGReolbH8wiIiIiUjelpaXk5ORQVFQU76oc8dLS0ujUqRPJycnxroqIiIiIHATFyA3nYGPkA0owm1ka8CGQGq77grv/ysy6A88ALYE5wLfdvaReNRIRERGRCnJycsjKyqJbt26YWbyrc8Ryd7Zt20ZOTg7du3ePd3VERERE5CAoRm4YDREjH2gXGcXAme4+GBgCnG9mJwP3APe7e29gB/DdetVGRERERPZTVFREq1atFDgfJDOjVatWauUiIiIichRQjNwwGiJGPqAEswfyw5fJ4cOBM4EXwumPA1+vd41EREREZD8KnBuG9qOIiIjI0UOxXcM42P14wIP8mVmimc0DtgBvAyuAne5eFi6SA3Q8qFqJiIiIiIiIiIiISKN3wIP8uXsEGGJmzYGXgX5VLVbVumZ2HXAdQJcuXQ500yIiIlKTBc/BpoVw7m/iXRM5xP71ydoGLe/yEYrLRETkCLd7Mzx9GXgUUptCWjNIbw4jb4I2feNdOxE5DBQjx88Bt2Au5+47gfeBk4HmZlaerO4EbKhmnb+6+3B3H966dev6blpERESqsuwt+OyleNdCjlKZmZmHpFwzY/z48Xtf33fffUyYMKHGdR577DESEhJYsGDB3mkDBgxg9erVh6SOIiJyBMj9AjbMhYQkiJTC9pUw90lY+Hy8ayYiRzHFyIEDSjCbWeuw5TJmlg6cDSwB3gMuDhe7Cvh3Q1ZSRERE6iBSCtHSeNdC5ICkpqby0ksvkZube0DrderUibvuuusQ1UpERI445THQuXfBdyfD9dMhMRUiJfGtl4hIPRxpMfKBtmBuD7xnZguAmcDb7v468FPgx2a2HGgFTGrYaoqIiEitomVBklnkEHJ3fvKTnzBgwAAGDhzIs88+C8D111/Pq6++CsBFF13ENddcA8CkSZO4/fbbqy0vKSmJ6667jvvvv3+/ea+99hojRoxg6NChnH322WzevHnvvDFjxrBo0SKWLl3akG9PRESOVJFwWKjE5H3TEpOD+EhE5BA71mPkA0owu/sCdx/q7oPcfYC7/zqcvtLdT3L3Xu5+ibsXH5rqioiISLUipTqJkkPupZdeYt68ecyfP5933nmHn/zkJ2zcuJHTTjuNqVOnArB+/XoWL14MwLRp0xg9enSNZd5www089dRT5OXlVZg+atQoZsyYwdy5cxk7diwTJ07cOy8hIYHbbruNu+++u4HfoYiIHJHKWzAnxAw1Vd5dhojIIXasx8j17oNZREREGploqW4DlUNu2rRpjBs3jsTERNq2bcuXvvQlZs6cyejRo5k6dSqLFy/m+OOPp23btmzcuJHp06dzyimn1Fhm06ZNufLKK3nooYcqTM/JyeG8885j4MCB3HvvvSxatKjC/Msvv5wZM2awatWqBn+fIiJyhClPJO/XglkJZhE59I71GFkJZhERkaNFpFStdOSQc/cqp3fs2JEdO3bw5ptvctpppzF69Giee+45MjMzycrKqrXcH/3oR0yaNImCgoK902666SZuvPFGFi5cyCOPPEJRUVGFdZKSkhg/fjz33HPPwb0pERE58pXfxZUQk2BOSN7XdYaIyCF0rMfISbUvIiIiIkeEaBl4BNzBLN61kUPo8hFd4rbt0047jUceeYSrrrqK7du38+GHH3LvvfcCMHLkSB544AHeffddtm3bxsUXX8zFF19cS4mBli1bcumllzJp0qS9fdPl5eXRsWNHAB5//PEq17v66quZOHEiu3fvboB3JyIiR6y9LZhj0hyJSWrBLHIMUYy8z+GOkdWCWURE5GhRfmKlVsxyCF100UUMGjSIwYMHc+aZZzJx4kTatWsHwOjRoykrK6NXr16ccMIJbN++vda+5WKNHz++wkjZEyZM4JJLLmH06NFkZ2dXuU5KSgo333wzW7ZsObg3JiIiR7a9fTBXbsGsuEhEDr1jPUa26ppwH2rDhw/3WbNmxWXbIiIiR6VHToON8+HnGyClSbxrIw1oyZIl9OvXL97VOGpUtT/NbLa7D49TlfZSjCwiUk8zJ8EbP4bxSyErSOrw5xGQ3RsuezK+dRORQ0IxcsM6mBhZLZhFRESOFuV9DKqljoiIiBxrquqDOVF9MIuIHA7qg1lERORoUX5raFQnUtK4bNu2jbPOOmu/6VOmTKFVq1ZxqJGIiBx1quqDOSFZfTCLSKN1NMXISjCLiIgcLdQHszRSrVq1Yt68efGuxiFlZo8CY4At7j6g0rxbgXuB1u6eW9X6IiJykKrqgzlRfTCLSON1NMXI6iJDRETkaFHecjlSEt96iBybHgPOrzzRzDoD5wBrD3eFRESOKeVdYSRWGuRPd3aJiBxySjCLiIgcLSLqIkMkXtz9Q2B7FbPuB24D4jOytojIsWJvC+aYG7UTk9SCWUTkMFCCWURE5GgRVRcZIo2JmX0NWO/u8+NdFxGRo16kNEgum+2bpj6YRUQOC/XBLCIicrQovzVUJ1JHv1n/aNjyhn+nYcsTzCwD+AVwbh2WvQ64DqBLly6HuGYiIkepaGnF/pch7INZd3aJHDMUI8eNWjCLiIgcLdSCWQ6h1atXM2DAgP2m33HHHbzzzjs1rjthwgTuu+++Om/LzBg/fvze1/fddx8TJkyocZ3HHnuMhIQEFixYsHfagAEDWL16dZ2328B6At2B+Wa2GugEzDGzdpUXdPe/uvtwdx/eunXrw1xNEZGjRKS0Yv/LELRo1oV3ETmEFCMHlGAWERE5WqgPZomDX//615x99tkNWmZqaiovvfQSubm5B7Rep06duOuuuxq0LvXl7gvdvY27d3P3bkAOcIK7b4pz1UREjk7lXWTESkzWhXcRiYtjLUZWgllERORo4K4WzHLIRSIRvve979G/f3/OPfdcCgsLufrqq3nhhRcA+M9//kPfvn0ZNWoUN998M2PGjNm77uLFizn99NPp0aMHDz30UI3bSUpK4rrrruP+++/fb95rr73GiBEjGDp0KGeffTabN2/eO2/MmDEsWrSIpUuXNtA7rjszexqYDvQxsxwz++5hr4SIyLEsWgqJKRWnqQ9mETkMFCMrwSwiInJ0iEZinutESg6NZcuWccMNN7Bo0SKaN2/Oiy++uHdeUVER3//+9/nvf//LtGnT2Lp1a4V1P//8cyZPnsynn37KnXfeSWlpzZ/TG264gaeeeoq8vLwK00eNGsWMGTOYO3cuY8eOZeLEiXvnJSQkcNttt3H33Xc3wLs9MO4+zt3bu3uyu3dy90mV5ndz9wNrbiIiInUXKdu/i4zEJPXBLCKHnGJkJZhFRESODrFJZbVglkOke/fuDBkyBIBhw4ZV6Lvt888/p0ePHnTv3h2AcePGVVj3q1/9KqmpqWRnZ9OmTZsKrSqq0rRpU6688sr9WnLk5ORw3nnnMXDgQO69914WLVpUYf7ll1/OjBkzWLVqVX3fpoiIHImiVXSRoRbMInIYKEZWgllEROToEFGCWQ691NTUvc8TExMpK9vXKszd671udX70ox8xadIkCgoK9k676aabuPHGG1m4cCGPPPIIRUVFFdZJSkpi/Pjx3HPPPbWWLyIiR5GqBvlTH8wichgoRoak2hcRERGRRi92YD+11Dn6Df9OvGuwn759+7Jy5UpWr15Nt27dePbZZw+6zJYtW3LppZcyadIkrrnmGgDy8vLo2LEjAI8//niV61199dVMnDiR3bt3H3QdRETkCBEtC1osx0pQglnkmKIYGYhPjKwWzCIiIkcDtWCWOEtPT+fhhx/m/PPPZ9SoUbRt25ZmzZoddLnjx4+vMFL2hAkTuOSSSxg9ejTZ2dlVrpOSksLNN9/Mli1bDnr7IiJyhIiUBn0ux0pM0oV3EYmrYyVGttqaah8qw4cP91mzZsVl2yIiIkedvBy4v3/w/KJHYPDY+NZHGtSSJUvo169fvKtRq/z8fDIzM3F3brjhBnr37s0tt9wS72rtp6r9aWaz3X14nKq0l2JkEZF6+udFULQLvjdl37Qpv4Gpf4AJO+NXLxE5ZBQjN6yDiZHVgllERORooBbM0gj87W9/Y8iQIfTv35+8vDy+//3vx7tKIiJyrKiuD2YcopG4VElEBI6NGFl9MIuIiBwN1AezNAK33HJLnVtjbNu2jbPOOmu/6VOmTKFVq1YNXTURETnaRcvg/7N33/FxXXX6x58zo1FvtiRbtiy5l7jFDk4nnfRAEnogENqG0LKU5UfZwgLLLgssLLvUQEJgSSGNhPRmp8d23Hsvsmw1W71Ouef3x51Rs2RL8YxGM/N5v15mZu6MZo5iI53zvd/7HM+AEkfkcSggebyjPyYAUGrMkSkwAwCQDPoWmOlgTkrWWhlj4j2MqCkqKtKGDRtG/XPjFQ8HAIixUEDyZfU/FulodgKSMkd9SABijzlydJzqHJmIDAAAkgERGUktMzNTx44dozh6iqy1OnbsmDIzKTIAQNJxApJnQERG5DFzIyApMUeOjmjMkelgBgAgGfSNxSAiI+lMmTJFVVVVqq+vj/dQEl5mZqamTJkS72EAAKItFBwig1n9r/QCkDSYI0fPqc6RKTADAJAMQsHB7yMp+Hw+TZ8+Pd7DAABg7HICJ85gBpB0mCOPHURkAACQDOhgBgAAqSwUOEEHM3MjAIilERWYjTHlxpgVxpjtxpitxpi/Dx//V2PMYWPMhvCfa2IzXAAAMCgymAEAQCpzgifIYObqLgCIpZFGZAQlfc1au84YkydprTHm+fBzP7PW/iS6wwMAAMPSN1uQLh0AAJBqQgHJO6DEEXnM3AgAYmpEBWZrbbWk6vD9VmPMdkllsRgYAAAYgX4dzHTpAACAFOMETtDBTIEZAGLpbWcwG2OmSVoqaVX40BeNMZuMMXcZY8YN8TW3GmPWGGPWsMMjAABR1LczJ+SP3zgAAADiIRQkgxkA4uRtFZiNMbmSHpb0ZWtti6RfS5opaYncDuf/GuzrrLV3WGuXWWuXlZSUvM0hAwCA44TY5A8AAKQwJyB5BlykTQYzAIyKEReYjTE+ucXle6y1j0iStbbWWhuy1jqSfifprOgOEwAAnFDfDGYWUQAAINWEAoN0MJPBDACjYUQFZmOMkXSnpO3W2p/2OT6pz8tulLQlOsMDAADDEulg9vhYRAEAgNRi7eAZzN5095YMZgCIqRFt8ifpfEkfk7TZGLMhfOzbkm4yxiyRZCUdkPTZqI0QAACcXKSo7MtmEQUAAFKLE3JvB3YwRwrODld3AUAsjajAbK19TZIZ5KmnojMcAADwtkRiMXxZLKKAODDG3CXpOkl11tqF4WM/lvRuSX5JeyV90lrbFL9RAkCSipxoH5jBHInI4OQ7AMTU29rkDwAAjDE9HcxZLKKA+Lhb0lUDjj0vaaG1drGkXZK+NdqDAoCUEJn7DNnBzNwIAGKJAjMAAMkg1Ccig0UUMOqsta9Iahhw7DlrbeSSgpWSpoz6wAAgFUSu3jougzn8mJPvABBTFJgBAEgGkaJyOhnMwBj1KUlPD/aEMeZWY8waY8ya+vr6UR4WACSBng7mAREZkcgM4sMAIKYoMAMAkAwiGcxpmRSYgTHGGPOPkoKS7hnseWvtHdbaZdbaZSUlJaM7OABIBj0ZzHQwA0A8jGiTPwAAMEY5Acl4JW+6FOiI92gAhBljbpG7+d9l1lob7/EAQFIigxkA4ooCMwAAySAUcBdVXh9dOsAYYYy5StI3JF1kreXMDwDEykkzmInIAIBYIiIDAIBk4ATdRZUnjZxBIA6MMfdJelPSXGNMlTHm05J+ISlP0vPGmA3GmN/EdZAAkKxOmsHMyXcAiCU6mAEASAahgLuoooMZiAtr7U2DHL5z1AcCAKmIDGYAiCs6mAEASAZOINzB7KNLBwAApJZIBAYZzAAQFxSYAQBIBqFgOIM5nZxBAACQWno6mAdcpE0GMwCMCgrMAAAkAyfgLqq8aVLIH+/RAAAAjJ6eDOYBHczGSMZLBzMAxBgFZgAAkkEo4C6qiMgAAACpJnJyfWAGs8T+FAAwCigwAwCQDCIZzF4fl4ECAIDU4gyRwSyFT74zNwKAWKLADABAMggF3XgMTxodzAAAILWEhshglogPA4BRQIEZAIBk0K+DmQIzAABIIc4QGcySOz9ibgQAMUWBGQCAZNA3g9mGJGvjPSIAAIDREYkHGyqDmYgMAIgpCswAACQDJxjuYA5fGkqnDgAASBU9HcyDRGR40pgXAUCMUWAGACAZhALhDOZw5w45zAAAIFVECsje9OOf8/qYFwFAjFFgBgAgGfRkMIcXVmxmAwAAUkWkgDxYRAYZzAAQcxSYAQBIBqGg26GMb/txAAAgAElEQVQT2dwmRNYgAABIEZF5z2Cb/HnTyGAGgBijwAwAQDJwApLH6+YMRh4DAACkgp4O5sEymOlgBoBYo8AMAEAyCEUiMny9jwEAAFJBTwbzYB3MZDADQKxRYAYAIBk4AXcB1bPJH5eCAgCAFBGZ9wyZwcy8CABiiQIzAADJIBQMdzCHLw2lgxkAAKSKyLzH4z3+OW8aHcwAEGMUmAEASAZOwF1A9XQws5ACAAApwglHhRlz/HNkMANAzFFgBgAgGZDBDAAAUlUoMHj+shTOYCYiAwBiiQIzAADJwAm6CygKzAAAINU4wcHzlyXJk8a8CABibEQFZmNMuTFmhTFmuzFmqzHm78PHxxtjnjfG7A7fjovNcAEAwKBCAXcBRUQGAABINaFA7z4UA3nTmRcBQIwN8RN4SEFJX7PWrjPG5Elaa4x5XtInJL1orf2hMeabkr4p6RvRHSoAABiSE6CDGYgjY8xdkq6TVGetXRg+Nl7SXyRNk3RA0gettY3xGiMAJK1IBnNfa/7g3jYdlDobex8PtOyTsR0bAKSAEXUwW2urrbXrwvdbJW2XVCbpekl/DL/sj5JuiOYgAQDACTiOZB13YdXTwUzWIDDK7pZ01YBj35TbhDFb0ovhxwCAaAsFh85gNl53rgQAiJm3ncFsjJkmaamkVZImWmurJbcILWnCEF9zqzFmjTFmTX19/dv9aAAA0Ffksk9vWu/loXQwA6PKWvuKpIYBh2nCAIDR4ISjwgbj8Ug2NLrjAYAU87YKzMaYXEkPS/qytbZluF9nrb3DWrvMWruspKTk7Xw0AAAYKFJM7tfBTIEZGANowgCA0RAKnLiDmQIzAMTUiAvMxhif3OLyPdbaR8KHa40xk8LPT5JUF70hAgCAE+rpYCaDGUhENGEAwClygsdnMEd4vJJDgRkAYmlEBWZjjJF0p6Tt1tqf9nnqb5JuCd+/RdJj0RkeAAA4qVA4b9nj6708lAxmYCygCQMARkMo0BsTNhAdzAAQcyPtYD5f0sckXWqM2RD+c42kH0q63BizW9Ll4ccAAGA0RIrJ3jTJm+7eD/njNx4AETRhAMBocAIn6WBmkz8AiKUhTvENzlr7miQzxNOXnfpwAADAiDl9MpiJyADiwhhzn6SLJRUbY6okfUdu08UDxphPS6qU9IH4jRAAktgJM5g9kqxknfB9AEC0jajADAAAxqBQnwzmnk3+iMgARpO19qYhnqIJAwBizQn2xoQN5PGGXxOSvBSYASAW+OkKAECiixSTPWm9+YN0MAMAgFRxwg7mcIGZHGYAiBkKzAAAJLpBO5gpMAMAgBRxogxm06eDGQAQExSYAQBIdINmMBORAQAAUkQoOHQHcyQiw7LRHwDECgVmAAASXaSY7PX15g/SwQwAAFKFExg6g5kOZgCIOQrMAAAkup4O5jTJGPeWDGYAAJAqTpTB7AmXPchgBoCYocAMAECi65vBLEnedCnkj994AAAARpMTJIMZAOKIAjMAAImubwZz5NYhgxkAAKSIUEDyDhGR0ZPBTIEZAGKFAjMAAImuJ4M5rfeWiAwAAJAqnAAdzAAQRxSYAQBIdIN2MFNgBgAAKSIUPEEGMwVmAIg1CswAACS64zKYfb1dzQAAAMnOCbibHA/GsMkfAMQaBWYAABJdJG+5p4M5jQ5mAACQOkIBOpgBII4oMAMAkOh6OpgjGcw+MpgBAEBqsHZ4GczWGb0xAUCKocAMAECiGzSDmYgMAACQAiJzHjqYASBuKDADAJDoBs1g9sdvPAAAAKMlMg8aMoM50sFMgRkAYoUCMwAAia4ng5mIDAAAkGKcASfaB6KDGQBijgIzAACJbmAHMxEZAAAgVYQGbHY8kAmXPehgBoCYocAMAECiG5jB7E2jgxkAAKQGZ8BmxwN5iMgAgFijwAwAQKILDdjcxuPrXWwBAAAks9CAE+0DRTKYHWd0xgMAKYgCMwAAic4JSDK9HTpkMAMAgFQx3AxmOpgBIGYoMAMAkOhCgf6LKk8aGcwAACA19FzJlT7482zyBwAxR4EZAIBE5wT7XxZKBzMwphhjvmKM2WqM2WKMuc8YkxnvMQFA0ujZi2KIDGZDBzMAxBoFZgAAEl0o0H9jG49PCvnjNx4APYwxZZJul7TMWrtQklfSh+M7KgBIIqFhRmTQwQwAMUOBGQCAROcEBnQwpxORAYwtaZKyjDFpkrIlHYnzeAAgeUTmPCfb5M+yyR8AxAoFZgAAEt3ADGZvGhEZwBhhrT0s6SeSKiVVS2q21j7X9zXGmFuNMWuMMWvq6+vjMUwASFw9HcxDRWSEyx50MANAzFBgBgAg0Q3MYPb4evMIAcSVMWacpOslTZc0WVKOMebmvq+x1t5hrV1mrV1WUlISj2ECQOLqyWAeqoPZuEVmMpgBIGYoMAMAkOgGZjB7fb07qgOIt3dJ2m+trbfWBiQ9Ium8OI8JAJLHyTKYJTcmgw5mAIiZEReYjTF3GWPqjDFb+hz7V2PMYWPMhvCfa6I7TAAAMKSBGcyeNDqYgbGjUtI5xphsY4yRdJmk7XEeEwAkj5NlMEvuRn90MANAzLydDua7JV01yPGfWWuXhP88dWrDAgAAwxYKDshg9pHBDIwR1tpVkh6StE7SZrnz7zviOigASCYny2CWwh3MbPIHALFygp/Ag7PWvmKMmRb9oQAAgLfFCbhdyxEen9ul4ziShzQsIN6std+R9J14jwMAktLJMpglOpgBIMaiuer8ojFmUzhCY9xgL2CHbAAAYiAUGNDBHC42E5MBAACSXWTfiRNmMHvIYAaAGIpWgfnXkmZKWiKpWtJ/DfYidsgGACAGnGD/rh1vuntLTAYAAEh2PR3MJ7hAmw5mAIipqBSYrbW11tqQtdaR9DtJZ0XjfQEAwDCEAv1zByPFZjqYAQBAsuvJYD5RB7OXDmYAiKGoFJiNMZP6PLxR0pZovC8AABgGJzCggzl8P3LJKAAAQLIadgYzm/wBQKyMeJM/Y8x9ki6WVGyMqZK7YcnFxpglkqykA5I+G8UxAgCAExmYwewhgxkAAKSIYWUw08EMALE04gKztfamQQ7fGYWxAACAt8MJ9s8d7OlgpsAMAACS3LAymD1kMANADEVrkz8AABAvoUD/RVVPBjMRGQAAIMmRwQwAcUeBGQCAROcMiMiIbPhHBzMAAEh2kRPqJ81gpsAMALFCgRkAgEQXCvZfVEXuh/zxGQ8AAMBoiZxQ93iHfg0dzAAQUxSYAQBIdE6gt2tZkrzpvccBAACSmRNwT64bM/RrPF7JOqM3JgBIMRSYAQBIdKFA/w7mnogMMpgBAECSCwVOnL8sScZDBzMAxBAFZgAAEp0T7L+w6tnkjw5mAACQ5JzgifOXpXAHMyfeASBWKDADAJDoQgHJ0zciw9d7HAAAIJmFBkSFDcZ4JYeIDACIFQrMAAAkOicwRAcznToAACDJOYFhdjATkQEAsUKBGQCARGbt8ZeG9mQw08EMAACSXCg4jAxmLxnMABBDFJgBAEhkkS5lMpgBAEAqcgZEhQ3G46GDGQBiiAIzAACJLFJgJoMZAACkolCADmYAiDMKzAAAJLJIEbnvwooCMwAASBUDo8IG4/FKlk3+ACBWKDADAJDIejqYicgAAAApKBTo3X9iKHQwA0BMUWAGACCR9XQwE5EBAABSUMg/zA7mkLs5MgAg6igwAwCQyCJdyoN2MAdHfzwAjmOMKTTGPGSM2WGM2W6MOTfeYwKApOEEh5fBLBGTAQAxcpLrSAAAwJg2aAZzWv/nAMTbzyU9Y619vzEmXVJ2vAcEAEljOJv8eSIF5pAkb8yHBACphgIzAACJjAxmYEwzxuRLulDSJyTJWuuX5I/nmAAgqTgBKf0k5+0iHcyOQ30ZAGKAiAwAABLZCTOYicgAxoAZkuol/cEYs94Y83tjTE7fFxhjbjXGrDHGrKmvr4/PKAEgUYUCw8hgDpc+LBv9AUAsUGAGACCRDZrBHInIoEkSGAPSJJ0h6dfW2qWS2iV9s+8LrLV3WGuXWWuXlZSUxGOMAJC4RpLB7FBgBoBYoMAMAEAii3Qp911YGeMWmYnIAMaCKklV1tpV4ccPyS04AwCiIRToPbk+lH4ZzACAaKPADABAIuvpYB6wsPKms8kfMAZYa2skHTLGzA0fukzStjgOCQCSizOMTf7oYAaAmGKTPwAAEllPBvOAhZXH17sBIIB4+5Kke4wx6ZL2SfpknMcDAMkjFBxGBjMdzAAQSxSYAQBIZINlMEvupn90MANjgrV2g6Rl8R4HACQlJ9B/s+PB9HQwO7EfDwCkICIyAABIZINlMEvhDmYKzAAAIMmFAsPoYA6XPuhgBoCYoMAMAEAiGzKD2ddbfAYAAEhWTtDde+JEyGAGgJiiwAwAQCIbMoM5jQ5mAACQ/ELDiMgggxkAYooCMwAAiSyykd9xGcw+KeQf/fEAAACMJmcYERl0MANATI14kz9jzF2SrpNUZ61dGD42XtJfJE2TdEDSB621jdEbJgAAGNTeF93bbY9JOcW9x7tbpWN7pTV/6D227JOjOzYAAIBYsjYckXGyDOZIBzOb/AFALLydDua7JV014Ng3Jb1orZ0t6cXwYwAAEGuR3dAjC6cITxqLKAAAkNyGupJrIBMufdDBDAAxMeICs7X2FUkNAw5fL+mP4ft/lHTDKY4LAAAMRyRL0Az4lW48LKIAAEBy69mLggxmAIinaGUwT7TWVktS+HZClN4XAACcSKSIfFwHs5dFFAAASG6RDY3JYAaAuBrVTf6MMbcaY9YYY9bU19eP5kcDAJCcejqYBxSYjZdFFAAASG6hcETGsDOYmRsBQCxEq8Bca4yZJEnh27rBXmStvcNau8xau6ykpCRKHw0AQAqjgxkAAKSqng7mk0RkGArMABBL0Sow/03SLeH7t0h6LErvCwAATiSykd+gHcxs8gcAAJJYTwbzMDuYmRsBQEyMuMBsjLlP0puS5hpjqowxn5b0Q0mXG2N2S7o8/BgAAMSaM8Qmfx6vZIOjPx4AAIDRMuwM5vA8iQ5mAIiJk1xHcjxr7U1DPHXZKY4FAACMlA25iyZj+h83HjKYAQBAchtpBjNzIwCIiVHd5A8AAESZEzo+HkNyswgtl4ECAIAkRgYzAIwJFJgBAEhkNnT8Bn+S5KGDGQAAJLkRZzAzNwKAWKDADABAInOcwTuYjZcuHQAAkNyccETGSTOYKTADQCxRYAYAIJEN1cFsvCyiAABAcuvpYD5ZRAab/AFALFFgBgAgkTmh3kVTXx46mAEAQJLryWA+WQezYQNkAIghCswAACQyOpgBAECqGm4GsxSOD2MDZACIBQrMAAAkMic0eAazhwIzMFYYY7zGmPXGmCfiPRYASCrDzWCWmBsBQAxRYAYAIJFZZ4gOZo8kS6cOMDb8vaTt8R4EACSd4WYwS8SHAUAMUWAGACCRDdnBHF5oORSYgXgyxkyRdK2k38d7LACQdIabwSwRHwYAMUSBGQCARGZDkmeITf4izwOIp/+W9P8kcbYHAKItFI7IGE4GMx3MABAzFJgBAEhkQ3UwG0/v8wDiwhhznaQ6a+3ak7zuVmPMGmPMmvr6+lEaHQAkgZ4O5mFEZBgvV3YBQIxQYAYAIJHZ0OAZzHQwA2PB+ZLeY4w5IOl+SZcaY/488EXW2justcustctKSkpGe4wAkLh6MpiH08HsYV4EADFCgRkAgEQ2ZAezt/d5AHFhrf2WtXaKtXaapA9LWm6tvTnOwwKA5EEGMwCMCRSYAQBIZNahgxkAAKQmMpgBYEwYRlARAAAYs5xQb95yX3QwA2OKtfYlSS/FeRgAkFxCfvd2uBnMFJiBlHPvqsqTvqawZaeuKaqRFr1f8mWNwqiSDx3MAAAksiEzmD29zwMAACQjZyQZzGzyB2Bwk+tflf72RffqULwtFJgBAEhkdDADAIBUFYnIGG4GMyfeAQwiw98opWVJ6TnxHkrCosAMAEAis0Ns8he5VJSz8AAAIFn1bPI3yFxoII+HE+8ABpXpb5RyiuM9jIRGgRkAgETmDLHJX6SrmYUUAABIVqGA271szMlfSwczgCFk+Buk7KJ4DyOhUWAGACCRDdnB7O19HgAAIBk5weHlL0vufIkT7wAGkUEH8ymjwAwAQCIbapM/MpgBAECyi3QwD4eHDmYAg8v0N0rZFJhPBQVmAAAS2VCb/HkoMAMAgCTnBCRv2vBea7xutBgADEAH86mjwAwAQCKzQ2UwE5EBAACS3Ig6mD3MiwAcxxvsUJrTRQbzKaLADABAInNOksFMBzMAAEhWZDADOEWZ/kb3Dh3Mp2SY15IAAIAxx3Ek2SE6mMPnkOnUAQAAySoUkDzDLGuQwZxy7l1VOezXfuTsihiOBGNZRqTATAbzKaGDGQCAROUE3NtBO5jDiy0WUgAAIFk5ATqYAZySTH+De4cO5lNCgRkAgEQVCheYB+tg7onIYDMbAACQpELBEWQwe929KwCgj4xAk3uHDOZTQoEZAIBE1dPBPMivcyIyAABAshtRB7OHDmYAx8kggzkqoprBbIw5IKlVUkhS0Fq7LJrvDwAA+ggF3dsTdjCzkAIAAEkqNIICcySD2VrJmNiOC0DCyPA3KGTS5M3Ij/dQElosNvm7xFp7NAbvCwAA+jpRBrOhwAwAAJKcM4KIjMjcyDqDz50ApKRMf6O608crmxNPp4SIDAAAEtVwMpiJyAAAAMkqFJC8w+ybY24EYBAZ/kZ1p4+L9zASXrQLzFbSc8aYtcaYWwc+aYy51Rizxhizpr6+PsofDQBAinHCERl0MAMAgFTkBEbewcwGyAD6yPQ3qIsC8ymLdoH5fGvtGZKulvQFY8yFfZ+01t5hrV1mrV1WUlIS5Y8GACDF9HQws8kfAABIQSPKYGZuBOB4dDBHR1QzmK21R8K3dcaYv0o6S9Ir0fwMAABS1lt3SrOvkArL3ccnzGA27nE6mAEAQLJygpJn8LLGqv0N/R5PaOjWdEnrDhxVwNfdc/zsZbEcIICxLiOcwYxTE7UOZmNMjjEmL3Jf0hWStkTr/QEASGmttdKTX5XW/an32IkymCPHLZeBAgCAJDWCDmYbvrrLMDcCEOYJ+ZUebCMiIwqiGZExUdJrxpiNklZLetJa+0wU3x8AgNTVVNn/VjpxBrPkxmTQwQzElTGm3Bizwhiz3Riz1Rjz9/EeEwAkjRFkMNvwfMmIuVGqMpGr/4CwjECjJBGREQVRi8iw1u6TdHq03g8AAPTRdLD/rTTMDmYWUUCcBSV9zVq7Lny131pjzPPW2m3xHhgAJLxQcPgdzKKDOZVNOLZaF6/5gh6/6El1Zk6I93AwRmT43QJzFxEZpyzam/wBAIBY6Ckw9+1gPkEGc+Q4HcxAXFlrq62168L3WyVtl1QW31EBQJJwAkNmMA/U08HMyfeUVNy0SWlOlwpbd8Z7KBhDMv1uVjsdzKeOAjMAAImgMVxgbjkiBcMb04TCERmeIX6d08EMjCnGmGmSlkpaNeD4rcaYNcaYNfX19fEYGgAkphFlMEcKzHQwp6LcjkOSpLz2ypO8Eqkk0sFMgfnURS0iA9F376rh/eD7yNkVMR4JACDueqIxrNRcJRXNpIMZSCDGmFxJD0v6srW2pe9z1to7JN0hScuWLbNxGB4AJCYnOOwMZqdnkz/mRqkor6Oy3y0gSZlEZEQNHcwAACSCpkoptzR8P1xsPmkGs4cOZmAMMMb45BaX77HWPhLv8QBA0ggFJO9wIzLIYE5lkc5lCszoK8PfIEce+X358R5KwqPADADAWOeEpKZD0vQL3MeRuIyTdjCn0cEMxJkxxki6U9J2a+1P4z0eAEgqTmDYHcyRiAyPmBulGm+oS9nddZKIyEB/Gf5G+dMLJUN59FQRkQEAwFjXWuMuoMrPlrY+2rvRXySDeagCs8cr0aUDxNv5kj4mabMxZkP42LettU/FcUxAwhlufKBEhGDKsNaNyBh2BjMdzKkqt6NKktSWVaacziMyTkB2mCcmkNwy/Y3qIn85KigwAwAw1kUiMcZPlwqm9D6OdDAPtcmf8dDBDMSZtfY1SSbe4wCApONENjseZoFZkU3+mBulmsgGf0dK3qk5lX9RTme12nI4EQW3g5kN/qKDAjMAAGNdpGO5cKpUWNGng/kkERkeLxnMAJIC3asAjhOZB5HBjJOI5C5XF5+vOZV/UV5HJQVmSHIzmJvzZsd7GEmBkBEAAMa6SOZyQbk0bmqfDOZI585QGcxeOpgBAEBy6rmSa2QZzBSYU09uxyH50/J0rHCRJCmv/WCcR4Sxwo3IGB/vYSQFCswAAIx1TQel3FLJl+l2MLfXSf6O4XUwU2AGACSxhbt/rSm1L8Z7GIiHyF4UI85gZm6UavI6Dqk1p0Jd6UUKeLOVF47MQGozTlAZgWYiMqKEAjMAAGNdU6XbuSxJhdPc2+ZDfTp3TtDBzCIKAJCkvKEuLdx7h+YcuDfeQ0E89MyDhhuREe5gFh3MqSa345DassslY9SaM1V5HXQwQ8oINEkSBeYoocAMAMBY13jQzV+WegvNjQdHnsFsrbTvJclhYQUASHzjm7fJY4Mqat4qEXuQ/J7/F+npb/Q+7slgfvsdzNmd1dLyH7hzJCQl4wSU03lErdnlkqTW7HLlttPBDHeDP0nqSh+vTn9IWw43yx/kd8nbxSZ/AACMZaGg1HLYjcaQem+bDvbJYB7ifPHAiIwdT0p/+aj0wf+T5r8ndmMGAGAUFDVtkiT5Qu3Kbz+gltwZcR4RYsZaaf09UsgvXfnv4TnOCDOYdXwGc2nDamnfRmnpzb0n8ZFUcjpr5LEht4NZUmvOVJXXLpdxgrLD7H5H8mntCqjtSJUk6f6tHXqydZuspCyfV8umjdM5M4p07swiLS4rUJqX3tzh4P9NwBg13N3S2SkdSHItVW4XcmTRkztRSst0C8zeDElGMkNMeoy3f0fXxvvc24OvU2AGACS84qZNCnoyleZ0qahpMwXmZNawT+o46t6v2y6VLoxKBnNPFm/lSgrMSSqvw11Xt2a76+a27HJ5bFA5ndVqyymP59AQB93BkJ7fVqs39x7T1Z49UrrU7Runy06boHefPlnrK5v05t5j+vGzOyVJJXkZ+t57FujqRZPiPPKxjwIzAABjwcE3pJW/kt7/h/4LpabwyaZI57IxUkF5OJd52tD5y5Lb2RzpYPa3S7uede9Xvhn14SM+hnsyUuKEJIDkU9y0UYcnXqzJda+qqHmz9k+5Pt5DQqxUruy9f2hluMDsdx+PNIM5fPLdF2hTpr8h/P5vSqd/KGrDxdiRO6DA3JrtnkjI6zhIgTnF7Kpt1aMbDqupI6Czpo/XDRkZ0gHp6nMWqSujWNcvKdP1S8okSUfburVy3zH9+qW9+tw963T1wlJ99/oFmpCXGd9vYgyjwAwACY5u9ySx5g/S9selqrekqef1Hm8Mb0JS2KerZtxU93j+lKHzl6X+m/wdWe9eSjrnKmn3c1J3q5SRF/3vAwCAUZDdWaPs7jrVFy5RRneDm8OM5HVopZRZ4F7FVblKOvMzvREZw+xgdgZ0MOd2hruXcyb0L2AjqWS3VcrvydQDO7pV17ZPU3weXS6p7uB2bepeqNKCTIqGSa6jO6gnN1dr/aEmleRm6LMXztDUohyV7m6XJHX7Co77muLcDF23eLKuXFCqO17Zp5+/uFtv7D2mf7luvt57RpmMMaP9bYx5FJgBAIg3x5H2rXDv713ev8DcVOlGYBRM6T1WWCEdXiuVn6WgjNbubxj0bae2BVUcDGjt/gbN37dSeRPmS2fdKu16Rjq0Wpp1WQy/KQAAYqeoaaMk6ei4JcrqrtO8/X+SJ+SX402P88gQE5WrpPKz3QLzoXAxOBKRMdwM5gEdzHkdlXJMmjzLPim9/J9SZ6OUNS7qQ8foa+4I6MG1h3T/6kP6ZvM2eVSitZVNKs3P1Jb2TLXZTHVU79T9h9yTDLMm5KpsXJYunF1M4TDJrNp3TD97cbc6/UFdMrdEF8+dIF84UznT36BuX77sCX6G+LwefeGSWbpyQam+8fAmfe3BjXp6S7V++qElys8c3s+eVEFSNQAA8Va7RWqvdzuO9y7v/1zTQSm/rH93TuFUdxHU2dizYc1grDwy1lFGd4PyOqukxR+Sys9yC9Z06gAAEpibv5yhprw5aihYKK8NalzrzngPC7HQ0SAd3ekWmCvOcU++txzp08E83L45E/5ft4M5r+OQ2rLKpOkXuk8fWh3lgWO0BUKO/vjGAV38kxX6tye3q6UroHnpx2TGT9c/Xztfn7t4lv7hynny50/TZRPbdfuls3XF/ImqbenSLXet1lX//aoeXHNI3cHQyT8MY96fVx7UR3+/Slk+r75wySxdPr+0p7gsSRn+RnWnjx/We82akKsHPnuu/una0/TSznrd8MvXta++LVZDT0h0MAMJZHrVo5pa/YxeWvaroTf1ApB49r7o3p7xcWnt3e5CKjs82Wk82D8eQ+rdhObY3p4NawZjjUfGhlTcvElWkln0ATcWo3TR0DnMO5+WMvKlaeef0reEscM4QV207kvSuNulOVfGezgAEBXFTZvUUDBfjsenYwWLJElFzZt1rHBRnEeGqIsUfivOkXxZ7v3KlVJ2kSTJetK0q6ZVy3fUaVdta8+XHT3obspV6AtqSUG75uV2yDEeeawjj+NXTme1jhSfr/zJZ7hd0JVv8nsyQVlrtWJnnX7w5HbtrW/XeTOL9I/XnqaNlY2a9Fy1do67RGl9CottOeUqbNml0oJMlRZk6p2zipWdkabfvbJPX39ok/53+R794MaFumB2SRy/K7xd/qCjf318q+5dValL5pbo41mvK7t5o9Tc/3WFrTtlbEgzKx90D5z9tRO+r9dj9JkLZmhhWYE+f886Xf/L1/W/Ny3VxXMnxOg7SSxUqIBEYR0t3HuHJhyneSMAACAASURBVB99XRMa1sZ7NACiae9yacICaclHJFlp30u9zzVV9m7wFxF5fGyvnBNkMFvjlbGOips2qyVnulTgblqhinOlqjVS0N//C/zt0sOfkR77gmTtKX9bGBtKj72pyfWvScHueA8FAKLC4wQ0vmWbjhUuliR1ZE5UZ0axxpPDnJwOrXQ38pt8hlS6WPJlK1S5UusO1EuSPnffZl3536/oP5/ZodX7G7T2YKPWHmzUrrYs7WzL0tN14/T9XRX6zIbZ8ts0HepIk6f1iIysu/FberY0eQlXdyWo/Ufb9fG7VutTd6+RtdLvP75M93zmbC2YXKCsrjp5Hb/asvtv5teaPVW5nYdlHDdmJc3r0fvfMUXPfPkC/eGTZyrNY/SxO1frq3/ZoGNtzJ8SydG2bt38+1W6d1Wlbrtopn5/y5nK9jqDvtYX7FDAmz3izzhnRpEe+8L5KivM0qfufku/e2WfLGsnOpiBRDGx4S3ldbgZUdMPP6a6ojPjPCIAI3LwDXcTmeJZ/Y/7290FzdmfdRdOmQVuR/PC97oFwdbq3o7liMJp7m13s+wJLuuyxiMjq8xAow5PuFA921dUnCut+o1Us0masqz3C7b9TfK3uX8OvkEXc5KYUfWounyFypxzVbyHAkRFceN6Ta5/VZtmf0kiKzMljWvZIa/j19ECt8AsY3SsYKGKmjbHd2CIjcqV0qTTpfRsdfpDasqdr6a3XtAvuvN0V7o0taRAP3zXIl08d4JKC3o3a1v14H9JkrpCRptbc7S+OVeB1jRta8lUa0u7lqYZHUqbpnmS2x296rdSoEvyseFbIgg5Vne9tl8/eW6n0tM8+s675+vmc6b2i0CYe+D/JEl5bft7u1QlZXXVymODOm3fnb0RCWd/TcYYXTJ3gs6dUaRfrdijX7+8Vyt21ukfr52v97Gx25i3rrJRX7xnnY61+/XzDy/R9UvCzTXWakrdCrXkTFNL7oye16eF2hVMmzLEu51Y+fhsPfL58/QPD27UD57ari1HmvXvNy5STkbqlllT9zsfA6y1qmrs1KaqZm063KQth5t1rM2vkGMVslbNHQFZSfmZPk0qyNSk8OUbE/Mz+/3QRGqYeehh+dPydKTkAlXUPK8187+tUNrIz7YBiIP2Y9Kf3yeNnynd9mr/gsiB16WQX5p5qZshOP0iae8Kt4O4uUqSPb6DOXu85MuRAu09G9YMJvJcyPjUkDdPMyNPVJzr3h58o3+Bef2fe/Od1/+ZAnMSSPc3a0rtCu2p+KDmprHxFRJLdzCkupZuVTd3qfJYu/KzfMrLSNOZW3+gca07VVN0juqKzor3MDEKrLWqaenS3ro21bV26/KWlyVJP9uRr8otu5SR5pH1lOujHS9pX9URHZxVpPJx2fJ4KAYlvGC3dHidus/4pH6/Yo/uem2/PtE9RZ9PW6evXjBRWiV967pF0qSKId8i02t1ZmGbzixsU+YO6Z35jepqr9SOYLlu275AH3xkk748ZakmhvxS9Qa32IwxbVdtq77+0CZtPNSkd502UT+4caEm5h9/YiDT3yhJ6hrQkBF5nOlvGDSDN9Pn1VevmKt3nz5Z33pks/7hwY16ZF2VfnDjIk0vzonBd4RTYa3Vna/t1w+f3qHSgkw9dNt5WjSlp7VGeR2VKjv6msa3bNemWZ9312LWvu0O5ojs9DT98iNn6Jcr9uinz+/S5sPN+tVHz9C80vxofFsJhwLzKGvtCmj5jjo9vblGK/cfU1OHuzGBz2s0rzRf5eOzleYx8niMDjV0yEhq7Aho7cFG+UNuW7/HSFOLcjR/Ur4WTE7Nf7ipJt3fpPKaF7Sn4gOqLL1c06qfUnntizpQ9u54Dw3AcKz6jRTokGo3S3tekGZf3vvc3uXujugV57mPZ10mbf+bdHRXuMCs4zOYjXG7muu2nSSD2S0wN+bPk+PN6H0ib6I0fobbEXT+7e6xhn3SwdekS/9JajokbX5QuuZHbmZzXy3V7vjO/IzkGbq4jbFhavVT8tqA9pVdr7nxHgxwAs2dAb22+6he3lWn7dWtqm7u0tFBLku+yLNRH0vfqZA8Ktr4a700b55mluQow8fPo2TT0O7XC9tr9fqeo3pj7zHVt7r/HnLSvfq0d6tqTZFa0yeqNMerrkBIr7RU6KOStq19WRe9dUyTCjJ19cJJumZRqc6oGEexORFYe9xVCW0H1io31K1vrs7WX7t36uK5Jbpi1vXyvvioFoa2uy/y+AZ5syE+wnhU4PVrhvaqctwSXeY06eF1Xj3/ll9rMqSG7S9rPAXmMasrENIdr+zT/y7frbxMn/7npqV69+JJQ3YWZ/ob5BiP/L7+dZOeAnN3g5pzh/682RPz9MBnz9V9b1Xqh0/t0JX//Ypuv3SWbr1wptLTaPobC5o7Avr6Qxv13LZaXT5/on7y/tNVkN3/Z8Kko2/IyijLf0zjWnepMX+uvKEuGVkFT7FpzxijL146W++YOl63379e1//idX3v+gX64LLylOt4p8A8Clq6AnphW62e2lyjV3bXyx90NCEvQ1fMn6jTywu1uKxQc0pzlZHWf2J876rKnvuOtWps96u6uUuHmzq1o6ZFT26u1pObq/X0lhpdtbBU7148WRVFdLQmo+mHH5fXBrR3ynvVlDdbbVllmn74cQrMwFgRCrobw0w97/iia3ertPq30uwrpdqt0qs/HVBgflGaen7v5ZgzLnFv97zYu5HNwA7myLG6bbI6UQezO/E9OtiGRxXnuhv6RRZzG+6VZKTTP+LuzL7uj9LWv7obD/a8oZUe+7xbFDce6ay/O8l/GMTbjKpH1Zg3V40Fp8V7KMBx9tS16dmtNXppZ53WVTYp5FjlZaZpSXmhFkzO16SCLE0qyNTEgkwt316rls6gbj3wIx3zF+kJ3xW6pfs+fXv1K7pP01RRlK05E/O0kOaLhOYPOlq+o04Pr6vSih11CjpWxbnpOn9WsSY1rtXCvHaVZAR1+q4tas+apH8rX9Pztd6gX9opfX3CGm0/9yYt31GnP686qLte36+J+Rm6euEk3bC0TKdPKUi5RX9COPim9OAt0g2/kma9S23dQd39+n51v3KPvibJN+08PXHFWVpYViB1zpFeNNKB19yv9Y6kwOxVTme1vI5fobwp+nRBrf7t1vfrzlf3a9+qydr/+jN6uuUq3X7pbHdtHeyWnvyatOBGtwkAcWGt1bNba/SDp7brUEOnrl08Sd97zwIV5Wac8Osy/A3q9o1z5619BNJyFfL4lBHucD4Rj8foo2dP1btOm6jvPr5VP3lul/628Yj+472L9I6pQ0fVIfY2HGrSF+9dp5rmLv3Ttafp0++cfvzP99ptGte2W1UlF6i4abMmHX1djXlz5Au1S5IC3uh0pJ87s0hP3X6BvvKXDfrGw5u1cl+Dvn/DQuWmUGRG6nyno8wfdPTyrno9uv6wnt9eK3/Q0aSCTN189tRhn0XvmxHUT577p7rLp9VNedrmKdSPn92pHz+7U++YOk43Li3TtYsmaVwOl8ImmpBjVdnQoZ01rXptd706AyF1+oP6Ud392uGdo59tTlemr0o+z8V637F7tWHrVmWnezVrQq5mTchVJt07Sa+9O6jq5i41dfRuznbwmPvL0ef1KD/Lp5x0Lwun0bb8e9LrP5cu/rZ08Tf6P7fmD1JXs3TRN6Sq1dIz33Q7hyvOcTuFj+6Szril9/XjpkpFs9wibulCtysnf/Lxnxnuaj5RB3Nj3hylBTvUnDPj+CcrzpE23ON+ftEsacN97sKpoMz9vOK50vp7+heYtz/ujitrvLT8++5iK6f4+Pc+8JpUcpqUU3Si/2qIgaDjqLUrqJbOgDIbd6ioZZseLP6C3th7VP5gSB6Pkc/rUWlBpqYUZmlyYVZKZ8Vh9NW1dOlvG4/o0Q2HteVwiyRpYVm+PnfRTF00t0RLywuVNkgU3OHGTo1v2qLTdmzQurlfU3b5jQqseEw/nrRCP8r5unbXterZrTV6dmuNlu+s0w1LynTd4kknLT4g/qy12ljVrEfWVelvG4+oqSOgkrwMfeqd03X9ksmaPylfxhitenC5JCkt2KbMQJNqx/ffjySUlqXO9PGaGqrU0rMr9JGzK3quHn1qc7XuW12pu984oLkT8/SBZVN049Iy/n2MFYFOd4Phtlo5j31Jd51+v375Rq0aOwJ6ZPx++b0V+tEn+5yczyqUJpwm1W1zH3uG/3vMGo9yumslSa3hjd8m5GXqW9ecps6OS1S6/XF9YWOVHl1/WB9YNkXf8D2gwvX/J+14Uvr8SvcqMIyqbUda9L0ntmrlvgbNmZirP3/6bL1z9iDzz5Zqd8PGzN5ohEx/o7rSxx3/WmPUlT5emf5jwx7HxPxM/eqj79AL22r1L49t0ft/86bec/pkfenSWZo1Ie/kb4CoqW7u1E+e3aVH1ldpckGWHrjtXJ1RMcjfsyS98b8KGZ9qx5+toDdH02qeUW54bytJp9zB3FdJXob++Kmz9KsVe/SzF3bptT1H9dXL5+gD75gy6Nwm2bCiiKJgyNHqAw16anO1nthUraaOgIpy0vWRsyr0niWTtWRKYVQvzZqUGdD1pQ369w+cr8NNnXpsw2H9dd1h/dOjW/Tdx7fqojkTdO3iUl06b6IKsoZ/Vhejwx90tL26ResrG7XpcLN21bZqd22buoO9O5waSef49qjCW6kfpn1enYGQGjv8uitwtj5g7lHR3kf15V290SkV47M1e2KeFpUVaGlFoU4vL1R+Jn/3iSbkWO2rb9PWIy3aeqRZO2vbVNPcqermLrV2BZWmoN7lWadDdoK22qly/6X08nqMCrJ8ys/0qTg3PZzhnqXWroDy+Pfw9mx+SGqvl86+7fgNpfaucIvL2UXSyz+UZlzUm90X7Jbe/KU0/UJpyjukCfOkl38kvfYz6SN/kfatcF83sCNm5mXSuj9JaRlSwZR+XdGtXQEdPNYhT9c4zZd0pDtT/7N/krpCHnU5HnWGPApaoyyPoyzvFGV5Fyq70lFeWlAH3zqkacU5mlaUrZKKc91/OZVvulEcLVXSFd93P8QYaelHpef/RTq6WyqeLfk7pGe/LU1cKL33d9JvL5Be+I50/S/7j33DfdKjt0kT5kufeqbfJL+HtW7udBoL+7fLWqsjzV3adqRZh5u6dKSpU9XNnWrpCva85h/THlDA69V/VC1SQ5U7NxlMYbZPU8dna/7kAi0sy9fCyQWaW5rHSUtETUO7X89vq9ETm6r1+p6jcqy0eEqB/vm6+bpu8aRBczMHM3//XfKn5WlP+fsV9OVqT8UHNP/An/W+RV9V+8LZaurwa1NVs7rr9mjZM5/Tr566UHtnflzXLZ6sy0+beNwls4iv6uZO/XX9YT28tkp769uVkebRFQtK9b4zyvTOWcVDLsbzOg5Lktqyy457rj1rsvLbD/a+NtOn65eU6folZWrtCuiJTdV6YM0h/duT2/XDp3fostMm6IYlZbp47gRlpfMzL25W/LvUsFfPlH5Wl1ffocyXvqvTZ35TX7lstk6//3Zp+ruO/5rys3sLzCPpYA5f+dXtK5Df13+OkjXzfGnLPXrt01P0i81p2rr6ReV5f6ENWWdpkX+jPI/fLnPT/WwuOkq2HmnWH984oIfWVqkgy6fvX79AN51VMfjPhtqt0l1Xu80Nn3pOyi2RrFWmv0Gt2YPnc3elj1d2V92Ix/Wu+RN1zswi/WL5Hv3pzQP628YjunbRJH3p0tmaW0qhOZbauoP67ct79btX98lxpFsvnKHPXzxLBWt/Kf31bunD90gTF/R+QXOVtPkB1Y87Q8G0bNWPW6qy+pc1+ejrqh+3VJJOKYN5MF6P0Zcum613zi7Wvz25Xd96ZLPuem2/vnXNPF0yd0JSN4JRYD5FHf6gXtl1VM9tq9HyHXVq6ggo0+fRFfNLdePSMr1zdvGobMhXVpilz188S5+7aKa2HmnRo+sP6/FNR/TC9lqleYzOm1WsqxaU6l3zJ2hCHrvijjZrraqbu7S+sknrKxu1/lCTNh9ulj9cTC7Jy9C80jx97JypmlOap3mleap/9Q/K9jqadeQxhVrSddmsfF3iXd/znq37y/Wl4At676d+rF117W6Buq5VO2ta9cL22p6r3mdPyNXS8nFaWlGopRXjNGtCrrxk0I0Z3cGQdtW0aeuRZm050qytR1q0o7pVnYGQJCk9zaM5E3M1vThH580s1pRcq+t2fkulta+4X581Qduyz9TugnO1JftcNfg9aukMqDn8Z1t1i9YcdC/9uuPVfZpalK0Fk/M1f1K+5k/O1/xJBZqYn5HUv+hOqrNJWvkrad617g7lA626Q3r66+79pkPSlT/oXVi0H5X+epvb7XvL49Kdl0sP/527kV9WobTxPqmtRrrxN+7r03Okcz4nrfiBOxHe86KUN0kqmdf/M2deKq3+rZzdz6t+3FLd8cQ27ahp0c6aVh1tc7vXr/R067fpUmMwXXvas5TpcZTldZSfFlKasepyPGoJelXb7VNnyKvWoFePPryp5yOy0z16w1ugPa88pXGZRhXphWotv1w9F/ot/rD0wnfdzf4u/6706n9JzYek994hTZwvnfN56Y3/kc74hFQe7iLb/4r0ty9JpYukuu3SA7dIH32w/8Kvo0F64OPuovBDf3ZjRQayVqre6E4QR7BoTFbWule3bDncoi1HmrXlsPunMbyPg5E0IT9DM0tyNT4nXQVZPhVmGn1ky0pVFV6kLyw+R1ZW7z1jiqy16go6qmnuVFVjpw43depwY6f2H23v6fCTpDSP0exw3MDCMrfwfNqkfGWnM3XE8Bxt69azW2v09OYavbnvmEKOVcX4bH3xklm6fmmZZpacIPRyEHntB1Ve84K2zfi0gj73a3dMvVlzDtyjeQf+pLXzv6XC7HRdVR7QDUe/L+s9rAWe/9NPD6frH3ZepjSP0fmzinXtokm6fP5Ejcv0SP5WKWuIjifExIGj7XppZ52e316rN/Yek7XSmdPG6e8umKFrFk8aVmNEbmeVHHnUnjnpuOfasspU3LzFjXqKXP3T0SAdWqW82VfoprMqdNNZFdpd26oH11bpkXVVenZrrbLTvbrstIm6bvEkXTSnhBNs0eY40lu/d/ejOPeL7qbGcqMk17/5oi544xe6P3iJvnf4Ev1uYpNuPvoX3XzxV6ScBqnjqFRx9vHvWXGOtPYP7v0RZjBLvd3Lx72npOJj6/SvV31Iwf13q71jor4S/JIu7XpW/7zrz3rpgZ9r1hW3asq4PkWp5ip33jPrXRSfT1FXIKQnNlXrnlUHtb6ySZk+j245b5q+fNmcoU8SNh1yN9P2ZbpdzPd+0J2XB7vkdfyDdzBL6k4fr3EtOyXr9EZodDW7e6OcpAkiNyNN37x6nm69cIbufG2f/vjGQT2xqVpXLSjVB8+covNnFR8XgYq3r7pyj57c06XfvFGjo23des/pk/X1K+eqfHy2tPLXbtOLJ0360w3SJ5+Wime5X7jy15K1qi5y/7/teHyqHX+WptS/3JPDHUyLzaaNSyvG6aHbztWzW2v0n8/s1KfuXqPzZhbpE+dN04VJ+nsmqqsEY8xVkn4uySvp99baH0bz/ceCo23dWnewUWsrG7XuYKM2VrlFwoIsny47bYKumF+qC+cUR3UBlt+2X5OPvqpAWp4OTbhE/vTCIV9rjAkvBAv07WtO04aqJj27pUZPb6nRt/+6Wd/+qzSjJEdnTRuvM6eN11nTx2vKuKzULi5FmbVWh5s6tbu2TTtqWrXxUJPWH2pUbYu7MUlGmkeLygp0y7lTtbTCLfxOKsg67n1WpTnyhro0vnmbjhUukuPtH3lSX3C6ZlQ/odmh3Zq9+B26Vr2T7JaugPu54YL2s9tq9Jc17mUguRlpOr28QEvKCzWvNF9zS/M0vThnVE6EpDJrrerburWjulU7atwi8vaaVu2ubVXQsZKkvIw0nTY5Xx8+q1wLJxdoQVm+Zpbk9v7ddDZJ931Yql0pXfWfUmaBMnY/p/m7XtDSY0/q6uxyrV7wz6qdeW6/z23pCqq6uVMT8jK0rbpFW4+06KnNNT2vKcpJDxeb83tupxfnjJ3LeEKBoYuMoUC4SDtRmrz0+Ofb6qSX/sMt5p53u1tE7vvzrnKV9PBnpOZKNxv58u+5BeDIa978pdu1O+86Kb9MWvlLyQlIV//Iff6xL0qdDdLND7ljeN+d0l1XSk98RXrf793O5klLpBkX937mmZ9xj7/yE2nfS7Jzr+n5mbG9pkXbq1t14Ij0sPUqPdSt5TVZuqf2oOZOzNOl8yZoenGuphVla44dJz3yM83L79b/TN138v+MViq77DYdONahg8fatf9ou3ZvW6hpzauV39Sme0KX6F//4xVNyMvQvEn5Oq00T5+YeIGK198rZ+GHlfHG/0iLP9RbEL7o/7kbAT71NenvVkjH9kj33ywVzZRueULa8YR7uesTX5He87/uf9Oje9xJf/MhKa9U+tP17nOnf7h3oE2V0uNfdrOpSxdL1//i+MJ/W537d2M8bqE7t+T4b7iz0S1SV5w79CIhFHS7w8fQ78CQY7X/aLt70ulws7YcbtGuI/Vq6rIKySuf12jOxDxdMb9UC6cUqL16r66q/oWKW3doW8kntbfi/bLGq7LaFcoJNKiy/MaerrzxfaKzygqz9I4Be0daa1XV2Ol+7hH3s5fvqNODa93NJj1GmlmS2zPPWDjZ/ZnBlREnlwpz5LrWLr21v1FvHWjQqv0N2lHTImul6cU5uu2iGbp64SQtmJz/tuec8/bfLcfj085pH+051plVqgOTr9XMQ49oy6zb5HH8umz1ZySnReYzL0iv/lRf3XGn3n/ZbN0TuFhPbq7W/3t4k87+6w79Z9bdmuLU/H/27jw+rrre//jrM5N9a5qke5u20JWWpaWALGUtmxQVBWS5sl7RnyAXRPCiiNUrKAUE8bpwtQoqCiiogAoIilChQKELXWihK93TNE2zZ5bv74/vSTqZTtq0mTppeD8fj3kkOcv3fM/JyeQzn/NdWDX+85SfeQtlfZJanTnnH4SVVu462al0WVVdC4vW1/Lye1W8tKyKVVv9sF4HVRRy/amj+eTkIQwv37sP90WN62jMH4hLkVRsyA+Syuvf9gnmZc/C09dD/Wbf4vXjP4KKUYweUMxXPzqeW84cyxurtvHMOxt5dtEmnl6wgYKcMEcO7+s/L40s44hhpb0yEZBW0VbYON8/YM5O+lxTX+V7Nr3/AgCNi//M74d/k2fWGAvXVPHHrK+yNdSXllNmMOfYQyjNOgl+/IZ/YH3stb6MYSkm3RuWkHTeyzGYgdStWssOgsL+fiizqmVk1ayg5LI/8fzwE3n2ncks/vN8Ji/5LmfN60th/xGcMrYfF4Zf4uC378Ra62D0GXDuA1Cy68MP6dzG2ibeWLWNOSt9b/DapggH9Svk9umH8KnJQ+mTF/K9Cd9/wceMB5+6M3Zr3Aa//qTvaXfVX30c+eglfjzvE74EQEsnCebmnDJCxMmN1BKONcOT18CiJ6GwH5x9F4w/d9cYMdIM21b4oeBCIcoKc7j5zHF8dupB/Pxfq3noX6t4dvEmCnPCnDKuP2dOGMgp4/p/qMbh3Wt1m/z/2+EnQNbOWHVTbTPPzl9F8ev38rGGJzmLvlSXfZEzLrucSW3DYbz1sB9+cNx0OOVr8PC58MuP+SRzXgm89RBM/BSt8Z05tM1lRzFo66sMqHkTSH8L5kRmxlkTB3Ha+AH85vW1/ODv73HNr96iKDeLaeP7c85hg5k6uqLX/I8x51x6CjILA8uB04F1wJvAxc65Jam2nzJlips7d26qVRkVicXZ3hihprGV9TVNrK5uYE11I6urG1hZ1cDabY0AZId9IvfIyr6cOr4/R40oS3uCbsGvb6Vy8wv0rVtOa1YxWbEmwD992VhxArFwLuFYE1PGj/LjYtasgdHT/Bvh4Mk73wxbG3Eb3mbzsjeYv6OI52oreXGdtXejLS/MYUxFNkcXbaWyTzZ9hk9kYEUFfQtzKC/M2XmzR1uhYYv/p5vVyfjOsQhYGEI9JDG1H0RjfnzLLXUtbKhtYlNtMxu3N7F+ezMrqup5f0s99S07uygPLy9g0rDS9mTyuIElqWecjcdgx3pobYB4lEXPP0Rp3XKGVr3MopFX05DUDTAca2bysnsJHXU1nPFtPy7qO7/3XwcdDhM/5e+F/FKcc6yubvStp9fUMO+DGpZuqicWJDazw8ZBFX4c5yF989uHVBhcmseAkjz65Gf3mje9/aWpNUZ1QwvbGlqprm9mY20ra7c18sG2Rj6oaWTttka2N0YARzk7KCguZeSgCiYM9t3RJwwuobKsgFBrHWx6x98HAyf61q1mPqH2q09C1bu+BenET7Yf+/XH76ZP/UqGb3qO/NZqqvocxtqBp+/yNPaYC25q/76uoZH3165j4bYsFm+sY8nGHSzfVE9rzLeqH5BVzwklWyjsU05W/3EMrihlaN8CKopyKCvMoSK+jeLqeVhTjU/s9p/Q3hoF8C2HVr0CG9724/uOPMkPs9D2vlS3GZY/618uDmPOhDFn7Wxt1FANi57wLYA3zPNDTBx+sb+nc4t864K3fwlzfuKHdgA/Ud6x1/lyYq2+VfIr34Nok08Ob18DI6b6v5eBh8Ls78E/vuOHoDjnezB3Fiz7i/9w8Ikfw7xfwQsz4JCP+8RxKAuevw1e+1848ko/9t9fb4GzvuuT0m1evsePTzz+XFj6NE3n/YKNg8+gqq6FqvoWqupaGLfwLo7d/FsAboxdzx8iOz84De2bz7iBJdxefQuVO96i+phbKD3zq7v2PGjaDncNp6Z4DMsrL6IrEu8BYGcCHZh39lO81TqMpcFDkPc213Oye53/y7mP9a6CUmvgpgE/o7R/JZXlBQwozuOQ6r8x/tUbaDjuFgqWPIpFcoGAhgAAIABJREFUmuE/X/DjSAP8/dvw8t1w2u0w9Gh47D98QvfTj0C/sb4l8+pXYOqX4ZSvwtyf+2vuHBx1NSx8zLcSP+EGOPEW3/Lp1Qfg9Qf98CM4yMr31/+4L/pW41uW+vULH/PbF/bzv68pV/kPfLEorHzJ31vvPgNF/X1r7cMv8slx8B9W3v0zLH3K38ujT4fxH/P3upmv36aFPnGx5l++BfrYszoGxo3b/Pvxe3/zDyVGTcONmkZ9Vl+q61uprq0jvmo2RWuep2jbIpaEx/OXyGSe3TGclpgRIs4p2Yu4ouBVPtI6h2h2MQ1jP0XJsZeRM/hQ/0HqX/cTf/leHEZTXj+KmjbQkDeANQPPYmD16xQ3fsC8sTe0f5je5fffBc45Nu1o9q2ng5bTizbUtj80BRjUJ4/KsoKdr/IC+hXnUl6YS1lhDn0LsnvOA6vdMLO3nHNT9kO5vSJGds5R1xKlpqGVD7Y1saq6gdVb/cOqFVX1rKn2MXJ+dpCcG1nG6YcMYNzA4u43ZKjbTPx746kqPZzVg6d3WJXXUsXh7/+YTWVH0ad+FTnRHYSv+isMneLfJx691CclznsQN+o0av50K2XLH6cqPIAFkWFMC81lRXwQPyz4AjsGHcv40hgnNf+DQzb8noLa93E5hXDohdiUq2DQYf6grQ2+x8Z7f/Ox8UEnw6jTd773gY+JNy+GzYv8//LBk6AgaTIo56BuI7TUQ98Ru4mve97DsDZtD7K37Ghm844WNu1o5r0tdSzdWMeSDTvYWr+zgcVxB5dz8tj+nDy2n08qN++AqmW+1WHFmI4PA5tr/Xvskj/C+regqD/bGlppze5Dv5p5VPWdxJpBZ+1SH4tHmLL0LkJHXeXfJxf8xscoR1zi/x9Fm+G0b/ghr0IhH4+sfAlWvUw8t4QlBUfyZPUIXl3byLLNdTgHOeEQEwYVMr7MGNBvACP6FTKivJAhffMpzT8w3t92q6Eaalb7ORiKBnS8zyLNfv6I1bP9PT3ieP/gNieIMbd/4BM4bz/shxLLK8VN+g8aD7ucbblDqV3yAge98iWyW2t5uPgaVteFuDX+IC1k80DRlzi9dAPHr/8ZsYseJTzu7J3HXfUKPDwdsgt9fHnL6l0/XzoH9471Dw/++wOfTEry+u/u3WXZhJWzKGpaz8KDP0dTnh9PucP/x8c+4++Jlh1w9DXw0bt3rtu2iviPjmNjyaHclXM9n9xwNyeH5vO6O4R3C4/i4qZHiYdzef/I28mbfBEVBSFKNswmtPgJP7Fy2Ug49AKY8El/vdu0NvoEW+M230usZEiP/HvvVCziH/onT3Tdtm7rclzNGmoLR/CBG8j6HS2sq2ni3U11rFnxLmPrXuPk0HzioVw2DTyJcSd+iqMOGeOHclv6tB9CpWqpb1kcbYbK4+C0r/tGHL/8uG9Q8JknYcQJ/phvPewfLJUOh+1rWDDqCzTn7jpmc3HDGg5Z/TBNOeXkt1ZDTpGPB9fO8e/dY86Gj870Dxq3vu9bzM//jW9c0nckHHkFHHHpzoYOdZuJvPcC1QufZ1Wd8buaMTzXOIaWUAEjKwoZO7CYw8tiTM5aSWV4K/lDDqdwxGQsJynBWV8FWxZDdoEfci43qcdPPO4bx9Rt8p+zUs2H0lIHVcv9mNTlo3Z9CBOL+GHw6jb4998+wzrec/G4bzyycYF/yDp4Usfxx53z87isnu0/Yw2e7D+LtV0L5/w1fPfPsPw5X8dx02HsR3dus24u7vWfwOI/YvEIzXn9mN//PP4YPoM3tmYzuHoO3876OSNCm1nW/2xGRleSs20ZHPIJ/wBg9WzfUGjUaXDRb/z/kE3vwEPn+PliRp/hJ1r//L94/ZXnOpz+8I3PMnDbG8RCOcwd/9/ty/clVt4bkVic11ZU8+eFG3luySa2N0YoyAkzflAJYwYUM2ZAEWMHFHNw/yL6FuSkzhtlQFdj5HQmmI8FZjjnzgx+vhXAOfedVNtnKnh+8J8reHttDS3ROC2ROC3RGM2ROHUtEbY3RKhLSAy2KcrNYnh5ASMqCjlsSB+OHN6XiUP67L+EW+M2eOm7xN/4KS6UxfqKE9hUfgzZsUaGbf47FbXvEAkX0pjXn+KGNYSIQ/Fg/8T1gzkQj0LJUP8Gu3WZ/yOLdzwvVzqcHRWTqKlvJL9mGRUtHxBm59i/a+L9WeaGUeVKGR6u4qDQZga4KsLEiRFia3gAm7OHUJ09mCIaKI9uoSyymZLoVuIWYkd2f7bnDKQ2ZyDRUC7FkSpKIlspbq0iN95AfVYZdTn9qMvuT0N2X/JjOyiM1FAUqaYgup1IKI+G7DLqs8tpzC4DHAXRWgoiNeRHawm5CE1ZpTRmldIQ7kNrOJ/caD35sTryYnXkxBppDeXTFC4OXkWEXJS8WAM58UZyY404jJZQAc2hAppDhUQti+x4C1nxZsKxZiwepYVsGl0OjfFs6mJZtETjRGJxDDD8347hMKAgJ+y7JRfkUFqYS9/CXPrmhciLNUBLrQ+io80+IMst8UFQdoHvUlW9ArathFgLyRryBrLooM+mDDJGffAE5U2rfeDVXOu7eR58qg/Ea1ZDOMeP5VpQ5lsM1q73SWzniJcMpilvINVZ/dkcKaCuoYGmpgbirc2EXIRWsmlx2TSTQwvZWChEblaY3KwQ2WEjbEYoZIQs4fuQETYImZFtcXJcM9lx/8qKR4iGcoiE8omEcomEcsl2reTEGsiLNZIbayBuIVrDhbQEL4C8WB15sXryonVkuQgt4UKaw8W0ZBXRGiogN1ZPfrSW/GgtedE6IqE8GrNKaMrqQ2O4D2EXoSC6ncJoDQWR7YSI0ZBV6l/hUiKhfAqj2yiM1lAc2UZerI6mcDH1WX2pyyqjLlxKTqyBkkgVJZFq+sSqiRGi2sqoooyNri/NUaOf28pg28pgq6aYJqrow0ZXQU12f6L5FQwOb2dQbCN9mtYRjjUB5pOpZQf5D5Wt9f4f+LaklqiF/Xwrzm0rfJL507/y3e8StAXPFo8yeOsrDN76L2KhXKr7TMQRAvN36eCR42HbKqhZ5T8QuJgP1PqOgL4jiRUPomnLCsJbFpPfsrW9/IgLs8INZqmrJIcIk0LvM9i2dahDM7msyB5NdfYgxkSWMjDik74Ryybb+W78O7LKWF00ib6tGxnW6PMq23MGEidMWasfT3F9wXgasvtycO3rhImxMX80a4onMab2X5S1rKc1lMfq4iMZXjeP3Hgjq4sn83r/T9O3ZT1Hb3mc0tZNVOdWEnatlLZuYlnpVF4ceh01uYOZVPUUJ234KQXRWqrzhlHRvJbFZafzl8pbaMkqAuc4suoJTv/gB0RDOeTF6nmn7zSeGP51ooSJO3DxOGdsfJBTtj4CwOLCY3hgwB20xhytMf9/pbG5hW/X3cbk+CJWuMFMa5npfw8JKrO380L4erKI8cCkv9J/4BBGDyhi7MDind2DX7kXXvwWfPJncNgFu/z9A/DdSrZlD+K9ygtTr0+yS9C0/i346akw4FD4f7M7rIrE4qzavJ3Kh6eQ11LNHwdcy6+YzprqhvZhOsDxm+w7OC68hCaXw5XMYFXuWIpysyjKzSI/O8T1tTM5rvEfRAlTlT2EB4d+lx15QwiHjFyLcd7Gezmy+hl2ZFdQEtnKiuKjeLryv6nNG0RedAdnrHuASdV/oTp3KIXR7eTGGljUdxr/HPxZjBgnb/gZE2pepClcTFX+QVTWLyBiOSwqP5NVJUczsfo5Rtf+i7iFWFVyDAMbl1EUqaYpXMy7ZafRp2UjI3a8SYg464oOJWK5DK+bR4gY23MGUpMzmOH1CwgRoyZ7IB8UTmB4w0L6RKqIY2zOG0lFyzqyXSvNoQKW5k+mMLqdUS1LCBGn1kqIEqbc1RB3xkI3kg2ughNC71BiTTS5HJYxnAm2imyiNGaVUt3vaAbWLiC7cbN/X5/4Kf8BetmzPlk96HAfK9R+wNaSCXww8HRas4op27GUys3PkxvZgQM2lX+EtQPP6Pz33w1b6pr9OPHra1m5tYEPtvkHaYmJ5zZmUJKX7e+JnDD52eH2r9lhIxwyskKh4Ksx8/zDMpKw2Y8J5gMiRv7Bi++xYF0tkVi8/dUajVPXEqW2McL2pkj7g+k2edkhRpQXMrKikEmVpRw9spwJg0vS3yvqhW/iZn+PBaOupSV318lDx6x9lL51y4lZFsuGX8ohVzywc2WkyfecWD3bx1+t9f6B1Ik30xrKZ80bf6L/y7fRp3kd74QnMCr6HvnWyvz4wTwRm8phtpJzw6+RZxGWhsfSEipgQmQR2URotjwawiWUR/0YnptzK1lXMJH+rWsY1PgeWa61Qz2rc4eyofAQHCHKm9dQ0byG3LhPzMcJU5M7hK35w9mR3Z+i6DZKWjbRp3UzRdFttIQKqM0dyI6cAezI8UnAgkgNhZEaCqI1hOOtNGX3pSGrL43ZfWkOF5MdbyI3Vk9urIGcWCNRy6E5XBTEUoU4jHA8QpZrJSveCjiilk3MsolYNhGyiTmIO4g7RyzugnvDEQ3ukaZIjNjOjxDk0kpFqI6hOY30z6qn2FoIFZSS36c/4aJ+Pv6tWeMfBtau3bmjhX0SZMAhPtm24u/+/a5kiH/I3FhN49p5vqVhvJVllRexvXhMyttl4oqfUti80Zc59Uv+AWVWju86/8wN/uH24MnBQ4B3/E75Zf7BQazFx8+Vx9JaPIy6zSsJbV9LccsmsohS7/L4wPXnA9eP9a6CHKL0DTdTGm6mT6gJQlk0hQrbr3M8nEsI3wMkbBAO+e99VN/29xR8sjDDESLsImTHW9pjaMMFsXMekVAejhB5sTryozvIi9WRG6unNZRPc1YJTVklNIdLyI03UNy6hZLWLRS3VhEixo7s/tTl+FcklOfvwaZVFEZr2q9dU7iYLXkj2ZpXSd+WDQxrWES2ayVOyN8vxIhaFmvzxtMQKmRCwxuA4+3co/ln+DgOb36dk2Kvk20x3o6P4ghbwSo3kFtCN8KAQxndv4ipfbdzxtJbya5a5JOSEz/le30le+ZG/+B51Om+p1gqj18GS/4EX9u0a8tpUieYx696iILmzbw17pb2z1cd/j++9iN47lYfo39+9s5kepu5P/d1C+fiLMS7E2/id6Gzea+qgeZNy/lKywNMCS3nzfgYDrYNlFk9tRTxevZRjHTrGR1dThxjee6h7MiuoLL1ffq3rvOf6QON4T5sKhhNdV4lubEGiiNbKYpUUxipoTWcT312efsrZtlkxVvIdi1kxVsIuTjR4LNWNJRL1HI6TAhtOMIuQla8lXC8NXifMqKhHGKhHKKWC7jg89UO8qK15MQaaQ4X0hjuQ2O4mKZQEQXRWkojmyht3USfaDUOY3t2P6qzBlAd7k9LPMTQ1hVURteQQ6T9+A0ul3ddJSvigzkiaxVj8O8DLSUjyHEtWN1G/zcx7Gj/2XnjAigf7RsijP2obwDy8t0+Lioe7B/SXfiwbxCS6J8z4R934IA3x38Vl2IiyKxoI5OW308kq5BNZUcz/MpZfu6QWMQPrfBS8G964GE+zxLK8r0hR0yFxX/wjQxC2T6RWbvW510ACir8/55IA/FQNuuKDmNDrJQhjUsY5jrOhxFxYd634azOPpiBVsOI6Er6xnd+zopjbM0Zwsa80UTD+QxoWU3/5lXkxJvat6nP6ktV/kFU5w6jJFJFv6aV9G3deZyYZVGdV0lV/sFELZsBje9R0byaLLfz99IULmZLwcFsyxtO3+Z1DGx81+cxEuzI6c/GwvHELYvKHW+3v3fECRPCD++4JW8EG/LHUtnwDmWtG4hjrM0/hKJINRXRTcQJsSznEEKxFsbG3qPe5fN47CTmxsdwQfifnBJeQIQs1uSOZVTLYlr7jCDn4w/4OW6irfDq9+Gfd/tkcmuDf+B16e98Ir3Nurn+wUNrvf/c/B9P7PJekNO6nSPe+wEt2aUsGPPF9uX7O8GcKBKL8+qKal5cupl3N9axfEtd0CBtp8KcMKUFwfB3Bdn86upjMjLcaSYSzOcDZznn/jP4+TPAMc656xK2uQa4JvhxLLAsLQfvvSqArXvcSj5MdE9IKrovJJnuCUlF98XuDXfOpRhvpXsUI6eN7l/pjO4N6YzuDUlF94V0RvdGal2KkdM5EEyqNHqH7LVz7v+A/0vjMXs1M5u7P1rSyIFL94SkovtCkumekFR0X2SMYuQ00P0rndG9IZ3RvSGp6L6Qzuje6J509l9bByROxToU2JDG8kVEREREDjSKkUVERESkV0tngvlNYLSZjTSzHOAi4Kk0li8iIiIicqBRjCwiIiIivVrahshwzkXN7DrgOSAM/Nw5tzhd5X9IqaukJNM9IanovpBkuickFd0XGaAYOW10/0pndG9IZ3RvSCq6L6Qzuje6IW2T/ImIiIiIiIiIiIjIh0s6h8gQERERERERERERkQ8RJZhFREREREREREREZJ8owdwDmdnPzWyLmS3KdF2kZzCzYWb2DzNbamaLzey/Ml0nySwzyzOzN8xsQXBPfDPTdZKewczCZjbPzJ7JdF2kZzCz1Wb2jpnNN7O5ma6PyN5QXCypKDaWzihGlj1RrCypKF7uPo3B3AOZ2YlAPfBL59zETNdHMs/MBgGDnHNvm1kx8BbwCefckgxXTTLEzAwodM7Vm1k2MBv4L+fcnAxXTTLMzL4ETAFKnHPTM10fyTwzWw1Mcc5tzXRdRPaW4mJJRbGxdEYxsuyJYmVJRfFy96kFcw/knHsZ2JbpekjP4Zzb6Jx7O/i+DlgKDMlsrSSTnFcf/JgdvPTE8EPOzIYC5wA/y3RdRETSQXGxpKLYWDqjGFl2R7GyyP6jBLPIAcbMRgCTgNczWxPJtKB713xgC/A355zuCbkfuAWIZ7oi0qM44Hkze8vMrsl0ZURE0kmxsSRTjCy7oVhZOqN4uZuUYBY5gJhZEfAEcINzbkem6yOZ5ZyLOeeOAIYCR5uZug5/iJnZdGCLc+6tTNdFepzjnXOTgbOBa4MhB0REDniKjSUVxciSimJl2QPFy92kBLPIASIYQ+wJ4BHn3JOZro/0HM657cBLwFkZropk1vHAx4Lxwx4FTjWzX2e2StITOOc2BF+3AH8Ajs5sjUREuk+xseyJYmRJolhZOqV4ufuUYBY5AASTVcwCljrnvpfp+kjmmVk/MysNvs8HpgHvZrZWkknOuVudc0OdcyOAi4C/O+f+I8PVkgwzs8JgAizMrBA4A1iU2VqJiHSPYmPpjGJk6YxiZemM4uX0UIK5BzKz3wKvAWPNbJ2ZXZ3pOknGHQ98Bv+UdX7w+mimKyUZNQj4h5ktBN7Ejy/3TIbrJCI9zwBgtpktAN4A/uycezbDdRLpMsXF0gnFxtIZxcgisrcUL6eBOacJVUVERERERERERERk76kFs4iIiIiIiIiIiIjsEyWYRURERERERERERGSfKMEsIiIiIiIiIiIiIvtECWYRERERERERERER2SdKMIuIiIiIiIiIiIjIPlGCWURERERERERERET2iRLMIiJ7YGYxM5uf8BphZieb2TPB+ivMLG5mhyXss8jMRiT8PMnMnJmdmVS2M7N7E37+spnNCL5/Lum4G8zs9d3U8yNm9nqw7dKEcq4ws6pg+RIz+2zSfn8ys9eSls0ws/UJ+1ycsO4hM1uVUK9XUxznXTO7setXWUREREQy6UCIeYM6/DZpWUUQg+aaWY6Z3W9mK8zsvSDOHZriHBeZ2dNmVhosD5nZA8Hyd8zsTTMbGazrY2a/DMpcEXzfJ1g3wsyaEmLmX5pZdsLxjjazl4K6vG1mfzazQ4N1ifF226s0uOa1ZjYviKnv2ctfpYjIv50SzCIie9bknDsi4bU6xTbrgK/tpoyLgdnB10QtwCfNrCJ5B+fcmW3HBI4HdgC37eYYDwPXBNtPBB5PWPdYsPxk4E4zGwAQBNWTgdK2IDrBfcE+HwceTAyWgZsTrsdxKY5zPPA1Mxu2m/qKiIiISM9xIMS8TwKnm1lBwrLzgaeccy3AnUAxMMY5Nxr4I/CkmVnSOU4EtgHXBss/DQwGDnPOHQqcB2wP1s0CVjrnDnbOHQysAn6WcPwVQd0PBYYCFwIE8fbjwFedc6Odc5OB7wAHJ+x7X9I1bzvmK865ScAkYLqZHd/J9RAR6RGUYBYRSY9ngAlmNjZ5RRDQng9cAZxhZnkJq6PA/wF7au37feAvzrm/7Wab/sBGAOdczDm3JHkD59wWYAUwPFj0KeBp4FHgolSFOufeAxqBvnuoY+I+1cD7wKCu7iMiIiIiPV5GY17n3A7gZeDchMUXAb8Nks5XAjc652LB9r/AJ7dPTVHca8CQ4PtBwEbnXDzYb51zrsbMRgFHAv+TsN+3gClmlpgoJjjmGwllXgc87Jx7NWGb2c65P+7hGiSW2QTMTyhTRKRHUoJZRGTP8hO6rf2hk23iwEzgqynWHQ+scs6tAF4CPpq0/ofApW1d7ZKZ2XnAFODWPdTzPmCZmf3BzD6XFNS3lXUQcBA++Qu+dclvg1dyS5O2fSYD7wXJ6TZ3J1yTR1LsUwnkAQv3UGcRERER6RkOlJj3twQNI8xsMDAG+AcwClgbJKETzQUmJB0rDJwGPBUsehw4Nzj3e81sUrD8EGB+W8Ia2hPJ81OUmQccAzwbLJoAvL2Hc7kx4Zr/I3mlmfUFRuOT6iIiPZYSzCIie5bYXfC83Wz3G+AjKYaauBjfQpjga4dEbhAE/xK4PrlAMxsCPABcEnT765Rz7lv4oPx54BJ2BrcAnzaz+fiA/HPOuW1Bt71RwGzn3HIgamYTE/a50cyWAa8DM5IOlzhExqVJx1kMrAS+75xr3l2dRURERKTHOCBiXnwr6hPMrAQ/HMXvg6SvAS7F9onL84OYuBooA/4W1G0dMBaf3I4DL5rZaV0s8+CEMtc651I2sDA/V8pSM/t+wuLEITJOSVg+1cwWApuAZ5xzm3Z3QUREMk0JZhGRNHHORYF7ga+0LQtaR3wKuN3MVgM/AM42s+Kk3e8HrgYKE/Y1/LjK30013EUndVjhnPsxvkXG4WZWHqx6LAhcj3HOtbVI+TR+2ItVQd1G0HGYjPucc2OD7X6ZqkV0Co855yYAU4F7zWxgV+otIiIiIgeGTMe8wbARz+LHSb4I34ACfA+94SmOORloK7cpGC95OJDDzjGYcc61OOf+6py7GT+W8yeAxcAkM2vPnQTfHw4sDRa1jcE8Cp94/1iwfHFw7LbyjwG+DqRswZ3kFefcYfhxnf+fmR3RhX1ERDJGCWYRkfR6CJgG9At+ngYscM4Nc86NcM4NB57AB6ztnHPb8F3zrk5Y/GWg2Tn3w64c2MzOSZjAZDQQY+fkJKlcDJwV1GsEfny5XcZhds49ie9aeHlX6hHs8xrwK+C/urqPiIiIiBwwHiJDMW/gt8CXgAHAnKDsBnyi+ntBwhszuwwoAP6eVI9afEvqL5tZtplNDobbaEsgHwascc69D8yj46SDtwFvB+sSy9wI/Dc7h/j4IXCFmSVOiJ04OeEeBb0Mv0NCMl9EpCdSgllEJI2cc6347n39g0UXA8lj2D2BH8Ii2b1A4sza3wbGJ4zLlnJstgSfwY/BPB+f3L00cby4RGY2AqgkCMiDuq8CdpjZMSl2+RbwpYTWG3cn1SsnxT53AVemaEUiIiIiIgewDMe84IeEG4zvPZc4hMWtQDOw3MzeAy4Azkvapu0c5gEL8A0s+gNPm9ki/BwiUeB/g02vBsaY2ftmtgI/5vPVyeUF/ggUmNnUYFiLTwPfCfZ9FT8J4v8mbH9j0nmPSFHmT4ATUwxJIiLSY1iK91kRERERERERERERkT1SC2YRERERERERERER2SdZma6AiIjsHTP7IXB80uLvO+d+kYn6iIiIiIikm2JeEZEDh4bIEBEREREREREREZF9oiEyRERERERERERERGSfKMEsIiIiIiIiIiIiIvtECWYRERERERERERER2SdKMIuIiIiIiIiIiIjIPlGCWURERERERERERET2iRLMIiIiIiIiIiIiIrJPlGAWERERERERERERkX2iBLOIiIiIiIiIiIiI7BMlmEVERERERERERERknyjBLCIiIiIiIiIiIiL7RAlmkQ8ZM/uJmX09TWVVmlm9mYWDn18ys/9MR9lBeX81s8vTVV5Cuflm9rSZ1ZrZ79Jd/l7UY4aZ/TpTxxcRERE5ECh+VfwqIiI9mxLMIr2Ima02syYzqzOz7Wb2qpl93sza/9adc593zv1PF8uatrttnHNrnXNFzrlYGuq+S7DqnDvbOfdwd8tO4XxgAFDunLtgP5R/QDOzk80sHnz4ans9Hazr8HsyM2dm7yTeY2b2bTN7KKnMwqCcv6Q43moz22xmhQnL/tPMXgq+/2pSXerNrCE49mVdPKeXzKzGzHKTlj8UlHN0wrJRZuaS9t3lg6eZjQj2Ta7bpxO2mWJmzwTH3m5mS8zsDjPrG6y/wsxiKcoYnHBtmoJlm4L6FnXlnEVERA4Eil+7TPHrbvSm+DXYZlTSsvZzCM7VmdkPk7aZbWZXpLguzsxuSbF8XYpjt8e9wTEjSedwS8J2zUnrjk0ox8xsjZktTHGM2Sn2/UOwbpqZre7kuvzazGakWJ4VnOOIhGVjzexxM9tqZjvMbLmZfT8hxp6W4n6pN7OjUtSxysx+b2YDUtVLRDwlmEV6n3Odc8XAcOC7wFeAWek+iJllpbvMf6PhwHLnXHRvdzzAz3tvbAg+fLW9zt3NtoOBi/ZQ3vlAC3CGmQ1KsT4L+K9UOzrn7kyqSxFwH7AEeGJPJxIEm1MBB3wsxSbbgG/vqZzdKE2q32PBcY8DXgL+BYxzzpUCZwFR4PCE/V9LPj/n3IaE9ecG53wEMAm4tRt1FRER6YkUv+6Z4tc96zXxaxc0AJclJlU7cTk+1t3XVvWPJZ3HzIR11yWtey0ONslSAAAgAElEQVRh3alAGTDOzCalKPfzSfuet4/124WZjQFeB9YAhzvnSvCfBdYCxydsujZFDP5mch2BMUBfIPHcRSSJEswivZRzrtY59xTwaeByM5sI7S02vx18X2G+deV2M9tmZq+YWcjMfgVUAk+3Pam2na01rzaztcDfE5YlBq0Hm9kb5rvv/cnMyoJj7fKUPHjyP83MzgK+Cnw6ON6CYH3iE/SQmd0WPAnfYma/NLM+wbq2elxuZmuDJ9VfS3VdzOybwO0Jx7q6i2W3n3cn5X7czOYHT8hXBOeEmQ02s6eC6/u+mX22k/07vT7B9zPM7HfBk/s6860uxpjZrUGdPzCzMxL2fcnM/sfM/hVs/7yZVaQ6dhrMBL65hw8vlwM/ARYCl6ZYfzfwZTMr3dPBzOyjwPXA+c65hi7U7zJgDvAQqYPrh4HDzOykLpS1N2YCv3DOfcc5txnaW019wzn30t4W5pzbBDyHTzSLiIj0OopfFb8qfu2y7fjY9hu7OWYBPkl+LTDazKak4bhddTnwJPAs+57c3lffAv7hnLvZObcewDm32Tl3r3Nur4eXcc7VAH9CMbjIbinBLNLLOefeANbhn9omuylY1w/f5e6rfhf3GfwT3nNTPKk+CRgPnNnJIS8DrsK3CogCD3Shjs8Cd7LzCfnhKTa7InidAhwEFAH/m7TNCcBY4DTgdjMbn+JY30g61qwult3peZsfXuGXwM1AKXAisDpY/Vv8NR6MD/DuNLPTUl2HLjgX+BX+Cfo8fLIxBAzBB1IPJm1/CXAl0B/IAb6cUOeFZnbJPtYj2ZPADvw13IWZVQInA48Er1TdAufiW/t+OcW6xLJG4K/BNc65pV2s32UJxz7Tdu3e1oi/J+7oYnl7ZL675LGkp4VKW5lDgbOB99NVpoiISE+k+HWXYyl+5UMXv3bFHcCnzGxsJ+s/BdQDv8Nf9y4NLddd5odz+yQ7r93Fe0jkp9s00huDVwDnoRhcZLeUYBb5cNiA76KULAIMAoY75yLOuVeccy7FdolmOOcanHNNnaz/lXNuUfBk/uvAhRZMotJNlwLfc86tdM7V44cJuCgpWPmmc67JObcAWEDHYQi6W/buzvtq4OfOub855+LOufXOuXfNbBj+Q8NXnHPNzrn5wM+Az+zVme/0inPuuaBr5O/wH6y+65yLAI8CI5JaUPzCObc8qPPjJDx1d84d5pz7zW6ONThoGdT2unA32zr87/p2SxrjOHAZsNA5twT/gWWCpe4qdzvwRTPrl+ogQdm/Ax5xzj26m/ok7nMCvkvp4865t4AV+A8uyR4EKs3s7K6Um2Rr0rUaj/8QFQI2JdRlZrC+wcxuS9j/I0n7r0gq/49mVgd8AGxhNy1VREREehHFr90vW/Fr53ps/NpVQe+2n+AT9alcjn8oEQN+g0/0Zu/lYS5MuqaDE9Y9kLD87YTl5+MT2y8CTwEF+EYSiX6UVG4649syOsbgNwTHqDezHydsV5lUh+1J98KPzKwWqAJK6GQ4FBHxlGAW+XAYgh97K9nd+Cexz5vZSjP77y6U9cFerF8DZAPp6No2OCgvsewsfMuVNpsSvm/Et+RIV9m7O+9h+MRlqnK3Oefqksoe0sV6Jduc8H0TsNXtnKCm7YND4jnv6/UAP4ZdacLr8d1t7Jz7C77V0DUpVre1IMb5sYX/SYqucs65RcAzQGf34ffxrYpu6vJZ+OM875zbGvz8m06O3QL8T/CyvSgfoCLpWi0FaoA4/gNw2zFucX4c5j/g7682c5L2Pzip/E84Py7lycA40vP3JCIi0tMpfu1+2Ypfd6MHx68x/D2YKBv/cCXZXfgeeh0eTAQPCk4hOAf8EA95wDnBz9EUx0h1nMeTrmniPCHXJyyfnLC8PbEdPCj4A7teuy8klfvNFHXZVzV0jMHvD2Lw/6XjOa9NqkNp8JkgsY598A85+rHvfwMiHwpKMIv0cuZnwh0CzE5e55yrc87d5Jw7CN997UsJ3d86awmypxYiwxK+r8QHKFvxE1EUJNQrjP9H3dVyN+BboiaWHaVj0LqvulL27ur3AZCcFGwrt8zMipPKXp9i2z1dnwPBbcDX6HgexwGjgVvNbJOZbQKOofOuct8APktSAGdmn8F387swaPGyR2aWD1wInJRw7BuBw5OD8MAvgD74LnDdErSAeh3fPTAtnHP/xI+1d0+6yhQREemJFL92ieLX9OhR8WtgLTAiadlIOj5QAMA5Vw3cj28kkegz+HzP00H9V+ITzG3DZKwFKoLhLNrqa/h7apfjdJWZDccPzXJFwrX7BDDdzPrua7l76UXSG4MvAL7DrkPQiEgCJZhFeikzKzGz6fiuZ792zr2TYpvpZjYqCCZ24J+Wt7Uo2Iwfz21v/YeZHWJ+UolvAb8PWiksB/LM7Jyga9ZtQGIXpM34LnKdvS/9FrjRzEYGgVDbOHR7PZP2fih7FnClmZ1mfsKVIWY2zjn3AfAq8B0zyzOzw/DdER9JUcaerk+P5/zEde/QsYXC5cDfgEPwT/+PACaSuqsczrn3gcfwk6AAYH6Cnx8BlwbXtKs+gb+fE489HniFFGPQBb/vGfiZ65NlBb/DtldXuhfeAlxlZv9tZv2DcxmK/4Cwr+4HTjczTTIiIiK9juLXvaL4NQ16YPxKUNZtZjY0+N1Mwz9M+X0n238POA4f57a5DPhmQv2PwCe7zzGzcufcWnxjiLvMrCgYGuJm/EOKOXtZ30SXAUvw44q3HXcs/m/loi6WYUlxd17w9w67xuQ5Kfa/HTjVzO62YEgP80OYjOvGef0cGGZm5+xxS5EPKSWYRXqfp23neK1fwwccV3ay7WjgBfwYWa8BPwqCLPBPaW8LxqLa7cQVSX6Fb2W5Cf+U/Hrws4IDX8CP4bYe3+Ihcdbpthl9q63jGF5tfh6U/TKwCmgGvrgX9dqdbpXt/EQ0VwL3AbX4LnRtLUouxrdA2IDvHvYN59zfUpSxp+uTVma22MxSzYbdXbcRjJdoZnn4FsQ/cM5tSnitwl/vzmaU/hZQmPDzl4KfnzQ/dlri66u7qcvl+HH81iYeH9/64NJOWqD8FtiYYvmP8d04216/SFi3PalOXwJwzs0GTsVPmrPczLbjZ9J+CfhBwv7Hpjivo1KdkHOuCj8hz9d3c94iIiIHGsWve0/xa/r0pPi1raxX8S34a4CZ+ET1olQbO+d2BNu0ncNH8L+/Hyadw1P44WUuDnb9NH4yxffxv7/TgI8655r3UL/duSzFcTfi5ztJvHY/SbombySsq6Rj3N3Eznvza0nLn09xPd4FPoJv1PFO8N4yG98ye0bicVL8bj6R6qSCoTN+gGJwkU6Z2+N8CCIiIiIiIiIiIiIiu1ILZhERERERERERERHZJ6m6B4uIiBwQzKwSP85bKocE48uJiIiIiPQIil9FpDfSEBkiIiIiIiIiIiIisk8y1oK5oqLCjRgxIlOHFxERERFp99Zbb211zvXLdD0UI4uIiIhIT9HVGDljCeYRI0Ywd+7cTB1eRERERKSdma3JdB1AMbKIiIiI9BxdjZE1yZ+IiIiIiIiIiIiI7BMlmEVERERERERERERknyjBLCIiIiIiIiIiIiL7JGNjMKcSiURYt24dzc3Nma7KAS8vL4+hQ4eSnZ2d6aqIiIiISDcoRk4fxcgiIiIi6dejEszr1q2juLiYESNGYGaZrs4ByzlHdXU169atY+TIkZmujoiIiIh0g2Lk9FCMLCIiIrJ/9KghMpqbmykvL1fg3E1mRnl5uVq5iIiIiPQCipHTQzGyiIiIyP7RoxLMgALnNNF1FBEREek9FNulh66jiIiISPr1uASziIiIiKRZLALxeKZrISIiIiLS88Qi4Fyma3FA61FjMCf7zetr01reJcdUprU8ERERkQPCQ+dAVi5c/nSmayJpoBhZREREJE2cg/+pgJO+Aqd8NdO1OWCpBXOSoqKi/VKumXHTTTe1/3zPPfcwY8aM3e7z0EMPEQqFWLhwYfuyiRMnsnr16v1SRxEREemlYhEIZWe6FnIAU4wsIiIivVI86r8qVu4WJZj/TXJzc3nyySfZunXrXu03dOhQ7rjjjv1UKxEREflQiEcgrKBZeh7FyCIiIpJRsYj/Gu7Rgzz0eEowd8I5x80338zEiRM59NBDeeyxxwD4whe+wFNPPQXAeeedx1VXXQXArFmzuO222zotLysri2uuuYb77rtvl3VPP/00xxxzDJMmTWLatGls3ry5fd306dNZvHgxy5YtS+fpiYiIyIdJLAohBc3SfYqRRUREpFeJBwlmtWDuFiWYO/Hkk08yf/58FixYwAsvvMDNN9/Mxo0bOfHEE3nllVcAWL9+PUuWLAFg9uzZTJ06dbdlXnvttTzyyCPU1tZ2WH7CCScwZ84c5s2bx0UXXcTMmTPb14VCIW655RbuvPPONJ+hiIiIfGioBbOkiWJkERER6VViwRAZipW7RQnmTsyePZuLL76YcDjMgAEDOOmkk3jzzTeZOnUqr7zyCkuWLOGQQw5hwIABbNy4kddee43jjjtut2WWlJRw2WWX8cADD3RYvm7dOs4880wOPfRQ7r77bhYvXtxh/SWXXMKcOXNYtWpV2s9TREREPgQ0BrOkiWJkERER6VXaWzCrt193KMHcCedcyuVDhgyhpqaGZ599lhNPPJGpU6fy+OOPU1RURHFx8R7LveGGG5g1axYNDQ3ty774xS9y3XXX8c477/Dggw/S3NzcYZ+srCxuuukm7rrrru6dlIiIiHw4xWNqlSFpoRhZREREepX2MZgVK3dHj07PX3JMZcaOfeKJJ/Lggw9y+eWXs23bNl5++WXuvvtuAI499ljuv/9+/v73v1NdXc3555/P+eef36Vyy8rKuPDCC5k1a1b72HS1tbUMGTIEgIcffjjlfldccQUzZ86krq4uDWcnIiIiHyrxiFpl9CKKkXdSjCwiIiLdojGY00ItmDtx3nnncdhhh3H44Ydz6qmnMnPmTAYOHAjA1KlTiUajjBo1ismTJ7Nt27Y9ji2X6KabbuowU/aMGTO44IILmDp1KhUVFSn3ycnJ4frrr2fLli3dOzERERH58IlpDGZJD8XIIiIi0qtoDOa0sM66ue1vU6ZMcXPnzu2wbOnSpYwfPz4j9emNdD1FREQEgO8MgyMuhbO/m+ma9Fhm9pZzbkqm66EYef/T9RQREZF2mxfDj4+DCx6GCZ/IdG16nK7GyGrBLCIiItLbxSIQ1hAZIiIiIiIdaAzmtNAnjTSqrq7mtNNO22X5iy++SHl5eQZqJCIiIkIwBrOCZskMxcgiIiLSY8WDITIUK3eLEsxpVF5ezvz58zNdDREREZGdnPOBs1plSIYoRhYREZEeq70Fs1Kk3aEhMkRERER6M7XKEBERERFJLR4kmBUrd4sSzCIiIiK9mVpliIiIiIikpjGY00IJZhEREZHeTK0yRERERERSU2+/tOjZTVnm/iK95U25Mr3liYiIiPR0sSBoVquM3kMxsoiIiEh6qLdfWqgFc5LVq1czceLEXZbffvvtvPDCC7vdd8aMGdxzzz1dPpaZcdNNN7X/fM899zBjxozd7vPQQw8RCoVYuHBh+7KJEyeyevXqLh9XREREPkTaWzAraJZ9pxhZREREeiX19ksLJZi76Fvf+hbTpk1La5m5ubk8+eSTbN26da/2Gzp0KHfccUda6yIiIiK9lMaVk/1IMbKIiIgc0NTbLy2UYE4hFovx2c9+lgkTJnDGGWfQ1NTEFVdcwe9//3sA/vKXvzBu3DhOOOEErr/+eqZPn96+75IlSzj55JM56KCDeOCBB3Z7nKysLK655hruu+++XdY9/fTTHHPMMUyaNIlp06axefPm9nXTp09n8eLFLFu2LE1nLCIiIr2WWmVImihGFhERkV5Hvf3SQgnmFN577z2uvfZaFi9eTGlpKU888UT7uubmZj73uc/x17/+ldmzZ1NVVdVh33fffZfnnnuON954g29+85tEIpHdHuvaa6/lkUceoba2tsPyE044gTlz5jBv3jwuuugiZs6c2b4uFApxyy23cOedd6bhbEVERKRXU6sMSRPFyCIiItLrqLdfWuxTgtnMwmY2z8yeCX4eaWavm9l7ZvaYmeWkt5r/XiNHjuSII44A4Mgjj+wwdtu7777LQQcdxMiRIwG4+OKLO+x7zjnnkJubS0VFBf379+/QqiKVkpISLrvssl1acqxbt44zzzyTQw89lLvvvpvFixd3WH/JJZcwZ84cVq1ata+nKSIiIh8GapUhaaIYWURERHod9fZLi31twfxfwNKEn+8C7nPOjQZqgKu7W7FMys3Nbf8+HA4TjUbbf3bO7fO+nbnhhhuYNWsWDQ0N7cu++MUvct111/HOO+/w4IMP0tzc3GGfrKwsbrrpJu666649li8iIiIfYmqVIWmiGFlERER6HfX2S4u9bspiZkOBc4A7gC+ZmQGnApcEmzwMzAB+3O3aTbmy20Wk27hx41i5ciWrV69mxIgRPPbYY90us6ysjAsvvJBZs2Zx1VVXAVBbW8uQIUMAePjhh1Pud8UVVzBz5kzq6uq6XQcRERHppeJB0KxWGb2HYmRAMbKIiIikgXr7pcW+tGC+H7gFiAc/lwPbnXNtzRDWAUNS7Whm15jZXDObmzwu24EiPz+fH/3oR5x11lmccMIJDBgwgD59+nS73JtuuqnDTNkzZszgggsuYOrUqVRUVKTcJycnh+uvv54tW7Z0+/giIiLSS7W3YFbQLPuPYmQRERE5IKm3X1rYnrqzddjYbDrwUefcF8zsZODLwJXAa865UcE2w4C/OOcO3V1ZU6ZMcXPnzu2wbOnSpYwfP37vziAD6uvrKSoqwjnHtddey+jRo7nxxhszXa1dHCjXU0RERPajVS/Dw+fC5c/AyKmZrk2PZWZvOeemdLOMMDAXWO+cm25mI4FHgTLgbeAzzrnW3ZWhGHn/O1Cup4iIiPwb/HMm/OMO+Hq1GmSk0NUYeW9bMB8PfMzMVuOD5VPxLZpLzazttzAU2LCX5R5QfvrTn3LEEUcwYcIEamtr+dznPpfpKomIiIikplYZ/069ep6SPVGMLCIiIgectlg5FM5sPQ5we5Wad87dCtwK0NaC2Tl3qZn9Djgfn3S+HPhTmuvZo9x4441dbo1RXV3NaaedtsvyF198kfLy8nRXTURERKQjjcH8b/Fvnaekh1KMLCIiIgeceMTHyWaZrskBLV1tv78CPGpm3wbmAbP2tSDnHNaLfqnl5eXMnz//337cvRn6RERERHoxjcH879I2T0lx8PNezVMCXANQWVmZsnDFyOmhGFlEREQ6iEXU0y8N9mWSPwCccy8556YH3690zh3tnBvlnLvAOdeyL2Xm5eVRXV2twK+bnHNUV1eTl5eX6aqIiIhIprXPjK3AeX8J5inZ4px7K3Fxik1TBrnOuf9zzk1xzk3p16/fLusVI6eHYmQRERHZRTyqODkNelRTlqFDh7Ju3TqqqqoyXZUDXl5eHkOHDs10NURERCTTYkEDWrXM2J/a5in5KJAHlJAwT0nQinmf5ylRjJw+ipFFRESkg1hEPf3SoEddwezsbEaOHJnpaoiIiIj0Hu0tmHtU2Ner7O95ShQji4iIiOwnbWMwS7fs8xAZIiIiInIAaB+DWYFzBnwFP+Hf+/gxmfd5nhIRERER2Q9iUcXJaaCmLCIiIiK9mcZg/rdyzr0EvBR8vxI4OpP1EREREZHdiEfU0y8N1IJZREREpDfTGMwiIiIiIqnFIoqT00AJZhEREZHeTGMwi4iIiIikFo+qp18aKMEsIiIi0ptpDGYRERERkdRiEQirIUZ3KcEsIiIi0pvFgyEy1DJDRERERKSjeERxchoowSwiIiLSm7W1YA6FM1sPEREREZGeRmMwp4USzCIiIiK9WVurDLNM10REREREpGeJRzVXSRoowSwiIiLSm6lVhoiIiIhIaoqV00IJZhEREZHeTDNji4iIiIikpjGY00IJZhEREZHeTDNji4iIiIikFouqBXMaKMEsIiIi0pupVYaIiIiISGrxiMZgTgMlmEVERER6M7XKEBERERFJTWMwp4USzCIiIiK9mVpliIiIiIikpvlK0kIJZhEREZHeTK0yRERERERS03wlaaEEs4iIiEhvplYZIiIiIiKpab6StFCCWURERKQ3U6sMEREREZHUNF9JWijBLCIiItKbqVWGiIiIiEhqmq8kLZRgFhEREenNNAaziIiIiEhqipXTQglmERERkd4sHlWrDBERERGRZM6pt1+aKMEsIiIi0pvFNa6ciIiIiMgu4jH/VbFytynBLCIiItKbxdQqQ0RERERkF/GI/6reft2mBLOIiIhIb6YWzCIiIiIiu4oFCWbFyt2mBLOIiIhIbxaLQCic6VqIiIiIiPQs8aj/qt5+3aYEs4iIiEhvpolLRERERER21d6CWUNkdJcSzCIiIiK9WUxDZIiIiIiI7CLW6r+qMUa3KcEsIiIi0pvFI5q4RERERET+P3t3HiZXWeZ9/Htq6+rqfU86nY2shCUEAwFE2RVQkRFx32ZQ33EcR0dn3J0ZfZ0ZeNUZd0dHHRFFEGFAWVVkhwRCEpbsC0nve3dVd+11znn/eKq6eqkogUq6U/4+19VXVZ1Tfep0dS33uc/93I9M56gHc7EowSwiIiJSyuy0gmYRERERkels9WAuFiWYRUREREqZejCLiIiIiMzkqAdzsSjBLCIiIlLK1INZRERERGSm3CR/KsZ42ZRgFhERESll6sEsIiIiIjKTk22RoWKMl00JZhEREZFSph7MIiIiIiIzTVQwqxjj5VKCWURERKRUOTbgatifiIiIiMh0Ez2YFSu/XEowi4iIiJQqWxOXiIiIiIgUpB7MRaMEs4iIiEipchQ0i4iIiIgUpB7MRaMEs4iIiEipsjXsT0RERESkIPVgLholmEVERERKVa4qQ0GziIiIiMhU6sFcNEeUYLYsK2hZ1pOWZT1jWdZ2y7K+mF2+1LKsTZZl7bUs62bLsgJHZ3dFRERE5EVTBbOIiIiISGF2rhhDsfLLdaQVzEngQtd11wKnAZdalnUWcB3wn67rrgBGgGuKu5siIiIicsTUg1lEREREpDBHE2IXyxElmF1jPHvTn/1xgQuBX2WXXw9cWbQ9FBEREZGXxtbEJSIiIiIiBdkqxiiWI+7BbFmW17KsbUA/8DtgPzDqum72CIZOYEHxdlFEREREXhJHE5ccC2ojJyIiInIcmqhgVoj2ch1xgtl1Xdt13dOANuBM4MRCdyv0u5ZlfdCyrM2WZW0eGBg40ocWERERkSOhHszHitrIiYiIiBxvNNqvaI44wZzjuu4o8CBwFlBrWVauNKYN6D7M7/zAdd31ruuub2pqeqkPLSIiIiIvhnowHxNqIyciIiJyHNJov6I5ogSzZVlNlmXVZq+XAxcDO4EHgDdn7/Ze4I5i7qSIiIiIvAQTVRkKmo82tZETEREROc5otF/RHGkF83zgAcuyngWeAn7nuu6dwKeAj1uWtQ9oAH5U3N0UERERkSOmCuZjRm3kRERERI4zTrYOQLHyy3ZE5Syu6z4LrCuw/AAmkBYRERGRuUJVGcec67qjlmU9yKQ2ctkq5j/aRg74AcD69esLJqFFREREpMhysbLHO7v7UQJecg9mEREREZnjVJVxTKiNnIiIiMhxyEmbONmyZntPjntqyCciIiJSqiYqmBXyHWXzgesty/JiCjh+6brunZZl7QBusizry8BW1EZOREREZO6w0xrpVyQ62hAREREpVerBfEyojZyIiIjIccjJKE4uErXIEBERESlV6sEsIiIiIlKYndZIvyJRgllERESkVE30YFbgLCIiIiIyRa4Hs7xsSjCLiIiIlCpVMIuIiIiIFGZnFCcXiRLMIiIiIqVKPZhFRERERApz0hrpVyRKMIuIiIiUKjvbIkOVGSIiIiIiU9lpxclFogSziIiISKmaqGBWZYaIiIiIyBRORiP9ikQJZhEREZFSpR7MIiIiIiKF2WnwqhCjGJRgFhERESlV6sEsIiIiIlKYk1acXCRKMIuIiIiUKvVgFhEREREpTD2Yi0YJZhEREZFS5aTB8oJlzfaeiIiIiIjMLU5Gc5UUiRLMIiIiIqVKVRkiIiIiIoUpVi4aJZhFRERESpVmxhYRERERKUw9mItGCWYRERGRUqWZsUVERERECrMzqmAuEiWYRUREREqVqjJERERERApz0urBXCRKMIuIiIiUKlVliIiIiIgUph7MRaMEs4iIiEipUlWGiIiIiEhhmq+kaJRgFhERESlVqsoQERERESlM85UUjRLMIiIiIqVKVRkiIiIiIoVpvpKiUYJZREREpFQ5GVVliIiIiIgUovlKikYJZhEREZFSZasqQ0RERESkIM1XUjRKMIuIiIiUKkc9mEVERERECtJ8JUWjBLOIiIhIqbLVg1lEREREZAbXVQ/mIlKCWURERKRUOZoZW0RERERkBsc2l6pgLgolmEVERERKlXowi4iIiIjM5KTNpXowF4USzCIiIiKlSj2YRURERERmsrMJZsXKRaEEs4iIiEipsjOqyhARERERmc7JmEuN9isKJZhFRERESpUqmEVEREREZpqoYFYxRjEowSwiIiJSqtSDWURERERkpokezIqVi0EJZhEREZFS5ahFhoiIiIjIDOrBXFRKMIuIiIiUKjutYX8iIiIiItOpB3NRKcEsIiIiUqoctcgQEREREZlBPZiLSglmERERkVJlZzTsT0RERERkOvVgLiolmEVERERKlZNWD2YRERERkensbIsMFWMUhRLMIiIiIqXKTitoFhERERGZbqKCWcUYxaAEs4iIiEgpcl1wbQ37ExERERGZbqIHs2LlYlCCWURERKQUaeISEREREZHC1IO5qJRgFhERESlFCppFRERERApTD+aiOmDJbwAAACAASURBVKIEs2VZCy3LesCyrJ2WZW23LOuj2eX1lmX9zrKsvdnLuqOzuyIiIiLyomjYn4iIiIhIYerBXFRHWsGcAT7huu6JwFnAhy3LWgN8Grjfdd0VwP3Z2yIiIiIyW5xsVYYqmI86FWGIiIiIHGdUjFFUR5Rgdl23x3XdLdnrY8BOYAHwRuD67N2uB64s5k6KiIiIyBFSD+ZjSUUYIiIiIscTFWMU1UvuwWxZ1hJgHbAJaHFdtwdMEhpoPszvfNCyrM2WZW0eGBh4qQ8tIiIiIn+KejAfMyrCEBERETnOqBijqF5SgtmyrErgVuBjrutGXuzvua77A9d117uuu76pqemlPLSIiIiIvBga9jcrXkoRhoiIiIgcY3bKXKoYoyiOOMFsWZYfk1z+ueu6t2UX91mWNT+7fj7QX7xdFBEREZEjNjHsT1UZx8pLLcLQKD8RERGRY8xRMUYxHVGC2bIsC/gRsNN13f+YtOrXwHuz198L3FGc3RMRERGRl0QVzMfUyynC0Cg/ERERkWPMVg/mYjrSCuZXAu8GLrQsa1v253LgWuASy7L2Apdkb4uIiIjIbFEP5mNGRRgiIiIixxlHPZiL6YieRdd1HwWsw6y+6OXvjoiIiIgURa4qQxXMx0KuCOM5y7K2ZZd9FlN08UvLsq4B2oGrZ2n/RERERGQyW8UYxaQ0vYiIiEgpmqhgVrh3tKkIQ0REROQ446gYo5iOeJI/ERERETkOqAeziIiIiEhhtooxikkJZhEREZFSpB7MIiIiIiKFOWmTXLYONwhNjoQSzCIiIiKlaKIHs6oyRERERESmsNMqxCgiJZhFRERESpEqmEVERERECnMyaiVXREowi4iIiJQi9WAWERERESnMTqv/chEpwSwiIiJSinIzY6uCWURERERkKietQowiUoJZREREpBRNVDCrMkNEREREZAo7o0KMIlKCWURERKQUqQeziIiIiEhhTlqFGEWkBLOIiIhIKVIPZhERERGRwuy0CjGKSAlmERERkVI00YNZlRkiIiIiIlM4GRViFJESzCIiIiKlSBXMIiIiIiKF2WkVYhSREswiIiIipUg9mEVERERECnPSKsQoIiWYRUREREqRnW2RocBZRERERGQq9WAuKiWYRUREREqRkwYs8Hhne09EREREROYW9WAuKiWYRUREREqRrWF/IiIiIiIFqQdzUSnBLCIiIlKKnIyG/YmIiIiIFKIezEWlBLOIiIhIKXIy4FVVhoiIiIjIDHYGvIHZ3ouSoQSziIiISCnSxCUiIiIiIoU5apFRTEowi4iIiJQiDfsTERERESlM85UUlRLMIiIiIqXIVg9mEREREZGCNF9JUSnBLCIiIlKKnLR6MIuIiIiIFGIrVi4mJZhFRERESpF6MIuIiIiIFOYoVi4mJZhFRERESpGTUV85EREREZFCbMXKxaQEs4iIiEgpsjUztoiIiIhIQY5i5WJSgllERESkFDmaGVtEREREpCBbsXIxKcEsIiIiUorUg1lEREREZCbXVQ/mIlOCWURERKQUORnNjC0iIiIiMp1jm0tVMBeNEswiIiIipUgVzCIiIiIiMzlpc6kezEWjBLOIiIhIKVIPZhERERGRmexsglmxctEowSwiIiJSiuyMqjJERERERKZzMuZSo/2KRglmERERkVKkCmYRERERkZkmKphVjFEsSjCLiIiIlCL1YBYRERERmWmiB7Ni5WJRgllERESkFDkZVTCLiIiIiEynHsxFpwSziIiISCmy0+rBLCIiIiIynXowF50SzCIiIiKlSD2YRURERERmUg/molOCWURERKQU2RlVMIuIiIiITKcezEWnBLOIiIhIKXLUIkNEREREZAY72yJDo/2KRglmERERkVJkq0WGiIiIiMgMExXMKsYoliN+Ji3L+jHweqDfdd2Ts8vqgZuBJcBB4C2u644UbzdFREREZIbN/1N4ueuawLlve/4+6//y2O2XiIiIiMhsO1ysPLjXXO67H0bbzXXFyi/LS6lg/glw6bRlnwbud113BXB/9raIiIiIzAbXMZeWd3b348+IZVk/tiyr37Ks5yctq7cs63eWZe3NXtbN5j6KiIiICPlY2aNYuViOOMHsuu7DwPC0xW8Ers9evx648mXul4iIiIi8VK5tLhU0H0s/QUUYIiIiInOfk42VLXUOLpZiPZMtruv2AGQvmwvdybKsD1qWtdmyrM0DAwNFemgRERERmcJRBfOxpiIMERERkeOEijGK7pim6l3X/YHruutd113f1NR0LB9aRERE5M/HRNCsqoxZpiIMERERkblmooJZCeZiKdZRR59lWfMBspf9RdquiIiIiBwpBc3HFRVhiIiIiBxDqmAuumIlmH8NvDd7/b3AHUXaroiIiIgcKQXNc4WKMERERETmGlfFGMV2xAlmy7J+ATwBrLIsq9OyrGuAa4FLLMvaC1ySvS0iIiIis0EVzHOFijBERERE5prcfCUqxiga35H+guu6bz/Mqote5r6IiIiISDGogvmYyxZhnA80WpbVCfwzpujil9mCjHbg6tnbQxEREREBJlUwa76SYjniBLOIiIiIzHG5qgxVMB8zKsIQEREROU44KsYoNqXqRURERErNRAWzQj0RERERkSnUg7nodNQhIiIiUmrUg1lEREREpDBVMBedEszHg/gI/OwqGO2Y7T0RERGRuSYdg03fg9hQfpl6MEspc124/cNw4MHZ3hMRERGZ64YPwNYbwHXyy1y1kys2JZiPB91bYd/v4eCjs70nIiIiMteEO2FgNwzuzS9TBbOUsmQEtv0Mdt0123siIiIic13vc9D1NCTH88scTfJXbHomjwfhLnMZ6Zzd/RAREZG5Jz5qLhOj+WW5qgxVMEspysXGuUsRERGRw0kUipVtk1y2rNnZpxKkBPPxINI99VJEREQkZyJoDueXqSpDStlEbKwEs4iIiPwJuWKM+KQEs2NrpF+R6ajjeJALnpVgFhERkelyCeb4SH6ZejBLKcuN6lOCWURERP6UiWKMabGy4uSiUoL5eKAqDRERETmc+B+rYFbgLCUoFxtHByCTnN19ERERkbnLdfIxcnxyrOwoTi4yJZiPBxH1mRMREZHDOFxfOVBlhpSmyTGxRviJiIjI4STH8nOTqIL5qFKC+XiQSzDHhyEdn919ERERkbklV42RjuerOV1VMEsJi3QB2Ul5lGAWERGRw5nou2zNHO2nuUqKSs/mXJccN2+CptXmtoJoERERybFTkI5C5TxzO1fF7GQrNVSZIaUo0gXNJ+avi4iIiBSSi42r5k2d5E8VzEWnBPNcN9ZjLtvWm0slmEVERCQnV71ctzh7Oxs4T1QwK9STEuO6pkXGRGysBLOIiIgcRi42rltiijdz7TIcWyP9ikxHHXNdODtLdtsZ5lJBtIiIiOTkqjJql0y97agHs5SoRNhU7TeuhGCt5igRERGRw0uMgscP1a2mACM5bpY7qmAuNiWY57pcxbISzCIiIjJdLqGcq2DO9ZZTD2YpVbnYuLoVqhdodJ+IiIgcXmIUgjUQrMvfBhMrK04uKiWY57pc0Fy/DMrrFESLiIhIXi5IrmiEQCXEs7NjqwezlKqJBHObSTJHOmd3f0RERGTuio9Cea35yd2GbA9mpUSLSc/mXBfpglAj+IOq0hAREZGp4qPgD4E3YNoFqIJZSl0uoVzdCjWKjUVEROSPSIyaGDmYTTAnJhVjKE4uKiWY57pIlwmgwVyGVaUhIiIiWbmgGaC8Jl+V4WiSPylRkW7zuq6aZ4ovogOQSc72XomIiMhc49im+KK8FgIV4PHlJ8h21YO52HTUMddFuk3wDNlhgKrSEBERkax4OD/kL1g7qa+cY5JwljV7+yZyNIS7oLIFvP58jKz4WERERKYb7zcxcbDWxMSTY2VHPZiLTQnmuS7SZYb/gek1FxuEdGJ290lERETmhikVzLWQjoGdUtAspWv66D5QgllERERminSZy4lijJqpk/ypgrmolGCey1IxM1nP9CB6rGf29klERETmhnQCUuNTK5jBtMlQ0CylKtKVr1yuacsvExEREZksFx9MFGPUTW0np1ZyRaVncy6bmCV7UosMUBAtIiIiMJaNE4I12cvc5CWjqmCW0uS6pkWGYmMRERH5U3I5tSkVzGHTNkPFGEWnBPNclguWJyqY1WdOREREsnLxQFAVzPJnIhGGdDTfPi5QYV73YSWYRUREZJpwJ3j84K8wt8vrTIycHAfHUTFGkSnBPJcdSQXzL98Dd3z42OyXiIiIHDt/+DJ8/7yZy6dXZZRnK5kT2QSzhv1JqZmIjVvzy6oXFC6+eOpH8M3Twc4cm30TERGRY8914TtnwRPfmbku0m2qlnOTXk8e7adijKLTkcdcNr2CuazSvDmmB9GZFOy5D3bdbd5cIiIiUjp23wM92yAybQ6GcKe5zAXL3oCp0EiMmqoMBc1SaiZi47b8supWiHTOvO+uO2F4P/RvPzb7JiIiIsfe4F4Y2Am77pq5LtKVL8SA/PW42skdDUowz2WRLiivB395fln1gpnDAHufhUwC4sPmzSUiIiKlIRGBvmyCrGPT1HWRbhMj+Mryy8prIB7OVjAraJYSM734Aky7jOnFF44NHU+Z6+3T3jciIiJSOjo2msuup03x5WSR7nwhBhSoYFZKtJh8s70DUtiNm9o579B+Qr4m7tnUPrH8fKeOsp4XuG/SsnfYG/O/2LEJmlYey10VERGRo6XzKSA7OqljE5x0ZX7d9KAZzO3ECIQaVcEsJee5HTs4CQ8370zhekwsfHKkglOjA9z0+D4cbwCAdywOQ2rM/FLHRtjwwdnaZRERETma2rP5sEzCFF+2rTe3HdvEyk2r8vcNVIDHpwrmo0Tp+jmsPNFHLDhvyrJY+TxCib6pd+zYBDWLTMPyjo2IiIhIiejYZHopzzs1H0DnRLpmJpjLa1XBLCUrlOgjUdaI6/FPLItmY+Xy5KT4OPdeaT1dFcwiIiKlrH0jLHhF/nrOeL+JhyfHypaVLcYYBVft5IpNFcxzWCjRx1DtqVOWxcpaKE8N4XHSOB6/6bncsQmWngfJiIJoERGRUtKxCVpOguUXw+PfhFQMAiGzLtIFDcun3j9YC+kopBMa9iclJ1So+CLYAkBFvJdoaKFZ2LEJKlvg1LfCvZ8y/cpr2qZvTo5DN04axfnHvGPDoqO8JyIiMuvGB8x8Cxd/EWJD0P4EnPO3Zl2urVb59NF+Ndn5SlSMUWw68pijvHaCYHpkImjOyQXV5dkq5op4J4z3waINsHADDO2F6NAx318REREpMjsDnZth4VnmO97JQPcWsy6ThOiACZIny1VpxIcVNEvJCSV6DxsbTxnh174JFp1l4mOYWf0vIiIix7/c/CSLzjLxcscmU4QJ+QTzjNF+dRAfyfZgVqxcTKpgnqPKE/0ABYJoczuU6CMaaqNpZJtZsfAsU8EM0PkkrLps6gYjPbD5R/DqT4IvcFT3XYpDFRoicsRGO+Dgo3Da22d7T+TFcl146Do48QpoWTN1Xf92SI2b5PLCM82y9o2w5FwY6wHgwLifgReGJ36letzDiYAbH2XMqmbnpHUb1h/tP0bkKHJdQoleeppeOWVxPjbuBaA83gvhdjjrQ9ByCvgrzAHnKW+esT0e+zqsfgM0ThsJICIlQcdTIiVg+AV47hZ41T/MHJ3XsRG8AZh/mjmp/OxNMHwAGpZB+I9VMIfNdUs1t8WkZ3OOylVhxMpn9mCevL5pZBuUVUPzidC6Djz+wlUaj38THv4K7Lrz6O64iIjMnvu/CLf/NfQ+N9t7Ii9W+0Z48N/hgX+dua7jSXO5aAOE6qFxVX5ZpBuApL96yq+k/Kai2cLFVQWzlJJEGL8dn9Eiw/aFSPqr87HxaLb4YtEG8PrMZD/tT8zcXvsT8Pt/KfzeExERkbnhoevMd/WBB2aua99k8mD+oCm6hHxVc6QLfEFzonmy8jrTfxlUwVxkqmCeo3JVGH+sghmgcXSrCZw9XvCUw/y1+YPPnEwSnrnJXN96A5z8pqO78yJyTKk6489b7v/vT4d50/Y78AK77/kuT6/5zIz76jUwB229wVzuuddMRlLZnF/XvhGqWqEm21d24Zmw8zfgOBNVGakZCeaqieuOqjKklGRPqkyPjXPLcrFz08hW8IfMxJhghs0+/BVIjkFZ/v3Blux7b9edEBs2J3FE5PiWiJhkVNU8mHcqgVQ9qUDNn/49EZmbEmHYfru5vvUGWH5Rfl06Dt1bzYglgKbVpjq5fSOc9g6TYK5uNRP7TTZl0j8lmItJRx5z1EQFc1nzlOUZXwUpXyWhRC/+dITasX35MzVggujuLZBJ5Zftvtv0Ylx0Nux/AEZfXDJKRESOH0u778LrpBiuWs2S7jvx2MnZ3iX5U5JjsP1/YfErTX/lZ2+eur5jk0kq5wLjRWeZSUkG90z0lUv5piaYHU+AjDcIoApmKS3Z1/z0CmaAeLBl0ui+rWY2ea/frFy4wVQqdT6V/4VE2Lz3Fp0Ddmrme09Ejk8bvwdPfBt++3n46RW8+f5zueLBS5k/8Mhs75mIvBTP3wqZuImVd901db6x7q3gpE18DKZ9RtuZkyqYu6F6AZteGJ7y89xwPuF8cCQ5ZZ28PEowz1GhRC9JfzW2LzRjXSwbRDeOPouFm5/ABMyBaCYBPc/kl239GVS3wZXfM7e3/WLmA6YT8JPXw8b/KvJfIiIiR53rsqzjNoarT2Tr6o9Tlo6wsO/+2d4rAXj2l/DdsyE6OHPd87dBOmZmvm47w1RU5iYmCXdBuCMfNMPUoX+RbiirxvGWzdhsLunsqoJZSskfSTBHg/MIJfrwZWLUju0xSeWctjNMj8X2TflluQPW134ZWk+HLT/Nv/cm2/hfcP0bzGhAEZnbkuOw6Xuw8lL4x/3wrtvYuvJjuJaXM7Z/GY+d+tPbkJJz46b2F/0js8BOw0/fCI/+Z+H1W34KzSfB5V+ZeUI41xp28nf+orNgYJcZmRTuguoFMzY5efSfYuXi0rM5R4USfQUDaDCBdSjRR+PINhw8pkojZ+LgM/tmC3fCvvvNEIH6pXDCebDtZ2Z47WQbvwsHH4Hf/zOMHDoKf5GIiBwtdZEd1I3tZn/bm+hr2MB4+QKWdd4227slsWG451PQvwP+8OWZ67f+zPRVblsP694Ng7uhc7NZl6u+mBw0NyyDUEM2wVw4aIZ8X2ZVMEtJCXfh4CFe1jhjVTzYQjA1TPPwZjyubUbt5QSrzcFpx6Q5SrbcYJa1ng6nv9u8R7u2TN3oyEH43T/BCw+bOFlE5rYt10N8BF71CahohOUXsXPZNWxe81kq490s67x1tvdQRKZ78gdw4EETJw/smbqu93lTpXz6e6DlJJP32jqpGKNjEzSsMO/3nFxhRvtGMyF2deuMh8x4QzjZGNlFsXIxqQfzLHJdl0g8Q3c4Tm84QU84QSyVAeA14S66vPU8um+QUMBLTbmf2nI/1eV+YsEWasf20DS6jdHqVdRP7idX1QJ1S7IHph+BbTcCLqx7p1m/7t1w6zXwwkOw7AKzLNIDD38VFp8LXU/D774Ab/npzB3ecgPs/DVc9SMTrMsxt6zjVnyZKLuXvme2d0VE5pDlHbeR8ZRxsPVysDwcaLuSU/d+h4pYB9HQwtnevdKVTsCv/hKa18BFX5i5/qHrTEuLlZfC0z+B9X8F87N9YQd2Q+eT8JovmxYYJ/0F3Ptp2PpTWHiG+R73h2DeKfntWZZJOLdvNN/DBYJmyFdmuKojkONILJXhuc4w2zpGeWEwyuB4ksHxFEPRJINjKb5sPckrrVq++vv9BP1eynwe6isCtFQHqUjXcSqwqPc+XCyshWdM3fiiDWY+EjtjKpu6t8Cl15r31Mlvhvs+Z957bZOKNu77nJnjpO1ceOgrcOrboHr+1O2O9cJtH4QNfw2rLz/qz5FM5bguGdslYzt4PBZlPg/W9F6b8uchk4THv22OZxeeObF4Wfst4LpEQotYu+eb4Lo4Hv/M39/wiWO4syJ/Rvb8Fh77uhlNX7d46rrxAXjwWtOuqu95uO8z8M5f5VvDbb0BvAE49S3m9rp3w50fMyeEW9eZWHn166Zus/V08PjMnCWuDTULYCQ89T6WRcpfTTA1ogrmIlOC+RhJpG129kR4vivM810Rnu8Oc2AgSjxtF7z/lWV9PGov5O7hnhnr/rHMw4esQTypCJvrXkdT7xjLmirwebNvjoUbTK9lxzHVUUtfbZLOAKtfb5qab/1ZPsF8/xdN75o3fguevQUe/Dd44RFY+qr8gx54EH7zUfMmve2D8LYbTY+bydJx2PR9OPENpspKXhLbcekfS9A1Eidp26QyDqm0zQW9P2bD4E8A6B53eWbeVQR8HrpH47RUB/F6FFD/WbEz4NVHuIDXjrO452465l1COptYPLDgSk7e+z2Wdd7Osys/Mst7eBwbbYenfgRnf3jq5Htgqifu+riZ52D33SZoPn3Syb/+XfDkf8Mr3gcX/RN883S49zPwvjtN4Lz1BhMAn/pWc/9gNay50rTNuPRaEzRP7iObs/BM83j+CjNpb4ERv7kWGTHHz75oECdb6OE7NAKA12NRF/JTXxGgssynhIwcc67r8sJglK3to2ztGGFr+yi7esewsy/WpqoyGivLaKwMsLSxgvqKAOv2RInEm1lQU04y7RBP2+zoibD50AijHps3BqCp83cc9C3mt5sGWb/E5uQFNZT5vGaE31M/hP7tpvjCG5j53nvuVnjtv0GgwsTRu+6EC79g3mff2QC//xd40/fzf0Q6ATe9E7o2m/7O1/x26gmhnEOPw+Be8/mg99oR6R9LsKd3nINDUQ4NRTk4FOOZjlHGkxnStkPantrWxGtZhMq8VAR8/OaZbhbWl7OypYoVLVWsbKlkXnVQn3clJpG2GRhLYj99PUvGuvn9yi+w+4F9E+s7ehoAeN5/BdfEvg09W+luOJdaf4Zyj6O3pMjL4bqmhUVN29TJ93L6d5pCjNQ43PQO+Kv7oKwyv/4PXzKt4t7wDdj3O7jvs7DnPlh1qfmOfeYmk7/KTcJ78lXmPluuN9uJj0ydjwwgEIL5a813OGRH+01LMGNiZZNgVgVzMSk7cRREkxl29kR4LptM3t4dZm//+ETQXBfyc/KCGjYsbaC1Nsj8mnLm1QSZXxOkMuiDTILqr0ZYtXI1X1i6hmgqQzieJhxLMxpP4xlcgGfMJegmubm3ld98/WHKfB5Wz6tiTWsNV2ZWsCF6M6nN1xMYPWSC4xx/0JwBejo7hGhoPzzzCzj376H+BHjl35mD3ns/DR98yCSwhl+AW94HjSth7VtNgP3Av06t1kqE4Rdvh0OPwePfgnffZt7Ykzm2GWIYH4FXf9LsSyGuW/IBeNp2ODQUY1//OPsHxjk4GKVzJE7XaJzu0TgZZ3LA7PJZ34281ncXt2ReTb01xmUd/8HNB/w86pzCdx/cj89jMb82SFttiLa6cpY0VrCsqZLlzRUsbqjA79WZueOJ7bj0hON0jsTpGI7RE04wEksxGkszEk1y7tCveE/0x4xZ1ezxLmPcOYFd1gls8Z6KtyxEKOCjPOAlFPBSHfRTF/JTFwoQT9mUB/Qlerya/rroHInTMRKj6cBtBDLjXNt3Jk/et4u07WI7LvXetaza/yu+03EJXp+foN/Ds52jtFSb75uWmiALastZVB8i6C/R10V0EMrrTBXidJmk+S5LRU0P5MkBL5hhejdcaVpR7PwNvOcOqJ1UDf7kD2Dbz81Q3O6tcNcnzOzVC88032P3ftps84LPmX248PMmIb3jDlNt8cxNprJ5cuL69HfDMzeaBFjPs+a7ebpcIJ2O0mnX8YfBGvqSfvqSAfqSfsJpHxc7PVznf5BHRmr5/MCS/O/ufnzG5gI+Dw0VAZqqyljcUMHShhCLGypY0ljBkoYQ9RUBJWTkZQvH0zzTMTqRUN7WMcpoLA1AZZmP0xbW8jfnL2PdolpOW1hHfUVg5ka+HaY9tJi3rVs0sch1XZNs7LdgO1RZce5zVvLv9+wCzOt7bVsNF85v5ENAfPcDlD9789QDVsi/97bfbuLkez9tijPO/lsTr57zEXjka3DGNfn3+G8+apLLr/sPs+4Xb4cPPACVTfntPvcr+N+/NoUc3VvhdV+b+Xk0tN/8/qlvNa3sCskNBy7R96LrunSOxNnenT92er47wsBYvvd1mc/D4oYQDZVlLG2sIODz4Pd68HssVqZ34rUTbPGcSixtE03ZJDM29+/s55ebOye2UVXmY/X8Kk5qrWHN/GrWtFazoqXSnISQOS2SSLOvf5x9fePs7R9jT984+/rH6RqN48Hh/sB/8ixLef9j1cDuSb/ZNHF5gv8R1o08wl/1vpkxQlR6bVqDSRYEUzzz8H6WN1eyrKmStrqQCneOc67rMpbM0B9J0jEcI2U7JNMOgUQ/wdQoA6FleD2W+bEsAj4PFWU+hqMpasv9eP5c/v92GrAOX7T0wsMmgfyqT0DziVPXOQ7c+ykTD1sekySeXGgRG4ZfvM2ctL30WvjN38HtH4KrrzeFit1bzQj5sz8MTStNO9enf2KqmJddYBLEidGp2wxWmxF/z9+W359F0xLMYGLlrqfN9eoFwA4AErbFaMbHaNpHjdtANYfYHKlmW7yBtGuRdix+d+cOQgEvoTIfFQEvoYCP6nI/86qDtNSU0VhR9ufz+ngJippgtizrUuAbgBf4oeu61xZz+3ON7bh0j8bZ1TvGrp4IO3sj7OoZ44Wh6EQc2FhZxikLqrlkTQsnL6jh5AU1tNb8ibPnwyYQSlXMpzzgpTzgpbEyP4nP/IETIduice05r+XCBSewvSvC9u4Idz3bzZZkJfeVQeyuz5G0KvjS8wtZOXqAk1qrWT2/mrp178J68gdm4qFnfwmV88yHBoC/HF7zf01CEdBnuwAAIABJREFUecv1Jsj+xdtNYPv2G6FuKQwfgEe+avrgnPwmGO+Hn73JnKF6zZdNFfP/vA7e/ot8FXS4E277P3DoUXN79z1w1Q/NNnJG203y+vnbzONe9M9mSMNk4S7Y/GPz4fKK90GwZubzNz5ghj8uOmtm5dfEPy9jAvyjHKiH42kODIyzfyDK/oHxiYRy+1BsShK5uaqMhfUhTltYy+tOnU9bXTm7esYIeOH1HV9jXf9dPNf6FgaW/yP9ySinPPd+fpj8Ft9f8QOalp5C50iMrlGTeHpozwC3PJ0Ppn0ei0UNIZY1VWaTzpUsa6rghKZKasoP8/zIUeU4Lv1jSTpHYnSMxOgcNonCXMKwZzQx7SSDOQBvLnf4gvN9Lkg9wPbQmYx7a1iS3MOGzGY8uPQ5zXyDv+K3sVcQSzvEUzaTt/L1+/fSWFlGW105C+vNyYiFdaGJ2621QR1kzSLHcRkIj9MZSdMxPDWJ3Dkc49TIQ3zQewdjbj0/zFzNHhYxrzrI95P30E0zI75mTvYOUuZx8FrwbGo9r05u5XLnITam1zKe8PLIjnHWJJ7mNqeNDrdl4rHnVQdZ1BBicX2IZbUWi2t9tM5rZXFDiNpQgURPcgwmt2eaLhE21bmBisOsj8DgHph/WuGgNhE2Sd3aRbDkVTM/q6NDsPE75gTomR+ExWdPXR/uMjPIb7/NDKG7/Kumz3HO4D5TUdH7LGCZasWr/yd/YrR7m/les7xwxbfNUPkfvxbefbsJgl94xFQjr7wMLvi8CYD/+wK4+d3wwQehZxsceMAE1Lm+cK94n/n++u0XwMlAdMAM9Zts0dlQv8z0oXNtRhvWsfvAEAeHorwwGOPgYJSuwSi3ul4Cls03n47zS3s+Xlyay9K0lKVYVJ5kIQGIwonVST5V3zHRKGP1q68CIGM7jMTSDEeTDI2nGIqm6Isk2NYxwl3PdjP546cq6GNJQwWLG0IsbTQnLZc2miR0g5LPL9qfU4w8Gkuxr3+c3X1jbGsfZWvHKPv6xwHzVl7ZXMWlJ81j3aJa1i2qY1lT5Z9O5rguhLuILThzymLLsqgK+vG2ngDbzbLWU87nqUsu5ulDI2w+OMzmQyN8bVOMK/31lD3wFcqtcX4YPRffYy+wal41q+dVUbfobGhYbooskmMmjnzbjfliiHM/bk783P2PJom88Tvw7E3mBNIZ18CC0+HHl8Iv32NORvkCZoLAez9lhuwvWGcKMGJDJv71lZm/advP4e5PQjpqtn/OR8zJKF9Z/u/edaf53EiMwvmfhfV/OTW+dV0zj8r2281B+arXzRxlCNC33UyQNP+0w8e/6bg5FjiKxhJpXhg0cXHu+GV7d5hIwrQJ9HosVjRX8qoVjZzUWsOJ86pY2lRBS1UQj8eamIjLY6dY1Hsfqw7+jIaISR50NZ3Hkyd9nnh5G+/YYE5EDI0n2ds/zt4+k5Tc2RPhls0dRFNmFKnfa7G8uYo186s5qdUknU+cX60YeRa4rktfJMkLg1EODkXZm00m7+sfpyecmLhfmc/DsqZK1i+p4y2NCzkj+gBLt/bRfvF/8eTai6kp92NhXuNP3WomDks4HpzxV1Lb9Qzfb7iZu4Kvoy/ppztRxpZwJQ/cvWti+wGfhxMaK1jWnD92Wt5UyQlNFaV7Qv44M5ZI05NtMdozGs9ej09ZlnuPA3ixeY/3t3zcdwuVVoIbMxdyXeZthJlaXPCN+/fisaC+oozmqjJaa8tpqyuntTZIa225uV1bTmPlUU4yuq7JsVQ2F/68dl1ofwKwTM5j+n0c2xQ07P0drLkCVrx26veCnTEx6QP/atqxXfJFOOXq/HYyKbPusW8AronJX/NlOOP95j6ZFNz+12bC3LM+bOYQ+fVHzHfcKz9mHv+W95kJqd93t2n9lhg1sfnDX4HzPmnmKKloNNfBfK9deq2Jvzd+14ygr10ES6edeF33bvPd+dD/M/OSNCyfWBWOp+kYjpFmFeuyyz52zwBbOpYwkPQTd/Lv30/62ljt28IDQ/Xc55gTUX7LwT/STjxtF5z3F0xuJVes01ZXTltdiAV15RPX/9yPpy33cM/ckW7IsrzAHuASoBN4Cni767o7Ct1//fr17ubNm4vy2MWWsR1iaZvRaJqhaJLhqDn4GhpP0TUao33iwD82ZWjW4oYQJ86rZvX8Kk7JJpNbqg9TpfvHHHwMfnI595/xA/oaz56xumZsH6979C+IBlu444LfTwRQkK0AGBpn3n+txp8Z5w9VV/DZ5PvojeS/lKvKfPyv7zO0OV0E3QQPr/m/jKy8ivqKgPkJ+Wm+7c14BnZitZ1hhiu867Z8S41M0syo3fscvOm/Tc/msV54yw2w4mJzYP+zN5kD/zf/2FRt/Oaj5oPm8q9ARbM5e5UIwyVfMv2hH/uGCbwBVl0Ou+4yZ8LO/Ric83cQ7oDHvmlmDXVtcB0oqzb9LM/6EIQaYf/95gzbnnvNwXtFE6x9G6x7j0kIxIZNYnvHHebAv3qBaeex5o35iRKH9sP+P5if+IhJkC+70MxA7vXjZlKkup/DPrQRu+c5wuWL6K0+lUNlq+hPeOgNx+kbDuMb2k3t+F6S6Qw7nUXsddtwvGUsachWFjeFOKk6xrKKJAsWLqWibl7+A911YWgfB277IvWRndSO76O78Rw6mi+auE8gNcrJB36E7Q0Q/OjTUNEw5TUynsxkE9vj7O/PJ7cPDkWnvGargj7aaspoqwvSWlfJ/Nry7GsgQF1FgIaKADUBCLljBCrqsHxlzODYpkrQ4zUf8tO/4FwXkhGTUKpoKly5bqdhvM98wZXXFd5GfAQyCbONQicOMkmzH8GamZWIYM6yxofNayfUWPjgKxUz9wk1FD7AsrNJIW/AVD5N2s9UxmE8kSYeHiA2NsqgVcdAwmJwLDnRP7I7HGdkeAB3tJsRu4w+6rCzEws0VZWxsDbIKVXjnBToZV7IJdS6msaFJzK/oYqy8W64+Z2mqvGCz8Kr/mHib3jqpn+nKnaIRX33E0r2M1K5nEPzLiUeqGc07WMg5ac/6Se48vx8wnIkTtfI1Gp5y4KWquyXZW2QBRU2NVWV1FVV0lhZRn1FgKqgj8qgj6oyP0EnipWOm+erUJIwFYNov/mfBGtn/l/ttJl8wclAVevM14bjmN8f74fKFvO/n/x/y70uRttNMrNu8cykZypqPotiQyZIqVk4dV8zSbN+eL/Zz8aV5nFy++rYZn3/djM7edNqaF6dT5y6rjnp1rPN9NStXWQSmo2rcD1eMrZDom8/qQOPYLU/Qdq26alZx77QWvamm+iLJPEM7WVN+EHOST7Gag6x2V3JPfaZ3GufSbqylcsrd3NN4noWJ3YTqVhCeXoYX2oM55S34F37VvjZm2hvvpCepnOn/OmWY7Nuz9cZCy2ks/l8mke2MC+6G5JmmFi49VXsbnszT/o30D4cpaHnYdaFf8+rnKcIkOZx5yR+7ZzDY/5zqK5t4NTQEBfaj3P6+IM0R/cwVr2S0RNeR3rVFYRa11DtTRDcfx+e5281n8ceH6x8relvuuI15n1z8BETFO74NWTi5gTn2rfCae+EplXZthI/MNW96aj5Q5rXwJkfMNV96Tg8/k148odmSF2wxgSsS18N533aJJGf+I4JXl3HbHfXXTDeC+veBRf9C+z7vak29gXgjd81Jyxv/QDEBs130rxTTcVFsBbec7tp+dT7HNzwF2abb/imqcIINcD778/PSdC3A354samoiA+bv/9Dj0/5vHIPPIT10ytw/CEcfyXPXP04owmH/rEk/ZEk/WMJTm//CVeN/BCAUxM/IJI9+PF7LRbWh1jaUMEXBz5GW/R5nrvgxxzs6KIhkMY76e0VTA6ydt936Wk4i/Z5r5lYvuHqP91jMpVx6ByJcXAoysHB7OVQLDvSJjY1+VzmY3FjiNaacpqry2iuCtJcVUZzdRk15QFqyn1UBf1UB00F/VxPRluW9bTruuv/9D2PeLslESM7jks0lWE8mWFoPEX/WCL7uk3SG0lwYGCcff2md3JOfUWAdQtrJ5LJp7bVUBV8CYm7+Chct5gtq/+BXUvfW/AuV/3+lZSlI9xx3j288YJzpv56yib283fTcOguBr0tXOJ8k5F4PvnQXFXG34fu5u3hH5HylDNUfxq7L76epuog1dnXcNXe/8Xzvx+Ate8wyeUTr4Crf5L/vnjuV2aek9Pfa5ICD3/FVEpf9SPz/fbEd8zQ3qWvhjd+xxxo77jDnER7/dfNAfXmH5nPoKt+aGKeez9rijOaVpvvpoOPmIlBX/tv+QqvR79uekpbXhMjN6wwIxJzn5nP/8rExz3PmP1sWm0O0Ne+zRzcD+03c6zsuMNUlM07xcTGa66ExhXme7B7q5k0fP8fzDaWXwTLLoLW08DjxU1FSR96kswLj+MO7GK0aiWdVWvZ519JT9SifyzBwEAf5QPP0Zbci42X552l7PUuZeH8eZzUWs3JrTWc0uCy0tdLWXzAjBhpWD71RGWkm4cfuJfG0WdY2vUbylNDhCuWsnvxO/E5CU7d+x1cPGxd/XHOvOrjhWM9zGv50OAY23vG2NEzlk1yRya9dl0WlqdZUetS1dDK/IZaFtSV05A7VqoIUOe3qbUH8ZdVmP/39Mr0XAyTHIeqeYVj01TMxEGBypkxTm4bsSEzvLxqfuEYOh03x2Fl1TNiU/OnuGYb6biJpXwFThqn4ybWClYXjtccx8S/dtJ8bxfaRiKc3UateV1N2kbadhgbixAfOEgsnqDP00x3ws/AWJK+SML8DA7hHd5Pk91Hn1vHfnc+aX8Ny5srWdFUwZmVvaxLP8PC8FOmtcUJ58EJ55vX6/fPM/v2N5tmPIebbvnalNvLO35F7fg+tq34CBlf/rW16vK/Zf9gNFsMFJ0oCuoYzn/vWRa01ZVzQmMl82uCNFeb77yW6iCNlQGqy/1UBX1UB/2UeS0s1z58sZPrmv+rx3f4kzrpuDl+CtUX3o6dMcdPvrLDH4PFhsz/prKl8GswOWaO3QMh8xqb/jiZFEQ6zTZqFs58HMcxr+HRdvP6qV0883FiwzC0z/wt9UtNrDz5cZLjZPp3k+zbS9xfy3BoKf1uPcPxNMPjSUbHY9C/i9DIDjLREZ5OLODp5AJGMTG/jwwnew5yfnA/6/wHiZW3Mdx4OqnW9dTXN9G74zGu6PwK82N7OFh3DuHQYk7p+iUJfzUPLf47nmu4jEw6Sd3o85zp2UXlyHZ6PPPZap3EI8kT2DvqYSyZZqHVz1menZzl2UGFlWJ32al01Z5Oqn41DVXlLAimWJ3YxuLRTdSOPo/duBr3hAsIrLiIYG0zlmObyWZ335PPV6x4Day6zJyI9AVMHPz8reZneL8p7jv5KvPTssYc6277uan0HT5gnr+G5aaIYe07zHHQszeZ74Xh/eAtM++N5jXmROlJf2Hm4rrvs+ZE6tJXm/9LzzaT77j0OhNb33qNWXb6e81ourv/wcTPKy+Dy66DO//exPqXfAle+dFswvlD5vvm7L81x3hPft/E2bm5wFzXjOh59iazr8/cCFd8a2qFMpjixgMPQjqGc95nGD/7E4wlMkTiaSJxM5r37HsvoyZ6kB3V5/K1+n+hO5ygcyTGWPZEZROjPBX8GxKunytrb6Ui0UNjIEN9IE2dP0ONz2Z9/FFeMfQbnm17O2NVK/Ba5qW94epP4LouibRDNJUhmswwGkvTF0nQG0nQGzY/3dlRpT3hxESngpyW6jKTeK4tp6W6jIbKMhoqAjRWmcvqoJ/KoI/KMt9xM3fAi42Ri5lgPhv4F9d1X5u9/RkA13X/vdD9Zyt4/vd7drLpwDAZxzGTQjhmYohE2iGWyhBP2zP6eU1WG/KzqD7EwvoQi7I/K1uqWD2vioqyIhWEP3sL3PZ+nln+NyQKzJTttROs3/X/GKo+iX0Lryp8wHjDm8yb/oMPQetpDI0n2d4dYU/fGJ0jcZYf/AXvGv4Wz7jLuDL5xRkTAZ1oHeLOwGfxWi5ftd7H7cE3EvB58FpmKEmjNcrXw39PozPIuFXJdQ1f4oXgyXg85nxxhR3h4wOf54TULjy47A+s5r8aPk2/31QkV9kjvH/oa5yW2ETSKqPMTfJE6EJuqbuGYV8zjZlerh75IRtiDzHmqabCGSNtBXi48jLurX4zlXaEyyO/5IzYw9h4GfdWUWcPE/HU8njlxRwIrGZD9AHWxjfiw6bLv4iWdBc+bAa9zWwpP5t56S7WJLeaZZ4mHCyanX4Auj3zCVvVrLT34sUhSjn7WcByt4OQZYLPEbeSOstU5KRdLzvcxVR6kiyhBy/OlOfTtTzQsAKrstlUc4c7TeI9x1duehdVtsDAThMMAGlvOT2N59DTcM6MoKEy1smJB6/HU7fYJOfSMROIZBImyPX4zZe31z9x3fH4SNgekrEx3NgQ/uQIocwoFi4RKhhyqhimioQboNEK02SFqWcMj+Vm/+Yqhjx1jFq1hEjQ6A7T4A5P/L1pfIx46xnxNmJbfmrtYertAYJu/gTHmKeaEW8jEW8tlfYYtfYg1c4onmytbcoKMOJtZMTbgAXU2kPU2YMEXNNo1MEi4q1jxNtAzFNFlT1KnT1ElZPvrRS3Qoz4Ghn1NhBwk9Rmhqi1h/BhvnQyeAl76xnxNpCwyqm1h6mzB6lwo/n9tKoY9jYwYtUScmM0OEPUOiMTf2sKH4PU00cdKddDszvMfGuYoJX/vw65VfS59QxTxTxPhPnWIBVubNLrwkumcj7e2oV4MjFTVZnO7wOQTZwuNckvxzYndVZdOuUuueDZcm1ahp+irf9BLNcmXLHUnKjJqmtuMwnZ+CjER3BTUZyyKlL+GmLeasbdcqxkmPLUENX2CGWYvyXshhhwaxnEjBhoZoQWa4SK7HvBxsMwNQxZ9YxbldQRpskdotodm3jshFU28X/1u2ka7AFq7WE8k94rYU8tw74m4lYF9XY/DZkB/OSfzzR+hnzNjHibqHDGaMz0EJr0fJrXVw0DvnlkLD9NmW7q7OEp6zN4GfDNY8TbSL09QHOmd8o+AEStSrp9bfjIsCDdTqBAk9te73xGPXW0ZQ5R6UZnrI+7AXa7C2mxRphvmX0YditxsWiwzPPS59YR94RY4nYB0Fl5CpGGtSwafYrKcHZ4Z90SGDkI1W3mxMLat5kTNo/+pxktkkngYrF15cdI+2dWFC/s/R2tQ08A4FhePCf9hdlG1xYzSiXSZT53MkmTqA01kF79RsJUENp9B6FoO2krwIBvHq1pUzW2xVnO485JnOHZzRnWbjyWy35nPq3WEOVWih63nnt4JSErxWt4gnoiRAkR9VTQ7AwQtSp4vPw89vjXsCH5KOsST+LFodfbyjy7mxR+NobO5w+Vr6c13c5rxu9gSXofUasSHxn8bpInQ+dzZ+27GPK1cP7YnVwavpkaZ4RxTxWVzhhbys/hF3V/zaB/PmVOjCvCP+c1kVtxLC8BN8WuslP4XsNnGPE14boulXaYDwx/jdMTG3Gw6PG1cV3jtQz7TEWDi0tLuovPDH6GJruPmBXic03fotfXNrE+Y7u8IvYInxn7NwD+seyfeIzTSNlutleoQyrj8E3vf3K590m+l3kD12XePuX/VRfys7oiys/H/orB4GLuefXtLGmsYGlDBa21wfxcC/d9Dp74NvzNRjY9dPeM/7vHTnHGrmvNycmWiyeWv5gE8x+TSz4fGsoloE3yuTecoH8swUgsfdjf9XosAl4Pfq8ZiurzePD7LPweM8zd57XMcHevxY0fOGtWWjsdxQTzcREjf/nOHTzdPkLaNjFyrsdtPG0TTWaITaoGm64u5GdpY4Wp9Mv+rGiuoq2uvDgHTn074Htn8+hpX6F9/qUF73LZo2+iLDXK7RfczzvOWjzzDpu+D/d8Es7/DO55n6J/LMmu3jF290bY1TvGSF8H/z30XnBdLk1dyz63bdoGXG4r+xKnW7vZYy3lw8F/w/aG8Hkt83r2Wrwn/lOuit4MwIMVl/I/dR+dmKke4JWx+3n/0Ffx4ODg4dba93FP1dUTPSBPiz3BNcNfo9yJ4SVD1FPFbbXv46HKy3HxsC7+BG8d+T4tmW4inlqqnVH6fK3cW/MWNlZcxNr4Ri4L38zi1D7CnjrK3SgBN0W7/wQeqryMlFXGq8fvZUVqBxl89Pvm0Zoxo972+Vexo+xUViWfZ1V6JwCd3oXUOKNUuWM4WOzzrcB1XVbY+/DgMkoV3TSxwj2E37JxXItuGmizBgFIuV52uEto9ozRSn/h/23DcpNcHdpnkpjTVS8w34XDB0wiC3AsD+GKE+hrOJNwxbKJGLksNcLS7jupib5gEo/Vk/6HrmO+52JD5ic+mi+OyP6kbJvMaDf+aC/+/8/encfJUdf5H39/umdyQBJyDVcCTBAWkICgEXA5RImCGg92AQkKRJCwP8MdZD1QoyuoCIIsiysaBBURFFyBBeVQFtgNYoBwhICoRAyEZHIfJDDT/fn9UdU9PTXVMz0zNdNVM6/n49GP6a6q/ta3azqTT3/6U58qtseua3yUVvh4rfIxGm8btZOt1vjwM4AktSqvFo3X6txEueXU5Ks0sbhajWHcKUmbbJTW5Ju0Pj9WY4rrNaHQolHF9jipTQ1am59Y/n9nXKFF49pWd4iDNuS209r8RG3Mb6cxhXUaV1il0cUNFfNo1JqGJq3NT5SpqHGFVZ3GWJ8bq7UNTdqUG60xhXUaH5nHG+V4baLyatP4tlUaW1ilBrX/+1+XG6fV+YnabKM0rrBaE4ot2sa3tI+hRq3UBL2mCRrmW7WzWtRk7fOUpHW+rV7xiXo9t62abYWafHWnX71v2yQb/5bgd785fP9M2CP4XNMSvEc1fEwQF1UmsipEE8wj3lil/f/8PW0dNkHFXF4NbVvUUHg9iO9HjA0KXbYZHyTZLK+Cu7a0FrX5jVa9uWWTtHWDGlo3amRxs9rctMlHaqNGaqNvo4JyGm8bNc42arw2aoS16nWN0HqN1rrcdtpko7Sttmi8r9O44joNVxBHv27baH1+nDbkxqnNGjS2sEZjC6vLn0uKMm3Mbad1+fHamB+rbYsbNa5ttcYU15Y/P7WqUevyE7S2YYKKymtcYZXGR+LozbngPbghP06jC+s1obBC2xbb38dF5bQ2P0Gr801y5TSx8JrGFVaX9yEF8fyq/A5alxuvcYXVaiq8pmHq+H//2tw4rcjvqJwXtVPhFY32TR3Wtymv5baDWjROO/oK7axVnX5vG3yk/uKTNFyt2sOWaZh1/v/n9ZE7qThqJ22zdolybeH7b/ROwRcdXpBk0sR/kK96Qa0No/W3HY/WmjH7SGbaZutran71vzV6yyvaOmy8hrWuV87DfYzdLYiPi23B56jt91VxyxrlNgTx+pZh4/WGDdfYN4K/R+s1Wst8ovbWUuXNtdmH61mfor3s7xprwe/w+eIu2snWaDvbrDfVoCdtqgq5Br298LRG6E29biO1NjdBkwrLVFROi4ftr8UjDtRb33hKU99YpJyKeq1hZ01sW6kGtemFYVP1+1EflOR67+a79Q9vLFarGrU5N0pji2u1dNge+u8xM7Vom0P0ztcf1ofW36xJrX/TxtwYjS5u0IqGnXXL+DO1aOQ/yuR616Z7ddy66zW2sEZv2jC9acN1/YQL9PjIoHjFvKjpG/9LH1/3Q+XVJsk0f9x5emhU+//H5kV9ct339P5Nv5Yk3TPqWP107L+U17tLjf6mvtRyofZofUF/adhTc7f7jt4omt5sC3JwrYWitm97Vb8onKcGL+jwN7+rV7xzPmx2/k59ofFmXZ07Wb/Z7uPacbsR2qXiDN3J47bRPr84QrlcTnbOk53+DkjS2A0vaK+/36LndztJ60e1V0H3NFZuKxS1YuMbWhaeeRrcwvvrXlfLxje0tbVY9fkNOdO2wxuCWDhnYUxhaujwuP3+zWccUpeEdD0SzMdJOsbdPx0+PlnSwe5+VsU2syXNDh/upY4NktDRRCnmry2GOt4XiMP7AlG8JxCH90XXdnP3pu436xli5ETxHkYc3heohvcG4vC+QDW8N+LVFCMn2YM5Lo3eIXvt7tdJui7BfQ5aZrawP6pokG28LxCH9wWieE8gDu+LuiFGTgjvYcThfYFqeG8gDu8LVMN7o2+SPP9wmaSKy6trsqRXExwfAAAAyBpiZAAAAAxqSSaY/yhpTzObYmbDJJ0o6Y4ExwcAAACyhhgZAAAAg1piLTLcvc3MzpL0W0l5Sde7++Kkxh+COE0ScXhfIA7vC0TxnkAc3hd1QIycKN7DiMP7AtXw3kAc3heohvdGHyR2kT8AAAAAAAAAwNCSZIsMAAAAAAAAAMAQQoIZAAAAAAAAANArJJhTxsyuN7OVZvZsveeC9DCzXczs92a2xMwWm9m59Z4T6svMRpjZY2b2VPie+Gq954T0MLO8mT1pZnfVey5IBzNbambPmNkiM1tY7/kAtSI2RjXEx4hDjIzuECcjijg5GfRgThkzO0LSJkk/dvep9Z4P0sHMdpK0k7s/YWajJT0u6WPu/lydp4Y6MTOTtK27bzKzRkmPSDrX3R+t89SQAmZ2gaRpksa4+4x6zwf1Z2ZLJU1z91X1ngvQE8TGqIb4GHGIkdEd4mREEScngwrmlHH3hyStqfc8kC7uvtzdnwjvb5S0RNKk+s4K9eSBTeHDxvDGN4aQmU2W9CFJP6z3XACgr4iNUQ3xMeIQI6MrxMlA/yHBDGSMmTVLOlDSH+o7E9RbeHrXIkkrJd3n7rwnIElXSbpIUrHeE0GquKR7zexxM5td78kAQJKIj1GJGBldIE5GHOLkBJBgBjLEzEZJuk3See6+od7zQX25e8HdD5A0WdJBZsapw0Ocmc2QtNLdH6/3XJA6h7r72yV9QNKcsO0AAGQe8TGiiJERhzgZXSBOTgAJZiAjwh5it0m6yd2Rf5XCAAAgAElEQVRvr/d8kB7uvk7Sg5KOqfNUUH+HSvpI2Efs55Lea2Y/re+UkAbu/mr4c6WkX0k6qL4zAoC+Iz5GV4iREUGcjFjEyckgwQxkQHixivmSlrj7d+o9H9SfmTWZ2djw/khJ0yU9X99Zod7c/fPuPtndmyWdKOl37v7JOk8LdWZm24YXwJKZbSvp/ZKere+sAKBviI8RhxgZ1RAnIw5xcnJIMKeMmd0saYGkvcxsmZmdXu85IRUOlXSygm9ZF4W3D9Z7UqirnST93syelvRHBf3l7qrznACk0w6SHjGzpyQ9Jum/3f03dZ4TUBNiY3SB+BhxiJEB9ARxckLMnQuqAgAAAAAAAAB6jgpmAAAAAAAAAECvkGAGAAAAAAAAAPQKCWYAAAAAAAAAQK+QYAYAAAAAAAAA9AoJZgAAAAAAAABAr5BgBgAAAAAAAAD0CglmAIgws4KZLaq4NZvZkWZ2V7h+lpkVzWz/iuc8a2bNFY8PNDM3s6MjY7uZXVHx+EIzmxfe/21kv6+a2R+6mWuDma0ys29Elj9oZgsrHk8zswfD++XXEvOcFyr2/8uKdZ80s6fNbLGZPWVmPzSzsV09z8zmmdkr4bLnzGxmV68FAAAAychKPBvGkdMqHjeb2bPh/SPDfX24Yv1dZnZkxeMmM2s1szMj426KPJ5lZteE9ytj1EVm9s2KuVTGtMdVPP/YcC57R+a6JfJ6TwnXLTWzidXmEFle3tbMdjCzn5nZX83scTNbYGbHVhyP9ZH9TQ/XlX7fz5rZnaU4HQAGCglmAOhsi7sfUHFbGrPNMklf7GKMmZIeCX9WekPSP0UDTkly96NL+5R0qKQNki7uZq7vl/SCpBPMzCLrtjezD3Tz/KhPVLzu4yTJzI6RdL6kD7j7vpLeLun/JO3Q1fNCV4av56OSvm9mjT2cDwAAAHouS/FsV7qb4/GSHo2ZY3eurDg2n6tYXhnT/rJieelYnBgZ5y+R4/zjHs6jLIzl/0vSQ+6+u7u/I9zf5IrNHo7s7/5ween3PVXSGklzejsPAOgNEswA0Dt3SdrXzPaKrgiDw+MkzZL0fjMbUbG6TdJ1ChK2XfmupLvd/b5utpsZbvuypEMi676tvgX0JV+UdKG7vyJJ7l5w9+vd/YVaB3D3FyW9LmlcAvMBAABA36Ulnu3KU5LWm9n7qqyfKWmupMlmNqkP+6nKzEYpSJafrs4J5iS9V9Kb7v6fpQXu/jd3//cejrNAUr8cCwCohgQzAHQ2suK0s19V2aYo6TJJX4hZd6ikl9z9L5IelPTByPr/kPQJM9subuDwNLhpkj7f1STNbKSkoxR8OLhZnSs3Fkh6w8ze09U4ETdVvPZvh8v2lfREL55XOde3S3rR3Vf2YC4AAADonUzEszX6umKKJsxsF0k7uvtjkm6V9PEejHl+xfGpbAFSGdNOCJd9TNJv3P1PktaEcW3JWyItKw7v2UvroJaY+/DI/t5SudLM8go+H9zRh3kAQI811HsCAJBCW8LT+rrzM0lfNLMpkeUzJf08vP9zSSdLur200t03mNmPJZ0jaUvlE8PKi6slHe3ub3Sz/xmSfu/ur5vZbZK+ZGbnu3uhYptSQP6vNbweKTgtcGG1lWa2n6SfSBot6Qvufks3zzvfzM6QtLukY2qcAwAAAPomK/Gsd7fM3R82M8Ukb09UkFguzXG+pO/UuK8r3f3ymG3iYtqZkq6q2M9MtSeC/1Ljce4xM/sPSYcpqGp+Z7j4YXefEbP5SDNbJKlZ0uOS+lI1DgA9RgUzAPSSu7dJukIVyduwauCfJX3ZzJZK+ndJHzCz0ZGnX6XgNLttK55rkm6U9E13f66GKcyUND3cz+OSJkjqUK3s7r+TNEKd22f0xGIFfZfl7s+EQfQ9kkbW8Nwr3X0vBRUlP46cXgkAAIA6SkE8u1odW6iNl7QqZrtL1LkX80xJs8I53iHpbWa2Z7hui5kNq2HcLoVVzO+V9MNwP5+V9PGYa58koRxzS5K7z1FQjdxUw3NLXyjsJmmY6MEMYICRYAaAvrlB0nS1B37TJT3l7ru4e7O77ybpNgWn1pW5+xoFFRenVyy+UNJWd/+P7nZqZmMUVDTsGu6nWUEgGXeBk0skXdSTFxXxDUmXm1nlBUZqSS6XufvtkhZKOrUP8wAAAEDyblAd4tnQg5I+WZGwPVXS76Mbufu9ChLRb5OksG/0tu4+qSIW/obaeyT/j6RPhtuOlHRC3Lg1OE7Sj919t3A/u0h6SUEcnrTfSRphZv+vYtk2PRnA3dcrqCq/kItrAxhIJJgBoA/c/U0FpwBuHy6aKSna5+42SSfFPP0KSZVX3/66pH0ifdWqBcL/JOl3kdMOfy3pI2Y2PDLHuyW1RJ5/lJktq7i9K1xe2Xfu/ornXy3pHjN7zsz+T1JB0m8rxuv0vBhfk3SBmfF/DwAAQErUMZ6VgosFbpT0lJk9JWmUpLjWFVJQNFEqeKg2x1KxxbmS/ilsG/GopF+4+0NdzKOa7o5FtAfzORXbPV0Ra5dad8yKxODlAg53dwVJ/Heb2Utm9piCavDKVnfRHszHRSfs7k8quDhif16QEAA6sOBvGAAAAAAAAAAAPUMVGQAAAAAAAACgVxrqPQEAQNfCK0gfGln8XXf/UT3mAwAAAPQE8SwADG60yAAAAAAAAAAA9AotMgAAAAAAAAAAvUKCGQAAAAAAAADQKySYAQAAAAAAAAC9QoIZAAAAAAAAANArJJgBAAAAAAAAAL1CghkAAAAAAAAA0CskmAEAAAAAAAAAvUKCGQAAAAAAAADQKySYAQAAAAAAAAC9QoIZAAAAAIAEmdl/mtmXEhprVzPbZGb58PGDZvbpJMYOx7vHzE5NaryKcUea2Z1mtt7MfpH0+D2Yxzwz+2m99g8AQwEJZmAQIZAlkAUAAED/MrOlZrbFzDaa2Toz+z8z+xczK3++dvd/cfd/q3Gs6V1t4+4vu/sody8kMPdOMaq7f8Ddb+zr2DGOk7SDpAnufnw/jJ9pZnakmRXDz1yl253hug6/JzNzM3um8j1mZl83sxsiY24bjnN3zP6WmtkKM9u2YtmnzezB8P4XInPZZGabw32fElleDP8NlB6fGo49sWLs4Wa2xMzONLPmcJzS9kvN7HNJHk8A9UWCGcgIAtmaEch2IWuBbDevpdP72MxmmdkjMds+aGZrzWx4zPJPR5YdaWbLKh5/1MwWmdkGM1tlZg+YWXPFMWutmPsSM/vnmP1PCY/7tZHlpWC7IbL8BjP7esVrKsQcp50rjkMpwH8tfO6oro4dAADosw+7+2hJu0n6pqR/lTQ/6Z1EY4SM2U3Sn9y9radPzPjr7olXw89cpduHu9h2Z0kndjPecZLekPR+M9spZn2DpHPjnujul0bmMkrSlZKek3RbZPnLCv4NlJbdKOkuSd+tGPJiScslXVexbGz4/JmSvmxmx3TzegBkBAlmIFsIZLtHINu9zASy3b+U7oXJ4MMluaSP9PC5e0j6saS5kraTNEXStZKKFZvdUjH38yT91Mx2iAx1iqS1kk6MJrlrtCB6nNz91Yr1Hw73f4CkAyV9vhf7AAAAPeTu6939Dkkfl3SqmU2VOn1ZPNHM7gqLRNaY2cNmljOzn0jaVdKd4RfFF1V8+Xy6mb0s6XdVvpB+i5k9ZsFZe782s/Hhvjp8UR4uW2pm08Nk3hckfTzc31Ph+vIX7uG8Ljazv5nZSjP7sZltF64rzeNUM3s5/OL9i3HHxcy+KunLFfs6vcaxy6+7yriVX/z/pZSgNLOdzeyO8Pj+2czOqPL8qscnvD/PzH5hZj+1oLDnGTP7BzP7fDjnv5vZ+yue+6CZ/ZuZ/W+4/b1WUcWbsMskfbWbzyynSvpPSU9L+kTM+m9LutDMxna3MzP7oKRzJB3n7ptrmN8Fkt5tZh8K/x2cJekMd/fohu6+QNJiSVNrGBdABpBgBjKIQJZAlkC2R06R9KikG8K59sQBkl5y9wc8sNHdb3P3l+M2dvffStoo6S0xc7hYUqukrhL6feLur0n6bThvAAAwQNz9MUnLFHypHTU3XNek4Ey7LwRP8ZPVsRL0sornvFvSPpKOrrLLUySdpqAYoE3S1TXM8TeSLlX7l+Nvi9lsVnh7j6TdJY2SdE1km8Mk7SXpKAVVqPvE7OsrkX3Nr3Hsqq/bzA5S8MX/ZyWNlXSEpKXh6psVHOOdFRQ/XGpmR8Udhxp8WNJPJI2T9KSC2ConaZKkr0n6fmT7kyR9StL2koZJurBizk+b2Um9nEfU7ZI2KDiGnZjZrpKOlHRTeIs7G3ChpAcr51hlrGYFx2C2uy+pZXLuvl7S/1PwueB6SV9197/EjG1mdqikfRUcXwCDAAlmIMMIZDvti0BWQyuQrdEpFfM72jpXF3flCUl7m9mVZvYe66L1RBgsf0jB7+O5iuWHS5os6eeSblX8MUqEmU2W9AFJf+6vfQAAgKpelTQ+ZnmrpJ0k7ebure7+cFxVZ8Q8d9/s7luqrP+Juz8bfiH/JUknWHjtlD76hKTvuPtf3X2TgrOiTowUG3zV3be4+1OSnpIUF9/3duyuXvfpkq539/vcvejur7j782a2i4LPCv/q7lvdfZGkH0o6uUevvN3D7v7b8IzIXyj4PPVNd29VEM81RwonfuTufwrnfKsqvuh39/3d/Wdd7GvnsCCodDuhi21dwe/6yxZ/Rtwpkp529+cUfE7Z18wOjNnuy5LONrOmuJ2EY/9C0k3u/vMu5tN5gu53KijsyCn+s+IqSWsU/H4+5+4P9GR8AOlFghnIPgLZvo9NIFtd2gPZ/6p8LQraV1SOe5iCtim3uvvjkv6iIDlfE3f/q4IE+iQFx3mVde5xfEK4782S7pB0qbuvq1h/qqR73H2tpJ9J+oCZbd+D1yhJh0R+Z9FqkP8ys42S/i5ppaSv9HB8AADQd5MUJM+ivq3gy997zeyvVtvFzf7eg/V/k9QoKYkz2nYOx6scu0FBwUrJaxX3X1dQwJHU2F297l0UxHJx465x942RsSfVOK+oFRX3t0ha5e3XpSl9Xqh8zb09HlLQum5sxe3WrjZ297sVFAvNjlldKqpQ2ErtfxRz9p67P6ugX3K19+F3FRQTza35VXS0WNLz7l6MWTfR3ce5+z7u3m2xEoDsIMEMZB+BbN/HJpDtQsoD2Y9VvhZJn4msP1XSve6+Knz8s8j82hS8jys1KviCpjT3R939BHdvUnC2wBGSKtu03BrufxsFrTFOMbMzJcnMRko6Xu3HaIGCY1lKcpd6hXc5B0mPRn5n0RYcH/OgP/uRkvZWMv8uAQBAjczsnQriwE4XG/agxdZcd99dwVlrF1Sc9VatAKS7wpBdKu7vqiBuWKXgC+9tKuaVV1C4UOu4ryr4cr5y7DZ1jFV7q5axu5rf39W5DVlp3PFmNjoy9isx23Z3fLLgYgWxaOXr+EdJe0r6vAUXfX5N0sGSZlZpdfcVSWco8tnFzE6W9M+STggLXQCgJiSYgQwjkK0JgWwyMhfIhsndExRcbKQ0v/Mlvc3MShXwL0tqjjx1ijp+KVHm7n9U0DYk9oIk7r5U0j1q77N8rKQxkq6tmMMktbfJWK7g31HNc+iKu/+Pgl7Tl/f0uQAAoOfMbIyZzVBwxtlP3f2ZmG1mmNkeZmYKWo8VwpsUxKS792LXnzSzt5rZNgraqf0yLE74k6QRFlxorVFBDFd5FtoKBWfGVcsF3CzpfDObEp6xVWo/1+MLaPfD2PMlfcrMjrLgOiuTzGxvd/+7pP+T9A0zG2Fm+ys4C/GmmDG6Oz6p5+4PSnpGHYsmTpV0n6S3Kjiz8QAF8eo2CtqnRcf4s6RbFFz7RJJkwXV9rpX0ifCYAkDNSDADGUQg2yMEsgnIaCD7MQXv+cr57SPpYbUneG9R8Ps9yAL/oCAJ/fNwfoeZ2RmllhZmtrekjyjoLdeJBT2Qj1FwaqAUHKPrJe1XMYdDJR1gZvuF/35uk3SJmU0ws0YzmxnO+Z5evu6rJL3PzLjQHwAA/efOivZUX5T0HQXXx4izp6T7JW2StEDStWFsJUnfkHRx2AKry+tVRPxEwZfKr0kaoTC+8uBCa59R0LrtFQWFDpUXm/5F+HO1mT0RM+714dgPSXpJ0lZJZ/dgXl3p09geXH/mU5KulLRewZlzpUKSmQq+sH9V0q8kfcXd74sZo7vjkygzW2xmcRfB7quLFbZJNLMRCooq/t3dX6u4vaTgeFe7yPXXJG1b8fiC8PHtFlwwvfL2hX54DQAGE3fnxo1bBm4KLiy3RdJGBQHVAklzJOUrtrlB0tfD++eHzykFTV+q2O6jCio31ym48FqzgirehoptOixTcJG2b0h6TEHC+k4FPbRK289SUI25MhxzqaTp4boJCqqs10p6omK8T4f3cwp69P5dUoukn0oaFzeP6HNjjtM8BUl39WbsKmMeK+np8Nj/WdLR4fLJCto+rFHQRuNfuphHV8cnuu10SUsrHjeE85wc9/rDsR+peLxYQcI27rUcKWlZjcfOJe1R8fjgcNkNCj7IrFVwscjoONcq+PJBla8zfLyLgg8TD4aPr5dUVPCBK3r7Qg3/JqZHlpWPhaTfSLoi5nknKPgwVnpvnxYesw3h7/dzknLhuqkK3usrwjktlfQtSY0Vx6y1Ys7LFVw5exsFlcptkvaLmcPdki4P749T+4ectZL+V9KhkddUiDk+7+ziOHxP0m31/JvFjRs3bty4cePGjRs3btyGxs3cuztzHQAAAAAAAACAzmiRAQAAAAAAAADolbiLMAEAUHdmtquk56qsfqu7vzyQ8wEAAAAAAJ3RIgMAAAAAAAAA0Ct1q2CeOHGiNzc312v3AAAAQNnjjz++yt2b6j0PYmQAAACkRa0xct0SzM3NzVq4cGG9dg8AAACUmdnf6j0HiRgZAAAA6VFrjMxF/gAAAAAAAAAAvUKCGQAAAAAAAADQKySYAQAAAAAAAAC9UrcezAAAAKhNa2urli1bpq1bt9Z7Kpk3YsQITZ48WY2NjfWeCgAAAPqAGDk5fY2RSTADAACk3LJlyzR69Gg1NzfLzOo9ncxyd61evVrLli3TlClTEhvXzHaR9GNJO0oqSrrO3b9rZvMknSGpJdz0C+5+d2I7BgAAGMKIkZORRIxMiwwAAICU27p1qyZMmEDg3EdmpgkTJvRHlUubpLnuvo+kQyTNMbO3huuudPcDwhvJZQAAgIQQIycjiRiZCmYAAIAMIHBORn8cR3dfLml5eH+jmS2RNCnxHQEAAKADYuRk9PU4UsEMAFlUaJO2rpc2rpDWLpVWvSgVC/WeFQAMeWbWLOlASX8IF51lZk+b2fVmNq5uEwMAZJO7tHVDvWcBAF2ighkAsmb9K9I175RaN3dcPn2edNj59ZgRgAH2sz+8nOh4Jx28a7fbjBo1Sps2bUp0v1JQLXHBBRfoiiuukCRdfvnl2rRpk+bNm1f1OTfccINOO+00LVq0SPvvv78kaerUqbrrrrvU3Nyc+BxrZWajJN0m6Tx332Bm35P0b5I8/HmFpNNinjdb0mxJ2nXX7n8XAIAh5Pm7pNvPlC58QRo+ut6zAVKNGLl+MTIVzACQNRteCZLL75glfeg70se+J+WHS5tX1XtmANBjw4cP1+23365Vq3r2N2zy5Mm65JJL+mlWPWdmjQqSyze5++2S5O4r3L3g7kVJP5B0UNxz3f06d5/m7tOampoGbtIAgPRbH8b+W9fXeyYABlDWYmQSzACQNYXW4Oe+x0rvPF064CSpYUT7cgDoR+6uz372s5o6dar2228/3XLLLZKkz3zmM7rjjjskSccee6xOOy0o1J0/f74uvvjiquM1NDRo9uzZuvLKKzutu/POO3XwwQfrwAMP1PTp07VixYryuhkzZmjx4sV64YUXknx5vWJB07r5kpa4+3cqlu9Usdmxkp4d6LkBADKu2NbxJ4BUGuoxMglmAMiaYphIzjW2L8s3tC8HgH50++23a9GiRXrqqad0//3367Of/ayWL1+uI444Qg8//LAk6ZVXXtFzzz0nSXrkkUd0+OGHdznmnDlzdNNNN2n9+o7VWYcddpgeffRRPfnkkzrxxBN12WWXldflcjlddNFFuvTSSxN+hb1yqKSTJb3XzBaFtw9KuszMnjGzpyW9RxJ9jAAAPVOK8QskmIE0G+oxMj2YASBrSsFlviLBnGukghnAgHjkkUc0c+ZM5fN57bDDDnr3u9+tP/7xjzr88MN11VVX6bnnntNb3/pWrV27VsuXL9eCBQt09dVXdznmmDFjdMopp+jqq6/WyJEjy8uXLVumj3/841q+fLnefPNNTZkypcPzTjrpJF1yySV66aWX+uW11srdH5EUd+ntuwd6LgCAQaYU+1NMAqTaUI+RqWAGgKwpVzBXfEeYb+S0OQADwt1jl0+aNElr167Vb37zGx1xxBE6/PDDdeutt2rUqFEaPbr7ixKdd955mj9/vjZvbr+A6dlnn62zzjpLzzzzjL7//e9r69atHZ7T0NCguXPn6lvf+lbfXhQAAGlVrmAmwQyk2VCPkUkwA0DWlILLDhXMDQSdAAbEEUccoVtuuUWFQkEtLS166KGHdNBBwbXr3vWud+mqq64qB8+XX355t6f+lYwfP14nnHCC5s+fX162fv16TZo0SZJ04403xj5v1qxZuv/++9XS0tLHVwYAQAqVYnwqmIFUG+oxMi0yACBrYnswNxJ0AkPISQfvWrd9H3vssVqwYIHe9ra3ycx02WWXaccdd5QkHX744br33nu1xx57aLfddtOaNWtqDp4lae7cubrmmmvKj+fNm6fjjz9ekyZN0iGHHBJ7mt+wYcN0zjnn6Nxzz+37iwMAIG3owQzUjBi53UDHyFathLu/TZs2zRcuXFiXfQNApj11i/Sr2dLZT0gT3hIsu/YfpXHN0syf1XVqAPrHkiVLtM8++9R7GoNG3PE0s8fdfVqdplRGjAwA6OCez0l/+J4067+l5sPqPRsgVYiRk9WXGJkWGQCQNbE9mBuoYAYAAAAGG3owA8gAWmQAQNbE9mBuJOgEkFqrV6/WUUcd1Wn5Aw88oAkTJtRhRgAAZES5BzMtMoDBZjDFyCSYASBrSsFlpx7MBJ0A0mnChAlatGhRvacBAED2FAvhT2J9YLAZTDEyLTIAIGvKFcwV3xHmGqhgBgAAAAYbWmQAyAASzACQNeUezNEKZoJOAAAAYFApt8gg1geQXj1OMJvZ9Wa20syejVl3oZm5mU1MZnoAgE7owQwAAAAMDeUKZlpkAEiv3lQw3yDpmOhCM9tF0vskvdzHOQEAukIPZgAAAGBoKCWWqWAGkGI9vsifuz9kZs0xq66UdJGkX/dxTgCArhRaJctLuYrvCOnBDAwtC3+U7HjTPtXtJkuXLtWMGTP07LMdT2L78pe/rCOOOELTp0+v+tx58+Zp1KhRuvDCC2uajpnpggsu0BVXXCFJuvzyy7Vp0ybNmzev6nNuuOEGnXbaaVq0aJH2339/SdLUqVN11113qbm5uab9AgCQOvRgBmpHjNzJQMXIifRgNrOPSHrF3Z/qZrvZZrbQzBa2tLQksWsAGHqKrR3bY0j0YAZQN1/72te6DJx7Y/jw4br99tu1atWqHj1v8uTJuuSSSxKdCwAAdVXuwczZikCWDLUYuc8JZjPbRtIXJX25u23d/Tp3n+bu05qamvq6awAYmgptHdtjSGEPZoJOAP2rUCjojDPO0L777qv3v//92rJli2bNmqVf/vKXkqS7775be++9tw477DCdc845mjFjRvm5zz33nI488kjtvvvuuvrqq7vcT0NDg2bPnq0rr7yy07o777xTBx98sA488EBNnz5dK1asKK+bMWOGFi9erBdeeCGhVwwAQJ2VEstUMAOpRYycTAXzWyRNkfSUmS2VNFnSE2a2YwJjAwCiiq1SPtLhKN9ABTOAfvfiiy9qzpw5Wrx4scaOHavbbrutvG7r1q0688wzdc899+iRRx5R9Gy1559/Xr/97W/12GOP6atf/apaW7v+mzVnzhzddNNNWr9+fYflhx12mB599FE9+eSTOvHEE3XZZZeV1+VyOV100UW69NJLE3i1AACkQLmCmVgfSCti5AQSzO7+jLtv7+7N7t4saZmkt7v7a32eHQCgs0JrlQpmgk4A/WvKlCk64IADJEnveMc7tHTp0vK6559/XrvvvrumTJkiSZo5c2aH537oQx/S8OHDNXHiRG2//fYdqirijBkzRqecckqnSo5ly5bp6KOP1n777advf/vbWrx4cYf1J510kh599FG99NJLvX2ZAACkR5EWGUDaESP3IsFsZjdLWiBpLzNbZmanJz8tAEBVVXswE3QC6F/Dhw8v38/n82pra/+74+69fm415513nubPn6/NmzeXl5199tk666yz9Mwzz+j73/++tm7d2uE5DQ0Nmjt3rr71rW91Oz4AAKlXbpFBrA+kFTFyLxLM7j7T3Xdy90Z3n+zu8yPrm929Z92mAQC1K7RJuUiLjFwDFcwA6mrvvffWX//613LFxi233NLnMcePH68TTjhB8+e3h5vr16/XpEmTJEk33nhj7PNmzZql+++/v9MpiAAAZE4psUyLDCCThkqM3ND9JgCAVKlawUzQCQwZ0z5V7xl0MnLkSF177bU65phjNHHiRB100EGJjDt37lxdc8015cfz5s3T8ccfr0mTJumQQw6JPc1v2LBhOuecc3TuuecmMgcAAOqmFONTTAJ0jxi5bjGydVeq3V+mTZvmCxcurMu+ASDTbjlZWvWiNOfR9mW/u0R66DLpK+sks/rNDUC/WLJkifbZZ596T6NbmzZt0qhRo+TumjNnjvbcc0+df/759Z5WJ3HH08wed/dpdZpSGTEyAKCDq/aX1v1NetdZ0tGX1Hs2QKoQIyerLzFyny/yBwAYYMU2KR85AaVU0ZBP8MAAACAASURBVEwfZgB19IMf/EAHHHCA9t13X61fv15nnnlmvacEAEC2lXswU8EMZNVQiJFpkQEAWVNolXKRFhmlnsyFmPYZADBAzj///JqrMVavXq2jjjqq0/IHHnhAEyZMSHpqAABkUymxTDs8ILOGQoxMghkAsqZaD+bSOgDIgAkTJmjRokX1ngYAAOlGD2ZgSMlqjEyLDADImkJbTAVzY/s6AINSva6bMdhwHAEAmVKK72mFB8QitktGX48jCWYAyJpia0wP5ob2dQAGnREjRmj16tUE0H3k7lq9erVGjBhR76kAAFAbKpiBqoiRk5FEjEyLDADImkKrNHxMx2XlCmYCT2Awmjx5spYtW6aWlpZ6TyXzRowYocmTJ9d7GgAA1KZIBTNQDTFycvoaI5NgBoCsoQczMOQ0NjZqypQp9Z4GAAAYSO4kmIEuECOnBy0yACBrCm1SLvL9ID2YAQAAgMGlMqnMmYoAUowEMwBkTWwFMz2YAQAAgEGlMqlMnA8gxUgwA0DWFFrbK5ZL6MEMAAAADC6VSWXifAApRoIZALKm2NZesVxS7sFMiwwAAABgUKhsf0ecDyDFSDADQNbEVjA3tK8DAAAAkH1UMAPICBLMAJA1sT2YG9vXAQAAAMg+ejADyAgSzACQNfRgBgAAAAa/DhXMtMgAkF4kmAEgawqt9GAGAAAABjt6MAPICBLMAJA1RXowA0CamNkuZvZ7M1tiZovN7Nxw+Xgzu8/MXgx/jqv3XAEAGVJKKucaaZEBINVIMANAlhSLkhfpwQwA6dImaa677yPpEElzzOytkj4n6QF331PSA+FjAABqU4rtG7ehRQaAVCPBDABZUgoyc5EWGeUezASeADDQ3H25uz8R3t8oaYmkSZI+KunGcLMbJX2sPjMEAGRSKbZvHEkhCYBU63GC2cyuN7OVZvZsxbJvm9nzZva0mf3KzMYmO00AgKT2FhidKpjDhDOBJwDUlZk1SzpQ0h8k7eDuy6UgCS1p+yrPmW1mC81sYUtLy0BNFQCQduUK5pG0wgOQar2pYL5B0jGRZfdJmuru+0v6k6TP93FeAIA45QrmaA/mUgUzgScA1IuZjZJ0m6Tz3H1Drc9z9+vcfZq7T2tqauq/CQIAsqVQ0SKDQhIAKdbjBLO7PyRpTWTZve5eOi/7UUmTE5gbACCqdJocPZgBIFXMrFFBcvkmd789XLzCzHYK1+8kaWW95gcAyKAOFcy0wgOQXv3Rg/k0SffEreD0PwDoI3owA0DqmJlJmi9pibt/p2LVHZJODe+fKunXAz03AECG0YMZQEYkmmA2sy8quIr2TXHrOf0PAPqIHswAkEaHSjpZ0nvNbFF4+6Ckb0p6n5m9KOl94WMAAGpTrGiRQSs8ACnW0P0mtTGzUyXNkHSUu3tS4wIAKhTDKgZ6MANAarj7I5KsyuqjBnIuAIBBpFDRIkMuFQtSLl/XKQFAnEQSzGZ2jKR/lfRud389iTEBADHKFcyRP9/lHsy0yAAAAAAGhWJFi4zSYxLMAFKoxy0yzOxmSQsk7WVmy8zsdEnXSBot6b7wlMD/THieAACpogdztII5TDhTwQwAAAAMDqUEc8OI4CexPoCU6nEFs7vPjFk8P4G5AAC6U60Hs1mQZKYHMwAAADA4FCp6MEvE+gBSK9GL/AEA+lm1HsySlB9GVQMAAAAwWBQrezBLKtAOD0A6kWAGgCyp1oNZCpLO9GAGAAAABodCtAczxSQA0okEMwBkSbUezFKQdKaCGQAAABgcipEWGcT6AFKKBDMAZEmpiiHag1kKK5gJOgEAAIBBoRBpkcHZigBSigQzAGRJuYI5pkVGvpG+bAAAAMBgQQUzgIwgwQwAWVLuwRxXwdxABTMAAAAwWJR7MI8IfhLrA0gpEswAkCVd9mBupKoBAAAAGCyKbZLlpfyw9scAkEIkmAEgS7rtwUzQCQAAAAwKxdbgLMVSezza4QFIKRLMAJAlXfZgbqCCGQAAABgsCm1BYUkp9qdFBoCUIsEMAFnSZQ/mRoJOAAAAYLAoVTCXYn+KSQCkFAlmAMiSUgsMejADAAAAg1uhNaxgDmN/ikkApBQJZgDIknIFc0yLjFwDPZgBAACAwaLYGiSX8/RgBpBuJJgBIEvKPZipYAYAAAAGtUJbkFymghlAypFgBoAsoQczAAAAMDSUK5jpwQwg3UgwA0CWdNuDmdPmAAAAgEGhUw9mYn0A6USCGQCypNAqWU7Kxfz5zjVQwQwAAAAMFsW2jj2YSTADSCkSzACQJaXT5OLQgxkAAAAYPIqRHszE+gBSqqHeEwAA9EChrXP/5YU/Cn6uXSptXdf+OGrap/p1agAAAAASVGgNzlLMlSqYSTADSCcqmAEgS4qt7QFmlOWlYmFg5wMAAACgf5RbZJQqmGmRASCdepxgNrPrzWylmT1bsWy8md1nZi+GP8clO00AgKT2C33EyeUlJ8EMAAAADAqF1rBFBhXMANKtNxXMN0g6JrLsc5IecPc9JT0QPgYAJK2rHsxUMAMAAACDRyn2z9ODGUC69TjB7O4PSVoTWfxRSTeG92+U9LE+zgsAEKfQ1n4V6ahcXvLiwM4HAAAAQP8onb1YKjAp0iIDQDol1YN5B3dfLknhz+3jNjKz2Wa20MwWtrS0JLRrABhCiq1Sflj8OstRwQwAAAAMFsW28CJ/+eAxFcwAUmpAL/Ln7te5+zR3n9bU1DSQuwaAwaHQRYuMUg9m94GdEwAAAIDklSqYzYLPAPRgBpBSSSWYV5jZTpIU/lyZ0LgAgErFLlpkWFjZQJsMAAAAIPsqr7+Sb6SCGUBqJZVgvkPSqeH9UyX9OqFxAQCVuqtgloIqZgAAAADZViy0X+Av10g7PACp1eMEs5ndLGmBpL3MbJmZnS7pm5LeZ2YvSnpf+BgAkLRia3uQGVWqYC5SwQwAAABkXqG1vYgkl6dFBoDUqnKedXXuPrPKqqP6OBcAQHcKbV1UMIffGVLBDAAAAGQfLTIAZMSAXuQPANBHxdbuezBz6hwAAACQfYW2SIuMtvrOBwCqIMEMAFlCD2YAAABgaCi2SrmwuCTfQAUzgNQiwQwAWVJTD2YSzAAAAEDmFVojFcwkmAGkEwlmAMiSQlt7FUMUFcwAUDdmdr2ZrTSzZyuWzTOzV8xsUXj7YD3nCADIEHd6MAPIDBLMAJAlNVUwFwduPgCAkhskHROz/Ep3PyC83T3AcwIAZFXprMRyBXMDPZgBpBYJZgDIki57MId/0qlgBoAB5+4PSVpT73kAAAaJUjuMcg9mKpgBpBcJZgDIkmJbcIGPOPRgBoA0OsvMng5baIyr92QAABlRSiZ36MFMBTOAdCLBDABZ0mUFMz2YASBlvifpLZIOkLRc0hVxG5nZbDNbaGYLW1paBnJ+AIC0KiWTK3swk2AGkFIkmAEgS2rqwUyCGQDSwN1XuHvB3YuSfiDpoCrbXefu09x9WlNT08BOEgCQTuUEc779Jy0yAKQUCWYAyJJCWw0VzFzkDwDSwMx2qnh4rKRn6zUXAEDGxLbIIMEMIJ2qNPIEAKRSsbWLHszhd4ZUMAPAgDOzmyUdKWmimS2T9BVJR5rZAZJc0lJJZ9ZtggCAbClf5K+iRUaBFhkA0okEMwBkCT2YASCV3H1mzOL5Az4RAMDgUEomlyuYG6hgBpBatMgAgKxwD5LH9GAGAAAABrdyBXNYF5hvpAczgNQiwQwAWVGIBJlRVDADAAAAgwM9mAFkCAlmAMiKYiTIjDISzAAAAMCg0KkHcwM9mAGkFglmAMiKQiTIjCpVMBeLAzMfAAAAAP2j3IM5PHsx1ygVSTADSCcSzACQFcXIhT6iLPyTTgUzAAAAkG2l2L9cwUyLDADpRYIZALKi1h7MXOQPAAAAyLboRf5ytMgAkF4kmAEgK+jBDAAAAAwNhcjZi7kGKpgBpBYJZgDIipp7MJNgBgAAADItWsGcb2z/PAAAKZNogtnMzjezxWb2rJndbGYjkhwfAIa0YuRCH1FGghkAAAAYFAqRsxdzjcGZiu71mxMAVJFYgtnMJkk6R9I0d58qKS/pxKTGB4Ahr7sKZi7yBwAAAAwOxUjsXyoyoYoZQAol3SKjQdJIM2uQtI2kVxMeHwCGrm57MFuQZKaCGQAAAMi2Tj2Yw5/0YQaQQoklmN39FUmXS3pZ0nJJ69393sptzGy2mS00s4UtLS1J7RoAhobuKpiloE2GFwdmPgAAAAD6R1wPZokKZgCplGSLjHGSPippiqSdJW1rZp+s3Mbdr3P3ae4+rampKaldA8DQUO7DVqUHsxRc6I8KZgAAACDb4nowS+3XZQGAFEmyRcZ0SS+5e4u7t0q6XdI/Jjg+AAxt0T5scSxPD2YAAAAg60qJ5GgPZhLMAFIoyQTzy5IOMbNtzMwkHSVpSYLjA8DQFu3DFocKZgAAACD7SonkUmI5R4sMAOmVZA/mP0j6paQnJD0Tjn1dUuMDwJAX7cMWx3JUMAMAAABZV4jE/qWfXOQPQAp1kaXoOXf/iqSvJDkmACAU7cMWJ5eXilzkDwAAAMi0aHu88kX+aJEBIH2SbJEBAOhP9GAGAAAAhoZoezwqmAGkGAlmAMgKejADAAAAQ0OxVZIF8b1UUcFMghlA+pBgBoCsqKkHMxXMAAAAQOYVWjsWlpTOYizSIgNA+pBgBoCsqKkHMxf5AwAAADKv2NaxNV4+LDKhghlACpFgBoCsKFUrdNeDmYv8AQAAANlWaG1PKksVFcwkmAGkDwlmAMiKcgVzFy0ycrTIAAAAADKv2BqpYKZFBoD0IsEMAFlR7sHcXQUzCWYAAAAg04pt8T2YCySYAaQPCWYAyIqaejBTwQwAAABkXqFKD2ZaZABIIRLMAJAVNfVgzlHBDAAAAGRdsTUoHinJcZE/AOlFghkAsqLQGiSQc1386aaCGQAAAMi+Qmt8iwx6MANIIRLMAJAV0Qt9xLEGqVgcmPkAAAAA6B/FKi0yqGAGkEIkmAEgKwptXfdfloLqZiqYAQAAgGwrtLYnlaWKCmYSzADShwQzAGRFsbW991o1lqcHMwAAAJB10bMXS4UmVDADSCESzACQFdE+bHHowQwAAABkHz2YAWQICWYAyIqaejBTwQwAAABkXrGt49mLpXYZJJgBpBAJZgDIikJbxz5scXJ5ybnIHwAAAJBpxbb4CmZaZABIIRLMAJAVNVUw56hgBgAAALKuUKUHMxf5A5BCJJgBICt60oPZfWDmBAAAACB50RYZpfsFWmQASB8SzACQFcW22nowS7TJAIABZmbXm9lKM3u2Ytl4M7vPzF4Mf46r5xwBABlSaO3YHs8svN4KFcwA0ifRBLOZjTWzX5rZ82a2xMzeleT4ADCkRYPMOLlSgpk2GQAwwG6QdExk2eckPeDue0p6IHwMAED34trj5RvpwQwglZKuYP6upN+4+96S3iZpScLjA8DQVVMP5jDBXKSCGQAGkrs/JGlNZPFHJd0Y3r9R0scGdFIAgOwqtHVuj5drDM5qBICUSSzBbGZjJB0hab4kufub7r4uqfEBYMiLCzKjcuGfdSqYASANdnD35ZIU/tw+biMzm21mC81sYUtLy4BOEACQUsXWjj2YpeBsRiqYAaRQkhXMu0tqkfQjM3vSzH5oZttWbkDwDAB9EBdkRpUrmEkwA0BWuPt17j7N3ac1NTXVezoAgDSIu8B3rpEezABSKckEc4Okt0v6nrsfKGmzIn3mCJ4BoA8KrVJ+WNfb0IMZANJkhZntJEnhz5V1ng8AICuq9mCmRQaA9EkywbxM0jJ3/0P4+JcKEs4AgCQUY6oYoqhgBoA0uUPSqeH9UyX9uo5zAQBkSWwP5gZ6MANIpcQSzO7+mqS/m9le4aKjJD2X1PgAMOQV2rpvkUEFMwDUhZndLGmBpL3MbJmZnS7pm5LeZ2YvSnpf+BgAgO4VY2L/PC0yAKRTN5mKHjtb0k1mNkzSXyV9KuHxAWDo6lEFc7H/5wMAKHP3mVVWHTWgEwEADA5xsX+ukYv8AUilRBPM7r5I0rQkxwQAhAoxfdiicuGJKVQwAwAAANlULEpe7FzBTIsMACmVZA9mAEB/KrZJ+W6+F6QHMwAAAJBtpTYYnVpkNFDBDCCVSDADQFbUVMFMD2YAAAAg00pJ5LgWGfRgBpBCJJgBICt61IOZBDMAAACQSeUK5kjsn28MLvwNAClDghkAsqLQ1oMKZi7yBwAAAGRSKYncqYK5gQpmAKlEghkAsqLYWkMP5vDPOhXMAAAAQDZV7cHcSA9mAKlEghkAsoIezAAAAMDg12UPZlpkAEgfEswAkAXuQdKYHswAAADA4FZKInfqwdxAghlAKpFgBoAsKFQ5TS6KCmYAAAAg20pJ5Gh7vBwtMgCkEwlmAMiCYpXT5KKMBDMAAACQadWKS7jIH4CUIsEMAFlQDjJr7MFcLPbvfAAAAAD0j2KV2D/fKBVokQEgfUgwA0AWlE+T666COfyzTgUzAAAAkE2FKrE/FcwAUqqbZp4AgFToaQ9mLvIHAAAAZFMxJvZf+CNp9V+kNzYF97sy7VP9NzcAiEEFMwBkAT2YAQAAgKGhUCX2z+WI8wGkEglmAMiCHvdgJvAEAAAAMqlaD2bLE+cDSCUSzACQBeUezN20yChXMHORPwAAACCTClVi/1yeOB9AKpFgBoAsqLWCuXSRPyobAAAAgGzqqoLZC5L7wM8JALpAghkAsqDmHswWJJnpzQYAAABkU/nsxWgPZs5WBJBOJJgBIAtKp8l1V8Es0ZsNAAAAyLJqsX/pbEWKSQCkDAlmAMiCcgVzNz2YpbA3G0EnAAAAkEnlFhn5jstL11spUsEMIF1IMANAFtTag1kKK5gJOgEAAIBMKlRpj5ejghlAOiWaYDazvJk9aWZ3JTkuAAx5tfZglqhgBgAAALKsq4v8SbTDA5A6SVcwnytpScJjAgDKfdhqaJFhOYJOAAAAIKsK3V3kj1gfQLoklmA2s8mSPiTph0mNCQAIUcEMAAAADA3lCuZIcQkVzABSKskK5qskXSSpauNPM5ttZgvNbGFLS0uCuwaAQa7HPZgJOgEAAIBMqtqDmQpmAOmUSILZzGZIWunuj3e1nbtf5+7T3H1aU1NTErsGgKGhWOU0uTi5vORc5A8AAADIpFLsX60HM7E+gJRJqoL5UEkfMbOlkn4u6b1m9tOExgYAFKqcJheHCmYAAAAgu8oJ5nzH5bkwhUOsDyBlEkkwu/vn3X2yuzdLOlHS79z9k0mMDQBQD3sw5zhtDgAAAMiqQmtQvWzWcTkVzABSKskezACA/kIPZgAAAGBoKLbGn7loVDADSKcazrXuGXd/UNKDSY8LAENauQdzDX+2c3kqmAEAAICsKrTFn7nIRf4ApBQVzACQBVQwAwAAAEND1QrmMMFMrA8gZUgwA0AWFN4MftbUgzlPXzYAAAAgqwqtVDADyBQSzACQBeUrSddSwZyjqgEAAADIqmJbfNxfrmCmmARAupBgBoAsKLQGieNcDX+26cEMAAAAZFehNf7aK1QwA0ipxC/yBwDoB8XW2qqXJXowA0DKmNlSSRslFSS1ufu0+s4IAJBq1WJ/C4tNSDADSBkSzACQBdWuJB2HCmYASKP3uPuqek8CAJAB3fVgpkUGgJShRQYAZEG1K0nHsTxBJwAAAJBVxUJ87G+0yACQTiSYASALqlUxxMnlCDoBIF1c0r1m9riZzY6uNLPZZrbQzBa2tLTUYXoAgFQpdlfBTKwPIF1IMANAFvS0BzMJZgBIk0Pd/e36/+zdd3gc1bn48e/ZrpVWq96s4l7BvVJM7+QCgRD6TUgCCaTc3PSb/O5NSEjvBQIhhFBD6Am92xjcjQ3uQrYsybJ6XWn7nN8fZ9VsydhGttb2+3mefbbM7O7ZndnZM++88x64ALhVKbW4/0St9d1a67la67m5ubkj00IhhBDJIz7E2YtSg1kIkaQkwCyEEEeDeGzwkaQHY5NB/oQQIplorWsT1w3AU8D8kW2REEKIpGbFhhjkTzKYhRDJSQb5E0KIo4FkMAsh9uPhlVUHPO81C0oPY0vE3pRSqYBNa92ZuH0ucNsIN0sIIUQyi0fB5d338Z4SGVrGWxFCJBcJMAshxNHgoGowyyB/QgiRRPKBp5RSYPreD2utXxzZJgkhhEhqQyWXSAazECJJSYBZCCGOBkOdJjcYZQO0yWxQUglJCCFGktZ6BzBjpNshhBDiKBKPDTHIn9RgFkIkJ4k8CCHE0SAePbgazCCZDUIIIYQQQhyNrP0N8qekRIYQIulIBrMQSUhqaYp9HGwNZkhkNhzgc4QQQgghhBDJYX/l8WRAbyFEEpIMZiGEOBoMdZrcYKQ2mxBCCCGEEEeHeBRi4YGP7a88ngzoLYRIQpLBLIQQRwMrCg7PoJNW7mwZcD+vNcQYYO2uZmKOvs7qgrmHs4FiJB3oWQ9yxoMQQgghRJJ5/uvQsgP+8999j1mxocvjSQazECIJSYBZCCGOBvEouH0HNKvGZDDbpDabEEIIIYQQya12PbTuHPhYfD/l8ZRNMpiFEElHSmQIIcTR4CBqMGtlNu1KOp5CCCGEEEIkt/ZqCLVDqKPvsaEG+QNTIsOSRBIhRHIZtgxmpVQJcD9QAFjA3Vrr3w3X6wtxvHPEuvF1VdLqnzrSTREjIb6f0+T2ohM1mJVkMAshhBBCCJG8Il3Q3Wxut1eDZ5q5vb/xV2ySwSzE3qRk4MgbzgzmGPA1rfUUYCFwq1JKImFCDJNJlQ9w7vJrccS6R7opYiQcVAZzT4BZOp5CCCGEEEIkrfaavttt1X23PzSDWfr5QojkMmwZzFrrPcCexO1OpdQWYBSwebje43hxoEdeQI6+HE/8gQrsOkZadzVt6ZNGujniSItHh85i2EtfiQzJYBZCCCGEECJp9Q8qt/e7vb++v80uGcxCiKRzWGowK6VGA7OAlXs9fpNSao1Sak1jY+PheGshjlm+bnPgIa27+kPmFMckK3bwNZiRjqcQQgghhBBJq71fcllb4rbWJoA85CB/ksEshEg+wx5gVkqlAU8A/6W17ug/TWt9t9Z6rtZ6bm5u7nC/tRDHLq3xdZkOR0+gWRxn4lGpwSyEEEIIIcSxpL3GBIwzSvsymONRcz1U399mB+nnCyGSzLAGmJVSTkxw+SGt9ZPD+dpCHM/c0TZcsU5AMpiPW1YU7K4DmrWvRIZkNgghhBBCCJG02qohfRRkju6rx2zFzPWQGcwyyJ8QIvkMW4BZKaWAvwJbtNa/Hq7XFUKAr2sXABqFTwLMx6f4gZfIsJAMZiGEEEIIIZJeezVklIC/tK8es9WTwbyfGsyW9POFEMllODOYTwauB85USq1PXC4cxtcX4rjVUxajxT9VMpiPV9bBlMiQDGYhhBBCCHEQHrwcnvv6SLfi+NNWDf4SE2QO1EEsbBJLYP81mKWfL4RIMsMWYNZaL9NaK631dK31zMTl+eF6fSGOZ2ldVVjYqMteRGpwD7Z4ZKSbJI60ePQgBvmTDGYhhBBCCHGArDhULoPKt0a6JceXeBQ6axMZzCXmsfaavgxmm33w59lsAwf5C3fCS9+Bxm2Ht71CCLEfwz7InxBi+Pm6q+hKKaI9bSwKTVqwZqSbJI6knpGkhzpNbu/ZezKYkQCzEEIIIYT4EK2VEAtB8wcmg1YcGR21ZrC+ngxmMCUz4h9SImPvDOa2KogGobn88LZXCCH248DOtxZCjChf1y4CqaUEvKbjkdZdQ0fa2BFulThiejqZtgMtkdGTwSynzgkhhDj2Pbyy6oDmu2ZB6WFuiRBHqYbN5tqKmSBz/rSRbc/xoj1R+rB/BnNbNWQktlX7K5HRP4O5s9Zcd9QennYKIcQBkAxmIZKd1vi6q+n0ltKZCDD31GQWx4kPG+hjLz0ZzDYpkSGEEEIIIT5Mw9Z+t7eMXDuONz2D+vlLIX0UoBIZzIkazPsb5K9/IknHHnPdueewNVUIIT6MBJiFSHLuSCuuWCedqWWEXVlE7V4Z6O9405vBfIABZvbNYFZWHDrrhr1pQgghhBDiKNewGXxF5mw5CTAfOe2Jsof+UeBwga/ABJ2tDzl7UdnB6pdI0pPBHGyFaPfha68QQuyHlMhIQuOqHjvwmRd87fA1RCSFnmzlTm8pKEWnt1QymI91z30NmivghqfNfetDshj20luDuV8Gc2HzO/CH38PXt4PLO6zNFUIIIYQQR7GGLVA4HVp2SoD5SGqvgtRccKaY+/6SA6vBbLP1ZTDHYxBoMBnQHbtNQkmWlFIUQhx5ksEsRJLrDTCnmlpcAW8Jad0yyN8xS2vY+jzsXGJGhIZhqcHsD+yASCfUrhvW5gohhBBCiKNYLGIGh8ubYi499ZjF4ddW3Vd7GUwt5vbqvuSS/dZgTiSSdNWbgQJHzTX3pQ6zEGKESIBZiCTn69qFpex0pYwCoDO1hNTuGhnA7VjVXm1Oc9MW1Kwxjx1iDeaeDGal46QGd5uJVSuGtblCCCGEEOIo1lJhApp5U82ltRIiUmZhWP37K7DtxX0fb682QeUe/hJo3w3xiLlvHyK5pH8N5p6Act4UkwktAWYhxAiRALMQSc7XXUWXpxArcQQ74C3BrmN4g1JP95jUPwDcc/tgazD3ZDBjOp7eYB12nciEqF45LM0UQgghhBDHgJ6M5bwpkDcZ0NC0bUSbdExprYS198Hqvwx8XGtTg3nvDGYraoLMsP8M5v4BZpvdlNrwFclAf0KIESMBZiGSXFpXFZ2pZb33O72mVEaa1GE+NlWtAJcP8qZBdSLA3FuD+UBLZAzMYPb1DAo54VwTYO4/KIgQ4rhiaU1XODbSzRBCCJEsGraAskH2BJPB3POYGB47l5rrXe+YciQ9uhohFoKM0r7H/InbLTvM9ZA1mO1gJQLMnXsgrdA85itMnAmph/czCCHEAZBB/oRIZlrj666iKXNG++yJ3gAAIABJREFU70OBRIDZ111NPYtGqmXicKlaASXzIGscrH/YDNxxsBnM9ASYTcfTF6wm5MzAM/VSKH/ZZKXkTTkszRdCJI/mQJgNNW20dEVo7Y7S1h2hIxjje09vZGxOKnPKMpk7OpM5ZZmMy01DKTXSTRZCCHGkNWwx/U6nBzLHgN0tdZiHU0+AOdoNu9dCWWL/rS2RALJ3BjP0BZiHGn9F2QZmMOdOMrfTi2BXGIIt+z5n6S/MQYRplx76ZxFCiP2QALMQSagjFKWquZt4ZwPXxAIsbfLx4NIKnHYbxX43FykXro5KtBydPrYE20yHfuolkD3OnEpX/37f9AOswYxSWNhMBrPWpHVX05E6Bk/pQjO9aoUEmI9hcUvTHYnhdTmw2yRgeLyxtKa8PsDyHU1srw+ggPQUJxkpTkqyvGR6Xcwty2RDTTuvbqnnsbVm0NiCdA83LR7LNQtK8TjtI/shhBDHBq2hqwnScke6JWJ/GrZA/jRz2+6AnInQsHVk23Ss0NoEmMefDR+8Zm73BJjbE2ejDqjBXGyuPyzAbEsM8hcJQLjDlMYAE2AG6NirTEZXM7zxE8iRALMQ4vCRALMQSSISs3h9awOPranm9a0NaGC22g5ueD+Yg82r6A7HWPpBgEpnLnWVW/lp1VY21rbzmVPGMD7PN9IfQXxUNasBDaULTYAZoGoljJoDwI6WMM+9Vs7S8ka21nX2Pi0enQCA3xFjeno3M/0B5iobSsdxR9twxQJ0ekvIyRoL3hxTJmPup4/0pxOHgdaaTbUdrK5sobYtSG1bkD3tIWKWRgFpbge+FAfpHid5PjczSvxMLUyXTNVjUCxusaqyheUVzTR3RfC5HZw1OY95Y7JI9ww8OHXNAnMmjNaaHU1drK1s5cl3a7jt2c3cuaSCmxeP5doFZaS4JNAshPgI3nsUnv4CXP80DzeOOeCn9WyjxBEQDZpg5olX9D2WN8WUcxAfXeM2CNTD1O+Zgy07l8Dp3zLT2s0B3t6gMoDbB56MDy+RoWyA7qvV3BNY9hWY6869Bvrb/oLJeG7cCo3bIXfiR/5oYnAPrzzwMpayrRPHGgkwJ4GucIzyhgDl9Z3ELE1LWxrpjhh+Zxy/I4bHLlmqx7Ka1m7+/k4lT67bTXNXhDyfm0sKmpifEWBGaD3UwY1j2wm71wIQsRQplT5mRWqY7m7myXUWj6yq5oxJuXzu1LEsGpctwaOjVdUKM2hH8VxwpaL9xTRvWcLDm1P4MvC9f2/jHcvJiaP8XDpzFA67Wc515esAqA+7eLPZz0uNmVzndrKqNRUVaWIm0OktAaVM8Lr/QILiqBQIx3hyXQ33L9/FBw0BANwOG0UZKSwcm02m10lXJE5HMEpHKEpbd5Ty+gAX/X4Zk/J9XDZ7FJfMLKLQnzLCn0R8VFprXtlcz29fK6elK0Jplpezp+QzbVQ6Dtv+h9pQSjEuN41xuWlcOa+EFTua+d2r5fzouS38eckObjl9HNcvKsNplyE7hBCHYN39oC146vO45v+TiMs/0i0Se2vcBuiBZ7blTYH3/wmhdvDIMjsgkW5wefd9vKc8xpjF0FQOK+7sm7et2oy74skY+JyMEqhLnME4VHk8W+IAcHuizIav0Fw7PODNNmUz+tv8L5Nk0t0EW/4FuV8/+M8ohBAfQgLMI2B3W5Bn1u9m9c4WttcH2N0W3GuO4gH3RnnCzM0IMNffyfjUEHLG87GhoSPEH9/4gEdWVaE1nD0lnyvnFbN4Qi5rn/wNABmBZjSKiKuv4+GyaewpfnJCFXx5dC3jL7ycB1dU8cCKSq65ZyVTC9P50pnjOf+EAgk0H22qVkDhdOIOLy+9v4fU0DgmVS5no20uAF84YzK/X3Q2OWnuAU9b+diLvbcjlmJrIAVVYyNuWYRaG+i0p/BIy2TKuqP4SxbA1mch0ABpeUf044mPrqIxwAPLd/H42hoC4RjTi/387PITaegIk5nqwraf33x3OIbbZeepdTX89IWt/OzFrZw+MZcvnjmBOWWZR/BTiOGyra6T257dxNsfNJPnc/Ppk0YzIf/Qz2ZZODabhTdls2pnC799dTu3PbuZf6yu4gf/cQKLxmUPY8uFEMe81l2w622Ydhls+TfzNt3G2zN/aQ52i+TRmCiF0TO4H/QFmxu2QumCI9+mo031KvjbhXDd4zD29IHTdi4xg/hljoYxp8E7vzeDeI870wSHM0r2/U34S/sCzEMN8K16Asw14Eozmc89egb66xHqgB1vwPybzL7Gln/DYgkwi+NHdzhGYyBMNK7RaJZsb8TSGptSFGemUJrllWSKYSIB5iOkPRjlhff38NS7u1m50xTdn1zgY+7oTK7OK2FCvo8JeWl4nHbefPpeOmJ22qMOWqMONnZ6ebYui2fqsvE7Ysz2Bzgtu53JaXsHpsXRoLUrwp+XVvD3dyqJxjVXzi3hS2eOpyhj30xCT6SFsCsDrQaephx2ZWLXUZyxANlpbr5y9gRuPm0sz6zfzV1Ld/CFh9YxsySD71wwmQVjJShwVIhF0LvXsnXUx/n8r95kV3M3X0mfxGlqCX86PwNegFMnF8JeweW9uWya6enduOyKU9LacHbtpjw2lkf25PPMT1/j61PzuRFMB3PqfxyRjyY+uvL6Tn77WjnPvbcHl93GxdMLueGk0cwsMQefDuR0PK/bwTULSrl+YRmVTV08+e5uHlyxi8vvfIdTxufwpTPHy/biKNHaFeE3r27nwRW78HmcfP9jU7HbbMNWc3v+mCwe+uwCXtlcz23Pbubqv6zgYzOK+O6FUyjwe4blPYQQx7j3/mmuz7kNCqZT9toPqN39L3YWXzKy7RIDNWwGuwuyxvY91hNgbtwiAeYD8fbvwIrCst8MDDBbcah8C6Yk+ttli0xN5R1LTIC5rXrgAH89+tdkHiqDuX+A2Vc4MEidXgT1myAaMgM3lr8M8QhM+Rik5sKr/wdtVSbw3d/6h6FyGVzyJzkQJI5agXCM7XWd1LYHqe8I0dARpjMcGzDP396uHHDfYVOUZXsZl5vGxHwfJ43PZm5ZFi6HBJ0PlgSYD7MN1W387e2dPL+xjkjMYmxOKv99zkQunTmK0uxBTqMBxqWGBty/rLCZQMzGho5UVrf5WNHq443mDEpTQty8cheXzhxFqlsWZbILhGP89a2d3PPWDgKRGJfMKOK/zp7I6JzUIZ/jibQQcmXt83jPY55I3wjBHqedT84r5fLZxTy5bje/fmU7n7x7BWdNzuOb509mUoHUaE4a4c4BmQbhWJzXX3mRC2JBfl+eQ0aRk29fO5tzc0rhrrtxVi0zMw410McgtLLhjHeTGa2nK3cKP0/byXLPKfxiQ5hrnU7WvvEcY0edOzBY1LDF7GA49h/EFkdORWOA379Wzr821OJ12vniGeP51Mmj98liP1ijE/9Fnz9tLA+tqOKupTv45N0rWDAmi6+cNUFK7SSpaNzioRW7+M2r5QTCMa5bWMZXz55IZqrroGr+HQilFOdOK2DxxFzueLOCPy+p4PUt9dx65nhuPHmMDAQohBia1rDhERh9qglinfwV6tc9y9zNP6YhazZd3kGCamJkNGyB7Amm1u+av5nHtGWCzu8/YZblYGQsD6O5ArY+ZwLFO940mccFJ5ppde+ZMiNjTjP3XalQPK+vbEZ71eAB/P5B56FqMPeUyOhu6hugsYevCNAmO71opslYTsuH4vl9AeYtz8KiW/qeE2yDF78DoTaYfJG5CHGUqGgM8Mrmeh5eWUV1SzcacNlt5KW7mZjvIy/dTZ7Pjcthx6bg3GkF2BTELM2u5m52NAaoaAxQ0djF61sb+OMbH+B12Vk4NpvFE3I4fVLefmM2oo9EJQ+DaNzixY11/O3tnayraiPN7eCqeSVcPruY6cX+Q9ppT3NYnJzVyclZnYTiirdb0nmpMZPvPrWRnz6/lcvnFHP9ojLG5aYdhk8kPopQNM4Dy3dx55IKWroinDs1n6+dO+nDA75a4460mNq5e7+my5zO7o607jPNYbdx5bwSPjajiPveqeSONz/g/N8t5WPTi/jimeOZ+BFOnxbDoHIZ3H8pnHMb4Xk388/V1dzxZgUXBl7kAid86qqrmH/iFLOdsPLAnW6eA0N3MgehsePrrkIBAW8JZd4wV35iJv99zkRq75mCt34Ni3/+BlfPL+ELp4+noG0d/O0CmHoJXHn/4fns4oBtr+/kz0sqePrd3bgddm5ePI6bFo8lK9U1rO/jdTn43OKxXLewjEdWVXHX0gquuWclc8sy+fJZEzh1Qo4EmpPE0u2N/PDZzZQ3BDh5fDb/e/G0I3Lg0OO089/nTOTy2aP44bOb+fmL27j/nV3msTnFw5Y1LcT+hKJxalq7qWsPU9cRoj5xicYtQFHRGEBhDoykuR1keJ34U5xkpJhrh5z6emTtXgstFXDKV819m53lM37Chcsu56QN3+HVBfehD+KguTiMGrZAyV5BTmWDtALo3DMybUo2m/8F7z8Gl921b53lFXea/vl1T8Ddp8PyO+CyO820/vWXe4w5DZb+3GQvh9o/egYz9A3w13s/UY+5fhPkToLyV2DGVWCzmUHE808wQef+AeblfzLB5bR8eP12mHiBmb8/rU3mc2bZ4G0S4giJW5r11a28vLmeVzbXs6OxC4Aiv4czJ+cxpTCdAr9nyNKBc0algsPsU80bPTCZLxCOsbyimaXbG3mrvJHXtzbAvzczKd/H+ScUcP4JBUwu8Mn+0RDkn30Y7W4L8tiaav6xqpq6jhBl2V7+9+KpfGJuMT7PgQeGPozHrjkrt50zc9pxLvg09y/fxUMrd3HfO5WcMj6H6xeVcdbkPOlMj7BgJM7j62r44+vl1HeEOXVCDl8/dxIzSjI+/MmAI96Nwwr3BpP7izgz0KgBGcxoDdueh6LZkF5IisvOF04fx9XzS/jzkh08sLySf22o5YITCvjimeOZViSDdhxx4QA8fQtYUWIv/x83vJ7Kys4c5pZlckteIzowmgXT+9XAs9mhZD588Gri/kEEmBMZzBpFwDuq9/GSLC/MOhO9/E98cnoOD62s4snVFbzu/R+ybS5sm58xHWkpn3HEaa1ZXdnKXUsqeG1rAx6njU+fPIbPnzaOXN/hzSpPcdm58ZQxXLOglMfWmIMeN9y7ihklGXzlrPGcMSlPOlIjQGvNOxXN3LV0B0u3N1KW7eXu6+dwztT8I748yrJTuec/57FiRzM/eWEr33ziPe5ZtoNvnjeZs6bI+iGGTzRusb2+k/dq2tlQ3caGmna213cStwZmUqZ7HLiddrSGYDSO1hpLa0JRa8B8CsjxuSnye+gKx5hWlM60Ij9+7/D1zcVeNjxiBhub2lcOo7BxGVUF5zC+5kkWr/0SNflnDv38BV87Ao0UhDpMHeA5n9p3WnoB1G8+4k1KOq2V8PQXIBIwpSgu/HnftO4WWP8QnHilCeTOus5kgZ/9f+ArMKUwcieDL7/vOWMWw5KfwoZ/mPsZgwSYB2QwDwzX9JTazGkNMi7x2MbOVLp29t8ntDNPObA1bIaKTIh2mfIYPaZ8DN78ad94LF1NsOIO83ud/DF48rOw6Uk48YqB7Xrjdlj6C1NCY9Z1B/b9CTFMQtE4y8qbeGVzPa9tracpEMFhUywcm81/LhrN2VPzWbKt8UNfp3TPi/Dne+CGp/c9OAOkuR2cMzWfc6aa321VczevbqnnxU11/P71cn73Wjll2V7On1bAeScUMLM4A5skW/SSAPNHFIlZvL61nkdWVbO03KzQp4zP4fbLTuCMSXmHdWVTCuaUZTGnLIvvXTSVR1dX8dDKKm5+YC1Ffg/XLCjl4ulFks5/hJXXd/LQyiqeWFdDZyjG3LJMfnfVLBYeZG1TT6QZYNASGdpmJ+z0D8xgXvYbeO0HkDkGPvNy7wBuGV4X375gMjcvHsu9b+/kvrcreWFjHWdPyePq+aUsnpgrRe2PkKanv01WWxVfjn2VH9r/wu3qDupv/Bcnjc9F/epdGHfWvk8qWdgXYD6YDOZEZkOXpwDLtlfWa+lC1Nu/5YdzI9x05ulU/uNr5DbUcEPkW/zA+xiFz3wVW8nJuHxSj/dICEbivLqlnnvf3sm7VW1kpbr46tkTuX5R2aFlLPecznoIAT+P0871i0Zz5bwSnli7mz+98QE33reGMTmpXDGnmMtnF0sN3iMgErN49r1a7nlrJ5v3dJCT5uI7F0zmUyePxu0Y2dIUC8dm8/QtJ/Hixjp+/tI2Pnv/GqYUpnPN/BIumTWK9GE8oC6OfVqb01M31LSxobqdDTVtbKpt7w0S+1OcTC/2c/aUcYzPSyM/3UNBuof8dA8prr7fQv8SMdG4RUcwSlswSlt3lJauCHXtQSqbu7n9+S29843NTWVWSSazSjOYWZLB5AKfJGcMh1gENj5hTrH3pA+Y1Ow/gfTATkY1LaPTW0K7b8IINVIA0LjNXCcG+FvZL0hZEPFTFgmwtryamGPffckFc49ICw+/cCf88wZzQOTyvw7MULbi8NQXTEb3CVfAqrtg0vmmfjLA2r9BtLsvE3jB52HVX2DV3XDat6Fq+b6B2OK54EgxgWkwA/rtrX9t5CEy/bUy2yqNIujOHThR2eh255JWvxG6m8GTAaNP6Zs+5WPw5k9MaY+5nzb7kNFuOOO7plzKsl/DGz+GqZf2BbjLXzHBZVcaPPc1KJzRVwqkR7AVXv5/MO1SGH/2oO0+1mmtiVsam0IOvA+D5kCY17Y28Mrmet4qbyQUtfC5HZw2KZdzpuZz+qQ8/CkH3u/Mat/Ewve+B8WzwXtg+7ml2V5uPGUMN54yhsbOsAk2b6zj3rd3ctfSHRSkezhvWj7nnVDA/NFZx30/QgLMh6AjFGVZeRNvbG3g9a0NNHdFKEj38KUzxvOJuSUmQ/AIy/W5+eKZE/j8aeN4dUsDD6yo5Jcvb+eXL29nSmE6F5xQwAUnFHyk0eXF0Bo7wyzd3sija6pZtbMFp11xwQmFXLOglAVjsg7sD6bufVMOIXHakScRPA65Bt/4hV1ZvfOw9j4TXB53lunMPPBx+NSzkNKXLZ2Z6uJr507is6eO5b63K/n78kpe3dJAVqqLi6cXcumsUcwqyZA/w48iHoW3fgUtO+HcH6JTc9lU28FrWxpoeO9Fbu94gPv0RWTNu4JwzgTGv3IL4xsegeyPQVcjlC7c9zX7P3aQNZjBlMfYR8+pkNUrKBnroaTxH0RmXM9puVfzo2X5/Dn4DV769Y2smPEjzpycx0njcvBY3fDCt2DPBviPP8Co2QfzzYi9hKJxlmxv5Nn39vDalnq6I3FKslL44SXTuGJOyYDAycHwBms5acO3cUU7WT79dlr9Uz/8SYNwO+xcs6CUT8wt5t8bavnH6mp+8dI2fvXyNhZPzOUTc0o4bVIuaVL/f9j0nO73+tYGHl9bQ31HmAl5afzs8hO5ZOaopKp5rJTighMLOXtqPk+sreH+5bv4f89s4sfPb+Xi6YVcNb+U2aXyfyL21dARYn11m8lOrjHX7cEoAB6njROK/Fy7oIzpxX5mlmRQmuU96PXIabeRneYme5Ba9edNy2dTbQfv727n3ao2lmxv4Il1NQCkOO2cWOxnVmlGb+A5P10OqO1Na01zV4TatiC7W4M0dIZpCoRpCkRoCoQZ1/wG3w628o3yqSy5/VVC0TjhmAXWRJxKk2a7hfttt1FQ9W9+aPtfwk4/6c446Y4YfkecbFcMd3UbRX4POWluyQw7nBoSGcp5k/eZFPSYoGVKuJFORypoTUHzSlJDtVTlH0XBw6YPTM3hCefC7BsGHnwPd8KDV0DNalN3+pGr4Op/9AWZl/8Rqt6BS++EaZeZmspP3wq3vAPOVFh5twk299RAzh5nDqysuRfKTjJB2576yz0cbjPYX8Xr5r6/eN82e7NNEDoWHPLsxZ5EkpArC2uQebo9+aTVvQ+178KkiwYmqeRNhaxxsOVfMPF8WH0PTP+kycIGE2h+9FpzJsLs601ZjCc/B/knwtUPw1/PNUH5m94ET+KM2M56eOAyaNhknnfZXftmQIcD8PoPTVvO+C449x3gHssyAyYmwXgwlqVp6gpT2xaiti1IbVuQxs4wzV0RWroiNAfCtHRHCEbMNi4Ss4jELbQ2Z8447TacdmWuHTZSXQ7SPA7S3Obi8zgYlZnCqAwPhf4UGVMLE2dbvbOFlTtbWLmjmfd3t2NpKPR7uHJuCWdPyWfh2OxDGnwvJVTP4rVfJuTOJu3KBw5pHcv1ubl6filXzy+lPRjl9a0m2Pzommr+vnwXPreDeWOyOGlcNgvHZjO1MP24+w8b1rVYKXU+8DvADtyjtf7pcL7+SNBaU9cRYmtdJ5trO1iyvZG1u1qJW5p0j4PFE3P5+OxRnDYxLynqEDrstt7aMDWt3by4sY4XN9bx61e28+tXtlOW7WV2aSYziv3MKMlgalH6iGdDHY3ag1HWVLaw7IMm3vmgmW31nQCUZXv5zgWTuWJO8cAdm9ZdpsbZ6FPAvVed7Jad8NJ3Ydtz5n7pIvKimaSEG9EoIq7BS1mEXFlkd2yEzc/As1+F8efA1Y/AziXw8FXwyNVw/ZN9f9573oO3fom/tZKvzPsct3zrSt7a0cZT79by6Opq7l++i+LMFOaNzmJ2aQazSjMlm2dvwVYzgEfeNMidOHBaw1b0kzeh6jZgKQfdm17gNnUz/wzMxKe6eS3ld7R7y7j8C3/G50sHPQ1qXjSnm3WbbPVBA8yj5pjAshU7yAxms9wGq+GNNwtyJsLOt+C9xyCtANcFt/MZj59Pn3QjVY+Xc/HmO/n3ume5ccV05jgrucP9J3Jje4h7snD89VzU2d+HhbcMrM+25z3TWS2caTqsduko9egIRdlY086GxCnfb3/QRGc4RqbXySUzR/Gx6YUsGJv9kf5HRtW/wcL3v4fSFjG7l/OWX8t7E25ly9hP9+6IHCyn3cbHZxfz8dnFVDZ18fjaGh5fW8OtD6/DblOcUJTOwrHZLBibxdzRWZK9ehAsS1PTGuTd6lbe2NrAku2NtHZHsdsUJ43L5qeXT+f0iblJHaR12m1cNb+UT84r4f3d7Tyyqopn1tfy2NoaslNdzB+TxYIxWcwfk83kAt9x18nu71jsIw9Fa01jIEx1Szfb6wNsq+tkW10n2+s7ae6KAGC3KSbl+7jwxAJmFGcwvTiDiflph9znGFf12AHNl73gayyemMviibm9ba1pDbKuqpX11W28W9XGvct2Eo3vACDPZwYIMpc0JuT7GJOTSqbXmdS/zUOltaY9GGVPe4g97UFz3RYacH93W5BIrKcMiUahUcpGVqqbnDQXn4u8Qbs9k/jo0zjD6cbjtOF22tm9dQ0xrYhqxV9in+G20E/4nr6LL4S/RXlXGp0xOxbmO/31n94GwGlXFPhN8KXI76EwI4WijMRtfwpFGR78KcfmshhWloUufwlrw6PECucQmHYNEbuX1Kr3SHN42BHNgYZOqoPmrCmH0nTaipgMOION2BxZTKx9Gn93JRpFRuAD2DTLZKoebp11pv+dO3nfM7M69sDbvzXZtSd+AhZ+oS+5RmuTgPPS/0AsDFufhe0vmSSJ1GxTHuShK6BmDVxxL8RC8NTn4ZFPwtWPQutOeP1HMPlimHG1ee/L7oK/ngPPf8Mk9QTq4NI/DWzToi+a93ru6ybzefTJ+36mMYtNgNnuQqflEYrECYRjdIVjvdcnpBSQ2rmTR9buoSsSpyscR6PZXZuNUnBixM8EYAfFvNHkx2O38NjMJcVu4bUXkte9HgBr8sUM2LIqZbKYl/8RXvl/Zh/jtG/1TZ98kSm5uORnZhn/8z9NNveVfzfZ1Vf8De67CJ65Fa58wJRZuf8SE2T+5IOmLvUTnzUB/J7BIGvWmCB1y05Aw/aX4fK/mEzoHh+8Ci//LzRth/k3weKvm32WHpYF5S/Bthdg/Flm2dj26t/Go7DrHUgfBTnjB1+nYhHQFp1xuwket5vg8Z6eQHK/bV8kPrDkksthIzvVRXaai6xUN2Nz00hx2XHZbbgdNlwOG5v3dBC3NLG4JhK3iMUtwjGLrnCcuvYQgXC09yydp97d3fva/hQnhX6P2cYlgs6jMlJ6Hyvwe46ps46bA2G21ff1D97f3c7m2g4sbQbom1mSwZfOnMA5U/OZVpT+kbbz9niQxeu+gjMW4OVFD3BRWu6HP+lD+FOcXDarmMtmFdMdibF0eyNLy5tYUdFs6jbTdxbW1MJ0Jhf6mFyQzrjctEMKkB8tlB5qZNiDfSGl7MB24BygBlgNXK21HrR409y5c/WaNWuG5b0PhtaacMwiHLUIxeKEonFCUYv2YDRx9D1MU2eYxkCYioYuttZ10BGK9T5/amE6Z0zO5YxJecwsyTgswbeVj/3qgOdd8IkDq1FW3xHipU11LN3exIaaNho7w4DpuI3LTWNUT4ctsUHLSXObo2seB77EtTsx6uYB/7i13v8p2nGTsTJk0CwWNkd+nd59jzBpDdGgGRwBzNFTZ0rf+2lt/tS6m8y1x29GzXWl9k0PtUPHbuioJY6dWFoh0bRCgqTQHowSaK0n1rAd1fwBkbY9VESzWR/MZXlbJru7bWTSwXxnBRdlVDPbXoEvPRPfhJOxlc6HolmmfZuegvf+CdUrzPs6veZPe/onzSjCb//O/LnbnHDqVwFl5m8yp6yFXFlsmPDFQb+egqbllNW/Yp47ag5c/1TfEff3Hzd/7BPPg1O/bk512va8yZD2l5ijy/5S854zr6VrzzbKl/8Lx843GRN8n11WPkutE1mtZhAomEtuRjpTvO1MVLsptarx2SLonAnY86bgKZhIamoqTpsyHcBAvanl5Uk3A4Sk5gz889faLFdtmaP/ew8gcSi0hkgXxCPm1C1Hv7IClmWygztqTEfUnWa+g/Qis87EoyaDY/c6qF1nRlEunG4NR9SeAAAa9UlEQVQ6VkWzsJxpRLe/inrvEZwfvISKm99Oa+aJbMu7iHW+0ynY9W8ubrybgPbwnehnqdCF/N51J1PVTiqLL6Eg3Y1ny+Nw48tQMq+vbYFGuGNB3+lr39w5+PfxlzPNgDlf/wAG+UMcbJsxdeff8HVXs27ifxF1mlNUB2wvnvkivPuAuX31o+aUvx6xMPz5VHS4k53jrqVsw29ows+XQrewTZfwc+fdnGdfw3sp83lzwv9wQvQ9ZtQ9QXbrht6XiKcVEZt1A/Y5N+Dw+Exm/c6lUPmWyWAoO8l0sEefagYjCbaZ0a4btphOva/IZNTkTjHlXpQyv9n2GmjfbTrD/mJzSck003vWg+4m83qe9MTvPm3gtigWNjsWYLYNjkHKUFiWWZ8c7iG3Y1prglHT6e+OmJ2C9mCU+g6zM17Xbq4rGgO9g0+AORC1YEwWF08vYtG47APrLGoN4Q5TL6+7JfH7yjefPR5l64P/zeTKB2hJn8Kymb8i4kxn3qbbKKt7mYbM2aw88Ta63bl8cl4JoMxncngOqYxG3NKs3NnM8opmVu5o4d3qVqJx05fI87kpy/ZSmpVKWbaX4syU3sG2/ClO0j1OUt0OHHaF02Y75gKOWmuicU04FicYjdPeHaU1cYp+a3eEho4wFY0BPmgwo1WHE0GarFQXp0/M5YzJeSyekHtItWH7lwj4MNcsGOSU3GESCMd44f09Zv3Y2cLutiAAPo+DMTmpFGemUJxp1o1CfwrpHgepiWyenswet8M2IgEjpdRarfWwn/h9tPSR+7MsTcwyp/r2rM/dkTjBSP91O5K4RGntirA7kdG6uy3Yu24DeF12Jub7mJTvY1KBjxklfqYW+g/5TI3BrPznL3HGOkkJN5ESbkLpGCF3DkFXDmFXhgn0kPgf/JDteygaZ/OeDt6tMuU6yusDlDd0DqjtnOK0U5RhdvpHZaSQk+Ymw+skw+si0+skw+vE47TjdthxO/oCDy6HDbfDfliSUixLE7UsYnFNNG4RjWtiljXgP6o7bAJZbd0RWrqjtCWWX0tXuDegEozGcRElh3ZyVDsxmxOVmkeKP5f8zDTGpcWZHVvPhM4V5NW/hT3WhRp7BmrCOeb0/7tOM8Gh8388cBnt1VfJat/EhJonqM1eRHXBOWgNXVGLzrBFxuyPU91tp7Yj0hf4aQ9S1x4itlctbq/LTqHfQ57PQ1aaiyyvi8xUF1leJz6PkxSX3VycdryJ6xSnjRSXA6/Lgcc5yPbmI5SaOhT9l10sPnA5BiMxgsEuop1NRCIROpSPDstDMGqm2QJ7yGjfTGbHNlJDdVTbSyhXY9iiy2iOODg19AZXxZ5mHLtp1178qpt27eWh+Nkssm3GhsUlkR8N0irNBvfnqNQFlKl6nMS4PXY965nIzx13coLaycuus7gv/Qt43U7KVD2l1JJrtRBKLSDoG0skvQyPNw2vy06qy4HXba5TVBRvxwd4W7bgbtmCzZmCKjgRW9GJOLLHYwu3mQSaTU8lBrrWkDUWpn0cTrjcZPi+/VuTKRyPmrPqalabPt2iL5rM2Ze+Z5J3xp4Ol9xhXuu1H5h+04W/NPtfu9ea4HJPrfANj8LTnzcJQd2JfZpblqO92SZ+ELOwv/Vz0t75OXF3BlFvHlsve5lwzCIUs0yAOBTljLeuJrdjI3tSp3DHhHv6AseRGIFwnFHdW7ij62tUkc/p4d9gDRKOud/5E06ybWJ8+MFB15mzbGv5q+tX/Cp6BX+If3yf6SfZNvKw68d0aTdzInfhdHtJ9zhJT3Hi8ziYoSr4bu2t5mPnX8aqaf9LqttBistGitNOYdM7zHjzRkL+cXjaK2i66F6YcjEepx2HTeFcdQf2V74HC2+FzU+bGtXXPm7GjokGTYZz+ctw9vdNQHfJz8y+12V3QTxsxqTpaoIzv2eW0avfhx1vQOZos4+88Qlw+8x+7KzrzL7tyjuhZQfa5kRZUSx/KeHZnyMw7Sp0UznuTY+RWv4MjpAp9dKacSLlBRey3n82Ld1RihqXMan9LU4IrsGhY7xjTeVVaw6vxWfRSAYz7JWcm7KNRbZNjNJ11KefSEfBIqzRp5JZNJFRznbSm9ajdq81AyhmlJq2Fs8z2etdjbBjCRWrniO/eRVRh5f67IXUZS+kIWsuMbuH1OBuMjrLSe/Yjg4HyBg9nV320Wy3RlHTEaeltRlbyw7SAjtIizSxW+ewQxdRqfMJKzf5qQ5OTO9ikruZUlszNm8mOqMMe9YYPGnppDjtpOou0iMNpIX24HLYsWeV4MoqJSUtEbuKx6CrwewXR7vM/oSvwMQJ+u9PdTUk9qcScRS3r2+baMXNflmwFW1zEHZlELGlEIlrusIxOkMxOrpDBANtBIIRaoMOajvjvYP01rYFaQpEerc1GV4XUwrSmTcmi4Vjs5hdmnlwZ+5pDZ17eHXZO6R3VaK0RUdqGZ2pYwh6cjlpw7cp3fMSS2f/jt35ZxzWfjBAXXuI5TuaWF7RzKbaDsrrA70HKxw2c+C0yG8OGhRmeChM9+D3Ogf8RtPcDtNnsNt7+w8jmdB6oH3k4QwwLwK+r7U+L3H/OwBa658MNv9IdZ4/c99qXkscURiKUpCd6qIsO5VJBT6mFPiYVJDOpHzf4R8Q5FdTiAQ70UoBCo0Nkx1gobS5gE5kJ9pwpWaagJQVN3+wVtQEXpTNBB7tzsRp9dpMj0fQ8SjaihNTDiLaQdiyE9EKZcVw6CguoriIEcNOBAcRnES0A5vSuIn2XmxYhJXLTMdFHDtuIrgJ4yaCixhRHIRxmYty4dBxPIRIIYwTE7iPYSeImxAeYthJIYSXIC76AvtRHHTjIUgKLiL46Op9fo8IDgKkEsOBnw7cRPf5ekO4aMNHOgG8hAddBB3aSwwbWSow5GIKOjNJiSbKU9gcZjTecKcZMbvnMTDLIneyOapeONMc0d70lBmlF2WWy/RPmj/fniLzWvP+A98gu30jQU8eTRkzGExmxzYmVj9qsmk//ZzpMPW3+h5TIwtM8HLhLbDgZvMnUf6KGcG4ZrVZT6zEd5UzEV16EuGGcly7V2HT0cSyVaQM8X3FtaKRDDIJ4Fb7fucxbLTiBxRegqQQwkbfdqcbD92kEFYmyKfQKK3NaPCJXBaVyJDRKOLYzC9C2bDrOKl0k0pwwGuGcZrXxEUWbQPWpf5aScdLCDfmD66dNNpJo5S63nm6tJtUFaZZ+/hX/CSejy9gum0HH7e/xTTbrt751qcs5K0p/0tJyWgm5vuYkOPCueyXpmSGtuCkL8O5P9y3EZuegsc+BRPOg2v/OWg7eem7piP8rcp9lzODB5gnV96PJ9LK+olf6X1sQID53QdN5sGJV5oMgr3VrDFZGtqCSReh/+MP1IRTEploHeRte4BL6u/AlfidVViFPBQ/m6fjJzPXto3r7K+y2P4+MW126h3KIqIdbGAiAeVlDltIxwRd20gjg77fWww7DuK999vxYSdOGt2Dfj1B3HSQhp9OPET2mR7GmXgNizS6e5d3jxAuAniJ4OrdPvVf3822x00INzbiOHUssZ2MmrMMEtvJME5i2t67JtpsCodN4bD1BRfch9I5iEdNYDk+yG/Q5jAHasLt1GXNpyr/bHTP9kdrctrfZ/Se57Fb+34vgDno5fSag1Masy3o+S+Bfv8jzn2zRBIsIJw4BTqWCGhE4xrLsgadH8xvOnEDlchY6+mzqsSFQb4mNUSfZaj5B7zXoPbzevvMMtTraDNFDz1P/9ezKYXDrrD3rh/mst+1ItEnGPoaAuE4uucxrft9bt3ve9OAJvWKO45YfcSa1m5W7WxhXVUrVS1Balq7qWntnwG5r+0/umBEsjsOY4D5qOgjf/bvq1myvZGYpTmY3QOHTZGZ6qLI72FU4gDCqIwUijNTmJjvY1RGyuE9mPTPG4hvfWHI7Zyl7EQdadisKE6lzWnnACiTeOD07pukgB7wmzZ1NS1i8ThxyyJuaSzLbOcsrdGJ5/TvsyTeofd+3zdgbtvQiZ9v37QYdqI4ei82NA7iOIjhwPzGY9h7L3FtMz0lrfe7pRuKzaawK9Xv/wpS4x24Y537zqxs4M0xB8V1HNx+GHeGOeD5wWsmaaPHzUsHZiYyeF9ldO3z5LeuIWpPxW6FsOn4wBlcPvP6yg6xEDoWMtdaE7e5iCkXYeUmZinsVginFcFFGIeOE8ZJCBchXES1HZeKkUIYD1HcKkpE2wkl9lFCuHAQx0MUD2HcRNFAWLkI4SaMC41K7ANFcBHBhk7895vXiGPvXU7mOobG1ttvje99W9uwYWHDwo6FnTgWyiS9JJatR0XIoGuf/nVM22gjDRtW7/6KhaIz0RfqEVYe3DpEXcoE1oy6jqqC8ygMbWdW9QOU1r+GDYuqkkt5d86PsSlFxYpnE69vMs2vbf4jpbGdVNnLeNB7PXts+UQthWVpLves4pzmh4goJx49+D4CwB6dRUj37T/bsShSzTiU+Q/o1m6cxHCqeO99F1EcyqJCF/ESJ9GosjiX5cxnI3Y0sUQ+7gvqNO61X8EeWyGT9A4+G3+UU63VgNkv/LP9Ov5hv5g4Cq1hnFXJj+K/YRw1RLHzLfVVXtXze3/qltZcxFv8RP0Ju9LcHP8Gr1uzew+im/bHecL1fWbaKvhG9CYei5++z2e+2LacP7r+wN3xj3Gn84beg6ipiYvPBb/YcRl1qZN48oQ7E9PtvdPT3A4mrf4eWR88Sf2Xd5nHXQ5sClY89ms04O/8gGnVD7O+6CqqvdMIWYpQ3EbIshG2bKhoN7c2fJ+KvHN4ZvztdIRidISiJvAXjNIZjPC3tk+TQQenh3/NHr13SUbNo64fssC2lbtiF/GT2LX7TL/T+VsusK+mUfv5rPVddthG925LXCrGD+J/4Fz9dmJZLeZnts8SUCbZy687+R/rz5ylTRJWG2n81XYFj3EeUeVknK7iy9YDnMK7ve/4nh7PvfELeCE2h9Nt6/mM4wXm27YR0zYcyiKsnbxizebf8ZMoUQ1cZl/GNNsuotqOTVnY0bTYstiYughnShpTO9/GHzQlkixHCrae/4a8qeaARs1qc5ABzPYunEhssznNmaBtVRBJ/N5cvt7bYWc6DVnzcMS6yGtdh92KYCkHcZsLZ7xvXyauHNh1Yh9V2c0+XnfTPutTj4AzG0+sHYcefL+2SafjIkq6Cg46vV17CeMih3Zsat9/jG48dJBKxhD7UyGctJGOhzDpumuf1whrJ62YM7V9dJOqBm4XgrjpVl4smxuPiuDWERw6jM2Koh0elMNj/osd7t6DwgdEa7OfFO0adLKl7Nh0nKq8s9iTa84oONAkzeESjVtUNnWxJbEfvbs1SG0iGamufd9M+aE4bIry2y9I6iSM4QwwXwGcr7X+bOL+9cACrfUX+81zE3BT4u4kYNuwvPmxJwcYeusijleyXoihyLohBiPrhRiKrBuDK9Naf/TzJvcifeQjQtbp5CfLKPnJMkp+soySnyyj5CfL6OAdUB95OAtkDhZGHxC91lrfDdw9jO95TFJKrTkcGTTi6CbrhRiKrBtiMLJeiKHIunHESR/5MJN1OvnJMkp+soySnyyj5CfLKPnJMjp8hvP8wxqg/2hSxUDtML6+EEIIIYQQRxvpIwshhBBCiGPacAaYVwMTlFJjlFIu4CrgX8P4+kIIIYQQQhxtpI8shBBCCCGOacNWIkNrHVNKfRF4CbAD92qtNw3X6x9n5BRJMRhZL8RQZN0Qg5H1QgxF1o0jSPrIR4Ss08lPllHyk2WU/GQZJT9ZRslPltFhMmyD/AkhhBBCCCGEEEIIIYQ4vgxniQwhhBBCCCGEEEIIIYQQxxEJMAshhBBCCCGEEEIIIYQ4JBJgTiJKqXuVUg1KqY0j3RaRPJRSJUqpN5RSW5RSm5RSXxnpNomRp5TyKKVWKaU2JNaLH4x0m0TyUErZlVLvKqWeHem2iOShlKpUSr2vlFqvlFoz0u0R4qOSvnPyk35s8pM+5dFB+nbJT/pZyU8plaGUelwptTXxv7RopNt0LJEazElEKbUYCAD3a61PGOn2iOSglCoECrXW65RSPmAtcKnWevMIN02MIKWUAlK11gGllBNYBnxFa71ihJsmkoBS6r+BuUC61vrikW6PSA5KqUpgrta6aaTbIsRwkL5z8pN+bPKTPuXRQfp2yU/6WclPKfV34C2t9T1KKRfg1Vq3jXS7jhWSwZxEtNZLgZaRbodILlrrPVrrdYnbncAWYNTItkqMNG0EEnediYscMRQopYqBi4B7RrotQghxOEnfOflJPzb5SZ8y+UnfToiPTimVDiwG/gqgtY5IcHl4SYBZiKOIUmo0MAtYObItEckgcarceqABeEVrLeuFAPgt8E3AGumGiKSjgZeVUmuVUjeNdGOEEMcX6ccmL+lTJj3p2x0dpJ+V3MYCjcDfEuVm7lFKpY50o44lEmAW4iihlEoDngD+S2vdMdLtESNPax3XWs8EioH5Sik5Pfg4p5S6GGjQWq8d6baIpHSy1no2cAFwa6K8gBBCHHbSj01u0qdMXtK3O6pIPyu5OYDZwJ1a61lAF/DtkW3SsUUCzEIcBRL10J4AHtJaPznS7RHJJXFqz5vA+SPcFDHyTgb+I1ED7h/AmUqpB0e2SSJZaK1rE9cNwFPA/JFtkRDieCD92KOH9CmTkvTtjhLSz0p6NUBNvzM0HscEnMUwkQCzEEkuMfDGX4EtWutfj3R7RHJQSuUqpTISt1OAs4GtI9sqMdK01t/RWhdrrUcDVwGva62vG+FmiSSglEpNDLBF4nTAc4GNI9sqIcSxTvqxyU/6lMlN+nZHB+lnJT+tdR1QrZSalHjoLEAGnB1GEmBOIkqpR4DlwCSlVI1S6jMj3SaRFE4GrsccrV6fuFw40o0SI64QeEMp9R6wGlMv79kRbpMQInnlA8uUUhuAVcBzWusXR7hNQnwk0nc+Kkg/NvlJn1KIj076WUeHLwEPJbZ3M4Efj3B7jilKaxkgVgghhBBCCCGEEEIIIcTBkwxmIYQQQgghhBBCCCGEEIdEAsxCCCGEEEIIIYQQQgghDokEmIUQQgghhBBCCCGEEEIcEgkwCyGEEEIIIYQQQgghhDgkEmAWQgghhBBCCCGEEEIIcUgkwCyEEEIIIYQQQojDTikVV0qt73cZrZQ6XSn1bGL6p5RSllJqer/nbFRKje53f5ZSSiulztvrtbVS6lf97n9dKfX9xO2X9nrfWqXUSqXUnxL3Nyulgv2mf1Mp9Wi/10pXSlUopcYope5TSu1MzLdOKbXosH1hQghxlJAAsxDiuJNsHdv9tPM+pdQVez0W2Ov+V5VSIaWUv99jn1JK/XGv+d5USs1N3L5RKfW+Uuq9xOe6pN/79XSWtyql/m+v18hVSkWVUjd/SJt6318p9X2l1O69PndG4vtuV0q9m3ivXw71PQghhBBCiGNGUGs9s9+lcpB5aoDv7uc1rgaWJa77CwMfV0rl7P0ErfV5Pe8JnAx0AN/TWt+aeOxCoKLfPL8AipVSZyde4jbgXq31zsT9byTm+zZw14F8cCGEOJZJgFkIcTxKqo7tIX2Cge1YDVx2IDMrpYoxn+sUrfV0YCHwXr9ZejrLM4H/VEqN6TftE8AK9v3MH+Y3e33fbYnH39JazwJmARcrpU4+yNcVQgghhBDHnmeBaUqpSXtPUEqp/9/e3YVYVUUBHP8vElO0B4NMNNEoiYI+CKFIo0KLhCzDKMd6EKJICEEyrB5qHoLKsNQYqF40H0okCyKCkOjDl/Ah0iJCkiki0D4pJ9QwVw9nXzoc73jHS+E49/+DgTNnn732PvO0WbPP2sBdwArgloiYUGs+BrwKrO4QfyPwXmbuHO6BzExgJbChbNJYQJV0bvoEuLjDeJI05plglqT2TvvCtpOIuAiYTJWkHmnSdypwCBgCyMyh2k6MutY7/Vm71wc8QrWbY0ZXk24jMw8DnwP/WUxJkiSNShNrX7W9Pcwzx4F1wBNt2uYBg5m5H/iIaudx3QBwb/3rvrqIuBOYCzzeaaKZuRd4H/gAWJWZf7V5bDHwRadYkjTWmWCW1IvOmIUt8Hy9vESjrQ94A9gFXBIRU0cQbw9wEBiMiM0RsbjdeFQ7uLdl5o9lzjOBaZm5G9gO3DOCsVpW197hw2ZjREwB5lDtAJEkSdLYVf+S8GRf4L0OXNv4mg6q9e+2cr2NxiaLzPwD2AqsagYsGyQ2Acsz8+gI5zsA/JCZzTVsa838IHD/CGNJ0phlgllSLzqTFraP1stLNNqWUSWBjwNvUZWwAMhhYmVm/g3cSrUDex/wYqtGdH08YBqwICKuq421vVyf8M7txqpd10tk3FS7f31E7AUOAO9m5oEOMSVJktQDMvMYsB5Y27oXEWcBS4EnI+Jb4CVgUUSc0+i+gSrpO6nWN4DXgGcz86tTmMrx8tPUWqPfnJlfnkI8SRqTTDBL0jBG0cL2BOUAwjnAzjKPZfyb9P0FmNLoci7wc3mvzMzdmflM6be0GT8zh6h2Z88vt/qAFWWsd4ArI2JOaTscEePbjdXBrlIH+nJgZUQ0E+iSJEnqXVuAhcB55feFwJ7MnJmZszNzFrADWFLvlJm/Um2MqO8sXgMcycyB/33WktSDTDBL0sltYXQubPuA/jKH2Zk5HZgREbOoDv2bFxHTAMrBJGcD30fE9Ii4uhbnKuC7ZvCIGAdcA+wvdagnZeaM1nhAKzkN8DFwX+k3EbgbOKEUxnAyc1+Jt7bTs5IkSeoNpebxJqozRKBa/zbL2+0Alrfpvh6oH7r9NHBpvfRcu9JtkqTuRHU4qiT1jogYyszJjXs3Amsy87aIWAHMzcyHS9sqqkP5LgT6gU8z8+Va39uBlZm5qB47Is4HBoF1mdkfEUepahsfqg39W6NsRH1OW6hKR7zZnHtEDAKLMvPrWtsLwMHMfC4i7gCeovpH4hDVwSSflQT0ZmA6cAT4CXgoM/eX8W4AfgfGUw40KXEmZOZjtbGuoCrPcVkp+/EKcAEQwNbMXF+e6wceKOO0LAFmt/7e5bmJwDfA/GEOHZQkSZIkSaOQCWZJkiRJkiRJUlcskSFJkiRJkiRJ6sq40z0BSep1ETEAzGvc3piZm0/HfCRJkiRJkkbKEhmSJEmSJEmSpK5YIkOSJEmSJEmS1BUTzJIkSZIkSZKkrphgliRJkiRJkiR1xQSzJEmSJEmSJKkr/wDTe1GTu3Yb4wAAAABJRU5ErkJggg==
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the distribution of values for at least five columns where there are</span>
<span class="c1"># no or few missing values, between the two subsets.</span>

<span class="c1">#subplot params: num_rows, num_cols, which_subplot</span>
<span class="k">def</span> <span class="nf">graph_value_dist</span><span class="p">(</span><span class="n">column</span><span class="p">):</span>
    <span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;low NaN data&#39;</span><span class="p">)</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;high NaN data&#39;</span><span class="p">)</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">[</span><span class="n">column</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">azdias_high</span><span class="p">[</span><span class="n">column</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</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>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#get column names for distribution analysis</span>
<span class="n">cols</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">11</span><span class="p">]</span>
<span class="n">col_names</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">:</span>
    <span class="n">null1</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="n">i</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">sum</span><span class="p">()</span>
    <span class="n">null2</span> <span class="o">=</span> <span class="n">azdias_high</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="n">i</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">sum</span><span class="p">()</span>
    <span class="n">col_name</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
    <span class="k">if</span><span class="p">(</span><span class="n">null1</span><span class="o">+</span><span class="n">null2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
        <span class="n">col_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col_name</span><span class="p">)</span>
        
<span class="nb">print</span><span class="p">(</span><span class="n">col_names</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>[&#39;ANREDE_KZ&#39;, &#39;FINANZ_MINIMALIST&#39;, &#39;FINANZ_SPARER&#39;, &#39;FINANZ_VORSORGER&#39;, &#39;FINANZ_ANLEGER&#39;, &#39;GREEN_AVANTGARDE&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">col_names</span><span class="p">:</span>
    <span class="n">graph_value_dist</span><span class="p">(</span><span class="n">col</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_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_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_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">
<h4 id="Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">Discussion 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4><p>I looked at 6 features where the data had no missing values, and quite interestingly some of these features had even distributions of values between the two datasets, and some were quite uneven.</p>
<p>The highest difference in distributions was seen in the columns:</p>
<ul>
<li>FINANZ_ANLEGER</li>
<li>FINANZ_SPARER</li>
<li>FINANZ_VORSORGER</li>
</ul>
<p>This means that 3 out of the 6 features I looked at have a very different distribution of values between the two splits of data- NaN heavy data and NaN light data.
This means that it might not be the best idea to drop a lot of the NaN datum, as it could disort the data. We should revist the high NaN rows later as some seem to be qualitatively different.</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">
<h3 id="Step-1.2:-Select-and-Re-Encode-Features">Step 1.2: Select and Re-Encode Features<a class="anchor-link" href="#Step-1.2:-Select-and-Re-Encode-Features">&#182;</a></h3><p>While almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (<code>feat_info</code>) for a summary of types of measurement.</p>
<ul>
<li>For numeric and interval data, these features can be kept without changes.</li>
<li>Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).</li>
<li>Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How many features are there of each data type?</span>
<span class="c1">#feat_info.groupby(&#39;type&#39;)[&#39;attribute&#39;].nunique()</span>
<span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</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[26]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ordinal        49
categorical    21
numeric         7
mixed           7
interval        1
Name: type, 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">
<h4 id="Step-1.2.1:-Re-Encode-Categorical-Features">Step 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Step-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:</p>
<ul>
<li>For binary (two-level) categoricals that take numeric values, can keep them without needing to do anything.</li>
<li>There is one binary variable that takes on non-numeric values. For this, need to re-encode the values as numbers or create a dummy variable.</li>
<li>For multi-level categoricals (three or more values), can choose to encode the values using multiple dummy variables (e.g. via <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html">OneHotEncoder</a>), or (to keep things straightforward) just drop them from the analysis.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Assess categorical variables: which are binary, which are multi-level, and</span>
<span class="c1"># which one needs to be re-encoded?</span>
<span class="n">cat_vars</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;categorical&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">cat_vars</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">var</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">sum</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>count    205378.000000
mean          1.743410
std           0.674312
min           1.000000
25%           1.000000
50%           2.000000
75%           2.000000
max           3.000000
Name: AGER_TYP, dtype: float64
685843
count    891221.000000
mean          1.522098
std           0.499512
min           1.000000
25%           1.000000
50%           2.000000
75%           2.000000
max           2.000000
Name: ANREDE_KZ, dtype: float64
0
count    886367.000000
mean          3.632838
std           1.595021
min           1.000000
25%           2.000000
50%           4.000000
75%           5.000000
max           6.000000
Name: CJT_GESAMTTYP, dtype: float64
4854
count    891221.000000
mean          3.790586
std           1.987876
min           1.000000
25%           2.000000
50%           4.000000
75%           6.000000
max           6.000000
Name: FINANZTYP, dtype: float64
0
count    886367.000000
mean          7.350304
std           3.525723
min           1.000000
25%           5.000000
50%           8.000000
75%          10.000000
max          12.000000
Name: GFK_URLAUBERTYP, dtype: float64
4854
count    891221.000000
mean          0.196612
std           0.397437
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           1.000000
Name: GREEN_AVANTGARDE, dtype: float64
0
count    813429.000000
mean          3.922339
std           3.941285
min           1.000000
25%           1.000000
50%           1.000000
75%           8.000000
max          11.000000
Name: LP_FAMILIE_FEIN, dtype: float64
77792
count    813429.000000
mean          2.381976
std           1.701527
min           1.000000
25%           1.000000
50%           1.000000
75%           4.000000
max           5.000000
Name: LP_FAMILIE_GROB, dtype: float64
77792
count    886367.000000
mean          4.791151
std           3.425305
min           1.000000
25%           2.000000
50%           4.000000
75%           9.000000
max          10.000000
Name: LP_STATUS_FEIN, dtype: float64
4854
count    886367.000000
mean          2.432575
std           1.474315
min           1.000000
25%           1.000000
50%           2.000000
75%           4.000000
max           5.000000
Name: LP_STATUS_GROB, dtype: float64
4854
count    782906.000000
mean          1.168889
std           0.475075
min           1.000000
25%           1.000000
50%           1.000000
75%           1.000000
max           3.000000
Name: NATIONALITAET_KZ, dtype: float64
108315
count    780025.000000
mean          1.590134
std           1.027972
min           0.000000
25%           1.000000
50%           2.000000
75%           2.000000
max           3.000000
Name: SHOPPER_TYP, dtype: float64
111196
count    817722.000000
mean          0.008423
std           0.091392
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           1.000000
Name: SOHO_KZ, dtype: float64
73499
count    2160.000000
mean        1.318519
std         0.999504
min         1.000000
25%         1.000000
50%         1.000000
75%         1.000000
max         5.000000
Name: TITEL_KZ, dtype: float64
889061
count    780025.000000
mean          1.511166
std           0.499876
min           1.000000
25%           1.000000
50%           2.000000
75%           2.000000
max           2.000000
Name: VERS_TYP, dtype: float64
111196
count    891221.000000
mean          3.362438
std           1.352704
min           1.000000
25%           3.000000
50%           3.000000
75%           4.000000
max           6.000000
Name: ZABEOTYP, dtype: float64
0
count    306609.000000
mean          3.410640
std           1.628844
min           1.000000
25%           2.000000
50%           3.000000
75%           5.000000
max           6.000000
Name: KK_KUNDENTYP, dtype: float64
584612
count    798073.000000
mean          2.798641
std           2.656713
min           1.000000
25%           1.000000
50%           1.000000
75%           3.000000
max           8.000000
Name: GEBAEUDETYP, dtype: float64
93148
count     798073
unique         2
top            W
freq      629528
Name: OST_WEST_KZ, dtype: object
93148
count     791869
unique         9
top            8
freq      134441
Name: CAMEO_DEUG_2015, dtype: object
99352
count     791869
unique        44
top           6B
freq       56672
Name: CAMEO_DEU_2015, dtype: object
99352
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</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[28]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>W    629528
O    168545
Name: OST_WEST_KZ, 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&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-encode categorical variable(s) to be kept in the analysis.</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;WEST_KZ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;WEST_KZ&#39;</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[29]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    628987
0    168439
Name: WEST_KZ, 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&nbsp;[30]:</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">imputer_freq</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span> <span class="c1">#fill in NaN with most common value in column</span>
    <span class="n">most_freq</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span>
    <span class="k">return</span> <span class="n">df</span><span class="p">[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">most_freq</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&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">one_hot_vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;CJT_GESAMTTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;FINANZTYP&#39;</span><span class="p">,</span><span class="s1">&#39;GFK_URLAUBERTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;LP_FAMILIE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;NATIONALITAET_KZ&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;SHOPPER_TYP&#39;</span><span class="p">,</span> <span class="s1">&#39;ZABEOTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBAEUDETYP&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">one_hot_vars</span><span class="p">:</span>
    <span class="n">imputed_data</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">,</span> <span class="n">var</span><span class="p">)</span> <span class="c1">#fill na-- imputation</span>
    <span class="n">encode</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">imputed_data</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="n">var</span><span class="p">)</span> <span class="c1">#one hot encode</span>
    <span class="n">azdias_low</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">var</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="c1"># remove column we onehot</span>
    
    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">encode</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span> <span class="c1">#insert new encoded columns into the dataset</span>
        <span class="n">azdias_low</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">encode</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> 
        <span class="nb">print</span><span class="p">(</span><span class="n">azdias_low</span><span class="o">.</span><span class="n">shape</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>(797426, 80)
(797426, 81)
(797426, 82)
(797426, 83)
(797426, 84)
(797426, 85)
(797426, 85)
(797426, 86)
(797426, 87)
(797426, 88)
(797426, 89)
(797426, 90)
(797426, 90)
(797426, 91)
(797426, 92)
(797426, 93)
(797426, 94)
(797426, 95)
(797426, 96)
(797426, 97)
(797426, 98)
(797426, 99)
(797426, 100)
(797426, 101)
(797426, 101)
(797426, 102)
(797426, 103)
(797426, 104)
(797426, 105)
(797426, 106)
(797426, 107)
(797426, 108)
(797426, 109)
(797426, 110)
(797426, 111)
(797426, 111)
(797426, 112)
(797426, 113)
(797426, 114)
(797426, 115)
(797426, 115)
(797426, 116)
(797426, 117)
(797426, 118)
(797426, 119)
(797426, 120)
(797426, 121)
(797426, 122)
(797426, 123)
(797426, 124)
(797426, 124)
(797426, 125)
(797426, 126)
(797426, 127)
(797426, 128)
(797426, 128)
(797426, 129)
(797426, 130)
(797426, 130)
(797426, 131)
(797426, 132)
(797426, 133)
(797426, 133)
(797426, 134)
(797426, 135)
(797426, 136)
(797426, 137)
(797426, 138)
(797426, 138)
(797426, 139)
(797426, 140)
(797426, 141)
(797426, 142)
(797426, 143)
(797426, 144)
(797426, 144)
(797426, 145)
(797426, 146)
(797426, 147)
(797426, 148)
(797426, 149)
(797426, 150)
(797426, 151)
(797426, 152)
(797426, 152)
(797426, 153)
(797426, 154)
(797426, 155)
(797426, 156)
(797426, 157)
(797426, 158)
(797426, 159)
(797426, 160)
(797426, 161)
(797426, 162)
(797426, 163)
(797426, 164)
(797426, 165)
(797426, 166)
(797426, 167)
(797426, 168)
(797426, 169)
(797426, 170)
(797426, 171)
(797426, 172)
(797426, 173)
(797426, 174)
(797426, 175)
(797426, 176)
(797426, 177)
(797426, 178)
(797426, 179)
(797426, 180)
(797426, 181)
(797426, 182)
(797426, 183)
(797426, 184)
(797426, 185)
(797426, 186)
(797426, 187)
(797426, 188)
(797426, 189)
(797426, 190)
(797426, 191)
(797426, 192)
(797426, 193)
(797426, 194)
(797426, 195)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">drop_categorical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;AGER_TYP&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">category</span> <span class="ow">in</span> <span class="n">drop_categorical</span><span class="p">:</span>
    <span class="k">try</span><span class="p">:</span> <span class="n">azdias_low</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">category</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="k">except</span> <span class="ne">ValueError</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{0}</span><span class="s1"> already cleaned from dataset&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">category</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>KK_KUNDENTYP already cleaned from dataset
TITEL_KZ already cleaned from dataset
AGER_TYP already cleaned from dataset
</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">
<h4 id="Discussion-1.2.1:-Re-Encode-Categorical-Features">Discussion 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Discussion-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><ul>
<li>The non-numerical binary variable is: <code>OST_WEST_KZ</code></li>
<li><code>CAMEO_DEU_2015</code> and <code>CAMEO_DEUG_2015</code> is a multi-level categorical: alphanumeric</li>
<li><code>SOHO_KZ</code> and <code>GREEN_AVANTGARDE</code> is a regular binary variable<ul>
<li><code>ANREDE_KZ</code> is binary, but takes values <code>[1,2]</code></li>
</ul>
</li>
</ul>
<p>The variables that need to be re-encoded are:</p>
<ul>
<li>non-numerical binary var: <code>OST_WEST_KZ</code> will be converted to regular binary</li>
<li>all multi-level categoricals will be one hot encoded</li>
</ul>
<p>The variables I dropped are:</p>
<ul>
<li><code>KK_KUNDENTYP</code></li>
<li><code>TITEL_KZ</code></li>
<li><code>AGER_TYP</code></li>
</ul>
<p>I dropped them because they had very few (relative to length of dataset) non-missing values. Which wouldn't be useful for analysis anyway.</p>
<h5 id="Engineering-Steps">Engineering Steps<a class="anchor-link" href="#Engineering-Steps">&#182;</a></h5><p>For alphanumeric binary var <code>OST_WEST_KZ</code>:</p>
<ul>
<li>converted <code>OST_WEST_KZ</code> to binary variable: <code>WEST_KZ</code><ul>
<li><code>W</code> maps to <code>1</code> (signifies True/1 for "west")</li>
<li><code>O</code> maps to <code>0</code></li>
</ul>
</li>
</ul>
<hr>
<p>For categorical variables: <code>['CJT_GESAMTTYP', 'FINANZTYP','GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_FAMILIE_GROB', 'LP_STATUS_FEIN', 'LP_STATUS_GROB', 'NATIONALITAET_KZ', 'SHOPPER_TYP', 'ZABEOTYP', 'GEBAEUDETYP', 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015']</code></p>
<ol>
<li><p>First, I imputed each variable column to prevent getting a one hot encoded column for NaN - missing values.</p>
<ul>
<li>to do this I find the highest frequency value in each common (most likely category), and then fill the missing values with that instead of a mean for example. The mean would be a bad choice to impute categorical variables as it makes the variable continuous rather than discrete.</li>
</ul>
</li>
<li><p>Secondly, I used pandas.get_dummies to one hot encode each column, dropped the original column from the dataset, and then added the new encoded data columns to the dataset.</p>
</li>
</ol>

</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">
<h4 id="Step-1.2.2:-Engineer-Mixed-Type-Features">Step 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Step-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:</p>
<ul>
<li>"PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.</li>
<li>"CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).</li>
<li>If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.</li>
</ul>
<p>Be sure to check <code>Data_Dictionary.md</code> for the details needed to finish these tasks.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;PRAEGENDE_JUGENDJAHRE&quot; and engineer two new variables.</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="n">decade_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span><span class="mi">10</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">90</span><span class="p">}</span>
<span class="n">avantgarde_mapping</span> <span class="o">=</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">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">9</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>

<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;AVANTGARDE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">avantgarde_mapping</span><span class="p">)</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;YOUTH_DECADE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">decade_mapping</span><span class="p">)</span>

<span class="c1">#impute null values</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;AVANTGARDE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">,</span> <span class="s1">&#39;AVANTGARDE&#39;</span><span class="p">)</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;YOUTH_DECADE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">,</span> <span class="s1">&#39;YOUTH_DECADE&#39;</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>14.0    188697
8.0     145988
5.0      86416
10.0     85808
3.0      55195
15.0     42547
11.0     35752
9.0      33570
6.0      25652
12.0     24446
1.0      21282
4.0      20451
2.0       7479
13.0      5764
7.0       4010
Name: PRAEGENDE_JUGENDJAHRE, 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&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;CAMEO_INTL_2015&quot; and engineer two new variables.</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;number of nulls: </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="k">azdias_low</span>[&#39;CAMEO_INTL_2015&#39;].isnull().sum())
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;value counts: </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</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>number of nulls: 6003
value counts: 51    133673
41     92309
24     91099
14     62851
43     56653
54     45376
25     39605
22     33137
23     26667
13     26311
45     26126
55     23939
52     20538
31     18979
34     18517
15     16966
44     14817
12     13234
35     10352
32     10346
33      9928
Name: CAMEO_INTL_2015, 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&nbsp;[35]:</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">decode_wealth</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
    <span class="k">if</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">))</span><span class="o">==</span><span class="mi">2</span><span class="p">):</span>
        <span class="n">tens</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
        <span class="k">return</span> <span class="n">tens</span>
    <span class="k">else</span><span class="p">:</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
    
<span class="k">def</span> <span class="nf">decode_life</span><span class="p">(</span><span class="n">key</span><span class="p">):</span>
    <span class="k">if</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">))</span><span class="o">==</span><span class="mi">2</span><span class="p">):</span>
        <span class="n">ones</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
        <span class="k">return</span> <span class="n">ones</span>
    <span class="k">else</span><span class="p">:</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</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&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;ENGINEERING OF `CAMEO_INTL_2015` &#39;&#39;&#39;</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_WEALTH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">decode_wealth</span><span class="p">)</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_LIFE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">decode_life</span><span class="p">)</span>

<span class="c1">#impute nan values</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_WEALTH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">,</span> <span class="s1">&#39;CAMEO_INTL_2015_WEALTH&#39;</span><span class="p">)</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_LIFE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">,</span> <span class="s1">&#39;CAMEO_INTL_2015_LIFE&#39;</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&nbsp;[37]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mixed_feat</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;mixed&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">mixed_feat</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">)</span>
<span class="n">mixed_feat</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mixed_feat</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>[&#39;LP_LEBENSPHASE_FEIN&#39;, &#39;LP_LEBENSPHASE_GROB&#39;, &#39;WOHNLAGE&#39;, &#39;KBA05_BAUMAX&#39;, &#39;PLZ8_BAUMAX&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[38]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#INVESTIGATE REMAINING MIXED-VARS</span>
<span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">mixed_feat</span><span class="p">:</span>
    <span class="k">try</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{0}</span><span class="s1"> has </span><span class="si">{1}</span><span class="s1"> null values&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feat</span><span class="p">,</span> <span class="n">azdias_low</span><span class="p">[</span><span class="n">feat</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">sum</span><span class="p">()))</span>
    <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span> <span class="n">mixed_feat</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">feat</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>LP_LEBENSPHASE_FEIN has 49651 null values
LP_LEBENSPHASE_GROB has 46830 null values
WOHNLAGE has 0 null values
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">mixed_feat</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{0}</span><span class="s1"> has </span><span class="si">{1}</span><span class="s1"> null values&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feat</span><span class="p">,</span> <span class="n">azdias_low</span><span class="p">[</span><span class="n">feat</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>LP_LEBENSPHASE_FEIN has 1.0     58722
5.0     52400
6.0     43334
2.0     37148
8.0     28837
11.0    25369
7.0     24950
29.0    24880
13.0    24811
10.0    24469
12.0    22275
31.0    22261
30.0    21147
15.0    18966
3.0     18756
19.0    18586
37.0    17500
4.0     16478
14.0    16354
20.0    16287
32.0    15763
39.0    15132
40.0    14108
27.0    13800
16.0    13633
38.0    13099
35.0    13011
34.0    12422
9.0     12111
21.0    11966
28.0    11543
24.0    11309
25.0     9849
36.0     9656
23.0     8628
22.0     6831
18.0     6805
33.0     5724
17.0     5487
26.0     3368
Name: LP_LEBENSPHASE_FEIN, dtype: int64 null values
LP_LEBENSPHASE_GROB has 2.0     149521
1.0     131104
3.0     109623
12.0     69495
4.0      51186
5.0      47165
9.0      46027
10.0     38024
11.0     31157
8.0      28711
6.0      27425
7.0      21158
Name: LP_LEBENSPHASE_GROB, dtype: int64 null values
WOHNLAGE has 3.0    249592
7.0    169269
4.0    135906
2.0    100328
5.0     74305
1.0     43891
8.0     17394
0.0      6741
Name: WOHNLAGE, dtype: int64 null values
PLZ8_BAUMAX has 1.0    499542
5.0     97333
2.0     70406
4.0     56684
3.0     50732
Name: PLZ8_BAUMAX, dtype: int64 null values
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rural_map</span><span class="o">=</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">2</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
<span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;RURAL&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="p">[</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">rural_map</span><span class="p">)</span>
<span class="n">azdias_high</span><span class="p">[</span><span class="s1">&#39;RURAL&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_high</span><span class="p">[</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">rural_map</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">
<h4 id="Discussion-1.2.2:-Engineer-Mixed-Type-Features">Discussion 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Discussion-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><hr>
<h6 id="Engineering--PRAEGENDE_JUGENDJAHRE">Engineering  <code>PRAEGENDE_JUGENDJAHRE</code><a class="anchor-link" href="#Engineering--PRAEGENDE_JUGENDJAHRE">&#182;</a></h6><ol>
<li>created mapping from the current variable to a categorical <code>YOUTH_DECADE</code> variable<ul>
<li>denotes which decade the persons childhood was in. for example 60s became <code>60</code></li>
</ul>
</li>
<li>created mapping from the current variable to a binary <code>AVANTGARDE</code> variable<ul>
<li>if the predominant movement during this persons childhood was avantgarde, the binary var denotes <code>1.0</code> otherwise it denotes <code>0.0</code></li>
</ul>
</li>
</ol>
<hr>
<h6 id="Engineering--CAMEO_INTL_2015">Engineering  <code>CAMEO_INTL_2015</code><a class="anchor-link" href="#Engineering--CAMEO_INTL_2015">&#182;</a></h6><ol>
<li><p>first defined two mapping functions: <code>decode_wealth</code> and <code>decode_life</code></p>
<ul>
<li><code>decode_wealth</code> returns the first digit of the orginial variable which shows wealth status</li>
<li><code>decode_life</code> returns the second digit of the original variable showing life stage</li>
</ul>
</li>
<li><p>I applied this function to the data set and created a new ordinal variable for each wealth and life stage</p>
<ul>
<li><code>CAMEO_INTL_2015_WEALTH</code></li>
<li><code>CAMEO_INTL_2015_LIFE</code></li>
</ul>
</li>
</ol>
<h6 id="Other-mixed-variables">Other mixed variables<a class="anchor-link" href="#Other-mixed-variables">&#182;</a></h6><p>the remaining mixed variables that were still in data set after engineering step were:
<code>['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE','PLZ8_BAUMAX']</code></p>
<ul>
<li><p><code>LP_LEBENSPHASE_FEIN</code></p>
<ul>
<li>this variable essentially encodes the same things as <code>CAMEO_INTL_2015</code>, so I decided to drop it</li>
</ul>
</li>
<li><p><code>LP_LEBENSPHASE_GROB</code></p>
<ul>
<li>this variable is the same as the variable above but on rougher scale, dropping as well</li>
</ul>
</li>
<li><p><code>WOHNLAGE</code></p>
<ul>
<li><code>CAMEO_DEU_2015</code> contains similar but more defined information. <code>WOHNLAGE</code> is encoded as 1: rural, 0: not-rural</li>
</ul>
</li>
<li><p><code>PLZ8_BAUMAX</code></p>
<ul>
<li>this variable is encapsulated by the information in other <code>PLZ8</code> variables, is dropped.</li>
</ul>
</li>
</ul>

</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">
<h4 id="Step-1.2.3:-Complete-Feature-Selection">Step 1.2.3: Complete Feature Selection<a class="anchor-link" href="#Step-1.2.3:-Complete-Feature-Selection">&#182;</a></h4><p>The dataframe should consist of the following:</p>
<ul>
<li>All numeric, interval, and ordinal type columns from the original dataset.</li>
<li>Binary categorical features (all numerically-encoded).</li>
<li>Engineered features from other multi-level categorical features and mixed features.</li>
</ul>
<p><em>NB</em> data should only be from <strong>the subset with few or no missing values</strong>.</p>
<hr>
<p>Some variables have been dropped already. Below, I will go through all features that have been encoded or need to be dropped again to double check</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#double check that features have been dropped</span>
<span class="n">one_hot_vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;CJT_GESAMTTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;FINANZTYP&#39;</span><span class="p">,</span><span class="s1">&#39;GFK_URLAUBERTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;LP_FAMILIE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;NATIONALITAET_KZ&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;SHOPPER_TYP&#39;</span><span class="p">,</span> <span class="s1">&#39;ZABEOTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBAEUDETYP&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span>
<span class="n">drop_categorical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span>
                       <span class="s1">&#39;LP_LEBENSPHASE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_LEBENSPHASE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">,</span> <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">,</span><span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">]</span> <span class="c1">#high error categoricals</span>

<span class="n">mixed_cat</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">one_hot_vars</span><span class="o">+</span><span class="n">drop_categorical</span><span class="o">+</span><span class="n">mixed_cat</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">]:</span>
    <span class="k">if</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;cleaning </span><span class="si">{0}</span><span class="s1">:&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feature</span><span class="p">))</span>
        <span class="n">azdias_low</span> <span class="o">=</span> <span class="n">azdias_low</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</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>cleaning LP_LEBENSPHASE_FEIN:
cleaning LP_LEBENSPHASE_GROB:
cleaning WOHNLAGE:
cleaning PLZ8_BAUMAX:
cleaning PRAEGENDE_JUGENDJAHRE:
cleaning CAMEO_INTL_2015:
cleaning OST_WEST_KZ:
</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">
<h3 id="Step-1.3:-Create-a-Cleaning-Function">Step 1.3: Create a Cleaning Function<a class="anchor-link" href="#Step-1.3:-Create-a-Cleaning-Function">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[42]:</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">clean_data</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Perform feature trimming, re-encoding, and engineering for demographics</span>
<span class="sd">    data</span>
<span class="sd">    </span>
<span class="sd">    INPUT: Demographics DataFrame</span>
<span class="sd">    OUTPUT: Trimmed and cleaned demographics DataFrame</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="sd">&#39;&#39;&#39;all main cleaning steps:&#39;&#39;&#39;</span>
    <span class="c1"># convert missing value codes into NaNs, ...</span>
    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="p">:</span>
        <span class="n">missing_rep</span> <span class="o">=</span> <span class="n">define_missing</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">==</span><span class="n">col</span><span class="p">][</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="k">if</span><span class="p">(</span><span class="n">missing_rep</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">):</span> 
            <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">replace_with</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">missing_rep</span><span class="p">]</span> <span class="c1">#create list of None same length as missing_rep</span>
            <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">missing_rep</span><span class="p">,</span> <span class="n">replace_with</span><span class="p">)</span>
    <span class="c1"># remove selected columns and rows, ...</span>
    <span class="n">features_to_clean</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span><span class="s1">&#39;GEBURTSJAHR&#39;</span><span class="p">,</span><span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;ALTER_HH&#39;</span><span class="p">,</span> <span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span>
                         <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">]</span>
    
    <span class="n">df</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="n">features_to_clean</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="n">df</span><span class="p">[</span><span class="s1">&#39;num_null&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</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="c1">#count null for each row</span>
    
    <span class="c1">#keep only low missing value rows</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;num_null &lt; 21&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;num_null&#39;</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="c1"># select, re-encode, and engineer column values.</span>
    <span class="n">cat_vars</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;categorical&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    <span class="n">mixed_vars</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;mixed&#39;</span><span class="p">][</span><span class="s1">&#39;attribute&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    
    <span class="c1">#re-encode binary var</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;WEST_KZ&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;W&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;O&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">})</span>
    
    <span class="c1">#one hot encode</span>
    <span class="n">one_hot_vars</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;CJT_GESAMTTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;FINANZTYP&#39;</span><span class="p">,</span><span class="s1">&#39;GFK_URLAUBERTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;LP_FAMILIE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;NATIONALITAET_KZ&#39;</span><span class="p">,</span> 
                <span class="s1">&#39;SHOPPER_TYP&#39;</span><span class="p">,</span> <span class="s1">&#39;ZABEOTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBAEUDETYP&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span>
    
    <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">one_hot_vars</span><span class="p">:</span>
        <span class="n">imputed_data</span> <span class="o">=</span> <span class="n">imputer_freq</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">var</span><span class="p">)</span> <span class="c1">#fill na-- imputation</span>
        <span class="n">encode</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">imputed_data</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="n">var</span><span class="p">)</span> <span class="c1">#one hot encode</span>

        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">encode</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span> <span class="c1">#insert new encoded columns into the dataset</span>
            <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">encode</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> 
            
    <span class="c1">#engineer mixed vars</span>
    <span class="n">decade_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span>
                      <span class="mi">9</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span><span class="mi">10</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">90</span><span class="p">}</span>
    <span class="n">avantgarde_mapping</span> <span class="o">=</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">2</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span>
                         <span class="mi">9</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">11</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">12</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">13</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">14</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">15</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
    
    <span class="n">rural_mapping</span> <span class="o">=</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">2</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">:</span><span class="mi">1</span><span class="p">}</span>
        <span class="c1">#map variables</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;RURAL&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">rural_mapping</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;AVANTGARDE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">avantgarde_mapping</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;YOUTH_DECADE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">decade_mapping</span><span class="p">)</span>
    
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_WEALTH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">decode_wealth</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015_LIFE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">decode_life</span><span class="p">)</span>
    
    <span class="c1">#remove all features either re-encoded, or not needed for analysis</span>
    <span class="n">drop_categorical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span>
                       <span class="s1">&#39;LP_LEBENSPHASE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_LEBENSPHASE_GROB&#39;</span><span class="p">,</span>
                        <span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">,</span> <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">,</span><span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">]</span> <span class="c1">#high error categoricals</span>
    
    <span class="n">shape_before</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="n">mixed_cat</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span>
    <span class="n">to_drop</span> <span class="o">=</span> <span class="n">one_hot_vars</span><span class="o">+</span><span class="n">drop_categorical</span><span class="o">+</span><span class="n">mixed_cat</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">]</span>

    <span class="c1">#already removed in high NaN column cleaning, make sure we dont try to drop again</span>
    <span class="k">for</span> <span class="n">removed</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span><span class="s1">&#39;GEBURTSJAHR&#39;</span><span class="p">,</span><span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;ALTER_HH&#39;</span><span class="p">,</span> <span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span>
                         <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">]:</span>
        <span class="k">if</span> <span class="n">removed</span> <span class="ow">in</span> <span class="n">to_drop</span><span class="p">:</span>
            <span class="n">to_drop</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">removed</span><span class="p">)</span>
    
    <span class="n">df</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="n">to_drop</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="c1"># Return the cleaned dataframe.</span>
    
    <span class="k">return</span> <span class="n">df</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">
<h2 id="Step-2:-Feature-Transformation">Step 2: Feature Transformation<a class="anchor-link" href="#Step-2:-Feature-Transformation">&#182;</a></h2><h3 id="Step-2.1:-Apply-Feature-Scaling">Step 2.1: Apply Feature Scaling<a class="anchor-link" href="#Step-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features.</p>
<ul>
<li>sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html">Imputer</a> to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.</li>
<li>For the actual scaling function, a <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> instance is suggested, scaling each feature to mean 0 and standard deviation 1. (RobustScaler also ok)</li>
<li>For these classes, you can make use of the <code>.fit_transform()</code> method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[43]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If you&#39;ve not yet cleaned the dataset of all NaN values, then investigate and</span>
<span class="c1"># do that now.</span>
<span class="n">azdias_low</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">describe</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[43]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>count      193.000000
mean      3573.564767
std      11363.386643
min          0.000000
25%          0.000000
50%          0.000000
75%          0.000000
max      64433.000000
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[44]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</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[44]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ordinal        49
categorical    21
numeric         7
mixed           7
interval        1
Name: type, 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&nbsp;[45]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#impute all variables using mode since many are categorical or binary, very few purely continuous vars</span>
<span class="c1">#highest missing value is 38,000 which is still only about 5.5% of values</span>
<span class="n">imputer</span> <span class="o">=</span> <span class="n">Imputer</span><span class="p">(</span><span class="n">missing_values</span><span class="o">=</span><span class="s1">&#39;NaN&#39;</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;most_frequent&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">azdias_imputed</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">imputer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">azdias_low</span><span class="p">),</span>
                              <span class="n">index</span><span class="o">=</span><span class="n">azdias_low</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">azdias_low</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>

<span class="c1">#end result is new imputed DF</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&nbsp;[46]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_imputed</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</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">describe</span><span class="p">()</span>
<span class="c1">#check that no more null</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[46]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>count    797426.0
mean          0.0
std           0.0
min           0.0
25%           0.0
50%           0.0
75%           0.0
max           0.0
dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[47]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply feature scaling to the general population demographics data.</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>

<span class="n">azdias_scaled</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">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">azdias_imputed</span><span class="p">),</span>
                              <span class="n">index</span><span class="o">=</span><span class="n">azdias_imputed</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">azdias_imputed</span><span class="o">.</span><span class="n">columns</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&nbsp;[48]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_scaled</span><span class="o">.</span><span class="n">describe</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[48]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>CAMEO_DEU_2015_9A</th>
      <th>CAMEO_DEU_2015_9B</th>
      <th>CAMEO_DEU_2015_9C</th>
      <th>CAMEO_DEU_2015_9D</th>
      <th>CAMEO_DEU_2015_9E</th>
      <th>AVANTGARDE</th>
      <th>YOUTH_DECADE</th>
      <th>CAMEO_INTL_2015_WEALTH</th>
      <th>CAMEO_INTL_2015_LIFE</th>
      <th>RURAL</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>...</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
      <td>7.974260e+05</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>8.509483e-17</td>
      <td>8.277811e-17</td>
      <td>1.346548e-16</td>
      <td>-8.425725e-17</td>
      <td>-3.776250e-17</td>
      <td>-9.630418e-17</td>
      <td>1.835286e-16</td>
      <td>-8.126334e-17</td>
      <td>-1.561111e-17</td>
      <td>-8.625319e-17</td>
      <td>...</td>
      <td>-2.060988e-17</td>
      <td>3.715659e-18</td>
      <td>-1.407852e-18</td>
      <td>1.045464e-16</td>
      <td>5.556559e-17</td>
      <td>-1.561111e-17</td>
      <td>-3.827753e-16</td>
      <td>4.856197e-17</td>
      <td>4.130886e-17</td>
      <td>3.727688e-17</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>...</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
      <td>1.000001e+00</td>
    </tr>
    <tr>
      <th>min</th>
      <td>-1.766334e+00</td>
      <td>-1.043765e+00</td>
      <td>-1.494609e+00</td>
      <td>-1.155304e+00</td>
      <td>-1.766955e+00</td>
      <td>-1.249881e+00</td>
      <td>-1.184467e+00</td>
      <td>-1.502317e+00</td>
      <td>-5.305674e-01</td>
      <td>-1.633717e+00</td>
      <td>...</td>
      <td>-1.625923e-01</td>
      <td>-1.895778e-01</td>
      <td>-1.798523e-01</td>
      <td>-1.928439e-01</td>
      <td>-8.974298e-02</td>
      <td>-5.305674e-01</td>
      <td>-2.316187e+00</td>
      <td>-1.551476e+00</td>
      <td>-1.249157e+00</td>
      <td>-5.528315e-01</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>-7.828722e-01</td>
      <td>-1.043765e+00</td>
      <td>-7.686897e-01</td>
      <td>-1.155304e+00</td>
      <td>-1.040726e+00</td>
      <td>-1.249881e+00</td>
      <td>-1.184467e+00</td>
      <td>-7.920337e-01</td>
      <td>-5.305674e-01</td>
      <td>-3.116745e-01</td>
      <td>...</td>
      <td>-1.625923e-01</td>
      <td>-1.895778e-01</td>
      <td>-1.798523e-01</td>
      <td>-1.928439e-01</td>
      <td>-8.974298e-02</td>
      <td>-5.305674e-01</td>
      <td>-9.502996e-01</td>
      <td>-8.696736e-01</td>
      <td>-1.249157e+00</td>
      <td>-5.528315e-01</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>2.005893e-01</td>
      <td>9.580705e-01</td>
      <td>-4.276997e-02</td>
      <td>1.914482e-01</td>
      <td>4.117312e-01</td>
      <td>1.082835e-01</td>
      <td>-4.697522e-01</td>
      <td>-8.175029e-02</td>
      <td>-5.305674e-01</td>
      <td>-3.116745e-01</td>
      <td>...</td>
      <td>-1.625923e-01</td>
      <td>-1.895778e-01</td>
      <td>-1.798523e-01</td>
      <td>-1.928439e-01</td>
      <td>-8.974298e-02</td>
      <td>-5.305674e-01</td>
      <td>4.155877e-01</td>
      <td>4.939317e-01</td>
      <td>9.484474e-02</td>
      <td>-5.528315e-01</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>1.184051e+00</td>
      <td>9.580705e-01</td>
      <td>6.831497e-01</td>
      <td>8.648242e-01</td>
      <td>1.137960e+00</td>
      <td>7.873659e-01</td>
      <td>9.596769e-01</td>
      <td>6.285331e-01</td>
      <td>-5.305674e-01</td>
      <td>1.010368e+00</td>
      <td>...</td>
      <td>-1.625923e-01</td>
      <td>-1.895778e-01</td>
      <td>-1.798523e-01</td>
      <td>-1.928439e-01</td>
      <td>-8.974298e-02</td>
      <td>-5.305674e-01</td>
      <td>1.098531e+00</td>
      <td>1.175734e+00</td>
      <td>7.668454e-01</td>
      <td>-5.528315e-01</td>
    </tr>
    <tr>
      <th>max</th>
      <td>1.184051e+00</td>
      <td>9.580705e-01</td>
      <td>1.409069e+00</td>
      <td>1.538200e+00</td>
      <td>1.137960e+00</td>
      <td>1.466448e+00</td>
      <td>1.674391e+00</td>
      <td>1.338816e+00</td>
      <td>1.884775e+00</td>
      <td>1.010368e+00</td>
      <td>...</td>
      <td>6.150354e+00</td>
      <td>5.274880e+00</td>
      <td>5.560118e+00</td>
      <td>5.185540e+00</td>
      <td>1.114293e+01</td>
      <td>1.884775e+00</td>
      <td>1.098531e+00</td>
      <td>1.175734e+00</td>
      <td>1.438846e+00</td>
      <td>1.808870e+00</td>
    </tr>
  </tbody>
</table>
<p>8 rows × 193 columns</p>
</div>
</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">
<h3 id="Discussion-2.1:-Apply-Feature-Scaling">Discussion 2.1: Apply Feature Scaling<a class="anchor-link" href="#Discussion-2.1:-Apply-Feature-Scaling">&#182;</a></h3><ol>
<li><p>I first had to make sure that there were no null values in the data set. Most of the data had very few nulls left. Only two features had more than 20,000 NaN, which is only about 5% of all the values.</p>
<ul>
<li>I decided that the best imputation method was mode instead of mean. Most of the values are categorical, and even some of the numeric values like birth year wouldnt make sense if they were continuous. Therefore, I imputed using most_frequent</li>
</ul>
</li>
<li><p>I used the StandardScaler() provided by sklearn.preprocessing to scale all the values to mean 0 and stdDev = 1. This was suggested in the writeup.</p>
</li>
</ol>

</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">
<h3 id="Step-2.2:-Perform-Dimensionality-Reduction">Step 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Step-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>Apply PCA on scaled data</p>
<ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html">PCA</a> class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).</li>
<li>Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html"><code>plot()</code></a> function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.</li>
<li>Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[49]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply PCA to the data.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="n">pca_fit</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_scaled</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&nbsp;[50]:</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">scree_plot</span><span class="p">(</span><span class="n">pca</span><span class="p">):</span> <span class="c1">##code from helper_functions.py in PCA lesson</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Creates a scree plot associated with the principal components </span>
<span class="sd">    </span>
<span class="sd">    INPUT: pca - the result of instantian of PCA in scikit learn</span>
<span class="sd">            </span>
<span class="sd">    RETURN: None</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">num_components</span><span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
    <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_components</span><span class="p">)</span>
    <span class="n">vals</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</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">15</span><span class="p">,</span> <span class="mi">10</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">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
    <span class="n">cumvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">vals</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">cumvals</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_components</span><span class="p">):</span>
        <span class="k">if</span><span class="p">(</span><span class="n">i</span><span class="o">%</span><span class="k">20</span> == 0 or i&lt;6):
            <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;</span><span class="si">%s%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">((</span><span class="nb">str</span><span class="p">(</span><span class="n">vals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">)[:</span><span class="mi">4</span><span class="p">])),</span> <span class="p">(</span><span class="n">idx</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">vals</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
 
    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">gridOn</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">gridOn</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Principal Component&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Variance Explained (%)&quot;</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">&#39;Explained Variance Per Principal Component&#39;</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&nbsp;[51]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the variance accounted for by each principal component.</span>
<span class="n">scree_plot</span><span class="p">(</span><span class="n">pca_fit</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&nbsp;[65]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-apply PCA to the data while selecting for number of components to retain.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">75</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_scaled</span><span class="p">)</span>
<span class="n">pca_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">azdias_scaled</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&nbsp;[63]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pca_data</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[63]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(797426, 75)</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">
<h3 id="Discussion-2.2:-Perform-Dimensionality-Reduction">Discussion 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Discussion-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>The Principal Component Analysis with all of the Principal Components showed that the first 6 Principal Components each explain more than 3% of the total variance, with the highest explained variance for a single Principal Component being ~8%. The first 15 Principal Components explain 40% of all the variance. I am retaining 75 of the 185 Prinicpal Components for the analysis, as they explain about 75% of the total variance.</p>
<p>It is interesting to note that the last 50 components (#125-175) explain very little of the variance.</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">
<h3 id="Step-2.3:-Interpret-Principal-Components">Step 2.3: Interpret Principal Components<a class="anchor-link" href="#Step-2.3:-Interpret-Principal-Components">&#182;</a></h3><ul>
<li>To investigate the features, map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.</li>
<li>Investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the <em>i</em>-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[64]:</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">investigate_PC</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="n">component</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">):</span>
    <span class="c1">#code inspired by helper_functions.py in PCA lesson</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Prints association of a feature to the weights of its components</span>
<span class="sd">    INPUT: pca: PCA object, compnent: which component is to be investigated (0 indexed),</span>
<span class="sd">            feature_names: the corresponding feature names to PCA</span>
<span class="sd">    OUTPUT: None</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">num_out</span> <span class="o">=</span> <span class="mi">10</span>
    
    <span class="k">if</span><span class="p">(</span><span class="n">component</span> <span class="o">&lt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">)):</span>
        <span class="n">pca_feature_map</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">&#39;weight&#39;</span><span class="p">:</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="n">component</span><span class="p">],</span>
                                        <span class="s1">&#39;name&#39;</span><span class="p">:</span> <span class="n">feature_names</span><span class="p">})</span>
        
        <span class="n">pca_feature_map</span> <span class="o">=</span> <span class="n">pca_feature_map</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">&#39;weight&#39;</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="nb">print</span><span class="p">(</span><span class="s1">&#39;Principal Component </span><span class="si">{}</span><span class="se">\n</span><span class="s1">---------------</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">component</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="s1">&#39;TOP </span><span class="si">{0}</span><span class="s1"> PRINCIPAL COMPONENTS </span><span class="se">\n</span><span class="s1"> </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_out</span><span class="p">,</span> <span class="n">pca_feature_map</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="n">num_out</span><span class="p">,:]))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> BOTTOM </span><span class="si">{0}</span><span class="s1"> PRINCIPAL COMPONENTS </span><span class="se">\n</span><span class="s1"> </span><span class="si">{1}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_out</span><span class="p">,</span> <span class="n">pca_feature_map</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">num_out</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="s1">&#39;Error in selecting component&#39;</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&nbsp;[65]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the first principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="c1"># HINT: Try defining a function here or in a new cell that you can reuse in the</span>
<span class="c1"># other cells.</span>
<span class="n">investigate_PC</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">azdias_scaled</span><span class="o">.</span><span class="n">columns</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>Principal Component 1
---------------

TOP 10 PRINCIPAL COMPONENTS 
                        name    weight
110      LP_STATUS_GROB_1.0  0.196198
29       HH_EINKOMMEN_SCORE  0.184257
190  CAMEO_INTL_2015_WEALTH  0.184198
52               PLZ8_ANTG3  0.182683
53               PLZ8_ANTG4  0.175970
57              ORTSGR_KLS9  0.158971
42                 EWDICHTE  0.156777
7          FINANZ_HAUSBAUER  0.147975
39              KBA05_ANTG4  0.129277
100      LP_STATUS_FEIN_1.0  0.126992

 BOTTOM 10 PRINCIPAL COMPONENTS 
                    name    weight
113  LP_STATUS_GROB_4.0 -0.112923
192               RURAL -0.115940
43           INNENSTADT -0.129658
55             PLZ8_GBZ -0.132682
34          KONSUMNAEHE -0.137710
36          KBA05_ANTG1 -0.180067
40            KBA05_GBZ -0.181211
50           PLZ8_ANTG1 -0.183000
46           MOBI_REGIO -0.187957
2     FINANZ_MINIMALIST -0.191273
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[66]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the second principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="n">investigate_PC</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">azdias_scaled</span><span class="o">.</span><span class="n">columns</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>Principal Component 2
---------------

TOP 10 PRINCIPAL COMPONENTS 
                      name    weight
0    ALTERSKATEGORIE_GROB  0.232889
4        FINANZ_VORSORGER  0.218994
124            ZABEOTYP_3  0.199978
17              SEMIO_ERL  0.180259
16             SEMIO_LUST  0.163505
10         RETOURTYP_BK_S  0.155426
30         W_KEIT_KIND_HH  0.123088
61      CJT_GESAMTTYP_2.0  0.108279
70            FINANZTYP_5  0.098996
100    LP_STATUS_FEIN_1.0  0.097016

 BOTTOM 10 PRINCIPAL COMPONENTS 
                       name    weight
47       ONLINE_AFFINITAET -0.161964
18              SEMIO_KULT -0.165087
19               SEMIO_RAT -0.168245
5           FINANZ_ANLEGER -0.204721
23           SEMIO_PFLICHT -0.205375
24             SEMIO_TRADV -0.208003
13               SEMIO_REL -0.214557
6    FINANZ_UNAUFFAELLIGER -0.214972
3            FINANZ_SPARER -0.227059
189           YOUTH_DECADE -0.230622
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[67]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the third principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="n">investigate_PC</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">azdias_scaled</span><span class="o">.</span><span class="n">columns</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>Principal Component 3
---------------

TOP 10 PRINCIPAL COMPONENTS 
                   name    weight
15          SEMIO_VERT  0.316899
12           SEMIO_FAM  0.258536
11           SEMIO_SOZ  0.255493
18          SEMIO_KULT  0.249721
70         FINANZTYP_5  0.137373
2    FINANZ_MINIMALIST  0.130359
118    SHOPPER_TYP_0.0  0.122717
122         ZABEOTYP_1  0.119285
13           SEMIO_REL  0.113028
14           SEMIO_MAT  0.088319

 BOTTOM 10 PRINCIPAL COMPONENTS 
                    name    weight
101  LP_STATUS_FEIN_2.0 -0.079073
120     SHOPPER_TYP_2.0 -0.092057
66          FINANZTYP_1 -0.099714
19            SEMIO_RAT -0.152760
5        FINANZ_ANLEGER -0.155240
17            SEMIO_ERL -0.207281
20           SEMIO_KRIT -0.266130
21            SEMIO_DOM -0.279330
22           SEMIO_KAEM -0.311478
1             ANREDE_KZ -0.342268
</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">
<h3 id="Discussion-2.3:-Interpret-Principal-Components">Discussion 2.3: Interpret Principal Components<a class="anchor-link" href="#Discussion-2.3:-Interpret-Principal-Components">&#182;</a></h3><p><em>Each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.</em></p>
<h5 id="1st-Principal-Component">1st Principal Component<a class="anchor-link" href="#1st-Principal-Component">&#182;</a></h5><p>The first principal component is explained mostly by the <code>LP_STATUS_GROB_1.0</code> variable which shows the social status: <em>low-income earner</em> of a specific person. The next three features with highest weight: <code>HH_EINKOMMEN_SCORE</code>, <code>CAMEO_INTL_2015_WEALTH</code>, <code>PLZ8_ANTG3</code>, are all also a certain measure of wealth or income. We can reconcile this with the fact that features with large weights in the same sign are a sign of correlation between the variables. Realistically, we would expect that each feature portraying wealth would be correlated.
The three most negatively correlated features are: <code>FINANZ_MINIMALIST</code>,<code>KBA05_ANTG1</code>, <code>MOBI_REGIO</code>. For example: <code>FINANZ_MINIMALIST</code>, shows the lack of interest of someone in finance, which has an obvious negative correlation to wealth.</p>
<h5 id="2nd-Principal-Component">2nd Principal Component<a class="anchor-link" href="#2nd-Principal-Component">&#182;</a></h5><p>The second principal component is explained mostly by: <code>ALTERSKATEGORIE_GROB</code>,  <code>FINANZ_VORSORGER</code>, <code>ZABEOTYP_3</code>, and the least by: <code>YOUTH_DECADE</code>, <code>FINANZ_SPARER</code>, <code>SEMIO_REL</code>. This is a mixture of age, financial awareness, and environmental/religious awareness respectively.</p>
<h5 id="3rd-Principal-Component">3rd Principal Component<a class="anchor-link" href="#3rd-Principal-Component">&#182;</a></h5><p>I find this third component most interesting, as many of the features that explain this principal component relate to personality traits. The most explanatory feature is <code>SEMIO_VERT</code> which is a measure of 'dreamfulness', followed by <code>SEMIO_FAM</code>: family-mindedness, <code>SEMIO_SOZ</code>: socially-mindedness and so forth. The least explanatory features, and those most negatively correlated are interestingly: <code>ANREDE_KZ</code> which shows gender, <code>SEMIO_KAEM</code> which is a combative-minded feature, <code>SEMIO_DOM</code> which is a dominant-minded feature and so forth.</p>
<hr>
<p>What is interesting is that as we move through the principal components, at least the first three, the weights on features become larger. This means that those features more strongly explain the principal component, after accounting for the variance captured by earlier principal components.</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">
<h2 id="Step-3:-Clustering">Step 3: Clustering<a class="anchor-link" href="#Step-3:-Clustering">&#182;</a></h2><h3 id="Step-3.1:-Apply-Clustering-to-General-Population">Step 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Step-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>I've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[69]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_score</span><span class="o">=</span><span class="p">[]</span>
<span class="n">clusters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="mi">16</span><span class="p">,</span><span class="mi">18</span><span class="p">,</span><span class="mi">20</span><span class="p">]</span>
<span class="c1"># Over a number of different cluster counts...</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">:</span>
    <span class="c1"># run k-means clustering on the data and...</span>
    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">pca_data</span><span class="p">)</span>
    
    <span class="c1"># compute the average within-cluster distances.</span>
    <span class="n">model_score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">pca_data</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">
<p>Could also use <code>Silhouette Coefficient</code> to analyze number of clusters: <a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html">http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html</a></p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[101]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the change in within-cluster distance across number of clusters.</span>
<span class="c1"># HINT: Use matplotlib&#39;s plot function to visualize this 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">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">clusters</span><span class="p">,</span> <span class="n">model_score</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;avg distance&#39;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;# Clusters: k&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">21</span><span class="p">,</span><span class="mi">2</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&nbsp;[71]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pca_data</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[71]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(797426, 75)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[66]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-fit the k-means model with the selected number of clusters and obtain</span>
<span class="c1"># cluster predictions for the general population demographics data.</span>
<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">pca_data</span><span class="p">)</span>
<span class="n">kmeans_pred</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_data</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">
<h3 id="Discussion-3.1:-Apply-Clustering-to-General-Population">Discussion 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Discussion-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>When investigating clustering and drawing scree-plot, I did not find a specific kink in the graph, but rather the average distance kept decreasing. The largest decrease happened in the first 5 clusters, before the benefit of adding an extra cluster starts levelling out. This is why I chose to segment the population into 10 clusters, a nice round number where the average distance is starting to level out on the scree plot.</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">
<h3 id="Step-3.2:-Apply-All-Steps-to-the-Customer-Data">Step 3.2: Apply All Steps to the Customer Data<a class="anchor-link" href="#Step-3.2:-Apply-All-Steps-to-the-Customer-Data">&#182;</a></h3><p>Now that I have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, I interpret how the general population fits apply to the customer data.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[73]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the customer demographics data.</span>
<span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_CUSTOMERS_Subset.csv&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;;&#39;</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&nbsp;[74]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply preprocessing, feature transformation, and clustering from the general</span>
<span class="c1"># demographics onto the customer data, obtaining cluster predictions for the</span>
<span class="c1"># customer demographics data.</span>
<span class="n">customers_cleaned</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">customers</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&nbsp;[77]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">azdias_scaled</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">column</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">customers_cleaned</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">column</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>GEBAEUDETYP_5.0
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[78]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers_cleaned</span><span class="p">[</span><span class="s1">&#39;GEBAEUDETYP_5.0&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1">#this column was missing from customer dataset after one-hot encoding</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&nbsp;[79]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">kmeans</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[79]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>KMeans(algorithm=&#39;auto&#39;, copy_x=True, init=&#39;k-means++&#39;, max_iter=300,
    n_clusters=10, n_init=10, n_jobs=1, precompute_distances=&#39;auto&#39;,
    random_state=None, tol=0.0001, verbose=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&nbsp;[80]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customer_reduced</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">imputer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">customers_cleaned</span><span class="p">)))</span>

<span class="n">customer_clusters</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">customer_reduced</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">
<h3 id="Step-3.3:-Compare-Customer-Data-to-Demographics-Data">Step 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Step-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>At this point, I have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, I compare the two cluster distributions to see where the strongest customer base for the company is.</p>
<p>Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[81]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the proportion of data in each cluster for the customer data to the</span>
<span class="c1"># proportion of data in each cluster for the general population.</span>
<span class="n">gen_pop</span> <span class="o">=</span> <span class="n">azdias_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">gen_pop_all</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1">#including all dropped high NaN data</span>

<span class="n">customer_pop</span> <span class="o">=</span> <span class="n">customers_cleaned</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">customer_pop_all</span> <span class="o">=</span> <span class="n">customers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1">#including all customers with high NaN data</span>

<span class="n">gen_pop_cluster_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">kmeans_pred</span><span class="p">)</span>
<span class="n">customer_cluster_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">customer_clusters</span><span class="p">)</span>

<span class="n">gen_pop_cluster_prop</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">/</span><span class="n">gen_pop</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">gen_pop_cluster_count</span><span class="p">]</span>
<span class="n">customer_cluster_prop</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">/</span><span class="n">customer_pop</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">customer_cluster_count</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&nbsp;[82]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prop_data</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">&#39;gen_pop&#39;</span><span class="p">:</span> <span class="n">gen_pop_cluster_prop</span><span class="p">,</span> <span class="s1">&#39;customers&#39;</span><span class="p">:</span> <span class="n">customer_cluster_prop</span><span class="p">},</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">))</span>
<span class="n">prop_data</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Cluster&#39;</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&nbsp;[83]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#create data frame where each entry in `gen_pop` is % of gen_pop in a cluster</span>
<span class="c1"># each entry in `customers` is% of customer population in a cluster</span>
<span class="n">prop_data_all</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">&#39;gen_pop&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">x</span><span class="o">/</span><span class="n">gen_pop_all</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">gen_pop_cluster_count</span><span class="p">],</span> 
                              <span class="s1">&#39;customers&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">x</span><span class="o">/</span><span class="n">customer_pop_all</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">customer_cluster_count</span><span class="p">]},</span>
                             <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">))</span>
<span class="n">prop_data_all</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Cluster&#39;</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&nbsp;[107]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;CLUSTER COUNT - LOW NAN DATA&#39;&#39;&#39;</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">10</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> <span class="c1"># this creates a figure 14 inch wide, 4 inch high</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;Cluster COUNT - Low NaN Data&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;General Population&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Customer Population&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">kmeans_pred</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">customer_clusters</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</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_text output_subarea ">
<pre>&lt;matplotlib.figure.Figure at 0x7f0aac8cee10&gt;</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&nbsp;[106]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;CLUSTER PROPORTION - LOW NAN DATA&#39;&#39;&#39;</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">14</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span> <span class="c1"># this creates a figure 14 inch wide, 4 inch high</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;Cluster PROPORTION - Low NaN Data&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;General Population&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Customer Population&#39;</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="n">prop_data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">prop_data</span><span class="o">.</span><span class="n">gen_pop</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</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="n">prop_data</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">prop_data</span><span class="o">.</span><span class="n">customers</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</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_text output_subarea ">
<pre>&lt;matplotlib.figure.Figure at 0x7f0aacce49b0&gt;</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&nbsp;[86]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;CLUSTER PROPORTION - ALL DATA&#39;&#39;&#39;</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">14</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span> <span class="c1"># this creates a figure 14 inch wide, 4 inch high</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;Cluster Proportion - All Data&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;General Population&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Customer Population&#39;</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="n">prop_data_all</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">prop_data_all</span><span class="o">.</span><span class="n">gen_pop</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</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="n">prop_data_all</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">prop_data_all</span><span class="o">.</span><span class="n">customers</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</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_text output_subarea ">
<pre>&lt;matplotlib.figure.Figure at 0x7f0ab14acf28&gt;</pre>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYsAAAEjCAYAAADDry0IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xm8HFWZ//HPN2GHAIEEHUJCQBABhYAhiDABBQEXlhlBwAHBQZFRHB1QFnEggyICI+ooiowwsi9G0YgZkQGCMyxK2AmYH2FLQlgCYQ1r4Pn9cc4llabTVX1zK9335vt+vfp1u6rOqX769ul+6tRyShGBmZlZK4M6HYCZmXU/JwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WVkrSBEkXdjqO/k7SKEkvShrc6VgaFT9jSaMlhaTlOh2XdQ8nCwNA0qclTc0/Zo9J+m9JO/Th+vv8B6iwzhfz42FJx/bV+pdUjmeXnumImBkRq0XEGx2M6ReSFkhadwnW8bCklyW9IOlZSTdKOlxSpd8TJ6P+ycnCkHQk8APgO8A7gFHAT4C9OhlXUckPy5oRsRpwAHCCpN3brN+nuvVHUNKqwCeB54B/WMLV7RERQ4D1ge8CxwDnLOE6rYs5WSzjJK0BnAR8KSJ+HRHzI+L1iPhdRHy9SfmdJM1umPfWFrSkcbmH8rykJySdkYv9Kf99NvcCtsvl/1HSfZKekXSVpPUL6w1JX5J0P3B/2XuJiJuAacB7F1df0gcl3SLpufz3g4XXmyLpFEl/yct/K2mtwvI9JU3LW9NTJG3a8D84RtJdwHxJl5CS7u/y+z26cYta0rqSJkmaJ2mGpM8X1jdB0uWSzs9b8NMkjS37H5T4JPAs6fM+eAnXBUBEPBcRk4D9gIMl9fzvPy7p9twOZkmaUKj2trYg6V2SrpX0tKSnJF0kac2+iNH6SET4sQw/gN2BBcByLcpMAC7Mz3cCZjcsfxjYJT+/CTgoP18N+EB+PhqI4usAewMzgE2B5YBvAjcWlgdwNbAWsHKTuN5aJyBge+AlYOdm9fPfZ4CDcp0D8vTaufwU4FFSslkV+FXhfb8bmA98BFgeODrHvkLhf3AHMLIn1uL/pdn/ALie1INbCRgDzC3EPgF4BfgYMBg4Bbh5CT/ra4DTSL3HBcDWi/mM3/ZZLe7zbpg/E/inQjt5H2mDdAvgCWDvFm1ho/y/XREYTkooP+j098OPhQ/3LGxt4KmIWNBH63sd2EjSsIh4MSJublH2C8ApEXFffv3vAGOKvYu8fF5EvNxiPU8B84CfA8dGxDWLqf9x4P6IuCAiFkTEJcBfgT0K5S+IiHsiYj7wr8Cn8gHp/YDfR8TVEfE68O+kBPTBQt3/iIhZJbECIGkksANwTES8EhF35PgPKhT7v4iYHOkYxwXAlmXrbfF6o4APARdHxBOkxNEnvYuCOaSETERMiYi7I+LNiLgLuATYcXEVI2JG/t++GhFzgTNalbelz8nCngaG9eF+9kNJW+F/zbt5PtGi7PrAD/NunWdJP/gCRhTKzKrwmsMiYmhEbBoR/9GwrFh/XeCRhuWPtHi9R0i9iGGNdSPizVy23ViLscyLiBdaxPJ44flLwErNPidJ3ygc5D9rMa93EHBfTkoAFwGflrR8GzGXGUH6DJG0raTrJM2V9BxwOOn/2JSkdSRdKulRSc8DF7Yqb0ufk4XdRNrdsXfF8vOBVXom8lb38J7piLg/Ig4A1gFOBSbmA6vNhjeeBXwhItYsPFaOiBsLZZZ0WORi/TmkBFU0irTrqcfIhmWvk3oui9SVpFy2WLcx1laxzwHWkjSkRSyVRMR3Ip1ltVpEHL6YYp8BNpT0uKTHSVvuw4CPtvt6zUjahpQs/i/PuhiYBIyMiDWAs0gbAtD8/3JKnr9FRKwOHFgob13AyWIZFxHPAScAZ0raW9IqkpaX9FFJpzWp8v9IW7gfz1ul3yTtZwZA0oGShuct72fz7DdI++PfBDYsrOss4DhJm+e6a0jat8/f5EKTgXcrnSa8nKT9gM2AKwtlDpS0maRVSAeCJ+bdQJcDH5e0c37fRwGvAjeyeE+w6Pt9S0TMynVPkbSSpC1IvbKLlvA9vk0+meBdwDjSsZExpOMyF7OEu6IkrZ57j5eSjnncnRcNIfWcXpE0Dvh0oVqztjAEeJF00HsE8LaTK6yznCyMiDgDOJL0wz+XtMV/BPCbJmWfA75I2r/+KKmnUTw7andgmqQXgR8C++d98i8BJwM35N1OH4iIK0i9j0vzrod76KMt3WYi4mngE6Qf+qdJB6k/ERFPFYpdAPyCtAtoJeCfc93ppK3dH5F6GnuQTh99rcVLngJ8M7/frzVZfgDpYO8c4ArgxIi4urfvr4WDgd/mYwiP9zxIn88nimd8teF3kl4gtZXjST2VzxaWfxE4KZc5gZRsAWjWFoB/A7Ymndb7e+DXvYjJaqQI3/zIDNKps6St4593OhazbuOehZmZlXKyMDOzUk4WA0S+ovhznY6jSA1jI7VZ928lTe/rmFqJiJ28C8p6q7+193Y5WbQgaX9Jf5Y0X9KT+fkX82mT/UYeOuL1fB5+z8Bv23U6rqI8DMZGPdMR8b8RsUknYxoIVMMAkeryUYjd3uvhZLEYko4inS1yOvBO0hAJh5OGlFhhKcfSFxfMXRZpsL3hpHPhf93fkp61R/1ggMgl1eK74fbe1zo93kg3PoA1SKeEfrKk3IqkYR9mks6pP4uF4wLtRDql9CjgSeAx4LNt1j2GdArnBcBQ0vUAc0njGV0JrFdY3xTgc4uJcwJ53J88vTnpAqhhpA2Gb5KuHn4SOB9YI5cbncsdRjq98zHgqMJ6fgF8uzC9E4Vxo1h0zKhxpAsAn83r+TELx1X6U36d+aRz7fdrsq5N83t8ljRY4J4NcZxJOuXyBeDPwLs63Y66oA2/COzbokzZ53cM6fToF4DpwM6kU6NfI12s+CJwZy67LukivHmkMbM+39D+fkm6KvsF4G7SVf7H5TY3C9i1IfZzcjt5FPg2MDgvOwS4Afh+fq1vN3lfbu81PNyzaG470o/5b0vKnUpq9GNIA6GNIJ1T3uOdpIY/gnTB1ZmShrZRdy3SVcOHkRr5f+XpUcDLpAbYFkkrkr5wsyNdX3BIfnyIdJHUak3W+yFgY2BX4Nhe7pd9A/gX0hd2O9IPzxcBImJ8LrNlpKuQL2uIeXngd8AfSVeGfxm4SFKx234A6Vz9oaQfq5N7EeNAsh3pOpErelM5/2+PALaJNBT5bsDDEfEHUk/lsvxZ9YxXdQlpA2ddYB/gO5J2LqxyDxZu9NwOXEVq0yNIFz/+rFD2PNJAhxsBW5HaXfF43LbAg6S20PJzdnvvQ53eAurGB+niq8cb5t1IyvIvA+NJQxHMp5DRSY3iocJWx8ssOrLmk8AHKtZ9DVipRYxjgGcK01No3bN4Lcf/JHAt8P687Brgi4Wym5C2Gpdj4ZbWewrLTwPOiWhvS6tJTF8FrihMB7BRs3UBf0vqYQ0qLL8EmFCI4+eFZR8D/trpdtThNvwPjW24SZnFfn6kH+ongV2A5Zu0p+KW+0jSj+OQwrxTgF8Uyl9dWLYHaYu6p7cwJH/+a5J2l71KYZRh0g/jdfn5IcDMkvfl9l7Doytv0tIF3hpcL/JorBHxQQClezkMIu0LXQW4tbArVKThpN9aTyw6mutLpC2ZKnXnRsQrby1Mw098n7QboKd3MkTS4Kh257XLI+LAJvMbB9d7hPTFeUdhXuPgeu+r8HqLkPRu0lW+Y0nvfTng1orV1wVmRRpCpBhHq0H3Vms3xgHmbW24HRExQ9JXST+8m0u6CjgyIuY0Kb64QRGL9994ovD8ZdJIx28UpiF9ZuuSBm98rPDdGMSibbDKgI1u733Mu6Gau4m0ddPqQOBTpEa+eSwcBG+NSAfVylSp23hp/VGkraBtIw201tOVXdKDdo2D640i7QIofrkbB9fr+cFYZFBB0q6zxfkpaTjwjXP836B67HOAkVr0tp29GnRvGVJlgMiWn19EXBwRO5DaR5B2ncLb22afDYpI+qF+lTSScM93Y/WI2LwYWi/WW4zV7b0XnCyaiIhnSfsDfyJpH0mrSRokaQzppjjkrP+fwPclrQMgaYSk3Sqsvzd1h5ASzLN5LJ8Tl+AtFl0C/IukDSStxsL90cWt0X/NAwxuThr/p2cf6x3AxyStJemdpK52q/ifB16U9B7gnxqWL3bQPdIBvPnA0UqDHO5E2pVxaeV3uYyJagNELvbzk7SJpA/nff6vkNpeT0/gCWB0z49Z9OGgiBHxGGlf/ffyIIWDlO6i11f3tnB77yUni8WIiNNIg+sdTdrv+QTpINwxLBxp9BjSwaWblQbC+x/S1n8V7db9AelmO08BNwN/aOf9tHAu6cDjn4CHSD8MX24oc32O9Rrg3yPij3n+BcCdpH21f2Thl6qZr5FGHn2BlCgby04AzsvnxX+quCDSYH17kgYZfIp0+udnIuKvVd/ksijKB4hs9fmtSLq39lOkXR7rkLaOIZ3ZBPC0pNvy874cFPEzpNPT7yWd+TcR+JterquR23sveSBBWyxJo0lfqOV7s9/brD9xe2/NPQszMyvlZGFmZqW8G8rMzEq5Z2FmZqUGzEV5w4YNi9GjR3c6DBvAbr311qciYvjSfl23batT1XY9YJLF6NGjmTp1aqfDsAFM0iPlpfqe27bVqWq79m4oMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMrNWCu4DYz6wYTJkyotXynuGdhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalnCzMzKxUrclC0u6SpkuaIenYJsuPlHSvpLskXSNp/cKyNyTdkR+T6ozTzMxaq+0e3JIGA2cCHwFmA7dImhQR9xaK3Q6MjYiXJP0TcBqwX172ckSMqSs+MzOrrs6exThgRkQ8GBGvAZcCexULRMR1EfFSnrwZWK/GeMzMrJfqTBYjgFmF6dl53uIcCvx3YXolSVMl3Sxp7zoCNDOzamrbDQWoybxoWlA6EBgL7FiYPSoi5kjaELhW0t0R8UBDvcOAwwBGjRrVN1GbdQG3bes2dfYsZgMjC9PrAXMaC0naBTge2DMiXu2ZHxFz8t8HgSnAVo11I+LsiBgbEWOHDx/et9GbdZDbtnWbOpPFLcDGkjaQtAKwP7DIWU2StgJ+RkoUTxbmD5W0Yn4+DNgeKB4YNzOzpai23VARsUDSEcBVwGDg3IiYJukkYGpETAJOB1YDfikJYGZE7AlsCvxM0pukhPbdhrOozMxsKarzmAURMRmY3DDvhMLzXRZT70bgfXXGZmZm1fkKbjMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVqjVZSNpd0nRJMyQd22T5kZLulXSXpGskrV9YdrCk+/Pj4DrjNDOz1mpLFpIGA2cCHwU2Aw6QtFlDsduBsRGxBTAROC3XXQs4EdgWGAecKGloXbGamVlrdfYsxgEzIuLBiHgNuBTYq1ggIq6LiJfy5M3Aevn5bsDVETEvIp4BrgZ2rzFWMzNroc5kMQKYVZienectzqHAf/eyrpmZ1Wi5GtetJvOiaUHpQGAssGM7dSUdBhwGMGrUqN5FadaF3Lat29TZs5gNjCxMrwfMaSwkaRfgeGDPiHi1nboRcXZEjI2IscOHD++zwM06zW3buk2dyeIWYGNJG0haAdgfmFQsIGkr4GekRPFkYdFVwK6ShuYD27vmeWZm1gG17YaKiAWSjiD9yA8Gzo2IaZJOAqZGxCTgdGA14JeSAGZGxJ4RMU/St0gJB+CkiJhXV6xmZtZanccsiIjJwOSGeScUnu/Sou65wLn1RWdmZlX5Cm4zMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMyWMknbS1o1Pz9Q0hnFWwqbdSMnC7Ol76fAS5K2BI4GHgHO72xIZq05WZgtfQsiIki3Gf5hRPwQGNLhmMxaqnXUWTNr6gVJxwEHAuMlDQaW73BMZi25Z2G29O0HvAocGhGPk+4vf3pnQzJrrVLPQtLawARge9K9sP+PdEOip+sLzWzgyb2IC4v3comImfiYhXW5qj2LS4EngU8C+wBzgcvqCspsoIqIN0gHt9fodCxm7ah6zGKtiPhWYfrbkvauIyCzZcArwN2Srgbm98yMiH/uXEhmrVVNFtdJ2h+4PE/vA/y+npDMBrzf4++P9TNVk8UXgCOBC/P0IGC+pCOBiIjV6wjObCCKiPMkrQyMiojpnY7HrIpKxywiYkhEDIqI5fJjUJ43xInCrD2S9gDuAP6Qp8dImtTZqMxaq3ydhaQ9gfF5ckpEXFlPSGYD3gRgHDAFICLukLRBJwMyK1OpZyHpu8BXgHvz4yt5npm1b0FEPNcwLzoSiVlFVXsWHwPGRMSbAJLOA24Hjq0rMLMB7B5JnwYGS9oY+Gfgxg7HZNZSO1dwr1l47nPEzXrvy8DmpKu4LwGeB77a0YjMSlTtWZwC3C7pOkCkYxfH1RaV2QAWES8Bx+eHWb9QKVlExCWSpgDb5FnH5DFtzKxNksYC3wBGU/gORsQWnYrJrEw7o85uB+xAOhA3GLiilojMBr6LgK8DdwNvdjgWs0qqng31E+BwUuO+B/iCpDMr1Ntd0nRJMyS97WC4pPGSbpO0QNI+DcvekHRHfvgcdBtI5kbEpIh4KCIe6Xl0OiizVqr2LHYE3ptv2NJzNtTdrSrk0TXPBD4CzAZukTQpIu4tFJsJHAJ8rckqXo6IMRXjM+tPTpT0c+Aa0kFuACLi150Lyay1qsliOjCKdPtHgJHAXSV1xgEzIuJBAEmXku4M9layiIiH8zJ3xW1Z8lngPaQbHvW0/QCcLKxrVU0WawP3SfpLnt4GuKln91BE7NmkzghgVmF6NrBtG7GtJGkqsAD4bkT8prGApMOAwwBGjRrVxqrNOmrLiHhfqwJu29ZtqiaLE3qxbjWZ185VqqMiYo6kDYFrJd0dEQ8ssrKIs4GzAcaOHesrYK2/uFnSZg27ZBfhtm3dpuqps9e3Wi7ppojYrmH2bNLuqh7rAXOqBhYRc/LfB/Npu1sBD7SsZNY/7AAcLOkh0jELkUZv9qmz1rXaOXW2lZWazLsF2DgPkPYosD/w6SorkzQUeCkiXpU0jHQ719P6KFazTtu90wGYtaud4T5aeVs3OSIWAEcAVwH3AZdHxDRJJ+URbJG0jaTZwL7AzyRNy9U3BaZKuhO4jnTMYrFddrP+JJ8muyawR36s6VNnrdv1Vc+iqYiYDExumHdC4fktpN1TjfVuBFoeADTrryR9Bfg8C89+ulDS2RHxow6GVYvrx+/YVvkd/9Ryj7d1UF8li2YHs82suUOBbSNiPoCkU4GbgAGXLGzg6KvdUAf10XrMlgUC3ihMv4E3uKzLVepZSPp74FRgHVKj7jl7Y3XSk3tqi9Bs4Pkv4M+SesZX2xs4t4PxmJWquhvqNGCPiLivzmDMlgURcUY+HXwH0obXZyPi9s5GZdZa1WTxhBOFWd+QdEFEHATc1mSeWVeqmiymSroM+A0e+MxsSW1enMiDbr6/Q7GYVVI1WawOvATsWpjngc/M2iDpONJNj1aW9HzPbOA18tAeZt2q6nAfn607ELOBLiJOAU6RdEpE+LbE1q9UvfnRuyVdI+mePL2FpG/WG5rZgHWlpFUBJB0o6QxJ63c6KLNWql5n8Z/AccDrABFxF2msJzNr30+BlyRtCRxNuk/M+Z0Nyay1qslilYj4S8O8BX0djNkyYkG+6+RewA8j4ofAkA7HZNZS1QPcT0l6F3nAwHy/7Mdqi8psYHshH+w+EBifz4ZavsMxmbVUNVl8iXS2xnskPQo8BPxDbVGZDWz7kYbrPzQiHpc0Cji9wzGZtVQ1WexNGj32OtKuq/nALpJujYg76grObCCKiMeBMwrTM/ExC+tyVY9ZjAUOB4aSxuE/DNgJ+E9JR9cTmtnAJOkFSc/nxyuS3pD0XKfjMmulas9ibWDriHgRQNKJwERgPHArvoudWWURscjBbEl7A+M6FI5ZJVV7FqNIV5n2eB1YPyJepjD8h5m1LyJ+A3y403GYtVK1Z3ExcLOk3+bpPYBL8oVFvt2pWRvykP89BpF2877t1sRm3aTqcB/fkjSZhUMqHx4RU/NinxVl1p49Cs8XAA8De3YmFLNqKt9WNSJuJR2fsCa2/9H2bde54cs31BBJ/zBhwoSlUqdLDQK+EhHPAkgaCnwP+MeORmXWQl/dVtXMqtuiJ1EARMQzwFYdjMesVOWehXWvHx/1u7bKH/G9PcoLWZ0GSRqakwSS1sLfRetybqBmS9/3gBslTSQd2P4UcHJnQzJrzcnCbCmLiPMlTSWdLivg7yPCZxVaV3Oy6BLXj9+xrfI7/un6miKxpSEnBycI6zd8gNvMzEo5WZiZWSknCzMzK1VrspC0u6TpkmZIOrbJ8vGSbpO0IN9QqbjsYEn358fBdcZpZmat1ZYs8t2/zgQ+CmwGHCBps4ZiM4FDSGNPFeuuBZwIbEsajfPEfJWrmZl1QJ09i3HAjIh4MCJeAy4l3XP4LRHxcETcBbzZUHc34OqImJcvXLoa2L3GWM3MrIU6k8UIYFZhenae12d1JR0maaqkqXPnzu11oGbdxm3buk2dyUJN5lUdhrlS3Yg4OyLGRsTY4cOHtxWcWTdz27ZuU+dFebOBkYXp9YA5bdTdqaHulD6JyhZx8oH7lBcqOP7CiTVFYmbdrM6exS3AxpI2kLQCsD8wqWLdq4BdJQ3NB7Z3zfPMzKwDaksWEbEAOIL0I38fcHlETJN0kqQ9ASRtI2k2sC/wM0nTct15wLdICecW4KQ8z8zMOqDWsaEiYjIwuWHeCYXnt5B2MTWrey5wbp3xmZlZNb6C28zMSjlZmJlZKScLMzMr5WRhZmalnCzMzKyUk4WZmZVysjAzs1JOFmZmVqrWi/I64f1fP7+t8ree/pmaIjEzGzgGXLIws7ebedL72io/6oS7a4rE+isnC1vmbDmxvTEp79xnt5oiMes/nCzMrCv9+KjftV3niO/tUUMkBk4WtgTuO/natspvevyHa4rEzOrms6HMzKyUk4WZmZVysjAzs1JOFmZmVsrJwszMSjlZmJlZKScLMzMr5WRhZmalfFGe9UuX/3JcW+U/te9faorEbNngnoWZmZVyz8LMBqSTD9ynrfLHXzixpkgGBvcszMyslJOFmZmVcrIwM7NSThZmZlaq1mQhaXdJ0yXNkHRsk+UrSrosL/+zpNF5/mhJL0u6Iz/OqjNOMzNrrbazoSQNBs4EPgLMBm6RNCki7i0UOxR4JiI2krQ/cCqwX172QESMqSs+MzOrrs6exThgRkQ8GBGvAZcCezWU2Qs4Lz+fCOwsSTXGZGZmvVBnshgBzCpMz87zmpaJiAXAc8DaedkGkm6XdL2kv232ApIOkzRV0tS5c+f2bfRmHeS2bd2mzmTRrIcQFcs8BoyKiK2AI4GLJa3+toIRZ0fE2IgYO3z48CUO2KxbuG1bt6nzCu7ZwMjC9HrAnMWUmS1pOWANYF5EBPAqQETcKukB4N3A1BrjNTPr17aceFVb5e/cZ7fKZevsWdwCbCxpA0krAPsDkxrKTAIOzs/3Aa6NiJA0PB8gR9KGwMbAgzXGamZmLdTWs4iIBZKOAK4CBgPnRsQ0SScBUyNiEnAOcIGkGcA8UkIBGA+cJGkB8AZweETMqytWMzNrrdaBBCNiMjC5Yd4JheevAPs2qfcr4Fd1xmZmZtX5Cm4zMyvlZGFmZqV8P4uCmSe9r63yo064u6ZIzMy6i3sWZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKebgPM7Mucfkvx7Vd51P7/qWGSN7OPQszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqU83IeZWYP7Tr62rfKbHv/hmiLpHk4WZtbS9j/avq3yN3z5hpoisU5ysjDrJ97/9fPbKn/r6Z+pKRJbFtV6zELS7pKmS5oh6dgmy1eUdFle/mdJowvLjsvzp0varc44zcystdqShaTBwJnAR4HNgAMkbdZQ7FDgmYjYCPg+cGquuxmwP7A5sDvwk7w+MzPrgDp7FuOAGRHxYES8BlwK7NVQZi/gvPx8IrCzJOX5l0bEqxHxEDAjr8/MzDqgzmQxAphVmJ6d5zUtExELgOeAtSvWNTOzpUQRUc+KpX2B3SLic3n6IGBcRHy5UGZaLjM7Tz9A6kGcBNwUERfm+ecAkyPiVw2vcRhwWJ7cBJjeIqRhwFO9fDudqtvJ1/Z7frv1I2J4L9fdlmWgbQ/UNtKNdcvqV2rXdZ4NNRsYWZheD5izmDKzJS0HrAHMq1iXiDgbOLtKMJKmRsTYytF3Qd1Ovrbfc2cN9La9LLaR/vr/6lHnbqhbgI0lbSBpBdIB60kNZSYBB+fn+wDXRurqTAL2z2dLbQBsDCydG82amdnb1NaziIgFko4ArgIGA+dGxDRJJwFTI2IScA5wgaQZpB7F/rnuNEmXA/cCC4AvRcQbdcVqZmat1XpRXkRMBiY3zDuh8PwVYN/F1D0ZOLkPw6nUpe+yup18bb/n/qM//r+WxTbSX/9fQI0HuM3MbODwqLNmZlZqwCcLSedKelLSPb2oO1LSdZLukzRN0lfaqLuSpL9IujPX/bdevP5gSbdLurIXdR+WdLekOyRNbbPumpImSvq53YkbAAAF6klEQVRrfu/bVay3SX69nsfzkr7axuv+S/5f3SPpEkkrtVH3K7netCqv2axdSFpL0tWS7s9/h1Z9/U7obdteknad63esbXeiXee6/aJt19quI2JAP4DxwNbAPb2o+zfA1vn5EOD/AZtVrCtgtfx8eeDPwAfafP0jgYuBK3sR+8PAsF7+z84DPpefrwCs2Yt1DAYeJ53DXaX8COAhYOU8fTlwSMW67wXuAVYhHYf7H2DjdtsFcBpwbH5+LHDq0mqnvfycetW2l6Rd5zoda9udbte5bte27Trb9YDvWUTEn0hnWvWm7mMRcVt+/gJwHxWvJI/kxTy5fH5UPkAkaT3g48DP2wp6CUlandTgzgGIiNci4tlerGpn4IGIeKSNOssBK+drblahybU1i7EpcHNEvBRpJIDrgb9rVWEx7aI4/Mx5wN5VA++E3rbtJWnXuU6/a9t92K6hi9t2ne16wCeLvqI0Iu5WpK2oqnUGS7oDeBK4OiIq1wV+ABwNvNlGnaIA/ijpVqWrgavaEJgL/FfeTfBzSav24vX3By6pWjgiHgX+HZgJPAY8FxF/rFj9HmC8pLUlrQJ8jEUv6qzqHRHxWI7nMWCdXqyjX+lNu871OtW2O92uof+17T5p104WFUhaDfgV8NWIeL5qvYh4IyLGkK5AHyfpvRVf7xPAkxFxa68CTraPiK1Jo/5+SdL4ivWWI3VjfxoRWwHzSV3XypQuwtwT+GUbdYaStoA2ANYFVpV0YJW6EXEfacTiq4E/AHeSrs+xFnrbrqGjbbtj7RqW7bbtZFFC0vKkL9RFEfHr3qwjd3enkIZbr2J7YE9JD5NG6/2wpAvbfM05+e+TwBVUH7V3NjC7sKU4kfQla8dHgdsi4ok26uwCPBQRcyPideDXwAerVo6IcyJi64gYT+qG399WxMkTkv4GIP99shfr6Bf6ol3D0m/bHW7X0D/bdp+0ayeLFiSJtI/zvog4o826wyWtmZ+vTGowf61SNyKOi4j1ImI0qct7bURU2hLJr7eqpCE9z4FdSd3ZKq/9ODBL0iZ51s6kK+nbcQBtdNOzmcAHJK2S/+87k/alVyJpnfx3FPD3vXh9WHT4mYOB3/ZiHV1vSdp1rt+Rtt0F7Rr6Z9vum3bdm6Pi/emR/7GPAa+Tti4ObaPuDqR9pHcBd+THxyrW3QK4Pde9Bzihl/HvRPtnjGxI6q7eCUwDjm+z/hhgao79N8DQNuquAjwNrNGL9/pvpB+de4ALgBXbqPu/pC//ncDOvWkXpOHxryFtuV0DrNXp9tvue6hYr9ftOtfvSNvuZLvO9bu+bdfZrn0Ft5mZlfJuKDMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThb9lKR3SrpU0gOS7pU0WdK71YvRdfP6DpG0bl/HadYut+3u5GTRD+ULe64ApkTEuyJiM+AbwDuWYLWHkIYiaCeOWu+0aMset+3u5WTRP30IeD0izuqZERF3ALN6pvPW1I8L01dK2ikPAPeLPD7+3Xmc/X2AscBFSmP1ryzp/ZKuzwO2XVUYLmCKpO9Iuh5o6z4IZhW4bXcpZ8/+6b1AbwdiGwOMiIj3QrohTEQ8K+kI4GsRMTWPG/QjYK+ImCtpP9L90P8xr2PNiNhxCd+DWTNu213KyWLZ8yCwoaQfAb8Hmg2VvAnpS3t12ivAYNIQAj0uqztIs15w266Rk0X/NA3Yp6TMAhbdzbgSQEQ8I2lLYDfgS8CnWLhV1UPAtIhY3G0n57cdsVk1bttdyscs+qdrgRUlfb5nhqRtgPULZR4GxkgaJGkkeShnScOAQRHxK+BfWThM8wukW2wCTAeGK9+jWNLykjav8f2Y9XDb7lLuWfRDERGS/g74gaRjgVdIX6DizdxvIN33927SSJe35fkjSHcL69lQOC7//QVwlqSXge1IW3f/IWkNUjv5AWmrz6w2btvdy6POmplZKe+GMjOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlfr/QW1YUAr6jN4AAAAASUVORK5CYII=
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[89]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39; What kinds of people are part of a cluster that is overrepresented </span>
<span class="sd">in the customer data compared to the general population?&#39;&#39;&#39;</span>

<span class="n">rep</span> <span class="o">=</span> <span class="p">{}</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> Cluster Representation - LOW NaN Data</span><span class="se">\n</span><span class="s1">----------</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">customer_cluster_prop</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">b</span> <span class="o">=</span> <span class="n">gen_pop_cluster_prop</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">rep</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="o">-</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span>

<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">rep</span><span class="p">:</span>
    <span class="k">if</span><span class="p">(</span><span class="n">rep</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cluster </span><span class="si">%s</span><span class="s1">: OVER represented by </span><span class="si">%.2f%%</span><span class="s1">&#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rep</span><span class="p">[</span><span class="n">x</span><span class="p">]))</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">rep</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">0</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cluster </span><span class="si">%s</span><span class="s1">: UNDER represented by </span><span class="si">%.2f%%</span><span class="s1">&#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rep</span><span class="p">[</span><span class="n">x</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>
 Cluster Representation - LOW NaN Data
----------

Cluster 1: UNDER represented by -8.32%
Cluster 2: OVER represented by 6.42%
Cluster 3: UNDER represented by -11.93%
Cluster 4: OVER represented by 7.32%
Cluster 5: UNDER represented by -2.24%
Cluster 6: UNDER represented by -2.78%
Cluster 7: UNDER represented by -7.32%
Cluster 8: OVER represented by 24.98%
Cluster 9: UNDER represented by -3.78%
Cluster 10: UNDER represented by -2.34%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[90]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">full_rep</span> <span class="o">=</span> <span class="p">{}</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> Cluster Over Representation - FULL Population</span><span class="se">\n</span><span class="s1">----------</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">prop_data_all</span><span class="o">.</span><span class="n">customers</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">b</span> <span class="o">=</span> <span class="n">prop_data_all</span><span class="o">.</span><span class="n">gen_pop</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">full_rep</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="o">-</span><span class="n">b</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span>

<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">full_rep</span><span class="p">:</span>
    <span class="k">if</span><span class="p">(</span><span class="n">full_rep</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cluster </span><span class="si">%s</span><span class="s1">: OVER represented by </span><span class="si">%.2f%%</span><span class="s1">&#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">full_rep</span><span class="p">[</span><span class="n">x</span><span class="p">]))</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">full_rep</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">0</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cluster </span><span class="si">%s</span><span class="s1">: UNDER represented by </span><span class="si">%.2f%%</span><span class="s1">&#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">full_rep</span><span class="p">[</span><span class="n">x</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>
 Cluster Over Representation - FULL Population
----------

Cluster 1: UNDER represented by -7.57%
Cluster 2: OVER represented by 4.06%
Cluster 3: UNDER represented by -10.84%
Cluster 4: OVER represented by 3.51%
Cluster 5: UNDER represented by -3.65%
Cluster 6: UNDER represented by -3.76%
Cluster 7: UNDER represented by -6.74%
Cluster 8: OVER represented by 16.42%
Cluster 9: UNDER represented by -3.91%
Cluster 10: UNDER represented by -3.09%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[68]:</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">cluster_centers</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
    <span class="n">cluster_center</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="n">cluster</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">clusters_sorted</span> <span class="o">=</span> <span class="n">cluster_center</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">cluster_center</span><span class="p">)[:]]</span>
    <span class="n">pca_undone</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="n">cluster</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> 
                                                 <span class="n">decimals</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">pca_undone_sorted</span> <span class="o">=</span> <span class="n">pca_undone</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">pca_undone</span><span class="p">)[:]]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Cluster </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">cluster</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;sorted array</span><span class="se">\n</span><span class="s1">-----</span><span class="se">\n</span><span class="s1"> Top 3: </span><span class="si">%s</span><span class="s1"> </span><span class="se">\n</span><span class="s1"> Bottom 3: </span><span class="si">%s</span><span class="s1"> &#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">clusters_sorted</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:],</span><span class="mi">0</span><span class="p">),</span> 
                                                               <span class="n">clusters_sorted</span><span class="p">[:</span><span class="mi">3</span><span class="p">]))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> original array</span><span class="se">\n</span><span class="s1">-----</span><span class="se">\n</span><span class="s1"> </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span> <span class="n">cluster_center</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;PCA sorted</span><span class="se">\n</span><span class="s1">-----</span><span class="se">\n</span><span class="s1"> Top 3: </span><span class="si">%s</span><span class="s1"> </span><span class="se">\n</span><span class="s1"> Bottom 3: </span><span class="si">%s</span><span class="s1"> &#39;</span><span class="o">%</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">pca_undone_sorted</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:],</span><span class="mi">0</span><span class="p">),</span> 
                                                               <span class="n">pca_undone_sorted</span><span class="p">[:</span><span class="mi">3</span><span class="p">]))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> PCA Undone</span><span class="se">\n</span><span class="s1">-----</span><span class="se">\n</span><span class="s1"> </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span> <span class="n">pca_undone</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&nbsp;[140]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_centers</span><span class="p">(</span><span class="n">cluster</span><span class="o">=</span><span class="mi">3</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>Cluster 3
sorted array
-----
 Top 3: [ 4.62  0.72  0.38] 
 Bottom 3: [-2.43 -2.14 -0.53] 

 original array
-----
 [ 4.62 -2.14 -2.43  0.72 -0.26 -0.21 -0.29 -0.4   0.1   0.38 -0.47 -0.15
  0.14 -0.05 -0.05 -0.04 -0.33  0.22 -0.05  0.02  0.07 -0.27  0.3   0.01
 -0.53  0.03 -0.06  0.2  -0.04  0.11  0.37 -0.06  0.02  0.14  0.1  -0.01
  0.05 -0.22 -0.25 -0.01 -0.08 -0.04  0.05 -0.04 -0.1  -0.03 -0.02 -0.01
  0.02 -0.01 -0.07 -0.03 -0.02  0.02  0.04 -0.04  0.    0.01  0.    0.02
  0.    0.01  0.01  0.04  0.01 -0.    0.01 -0.   -0.    0.04 -0.03 -0.01
  0.01  0.05  0.02]
PCA sorted
-----
 Top 3: [ 1.46  1.31  1.24] 
 Bottom 3: [-1.4  -1.02 -0.97] 

 PCA Undone
-----
 [-0.97  0.93 -1.4   1.15 -0.88  1.03  0.84  0.82 -0.43  0.13 -0.63 -0.47
 -0.13  0.66  0.42 -1.02 -0.55 -0.11 -0.05  1.24  0.53  0.84  0.93  0.97
  0.91 -0.02 -0.02 -0.26 -0.06  0.73 -0.04 -0.38  0.35  0.04 -0.48 -0.25
 -0.6   0.14  0.47  0.4  -0.62 -0.31  0.56 -0.43 -0.29  0.11 -0.63  0.17
  0.14 -0.18 -0.6   0.45  0.63  0.6   0.18 -0.4   0.38  0.57  0.36 -0.07
 -0.26 -0.29  0.13 -0.07  0.24  0.27  1.31 -0.35 -0.17 -0.11 -0.39 -0.52
 -0.03 -0.12 -0.12 -0.1  -0.15 -0.1  -0.17 -0.01  0.15 -0.04  0.07  0.4
  0.21 -0.14  0.12  0.16  0.06  0.01  0.   -0.09  0.04 -0.12 -0.16  0.21
 -0.14  0.2  -0.06 -0.19  0.02  1.46 -0.24 -0.25 -0.16 -0.15 -0.11 -0.15
 -0.4  -0.38  1.05 -0.42 -0.19 -0.44 -0.38 -0.33  0.15  0.33 -0.27  0.02
  0.4  -0.22 -0.44 -0.17 -0.51  0.57  0.66 -0.05 -0.27 -0.06  0.16 -0.04
 -0.    0.    0.19 -0.15 -0.29 -0.24 -0.26 -0.03 -0.    0.13  0.29  0.4
 -0.06 -0.   -0.03 -0.13 -0.08 -0.12 -0.11 -0.15 -0.17 -0.13 -0.08 -0.14
 -0.11 -0.17 -0.07 -0.17 -0.03 -0.03  0.03 -0.05 -0.03  0.02 -0.03 -0.04
  0.05 -0.03  0.04 -0.04 -0.02 -0.02  0.08  0.12  0.01  0.02 -0.02  0.24
  0.14  0.12  0.04  0.24  0.2   0.17  0.2  -0.04 -0.43  0.93  0.62 -0.45
 -0.49]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[141]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_centers</span><span class="p">(</span><span class="n">cluster</span><span class="o">=</span><span class="mi">8</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>Cluster 8
sorted array
-----
 Top 3: [ 3.26  1.06  0.69] 
 Bottom 3: [-4.43 -1.01 -0.98] 

 original array
-----
 [-4.43  0.5   1.06  3.26 -0.9  -1.01 -0.98 -0.7  -0.01  0.1   0.45  0.44
  0.25  0.    0.39 -0.23  0.09  0.21 -0.1   0.69 -0.66 -0.55  0.14 -0.03
  0.19  0.2   0.07  0.18 -0.03 -0.13  0.16 -0.04 -0.07  0.26  0.08  0.06
  0.05  0.05 -0.08 -0.01  0.02 -0.02 -0.05 -0.06  0.01  0.03  0.01 -0.02
 -0.01  0.07  0.01  0.    0.06  0.   -0.02  0.01 -0.03  0.05 -0.04  0.05
  0.02  0.01 -0.02 -0.01  0.01 -0.02  0.01  0.04 -0.01 -0.03  0.02  0.05
 -0.03  0.04 -0.03]
PCA sorted
-----
 Top 3: [ 2.28  2.28  1.9 ] 
 Bottom 3: [-1.34 -0.84 -0.81] 

 PCA Undone
-----
 [ 0.4  -0.08  1.06 -0.58  0.4  -0.58 -0.24 -0.81  1.9  -0.17  0.09  0.02
 -0.24 -0.39 -0.28  0.24  0.33  0.19 -0.19 -0.34 -0.14  0.05 -0.07 -0.4
 -0.25  0.02 -0.1   0.45 -0.1  -1.34 -0.2   0.33 -0.43 -0.12  0.29 -0.06
  0.99 -0.15 -0.61 -0.45  0.77  0.01 -0.05  0.11  0.35 -0.51  0.91  0.35
 -0.52  0.2   0.62 -0.32 -0.47 -0.45  0.06  0.48 -0.22 -0.04 -0.19  0.28
  0.02  0.18 -0.02 -0.11 -0.07  0.02 -0.58  0.37  0.35 -0.12  0.13  0.07
  0.01 -0.04  0.14 -0.07  0.19  0.07  0.24  0.15 -0.1   0.04 -0.13 -0.33
 -0.46  0.17 -0.05 -0.04 -0.01 -0.03  0.06  0.13  0.01  0.26  0.25 -0.46
  0.17 -0.05  0.11  0.37 -0.52 -0.36 -0.23 -0.3  -0.23 -0.2  -0.11 -0.16
 -0.45  2.28 -0.72 -0.47 -0.23 -0.49  2.28  0.24 -0.18 -0.14  0.06 -0.14
 -0.13  0.24  0.73  0.42 -0.1  -0.3  -0.34 -0.22  0.45  0.07 -0.41  0.03
  0.03 -0.   -0.15  0.49  0.57  0.13  0.15 -0.03 -0.11 -0.19 -0.37 -0.36
  0.14  0.09  0.12  0.45  0.19  0.23  0.12  0.25  0.43  0.06  0.04  0.06
  0.08  0.03  0.01  0.13  0.09  0.06 -0.07  0.   -0.01 -0.01  0.02  0.03
 -0.03 -0.06 -0.02  0.04 -0.14 -0.01 -0.12 -0.12 -0.05 -0.06 -0.04 -0.21
 -0.18 -0.17 -0.11 -0.14 -0.19 -0.16 -0.18 -0.06  1.9  -0.41 -0.84  0.49
 -0.17]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[69]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_centers</span><span class="p">(</span><span class="n">cluster</span><span class="o">=</span><span class="mi">2</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>Cluster 2
sorted array
-----
 Top 3: [ 2.17  1.94  0.72] 
 Bottom 3: [-1.49 -1.42 -1.41] 

 original array
-----
 [-1.49  2.17 -1.42 -1.41 -1.    1.94 -0.64  0.65 -0.12 -0.21  0.7   0.41
 -0.5   0.09  0.04  0.06  0.72 -0.39  0.04 -0.26 -0.06  0.42  0.22  0.07
 -0.3  -0.09 -0.   -0.17 -0.03 -0.13 -0.39 -0.14 -0.1   0.19  0.1   0.2
 -0.07  0.17 -0.01  0.05  0.07 -0.12  0.12 -0.13  0.11  0.07  0.05  0.
  0.09  0.07 -0.05  0.08  0.07  0.02  0.01 -0.07  0.04  0.01  0.05 -0.01
 -0.03  0.03  0.01  0.04 -0.06 -0.01 -0.01 -0.03  0.02 -0.06  0.01 -0.02
 -0.    0.11 -0.04]
PCA sorted
-----
 Top 3: [ 1.61  1.4   0.9 ] 
 Bottom 3: [-0.73 -0.68 -0.68] 

 PCA Undone
-----
 [ 0.63  0.39  0.01 -0.61  0.61 -0.21 -0.61  0.54 -0.15 -0.14  0.35 -0.44
 -0.58 -0.68 -0.38 -0.29  0.41  0.67 -0.68 -0.37  0.41  0.14  0.21 -0.56
 -0.55 -0.04 -0.02 -0.27 -0.09  0.3   0.31  0.16 -0.17 -0.08  0.28 -0.15
  0.02  0.36  0.17 -0.28  0.11  0.39 -0.61  0.47  0.18 -0.05  0.14 -0.56
 -0.14  0.27  0.34 -0.19 -0.37 -0.43 -0.    0.38 -0.32 -0.67 -0.37 -0.11
  0.16  0.4  -0.15 -0.04 -0.2  -0.18 -0.28  0.37 -0.42 -0.2   0.05  0.27
  0.04 -0.18 -0.04  0.04  0.14  0.05 -0.06  0.05 -0.2   0.04 -0.04  0.08
  0.25  0.04 -0.07 -0.03 -0.05 -0.08 -0.11 -0.14 -0.03 -0.08 -0.19  0.25
  0.04 -0.08 -0.2  -0.18 -0.45 -0.45  0.9   1.4  -0.01 -0.15 -0.11 -0.11
 -0.36 -0.32 -0.73  1.61 -0.19 -0.38 -0.32  0.21 -0.17 -0.11 -0.18 -0.04
  0.1   0.09 -0.44 -0.24  0.66 -0.18 -0.26  0.16  0.06 -0.17 -0.11 -0.09
  0.02 -0.04  0.09 -0.02 -0.05  0.15  0.11  0.03  0.2   0.02 -0.14 -0.26
  0.06  0.04  0.   -0.11 -0.01 -0.09 -0.06 -0.06  0.07 -0.05 -0.07  0.15
  0.13  0.03 -0.01  0.12  0.07 -0.04  0.05  0.01 -0.02  0.    0.03  0.02
  0.02  0.17  0.03  0.04  0.07  0.    0.05 -0.04  0.01 -0.02  0.07 -0.12
 -0.13  0.    0.01 -0.1  -0.1  -0.17 -0.16  0.04 -0.15 -0.6  -0.16  0.28
  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">
<h3 id="Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">Discussion 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>When including the population count for all including those we dropped for analysis as they had missing data, the culsters: <code>8</code>,<code>2</code>,<code>4</code> were the most over represented in the customer data compared to the general population data (<code>16.42%</code>, <code>4.06%%</code>, <code>3.51%</code>. These percentages were higher for low-NaN data. Based on the full general population data, it seems that clusters <code>4</code>, <code>5</code>, <code>6</code>, <code>9</code>, <code>10</code> were closely represented in both general population and customer data (difference of &lt; 4%).</p>
<p>The must under-represented compared to full population data were clusters: <code>3</code>, <code>1</code>, and <code>7</code> by a substantial <code>10.84%</code>, <code>7.57%</code>, and <code>6.74%</code> respectively.</p>
<p>To see which features define each cluster the most, I took the inverse transformation of the PCA, which gives us back the original features but on a standard scale. This is much easier to interpret than the principal components themselves.</p>
<h4 id="Over-Represented-Cluster:-#8">Over-Represented Cluster: #8<a class="anchor-link" href="#Over-Represented-Cluster:-#8">&#182;</a></h4><ul>
<li><code>LP_STATUS_FEIN_10.0</code> is the highest impact feature in the 8th cluster. It represents the highest income housholds and highest social status.<ul>
<li><code>'LP_STATUS_GROB_5.0</code> represents the same aspect but on a less refined scale</li>
</ul>
</li>
<li><code>GREEN_AVANTGARDE</code> is the feature with the third highest impact in the 8th cluster. It represents participation and interest in environmental issues during a person's youth.</li>
<li>the least impactful feature in the 8th cluster is <code>HH_EINKOMMEN_SCORE</code>, which corroberates our first point as it is correlated with low income, but high income and status is the biggest definer of our cluster.</li>
</ul>
<p>Therefore, people with a high income and high social status who are also environmentally conscious are significantly over represented in the customer data in comparison to the general population. This is a good target audience for any marketing campaing.</p>
<hr>
<h4 id="Over-Represented-Cluster:-#2">Over-Represented Cluster: #2<a class="anchor-link" href="#Over-Represented-Cluster:-#2">&#182;</a></h4><p>Two of the features cluster two is characterized by are:</p>
<ul>
<li><code>LP_STATUS_GROB_2.0</code>: average income and social status</li>
<li><code>LP_STATUS_FEIN_4.0</code>: villagers</li>
</ul>
<p>While average earning villagers are over-represented in the customer data, they are more closely represented than the people in cluster #8 above. Nevertheless, it could still probably be worth to target these people in a campaign</p>
<hr>
<h4 id="Under-Represented-Cluster:-#3">Under-Represented Cluster: #3<a class="anchor-link" href="#Under-Represented-Cluster:-#3">&#182;</a></h4><ul>
<li><code>LP_STATUS_FEIN_2.0</code> is the biggest factor in the third cluster, and that represents orientation-seeking low-income earners. </li>
<li><code>FINANZTYP_1</code> is the second most important factor, and it represents people who are not interested in finance.</li>
<li><code>SEMIO_RAT</code> is a variable that portrays rationality (higher values means less rational).</li>
</ul>
<p>This means that low income people who are both irrational and do not care for finance much are under-represented in the customer population (which makes sense since this is a financial services company).</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">
<blockquote><h4 id="Could-Also-Visualize-Clustering-in-2-Dimensions:">Could Also Visualize Clustering in 2 Dimensions:<a class="anchor-link" href="#Could-Also-Visualize-Clustering-in-2-Dimensions:">&#182;</a></h4>
</blockquote>
<div class="highlight"><pre><span></span><span class="n">reduced_data</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">clusters</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">best_k</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="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span> <span class="o">=</span> <span class="n">reduced_data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">reduced_data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span> <span class="o">=</span> <span class="n">reduced_data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">reduced_data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">hx</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_max</span><span class="o">-</span><span class="n">x_min</span><span class="p">)</span><span class="o">/</span><span class="mf">1000.</span>
<span class="n">hy</span> <span class="o">=</span> <span class="p">(</span><span class="n">y_max</span><span class="o">-</span><span class="n">y_min</span><span class="p">)</span><span class="o">/</span><span class="mf">1000.</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="n">hx</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="n">hy</span><span class="p">))</span>

<span class="n">Z</span> <span class="o">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="n">centroids</span> <span class="o">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">cluster_centers_</span>

<span class="k">def</span> <span class="nf">PCA_plot</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">centroids</span><span class="p">):</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</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="mi">1</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">10</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span>
               <span class="n">extent</span><span class="o">=</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">()),</span>
               <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span><span class="p">,</span>
               <span class="n">aspect</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">origin</span><span class="o">=</span><span class="s1">&#39;lower&#39;</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">reduced_data</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">reduced_data</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;k.&#39;</span><span class="p">,</span>
             <span class="n">markersize</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">centroids</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">centroids</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
                <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">169</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                <span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">10</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">&#39;Clustering on the dataset (PCA-reduced data)</span><span class="se">\n</span><span class="s1">&#39;</span>
              <span class="s1">&#39;Centroids are marked with white cross&#39;</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="n">x_min</span><span class="p">,</span> <span class="n">x_max</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="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">)</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">yticks</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">PCA_plot</span><span class="p">(</span><span class="n">Z</span><span class="p">,</span> <span class="n">centroids</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</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>