tfp_bnn.html

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

<title>tfp_bnn</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/2.7.5/latest.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="Uncertainty-Estimation-using-TensorFlow-Probability">Uncertainty Estimation using TensorFlow Probability<a class="anchor-link" href="#Uncertainty-Estimation-using-TensorFlow-Probability">&#182;</a></h1>
</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="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">tensorflow_probability</span> <span class="k">as</span> <span class="nn">tfp</span>
<span class="n">tfd</span> <span class="o">=</span> <span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span>
<span class="kn">from</span> <span class="nn">tensorflow_probability.python.layers</span> <span class="k">import</span> <span class="n">DenseVariational</span><span class="p">,</span> <span class="n">DenseReparameterization</span><span class="p">,</span> <span class="n">DenseFlipout</span><span class="p">,</span> <span class="n">Convolution2DFlipout</span><span class="p">,</span> <span class="n">Convolution2DReparameterization</span>
<span class="kn">from</span> <span class="nn">tensorflow_probability.python.layers</span> <span class="k">import</span> <span class="n">DistributionLambda</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="k">import</span> <span class="n">Model</span><span class="p">,</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="k">import</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">BatchNormalization</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">LeakyReLU</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.utils</span> <span class="k">import</span> <span class="n">plot_model</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.optimizers</span> <span class="k">import</span> <span class="o">*</span>
<span class="n">tf</span><span class="o">.</span><span class="n">compat</span><span class="o">.</span><span class="n">v1</span><span class="o">.</span><span class="n">enable_eager_execution</span><span class="p">()</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="k">import</span> <span class="n">softmax</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;TensorFlow version:&#39;</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;TensorFlow Probability version:&#39;</span><span class="p">,</span> <span class="n">tfp</span><span class="o">.</span><span class="n">__version__</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>WARNING:tensorflow:

  TensorFlow&#39;s `tf-nightly` package will soon be updated to TensorFlow 2.0.

  Please upgrade your code to TensorFlow 2.0:
    * https://www.tensorflow.org/beta/guide/migration_guide

  Or install the latest stable TensorFlow 1.X release:
    * `pip install -U &#34;tensorflow==1.*&#34;`

  Otherwise your code may be broken by the change.

  
TensorFlow version: 1.15.0-dev20190821
TensorFlow Probability version: 0.8.0-dev20190828
</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="Build-the-dataset-for-regression">Build the dataset for regression<a class="anchor-link" href="#Build-the-dataset-for-regression">&#182;</a></h2>
</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="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">w0</span><span class="p">,</span> <span class="n">b0</span><span class="p">,</span> <span class="n">x_low</span><span class="p">,</span> <span class="n">x_high</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">s</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
        <span class="n">g</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">x_low</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">x_high</span> <span class="o">-</span> <span class="n">x_low</span><span class="p">)</span>
        <span class="k">return</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">(</span><span class="mf">0.25</span> <span class="o">+</span> <span class="n">g</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
    <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">w</span> <span class="o">*</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="n">b</span>
    <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">x_high</span> <span class="o">-</span> <span class="n">x_low</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="n">x_low</span>  <span class="c1"># N(x_low, x_high)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">w0</span><span class="p">,</span> <span class="n">b0</span><span class="p">)</span> <span class="o">+</span> <span class="n">eps</span>
    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</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="n">n_data</span> <span class="o">=</span> <span class="mi">500</span>
<span class="n">n_train</span> <span class="o">=</span> <span class="mi">400</span>
<span class="n">w0</span> <span class="o">=</span> <span class="mf">0.125</span>
<span class="n">b0</span> <span class="o">=</span> <span class="mf">5.0</span>
<span class="n">x_low</span><span class="p">,</span> <span class="n">x_high</span> <span class="o">=</span> <span class="o">-</span><span class="mi">20</span><span class="p">,</span> <span class="mi">60</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="n">n_data</span><span class="p">,</span> <span class="n">w0</span><span class="p">,</span> <span class="n">b0</span><span class="p">,</span> <span class="n">x_low</span><span class="p">,</span> <span class="n">x_high</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</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="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

<span class="n">idx_randperm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">n_data</span><span class="p">)</span>
<span class="n">idx_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">idx_randperm</span><span class="p">[:</span><span class="n">n_train</span><span class="p">])</span>
<span class="n">idx_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">idx_randperm</span><span class="p">[</span><span class="n">n_train</span><span class="p">:])</span>

<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">idx_train</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">idx_train</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">idx_test</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_train.shape =&quot;</span><span class="p">,</span> <span class="n">X_train</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="s2">&quot;y_train.shape =&quot;</span><span class="p">,</span> <span class="n">y_train</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="s2">&quot;X_test.shape =&quot;</span><span class="p">,</span> <span class="n">X_test</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">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</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;y&#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;Noisy training data and ground truth&#39;</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_subarea output_stream output_stdout output_text">
<pre>X_train.shape = (400, 1)
y_train.shape = (400, 1)
X_test.shape = (100, 1)
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[3]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x22645118630&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 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="Traditional-point-estimate-neural-network">Traditional point-estimate neural network<a class="anchor-link" href="#Traditional-point-estimate-neural-network">&#182;</a></h2>
</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="Define-the-loss-function-of-negative-log-likelihood-(input-as-prediction)">Define the loss function of negative log-likelihood (input as prediction)<a class="anchor-link" href="#Define-the-loss-function-of-negative-log-likelihood-(input-as-prediction)">&#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;[4]:</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">neg_log_likelihood_with_dist</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
    <span class="k">return</span> <span class="o">-</span><span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">y_true</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="Define-and-train-the-model">Define and train the model<a class="anchor-link" href="#Define-and-train-the-model">&#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;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">3000</span>
<span class="n">lr</span> <span class="o">=</span> <span class="mf">5e-3</span>

<span class="k">def</span> <span class="nf">build_point_estimate_model</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="n">model_in</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">)(</span><span class="n">model_in</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out</span> <span class="o">=</span> <span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_in</span><span class="p">,</span> <span class="n">model_out</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">pe_model</span> <span class="o">=</span> <span class="n">build_point_estimate_model</span><span class="p">()</span>
<span class="n">pe_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">neg_log_likelihood_with_dist</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
<span class="n">pe_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">pe_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</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>Model: &#34;model&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 1)]               0         
_________________________________________________________________
dense (Dense)                (None, 16)                32        
_________________________________________________________________
leaky_re_lu (LeakyReLU)      (None, 16)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                1088      
_________________________________________________________________
leaky_re_lu_1 (LeakyReLU)    (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 16)                1040      
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 16)                0         
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 17        
_________________________________________________________________
distribution_lambda (Distrib ((None, 1), (None, 1))    0         
=================================================================
Total params: 2,177
Trainable params: 2,177
Non-trainable params: 0
_________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-loss-and-predict-the-test-data">Plot the training loss and predict the test data<a class="anchor-link" href="#Plot-the-training-loss-and-predict-the-test-data">&#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;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</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">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Training loss&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;mean_squared_error&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Mean squared error&#39;</span><span class="p">)</span>
<span class="n">y_test_pred_pe</span> <span class="o">=</span> <span class="n">pe_model</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-and-test-data">Plot the training and test data<a class="anchor-link" href="#Plot-the-training-and-test-data">&#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;[7]:</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">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#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">X_test</span><span class="p">,</span> <span class="n">y_test_pred_pe</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="s1">&#39;r-&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</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;y&#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;Noisy training data and ground truth&#39;</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 output_prompt">Out[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x22715cc0518&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 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="Estimate-aleatoric-uncertainty">Estimate aleatoric uncertainty<a class="anchor-link" href="#Estimate-aleatoric-uncertainty">&#182;</a></h2>
</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="Define-and-train-the-model">Define and train the model<a class="anchor-link" href="#Define-and-train-the-model">&#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;[8]:</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">build_aleatoric_model</span><span class="p">():</span>
    <span class="n">model_in</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">)(</span><span class="n">model_in</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out_loc</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out_scale</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out</span> <span class="o">=</span> <span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
                                                        <span class="n">scale</span><span class="o">=</span><span class="mf">1e-7</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">softplus</span><span class="p">(</span><span class="mf">1e-3</span> <span class="o">*</span> <span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">])))([</span><span class="n">model_out_loc</span><span class="p">,</span>
                                                                                                      <span class="n">model_out_scale</span><span class="p">])</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_in</span><span class="p">,</span> <span class="n">model_out</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">al_model</span> <span class="o">=</span> <span class="n">build_aleatoric_model</span><span class="p">()</span>
<span class="n">al_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">neg_log_likelihood_with_dist</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
<span class="n">al_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">al_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</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>Model: &#34;model_1&#34;
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_2 (InputLayer)            [(None, 1)]          0                                            
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 16)           32          input_2[0][0]                    
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU)       (None, 16)           0           dense_4[0][0]                    
__________________________________________________________________________________________________
dense_5 (Dense)                 (None, 64)           1088        leaky_re_lu_3[0][0]              
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU)       (None, 64)           0           dense_5[0][0]                    
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 16)           1040        leaky_re_lu_4[0][0]              
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU)       (None, 16)           0           dense_6[0][0]                    
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 1)            17          leaky_re_lu_5[0][0]              
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 1)            17          leaky_re_lu_5[0][0]              
__________________________________________________________________________________________________
distribution_lambda_1 (Distribu ((None, 1), (None, 1 0           dense_7[0][0]                    
                                                                 dense_8[0][0]                    
==================================================================================================
Total params: 2,194
Trainable params: 2,194
Non-trainable params: 0
__________________________________________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-loss-and-predict-the-test-data">Plot the training loss and predict the test data<a class="anchor-link" href="#Plot-the-training-loss-and-predict-the-test-data">&#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;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</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">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Training loss&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;mean_squared_error&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Mean squared error&#39;</span><span class="p">)</span>
<span class="n">y_test_pred_al</span> <span class="o">=</span> <span class="n">al_model</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">y_test_pred_al_mean</span> <span class="o">=</span> <span class="n">y_test_pred_al</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">y_test_pred_al_stddev</span> <span class="o">=</span> <span class="n">y_test_pred_al</span><span class="o">.</span><span class="n">stddev</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-and-test-data">Plot the training and test data<a class="anchor-link" href="#Plot-the-training-and-test-data">&#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;[10]:</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">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#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">X_test</span><span class="p">,</span> <span class="n">y_test_pred_al_mean</span><span class="p">,</span> <span class="s1">&#39;r-&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test data&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> 
                 <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">y_test_pred_al_mean</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_test_pred_al_stddev</span><span class="p">),</span>
                 <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">y_test_pred_al_mean</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_test_pred_al_stddev</span><span class="p">),</span>
                 <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Aleatoric uncertainty&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</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;y&#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;Noisy training data and ground truth&#39;</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 output_prompt">Out[10]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x2271887fba8&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 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="Estimate-epistemic-uncertainty">Estimate epistemic uncertainty<a class="anchor-link" href="#Estimate-epistemic-uncertainty">&#182;</a></h2>
</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="n">n_epochs</span> <span class="o">=</span> <span class="mi">10000</span>
<span class="n">lr</span> <span class="o">=</span> <span class="mf">5e-3</span>
<span class="n">n_test</span> <span class="o">=</span> <span class="mi">10</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="Specify-the-surrogate-posterior-over-kernel-and-bias-of-DenseVariational">Specify the surrogate posterior over <code>kernel</code> and <code>bias</code> of <code>DenseVariational</code><a class="anchor-link" href="#Specify-the-surrogate-posterior-over-kernel-and-bias-of-DenseVariational">&#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;[12]:</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">posterior_mean_field</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">bias_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="n">n</span> <span class="o">=</span> <span class="n">kernel_size</span> <span class="o">+</span> <span class="n">bias_size</span>
    <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expm1</span><span class="p">(</span><span class="mf">1.0</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">Sequential</span><span class="p">([</span><span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">VariableLayer</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
                       <span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Independent</span><span class="p">(</span>
                           <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">n</span><span class="p">],</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1e-7</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softplus</span><span class="p">(</span><span class="n">c</span> <span class="o">+</span> <span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">n</span><span class="p">:])),</span>
                           <span class="n">reinterpreted_batch_ndims</span><span class="o">=</span><span class="mi">1</span><span class="p">))</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="Specify-the-prior-over-kernel-and-bias-of-DenseVariational">Specify the prior over <code>kernel</code> and <code>bias</code> of <code>DenseVariational</code><a class="anchor-link" href="#Specify-the-prior-over-kernel-and-bias-of-DenseVariational">&#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;[13]:</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">prior_trainable</span><span class="p">(</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">bias_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="n">n</span> <span class="o">=</span> <span class="n">kernel_size</span> <span class="o">+</span> <span class="n">bias_size</span>
    <span class="k">return</span> <span class="n">Sequential</span><span class="p">([</span><span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">VariableLayer</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
                       <span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Independent</span><span class="p">(</span>
                           <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">),</span> <span class="n">reinterpreted_batch_ndims</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</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="Define-and-train-the-model">Define and train the model<a class="anchor-link" href="#Define-and-train-the-model">&#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;[14]:</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">build_epistemic_model</span><span class="p">(</span><span class="n">train_size</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="n">model_in</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">model_in</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out</span> <span class="o">=</span> <span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">))(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_in</span><span class="p">,</span> <span class="n">model_out</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">ep_model</span> <span class="o">=</span> <span class="n">build_epistemic_model</span><span class="p">(</span><span class="n">n_train</span><span class="p">)</span>
<span class="n">ep_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">neg_log_likelihood_with_dist</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
<span class="n">ep_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">ep_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</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>Model: &#34;model_2&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 1)]               0         
_________________________________________________________________
dense_variational (DenseVari (None, 16)                96        
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 16)                0         
_________________________________________________________________
dense_variational_1 (DenseVa (None, 64)                3264      
_________________________________________________________________
leaky_re_lu_7 (LeakyReLU)    (None, 64)                0         
_________________________________________________________________
dense_variational_2 (DenseVa (None, 16)                3120      
_________________________________________________________________
leaky_re_lu_8 (LeakyReLU)    (None, 16)                0         
_________________________________________________________________
dense_variational_3 (DenseVa (None, 1)                 51        
_________________________________________________________________
distribution_lambda_2 (Distr ((None, 1), (None, 1))    0         
=================================================================
Total params: 6,531
Trainable params: 6,531
Non-trainable params: 0
_________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Show-all-layer-names,-weight-names-and-shapes">Show all layer names, weight names and shapes<a class="anchor-link" href="#Show-all-layer-names,-weight-names-and-shapes">&#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;[15]:</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">l</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">l</span><span class="o">.</span><span class="n">weights</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">ep_model</span><span class="o">.</span><span class="n">layers</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;input_3&#39;: [], &#39;dense_variational&#39;: [&lt;tf.Variable &#39;dense_variational/constant:0&#39; shape=(64,) dtype=float32, numpy=
array([-0.04704537, -0.09531302, -0.05468397, -0.11580291, -0.08971833,
       -0.07729009, -0.05076814, -0.08324057, -0.12371637, -0.05294088,
       -0.08828061, -0.09085072, -0.14504395, -0.1380819 , -0.0870083 ,
       -0.27899066, -1.9161842 , -1.8248898 , -2.0795515 , -2.4224415 ,
       -2.1526618 , -2.125303  , -1.8890059 , -1.7628058 , -2.7586875 ,
       -1.9747292 , -1.9358826 , -1.620749  , -3.2749026 , -2.855188  ,
       -2.1074066 , -3.862741  , -4.304486  , -4.014289  , -4.6453576 ,
       -4.387102  , -4.175872  , -4.225246  , -3.7834823 , -4.292671  ,
       -4.809787  , -3.940326  , -4.010811  , -4.3495092 , -5.026194  ,
       -4.5197973 , -3.9998908 , -5.616435  , -0.9938686 , -0.58735746,
       -0.9358099 , -1.1768152 , -0.9848648 , -1.0890893 , -0.36814827,
       -0.8628556 , -1.5795509 , -0.64255416, -0.7174097 , -0.7457821 ,
       -1.5089829 , -1.5389388 , -0.77139425, -2.554386  ], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational/constant:0&#39; shape=(32,) dtype=float32, numpy=
array([-0.03552532, -0.09396114, -0.05786705, -0.10888591, -0.09632728,
       -0.07216997, -0.03780396, -0.0857416 , -0.11594326, -0.03910633,
       -0.07292157, -0.08665093, -0.15043956, -0.14847077, -0.09428117,
       -0.26843002, -1.9407692 , -1.8078051 , -2.1089375 , -2.379748  ,
       -2.1603942 , -2.217171  , -1.9734799 , -1.740873  , -2.7568405 ,
       -2.0162845 , -2.0360692 , -1.5466782 , -3.2942357 , -2.9302049 ,
       -2.0141025 , -3.8510332 ], dtype=float32)&gt;], &#39;leaky_re_lu_6&#39;: [], &#39;dense_variational_1&#39;: [&lt;tf.Variable &#39;dense_variational_1/constant:0&#39; shape=(2176,) dtype=float32, numpy=
array([-0.35227278, -0.22739749,  0.00543146, ..., -0.15598762,
       -1.1102257 , -0.4004959 ], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_1/constant:0&#39; shape=(1088,) dtype=float32, numpy=
array([-0.38646337, -0.14000496, -0.1035843 , ..., -2.9416277 ,
       -4.947252  , -3.6286163 ], dtype=float32)&gt;], &#39;leaky_re_lu_7&#39;: [], &#39;dense_variational_2&#39;: [&lt;tf.Variable &#39;dense_variational_2/constant:0&#39; shape=(2080,) dtype=float32, numpy=
array([-0.20594649, -0.5097729 , -0.44125563, ..., -0.30167702,
       -0.05558572, -0.003965  ], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_2/constant:0&#39; shape=(1040,) dtype=float32, numpy=
array([-0.17168942, -0.53252125, -0.35130063, ..., -2.4285772 ,
       -5.924332  , -6.627571  ], dtype=float32)&gt;], &#39;leaky_re_lu_8&#39;: [], &#39;dense_variational_3&#39;: [&lt;tf.Variable &#39;dense_variational_3/constant:0&#39; shape=(34,) dtype=float32, numpy=
array([ 3.8052346e-03, -8.4818482e-02, -4.8954707e-02, -4.6195488e-02,
       -4.8472621e-02, -5.2426271e-02, -5.6273747e-02, -2.3019690e-02,
       -2.5252184e-02, -4.0765688e-02, -4.5673251e-02, -3.7789512e-02,
       -1.9367348e-02, -6.7534581e-02, -1.0069254e-02, -2.1955183e-02,
        6.0810575e+00, -4.9888983e+00, -5.2000089e+00, -4.8150182e+00,
       -5.1294537e+00, -5.0586605e+00, -5.9611487e+00, -5.3447561e+00,
       -4.8540487e+00, -5.1902680e+00, -4.9880152e+00, -5.2330332e+00,
       -4.8953261e+00, -4.9589343e+00, -5.5525970e+00, -4.8847165e+00,
       -5.0921049e+00, -3.6405253e+00], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_3/constant:0&#39; shape=(17,) dtype=float32, numpy=
array([ 3.0475669e-03, -8.7187938e-02, -5.3479671e-02, -5.3049676e-02,
       -4.1727580e-02, -5.1039308e-02, -5.0959606e-02, -1.7846076e-02,
       -3.1419937e-02, -3.1960133e-02, -4.2406023e-02, -3.1950708e-02,
       -1.7996188e-02, -6.4437412e-02,  3.7976943e-03, -2.0693699e-02,
        6.0708361e+00], dtype=float32)&gt;], &#39;distribution_lambda_2&#39;: ListWrapper([])}
</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="Plot-the-training-loss-and-predict-the-test-data">Plot the training loss and predict the test data<a class="anchor-link" href="#Plot-the-training-loss-and-predict-the-test-data">&#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;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</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">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Training loss&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;mean_squared_error&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Mean squared error&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
<span class="n">y_test_pred_ep_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">ep_model</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_test</span><span class="p">)]</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-and-test-data">Plot the training and test data<a class="anchor-link" href="#Plot-the-training-and-test-data">&#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;[17]:</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">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#39;</span><span class="p">)</span>
<span class="n">avg_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y_test_pred_ep_list</span><span class="p">):</span>
    <span class="n">y_mean</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">mean</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">X_test</span><span class="p">,</span> <span class="n">y_mean</span><span class="p">,</span> <span class="s1">&#39;r-&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ensemble tests&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
    <span class="n">avg_mean</span> <span class="o">+=</span> <span class="n">y_mean</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">avg_mean</span><span class="o">/</span><span class="n">n_test</span><span class="p">,</span> <span class="s1">&#39;g-&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Averaged tests&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</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;y&#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;Noisy training data and ground truth&#39;</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 output_prompt">Out[17]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x2273848c320&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 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="Estimate-aleatoric-+-epistemic-uncertainty">Estimate aleatoric + epistemic uncertainty<a class="anchor-link" href="#Estimate-aleatoric-+-epistemic-uncertainty">&#182;</a></h2>
</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="n">n_epochs</span> <span class="o">=</span> <span class="mi">30000</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="Define-and-train-the-model">Define and train the model<a class="anchor-link" href="#Define-and-train-the-model">&#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;[19]:</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">build_aleatoric_epistemic_model</span><span class="p">(</span><span class="n">train_size</span><span class="p">):</span>
    <span class="n">model_in</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">model_in</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out_loc</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out_scale</span> <span class="o">=</span> <span class="n">DenseVariational</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">,</span> <span class="n">kl_weight</span><span class="o">=</span><span class="mi">1</span><span class="o">/</span><span class="n">train_size</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">model_out</span> <span class="o">=</span> <span class="n">DistributionLambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">tfd</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
                                                        <span class="n">scale</span><span class="o">=</span><span class="mf">1e-7</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">softplus</span><span class="p">(</span><span class="mf">1e-3</span> <span class="o">*</span> <span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">])))([</span><span class="n">model_out_loc</span><span class="p">,</span>
                                                                                                      <span class="n">model_out_scale</span><span class="p">])</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_in</span><span class="p">,</span> <span class="n">model_out</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="n">ae_model</span> <span class="o">=</span> <span class="n">build_aleatoric_epistemic_model</span><span class="p">(</span><span class="n">n_train</span><span class="p">)</span>
<span class="n">ae_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">neg_log_likelihood_with_dist</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
<span class="n">ae_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">ae_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</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>Model: &#34;model_3&#34;
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_4 (InputLayer)            [(None, 1)]          0                                            
__________________________________________________________________________________________________
dense_variational_4 (DenseVaria (None, 16)           96          input_4[0][0]                    
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU)       (None, 16)           0           dense_variational_4[0][0]        
__________________________________________________________________________________________________
dense_variational_5 (DenseVaria (None, 64)           3264        leaky_re_lu_9[0][0]              
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU)      (None, 64)           0           dense_variational_5[0][0]        
__________________________________________________________________________________________________
dense_variational_6 (DenseVaria (None, 16)           3120        leaky_re_lu_10[0][0]             
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU)      (None, 16)           0           dense_variational_6[0][0]        
__________________________________________________________________________________________________
dense_variational_7 (DenseVaria (None, 1)            51          leaky_re_lu_11[0][0]             
__________________________________________________________________________________________________
dense_variational_8 (DenseVaria (None, 1)            51          leaky_re_lu_11[0][0]             
__________________________________________________________________________________________________
distribution_lambda_3 (Distribu ((None, 1), (None, 1 0           dense_variational_7[0][0]        
                                                                 dense_variational_8[0][0]        
==================================================================================================
Total params: 6,582
Trainable params: 6,582
Non-trainable params: 0
__________________________________________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Show-all-layer-names,-weight-names-and-shapes">Show all layer names, weight names and shapes<a class="anchor-link" href="#Show-all-layer-names,-weight-names-and-shapes">&#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;[20]:</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">l</span><span class="o">.</span><span class="n">name</span><span class="p">:</span> <span class="n">l</span><span class="o">.</span><span class="n">weights</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">ae_model</span><span class="o">.</span><span class="n">layers</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;input_4&#39;: [], &#39;dense_variational_4&#39;: [&lt;tf.Variable &#39;dense_variational_4/constant:0&#39; shape=(64,) dtype=float32, numpy=
array([-0.09862926, -0.11476649, -0.07899566, -0.04197057, -0.02217351,
       -0.11380268, -0.0481223 , -0.09360223, -0.03561402, -0.03081092,
       -0.33977365, -0.05825329, -0.06220264, -0.05583094, -0.08798434,
       -0.12000691, -2.5273845 , -2.4766982 , -2.3864813 , -2.483623  ,
       -2.381097  , -2.2686546 , -2.4497914 , -2.4368212 , -2.5325873 ,
       -2.433751  , -3.2118866 , -2.3995125 , -2.2658179 , -2.3294208 ,
       -2.441587  , -2.4014895 , -3.1474376 , -3.046749  , -3.3582273 ,
       -3.1774943 , -3.0754395 , -3.0821257 , -3.068309  , -3.1905553 ,
       -3.0303633 , -3.1168997 , -4.9372067 , -3.0782003 , -3.0511806 ,
       -3.1341414 , -3.0179408 , -3.1620212 , -0.41232404, -0.23841205,
       -0.47780365, -0.3329982 , -0.1601861 , -0.29951492, -0.15853305,
       -0.37995142, -0.05261213, -0.1921717 , -1.9523746 , -0.28087547,
       -0.24126856, -0.26046717, -0.19583614, -0.22576518], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_4/constant:0&#39; shape=(32,) dtype=float32, numpy=
array([-0.09596992, -0.10569854, -0.09335621, -0.0595726 ,  0.0146834 ,
       -0.10611212, -0.08417413, -0.1043053 , -0.06240533, -0.05138741,
       -0.34233236, -0.11072929, -0.03926694, -0.0545993 , -0.09282467,
       -0.10566173, -2.4582586 , -2.4655597 , -2.385962  , -2.4510012 ,
       -2.3168685 , -2.3953304 , -2.327874  , -2.3989172 , -2.479299  ,
       -2.555362  , -3.2063603 , -2.354704  , -2.3181937 , -2.2886453 ,
       -2.490134  , -2.3655572 ], dtype=float32)&gt;], &#39;leaky_re_lu_9&#39;: [], &#39;dense_variational_5&#39;: [&lt;tf.Variable &#39;dense_variational_5/constant:0&#39; shape=(2176,) dtype=float32, numpy=
array([-0.07682855, -0.10121486,  0.2486316 , ...,  0.00329416,
       -0.04051789, -0.11259051], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_5/constant:0&#39; shape=(1088,) dtype=float32, numpy=
array([-0.03216608, -0.02258643,  0.3282548 , ..., -2.2870789 ,
       -2.5762296 , -1.3740698 ], dtype=float32)&gt;], &#39;leaky_re_lu_10&#39;: [], &#39;dense_variational_6&#39;: [&lt;tf.Variable &#39;dense_variational_6/constant:0&#39; shape=(2080,) dtype=float32, numpy=
array([-0.137996  , -0.9982297 , -0.68139344, ..., -0.02119915,
        0.01147775, -0.01906822], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_6/constant:0&#39; shape=(1040,) dtype=float32, numpy=
array([-0.07852437, -0.94465953, -0.7332415 , ..., -2.213003  ,
       -2.8716516 , -2.2289624 ], dtype=float32)&gt;], &#39;leaky_re_lu_11&#39;: [], &#39;dense_variational_7&#39;: [&lt;tf.Variable &#39;dense_variational_7/constant:0&#39; shape=(34,) dtype=float32, numpy=
array([-0.00993999,  0.13302565, -0.05715121,  0.01200709, -0.03035622,
       -0.03412971, -0.01329443, -0.0100791 ,  0.02336478, -0.02682818,
       -0.01447157,  0.01115929,  0.00999235, -0.0528665 , -0.03448028,
       -0.03277865,  4.297745  , -3.8062973 , -5.5671377 , -3.8215764 ,
       -4.1935534 , -3.8484457 , -4.041553  , -4.1952405 , -4.2259684 ,
       -7.6016836 , -3.941422  , -3.8715405 , -3.911588  , -4.1214776 ,
       -3.9944503 , -3.907611  , -4.1094184 , -2.6508656 ], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_7/constant:0&#39; shape=(17,) dtype=float32, numpy=
array([-0.01208552,  0.13188832, -0.05139474,  0.0077449 , -0.00782503,
       -0.03098016, -0.00436794, -0.00431121,  0.01966239, -0.04230103,
       -0.00754995, -0.0179634 ,  0.02300935, -0.03148894, -0.0397683 ,
       -0.02528351,  4.3014326 ], dtype=float32)&gt;], &#39;dense_variational_8&#39;: [&lt;tf.Variable &#39;dense_variational_8/constant:0&#39; shape=(34,) dtype=float32, numpy=
array([-4.7227371e-01, -6.2047906e+00, -6.4698035e-01, -3.0392554e-01,
       -6.3928866e-01, -2.0529075e+00, -4.9356914e-01, -8.4995079e-01,
        1.5248514e+01, -5.4005849e-01, -2.3926420e+00, -9.0026522e-01,
       -1.1381882e+00, -1.5625995e+00, -4.9507833e-01, -9.1090596e-01,
        4.5232415e+00,  9.1445476e-02, -1.4099446e-02, -4.9302932e-02,
       -9.3029387e-02, -6.0380854e-02, -1.8556746e-02,  2.9846998e-03,
        3.8176846e-02, -8.9852488e-01, -3.5951409e-02, -4.6424832e-02,
       -4.9651567e-02, -6.4862065e-02,  5.8297817e-02,  3.0502148e-02,
        1.0854327e-02, -1.0304969e-02], dtype=float32)&gt;, &lt;tf.Variable &#39;dense_variational_8/constant:0&#39; shape=(17,) dtype=float32, numpy=
array([-0.4323448 , -6.1359973 , -0.6771927 , -0.36630574, -0.68904454,
       -1.8776996 , -0.49424997, -0.9684594 , 15.205367  , -0.56479084,
       -2.3902519 , -1.0373361 , -1.1890415 , -1.5385239 , -0.66423213,
       -0.9370167 ,  4.464679  ], dtype=float32)&gt;], &#39;distribution_lambda_3&#39;: ListWrapper([])}
</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="Plot-the-training-loss-and-predict-the-test-data">Plot the training loss and predict the test data<a class="anchor-link" href="#Plot-the-training-loss-and-predict-the-test-data">&#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;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</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">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Training loss&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">),</span> <span class="n">hist</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;mean_squared_error&#39;</span><span class="p">])</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Mean squared error&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
<span class="n">y_test_pred_ae_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">ae_model</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_test</span><span class="p">)]</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Plot-the-training-and-test-data">Plot the training and test data<a class="anchor-link" href="#Plot-the-training-and-test-data">&#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;[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">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training data&#39;</span><span class="p">)</span>
<span class="n">avg_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y_test_pred_ae_list</span><span class="p">):</span>
    <span class="n">y_mean</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
    <span class="n">y_stddev</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">stddev</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">X_test</span><span class="p">,</span> <span class="n">y_mean</span><span class="p">,</span> <span class="s1">&#39;r-&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;+&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Ensemble tests&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> 
                     <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">y_mean</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_stddev</span><span class="p">),</span>
                     <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">y_mean</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y_stddev</span><span class="p">),</span>
                     <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Aleatoric uncertainties&#39;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
    <span class="n">avg_mean</span> <span class="o">+=</span> <span class="n">y_mean</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;x&#39;</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;y&#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;Noisy training data and ground truth&#39;</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 output_prompt">Out[22]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x22747ed3c88&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 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="Uncertainties-in-CNNs">Uncertainties in CNNs<a class="anchor-link" href="#Uncertainties-in-CNNs">&#182;</a></h2>
</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="Define-the-loss-function-of-negative-log-likelihood-(input-as-logits)">Define the loss function of negative log-likelihood (input as logits)<a class="anchor-link" href="#Define-the-loss-function-of-negative-log-likelihood-(input-as-logits)">&#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;[23]:</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">neg_log_likelihood_with_logits</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
    <span class="n">y_pred_dist</span> <span class="o">=</span> <span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">Categorical</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">y_pred</span><span class="p">)</span>
    <span class="k">return</span> <span class="o">-</span><span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">y_pred_dist</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_true</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 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="Load-MNIST-dataset">Load MNIST dataset<a class="anchor-link" href="#Load-MNIST-dataset">&#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;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_class</span> <span class="o">=</span> <span class="mi">10</span>

<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">n_epochs</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-3</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Loading MNIST dataset&#39;</span><span class="p">)</span>
<span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">n_train</span> <span class="o">=</span> <span class="n">X_train</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">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>

<span class="c1"># Normalize data</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_train.shape =&quot;</span><span class="p">,</span> <span class="n">X_train</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="s2">&quot;y_train.shape =&quot;</span><span class="p">,</span> <span class="n">y_train</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="s2">&quot;X_test.shape =&quot;</span><span class="p">,</span> <span class="n">X_test</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="s2">&quot;y_test.shape =&quot;</span><span class="p">,</span> <span class="n">y_test</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">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gist_gray&#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>Loading MNIST dataset
X_train.shape = (60000, 28, 28, 1)
y_train.shape = (60000, 10)
X_test.shape = (10000, 28, 28, 1)
y_test.shape = (10000, 10)
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[24]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.image.AxesImage at 0x227480fc080&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 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="Define-the-kernel-divergence-function-that-comes-with-a-weight">Define the kernel divergence function that comes with a weight<a class="anchor-link" href="#Define-the-kernel-divergence-function-that-comes-with-a-weight">&#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;[25]:</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">get_kernel_divergence_fn</span><span class="p">(</span><span class="n">train_size</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Get the kernel Kullback-Leibler divergence function</span>

<span class="sd">    # Arguments</span>
<span class="sd">        train_size (int): size of the training dataset for normalization</span>
<span class="sd">        w (float): weight to the function</span>

<span class="sd">    # Returns</span>
<span class="sd">        kernel_divergence_fn: kernel Kullback-Leibler divergence function</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">kernel_divergence_fn</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>  <span class="c1"># need the third ignorable argument</span>
        <span class="n">kernel_divergence</span> <span class="o">=</span> <span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">kl_divergence</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="o">/</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">train_size</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">w</span> <span class="o">*</span> <span class="n">kernel_divergence</span>
    <span class="k">return</span> <span class="n">kernel_divergence_fn</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;[26]:</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">add_kl_weight</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="n">train_size</span><span class="p">,</span> <span class="n">w_value</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
    <span class="n">w</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">add_weight</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;/kl_loss_weight&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(),</span>
                         <span class="n">initializer</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">initializers</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">w_value</span><span class="p">),</span> <span class="n">trainable</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="n">layer</span><span class="o">.</span><span class="n">kernel_divergence_fn</span> <span class="o">=</span> <span class="n">get_kernel_divergence_fn</span><span class="p">(</span><span class="n">train_size</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">layer</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="Build-and-train-the-Bayesian-CNN-model">Build and train the Bayesian CNN model<a class="anchor-link" href="#Build-and-train-the-Bayesian-CNN-model">&#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;[27]:</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">build_bayesian_bcnn_model</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">train_size</span><span class="p">):</span>
    <span class="n">model_in</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">input_shape</span><span class="p">)</span>
    <span class="n">conv_1</span> <span class="o">=</span> <span class="n">Convolution2DFlipout</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                  <span class="n">kernel_divergence_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="n">conv_1</span> <span class="o">=</span> <span class="n">add_kl_weight</span><span class="p">(</span><span class="n">conv_1</span><span class="p">,</span> <span class="n">train_size</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">conv_1</span><span class="p">(</span><span class="n">model_in</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">conv_2</span> <span class="o">=</span> <span class="n">Convolution2DFlipout</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                  <span class="n">kernel_divergence_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="n">conv_2</span> <span class="o">=</span> <span class="n">add_kl_weight</span><span class="p">(</span><span class="n">conv_2</span><span class="p">,</span> <span class="n">train_size</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">conv_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">Flatten</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">dense_1</span> <span class="o">=</span> <span class="n">DenseFlipout</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span>
                           <span class="n">kernel_divergence_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="n">dense_1</span> <span class="o">=</span> <span class="n">add_kl_weight</span><span class="p">(</span><span class="n">dense_1</span><span class="p">,</span> <span class="n">train_size</span><span class="p">)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">dense_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    <span class="n">dense_2</span> <span class="o">=</span> <span class="n">DenseFlipout</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                           <span class="n">kernel_divergence_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
    <span class="n">dense_2</span> <span class="o">=</span> <span class="n">add_kl_weight</span><span class="p">(</span><span class="n">dense_2</span><span class="p">,</span> <span class="n">train_size</span><span class="p">)</span>
    <span class="n">model_out</span> <span class="o">=</span> <span class="n">dense_2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>  <span class="c1"># logits</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">model_in</span><span class="p">,</span> <span class="n">model_out</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>
    
<span class="n">bcnn_model</span> <span class="o">=</span> <span class="n">build_bayesian_bcnn_model</span><span class="p">(</span><span class="n">X_train</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">n_train</span><span class="p">)</span>
<span class="n">bcnn_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="n">neg_log_likelihood_with_logits</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;acc&#39;</span><span class="p">],</span>
                   <span class="n">experimental_run_tf_function</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">bcnn_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">hist</span> <span class="o">=</span> <span class="n">bcnn_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.1</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>WARNING:tensorflow:From C:\ProgramData\Anaconda3\envs\nightly\lib\site-packages\tensorflow_probability\python\layers\util.py:103: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.add_weight` method instead.
Model: &#34;model_4&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
conv2d_flipout (Conv2DFlipou (None, 14, 14, 32)        609       
_________________________________________________________________
batch_normalization (BatchNo (None, 14, 14, 32)        128       
_________________________________________________________________
activation (Activation)      (None, 14, 14, 32)        0         
_________________________________________________________________
conv2d_flipout_1 (Conv2DFlip (None, 7, 7, 64)          36929     
_________________________________________________________________
batch_normalization_1 (Batch (None, 7, 7, 64)          256       
_________________________________________________________________
activation_1 (Activation)    (None, 7, 7, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 3136)              0         
_________________________________________________________________
dense_flipout (DenseFlipout) (None, 512)               3211777   
_________________________________________________________________
dense_flipout_1 (DenseFlipou (None, 10)                10251     
=================================================================
Total params: 3,259,950
Trainable params: 3,259,754
Non-trainable params: 196
_________________________________________________________________
Train on 54000 samples, validate on 6000 samples
Epoch 1/20
54000/54000 [==============================] - 13s 244us/sample - loss: 67.6189 - acc: 0.8352 - val_loss: 64.8947 - val_acc: 0.9190
Epoch 2/20
54000/54000 [==============================] - 10s 186us/sample - loss: 62.1857 - acc: 0.9376 - val_loss: 59.3274 - val_acc: 0.9502a
Epoch 3/20
54000/54000 [==============================] - 10s 185us/sample - loss: 56.3240 - acc: 0.9561 - val_loss: 53.2225 - val_acc: 0.9647
Epoch 4/20
54000/54000 [==============================] - 10s 186us/sample - loss: 50.0751 - acc: 0.9654 - val_loss: 46.8906 - val_acc: 0.9678
Epoch 5/20
54000/54000 [==============================] - 10s 185us/sample - loss: 43.7388 - acc: 0.9709 - val_loss: 40.6024 - val_acc: 0.9720
Epoch 6/20
54000/54000 [==============================] - 10s 185us/sample - loss: 37.5904 - acc: 0.9739 - val_loss: 34.6494 - val_acc: 0.9740
Epoch 7/20
54000/54000 [==============================] - 10s 185us/sample - loss: 31.8904 - acc: 0.9759 - val_loss: 29.2451 - val_acc: 0.9780.9775 - a
Epoch 8/20
54000/54000 [==============================] - 10s 186us/sample - loss: 26.8405 - acc: 0.9766 - val_loss: 24.5636 - val_acc: 0.9758 1s - loss: 27.1942 - acc - ETA: 1s - loss: 27.1280 - - ETA: 0s - loss: 27. - ETA: 0s - loss: 26.8883 - acc: 0. - ETA: 0s - loss: 26.8613 - acc: 0.97 - ETA: 0s - loss: 26.8454 - acc: 0.97
Epoch 9/20
54000/54000 [==============================] - 10s 186us/sample - loss: 22.5185 - acc: 0.9778 - val_loss: 20.6052 - val_acc: 0.9783
Epoch 10/20
54000/54000 [==============================] - 10s 186us/sample - loss: 18.9320 - acc: 0.9789 - val_loss: 17.3798 - val_acc: 0.9785
Epoch 11/20
54000/54000 [==============================] - 10s 186us/sample - loss: 16.0087 - acc: 0.9801 - val_loss: 14.7672 - val_acc: 0.9767
Epoch 12/20
54000/54000 [==============================] - 10s 186us/sample - loss: 13.6396 - acc: 0.9793 - val_loss: 12.6266 - val_acc: 0.9780ETA: 1s - loss: 13.7973 - acc: 
Epoch 13/20
54000/54000 [==============================] - 10s 186us/sample - loss: 11.7202 - acc: 0.9798 - val_loss: 10.8952 - val_acc: 0.9788- a
Epoch 14/20
54000/54000 [==============================] - 10s 186us/sample - loss: 10.1429 - acc: 0.9803 - val_loss: 9.4645 - val_acc: 0.9793
Epoch 15/20
54000/54000 [==============================] - 10s 187us/sample - loss: 8.8226 - acc: 0.9805 - val_loss: 8.2442 - val_acc: 0.9792- ETA: 3s - loss: 9.0570 - acc: 0.981 - ETA: 3s - loss - ETA: 2s - loss: 8.9605 - acc: 0. - ETA: 2s - loss: 8.9449 - ETA: 1s - loss: 8.88
Epoch 16/20
54000/54000 [==============================] - 10s 186us/sample - loss: 7.7130 - acc: 0.9809 - val_loss: 7.2372 - val_acc: 0.97730733 - acc: 0. - ETA: 6s - loss: 8.0614 -  - ETA: 5s - loss: 8.0244 - -
Epoch 17/20
54000/54000 [==============================] - 10s 186us/sample - loss: 6.7763 - acc: 0.9811 - val_loss: 6.3775 - val_acc: 0.9790
Epoch 18/20
54000/54000 [==============================] - 10s 186us/sample - loss: 5.9771 - acc: 0.9811 - val_loss: 5.6293 - val_acc: 0.9788
Epoch 19/20
54000/54000 [==============================] - 10s 186us/sample - loss: 5.2980 - acc: 0.9804 - val_loss: 5.0170 - val_acc: 0.9783
Epoch 20/20
54000/54000 [==============================] - 10s 186us/sample - loss: 4.7217 - acc: 0.9806 - val_loss: 4.4656 - val_acc: 0.9820
</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="Quantify-the-uncertainty-in-predictions">Quantify the uncertainty in predictions<a class="anchor-link" href="#Quantify-the-uncertainty-in-predictions">&#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;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_mc_run</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">med_prob_thres</span> <span class="o">=</span> <span class="mf">0.2</span>

<span class="n">y_pred_logits_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">bcnn_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_mc_run</span><span class="p">)]</span>  <span class="c1"># a list of predicted logits</span>
<span class="n">y_pred_prob_all</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">softmax</span><span class="p">(</span><span class="n">y</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="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred_logits_list</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">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">med_prob_thres</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred_prob</span><span class="p">]</span> <span class="k">for</span> <span class="n">y_pred_prob</span> <span class="ow">in</span> <span class="n">y_pred_prob_all</span><span class="p">]</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>

<span class="n">idx_valid</span> <span class="o">=</span> <span class="p">[</span><span class="nb">any</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of recognizable samples:&#39;</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">idx_valid</span><span class="p">))</span>

<span class="n">idx_invalid</span> <span class="o">=</span> <span class="p">[</span><span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Unrecognizable samples:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">idx_invalid</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy on MNIST (recognizable samples):&#39;</span><span class="p">,</span>
      <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_valid</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx_valid</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="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_valid</span><span class="p">]))</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy on MNIST (unrecognizable samples):&#39;</span><span class="p">,</span>
      <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_invalid</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx_invalid</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="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_invalid</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 recognizable samples: 9992
Unrecognizable samples: [1039 1319 1790 2293 3808 4248 6572 6651]
Test accuracy on MNIST (recognizable samples): 0.9885908726981585
Test accuracy on MNIST (unrecognizable samples): 0.125
</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="Define-the-function-that-plots-the-histogram-of-predicted-probabilities-across-all-possible-classes">Define the function that plots the histogram of predicted probabilities across all possible classes<a class="anchor-link" href="#Define-the-function-that-plots-the-histogram-of-predicted-probabilities-across-all-possible-classes">&#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;[29]:</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">plot_pred_hist</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">n_class</span><span class="p">,</span> <span class="n">n_mc_run</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">med_prob_thres</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">n_subplot_rows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">10</span><span class="p">)):</span>
    <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="n">n_bins</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</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">n_subplot_rows</span><span class="p">,</span> <span class="n">n_class</span> <span class="o">//</span> <span class="n">n_subplot_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</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">n_subplot_rows</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_class</span> <span class="o">//</span> <span class="n">n_subplot_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
            <span class="n">idx</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="p">(</span><span class="n">n_class</span> <span class="o">//</span> <span class="n">n_subplot_rows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">j</span>
            <span class="k">if</span> <span class="n">idx</span> <span class="o">&lt;</span> <span class="n">n_class</span><span class="p">:</span>
                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">bins</span><span class="p">)</span>
                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_mc_run</span><span class="p">])</span>
                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">set_text</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> (median prob: </span><span class="si">{:.2f}</span><span class="s2">) (</span><span class="si">{}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span>
                                                                               <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">]),</span>
                                                                               <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span> <span class="o">&gt;=</span> <span class="n">med_prob_thres</span><span class="p">)))</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#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>
<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="A-recognizable-example">A recognizable example<a class="anchor-link" href="#A-recognizable-example">&#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;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gist_gray&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;True label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx</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">plot_pred_hist</span><span class="p">(</span><span class="n">y_pred_prob_all</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">n_class</span><span class="p">,</span> <span class="n">n_mc_run</span><span class="p">,</span> <span class="n">med_prob_thres</span><span class="o">=</span><span class="n">med_prob_thres</span><span class="p">)</span>

<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">]):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</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">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;I don&#39;t know!&quot;</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>True label of the test sample 0: 7
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABZsAAAJSCAYAAAClThubAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZg0Z1kv/u8NYZEEzEJASAhrkEVZw+KCICAcNomCiKAExB+KqEf0ILgBIgp4eRRREDkCYVcMS9j0HAQC4gKEnciSEJZEAoQlIQthic/vj6eGdCbd8868NUvXzOdzXXPNTHd1991d9a2uuvup6mqtBQAAAAAAxrjcThcAAAAAAMD0aTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAw2p5pNlfVzarq5G16rCdX1UuHv4+qqvOr6vLb8dhjVNWnq+ruO13HWqrqmlX10aq60j6mu0dVvXYTHu+dVfXwkffxrKr6hbG17FVVdXhVfbyqrrwNj/XwqnrnzP/nV9UNtvpxx6qqk5Z9GauqK1XVx6rqGvuYblPW1VX10qp68sj7+I2qeurYWvYq2d032Z17P7K7w2R332R37v3I7g6zv7tv9nfn3o/93SUgv/smv3PvZ2nzO9lmc1UdWlWvqaoLquozVfWQfdzkD5P86XbUNqu19tnW2kGttYu3+7G3W1U9ZJgXF1TVa6vq0DWmvVVVvbeqLhx+32rmuqqqZ1TVl4efP6mqSpLW2heSvC3Jo/ZRzh8nefrMfbahrvOHn3PGPdsN+ZMkT6yqA7bxMZdWVf1KVZ1cVd+oquPXcZMnJHlha+2iLS7tMobsnr7dj7vdqupuww7phVX1tqq67hrTXm+Y5sLhNndfdf1jq+rzVXVuVb1g5Y22tfaNJC9I8vh9lHOpdfWwUfH1meyeX1XX3v9nuyHPTfKIqjpsmx5vaQ1Ni+cP6/jzqur9VXWvfdxMdreY7C4kuzOqNwHPqqqvVdUn1rFDIbtbTHYXkt05quroqrqohubQGuzvbjH7uwvZ312l+oeSF83Mj4/v4ybyu8Xkd6Etye9km81Jnp3km0mumeShSf66qm4+b8KqulaSH00y+pODqdrqFf/w2v9Nkp9LnycXJnnOgmmvmOTEJC9NckiSFyU5cbg86cE8Nsktk9wiyX2T/OLMXbxs1f+r7/92Sb67tfYfq6665bAiPai1dvDGnuH+a62dmeST6c+D5HNJnpq+A7SmYYfpuPRlZU/ahuxePcmrk/x+kkOTnJzk79e4ySuSvD/JYUl+N8kJVXX4cF/3TG9S3C3J9ZLcIMkfzNz25UmOqwWf9K6xrr7fTHYPaq19bkNPcj+11i5M8v/S12t73QFJzkhy5yTfnb68vLKqrjdvYtmV3YHsLoenJblea+1qSX48yVOr6rbzJpRd2R3I7nJ5dpL3rDWB/V37uwP7u8vlV2bmx/cumkh+5Xewq/I7yWZzVR2Y5AFJfr+1dn5r7Z1JXpfFGyY/luR9syM0hk/tH1dVHxo+QXh+9SHv/1h91NY/V9UhM9Pfsar+rarOqaoPVtVdZq67flW9fbjdm5Ncfea66w2fUhww/P+I6sPqz6uq06vqF2emvUtVnVlVv1lVX6w+CuURa7wOJ1XV06rq3dVHM5y48unMzOM+sqo+m+Stw+U/XlWnDM/jpKq66aq7vV1V/WdVfbWqXljrP4TyoUle31p7R2vt/PQN6J+sqqvOmfYu6Y2LZ7bWvtFae1aSSnLX4frjkvzv1tqZrbX/SvK/kzx85vbvSnKDWjwK5F5J3r6eoqvqsKp6U1WdPTzn11fVEQumvXFVvWN4rb9UVS+fue5mwzLzleqjTh6w6uYnJbnPemra7Vprr26tvTbJl9cx+R2SnDOsAJN8Z7l/6pDH84d5dlhVvaz6qK33zDbAquomVfXmYd58vKoeNHPdYVX1uuF2705yw9kHHzJ0o+Hv+1Qfzfm1qjqjZg4VncnbcVX12WH5+N1FT6qqjq+q5w51nTesP647c32rqsdU1alJTh0u+8HhuZ07/P7BVXd7w3nrgnX4ySSntNb+YVhHPjnJLavqJnPqvnGS2yR5Umvt6621VyX5cPr6OOnZfX5r7ZTW2lfTP6F/+Mrth/n41SR3XFDLZdbVi1TV5arqhOqjuRatz1amvcaQ83OG5eAdM9cdWf0ombOr6lNV9ZhVNz8pspvW2gWttSe31j7dWvvv1tobknwqydyGVWRXdheQ3e03zNdvrPw7/NxwweSyK7tzye7OqKoHJzknyVv2Man9Xfu7c5X93SmQX/mda8r5nWSzOcmNk1zcWvvEzGUfTDJ3ZHOS708y77CFB6QH+8ZJ7pfkH5P8Tnr4Lpfk15JkmJlvTB+NeWiS/5XkVTWMKkgfNfDe4XZ/mL7wLfLF9E8MrpbkEUn+vKpuM3P996SPGjsiySOTPHt2JTLHw5L8fJJrJ/l2kmetuv7OSW6a5J7VN1hfkeTXkxye5E1JXl+XfEKT9BDeM33j/8ZJfm/liiHwP7ygjpunz4MkSWvtk+kjz2+8YNoPtdbazGUfyiXz71L3lVXztrX27SSnpX+SNM+i+T3P5ZL8nyRHJblukm8l+YsF0/5R+nJwSJIj00cZZFhBvTnJi5NcI/01fF5VzX56+dE16mWxRfPywekfLh2Rvqz+e5IXpufzo0melHzng6k3p2f0Gkl+Jslz6pKjIJ6d5KIk10rP0c+vUcsF6Xk7OH1F/OiqOnbVND+c5HvTRxg9cc6b46yHpq8vrp7kA+mfgM46Nn2n/2bDm/Ib0/N9WJI/S/LGuvRhpgvXBcNGyqJTDa3O7gXpn2zOW5/ePMnprbXzZi6bzee87F5zVZ1rZWEj2U2SNyQ5On29+ZEkL1kw3eOSnJ6+3vue9I2LVD832RvSRwsdkf5+8Liquts6692zquqa6ev3UxZMIruyuxbZ3WZV9ZyqujDJx5Kclb4NOI/syu5aZHcbVdXVkjwlyW+uY3L7u/Z3F7G/u3OeNjT9/nW2+TuH/MrvIpPN71SbzQclOXfVZecmmfepRNI3Us+bc/lftta+MHwa8S9J3tVae/8w+uM1SW49TPezSd7UWnvTMKLrzemHvN27qo5Kcrv0UdbfaK29I8nrFxXeWntja+2TrXt7+qFid5qZ5FtJntJa+1Zr7U1Jzk/fiF7kJa21jwwbqr+f5EF16ZO7P3kYkfb1JD+d5I2ttTe31r6Vfk6g70oyO1Ljr1prZ7TWvpK+sP7MTO0HD6PI59nIPNnXtKuvPzfJQVX9PDiD89Ln6zyL5vf7hhXQOVX1rCRprZ3dWnvNMFrka+nnzrnzgvv9VvohitdqrV3UWvvX4fIfT/KJ1tqLW2vfbq29N/0QmAeus14WWzQvXzjk6Nz0N91Pttb+eVix/0Muye59k3y6tfbCYd68L8mrkjxwyMkDkjxxyMhH0g+Rmau1dlJr7cPDOuBD6W+Eq5eVPxiWpQ+mv+mstcJ+4/DJ6jfSD4v9gaq6zsz1T2utfWXI7n2SnNpae8nwPF6R3jC438z0C9cFrbVbtNZenvm2OrtZdV/7k93XzmT3tUkyzIfjW2vnzYwMu+3Q6FjtW+kbOEe11r45rHuTPtLraq21Px4uPy3J89ObKuupd0+qqiukN2le1Fr72ILJZFd2E9ldGq21X06fn3dKP4XDNxZMKruym8jusvjD9JHrZ6xjWvu79ncT+7vL5PHppzY6Isnz0pumi44qkl/5TXZZfqfabD4//ZOWWVfL/BmW9MPH5i1EX5j5++tz/j9o+Pu6SX5qZsafkz6S4lrpG1JfHcKz4jOLCq+qe1XVfwzD189Jcu/MHMaQ5MvDhvuKC2fqmGd24+MzSa6w6v5mr7/2bG2ttf8erj9iwfSfGW6zHhuZJ/uadvX1V0tyfmuX+mTpqumHlM2zaH7fZlgBHdxaW/kU8MCq+tvqh2B+Lf3wjavPuW3SRxVcIcnJVfXhqlr5RPC6SX5o1fLx0+nLx3rqZbHNyO4dVs2bh6Z/Int4LjkP7Yq1snuH6l/Qc3ZVnZvkl3LZZeXzM3+vO7utH8rzlVw6bwuzO1PrWtldvS5YZKuzm1X3tT/ZPXYmu8cmfXRU9S9jOH3I7mnDtPOe89PTX5O3VNUnq+pxw+XXTXLUquXjt9KXj/XUu+dU1eXSR7J9M8mvrDGp7HayK7tLo7V28bATdmSSRy+YTHY72ZXdHVX9y6junuTP13kT+7ud/V37u0uhtfau4cO5b7TWXpTkX9OzMI/8dvK7i/I71WbzJ5IcUFVHz1x2yyw+nPdDmT88fr3OSP9E5uCZnwNba09PPxTxkFWf6h81706qfznHq9I/oblm6yf9flP6+V/21+yIjKPSP8340sxlswv859IXtJV6arj9f61xf+v9QpBTMjOapKpukORK6fNq3rS3WPXJzy1yyfy71H1l1bytfj6hG+XShy7M2sj8/q0k109y+9a/OOeuiyZsrZ3VWvuF1tq1kjwm/dCD66cvH29ZtXwc1FqbbcTcdI16WWwzsvv2OfPm0UnOTj+UZ/Uyv8jL088Nf53W2nenf2P6pmS3qg5KP+RpNm8LsztT61rZXb0uWGR1dg9MPyxp3vr0lPTzT82+Oc7mc152v9Bamz0/91pZ2Mj8flj6xs9d0w/lutHKU1g9YWvta621x7bWrpd+mPTjq+rO6cvHqauWj6u21mZHrsnuYFhnPz/9SzUeMIw4WER2O9m9LNndeQdk8TmbZbeT3cuS3e11l/QRap+tqs+nHxb/gKp634Lp7e929ncvy/7ucmhZnAP57eT3siab30k2m4dPZV6d5ClDp/+Hktw/i88b9uYkt6n1nzx8tZcmuV9V3XP4VP/K1U+OfmRr7TPphyj8QVVdsfo5Yu634H6umL5An53k21V1ryT32M+aVvxs9ZN9XyX9nF4ntNYuXjDtK5Pcp6ruVv0w6N9MP4zy32ameUz1L+44NP18QGt9O/asl6W/RncaVmRPSfLqdulzzK04KcnFSX6tqq5UVSsL+VuH3y9O8htVdURVXXuo8/iZ298+/RDNRZ/IvSmLDy1Y7arpn8Z9tfr57Z64aMKqelBdcjL2c9JXjBen7wjdvKoeUlVXGH5uX5c+B86d0w873fOq6oAhi5dPspKnRd8+++4kB9eCk+CvwxuS3Liqfm5m3tyuqm465OTVSZ5cVVepqptl7fNXXTXJV1prF1XV7ZMsOhfjet27qn64+jmo/jD9sKhFh0m+aXgeDxlev59OcrPh+a3YyLpg1muSfF9VPWCYL09MP0fVZU6P0Pp58j+Q5EnDfPuJ9DfeVw2TvDjJI4c6Dkk/h9bxK7cf5uOhSVZ/8+6Kjayrr5q+/vpykqukH0Y1V1Xdr6puOGwwnJue24vTzzn6zepfcnHlYf3+/VU1+6V3snuJv07fELlf64e6rUV2O9m9LNndRtW/qO3BVXXQ8DrdM/2Q07cuuInsdrJ7WbK7vZ6X/iHErYaf56afi/OeC6a3v9vZ370s+7vbrKoOHrJ05eE95KFJfiTJ/11wE/nt5PeyJpvfSTabB7+cfv6WL6afw+3RrbW5I5tba19IXzDuvz8PNGyI3j99YT47/ZOBx+WS1+8h6V8o8pX0L0h58YL7OS/9JO6vTB86/5D0GT/GS9IX7M8nufJw/4uex8fTz+fzl+mfJt0vvWnwzZnJXp5+Xp7Th5+nrlxR/RvIZ8/XM3vfp6Qf3viy9Hly1fR5tHLbf6yq3xmm/Wb6KIeHpYfg59MP11up42/SzyP04fQvH3njcNmKh6ZvcC16nu9Lcm5V3WHRNDP+LH10xpfTV2JrBewOSd5TVSsfdjymtfbZ1s9feM/01/as9HnxtPQV9cqG/tFZ49xIe8zvpR/284T01+zrmTmx/6xhmTh+mG7DhszdI/1cgJ9LnzfPyDBv0k8DcNBw+fHpX3a0yC+nf8B1XvpK/pX7U9OMl6evL76S5Lbpy/Vcwwil+6a/kX05/RPO+7bWZj8VXrguqP6NvnPvv7V2dvo5NP8ofb10h8ycO7GqnltVs3l7cJJjhmmfnuSBw32ktfZPSf4kydvSD2v6zPAcVzwk/Ty/c88VusF19QvT5+nn0j9J/rc1pv3e4X7PTz+E7S9aa+9s/RCwe2fYIEhfL/5NhsOiquq7kvyPLFin7yXVv035F9N3eD8/vB+cv8ZyJbuR3QVkd3u19FNmnJk+7/80ya+31k6cO7HsJpHdBWR3G7XWLmytfX7lJ/21vGhl3s+Z3v5u7O8uYH93+10hfbk6O305/NX0+T/3S+Hk9zuPL7+XNdn8VrvUaUV2r+qjJ16UPvx8VzzpqjopyUtba3+707Vsl6q6RpK3J7l1619Osmi6eyT55TacZ24nVdVfJDmltfa8na5liqp/i+6/pM/zfY2mnISqOj7Jma21uU323aj6YVkfTPIjrbUvrjHd0qyrq+qxSQ5vrf3OTtYxVbK7O8ju3iO7u4Ps7j3LNC83i/1d+7t7hfzuDvI7c7+7ZDnek/ZieGE32Is7vbAbyC5Mk+zCNNnfhemS371tn6fRqKoXVNUXq+ojM5cdWlVvrqpTh9+HDJdXVT2rqk6rqg9V1W22snhgMdmF6ZJfmCbZhWmSXZgm2YXltJ5zNh+ffu6sWU9I/zbDo5O8Zfg/Se6Vfq6Po5M8Kv3LhNgirbW7+JSINRwf2V1KrbWHG13FPhwf+V06sss6HB/ZXTqyyzocH9ldOvZ3WYfjI7tLSX73tn02m1tr70g/kfis+6efTybD72NnLn9x6/4j/dusr7VZxQLrJ7swXfIL0yS7ME2yC9Mku7CcDtjP212ztXZWkrTWzhpOgp0kR6R/8+WKM4fLzlp9B1X1qPRPk3LggQfe9iY3ucl+lgK713vf+94vtdYO38S7lF3YJsuWX9mF9Vm27CbyC+shuzBNsgvTtFZ297fZvEjNuWzuNxAO33T4vCQ55phj2sknn7zJpcD0VdVntuuh5lwmuzDCsuVXdmF9li27ifzCesguTJPswjStld31nLN5ni+sHG4w/P7icPmZSa4zM92RST63n48BbD7ZhemSX5gm2YVpkl2YJtmFHba/zebXJTlu+Pu4JCfOXP6w4Vs+75jk3JXDF4ClILswXfIL0yS7ME2yC9Mku7DD9nkajap6RZK7JLl6VZ2Z5ElJnp7klVX1yCSfTfJTw+RvSnLvJKcluTDJI7agZmAdZBemS35hmmQXpkl2YZpkF5bTPpvNrbWfWXDV3eZM25I8ZmxRwHiyC9MlvzBNsgvTJLswTbILy2l/T6MBAAAAAADfodkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjDaq2VxVj62qU6rqI1X1iqq6clVdv6reVVWnVtXfV9UVN6tYYHPILkyT7MJ0yS9Mk+zCNMku7Jz9bjZX1RFJfi3JMa2170ty+SQPTvKMJH/eWjs6yVeTPHIzCgU2h+zCNMkuTJf8wjTJLkyT7MLOGnsajQOSfFdVHZDkKknOSnLXJCcM178oybEjHwPYfLIL0yS7MF3yC9MkuzBNsgs7ZL+bza21/0ryp0k+mx7ac5O8N8k5rbVvD5OdmeSIebevqkdV1clVdfLZZ5+9v2UAGyS7ME2yC9MlvzBNsgvTJLuws8acRuOQJPdPcv0k105yYJJ7zZm0zbt9a+15rbVjWmvHHH744ftbBrBBsgvTJLswXfIL0yS7ME2yCztrzGk07p7kU621s1tr30ry6iQ/mOTg4TCFJDkyyedG1ghsLtmFaZJdmC75hWmSXZgm2YUdNKbZ/Nkkd6yqq1RVJblbkv9M8rYkDxymOS7JieNKBDaZ7MI0yS5Ml/zCNMkuTJPswg4ac87md6WfWP19ST483Nfzkjw+yW9U1WlJDkvy/E2oE9gksgvTJLswXfIL0yS7ME2yCzvrgH1Pslhr7UlJnrTq4tOT3H7M/QJbS3ZhmmQXpkt+YZpkF6ZJdmHnjDmNBgAAAAAAJNFsBgAAAABgE2g2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjjWo2V9XBVXVCVX2sqj5aVT9QVYdW1Zur6tTh9yGbVSywOWQXpkl2YbrkF6ZJdmGaZBd2ztiRzX+R5J9aazdJcsskH03yhCRvaa0dneQtw//AcpFdmCbZhemSX5gm2YVpkl3YIfvdbK6qqyX5kSTPT5LW2jdba+ckuX+SFw2TvSjJsWOLBDaP7MI0yS5Ml/zCNMkuTJPsws4aM7L5BknOTvLCqnp/Vf1tVR2Y5JqttbOSZPh9jXk3rqpHVdXJVXXy2WefPaIMYINkF6ZJdmG65BemSXZhmmQXdtCYZvMBSW6T5K9ba7dOckE2cAhCa+15rbVjWmvHHH744SPKADZIdmGaZBemS35hmmQXpkl2YQeNaTafmeTM1tq7hv9PSA/zF6rqWkky/P7iuBKBTSa7ME2yC9MlvzBNsgvTJLuwg/a72dxa+3ySM6rqe4eL7pbkP5O8Lslxw2XHJTlxVIXAppJdmCbZhemSX5gm2YVpkl3YWQeMvP2vJnlZVV0xyelJHpHewH5lVT0yyWeT/NTIxwA2n+zCNMkuTJf8wjTJLkyT7MIOGdVsbq19IMkxc66625j7BbaW7MI0yS5Ml/zCNMkuTJPsws4Zc85mAAAAAABIotkMAAAAAMAm0GwGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEYb3WyuqstX1fur6g3D/9evqndV1alV9fdVdcXxZQKbTXZhmmQXpkl2YbrkF6ZJdmFnbMbI5v+Z5KMz/z8jyZ+31o5O8tUkj9yExwA2n+zCNMkuTJPswnTJL0yT7MIOGNVsrqojk9wnyd8O/1eSuyY5YZjkRUmOHfMYwOaTXZgm2YVpkl2YLvmFaZJd2DljRzY/M8lvJfnv4f/DkpzTWvv28P+ZSY4Y+RjA5pNdmCbZhWmSXZgu+YVpkl3YIfvdbK6q+yb5YmvtvbMXz5m0Lbj9o6rq5Ko6+eyzz97fMoANkl2YJtmFaRqb3eE+5Bd2gPdemCbZhZ01ZmTzDyX58ar6dJK/Sz8c4ZlJDq6qA4ZpjkzyuXk3bq09r7V2TGvtmMMPP3xEGcAGyS5Mk+zCNI3KbiK/sIO898I0yS7soP1uNrfWfru1dmRr7XpJHpzkra21hyZ5W5IHDpMdl+TE0VUCm0Z2YZpkF6ZJdmG65BemSXZhZ409Z/M8j0/yG1V1Wvo5cZ6/BY8BbD7ZhWmSXZgm2YXpkl+YJtmFbXDAvifZt9baSUlOGv4+PcntN+N+ga0luzBNsgvTJLswXfIL0yS7sP22YmQzAAAAAAB7jGYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADDaATtdAAAAAFvjek9449zLP/30+2xzJQDAXmBkMwAAAAAAoxnZDAAAAADsaY4G2hxGNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACj+YJAAEbxJQoAAEyFbVeArWVkMwAAAAAAo2k2AwAAAAAwmtNoALAuiw45BAAAAEiMbAYAAAAAYBMY2QwAwHf44iQAAGB/GdkMAAAAAMBo+91srqrrVNXbquqjVXVKVf3P4fJDq+rNVXXq8PuQzSsXGEt2YbkP58kAACAASURBVJpkF6ZLfmGaZBemSXZhZ40Z2fztJL/ZWrtpkjsmeUxV3SzJE5K8pbV2dJK3DP8Dy0N2YZpkF6ZLfmGaZBemSXZhB+13s7m1dlZr7X3D3+cl+WiSI5LcP8mLhslelOTYsUUCm0d2YZpkF6ZLfmGaZBemSXZhZ23KFwRW1fWS3DrJu5Jcs7V2VtIDXlXXWHCbRyV5VJIcddRRm1EGsEGyC9MkuzBd8stWWfTlnmwO2YVpkl3YfqO/ILCqDkryqiS/3lr72npv11p7XmvtmNbaMYcffvjYMoANkl2YJtmF6ZJfmCbZhWmSXdgZo5rNVXWF9OC+rLX26uHiL1TVtYbrr5Xki+NKBDab7MI0yS5Ml/zCNMkuTJPsws7Z79NoVFUleX6Sj7bW/mzmqtclOS7J04ffJ46qENhUsgvTJLswXfIL0yS7e8uiU9F8+un32eZKGEt2YWeNOWfzDyX5uSQfrqoPDJf9TnpoX1lVj0zy2SQ/Na5EYJPJLkyT7MJ0yS9Mk+zCNMku7KD9bja31t6ZpBZcfbf9vV92nk90dzfZhWmSXZgu+YVpkl2YJtmFnTX6CwIBAAAAAECzGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDR9vsLAmFffNEgACyvRe/TwHKTXQC4xFrvi/pPO8PIZgAAAAAARjOyGQCAfXLEEgAAU+JooJ1hZDMAAAAAAKNpNgMAAAAAMJrTaAAAAOwxTo3DbufweYCdYWQzAAAAAACjGdnMaD4xBjbCSCoAAADYnYxsBgAAAABgNM1mAAAAAABGcxoN1s3pMmBvkHXYXWQaAPafU8ABbIyRzQAAAAAAjGZkMwBbwmhK2BuM+AJgJ9nmBFguRjYDAAAAADCaZjMAAAAAAKM5jcYetlOHGzncFgAAAAB2HyObAQAAAAAYzchmAAAAAGBp+TLQ6TCyGQAAAACA0TSbAQAAAAAYzWk09gCHGgDzWDfA7rJsmfaFwAAAsPcY2QwAAAAAwGhGNgMAAAAAbIAj+eYzshkAAAAAgNE0mwEAAAAAGM1pNFgaDj8AAIDlZXsdLiEPAPMZ2QwAAAAAwGhGNgMAACyZRaMmd+vjwoqpjxieev0AYxnZDAAAAADAaJrNAAAAAACM5jQaACwFhxwCsBc5bQUALDfv1RtjZDMAAAAAAKMZ2byL+KQFAHY/7/cAAOxWtnWnz8hmAAAAAABG02wGAAAAAGA0p9GYIIcUAPNYN8DuItPAVPiSX7aD90XYXXZzpvf6++KWjGyuqv9RVR+vqtOq6glb8RjA1pBfmCbZhWmSXZgm2YVpkl3Yeps+srmqLp/k2Ul+LMmZSd5TVa9rrf3nZj8We8NGPxHa658gjSG/7AZ7cR0gu9O2m0d1bMT+vA5Tz7XssldN/b1adjdm6vN7s6z1PrfXXoudIrvstL2yPtyKkc23T3Jaa+301to3k/xdkvtvweMAm09+YZpkF6ZJdmGaZBemSXZhG2zFOZuPSHLGzP9nJrnD6omq6lFJHjX8e35VfXwLatmIqyf50g7XsNqy1bRU9dQzNlZPPWMLi+m24vW57ibf377sM7+yuy7LVtOk69ms7K5xP1v1+mxnfqeY3WTiy+Y22JX1bOb78Ua3BdZpqbKbLGV+d+WyuYnUM9joe+/I9YPs7ttSLZtbtA4fYzJZ2UHed3fGXlgOxli2epId2uddw7ZmdyuazTXnsnaZC1p7XpLnbcHj75eqOrm1dsxO1zFr2WpSz9qWrZ79tM/8yu6+LVtN6lnbstWznyaX3WT5Xnv1rG3Z6kmWs6YNst28CdSztmWrJ1nOmjZIdjeBevZt2Wpatnr2g+xuAvXs27LVtN31bMVpNM5Mcp2Z/49M8rkteBxg88kvTJPswjTJLkyT7MI0yS5sg61oNr8nydFVdf2qumKSByd53RY8DrD55BemSXZhmmQXpkl2YZpkF7bBpp9Go7X27ar6lST/N8nlk7ygtXbKZj/OFliaQyRmLFtN6lnbstWzYRPN7zK+7stWk3rWtmz1bNhEs5ss32uvnrUtWz3Jcta0brK7adSztmWrJ1nOmtZNdjeNevZt2Wpatno2RHY3jXr2bdlq2tZ6qrXLnJ4GAAAAAAA2ZCtOowEAAAAAwB6j2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNa6iqY6vq/1TViVV1j+Gyy1XVH1XVX1bVcUtQz4FV9aLh8oducz03qKrnV9UJqy4/sKreW1X33c56VtVwVFW9rqpeUFVP2Kk6Zuq5zLxj68juPuuR3fXXI7vbSHb3WY/srr8e2d1GsruumpYyv7K7ty1bdteoyXvvZetaquwm8rudZHdd9cjuOm1Jdltru/InyQuSfDHJR1Zd/j+SfDzJaUmesM77OiTJ84e/fyLJ8Un+LMndlqCen0tyv+Hvv9+hek5Y9f9Tkjw+yX13at4luXuSXxz+fvEyLkt+tvf1lt259yW7srtpP7K7rfXIruxu2o/sbntNm5Zf2d3bP8uW3S2uaVe99y5bdrdy3vnZvtdadhfel+zuQHY3JSzL+JPkR5LcZvYFT3L5JJ9McoMkV0zywSQ3S/L9Sd6w6ucaM7f730luM/z9hJkF44QlqOe3k9xq+PvlO1TPCTN/3z3Jg5M8fH/Cu1m1JTksyduSvDXJI5ZxWfIju7Iru7vpR3ZlV3an+SO7082v7O7tn2XL7hbXtKvee5ctu1s57/zIruzuzewekF2qtfaOqrreqotvn+S01trpSVJVf5fk/q21pyW5zBD6qqokT0/yj6219w0Xn5nkm8PfFy9JPUcm+UA2cFqUzahngR9NcmD6gvz1qnpTa+2/11vXZtVWVf8ryZOG+zohyQs3UsMW1DNv3jGH7G59PQvIruyOIrtbX88Csiu7o8ju9tS0wKj8yu7etmzZ3Yaads1777JldxNrkt91kN3tqWcB2d2m7O7aZvMCRyQ5Y+b/M5PcYY3pfzX9k4/vrqobtdaem+TVSf6yqu6U5B1LUs9fVdV9krx+O+upqsOS/FGSW1fVb7fWntZa+93huocn+dJGd3g3q7Yk/5TkyVX1kCSf3qQaxtQzb96xfrK7ifXIruxuI9ndxHpkV3a3kexuck3bmF/Z3duWLbubWdNuf+9dtuzuT03yu/9kd5Prkd2dze5eazbXnMvaoolba89K8qxVl12Y5JFLVM8FSR6xQ/V8OckvLbju+E2qacVGa/tIkgducg2zRs87NkR2N7ce2V31sIsmlt3RZHdz65HdVQ+7aGLZHU12N7+m7cqv7O5ty5bdzappL7z3Llt2E/ndTrK7+fXI7qqHXjTxVmR3Q4eQ7QJnJrnOzP9HJvncDtWSqGcjlq22Zatnt1u211s967dstS1bPbvdsr3e6lm/Zatt2erZ7Zbt9V62epLlrClZvrqWrZ7dbhlf72WradnqWbGMdS1jTbvVMr7Wy1bTstWzYhnr2vGa9lqz+T1Jjq6q61fVFdNPDP469SxtPbOWrbZlq2e3W7bXWz3rt2y1LVs9u92yvd7qWb9lq23Z6tntlu31XrZ6lrWmZPnqWrZ6drtlfL2XraZlq2fFMta1jDXtVsv4Wi9bTctWz4plrGvna2pL8G2cW/GT5BVJzkryrfSu/iOHy++d5BPp38z4u+pZjnqWubZlq2e3/yzb662e6da2bPXs9p9le73VM93alq2e3f6zbK/3stWzrDUtY13LVs9u/1nG13vZalq2epa5rmWsabf+LONrvWw1LVs9y1zXMtbUWksNRQAAAAAAwH7ba6fRAAAAAABgC2g2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACj7Zlmc1UdXlUfr6orb8NjPbyq3jnz//lVdYOtftyxquqkqvqFna5jLVV1par6WFVdYx/T3ayqTt6Ex3tpVT155H38RlU9dWwte50M79sUMpwkVfXuqrr5PqbZtvk957F/sqpeut2Pu9sM6+v/rKrv2YbHuktVnTnz/ylVdZetftyxqur4Kbw/bGdmq+qpVXX8yPuQ4RG83+7bFN5vbTMDADtl0s3mqnpwVX20qi6oqk9W1Z3WmPwJSV7YWrtou+pb0Vo7qLV2+nY/7narqrsNG7UXVtXbquq6a0x7vWGaC4fb3H3V9Y+tqs9X1blV9YKqulKStNa+keQFSR6/j3L+MMmfztzfp6vq68NOzMrPtff/2W7Ic5M8oqoO26bHm4RV8+L8qrq4qv5yjZvI8BaqqitW1QlDVtq+GnVVdWhVvWZY/36mqh6y6vqHDJdfUFWvrapDZ67+0yRP2UdJ35nfQ+Nwdjm5aOb/39mvJ7y21yS57b6aa3vNsN5+U1V9dVg//1VVHbDGTR6V5B2ttc9vV40rWms3b62dtN2Pu92q6lZV9d7hvfS9VXWrNabdtswO93fSqqyeX1U/sL/PdYNkeEZV3bSq3jpsU51WVT+xj5t4v91itpkXss0MALvAZJvNVfVjSZ6R5BFJrprkR5LM3TgdNrqOS7JnR7nsoyGwGfd/9SSvTvL7SQ5NcnKSv1/jJq9I8v4khyX53SQnVNXhw33dM31H525JrpfkBkn+YOa2L09y3MrG9JxarpXkR5O8dtVV9xt2YlZ+PrehJ7mfWmsXJvl/SX5uOx5vKmbnRZJrJvl6kn+YN60Mb32GB+9M8rNJ1tMcfHaSb6bPu4cm+euVxs7w+2/Sl/lrJrkwyXNmbvu6JD86ZPUyVs/voXG4sqz8S5JfmVl+/njO7Ue9Vq21luTvkvx/Y+5nF3pOki8muVaSWyW5c5JfXmP6X0zykm2oayltw/vuFZOcmJ6TQ5K8KMmJw+XzbFtmZ/zKqvfdf9/4M904Gb7EsByemOQN6dtnj0ry0qq68YLpvd/aZk5sMwMAI0y22Zy+IfWU1tp/tNb+u7X2X621/1ow7R2SnNNamz3E9qTqh2r+2/CJ/eur6rCqellVfa2q3lNV15uZ/iZV9eaq+kr1QwsfNHPdYVX1uuF2705yw9kHrz5K8EbD3/epqvcP055RM4ebDSMXWlUdV1WfraovVdXvLnoBqh9++9yhrvOq6u2zIyOG+3pMVZ2a5NThsh8cntu5w+8fXHW3N6x+uOy5VXXiqpFNa/nJJKe01v5hGAnz5CS3rKqbzKn7xkluk+RJrbWvt9ZeleTDSR4wTHJckue31k5prX01fcTFw1duP8zHrya544JafizJ+9YzIqeqLld9NOfnq+qcYbm46YJpr1F9VN85w3Lwjpnrjqw+YuzsqvpUVT1m1c1PSnKffdWzhz0wvYn1Lwuul+EtznBr7ZuttWe21t6Z5OK1pq2qA9Pz+vuttfOH27wul+wcPjTJ61tr72itnZ++Q/2TVXXV4bEuSvLeJPdY8BCXmd/7qOcXquodVfWsqvpKkt+rVYfiV9WNqqrN/H9wVb2wqs6qqjOr6ilVNfueeFJkdrXrJ3lla+2iYbTyPyWZO3K0qo5Kz9G7Zi47vqqeU1X/OGT2X6vqe6rqmdVHS3+sqm49M/21q+pVM+vVX5u57ruG+/tqVf1nktutevxP1zD6r6puX1X/Pqy7z6o+IvuKM9O2qvqlqjp1uL9nV1UteF5PHt4z/n7I7Puq6parHvfxVfWhJBdU1QHVR5WeNDz+KVX146vu9uqL1gH7cJckByR5ZmvtG621ZyWpJHedU/eyZfavhtytrKtXr8dWprtKVb28qr48vH7vrt6ok+H1u0mSayf589baxa21tyb51yxu5nm/tc08V9lmBgDWaZLN5qq6fJJjkhxe/XDAM4cdl+9acJPvT/LxOZc/OH1j+4j0jd1/T/LC9FEGH03ypOHxDkzy5vTRAddI8jNJnlOXHJ757CQXpY/2+vnhZ5ELkjwsycHpG1KPrqpjV03zw0m+N32UwhMXbcgNHpq+YXn1JB9I8rJV1x+bvuNws2Ej+I1JnpU+OuLPkryxLn2o2sOG+q+d5NvDtBlehw/VqsNuZ9w8yQdX/mmtXZDkk5nfiLh5ktNba+fNXPbBmWkvdV/D39dcVedHk9wy8y2a34u8IcnRSb4nyUeyeCTe49JHzx8+TPv7yXeWxzckeU/6svRjSR5XVXdbZ730naUXD6PR5pHhrc/wRtw4ycWttU/MXLYww621T6aPqJwdSbeZGU6SHxzu8/D0o1725aXpo+lvmP5+cp/0I2Vm67tRVV1lg3XsZn+R5MFDA/CIJPdKbzjP8/3p6/lvr7r8QUl+L315/0Z6Zt83/H9C+jKdoWn4+vTl6Ij0LP169VF8Sc/2DYefe6avQxa5OMljh8f4geG+Vo/Ivm96w/qWQ433zGL3Tz8K49D0dcprq+oKM9f/TPrydHB68/f16SP1rpHkV5O8rKq+d2b6heuAqnpDVT1hQR03T/KhVevND2X+++6yZfZdSW6R/hqekOQfav7Iy0ckuUqSI9PXeb+cvq5OZHi95n1wUkm+b8H03m9tM6/FNjMAsE+TbDanH+J5hfTRkHdKP5z31uk7sPMcnOS8OZe/sLX2ydbauUn+McknW2v/POwc/8Nwn0nfCf10a+2FrbVvt9bel+RVSR44bDQ9IMkTW2sXtNY+kn4o61yttZNaax8eRmN/KP3QuDuvmuwPhtELH0zfaFxrg+uNw0ikb6QfWvcDVXWdmeuf1lr7Smvt6+kb6qe21l4yPI9XJPlYkvvNTP+S1tpHhg3f30/yoOE5prV2i9bayxfUcVCSc1dddm76KU42Ou3q61f+nr2v89Ln6zyL5vdrhxEW51TVa5NkmA/Ht9bOmxldctthZ2m1b6XvUBw1jAJ9+3D5HZNcrbX2x8PlpyV5fvqO2Xrq3dOqj4C8c9bITWR4OzK8ERvN8Orrk/3L8Fo+21r762Hk3tfXmnBolN4tyWNbaxcOo3SfmctmdqUWurenNza+luTM9EO/Vx96vWLRPHxNa+29w/r2NUkuaq29uLV2cfph5CuZvV2Sw1trTxnWq6cn+T+5ZB49KMkfDdk4IzNNntWGx/uPITOfTj9dxOrMPr21dk5r7bNJ3pa+XbHIe1trJ7TWvpXegLpyLj1q8FmttTOG5fCO6Xl4+vA83preaPmZmekXrgNaa/dtrT19QR1b+b477772J7PPmnnffd/KhcM67CvDuvpPklwtyY3m3P5b6Y3BGw3ZPrm1dr4Mb8jH0o8celxVXaGq7pG+/C9qwnu/tc2c2GYGAEaYarN5pZHwl621s1prX0rf4bv3gum/mvkbcF9YdZ+r/z9o+Pu6Se4ws9F1TvroiO9J/8T+gCRnzNz2M4sKr6o7VP+Sj7Or6twkv5S+IzVr9nypF87UMc93Hrf1Q1+/kr5xd5nrh8tX1/aZ9JEF86b/THpTf3V985yfvrM462qZvwG7r2lXX7/y9+x9XTXJOQtqWTS/j22tHTz8HJv0ERZV9SdVdXpVfS3JacO0857z09Nfk7dU/0LKxw2XXzfJUauWj99KXz7WU+9e97Ak72ytfWqNaWS428oMb8RGM7z6+mT/MryWM/Y9yXdcN8mVknxhZnl4dvoHmbP1ZY0a95RhpPH/TT/P6IHpy9QhWTyKfDMye+1Vmf2dXDKPrp31Z/bGwwjhzw/r+T/O5mX2v9Mb72tl9oxhutla52Z2wTpgka183513X/uT2V+bed+9zcqFVfVb1U+bcu5w25VlarXjk/xzkldW1X9V1dOrn09Xhtdp+FDk2PTm6eeT/GaSV6Yvt/N4v+1sM9tmBgD20ySbza2fk+zMJIsOuV/tQ7n0oaAbdUaSt89sdB3c+pdlPDrJ2emHzs2OjDhqjft6efp5Eq/TWvvu9G9dnntuyHX6zuNW1UHphzPOfonH7Gv0ufSNvFlHJZk91/Xq5/GtJF9aRx2nZGY0yTDK4YbD5fOmvUEN54Ic3HJm2kvd1/D3F1prX5657Ka59GGDszYyvx+W/iHFXZN8dy4ZWXWZedJa+1pr7bGtteul77g9vqrunL58nLpq+bhqa2129Mta9e51D8vao5oTGV6xlRneiE8kOaCqjp65bGGGq+oG6Y2h2UP4NyvDK1a/H1yQS4/cm92RPSO9KXHozPJwtdbaLVbVd1rrX1ZEXy6vk+SvWj8/8JfTD6Ff9CHvh9LX8/v7RVtnJPnUnPXqyuOdlfVn9q/TRyQe3Vq7WnrTerMye7n0Uzysldnr1KXPJbwwswvWAYuckuQWVZc6v/QtMv99d2kyW1U/muQ30ke4Hpz+ocX5mf+++83W2pNbazdNP13CT6Q3LmV4A1prH2qt3bm1dlhr7Z7pXyL37gWTe7/tbDNflm1mAGBdJtlsHrwwya9W/wKKQ5L8evqhqfO8O8nBw2GX++MNSW5cVT83HIJ4haq6XVXdtPVDf1+d5MnVz2N5s6x97sirJvlKa+2iqrp9krHnT713Vf1w9S87+sMk72r9kOJ53jQ8j4dU/9Kin05ys1z6dfvZqrpZ9XMcPiXJCcNz3JfXJPm+qnpAVV05yRPTzyX5sdUTtn7OyA8keVJVXbmqfiJ9B/lVwyQvTvLIoY5D0k+PcvzK7Yf5eGiS/1hQy5uT3GaoY1+umn7e0C+nN6b+aNGEVXW/qrrhsGN/bvp5QC9OP2/hN6vqN4fnc/mq+v6quu3Mze+cftgpM6p/2c4R6YfgrkWGu63McKrqSjO5ueKwPM/bibwg/TV7SlUdWFU/lH4e25VzN74syf2q6k7DTvRTkry6DeecrH5u1tumZ3WesfM76euYO1fVdarq4CTfOe/t8Pq+PcmfVtXVqn/p0Y2q6kdmbi+zM4YjiD6Vfs7UA4bX9LgsaAi0/qVUpya5/X4+5LuTfK36l+1917Be/b6qWvkiwFcm+e2qOqSqjkw/F/IiV00/9cf51b+A69H7WdOK21bVTw6N9F9Pfw9Z9H70rvQPPn5rWO/c5f9v7+5jLDvr+4B/f/HWSnCT8JIFkTXURnWhqGkLXRnaqKjFSQTYxRYCxQ0hC7XqtgkhTYrKQv+AqkIxUts0JG2TLQY7EQFcF9VuTIioMaJRW4s1kGLjUrvGMRsbvKS85UUybp7+cY7TYT0zOzPPvTPn3Pv5SFczc+a+/Hxnv3PvfP2cczLshv/+DdfZze+AjT6W4TXoDWN2Xz9u/+iZV5xYZr8zQ+H45QwrQd+WYWXz41TVi8ef+7dl+Bl+M8Oxp2V4F6rqL46/z59QVW/McLzk67a4utfbgffMj+c9MwCwI3Mum/9ZhpNL/K8MJ5L4VLZ409NaeyTDG68f3csDjX9s/VCG44k9mGGXvXdkWPWTJK/PsNveF8fHec82d/fjGf7Y+0aGN5c37GWmDX4tw0lZ/k+GPwRfvdUVx1UOl2XYhfL3Muy2dtlYIjzmVzP8N3wxw3Eo3/DYN6rqrqra9P5ba6czrFJ6e4Zd8l6QDcdfq+EM4L+04SZXZjihz1cy7Gr3yvE+0lr7cIZjON6WYRe83xn/Gx/zI0mub8Mx9zab5UsZ/ti+fKvnYoP3ZPiZPphhdch/3ea6zx7v9/cznMn951trv9WG4xW+LEOpcn+GP6B/OeOujDWcuPIlGf4g4Fsdy4ZCYysyPFhmhkefy7A79JEMh0z4o4wru6rqLVW18Y+/H0/yHRmOBfq+JP+gtXbXOOddGXZ3fu/4/e/Mt56Q7eVJPtZa23T1Zu/Pe/ThDH/QfyZDeXLzGd//0QwF12cz/B769xlXP49/HF+Z5ETH46+iV2T4XXY6w+7Tj2Y48d5WfjnDCcV2bSxs/laGYyd/PsPv1XdlWE2XJP80w2vD5zOcfG+rk1QlyRszvG58I8Nxnz+wl5k2uCnJD2f4d/OaJK8YD1Ww2X/HIxn+vb90/G/4N0l+7IxSacvfAVX1G1X1lm3u+4oMqw2/muFEZVeM26ec2Q9lODTGPRleM7+eYaX6Zr43Qzn59Qyv0f95nD2R4d14TYbn+OEMx7r+wW3eQ3m9jffMW/CeGQDYkWrfchLz1VVVh5P8lyTPa2c5edRcVNV1SU611rY6MeLKGVdX/XaSF7XWHt7mes/NcGiGi9sB/yOvqp/OcKKrTQsDdkaGV0dV3Z7kqjacHGqr6xzYz3tcOfaq1lrvKrq1Nv6+/lSSS1prW5WJs1JVb8twsrqe/xEyO1PP7CazyHCHKf0sF2UdX2+9ZwYADsralM2raB3fOMMqkWGYl3Utm2HuvN4CAOyfsx5Go6reXVUPV9WdG7Y9uao+UlX3jB+fNG6vqnpnVd1bVf+jqp6/9T0DyyS7MF/yC/MkuwAArLudHLP5ugzHztroeJJbW2sXJbk1///ESy9NctF4uTrDGeBZktbaa63QYBvXRXYnTYbZxnWR38lprb3NqmbO4rrI7uR4vQUA2D9nLZtbax/PcCKNjS7PcGyvjB+v2LD9V9rgv2c4m/XTFzUssHOyC/MlvzBPsgsAwLo7tMfbPe2xk/201h6qqqeO248k+cKG650atz3uxEBVdXWGVRw577zz/spznvOcPY4Cq+uOO+74cmvt8ALvUnZhhz7zu1/bdPv3HfnuHd1+avmVXdiZqWU3kV/YiSVkFwDYg72WzVupTbZtegbC1tqJJCeS5OjRo+3kyZMLHgXmr6p+Z78eapNtsstau+D4LZtuP3nNpTu6/dTyK7uwM1PLbiK/sBP7mF0AYBs7OWbzZr702G5+48eHx+2nkjxjw/XOT/Lg3scDFkx2Yb7kF+ZJdgEAWBt7LZtvTnJs/PxYkps2bP+x8ezaL0zytcd2GwQmQXZhvuQX5kl2AQBYG2c9jEZVvS/J30jyPVV1Kslbk1yT5IaquirJA0leNV79Q0leluTeJH+Y5HVLmBnYAdmF+ZJfmCfZ63MykQAAE9ZJREFUBQBg3Z21bG6t/e0tvnXJJtdtSX6idyign+zCfMkvzJPsAgCw7vZ6GA0AAAAAAPgTymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuymYAAAAAALopmwEAAAAA6KZsBgAAAACgW1fZXFU/XVV3VdWdVfW+qvr2qrqwqm6vqnuq6gNVde6ihgUWQ3ZhnmQX5kt+AQBYB3sum6vqSJI3JDnaWvsLSc5JcmWSdyT5udbaRUm+kuSqRQwKLIbswjzJLsyX/AIAsC56D6NxKMl3VNWhJE9I8lCSFye5cfz+9Umu6HwMYPFkF+ZJdmG+5BcAgJW357K5tfa7Sf55kgcyvFn+WpI7kny1tfboeLVTSY5sdvuqurqqTlbVydOnT+91DGCXZBfmSXZhvuQXAIB10XMYjScluTzJhUm+N8l5SV66yVXbZrdvrZ1orR1trR09fPjwXscAdkl2YZ5kF+ZLfgEAWBc9h9H4gSSfb62dbq19M8kHk/y1JE8cdw9MkvOTPNg5I7BYsgvzJLswX/ILAMBa6CmbH0jywqp6QlVVkkuSfDbJbUleOV7nWJKb+kYEFkx2YZ5kF+ZLfgEAWAs9x2y+PcMJTT6Z5DPjfZ1I8qYkP1NV9yZ5SpJrFzAnsCCyC/MkuzBf8gsAwLo4dParbK219tYkbz1j831JLu65X2C5ZBfmSXZhvuQXAIB10HMYDQAAAAAASKJsBgAAAABgAZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQLeusrmqnlhVN1bV/6yqu6vqr1bVk6vqI1V1z/jxSYsaFlgM2YV5kl2YL/kFAGAd9K5s/vkkH26tPSfJX0pyd5LjSW5trV2U5Nbxa2BaZBfmSXZhvuQXAICVt+eyuaq+K8mLklybJK21R1prX01yeZLrx6tdn+SK3iGBxZFdmCfZhfmSXwAA1kXPyuZnJTmd5D1V9amqeldVnZfkaa21h5Jk/PjUzW5cVVdX1cmqOnn69OmOMYBdkl2YJ9mF+ZJfAADWQk/ZfCjJ85P829ba85L8QXax619r7URr7Whr7ejhw4c7xgB2SXZhnmQX5kt+AQBYCz1l86kkp1prt49f35jhTfSXqurpSTJ+fLhvRGDBZBfmSXZhvuQXAIC1sOeyubX2xSRfqKpnj5suSfLZJDcnOTZuO5bkpq4JgYWSXZgn2YX5kl8AANbFoc7b/2SS91bVuUnuS/K6DAX2DVV1VZIHkryq8zGAxZNdmCfZhfmSXwAAVl5X2dxa+3SSo5t865Ke+wWWS3ZhnmQX5kt+AQBYBz3HbAYAAAAAgCTKZgAAAAAAFkDZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdusvmqjqnqj5VVb8+fn1hVd1eVfdU1Qeq6tz+MYFFk12YJ9mFeZJdAADWwSJWNv9Ukrs3fP2OJD/XWrsoyVeSXLWAxwAWT3ZhnmQX5kl2AQBYeV1lc1Wdn+TSJO8av64kL05y43iV65Nc0fMYwOLJLsyT7MI8yS4AAOuid2Xzv0ryj5P88fj1U5J8tbX26Pj1qSRHNrthVV1dVSer6uTp06c7xwB2SXZhnmQX5mnP2U3kFwCA+dhz2VxVlyV5uLV2x8bNm1y1bXb71tqJ1trR1trRw4cP73UMYJdkF+ZJdmGeerObyC8AAPNxqOO235/k5VX1siTfnuS7MqzaeGJVHRpXapyf5MH+MYEFkl2YJ9mFeZJdAADWxp5XNrfW3txaO7+1dkGSK5N8tLX26iS3JXnleLVjSW7qnhJYGNmFeZJdmCfZBQBgnfQes3kzb0ryM1V1b4bj0V27hMcAFk92YZ5kF+ZJdgEAWDk9h9H4E621jyX52Pj5fUkuXsT9AssluzBPsgvzJLsAAKy6ZaxsBgAAAABgzSibAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKDboYMeAAAAgG91wfFbNt1+/zWX7vMkAAA7Z2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0O3QQQ/Awbng+C2bbr//mkv3eRIAAAAAYO72vLK5qp5RVbdV1d1VdVdV/dS4/clV9ZGqumf8+KTFjQv0kl2YJ9mF+ZJfAADWRc9hNB5N8o9aa38+yQuT/ERVPTfJ8SS3ttYuSnLr+DUwHbIL8yS7MF/yCwDAWthz2dxae6i19snx828kuTvJkSSXJ7l+vNr1Sa7oHRJYHNmFeZJdmC/5BQBgXSzkBIFVdUGS5yW5PcnTWmsPJcMb6yRPXcRjAIsnuzBPsgvzJb8AAKyy7hMEVtWfTvIfkvzD1trXq2qnt7s6ydVJ8sxnPrN3DGCXZBfmSXZxgt/5kl8AAFZd18rmqvpTGd4wv7e19sFx85eq6unj95+e5OHNbttaO9FaO9paO3r48OGeMYBdkl2YJ9mF+ZJfAADWwZ7L5hqWYlyb5O7W2r/c8K2bkxwbPz+W5Ka9jwcsmuzCPMkuzJf8AgCwLnoOo/H9SV6T5DNV9elx21uSXJPkhqq6KskDSV7VNyKwYLIL8yS7MF/yCwDAWthz2dxa+60kWx1o7pK93i+wXLIL8yS7MF/yCwDAuug+QSAAANPlhIIwbVtlFABgjrpOEAgAAAAAAImVzQAAALNnLwYAYAqsbAYAAAAAoJuVzWvAceAAAAAAgGWzshkAAAAAgG7KZgAAAAAAujmMBgDABDkMFgAAMDdWNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAt0MHPQAAAP0uOH7LQY8AAACsOSubAQAAAADopmwGAAAAAKCbw2iwY1vtnnv/NZfu8yQAwLJ4vYdpc8gcAGDKrGwGAAAAAKCblc08jtUSAAAAAMBuWdkMAAAAAEA3ZTMAAAAAAN0cRmOFHNThL5xICAAAAACwshkAAAAAgG5WNs+QE/gBwOrwug7rQdYBgHVgZTMAAAAAAN2UzQAAAAAAdHMYDQCANWSXfgAAYNGsbAYAAAAAoJuVzQAAAAtirwEAYJ1Z2QwAAAAAQDdlMwAAAAAA3RxGg3231a6F919z6T5PAgAAAAAsipXNAAAAAAB0s7KZpXFyFADWkT14gCnxOwkA2E9WNgMAAAAA0E3ZDAAAAABAN4fRmDCHoQAAgIO1qoeh2MvfGnP/bwYAls/KZgAAAAAAulnZDACwD+a+x9Kqru6Ex8w9owAAU2BlMwAAAAAA3ZZSNlfVS6rqc1V1b1UdX8ZjAMshvzBPsgvzJLsAAKyShR9Go6rOSfKvk/xgklNJPlFVN7fWPrvox5oiu98xZ+ueX5gr2T0YXvPpJbsAAKyaZaxsvjjJva21+1prjyR5f5LLl/A4wOLJL8yT7MI8yS4AACtlGScIPJLkCxu+PpXkBWdeqaquTnL1+OXvV9XnljDLbnxPki8f8AxnmtpMS52n3rHrm6zD8/NnFnx/Z3PW/MrujkxtJvNsb1fz7OJ31X7md47ZTWb+b2EfzGKePbx+L9LcX3u9b16MA5lnm3/7K/v8LDDvc88uALCFZZTNtcm29rgNrZ1IcmIJj78nVXWytXb0oOfYaGozmWd7U5tnj86aX9k9u6nNZJ7tTW2ePZpddpPpPffm2d7U5kmmOdMued+8AObZ3tTmSaY5EwCwGMs4jMapJM/Y8PX5SR5cwuMAiye/ME+yC/MkuwAArJRllM2fSHJRVV1YVecmuTLJzUt4HGDx5BfmSXZhnmQXAICVsvDDaLTWHq2q1yf5zSTnJHl3a+2uRT/OEkxm18QNpjaTebY3tXl2bab5neLzPrWZzLO9qc2zazPNbjK9594825vaPMk0Z9ox2V0Y82xvavMk05wJAFiAau1xh4UDAAAAAIBdWcZhNAAAAAAAWDPKZgAAAAAAuimbAQAAAADopmwGAAAAAKCbsnkbVXVFVf27qrqpqn5o3PZtVfX2qvqFqjo2gXnOq6rrx+2v3ud5nlVV11bVjWdsP6+q7qiqy/ZznjNmeGZV3VxV766q4wc1x4Z5HvezY3lk96zzyO7O55HdfSS7Z51Hdnc+j+zuI9nd0UyTzK/sAgCLtrJl8/iG6eGquvOM7S+pqs9V1b1ne0PVWvuPrbW/m+S1SX543Hx5kiNJvpnk1ATmeUWSG8ftL9/nee5rrV21ybfelOSGnc6yjNmS/Lkkt7TW/k6S5+51lkXNs8XPjk3I7r7MI7uyu3Cyuy/zyK7sLpzs7ttMC8+v7AIAk9RaW8lLkhcleX6SOzdsOyfJ/07yrCTnJvntDG+qvi/Jr59xeeqG2/2LJM8fPz+e5O+Nn984gXnenOQvj5//2gHNc+OGz38gyZUZ3iBedlA/uyRPSXJbko8med0U/y25yK7syu4qXWRXdmV3nhfZnW9+ZdfFxcXFxcVlipdDWVGttY9X1QVnbL44yb2ttfuSpKren+Ty1trPJnncrmtVVUmuSfIbrbVPjptPJXlk/Pz/TmSe85N8OrtYqb6IebbwN5Ocl+FN5B9V1Ydaa3+807kWNVtVvTHJW8f7ujHJe3YzwxLm2exnxyZkd/nzbEF2ZbeL7C5/ni3Irux2kd39mWkLXfmVXQBgila2bN7CkSRf2PD1qSQv2Ob6P5lhxcF3V9Wfba39UpIPJvmFqvrrST4+kXl+saouTfKf9nOeqnpKkrcneV5Vvbm19rOttX8yfu+1Sb682z94FzVbkg8neVtV/UiS+xc0Q888m/3s2DnZXeA8siu7+0h2FziP7MruPpLdBc+0j/mVXQDgQK1b2VybbGtbXbm19s4k7zxj2x8m2ex4awc1zx8ked0BzfN7Sf7+Ft+7bkEzPWa3s92Z5JULnmGj7p8duyK7i51Hds942K2uLLvdZHex88juGQ+71ZVlt5vsLn6m/cqv7AIAB2plTxC4hVNJnrHh6/OTPHhAsyTm2Y2pzTa1eVbd1J5v8+zc1Gab2jyrbmrPt3l2bmqzTW2eVTe153tq8yTTnCmZ3lxTmwcAWLJ1K5s/keSiqrqwqs7NcEKOm80z2Xk2mtpsU5tn1U3t+TbPzk1ttqnNs+qm9nybZ+emNtvU5ll1U3u+pzbPVGdKpjfX1OYBAJZsZcvmqnpfkv+W5NlVdaqqrmqtPZrk9Ul+M8ndSW5ord1lnoOfZ8qzTW2eVTe159s8851tavOsuqk93+aZ72xTm2fVTe35nto8U51pinNNbR4A4GBUa1seMgsAAAAAAHZkZVc2AwAAAACwf5TNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQLf/B90lwP1XBb4lAAAAAElFTkSuQmCC
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Predicted label of the test sample 0: 7
</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="Unrecognizable-examples">Unrecognizable examples<a class="anchor-link" href="#Unrecognizable-examples">&#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;[31]:</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">idx</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">idx_invalid</span><span class="p">)[</span><span class="mi">0</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">X_test</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gist_gray&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;True label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx</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">plot_pred_hist</span><span class="p">(</span><span class="n">y_pred_prob_all</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">n_class</span><span class="p">,</span> <span class="n">n_mc_run</span><span class="p">,</span> <span class="n">med_prob_thres</span><span class="o">=</span><span class="n">med_prob_thres</span><span class="p">)</span>

    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">]):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</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">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;I don&#39;t know!&quot;</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>True label of the test sample 1039: 7
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 1319: 8
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 1790: 2
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 2293: 9
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 3808: 7
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 4248: 2
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 6572: 1
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
True label of the test sample 6651: 0
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABZsAAAJSCAYAAAClThubAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZhsZ1kv7N8DYZBACENACIQwBBmUMQKKCBIGISBREBGOBMSDCugBFYmigggS/TiKKIocgYQhKISZoOcgEBAHJIAMYUqIgQQCBMhIwvx+f7yrk0qnqnf3Xr27a+2+7+vqq7urVlU9Vat+a1U99a63qrUWAAAAAAAY4wrbXQAAAAAAANOn2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoO6bZXFW3qaqTtui2nllVrxz+PqiqLqyqK27FbY9RVadX1X22u461VNVVquqTVXW9XSy3Keu7ql5ZVc8ceR2/UVXPHlvLTlVVB1TVp6rqqltwW4+pqvfO/H9hVd1sT9/uWFV1YlX90nbXsStV9Z9VddtdLLMp67uqnl1Vx4y8jp9Z2ZazcbK7a1PIrv3uziO7uya7c69HdreZ97u7NpH3u9evqk9U1VV2sdz9quqNm3B7762qx4y8jhcs+zZxmcnurk0ku/a7MybbbK6qa1fVG6rq61X12ap65C4u8kdJnrcVtc1qrX2utXb11tp3t/q2t1pVPXJYF1+vqjdW1bXXWPYOVfWBqrpo+H2HmfOeWlUfq6oLquq/q+qpK+e11r6Z5KVJnraLci6zvoeN08XDxnTl54a7f2835EVJHltV19mi21tqVfWkqjqpqr65zmbgUUle1lr7xh4u7XKG7J621be71arqsGHHeFFVvauqbrLGsgcPy1w0XOY+M+c9YmhQnFdVX66qY6tqv5mLPy/Js3ZRzmXW9/CG/hursvsjY+7vBrwhyZ1rFw3ynWB48fSSYRt/QVV9qKoesIuLye4etlnZHc5/SlV9ccjvS1fe5NrvTt/wZuSsqjq/qj69joaA7O5hsruQ7M5RVYcMr4V29QG497t72Ca+362q+pOq+urw86dVVUnSWvtSknclefwuyvnjJEfPXGcb6lrJ7bnj7u2G/GmSP6iqfbbwNpfWnPcvn9rFRWR3D9vE7D65qk4bXlN9oar+fOV5b797WZNtNid5YZJvJbl+kkcl+ZtFDYGqukGSn0gy+pO/qdrTG/7hsf/bJL+Qvk4uSvLXC5a9cpI3JXllkmslOTbJm4bTk6SSPHo47yeTPKmqHjFzFcclObIWfNq7xvp+8LAxXfn5wsbv6ca11i5K8v/SHxuSLyR5dvqGeE3DOj4y/bmyI21Bdq+b5PVJfj/JtZOclOQf1rjIq5N8KMl1kjw9yfFVdcBw3r8muXtr7ZpJbpZkn/R1veLNSX5iyOi8What7yetyu6/b+Q+7q7WWkvy90n+51bc3pLbJ8kZSe6Z5Jrpz5fXVNXB8xaW3Wllt6run95gPCzJwen5/cOZy9rvTttzkxzcWtsvyU8leXZV3XnegrIruwPZXS4vTPL+tRbwfndy73cfn+SIJLdPcrskD0ryyzNX8apV/6++/h9Ocs3W2n+sOuv2M7ndf2P3cPe11s5M8pn0+0E3+/7lBxYtJLuTy+5bktxpeE31g+kZ/vWZq7DfHUyy2VxV+yZ5aJLfb61d2Fp7b3oTY9EDdN8kH5wdoTF8evDUqvrI8OnGS6ofsvKP1Udt/XNVXWtm+btV1b9V1blV9eGqutfMeTetqncPl3t7kuvOnHfw8CnjPsP/j61+WMwFwycivzyz7L2q6syq+s3qowLPqqrHrvE4nFhVz61+aPp5VfWmlU9oZm73cVX1uSTvHE7/qao6ebgfJ1bVrVdd7Q9X1cer6pyqelmt/xDKRyV5S2vtPa21C9NfQP9MVV1jzrL3Sm9cPL+19s3W2gvSG8z3TpLW2p+21j7YWvtOa+1T6WG/+8qFh53ZOUnutqCWy63vRarqClV1fPURIYsek5Vlr1dVbxuW+1pVvWfmvBtVH2l/dvXR2E9cdfETkxy+q3p2gtba61trb0zy1XUsftck5w7rPMklz/tnD3m8sKreUlXXqapXVf+E8f2zDbCqulVVvX1YZ5+qqofPnHedqnrzcLn/THLz2RsfMnSL4e/Dq4/mPL+qzqiZQ1Zm8nZkVX2uqr5SVU9fdKeq6piqetFQ1wXD9uMmM+e3qnpiVZ2S5JThtB8d7tt5w+8fXXW1N5+3LViHn0lycmvttUNmnpnk9lV1qzl13zLJnZI8o7V2cWvtdUk+mr49TmvtjNbaV2Yu8t0kt1j5Z7j+DyS534JaLre+11JVfzVsM89f8JisLHe1qjqu+siRc4fH6brDefsP27qzhut6VlXN7htPjOymtfb11tozW2unt9a+11p7a5L/TjK3YRXZnVR205uLL2mtndxaOyd9xMVjVi5vvzttw3r95sq/w8/NFywuu7I7l+xuj+oDbs5N8o5dLOr97oTe76Zn93+31s5srX0+yf/OTHaTvC/JzWrxUQ8PSPLu9RQ9bHffNmTunGEbfuCCZW9ZVe8ZHuuvVNVxM+fdZnjOfK36URYPXXXxEyO7u0N2J5Td1tpnWmsrRw1Uku/lsu937XcHk2w2J7llku+21j49c9qHkyw61PmHksw7dOGh6Sv7lkkenOQfk/xuegCvkOETimFjfEL6CL1rJ/mtJK+rS0fzHZfeQLlu+ou8I9eo/cvpn/jtl+SxSf68qu40c/73p48aOzDJ45K8cHZDMsejk/xikhsm+U6SF6w6/55Jbp3k/tVfsL46yZOTHJDkbUneUpd+SpP0IN4//cX/LZP83soZwxP3xxbUcdv0dZCkhzB95PktFyz7kWHU4IqPZM76q6pKco8kJ6866xPpnyLNs2h9L/LWJIekP/YfS/KKBcs9Nclp6Y/d96dvpFJ9jqO3po84ODD9OfXUqjpsnfWy2KJ1+Yj0D5cOTH+u/nuSl6Xn8xNJnpFc8sHU29Mzer0kP5/kr+vSoyBemOQbSW6QnqNfXKOWr6fnbf/0jfGvVtURq5b5sSQ/kD7C6A8W7QwGj0rfXlw3yX+lj2CYdUT6m/7bDDvmE9LzfZ0kf5bkhLrs4S4LtwXDC5VFUw2tzu7X00cmzNue3jbJaa21C2ZOu8y2t6p+rKrOS3JB+jb2+auuYzOz+7700SDXTnJ8ktfW/E+RH5vkaklulP74PSF9vSf9U+uL059Hh6av29kXTp9IcouqutoG6trrVdX107fvq7fNK2R3Wtm9zHUNf19/VZ32uxNWVX9dVRcl+WSSs9JfA84ju7K7FtndQtWnIntWkt9cx+Le707r/e687F6yDWitfSfJqdmc7F4hyf9JclCSmyT5dpK/WLDsc9KfB9dKf938wiQZmnJvT/Ly9G37o5K8uKpmR+zK7mU9t3rD/l9nm79zyO60spvqU3Kcn+Qr6c/5v111Hfa7mW6z+epJzlt12nlJ5n0ykfQXqRfMOf0vW2tfGj5N/Jck72utfWgY/fGGJHcclvsfSd7WWnvbMKLr7emHvD2wqg5K8sPpo6y/2Vp7T/rQ+rlaaycMn4a01tq704es32NmkW8neVZr7duttbcluTD9RfQir2itfWx4ofr7SR5el53g/ZnDiLSLk/xckhNaa29vrX07fZ6Y70syO1Ljr4bRiV9L39n8/Ezt+w+jyOfZyDrZyLLPTH+evmzV6Rekr9d5Fq3vNw4boXNr+DKFYX0e01q7YGZ0yZ2HN0urfTt9Q3lQa+1bw/pL+qdW+7XW/ng4/dQkL0l/Y7aeells0bp82ZCj89J3vJ9prf3z8MLstbk0uw9Kcnpr7WXDSPkPJnldkocNOXlokj8YMvKx9MNk5mqtndha++jwnPlI+s7wnqsW+8Nh5NGH03doa220Txg+Xf1m+mGxP1JVN545/7mtta8N2T08ySmttVcM9+PV6Q2DB88sv3Bb0Fq7XWvtuMy3qdltrb239Wk0bpTk/0ty+qrldye7L5jJ7gdnbusVw2P0nfS54vbLzCfLM76d/gLrFq2177bWTmqtXTi8ODssyVNaaxe11r6Y3hxfnd2V2khSVVdKb9Ic21r75ILFZHda2V19/srfs9dlvzthrbUnpK/Pe6RP4fDNBYvKruwmsrss/ih95PoZ61jW+91pvd+dl92rDwOtVuxOdj84k90XJElr7ezW2huGbeX56XM9r96Wrvh2+pQ8N2itfaO19q/D6T+V5NOttZcP28QPpE8D8LB11rvTPC19WqMDk7w4vWm66Igi2Z1WdtNaO671aTRumT7n8ZdWLW+/m+k2my9MbyrM2i/zV1rSh7HPeyLNPikunvP/1Ye/b5LkZ2dW/rnpIylukL5CzxkCtOKziwqvqgdU1X9UH9p+bpIHZuZQhiRfHV64r7hopo55Zl98fDbJlVZd3+z5N5ytrbX2veH8Axcs/9nhMuuxkXWyrmWr6knpn4Yd3i49/HPFNdIPKZtn0fo+YtgI7d9aO2K4jStW/0KG04ZPp04dlr3unMsfnf6YvKOqPlOXfnHhTZIctOr58dvpnyitp14W24zs3nXVunlU+ro5IJfOQ7tirezetfoX9Jw9jNz9lVz+efLFmb/Xnd3WD+f5Wi6bt4XZnal1reyu3hYssunZTZLhhdE/pc95PGt3svvrM9m95NP1qvrt6ofxnTdcdt/Mv8/HJPnn9DmGP19VR1c/XOwmSa6S5Eszz48Xps/lNVtv1qh5R6k+xcgr0kcDPGmNRWW3m0p2V5+/8vfsddnvTlzrH7a9N/3DwF9dsJjsdrIru9uq+hdS3SfJn6/zIt7vdlN5vzsvuxe2dpnRlLuT3TvNZHdl5Ou+VfV31accOj99yoJF26rfTH98T6qqj1bVyijYmyS5+6rnx8+lPz/WU++O0lp739Ag/GZr7dj077V54ILFZbebSnYv0Vo7Jf0oz9XzP9vvZrrN5k8n2aeqDpk57fZZfDjvRzJ/iPx6nZH+qcz+Mz/7ttaOTj8U8VqrPl04aN6VVD+8+3Xpn9Jcv/VJ+9+WPtfL7podkXFQ+icas3Omzu6wvpD+ZFupp4bLf36N61vvxOQnZ2Y0SVXdLL2J8+kFy95u1Se3t8vM+quqX8zwhSdt/vytt85lDz2atZH1/ej0jei90w8JWRkVebl10lo7v7X2lNbawemHWj6tqu6Z/vw4ZdXz4xqttdnRL2vVy2Kbkd13r1o3V2+t/WqSs9MP51n9nF/kuPS54W/c+sjdF2WTsltVV08/7Gk2bwuzO1PrWtldvS1YZHV2900/NGne9vTk9PnjZneQa21798nl5wXdlOxW1U8k+Y30UXL7px/ud2HmZ/dbrc83fOv0F18/nd78OCP9RdK1Z54f+7XWbreq3lNb//KEHW3YZr8kvRn/0GHUwSKy200lu5e5ruHvL7XWZufWt9/de8zbNq+Q3U52L092t9a90keYfq6qvph+aPxDa+YIr1W83+2m8n53XnZn3wvvk56xzcjubye5aZK7DKMx771owdbaWa21X2qt3SDJE9Onyrhp+vPjHXO27bMDD2R3sZbFGZDdbirZXW2Pvd/NxPe7k2w2D5/MvD7Js4ZP6u6e5CFZPH/J25PcqdY/gfhqr0zy4Kq6//DpwlWrT5B+o9baZ9MPU/jDqrpy9XliHrzgeq6c/qQ+O8l3quoBWfxFWev1P6pP1n+19Dm9jm+tfXfBsq9JcnhVHVb9MOjfTD+M8t9mlnli9QnEr50+J9Ba344961Xpj9E9ho3Zs5K8vl12jrkVJ6Z/cdivV9VVqo9gTi6dGP5R6Yf33Le1dtrqC1c/9P3aSVZ/++6Kjazva6Q/Bl9Nn9P1OYsWrKoHV9XNhw3PecN9+G76vIXfqj5Z/lWH58gP1WW/6f2e6Yed7nhVtc+wbq6YZCVPi76B9j+T7F8LvsRiHd6a5JZV9QtVdaXh54er6tZDTl6f5JnVv0DuNll7DqtrJPlaa+0bVXWXJIvmYlyvB1af3/jK6YdJvq8tPkzybcP9eOTw+P1cktsM92/FRrYFs96Q5Aer6qHDevmD9HmqLjc9Quvz5P9XkmcM6+2n03e+r0t6dqvqoOpukp6nS77QZngRc+f0jM6zkfV9jfSmxVfSPyV/ZvrI5supqntX1Q9WH5V7fvoLne8Oj/e7kzyvqvar/iUMt6iqH5+5uOxe6m/SX4w8uPXD3dYiu90ksps+B+PjhjqulT4H3jErl7ffna7qXxjziKq6+vA43T/9sNN3LriI7Haye3myu7VenN7AuMPw86L0eVnvv2B573e7SbzfTc/ub1TVgVV1w6HOY2Yuf5f0KYkWjUJ9WxZPhbHaNdIHV5xTfT73P1i0YFU9fGb7f256M/C76R/83XbYJq5s2+9Sl52zWXaTVP/y8fsPGdqnem/jx5P83wUXkd1uEtmtql+qqusNf98mye/ksu937XcHk2w2D56QPofLl9PncPvV1trcTxtaa19Kf3I8ZHduaHgh+pD0J/TZ6Z8OPDWXPn6PTP9Cka+lf0HKyxdczwXpE7m/Jn34/CPTN9xjvCJ9x/TFJFcdrn/R/fhU+pw+f5neoHlwetPgWzOLHZc+N89pw8+zV86o/g3ks3P2zF73yemHN74qfZ1cI30drVz2H6vqd4dlv5X+acuj03div5h+2MBKHc9O/zKW9w+3eWFVvWjm5h6ZPlfo3PkGN7i+X5b+idgX0j+t+rc1lv2B4XovTD8U5i9an5/2O+mfON0lfX7ar6RPEr/fcN+/L8lPZsHzYgf6vfRDf45Kfz5enJnJ/WcNz4ljhuU2bMjc/dLnJPpCek7+JH1HmvRpAK4+nH5MLj83+KwnpH/AdUH6i7TX7E5NM45L3158Lb0B+6hFCw4jlB6UvtP9avoIhQe11mY/GV64Laj+rb5zr7+1dnb66ODnpG+X7pqZOZyq6kWr8veI9C/SOyf9cJ2HDdeR9Dfi/5ZLM/KpJP9z5rI/leTE1trcT6E3uL7flj41xinpuTs//dP7eW6Y3uA4Pz3n/5y+38hwW/sm+fhwn16b4bCiYWf9iPQ3fDta9Q8Pfjn9De8XZ7bNi55XspvpZLe19k/p856/K/0QvM8O93GF/e50tfQpM1a+Hf15SZ7cWnvT3IVlN4nsLiC7W6gN3yWx8pP+WH5j5jXX6uW9382k3u/+bfrcuR9N/9KvE3LZLxl7VPoHDIvu5weTnFdVd120zIw/Sx8V+dX03K7VVLpr+nvwlcF9T2ytfa71+frvn/7YnpW+Lp6bYds+NNgOyRrzAe8gV0p/Tp2d/hz8tfR1P/dL4WT3ktufSnbvnuSjQ0beNvz87szN2e8Oql1mWqC91/Cpw7Hph4/sFXe6qk5M8srW2t9tdy1bpfrIyA8n+fHW2pfXWG5p1ndVPSXJAa21393lwlxO9W/S/Zckd1zHaMpJqKpjkpzZWpvbZN9bVdX7kjyu9S+FWrTM0qzv6iPIfra1NnY03Y60TOtys+zE7Nrv7jyyu3eQ3Z1nmdblZtmh73evl37k3R1b/1KwRcvdL8kT2jC/63aqqr9IcnJrbccP0Ngdsrt3sN9ddd17yXN5R9qJAYa9wU580wt7A9mFaZJdmCbvd2GaZJddTqNRVS+tqi9X1cdmTrt2Vb29qk4Zfl9rOL2q6gVVdWpVfaSq7rQniwcWk12YLvmFaZJdmCbZhWmSXVhO65mz+Zj0OTxmHZX+baSHpE+GfdRw+gPS5+o5JMnj079MiD2ktXYvnxSxhmMiu0uptfYYo6vYhWMiv0tHdlmHYyK7S0d2WYdjIrtLx/td1uGYyO7SkV122Wxurb0nfTLxWQ9Jn2Mkw+8jZk5/eev+I/3brG+wWcUC6ye7MF3yC9MkuzBNsgvTJLuwnPbZzctdv7V2VpK01s4aJrFPkgPTv/1yxZnDaWetvoKqenz6p0nZd99973yrW91qN0uBvdcHPvCBr7TWDtjEq5Rd2CLLll/ZhfVZtuwm8gvrIbswTbIL07RWdne32bxIzTlt7jcQDt9U+uIkOfTQQ9tJJ520yaXA9FXVZ7fqpuacJrswwrLlV3ZhfZYtu4n8wnrILkyT7MI0rZXd9czZPM+XVg43GH5/eTj9zCQ3nlnuRkm+sJu3AWw+2YXpkl+YJtmFaZJdmCbZhW22u83mNyc5cvj7yCRvmjn90cO3fN4tyXkrhy8AS0F2YbrkF6ZJdmGaZBemSXZhm+1yGo2qenWSeyW5blWdmeQZSY5O8pqqelySzyX52WHxtyV5YJJTk1yU5LF7oGZgHWQXpkt+YZpkF6ZJdmGaZBeW0y6bza21n19w1mFzlm1Jnji2KGA82YXpkl+YJtmFaZJdmCbZheW0u9NoAAAAAADAJTSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNFGNZur6ilVdXJVfayqXl1VV62qm1bV+6rqlKr6h6q68mYVC2wO2YVpkl2YLvmFaZJdmCbZhe2z283mqjowya8nObS19oNJrpjkEUn+JMmft9YOSXJOksdtRqHA5pBdmCbZhemSX5gm2YVpkl3YXmOn0dgnyfdV1T5JrpbkrCT3TnL8cP6xSY4YeRvA5pNdmCbZhemSX5gm2YVpkl3YJrvdbG6tfT7J85J8Lj205yX5QJJzW2vfGRY7M8mB8y5fVY+vqpOq6qSzzz57d8sANkh2YZpkF6ZLfmGaZBemSXZhe42ZRuNaSR6S5KZJbphk3yQPmLNom3f51tqLW2uHttYOPeCAA3a3DGCDZBemSXZhuuQXpkl2YZpkF7bXmGk07pPkv1trZ7fWvp3k9Ul+NMn+w2EKSXKjJF8YWSOwuWQXpkl2YbrkF6ZJdmGaZBe20Zhm8+eS3K2qrlZVleSwJB9P8q4kDxuWOTLJm8aVCGwy2YVpkl2YLvmFaZJdmCbZhW00Zs7m96VPrP7BJB8druvFSZ6W5Deq6tQk10nykk2oE9gksgvTJLswXfIL0yS7ME2yC9trn10vslhr7RlJnrHq5NOS3GXM9QJ7luzCNMkuTJf8wjTJLkyT7ML2GTONBgAAAAAAJNFsBgAAAABgE2g2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjaTYDAAAAADCaZjMAAAAAAKNpNgMAAAAAMJpmMwAAAAAAo2k2AwAAAAAwmmYzAAAAAACjjWo2V9X+VXV8VX2yqj5RVT9SVdeuqrdX1SnD72ttVrHA5pBdmCbZhemSX5gm2YVpkl3YPmNHNv9Fkn9qrd0qye2TfCLJUUne0Vo7JMk7hv+B5SK7ME2yC9MlvzBNsgvTJLuwTXa72VxV+yX58SQvSZLW2rdaa+cmeUiSY4fFjk1yxNgigc0juzBNsgvTJb8wTbIL0yS7sL3GjGy+WZKzk7ysqj5UVX9XVfsmuX5r7awkGX5fb96Fq+rxVXVSVZ109tlnjygD2CDZhWmSXZgu+YVpkl2YJtmFbTSm2bxPkjsl+ZvW2h2TfD0bOAShtfbi1tqhrbVDDzjggBFlABskuzBNsgvTJb8wTbIL0yS7sI3GNJvPTHJma+19w//Hp4f5S1V1gyQZfn95XInAJpNdmCbZhemSX5gm2YVpkl3YRrvdbG6tfTHJGVX1A8NJhyX5eJI3JzlyOO3IJG8aVSGwqWQXpkl2YbrkF6ZJdmGaZBe21z4jL/9rSV5VVVdOclqSx6Y3sF9TVY9L8rkkPzvyNoDNJ7swTbIL0yW/ME2yC9Mku7BNRjWbW2v/leTQOWcdNuZ6gT1LdmGaZBemS35hmmQXpkl2YfuMmbMZAAAAAACSaDYDAAAAALAJNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNFGN5ur6opV9aGqeuvw/02r6n1VdUpV/UNVXXl8mcBmk12YJtmFaZJdmC75hWmSXdgemzGy+X8l+cTM/3+S5M9ba1PRq+cAACAASURBVIckOSfJ4zbhNoDNJ7swTbIL0yS7MF3yC9Mku7ANRjWbq+pGSQ5P8nfD/5Xk3kmOHxY5NskRY24D2HyyC9MkuzBNsgvTJb8wTbIL22fsyObnJ/ntJN8b/r9OknNba98Z/j8zyYEjbwPYfLIL0yS7ME2yC9MlvzBNsgvbZJ/dvWBVPSjJl1trH6iqe62cPGfRtuDyj0/y+CQ56KCDdrcMYINkF6ZJdmGaxmZ3uA75hW1g3wvTJLtwWQcfdcLc008/+vA9cntjRjbfPclPVdXpSf4+/XCE5yfZv6pWmtg3SvKFeRdurb24tXZoa+3QAw44YEQZwAbJLkyT7MI0jcpuIr+wjex7YZpkF7bRbjebW2u/01q7UWvt4CSPSPLO1tqjkrwrycOGxY5M8qbRVQKbRnZhmmQXpkl2YbrkF6ZJdmF7jZ2zeZ6nJfmNqjo1fU6cl+yB2wA2n+zCNMkuTJPswnTJL0yT7MIW2O05m2e11k5McuLw92lJ7rIZ1wvsWbIL0yS7ME2yC9MlvzBNsgtbb0+MbAYAAAAAYIfRbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG02wGAAAAAGA0zWYAAAAAAEbbZ7sLAIBldPBRJ8w9/fSjD9/iSgAAAGAajGwGAAAAAGA0zWYAAAAAAEbTbAYAAAAAYDTNZgAAAAAARtNsBgAAAABgNM1mAAAAAABG22e7CwAAAGD5HXzUCXNPP/3ow7e4EgBgWRnZDAAAAADAaJrNAAAAAACMZhoNAAAAAIAdZE9Nj2VkMwAAAAAAoxnZDJtk0SdCi/giFQAAAAD2JkY2AwAAAAAwmmYzAAAAAACjmUYDgB1to1PgAAAAAPMZ2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIy2z3YXAAAABx91wtzTTz/68C2uBNgo+QUAVhjZDAAAAADAaJrNAAAAAACMZhoNAAAAgB1m0RQ4i5gaB1gPI5sBAAAAABjNyGbYoI1++gsAAAAAO4GRzQAAAAAAjKbZDAAAAADAaKbRAABgae3O9FW+wAgAALaHkc0AAAAAAIxmZDMAAADAXsqX3ANbychmAAAAAABG2+1mc1XduKreVVWfqKqTq+p/Dadfu6reXlWnDL+vtXnlAmPJLkyT7MJ0yS9Mk+zCNMkubK8xI5u/k+Q3W2u3TnK3JE+sqtskOSrJO1prhyR5x/A/sDxkF6ZJdmG65BemSXZhmmQXttFuN5tba2e11j44/H1Bkk8kOTDJQ5IcOyx2bJIjxhYJbB7ZhWmSXZgu+YVpkl2YJtmF7bUpXxBYVQcnuWOS9yW5fmvtrKQHvKqut+Ayj0/y+CQ56KCDNqMMYINkl51kb/piFNmF6ZJfmCbZhcWvp08/+vAtrmT9ZBe23ugvCKyqqyd5XZInt9bOX+/lWmsvbq0d2lo79IADDhhbBrBBsgvTJLswXfIL0yS7ME2yC9tjVLO5qq6UHtxXtdZeP5z8paq6wXD+DZJ8eVyJwGaTXZgm2YXpkl+YJtmFaZJd2D67PY1GVVWSlyT5RGvtz2bOenOSI5McPfx+06gKgU0luzBNsgvTJb/sVFM85H6W7MI0yS5srzFzNt89yS8k+WhV/ddw2u+mh/Y1VfW4JJ9L8rPjSgQ2mezCNMkuTJf8wjTJLkyT7MI22u1mc2vtvUlqwdmH7e71AnuW7MI0yS5Ml/zCNMkuTJPsslMtOqJoq43+gkAAAAAAABgzjQYAAGzIsoy4AAAANp+RzQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaJrNAAAAAACMptkMAAAAAMBoms0AAAAAAIym2QwAAAAAwGiazQAAAAAAjKbZDAAAAADAaPtsdwEAAADsHAcfdcLc008/+vAtrgQA2GxGNgMAAAAAMJpmMwAAAAAAo5lGAxZYdHgfAAAAAHB5RjYDAAAAADCakc0A7FUclQAAAADbw8hmAAAAAABG02wGAAAAAGA002gAAACQxHRUAMA4RjYDAAAAADCakc0AAGw6oyMBAGDnMbIZAAAAAIDRNJsBAAAAABjNNBrseA7zhWmSXQAAAFguRjYDAAAAADCakc0AAAAAABOw7Ef5GtkMAAAAAMBoms0AAAAAAIxmGg0AAPYqiw4tPP3ow7e4EgAA2D3LPl3GIkY2AwAAAAAwmpHNAAAAO8xUR0sBy8cRRcAsI5sBAAAAABhNsxkAAAAAgNFMowEAAAAwEabBgb3L3pbpPTKyuap+sqo+VVWnVtVRe+I2gD1DfmGaZBemSXZhmmQXpkl2Yc/b9JHNVXXFJC9Mct8kZyZ5f1W9ubX28c2+LWBzyS/LaG/7lHdPkF2YJtmFy5rKl4zJLlvF6+DNJbuwNfbEyOa7JDm1tXZaa+1bSf4+yUP2wO0Am09+YZpkF6ZJdmGaZBemSXZhC+yJOZsPTHLGzP9nJrnr6oWq6vFJHj/8e2FVfWoP1LIR103ylW2uYbVlq0k9a9tQPfUn61rsJrtbzG7aZX5ld12WrSb1rG1PZDfZ2vxOMbvJxJ8LW0A9uzb1fa/XzZtDPWtbtnoS2d0uy/ZcUM+uLVtNsrs9Jv082ALLVk+yfDVtaXb3RLO55pzWLndCay9O8uI9cPu7papOaq0dut11zFq2mtSztmWrZzftMr+yu2vLVpN61rZs9eymyWU3Wb7HXj1rW7Z6kuWsaYO8bt4E6lnbstWTLGdNGyS7m0A9u7ZsNS1bPbtBdjeBenZt2Wra6nr2xDQaZya58cz/N0ryhT1wO8Dmk1+YJtmFaZJdmCbZhWmSXdgCe6LZ/P4kh1TVTavqykkekeTNe+B2gM0nvzBNsgvTJLswTbIL0yS7sAU2fRqN1tp3qupJSf5vkismeWlr7eTNvp09YGkOkZixbDWpZ23LVs+GTTS/y/i4L1tN6lnbstWzYRPNbrJ8j7161rZs9STLWdO6ye6mUc/alq2eZDlrWjfZ3TTq2bVlq2nZ6tkQ2d006tm1ZatpS+up1i43PQ0AAAAAAGzInphGAwAAAACAHUazGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJvXUFVHVNX/qao3VdX9htOuUFXPqaq/rKojl6Cefavq2OH0R21xPTerqpdU1fGrTt+3qj5QVQ/aynpW1XBQVb25ql5aVUdtVx0z9Vxu3bHnyO4u65Hd9dcju1tIdndZj+yuvx7Z3UKyu66aljK/sruzLVt216jJvvfydS1VdhP53Uqyu656ZHed9kh2W2t75U+Slyb5cpKPrTr9J5N8KsmpSY5a53VdK8lLhr9/OskxSf4syWFLUM8vJHnw8Pc/bFM9x6/6/1lJnpbkQdu17pLcJ8kvD3+/fBmfS3629vGW3bnXJbuyu2k/srul9ciu7G7aj+xueU2bll/Z3dk/y5bdPVzTXrXvXbbs7sl152frHmvZXXhdsrsN2d2UsCzjT5IfT3Kn2Qc8yRWTfCbJzZJcOcmHk9wmyQ8leeuqn+vNXO5/J7nT8PdRM0+M45egnt9Jcofh7+O2qZ7jZ/6+T5JHJHnM7oR3s2pLcp0k70ryziSPXcbnkh/ZlV3Z3Zt+ZFd2ZXeaP7I73fzK7s7+Wbbs7uGa9qp977Jld0+uOz+yK7s7M7v7ZC/VWntPVR286uS7JDm1tXZaklTV3yd5SGvtuUkuN4S+qirJ0Un+sbX2weHkM5N8a/j7u0tSz42S/Fc2MC3KZtSzwE8k2Tf9iXxxVb2ttfa99da1WbVV1W8lecZwXccnedlGatgD9cxbd8whu3u+ngVkV3ZHkd09X88Csiu7o8ju1tS0wKj8yu7OtmzZ3YKa9pp977JldxNrkt91kN2tqWcB2d2i7O61zeYFDkxyxsz/Zya56xrL/1r6Jx/XrKpbtNZelOT1Sf6yqu6R5D1LUs9fVdXhSd6ylfVU1XWSPCfJHavqd1prz22tPX047zFJvrLRN7ybVVuSf0ryzKp6ZJLTN6mGMfXMW3esn+xuYj2yK7tbSHY3sR7Zld0tJLubXNMW5ld2d7Zly+5m1rS373uXLbu7U5P87j7Z3eR6ZHd7s7vTms0157S2aOHW2guSvGDVaRcledwS1fP1JI/dpnq+muRXFpx3zCbVtGKjtX0sycM2uYZZo9cdGyK7m1uP7K662UULy+5osru59cjuqptdtLDsjia7m1/TVuVXdne2ZcvuZtW0E/a9y5bdRH63kuxufj2yu+qmFy28J7K7oUPI9gJnJrnxzP83SvKFbaolUc9GLFtty1bP3m7ZHm/1rN+y1bZs9eztlu3xVs/6LVtty1bP3m7ZHu9lqydZzpqS5atr2erZ2y3j471sNS1bPSuWsa5lrGlvtYyP9bLVtGz1rFjGura9pp3WbH5/kkOq6qZVdeX0icHfrJ6lrWfWstW2bPXs7Zbt8VbP+i1bbctWz95u2R5v9azfstW2bPXs7Zbt8V62epa1pmT56lq2evZ2y/h4L1tNy1bPimWsaxlr2lst42O9bDUtWz0rlrGu7a+pLcG3ce6JnySvTnJWkm+nd/UfN5z+wCSfTv9mxqerZznqWebalq2evf1n2R5v9Uy3tmWrZ2//WbbHWz3TrW3Z6tnbf5bt8V62epa1pmWsa9nq2dt/lvHxXraalq2eZa5rGWvaW3+W8bFetpqWrZ5lrmsZa2qtpYYiAAAAAABgt+20aTQAAAAAANgDNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNE0mwEAAAAAGE2zGQAAAACA0TSbAQAAAAAYTbMZAAAAAIDRNJsBAAAAABhNsxkAAAAAgNF2TLO5qg6oqk9V1VW34LYeU1Xvnfn/wqq62Z6+3bGq6sSq+qXtrmNXquo/q+q2u1hmU9Z3VT27qo4ZeR0/U1WvHHMdO5ns7toUsltVV6mqT1bV9Xax3G2q6qRNuL1XVtUzR17Hb1TVs8fWslMN6/zjVfX9W3Bb96qqM2f+P7mq7rWnb3esqjpmCs+xqnp9Vf3kLpbZlPVdVb9UVSeOvI47VdW/jLmOncx+d9fsd+dej/0uAJBk4s3mqnpEVX2iqr5eVZ+pqnussfhRSV7WWvvGVtW3orV29dbaaVt9u1utqg4bXtReVFXvqqqbrLHswcMyFw2Xuc/MeUdW1Qeq6vyqOrOq/rSq9pm5+POSPGsX5VxmfQ9vCr4xvIlZ+fmRMfd3A96Q5M67apDvFKvWwYVV9d2q+ss1LiK7e9hmZXc4/ylV9cWqOq+qXlpVV0mS1to3k7w0ydN2Uc4fpWd85fpOr6qLVz1nbrj793ZDXpTksVV1nS26vaU2rPu3VdU5wzr+q1Xb5tUen+Q9rbUvblWNK1prt22tnbjVt7vVquoOw/7youH3HRYsd5WqeklVfbaqLqiqD1XVA1Yts9Z24Ogkz9lFOZdZ30Mj/VursvtzY+7verXWPpjk4tX3caeqqltX1TuH7fKpVfXTu7iI/e4eZr+7kP0uAOwFJttsrqr7JvmTJI9Nco0kP55k7ovT4UXXkUl27OjSXTQENuP6r5vk9Ul+P8m1k5yU5B/WuMirk3woyXWSPD3J8VV1wHDe1ZI8Ocl1k9w1yWFJfmvmsm9O8hNVdYMFtSxa308a3sSs/Pz7Bu7ibmuttSR/n+R/bsXtLbvZdZDk+kkuTvLaecvK7rSyW1X3T29SHJbk4CQ3S/KHM5c9LsmRK2+E59RygyQ/keSNq8568KrsfmFDd3I3tdYuSvL/kvzCVtzeBPx1ki8nuUGSOyS5Z5InrLH8Lyd5xRbUtZS2ILtXTvKm9O3jtZIcm+RNw+mr7ZPkjPR1ds30vL+mqg4ermvN7UBr7T+T7FdVh65R0rz1/aersrvWtmWzvWqoaUcbnodvSvLW9HX7+CSvrKpbLljeftd+N7HfBQBGmGyzOf2F1LNaa//RWvtea+3zrbXPL1j2rknOba3NHmJ7YvUpEv5t+MT+LVV1nap6VfURte9feRM2LH+rqnp7VX2t+qGFD5857zpV9ebhcv+Z5OazN15VrapuMfx9+DCi6PyqOqNmDjcbRi606iN7P1dVX6mqpy96AIZRQy8a6rqgqt49OzJiuK4nVtUpSU4ZTvvR4b6dN/z+0VVXe/Pq01ScV1VvqqprL7r9VX4mycmttdcOI2GemeT2VXWrOXXfMsmdkjyjtXZxa+11ST6a5KFJ0lr7m9bav7TWvjWs01clufvK5Yfr/0CS+y2o5XLrey3VR+edObPeVz8mK8tdraqOq6qvVtW5w+N03eG8/avqZVV11nBdz6qq2XydmOTw9dSzwzwsvXm16HBn2Z1QdtMbFC9prZ3cWjsnfbTUY1YuP6zHc5LcbUEt903ywfWMpquqK1TV8dVHc507PC9uvWDZ61UfkXvu8Dx4z8x5N6qqN1TV2VX131X1xFUXPzGyu+KmSV7TWvvGMHr1n5LMPWKjqg5Kz9P7Zk47pqr+uqr+ccjuv1bV91fV86uPlv5kVd1xZvkbVtXrZtbNr8+c933D9Z1TVR9P8sOrbv/0Gkb/VdVdqurfh/V/1rDNv/LMsq2qfqWqThmu74VVVQvu1zOH590/DNn9YFXdftXtPq2qPpLk61W1T/VRpScOt39yVf3Uqqu97qJtwS7cK72J/PzW2jdbay9IUknuvXrB1trXW2vPbK2dPrxmemuS/05y52GR9WwHTsyCLMxb32upqt+rqtOG+zzvMVlZ7gpV9YKq+vKwbftIVd1mOO+qVfVnw/b4S8Nza3bahxOT3LeqrrSemvZit0pywyR/3lr7bmvtnUn+NYubefa79rtzlf0uALBOk2w2V9UVkxya5IDqhwOeObx5/L4FF/mhJJ+ac/oj0l9sH5j+Yvffk7wsfZTBJ5I8Y7i9fZO8PX10wPWS/HySv65Lp0V4YZJvpI/2+sXhZ5GvJ3l0kv3TX0j9alUdsWqZH0vyA+mjFP5g0Qu5waPSX1heN8l/pTdmZx2R/sbhNsOL4BOSvCB9dMSfJTmhLnuo2qOH+m+Y5DvDshkeh49U1SMX1HHbJB9e+ae19vUkn8n8RsRtk5zWWrtg5rQPL1g26aPWT1512ieS3H7Ossni9b3I+5LcLn29H5/ktTV/BMhj00dd3yj98XtC+npP+gigi9OfR4emr9vHrqr3FlV1tQ3UtRMcmeTlw+jveWR3Wtm9zHUNf19/VZ2bmd23Jjkkyfcn+VgWj6J9avqRLwcMy/5+csm+5K1J3p/+XLpvkqdW1WHrrHen+Yskj6j+wduBSR6Q3nCe54fSnyvfWXX6w5P8Xvrz/pvp2f3g8P/x6c/tVP+w7i3pz6ED0zP15Oqj+JKe8ZsPP/dP35Ys8t0kTxlu40eG61o9IvtB6Q3r2w813j+LPST9aIxrp29b3riqofnz6duI/dObv29JH6l3vSS/luRVVfUDM8sv3BZU1Vur6qgFddw2yUdWbT8/ksX70ktU1fWT3DKX7lvXsx3YVXbnre9FPp3+IfI106fnOG6oabUHpDfJDkkfvf2IJF8bznte+gcgtxvOPzh91OfKffhs+uN/yDpr2lvN++CkkvzgguXtd+1312K/CwDs0iSbzemH3l8pfVTkPdIP571j+hvYefZPcsGc01/WWvtMa+28JP+Y5DOttX8e3iy9drjOpL8JPb219rLW2neGuQBfl+Rhw4umhyb5g2Hk0MfSD2Wdq7V2Ymvto8PIoo+kHxp3z1WL/eEweuHD6S8a13rBdUJr7T3DvGxPT/IjVXXjmfOf21r7Wmvt4vQX6qe01l4x3I9XJ/lkkgfPLP+K1trHhhe+v5/k4cN9TGvtdq214xbUcfUk56067bz0KU52e9mqemx68/Z5q866IH29zrNofb9gGGFxblV9cOXE4fH42rDe/zTJfkluMefy305/g3KLYXTQSa21C4emy2FJntJau2gY8ff89Ddms/Wu1EYuGQl3z6yRl8ju1LK7+vyVv2eva3ey+8aZ7L4xSYb1cExr7YKZkWF3Hhodq307vRlw0HDExLuH0++WZL/W2h8Pp5+a5CW5fHbltnt3emPj/CRnph/6vfrQ6xWL1uUbWmsfGNbZG5J8o7X28tbad9MPI1/J7g8nOaC19qxh3ZyW5P/k0nXz8CTPGTJyRmaaPKsNt/cfQ3ZOT/K3uXx2j26tndta+1ySd6W/rljkA62141tr305vQF01lx01+ILW2hlDdu+Wnoujh/vxzvRGy8/PLL9wW9Bae1Br7egFdWwku5cYGuOvSnJsa+2TG7iu3cnub81k9ysrJ7bWXtNaO2vI8XFJTk/f16/27fR98q2Gy328tfbF4cOIX0ry5NbaOa2185M8N5fN7q5q3ik+mX4E0VOr6kpVdb/05/+iD7/td+13E/tdAGCEqTabLx5+/+XwZuUr6W/4Hrhg+XMy/wXcl1Zd5+r/rz78fZMkd5150XVu+uiI70//xH5lLsQVn11UeFXdtfqXfJxdVecl+ZX0Buas2S9Tumimjnkuud3W2oXpI35uOO/84fTVtX02fWTBvOU/m97UX13fPBemvyGctV/mv4Bd17LD6JWjkzxgWMezrpHk3AW1LFrfv95a23/4udPM7fx29cO3zxsuu2/m3+djkvxz+jyXn6+qo6vP63eTJFdJ8qWZ58cL0z8Uma03a9S8Ez06yXtba/+9xjKy200lu6vPX/l79rp2J7tHzGT3iKSPjqr+5aGnVdX5SU4dlp13n49Of0zeUf3LZJ86nH6TJAeten78dvrzYz317hhDc+//ps8zurKNvFb6dyfMsxnZveGqdfO7uXS7esOsP7u3HEYIf3F4rvxxNi+730tvvK+V3TOG5WZrnZvdBduCRTaS3SSXrMdXJPlWkidt8Lp2J7vPm8nuJY95VT2mqj48s25vlTnZba39v/QvDPub9H3si6rqGukZvUqS2et4a/pI2lk7Pr/DhyJHpDdPv5jkN5O8Jv15O4/9bme/a78LAOymSTabW5+T7Mwkiw69X+0j6YeL7q4zkrx75kXX/q1/WcavJjk7/dC52ZERB61xXcelf8HdjVtr10x/E/X/t3f3UbaddX3Avz9zTZXwkoAXFibQgEYgC7HANdBqQYlaSoBEXhYUhEDTxloFeVtyoWsVqotlWMsWRFs0JZDYpUAMtEkJYpGXUtsaCC8CIWJSiHCbQC4IBLAtBJ/+sffFk5s5c2fmOTNz9pzPZ61Z986ePXN+95z5zjnzvXs/e821ITfo27dbVXfMcDrj7EU8Zu+jGzO8yJt17ySza10f/e/4ZpKji961XJOZo0nGoxy+L7df/uLIvvcdf2E84odm962qR2c4iu1xrbWPrfE1HpDbnjY4a8OPd1X9eJIXZDjS5sQM5cnXssZjMh6B8fLW2gMynLb50xl+gfpshl9w7jrz/XHn1tqDjpr3+jZc+ITBM7P+Uc2J7B4xleze5muNf/98a+2LM9sWkt0M3z+PybA+7V3yN2cjrJXdW1prz2+tnZqhdHlxVT0yw/fHdUd9f9yptTZ75Np6866Su2b4HvvNNqwP/MUMp9DP+0/ej2b4XtnqhbY+m+TTazw2R27vpmw8u6/NcETiaa21O2corReV3e/IsLTSetm9V912Df+52Z3zs2Cea5I8qOo260s/KGtnN+N+F2Uo7J84lpCzX+tYPweOld0NPd5Vdd8Mj8nPJblba+3EDI/Pmo9Ja+3V438QPzDJ6Rmesz+foTC/38z3x13Gn81HbufIz8zrjjXTXtda+2hr7ZGttbu11v5BhovIvX/O7p53B553b8/zLgCwIZMsm0dvSPKcGi5AcVKS52U4qmUt709y4rjcwVa8LckPVNUzxlMQv7OqfriqHtCGU3/fmuTlNaxjeXrWXzvyTkn+srX2f6vqjCTz1nPbqMdU1Y/WcLGjX0lyVRtOKV7L28d/x9NquGjRUzL84jZ7v/1MVZ1ew9rCv5zksvHfeCz/MckDq+qJNVyg519mWEvyz47esbX25xnWyntZDRf4+ekMvyC/JUmq6lEZTvF9Ymvtdr8M1bCe8kMzrAm4ls083nfK8IvPFzIckfLyDEft3U5VPaqqHjiWBrdk+KXiW+P9/V+T/FpV3bmGC6h8f1U9YubTH5nhtFMyXHQnw9FBv3+MXWV3MInsJvmdJOeNc5yUYWmji498/vg43jXJn8yZ5Z1JHlK3vcjXPHfKsObvFzOcDv6KeTtW1eOq6vvGsu0rGdbw/VaGNUe/UVUvHP89x1XVD1bVQ2c+XXaTjGeXfDrDmqn7qurEDHlZsxBow0WprktyxhZv8v1JbqnhYnvfPT42D6yqIxcCvDTJS6rqpKo6JcNayPPcKcPP7K/VcAGun9viTEc8tKqeMBarz8vwfTjve/qqDOvO/tL48+fHMpyG/6aZfTbzs2DWezN8Hz+3qv5WVR05Uvndc/Z/bYYS53HjMgGzNvJzYG4WNvl43zFDqXc4Qwf+TzIuk3G0Gi7ueMZ4X389Q8H8rfFn2+uSvLqq9tfglBqWiJid94+OKtVXUlU9aPwZd4eqelGG9ZIvnrO7592B593b87wLAGzIlMvmX8lwcYk/z3AhiQ9nzoue1to3Mrzw+pmt3FAbLsrxUxnWE7sxwyl7r8xwCmcynIp6x3H7xRmK8Hn+eZJfrqqvZnhxeelWZprxexkuyvKXGQrYp8/bcTzK4bEZTqH8YobT1h571BIV/yHDv+FzGdahfO6RD9Rwxfg1v35r7XCGo4NfkeGUvIdlZv21Gk59/a2ZT3lqhvUZv5ThVLsnjV8jGda9u0uSt9dw1fOvVdXsi87HJ3lva23NI782+Xi/PcPSCfxIEwAAF/tJREFUGNdlWDPylgxHzK3lezP8knRLhiNJ/ijD+oEZb+uEJJ8Y/02/n/GUwPGF9lOTXLiBeVbFuUne2m57wZvbkd3BVLLbWntHhnXP35Ph9Nm/GP+NRzwtwzqx/2/OLJ/PUJSdPe++mPGGDI/pjRny+D/W2fd+49f9WpL/nuTXW2t/3Ia1Rh+ToSC7IcN/Ov12xtOQa7jo7KMz/DJP8oQM98fhDKdP35rhwnvz/HaGC4pt2ljYPC7D2smfzvDYvC7Dc0OS/KsM31+fznDxvXkXqUqSF2X43vtqhjNm3ryVmWZcnuQpGTLwjCRPmFdojj/DHp/hQndfSPLvkjzzqFJp7s+CqvqDqnrpOl/7nAxHG345w4XKzhm3p6peeuS5s4ajfH82w/35uZnn1qePX+tYPwd+OMnX1/oP4BkberzbsPbuazKUmjdlKJqvmrP7iRmOxv5yhozelORV48demOF74P0Zyqz/ktteDPDpGY6EZXhcbsqwdvOZSX5ynZ/DnnfjeXcOz7sAwIZUu81FzPeuqtqf5L8lefAaR/RMUlVdnORQa23ehRH3pKq6Ksl5bbiwzLx9lubxHo9CeXJrrfeInJW0TI/loqxidms4I+FPkzyitXbzOvudnmF5lTPaLj9BVdXzM1ykbs2yj/WNj/mHk5zZWpv3n3iTUlUvz3CR2C0VcVNVVW9JclFr7e3r7LM0j3dVPTjDdT1+dDfnmCrPu3uD510AYLesTNm8F63iC2fYC2QXpmlVy2aYOs+7AAA755jLaFTV66vq5qr6+My2u1bVO6vquvHPk8btVVWvqarrq+qjVfWQ7RwemE92YbrkF6ZJdgEAWHUbWbP54gxrZ806mORdrbXTkrxrfD8Z1iQ8bXw7P8PFaNgmrbVnOUKDdVwc2V1KsssGXBz5XTqttZc7qpljuDiyu3Q87wIA7Jxjls2ttfdluJDGrLMzrO2V8c9zZrb/Thv8SYarWd9zUcMCGye7MF3yC9MkuwAArLp9W/y8exy5+Etr7aaquvu4/eQkn53Z79C47XYXiqmq8zMcxZETTjjhofe///23OArsXR/84Ae/0Frbv8AvKbuwQ5Ytv7ILG7Ns2U3kFzZiG7ILAGzBVsvmeWqNbWtegbC1dmGSC5PkwIED7eqrr17wKDB9VfUXO3VTa2yTXeiwbPmVXdiYZctuIr+wETuYXQBgHRtZs3ktnz9ymt/4583j9kNJ7jWz3ylJbtz6eMCCyS5Ml/zCNMkuAAArY6tl8xVJzh3/fm6Sy2e2P3O8uvbDk3zlyGmDwFKQXZgu+YVpkl0AAFbGMZfRqKo3JvmxJN9TVYeSvCzJBUkurarzknwmyZPH3d+e5DFJrk/yV0mevQ0zAxsguzBd8gvTJLsAAKy6Y5bNrbV/NOdDZ66xb0vy871DAf1kF6ZLfmGaZBcAgFW31WU0AAAAAADg25TNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQLeusrmqnl9V11TVx6vqjVX1XVV1n6q6qqquq6o3V9XxixoWWAzZhWmSXZgu+QUAYBVsuWyuqpOTPDfJgdbaA5Mcl+SpSV6Z5FWttdOSfCnJeYsYFFgM2YVpkl2YLvkFAGBV9C6jsS/Jd1fVviR3SHJTkkcluWz8+CVJzum8DWDxZBemSXZhuuQXAIA9b8tlc2vtfyf5tSSfyfBi+StJPpjky621W8fdDiU5ea3Pr6rzq+rqqrr68OHDWx0D2CTZhWmSXZgu+QUAYFX0LKNxUpKzk9wnyfcmOSHJP1xj17bW57fWLmytHWitHdi/f/9WxwA2SXZhmmQXpkt+AQBYFT3LaPxEkk+31g631r6Z5K1J/l6SE8fTA5PklCQ3ds4ILJbswjTJLkyX/AIAsBJ6yubPJHl4Vd2hqirJmUk+keQ9SZ407nNuksv7RgQWTHZhmmQXpkt+AQBYCT1rNl+V4YImH0rysfFrXZjkxUleUFXXJ7lbkosWMCewILIL0yS7MF3yCwDAqth37F3ma629LMnLjtr8qSRn9HxdYHvJLkyT7MJ0yS8AAKugZxkNAAAAAABIomwGAAAAAGABlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0G3fbg8AAACLdOrBK9fcfsMFZ+3wJAAAsFoc2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANCtq2yuqhOr6rKq+rOquraq/m5V3bWq3llV141/nrSoYYHFkF2YJtmF6ZJfAABWQe+Rzb+e5B2ttfsn+aEk1yY5mORdrbXTkrxrfB9YLrIL0yS7MF3yCwDAnrflsrmq7pzkEUkuSpLW2jdaa19OcnaSS8bdLklyTu+QwOLILkyT7MJ0yS8AAKui58jm+yY5nOQNVfXhqnpdVZ2Q5B6ttZuSZPzz7mt9clWdX1VXV9XVhw8f7hgD2CTZhWmSXZgu+QUAYCX0lM37kjwkyWtbaw9O8vVs4tS/1tqFrbUDrbUD+/fv7xgD2CTZhWmSXZgu+QUAYCX0lM2HkhxqrV01vn9ZhhfRn6+qeybJ+OfNfSMCCya7ME2yC9MlvwAArIQtl82ttc8l+WxV3W/cdGaSTyS5Ism547Zzk1zeNSGwULIL0yS7MF3yCwDAqtjX+fnPSfK7VXV8kk8leXaGAvvSqjovyWeSPLnzNoDFk12YJtmF6ZJfAAD2vK6yubX2kSQH1vjQmT1fF9hesgvTJLswXfILAMAq6FmzGQAAAAAAkiibAQAAAABYAGUzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHTrLpur6riq+nBVvW18/z5VdVVVXVdVb66q4/vHBBZNdmGaZBemSXYBAFgFiziy+ReTXDvz/iuTvKq1dlqSLyU5bwG3AUvv1INXrvm2xGQXpkl2YZpkFwCAPa+rbK6qU5KcleR14/uV5FFJLht3uSTJOT23ASye7MI0yS5Mk+wCALAqeo9sfnWSX0ry1+P7d0vy5dbareP7h5KcvNYnVtX5VXV1VV19+PDhzjGATZJdmCbZhWnacnYT+QUAYDq2XDZX1WOT3Nxa++Ds5jV2bWt9fmvtwtbagdbagf379291DGCTZBemSXZhmnqzm8gvAADTsa/jc38kyeOr6jFJvivJnTMctXFiVe0bj9Q4JcmN/WMCCyS7ME2yC9MkuwAArIwtH9ncWntJa+2U1tqpSZ6a5N2ttacneU+SJ427nZvk8u4pgYWRXZgm2YVpkl0AAFZJ75rNa3lxkhdU1fUZ1qO7aBtuA1g82YVpkl2YJtkFAGDP6VlG49taa+9N8t7x759KcsYivi6wvWQXpkl2YZpkFwCAvW47jmwGAAAAAGDFKJsBAAAAAOimbAYAAAAAoJuyGQAAAACAbspmAAAAAAC6KZsBAAAAAOimbAYAAAAAoJuyGQAAAACAbspmAAAAAAC6KZsBAAAAAOimbAYAAAAAoJuyGQAAAACAbspmAAAAAAC6KZsBAAAAAOimbAYAAAAAoJuyGQAAAACAbspmAAAAAAC6KZsBAAAAAOimbAYAAAAAoNu+3R4AALbi1INXrrn9hgvO2uFJAGA1eO4FAI7Fkc0AAAAAAHRTNgMAAAAA0E3ZDAAAAABAN2UzAAAAAADdlM0AAAAAAHRTNgMAAAAA0G3fbg8AAADAdJ168Mo1t99wwVk7PAkAsNsc2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQLd9uz0ATM2pB69cyP43XHDWIsYBAAAAgKXgyGYAAAAAALopmwEAAAAA6GYZDQD2FEvXAMDWbXbJOACAWVs+srmq7lVV76mqa6vqmqr6xXH7XavqnVV13fjnSYsbF+gluzBNsgvTJb8AAKyKnmU0bk3ywtbaA5I8PMnPV9XpSQ4meVdr7bQk7xrfB5aH7MI0yS5Ml/wCALAStlw2t9Zuaq19aPz7V5Ncm+TkJGcnuWTc7ZIk5/QOCSyO7MI0yS5Ml/wCALAqFnKBwKo6NcmDk1yV5B6ttZuS4YV1krsv4jaAxZNdmCbZhemSXwAA9rLuCwRW1R2TvCXJ81prt1TVRj/v/CTnJ8m9733v3jGATZJdmCbZhb8xtQuZyS8AAHtd15HNVfWdGV4w/25r7a3j5s9X1T3Hj98zyc1rfW5r7cLW2oHW2oH9+/f3jAFskuzCNMkuTJf8AgCwCrZ8ZHMNh2JclOTa1tq/mfnQFUnOTXLB+OflXRMCCyW7TM3UjlzcLrIL0yW/AACsip5lNH4kyTOSfKyqPjJue2mGF8uXVtV5ST6T5Ml9IwILJrswTbIL0yW/AACshC2Xza21P04yb6G5M7f6dYHtJbswTbIL0yW/AACsiq41mwEAAAAAIFE2AwAAAACwAD1rNgPAZMy70OANF5y1w5MAAADA3uTIZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG7KZgAAAAAAuimbAQAAAADopmwGAAAAAKCbshkAAAAAgG77dnsAAACY59SDV+72CMAWzcvvDRectcOTAAA7xZHNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQLd9uz0ALKtTD1652yMAO2Be1m+44KwdngRWm+ddAACYPkc2AwAAAADQTdkMAAAAAEA3ZTMAAAAAAN2UzQAAAAAAdHOBQAAAgBXjopwAwHZwZDMAAAAAAN0c2czKc1QHLAdZBAAAgGlzZDMAAAAAAN2UzQAAAAAAdLOMBgAAwB61jMtUzZvphgvO2uFJAIBFc2QzAAAAAADdHNnMyljGozoAAAAAYK9wZDMAAAAAAN22pWyuqkdX1Ser6vqqOrgdtwFsD/mFaZJdmCbZBQBgL1n4MhpVdVySf5vkJ5McSvKBqrqitfaJRd8WsFjyyyK5+M/OkV2YJtkFAGCv2Y4jm89Icn1r7VOttW8keVOSs7fhdoDFk1+YJtmFaZJdAAD2lO24QODJST478/6hJA87eqeqOj/J+eO7X6uqT27DLJvxPUm+sMszHG3ZZjLP+jY1T71yQ7v97a0Os0XHzK/sbsiyzbRU89Qrl2uezLl/NpjR9exkfqeY3WTJvjdjnmNZtnmS6T/3et28GOZZ37LNk2zPc+9Ov24GANawHWVzrbGt3W5DaxcmuXAbbn9Lqurq1tqB3Z5j1rLNZJ71Lds8W3TM/MrusS3bTOZZ37LNs0WTy26yfPe9eda3bPMkyznTJnndvADmWd+yzZMs50wAwGJsxzIah5Lca+b9U5LcuA23Ayye/MI0yS5Mk+wCALCnbEfZ/IEkp1XVfarq+CRPTXLFNtwOsHjyC9MkuzBNsgsAwJ6y8GU0Wmu3VtUvJPnDJMcleX1r7ZpF3842WJpTE2cs20zmWd+yzbNpE83vMt7vyzaTeda3bPNs2kSzmyzffW+e9S3bPMlyzrRhsrsw5lnfss2TLOdMAMACVGu3WxYOAAAAAAA2ZTuW0QAAAAAAYMUomwEAAAAA6KZsBgAAAACgm7IZAAAAAIBuyuZ1VNU5VfXvq+ryqvqpcdt3VNUrquo3qurcJZjnhKq6ZNz+9B2e575VdVFVXXbU9hOq6oNV9didnOeoGe5dVVdU1eur6uBuzTEzz+0eO7aP7B5zHtnd+Dyyu4Nk95jzyO7G55HdHSS7G5ppKfMruwDAou3Zsnl8wXRzVX38qO2PrqpPVtX1x3pB1Vr7T621f5rkWUmeMm4+O8nJSb6Z5NASzPOEJJeN2x+/w/N8qrV23hofenGSSzc6y3bMluQHklzZWvvHSU7f6iyLmmfOY8caZHdH5pFd2V042d2ReWRXdhdOdndspoXnV3YBgKXUWtuTb0kekeQhST4+s+24JP8ryX2THJ/kTzO8qPrBJG876u3uM5/3r5M8ZPz7wSQ/O/79siWY5yVJ/s7499/bpXkum/n7TyR5aoYXiI/drccuyd2SvCfJu5M8exm/l7zJruzK7l56k13Zld1pvsnudPMru968efPmzZu3ZXzblz2qtfa+qjr1qM1nJLm+tfapJKmqNyU5u7X2q0lud+paVVWSC5L8QWvtQ+PmQ0m+Mf79W0syzylJPpJNHKm+iHnm+PEkJ2R4Efl/qurtrbW/3uhci5qtql6U5GXj17osyRs2M8M2zLPWY8caZHf755lDdmW3i+xu/zxzyK7sdpHdnZlpjq78yi4AsIz2bNk8x8lJPjvz/qEkD1tn/+dkOOLgLlX1/a2130ry1iS/UVV/P8n7lmSe36yqs5L8552cp6ruluQVSR5cVS9prf1qa+1fjB97VpIvbPYX3kXNluQdSV5eVU9LcsOCZuiZZ63Hjo2T3QXOI7uyu4Nkd4HzyK7s7iDZXfBMO5hf2QUAdtWqlc21xrY2b+fW2muSvOaobX+VZK311nZrnq8nefYuzfPFJP9szscuXtBMR2x2to8nedKCZ5jV/dixKbK72Hlk96ibnbez7HaT3cXOI7tH3ey8nWW3m+wufqadyq/sAgC7as9eIHCOQ0nuNfP+KUlu3KVZEvNsxrLNtmzz7HXLdn+bZ+OWbbZlm2evW7b72zwbt2yzLds8e92y3d/LNk+ynDMlyzfXss0DAGyzVSubP5DktKq6T1Udn+GCHFeYZ2nnmbVssy3bPHvdst3f5tm4ZZtt2ebZ65bt/jbPxi3bbMs2z163bPf3ss2zrDMlyzfXss0DAGyzPVs2V9Ubk/zPJPerqkNVdV5r7dYkv5DkD5Ncm+TS1to15tn9eZZ5tmWbZ69btvvbPNOdbdnm2euW7f42z3RnW7Z59rplu7+XbZ5lnWkZ51q2eQCA3VGtzV0yCwAAAAAANmTPHtkMAAAAAMDOUTYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3ZTNAAAAAAB0UzYDAAAAANBN2QwAAAAAQDdlMwAAAAAA3f4/tlvGFH85CXYAAAAASUVORK5CYII=
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
</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="Load-Fashion-MNIST-dataset">Load Fashion-MNIST dataset<a class="anchor-link" href="#Load-Fashion-MNIST-dataset">&#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;[32]:</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="s1">&#39;Loading Fashion-MNIST dataset&#39;</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fashion_mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>

<span class="n">X_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;X_test.shape =&quot;</span><span class="p">,</span> <span class="n">X_test</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="s2">&quot;y_test.shape =&quot;</span><span class="p">,</span> <span class="n">y_test</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>Loading Fashion-MNIST dataset
X_test.shape = (10000, 28, 28, 1)
y_test.shape = (10000, 10)
</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="Quantify-the-uncertainty-in-predictions">Quantify the uncertainty in predictions<a class="anchor-link" href="#Quantify-the-uncertainty-in-predictions">&#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;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_pred_logits_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">bcnn_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_mc_run</span><span class="p">)]</span>  <span class="c1"># a list of predicted logits</span>
<span class="n">y_pred_prob_all</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">softmax</span><span class="p">(</span><span class="n">y</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="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred_logits_list</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">y_pred</span> <span class="o">=</span> <span class="p">[[</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">med_prob_thres</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred_prob</span><span class="p">]</span> <span class="k">for</span> <span class="n">y_pred_prob</span> <span class="ow">in</span> <span class="n">y_pred_prob_all</span><span class="p">]</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span>

<span class="n">idx_valid</span> <span class="o">=</span> <span class="p">[</span><span class="nb">any</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of recognizable samples:&#39;</span><span class="p">,</span> <span class="nb">sum</span><span class="p">(</span><span class="n">idx_valid</span><span class="p">))</span>

<span class="n">idx_invalid</span> <span class="o">=</span> <span class="p">[</span><span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Unrecognizable samples:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">idx_invalid</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy on MNIST (recognizable samples):&#39;</span><span class="p">,</span>
      <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_valid</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx_valid</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="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_valid</span><span class="p">]))</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy on MNIST (unrecognizable samples):&#39;</span><span class="p">,</span>
      <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_invalid</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">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx_invalid</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="nb">len</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx_invalid</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 recognizable samples: 1055
Unrecognizable samples: [   1    2    3 ... 9997 9998 9999]
Test accuracy on MNIST (recognizable samples): 0.02085308056872038
Test accuracy on MNIST (unrecognizable samples): 0.1106763555058692
</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="An-unrecognizable-example">An unrecognizable example<a class="anchor-link" href="#An-unrecognizable-example">&#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;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">idx_invalid</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="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gist_gray&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;True label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_test</span><span class="p">[</span><span class="n">idx</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">plot_pred_hist</span><span class="p">(</span><span class="n">y_pred_prob_all</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">n_class</span><span class="p">,</span> <span class="n">n_mc_run</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">med_prob_thres</span><span class="o">=</span><span class="n">med_prob_thres</span><span class="p">)</span>

<span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</span><span class="p">]):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted label of the test sample </span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[</span><span class="n">idx</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">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;I don&#39;t know!&quot;</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>True label of the test sample 1: 2
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I don&#39;t know!
</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="Generate-an-HTML-version-of-this-notebook">Generate an HTML version of this notebook<a class="anchor-link" href="#Generate-an-HTML-version-of-this-notebook">&#182;</a></h2>
</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="o">!!</span>python -m nbconvert *.ipynb
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[35]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;[NbConvertApp] Converting notebook tfp_bnn.ipynb to html&#39;,
 &#39;[NbConvertApp] Writing 1138460 bytes to tfp_bnn.html&#39;]</pre>
</div>

</div>

</div>
</div>

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

 
</html>