index.html

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

<title>stock_prediction</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">
    /*This file contains any manual css for this page that needs to override the global styles.
This is only required when different pages style the same element differently. This is just
a hack to deal with our current css styles and no new styling should be added in this file.*/

#ipython-main-app {
    position: relative;
}
#jupyter-main-app {
    position: relative;
}

    </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="Stock-Prediction-with-Deep-Learning-Models-in-TensorFlow">Stock Prediction with Deep Learning Models in TensorFlow<a class="anchor-link" href="#Stock-Prediction-with-Deep-Learning-Models-in-TensorFlow">&#182;</a></h1><h2 id="MA2132-Journal-Project">MA2132 Journal Project<a class="anchor-link" href="#MA2132-Journal-Project">&#182;</a></h2><blockquote><p>Lee Jia Jie, Prannaya Gupta</p>
<p>M19207</p>
<p>NUS High School</p>
<p><strong>20 September 2019</strong></p>
</blockquote>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="kn">from</span> <span class="nn">dateutil.relativedelta</span> <span class="kn">import</span> <span class="n">relativedelta</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Conv1D</span><span class="p">,</span> <span class="n">AveragePooling1D</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.optimizers</span> <span class="kn">import</span> <span class="n">Adam</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.callbacks</span> <span class="kn">import</span> <span class="n">TensorBoard</span>

<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>

<span class="nb">print</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="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">is_gpu_available</span><span class="p">():</span>
    <span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">CuDNNLSTM</span> <span class="k">as</span> <span class="n">LSTM</span><span class="p">,</span> <span class="n">CuDNNGRU</span> <span class="k">as</span> <span class="n">GRU</span>
<span class="k">else</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GPU is not available&quot;</span><span class="p">)</span>
    <span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">LSTM</span><span class="p">,</span> <span class="n">GRU</span>

<span class="n">DATA_DIR</span> <span class="o">=</span> <span class="s2">&quot;/content/data&quot;</span>
<span class="n">FILE</span> <span class="o">=</span> <span class="s2">&quot;NFLX.csv&quot;</span>
<span class="n">PATH</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="n">FILE</span><span class="p">)</span>

<span class="n">WINDOW</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">FEATURE_NAMES</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;High&quot;</span><span class="p">,</span> <span class="s2">&quot;Low&quot;</span><span class="p">,</span> <span class="s2">&quot;Open&quot;</span><span class="p">,</span> <span class="s2">&quot;Close&quot;</span><span class="p">,</span> <span class="s2">&quot;Volume&quot;</span><span class="p">,</span> <span class="s2">&quot;10ma&quot;</span><span class="p">,</span> <span class="s2">&quot;30ma&quot;</span><span class="p">]</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">64</span>
<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">20</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>1.14.0
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># download and / or import data</span>

<span class="kn">from</span> <span class="nn">pandas_datareader.data</span> <span class="kn">import</span> <span class="n">DataReader</span>

<span class="n">YEARS</span> <span class="o">=</span> <span class="mi">5</span>  <span class="c1"># how many years of data to download</span>
<span class="n">NAME</span> <span class="o">=</span> <span class="s2">&quot;NFLX&quot;</span>
<span class="n">DATA_SOURCE</span> <span class="o">=</span> <span class="s2">&quot;yahoo&quot;</span>

<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">PATH</span><span class="p">):</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">PATH</span><span class="p">)</span>
    <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">DataReader</span><span class="p">(</span><span class="n">NAME</span><span class="p">,</span> <span class="n">DATA_SOURCE</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">dt</span><span class="o">-</span><span class="n">relativedelta</span><span class="p">(</span><span class="n">years</span><span class="o">=</span><span class="n">YEARS</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="n">dt</span><span class="p">)</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">):</span>
        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">)</span>
    <span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">PATH</span><span class="p">)</span>

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[&nbsp;]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>High</th>
      <th>Low</th>
      <th>Open</th>
      <th>Close</th>
      <th>Volume</th>
      <th>Adj Close</th>
    </tr>
    <tr>
      <th>Date</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2014-09-22</th>
      <td>65.278572</td>
      <td>62.697144</td>
      <td>65.181427</td>
      <td>63.254284</td>
      <td>16169300.0</td>
      <td>63.254284</td>
    </tr>
    <tr>
      <th>2014-09-23</th>
      <td>64.001427</td>
      <td>62.928570</td>
      <td>63.032856</td>
      <td>63.414288</td>
      <td>11471600.0</td>
      <td>63.414288</td>
    </tr>
    <tr>
      <th>2014-09-24</th>
      <td>64.522858</td>
      <td>63.220001</td>
      <td>63.494286</td>
      <td>64.365715</td>
      <td>9551500.0</td>
      <td>64.365715</td>
    </tr>
    <tr>
      <th>2014-09-25</th>
      <td>64.621429</td>
      <td>63.202858</td>
      <td>64.271431</td>
      <td>63.355713</td>
      <td>9839900.0</td>
      <td>63.355713</td>
    </tr>
    <tr>
      <th>2014-09-26</th>
      <td>64.377144</td>
      <td>63.412857</td>
      <td>63.571430</td>
      <td>64.107140</td>
      <td>10404800.0</td>
      <td>64.107140</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># preprocessing pipeline</span>

<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span>

<span class="k">class</span> <span class="nc">FeatureUpdater</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>

    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span>
        <span class="k">assert</span> <span class="s2">&quot;Close&quot;</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">columns</span>

        <span class="k">if</span> <span class="s2">&quot;Adj Close&quot;</span> <span class="ow">in</span> <span class="n">X</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
            <span class="n">X_copy</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s2">&quot;Adj Close&quot;</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="n">X_copy</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="n">X_copy</span><span class="p">[</span><span class="s2">&quot;10ma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">X_copy</span><span class="p">[</span><span class="s2">&quot;Close&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
        <span class="n">X_copy</span><span class="p">[</span><span class="s2">&quot;30ma&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">X_copy</span><span class="p">[</span><span class="s2">&quot;Close&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">rolling</span><span class="p">(</span><span class="n">window</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>

        <span class="k">return</span> <span class="n">X_copy</span>

<span class="k">class</span> <span class="nc">NanDropper</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">TransformerMixin</span><span class="p">):</span>

    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</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="kc">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
            <span class="k">return</span> <span class="n">X</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">dropna</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>

<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;feature_updater&quot;</span><span class="p">,</span> <span class="n">FeatureUpdater</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;nan_dropper&quot;</span><span class="p">,</span> <span class="n">NanDropper</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;scaler&quot;</span><span class="p">,</span> <span class="n">MinMaxScaler</span><span class="p">())</span>
<span class="p">])</span>

<span class="n">df_tr</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">FEATURE_NAMES</span><span class="p">)</span>

<span class="n">df_tr</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[&nbsp;]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>High</th>
      <th>Low</th>
      <th>Open</th>
      <th>Close</th>
      <th>Volume</th>
      <th>10ma</th>
      <th>30ma</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.025150</td>
      <td>0.026576</td>
      <td>0.025368</td>
      <td>0.029174</td>
      <td>0.183252</td>
      <td>0.020205</td>
      <td>0.035485</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.025814</td>
      <td>0.027007</td>
      <td>0.028723</td>
      <td>0.027508</td>
      <td>0.155173</td>
      <td>0.021367</td>
      <td>0.034735</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.022676</td>
      <td>0.024546</td>
      <td>0.026326</td>
      <td>0.025356</td>
      <td>0.094994</td>
      <td>0.022035</td>
      <td>0.033892</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.022900</td>
      <td>0.023622</td>
      <td>0.025828</td>
      <td>0.024439</td>
      <td>0.116922</td>
      <td>0.022263</td>
      <td>0.032924</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.021947</td>
      <td>0.023994</td>
      <td>0.022937</td>
      <td>0.025555</td>
      <td>0.081407</td>
      <td>0.022273</td>
      <td>0.032094</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plot data</span>

<span class="k">def</span> <span class="nf">plot_data</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</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">rowspan</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
    <span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>

    <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;High&quot;</span><span class="p">,</span> <span class="s2">&quot;Low&quot;</span><span class="p">,</span> <span class="s2">&quot;Open&quot;</span><span class="p">,</span> <span class="s2">&quot;Close&quot;</span><span class="p">,</span> <span class="s2">&quot;10ma&quot;</span><span class="p">,</span> <span class="s2">&quot;30ma&quot;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
    <span class="n">df</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="s2">&quot;Volume&quot;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Volume&quot;</span><span class="p">)</span>

    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plot_data</span><span class="p">(</span><span class="n">df_tr</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,iVBORw0KGgoAAAANSUhEUgAAAXgAAAD8CAYAAAB9y7/cAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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=
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># split data into training, validation and test sets</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">from sklearn.model_selection import train_test_split</span>

<span class="sd">train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=TEST_RATIO)</span>
<span class="sd">train_X, val_X, train_y, val_y = train_test_split(train_X, train_y, test_size=VALIDATION_RATIO)</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="n">TRAIN_RATIO</span> <span class="o">=</span> <span class="mf">0.7</span>
<span class="n">VALIDATION_RATIO</span> <span class="o">=</span> <span class="mf">0.15</span>

<span class="k">def</span> <span class="nf">split_input_output</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">target_feature</span><span class="o">=</span><span class="s2">&quot;Open&quot;</span><span class="p">):</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">FEATURE_NAMES</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">array</span><span class="p">([</span><span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">WINDOW</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">-</span> <span class="n">WINDOW</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">df</span><span class="p">[</span><span class="n">target_feature</span><span class="p">][</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</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">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>

<span class="k">def</span> <span class="nf">split_train_val_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="k">assert</span> <span class="n">X</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="o">==</span> <span class="n">y</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">train_val_split</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">TRAIN_RATIO</span> <span class="o">*</span> <span class="n">X</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">val_test_split</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">TRAIN_RATIO</span> <span class="o">+</span> <span class="n">VALIDATION_RATIO</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</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">train_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:</span><span class="n">train_val_split</span><span class="p">]</span>
    <span class="n">train_y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:</span><span class="n">train_val_split</span><span class="p">]</span>
    <span class="n">val_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_val_split</span> <span class="p">:</span> <span class="n">val_test_split</span><span class="p">]</span>
    <span class="n">val_y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">train_val_split</span> <span class="p">:</span> <span class="n">val_test_split</span><span class="p">]</span>
    <span class="n">test_X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">val_test_split</span><span class="p">:]</span>
    <span class="n">test_y</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">val_test_split</span><span class="p">:]</span>

    <span class="k">return</span> <span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">split_input_output</span><span class="p">(</span><span class="n">df_tr</span><span class="p">)</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">split_train_val_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training input shape: &quot;</span><span class="p">,</span> <span class="n">train_X</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;Training output shape: &quot;</span><span class="p">,</span> <span class="n">train_y</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;Validation input shape: &quot;</span><span class="p">,</span> <span class="n">val_X</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;Validation ouput shape: &quot;</span><span class="p">,</span> <span class="n">val_y</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;Test input shape: &quot;</span><span class="p">,</span> <span class="n">test_X</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;Test output shape: &quot;</span><span class="p">,</span> <span class="n">test_y</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>Training input shape:  (791, 100, 7)
Training output shape:  (791, 1)
Validation input shape:  (169, 100, 7)
Validation ouput shape:  (169, 1)
Test input shape:  (170, 100, 7)
Test output shape:  (170, 1)
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the models and tensorboard logs will be saved to the following folders</span>

<span class="n">LOG_DIR</span> <span class="o">=</span> <span class="s2">&quot;/content/logs&quot;</span>
<span class="n">MODEL_DIR</span> <span class="o">=</span> <span class="s2">&quot;/content/models&quot;</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">):</span>
    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">)</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">):</span>
    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># feedforward neural network</span>

<span class="n">name_ffnn</span> <span class="o">=</span> <span class="s2">&quot;FFNN-&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>

<span class="n">model_ffnn</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_X</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">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_ffnn</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;adam&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_ffnn</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="n">history_ffnn</span> <span class="o">=</span> <span class="n">model_ffnn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name_ffnn</span><span class="p">))])</span>

<span class="n">model_ffnn</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_ffnn</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="n">name_ffnn</span> <span class="o">+</span> <span class="s2">&quot;.h5&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>WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
Model: &#34;sequential&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 700)               0         
_________________________________________________________________
dense (Dense)                (None, 128)               89728     
_________________________________________________________________
dense_1 (Dense)              (None, 64)                8256      
_________________________________________________________________
dense_2 (Dense)              (None, 32)                2080      
_________________________________________________________________
dense_3 (Dense)              (None, 16)                528       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 17        
=================================================================
Total params: 100,609
Trainable params: 100,609
Non-trainable params: 0
_________________________________________________________________
Train on 791 samples, validate on 169 samples
Epoch 1/20
 64/791 [=&gt;............................] - ETA: 34s - loss: 0.2721WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.104016). Check your callbacks.
791/791 [==============================] - 3s 4ms/sample - loss: 0.0288 - val_loss: 0.0280
Epoch 2/20
791/791 [==============================] - 0s 62us/sample - loss: 0.0017 - val_loss: 0.0139
Epoch 3/20
791/791 [==============================] - 0s 57us/sample - loss: 0.0014 - val_loss: 0.0264
Epoch 4/20
791/791 [==============================] - 0s 60us/sample - loss: 9.0126e-04 - val_loss: 0.0197
Epoch 5/20
791/791 [==============================] - 0s 59us/sample - loss: 8.3629e-04 - val_loss: 0.0069
Epoch 6/20
791/791 [==============================] - 0s 57us/sample - loss: 6.8322e-04 - val_loss: 0.0114
Epoch 7/20
791/791 [==============================] - 0s 58us/sample - loss: 5.4880e-04 - val_loss: 0.0065
Epoch 8/20
791/791 [==============================] - 0s 54us/sample - loss: 6.5188e-04 - val_loss: 0.0156
Epoch 9/20
791/791 [==============================] - 0s 65us/sample - loss: 7.8418e-04 - val_loss: 0.0186
Epoch 10/20
791/791 [==============================] - 0s 57us/sample - loss: 7.3360e-04 - val_loss: 0.0071
Epoch 11/20
791/791 [==============================] - 0s 57us/sample - loss: 4.5494e-04 - val_loss: 0.0050
Epoch 12/20
791/791 [==============================] - 0s 55us/sample - loss: 4.8384e-04 - val_loss: 0.0082
Epoch 13/20
791/791 [==============================] - 0s 57us/sample - loss: 3.7052e-04 - val_loss: 0.0047
Epoch 14/20
791/791 [==============================] - 0s 58us/sample - loss: 3.7644e-04 - val_loss: 0.0083
Epoch 15/20
791/791 [==============================] - 0s 70us/sample - loss: 4.5082e-04 - val_loss: 0.0065
Epoch 16/20
791/791 [==============================] - 0s 72us/sample - loss: 3.3785e-04 - val_loss: 0.0041
Epoch 17/20
791/791 [==============================] - 0s 62us/sample - loss: 3.7349e-04 - val_loss: 0.0060
Epoch 18/20
791/791 [==============================] - 0s 59us/sample - loss: 3.8623e-04 - val_loss: 0.0047
Epoch 19/20
791/791 [==============================] - 0s 60us/sample - loss: 4.6347e-04 - val_loss: 0.0071
Epoch 20/20
791/791 [==============================] - 0s 56us/sample - loss: 4.9343e-04 - val_loss: 0.0101
170/170 [==============================] - 0s 68us/sample - loss: 0.0085
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># lstm</span>

<span class="n">name_lstm</span> <span class="o">=</span> <span class="s2">&quot;LSTM-&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>

<span class="n">model_lstm</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>  <span class="c1"># accept input of arbitary length</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_lstm</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_lstm</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="n">history_lstm</span> <span class="o">=</span> <span class="n">model_lstm</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name_lstm</span><span class="p">))])</span>

<span class="n">model_lstm</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_lstm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="n">name_lstm</span> <span class="o">+</span> <span class="s2">&quot;.h5&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>Model: &#34;sequential_1&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
cu_dnnlstm (CuDNNLSTM)       (None, None, 128)         70144     
_________________________________________________________________
dropout (Dropout)            (None, None, 128)         0         
_________________________________________________________________
cu_dnnlstm_1 (CuDNNLSTM)     (None, None, 128)         132096    
_________________________________________________________________
dropout_1 (Dropout)          (None, None, 128)         0         
_________________________________________________________________
cu_dnnlstm_2 (CuDNNLSTM)     (None, 128)               132096    
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 128)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 64)                8256      
_________________________________________________________________
dropout_3 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_6 (Dense)              (None, 1)                 65        
=================================================================
Total params: 342,657
Trainable params: 342,657
Non-trainable params: 0
_________________________________________________________________
Train on 791 samples, validate on 169 samples
Epoch 1/20
791/791 [==============================] - 4s 5ms/sample - loss: 0.0100 - val_loss: 0.0051
Epoch 2/20
791/791 [==============================] - 0s 370us/sample - loss: 0.0028 - val_loss: 0.0051
Epoch 3/20
791/791 [==============================] - 0s 381us/sample - loss: 0.0022 - val_loss: 0.0049
Epoch 4/20
791/791 [==============================] - 0s 369us/sample - loss: 0.0020 - val_loss: 0.0063
Epoch 5/20
791/791 [==============================] - 0s 368us/sample - loss: 0.0021 - val_loss: 0.0062
Epoch 6/20
791/791 [==============================] - 0s 371us/sample - loss: 0.0015 - val_loss: 0.0055
Epoch 7/20
791/791 [==============================] - 0s 373us/sample - loss: 0.0020 - val_loss: 0.0070
Epoch 8/20
791/791 [==============================] - 0s 376us/sample - loss: 0.0018 - val_loss: 0.0095
Epoch 9/20
791/791 [==============================] - 0s 361us/sample - loss: 0.0023 - val_loss: 0.0100
Epoch 10/20
791/791 [==============================] - 0s 381us/sample - loss: 0.0024 - val_loss: 0.0046
Epoch 11/20
791/791 [==============================] - 0s 365us/sample - loss: 0.0019 - val_loss: 0.0052
Epoch 12/20
791/791 [==============================] - 0s 369us/sample - loss: 0.0018 - val_loss: 0.0045
Epoch 13/20
791/791 [==============================] - 0s 378us/sample - loss: 0.0016 - val_loss: 0.0114
Epoch 14/20
791/791 [==============================] - 0s 376us/sample - loss: 0.0017 - val_loss: 0.0040
Epoch 15/20
791/791 [==============================] - 0s 370us/sample - loss: 0.0014 - val_loss: 0.0052
Epoch 16/20
791/791 [==============================] - 0s 364us/sample - loss: 0.0017 - val_loss: 0.0089
Epoch 17/20
791/791 [==============================] - 0s 372us/sample - loss: 0.0021 - val_loss: 0.0080
Epoch 18/20
791/791 [==============================] - 0s 367us/sample - loss: 0.0017 - val_loss: 0.0109
Epoch 19/20
791/791 [==============================] - 0s 374us/sample - loss: 0.0014 - val_loss: 0.0041
Epoch 20/20
791/791 [==============================] - 0s 371us/sample - loss: 0.0013 - val_loss: 0.0038
170/170 [==============================] - 0s 315us/sample - loss: 0.0017
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># gru</span>

<span class="n">name_gru</span> <span class="o">=</span> <span class="s2">&quot;GRU-&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>

<span class="n">model_gru</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">GRU</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>  <span class="c1"># accept input of arbitary length</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">GRU</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">GRU</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
    
    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">64</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_gru</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_gru</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="n">history_gru</span> <span class="o">=</span> <span class="n">model_gru</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name_gru</span><span class="p">))])</span>

<span class="n">model_gru</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_gru</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="n">name_gru</span> <span class="o">+</span> <span class="s2">&quot;.h5&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>Model: &#34;sequential_2&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
cu_dnngru (CuDNNGRU)         (None, None, 128)         52608     
_________________________________________________________________
dropout_4 (Dropout)          (None, None, 128)         0         
_________________________________________________________________
cu_dnngru_1 (CuDNNGRU)       (None, None, 128)         99072     
_________________________________________________________________
dropout_5 (Dropout)          (None, None, 128)         0         
_________________________________________________________________
cu_dnngru_2 (CuDNNGRU)       (None, 128)               99072     
_________________________________________________________________
dropout_6 (Dropout)          (None, 128)               0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 128)               0         
_________________________________________________________________
dense_7 (Dense)              (None, 64)                8256      
_________________________________________________________________
dropout_7 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 65        
=================================================================
Total params: 259,073
Trainable params: 259,073
Non-trainable params: 0
_________________________________________________________________
Train on 791 samples, validate on 169 samples
Epoch 1/20
791/791 [==============================] - 1s 1ms/sample - loss: 0.0174 - val_loss: 0.0090
Epoch 2/20
791/791 [==============================] - 0s 361us/sample - loss: 0.0039 - val_loss: 0.0053
Epoch 3/20
791/791 [==============================] - 0s 332us/sample - loss: 0.0027 - val_loss: 0.0038
Epoch 4/20
791/791 [==============================] - 0s 335us/sample - loss: 0.0022 - val_loss: 0.0023
Epoch 5/20
791/791 [==============================] - 0s 330us/sample - loss: 0.0020 - val_loss: 0.0028
Epoch 6/20
791/791 [==============================] - 0s 335us/sample - loss: 0.0018 - val_loss: 0.0022
Epoch 7/20
791/791 [==============================] - 0s 334us/sample - loss: 0.0016 - val_loss: 0.0024
Epoch 8/20
791/791 [==============================] - 0s 331us/sample - loss: 0.0015 - val_loss: 0.0020
Epoch 9/20
791/791 [==============================] - 0s 330us/sample - loss: 0.0015 - val_loss: 0.0018
Epoch 10/20
791/791 [==============================] - 0s 349us/sample - loss: 0.0016 - val_loss: 0.0017
Epoch 11/20
791/791 [==============================] - 0s 332us/sample - loss: 0.0013 - val_loss: 0.0037
Epoch 12/20
791/791 [==============================] - 0s 333us/sample - loss: 0.0016 - val_loss: 0.0086
Epoch 13/20
791/791 [==============================] - 0s 335us/sample - loss: 0.0018 - val_loss: 0.0018
Epoch 14/20
791/791 [==============================] - 0s 334us/sample - loss: 0.0014 - val_loss: 0.0019
Epoch 15/20
791/791 [==============================] - 0s 334us/sample - loss: 0.0014 - val_loss: 0.0040
Epoch 16/20
791/791 [==============================] - 0s 341us/sample - loss: 0.0013 - val_loss: 0.0035
Epoch 17/20
791/791 [==============================] - 0s 350us/sample - loss: 0.0013 - val_loss: 0.0033
Epoch 18/20
791/791 [==============================] - 0s 326us/sample - loss: 0.0014 - val_loss: 0.0015
Epoch 19/20
791/791 [==============================] - 0s 329us/sample - loss: 0.0011 - val_loss: 0.0023
Epoch 20/20
791/791 [==============================] - 0s 333us/sample - loss: 0.0011 - val_loss: 0.0028
170/170 [==============================] - 0s 296us/sample - loss: 0.0019
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 1d cnn</span>

<span class="n">name_cnn</span> <span class="o">=</span> <span class="s2">&quot;CNN-&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>

<span class="n">model_cnn</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">Conv1D</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="n">kernel_initializer</span><span class="o">=</span><span class="s2">&quot;VarianceScaling&quot;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">train_X</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">AveragePooling1D</span><span class="p">(</span><span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>

    <span class="n">Conv1D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="n">kernel_initializer</span><span class="o">=</span><span class="s2">&quot;VarianceScaling&quot;</span><span class="p">),</span>
    <span class="n">AveragePooling1D</span><span class="p">(</span><span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>

    <span class="n">Conv1D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">,</span> <span class="n">kernel_initializer</span><span class="o">=</span><span class="s2">&quot;VarianceScaling&quot;</span><span class="p">),</span>
    <span class="n">AveragePooling1D</span><span class="p">(</span><span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>

    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;relu&quot;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_cnn</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s2">&quot;adam&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_cnn</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="n">history_cnn</span> <span class="o">=</span> <span class="n">model_cnn</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name_cnn</span><span class="p">))])</span>

<span class="n">model_cnn</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_cnn</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name_cnn</span> <span class="o">+</span> <span class="s2">&quot;.h5&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>Model: &#34;sequential_3&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv1d (Conv1D)              (None, 99, 128)           1920      
_________________________________________________________________
average_pooling1d (AveragePo (None, 98, 128)           0         
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 97, 64)            16448     
_________________________________________________________________
average_pooling1d_1 (Average (None, 96, 64)            0         
_________________________________________________________________
conv1d_2 (Conv1D)            (None, 95, 32)            4128      
_________________________________________________________________
average_pooling1d_2 (Average (None, 94, 32)            0         
_________________________________________________________________
flatten_3 (Flatten)          (None, 3008)              0         
_________________________________________________________________
dense_9 (Dense)              (None, 256)               770304    
_________________________________________________________________
dense_10 (Dense)             (None, 1)                 257       
=================================================================
Total params: 793,057
Trainable params: 793,057
Non-trainable params: 0
_________________________________________________________________
Train on 791 samples, validate on 169 samples
Epoch 1/20
791/791 [==============================] - 2s 2ms/sample - loss: 0.2449 - val_loss: 0.0285
Epoch 2/20
791/791 [==============================] - 0s 75us/sample - loss: 0.0091 - val_loss: 0.0184
Epoch 3/20
791/791 [==============================] - 0s 77us/sample - loss: 0.0034 - val_loss: 0.0338
Epoch 4/20
791/791 [==============================] - 0s 73us/sample - loss: 0.0017 - val_loss: 0.0388
Epoch 5/20
791/791 [==============================] - 0s 73us/sample - loss: 0.0014 - val_loss: 0.0496
Epoch 6/20
791/791 [==============================] - 0s 73us/sample - loss: 0.0012 - val_loss: 0.0355
Epoch 7/20
791/791 [==============================] - 0s 68us/sample - loss: 0.0010 - val_loss: 0.0244
Epoch 8/20
791/791 [==============================] - 0s 75us/sample - loss: 9.0494e-04 - val_loss: 0.0159
Epoch 9/20
791/791 [==============================] - 0s 81us/sample - loss: 6.7020e-04 - val_loss: 0.0177
Epoch 10/20
791/791 [==============================] - 0s 74us/sample - loss: 5.5483e-04 - val_loss: 0.0101
Epoch 11/20
791/791 [==============================] - 0s 77us/sample - loss: 7.1344e-04 - val_loss: 0.0158
Epoch 12/20
791/791 [==============================] - 0s 73us/sample - loss: 6.0090e-04 - val_loss: 0.0195
Epoch 13/20
791/791 [==============================] - 0s 71us/sample - loss: 5.2538e-04 - val_loss: 0.0225
Epoch 14/20
791/791 [==============================] - 0s 72us/sample - loss: 4.9215e-04 - val_loss: 0.0179
Epoch 15/20
791/791 [==============================] - 0s 78us/sample - loss: 4.7078e-04 - val_loss: 0.0262
Epoch 16/20
791/791 [==============================] - 0s 74us/sample - loss: 5.6437e-04 - val_loss: 0.0205
Epoch 17/20
791/791 [==============================] - 0s 74us/sample - loss: 4.6883e-04 - val_loss: 0.0113
Epoch 18/20
791/791 [==============================] - 0s 73us/sample - loss: 3.9596e-04 - val_loss: 0.0137
Epoch 19/20
791/791 [==============================] - 0s 68us/sample - loss: 3.3587e-04 - val_loss: 0.0120
Epoch 20/20
791/791 [==============================] - 0s 73us/sample - loss: 3.4992e-04 - val_loss: 0.0083
170/170 [==============================] - 0s 1ms/sample - loss: 0.0057
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># visualise model predictions</span>

<span class="k">def</span> <span class="nf">visualize_predictions</span><span class="p">(</span><span class="n">days</span><span class="p">,</span> <span class="n">actual</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</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">days</span><span class="p">,</span> <span class="n">actual</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Actual Price&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</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">days</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">visualize_predictions</span><span class="p">(</span>
    <span class="n">days</span><span class="o">=</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
    <span class="n">actual</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
    <span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">model_ffnn</span><span class="p">,</span> <span class="n">model_lstm</span><span class="p">,</span> <span class="n">model_gru</span><span class="p">,</span> <span class="n">model_cnn</span><span class="p">],</span>
    <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;FFNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">,</span> <span class="s2">&quot;CNN&quot;</span><span class="p">]</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_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-7-04d2ad154617&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold">     12</span>     days<span class="ansi-blue-fg">=</span>df_tr<span class="ansi-blue-fg">.</span>index<span class="ansi-blue-fg">[</span>WINDOW<span class="ansi-blue-fg">:</span><span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">.</span>values<span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold">     13</span>     actual<span class="ansi-blue-fg">=</span>y<span class="ansi-blue-fg">,</span>
<span class="ansi-green-fg">---&gt; 14</span><span class="ansi-red-fg">     </span>models<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">[</span>model_ffnn<span class="ansi-blue-fg">,</span> model_lstm<span class="ansi-blue-fg">,</span> model_gru<span class="ansi-blue-fg">,</span> model_cnn<span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold">     15</span>     X<span class="ansi-blue-fg">=</span>X<span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold">     16</span>     labels<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">&#34;FFNN&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;LSTM&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;GRU&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;CNN&#34;</span><span class="ansi-blue-fg">]</span>

<span class="ansi-red-fg">NameError</span>: name &#39;model_ffnn&#39; is not defined</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plot model losses and training</span>

<span class="k">def</span> <span class="nf">plot_losses</span><span class="p">(</span><span class="n">histories</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">histories</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>

    <span class="n">ax_loss</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</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="mi">2</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</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">ax_val_loss</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</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="mi">2</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>

    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">history</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">histories</span><span class="p">):</span>
        <span class="n">ax_loss</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">EPOCHS</span><span class="p">),</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
        <span class="n">ax_val_loss</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">EPOCHS</span><span class="p">),</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s2">&quot;val_loss&quot;</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">ax_loss</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">ax_val_loss</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plot_losses</span><span class="p">(</span>
    <span class="n">histories</span><span class="o">=</span><span class="p">[</span><span class="n">history_ffnn</span><span class="p">,</span> <span class="n">history_lstm</span><span class="p">,</span> <span class="n">history_gru</span><span class="p">,</span> <span class="n">history_cnn</span><span class="p">],</span>
    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;FFNN&quot;</span><span class="p">,</span> <span class="s2">&quot;LSTM&quot;</span><span class="p">,</span> <span class="s2">&quot;GRU&quot;</span><span class="p">,</span> <span class="s2">&quot;CNN&quot;</span><span class="p">]</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_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">NameError</span>                                 Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-8-eb59555420c7&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold">     15</span> 
<span class="ansi-green-intense-fg ansi-bold">     16</span> plot_losses(
<span class="ansi-green-fg">---&gt; 17</span><span class="ansi-red-fg">     </span>histories<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">[</span>history_ffnn<span class="ansi-blue-fg">,</span> history_lstm<span class="ansi-blue-fg">,</span> history_gru<span class="ansi-blue-fg">,</span> history_cnn<span class="ansi-blue-fg">]</span><span class="ansi-blue-fg">,</span>
<span class="ansi-green-intense-fg ansi-bold">     18</span>     labels<span class="ansi-blue-fg">=</span><span class="ansi-blue-fg">[</span><span class="ansi-blue-fg">&#34;FFNN&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;LSTM&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;GRU&#34;</span><span class="ansi-blue-fg">,</span> <span class="ansi-blue-fg">&#34;CNN&#34;</span><span class="ansi-blue-fg">]</span>
<span class="ansi-green-intense-fg ansi-bold">     19</span> )

<span class="ansi-red-fg">NameError</span>: name &#39;history_ffnn&#39; is not defined</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># experimenting with different architectures for the lstm</span>

<span class="n">lstm_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]</span>
<span class="n">lstm_layer_nums</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="n">dropout_rates</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]</span>
<span class="n">dense_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">]</span>

<span class="k">for</span> <span class="n">lstm_size</span> <span class="ow">in</span> <span class="n">lstm_sizes</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">lstm_layer_num</span> <span class="ow">in</span> <span class="n">lstm_layer_nums</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">dropout_rate</span> <span class="ow">in</span> <span class="n">dropout_rates</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">dense_size</span> <span class="ow">in</span> <span class="n">dense_sizes</span><span class="p">:</span>
                <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;LSTM-</span><span class="si">{</span><span class="n">lstm_size</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">lstm_layer_num</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">dropout_rate</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">dense_size</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">())</span><span class="si">}</span><span class="s2">&quot;</span>

                <span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

                <span class="k">if</span> <span class="n">lstm_layer_num</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">lstm_size</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout_rate</span><span class="p">))</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">lstm_size</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout_rate</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">lstm_layer_num</span> <span class="o">-</span> <span class="mi">2</span><span class="p">):</span>
                        <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">lstm_size</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
                        <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout_rate</span><span class="p">))</span>
                    
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LSTM</span><span class="p">(</span><span class="n">lstm_size</span><span class="p">))</span>
                    <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout_rate</span><span class="p">))</span>
                    
                <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>

                <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">dense_size</span><span class="p">))</span>
                <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout_rate</span><span class="p">))</span>

                <span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>

                <span class="n">model</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

                <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
                    <span class="n">train_X</span><span class="p">,</span>
                    <span class="n">train_y</span><span class="p">,</span>
                    <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">,</span>
                    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">TensorBoard</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">LOG_DIR</span><span class="p">,</span> <span class="n">name</span><span class="p">))],</span>
                    <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                <span class="p">)</span>

                <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
                <span class="nb">print</span><span class="p">(</span><span class="n">name</span><span class="o">.</span><span class="n">ljust</span><span class="p">(</span><span class="mi">37</span><span class="p">),</span> <span class="n">loss</span><span class="p">)</span>

                <span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;.h5&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>WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.113695). Check your callbacks.
LSTM-256-1-0-256-1569067585           0.000771384166471441
LSTM-256-1-0-512-1569067602           0.0005244185411891736
LSTM-256-1-0.2-256-1569067615         0.0013613891733043333
LSTM-256-1-0.2-512-1569067627         0.00048368569148485275
LSTM-256-1-0.4-256-1569067640         0.0005854476181625882
LSTM-256-1-0.4-512-1569067653         0.0004048840252353865
LSTM-256-2-0-256-1569067667           0.001741345419877154
LSTM-256-2-0-512-1569067694           0.001724570045959862
LSTM-256-2-0.2-256-1569067721         0.0017416157424176002
LSTM-256-2-0.2-512-1569067750         0.0013493808801285923
LSTM-256-2-0.4-256-1569067778         0.0007730655355022892
LSTM-256-2-0.4-512-1569067807         0.002853274502901032
LSTM-256-3-0-256-1569067836           0.0014699550619458452
LSTM-256-3-0-512-1569067880           0.01192138028495452
LSTM-256-3-0.2-256-1569067924         0.001133636458133183
LSTM-256-3-0.2-512-1569067969         0.0015488734078483984
LSTM-256-3-0.4-256-1569068016         0.0012772853999836917
LSTM-256-3-0.4-512-1569068062         0.0011163409022093916
LSTM-512-1-0-256-1569068110           0.0005519506833105184
LSTM-512-1-0-512-1569068138           0.0003458335901514682
LSTM-512-1-0.2-256-1569068167         0.0004661964119055911
LSTM-512-1-0.2-512-1569068196         0.00044594442160279655
LSTM-512-1-0.4-256-1569068227         0.000499082130023881
LSTM-512-1-0.4-512-1569068257         0.0004734005162711529
LSTM-512-2-0-256-1569068287           0.0007059807084528182
LSTM-512-2-0-512-1569068345           0.0006991481048958923
LSTM-512-2-0.2-256-1569068404         0.000888560847242308
LSTM-512-2-0.2-512-1569068464         0.004082041272126576
LSTM-512-2-0.4-256-1569068524         0.0011935651382801178
LSTM-512-2-0.4-512-1569068585         0.0007682317196775008
LSTM-512-3-0-256-1569068646           0.0011334099481567082
LSTM-512-3-0-512-1569068736           0.001307406705831561
LSTM-512-3-0.2-256-1569068826         0.0035181937081848872
LSTM-512-3-0.2-512-1569068919         0.0013878320275312838
LSTM-512-3-0.4-256-1569069012         0.0011858843578546143
LSTM-512-3-0.4-512-1569069106         0.0009717383766067488
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We will now use tensorboard to compare the models. This notebook was run on google colab to train the models using a hosted gpu server, and hence localtunnel is used to help run tensorboard.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># install localtunnel</span>

<span class="o">!</span>npm install -g localtunnel
</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>/tools/node/bin/lt -&gt; /tools/node/lib/node_modules/localtunnel/bin/lt.js
+ localtunnel@2.0.0
added 35 packages from 21 contributors in 2.303s
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># tensorboard</span>

<span class="n">get_ipython</span><span class="p">()</span><span class="o">.</span><span class="n">system_raw</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;tensorboard --logdir </span><span class="si">{</span><span class="n">LOG_DIR</span><span class="si">}</span><span class="s1"> --host 0.0.0.0 --port 6006 &amp;&#39;</span><span class="p">)</span>
<span class="n">get_ipython</span><span class="p">()</span><span class="o">.</span><span class="n">system_raw</span><span class="p">(</span><span class="s1">&#39;lt --port 6006 &gt;&gt; url.txt 2&gt;&amp;1 &amp;&#39;</span><span class="p">)</span>

<span class="o">!</span>cat url.txt
</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>your url is: https://ordinary-hound-56.localtunnel.me
/tools/node/lib/node_modules/localtunnel/bin/lt.js:81
    throw err;
    ^

Error: connection refused: localtunnel.me:36158 (check your firewall settings)
    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)
    at emitOne (events.js:116:13)
    at Socket.emit (events.js:211:7)
    at emitErrorNT (internal/streams/destroy.js:64:8)
    at _combinedTickCallback (internal/process/next_tick.js:138:11)
    at process._tickCallback (internal/process/next_tick.js:180:9)
your url is: https://odd-parrot-5.localtunnel.me
your url is: https://mean-falcon-4.localtunnel.me
/tools/node/lib/node_modules/localtunnel/bin/lt.js:81
    throw err;
    ^

Error: connection refused: localtunnel.me:41770 (check your firewall settings)
    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)
    at emitOne (events.js:116:13)
    at Socket.emit (events.js:211:7)
    at emitErrorNT (internal/streams/destroy.js:64:8)
    at _combinedTickCallback (internal/process/next_tick.js:138:11)
    at process._tickCallback (internal/process/next_tick.js:180:9)
/tools/node/lib/node_modules/localtunnel/bin/lt.js:81
    throw err;
    ^

Error: connection refused: localtunnel.me:37579 (check your firewall settings)
    at Socket.remote.on.err (/tools/node/lib/node_modules/localtunnel/lib/TunnelCluster.js:52:11)
    at emitOne (events.js:116:13)
    at Socket.emit (events.js:211:7)
    at emitErrorNT (internal/streams/destroy.js:64:8)
    at _combinedTickCallback (internal/process/next_tick.js:138:11)
    at process._tickCallback (internal/process/next_tick.js:180:9)
your url is: https://fluffy-chipmunk-54.localtunnel.me
your url is: https://mighty-vampirebat-2.localtunnel.me
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>It is apparent that the model with 256 lstm nodes, 1 lstm layer, a dropout rate of 0.4 and with 512 dense layers produces the best results, with the lowest test and validation losses (0.00042 and 0.00100 respectively)</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">BEST_MODEL</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="s2">&quot;LSTM-256-1-0.4-512-1568463412.h5&quot;</span><span class="p">)</span>  <span class="c1"># change the model name if the timestamp is different</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># download the best model</span>

<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">files</span>

<span class="n">files</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">BEST_MODEL</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test the final model on other companies&#39; stocks</span>

<span class="k">def</span> <span class="nf">test_company</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
    <span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">DataReader</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="s2">&quot;yahoo&quot;</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">dt</span><span class="o">-</span><span class="n">relativedelta</span><span class="p">(</span><span class="n">years</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="n">dt</span><span class="p">)</span>
    <span class="n">df_tr</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">FEATURE_NAMES</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">split_input_output</span><span class="p">(</span><span class="n">df_tr</span><span class="p">)</span>

    <span class="n">model</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">models</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">BEST_MODEL</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">visualize_predictions</span><span class="p">(</span>
        <span class="n">days</span><span class="o">=</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
        <span class="n">actual</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
        <span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">model</span><span class="p">],</span>
        <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
        <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LSTM Predictions&quot;</span><span class="p">],</span>
    <span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># apple stocks</span>

<span class="n">test_company</span><span class="p">(</span><span class="s2">&quot;AAPL&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>1130/1130 [==============================] - 0s 344us/sample - loss: 4.1550e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># google stocks</span>

<span class="n">test_company</span><span class="p">(</span><span class="s2">&quot;GOOGL&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>1130/1130 [==============================] - 0s 372us/sample - loss: 3.9997e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># nvidia stocks</span>

<span class="n">test_company</span><span class="p">(</span><span class="s2">&quot;NVDA&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>1130/1130 [==============================] - 0s 406us/sample - loss: 3.0817e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo
dHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xd4VFX6wPHvuVMy6T2UEEjoJUAC
CAioiKKI2MtiV9Z1dYvt56qooOu6u7q669p2V137KjZUUFEpioD0Ji2UAAECIb0nU+/5/XHDJCEh
CTCTSTmf5/Fx5t5z730Hwpsz5577HiGlRFEURelYtEAHoCiKovieSu6KoigdkEruiqIoHZBK7oqi
KB2QSu6KoigdkEruiqIoHZBK7oqiKB2QSu6KoigdkEruiqIoHZA5UBeOi4uTycnJgbq8oihKu7Rh
w4YCKWV8c+0CltyTk5NZv359oC6vKIrSLgkhDrSknRqWURRF6YBUclcURemAVHJXFEXpgAI25t4Y
l8tFdnY2drs90KEofmCz2ejRowcWiyXQoShKh9emknt2djbh4eEkJycjhAh0OIoPSSkpLCwkOzub
lJSUQIejKB1es8MyQog3hRB5QohtJ9gvhBAvCiEyhRBbhBAjTjUYu91ObGysSuwdkBCC2NhY9a1M
UVpJS8bc3wamNLH/IqBfzX93AP8+nYBUYu+41N+torSeZpO7lHIZUNREk8uAd6VhNRAlhOjmqwAV
RekYKh1uPlp3ELW0Z+vwxWyZROBQnffZNdsaEELcIYRYL4RYn5+f74NL+8cXX3yBEIKdO3c22/bt
t9/myJEjp3ytpUuXMm3atEa3R0ZGkpaWxqBBg/jjH//Y6PFHjhzh6quvPuXrK0prefDTLTw0dys/
Z5cGOpROoVWnQkopX5NSjpJSjoqPb/bp2YCZM2cOEyZMYM6cOc22Pd3k3pSzzjqLzZs3s379ev73
v/+xcePGevvdbjfdu3fn008/9cv1FcWXNh8qAaC4yhngSDoHXyT3w0BSnfc9ara1SxUVFaxYsYI3
3niDDz/8sN6+Z555hqFDhzJ8+HAefvhhPv30U9avX88NN9xAWloa1dXVJCcnU1BQAMD69euZOHEi
AGvXruXMM88kPT2dcePGsWvXrhbHFBoaysiRI8nMzOTtt9/m0ksvZdKkSZx33nlkZWWRmpoKgMfj
4YEHHiA1NZVhw4bx0ksvAbBhwwbOOeccRo4cyYUXXkhOTo4P/qQU5dQUlDsCHUKn4IupkPOB3wkh
PgTGAKVSytPOHn/8cjs7jpSddnB1De4eweOXDGmyzbx585gyZQr9+/cnNjaWDRs2MHLkSL755hvm
zZvHmjVrCAkJoaioiJiYGF5++WWee+45Ro0a1eR5Bw4cyPLlyzGbzSxevJhHHnmEuXPntijuwsJC
Vq9ezaxZs1i3bh0bN25ky5YtxMTEkJWV5W332muvkZWVxebNmzGbzRQVFeFyufj973/PvHnziI+P
56OPPuLRRx/lzTffbNG1FcVX9Jqx9qJK1XNvDc0mdyHEHGAiECeEyAYeBywAUsr/AAuAqUAmUAXc
5q9gW8OcOXO45557AJg+fTpz5sxh5MiRLF68mNtuu42QkBAAYmJiTuq8paWl3HLLLezZswchBC6X
q9ljli9fTnp6Opqm8fDDDzNkyBDWrVvH5MmTG73+4sWLufPOOzGbzd4Yt23bxrZt25g8eTJg9O67
dVP3uxX/+uOX23G6df58xVDvNo9uJPdKhztQYXUqzSZ3KeV1zeyXwG99FlGN5nrY/lBUVMT333/P
1q1bEULg8XgQQvDss8+2+Bxmsxld1wHqzemeNWsW5557Lp9//jlZWVne4ZqmnHXWWXz11VcNtoeG
hrY4HiklQ4YMYdWqVS0+RlFO11s/ZQHUS+5Ot4d7THOxlP4CGBCYwDoRVVumjk8//ZSbbrqJAwcO
kJWVxaFDh0hJSWH58uVMnjyZt956i6qqKsD4RQAQHh5OeXm59xzJycls2LABoN6wS2lpKYmJxiSi
t99+2y/xT548mVdffRW32+2NccCAAeTn53uTu8vlYvv27X65vqI0JdF9iPssc7lxl8/7gkojVHKv
Y86cOVxxxRX1tl111VXMmTOHKVOmcOmllzJq1CjS0tJ47rnnALj11lu58847vTdUH3/8ce655x5G
jRqFyWTynufBBx9k5syZpKene5Ovr91+++307NmTYcOGMXz4cD744AOsViuffvopDz30EMOHDyct
LY2VK1f65fqKAlBa3XDIUUpJsm6UIQ9zF7d2SJ2SCNQDBaNGjZLHL9aRkZHBoEGDAhKP0jrU33HH
996qLD6f/zlXmpZz9eyPsFktON06Tz5+P09Z3kJHoD1REugw2y0hxAYpZdMzOGhjhcMURWnfpJTM
mredLNsTAOzN3EJVRF+S40KIogIADQm6DpoaOPAn9aerKIrP7CuoBGpHA/71v4+55OUVlFS5iBIV
tQ3tqufubyq5K4riM/vyK+klcr3vh2t7Adh0qIQoUVnbsFqNu/ubSu6KovhMYYWD4WIfAEUyjLO1
LYwRGezPKyWSOj13ldz9TiV3RVF8ptzupp+WjS5MvOy+gmQtl4+C/kS3jLeJEhVUE2Q0rGqq0Kzi
Cyq5K4riM2V2F/3FYapCe/KTXvsgYlL+MqKoJNfc3dhQrZK7v6nkfpywsLAG23bt2sXEiRO95Xfv
uOMOvvvuO9LS0khLSyMsLIwBAwaQlpbGzTffzNKlSxFC8N///td7js2bNyOE8M6Pr+uJJ54gMTGR
tLQ0UlNTmT9//ml9hokTJ3JsmunUqVMpKTnxzasvvviCHTt2eN/Pnj2bxYsXn9b1lc6rrNpFb9NR
grsPYuSoM6kc+38cFl0YpB0gRpRREtLLaKiGZfxOTYVsgbvvvpv77ruPyy67DICtW7cydOhQLrzw
QsBIpnWLhy1dupTU1FQ+/vhjbr/9dsB4QGr48OEnvMZ9993HAw88QEZGBmeddRZ5eXlodaaKud1u
b82Yk7FgwYIm93/xxRdMmzaNwYMHA/Dkk0+e9DUU5Zhyu5t4SjFFdOUv09KAND7ZaefWklcA2BmW
jF4qEJWFqHW5/Ev13FsgJyeHHj16eN8PHTq0idaGXr16Ybfbyc3NRUrJt99+y0UXXdTscYMGDcJs
NlNQUOB9+nXMmDE8+OCDVFZWMmPGDEaPHk16ejrz5s0DoLq6munTpzNo0CCuuOIKqqurveerW4L4
3Xff9T69etNNN7Fy5Urmz5/PH/7wB9LS0ti7dy+33nqrtz78kiVLSE9PZ+jQocyYMQOHw+E95+OP
P86IESMYOnSod1GTH3/80fttJj09vV5ZBqX9SH9yIckPf828zYfZcODkhk8qq6uJogzCuni3BSXW
dmqCYxIpJZTSojyfxas0ru323L95GI5u9e05uw6Fi54+6cPuu+8+Jk2axLhx47jgggu47bbbiIqK
ava4q6++mk8++YT09HRGjBhBUFBQs8esWbMGTdM4tphJdnY2K1euxGQy8cgjjzBp0iTefPNNSkpK
GD16NOeffz6vvvoqISEhZGRksGXLFkaMaLhG+fbt23nqqadYuXIlcXFx3pLFl156KdOmTWuwmpPd
bufWW29lyZIl9O/fn5tvvpl///vf3HvvvQDExcWxceNG/vWvf/Hcc8/x3//+l+eee45XXnmF8ePH
U1FRgc1ma8kfr9KGON06xVVG+YB7P9yIFTf3ThnGXRP7tOwElUZHgrAE76byyP7e1xEpIynZEkpw
Wdtdia2jUD33FrjtttvIyMjgmmuuYenSpYwdO9bbi23KtddeyyeffMKcOXO47romi2vy/PPPk5aW
xgMPPMBHH33kXUz6mmuu8daoWbhwIU8//TRpaWlMnDgRu93OwYMHWbZsGTfeeCMAw4YNY9iwYQ3O
//3333PNNdcQFxcHNF+yeNeuXaSkpNC/v/EP85ZbbmHZsmXe/VdeeSUAI0eO9NaUHz9+PPfffz8v
vvgiJSUlpzSMpARW3bows83vsT7oLt7/bnmzxy3dlcdNb6zBWXrU2FCn524NjeJN9xTmDXiG8H7j
KSEcvUqNuftb2/3Xdwo9bH/q3r07M2bMYMaMGaSmprJt2zZGjhzZ5DFdu3bFYrGwaNEiXnjhhSYL
dh0bcz9e3fK+Ukrmzp3LgAGBL5d67FuIyWTyFkJ7+OGHufjii1mwYAHjx4/nu+++Y+DAgYEMUzlJ
pdVOLLiZYfqG28zfAXC16Ufg1iaPe+bbXWTklDFRywUrEFrbc58+uifPlz7Btef1w2bWyJChdHOo
J1T9TfXcW+Dbb7/1Lq5x9OhRCgsLveV7m/Pkk0/yzDPP1KsQeaouvPBCXnrpJe/q8Zs2bQLg7LPP
5oMPPgBg27ZtbNmypcGxkyZN4pNPPqGwsBA4ccniYwYMGEBWVhaZmZkAvPfee5xzzjlNxrd3716G
Dh3KQw89xBlnnNGiBcaVtqW02sUVpuXMtNSuH3yLaRHonhMes+1wKRk5xqpp8aJm8es6wzI2i4mZ
UwcRFmTGbNKwiyBMHntjp1J8SCX341RVVdGjRw/vf//4xz9YuHAhqampDB8+nAsvvJBnn32Wrl27
tuh848aN4/LLL/dJbLNmzcLlcjFs2DCGDBnCrFmzALjrrruoqKhg0KBBzJ49u9FvFEOGDOHRRx/l
nHPOYfjw4dx///2AsdrUs88+S3p6Onv37vW2t9lsvPXWW1xzzTUMHToUTdO48847m4zvn//8p3f9
VovF0qIbyEpgrdlXyItL9njfl1a7mKj9jNMSgbzlSxb2nkm0KEcvPfGyyNNeWoFA53emz5mkGR2O
usn9eA4RhFkld79TJX+VVqX+jtuW5Ie/BmDLExcQYbPw2cZs+n1xMX1S+hBy22d8+fkHXPLzXThu
+JKgfmfXO/bj9Yc4s3csZ/3tB8Zp2/jA+hcAHOYwgh478S+Dj5+4mqnm9YQ9luW3z9WRtbTkr+q5
K0qnJ7n1zbWA0XPvKooxRRrr7LpDjB64q6z+mvd2l4cHP93CWX/7AYBxWu3qXo6guCav5hQ2LLrq
uftb272hqihKq/jM+jhZOV2BrymrqCaWMoiquacUYsyq8lTWznc35r7XPoI0VOzjd+Z53vfu4Pgm
r+cUQVh0B0gJQj3K5C9tLrlLKb3TAJWOJVBDgB2drksKKhwkRNQ+V9DSf0ddKWSElskIjBvnpfnZ
aEJChNFzFyHG8xx6TXLfX1DJVf+uv9j6Raa19eMJbSa5azY0XQe3AyzqWQh/aVPDMjabjcLCQpUE
OiApJYWFherBJj94+YdMRv9lCUdKjCeT//DJz/R99JsWHXtedJ0nRSsLKM411jkl3EjuVmsw5TIY
WVVESZWTc59b6m2eLvYwQuymvziEPaovr7gvrTm2e5PXrNTCjRdVhS2KUTk1barn3qNHD7Kzs8nP
V0+vdUQ2m61eGQfFNxZnGItj5Jc76B4VzCcbsgE4869L+OK34+kS0fgvVCkloro2wf7ro/mYi/aA
BYg1nkgNMmuUEkpIdRFX/8fosSeLHP5sfpPxJmOcvVBGYEu+hJx8KwCm6KQm4y0y1YzJlx2GyJZN
KVZOXptK7haLhZSUlECHoSjtikc3vunuyCljeFIUwRYTse4cBpcfYMxf7GyaNZnoUGuD44qrXAS5
So1kDpTsXcsQUYjHFIwppjdgzFEvlmGE2UvIzKugjzjMkqA/1DtPrCiDxBF8sLoLldLG4yNubjLe
IrMxbLNgxXqmXjf6dD++cgJtalhGUZSTdyy5z/xsK0WVTmJDLawIupfXrM9ztvYz015a0ehxRZUO
okUFbqmRqXfnRtNizrbuRvQcDZrx0F2QWaNYhqPZi0iKCebvlv8AkBk+mr3BdQroJY5ER+Nz/SzC
Ipquu1RsNmbgbNy2TQ3B+pFK7orSzhRWOEh/ciE/HypBSsnOo8YTxl0p5P8+3MCAynXetu9an+Fw
SXWj5ympchFNBSWE8apnGj21fHrrWWi9xnvbhNssFBGOqaqAGHs2w7R9vOW+kLUT3uC58Do9+ITa
hTnMpqbTyuocD5UyiO6ikLzy5ms0KadGJXdFaWdWZBZQXOXi9eX7yMyrII5SsmzXs9r2e35d8nf6
6lkAlMtgAKIpa/Q8pdUuIkUFJTKMjXq/2h3pN3hfJkYHkyNjsVYdpad9FxqSC298gOtGJ7GqIJhn
Xdfyb/clYG447HNighwZSzdRSG6Zmu/uLyq5K0o789nGw9xr/pSRVSsorXYxVqtdSat32Xp6iaMU
EcEfXL8GoLtovCZ7abXRc4+MSWCv7M73njQWRN8IkbU3vcOCzByWcVhwk64Z0yW7pwxCCMHvJ/Xj
Fc/lbOh370l/hiMylotM6ygsqzzpY5WWaVM3VBVFad6q3Ud4x/YZZH/GSs/1JAujzO73jGIMW0kR
uZQGJ5HtMGal9BPZjZ6n2uUhSlQQEdOf1yaPYsZ7D3JeZAJTj2tXFdwN3DBGy6DaHElwkDGV8ZcT
Urh+dE+sZqOPuOT/zqHaeeICY8ckhAexozqZs9mKde9CGHzbKf5JKE1RPXdFaWf6i0Pe1y6ngx4i
H2dwAofD0wgVDoZp+4hNGsgOmUyujGKSaVOj57G7dCJFJSI4hohgY8pMVEjD4ZUyq1GbfYh2gIrg
+lMXg60mTJrxsFSf+DBSEyObjX/Zg+fyvPsqAGwle5pprZyqFiV3IcQUIcQuIUSmEOLhRvb3FEL8
IITYJITYIoQ4/pe/oig+8O6qLFK1LO/779dsoqsoxh3alUqLUSogVNiJ6H0Gj1w8hM16XwaLA42e
y+H2EEElWkgUY1Ji+NNlQ3j80sEN2uXbepEvjaTtjul72p/BZjFx1Zi+FMswbNW5p30+pXHNJnch
hAl4BbgIGAxcJ4Q4/ifgMeBjKWU6MB34l68DVRQF3lmZRarY732/flcWcaIUPTSeMq3OFMT+UxjZ
K5pCGUGkMMa1v9+Zy6cbaodonE4XEaIaLSQaIQQ3nZlMhM3S4Jq6sPCl50wAuqb7pozzU5elclTG
qOTuRy0Zcx8NZEop9wEIIT4ELgN21GkjgYia15HAEV8GqSiKoX+XcFLLanvi4aKaOFGKDInnYGW3
2oYxKaTHwI6gcCJqKjDOeNsosT2yVzQpcaFIh7Gwhhbc/HrAT7uv40PPuSwc3vRykS2laYI8Yujv
UAtl+0tLhmUSgUN13mfXbKvrCeBGIUQ2sAD4fWMnEkLcIYRYL4RYr0oMKMrJKysvZ7A4yP6wdADC
qTKqOIYlcECPZ7knlU3pT3nbC1s4QdIBntq1US983lgLV7PXrJpkazq5X5bWHScWdsskn1ZxzNdi
CHeqPOAvvrqheh3wtpSyBzAVeE8I0eDcUsrXpJSjpJSj4uObrhynKEpDCWVbseIkM2YiAImiAIvw
IMISqHJ5uMn1CCUDf+Ftb9fCjBcO40GnK7TlPC5eB0Bz1Mx/tzV9E/SXE1IIMmtcPLRbk+1OVqUW
gc3TcIlHxTdaMixzGKhbCahHzba6fglMAZBSrhJC2IA4QH3nUhQfiqw2xswLoobCQbzTILXwBOwu
3WgTXDtuXmmqGS2tMP4pPm/9d837fMzOmp57M8MyQgh2PeX7JRMdplDMLpdR+tcc5PPzd3Yt6bmv
A/oJIVKEEFaMG6bzj2tzEDgPQAgxCLAB6vuWoviQ060T4z6KjomKCKNq4xjNWITcHN2Dapcxx7xu
cs+19jJe5GegodeeK/MHZHWJ8aaZYRl/cZpCjRcO1Xv3h2aTu5TSDfwO+A7IwJgVs10I8aQQoqaA
M/8H/EoI8TMwB7hVqopAiuJTeeV2eogCqoO7YLZF4JGCQdpBAKzx/bwPENVN7jnWXnikYOO6n7jQ
tMG7fe6nH3DkSM3MmZDY1vsQdTjNx4aMGi+PoJyeFj2hKqVcgHGjtO622XVe7wDGH3+coii+c6TE
TpLIwxXeA5vVjEkY/aeDejw9wxKIC7dyqKi63nTG/aUesmRX8vduYpjohgcT6/T+DNYOUKqH4RYW
zM2snOQvbrPqufuTekJVUdqJnNJqeoscRFxfbJbaf7oz3bcD8MHtY/nnL9K85QAAyqrd7JRJ9BeH
6CVycUb0YpfegxSRQ5LINUoLaIFJAx7LsZ57RUCu39Gp2jKK0g68v+YAf/t8NT/bynF0HYi5TkJ+
8M47AEiKCSEpJqTecWXVLnbRk4vMRhlgLW4oOYWxRIhqLjatpbyL72+UtlRtclc9d39QPXdFaQce
/Xwbw7R9AAR1GYBZE5xpf4nro99neM+YEx53Zp9YtsoUNCFJ0XKxxvclj2jv/qCxM/we+4noVpXc
/Ukld0Vp4worHMRTzHvWp40N3YZh0gQ5xFIqmp6j/uJ16SzVh3vfi9i+HJbGGHvBgOuxDrjAb3E3
R1prFspWN1T9QiV3RWnj3ll1gEtNKwG4z3kXRHTHbGrZk6KRwRZk3X/mMSms0QdwjWM2cvKT/gi3
xUSQ6rn7k0ruitLGVdjdDNYOkCejmHKDsTDGsTH3k55wHNOb8wd1YZ0cSGxMnI8jPTmx0THoUlBe
VhzQODoqldwVpQ3IKqjE7jrxQhcTtG2slwO5cEhXAMzaydV4+Y97mvEiMomXrx/BqpmT0E7yHL42
ODGSCoIpKirkjRX71WLZPqaSu6IESGm1i2W785n52VYmPreU2fO2Nd6uKJcuooTREyZ7t5lOMjE/
457OQMc7YLJgs5joFhl8WrH7gkXTKCOEzZkH+dNXO9h2WI29+5KaCqkoAXL3nE38uNuo0jFFW0vF
tm1w9fB6bTy6ZPfObRAEcUkDvduPjbm3tK8r0Xh9xlifxO0rJk1QIYMJ0qsAKKpyBjiijkX13BUl
QLZklxBGFeO0bfzH+k/+xV8aDKJXOd3EipoebViCd7vpJB48evrKoUwcEM9Z/dpWJVazSVBBMBGi
GoCnvtrRzBHKyVA9d0UJACkl4dXZ/BB0v7eMAADVxRBSO2/d7tKJouYJzuDa7cfG3FsyTj19dE+m
j+7pm8B9yKQJymQwsVoF8RRzY9FbuO0jMdvCAh1ah6B67ooSAO+szGKytt6b2NfImpUrK+ovO2d3
eYgWNVMF6yR9H66ZETBmzei5h8gq/mJ5g1vMiyje+EWgw+owVHJXlFaWmVfBH7/cxizL+wDoM3PY
P+Ru43XZ0Xpt7S4PCaIEXVjqleYVtP/sbtIE5TKYPloOk00bAdDzdgY4qo5DJXdFaWXn/+NHxmnb
ve+1oBD0sC4AuEtz6rWtdnlIFAXYQwNX4MtfzJpGBfVn7ZiLMgMUTcfTsX5aFKWdGCV2A/C86yoA
PKHGzVJ3g567TldRhDO0/hJ3HWFY5thsmWNKZCjOkiMBjKhjUcldUVqZGTdXmFawRh/ICx4juZuD
I6iUQejl9ZN7tctDFBVIW3Rjpzr5J1TbELMmKKe2iuUumURVSR5fbVEJ3hdUcleUVjbNsoFkLZev
gqbx9JVDAbBZNPJkFJQ3HHOPEpXI4PrJ/VjN9phQa+sE7QcmTVBeZ1hmr96daFFORo56mMkX1FRI
RWlFVU43feQBdEz86aGHwGwkZ5vZRB7RJBw/W8bpJpIKqkLql/XtEx/GU5enMiW1a6vF7mvHD8vk
yBhiRAWaqzqAUXUcqueuKK0or8xBX3GYytAkb2IHCLJo5MsoTJV59dq77BUECTdacMNFrG8c24u4
sCC/x+wvx6ZCHrNbJgEw4ujHgQqpQ1HJXVFaUW6ZnT7iCI7ovvW228wm8mUk8rhhGb3KqJhoDg3M
Itb+ZEyFrB1zX6YbQ1SDChcFKqQORSV3RWkFui7545fbeX9lJsniKFp8/3r7I0Ms5MlogmUVOCu9
xyzauAsAc1jjN1TbM7OmcVDWllSoxsbXntGYpCuAUXUcKrkrSiv4+6JdHFn1CS9mXoBVeAjuPqTe
/sHdIrDbauqrlx/lHwt30fuRBZxbajyxaQk98VJ67ZXJJCjCWI3p5/hLAHBiwaSrAmK+oG6oKkor
WLruZz6zvOx9b+t9Zr39Qgi6JibDQaAil3dWVXCf+VOuN/8AgNY1tRWjbR1GfRxBf/s73JbcDw5l
4ZAWzFIld19QyV1RWsGQ6nUEWVxMdz6GBTfvxfZp0KYqyOi5Hzy4n/5WjXvkZwD8o9+73B8a2FWT
/MFiMgYOnFiQova1WVfDMr6ghmUUxc8KKxwMEVmUy2Auuvgq7rjt9kbbVdck9ze/Xc3wiuUATHT8
nftvuKzVYm1NJk0w8yKjRr2UksSoYBxYWmXM/dg9kC3ZJX6/VqCo5K4ofvb68v0M0Q7gSRjCLeN7
n7Cuustq3DR9wvIuj1neJ1/EMvWcCa0ZaqvTauoo6BI++804nJgx6w6/X3ddVhFHVn3M13PfJTOv
Al1vx4/6noBK7oriZ6/9uIfB4gCm7sObbGcxmzio1yZ+d0w/HpwysIkj2r9jNXKkhC4RNjDbMOEB
/cTryZ6uaqeHJ17/iFet/2Rm8Swu/sciXlyy22/XCxSV3BXFz/qKw4QIB2EpZzTZzmIS/NL1B15y
Xw7UFhPryIQ4tlyg0XN2C4uxw+2/3vvcjdn82fKG9/0u262ctebXfrteoKjkrig+tiQjl3mbDwPg
dOsM0/YBIBJHNnmcLmGP7MEr7sv4m+tackY/5vdYA+1YcctjBdC8yd3jv+R+JCeHNLGXd921C46P
dG9q31XYGqGSu6L42K/fWc0/P/oGMIYAhop9OE2hENu3yeOqncZQhJ0g/uW5nKSkXn6PNdCiQoxk
Hh1ilGJwUVOSwY89d8uRdWhC8pN1PNc6ZtXuqCzw2zUB5v98hA/XHvTrNepqUXIXQkwRQuwSQmQK
IR4+QZtrhRA7hBDbhRAf+DZMRWkfsouruNc8lx+C/g8K91LlctNT5FER2rPZxTYqHG4AUhMjiA21
0iWi/daNaanL0xL565VDuWuiMTXU1QrDMvElm3Bj5t5br2etHMQdzvuMHWXZfrsmwN1zNvHwZ1v9
eo26mk3uQggT8ApwETAYuE4IMfi4Nv2AmcB4KeUQ4F4/xKoobd7BwiomaNuMN3sWUeX0EC9KcQY3
P35eWGEktHvO68+GWZO949HNR5oEAAAgAElEQVQdmaYJrhvd01vC2K3V/ELzQ3LfX1DJ1uxSutn3
URKawqCeXfjp4UnoEYkAPPbudxRV+ucBKiklk7X1LLfeAyWH/HKN47Wk5z4ayJRS7pNSOoEPgeMn
3v4KeEVKWQwgpcxDUTohh1vHjDG84jm8kcU7ckkQJXhCGp/+WFdIkPFMYfcom19jbMvcoua5Sh+P
uZdUOTn3uaVc8vIKkkQe1rjeACRGBRPX3fjW8JT9r/y0aK5Pr3tMtcvDZaaVJGn5yA1v++Uax2tJ
ck8E6v6qya7ZVld/oL8Q4ichxGohxBRfBago7YnD7aG7MMZu8w/u5v1vfyRBlGCK79fssX++PJW/
XzOcId0j/R1mm+UW/hlzL7cbQ17B2EnRcolIqh188NhiyJcRAIzI+dCn1z0mv9xBF1EEgHPFyw2q
f/qDr26omoF+wETgOuB1IUSDAtRCiDuEEOuFEOvz8/N9dGlFaTscLjcxogIAS0kmD5vnoEtB8ODm
+ztRIVauGtnD3yG2aW7NP8nd7vIww/QNc6xPYcENybUPh8VH2Lja+QTb9V5ElO/z6XWPmTl3CwPE
IZZ5hmLRHRStfNcv16mrJcn9MJBU532Pmm11ZQPzpZQuKeV+YDdGsq9HSvmalHKUlHJUfHzzX1MV
pb3xOIxyvRl6ErGinKmmtaxNuo3I5LQAR9Y+eLw9d7tPz1vhcDPb8h5pNdNSSRrr3ZcSF8oB2ZWv
PGcSXnUQqotP+vzrsopwuE/84NXRw1lEiGoW6qO4yvkEO5JvPulrnKyWJPd1QD8hRIoQwgpMB+Yf
1+YLjF47Qog4jGEa//wKVJQ2TLcb638uM4/zbht769OBCqfd8Q7LeHx7Y/OVhbWzVH5Mfx6Cwrzv
pw3rzi9GJbFFphgbjmw+qXNnFVRyzX9W8dv3N52wzTCbcRvy99dcxCbZj5xy/9fPaTa5SyndwO+A
74AM4GMp5XYhxJNCiEtrmn0HFAohdgA/AH+QUhb6K2hFabNqknt8ciovuK9g/ZDHwNzxpzT6itvk
n5774b3GDKbfOu/mnMtm1NsXbDXx9FVD2an3NDbk7zypcx8preYibQ337Z2BPLCq0TYJTmN+e3TP
wfz67N707xJ+kp/g5LWo5K+UcgGw4Lhts+u8lsD9Nf8pSof2xor9hFpNTB/ds+FORzkAU0b2ZfnI
a0kf1KWVo2vfdO3YPHff9tzHRhaDHZ66/fJG9wshsEV2odgeRnT+rpM6d365g8cs/yNRFLLn3d/R
b9aGevullCS4c3CZg7BG92Dm1NaZ4qqeUFWUk/Snr3bw8GdbcXn0BvtMdmO81hYex4VDumLSOv5c
dV/yCONbjnTbufG/a1i43TezShI9xgNK0T1OXIjt2jN6sld2RxY0XURs1d5Cbn1rLZ6aSpJZ2dkk
ikKqZBB93HvrLZNYbndR7fIQR7FR0rkVn11QyV1RTlIU5fzR/BaLF37VYJ+syAVAC1c99lPhqRmW
cTvtrMgs4I73NjRzRPPK7C6i7QcpD+oC1tATtusWZWO/3hV3fmaT5/vN+xtYuiuf4ionLo/Oip9W
APCtNgFNSKjp+f/itVUMfWIhhRVO4inFYWvdBVdUcleUk3Sn+UtuMS+i9+HaeQU3vbGG+z/azIF9
e4wNYR2/oqM/6DU3VD0u3425bztcSh+RgyOyd5Ptzh2QwBHiMFflgefENzyPPTlc7fSw62g5E0zG
eH5Fz/ONBmWHsbs8bM/K4SnzG+zau5d4UYI7pHV/JtQye4rSAh8uXE7wiqfpctmfSNeMnl3GgcN0
rXYRbDExZv8rXKatJMFcwka9LyMswQGOuH06Ns9dd/ouuV//+mq2BOXgiBnXZLv48CBKLfEIKaEi
FyIbf+bg2MBKmd3FVS99z8agr/lOH01pzDA4AJQfpaTKxURtMzeal7BofgkJWgmVrVzCWSV3RWnG
2v1FXPDTdGK0CvLnX06wMB6wudy0kufffZNF9sG8YVpOt5onEKMufDSQ4bZrx26o6i7fPcQUSxkR
ooqSLv2bbesM6QqVQNmREyf3muy+L7+SidpmQoWDs66+m8y8ODxSQNlRSqtd3p+HySZjaKmqlYfq
1LCMomA8weh0N7xBml1cxSOvfeJ96jRelBImanuVUw6/jDy6lW6iiP16Fz5yT6T3mVe0Wtwdjcmk
4cSC7sOpkGeEG7Oyo3oMbqYluMO6GS/Kjn9Os1awtPOi5SUq96zgPvNcDtGVkMFTCLEFUUIY7ooC
SqtdJIv6N4PNyeNP/UOcApXcFQUYOOtbpr20vMH24koXN5iW4JIm7nLe491+geMZ5nomMEg7yDdB
MwE4dM4/ODDhmVadEdHRaELgEhZ0l51p2ipmm9897UU0Ej01iTq2T7NtPeE1vfXiAydsM0Ts41LT
KqZv+xUDtUPYxswAk4VQq5liGY6nkeS+TU8mLvW80/ocJ0sNyyhKjd25FQ22/fvHTGab1rJQH8k3
+hgecv2KChnMbpnEO+4LucpkzJS42/lbXjxvGme3dtAdjNWk4cQKLjsvW2uWwqvIgxYOaXy9JYcP
1x1kQt84bhzbC4+UhDtzkWaBOMEwS12hETHkEU1C7vYTtolw138+MzRpiPH/IDNFhNO9qojSahc9
RQk/itF84xxG93HXk9rK02JVcleUJqzcuoeutmI268YqSh95zuXuSX3h+0y2yN585xlFluxCr4m3
BDjSjmFgt3DK9gcRZi+v3Vh6qMXJ/bcfbARg+Z4CckrtXJrWna4U4bTFE2SyNHt8bKiVH91DuXrn
14jqEgiuX//Q4fYQ7iqEOqcKqRnLDwkyUSzDEdVGco8RZewyDeZDzyTmpjY9U8cf1LCMotSRW2bn
v8v3odc8oDLMYjz8csMlF3nb3Dnx2Nd7wa9d97Ms+W7+74IBrR1qhxQXFkSJDKW4qE7V2JITD5Gc
yHuWvzD6wGtUOz10E0W4Qru26LiYMCuf6xMQrko4srHB/rJqNz3EcRVto5MBCAsyUyTD0exFlFY5
iKaC8BjjugnhrV+CQvXcFQUw4cGKizP/sojbTN+ww3olqWPOZ0hQAbgheWA6838XTlGlE6uptk/0
1m1ncEZyTAAj71hMmqBMhuApzvd2PZ2FB46trNoiN5u+4yzTNijcxucl9zBYFOEOG9KiY8/uF8+c
oG4ggbKcBvvL7MZY+i6RQqxeiGa2EmM2oguxmigmHIu9GHdFIWahc1baQOZfOp6kmJCT+AS+oXru
SqcnpeQFyytk2GZwsbaaWZb36bpiFlJKQpw1vbTwrgzrEcXEAQn1SgqcOyCBsCDVR/IVTQhKCaWH
qF2setv2bSc+QPfA3h+8b7tTwOPm2lrpsfNuppfIRYZ3a9H1k2JCmDBiKLoUyOKsevtcHp2F23ON
NXFDkpjs+Bv/HDTHuz/UaqZQhqNJF1rxXgDM4V0Y1qPB0hatQiV3pdNzuHWmmVYDcIN5CQCRlXup
dLiJ1YuotkRDnfFaIQRXpCfynxtHBiTejsxc03NPECXebZ4TzFzJK7ez9LUH4L3LIcu4sT0p8ggm
IbnZ+RBHZAxnm7ZiEy5kRFKj52hMZHgEGbIn+sE19bb/feFunv12B0kiDxHbm2IiSIiJ9u4PDTJz
SBoPKp2R9bqxMSS2xdf1NZXclU7P4aqd3z5WywDAojsoPnqQBFGMo5HFrZ//RRpTUls2jqu0nKYJ
yqit/7JZ70OC42CjbX//wSYijiwz3tQU60q2VQGQPmo84xwveduK6F4tjiEi2MxqfTDi0Op6ZQg2
HiwmVezHKjx06TWI7pE2RvSsm9xNbNGNG6fnmLbUbAzcokQquSudnt3tYa9e+7V9lcd42MVxcB1d
RTGeMJXEW4vRczeSu0Oa+cwzgV5aHuQ3rNQoj25jRE0piGOlloNdxlOhd00dzaNTax9aMse0PLk7
3Trb9GQ0jwOK9nu3B+FkrvUJAOKSU1k58zzG9a0tBhZsMXGUWH7jvLv2ZOGB+9lRyV3p9MqqXQSJ
2h7a0p6/oZogLAeW00UUISK6BzC6zsWkCcowbj7myWgWeUYZO3bWr8C5NbuUUc613veOsnzmbsjG
Vp5FqSUemy2YacO74ZDG/RBLbMuTe7+EcDJlovGmwKjwKKWkv8jGIjwstU4kKKVhnZpjBcUW6LVL
+BEcuJvtKrkrnV5xlYtoynnTPYUR9v8gepzBRk9ftEOriBdlWKI696LVrcmkCfZK45dpRvhYcojl
Z703JRs/r9fuhSV7ONu0lXwZCcD6jL0snvsaU7W1VMcYM2Oigq38wjmbN91TCIpo+fDIhH5xDE4d
YbzJ38X2I6WkzFxAyQFjqOWs2/8GWuOpM7zm5vq1jlk87Zp+wnatQSV3pdMrKy8jVDgokJEUEYHV
rLFT9iTJacx4CI5Vyb21mIRglT6Eqx2zSbnhBQB+1IcRXrzNO/QCkOTOYqyWwVvuCymTwYTmruff
1hcIFk6c8amAsXzeZtmXJ903I04yyfZK7MIRGYMrdycbDhQzRGTxG9M8XFgwNVHG4NgzEA/d+Utu
+L/nT/bj+5RK7kqnt3+vsWZmPkYv8M5zerNT1s6uMEclBiSuzujYNNP1ciCxkcY6o6v0IZjQ4ecP
ve16uYxfvIv0UZTIMPq5apfG03ufe9pxDOwaTqaeiPPoTlwuF18HPUIfLQcTHjCdeOrrbyb2Yf9f
pzKyV3RA5rbXpZK70qld99pq8td/AdQkEU0QYjVjSxxW26iFc6SV06fVKbp27PmBVfoQ8kUMnpqp
iaP/vJjy7B24pUZs0kBKCCMUY5bMr5z303Xo6Sf31MRI9sruWIszmfPdCu/2w6GDmjxOCOEdew80
ldyVTu3Avl3cYf6arXoy2TKezbMnAyAS6pQTUDdUW03d+o9Wc2162unuTvEh4xtWXrmDfuIwBUE9
mDysJyUyDIBCGc4ifRQ2i8l7XIjVRGRw8zVljpcQbiPHkoRFr+YMtgLwR9dNLEv7xyl8qsBQj9Yp
nZaUkhvMi4kTZTzmmkHW0xd793WJjeET99n0CXczIkSVF2gt8rjyvllPX8zBwirWPP8f0qp3erf3
E9lUhA/AYhIUYwzfHDD14s9XpNY7fuOsyaccS2FwClTBBdp6AJaYz2Fa//ZTQ0gld6XTsrt0UkUW
O/RexI2+ut6+pJgQ7nbfyYTYOP4XoPg6o8Yqt/eMDWGBKY5QZwF43IRgp5fIZW/cZZg1jXJpLGmY
NHAkI8bUn/JYtxd/ssrDekMVnGv6mRwZw7Inrm5XtfrVsIzSad0zZyODtSwsPdL402X1e3zpSVEk
x4Zw/ZieAYquczrWcZ88uH6J32JTPBo6suIo47TtmIVOzJDzMJsE5TXz4t1JZ/o0FltUV+zSGNLZ
Gz2hXSV2UD13pZNyunVsuz4n3lpGdpdRDW6CJcWEsPQPp39jTjlZRnY/fl2LMmsC2MFZeIgxWgZu
LYj4wROxbM3jRfcVrNf789SgS30aydg+cdh2Gw+3jT7/Wp+euzWonrvSKW0/Usp00w/kywh2xk8J
dDhKjWM9d0H97F4dZBTgcpTlkiTyqQhOBLMVs6ZRjY0l+khCbSdTGLh5ybEhfO9JA8Dau3XXP/UF
1XNXOp2jpXZ+9Z/vWG/dwWfh1zM1LSXQISk1jo25Hz8CEhqVAKXgKi+kuyjAHmI8e2Ax1TYMtfo2
nSVGB3OJ6zfEusv5oR3eVFc9d6XTeeTzrfTHKCN75RW/IDLk5KfKKf6h13Tdj0/uXboazxq8+u06
EkUBznAjuacmRnrbmHy8RmnPmBCsYbHYI9rnL3/Vc1c6HZdHJ1XUVPvrktp0Y6VV2czG7Jbj56Zb
bBHYpYVHLMbiGOU1yb1HdAjx4UHEhfl+GTshBIvuO5uQoFOfcRNIKrkrnU6oBW4xL+SQHk9SaFzz
ByitZtLABB67eBDTR9efpRRsNZEt4+krjgBQmVw7f33FQ+c2GKP3lehQ347jt6YWDcsIIaYIIXYJ
ITKFEA830e4qIYQUQozyXYiK4luJrkMkikL+47kk0KEox9E0we1n9W6wdGGw1cTHnnMAmOD4J6UR
/b37gsymek+zKoZme+5CCBPwCjAZyAbWCSHmSyl3HNcuHLgHWNPwLIrSdpgdhQDe0rJK22ezmHjd
czGfes6hiAjSk6KbP6iTa8mvu9FAppRyn5TSCXwIXNZIuz8BzwB2H8anKD5nqTaS+7XnpAc4EqWl
bBYTEo3evXqR9fTFBFvb5zh4a2pJck8EDtV5n12zzUsIMQJIklJ+7cPYFMUvrA5jKbYrJ6QFOBKl
pUqrjIeJekQHBziS9uO0B6qEEBrwD+D/WtD2DiHEeiHE+vz8/NO9tKKckmBXEToaBKuv9u3FsCRj
yuPxN1qVE2tJcj8MJNV536Nm2zHhQCqwVAiRBYwF5jd2U1VK+ZqUcpSUclR8fOBWBVc6Lykloe4S
qs2RoKmv9u3FwK4R7P/rVMb2jg10KO1GS5L7OqCfECJFCGEFpgPzj+2UUpZKKeOklMlSymRgNXCp
lHK9XyJWlNNQ4XATQxmOoPb3xGFn11YWwWgvmk3uUko38DvgOyAD+FhKuV0I8aQQwreVehTFz0qr
XcSIMtwquSsdXIseYpJSLgAWHLdt9gnaTjz9sJTOauXeAoZ0izxhSYB3F69jVHcbgwcPPaXzl1a7
iKUMT0jv0wlTUdo8NfNfaTPK7S6uf30Nd72/odH9zy/M4OYV5zP44wmnfI3SahexogyhnkxVOjiV
3JU2o9LhASAzr6LR/btXfVX7xu08pWuUVjqJoApTiJopo3RsKrkrbUaFww3Q6KPkbo9OmmtT7Yai
fad0jcqKMjQhsYZGNt9YUdoxVThMaTMqHG7uM39CVzfApHr7Plh7kAnaNsplMOGiGgp2QcLAk75G
dUUJADaV3JUOTvXclTajwu7mHvPn/ML1OdjL6u0rLchhiHaAdz011QDzd5/SNVzVxnmtIRGnFaui
tHUquSttRrndVfvmUP36c7ZDywHQ+08lhzgOZ26mqPIUxt0dxni+sKnkrnRsKrkrbUZlVaX3tTtv
l/f1u6uymHj0bSoIpTwmld2e7sQcXMhT/1vQyFmaJpzlxgtr2OmGqyhtmkruSpvhKTta+zrXSO6V
DjfffPkx/bTDbDGnYrVY2SMTCRZO/pYzo97xn23M5tqXl/Dev/7Mkb+NgZKDDa4hnDW/QILC/fdB
FKUNUMldaTMcxTne1/t2beadlVlsPFjMJdoqAGJvfAOXR+crz5kAmPGAx5hh43TrrJz7InPyr+Km
vL/RvWonrh0Ne/YmV03PXSV3pYNTyV1pE6SUbNy8EYA1+kAGObYQ/c2dvLwog4mmzeR0O58ByUn8
ZmJfNsu+POL6pXFgRS4Ae/Mr+J3pC0xC8m/3JdilBefhzd7ze3TJgay92GoW6lDDMkpHp5K70iZk
5JQzQttDhbTxjvsCAC41reIXOc/QXRQRlTYNgMgQC6tmTuKINGrD7Mkw5r7vP3CAZC2XP7uu5xn3
dWzQ+yPyMgA4WFjF5Y++TNe3RnNN0avGBVXPXengVHJX2oRNh4oZqe0mO3QIP+rD+d5jLKRxpWkF
JTKU4GFXeNt2iwymsssZFMpwqhbMAo+b4r1GEdJrLpnGe78czW7ZA2vxHtB1Fu84ytOW1wkSxhDO
YRkLFrXog9KxqeSutAkVpcUMFAfpO2ISlQQzw/Ugv3beB8DK4X+F4Kh67Q9WmviT6yaGa/s4sm4e
xRk/ANB/2JkkRYewVU/B7K6EnM1kbP6JIdoBXrD9hnMdf+e+0GdAlY9VOjj1hKrSJpgKd2ESEnqk
s/cvU8kvdzD2rzDY/iY/Tp7WoH2F3c3X+lj+LN/gp6/e5gbTRnK7n0eXkBhiNBeL9RE4pQn7ho9J
LTiIR5i45/cP8FtbNCZNJXal41M9d6VN2LWtZm2X+IGYNEHXSBu3jkumChtxYdYG7R1uHRdm1uoD
uca8jGhRQex59wIQHmSmjDCW6cPQN77HFXxPUfJFEBqL2aSpRR+UTkEldyXgXB6dfuIwTqwQnezd
/vglg8n880WNJmO3LgF4xX0ZAMs8QzH3PgswVuxZfP85fOU5kygqiBDVxE+62/8fRFHaEJXclYAr
qHDQT2RTGdG73rqmQgjMpsZ/RIcmGoW/1suBJNs/IO6uBfXG0fsmhLFIH8levRuboyZD0mj/fghF
aWPUmLsSEBUON6FWE+uyinl+0W6e1Q7jiBrb4uPfnTGa/YWVlNvdJEYF0zeh4bz1ob0TOW/f33lh
YhppaihG6WRUclda3f0fb8b683vcYV7AB54rseih9LAWcLTL4BafIzrUSnRow7H4uj6840zK7S7C
gtSPudL5qJ96pVUVVDjYvWk5X1jfxCx0/ml+2bsvOHGYz68Xbmt8LVZF6ejUmLvSqlbtLeQG0xJ0
SyjvuCfX2xfe0/fJXVE6K5XclVZVXOVknLYd2Ws8y3Qjmf/PfR7PuKajRfcMcHSK0nGoYRml1Ugp
+e/CDdys5eFJGceS7X0YZ3+RC8aN4qLUruqpUUXxIZXclVaz/UgZ4fYcCAJTTAogOEIcM6cOJMhs
avZ4RVFaTg3LKK2qhygwXkQlkZpoLHWnErui+J7quSutxuH2kHgsuUf25KM7Iql0ugMblKJ0UCq5
K62myukhSeTh0mxYQmIIFYJQNQddUfxCDcsorabS4WGIloUrboi6eaoofqaSu9Jqql1ueolcPLH9
Ah2KonR4KrkrrabK4SKGcrTwhECHoigdXouSuxBiihBilxAiUwjxcCP77xdC7BBCbBFCLBFC9PJ9
qEp756ksxiI8mFRyVxS/aza5CyFMwCvARcBg4DohxPEVnjYBo6SUw4BPgb/5OlCl/ZNVxQBYw2ID
HImidHwt6bmPBjKllPuklE7gQ+Cyug2klD9IKatq3q4Gevg2TKUj8NjLANBsEQGORFE6vpYk90Tg
UJ332TXbTuSXwDenE5TScdhdHhbtyEVKiXRUGBuDGtZeVxTFt3w6yVgIcSMwCjjnBPvvAO4A6NlT
FYnqDJ75didv/ZTFLyeksH9HFr+0AtbwQIelKB1eS5L7YSCpzvseNdvqEUKcDzwKnCOldDR2Iinl
a8BrAKNGjZInHa3S7izflsV866PoazU2aX2Njarnrih+15Lkvg7oJ4RIwUjq04Hr6zYQQqQDrwJT
pJR5Po9SaRd0XfL+mgOE2cxckd4Dt0cntXIlw8z7AUjT9hoNw9RsGUXxt2bH3KWUbuB3wHdABvCx
lHK7EOJJIcSlNc2eBcKAT4QQm4UQ8/0WsdJmfbPtKFu+eoWUzy9h87oVrNxbyEViNVVBCazWBwFw
6PxXITg6wJEqSsfXojF3KeUCYMFx22bXeX2+j+NS2hm3R+dvHy/me/PrmISk7KtreMx1G89ZNiNT
Z5ARfD25pdu5bML0QIeqKJ2CqtqknLbdueVc8Pwy/miehxAa+6Z9SMiXd/Ci9RU8piBMY2ZwW8Ig
ID3QoSpKp6GSu9IiVU43C7fnMqxHJFmFlcSFBTF3QzYzpw7igueX8WvTl9xiXkTBwBvpPeoCyhIX
I3d+gmngxZAwKNDhK0qno5K70iJv/ZTFxwt/JIxqkkQ+q/VBnG/ayJ/2JHKNKZuZljk4g2KJu+RJ
ACK69YVuMwMctaJ0Xiq5Ky1SsGcdS6wPYBZ6/R0VgAWquo0hZMY8sAQHJD5FUepTyV1p1vqsIoIO
LMVs0dk34Fck9xlIdeFhfijtwqSM2YQIB5bxv1GJXVHaEJXclROyuzwcLqnmwU9+ZraWQbkljt7X
PQdAKBCdWcDFP5v5e3oeIwZf2vTJFEVpVSq5K43Sdcmg2d8iJVymrWCi9Wf08Y/WazO+bxwfzryR
LhG2AEWpKMqJqOSuNGrToWKGkcmfrW+SqmXhjB+K9ewHGrRTiV1R2iaV3JV6pJR8vzOP299Zy/Kg
F+khCnD2uxjr5FmgqYW7FKW9UMldqecPn27h2w27+Yv5fXqIArj6LaypVwY6LEVRTpJK7opXQYWD
JRt2sDDoEbqLIuToOxCDL2v+QEVR2hyV3BWvjJwyrjQtp7soglu+RKScHeiQFEU5RWoQtZPbeLCY
b7bm4NElW7JLmWJahzshFVRiV5R2TfXcO5ncMjsPz91CQriNj9YbqyfGUcr3iW6WHta4y7YHbcij
zZxFUZS2TiX3TiQjp4yLXljOFdpyBmuZ/MXsYZS2i/7aYSiEZZahaEgYdEmgQ1UU5TSp5N5JlNld
XPTCcq4xLeVZy2sA6JoFkXIWOZX96Xb0B842bYWRt0HCwABHqyjK6VLJvZN4cfEeztK2GIk9+Sy4
5AU0ayiEd6UbQNkRQEBEtwBHqiiKL6jk3gkUVDh4b8Uuloa+C1F94YZPGhb5iugemOAURfELldw7
gZ8yC7jD9BXdPEdg6ueqeqOidAJqKmQn8PPuLH5rmYc++HLoMynQ4SiK0gpUz70DkFJS5fQQYjXx
5ZYc9uSWExlsIdhq4oYxvQjNWYkNF4z9TaBDVRSllajk3s7ZXR4GzvoWgOTYELIKKwnChQMrAp3C
CieO/H3G37SaBaMonYZK7q2stMpFmM2MSRMAeHSJLiUW08mPkNldHv745XbMuDlT20Gv6io+ivmG
+Op9HLSkkOA4xOzvb2W4uQTdHIIWFOHrj6MoShulknsrWrj9KHe8twGAM3vHkl1Sha7D4ZJqPvjV
GMb1iWvyeLvLwxsr9jM6JYauETZufWstB/JL+S72n/Sp3Ag64AyGQZeQXH4UmZfLc45XjYOTzgYh
/PwJFUVpK1Ry96O1+4sY2C2c7YfLeHvlfr7bnksXihim7WPNvoE1QyeSiVoGj/83m0V//fUJz/XR
uoM8NHdrvW2pYh8/J7xFaNlemPIMdBkCcf0hvAsAwuOGn56HshyYONOvn1VRlLZFJXc/Wb2vkOmv
ra63baS2mznhL2B1FDvRkTMAAArYSURBVHu3SZMV4XHikGYuftzK+w/dQFSItd5xH649yMOfbSWG
Mp6KnE//4DL2hw5j4tG3sTjM8Iv3YdC0hkGYzHD2H/zy+RRFadtUcvch+f/t3XtwVNUdwPHvb7NJ
1pB3eCTyMIlGK1UUjBZGShGQVylKi5X4AKuFqYyjte1YGGZ8tNOqWC2oHYEitiqKL6zIQIFGi9rB
SChCeSe8QyEvSIghgTxO/7gn6TUCSYCwe5ffZ+ZOzj337N3zy9n8du+5N3uN4fW8fewtq2bllmIG
+9ZzR8Im8iOzmZCwiYyDf0cCnWHUk3BkN/j8yFfF1PoTCKx5lgcbXmdx/kD8kZE8+sFmHhyaxcPD
snj5s930kx28F/8ccrwKIlK4rOIziO8Okz6ElEuDHbpSKsRocj9HVu8oZdKCL5rXu3KE+YFn8dc0
MrxmOdTGQcplMP5l6HLF1x4bAKrq6xix9nkWrfwV0+qnAPB8bgFJMZGUlZfyduwcJCYZJi2BbldD
5T4nufujz2eYSimP0OR+lmrrGpj/6S5m5xYQTzUPdd/GwMBeuh9eg7+mEW6dA74IuHwEBBJOuZ+4
UU+welcxE8rfok/XSNL95ZwoKWDWsnFMpJrEuhL44Uq4uK/zgOTM8xShUsqLNLmfhZKjtdzw+1wA
JqcWMP3o7/CVn4CoWIiOhyvHwlU/An9UK3sCfD4G3f8iVXN20LtsFST2osKfwHTzBgWmBw1drsTf
8/oOjkgpFS48ndyrj9cT4RMCkRHN6w3GEB+IPKv91pxoYOKCPCqO1TH1pku59druVB2vJyrCx8HK
Wn793kZ2FFdRcawOoZH5PVcxtPRV50qV0TOdb130RbT7ecUfRdzkpVBdCknp1GzfQPSi73GV7IFL
7jurmJRSFxYxxrTeSGQkMBuIAOYbY55qsT0aeBW4DigHbjfG7DndPrOzs01+fn67O2yMYduhKvaW
H2PqwnU02u5ndY1lV1k1MVERzLs7m8wunUiKieLJ5Vt5/fO9pCYEeHjY5Xx1vJ6KY3VMGZTZ/KbQ
0pPLtjL3k12t9iWZo7zT9RUuPZoH3xoDwx6Hzlntjum0vvgz5M2FnEXQ+bJzu2+llOeIyDpjTHar
7VpL7iISAewAbgaKgLVAjjFmi6vNVKCPMeZnIjIBGGeMuf10+z3T5D7vk538ftm25vXvZnSiqqae
4hqhe0KAI/s3kykHWdV4HXDqf9pJiolk4oB0xvRJ43h9I5ERPrK6xrJ+/xHufOmfvBT/FwbHHyK3
OIYX6sexwTiJ9dudqpjdZw89SlYTVVGIr7YSRj0F2fe2OxallGqvtib3tkzL3AAUGmN22R0vAm4B
trja3AI8bsvvAi+KiJi2HBa009j4nfRP/CMFqaP5vu9zArtXgWmEi5KhykC0cw35a90eYWfnIfSp
+pRbK18F8VPSGE9p2iA6l+fz3KFreCF3ILNzC77xHDkRn3HTidWQMITsw+t53/cYBOLwiUBtJawH
OnWBlCwYMgPSB57rMJVS6qy0Jbl3B/a71ouA75yqjTGmXkQqgRSgzN1IRKYAUwB69ep1Rh1O9VWQ
2rCZPnvWQkyK802HFyU6dxJqOOFcbpj7W+4ungnFM50Hdb4CulxO6oF/k7r9BQCe8a/hsW7/Yrl/
KObKH7CrtIZX1pXx4I1dmVyyDcqS4K7FxB89iCz7JRJIhOg456YWGYOcq1b03/mVUiHqvJ5QNcbM
A+aBMy1zRjvp82PIHAwFKyFrBMR2+Waba3Jg64dwvMr5VD/gAYgMQP0J+O96SL0aNi8mdtWj3HZs
FhTPAmBap2j40kBDnfOJXARfwsWQ8+YZRqyUUsHRluR+AOjpWu9h607WpkhE/EACzonVjhHbFfre
dertcalww+Rv1vujoJc96Oh7l7MUrYOitc7VLYd3O28G19937k+MKqXUedSW5L4WyBKRDJwkPgG4
o0WbJcAkYA0wHvioI+bbO0SP65xFKaXCSKvJ3c6hPwCswLkUcoExZrOI/AbIN8YsAV4GXhORQuAw
zhuAUkqpIGnTnLsxZhmwrEXdo65yLXDbue2aUkqpM6U3yFZKqTCkyV0ppcKQJnellApDmtyVUioM
aXJXSqkwpMldKaXCUJu+8rdDnlikFNgblCdvn860+I6cMKFxeYvG5S0dGdclxpiTfO/K1wUtuXuF
iOS35es1vUbj8haNy1tCIS6dllFKqTCkyV0ppcKQJvfWzQt2BzqIxuUtGpe3BD0unXNXSqkwpJ/c
lVIqDF3wyV1EeorIxyKyRUQ2i8hDtj5ZRFaJSIH9mWTrRUSeF5FCEdkoIv2CG8GpiUiEiKwXkaV2
PUNE8mzf3xKRKFsfbdcL7fb0YPb7dEQkUUTeFZFtIrJVRAaEyVg9bF9/m0TkTREJeHG8RGSBiJSI
yCZXXbvHR0Qm2fYFIjIpGLG4nSKuZ+zrcKOIvC8iia5t021c20VkhKt+pK0rFJFpHdppY8wFvQBp
QD9bjgN2AL2BmcA0Wz8NeNqWRwPLAQH6A3nBjuE0sf0CeANYatffBibY8hzgflueCsyx5QnAW8Hu
+2li+ivwU1uOAhK9PlY49yDeDVzkGqd7vDhewCCgH7DJVdeu8QGSgV32Z5ItJ4VgXMMBvy0/7Yqr
N7ABiAYygJ0498KIsOVM+9rdAPTusD4H+8UQagvwAXAzsB1Is3VpwHZbngvkuNo3twulBed2iLnA
EGCp/QMqc70YBwArbHkFMMCW/badBDuGk8SUYJOgtKj3+lg13WA+2f7+lwIjvDpeQHqLJNiu8QFy
gLmu+q+1C5W4WmwbByy05enAdNe2FXb8msfwZO3O9XLBT8u42cPbvkAe0M0Yc9BuOgR0s+WmP8Qm
RbYu1MwCHgEa7XoKUGGMqbfr7n43x2S3V9r2oSYDKAVesdNN80WkEx4fK2PMAeAPwD7gIM7vfx3e
H68m7R0fT4xbC/fiHIVAiMSlyd0SkVjgPeDnxpij7m3GeZv1zGVFIjIGKDHGrAt2X84xP86h8UvG
mL5ANc5hfjOvjRWAnYO+BefN62KgEzAyqJ3qIF4cn9aIyAygHlgY7L64aXIHRCQSJ7EvNMYsttXF
IpJmt6cBJbb+ANDT9fAeti6U3AiMFZE9wCKcqZnZQKKINN1a0d3v5pjs9gSg/Hx2uI2KgCJjTJ5d
fxcn2Xt5rACGAbuNMaXGmDpgMc4Yen28mrR3fLwybojIPcAY4E77xgUhEtcFn9xFRHBu8L3VGPOc
a9MSoOks/SScufim+on2TH9/oNJ1yBkSjDHTjTE9jDHpOCfcPjLG3Al8DIy3zVrG1BTreNs+5D5d
GWMOAftF5ApbNRTYgofHytoH9BeRGPt6bIrL0+Pl0t7xWQEMF5Eke1Qz3NaFFBEZiTP1OdYYc8y1
aQkwwV7VlAFkAV8Aa4EsexVUFM7f5pIO62CwT1IEewEG4hwmbgS+tMtonDnMXKAA+AeQbNsL8Cec
s97/AbKDHUMr8Q3m/1fLZNoXWSHwDhBt6wN2vdBuzwx2v08Tz7VAvh2vv+FcTeH5sQKeALYBm4DX
cK608Nx4AW/inDeowznSuu9MxgdnDrvQLj8J0bgKcebQm/LGHFf7GTau7cAoV/1onCvydgIzOrLP
+h+qSikVhi74aRmllApHmtyVUioMaXJXSqkwpMldKaXCkCZ3pZQKQ5rclVIqDGlyV0qpMKTJXSml
wtD/AF74fMTxDgYIAAAAAElFTkSuQmCC
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># disney stocks</span>

<span class="n">test_company</span><span class="p">(</span><span class="s2">&quot;DIS&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>1130/1130 [==============================] - 0s 435us/sample - loss: 8.5428e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># dbs stocks</span>

<span class="n">test_company</span><span class="p">(</span><span class="s2">&quot;D05.SI&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>1125/1125 [==============================] - 1s 454us/sample - loss: 3.0266e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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">
<p>It is apparent that the model generates accurate predictions even for the stock prices of other companies it was not trained on.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get the actual price and predictions</span>

<span class="k">def</span> <span class="nf">unscale_price</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">feature</span><span class="o">=</span><span class="s2">&quot;Open&quot;</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;data is a 1-D array of opening prices&quot;&quot;&quot;</span>
    <span class="n">scaler</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s2">&quot;scaler&quot;</span><span class="p">]</span>
    <span class="n">scaler_copy</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span>
    <span class="n">col_no</span> <span class="o">=</span> <span class="n">FEATURE_NAMES</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span>
    <span class="n">scaler_copy</span><span class="o">.</span><span class="n">min_</span><span class="p">,</span> <span class="n">scaler_copy</span><span class="o">.</span><span class="n">scale_</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">min_</span><span class="p">[</span><span class="n">col_no</span><span class="p">],</span> <span class="n">scaler</span><span class="o">.</span><span class="n">scale_</span><span class="p">[</span><span class="n">col_no</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">scaler_copy</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">test_company_table</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
    <span class="n">dt</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">DataReader</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="s2">&quot;yahoo&quot;</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="n">dt</span><span class="o">-</span><span class="n">relativedelta</span><span class="p">(</span><span class="n">years</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="n">dt</span><span class="p">)</span>
    <span class="n">df_tr</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">FEATURE_NAMES</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">split_input_output</span><span class="p">(</span><span class="n">df_tr</span><span class="p">)</span>

    <span class="n">model</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">models</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">BEST_MODEL</span><span class="p">)</span>  <span class="c1"># change the model name if the timestamp is different</span>
    <span class="n">predictions_unscaled</span> <span class="o">=</span> <span class="n">unscale_price</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
    <span class="n">actual_unscaled</span> <span class="o">=</span> <span class="n">unscale_price</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>

    <span class="n">table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;Date&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span> <span class="o">+</span> <span class="mi">29</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="s2">&quot;Predicted&quot;</span><span class="p">:</span> <span class="n">predictions_unscaled</span><span class="p">,</span> <span class="s2">&quot;Actual&quot;</span><span class="p">:</span> <span class="n">actual_unscaled</span><span class="p">})</span>
    <span class="n">table</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;Date&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">table</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nflx_table</span> <span class="o">=</span> <span class="n">test_company_table</span><span class="p">(</span><span class="s2">&quot;NFLX&quot;</span><span class="p">)</span>
<span class="n">nflx_table</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[&nbsp;]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Predicted</th>
      <th>Actual</th>
    </tr>
    <tr>
      <th>Date</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2015-03-27</th>
      <td>58.167995</td>
      <td>59.330002</td>
    </tr>
    <tr>
      <th>2015-03-30</th>
      <td>57.471416</td>
      <td>59.715714</td>
    </tr>
    <tr>
      <th>2015-03-31</th>
      <td>57.224842</td>
      <td>60.110001</td>
    </tr>
    <tr>
      <th>2015-04-01</th>
      <td>57.108532</td>
      <td>59.642857</td>
    </tr>
    <tr>
      <th>2015-04-02</th>
      <td>56.827667</td>
      <td>59.071430</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># mean absolute error</span>

<span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">nflx_table</span><span class="p">[</span><span class="s2">&quot;Predicted&quot;</span><span class="p">]</span> <span class="o">-</span> <span class="n">nflx_table</span><span class="p">[</span><span class="s2">&quot;Actual&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 output_prompt">Out[&nbsp;]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>6.5992601824801005</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>As you can see, the actual error of the model in terms of money is around 20 cents, which is too high and not practical in stock trading.</p>
<p>However, predicting the range of prices for that day (the high and low prices) would be more useful as valuable information can still be obtained even if those predictions are not accurate.</p>
<p>We will now modify the lstm model to predict the highest and lowest prices of the day. A separate model will be trained for each feature.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># model to predict day high</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">split_input_output</span><span class="p">(</span><span class="n">df_tr</span><span class="p">,</span> <span class="n">target_feature</span><span class="o">=</span><span class="s2">&quot;High&quot;</span><span class="p">)</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">split_train_val_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="n">model_high</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_high</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_high</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">)</span>

<span class="n">model_high</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_high</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="s2">&quot;LSTM-high.h5&quot;</span><span class="p">))</span>

<span class="c1"># plot predictions</span>

<span class="n">visualize_predictions</span><span class="p">(</span>
    <span class="n">days</span><span class="o">=</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
    <span class="n">actual</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
    <span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">model_high</span><span class="p">],</span>
    <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LSTM&quot;</span><span class="p">]</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>Train on 791 samples, validate on 169 samples
Epoch 1/20
791/791 [==============================] - 1s 1ms/sample - loss: 0.0159 - val_loss: 0.0018
Epoch 2/20
791/791 [==============================] - 0s 229us/sample - loss: 0.0031 - val_loss: 0.0041
Epoch 3/20
791/791 [==============================] - 0s 203us/sample - loss: 0.0018 - val_loss: 0.0022
Epoch 4/20
791/791 [==============================] - 0s 208us/sample - loss: 0.0013 - val_loss: 0.0016
Epoch 5/20
791/791 [==============================] - 0s 203us/sample - loss: 0.0012 - val_loss: 0.0018
Epoch 6/20
791/791 [==============================] - 0s 202us/sample - loss: 9.1537e-04 - val_loss: 0.0014
Epoch 7/20
791/791 [==============================] - 0s 204us/sample - loss: 9.9727e-04 - val_loss: 0.0014
Epoch 8/20
791/791 [==============================] - 0s 205us/sample - loss: 9.8865e-04 - val_loss: 0.0013
Epoch 9/20
791/791 [==============================] - 0s 202us/sample - loss: 9.5909e-04 - val_loss: 0.0031
Epoch 10/20
791/791 [==============================] - 0s 213us/sample - loss: 0.0011 - val_loss: 0.0020
Epoch 11/20
791/791 [==============================] - 0s 204us/sample - loss: 9.3523e-04 - val_loss: 0.0011
Epoch 12/20
791/791 [==============================] - 0s 215us/sample - loss: 8.1261e-04 - val_loss: 0.0018
Epoch 13/20
791/791 [==============================] - 0s 204us/sample - loss: 6.9173e-04 - val_loss: 0.0012
Epoch 14/20
791/791 [==============================] - 0s 203us/sample - loss: 8.7234e-04 - val_loss: 0.0011
Epoch 15/20
791/791 [==============================] - 0s 207us/sample - loss: 7.6112e-04 - val_loss: 0.0019
Epoch 16/20
791/791 [==============================] - 0s 201us/sample - loss: 7.3311e-04 - val_loss: 0.0014
Epoch 17/20
791/791 [==============================] - 0s 204us/sample - loss: 6.4047e-04 - val_loss: 0.0020
Epoch 18/20
791/791 [==============================] - 0s 204us/sample - loss: 8.0069e-04 - val_loss: 9.5259e-04
Epoch 19/20
791/791 [==============================] - 0s 207us/sample - loss: 7.0957e-04 - val_loss: 0.0016
Epoch 20/20
791/791 [==============================] - 0s 206us/sample - loss: 6.9054e-04 - val_loss: 9.5337e-04
170/170 [==============================] - 0s 130us/sample - loss: 5.1280e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># model to predict day low</span>

<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">split_input_output</span><span class="p">(</span><span class="n">df_tr</span><span class="p">,</span> <span class="n">target_feature</span><span class="o">=</span><span class="s2">&quot;Low&quot;</span><span class="p">)</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">split_train_val_test</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="n">model_low</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="p">])</span>
<span class="n">model_low</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_low</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">)</span>

<span class="n">model_low</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_low</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="s2">&quot;LSTM-low.h5&quot;</span><span class="p">))</span>

<span class="c1"># plot predictions</span>

<span class="n">visualize_predictions</span><span class="p">(</span>
    <span class="n">days</span><span class="o">=</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
    <span class="n">actual</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
    <span class="n">models</span><span class="o">=</span><span class="p">[</span><span class="n">model_low</span><span class="p">],</span>
    <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
    <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;LSTM&quot;</span><span class="p">]</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>Train on 791 samples, validate on 169 samples
Epoch 1/20
791/791 [==============================] - 1s 2ms/sample - loss: 0.0231 - val_loss: 0.0217
Epoch 2/20
791/791 [==============================] - 0s 246us/sample - loss: 0.0035 - val_loss: 0.0108
Epoch 3/20
791/791 [==============================] - 0s 227us/sample - loss: 0.0020 - val_loss: 0.0030
Epoch 4/20
791/791 [==============================] - 0s 204us/sample - loss: 0.0016 - val_loss: 0.0020
Epoch 5/20
791/791 [==============================] - 0s 205us/sample - loss: 0.0015 - val_loss: 0.0029
Epoch 6/20
791/791 [==============================] - 0s 202us/sample - loss: 0.0012 - val_loss: 0.0047
Epoch 7/20
791/791 [==============================] - 0s 204us/sample - loss: 0.0014 - val_loss: 0.0025
Epoch 8/20
791/791 [==============================] - 0s 207us/sample - loss: 0.0011 - val_loss: 0.0017
Epoch 9/20
791/791 [==============================] - 0s 205us/sample - loss: 0.0012 - val_loss: 0.0020
Epoch 10/20
791/791 [==============================] - 0s 203us/sample - loss: 0.0010 - val_loss: 0.0027
Epoch 11/20
791/791 [==============================] - 0s 207us/sample - loss: 9.1126e-04 - val_loss: 0.0027
Epoch 12/20
791/791 [==============================] - 0s 204us/sample - loss: 9.6085e-04 - val_loss: 0.0016
Epoch 13/20
791/791 [==============================] - 0s 207us/sample - loss: 7.7065e-04 - val_loss: 0.0015
Epoch 14/20
791/791 [==============================] - 0s 203us/sample - loss: 0.0010 - val_loss: 0.0023
Epoch 15/20
791/791 [==============================] - 0s 206us/sample - loss: 7.8661e-04 - val_loss: 0.0019
Epoch 16/20
791/791 [==============================] - 0s 206us/sample - loss: 0.0011 - val_loss: 0.0016
Epoch 17/20
791/791 [==============================] - 0s 217us/sample - loss: 9.0151e-04 - val_loss: 0.0036
Epoch 18/20
791/791 [==============================] - 0s 207us/sample - loss: 8.1273e-04 - val_loss: 0.0021
Epoch 19/20
791/791 [==============================] - 0s 207us/sample - loss: 9.1088e-04 - val_loss: 0.0015
Epoch 20/20
791/791 [==============================] - 0s 205us/sample - loss: 8.1074e-04 - val_loss: 0.0015
170/170 [==============================] - 0s 141us/sample - loss: 7.2225e-04
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</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">plot</span><span class="p">(</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">model_high</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Predicted high&quot;</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">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">model_low</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Predicted low&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># model to predict the high and low of the next 10 days</span>
<span class="c1"># the number of output neurons is now 20, which may affect the accuracy of the model</span>

<span class="n">NEXT</span> <span class="o">=</span> <span class="mi">10</span>

<span class="n">X</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">df_tr</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">WINDOW</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_tr</span><span class="p">)</span> <span class="o">-</span> <span class="n">WINDOW</span> <span class="o">-</span> <span class="n">NEXT</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">array</span><span class="p">([</span><span class="n">df_tr</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="n">WINDOW</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">WINDOW</span> <span class="o">+</span> <span class="n">NEXT</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;High&quot;</span><span class="p">,</span> <span class="s2">&quot;Low&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_tr</span><span class="p">)</span> <span class="o">-</span> <span class="n">WINDOW</span> <span class="o">-</span> <span class="n">NEXT</span><span class="p">)])</span>

<span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">5</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="p">[:</span><span class="mi">5</span><span class="p">])</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>[[[0.0251502  0.02657588 0.02536826 ... 0.18325192 0.02020532 0.03548474]
  [0.02581434 0.02700677 0.02872307 ... 0.15517288 0.02136674 0.03473482]
  [0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]
  ...
  [0.04333636 0.0435905  0.04191442 ... 0.14844398 0.04105003 0.0496866 ]
  [0.04225476 0.04121474 0.04603291 ... 0.19474343 0.04031633 0.04924247]
  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]]

 [[0.02581434 0.02700677 0.02872307 ... 0.15517288 0.02136674 0.03473482]
  [0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]
  [0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]
  ...
  [0.04225476 0.04121474 0.04603291 ... 0.19474343 0.04031633 0.04924247]
  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]
  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]]

 [[0.02267579 0.02454561 0.02632569 ... 0.09499381 0.02203479 0.0338918 ]
  [0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]
  [0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]
  ...
  [0.03629264 0.03889333 0.0379061  ... 0.14018425 0.0391203  0.04873724]
  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]
  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]]

 [[0.0228997  0.02362171 0.02582798 ... 0.11692226 0.02226277 0.03292381]
  [0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]
  [0.02191676 0.02475136 0.02500351 ... 0.07471867 0.0222389  0.03120273]
  ...
  [0.03469869 0.03667285 0.03711205 ... 0.13836765 0.0381801  0.04815849]
  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]
  [0.03622052 0.0390719  0.03918649 ... 0.12278028 0.03813116 0.04682333]]

 [[0.02194712 0.02399437 0.02293667 ... 0.08140674 0.02227312 0.03209361]
  [0.02191676 0.02475136 0.02500351 ... 0.07471867 0.0222389  0.03120273]
  [0.02351071 0.0246349  0.02483635 ... 0.07867844 0.02255204 0.03034357]
  ...
  [0.03660383 0.03882345 0.03813786 ... 0.11329587 0.03820397 0.04755808]
  [0.03622052 0.0390719  0.03918649 ... 0.12278028 0.03813116 0.04682333]
  [0.0345355  0.03702611 0.0379441  ... 0.12658357 0.03773327 0.045969  ]]]
[[[0.03469869 0.03667285]
  [0.03660383 0.03882345]
  [0.03622052 0.0390719 ]
  [0.0345355  0.03702611]
  [0.03406111 0.037457  ]
  [0.03629264 0.03637782]
  [0.03735906 0.04066739]
  [0.04335913 0.04376906]
  [0.04343504 0.04629622]
  [0.04837626 0.05127678]]

 [[0.03660383 0.03882345]
  [0.03622052 0.0390719 ]
  [0.0345355  0.03702611]
  [0.03406111 0.037457  ]
  [0.03629264 0.03637782]
  [0.03735906 0.04066739]
  [0.04335913 0.04376906]
  [0.04343504 0.04629622]
  [0.04837626 0.05127678]
  [0.05977298 0.0597278 ]]

 [[0.03622052 0.0390719 ]
  [0.0345355  0.03702611]
  [0.03406111 0.037457  ]
  [0.03629264 0.03637782]
  [0.03735906 0.04066739]
  [0.04335913 0.04376906]
  [0.04343504 0.04629622]
  [0.04837626 0.05127678]
  [0.05977298 0.0597278 ]
  [0.05939348 0.06071768]]

 [[0.0345355  0.03702611]
  [0.03406111 0.037457  ]
  [0.03629264 0.03637782]
  [0.03735906 0.04066739]
  [0.04335913 0.04376906]
  [0.04343504 0.04629622]
  [0.04837626 0.05127678]
  [0.05977298 0.0597278 ]
  [0.05939348 0.06071768]
  [0.05822837 0.06056242]]

 [[0.03406111 0.037457  ]
  [0.03629264 0.03637782]
  [0.03735906 0.04066739]
  [0.04335913 0.04376906]
  [0.04343504 0.04629622]
  [0.04837626 0.05127678]
  [0.05977298 0.0597278 ]
  [0.05939348 0.06071768]
  [0.05822837 0.06056242]
  [0.09155702 0.08325246]]]
</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">split_train_val_test</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">reshape</span><span class="p">(</span><span class="n">y</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="o">-</span><span class="mi">1</span><span class="p">))</span>

<span class="n">model_next</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">([</span>
    <span class="n">LSTM</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Flatten</span><span class="p">(),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">),</span>
    <span class="n">Dropout</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>

    <span class="n">Dense</span><span class="p">(</span><span class="n">NEXT</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
<span class="p">])</span>

<span class="n">model_next</span><span class="o">.</span><span class="n">compile</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="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">decay</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">),</span> <span class="n">loss</span><span class="o">=</span><span class="s2">&quot;mse&quot;</span><span class="p">)</span>

<span class="n">model_next</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">val_X</span><span class="p">,</span> <span class="n">val_y</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">EPOCHS</span><span class="p">)</span>

<span class="n">model_next</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">test_X</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>

<span class="n">model_next</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_DIR</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;LSTM-next-</span><span class="si">{</span><span class="n">NEXT</span><span class="si">}</span><span class="s2">-high-low.h5&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>Train on 784 samples, validate on 168 samples
Epoch 1/20
784/784 [==============================] - 2s 2ms/sample - loss: 0.0217 - val_loss: 0.0083
Epoch 2/20
784/784 [==============================] - 0s 254us/sample - loss: 0.0051 - val_loss: 0.0070
Epoch 3/20
784/784 [==============================] - 0s 211us/sample - loss: 0.0037 - val_loss: 0.0051
Epoch 4/20
784/784 [==============================] - 0s 216us/sample - loss: 0.0030 - val_loss: 0.0050
Epoch 5/20
784/784 [==============================] - 0s 209us/sample - loss: 0.0028 - val_loss: 0.0049
Epoch 6/20
784/784 [==============================] - 0s 209us/sample - loss: 0.0026 - val_loss: 0.0049
Epoch 7/20
784/784 [==============================] - 0s 212us/sample - loss: 0.0023 - val_loss: 0.0049
Epoch 8/20
784/784 [==============================] - 0s 215us/sample - loss: 0.0022 - val_loss: 0.0049
Epoch 9/20
784/784 [==============================] - 0s 215us/sample - loss: 0.0022 - val_loss: 0.0048
Epoch 10/20
784/784 [==============================] - 0s 216us/sample - loss: 0.0021 - val_loss: 0.0047
Epoch 11/20
784/784 [==============================] - 0s 208us/sample - loss: 0.0020 - val_loss: 0.0051
Epoch 12/20
784/784 [==============================] - 0s 205us/sample - loss: 0.0019 - val_loss: 0.0047
Epoch 13/20
784/784 [==============================] - 0s 210us/sample - loss: 0.0019 - val_loss: 0.0051
Epoch 14/20
784/784 [==============================] - 0s 213us/sample - loss: 0.0017 - val_loss: 0.0053
Epoch 15/20
784/784 [==============================] - 0s 217us/sample - loss: 0.0018 - val_loss: 0.0099
Epoch 16/20
784/784 [==============================] - 0s 215us/sample - loss: 0.0018 - val_loss: 0.0052
Epoch 17/20
784/784 [==============================] - 0s 207us/sample - loss: 0.0016 - val_loss: 0.0046
Epoch 18/20
784/784 [==============================] - 0s 216us/sample - loss: 0.0015 - val_loss: 0.0058
Epoch 19/20
784/784 [==============================] - 0s 217us/sample - loss: 0.0018 - val_loss: 0.0047
Epoch 20/20
784/784 [==============================] - 0s 210us/sample - loss: 0.0018 - val_loss: 0.0056
168/168 [==============================] - 0s 135us/sample - loss: 0.0031
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>As you can see, the accuracy of the model is clearly affected due to the higher amount of predictions and hence greater complexity, thus a larger model may be required to make accurate predictions futher into the future.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predicted</span> <span class="o">=</span> <span class="n">model_next</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">df_tr</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">100</span><span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">high</span> <span class="o">=</span> <span class="n">predicted</span><span class="p">[:</span><span class="n">NEXT</span><span class="p">]</span>
<span class="n">low</span> <span class="o">=</span> <span class="n">predicted</span><span class="p">[</span><span class="n">NEXT</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">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df_tr</span><span class="p">[</span><span class="s2">&quot;High&quot;</span><span class="p">][</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Actual High&quot;</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">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df_tr</span><span class="p">[</span><span class="s2">&quot;Low&quot;</span><span class="p">][</span><span class="n">WINDOW</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Actual Low&quot;</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="nb">range</span><span class="p">(</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">NEXT</span><span class="p">),</span> <span class="n">high</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Predicted High&quot;</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="nb">range</span><span class="p">(</span><span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">df_tr</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">NEXT</span><span class="p">),</span> <span class="n">low</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Predicted Low&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo
dHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXd8VFX6/99nenpCCAESMPQinUiV
IopiQ1GxF6yrLivqrmVd/dpd/YGudUXXAusq7i42dEURpKqUgCC9hxpSSZ8+5/fHncxk0hMmyUw4
79eLF/eec+69z4Twuc8855znEVJKFAqFQtG20LW2AQqFQqEIPkrcFQqFog2ixF2hUCjaIErcFQqF
og2ixF2hUCjaIErcFQqFog2ixF2hUCjaIErcFQqFog2ixF2hUCjaIIbWenD79u1lWlpaaz1eoVAo
wpKNGzfmSSmT6hvXauKelpZGRkZGaz1eoVAowhIhxKGGjFNhGYVCoWiDKHFXKBSKNogSd4VCoWiD
tFrMvSacTidHjx7FZrO1timKBmKxWEhNTcVoNLa2KQqFohIhJe5Hjx4lJiaGtLQ0hBCtbY6iHqSU
5Ofnc/ToUbp169ba5igUikrUG5YRQnwghMgRQmyrpV8IIV4XQuwTQvwmhBjWVGNsNhuJiYlK2MME
IQSJiYnqm5ZCEYI0JOY+D5hSR/+FQC/vn7uAt0/FICXs4YX691IoQpN6xV1KuQooqGPIZcA/pcZa
IF4I0SlYBioUipYnp9jG4q1Z2onLDhkfgtvVukYpGkUwVsukAEcqnR/1tlVDCHGXECJDCJGRm5sb
hEc3D19++SVCCHbt2lXv2Hnz5nH8+PEmP2vFihVccsklDWqfMWMGCxcuBOCOO+5gx44ddd678niF
ojHc9dFG7vl4E/mldvjpdfjmftimfpfCiRZdCimlfFdKmS6lTE9Kqnf3bKuxYMECzj77bBYsWFDv
2FMV96by3nvv0b9//xZ/ruL0oGPez/zT+Ff2HcvFUaBtiMw/fqCVrVI0hmCI+zGgS6XzVG9bWFJa
WsqaNWt4//33+fTTTwP6XnrpJQYOHMjgwYN59NFHWbhwIRkZGdxwww0MGTIEq9VKWloaeXl5AGRk
ZDBx4kQA1q9fz+jRoxk6dChjxoxh9+7dp2TnxIkTfekb3n//fXr37s2IESO48847mTlzpm/cqlWr
GDNmDN27d1devKLBzJXPMV6/lfjt89mRo02YH9u9qZWtUjSGYCyFXATMFEJ8CowEiqSUWad606e/
3s6O48WnbFxl+neO5clLz6xzzFdffcWUKVPo3bs3iYmJbNy4keHDh7N48WK++uor1q1bR2RkJAUF
BbRr144333yTOXPmkJ6eXud9+/bty+rVqzEYDCxdupTHHnuMzz77rM5rVq9ezZAhQ3znhw8frhaq
OX78OM8++yybNm0iJiaGSZMmMXjwYF9/VlYWa9asYdeuXUydOpWrrrqqzmcqFJWxigicdisAEeVH
W9kaRWOoV9yFEAuAiUB7IcRR4EnACCClnAt8C1wE7APKgVuby9iWYMGCBcyaNQuAa6+9lgULFjB8
+HCWLl3KrbfeSmRkJADt2rVr1H2Lioq45ZZb2Lt3L0IInE5nvdeMGzeOb775xnc+Y8aMamPWr1/P
hAkTfPZMnz6dPXv2+Povv/xydDod/fv3Jzs7u1E2KxRlhnjMLs1z17nUktdwol5xl1JeV0+/BH4f
NIu81OdhNwcFBQX8+OOPbN26FSEEbrcbIQSzZ89u8D0MBgMejwcgYP33E088wTnnnMMXX3xBZmam
L1zT3JjNZt+x9k+lUNSPRwp0QlJcZiPJ5QDA4LG3slWKxqByy1Ri4cKF3HTTTRw6dIjMzEyOHDlC
t27dWL16NZMnT+bDDz+kvLwc0F4EADExMZSUlPjukZaWxsaNGwECwi5FRUWkpGiLiObNmxc0m886
6yxWrlzJyZMncblc9YZ6FIqGoBOaI7D4t6O4HFpYxiRtykEII5S4V2LBggVMmzYtoO3KK69kwYIF
TJkyhalTp5Kens6QIUOYM2cOoIVK7r77bt+E6pNPPsmsWbNIT09Hr9f77vPwww/z5z//maFDh+Jy
BW+9cEpKCo899hgjRoxg7NixpKWlERcXF7T7K05vDLiR3nCMBQc2p6eVLVI0FNFab+L09HRZtVjH
zp076devX6vYE86UlpYSHR2Ny+Vi2rRp3HbbbdVeUs2J+ndrgzylOQgPOe9iun4lI3S7sUoTJx84
TOf4iFY27vRGCLFRSln3Cg6U594meOqppxgyZAgDBgygW7duXH755a1tkiKMcbr93rkBN2a0yf8I
4eBkmZpUDRdCKiukomlUhIgUimBgdbqpSOCsx0M8pb6+0pO5kJLQOoYpGoXy3BUKRQA2p9t3bMRF
R3GS48auWkOB2qUaLihxVygUAZws8+/BeNL4EWbh5KCpNwDCmt9aZikaiRJ3hUIRwB3vLK3W5uwy
BgC3y12tTxGaKHFXKBQBvOp+vlrbwL59AXCrtL9hgxL3GgjVlL8KRUtgpLqA6yO1pZFK3MMHJe41
EA4pfxWK5sKDjlXugQFthogKcVdhmXBBiXsVQj3l77Jlyxg6dCgDBw7ktttuw263s2HDBq644gpA
y2oZERGBw+HAZrPRvXv3pv8wFKclcXo7WGID2oxGbXFkMHdXK5qX0F3nvvhROLE1uPfsOBAufLHO
IaGU8rcqNpuNGTNmsGzZMnr37s3NN9/M22+/zcyZM9m8eTOgpQkeMGAAGzZswOVyMXLkyEY9Q6Ew
SCex0dFw0t9mNGji7vG0Mc/d4wFnOZijG37N0Y0Q1R4Szmg+u4KA8tyrsGDBAq699lrAn/IXCErK
3+nTpzNgwAAeeOABtm/f3mjbdu/eTbdu3ejdW1uWdsstt7Bq1SoMBgM9evRg586drF+/ngcffJBV
q1axevVqxo0b1+jnKE5vDNIFeu82pt5T4O6f0Ok0qWhKWGb9wQJe+HZnSCYdK1o6G/6aAtaT9Q8G
yNsH702C1wY1r2FBIHQ993o87OYgnFP+jh8/nsWLF2M0GjnvvPOYMWMGbre7UbYrFFJKjDhBb4Kn
ivwdhYeBJkyonsxkxPzB7HCdT9mkT4i2GOu/pgXYejifvcdyGbRmHnE68Bzbgq7nxLovshXBm8Nb
xL5goDz3SoR6yt8+ffqQmZnJvn37APjoo4+YMGECoBX2ePXVVxk9ejRJSUnk5+eze/duBgwY0KRn
KU5PnG6JCRcYzIEdQstwmltsbdT9Sta8C8AMwxJKdy4Lio3BYOe7t3LF9yOJFJoDVpBzpP6Ljv/a
zFYFFyXulQi1lL/Lli0jNTXV9+fXX3/lww8/ZPr06QwcOBCdTsfdd98NwMiRI8nOzmb8+PEADBo0
iIEDByKECMaPRnGaYHe5MeJCpzcFdghNKm4v+Fuj7rf0oL/AR2lxwSnbFyym6n8GoLPQbDpZWH9Y
puzn95rVpmCjUv4qThn179Z2yC2x025OMtt73smgmyqF9EpzYE4v7bhyuKYelr9+F+cU/BuAX0e+
yg9iNG+v3M/Bv14cTLNrpXjLIlj+ArH3/QQ6v7N14sk0Ogq/oK84YxYTb32m5ptICStehJVVQsWN
+DkEE5XyV6FQNBqb3YFeyFo9d9C8+5pwuj3sy/GHKN9bfYDik7m+87LyUv6+Yj8t6U/qvryH2MKd
5OXlBrTbZWDs315eXPtNTmytLuyAxxN6E8SVUeKuUCh8OOzeYtjGqjF3v1QUltdc3P3rdbuxvDWE
X1d/DYsf5cfF/8XgKqUMbYWZ1TtfBS1Xz9cptbDk/qxc2LcMbJqI2wh8eemcZbXfxOoPJx2Tib7j
/OLSmkaHDErcFQqFD7tdmzDVGap47pVCGidLqgvh8UIrn33zFakijw4b5sC6t/nE9AIxWCkQ8QA4
7f7JWIe7mcr1eQLv6/FKnCtvP/zrCnixCwB2YQFgryeFXBmL0VW7UJ/My/IdF0n/evjifWuDZnZz
oMRdoVBoHNlA2jdXAyDMUYF9lTz34qLqk4/bjxfTQ2hpOBwe/9jx+q1kuWMAkC7/5KrDFXxx9+xf
Ac8kII9v9rd5PXfHiT3+gaU5eISeMiJYM/E/lBnbE+OsPZVxcc5R/0m7br7D1B9+FzTbmwMl7gqF
AgDPhxcRWbATAEdSYG6ZiqWQALb8o1Ql2mzgPN0mAPIdgdtnHEYtL01FoW1oHnHftVKbuN39y/8A
2LZuGUmiEABdfiVxn9MLg3RyPG4ot04aQJExiXiXFpO//9NfWbYzO+C+hTmHfcf7+97t/wy60K4l
q8RdoVAAoPM4fMee5MGBnZU8d+eJndWuFeW5jNVtA8DiCFzyODrVhBsdOCuJezOEZQqt2kRvl+1z
AZAb5/vtyw3M8BpPMU6hhZ7Kje2I8RQhpeTLzce5fX7gKj7HyWO+48FnTcDzxEnyZCzZZpV+IKzQ
6/W+YtPTp0/3bVpqCpXT9i5atIgXX6x9121hYSF///vfG/2Mp556qsYaqjW1V05qNmbMmHrvXXm8
4vTCbNIHNlSKua/bvIXZ3wUKvCg6gl5ok6QRnsCYvN7jxCFM4G6+sIzd5eb4CS02HuUpRno8dLT6
k/ON1wfmqUqikGKX9g1DmGOI8JTjdEsm6LbQES1Ec/0/1nL/p78SafevtOkUb0Gn15GjS8bprHli
OVRQ4l6FiIgINm/ezLZt2zCZTMydOzegX0rpSy/QGKZOncqjjz5aa39Txb2p/Pzzzy32LEV4UC61
FTJ2aaRDjCWws1JY5jHjAnJWfxDQ7XL4vfJYUWXC1ePCJYwIl4PHDB+Tabkee5DFfePyL7lKv8p3
Xph9GIs9MI6e6UlmluNeAMzChVWvTY5adZFEYeO1pbuYb3qJz8xP4fZIumX+m4wtW4hz5nFMJLNw
9BcY9ZpkCr0+5NMfK3Gvg3HjxrFv3z4yMzPp06cPN998MwMGDODIkSMsWbKE0aNHM2zYMKZPn05p
qTbb/t1339G3b1+GDRvG559/7rvXvHnzmDlzJgDZ2dlMmzaNwYMHM3jwYH7++WceffRR9u/fz5Ah
Q3jooYcAmD17NmeddRaDBg3iySef9N3r+eefp3fv3px99tlNTh0cHa39Yns8Hu6991769u3L5MmT
ueiii1i4cKFv3BtvvMGwYcMYOHBgg4qXKMITm7UMMw4yPclc5XiSdlFV17kH7nSuCMFU4Kks7lT5
tmuMxCVM6Nx27jJo8fBge+7G3/4VcP7mv/5NjCOXlXIYu5K1DVNFpg7cPMkfbipB+z/Qvn0SOiFJ
WKNtYkoR+ZRk7eN54wf80/hXOnmyONjxQq66YJLvWg96hAxtcQ/ZxGEvrX+JXQXBFZO+7fryyIhH
GjTW5XKxePFipkyZAsDevXuZP38+o0aNIi8vj+eee46lS5cSFRXFSy+9xCuvvMLDDz/MnXfeyY8/
/kjPnj255pprarz3fffdx4QJE/jiiy9wu92Ulpby4osvsm3bNl/q3iVLlrB3717Wr1+PlJKpU6ey
atUqoqKi+PTTT9m8eTMul4thw4YxfHjNyYz+9re/8a9/+X/payoq8vnnn5OZmcmOHTvIycmhX79+
3Hbbbb7+9u3bs2nTJv7+978zZ84c3nsvvLZgKxrGyczf6CQkLzmvpdeQGjKJVhH3CEvgZKLbG093
CBNm/LF7opNh2lzcb05G5/GHZWx2GxB3ynZ7/nEuIHCXay+LvckX0it7MU+UvQBAclIizpgukA05
tOOM2HjftUVoK4L6pibBdrjDsNjXV7biVeKB7roTWkOHvgHPlUKHnmZazhkklOdeBavVypAhQ0hP
T6dr167cfvvtAJxxxhmMGjUKgLVr17Jjxw7Gjh3LkCFDmD9/PocOHWLXrl1069aNXr16IYTgxhtv
rPEZP/74I/fccw+gxfjj4qr/ki9ZsoQlS5YwdOhQhg0bxq5du9i7dy+rV69m2rRpREZGEhsby9Sp
U2v9LA888ACbN2/2/encuXO1MWvWrGH69OnodDo6duzIOeecE9BfUQRk+PDhZGZm1v8DVIQl1iNb
ALjtikt45ZohdY49Yu5FrCdw673Hqa1hd+qrLKG84AWIS8WtM+Fy+Ne5lxSX0BQcLg//WnvI5/nr
jmWgO7aBUe6NHIvoTa+b3gi8YPJzGCO1wiMlMgJLlP//WveuXQEw9J1S7TknM38LOI+MSQg4l0J5
7k2moR52sKmIuVclKsr/SyulZPLkydXK8NV0XVORUvLnP/+Z3/0ucC3tq6++GrRnNASzWYvD6vV6
VYWnrVKSTfefH8EujcSl9K53uFtvxuBwsO+dGzB0H0fa5Ltxe8MybmMUuCqtg0/RvlWWuvVatsmK
R5bWsd2/Dj5Ys5+flixEcC03jOoW0Jdi3QMR8QFtfXr3Yf+xVABOymgiov0iPebSWwEQsdWdngHO
QHE3RQS+tKTQIZow99aSKM+9CYwaNYqffvrJl3q3rKyMPXv20LdvXzIzM9m/fz9ArTVYzz33XN5+
+21AK35QVFRULXXwBRdcwAcffOCL5R87doycnBzGjx/Pl19+idVqpaSkhK+//vqUPsvYsWP57LPP
8Hg8ZGdns2LFilO6nyL8cGZoSwbNwklyXFQ9o0HqTOikg55Z35D2k+aE+bxyk38Hp+emRb5NPzrp
YbJ+k6+vrIni3uXAv/nI9CLt939ZvbPvJaA3UiQjfU1CCOx9pvKw8042pdxIZIzfcxcm72fV159j
3hIRWHZQokNIJe5tjqSkJObNm8d1113HoEGDGD16NLt27cJisfDuu+9y8cUXM2zYMDp06FDj9a+9
9hrLly9n4MCBDB8+nB07dpCYmMjYsWMZMGAADz30EOeffz7XX389o0ePZuDAgVx11VWUlJQwbNgw
rrnmGgYPHsyFF17IWWeddUqf5corryQ1NZX+/ftz4403MmzYsBrDRIq2y8Z87dvZ485biY2o/8u8
1JuJrLzcsegYbm9qgehK4qlL9c8FdXMfDLiHy1ZHLpc6SHRo80b6vOpr7Zn2DgCFvwv8Bt2/czz9
Lvo9z183loiopv1uW6ICy/BJoUdHGwjLCCGmAK8BeuA9KeWLVfq7AvOBeO+YR6WU3wbZ1hahwlOu
TFpaGtu2Ba4OmDRpEhs2bKg2dsqUKTWuKpkxYwYzZswAIDk5ma+++qramE8++STgfNasWcyaNava
uL/85S/85S9/qfNzPPXUU9XaKsfMKz6nTqdjzpw5REdHk5+fz4gRIxg4cGC18enp6cqrb6NkF2i7
OM+78q768/9HJyMNJpKEP+Zevmsp0qGtkNF5C2t7pEBnrGMHp7Np+0ek11t2lhfj8Uifd5on42jv
rYN6RufkgGuEENw61hvCkXV76d+5zyJfxnKDIbCwSERUTKAdIvQ993rFXQihB94CJgNHgQ1CiEVS
yh2Vhj0O/EdK+bYQoj/wLZDWDPYqmoFLLrmEwsJCHA4HTzzxBB07dmxtkxQtiHRoL/qJA9LqHnjn
cojrgvjnvbQX/rBKQfZhDI4inBgwFmu7OZ2JfTDr9LXdCeFqXEUn33VubSWO0VPOyTIbFTkaXaK6
lB30JNOtaqP35eWMTqGyzDukHpNw0+H8+zEePwg7l2ETFixSm0uIqkHcdSG+WqYhnvsIYJ+U8gCA
EOJT4DKgsrhLoCIoFQdUX3OnCFmUR356I5xleKjH0wZIGab9XSVjZGn+cYS9iDJdDPE5miyY02+q
55mnJu4Wj5W8/FyfuFfeZAXw/wZ8Tc+UDtXFHeCeXzBGB3r3Ged/TqdfnmZY+lh+Wa6lTyjUJ9LR
pb2szDHtAsZLoUPXBlbLpACVCwweBUZWGfMUsEQI8QcgCjivphsJIe4C7gLo6l2GpFAoWhedsxyb
iCCygSUZRaX6qiVE4ig4QqL9JHZTLNi9K2WGVlkGHJsKxf6EY0313Cvy31g85Xgy/ulrj4oJjKU/
fNX42m+S3L9a05ixE2HsRAD6du0E6yEiKhaKNHEXNdSUFSHuuQdrQvU6YJ6UMhW4CPhICFHt3lLK
d6WU6VLK9KSkpCA9WqFQnAp6V3mjMhxWCJ1VmthhHkL78gMM8uyiLLKLf5ClysTljQsDTnWVMkQ2
hgpxj5BW+m3TygB6EnsRM61xtV3rIiFeWy4ZV8fkshR6dCEec2+IuB8DKv2rkeptq8ztwH8ApJS/
ABagfTAMVCgUzYvOVY5Lb6l/oBerRxO9AlNnrBHJJLuPYxYu8pJGwnlPQ2KvajtaaR+4fl7XRM9d
7xV3o3Twm1krI6q79xfoVsOu2qai84v6LMe9POOsIcTURmLuG4BeQohuaKJ+LXB9lTGHgXOBeUKI
fmjinotCoQhppJQYXOW4LfWvb6+ga1IcZEJs/3MxZtnQo2WDNETGw9n3wdn3V79Ip9dW0HgzRxrc
jV8t4/ZICorLQK+Je77NxoGIM+negHXqjaLixaQ38cjD/4fVWT223iY8dymlC5gJfA/sRFsVs10I
8YwQomLv+x+BO4UQW4AFwAzZUkUSg0xbTvmrOH3Z8d0/yH95BFWrU5eVFnOebiPJ5XtqubI6Ud50
wDHtOmE0+8M5xqiE2i4BwFnJlzS4G++5l9pcmNDS7JqFk2hZiqdq+CcYJA+EUffCle/ROT6CHknR
1YZoq2VCe0K1QTF3KeW3UsreUsoeUsrnvW3/J6Vc5D3eIaUcK6UcLKUcIqVc0pxGNyenS8pfxelF
/7V/IrFkN1nZgVWGSo7vbfpNBZgi/OJujq5b3M3Cn/88K7+w0Y9zuD0kCm0Xd2dRwEBdJrqIup/Z
JHQ6mPJXaNe9jjF6dHg4uf0Hcn/+OPg2BAG1Q7UO2mLK31deeYUBAwYwYMAAX56a2bNn8/rrrwNa
srFJk7TUpj/++CM33HBDE396ilDCLjWv+emPvX7Xzq/hqTgif3qp8Tfr6C3Bl9QPi8W/1T8ysuGh
HYu0UWpvXK4ih7WEfrrDAW2WnkGMtTcCbYeqh4T/XkXSkntbxYb6CNnEYSdeeAH7zuCm/DX360vH
xx5r0Ni2kPK3Khs3buTDDz9k3bp1SCkZOXIkEyZMYNy4cbz88svcd999ZGRkYLfbcTqdrF69mvHj
61hSpggpPvolkxe+3cW2py9Arwuc0MwR7enCCSbovAmxNmt5j+IOa2K/5+KF1J8yzMuAKyGpD3Qc
iCVzv685OiqyjosCicDBofwyzuzc8LCKw66FSEulhWhhI1/G0Pmcuxp8fTARXs/dh5TVJ5FbGeW5
V6Etpfytypo1a5g2bRpRUVFER0dzxRVXsHr1aoYPH87GjRspLi7GbDYzevRoMjIyWL16NePGtY5n
pGg8T3y1HavTTbG1evm3kzotfNHOo5VNzC0MTNwVkTqo4Q8Swue9R0T649FRkQ0Td2dEEhHCTnax
jZ1ZxfR5fDFHT9Y/t+W0aXF6j0nbLXrckBpQ/q8lkUKPQfq/eRw4Gnr7NkPWc2+ohx1sTseUv0aj
kW7dujFv3jzGjBnDoEGDWL58Ofv27aNfv35Bf56ieSm2OUmoUknJJLVCGTqHFrO25fvDG8UykviE
wB2YDaXy/wujqWHLKT0R7YgotWN1ePjHqgPYXR5+3pfP1WfV/XJwepOTiYh4cObSpX1sneObFaEj
WvjX6r/x9U/87d6rW8+eGlCeexMI15S/48aN48svv6S8vJyysjK++OILn2c+btw45syZw/jx4xk3
bhxz585l6NCh9SeSUoQM9+gX8ZDhU0ps1WPZRql58yZ3OVa7iwRnNv92TeRGx58Za3+daHPT/LzI
+Ep5iPSm2gdWQprjiMSOzekmq0gTyKQYcz1X+dMKeyK0F1G8ufXky2gIXH55hql6wsHWRol7EwiX
lL/PPfccqampvj/Dhg1jxowZjBgxgpEjR3LHHXcwdOhQQBP3rKwsRo8eTXJyMhaLRYVkwojMvDIe
MX7K7w2L6PTd7eAODM0YvIUyzO5yiooLiRY2DshOrPEMpITIJr/EdT0m+E8aKO7EJJMsTmJ1uilz
aHa5PfWvnK4owm1N8tZBNTV8AjfYREQHfmuYlF+zI9eaiNZajp6eni4zMjIC2nbu3KnCAGGI+ndr
ff700qvMsfpXVMlZWxAJab7zY0/2JEXksoVeRN/yH3rMH8rGAY+zq8vVxFqMXDq4ejWiBvOUd87o
4YMQWUd4xzvOPuo+xC9v8c9z17NqwyYeKXqOE5d+zLlnDazzMauWfcP41Tdw5KKP6GIohF7nQ0zr
ZDA98M0cumc86zv/1ZTO0MeW1XFF8BBCbJRSptc3TnnuCkUb4Enr/ws43/DRE0x+ZaXv3Oe5e2y+
KkiRUTHcMPKMUxP2ytS3U7SvtqHPENUOk3BTXl7O5JP/5kzdIZKOLK77WiAzS5sM7tw+AYbd3GrC
DqCL9K+v9yAwNmHHbXOjxF2haAMcjAz0ekcULGJvjj8ObPTu7LTgoLhYE3dzZGCO8lNGX0/c/NqP
4aki9N7Uwu+v2EmM0ETRpqt/pc2kzFe0x+hbfx2IIcr/DeWkPolIT9MKfjcnISfuYZq14LRF/XuF
BidFHHm6RHJ6XOVrE3jweCRuj/QVp44SNuRWLUOjpRGbjhpEQ3O8eLNKmnESgzZJatXVb4vB5S3N
177BK/KbjcqpFkojOhHtUROqdWKxWMjPz1eCESZIKcnPz8diaXhGQUUz4bLj1pkp7O/fURxDOU6P
hw2ZBb5le0miiHEntILYlsggLSW8+StIv73hm3gqxF04OFOXCYDHVfduVZvTzV53R7JiBkJUYp1j
WwJTtN9zt0V0JI5SHvxkHVsOnmhFqwJp/e83lUhNTeXo0aPk5qqEkuGCxWIhNTW1tc047dG77biE
ie4Jfu85XpThckvKVr9d4zWRUUEKy3SfqP1pKF5xv0P/LR2FVtzD43LUeUl+mYNEUYInosbaSi2O
Jdb/gnFEdcYsnLyy53zKdpvh6ZxWtMxPSIl7xWYahULROPTSiUdvwtB1BC59JAZ3OXGU4XR7GJq9
sMZrzB16trCVXryx+en6Vb4m6bLXeYnT5aGdKMZuadpmq2BjqZQkzR3jn5COEnYoyYaY5Joua1FC
KiyjUCiaht7jwK03gcHM3vPmwwsKAAAgAElEQVTnARAnynC6JSdLA6seFcooXnBeh4hshoyKDcG7
Hj5S+AW9Ps/d6XKTQAlOS+uHZACEKZJdAx8m4+z3aN8hJbDz5dafEwAl7gpFm8AoHXh0mmhaYjQB
vEn/Ay6Ph3I0Tzmzj5Yn6WDieP70dCuml3ZUX1nidtbjuVuLMQsX7hDx3AH6XvkX0s+bTsdRoZV2
oAIl7gpFG8AgnT5xT+nUCYAL9Bm43JJEXRm/Jkyhax9tN/LAlDhMhlb8r1/DTlaPs+6aqh5rEQDS
0or5ZGrBYKy+Skg2oeZDsFHirlC0AYzSgfTGsk3x/hiw0+0hQtpwG6PReTMoGnStnC/Iu5kJwOWd
9qsvLON2av06Q/05aEIBa1nNxUhKco5x56JbWHFkRbPboMRdoQhzXG4PRlzICo9YCA70uIkSGYHD
7SESGx5jpU1Crb3UWAjyEoYAYNVH40bHoRN1r5BzuyrEvYH5a1qZ/CPVi+iUlxVxdPx5PPjwekpt
xTVcFVyUuCsUYY7D7cEsnEiDf7+BNMUQhY0yq1XrM0YBoZPhU3pfNtnGFIplJLGU15nT3eWNyesN
QS6GHSRKpn7A8c7n+871K16Ahbez50Qx+aV2/rf9Mw4NH+Xr77ynoNltCqmlkAqFovFYHW7MOCmr
HMs2mNEJSXlRvnYekEGx9TcJegyauGcZu9KeImLLyth+vJjUhJrTEPg8d2Noeu4xw67kUHYBnY9r
la0656yCHLg6YzKG6ERe3Ts/YHyCqF50O9goz12hCHPKDqwjSRRhNFfaKez14p/9t7aW3G2Mgm7e
FM7pt7W0idVwG7T8Mm4MRMS2I44ySr156P/03y2s2hMYpqkQd0MIh2X6dqwu2Bfp1zO+fCmFPTbi
qqS2ce7m/waixF2hCGOklHT9/FIAjGa/1xsdrQnNLMNnAAzodybEpcJTRdB1VPUbtTBur+fuFgaI
6UQ3cYIyhwub083CjUe5+YP1AeM93glVfQiLu6GGqNcLxvd5xTSXBLOLm//oLwmoc7ib3R4l7gpF
GJOVl+87tlgifMeJcdqSwYv1mkjGdhnQsobVg8fnuevRdRlBmi4bW1kxxbbq9V8BPC6tXW8K4dUy
3tCXs9u51bo6uNy4DIJrHtHTY/NG4q+8otnNUeKuUIQxpTn+WqiJkX7P0FC1nmlskHK2B4mKmLsL
PYZIbxGP8hKKrS5qmhOoCMuE6oQqoC3xvHA2xms+ZL+nU0BXJ29iNKkTmCwNKyR+qihxVyjCGHul
9dQ6e5G/o+p68BCrhVsh7gIPwuvx7j6SzYmCQjItN3CL/vuA8dIr7kZjCHvuOh2MvAsscXwy4nNy
pX/DVbSUdHdYeGbMMy1nTos9SaFQBB1neSVBT6lUea3Sssi9Z85qQYsaRrs4LSNlj0SzL5zRo2AN
R7b+BMAM/XcB46W3JqzBFLox98o8cUl/EnuN9J0fMPflqewIpvWa1mI2KHFXKMIEKSUbMgsC6h1Y
dn8FwPFL/gWDr/MPrrRpqWen0MnHUkFCjCbovdtbfFkiH3C/j71EWyVjrVKZSbq9q2VC2XOvgu7K
9+D85+CmL3DqI9F76t6FG/Tnt+jTFApFk/lxVw7T5/7Cv9YeAiC3qIz+WV8AYEzqpYUFKoj0Z08U
obhlv2JNvtsBdv9uTUeZ97jKLlq3d0I1lJdCViMiHsb8AXpMQupM6GXNk8XNhRJ3hSJMKCjTPL+N
h7QCF/v2bPP1tUtsHzg4Ksl/7G5ZUWkQPnF3Qto4X3NZqRZmEtLD7hP+7JHCm+89JF9UDcFgwqg8
d4VCURPRZgNmHGw8rIm7bt9SX5++aum5yEqhGEdZS5jXOCrqrbodENuJvd1vpkRG4LZWCLrktnkb
sDndSCkRLm/WSGNEjbcLefRmDNLZoiVElbgrFGGCqWA3uy0zGFC0EoDiHC08IyPaVV8No9NDfFft
2BF6xZsDxB0wmKOIxIbRo+WXMeDhWKGVvk98x1vL9yHc3nzvYeq5J8bF0D4CPC2Y+aFB4i6EmCKE
2C2E2CeEeLSWMVcLIXYIIbYLIT4JrpkKhcJycg8At+u/BcBtLaJIF494+EDNF4y5T/vbWXtCrlbD
F5bR1n8bIqLRC0kC2ouok8hnv/kGxuu28N+NR9G5vZ67ITw996T4GGKNHvQtmG65XnEXQuiBt4AL
gf7AdUKI/lXG9AL+DIyVUp4J3N8MtioUpzc2bU17LFqYxewqxaaPqX0Ne//LIbEXjLq3pSxsOGeM
gXbdYeIjAJgs2uqZ9kKLuUcLG3ohudewiGizAZ3bhgND4KRxOGEwQz11YoP+yAaMGQHsk1IeABBC
fApcBuyoNOZO4C0p5UkAKWVolP9WKNoQBmseAGa02K3ZXYbdElX7BdFJ8IeMFrKukVji4L5ffadx
sdqGn0QRmOfcKfXEWoxQbseBmTBaKxOI3uQLQbUUDXkNpgBHKp0f9bZVpjfQWwjxkxBirRBiSrAM
VCgUGrZSzXOPEeW8v+YgUZ4SnIaYVrYqOFTkxUkgsL6qCz0dYs1IhxWnLmylvVU892B9xzEAvYCJ
wHXAP4QQ8VUHCSHuEkJkCCEycnPrrryiUCgCsXvXgLcTpXRe8js6UIDDHHoblJqENwafIAInf8/R
b8Fsz6e0tASXLjwnUwFtcjtlGLRgbdWGiPsxoEul81RvW2WOAouklE4p5UFgD5rYByClfFdKmS6l
TE9KSqrarVAo6sDo9i9pvEi/nk6iAJelbYl7kiii2BIYGOh/9L9YpBUr4TmZCsDwGXDH0hadM2jI
kzYAvYQQ3YQQJuBaYFGVMV+iee0IIdqjhWlqmcJXKBRNweix4hCB3qunjYk7QHHCmQFd5dZyLtBn
EB0b19JWhTX1iruU0gXMBL4HdgL/kVJuF0I8I4SY6h32PZAvhNgBLAceklLm13xHhULRFCzucoqN
Vb7xRrYRca+UViCiXWcYez9c8AIAI3U7AWhXsLlVTAtXGlRDVUr5LfBtlbb/q3QsgQe9fxQKRTNg
kVaspnbgOOprE1V3poYrlTz3dolJMOlxAMqWzSbRUVzbVYo6CNNFowrF6YXbI4mQVuxVJlB1bVDc
hdmfB92ljyBGaJuw3F3HtrhZ4YwSd4UiDMgvsxMlbHgsCQHt0QnJrWRRkKmccyUu1XfoNkSQKLTl
kbpLXm5pq8IaJe4KRRhw7KSVKGxYogInFTt3Tq3lijDD4/If97/M31wp3YAwt401/S2FEneFIgzI
K7EThQ1zZKDAmWLa13JFmNJzspb0zEtFOT7AV7FJ0TAaNKGqUChaF0vxfnRCIqOrhGHCNEtiNbqO
grGzYNTvA5o9xsrirjz3xqDEXaEIAyxF2rYRR8dhrWxJM6HTw+TqxaOdOi0s4xAmTHolV41BhWUU
ijCgokC0CKcyc0GgXGqf16mPrGekoipK3BWKMMDjzXuu15tg1pZWtqblOKOTtmmr6kSyon6UuCsU
4YDXc9cZjZCQ1rq2tCAW7wSy3qLi7Y1FibtCEQZI71JBg8HYypa0MBUbmnQq3t5YlLgrFOGA13PX
608zcbd4wzHS3bp2hCFK3BWKMEBWxNxPswlVn7i3YB70toL6rqNQhAOeSjF3gHvXnh6beizemj/e
z69oOErcFYpwwBuW8cXcO/RrRWNakDhv4Y5SVZa5saiwjEIRDni0mLP+dJtQbd9b+3vYza1rRxii
PHeFIhzwhiUMxtMs5q7Tw+O5cLpNJAcBJe4KRTjg9dzF6bgk8HSbRA4SKiyjUIQDHidOqQchWtsS
RZigxF2hCAOE24kLff0DFQovStwVihCnoMxBcbkVt1Dirmg4StwVihBn0ssr0BdmUqBrI/VSFS2C
EneFIsTpbt3OefpfOWLp29qmKMIIJe4KRYjzufkpAA5HD2pdQxRhhRJ3hSKEcVqLAdjp6UrUqBmt
a4wirFDirlCEMPmHdwFwfNBMLh3atZWtUYQTStwVihDmZG4WAEkdUxFqjbuiEShxVyhCmG6rHwAg
oX1yK1uiCDeUuCsUIYq0nsRizwegQ3KnVrZGEW4ocVcoQpT8fRsByIkbjDm+cytbowg3lLgrFCFK
ya7luKXg6MUfqZwyikajxF2hCFFKsw+STQI9uyqvXdF4lLgrFM3M/37L4mBeWaOvcxcdpcSUTKxF
5TJXNJ4GibsQYooQYrcQYp8Q4tE6xl0phJBCiPTgmahQhClSkn94J+0XXs67778NZXl4vn+cshf7
YD++rd7L27lysUZ0bAFDFW2ResVdCKEH3gIuBPoD1wkh+tcwLgaYBawLtpEKRVjyzf0kfjCKkbpd
TLCtwPFaOrpf3iDKdoKff/hcG7P/R8jeXv1aKUny5GGPVOKuaBoN8dxHAPuklAeklA7gU+CyGsY9
C7wE2IJon0IRvmyc5zucIldjcpz0nQ868i9YcB18NA3eHsOqTYECX16US4Rw4IpS8XZF02iIuKcA
RyqdH/W2+RBCDAO6SCn/F0TbFIo2xy2OR1jpHkSiKxt2f+trX730q4BxJTmHtIO41JY0T9GGOOUJ
VSGEDngF+GMDxt4lhMgQQmTk5uae6qMVipAmV8YCsNLtz+b46O/voSx1XLWx7YzOgPNSr7gbE5S4
K5pGQ8T9GNCl0nmqt62CGGAAsEIIkQmMAhbVNKkqpXxXSpkupUxPSkpqutUKRUsjZaOGu9weyrGw
JnISrqvmM8c5nSc6v0u/lAQ8KWcBMN81maG2uQBE67RoZvnOH5AfX83aDG3qytJeJQtTNI2GlFLf
APQSQnRDE/VrgesrOqWURUD7inMhxArgT1LKjOCaqlC0PHanC/Pz3gpIT+SBvp5liWX5kLWZoo5n
044Sytp3pP/g7ow78130Om0jUqeBE5j607MMGTGOuQOS4WMYV/wtnkOXEfnvqwC4wXu7Pj26N9Mn
U7R16hV3KaVLCDET+B7QAx9IKbcLIZ4BMqSUi5rbSIWitViyaQ+Xeo8L8k7QLrlLneOZrYlx4Z17
6CGsiCjN7zEZ/F+Sh5/RjsfvupEhXeJ97WnuTPjwgmq3M5tMp/wZFKcnDfHckVJ+C3xbpe3/ahk7
8dTNUihCA13uLt/xxj2HmFybuLscsPw532lO5g56AIbYDjUOH9GtXa3PfNt1Kd1FFp+5x/Fuk6xW
KNQOVYWiTizrXvcdT1hxNbhdNY5zbvoIfnrNdx61+T0ADIkNCKtM80t4uvsDLpg1l/Uj3+DC6Xc0
0WqFQom7QlErNqebNHECj9Ri5SZ3Gc71H9Q4dv3K7wBY5h4KwKDcbwCI6Nir/ge1014ADn0kGc9e
SfekaJ64pD/ThqqVMoqmo8RdoaiFUruLWFHO0eSJvjbj9w9h27sycKDLzlmly1juHox+zL0BXbEd
zqj/QeYYAEw9zzlVkxUKH0rcFYpacLrcxFJGaXQa9keOsc7TF4Df1i0LGFf8/fOYhJuYroM4e8SI
gL4IcwMmRJP6wOVvw7S5QbNdoWjQhKpCcTrislsxCxduUxzmiGhyrvgc5xcDcZX50whQdJTYDVqs
3TT2XgyJaXzX/0V+2rKLtZ7+/NCQPOxCwJDr6x+nUDQCJe4KRS24rCUASFMUAJcOSaHgyyiwFfnH
rHrF959oUH8tn96Uq+9h2BQbN5YH7jpVKFoSFZZRKGrB5bACIAwRvjarLhph94v77kwt7dI/Ul8M
uLZDrIU+HWNawEqFomaUuCsUteDxijtGv7hHGGB0+Qqs3klVT1EW6z19uHT6ra1hokJRK0rcFYpa
cDs1cdeZLL42Y4y249T48TSOfvoAXZwHMCek0DHOUuM9FIrWQom74vQmfz9snA8ed7Uujy8s4xfu
mJs+4XDkmRhwk7rrA+JFGRH6FrNWoWgwStwVpy/Hf4U3hsHX92nHVfA4ygHQmSL9jfFdSLhnccA4
W/fzm9VMhaIpqNUyitOTw+vgA78ob9l7kMGpgVmqPU4tDW/lsAxATEwc1j9sIz8vl92OJCYNUDtJ
FaGHEnfF6cXmBWCJJXP1AtIqNR/NOsHgKkOlT9wjqEpEYhdSE7ugZF0RqqiwjOL0oeQEfHk3fHo9
tiOb2e/pxE2mV7U+e0m14Yb83QCYEupJ86tQhCBK3BWnB9aTOP421HfaV3eEH+RZfPTH6QAIewm4
7BS+cwm2g2vB46FD5iK20pPUzim13VWhCFmUuCvaPO7SfHgpDZOnnDJpBqBYRpI98G4wReFCh95Z
TM7m74nPWk3ev/9A+YldJDsOszdlGjpdA1IIKBQhhhJ3RZvn20ULANjk6cncuPsB+NEzhEG9zgAh
sIpI2tkO0+GbmwA47ErgeOYeAFJ7DWkdoxWKU0RNqCraPMbDa3BJHfN6vsHzl/Vl2UfHSTv3Pob0
06ZDHcLMWWWrfONdwkBJ9kEAklJ7tIrNCsWposRd0ab58uetTLb+yGI5mtdvHgPAuTPfDBiT6Mn3
Hf/kPpPx/MTGzChKZQQpZzSg2IZCEYKosIyibbFyNuz82nf68//mEyXs/JY8rdZLpN6fc739pN8D
MLxoCYdMPTEZlf+jCE+UuCvaBiUnIGeXVqT63zeCxwMuOxck5lEqLTx2z221Xioe2odr0HV4rl/I
GWOv9rUXxvVtCcsVimZBuSWK8MBWBJ/fBQOnw8CrArrks0kIt4P89meR6G3z/GMSuqxfORfYZexH
X10dCWAscRiu0KogWYDF7W7iwoKPcMd1bZaPolC0BMpzV4QFzg0fwp7vcHx5H7w1Ep6Kg5WzkQUH
EW4HAIl5GzjsSaJQRqHL8ueKOWbu3qhndZn2NE9wD0kT7wnqZ1AoWhIl7oqQRErJ0h3ZeKzF8OvH
2FZ6S9m5yyB3lzZo+XOI1wOXKnbV5fIH5x8C2nIjezbq2QO6JPLsUy/Sr0tS0z+AQtHKqLCMIiRZ
tmU/4rM70Ok1DzwGWOoeigcdz7lu5A79t9xs+AGAt1xT2dXhYu7Oe4G8YX/g3KTzuHhVHz633YVZ
OKH7xFb7HApFa6HEXRGSROf9xiivsG/zpPGO6xK6jb2SEo+ZZ3onUZ47iBXf5/C46zbOTh/GXy/p
z+KtY5k6pDMTDHpmjO3GtnVxfLpkFfedPaaVP41C0fIocVeEJHqHVqf0TddlTLr3TVJ/O84tZ3cj
MVpLH+DplcSiqAW81ymGnknRGPQ6pqcHJvgaMPJcnht5bovbrlCEAkrcFSGJqfAAAJNvfow+nWPp
3zk2oF+nE1w+VCX0UihqQ4m7IqSQUpJVaGXwntcB6NSxYytbpFCEJ2q1TAvy+aajXPjaamzO6vU6
FRpfL5xP59c6+c4jI2Na0RqFInxR4t5CFJQ5eOg/m9iZVcSe7OqFIRQa7Xd95Dv+R9/3MBhU9WmF
oikoca/CwbwyHln4G4XljqDed++JYpaYHuZV41v8ergwqPcORaTHzd7Z55D9dC+yf/qo3vG2XUvY
8+oljHFn8F/XeG6K/DuXXXRpC1iqULRNGhRzF0JMAV4D9MB7UsoXq/Q/CNwBuIBc4DYp5aEg29oi
LN2Rzb8zDhNh8HBX3Hpi+p9HTHLjdjjWhGHta/TQZdGDLM78bifXjuiCuQ17pTv37qV/2SYA9i5/
jeSEWOhzMej9v3Ky6BhlViu5/7yVbuW/0dvb3mvoeD668oZWsFqhaDvU67kLIfTAW8CFQH/gOiFE
/yrDfgXSpZSDgIXA/wu2oS1F+5yfybTcwPWbrqfzyofInn/rKd9zzsvPM3zv677z5+Xr7H5mOFu2
bzvle4cquYe0XaR7PSn0cu2F/9zM0W8Dfy3K3hhL9NzhdCv/DYC/Oa9kp6cLqWOuaXF7FYq2RkPC
MiOAfVLKA1JKB/ApcFnlAVLK5VLKcu/pWgjfovAJBZsB6K07BoDedvKU7ldqc/KnEk3U9um1bwCX
639mkO4gZT+/d0r3DmUijqzEKfXYRj+IQ2rfUHL3rPf1u9weol3az3aDpzf/7PcO8Rc9QeEtK2nf
SSXsUihOlYaIewpwpNL5UW9bbdwOLD4Vo1oTq0sGnAuPEyllLaPrZ8uyBb7jno9v4kTKBb5zj0e7
7+vL9vLOyv1NfkYoInJ2cEyfQt/zb+OBXj+wztMXV/EJ7WcpJcePH8UldbzLFXw3Yj43X3Mtt47t
xugeifXfXKFQ1EtQJ1SFEDcC6cDsWvrvEkJkCCEycnNzg/nooCEdZbjRcbD79eRE9CBClrPrQCb5
Bfn1X1wDxUe2A+B5cA8IgeWsm319Z2fNg6fi+NcP6/jr4l3BMD8kcLvdnGHbTWlcH4x6HW/dOJwO
HToSLUsp3vkjPB1P1/cHYhAeRl54C09cUjXKp1AoTpWGiPsxoPK+7lRvWwBCiPOAvwBTpZT2mm4k
pXxXSpkupUxPSgqdjHvrNm1k30Ft/lfnKMUqIul289sUJI8hChv9PhqC/c2m5SdxFRyiVBeDLjYZ
gPiU3tXG3GxYwpOG+RRs/aHpHyKEOJl7nA7iJOXJw/yNlnjiRBkHFgXG3QcOP7uFrVMoTg8aIu4b
gF5CiG5CCBNwLbCo8gAhxFDgHTRhzwm+mc1HucPFyEWT6DhvJAAGZyl2faTWaY4mWtgA6Ow5UeP1
Uko2/7KMotzA993O40WUWu3EOU5QbK60yzIhDRLS+LWTf9JwlG4ntxq+p91ngUUowhWXQ/uZYYz0
tYmoRDqLAoba1rLKPdDXrjOoTdIKRXNQr7hLKV3ATOB7YCfwHynldiHEM0KIqd5hs4Fo4L9CiM1C
iEW13C7kOFGkCVG0sHI8J5e+jq3kGLQpBWEO3B3p9lSPvW/YeZAh319B8bsX+9rySqzEvTOE6Jc6
MJ5fKbP4d1xiMMGsLQy8Yy77b1xPuTSTrtsDgFVYgv3xWoUKcdcZzb62hKH+Ofi4Cb/HPnMzPNh2
QlEKRajRILdJSvkt8G2Vtv+rdHxekO1qMeyVUgEY5o6mg8jjf6n30Q/QWQLFvbC0nMTYKN+5lJK5
32UwAujiPIjL7cGg17H9t41MEAW+cW5L9UlCg15Hj559WKfrw0ipLQU8IM7gzFP5LGvfw3N0IxFX
vX0Kdzl1KsRdGPwvq7ieo7UDSxyDz7uuNcxSKE4rTvsdqg57ue+4g0eb5B0zShMifRVxzysoCDjf
m1NKXq4/XLP/wF4Acrd8FzBOZ4mu9fml0i+Ax51RZBfbGmO+j/KyYszf/ZGIbZ9QbmvaPYKF33M3
+RsNJvjzUXj4YCtZpVCcXpz24u4u01IBHIj2T/7Ft9di5NGx8QFjd2RmBZxnF5axyPyE77w8azcF
ZQ66nljCcdnO126Oiqv1+WnRHt/xZP0mrv7rx74lko3h8H5/iOPQgT2Nvj6YuJ3afLrOWCXMZI6B
ugpVKxSKoKHE3VasHaT5V22IaG1lS4fE9gFjc/Pz2HWimKvn/kKp3YXj6JaAfntpIVm/fs8I3W72
db6Mk4N/B0BK+8CXRGW69hoQcL7E9DC7G5lYzLl3OR2/ucl/XpzXqOuDxeHjx1n6zkM48jIB0BtM
dV+gUCiajdNe3D1ecdd3HkRRh5EcOvslMHgnAqtMqNqKcvh/X28m5vAPPDJ/KT/8uCSg315eTNZv
ywA484pHSWinvRwM1trXyBsvng03f0VusvZyMQsXe04UNdj+5SuWYvz4cuId/vCQ3VZa8+DCw7Di
JXA2T9hm3fzHOS/rXfqtfQgAvaltTBArFOHIaS/uokQTRV1EPHH3LuGM8+72dxojAsbKI+u5uOS/
vG96mbeOX8OLRi19gOyrZS/84dd9dMv5gcOWPiQmdYQBV2oX9qyj1JvBBN0nktB7tK/JuWdZvXa7
CzIp3bOac1Zoz3jTdRl2NE/ZYa1Z3EveGAcrXkC+0AnstbwAmoiUkv4ObWLYILRQk0GJu0LRapyW
4r4vp9QX1x6xbiYAhoga4uJ6f1ihiBhGuzPwFGQGDNkdPx5x5T8AGKbbSw+OUtbrcq0zsQc8WQi9
Jtdrk2HMTN9x7LHVNY7ZfiibJ557mo2ZBeS9M5XoTy4BYIluHDOf+yflt2jfJJy2suoXu53EuLX5
BSE95H31WED3ugP5HMyr4bra2PEVLHlCe0mU5XGy3EkHTzbuSr9SBp1o+P0UCkVQOW12kBSWO9Dr
BEfyy5n91huMPW8a143uScXCxsioGla0GPzrtJeJkVyhW8oI3e6AIbYOQ8EYgc0QyxWsASBt+Pn+
AaKBAhfhj8snltS8/vvQggd41vU/Tnz4IcnCn9Bs6PVPAxAVpdUZtZdX98oLjuykHfCNeySX6Nfh
3PU9SOmz75p31wKw5cxPKT1xgM5/XIWoafIzcw3275/EnJWhnf/8OnZhoeDC+fQUxezt/wd67XgD
AGO7tIZ9doVCEXROG899yDNLuOjFL4lY9igfmmYTsf1TcvasA8CmjyEupU/1i/R+cR+ZflaN9x10
4e0AWMZok6cHO5xHRNdhNY6tl+v/C0CiO5cNmYHLLpdtOcBFtv8B0NEr7Pc5ZvKm6zLadx8KgClC
e0F1OPiFJtyV2LNVy8jY+8r/Y2HyfXTynCD/wK/gduJyublBv5TvTI8Qt38RKWXbKC3WvPz1BwvI
2PAz7jfOgqfiYN7FmLMysEr/txqztNHz22sokpFEjpzBzku/YuXFK0hNDdvkoApF2HPaeO5PG+Zx
Cz/AAe38hvzX4Qstx/rh61fSuyYvtdJqj5RO/kSYjoRemGZpnqvPL5/4Zxj9e7pFJDTdyN7nc3zA
7+i09QPOmfsT390/kT4dtUndpK/9CcdyInux9uwPeGZIX/JKHQid9x1t1sR9qGsLB9d/Q7eR/kpG
MnsnbilI6zOUgoICyIbEj86hMLYPzql/53njBwGmOKxlEJtA9gfXcal+bUDfufbZDBoygstsX3Js
/3Zu0GnhoI8S/sDMM3qSckZP+jX9p6BQKILAaSHuUkpuMdSclOuQpwNpZ3Sr+cJKnjtnToMTWyEi
HlP3c6qP1enhVITdSwS6yygAAAqLSURBVLtO3TBvc3K/4XN+OzKIDZkFvLR4J1vFVm3AfZvpENeF
qd6KRvGRlZYbmqIo7Hsd8bsW4Fz9GlbrfkRiN8zdx2Ip3MNxXUe6RESR1Gckm1b0ZJhuH/HFu9lx
cBsVadwWy9FcKH7Bc3wzWz+51yfsb7gu5y3XZQzt3pnPbxxOXKQRGAL2EtY9fx4r3YO46vo/nPLn
VygUwaFti7vLDvn7KLW7qFjUOCvmFcZ2MXH1jpnc4niEPv0G8ZihluiU3ug/NkfDRc1fYMrSoadm
p+FzXt40hq2HsllvfA2ArZ2nM7BdLS8iL/HXzmX5nGLOKf0frNgAwKGowcSW5FAQ24MuQI+UZI7c
v5q1G//HqJ/uoHiDlnN+1ZQldCjYDut/Ienrm3yCPzPmNe6+dhq/7xSLruokqTmGEU+vpUuRjc7x
gauLFApF69Fmxd3642wiVj0H4BP2n0a8xcsXXE+Zw817ncby3pg0jPo6ph0aOhkaTLqfA2fdARve
Qxxey7vGLzAJLf9NQmJyg24ROekhWPQ/3/kZZVtAB5s6X+1r69Iukg4TppL3UzyjHGsplRYGnXkm
B9cFpgf4Yvy3vHHOGEQdPwshhBJ2hSLEaJMTqiU5h3zCDlAkI3nZ+DuGT74Og15HXISRO8Z1r1vY
Wwu9AS74KwAPGhf6hB0gpWPDxH3ksKF8nfIgyzve5msrkREMuOjegHFmk5n8ThMA2GwYTHxMNEaL
PzHaitR7uLweYVcoFKFJm/Tcd6/6L+nAFPuLdOw9nEEpcVw1PBWLsYl5TXpMCqp99VJl237hqIeJ
T05DDJze4FtceueTAJz8aRAJP9xPySXvEBPfsdo4S/8pkPUV9NQSe1auMhiZmKKEXaEIU8Sp1Ac9
FdLT02VGRkbQ75t7aBdJH2qFN5yP52M81WIQTivojJpH3YLs//QReuyaq51c/RH0n1r3BXVRaT17
NdxOTq58m4Sz7wBTJCcLi8h4eRoZnt7c/uDzdEhUNU0VilBCCLFRSple37iw99ydRVnYdv9ITGwC
Dg8k/UfLFf6VewyXBaPKj7F1YslJKd2hYi9T34vrHFsvdXnfeiMJk+7znSbExzH52R+pf0+tQqEI
ZcJe3HfOvZlBVm2DTkUwY7l7MB1m/Kv1jAoCsb3GwjLg/OdVmlyFQtFowlrcpZSYy46DDla5B2IS
LlJFLgfPepLbeoR5OKHjALjv/7d3v7FV3XUcx98fy2hxQ3o7CWllSLtNI8bEMlzAGV22yRiZIyZ7
ULIo6IyJJou6BwaCTzTxAbq4qTGyRWeWBbcpzo2QKdm/Rz5Au8xVHKsUtgkIo5s4zCQTxtcH5wc7
XAu9ZW3PPed+XskJv/M7p+3v2+/h23vOPfd3noXauW99NDMbS6mL+5snTtKj1/jDxTcze9UdPLHr
FXo6Z7H2YwuKHtrk6OoregRmVlKlLu7Hjh2jpmPM6pxH/4Ia/Qve+SdEzcyqoAlv9G7cm+kRefUP
1TAza3XVKO4dZ39GqZlZKyp1cX/ryAEA1PGegkdiZtZcSl3c33jsWwDE7J6CR2Jm1lxKXdznx0Ee
f2sxvR+5quihmJk1ldIW9+NH9lHj37RdejW1i9rH/wIzsxZS2uI+9PvsyUHv6vtUwSMxM2s+pbvP
PY7+g6P3f44rRgcZPPkBrrjSl2TMzOqV7pX70CN3Mmc0m03y0GUDzO64YJyvMDNrPaV75T7zug3c
9uCHWbDwUm77zMeLHo6ZWVMqXXH/UE8nP759bdHDMDNraqW7LGNmZuNrqLhLWiFpWNKIpHVjbG+X
9FDavkPSwskeqJmZNW7c4i6pDfgJcAOwCFgtaVHdbrcCRyLiMuBOYONkD9TMzBrXyCv3K4GRiNgb
Ef8FHgRW1e2zCrgvtbcA18pPVjYzK0wjxf19wL7c+v7UN+Y+EXECeB0o+aOQzMzKa1rfUJX0ZUmD
kgZHR0en80ebmbWURor7AeCS3Pr81DfmPpJmAHOA1+q/UUTcExFLImLJ3Llzz2/EZmY2rkaK+5+A
yyX1SpoJDABb6/bZCqxJ7ZuBpyIiJm+YZmY2EWqkBktaCdwFtAH3RsR3JX0HGIyIrZI6gPuBfuCf
wEBE7B3ne44CL7/TAKbBe4FXix7EFKhiXFWMCaoZl2M6f++PiHEvfTRU3FuZpMGIWFL0OCZbFeOq
YkxQzbgc09TzJ1TNzCrIxd3MrIJc3Md3T9EDmCJVjKuKMUE143JMU8zX3M3MKsiv3M3MKqili7uk
SyQ9Lel5SX+V9LXU3yXpcUm707+11C9JP0qzXw5JWlxsBOcmqU3Ss5K2pfXeNGvnSJrFc2bqL8Ws
npI6JW2R9IKkXZKWVSFXkr6Rjr+dkh6Q1FHGXEm6V9JhSTtzfRPOj6Q1af/dktaM9bOmy1li+n46
Bock/VZSZ27b+hTTsKTrc/3nnFl3SkREyy5AN7A4tWcDfyOb+fJ7wLrUvw7YmNorgd8BApYCO4qO
YZz4bgd+CWxL678i+wwCwCbgK6n9VWBTag8ADxU99rPEcx/wpdSeCXSWPVdk8zK9CMzK5WhtGXMF
fBJYDOzM9U0oP0AXsDf9W0vtWpPFtByYkdobczEtAp4D2oFeYA/ZZ4PaUrsvHbfPAYumfOxFHxDN
tACPAp8GhoHu1NcNDKf23cDq3P6n92u2hWyaiCeBa4Bt6T/Rq7mDchmwPbW3A8tSe0baT0XHUBfP
nFQEVddf6lzx9qR7Xel3vw24vqy5AhbWFcIJ5QdYDdyd6z9jv2aIqW7bZ4HNqb0eWJ/btj3l7nT+
xtpvqpaWviyTl05v+4EdwLyIOJg2HQLmpXYjM2Q2i7uAbwIn0/rFwL8im7UTzhx7GWb17AVGgV+k
S00/k3QhJc9VRBwA7gD+Dhwk+90/Q7lzlTfR/JQibzlfJDsDgSaLycUdkHQR8Bvg6xFxNL8tsj+1
pbqlSNKNwOGIeKbosUyiGWSnxz+NiH7gDbLT/NNKmqsa2fMQeoEe4EJgRaGDmiJlzM+5SNoAnAA2
Fz2WsbR8cZd0AVlh3xwRD6fuVyR1p+3dwOHU38gMmc3gKuAmSS+RPVzlGuCHQGeatRPOHHtDs3oW
bD+wPyJ2pPUtZMW+7Lm6DngxIkYj4jjwMFn+ypyrvInmpxR5k7QWuBG4Jf3RgiaLqaWLuyQBPwd2
RcQPcpvys1yuIbsWf6r/8+md/qXA67lTzqYREesjYn5ELCR70+2piLgFeJps1k74/7iaelbPiDgE
7JP0wdR1LfA8Jc8V2eWYpZLenY7HU3GVNld1Jpqf7cBySbV0VrM89TUNSSvILnneFBH/yW3aCgyk
O5p6gcuBP9LYzLqTr8g3KopegE+QnSYOAX9Oy0qya5hPAruBJ4CutL/Inie7B/gLsKToGBqI8Wre
vlumLx1sI8CvgfbU35HWR9L2vqLHfZZYPgoMpnw9QnY3RelzBXwbeAHYSTa7ansZcwU8QPa+wXGy
M61bzyc/ZNexR9LyhSaMaYTsGvqpmrEpt/+GFNMwcEOufyXZ3Xh7gA3TMXZ/QtXMrIJa+rKMmVlV
ubibmVWQi7uZWQW5uJuZVZCLu5lZBbm4m5lVkIu7mVkFubibmVXQ/wDDL6NCmlGzNgAAAABJRU5E
rkJggg==
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">high_low_predictions_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;High&quot;</span><span class="p">:</span> <span class="n">unscale_price</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">high</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">feature</span><span class="o">=</span><span class="s2">&quot;High&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
                                        <span class="s2">&quot;Low&quot;</span><span class="p">:</span> <span class="n">unscale_price</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">low</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">feature</span><span class="o">=</span><span class="s2">&quot;Low&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()})</span>
<span class="n">high_low_predictions_df</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[&nbsp;]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>High</th>
      <th>Low</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>302.169800</td>
      <td>293.899261</td>
    </tr>
    <tr>
      <th>1</th>
      <td>296.907776</td>
      <td>293.083130</td>
    </tr>
    <tr>
      <th>2</th>
      <td>292.739288</td>
      <td>295.500458</td>
    </tr>
    <tr>
      <th>3</th>
      <td>296.282959</td>
      <td>294.505859</td>
    </tr>
    <tr>
      <th>4</th>
      <td>303.337860</td>
      <td>294.262360</td>
    </tr>
    <tr>
      <th>5</th>
      <td>299.599915</td>
      <td>291.348450</td>
    </tr>
    <tr>
      <th>6</th>
      <td>300.364197</td>
      <td>297.488983</td>
    </tr>
    <tr>
      <th>7</th>
      <td>297.884888</td>
      <td>290.553009</td>
    </tr>
    <tr>
      <th>8</th>
      <td>301.414459</td>
      <td>296.423737</td>
    </tr>
    <tr>
      <th>9</th>
      <td>305.891571</td>
      <td>290.927673</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># predict the next day&#39;s opening price</span>

<span class="n">model_lstm</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">models</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">BEST_MODEL</span><span class="p">)</span>
<span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">unscale_price</span><span class="p">(</span><span class="n">model_lstm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">df_tr</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">100</span><span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">axis</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 output_prompt">Out[&nbsp;]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([[287.181]], dtype=float32)</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;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># sentiment analysis</span>

<span class="kn">import</span> <span class="nn">tweepy</span>
<span class="kn">from</span> <span class="nn">textblob</span> <span class="kn">import</span> <span class="n">TextBlob</span>

<span class="n">CONSUMER_KEY</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>  <span class="c1"># missing; the code therefore cannot run</span>
<span class="n">CONSUMER_SECRET</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
<span class="n">ACCESS_TOKEN</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
<span class="n">ACCESS_TOKEN_SECRET</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
<span class="n">auth</span> <span class="o">=</span> <span class="n">tweepy</span><span class="o">.</span><span class="n">OAuthHandler</span><span class="p">(</span><span class="n">CONSUMER_KEY</span><span class="p">,</span> <span class="n">CONSUMER_SECRET</span><span class="p">)</span>
<span class="n">auth</span><span class="o">.</span><span class="n">set_access_token</span><span class="p">(</span><span class="n">ACCESS_TOKEN</span><span class="p">,</span> <span class="n">ACCESS_TOKEN_SECRET</span><span class="p">)</span>
<span class="n">api</span> <span class="o">=</span> <span class="n">tweepy</span><span class="o">.</span><span class="n">API</span><span class="p">(</span><span class="n">auth</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">THRESHOLD</span> <span class="o">=</span> <span class="mi">0</span>

<span class="n">score</span> <span class="o">=</span> <span class="mi">0</span>

<span class="n">public_tweets</span> <span class="o">=</span> <span class="n">api</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="s2">&quot;Facebook&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tweet</span> <span class="ow">in</span> <span class="n">public_tweets</span><span class="p">:</span>
    <span class="n">analysis</span> <span class="o">=</span> <span class="n">TextBlob</span><span class="p">(</span><span class="n">tweet</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">analysis</span><span class="o">.</span><span class="n">sentiment</span><span class="o">.</span><span class="n">polarity</span> <span class="o">&gt;=</span> <span class="n">THRESHOLD</span><span class="p">:</span>
        <span class="n">score</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">score</span> <span class="o">-=</span> <span class="mi">1</span>

<span class="n">score</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_text output_error">
<pre>
<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">TweepError</span>                                Traceback (most recent call last)
<span class="ansi-green-fg">&lt;ipython-input-159-8f57ec5a5a99&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-intense-fg ansi-bold">      3</span> score <span class="ansi-blue-fg">=</span> <span class="ansi-cyan-fg">0</span>
<span class="ansi-green-intense-fg ansi-bold">      4</span> 
<span class="ansi-green-fg">----&gt; 5</span><span class="ansi-red-fg"> </span>public_tweets <span class="ansi-blue-fg">=</span> api<span class="ansi-blue-fg">.</span>search<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">&#34;Facebook&#34;</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">      6</span> <span class="ansi-green-fg">for</span> tweet <span class="ansi-green-fg">in</span> public_tweets<span class="ansi-blue-fg">:</span>
<span class="ansi-green-intense-fg ansi-bold">      7</span>     analysis <span class="ansi-blue-fg">=</span> TextBlob<span class="ansi-blue-fg">(</span>tweet<span class="ansi-blue-fg">.</span>text<span class="ansi-blue-fg">)</span>

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tweepy/binder.py</span> in <span class="ansi-cyan-fg">_call</span><span class="ansi-blue-fg">(*args, **kwargs)</span>
<span class="ansi-green-intense-fg ansi-bold">    248</span>             <span class="ansi-green-fg">return</span> method
<span class="ansi-green-intense-fg ansi-bold">    249</span>         <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">--&gt; 250</span><span class="ansi-red-fg">             </span><span class="ansi-green-fg">return</span> method<span class="ansi-blue-fg">.</span>execute<span class="ansi-blue-fg">(</span><span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">    251</span> 
<span class="ansi-green-intense-fg ansi-bold">    252</span>     <span class="ansi-red-fg"># Set pagination mode</span>

<span class="ansi-green-fg">/usr/local/lib/python3.6/dist-packages/tweepy/binder.py</span> in <span class="ansi-cyan-fg">execute</span><span class="ansi-blue-fg">(self)</span>
<span class="ansi-green-intense-fg ansi-bold">    232</span>                     <span class="ansi-green-fg">raise</span> RateLimitError<span class="ansi-blue-fg">(</span>error_msg<span class="ansi-blue-fg">,</span> resp<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">    233</span>                 <span class="ansi-green-fg">else</span><span class="ansi-blue-fg">:</span>
<span class="ansi-green-fg">--&gt; 234</span><span class="ansi-red-fg">                     </span><span class="ansi-green-fg">raise</span> TweepError<span class="ansi-blue-fg">(</span>error_msg<span class="ansi-blue-fg">,</span> resp<span class="ansi-blue-fg">,</span> api_code<span class="ansi-blue-fg">=</span>api_error_code<span class="ansi-blue-fg">)</span>
<span class="ansi-green-intense-fg ansi-bold">    235</span> 
<span class="ansi-green-intense-fg ansi-bold">    236</span>             <span class="ansi-red-fg"># Parse the response payload</span>

<span class="ansi-red-fg">TweepError</span>: [{&#39;code&#39;: 215, &#39;message&#39;: &#39;Bad Authentication data.&#39;}]</pre>
</div>
</div>

</div>
</div>

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

 
</html>