report.html

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

<title>dog_app</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>


<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">&#182;</a></h2><h2 id="Project:-Write-an-Algorithm-for-a-Dog-Identification-App">Project: Write an Algorithm for a Dog Identification App<a class="anchor-link" href="#Project:-Write-an-Algorithm-for-a-Dog-Identification-App">&#182;</a></h2><hr>
<p>In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'(IMPLEMENTATION)'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut.  Markdown cells can be edited by double-clicking the cell to enter edit mode.</p>
</blockquote>
<p>The rubric contains <em>optional</em> "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.</p>
<hr>
<h3 id="Why-We're-Here">Why We're Here<a class="anchor-link" href="#Why-We're-Here">&#182;</a></h3><p>In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app.  At the end of this project, your code will accept any user-supplied image as input.  If a dog is detected in the image, it will provide an estimate of the dog's breed.  If a human is detected, it will provide an estimate of the dog breed that is most resembling.  The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).</p>
<p><img src="images/sample_dog_output.png" alt="Sample Dog Output"></p>
<p>In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed.  There are many points of possible failure, and no perfect algorithm exists.  Your imperfect solution will nonetheless create a fun user experience!</p>
<h3 id="The-Road-Ahead">The Road Ahead<a class="anchor-link" href="#The-Road-Ahead">&#182;</a></h3><p>We break the notebook into separate steps.  Feel free to use the links below to navigate the notebook.</p>
<ul>
<li><a href="#step0">Step 0</a>: Import Datasets</li>
<li><a href="#step1">Step 1</a>: Detect Humans</li>
<li><a href="#step2">Step 2</a>: Detect Dogs</li>
<li><a href="#step3">Step 3</a>: Create a CNN to Classify Dog Breeds (from Scratch)</li>
<li><a href="#step4">Step 4</a>: Use a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step5">Step 5</a>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li>
<li><a href="#step6">Step 6</a>: Write your Algorithm</li>
<li><a href="#step7">Step 7</a>: Test Your Algorithm</li>
</ul>
<hr>
<p><a id='step0'></a></p>
<h2 id="Step-0:-Import-Datasets">Step 0: Import Datasets<a class="anchor-link" href="#Step-0:-Import-Datasets">&#182;</a></h2><h3 id="Import-Dog-Dataset">Import Dog Dataset<a class="anchor-link" href="#Import-Dog-Dataset">&#182;</a></h3><p>In the code cell below, we import a dataset of dog images.  We populate a few variables through the use of the <code>load_files</code> function from the scikit-learn library:</p>
<ul>
<li><code>train_files</code>, <code>valid_files</code>, <code>test_files</code> - numpy arrays containing file paths to images</li>
<li><code>train_targets</code>, <code>valid_targets</code>, <code>test_targets</code> - numpy arrays containing onehot-encoded classification labels </li>
<li><code>dog_names</code> - list of string-valued dog breed names for translating labels</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_files</span>       
<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">np_utils</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>

<span class="c1"># define function to load train, test, and validation datasets</span>
<span class="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">load_files</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
    <span class="n">dog_files</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">data</span><span class="p">[</span><span class="s1">&#39;filenames&#39;</span><span class="p">])</span>
    <span class="n">dog_targets</span> <span class="o">=</span> <span class="n">np_utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">]),</span> <span class="mi">133</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">dog_files</span><span class="p">,</span> <span class="n">dog_targets</span>

<span class="c1"># load train, test, and validation datasets</span>
<span class="n">train_files</span><span class="p">,</span> <span class="n">train_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;/data/dog_images/train&#39;</span><span class="p">)</span>
<span class="n">valid_files</span><span class="p">,</span> <span class="n">valid_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;/data/dog_images/valid&#39;</span><span class="p">)</span>
<span class="n">test_files</span><span class="p">,</span> <span class="n">test_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">&#39;/data/dog_images/test&#39;</span><span class="p">)</span>

<span class="c1"># load list of dog names</span>
<span class="n">dog_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">20</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;/data/dog_images/train/*/&quot;</span><span class="p">))]</span>

<span class="c1"># print statistics about the dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total dog categories.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">dog_names</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%s</span><span class="s1"> total dog images.</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">train_files</span><span class="p">,</span> <span class="n">valid_files</span><span class="p">,</span> <span class="n">test_files</span><span class="p">])))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> training dog images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_files</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> validation dog images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_files</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> test dog images.&#39;</span><span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_files</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_stderr output_text">
<pre>Using TensorFlow backend.
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Import-Human-Dataset">Import Human Dataset<a class="anchor-link" href="#Import-Human-Dataset">&#182;</a></h3><p>In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array <code>human_files</code>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">8675309</span><span class="p">)</span>

<span class="c1"># load filenames in shuffled human dataset</span>
<span class="n">human_files</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">glob</span><span class="p">(</span><span class="s2">&quot;/data/lfw/*/*&quot;</span><span class="p">))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">human_files</span><span class="p">)</span>

<span class="c1"># print statistics about the dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;There are </span><span class="si">%d</span><span class="s1"> total human images.&#39;</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">human_files</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>There are 13233 total human images.
</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">
<hr>
<p><a id='step1'></a></p>
<h2 id="Step-1:-Detect-Humans">Step 1: Detect Humans<a class="anchor-link" href="#Step-1:-Detect-Humans">&#182;</a></h2><p>We use OpenCV's implementation of <a href="http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html">Haar feature-based cascade classifiers</a> to detect human faces in images.  OpenCV provides many pre-trained face detectors, stored as XML files on <a href="https://github.com/opencv/opencv/tree/master/data/haarcascades">github</a>.  We have downloaded one of these detectors and stored it in the <code>haarcascades</code> directory.</p>
<p>In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">cv2</span>                
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>                        
<span class="o">%</span><span class="k">matplotlib</span> inline                               

<span class="c1"># extract pre-trained face detector</span>
<span class="n">face_cascade</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">CascadeClassifier</span><span class="p">(</span><span class="s1">&#39;haarcascades/haarcascade_frontalface_alt.xml&#39;</span><span class="p">)</span>

<span class="c1"># load color (BGR) image</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">human_files</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="c1"># convert BGR image to grayscale</span>
<span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>

<span class="c1"># find faces in image</span>
<span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>

<span class="c1"># print number of faces detected in the image</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Number of faces detected:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">))</span>

<span class="c1"># get bounding box for each detected face</span>
<span class="k">for</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">w</span><span class="p">,</span><span class="n">h</span><span class="p">)</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span>
    <span class="c1"># add bounding box to color image</span>
    <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">img</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">x</span><span class="o">+</span><span class="n">w</span><span class="p">,</span><span class="n">y</span><span class="o">+</span><span class="n">h</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span>
    
<span class="c1"># convert BGR image to RGB for plotting</span>
<span class="n">cv_rgb</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span>

<span class="c1"># display the image, along with bounding box</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv_rgb</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of faces detected: 1
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</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>Before using any of the face detectors, it is standard procedure to convert the images to grayscale.  The <code>detectMultiScale</code> function executes the classifier stored in <code>face_cascade</code> and takes the grayscale image as a parameter.</p>
<p>In the above code, <code>faces</code> is a numpy array of detected faces, where each row corresponds to a detected face.  Each detected face is a 1D array with four entries that specifies the bounding box of the detected face.  The first two entries in the array (extracted in the above code as <code>x</code> and <code>y</code>) specify the horizontal and vertical positions of the top left corner of the bounding box.  The last two entries in the array (extracted here as <code>w</code> and <code>h</code>) specify the width and height of the box.</p>
<h3 id="Write-a-Human-Face-Detector">Write a Human Face Detector<a class="anchor-link" href="#Write-a-Human-Face-Detector">&#182;</a></h3><p>We can use this procedure to write a function that returns <code>True</code> if a human face is detected in an image and <code>False</code> otherwise.  This function, aptly named <code>face_detector</code>, takes a string-valued file path to an image as input and appears in the code block below.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># returns &quot;True&quot; if face is detected in image stored at img_path</span>
<span class="k">def</span> <span class="nf">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
    <span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Assess-the-Human-Face-Detector">(IMPLEMENTATION) Assess the Human Face Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Human-Face-Detector">&#182;</a></h3><p><strong>Question 1:</strong> Use the code cell below to test the performance of the <code>face_detector</code> function.</p>
<ul>
<li>What percentage of the first 100 images in <code>human_files</code> have a detected human face?  </li>
<li>What percentage of the first 100 images in <code>dog_files</code> have a detected human face? </li>
</ul>
<p>Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face.  You will see that our algorithm falls short of this goal, but still gives acceptable performance.  We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays <code>human_files_short</code> and <code>dog_files_short</code>.</p>
<p><strong>Answer:</strong> 100% images of first 100 human_files were detected as human face
11.0% images of first 100 dog_files were dected as human face.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">train_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="c1"># Do NOT modify the code above this line.</span>

<span class="c1">## TODO: Test the performance of the face_detector algorithm </span>
<span class="c1">## on the images in human_files_short and dog_files_short.</span>
<span class="k">def</span> <span class="nf">checkPerformance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">detector</span><span class="p">):</span>
    <span class="n">result</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">file</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">detector</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> 
            <span class="n">result</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="mf">100.</span><span class="o">*</span><span class="n">result</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>

<span class="nb">print</span> <span class="p">(</span><span class="n">checkPerformance</span><span class="p">(</span><span class="n">human_files_short</span><span class="p">,</span> <span class="n">face_detector</span><span class="p">),</span> <span class="s1">&#39;</span><span class="si">% o</span><span class="s1">f the first 100 images in human_files have a detected human face&#39;</span><span class="p">)</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">checkPerformance</span><span class="p">(</span><span class="n">dog_files_short</span><span class="p">,</span> <span class="n">face_detector</span><span class="p">),</span> <span class="s1">&#39;</span><span class="si">% o</span><span class="s1">f the first 100 images in dog_files have a detected human face&#39;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>100.0 % of the first 100 images in human_files have a detected human face
11.0 % of the first 100 images in dog_files have a detected human face
</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><strong>Question 2:</strong> This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?</p>
<p><strong>Answer:</strong>it is fair ok if we detect the boundary and amrkings on the face inorder to detect the human in the given picture.
There is no need of clear image of a person inorder to identify as human being.Most probably for a not well trained data we need the whole clear picture in order to predict the image with high accuracy if we provide a wide variety of images with different features then the image will be easily detected by using the algorithm called the CNN for image classification.</p>
<p>We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :).  Please use the code cell below to design and test your own face detection algorithm.  If you decide to pursue this <em>optional</em> task, report performance on each of the datasets.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## (Optional) TODO: Report the performance of another  </span>
<span class="c1">## face detection algorithm on the LFW dataset</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<p><a id='step2'></a></p>
<h2 id="Step-2:-Detect-Dogs">Step 2: Detect Dogs<a class="anchor-link" href="#Step-2:-Detect-Dogs">&#182;</a></h2><p>In this section, we use a pre-trained <a href="http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006">ResNet-50</a> model to detect dogs in images.  Our first line of code downloads the ResNet-50 model, along with weights that have been trained on <a href="http://www.image-net.org/">ImageNet</a>, a very large, very popular dataset used for image classification and other vision tasks.  ImageNet contains over 10 million URLs, each linking to an image containing an object from one of <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">1000 categories</a>.  Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">ResNet50</span>

<span class="c1"># define ResNet50 model</span>
<span class="n">ResNet50_model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5
102858752/102853048 [==============================] - 1s 0us/step
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape</p>
$$
(\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}),
$$<p>where <code>nb_samples</code> corresponds to the total number of images (or samples), and <code>rows</code>, <code>columns</code>, and <code>channels</code> correspond to the number of rows, columns, and channels for each image, respectively.</p>
<p>The <code>path_to_tensor</code> function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN.  The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels.  Next, the image is converted to an array, which is then resized to a 4D tensor.  In this case, since we are working with color images, each image has three channels.  Likewise, since we are processing a single image (or sample), the returned tensor will always have shape</p>
$$
(1, 224, 224, 3).
$$<p>The <code>paths_to_tensor</code> function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape</p>
$$
(\text{nb_samples}, 224, 224, 3).
$$<p>Here, <code>nb_samples</code> is the number of samples, or number of images, in the supplied array of image paths.  It is best to think of <code>nb_samples</code> as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span>                  
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>

<span class="k">def</span> <span class="nf">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># loads RGB image as PIL.Image.Image type</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>
    <span class="c1"># convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="c1"># convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">paths_to_tensor</span><span class="p">(</span><span class="n">img_paths</span><span class="p">):</span>
    <span class="n">list_of_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="k">for</span> <span class="n">img_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">img_paths</span><span class="p">)]</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">list_of_tensors</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Making-Predictions-with-ResNet-50">Making Predictions with ResNet-50<a class="anchor-link" href="#Making-Predictions-with-ResNet-50">&#182;</a></h3><p>Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing.  First, the RGB image is converted to BGR by reordering the channels.  All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image.  This is implemented in the imported function <code>preprocess_input</code>.  If you're curious, you can check the code for <code>preprocess_input</code> <a href="https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py">here</a>.</p>
<p>Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions.  This is accomplished with the <code>predict</code> method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category.  This is implemented in the <code>ResNet50_predict_labels</code> function below.</p>
<p>By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">preprocess_input</span><span class="p">,</span> <span class="n">decode_predictions</span>

<span class="k">def</span> <span class="nf">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># returns prediction vector for image located at img_path</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ResNet50_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Write-a-Dog-Detector">Write a Dog Detector<a class="anchor-link" href="#Write-a-Dog-Detector">&#182;</a></h3><p>While looking at the <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from <code>'Chihuahua'</code> to <code>'Mexican hairless'</code>.  Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the <code>ResNet50_predict_labels</code> function above returns a value between 151 and 268 (inclusive).</p>
<p>We use these ideas to complete the <code>dog_detector</code> function below, which returns <code>True</code> if a dog is detected in an image (and <code>False</code> if not).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### returns &quot;True&quot; if a dog is detected in the image stored at img_path</span>
<span class="k">def</span> <span class="nf">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="n">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
    <span class="k">return</span> <span class="p">((</span><span class="n">prediction</span> <span class="o">&lt;=</span> <span class="mi">268</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">prediction</span> <span class="o">&gt;=</span> <span class="mi">151</span><span class="p">))</span> 
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Assess-the-Dog-Detector">(IMPLEMENTATION) Assess the Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Dog-Detector">&#182;</a></h3><p><strong>Question 3:</strong> Use the code cell below to test the performance of your <code>dog_detector</code> function.</p>
<ul>
<li>What percentage of the images in <code>human_files_short</code> have a detected dog?  </li>
<li>What percentage of the images in <code>dog_files_short</code> have a detected dog?</li>
</ul>
<p><strong>Answer:</strong> 0% images in first 100 human_Files were detected as dog
100% images of the first 100 dog files are detected as dog.(here i have choosen only 100 size dataset for fast results)</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Test the performance of the dog_detector function</span>
<span class="c1">### on the images in human_files_short and dog_files_short.</span>
<span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">train_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="n">count_humans</span> <span class="o">=</span> <span class="mi">0</span> 
<span class="n">count_dogs</span> <span class="o">=</span> <span class="mi">0</span>

<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">human_files_short</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
        <span class="n">count_humans</span> <span class="o">+=</span> <span class="mi">1</span>
        
<span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">dog_files_short</span><span class="p">:</span>
    <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
        <span class="n">count_dogs</span> <span class="o">+=</span> <span class="mi">1</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%.1f%%</span><span class="s1"> images of the first 100 human_files were detected as dog.&#39;</span> <span class="o">%</span> <span class="n">count_humans</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%.1f%%</span><span class="s1"> images of the first 100 dog_files were detected as dog.&#39;</span> <span class="o">%</span> <span class="n">count_dogs</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>0.0% images of the first 100 human_files were detected as dog.
100.0% images of the first 100 dog_files were detected as dog.
</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">
<hr>
<p><a id='step3'></a></p>
<h2 id="Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">Step 3: Create a CNN to Classify Dog Breeds (from Scratch)<a class="anchor-link" href="#Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">&#182;</a></h2><p>Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images.  In this step, you will create a CNN that classifies dog breeds.  You must create your CNN <em>from scratch</em> (so, you can't use transfer learning <em>yet</em>!), and you must attain a test accuracy of at least 1%.  In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</p>
<p>Be careful with adding too many trainable layers!  More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process.  Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.</p>
<p>We mention that the task of assigning breed to dogs from images is considered exceptionally challenging.  To see why, consider that <em>even a human</em> would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.</p>
<table>
<thead><tr>
<th>Brittany</th>
<th>Welsh Springer Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Brittany_02625.jpg" width="100"></td>
<td><img src="images/Welsh_springer_spaniel_08203.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).</p>
<table>
<thead><tr>
<th>Curly-Coated Retriever</th>
<th>American Water Spaniel</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Curly-coated_retriever_03896.jpg" width="200"></td>
<td><img src="images/American_water_spaniel_00648.jpg" width="200"></td>
</tr>
</tbody>
</table>
<p>Likewise, recall that labradors come in yellow, chocolate, and black.  Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.</p>
<table>
<thead><tr>
<th>Yellow Labrador</th>
<th>Chocolate Labrador</th>
<th>Black Labrador</th>
</tr>
</thead>
<tbody>
<tr>
<td><img src="images/Labrador_retriever_06457.jpg" width="150"></td>
<td><img src="images/Labrador_retriever_06455.jpg" width="240"></td>
<td><img src="images/Labrador_retriever_06449.jpg" width="220"></td>
</tr>
</tbody>
</table>
<p>We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.</p>
<p>Remember that the practice is far ahead of the theory in deep learning.  Experiment with many different architectures, and trust your intuition.  And, of course, have fun!</p>
<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">&#182;</a></h3><p>We rescale the images by dividing every pixel in every image by 255.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</span>                            
<span class="n">ImageFile</span><span class="o">.</span><span class="n">LOAD_TRUNCATED_IMAGES</span> <span class="o">=</span> <span class="kc">True</span>                 

<span class="c1"># pre-process the data for Keras</span>
<span class="n">train_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">train_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
<span class="n">valid_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">valid_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
<span class="n">test_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">test_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span>
</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_stderr output_text">
<pre>100%|██████████| 6680/6680 [01:25&lt;00:00, 48.26it/s] 
100%|██████████| 835/835 [00:09&lt;00:00, 86.97it/s] 
100%|██████████| 836/836 [00:09&lt;00:00, 87.40it/s] 
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

<pre><code>    model.summary()

</code></pre>
<p>We have imported some Python modules to get you started, but feel free to import as many modules as you need.  If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains &gt;1% test accuracy in 5 epochs:</p>
<p><img src="images/sample_cnn.png" alt="Sample CNN"></p>
<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.</p>
<p><strong>Answer:</strong> there are 3 convolutional layers are created with 3 max pooling layers in between them to learn hierarchy of high level features. Max pooling layer is added to reduce the dimensionality.Flatten layer is added to reduce the matrix to row vector. This is because fully connected layer only accepts row vector.
Filters were used 16, 32, 64 in each of the convolutional layers.
Dropout was used along with flattening layer before using the fully connected layer to reduce overfitting and ensure that the network generalizes well.
Number of noders in the last fully connected layer were setup as 133 along with softmax activation function to obtain probabilities of the prediction.
Relu activation function was used for all other layers.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>

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

<span class="c1">### TODO: Define your architecture.</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span> <span class="o">=</span> <span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</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">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span> <span class="o">=</span> <span class="s1">&#39;same&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</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">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">padding</span> <span class="o">=</span> <span class="s1">&#39;same&#39;</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">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</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="mf">0.4</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="mi">500</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">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="mf">0.2</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">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">summary</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>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 16)      208       
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 32)      2080      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 64)        8256      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 28, 28, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 50176)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 500)               25088500  
_________________________________________________________________
dropout_2 (Dropout)          (None, 500)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 133)               66633     
=================================================================
Total params: 25,165,677
Trainable params: 25,165,677
Non-trainable params: 0
_________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="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="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span>  

<span class="c1">### TODO: specify the number of epochs that you would like to use to train the model.</span>

<span class="n">epochs</span> <span class="o">=</span> <span class="mi">5</span>

<span class="c1">### Do NOT modify the code below this line.</span>

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</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">fit</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">,</span> <span class="n">valid_targets</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">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/5
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.8501 - acc: 0.0195Epoch 00001: val_loss improved from inf to 4.51009, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 32s 5ms/step - loss: 4.8501 - acc: 0.0196 - val_loss: 4.5101 - val_acc: 0.0383
Epoch 2/5
6660/6680 [============================&gt;.] - ETA: 0s - loss: 4.2436 - acc: 0.0676Epoch 00002: val_loss improved from 4.51009 to 4.19213, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 31s 5ms/step - loss: 4.2447 - acc: 0.0674 - val_loss: 4.1921 - val_acc: 0.0707
Epoch 3/5
6660/6680 [============================&gt;.] - ETA: 0s - loss: 3.5832 - acc: 0.1734Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 31s 5ms/step - loss: 3.5822 - acc: 0.1735 - val_loss: 4.2466 - val_acc: 0.0898
Epoch 4/5
6660/6680 [============================&gt;.] - ETA: 0s - loss: 2.5512 - acc: 0.3857Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 31s 5ms/step - loss: 2.5520 - acc: 0.3855 - val_loss: 4.7222 - val_acc: 0.0790
Epoch 5/5
6660/6680 [============================&gt;.] - ETA: 0s - loss: 1.4260 - acc: 0.6378Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 31s 5ms/step - loss: 1.4273 - acc: 0.6373 - val_loss: 5.3262 - val_acc: 0.0778
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[15]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;keras.callbacks.History at 0x7f57c5465da0&gt;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.from_scratch.hdf5&#39;</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images.  Ensure that your test accuracy is greater than 1%.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span>
<span class="n">dog_breed_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</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">tensor</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">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">test_tensors</span><span class="p">]</span>

<span class="c1"># report test accuracy</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</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>Test accuracy: 8.0144%
</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">
<hr>
<p><a id='step4'></a></p>
<h2 id="Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">Step 4: Use a CNN to Classify Dog Breeds<a class="anchor-link" href="#Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">&#182;</a></h2><p>To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning.  In the following step, you will get a chance to use transfer learning to train your own CNN.</p>
<h3 id="Obtain-Bottleneck-Features">Obtain Bottleneck Features<a class="anchor-link" href="#Obtain-Bottleneck-Features">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;/data/bottleneck_features/DogVGG16Data.npz&#39;</span><span class="p">)</span>
<span class="n">train_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3><p>The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model.  We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG16</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">VGG16_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">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

<span class="n">VGG16_model</span><span class="o">.</span><span class="n">summary</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>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_1 ( (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 133)               68229     
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">VGG16_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_VGG16</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_VGG16</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 11.7013 - acc: 0.1485Epoch 00001: val_loss improved from inf to 10.05394, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 314us/step - loss: 11.6792 - acc: 0.1494 - val_loss: 10.0539 - val_acc: 0.2491
Epoch 2/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 9.1709 - acc: 0.3315Epoch 00002: val_loss improved from 10.05394 to 9.20512, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 264us/step - loss: 9.1684 - acc: 0.3322 - val_loss: 9.2051 - val_acc: 0.3377
Epoch 3/20
6560/6680 [============================&gt;.] - ETA: 0s - loss: 8.5435 - acc: 0.4014Epoch 00003: val_loss improved from 9.20512 to 8.83477, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 261us/step - loss: 8.5508 - acc: 0.4009 - val_loss: 8.8348 - val_acc: 0.3509
Epoch 4/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 8.3013 - acc: 0.4374Epoch 00004: val_loss improved from 8.83477 to 8.65912, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 261us/step - loss: 8.2796 - acc: 0.4386 - val_loss: 8.6591 - val_acc: 0.3760
Epoch 5/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 8.0041 - acc: 0.4646Epoch 00005: val_loss improved from 8.65912 to 8.46969, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 263us/step - loss: 8.0089 - acc: 0.4645 - val_loss: 8.4697 - val_acc: 0.4072
Epoch 6/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 7.8620 - acc: 0.4846Epoch 00006: val_loss improved from 8.46969 to 8.43287, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 263us/step - loss: 7.8723 - acc: 0.4843 - val_loss: 8.4329 - val_acc: 0.4120
Epoch 7/20
6580/6680 [============================&gt;.] - ETA: 0s - loss: 7.8208 - acc: 0.4960Epoch 00007: val_loss improved from 8.43287 to 8.40851, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 263us/step - loss: 7.8194 - acc: 0.4960 - val_loss: 8.4085 - val_acc: 0.4216
Epoch 8/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 7.7120 - acc: 0.5024Epoch 00008: val_loss improved from 8.40851 to 8.29867, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 265us/step - loss: 7.7126 - acc: 0.5025 - val_loss: 8.2987 - val_acc: 0.4275
Epoch 9/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 7.6303 - acc: 0.5120Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 2s 262us/step - loss: 7.6136 - acc: 0.5130 - val_loss: 8.3386 - val_acc: 0.4228
Epoch 10/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 7.5812 - acc: 0.5174Epoch 00010: val_loss improved from 8.29867 to 8.25436, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 265us/step - loss: 7.5694 - acc: 0.5180 - val_loss: 8.2544 - val_acc: 0.4311
Epoch 11/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 7.4920 - acc: 0.5237Epoch 00011: val_loss improved from 8.25436 to 8.18213, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 267us/step - loss: 7.4823 - acc: 0.5243 - val_loss: 8.1821 - val_acc: 0.4335
Epoch 12/20
6560/6680 [============================&gt;.] - ETA: 0s - loss: 7.3832 - acc: 0.5320Epoch 00012: val_loss improved from 8.18213 to 8.08392, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 262us/step - loss: 7.3948 - acc: 0.5310 - val_loss: 8.0839 - val_acc: 0.4443
Epoch 13/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 7.2888 - acc: 0.5379Epoch 00013: val_loss improved from 8.08392 to 7.97171, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 264us/step - loss: 7.2917 - acc: 0.5376 - val_loss: 7.9717 - val_acc: 0.4407
Epoch 14/20
6500/6680 [============================&gt;.] - ETA: 0s - loss: 7.1644 - acc: 0.5482Epoch 00014: val_loss improved from 7.97171 to 7.95892, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 264us/step - loss: 7.1813 - acc: 0.5473 - val_loss: 7.9589 - val_acc: 0.4491
Epoch 15/20
6560/6680 [============================&gt;.] - ETA: 0s - loss: 7.1631 - acc: 0.5497Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 2s 262us/step - loss: 7.1694 - acc: 0.5493 - val_loss: 7.9977 - val_acc: 0.4455
Epoch 16/20
6520/6680 [============================&gt;.] - ETA: 0s - loss: 7.1571 - acc: 0.5521Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 2s 263us/step - loss: 7.1546 - acc: 0.5524 - val_loss: 7.9765 - val_acc: 0.4563
Epoch 17/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 7.1749 - acc: 0.5508Epoch 00017: val_loss improved from 7.95892 to 7.93084, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 263us/step - loss: 7.1513 - acc: 0.5522 - val_loss: 7.9308 - val_acc: 0.4599
Epoch 18/20
6560/6680 [============================&gt;.] - ETA: 0s - loss: 7.1155 - acc: 0.5552Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 2s 260us/step - loss: 7.1407 - acc: 0.5534 - val_loss: 8.0257 - val_acc: 0.4503
Epoch 19/20
6540/6680 [============================&gt;.] - ETA: 0s - loss: 7.1275 - acc: 0.5511Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 2s 260us/step - loss: 7.1147 - acc: 0.5518 - val_loss: 7.9616 - val_acc: 0.4515
Epoch 20/20
6620/6680 [============================&gt;.] - ETA: 0s - loss: 7.0476 - acc: 0.5548Epoch 00020: val_loss improved from 7.93084 to 7.84808, saving model to saved_models/weights.best.VGG16.hdf5
6680/6680 [==============================] - 2s 260us/step - loss: 7.0613 - acc: 0.5540 - val_loss: 7.8481 - val_acc: 0.4587
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[21]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;keras.callbacks.History at 0x7f57a3c1c2e8&gt;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.VGG16.hdf5&#39;</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">&#182;</a></h3><p>Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images.  We print the test accuracy below.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span>
<span class="n">VGG16_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">VGG16_model</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">feature</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">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_VGG16</span><span class="p">]</span>

<span class="c1"># report test accuracy</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</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>Test accuracy: 46.4115%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Predict-Dog-Breed-with-the-Model">Predict Dog Breed with the Model<a class="anchor-link" href="#Predict-Dog-Breed-with-the-Model">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span>

<span class="k">def</span> <span class="nf">VGG16_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="c1"># extract bottleneck features</span>
    <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_VGG16</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>
    <span class="c1"># obtain predicted vector</span>
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span>
    <span class="c1"># return dog breed that is predicted by the model</span>
    <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<p><a id='step5'></a></p>
<h2 id="Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">&#182;</a></h2><p>You will now use transfer learning to create a CNN that can identify dog breed from images.  Your CNN must attain at least 60% accuracy on the test set.</p>
<p>In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features.  In this section, you must use the bottleneck features from a different pre-trained model.  To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras.  These are already in the workspace, at /data/bottleneck_features.  If you wish to download them on a different machine, they can be found at:</p>
<ul>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz">VGG-19</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz">ResNet-50</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz">Inception</a> bottleneck features</li>
<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz">Xception</a> bottleneck features</li>
</ul>
<p>The files are encoded as such:</p>

<pre><code>Dog{network}Data.npz

</code></pre>
<p>where <code>{network}</code>, in the above filename, can be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.</p>
<p>The above architectures are downloaded and stored for you in the <code>/data/bottleneck_features/</code> folder.</p>
<p>This means the following will be in the <code>/data/bottleneck_features/</code> folder:</p>
<p><code>DogVGG19Data.npz</code>
<code>DogResnet50Data.npz</code>
<code>DogInceptionV3Data.npz</code>
<code>DogXceptionData.npz</code></p>
<h3 id="(IMPLEMENTATION)-Obtain-Bottleneck-Features">(IMPLEMENTATION) Obtain Bottleneck Features<a class="anchor-link" href="#(IMPLEMENTATION)-Obtain-Bottleneck-Features">&#182;</a></h3><p>In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:</p>

<pre><code>bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']</code></pre>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;/data/bottleneck_features/DogXceptionData.npz&#39;</span><span class="p">)</span>
<span class="n">train_Xception</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
<span class="n">valid_Xception</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span>
<span class="n">test_Xception</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">&#182;</a></h3><p>Create a CNN to classify dog breed.  At the end of your code cell block, summarize the layers of your model by executing the line:</p>

<pre><code>    &lt;your model's name&gt;.summary()

</code></pre>
<p><strong>Question 5:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step.  Describe why you think the architecture is suitable for the current problem.</p>
<p><strong>Answer:</strong> I have taken the Xception model model as my final dataset among all other models because i observed that the new data set was small and similar to training data of original model so, hence end of the network is sliced off and a fully connected layer that matches the number of classes in the new data set is added. Next the weights of the new fully connected layer are randomized; All the weights from the pre-trained network are frozen. Finally network is trained to update the weights of the new fully connected layer. Overall, I have used the Xception model which gave the best result. I also added a fully connected layer with 510 nodes and a ReLU activation function to detect more patterns and a Dropout to avoid overfitting.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Define your architecture.</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span>

<span class="n">Xception_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">Xception_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_Xception</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">Xception_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">510</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">Xception_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="mf">0.4</span><span class="p">))</span>
<span class="n">Xception_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">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

<span class="n">Xception_model</span><span class="o">.</span><span class="n">summary</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>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 510)               1044990   
_________________________________________________________________
dropout_3 (Dropout)          (None, 510)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 133)               67963     
=================================================================
Total params: 1,112,953
Trainable params: 1,112,953
Non-trainable params: 0
_________________________________________________________________
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Compile-the-Model">(IMPLEMENTATION) Compile the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Compile-the-Model">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Compile the model.</span>
<span class="n">Xception_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">&#182;</a></h3><p>Train your model in the code cell below.  Use model checkpointing to save the model that attains the best validation loss.</p>
<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Train the model.</span>


<span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span> 

<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">&#39;saved_models/weights.best.Xception.hdf5&#39;</span><span class="p">,</span> 
                               <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">Xception_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_Xception</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> 
          <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_Xception</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span>
          <span class="n">epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Train on 6680 samples, validate on 835 samples
Epoch 1/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 1.4831 - acc: 0.6385Epoch 00001: val_loss improved from inf to 0.60371, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 4s 610us/step - loss: 1.4749 - acc: 0.6407 - val_loss: 0.6037 - val_acc: 0.8048
Epoch 2/25
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.6585 - acc: 0.8036Epoch 00002: val_loss improved from 0.60371 to 0.58540, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 4s 541us/step - loss: 0.6596 - acc: 0.8034 - val_loss: 0.5854 - val_acc: 0.8216
Epoch 3/25
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.5385 - acc: 0.8402Epoch 00003: val_loss improved from 0.58540 to 0.57232, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 4s 526us/step - loss: 0.5393 - acc: 0.8397 - val_loss: 0.5723 - val_acc: 0.8335
Epoch 4/25
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.4423 - acc: 0.8672Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 3s 524us/step - loss: 0.4425 - acc: 0.8669 - val_loss: 0.6293 - val_acc: 0.8347
Epoch 5/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.4077 - acc: 0.8759Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 4s 526us/step - loss: 0.4074 - acc: 0.8762 - val_loss: 0.6331 - val_acc: 0.8347
Epoch 6/25
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.3455 - acc: 0.8964Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 3s 521us/step - loss: 0.3460 - acc: 0.8963 - val_loss: 0.6576 - val_acc: 0.8192
Epoch 7/25
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.3036 - acc: 0.9071Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 3s 512us/step - loss: 0.3031 - acc: 0.9070 - val_loss: 0.6978 - val_acc: 0.8347
Epoch 8/25
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.2854 - acc: 0.9146Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 3s 507us/step - loss: 0.2859 - acc: 0.9147 - val_loss: 0.7291 - val_acc: 0.8383
Epoch 9/25
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.2631 - acc: 0.9158Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 3s 499us/step - loss: 0.2632 - acc: 0.9156 - val_loss: 0.7449 - val_acc: 0.8335
Epoch 10/25
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.2423 - acc: 0.9263Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 3s 516us/step - loss: 0.2421 - acc: 0.9263 - val_loss: 0.8590 - val_acc: 0.8359
Epoch 11/25
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.2392 - acc: 0.9273Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 3s 521us/step - loss: 0.2399 - acc: 0.9268 - val_loss: 0.7412 - val_acc: 0.8455
Epoch 12/25
6560/6680 [============================&gt;.] - ETA: 0s - loss: 0.2126 - acc: 0.9375Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 3s 517us/step - loss: 0.2150 - acc: 0.9368 - val_loss: 0.7516 - val_acc: 0.8383
Epoch 13/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1988 - acc: 0.9423Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 3s 514us/step - loss: 0.1987 - acc: 0.9422 - val_loss: 0.7971 - val_acc: 0.8467
Epoch 14/25
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.1809 - acc: 0.9471Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 4s 555us/step - loss: 0.1805 - acc: 0.9473 - val_loss: 0.8718 - val_acc: 0.8491
Epoch 15/25
6620/6680 [============================&gt;.] - ETA: 0s - loss: 0.1646 - acc: 0.9492Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 4s 596us/step - loss: 0.1651 - acc: 0.9493 - val_loss: 0.9915 - val_acc: 0.8455
Epoch 16/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1636 - acc: 0.9532Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 4s 550us/step - loss: 0.1625 - acc: 0.9534 - val_loss: 0.9407 - val_acc: 0.8491
Epoch 17/25
6660/6680 [============================&gt;.] - ETA: 0s - loss: 0.1591 - acc: 0.9560Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 3s 518us/step - loss: 0.1590 - acc: 0.9560 - val_loss: 0.9330 - val_acc: 0.8443
Epoch 18/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1532 - acc: 0.9536Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 3s 509us/step - loss: 0.1533 - acc: 0.9537 - val_loss: 1.0585 - val_acc: 0.8407
Epoch 19/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1310 - acc: 0.9600Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 3s 514us/step - loss: 0.1323 - acc: 0.9594 - val_loss: 0.9794 - val_acc: 0.8443
Epoch 20/25
6600/6680 [============================&gt;.] - ETA: 0s - loss: 0.1288 - acc: 0.9638Epoch 00020: val_loss did not improve
6680/6680 [==============================] - 4s 526us/step - loss: 0.1300 - acc: 0.9633 - val_loss: 1.0043 - val_acc: 0.8491
Epoch 21/25
6560/6680 [============================&gt;.] - ETA: 0s - loss: 0.1254 - acc: 0.9665Epoch 00021: val_loss did not improve
6680/6680 [==============================] - 3s 520us/step - loss: 0.1261 - acc: 0.9663 - val_loss: 1.0172 - val_acc: 0.8455
Epoch 22/25
6560/6680 [============================&gt;.] - ETA: 0s - loss: 0.1292 - acc: 0.9649Epoch 00022: val_loss did not improve
6680/6680 [==============================] - 3s 520us/step - loss: 0.1280 - acc: 0.9653 - val_loss: 1.1375 - val_acc: 0.8395
Epoch 23/25
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.1376 - acc: 0.9623Epoch 00023: val_loss did not improve
6680/6680 [==============================] - 3s 520us/step - loss: 0.1371 - acc: 0.9624 - val_loss: 1.1798 - val_acc: 0.8323
Epoch 24/25
6580/6680 [============================&gt;.] - ETA: 0s - loss: 0.1345 - acc: 0.9652Epoch 00024: val_loss did not improve
6680/6680 [==============================] - 3s 521us/step - loss: 0.1345 - acc: 0.9648 - val_loss: 1.1473 - val_acc: 0.8407
Epoch 25/25
6640/6680 [============================&gt;.] - ETA: 0s - loss: 0.1107 - acc: 0.9690Epoch 00025: val_loss did not improve
6680/6680 [==============================] - 3s 506us/step - loss: 0.1103 - acc: 0.9690 - val_loss: 1.1706 - val_acc: 0.8323
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[28]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;keras.callbacks.History at 0x7f57a3910f60&gt;</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">(IMPLEMENTATION) Load the Model with the Best Validation Loss<a class="anchor-link" href="#(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Load the model weights with the best validation loss.</span>
<span class="n">Xception_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">&#39;saved_models/weights.best.Xception.hdf5&#39;</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">&#182;</a></h3><p>Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Calculate classification accuracy on the test dataset.</span>
<span class="n">Xception_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">Xception_model</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">feature</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">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_Xception</span><span class="p">]</span>
<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Xception_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">Xception_predictions</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">test_accuracy</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>Test accuracy: 82.6555%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">(IMPLEMENTATION) Predict Dog Breed with the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">&#182;</a></h3><p>Write a function that takes an image path as input and returns the dog breed (<code>Affenpinscher</code>, <code>Afghan_hound</code>, etc) that is predicted by your model.</p>
<p>Similar to the analogous function in Step 5, your function should have three steps:</p>
<ol>
<li>Extract the bottleneck features corresponding to the chosen CNN model.</li>
<li>Supply the bottleneck features as input to the model to return the predicted vector.  Note that the argmax of this prediction vector gives the index of the predicted dog breed.</li>
<li>Use the <code>dog_names</code> array defined in Step 0 of this notebook to return the corresponding breed.</li>
</ol>
<p>The functions to extract the bottleneck features can be found in <code>extract_bottleneck_features.py</code>, and they have been imported in an earlier code cell.  To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function</p>

<pre><code>extract_{network}

</code></pre>
<p>where <code>{network}</code>, in the above filename, should be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write a function that takes a path to an image as input</span>
<span class="c1">### and returns the dog breed that is predicted by the model.</span>

<span class="k">def</span> <span class="nf">Xception_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
    <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_Xception</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span>      
    <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">Xception_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span>       
    <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span>                       
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<p><a id='step6'></a></p>
<h2 id="Step-6:-Write-your-Algorithm">Step 6: Write your Algorithm<a class="anchor-link" href="#Step-6:-Write-your-Algorithm">&#182;</a></h2><p>Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither.  Then,</p>
<ul>
<li>if a <strong>dog</strong> is detected in the image, return the predicted breed.</li>
<li>if a <strong>human</strong> is detected in the image, return the resembling dog breed.</li>
<li>if <strong>neither</strong> is detected in the image, provide output that indicates an error.</li>
</ul>
<p>You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the <code>face_detector</code> and <code>dog_detector</code> functions developed above.  You are <strong>required</strong> to use your CNN from Step 5 to predict dog breed.</p>
<p>Some sample output for our algorithm is provided below, but feel free to design your own user experience!</p>
<p><img src="images/sample_human_output.png" alt="Sample Human Output"></p>
<h3 id="(IMPLEMENTATION)-Write-your-Algorithm">(IMPLEMENTATION) Write your Algorithm<a class="anchor-link" href="#(IMPLEMENTATION)-Write-your-Algorithm">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</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">cv2</span> 
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">def</span> <span class="nf">recognizer</span><span class="p">(</span><span class="n">imagePath</span><span class="p">):</span>
    
    <span class="n">img</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">img</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span> 
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;could not load image !&quot;</span><span class="p">)</span>
    <span class="n">rgb</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">rgb</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="k">if</span><span class="p">(</span><span class="n">dog_detector</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;HEY IT IS A DOG&quot;</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The Dog belongs to the following breed&quot;</span> <span class="o">+</span> <span class="n">Xception_predict_breed</span><span class="p">(</span><span class="n">imagePath</span><span class="p">))</span>
    <span class="k">elif</span><span class="p">(</span><span class="n">face_detector</span><span class="p">(</span><span class="n">imagePath</span><span class="p">)):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;I GUESS YOU ARE A  HUMAN&quot;</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The Human face resembles the following breed&quot;</span> <span class="o">+</span> <span class="n">Xception_predict_breed</span><span class="p">(</span><span class="n">imagePath</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The face neither Human nor a Dog Face!!!&quot;</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<p><a id='step7'></a></p>
<h2 id="Step-7:-Test-Your-Algorithm">Step 7: Test Your Algorithm<a class="anchor-link" href="#Step-7:-Test-Your-Algorithm">&#182;</a></h2><p>In this section, you will take your new algorithm for a spin!  What kind of dog does the algorithm think that <strong>you</strong> look like?  If you have a dog, does it predict your dog's breed accurately?  If you have a cat, does it mistakenly think that your cat is a dog?</p>
<h3 id="(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">(IMPLEMENTATION) Test Your Algorithm on Sample Images!<a class="anchor-link" href="#(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">&#182;</a></h3><p>Test your algorithm at least six images on your computer.  Feel free to use any images you like.  Use at least two human and two dog images.</p>
<p><strong>Question 6:</strong> Is the output better than you expected :) ?  Or worse :( ?  Provide at least three possible points of improvement for your algorithm.</p>
<p><strong>Answer:</strong> The results i predicted are wonderful according to me because if i gave a clear picture of human and dog it can easily predict the difference and outputs that the given model is a human or a dog.
the possible points of improvement of my algorithm was 
1.if i was able to give more training data it main predict the results more accurately
2.BY following the image agumentation better results can be drawn
3.If the parameters and depth of cnn classifier was then the accuracy will be more</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/dog1.jpg&#39;</span><span class="p">)</span>
<span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/human1.jpeg&#39;</span><span class="p">)</span>
<span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/human2.jpg&#39;</span><span class="p">)</span>
<span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/dog2.jpg&#39;</span><span class="p">)</span>
<span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/human3.jpg&#39;</span><span class="p">)</span>
<span class="n">recognizer</span><span class="p">(</span><span class="s1">&#39;./images/dog3.jpg&#39;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stderr output_text">
<pre>/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>HEY IT IS A DOG
The Dog belongs to the following breedin/078.Great_dane
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I GUESS YOU ARE A  HUMAN
The Human face resembles the following breedin/040.Bulldog
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I GUESS YOU ARE A  HUMAN
The Human face resembles the following breedin/023.Bernese_mountain_dog
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>HEY IT IS A DOG
The Dog belongs to the following breedin/056.Dachshund
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>I GUESS YOU ARE A  HUMAN
The Human face resembles the following breedin/023.Bernese_mountain_dog
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>HEY IT IS A DOG
The Dog belongs to the following breedin/096.Labrador_retriever
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Please-download-your-notebook-to-submit">Please download your notebook to submit<a class="anchor-link" href="#Please-download-your-notebook-to-submit">&#182;</a></h1><p>In order to submit, please do the following:</p>
<ol>
<li>Download an HTML version of the notebook to your computer using 'File: Download as...'</li>
<li>Click on the orange Jupyter circle on the top left of the workspace.</li>
<li>Navigate into the dog-project folder to ensure that you are using the provided dog_images, lfw, and bottleneck_features folders; this means that those folders will <em>not</em> appear in the dog-project folder.  If they do appear because you downloaded them, delete them.</li>
<li>While in the dog-project folder, upload the HTML version of this notebook you just downloaded.  The upload button is on the top right.</li>
<li>Navigate back to the home folder by clicking on the two dots next to the folder icon, and then open up a terminal under the 'new' tab on the top right</li>
<li>Zip the dog-project folder with the following command in the terminal:
<code>zip -r dog-project.zip dog-project</code></li>
<li>Download the zip file by clicking on the square next to it and selecting 'download'.  This will be the zip file you turn in on the next node after this workspace!</li>
</ol>

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

 
</html>