student_project.html

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

<title>student_project</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">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

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

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

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Overview">Overview<a class="anchor-link" href="#Overview">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>Project Instructions &amp; Prerequisites</li>
<li>Learning Objectives</li>
<li>Data Preparation</li>
<li>Create Categorical Features with TF Feature Columns</li>
<li>Create Continuous/Numerical Features with TF Feature Columns</li>
<li>Build Deep Learning Regression Model with Sequential API and TF Probability Layers</li>
<li>Evaluating Potential Model Biases with Aequitas Toolkit</li>
</ol>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="1.-Project-Instructions-&amp;-Prerequisites">1. Project Instructions &amp; Prerequisites<a class="anchor-link" href="#1.-Project-Instructions-&amp;-Prerequisites">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Project-Instructions">Project Instructions<a class="anchor-link" href="#Project-Instructions">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Context</strong>: EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical industry and regulators to <a href="https://www.fda.gov/news-events/speeches-fda-officials/breaking-down-barriers-between-clinical-trials-and-clinical-care-incorporating-real-world-evidence">make decisions on clinical trials</a>. You are a data scientist for an exciting unicorn healthcare startup that has created a groundbreaking diabetes drug that is ready for clinical trial testing. It is a very unique and sensitive drug that requires administering the drug over at least 5-7 days of time in the hospital with frequent monitoring/testing and patient medication adherence training with a mobile application. You have been provided a patient dataset from a client partner and are tasked with building a predictive model that can identify which type of patients the company should focus their efforts testing this drug on. Target patients are people that are likely to be in the hospital for this duration of time and will not incur significant additional costs for administering this drug to the patient and monitoring.</p>
<p>In order to achieve your goal you must build a regression model that can predict the estimated hospitalization time for a patient and use this to select/filter patients for your study.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Expected Hospitalization Time Regression Model:</strong> Utilizing a synthetic dataset(denormalized at the line level augmentation) built off of the UCI Diabetes readmission dataset, students will build a regression model that predicts the expected days of hospitalization time and then convert this to a binary prediction of whether to include or exclude that patient from the clinical trial.</p>
<p>This project will demonstrate the importance of building the right data representation at the encounter level, with appropriate filtering and preprocessing/feature engineering of key medical code sets. This project will also require students to analyze and interpret their model for biases across key demographic groups.</p>
<p>Please see the project rubric online for more details on the areas your project will be evaluated.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Dataset">Dataset<a class="anchor-link" href="#Dataset">&#182;</a></h3>
</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>Due to healthcare PHI regulations (HIPAA, HITECH), there are limited number of publicly available datasets and some datasets require training and approval. So, for the purpose of this exercise, we are using a dataset from UC Irvine(<a href="https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008">https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008</a>) that has been modified for this course. Please note that it is limited in its representation of some key features such as diagnosis codes which are usually an unordered list in 835s/837s (the HL7 standard interchange formats used for claims and remits).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Data Schema</strong>
The dataset reference information can be <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/</a>
. There are two CSVs that provide more details on the fields and some of the mapped values.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">

</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">

</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">

</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">

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Environment-Setup">Environment Setup<a class="anchor-link" href="#Environment-Setup">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>For step by step instructions on creating your environment, please go to <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/README.md">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/README.md</a>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="2.--Learning-Objectives">2.  Learning Objectives<a class="anchor-link" href="#2.--Learning-Objectives">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>By the end of the project, you will be able to</p>
<ul>
<li>Use the Tensorflow Dataset API to scalably extract, transform, and load datasets and build datasets aggregated at the line, encounter, and patient data levels(longitudinal)</li>
<li>Analyze EHR datasets to check for common issues (data leakage, statistical properties, missing values, high cardinality) by performing exploratory data analysis.</li>
<li>Create categorical features from Key Industry Code Sets (ICD, CPT, NDC) and reduce dimensionality for high cardinality features by using embeddings </li>
<li>Create derived features(bucketing, cross-features, embeddings) utilizing Tensorflow feature columns on both continuous and categorical input features</li>
<li>SWBAT use the Tensorflow Probability library to train a model that provides uncertainty range predictions that allow for risk adjustment/prioritization and triaging of predictions</li>
<li>Analyze and determine biases for a model for key demographic groups by evaluating performance metrics across groups by using the Aequitas framework </li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="3.-Data-Preparation">3. Data Preparation<a class="anchor-link" href="#3.-Data-Preparation">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[193]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from __future__ import absolute_import, division, print_function, unicode_literals</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">layers</span>
<span class="kn">import</span> <span class="nn">tensorflow_probability</span> <span class="k">as</span> <span class="nn">tfp</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">aequitas</span> <span class="k">as</span> <span class="nn">ae</span>
<span class="c1"># Put all of the helper functions in utils</span>
<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">build_vocab_files</span><span class="p">,</span> <span class="n">show_group_stats_viz</span><span class="p">,</span> <span class="n">aggregate_dataset</span><span class="p">,</span> <span class="n">preprocess_df</span><span class="p">,</span> <span class="n">df_to_dataset</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_columns&#39;</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span>
<span class="c1"># this allows you to make changes and save in student_utils.py and the file is reloaded every time you run a code block</span>
<span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</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>The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#OPEN ISSUE ON MAC OSX for TF model training</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;KMP_DUPLICATE_LIB_OK&#39;</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;True&#39;</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Dataset-Loading-and-Schema-Review">Dataset Loading and Schema Review<a class="anchor-link" href="#Dataset-Loading-and-Schema-Review">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Load the dataset and view a sample of the dataset along with reviewing the schema reference files to gain a deeper understanding of the dataset. The dataset is located at the following path <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/starter_code/data/final_project_dataset.csv">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/starter_code/data/final_project_dataset.csv</a>. Also, review the information found in the data schema <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/</a></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="n">dataset_path</span> <span class="o">=</span> <span class="s2">&quot;./data/final_project_dataset.csv&quot;</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">dataset_path</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>ndc_code</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2278392</td>
      <td>8222157</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[0-10)</td>
      <td>?</td>
      <td>6</td>
      <td>25</td>
      <td>1</td>
      <td>1</td>
      <td>?</td>
      <td>Pediatrics-Endocrinology</td>
      <td>250.83</td>
      <td>?|?</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>41</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>NaN</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149190</td>
      <td>55629189</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[10-20)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>3</td>
      <td>?</td>
      <td>?</td>
      <td>276</td>
      <td>250.01|255</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>59</td>
      <td>9</td>
      <td>18</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>2</th>
      <td>64410</td>
      <td>86047875</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[20-30)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>?</td>
      <td>?</td>
      <td>648</td>
      <td>250|V27</td>
      <td>2</td>
      <td>1</td>
      <td>0</td>
      <td>11</td>
      <td>6</td>
      <td>13</td>
      <td>5</td>
      <td>0378-1110</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>3</th>
      <td>500364</td>
      <td>82442376</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[30-40)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>?</td>
      <td>?</td>
      <td>8</td>
      <td>250.43|403</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>44</td>
      <td>7</td>
      <td>16</td>
      <td>1</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>4</th>
      <td>16680</td>
      <td>42519267</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>?</td>
      <td>?</td>
      <td>197</td>
      <td>157|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>5</th>
      <td>16680</td>
      <td>42519267</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>?</td>
      <td>?</td>
      <td>197</td>
      <td>157|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>6</th>
      <td>35754</td>
      <td>82637451</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[50-60)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>?</td>
      <td>?</td>
      <td>414</td>
      <td>411|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>31</td>
      <td>9</td>
      <td>16</td>
      <td>6</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>7</th>
      <td>55842</td>
      <td>84259809</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>4</td>
      <td>?</td>
      <td>?</td>
      <td>414</td>
      <td>411|V45</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>70</td>
      <td>7</td>
      <td>21</td>
      <td>1</td>
      <td>35208-001</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>8</th>
      <td>55842</td>
      <td>84259809</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>4</td>
      <td>?</td>
      <td>?</td>
      <td>414</td>
      <td>411|V45</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>70</td>
      <td>7</td>
      <td>21</td>
      <td>1</td>
      <td>16729-001</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>9</th>
      <td>55842</td>
      <td>84259809</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>3</td>
      <td>1</td>
      <td>2</td>
      <td>4</td>
      <td>?</td>
      <td>?</td>
      <td>414</td>
      <td>411|V45</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>70</td>
      <td>7</td>
      <td>21</td>
      <td>1</td>
      <td>47918-891</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>10</th>
      <td>63768</td>
      <td>114882984</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>5</td>
      <td>?</td>
      <td>?</td>
      <td>428</td>
      <td>492|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>73</td>
      <td>8</td>
      <td>12</td>
      <td>0</td>
      <td>0115-1742</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>11</th>
      <td>12522</td>
      <td>48330783</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>13</td>
      <td>?</td>
      <td>?</td>
      <td>398</td>
      <td>427|38</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>68</td>
      <td>8</td>
      <td>28</td>
      <td>2</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>12</th>
      <td>12522</td>
      <td>48330783</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>13</td>
      <td>?</td>
      <td>?</td>
      <td>398</td>
      <td>427|38</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>68</td>
      <td>8</td>
      <td>28</td>
      <td>2</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>13</th>
      <td>15738</td>
      <td>63555939</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[90-100)</td>
      <td>?</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>12</td>
      <td>?</td>
      <td>InternalMedicine</td>
      <td>434</td>
      <td>198|486</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>33</td>
      <td>8</td>
      <td>18</td>
      <td>3</td>
      <td>0173-0863</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>14</th>
      <td>15738</td>
      <td>63555939</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[90-100)</td>
      <td>?</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>12</td>
      <td>?</td>
      <td>InternalMedicine</td>
      <td>434</td>
      <td>198|486</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>33</td>
      <td>8</td>
      <td>18</td>
      <td>3</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>15</th>
      <td>28236</td>
      <td>89869032</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>9</td>
      <td>?</td>
      <td>?</td>
      <td>250.7</td>
      <td>403|996</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>47</td>
      <td>9</td>
      <td>17</td>
      <td>2</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>16</th>
      <td>36900</td>
      <td>77391171</td>
      <td>AfricanAmerican</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>7</td>
      <td>?</td>
      <td>?</td>
      <td>157</td>
      <td>288|197</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>62</td>
      <td>7</td>
      <td>11</td>
      <td>0</td>
      <td>0115-1743</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&lt;30</td>
    </tr>
    <tr>
      <th>17</th>
      <td>36900</td>
      <td>77391171</td>
      <td>AfricanAmerican</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>7</td>
      <td>?</td>
      <td>?</td>
      <td>157</td>
      <td>288|197</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>62</td>
      <td>7</td>
      <td>11</td>
      <td>0</td>
      <td>50090-0353</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&lt;30</td>
    </tr>
    <tr>
      <th>18</th>
      <td>40926</td>
      <td>85504905</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>7</td>
      <td>?</td>
      <td>Family/GeneralPractice</td>
      <td>428</td>
      <td>250.43|250.6</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>60</td>
      <td>8</td>
      <td>15</td>
      <td>0</td>
      <td>0378-7185</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&lt;30</td>
    </tr>
    <tr>
      <th>19</th>
      <td>40926</td>
      <td>85504905</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>7</td>
      <td>?</td>
      <td>Family/GeneralPractice</td>
      <td>428</td>
      <td>250.43|250.6</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>60</td>
      <td>8</td>
      <td>15</td>
      <td>0</td>
      <td>60429-484</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&lt;30</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[4]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>ndc_code</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>143404</th>
      <td>443841992</td>
      <td>184875899</td>
      <td>Other</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>13</td>
      <td>?</td>
      <td>?</td>
      <td>250.8</td>
      <td>730|731</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>9</td>
      <td>13</td>
      <td>2</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143405</th>
      <td>443842016</td>
      <td>183087545</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>9</td>
      <td>?</td>
      <td>?</td>
      <td>574</td>
      <td>574|250.02</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>50</td>
      <td>9</td>
      <td>33</td>
      <td>2</td>
      <td>0009-3449</td>
      <td>None</td>
      <td>&gt;7</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143406</th>
      <td>443842016</td>
      <td>183087545</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>9</td>
      <td>?</td>
      <td>?</td>
      <td>574</td>
      <td>574|250.02</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>50</td>
      <td>9</td>
      <td>33</td>
      <td>2</td>
      <td>47918-902</td>
      <td>None</td>
      <td>&gt;7</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143407</th>
      <td>443842022</td>
      <td>188574944</td>
      <td>Other</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>14</td>
      <td>MD</td>
      <td>?</td>
      <td>592</td>
      <td>599|518</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>73</td>
      <td>9</td>
      <td>26</td>
      <td>6</td>
      <td>0049-0178</td>
      <td>None</td>
      <td>&gt;8</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143408</th>
      <td>443842022</td>
      <td>188574944</td>
      <td>Other</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>14</td>
      <td>MD</td>
      <td>?</td>
      <td>592</td>
      <td>599|518</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>73</td>
      <td>9</td>
      <td>26</td>
      <td>6</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>&gt;8</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143409</th>
      <td>443842070</td>
      <td>140199494</td>
      <td>Other</td>
      <td>Female</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>MD</td>
      <td>?</td>
      <td>996</td>
      <td>585|403</td>
      <td>1</td>
      <td>1</td>
      <td>1</td>
      <td>46</td>
      <td>9</td>
      <td>17</td>
      <td>6</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143410</th>
      <td>443842136</td>
      <td>181593374</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>5</td>
      <td>?</td>
      <td>?</td>
      <td>491</td>
      <td>518|511</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>21</td>
      <td>9</td>
      <td>16</td>
      <td>1</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143411</th>
      <td>443842340</td>
      <td>120975314</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>5</td>
      <td>MC</td>
      <td>?</td>
      <td>292</td>
      <td>8|304</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>76</td>
      <td>9</td>
      <td>22</td>
      <td>1</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143412</th>
      <td>443842778</td>
      <td>86472243</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>MC</td>
      <td>?</td>
      <td>435</td>
      <td>784|250</td>
      <td>3</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>7</td>
      <td>15</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143413</th>
      <td>443847176</td>
      <td>50375628</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>6</td>
      <td>DM</td>
      <td>?</td>
      <td>345</td>
      <td>438|412</td>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>45</td>
      <td>9</td>
      <td>25</td>
      <td>1</td>
      <td>0173-0861</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143414</th>
      <td>443847176</td>
      <td>50375628</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[60-70)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>6</td>
      <td>DM</td>
      <td>?</td>
      <td>345</td>
      <td>438|412</td>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>45</td>
      <td>9</td>
      <td>25</td>
      <td>1</td>
      <td>50090-0353</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143415</th>
      <td>443847548</td>
      <td>100162476</td>
      <td>AfricanAmerican</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>3</td>
      <td>MC</td>
      <td>?</td>
      <td>250.13</td>
      <td>291|458</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>9</td>
      <td>16</td>
      <td>0</td>
      <td>42708-009</td>
      <td>None</td>
      <td>&gt;8</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143416</th>
      <td>443847548</td>
      <td>100162476</td>
      <td>AfricanAmerican</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>3</td>
      <td>MC</td>
      <td>?</td>
      <td>250.13</td>
      <td>291|458</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>9</td>
      <td>16</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>&gt;8</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>143417</th>
      <td>443847782</td>
      <td>74694222</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>1</td>
      <td>4</td>
      <td>5</td>
      <td>5</td>
      <td>MC</td>
      <td>?</td>
      <td>560</td>
      <td>276|787</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>33</td>
      <td>9</td>
      <td>18</td>
      <td>3</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143418</th>
      <td>443854148</td>
      <td>41088789</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>MC</td>
      <td>?</td>
      <td>38</td>
      <td>590|296</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>53</td>
      <td>13</td>
      <td>9</td>
      <td>0</td>
      <td>10631-019</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143419</th>
      <td>443854148</td>
      <td>41088789</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>MC</td>
      <td>?</td>
      <td>38</td>
      <td>590|296</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>53</td>
      <td>13</td>
      <td>9</td>
      <td>0</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143420</th>
      <td>443857166</td>
      <td>31693671</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>3</td>
      <td>7</td>
      <td>10</td>
      <td>MC</td>
      <td>Surgery-General</td>
      <td>996</td>
      <td>285|998</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>45</td>
      <td>9</td>
      <td>21</td>
      <td>2</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143421</th>
      <td>443857166</td>
      <td>31693671</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>3</td>
      <td>7</td>
      <td>10</td>
      <td>MC</td>
      <td>Surgery-General</td>
      <td>996</td>
      <td>285|998</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>45</td>
      <td>9</td>
      <td>21</td>
      <td>2</td>
      <td>0781-5421</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143422</th>
      <td>443857166</td>
      <td>31693671</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>3</td>
      <td>7</td>
      <td>10</td>
      <td>MC</td>
      <td>Surgery-General</td>
      <td>996</td>
      <td>285|998</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>45</td>
      <td>9</td>
      <td>21</td>
      <td>2</td>
      <td>47918-902</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>143423</th>
      <td>443867222</td>
      <td>175429310</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[70-80)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>6</td>
      <td>?</td>
      <td>?</td>
      <td>530</td>
      <td>530|787</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>13</td>
      <td>9</td>
      <td>3</td>
      <td>3</td>
      <td>NaN</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Determine-Level-of-Dataset-(Line-or-Encounter)">Determine Level of Dataset (Line or Encounter)<a class="anchor-link" href="#Determine-Level-of-Dataset-(Line-or-Encounter)">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 1</strong>: Based off of analysis of the data, what level is this dataset? Is it at the line or encounter level? Are there any key fields besides the encounter_id and patient_nbr fields that we should use to aggregate on? Knowing this information will help inform us what level of aggregation is necessary for future steps and is a step that is often overlooked.</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="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_text output_subarea output_execute_result">
<pre>143424</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[6]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>101766</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>Student Response: This is a line-level dataset as the number of rows in the dataset exceed the number of unique encounter ids. We should also aggregate on the primary_diagnosis_code field.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Analyze-Dataset">Analyze Dataset<a class="anchor-link" href="#Analyze-Dataset">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 2</strong>: Utilizing the library of your choice (recommend Pandas and Seaborn or matplotlib though), perform exploratory data analysis on the dataset. In particular be sure to address the following questions:</p>

<pre><code>- a. Field(s) with high amount of missing/zero values:
'ndc_code' has high amount of NaN values, 'weight', 'payer_code', 'medical_specialty' have high amount of missing values and 'race' has some missing values. 'max_glu_serum', 'A1Cresult' have a high number of 'None values'


- b. Based off the frequency histogram for each numerical field, which numerical field(s) has/have a Gaussian(normal) distribution shape?:
'num_lab_features', 'num_medications' seem to have a gaussian distribution and 'encounter_id' might have a gaussian distribution


- c. Which field(s) have high cardinality and why (HINT: ndc_code is one feature)
'medical_specialty', 'primary_diagnosis_code', 'other_diagnosis_codes', 'ndc_code' have high cardinality.


- d. Please describe the demographic distributions in the dataset for the age and gender fields.
The data contains about 10,000 more rows of female patients than male patients, with a few labelled as 'invalid'. In terms of age group, most patients are within the 50-60, 60-70, 70-80, and 80-90 age groups</code></pre>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[7]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>1.434240e+05</td>
      <td>1.434240e+05</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>1.674290e+08</td>
      <td>5.493608e+07</td>
      <td>2.027694</td>
      <td>3.655316</td>
      <td>5.701096</td>
      <td>4.490190</td>
      <td>0.362429</td>
      <td>0.600855</td>
      <td>0.195086</td>
      <td>43.255745</td>
      <td>7.424434</td>
      <td>16.776035</td>
      <td>1.349021</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.019046e+08</td>
      <td>3.857840e+07</td>
      <td>1.427585</td>
      <td>5.219279</td>
      <td>4.064532</td>
      <td>2.999667</td>
      <td>1.249295</td>
      <td>1.207934</td>
      <td>0.920410</td>
      <td>19.657319</td>
      <td>1.924872</td>
      <td>8.397130</td>
      <td>1.719104</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.252200e+04</td>
      <td>1.350000e+02</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>8.829596e+07</td>
      <td>2.357219e+07</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>32.000000</td>
      <td>6.000000</td>
      <td>11.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>1.547637e+08</td>
      <td>4.630783e+07</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>7.000000</td>
      <td>4.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>44.000000</td>
      <td>8.000000</td>
      <td>15.000000</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>2.320897e+08</td>
      <td>8.823627e+07</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>7.000000</td>
      <td>6.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>57.000000</td>
      <td>9.000000</td>
      <td>21.000000</td>
      <td>2.000000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>4.438672e+08</td>
      <td>1.895026e+08</td>
      <td>8.000000</td>
      <td>28.000000</td>
      <td>25.000000</td>
      <td>14.000000</td>
      <td>42.000000</td>
      <td>21.000000</td>
      <td>76.000000</td>
      <td>132.000000</td>
      <td>16.000000</td>
      <td>81.000000</td>
      <td>6.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">info</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>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 143424 entries, 0 to 143423
Data columns (total 26 columns):
 #   Column                    Non-Null Count   Dtype 
---  ------                    --------------   ----- 
 0   encounter_id              143424 non-null  int64 
 1   patient_nbr               143424 non-null  int64 
 2   race                      143424 non-null  object
 3   gender                    143424 non-null  object
 4   age                       143424 non-null  object
 5   weight                    143424 non-null  object
 6   admission_type_id         143424 non-null  int64 
 7   discharge_disposition_id  143424 non-null  int64 
 8   admission_source_id       143424 non-null  int64 
 9   time_in_hospital          143424 non-null  int64 
 10  payer_code                143424 non-null  object
 11  medical_specialty         143424 non-null  object
 12  primary_diagnosis_code    143424 non-null  object
 13  other_diagnosis_codes     143424 non-null  object
 14  number_outpatient         143424 non-null  int64 
 15  number_inpatient          143424 non-null  int64 
 16  number_emergency          143424 non-null  int64 
 17  num_lab_procedures        143424 non-null  int64 
 18  number_diagnoses          143424 non-null  int64 
 19  num_medications           143424 non-null  int64 
 20  num_procedures            143424 non-null  int64 
 21  ndc_code                  119962 non-null  object
 22  max_glu_serum             143424 non-null  object
 23  A1Cresult                 143424 non-null  object
 24  change                    143424 non-null  object
 25  readmitted                143424 non-null  object
dtypes: int64(13), object(13)
memory usage: 28.5+ MB
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[75]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Obtain numeric columns</span>
<span class="n">num_columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">column</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;int64&quot;</span><span class="p">]</span>
<span class="n">num_columns</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[75]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;encounter_id&#39;,
 &#39;patient_nbr&#39;,
 &#39;admission_type_id&#39;,
 &#39;discharge_disposition_id&#39;,
 &#39;admission_source_id&#39;,
 &#39;time_in_hospital&#39;,
 &#39;number_outpatient&#39;,
 &#39;number_inpatient&#39;,
 &#39;number_emergency&#39;,
 &#39;num_lab_procedures&#39;,
 &#39;number_diagnoses&#39;,
 &#39;num_medications&#39;,
 &#39;num_procedures&#39;]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Obtain categorical columns</span>
<span class="n">cat_columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">column</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">column</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">num_columns</span><span class="p">]</span>
<span class="n">cat_columns</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[10]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;race&#39;,
 &#39;gender&#39;,
 &#39;age&#39;,
 &#39;weight&#39;,
 &#39;payer_code&#39;,
 &#39;medical_specialty&#39;,
 &#39;primary_diagnosis_code&#39;,
 &#39;other_diagnosis_codes&#39;,
 &#39;ndc_code&#39;,
 &#39;max_glu_serum&#39;,
 &#39;A1Cresult&#39;,
 &#39;change&#39;,
 &#39;readmitted&#39;]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Check for NaN vals</span>
<span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[11]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>encounter_id                    0
patient_nbr                     0
race                            0
gender                          0
age                             0
weight                          0
admission_type_id               0
discharge_disposition_id        0
admission_source_id             0
time_in_hospital                0
payer_code                      0
medical_specialty               0
primary_diagnosis_code          0
other_diagnosis_codes           0
number_outpatient               0
number_inpatient                0
number_emergency                0
num_lab_procedures              0
number_diagnoses                0
num_medications                 0
num_procedures                  0
ndc_code                    23462
max_glu_serum                   0
A1Cresult                       0
change                          0
readmitted                      0
dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Check for vals filled with &#39;?&#39;(assuming this is missing data)</span>
<span class="p">(</span><span class="n">df</span> <span class="o">==</span> <span class="s1">&#39;?&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stderr output_text">
<pre>/opt/conda/lib/python3.7/site-packages/pandas/core/ops/array_ops.py:253: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  res_values = method(rvalues)
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[12]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>encounter_id                     0
patient_nbr                      0
race                          3309
gender                           0
age                              0
weight                      139122
admission_type_id                0
discharge_disposition_id         0
admission_source_id              0
time_in_hospital                 0
payer_code                   54190
medical_specialty            69463
primary_diagnosis_code          33
other_diagnosis_codes            0
number_outpatient                0
number_inpatient                 0
number_emergency                 0
num_lab_procedures               0
number_diagnoses                 0
num_medications                  0
num_procedures                   0
ndc_code                         0
max_glu_serum                    0
A1Cresult                        0
change                           0
readmitted                       0
dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Check for &#39;None&#39; vals since these indicate test not carried out</span>
<span class="p">(</span><span class="n">df</span> <span class="o">==</span> <span class="s1">&#39;None&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[13]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>encounter_id                     0
patient_nbr                      0
race                             0
gender                           0
age                              0
weight                           0
admission_type_id                0
discharge_disposition_id         0
admission_source_id              0
time_in_hospital                 0
payer_code                       0
medical_specialty                0
primary_diagnosis_code           0
other_diagnosis_codes            0
number_outpatient                0
number_inpatient                 0
number_emergency                 0
num_lab_procedures               0
number_diagnoses                 0
num_medications                  0
num_procedures                   0
ndc_code                         0
max_glu_serum               136409
A1Cresult                   117650
change                           0
readmitted                       0
dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#generate hist distributions for numerical columns</span>
<span class="n">df</span><span class="p">[</span><span class="n">num_columns</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</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/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: 
The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.
  layout[ax.rowNum, ax.colNum] = ax.get_visible()
/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:298: MatplotlibDeprecationWarning: 
The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.
  layout[ax.rowNum, ax.colNum] = ax.get_visible()
/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: 
The rowNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().rowspan.start instead.
  if not layout[ax.rowNum + 1, ax.colNum]:
/opt/conda/lib/python3.7/site-packages/pandas/plotting/_matplotlib/tools.py:304: MatplotlibDeprecationWarning: 
The colNum attribute was deprecated in Matplotlib 3.2 and will be removed two minor releases later. Use ax.get_subplotspec().colspan.start instead.
  if not layout[ax.rowNum + 1, ax.colNum]:
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[14]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([[&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cfaee150&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf80ab90&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf9b5250&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cfc818d0&gt;],
       [&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cfcb9f50&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf9a3610&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf959d10&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf91a310&gt;],
       [&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf754310&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf9acd50&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cf78ff90&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cd652650&gt;],
       [&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cd609cd0&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cd5c7390&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cd57ea10&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8cd53f0d0&gt;]],
      dtype=object)</pre>
</div>

</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">cat_columns</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_text output_subarea output_execute_result">
<pre>race                          6
gender                        3
age                          10
weight                       10
payer_code                   18
medical_specialty            73
primary_diagnosis_code      717
other_diagnosis_codes     19374
ndc_code                    251
max_glu_serum                 4
A1Cresult                     4
change                        2
readmitted                    3
dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[16]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7ff8cd23b710&gt;</pre>
</div>

</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[17]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7ff8cc043610&gt;</pre>
</div>

</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_text output_subarea output_execute_result">
<pre>array([&#39;Female&#39;, &#39;Male&#39;, &#39;Unknown/Invalid&#39;], dtype=object)</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">

</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">

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

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

</div>
<div 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="Reduce-Dimensionality-of-the-NDC-Code-Feature">Reduce Dimensionality of the NDC Code Feature<a class="anchor-link" href="#Reduce-Dimensionality-of-the-NDC-Code-Feature">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 3</strong>: NDC codes are a common format to represent the wide variety of drugs that are prescribed for patient care in the United States. The challenge is that there are many codes that map to the same or similar drug. You are provided with the ndc drug lookup file <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/ndc_lookup_table.csv">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/ndc_lookup_table.csv</a> derived from the National Drug Codes List site(<a href="https://ndclist.com/">https://ndclist.com/</a>). Please use this file to come up with a way to reduce the dimensionality of this field and create a new field in the dataset called "generic_drug_name" in the output dataframe.</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="c1">#NDC code lookup file</span>
<span class="n">ndc_code_path</span> <span class="o">=</span> <span class="s2">&quot;./medication_lookup_tables/final_ndc_lookup_table&quot;</span>
<span class="n">ndc_code_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">ndc_code_path</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ndc_code_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[20]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>NDC_Code</th>
      <th>Proprietary Name</th>
      <th>Non-proprietary Name</th>
      <th>Dosage Form</th>
      <th>Route Name</th>
      <th>Company Name</th>
      <th>Product Type</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0087-6060</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0087-6063</td>
      <td>Glucophage XR</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Extended Release</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0087-6064</td>
      <td>Glucophage XR</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Extended Release</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0087-6070</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0087-6071</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[21]:</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">student_utils</span> <span class="kn">import</span> <span class="n">reduce_dimension_ndc</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduce_dim_df</span> <span class="o">=</span> <span class="n">reduce_dimension_ndc</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">ndc_code_df</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Number of unique values should be less for the new output field</span>
<span class="k">assert</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;ndc_code&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">reduce_dim_df</span><span class="p">[</span><span class="s1">&#39;generic_drug_name&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduce_dim_df</span><span class="p">[</span><span class="s1">&#39;generic_drug_name&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[23]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>22</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduce_dim_df</span> <span class="o">=</span> <span class="n">reduce_dim_df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;NDC_Code&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Select-First-Encounter-for-each-Patient">Select First Encounter for each Patient<a class="anchor-link" href="#Select-First-Encounter-for-each-Patient">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 4</strong>: In order to simplify the aggregation of data for the model, we will only select the first encounter for each patient in the dataset. This is to reduce the risk of data leakage of future patient encounters and to reduce complexity of the data transformation and modeling steps. We will assume that sorting in numerical order on the encounter_id provides the time horizon for determining which encounters come before and after another.</p>

</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="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">select_first_encounter</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">first_encounter_df</span> <span class="o">=</span> <span class="n">select_first_encounter</span><span class="p">(</span><span class="n">reduce_dim_df</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># unique patients in transformed dataset</span>
<span class="n">unique_patients</span> <span class="o">=</span> <span class="n">first_encounter_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of unique patients:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unique_patients</span><span class="p">))</span>

<span class="c1"># unique encounters in transformed dataset</span>
<span class="n">unique_encounters</span> <span class="o">=</span> <span class="n">first_encounter_df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of unique encounters:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unique_encounters</span><span class="p">))</span>

<span class="n">original_unique_patient_number</span> <span class="o">=</span> <span class="n">reduce_dim_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="c1"># number of unique patients should be equal to the number of unique encounters and patients in the final dataset</span>
<span class="k">assert</span> <span class="n">original_unique_patient_number</span> <span class="o">==</span> <span class="n">unique_patients</span>
<span class="k">assert</span> <span class="n">original_unique_patient_number</span> <span class="o">==</span> <span class="n">unique_encounters</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Tests passed!!&quot;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of unique patients:71518
Number of unique encounters:71518
Tests passed!!
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">first_encounter_df</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[29]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>71518</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Aggregate-Dataset-to-Right-Level-for-Modeling">Aggregate Dataset to Right Level for Modeling<a class="anchor-link" href="#Aggregate-Dataset-to-Right-Level-for-Modeling">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>In order to provide a broad scope of the steps and to prevent students from getting stuck with data transformations, we have selected the aggregation columns and provided a function to build the dataset at the appropriate level. The 'aggregate_dataset" function that you can find in the 'utils.py' file can take the preceding dataframe with the 'generic_drug_name' field and transform the data appropriately for the project.</p>
<p>To make it simpler for students, we are creating dummy columns for each unique generic drug name and adding those are input features to the model. There are other options for data representation but this is out of scope for the time constraints of the course.</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="n">exclusion_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;generic_drug_name&#39;</span><span class="p">,</span> <span class="s1">&#39;ndc_code&#39;</span><span class="p">]</span>
<span class="n">grouping_field_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">first_encounter_df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclusion_list</span><span class="p">]</span>
<span class="n">agg_drug_df</span><span class="p">,</span> <span class="n">ndc_col_list</span> <span class="o">=</span> <span class="n">aggregate_dataset</span><span class="p">(</span><span class="n">first_encounter_df</span><span class="p">,</span> <span class="n">grouping_field_list</span><span class="p">,</span> <span class="s1">&#39;generic_drug_name&#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>/home/workspace/starter_code/utils.py:10: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  array_field].apply(lambda x: x[array_field].values.tolist()).reset_index().rename(columns={
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">agg_drug_df</span><span class="p">)</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">agg_drug_df</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[32]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>71518</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ndc_col_list</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[34]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;Acarbose&#39;,
 &#39;Glimepiride&#39;,
 &#39;Glipizide&#39;,
 &#39;Glipizide_And_Metformin_Hcl&#39;,
 &#39;Glipizide_And_Metformin_Hydrochloride&#39;,
 &#39;Glyburide&#39;,
 &#39;Glyburide_And_Metformin_Hydrochloride&#39;,
 &#39;Glyburide-metformin_Hydrochloride&#39;,
 &#39;Human_Insulin&#39;,
 &#39;Insulin_Human&#39;,
 &#39;Metformin_Hcl&#39;,
 &#39;Metformin_Hydrochloride&#39;,
 &#39;Miglitol&#39;,
 &#39;Nateglinide&#39;,
 &#39;Pioglitazone&#39;,
 &#39;Pioglitazone_Hydrochloride_And_Glimepiride&#39;,
 &#39;Repaglinide&#39;,
 &#39;Rosiglitazone_Maleate&#39;,
 &#39;Tolazamide&#39;,
 &#39;Tolbutamide&#39;]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">agg_drug_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[33]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
      <th>generic_drug_name_array</th>
      <th>Acarbose</th>
      <th>Glimepiride</th>
      <th>Glipizide</th>
      <th>Glipizide_And_Metformin_Hcl</th>
      <th>Glipizide_And_Metformin_Hydrochloride</th>
      <th>Glyburide</th>
      <th>Glyburide_And_Metformin_Hydrochloride</th>
      <th>Glyburide-metformin_Hydrochloride</th>
      <th>Human_Insulin</th>
      <th>Insulin_Human</th>
      <th>Metformin_Hcl</th>
      <th>Metformin_Hydrochloride</th>
      <th>Miglitol</th>
      <th>Nateglinide</th>
      <th>Pioglitazone</th>
      <th>Pioglitazone_Hydrochloride_And_Glimepiride</th>
      <th>Repaglinide</th>
      <th>Rosiglitazone_Maleate</th>
      <th>Tolazamide</th>
      <th>Tolbutamide</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>12522</td>
      <td>48330783</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>13</td>
      <td>?</td>
      <td>?</td>
      <td>398</td>
      <td>427|38</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>68</td>
      <td>8</td>
      <td>28</td>
      <td>2</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
      <td>[Insulin Human]</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>15738</td>
      <td>63555939</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[90-100)</td>
      <td>?</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>12</td>
      <td>?</td>
      <td>InternalMedicine</td>
      <td>434</td>
      <td>198|486</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>33</td>
      <td>8</td>
      <td>18</td>
      <td>3</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
      <td>[Human Insulin]</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>16680</td>
      <td>42519267</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>?</td>
      <td>?</td>
      <td>197</td>
      <td>157|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
      <td>[Glipizide]</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>28236</td>
      <td>89869032</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>?</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>9</td>
      <td>?</td>
      <td>?</td>
      <td>250.7</td>
      <td>403|996</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>47</td>
      <td>9</td>
      <td>17</td>
      <td>2</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
      <td>[Human Insulin]</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>35754</td>
      <td>82637451</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[50-60)</td>
      <td>?</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>?</td>
      <td>?</td>
      <td>414</td>
      <td>411|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>31</td>
      <td>9</td>
      <td>16</td>
      <td>6</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
      <td>[Insulin Human]</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Prepare-Fields-and-Cast-Dataset">Prepare Fields and Cast Dataset<a class="anchor-link" href="#Prepare-Fields-and-Cast-Dataset">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h3>
</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 5</strong>: After you have aggregated the dataset to the right level, we can do feature selection (we will include the ndc_col_list, dummy column features too). In the block below, please select the categorical and numerical features that you will use for the model, so that we can create a dataset subset.</p>
<p>For the payer_code and weight fields, please provide whether you think we should include/exclude the field in our model and give a justification/rationale for this based off of the statistics of the data. Feel free to use visualizations or summary statistics to support your choice.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Student response: we choose to remove the payer_code, weight and medical_specialty field as they have a high number of '?', indicating missing values. Also exclude 'max_glu_serum', 'A1Cresult' have a high number of 'None' values. ndc_code is also excluded as it is one-hot-encoded in the previous step. We also exclude 'readmitted' as it is not present in the schema</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[103]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd">Please update the list to include the features you think are appropriate for the model </span>
<span class="sd">and the field that we will be using to train the model. There are three required demographic features for the model </span>
<span class="sd">and I have inserted a list with them already in the categorical list. </span>
<span class="sd">These will be required for later steps when analyzing data splits and model biases.</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="n">exclude_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;payer_code&#39;</span><span class="p">,</span> <span class="s1">&#39;weight&#39;</span><span class="p">,</span> <span class="s1">&#39;medical_specialty&#39;</span><span class="p">,</span> <span class="s1">&#39;max_glu_serum&#39;</span><span class="p">,</span> <span class="s1">&#39;A1Cresult&#39;</span><span class="p">,</span> <span class="s1">&#39;ndc_code&#39;</span><span class="p">,</span> <span class="s1">&#39;readmitted&#39;</span><span class="p">]</span>
<span class="n">required_demo_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">]</span>
<span class="n">student_categorical_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cat_columns</span> <span class="k">if</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude_cols</span> <span class="ow">and</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">required_demo_col_list</span><span class="p">]</span> <span class="o">+</span> <span class="n">required_demo_col_list</span> <span class="o">+</span> <span class="n">ndc_col_list</span>
<span class="n">student_categorical_col_list</span> <span class="o">=</span> <span class="n">student_categorical_col_list</span> <span class="o">+</span><span class="p">[</span><span class="s1">&#39;admission_type_id&#39;</span><span class="p">,</span><span class="s1">&#39;discharge_disposition_id&#39;</span><span class="p">,</span><span class="s1">&#39;admission_source_id&#39;</span><span class="p">]</span>
<span class="n">student_numerical_col_list</span> <span class="o">=</span> <span class="n">num_columns</span>
<span class="n">exclude_num_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">,</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">,</span><span class="s1">&#39;admission_type_id&#39;</span><span class="p">,</span><span class="s1">&#39;discharge_disposition_id&#39;</span><span class="p">,</span><span class="s1">&#39;admission_source_id&#39;</span><span class="p">,</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">]</span>
<span class="n">student_numerical_col_list</span> <span class="o">=</span> <span class="p">[</span> <span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">student_numerical_col_list</span> <span class="k">if</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclude_num_cols</span><span class="p">]</span>
<span class="n">PREDICTOR_FIELD</span> <span class="o">=</span> <span class="s1">&#39;time_in_hospital&#39;</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[104]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">student_numerical_col_list</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">figsize</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">])</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[104]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([[&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c60b6fd0&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5b56990&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5b7ef10&gt;],
       [&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5b3d4d0&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5af2a50&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5a9db10&gt;],
       [&lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5a69610&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5a1ead0&gt;,
        &lt;matplotlib.axes._subplots.AxesSubplot object at 0x7ff8c5a1eb10&gt;]],
      dtype=object)</pre>
</div>

</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[105]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#remove &#39;number_outpatient&#39;,&#39;number_inpatient&#39;,&#39;number_emergency&#39; as they are mostly 0</span>
<span class="n">student_numerical_col_list</span> <span class="o">=</span> <span class="p">[</span> <span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">student_numerical_col_list</span> <span class="k">if</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;number_outpatient&#39;</span><span class="p">,</span><span class="s1">&#39;number_inpatient&#39;</span><span class="p">,</span><span class="s1">&#39;number_emergency&#39;</span><span class="p">]]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[106]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">student_categorical_col_list</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[106]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;primary_diagnosis_code&#39;,
 &#39;other_diagnosis_codes&#39;,
 &#39;change&#39;,
 &#39;race&#39;,
 &#39;gender&#39;,
 &#39;age&#39;,
 &#39;Acarbose&#39;,
 &#39;Glimepiride&#39;,
 &#39;Glipizide&#39;,
 &#39;Glipizide_And_Metformin_Hcl&#39;,
 &#39;Glipizide_And_Metformin_Hydrochloride&#39;,
 &#39;Glyburide&#39;,
 &#39;Glyburide_And_Metformin_Hydrochloride&#39;,
 &#39;Glyburide-metformin_Hydrochloride&#39;,
 &#39;Human_Insulin&#39;,
 &#39;Insulin_Human&#39;,
 &#39;Metformin_Hcl&#39;,
 &#39;Metformin_Hydrochloride&#39;,
 &#39;Miglitol&#39;,
 &#39;Nateglinide&#39;,
 &#39;Pioglitazone&#39;,
 &#39;Pioglitazone_Hydrochloride_And_Glimepiride&#39;,
 &#39;Repaglinide&#39;,
 &#39;Rosiglitazone_Maleate&#39;,
 &#39;Tolazamide&#39;,
 &#39;Tolbutamide&#39;,
 &#39;admission_type_id&#39;,
 &#39;discharge_disposition_id&#39;,
 &#39;admission_source_id&#39;]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[107]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">student_numerical_col_list</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[107]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[&#39;num_lab_procedures&#39;, &#39;number_diagnoses&#39;, &#39;num_medications&#39;, &#39;num_procedures&#39;]</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[108]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">select_model_features</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">categorical_col_list</span><span class="p">,</span> <span class="n">numerical_col_list</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">grouping_key</span><span class="o">=</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">):</span>
    <span class="n">selected_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">grouping_key</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">PREDICTOR_FIELD</span><span class="p">]</span> <span class="o">+</span> <span class="n">categorical_col_list</span> <span class="o">+</span> <span class="n">numerical_col_list</span>   
    <span class="k">return</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="n">selected_col_list</span><span class="p">]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[109]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">selected_features_df</span> <span class="o">=</span> <span class="n">select_model_features</span><span class="p">(</span><span class="n">agg_drug_df</span><span class="p">,</span> <span class="n">student_categorical_col_list</span><span class="p">,</span> <span class="n">student_numerical_col_list</span><span class="p">,</span>
                                            <span class="n">PREDICTOR_FIELD</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[110]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">selected_features_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[110]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>patient_nbr</th>
      <th>time_in_hospital</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>change</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>Acarbose</th>
      <th>Glimepiride</th>
      <th>Glipizide</th>
      <th>Glipizide_And_Metformin_Hcl</th>
      <th>Glipizide_And_Metformin_Hydrochloride</th>
      <th>Glyburide</th>
      <th>Glyburide_And_Metformin_Hydrochloride</th>
      <th>Glyburide-metformin_Hydrochloride</th>
      <th>Human_Insulin</th>
      <th>Insulin_Human</th>
      <th>Metformin_Hcl</th>
      <th>Metformin_Hydrochloride</th>
      <th>Miglitol</th>
      <th>Nateglinide</th>
      <th>Pioglitazone</th>
      <th>Pioglitazone_Hydrochloride_And_Glimepiride</th>
      <th>Repaglinide</th>
      <th>Rosiglitazone_Maleate</th>
      <th>Tolazamide</th>
      <th>Tolbutamide</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>48330783</td>
      <td>13</td>
      <td>398</td>
      <td>427|38</td>
      <td>Ch</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[80-90)</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>68</td>
      <td>8</td>
      <td>28</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1</th>
      <td>63555939</td>
      <td>12</td>
      <td>434</td>
      <td>198|486</td>
      <td>Ch</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[90-100)</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>3</td>
      <td>3</td>
      <td>4</td>
      <td>33</td>
      <td>8</td>
      <td>18</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2</th>
      <td>42519267</td>
      <td>1</td>
      <td>197</td>
      <td>157|250</td>
      <td>Ch</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>89869032</td>
      <td>9</td>
      <td>250.7</td>
      <td>403|996</td>
      <td>No</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[40-50)</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>47</td>
      <td>9</td>
      <td>17</td>
      <td>2</td>
    </tr>
    <tr>
      <th>4</th>
      <td>82637451</td>
      <td>3</td>
      <td>414</td>
      <td>411|250</td>
      <td>No</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[50-60)</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>31</td>
      <td>9</td>
      <td>16</td>
      <td>6</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Preprocess-Dataset---Casting-and-Imputing">Preprocess Dataset - Casting and Imputing<a class="anchor-link" href="#Preprocess-Dataset---Casting-and-Imputing">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We will cast and impute the dataset before splitting so that we do not have to repeat these steps across the splits in the next step. For imputing, there can be deeper analysis into which features to impute and how to impute but for the sake of time, we are taking a general strategy of imputing zero for only numerical features.</p>
<p>OPTIONAL: What are some potential issues with this approach? Can you recommend a better way and also implement it?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[111]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">processed_df</span> <span class="o">=</span> <span class="n">preprocess_df</span><span class="p">(</span><span class="n">selected_features_df</span><span class="p">,</span> <span class="n">student_categorical_col_list</span><span class="p">,</span> 
        <span class="n">student_numerical_col_list</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">categorical_impute_value</span><span class="o">=</span><span class="s1">&#39;nan&#39;</span><span class="p">,</span> <span class="n">numerical_impute_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stderr output_text">
<pre>/home/workspace/starter_code/utils.py:29: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[predictor] = df[predictor].astype(float)
/home/workspace/starter_code/utils.py:31: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[c] = cast_df(df, c, d_type=str)
/home/workspace/starter_code/utils.py:33: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[numerical_column] = impute_df(df, numerical_column, numerical_impute_value)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Split-Dataset-into-Train,-Validation,-and-Test-Partitions">Split Dataset into Train, Validation, and Test Partitions<a class="anchor-link" href="#Split-Dataset-into-Train,-Validation,-and-Test-Partitions">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 6</strong>: In order to prepare the data for being trained and evaluated by a deep learning model, we will split the dataset into three partitions, with the validation partition used for optimizing the model hyperparameters during training. One of the key parts is that we need to be sure that the data does not accidently leak across partitions.</p>
<p>Please complete the function below to split the input dataset into three partitions(train, validation, test) with the following requirements.</p>
<ul>
<li>Approximately 60%/20%/20%  train/validation/test split</li>
<li>Randomly sample different patients into each data partition</li>
<li><strong>IMPORTANT</strong> Make sure that a patient's data is not in more than one partition, so that we can avoid possible data leakage.</li>
<li>Make sure that the total number of unique patients across the splits is equal to the total number of unique patients in the original dataset</li>
<li>Total number of rows in original dataset = sum of rows across all three dataset partitions</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[117]:</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">student_utils</span> <span class="kn">import</span> <span class="n">patient_dataset_splitter</span>
<span class="n">d_train</span><span class="p">,</span> <span class="n">d_val</span><span class="p">,</span> <span class="n">d_test</span> <span class="o">=</span> <span class="n">patient_dataset_splitter</span><span class="p">(</span><span class="n">processed_df</span><span class="p">,</span> <span class="s1">&#39;patient_nbr&#39;</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[118]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_train</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_val</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_test</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">processed_df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test passed for number of total rows equal!&quot;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Test passed for number of total rows equal!
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[122]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">d_train</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">d_test</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()))</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">d_val</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())))</span> <span class="o">==</span> <span class="mi">0</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test passed for patient data in only one partition&quot;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Test passed for patient data in only one partition
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[123]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">d_train</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">+</span> <span class="n">d_val</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">+</span> <span class="n">d_test</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">())</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test passed for number of unique patients being equal!&quot;</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>Test passed for number of unique patients being equal!
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Demographic-Representation-Analysis-of-Split">Demographic Representation Analysis of Split<a class="anchor-link" href="#Demographic-Representation-Analysis-of-Split">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>After the split, we should check to see the distribution of key features/groups and make sure that there is representative samples across the partitions. The show_group_stats_viz function in the utils.py file can be used to group and visualize different groups and dataframe partitions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Label-Distribution-Across-Partitions">Label Distribution Across Partitions<a class="anchor-link" href="#Label-Distribution-Across-Partitions">&#182;</a></h3>
</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>Below you can see the distributution of the label across your splits. Are the histogram distribution shapes similar across partitions?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[124]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Histogram distributions are similar for labels</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">processed_df</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</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>time_in_hospital
1.0     10717
2.0     12397
3.0     12701
4.0      9567
5.0      6839
6.0      5171
7.0      3999
8.0      2919
9.0      1990
10.0     1558
11.0     1241
12.0      955
13.0      795
14.0      669
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[125]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</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>time_in_hospital
1.0     6430
2.0     7438
3.0     7621
4.0     5740
5.0     4103
6.0     3103
7.0     2399
8.0     1751
9.0     1194
10.0     935
11.0     745
12.0     573
13.0     477
14.0     401
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[126]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</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>time_in_hospital
1.0     2144
2.0     2480
3.0     2540
4.0     1913
5.0     1368
6.0     1034
7.0      800
8.0      584
9.0      398
10.0     311
11.0     248
12.0     191
13.0     159
14.0     134
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAWh0lEQVR4nO3dfbBkdX3n8fcnw1NAHlRAJ6gZVMR11UVyVzTuWlETxdGgMXGFlKuulrNmtcC4WQOLWZNNuRrddaNJSp0gBp+gXB8iG8GFYjWsVUFzB2EYBBTxIYDrhDIihi0V+O4f51xpmj739r23+56evu9XVVd3n/M7fX6/7pr7nXPO73y/qSokSRrlZ/rugCRpdhkkJEmdDBKSpE4GCUlSJ4OEJKnTfn13YJKOPPLI2rZtW9/dkKR9yq5du26rqqNGrZurILFt2zYWFxf77oYk7VOSfKtrnaebJEmdDBKSpE5zdbrpmltuZ9uZn1nXZ3zzbc+bUG8kad831SOJJOcm2Ztkz4h1v5OkkhzZse3JSW5IcmOSM6fZT0nSaNM+3fQXwMnDC5M8HPgV4NujNkqyBfgz4LnA44DTkjxuet2UJI0y1SBRVZcD3xux6r8DbwS6sgs+Gbixqm6qqh8DFwAvmE4vJUldNvzCdZJTgFuq6uplmh0D/N3A+5vbZaM+b0eSxSSLd995+wR7Kkna0AvXSQ4GzgaevVLTEctGHnVU1U5gJ8CBW48z77kkTdBGH0k8CjgWuDrJN4GHAVcmeehQu5uBhw+8fxhw64b0UJL0Uxt6JFFV1wBHL71vA8VCVd021PRvgeOSHAvcApwK/OZG9VOS1JhqkEhyPvBLwJFJbgbeXFXv72j7c8A5VbW9qu5K8jrgfwFbgHOr6tqV9veEYw5n0fscJGliphokquq0FdZvG3h9K7B94P1FwEVT65wkaUWm5ZAkdTJISJI6GSQkSZ0MEpKkTgYJSVIng4QkqdO075M4F3g+sLeqHt8u+0OaZH33AHuBV7TTX4e3PRl4F819EudU1dtW2t8k6kkMsraEpM2uj1Th76iqJ1bVCcBfAf9peCNThUvSbNjwVOFV9YOBt4cwOnGfqcIlaQb0Ur40yVuAlwG3A88Y0WRUqvCTNqBrkqQBvVy4rqqzq+rhwEeA141oMnaqcOtJSNL09D276aPAr49YPnaq8KraWVULVbWw5eDDp9BFSdq8+qhMd9zA21OA60c0+2mq8CQH0KQKv3Aj+idJuteGpwoHtic5nmYK7LeA17Rt150qXJI0Waman4qfCwsLtbi42Hc3JGmfkmRXVS2MWtf3NQlJ0gwzSEiSOhkkJEmdDBKSpE4GCUlSJ4OEJKlTL7mbpmXSqcKHmTpc0mYz1SOJJOcm2Ztkz8CydyS5PsnuJJ9KckTHticnuSHJjUnOnGY/JUmj9VFP4lLg8VX1ROCrwFnDG1lPQpJmQx/1JC6pqrvat1fQJO8bZj0JSZoBfV+4fiVw8Yjlo+pJHDPqA0wVLknT01uQSHI2cBdNTYn7rR6xbGSSKVOFS9L09FWZ7uXA84Fn1egMg2PXk5AkTU8f9SROBn4XOKWq7uxoZj0JSZoBfdSTOAs4ELg0CcAVVfWaSdSTeMIxh7PovQySNDHWk5CkTc56EpKkNTFISJI6GSQkSZ0MEpKkTgYJSVIng4QkqZP1JCbIehOS5k0f9SRenOTaJPckGTkvt21nPQlJ6lkf9ST2AC8CLu/ayHoSkjQb+qgncV1V3bDCptaTkKQZMKsXrq0nIUkzYFaDhPUkJGkGzGqQsJ6EJM2AWQ0S1pOQpBnQRz2J7wF/AhwFfCbJVVX1HOtJSNLssZ6EJG1y1pOQJK2JQUKS1MkgIUnqZJCQJHUySEiSOpkqfAOZSlzSvqaPVOEPSnJpkq+1zw/s2NZU4ZLUsz5ShZ8JXFZVxwGXte/vw1ThkjQbNjxVOE3K7/Pa1+cBLxyxqanCJWkG9HHh+iFV9R2A9vnoEW1MFS5JM2BWZzeZKlySZsCys5uSvGG59VX1zjXs87tJtlbVd5JsBfaOaGOqcEmaASsdSRy6wmMtLgRe3r5+OfDpEW1MFS5JM2CqWWAHU4UD36VJFf6XwMeARwDfBl5cVd8bTBXebrsd+GPuTRX+lpX2ZxZYSVq95bLAjnUzXZKDgFcB/xQ4aGl5Vb1yue2q6rSOVc8a0fZWYPvA+4uAi8bpnyRpOsa9cP0h4KHAc4C/prlGcMe0OiVJmg3jBolHV9XvAf9YVecBzwOeML1uSZJmwbhB4ift8/eTPB44HNg2lR5JkmbGuAn+drY5lt5EM8voAcDvTa1XkqSZMG6QuKyq/gG4HHgkQJJjp9YrSdJMGPd00ydGLPv4JDsiSZo9K91x/Viaaa+HJ3nRwKrDGJgKOytmvZ7EuKw7IWlWrHQkcTzwfOAI4FcHHicCr17PjpOckWRPkmuTvH7E+iR5d1tPYneSE9ezP0nS6i17JFFVnwY+neSpVfU3k9ppO0Pq1TQpwX8MfDbJZ6rqawPNngsc1z5OAt7TPkuSNshKp5veWFVvB34zyf3unq6q09e4338CXFFVd7b7+Wvg14C3D7R5AfDBavKGXJHkiKXEgGvcpyRplVaa3XRd+zzphEh7gLckeTDw/2jScQzvo6umxH2CRJIdwA6ALYcdNeFuStLmttLppv/ZPp8HkOSw5m2tKyVHVV2X5I+AS4EfAlcDdw01G6umRFXtBHYCHLj1uOllK5SkTWisKbBJFpJcA+wG9iS5OskvrGfHVfX+qjqxqp5OU+L0a0NNrCkhST0b9z6Jc4F/V1XbqurngdcCH1jPjpMc3T4/AngRcP5QkwuBl7WznJ4C3O71CEnaWOPecX1HVf2fpTdV9YUk680C+4n2msRPgNdW1T8keU37+e+lSRO+HbgRuBP4Nyt94BOOOZxF7zGQpIkZN0h8Kcn7aP63X8BLgM8v3btQVVeudsdV9S9HLHvvwOuiOWKRJPVk3CBxQvv85qHlv0gTNJ45sR5JkmbGWEGiqp4x7Y5IkmbPuLObzkhyWHsR+ZwkVyZ59rQ7J0nq17izm15ZVT8Ang0cTXMR+W1T65UkaSaMGySWbmzbDnygqq5m9M1ukqQ5Mu6F611JLgGOBc5Kcihwz/S6tTbzkip82kxFLmlc4x5JvAo4E/jnbVK+AxjjvoXlJPntNk34niTnJzloaL2pwiWpZ2MFiaq6hyYtxpuS/FfgF6tq91p3muQY4HRgoaoeD2wBTh1qNpgqfAdNqnBJ0gYad3bT24AzgK+0j9OTvHWd+94P+Nkk+wEHc/+8TD9NFV5VVwBHJNm6zn1KklZh3GsS24ET2iMKkpwHfBk4ay07rapb2iOSb9OkCr+kqi4ZamaqcEnq2bjXJKApYbrk8PXsNMkDaY4UjgV+DjgkyUuHm43YdGSq8KpaqKqFLQevq1uSpCHjHkm8Ffhyks/R/PF+Oms8imj9MvCNqvp7gCSfpEnx8eGBNqYKl6SejXvh+nzgKcAn28dTq+qCdez328BTkhycJMCzuLcK3hJThUtSz8Y9koAmoNzWbvOYJI+pqsvXstOq+mKSjwNX0lSk+zKw01ThkjRb0mTkXqFRU2r0JcC13HsTXVXVKVPs26otLCzU4uKky3FL0nxLsquqFkatG/dI4oXA8VX1o8l1S5I068ad3XQTsP80OyJJmj3LHkkk+ROaaad3AlcluQz46dFEVZ0+3e5Jkvq00ummpRP8u2hmG0mSNpFlg0RVnTfOhyT5RFX9+mS6JEmaFau543o5j5zQ50iSZshq7pNYzsrzaDeA9SRmj7UrpH3bpI4kViXJ8UmuGnj8IMnrh9pYT0KSejapI4lVlTKtqhuAEwCSbAFuAT411GywnsRJNPUkTlp3TyVJY5vUkcTvrmPbZwFfr6pvDS23noQk9WzcokNPS3Jpkq8muSnJN5LctLR+RC2I1TgVOH/E8q56EsN925FkMcni3Xfevo5uSJKGjXu66f3Ab9PcL3H3pHae5ADgFEanHR+7ngSwE+DArcfNxAV0SZoX4waJ26vq4ins/7nAlVX13RHrrCchST0b95rE55K8I8lTk5y49JjA/k9j9KkmsJ6EJPVu3COJpVlFg6lkC3jmWnec5GDgV4B/O7DMehKSNEPGChJV9YxJ77iq7gQePLTsvQOvC3jtpPcrSRrfSllgX1pVH07yhlHrq+qd0+mWJGkWrHQkcUj7fOi0OyJJmj0rZYF9X/v8B8u1S3JWVb11kh2TJPVvUndcv3hCnyNJmiGTChKryt0kSdo3mCpcc8f05NLk9HYkkeSIJB9Pcn2S65I8dWi9qcIlqWeTOpL4H2vY5l3AZ6vqN9ocTgcPrTdVuCT1bNwssI9JclmSPe37JyZ509L6qvovq9lpksOAp9MkDqSqflxV3x9qZqpwSerZuKeb/pwmU+tPAKpqN02K77V6JPD3wAeSfDnJOUkOGWpjqnBJ6tm4QeLgqvrS0LK71rHf/YATgfdU1ZOAfwTOHGozdqrwqlqoqoUtBx++ji5JkoaNGyRuS/Io2j/SSX4DWE9G1puBm6vqi+37j9MEjeE2pgqXpB6NGyReC7wPeGySW4DXA7+11p1W1f8F/i7J8e2iZwFfGWpmqnBJ6lmaZKtjNm6uG/xMVd2x7h0nJwDnAAcAN9GkAn8JNNlgkwT4U+Bk2lThVbW43GcuLCzU4uKyTSRJQ5LsqqqFUevGmgKb5AjgZcA2YL/m7zdU1elr7VRVXcV961MAmCpckmbIuPdJXARcAVwD3DO97kiSZsm4QeKgqhpZU0KSNL/GvXD9oSSvTrI1yYOWHlPtmSSpd+MeSfwYeAdwNvfeq1A0N8VJkubUuEHiDcCjq+q2aXZGkjRbxj3ddC3NNFRJ0iYy7pHE3cBVST4H/Ghp4XqmwE6D9SS0L7HuhfYF4waJv2wfE5Pkm8AdNAHoruEbOdqb6d4FbKc5inlFVV05yT5IkpY3VpCoqvOmtP9nLHOdw3oSktSzZYNEko9V1b9Kcg33z8BaVfXPpte1e+tJAFe0ley2mr9JkjbOSkcSZ7TP1wH/YWB5gLevc98FXJKkgPdV1c6h9V31JO4TJJLsAHYAbDnsqHV2SZI0aNkgMfC/9kdX1bcG1yV57Dr3/bSqujXJ0cClSa6vqssHdzGqSyP6uBPYCXDg1uPGz1YoSVrRslNgk/xWe6rp+CS7Bx7fAHavZ8dVdWv7vBf4FPDkoSbWk5Cknq10uumjwMXAW7lv5bg7qup7a93pYMrx9vWzgf881OxC4HVJLqC5YG09CUnaYCudbroduB04bcL7fQjwqTbl+H7AR6vqs0le0+73vTSZZ7cDN9LWk1jpQ59wzOEsOvdckiZm3PskJqqqbgLuNzOqDQ5Lr60nIUk9GzcthyRpEzJISJI6GSQkSZ0MEpKkTgYJSVIng4QkqVMvU2CXJNkCLAK3VNXzh9atOlW49SSkfZs1NmZP30cSZ9AkDxxlMFX4DppU4ZKkDdRbkEjyMOB5wDkdTX6aKryqrgCOSLJ1wzooSer1SOKPgTcC93Ss70oVfh9JdiRZTLJ49523T76XkrSJ9RIkkjwf2FtVu5ZrNmLZyFThVbVQVQtbDj58Yn2UJPV3JPE04JS2zvUFwDOTfHiojanCJalnvQSJqjqrqh5WVduAU4H/XVUvHWp2IfCyNJ6CqcIlacP1OgV2mKnCJWm29B4kqurzwOfb16YKl6QZ0vd9EpKkGWaQkCR1MkhIkjoZJCRJnQwSkqROBglJUqfep8BOkqnCJW1G00yx3lfupoOSfCnJ1UmuTfIHI9okybuT3Jhkd5IT++irJG1mfR1J/Ah4ZlX9MMn+wBeSXNymBF8yWE/iJJp6EidtfFclafPqK3dTVdUP27f7t4/hDK/Wk5CknvVZdGhLkquAvcClVfXFoSbWk5CknvUWJKrq7qo6gSYF+JOTPH6oifUkJKlnvU+Brarv0yT4O3lolfUkJKlnfc1uOirJEe3rnwV+Gbh+qJn1JCSpZ33NbtoKnJdkC02g+lhV/ZX1JCRptvQSJKpqN/CkEcutJyFJM6T3axKSpNllkJAkdTJISJI6GSQkSZ0MEpKkTgYJSVKnXqbAJnk48EHgocA9wM6qetdQmwDvorlX4k7gFVV15XKfaz0JSfNqmjUjltPXzXR3Af++qq5MciiwK8mlVfWVgTamCpeknvWVKvw7S0cFVXUHcB33z/BqqnBJ6lnv1ySSbKO5+3pNqcIlSdPTa5BI8gDgE8Drq+oHw6tHbHK/VOHWk5Ck6emz6ND+NAHiI1X1yRFNxkoVbj0JSZqevlKFB3g/cF1VvbOjmanCJalnfc1uehrwr4Fr2hKmAP8ReASYKlySZkVfqcK/wOhrDoNtTBUuST3rfXaTJGl2GSQkSZ0MEpKkTgYJSVIng4QkqZNBQpLUqa/7JKbCVOGSVquvFNz7ij7TcpybZG+SPR3rk+TdSW5MsjvJiRvdR0na7Po83fQXwMnLrB+sJ7GDpp6EJGkD9RYkqupy4HvLNLGehCT1bJYvXI9VT8JU4ZI0PbMcJMaqJ2GqcEmanlkOEmPVk5AkTc8sBwnrSUhSz3q7TyLJ+cAvAUcmuRl4M7A/WE9CkmZFb0Giqk5bYb31JCSpZ7N8ukmS1DODhCSpU5qzOvMhyR3ADX33oydHArf13YmeOPbNabOOfRrj/vmqOmrUirlK8AfcUFULfXeiD0kWHfvm49g339g3etyebpIkdTJISJI6zVuQ2Nl3B3rk2Dcnx775bOi45+rCtSRpsubtSEKSNEEGCUlSp7kJEklOTnJDW+70zL77M2lJvpnkmiRXJVlslz0oyaVJvtY+P3Cg/Vntd3FDkuf01/PVG1Xadi1jTfIL7Xd2Y1sKd1T6+ZnSMfbfT3JL+9tflWT7wLp5GvvDk3wuyXVJrk1yRrt8rn/7ZcY9G797Ve3zD2AL8HXgkcABwNXA4/ru14TH+E3gyKFlbwfObF+fCfxR+/px7XdwIHBs+91s6XsMqxjr04ETgT3rGSvwJeCpNLVJLgae2/fY1jj23wd+Z0TbeRv7VuDE9vWhwFfbMc71b7/MuGfid5+XI4knAzdW1U1V9WPgApryp/PuBcB57evzgBcOLL+gqn5UVd+gyaT75B76tyY1urTtqsbalro9rKr+ppp/PR8c2GZmdYy9y7yN/TtVdWX7+g7gOppqlHP92y8z7i4bOu55CRJjlTrdxxVwSZJdSXa0yx5SbY2N9vnodvk8fh+rHesx7evh5fuq1yXZ3Z6OWjrdMrdjT7INeBLwRTbRbz80bpiB331egsRYpU73cU+rqhOB5wKvTfL0Zdpuhu9jSddY5+k7eA/wKOAE4DvAf2uXz+XYkzwA+ATw+qr6wXJNRyzbZ8c/Ytwz8bvPS5CY+1KnVXVr+7wX+BTN6aPvtoeYtM972+bz+H2sdqw3t6+Hl+9zquq7VXV3Vd0D/Dn3njqcu7En2Z/mD+VHquqT7eK5/+1HjXtWfvd5CRJ/CxyX5NgkBwCn0pQ/nQtJDkly6NJr4NnAHpoxvrxt9nLg0+3rC4FTkxyY5FjgOJoLWvuyVY21PS1xR5KntDM8XjawzT5l6Q9k69dofnuYs7G3fX0/cF1VvXNg1Vz/9l3jnpnfve8r+5N60JQ6/SrNlf6z++7PhMf2SJrZDFcD1y6ND3gwcBnwtfb5QQPbnN1+FzcwwzM7OsZ7Ps3h9U9o/nf0qrWMFVho/2F9HfhT2gwDs/zoGPuHgGuA3e0fiK1zOvZ/QXN6ZDdwVfvYPu+//TLjnonf3bQckqRO83K6SZI0BQYJSVIng4QkqZNBQpLUySAhSepkkJAkdTJISJI6/X9e7i3EXTDssgAAAABJRU5ErkJggg==
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Demographic-Group-Analysis">Demographic Group Analysis<a class="anchor-link" href="#Demographic-Group-Analysis">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We should check that our partitions/splits of the dataset are similar in terms of their demographic profiles. Below you can see how we might visualize and analyze the full dataset vs. the partitions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[129]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Full dataset before splitting</span>
<span class="n">patient_demo_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span>
<span class="n">patient_group_analysis_df</span> <span class="o">=</span> <span class="n">processed_df</span><span class="p">[</span><span class="n">patient_demo_features</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">patient_group_analysis_df</span><span class="p">,</span> <span class="s1">&#39;gender&#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>gender
Female             38025
Male               33490
Unknown/Invalid        3
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[130]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training partition</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="s1">&#39;gender&#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>gender
Female             22692
Male               20216
Unknown/Invalid        2
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[131]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test partition</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="s1">&#39;gender&#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>gender
Female             7617
Male               6686
Unknown/Invalid       1
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
</div>
</div>

<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Convert-Dataset-Splits-to-TF-Dataset">Convert Dataset Splits to TF Dataset<a class="anchor-link" href="#Convert-Dataset-Splits-to-TF-Dataset">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We have provided you the function to convert the Pandas dataframe to TF tensors using the TF Dataset API. 
Please note that this is not a scalable method and for larger datasets, the 'make_csv_dataset' method is recommended -<a href="https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset">https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset</a>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[132]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Convert dataset from Pandas dataframes to TF dataset </span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">diabetes_train_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
<span class="n">diabetes_val_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_val</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
<span class="n">diabetes_test_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[133]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We use this sample of the dataset to show transformations later</span>
<span class="n">diabetes_batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">diabetes_train_ds</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">demo</span><span class="p">(</span><span class="n">feature_column</span><span class="p">,</span> <span class="n">example_batch</span><span class="p">):</span>
    <span class="n">feature_layer</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">DenseFeatures</span><span class="p">(</span><span class="n">feature_column</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">(</span><span class="n">example_batch</span><span class="p">))</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="4.-Create-Categorical-Features-with-TF-Feature-Columns">4. Create Categorical Features with TF Feature Columns<a class="anchor-link" href="#4.-Create-Categorical-Features-with-TF-Feature-Columns">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Build-Vocabulary-for-Categorical-Features">Build Vocabulary for Categorical Features<a class="anchor-link" href="#Build-Vocabulary-for-Categorical-Features">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Before we can create the TF categorical features, we must first create the vocab files with the unique values for a given field that are from the <strong>training</strong> dataset. Below we have provided a function that you can use that only requires providing the pandas train dataset partition and the list of the categorical columns in a list format. The output variable 'vocab_file_list' will be a list of the file paths that can be used in the next step for creating the categorical features.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[134]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab_file_list</span> <span class="o">=</span> <span class="n">build_vocab_files</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">student_categorical_col_list</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">
<h2 id="Create-Categorical-Features-with-Tensorflow-Feature-Column-API">Create Categorical Features with Tensorflow Feature Column API<a class="anchor-link" href="#Create-Categorical-Features-with-Tensorflow-Feature-Column-API">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 7</strong>: Using the vocab file list from above that was derived fromt the features you selected earlier, please create categorical features with the Tensorflow Feature Column API, <a href="https://www.tensorflow.org/api_docs/python/tf/feature_column">https://www.tensorflow.org/api_docs/python/tf/feature_column</a>. Below is a function to help guide you.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[156]:</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">student_utils</span> <span class="kn">import</span> <span class="n">create_tf_categorical_feature_cols</span>
<span class="n">tf_cat_col_list</span> <span class="o">=</span> <span class="n">create_tf_categorical_feature_cols</span><span class="p">(</span><span class="n">student_categorical_col_list</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>INFO:tensorflow:vocabulary_size = 642 in primary_diagnosis_code is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/primary_diagnosis_code_vocab.txt.
INFO:tensorflow:vocabulary_size = 11508 in other_diagnosis_codes is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/other_diagnosis_codes_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in change is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/change_vocab.txt.
INFO:tensorflow:vocabulary_size = 7 in race is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/race_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in gender is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/gender_vocab.txt.
INFO:tensorflow:vocabulary_size = 11 in age is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/age_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Acarbose is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Acarbose_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glimepiride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glimepiride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glipizide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glipizide_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Glipizide_And_Metformin_Hcl is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glipizide_And_Metformin_Hcl_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glipizide_And_Metformin_Hydrochloride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glipizide_And_Metformin_Hydrochloride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glyburide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glyburide_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glyburide_And_Metformin_Hydrochloride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glyburide_And_Metformin_Hydrochloride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Glyburide-metformin_Hydrochloride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glyburide-metformin_Hydrochloride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Human_Insulin is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Human_Insulin_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Insulin_Human is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Insulin_Human_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Metformin_Hcl is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Metformin_Hcl_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Metformin_Hydrochloride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Metformin_Hydrochloride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Miglitol is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Miglitol_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Nateglinide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Nateglinide_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Pioglitazone is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Pioglitazone_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Pioglitazone_Hydrochloride_And_Glimepiride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Pioglitazone_Hydrochloride_And_Glimepiride_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Repaglinide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Repaglinide_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Rosiglitazone_Maleate is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Rosiglitazone_Maleate_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Tolazamide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Tolazamide_vocab.txt.
INFO:tensorflow:vocabulary_size = 4 in Tolbutamide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Tolbutamide_vocab.txt.
INFO:tensorflow:vocabulary_size = 9 in admission_type_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/admission_type_id_vocab.txt.
INFO:tensorflow:vocabulary_size = 27 in discharge_disposition_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/discharge_disposition_id_vocab.txt.
INFO:tensorflow:vocabulary_size = 17 in admission_source_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/admission_source_id_vocab.txt.
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[158]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_cat_var1</span> <span class="o">=</span> <span class="n">tf_cat_col_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Example categorical field:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_cat_var1</span><span class="p">))</span>
<span class="n">demo</span><span class="p">(</span><span class="n">test_cat_var1</span><span class="p">,</span> <span class="n">diabetes_batch</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>Example categorical field:
EmbeddingColumn(categorical_column=VocabularyFileCategoricalColumn(key=&#39;primary_diagnosis_code&#39;, vocabulary_file=&#39;./diabetes_vocab/primary_diagnosis_code_vocab.txt&#39;, vocabulary_size=642, num_oov_buckets=0, dtype=tf.string, default_value=-1), dimension=10, combiner=&#39;mean&#39;, initializer=&lt;tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7ff8badbf3d0&gt;, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True)
tf.Tensor(
[[ 0.2048861  -0.2885621  -0.30195525 ...  0.0365615  -0.07080904
   0.20026635]
 [ 0.04605496  0.20073606  0.14133656 ... -0.44696814 -0.18480386
  -0.5679692 ]
 [ 0.12585497 -0.10560415  0.20002198 ...  0.51596874 -0.2154248
  -0.05594273]
 ...
 [-0.14416257  0.11873005  0.34053504 ...  0.21792197 -0.0480918
  -0.40483573]
 [-0.12409705  0.2411791   0.20034648 ... -0.4122797  -0.30944848
   0.15844817]
 [ 0.0403539   0.5664697  -0.18422596 ... -0.43804464 -0.01344522
   0.16393715]], shape=(128, 10), dtype=float32)
</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="5.-Create-Numerical-Features-with-TF-Feature-Columns">5. Create Numerical Features with TF Feature Columns<a class="anchor-link" href="#5.-Create-Numerical-Features-with-TF-Feature-Columns">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 8</strong>: Using the TF Feature Column API(<a href="https://www.tensorflow.org/api_docs/python/tf/feature_column/">https://www.tensorflow.org/api_docs/python/tf/feature_column/</a>), please create normalized Tensorflow numeric features for the model. Try to use the z-score normalizer function below to help as well as the 'calculate_stats_from_train_data' function.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[171]:</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">student_utils</span> <span class="kn">import</span> <span class="n">create_tf_numeric_feature</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>For simplicity the create_tf_numerical_feature_cols function below uses the same normalizer function across all features(z-score normalization) but if you have time feel free to analyze and adapt the normalizer based off the statistical distributions. You may find this as a good resource in determining which transformation fits best for the data <a href="https://developers.google.com/machine-learning/data-prep/transform/normalization">https://developers.google.com/machine-learning/data-prep/transform/normalization</a>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[161]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">calculate_stats_from_train_data</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
    <span class="n">mean</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()[</span><span class="s1">&#39;mean&#39;</span><span class="p">]</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()[</span><span class="s1">&#39;std&#39;</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span>

<span class="k">def</span> <span class="nf">create_tf_numerical_feature_cols</span><span class="p">(</span><span class="n">numerical_col_list</span><span class="p">,</span> <span class="n">train_df</span><span class="p">):</span>
    <span class="n">tf_numeric_col_list</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">numerical_col_list</span><span class="p">:</span>
        <span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">calculate_stats_from_train_data</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
        <span class="n">tf_numeric_feature</span> <span class="o">=</span> <span class="n">create_tf_numeric_feature</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
        <span class="n">tf_numeric_col_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tf_numeric_feature</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">tf_numeric_col_list</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[172]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tf_cont_col_list</span> <span class="o">=</span> <span class="n">create_tf_numerical_feature_cols</span><span class="p">(</span><span class="n">student_numerical_col_list</span><span class="p">,</span> <span class="n">d_train</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[173]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_cont_var1</span> <span class="o">=</span> <span class="n">tf_cont_col_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Example continuous field:</span><span class="se">\n</span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_cont_var1</span><span class="p">))</span>
<span class="n">demo</span><span class="p">(</span><span class="n">test_cont_var1</span><span class="p">,</span> <span class="n">diabetes_batch</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>Example continuous field:
NumericColumn(key=&#39;num_lab_procedures&#39;, shape=(1,), default_value=(0,), dtype=tf.float64, normalizer_fn=functools.partial(&lt;function normalize_numeric_with_zscore at 0x7ff8bacda0e0&gt;, mean=43.08548123980424, std=19.946571796092144))

tf.Tensor(
[[-1.8947369 ]
 [-0.05263158]
 [ 0.10526316]
 [ 0.31578946]
 [-0.10526316]
 [ 0.        ]
 [ 0.6315789 ]
 [-0.5263158 ]
 [ 0.36842105]
 [ 0.15789473]
 [-1.7368422 ]
 [ 0.42105263]
 [ 0.7894737 ]
 [ 0.        ]
 [ 0.15789473]
 [-0.31578946]
 [ 0.2631579 ]
 [-0.21052632]
 [-2.1578948 ]
 [-1.0526316 ]
 [ 0.68421054]
 [ 0.94736844]
 [-1.6315789 ]
 [ 0.31578946]
 [ 0.31578946]
 [ 0.7368421 ]
 [ 0.68421054]
 [ 1.1052631 ]
 [ 0.2631579 ]
 [ 0.68421054]
 [-0.84210527]
 [ 1.4210526 ]
 [-0.10526316]
 [ 0.6315789 ]
 [ 0.        ]
 [-0.2631579 ]
 [ 2.1578948 ]
 [-0.5263158 ]
 [ 0.7894737 ]
 [ 1.9473684 ]
 [-1.1578947 ]
 [-0.8947368 ]
 [ 0.        ]
 [ 0.42105263]
 [-2.2105262 ]
 [ 0.        ]
 [-0.2631579 ]
 [ 1.        ]
 [ 1.5789474 ]
 [-0.15789473]
 [ 1.3684211 ]
 [-0.7894737 ]
 [-0.8947368 ]
 [-0.2631579 ]
 [-1.3684211 ]
 [ 0.8947368 ]
 [ 1.1052631 ]
 [ 0.15789473]
 [-0.8947368 ]
 [-0.68421054]
 [ 0.84210527]
 [-0.15789473]
 [-1.        ]
 [-1.3684211 ]
 [ 0.68421054]
 [ 0.21052632]
 [ 0.6315789 ]
 [ 0.94736844]
 [-0.7894737 ]
 [-0.31578946]
 [-0.10526316]
 [-1.3684211 ]
 [-0.21052632]
 [ 0.15789473]
 [-0.21052632]
 [-2.2105262 ]
 [ 1.5263158 ]
 [-2.1578948 ]
 [-1.3157895 ]
 [ 0.68421054]
 [ 2.4736843 ]
 [-1.7368422 ]
 [ 0.94736844]
 [ 1.1052631 ]
 [-0.2631579 ]
 [-2.2105262 ]
 [ 1.4210526 ]
 [-0.05263158]
 [-0.05263158]
 [-2.2105262 ]
 [ 0.7894737 ]
 [ 1.2105263 ]
 [ 1.5789474 ]
 [-0.57894737]
 [ 0.36842105]
 [ 0.10526316]
 [ 1.1578947 ]
 [-0.84210527]
 [-0.47368422]
 [-1.1578947 ]
 [-0.6315789 ]
 [ 0.10526316]
 [-1.8421053 ]
 [ 0.5263158 ]
 [-0.31578946]
 [-0.15789473]
 [-1.2105263 ]
 [ 0.        ]
 [ 0.        ]
 [-2.2105262 ]
 [ 1.1578947 ]
 [ 0.21052632]
 [ 0.        ]
 [ 1.8947369 ]
 [ 0.42105263]
 [-0.7368421 ]
 [ 0.68421054]
 [-1.2631578 ]
 [-0.7894737 ]
 [ 1.1578947 ]
 [ 1.2105263 ]
 [ 0.31578946]
 [ 0.21052632]
 [-0.21052632]
 [-0.05263158]
 [ 0.21052632]
 [-2.2105262 ]
 [-2.0526316 ]], shape=(128, 1), dtype=float32)
</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="6.-Build-Deep-Learning-Regression-Model-with-Sequential-API-and-TF-Probability-Layers">6. Build Deep Learning Regression Model with Sequential API and TF Probability Layers<a class="anchor-link" href="#6.-Build-Deep-Learning-Regression-Model-with-Sequential-API-and-TF-Probability-Layers">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Use-DenseFeatures-to-combine-features-for-model">Use DenseFeatures to combine features for model<a class="anchor-link" href="#Use-DenseFeatures-to-combine-features-for-model">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Now that we have prepared categorical and numerical features using Tensorflow's Feature Column API, we can combine them into a dense vector representation for the model. Below we will create this new input layer, which we will call 'claim_feature_layer'.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[174]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">claim_feature_columns</span> <span class="o">=</span> <span class="n">tf_cat_col_list</span> <span class="o">+</span> <span class="n">tf_cont_col_list</span>
<span class="n">claim_feature_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DenseFeatures</span><span class="p">(</span><span class="n">claim_feature_columns</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">
<h2 id="Build-Sequential-API-Model-from-DenseFeatures-and-TF-Probability-Layers">Build Sequential API Model from DenseFeatures and TF Probability Layers<a class="anchor-link" href="#Build-Sequential-API-Model-from-DenseFeatures-and-TF-Probability-Layers">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Below we have provided some boilerplate code for building a model that connects the Sequential API, DenseFeatures, and Tensorflow Probability layers into a deep learning model. There are many opportunities to further optimize and explore different architectures through benchmarking and testing approaches in various research papers, loss and evaluation metrics, learning curves, hyperparameter tuning, TF probability layers, etc. Feel free to modify and explore as you wish.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>OPTIONAL</strong>: Come up with a more optimal neural network architecture and hyperparameters. Share the process in discovering the architecture and hyperparameters.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[175]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">build_sequential_model</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
        <span class="n">feature_layer</span><span class="p">,</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">150</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">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">75</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">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DenseVariational</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">),</span>
        <span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DistributionLambda</span><span class="p">(</span>
            <span class="k">lambda</span> <span class="n">t</span><span class="p">:</span><span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">1</span><span class="p">],</span>
                                             <span class="n">scale</span><span class="o">=</span><span class="mf">1e-3</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">softplus</span><span class="p">(</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">:])</span>
                                             <span class="p">)</span>
        <span class="p">),</span>
    <span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span> <span class="nf">build_diabetes_model</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">val_ds</span><span class="p">,</span>  <span class="n">feature_layer</span><span class="p">,</span>  <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">loss_metric</span><span class="o">=</span><span class="s1">&#39;mse&#39;</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">build_sequential_model</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_metric</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">loss_metric</span><span class="p">])</span>
    <span class="n">early_stop</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">EarlyStopping</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="n">loss_metric</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>     
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="n">val_ds</span><span class="p">,</span>
                        <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">early_stop</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="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">history</span> 
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[176]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">diabetes_model</span><span class="p">,</span> <span class="n">history</span> <span class="o">=</span> <span class="n">build_diabetes_model</span><span class="p">(</span><span class="n">diabetes_train_ds</span><span class="p">,</span> <span class="n">diabetes_val_ds</span><span class="p">,</span>  <span class="n">claim_feature_layer</span><span class="p">,</span>  <span class="n">epochs</span><span class="o">=</span><span class="mi">10</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 for 336 steps, validate for 112 steps
Epoch 1/10
336/336 [==============================] - 11s 32ms/step - loss: 26.2638 - mse: 26.1294 - val_loss: 21.2723 - val_mse: 20.7482
Epoch 2/10
336/336 [==============================] - 6s 18ms/step - loss: 16.3418 - mse: 15.7052 - val_loss: 13.8567 - val_mse: 13.1735
Epoch 3/10
336/336 [==============================] - 5s 16ms/step - loss: 13.5807 - mse: 12.6009 - val_loss: 12.7885 - val_mse: 11.7566
Epoch 4/10
336/336 [==============================] - 6s 18ms/step - loss: 11.2030 - mse: 10.2118 - val_loss: 10.3455 - val_mse: 9.4836
Epoch 5/10
336/336 [==============================] - 6s 18ms/step - loss: 10.3541 - mse: 9.4801 - val_loss: 11.1701 - val_mse: 10.3278
Epoch 6/10
336/336 [==============================] - 6s 18ms/step - loss: 9.8856 - mse: 9.0152 - val_loss: 9.8152 - val_mse: 9.0282
Epoch 7/10
336/336 [==============================] - 6s 18ms/step - loss: 9.7293 - mse: 8.9747 - val_loss: 9.0018 - val_mse: 8.2614
Epoch 8/10
336/336 [==============================] - 6s 18ms/step - loss: 8.8179 - mse: 7.9975 - val_loss: 8.8090 - val_mse: 8.0114
Epoch 9/10
336/336 [==============================] - 5s 16ms/step - loss: 8.5359 - mse: 7.6694 - val_loss: 8.8681 - val_mse: 7.9376
Epoch 10/10
336/336 [==============================] - 6s 18ms/step - loss: 8.5581 - mse: 7.7187 - val_loss: 8.9638 - val_mse: 7.8108
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Show-Model-Uncertainty-Range-with-TF-Probability">Show Model Uncertainty Range with TF Probability<a class="anchor-link" href="#Show-Model-Uncertainty-Range-with-TF-Probability">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 9</strong>: Now that we have trained a model with TF Probability layers, we can extract the mean and standard deviation for each prediction. Please fill in the answer for the m and s variables below. The code for getting the predictions is provided for you below.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[177]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feature_list</span> <span class="o">=</span> <span class="n">student_categorical_col_list</span> <span class="o">+</span> <span class="n">student_numerical_col_list</span>
<span class="n">diabetes_x_tst</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">d_test</span><span class="p">[</span><span class="n">feature_list</span><span class="p">])</span>
<span class="n">diabetes_yhat</span> <span class="o">=</span> <span class="n">diabetes_model</span><span class="p">(</span><span class="n">diabetes_x_tst</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">diabetes_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">diabetes_test_ds</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[186]:</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">student_utils</span> <span class="kn">import</span> <span class="n">get_mean_std_from_preds</span>
<span class="n">m</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">get_mean_std_from_preds</span><span class="p">(</span><span class="n">diabetes_yhat</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">
<h2 id="Show-Prediction-Output">Show Prediction Output<a class="anchor-link" href="#Show-Prediction-Output">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[187]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prob_outputs</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;pred&quot;</span><span class="p">:</span> <span class="n">preds</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
    <span class="s2">&quot;actual_value&quot;</span><span class="p">:</span> <span class="n">d_test</span><span class="p">[</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
    <span class="s2">&quot;pred_mean&quot;</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
    <span class="s2">&quot;pred_std&quot;</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="p">}</span>
<span class="n">prob_output_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">prob_outputs</span><span class="p">)</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[188]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prob_output_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[188]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pred</th>
      <th>actual_value</th>
      <th>pred_mean</th>
      <th>pred_std</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2.747450</td>
      <td>1.0</td>
      <td>4.729186</td>
      <td>0.684103</td>
    </tr>
    <tr>
      <th>1</th>
      <td>8.087453</td>
      <td>1.0</td>
      <td>3.554142</td>
      <td>0.684215</td>
    </tr>
    <tr>
      <th>2</th>
      <td>7.086816</td>
      <td>4.0</td>
      <td>7.480109</td>
      <td>0.689349</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2.562191</td>
      <td>1.0</td>
      <td>2.976534</td>
      <td>0.684892</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2.043322</td>
      <td>4.0</td>
      <td>10.816481</td>
      <td>0.690144</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Convert-Regression-Output-to-Classification-Output-for-Patient-Selection">Convert Regression Output to Classification Output for Patient Selection<a class="anchor-link" href="#Convert-Regression-Output-to-Classification-Output-for-Patient-Selection">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 10</strong>: Given the output predictions, convert it to a binary label for whether the patient meets the time criteria or does not (HINT: use the mean prediction numpy array). The expected output is a numpy array with a 1 or 0 based off if the prediction meets or doesnt meet the criteria.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[194]:</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">student_utils</span> <span class="kn">import</span> <span class="n">get_student_binary_prediction</span>
<span class="n">student_binary_prediction</span> <span class="o">=</span> <span class="n">get_student_binary_prediction</span><span class="p">(</span><span class="n">prob_output_df</span><span class="p">,</span> <span class="s1">&#39;pred_mean&#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="Add-Binary-Prediction-to-Test-Dataframe">Add Binary Prediction to Test Dataframe<a class="anchor-link" href="#Add-Binary-Prediction-to-Test-Dataframe">&#182;</a></h3>
</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>Using the student_binary_prediction output that is a numpy array with binary labels, we can use this to add to a dataframe to better visualize and also to prepare the data for the Aequitas toolkit. The Aequitas toolkit requires that the predictions be mapped to a binary label for the predictions (called 'score' field) and the actual value (called 'label_value').</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[195]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">add_pred_to_test</span><span class="p">(</span><span class="n">test_df</span><span class="p">,</span> <span class="n">pred_np</span><span class="p">,</span> <span class="n">demo_col_list</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">demo_col_list</span><span class="p">:</span>
        <span class="n">test_df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
    <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred_np</span>
    <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span><span class="mi">5</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">test_df</span>

<span class="n">pred_test_df</span> <span class="o">=</span> <span class="n">add_pred_to_test</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">student_binary_prediction</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">])</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[196]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred_test_df</span><span class="p">[[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;time_in_hospital&#39;</span><span class="p">,</span> <span class="s1">&#39;score&#39;</span><span class="p">,</span> <span class="s1">&#39;label_value&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[196]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>patient_nbr</th>
      <th>gender</th>
      <th>race</th>
      <th>time_in_hospital</th>
      <th>score</th>
      <th>label_value</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>46624</th>
      <td>53403669</td>
      <td>Female</td>
      <td>Other</td>
      <td>1.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4412</th>
      <td>28226448</td>
      <td>Male</td>
      <td>Caucasian</td>
      <td>1.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3655</th>
      <td>79076700</td>
      <td>Female</td>
      <td>Caucasian</td>
      <td>4.0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>8798</th>
      <td>19471599</td>
      <td>Male</td>
      <td>Hispanic</td>
      <td>1.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3979</th>
      <td>58055679</td>
      <td>Male</td>
      <td>Caucasian</td>
      <td>4.0</td>
      <td>1</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Model-Evaluation-Metrics">Model Evaluation Metrics<a class="anchor-link" href="#Model-Evaluation-Metrics">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 11</strong>: Now it is time to use the newly created binary labels in the 'pred_test_df' dataframe to evaluate the model with some common classification metrics. Please create a report summary of the performance of the model and be sure to give the ROC AUC, F1 score(weighted), class precision and recall scores.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>For the report please be sure to include the following three parts:</p>
<ul>
<li><p>With a non-technical audience in mind, explain the precision-recall tradeoff in regard to how you have optimized your model.</p>
</li>
<li><p>What are some areas of improvement for future iterations?</p>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[197]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">roc_auc_score</span>
<span class="n">y_true</span> <span class="o">=</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> 
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="c1"># AUC, F1, precision and recall</span>

<span class="c1"># Summary</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[198]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[198]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0.6404502237136466</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[199]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</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>              precision    recall  f1-score   support

           0       0.93      0.47      0.62      9077
           1       0.50      0.93      0.66      5227

    accuracy                           0.64     14304
   macro avg       0.72      0.70      0.64     14304
weighted avg       0.77      0.64      0.64     14304

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

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[200]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[200]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0.702785053555893</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="Summary">Summary<a class="anchor-link" href="#Summary">&#182;</a></h1><p>F1: 0.64
ROC: 0.70
Precision: 0.77
Recall: 0.64</p>
<p>Precision refers to the level of correct identification of a positive outcome. As precision rises, the true positive rate rises, however this might also lead to cases being falsely labelled as negative.</p>
<p>Recall refers to the percentage of positive outcomes that were actually correctly identified. A high recall score might lead to a high false positive rate where cases that are actually negative are labelled as positive.</p>
<p>In our case precision is 0.77. A high value is desirable for us as we rather correctly identify patients than include patients that might not fit our criteria. The recall is 0.64 which indicates that we might not  identify a fair amount of patients that actually fit our criteria.</p>
<p>IMPROVEMENTS: 
For further improvement on the model we should aim to have a higher recall score and a marginally higher precision score. We can try using a smaller learning rate, train for more epochs, use a different loss function that better captures our data or use a different optimizer function, to improve our model performance</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="7.-Evaluating-Potential-Model-Biases-with-Aequitas-Toolkit">7. Evaluating Potential Model Biases with Aequitas Toolkit<a class="anchor-link" href="#7.-Evaluating-Potential-Model-Biases-with-Aequitas-Toolkit">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Prepare-Data-For-Aequitas-Bias-Toolkit">Prepare Data For Aequitas Bias Toolkit<a class="anchor-link" href="#Prepare-Data-For-Aequitas-Bias-Toolkit">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Using the gender and race fields, we will prepare the data for the Aequitas Toolkit.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[201]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Aequitas</span>
<span class="kn">from</span> <span class="nn">aequitas.preprocessing</span> <span class="kn">import</span> <span class="n">preprocess_input_df</span>
<span class="kn">from</span> <span class="nn">aequitas.group</span> <span class="kn">import</span> <span class="n">Group</span>
<span class="kn">from</span> <span class="nn">aequitas.plotting</span> <span class="kn">import</span> <span class="n">Plot</span>
<span class="kn">from</span> <span class="nn">aequitas.bias</span> <span class="kn">import</span> <span class="n">Bias</span>
<span class="kn">from</span> <span class="nn">aequitas.fairness</span> <span class="kn">import</span> <span class="n">Fairness</span>

<span class="n">ae_subset_df</span> <span class="o">=</span> <span class="n">pred_test_df</span><span class="p">[[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;score&#39;</span><span class="p">,</span> <span class="s1">&#39;label_value&#39;</span><span class="p">]]</span>
<span class="n">ae_df</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">preprocess_input_df</span><span class="p">(</span><span class="n">ae_subset_df</span><span class="p">)</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">Group</span><span class="p">()</span>
<span class="n">xtab</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">get_crosstabs</span><span class="p">(</span><span class="n">ae_df</span><span class="p">)</span>
<span class="n">absolute_metrics</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">list_absolute_metrics</span><span class="p">(</span><span class="n">xtab</span><span class="p">)</span>
<span class="n">clean_xtab</span> <span class="o">=</span> <span class="n">xtab</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">aqp</span> <span class="o">=</span> <span class="n">Plot</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">Bias</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/python3.7/site-packages/aequitas/group.py:143: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[&#39;score&#39;] = df[&#39;score&#39;].astype(float)
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stdout output_text">
<pre>model_id, score_thresholds 1 {&#39;rank_abs&#39;: [9684]}
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stderr output_text">
<pre>/opt/conda/lib/python3.7/site-packages/aequitas/group.py:30: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  divide = lambda x, y: x / y if y != 0 else pd.np.nan
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Reference-Group-Selection">Reference Group Selection<a class="anchor-link" href="#Reference-Group-Selection">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Below we have chosen the reference group for our analysis but feel free to select another one.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[202]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test reference group with Caucasian Male</span>
<span class="n">bdf</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">get_disparity_predefined_groups</span><span class="p">(</span><span class="n">clean_xtab</span><span class="p">,</span> 
                    <span class="n">original_df</span><span class="o">=</span><span class="n">ae_df</span><span class="p">,</span> 
                    <span class="n">ref_groups_dict</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;race&#39;</span><span class="p">:</span><span class="s1">&#39;Caucasian&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">:</span><span class="s1">&#39;Male&#39;</span>
                                     <span class="p">},</span> 
                    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> 
                    <span class="n">check_significance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>


<span class="n">f</span> <span class="o">=</span> <span class="n">Fairness</span><span class="p">()</span>
<span class="n">fdf</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get_group_value_fairness</span><span class="p">(</span><span class="n">bdf</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>get_disparity_predefined_group()
</pre>
</div>
</div>

<div class="output_area">

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


<div class="output_subarea output_stream output_stderr output_text">
<pre>/opt/conda/lib/python3.7/site-packages/aequitas/bias.py:368: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  df = df.replace(pd.np.inf, fill_divbyzero)
/opt/conda/lib/python3.7/site-packages/aequitas/fairness.py:32: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  self.fair_eval = lambda tau: lambda x: pd.np.nan if pd.np.isnan(x) else \
/opt/conda/lib/python3.7/site-packages/aequitas/fairness.py:45: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  self.high_level_pair_eval = lambda col1, col2: lambda x: pd.np.nan if (pd.np.isnan(x[col1]) and pd.np.isnan(x[col2])) \
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Race-and-Gender-Bias-Analysis-for-Patient-Selection">Race and Gender Bias Analysis for Patient Selection<a class="anchor-link" href="#Race-and-Gender-Bias-Analysis-for-Patient-Selection">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 12</strong>: For the gender and race fields, please plot two metrics that are important for patient selection below and state whether there is a significant bias in your model across any of the groups along with justification for your statement.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[205]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot two metrics</span>
<span class="n">xtab</span><span class="p">[[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">xtab</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">absolute_metrics</span><span class="p">]]</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[205]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>model_id</th>
      <th>score_threshold</th>
      <th>k</th>
      <th>attribute_name</th>
      <th>attribute_value</th>
      <th>pp</th>
      <th>pn</th>
      <th>fp</th>
      <th>fn</th>
      <th>tn</th>
      <th>tp</th>
      <th>group_label_pos</th>
      <th>group_label_neg</th>
      <th>group_size</th>
      <th>total_entities</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>?</td>
      <td>225</td>
      <td>157</td>
      <td>109</td>
      <td>11</td>
      <td>146</td>
      <td>116</td>
      <td>127</td>
      <td>255</td>
      <td>382</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>AfricanAmerican</td>
      <td>1659</td>
      <td>842</td>
      <td>826</td>
      <td>69</td>
      <td>773</td>
      <td>833</td>
      <td>902</td>
      <td>1599</td>
      <td>2501</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>Asian</td>
      <td>48</td>
      <td>47</td>
      <td>24</td>
      <td>7</td>
      <td>40</td>
      <td>24</td>
      <td>31</td>
      <td>64</td>
      <td>95</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>Caucasian</td>
      <td>7410</td>
      <td>3345</td>
      <td>3664</td>
      <td>238</td>
      <td>3107</td>
      <td>3746</td>
      <td>3984</td>
      <td>6771</td>
      <td>10755</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>Hispanic</td>
      <td>196</td>
      <td>120</td>
      <td>108</td>
      <td>9</td>
      <td>111</td>
      <td>88</td>
      <td>97</td>
      <td>219</td>
      <td>316</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>race</td>
      <td>Other</td>
      <td>146</td>
      <td>109</td>
      <td>69</td>
      <td>9</td>
      <td>100</td>
      <td>77</td>
      <td>86</td>
      <td>169</td>
      <td>255</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>6</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>gender</td>
      <td>Female</td>
      <td>5218</td>
      <td>2399</td>
      <td>2552</td>
      <td>181</td>
      <td>2218</td>
      <td>2666</td>
      <td>2847</td>
      <td>4770</td>
      <td>7617</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>7</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>gender</td>
      <td>Male</td>
      <td>4466</td>
      <td>2220</td>
      <td>2248</td>
      <td>162</td>
      <td>2058</td>
      <td>2218</td>
      <td>2380</td>
      <td>4306</td>
      <td>6686</td>
      <td>14304</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
      <td>binary 0/1</td>
      <td>9684</td>
      <td>gender</td>
      <td>Unknown/Invalid</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
      <td>14304</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[206]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">xtab</span><span class="p">[[</span><span class="s1">&#39;attribute_name&#39;</span><span class="p">,</span> <span class="s1">&#39;attribute_value&#39;</span><span class="p">]</span><span class="o">+</span><span class="n">absolute_metrics</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

    <div class="prompt output_prompt">Out[206]:</div>


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

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute_name</th>
      <th>attribute_value</th>
      <th>tpr</th>
      <th>tnr</th>
      <th>for</th>
      <th>fdr</th>
      <th>fpr</th>
      <th>fnr</th>
      <th>npv</th>
      <th>precision</th>
      <th>ppr</th>
      <th>pprev</th>
      <th>prev</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>race</td>
      <td>?</td>
      <td>0.91</td>
      <td>0.57</td>
      <td>0.07</td>
      <td>0.48</td>
      <td>0.43</td>
      <td>0.09</td>
      <td>0.93</td>
      <td>0.52</td>
      <td>0.02</td>
      <td>0.59</td>
      <td>0.33</td>
    </tr>
    <tr>
      <th>1</th>
      <td>race</td>
      <td>AfricanAmerican</td>
      <td>0.92</td>
      <td>0.48</td>
      <td>0.08</td>
      <td>0.50</td>
      <td>0.52</td>
      <td>0.08</td>
      <td>0.92</td>
      <td>0.50</td>
      <td>0.17</td>
      <td>0.66</td>
      <td>0.36</td>
    </tr>
    <tr>
      <th>2</th>
      <td>race</td>
      <td>Asian</td>
      <td>0.77</td>
      <td>0.62</td>
      <td>0.15</td>
      <td>0.50</td>
      <td>0.38</td>
      <td>0.23</td>
      <td>0.85</td>
      <td>0.50</td>
      <td>0.00</td>
      <td>0.51</td>
      <td>0.33</td>
    </tr>
    <tr>
      <th>3</th>
      <td>race</td>
      <td>Caucasian</td>
      <td>0.94</td>
      <td>0.46</td>
      <td>0.07</td>
      <td>0.49</td>
      <td>0.54</td>
      <td>0.06</td>
      <td>0.93</td>
      <td>0.51</td>
      <td>0.77</td>
      <td>0.69</td>
      <td>0.37</td>
    </tr>
    <tr>
      <th>4</th>
      <td>race</td>
      <td>Hispanic</td>
      <td>0.91</td>
      <td>0.51</td>
      <td>0.08</td>
      <td>0.55</td>
      <td>0.49</td>
      <td>0.09</td>
      <td>0.92</td>
      <td>0.45</td>
      <td>0.02</td>
      <td>0.62</td>
      <td>0.31</td>
    </tr>
    <tr>
      <th>5</th>
      <td>race</td>
      <td>Other</td>
      <td>0.90</td>
      <td>0.59</td>
      <td>0.08</td>
      <td>0.47</td>
      <td>0.41</td>
      <td>0.10</td>
      <td>0.92</td>
      <td>0.53</td>
      <td>0.02</td>
      <td>0.57</td>
      <td>0.34</td>
    </tr>
    <tr>
      <th>6</th>
      <td>gender</td>
      <td>Female</td>
      <td>0.94</td>
      <td>0.46</td>
      <td>0.08</td>
      <td>0.49</td>
      <td>0.54</td>
      <td>0.06</td>
      <td>0.92</td>
      <td>0.51</td>
      <td>0.54</td>
      <td>0.69</td>
      <td>0.37</td>
    </tr>
    <tr>
      <th>7</th>
      <td>gender</td>
      <td>Male</td>
      <td>0.93</td>
      <td>0.48</td>
      <td>0.07</td>
      <td>0.50</td>
      <td>0.52</td>
      <td>0.07</td>
      <td>0.93</td>
      <td>0.50</td>
      <td>0.46</td>
      <td>0.67</td>
      <td>0.36</td>
    </tr>
    <tr>
      <th>8</th>
      <td>gender</td>
      <td>Unknown/Invalid</td>
      <td>NaN</td>
      <td>1.00</td>
      <td>0.00</td>
      <td>NaN</td>
      <td>0.00</td>
      <td>NaN</td>
      <td>1.00</td>
      <td>NaN</td>
      <td>0.00</td>
      <td>0.00</td>
      <td>0.00</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[208]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_group_metric_all</span><span class="p">(</span><span class="n">xtab</span><span class="p">,</span> <span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;tpr&#39;</span><span class="p">,</span> <span class="s1">&#39;fpr&#39;</span><span class="p">,</span> <span class="s1">&#39;ppr&#39;</span><span class="p">,</span> <span class="s1">&#39;pprev&#39;</span><span class="p">,</span> <span class="s1">&#39;fnr&#39;</span><span class="p">],</span> <span class="n">ncols</span> <span class="o">=</span> <span class="mi">5</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area">

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


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

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Is-there-significant-bias-in-your-model-for-either-race-or-gender">Is there significant bias in your model for either race or gender<a class="anchor-link" href="#Is-there-significant-bias-in-your-model-for-either-race-or-gender">&#182;</a></h1><p>There doesnt seem to be any bias based on gender. Caucasians seem to have a high PPR, indicating that the rate of true positives is hight. This in turn means that we can be very confident that the caucasian patients we select are actually the correct fit. On the other hand, Asians have a very low PPR indicating that our selection of ASian patients might not actually be fit for the program. Predictably we see a high FNR for Asians and it seems like they would be falsely for rejected by our model</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Fairness-Analysis-Example---Relative-to-a-Reference-Group">Fairness Analysis Example - Relative to a Reference Group<a class="anchor-link" href="#Fairness-Analysis-Example---Relative-to-a-Reference-Group">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Question 13</strong>: Earlier we defined our reference group and then calculated disparity metrics relative to this grouping. Please provide a visualization of the fairness evaluation for this reference group and analyze whether there is disparity.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[209]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Reference group fairness plot</span>
<span class="n">fpr_disparity</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_disparity</span><span class="p">(</span><span class="n">bdf</span><span class="p">,</span> <span class="n">group_metric</span> <span class="o">=</span> <span class="s1">&#39;fpr_disparity&#39;</span><span class="p">,</span> <span class="n">attribute_name</span> <span class="o">=</span> <span class="s1">&#39;race&#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_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>The biggest disparity in FPR, when compared to caucasians, is for asians. This indicates that the FPR for asians is low, indicating that asians that are slected for the program will actually fit the program.</p>

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

 
</html>