report.html

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

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

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.6 (http://getbootstrap.com)
 * Copyright 2011-2015 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;
    color: #000 !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: thin dotted;
  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: thin dotted;
  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: thin dotted;
  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 4.2.0 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=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#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;
}
.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;
}
@-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 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  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 {
  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:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.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 {
  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: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-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.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";
}
/*!
*
* 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;
}
/**
 * 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 {
  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;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
#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#login_widget {
  float: right;
}
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;
}
@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;
}
/*!
*
* 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 {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
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;
}
.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: baseline;
  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;
}
.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_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;
}
.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;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.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.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.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.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.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;
}
.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;
}
.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.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.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.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.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);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* 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;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  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 {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@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 {
  /* 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;
}
@-moz-document url-prefix() {
  div.inner_cell {
    overflow-x: hidden;
  }
}
/* 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. */
  padding: 0.4em;
}
.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. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

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;
}
/* 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: 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 {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.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;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.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;
}
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.unrendered .text_cell_render {
  display: none;
}
.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;
}
/* 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;
}
.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 {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#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;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -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.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.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;
}
.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;
}
#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;
}
#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;
}
#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.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.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.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.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.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.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: 20ex;
  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 {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  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;
}
#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;
}
.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;
}
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;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  display: none;
}
.command-shortcut:before {
  content: "(command)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
#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;
}
#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 {
  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-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 .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.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 .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

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

    </style>


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

div#notebook {
  overflow: visible;
  border-top: none;
}

@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

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

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

<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Unsupervised-Learning">Unsupervised Learning<a class="anchor-link" href="#Unsupervised-Learning">&#182;</a></h2><h2 id="Project-3:-Creating-Customer-Segments">Project 3: Creating Customer Segments<a class="anchor-link" href="#Project-3:-Creating-Customer-Segments">&#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>Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
</blockquote>

</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="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in <em>monetary units</em>) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.</p>
<p>The dataset for this project can be found on the <a href="https://archive.ics.uci.edu/ml/datasets/Wholesale+customers">UCI Machine Learning Repository</a>. For the purposes of this project, the features <code>'Channel'</code> and <code>'Region'</code> will be excluded in the analysis — with focus instead on the six product categories recorded for customers.</p>
<p>Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.</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-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">renders</span> <span class="kn">as</span> <span class="nn">rs</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>

<span class="c1"># Show matplotlib plots inline (nicely formatted in the notebook)</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># Load the wholesale customers dataset</span>
<span class="k">try</span><span class="p">:</span>
    <span class="n">data</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="s2">&quot;customers.csv&quot;</span><span class="p">)</span>
    <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Region&#39;</span><span class="p">,</span> <span class="s1">&#39;Channel&#39;</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;Wholesale customers dataset has {} samples with {} features each.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
    <span class="k">print</span> <span class="s2">&quot;Dataset could not be loaded. Is the dataset missing?&quot;</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>Wholesale customers dataset has 440 samples with 6 features each.
</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="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.</p>
<p>Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: <strong>'Fresh'</strong>, <strong>'Milk'</strong>, <strong>'Grocery'</strong>, <strong>'Frozen'</strong>, <strong>'Detergents_Paper'</strong>, and <strong>'Delicatessen'</strong>. Consider what each category represents in terms of products you could purchase.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display a description of the dataset</span>
<span class="n">display</span><span class="p">(</span><span class="n">data</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"></div>

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>12000.297727</td>
      <td>5796.265909</td>
      <td>7951.277273</td>
      <td>3071.931818</td>
      <td>2881.493182</td>
      <td>1524.870455</td>
    </tr>
    <tr>
      <th>std</th>
      <td>12647.328865</td>
      <td>7380.377175</td>
      <td>9503.162829</td>
      <td>4854.673333</td>
      <td>4767.854448</td>
      <td>2820.105937</td>
    </tr>
    <tr>
      <th>min</th>
      <td>3.000000</td>
      <td>55.000000</td>
      <td>3.000000</td>
      <td>25.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>3127.750000</td>
      <td>1533.000000</td>
      <td>2153.000000</td>
      <td>742.250000</td>
      <td>256.750000</td>
      <td>408.250000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>8504.000000</td>
      <td>3627.000000</td>
      <td>4755.500000</td>
      <td>1526.000000</td>
      <td>816.500000</td>
      <td>965.500000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>16933.750000</td>
      <td>7190.250000</td>
      <td>10655.750000</td>
      <td>3554.250000</td>
      <td>3922.000000</td>
      <td>1820.250000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>112151.000000</td>
      <td>73498.000000</td>
      <td>92780.000000</td>
      <td>60869.000000</td>
      <td>40827.000000</td>
      <td>47943.000000</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="Implementation:-Selecting-Samples">Implementation: Selecting Samples<a class="anchor-link" href="#Implementation:-Selecting-Samples">&#182;</a></h3><p>To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add <strong>three</strong> indices of your choice to the <code>indices</code> list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Select three indices of your choice you wish to sample from the dataset</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">39</span><span class="p">,</span> <span class="mi">275</span><span class="p">,</span> <span class="mi">333</span><span class="p">]</span>

<span class="c1"># Create a DataFrame of the chosen samples</span>
<span class="n">samples</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">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</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="bp">True</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Chosen samples of wholesale customers dataset:&quot;</span>
<span class="n">display</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Mean money spending on each category:&quot;</span>
<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">mean</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>Chosen samples of wholesale customers dataset:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>56159</td>
      <td>555</td>
      <td>902</td>
      <td>10002</td>
      <td>212</td>
      <td>2916</td>
    </tr>
    <tr>
      <th>1</th>
      <td>680</td>
      <td>1610</td>
      <td>223</td>
      <td>862</td>
      <td>96</td>
      <td>379</td>
    </tr>
    <tr>
      <th>2</th>
      <td>8565</td>
      <td>4980</td>
      <td>67298</td>
      <td>131</td>
      <td>38102</td>
      <td>1215</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>
Mean money spending on each category:
</pre>
</div>
</div>

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


<div class="output_text output_subarea ">
<pre>Fresh               12000.297727
Milk                 5796.265909
Grocery              7951.277273
Frozen               3071.931818
Detergents_Paper     2881.493182
Delicatessen         1524.870455
dtype: float64</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="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><p>Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.<br>
<em>What kind of establishment (customer) could each of the three samples you've chosen represent?</em><br>
<strong>Hint:</strong> Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying <em>"McDonalds"</em> when describing a sample customer as a restaurant.</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>Answer:</strong></p>
<p><strong>First customer</strong> prefers fresh food and some frozen as well. Money spending for these categories is much higher than average. Customer almost doesn't buy Milk, Grocery and Detergents Paper. Money spending for this category is much lower than average in a whole dataset. Another interesting point is Delicatessen. Money spending for this category is twice higher than mean value. Combining all this data I can assume that it might be some restaurant. Customer buys small amount of Grocey, but buys a log of Delicatessen, which is same is most restaurants does.</p>
<p><strong>Second customer</strong> looks like doesn't buy much products. Money spending on each category is much smaller than average. However, there is some interesting behaviour. Between all customers Fresh and Grocery categories have highest mean money spending, but speaking about this customer, he prefers milk products. And he almost doesn't buy any Grocery. But it seems like a very popular category between customers. So for me, it most likely might be some cafe.</p>
<p><strong>The third customer</strong> is quite interesting. It buys way much grocery and detergents paper than average customer does, but he almost doesn't buy any things. Money spending for other categories is below the average. From my perspective it might be some bar. On the other hand it might be some shop which cells only drings and detergents paper. But for me it defenetely doesn't seem like a restaurant, cafe, or hotel.</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="Implementation:-Feature-Relevance">Implementation: Feature Relevance<a class="anchor-link" href="#Implementation:-Feature-Relevance">&#182;</a></h3><p>One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign <code>new_data</code> a copy of the data by removing a feature of your choice using the <code>DataFrame.drop</code> function.</li>
<li>Use <code>sklearn.cross_validation.train_test_split</code> to split the dataset into training and testing sets.<ul>
<li>Use the removed feature as your target label. Set a <code>test_size</code> of <code>0.25</code> and set a <code>random_state</code>.</li>
</ul>
</li>
<li>Import a decision tree regressor, set a <code>random_state</code>, and fit the learner to the training data.</li>
<li>Report the prediction score of the testing set using the regressor's <code>score</code> function.</li>
</ul>

</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-ipython2"><pre><span></span><span class="c1"># TODO: Make a copy of the DataFrame, using the &#39;drop&#39; function to drop the given feature</span>
<span class="n">new_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Grocery&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Grocery&#39;</span><span class="p">]</span>


<span class="c1"># TODO: Split the data into training and testing sets using the given feature as the target</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cross_validation</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">new_data</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="c1"># TODO: Create a decision tree regressor and fit it to the training set</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
<span class="n">regressor</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># TODO: Report the score of the prediction using the testing set</span>
<span class="k">print</span> <span class="s2">&quot;Grocery score with correlated feature:&quot;</span><span class="p">,</span> <span class="n">regressor</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
</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>Grocery score with correlated feature: 0.795293171715
</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>Below is presented score for each feature as a target label. As we can see the values are completely different. Some products like delicatessen are compltely independent from others, while products like Grocery and Detergents_Paper are definitely related to other products which customer buys, which actually make  sence. It doesn't seem like someone will buy only DetergentsPaper. Normally people buy it with something else.</p>
<table>
<thead><tr>
<th>Label</th>
<th>Score</th>
</tr>
</thead>
<tbody>
<tr>
<td>Fresh</td>
<td>-1.13897180207</td>
</tr>
<tr>
<td>Milk</td>
<td>0.372557452052</td>
</tr>
<tr>
<td>Grocery</td>
<td>0.795293171715</td>
</tr>
<tr>
<td>Frozen</td>
<td>-0.696550018302</td>
</tr>
<tr>
<td>Detergents_Paper</td>
<td>0.763468783139</td>
</tr>
<tr>
<td>Delicatessen</td>
<td>-10.5864940608</td>
</tr>
</tbody>
</table>

</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="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><p><em>Which feature did you attempt to predict? What was the reported prediction score? Is this feature is necessary for identifying customers' spending habits?</em><br>
<strong>Hint:</strong> The coefficient of determination, <code>R^2</code>, is scored between 0 and 1, with 1 being a perfect fit. A negative <code>R^2</code> implies the model fails to fit the data.</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>Answer:</strong></p>
<p>As you can see above, I launched an algorithm for every feature in a dataset. And it seems like there are some habits in customers' behaviour. As far as I understand features with highest score are not necessary to identify customer's spending habbits.</p>
<p>Here is what I think. For instance, if model was able to predict values for <code>Grocery</code> based on some features, than we we can delete <code>Grocery</code> feature, because it seems like correlated with some other features and no new information is gained. And it seems so. The score of <code>Detergents_Paper</code> seems quite same. So from my perspective those features are correlated with each other. If these features are highly correlated then we can just leave one of them or compress them by applying PCA. First let's check how R^2 score will change for Grocery if we remove from dataset correlated features:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Remove both correlated features.</span>
<span class="n">new_data_test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Grocery&#39;</span><span class="p">,</span> <span class="s1">&#39;Detergents_Paper&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">label_test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Grocery&#39;</span><span class="p">]</span>

<span class="n">X_train_test</span><span class="p">,</span> <span class="n">X_test_test</span><span class="p">,</span> <span class="n">y_train_test</span><span class="p">,</span> <span class="n">y_test_test</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">new_data_test</span><span class="p">,</span> <span class="n">label_test</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">regressor</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_test</span><span class="p">,</span> <span class="n">y_train_test</span><span class="p">)</span>

<span class="c1"># TODO: Report the score of the prediction using the testing set</span>
<span class="k">print</span> <span class="s2">&quot;Grocery score without correlated feature:&quot;</span><span class="p">,</span> <span class="n">regressor</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_test</span><span class="p">,</span> <span class="n">y_test_test</span><span class="p">)</span>
</pre></div>

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

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


<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Grocery score without correlated feature: 0.383801818204
</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>So yes, here we see that after we have removed both correlated features, Rˆ2 score for <code>Grocery</code> dropped down twice. Thus only independent features (with low/negative rˆ2 score will be useful for identifying customer's spending.</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="Visualize-Feature-Distributions">Visualize Feature Distributions<a class="anchor-link" href="#Visualize-Feature-Distributions">&#182;</a></h3><p>To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix.</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-ipython2"><pre><span></span><span class="c1"># Produce a scatter matrix for each pair of features in the data</span>
<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#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,iVBORw0KGgoAAAANSUhEUgAAA00AAAH+CAYAAABAwuXeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><p><em>Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?</em><br>
<strong>Hint:</strong> Is the data normally distributed? Where do most of the data points lie?</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>Answer:</strong></p>
<p>So finally my guess was correct. If you take a look on a scatter matrix of Grocery and Detergents Paper, you see that it shows the strongest correlation. The more Grocery buys customer, the more he buys Detergents Paper. Also, there is another linear correlation of Grocery and Detergents Paper with Milk products. Although it is not as strong as correlation between Grocery and Detergents Paper, it defenetely has a place to be. This correlation is also presented in a table of scores for each type of products in Question 2.</p>
<p>Speaking about data distribution it seems like a lognormal one. The data is skewed right for every feature. It means that a majority of customers buys amount of products which is below the average(mean) money spending in a whole dataset. Knowing that, I would assume that if we cut off upper 10% of the data for each feature the mean value will be much lower.</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="Data-Preprocessing">Data Preprocessing<a class="anchor-link" href="#Data-Preprocessing">&#182;</a></h2><p>In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful.</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="Implementation:-Feature-Scaling">Implementation: Feature Scaling<a class="anchor-link" href="#Implementation:-Feature-Scaling">&#182;</a></h3><p>If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most <a href="http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics">often appropriate</a> to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a <a href="http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html">Box-Cox test</a>, which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign a copy of the data to <code>log_data</code> after applying a logarithm scaling. Use the <code>np.log</code> function for this.</li>
<li>Assign a copy of the sample data to <code>log_samples</code> after applying a logrithm scaling. Again, use <code>np.log</code>.</li>
</ul>

</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-ipython2"><pre><span></span><span class="c1"># TODO: Scale the data using the natural logarithm</span>
<span class="n">log_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

<span class="c1"># TODO: Scale the sample data using the natural logarithm</span>
<span class="n">log_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>

<span class="c1"># Produce a scatter matrix for each pair of newly-transformed features</span>
<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">log_data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#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,iVBORw0KGgoAAAANSUhEUgAAAzkAAAHqCAYAAAAnJ4CxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).</p>
<p>Run the code below to see how the sample data has changed after having the natural logarithm applied to it.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the log-transformed sample data</span>
<span class="n">display</span><span class="p">(</span><span class="n">log_samples</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_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>10.935942</td>
      <td>6.318968</td>
      <td>6.804615</td>
      <td>9.210540</td>
      <td>5.356586</td>
      <td>7.977968</td>
    </tr>
    <tr>
      <th>1</th>
      <td>6.522093</td>
      <td>7.383989</td>
      <td>5.407172</td>
      <td>6.759255</td>
      <td>4.564348</td>
      <td>5.937536</td>
    </tr>
    <tr>
      <th>2</th>
      <td>9.055439</td>
      <td>8.513185</td>
      <td>11.116886</td>
      <td>4.875197</td>
      <td>10.548022</td>
      <td>7.102499</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="Implementation:-Outlier-Detection">Implementation: Outlier Detection<a class="anchor-link" href="#Implementation:-Outlier-Detection">&#182;</a></h3><p>Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many "rules of thumb" for what constitutes an outlier in a dataset. Here, we will use <a href="http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/">Tukey's Method for identfying outliers</a>: An <em>outlier step</em> is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign the value of the 25th percentile for the given feature to <code>Q1</code>. Use <code>np.percentile</code> for this.</li>
<li>Assign the value of the 75th percentile for the given feature to <code>Q3</code>. Again, use <code>np.percentile</code>.</li>
<li>Assign the calculation of an outlier step for the given feature to <code>step</code>.</li>
<li>Optionally remove data points from the dataset by adding indices to the <code>outliers</code> list.</li>
</ul>
<p><strong>NOTE:</strong> If you choose to remove any outliers, ensure that the sample data does not contain any of these points!<br>
Once you have performed this implementation, the dataset will be stored in the variable <code>good_data</code>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># OPTIONAL: Select the indices for data points you wish to remove</span>
<span class="n">outliers</span>  <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># For each feature find the data points with extreme high or low values</span>
<span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">log_data</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="c1"># TODO: Calculate Q1 (25th percentile of the data) for the given feature</span>
    <span class="n">Q1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="mi">25</span><span class="p">)</span>
    
    <span class="c1"># TODO: Calculate Q3 (75th percentile of the data) for the given feature</span>
    <span class="n">Q3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span> <span class="mi">75</span><span class="p">)</span>
    
    <span class="c1"># TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)</span>
    <span class="n">step</span> <span class="o">=</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">Q3</span><span class="o">-</span><span class="n">Q1</span><span class="p">)</span>
    
    <span class="c1"># Display the outliers</span>
    <span class="k">print</span> <span class="s2">&quot;Data points considered outliers for the feature &#39;{}&#39;:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span>
    <span class="n">display</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">))])</span>
    
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">log_data</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">))]</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
        <span class="n">outliers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>    

<span class="n">outliers</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">print</span> <span class="s2">&quot;Sorted outliers indicies: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">outliers</span><span class="p">)</span>

<span class="c1"># Remove the outliers, if any were specified</span>
<span class="n">good_data</span> <span class="o">=</span> <span class="n">log_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">log_data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">outliers</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="bp">True</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>Data points considered outliers for the feature &#39;Fresh&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>65</th>
      <td>4.442651</td>
      <td>9.950323</td>
      <td>10.732651</td>
      <td>3.583519</td>
      <td>10.095388</td>
      <td>7.260523</td>
    </tr>
    <tr>
      <th>66</th>
      <td>2.197225</td>
      <td>7.335634</td>
      <td>8.911530</td>
      <td>5.164786</td>
      <td>8.151333</td>
      <td>3.295837</td>
    </tr>
    <tr>
      <th>81</th>
      <td>5.389072</td>
      <td>9.163249</td>
      <td>9.575192</td>
      <td>5.645447</td>
      <td>8.964184</td>
      <td>5.049856</td>
    </tr>
    <tr>
      <th>95</th>
      <td>1.098612</td>
      <td>7.979339</td>
      <td>8.740657</td>
      <td>6.086775</td>
      <td>5.407172</td>
      <td>6.563856</td>
    </tr>
    <tr>
      <th>96</th>
      <td>3.135494</td>
      <td>7.869402</td>
      <td>9.001839</td>
      <td>4.976734</td>
      <td>8.262043</td>
      <td>5.379897</td>
    </tr>
    <tr>
      <th>128</th>
      <td>4.941642</td>
      <td>9.087834</td>
      <td>8.248791</td>
      <td>4.955827</td>
      <td>6.967909</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>171</th>
      <td>5.298317</td>
      <td>10.160530</td>
      <td>9.894245</td>
      <td>6.478510</td>
      <td>9.079434</td>
      <td>8.740337</td>
    </tr>
    <tr>
      <th>193</th>
      <td>5.192957</td>
      <td>8.156223</td>
      <td>9.917982</td>
      <td>6.865891</td>
      <td>8.633731</td>
      <td>6.501290</td>
    </tr>
    <tr>
      <th>218</th>
      <td>2.890372</td>
      <td>8.923191</td>
      <td>9.629380</td>
      <td>7.158514</td>
      <td>8.475746</td>
      <td>8.759669</td>
    </tr>
    <tr>
      <th>304</th>
      <td>5.081404</td>
      <td>8.917311</td>
      <td>10.117510</td>
      <td>6.424869</td>
      <td>9.374413</td>
      <td>7.787382</td>
    </tr>
    <tr>
      <th>305</th>
      <td>5.493061</td>
      <td>9.468001</td>
      <td>9.088399</td>
      <td>6.683361</td>
      <td>8.271037</td>
      <td>5.351858</td>
    </tr>
    <tr>
      <th>338</th>
      <td>1.098612</td>
      <td>5.808142</td>
      <td>8.856661</td>
      <td>9.655090</td>
      <td>2.708050</td>
      <td>6.309918</td>
    </tr>
    <tr>
      <th>353</th>
      <td>4.762174</td>
      <td>8.742574</td>
      <td>9.961898</td>
      <td>5.429346</td>
      <td>9.069007</td>
      <td>7.013016</td>
    </tr>
    <tr>
      <th>355</th>
      <td>5.247024</td>
      <td>6.588926</td>
      <td>7.606885</td>
      <td>5.501258</td>
      <td>5.214936</td>
      <td>4.844187</td>
    </tr>
    <tr>
      <th>357</th>
      <td>3.610918</td>
      <td>7.150701</td>
      <td>10.011086</td>
      <td>4.919981</td>
      <td>8.816853</td>
      <td>4.700480</td>
    </tr>
    <tr>
      <th>412</th>
      <td>4.574711</td>
      <td>8.190077</td>
      <td>9.425452</td>
      <td>4.584967</td>
      <td>7.996317</td>
      <td>4.127134</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Data points considered outliers for the feature &#39;Milk&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>86</th>
      <td>10.039983</td>
      <td>11.205013</td>
      <td>10.377047</td>
      <td>6.894670</td>
      <td>9.906981</td>
      <td>6.805723</td>
    </tr>
    <tr>
      <th>98</th>
      <td>6.220590</td>
      <td>4.718499</td>
      <td>6.656727</td>
      <td>6.796824</td>
      <td>4.025352</td>
      <td>4.882802</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
    <tr>
      <th>356</th>
      <td>10.029503</td>
      <td>4.897840</td>
      <td>5.384495</td>
      <td>8.057377</td>
      <td>2.197225</td>
      <td>6.306275</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Data points considered outliers for the feature &#39;Grocery&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>75</th>
      <td>9.923192</td>
      <td>7.036148</td>
      <td>1.098612</td>
      <td>8.390949</td>
      <td>1.098612</td>
      <td>6.882437</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Data points considered outliers for the feature &#39;Frozen&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>38</th>
      <td>8.431853</td>
      <td>9.663261</td>
      <td>9.723703</td>
      <td>3.496508</td>
      <td>8.847360</td>
      <td>6.070738</td>
    </tr>
    <tr>
      <th>57</th>
      <td>8.597297</td>
      <td>9.203618</td>
      <td>9.257892</td>
      <td>3.637586</td>
      <td>8.932213</td>
      <td>7.156177</td>
    </tr>
    <tr>
      <th>65</th>
      <td>4.442651</td>
      <td>9.950323</td>
      <td>10.732651</td>
      <td>3.583519</td>
      <td>10.095388</td>
      <td>7.260523</td>
    </tr>
    <tr>
      <th>145</th>
      <td>10.000569</td>
      <td>9.034080</td>
      <td>10.457143</td>
      <td>3.737670</td>
      <td>9.440738</td>
      <td>8.396155</td>
    </tr>
    <tr>
      <th>175</th>
      <td>7.759187</td>
      <td>8.967632</td>
      <td>9.382106</td>
      <td>3.951244</td>
      <td>8.341887</td>
      <td>7.436617</td>
    </tr>
    <tr>
      <th>264</th>
      <td>6.978214</td>
      <td>9.177714</td>
      <td>9.645041</td>
      <td>4.110874</td>
      <td>8.696176</td>
      <td>7.142827</td>
    </tr>
    <tr>
      <th>325</th>
      <td>10.395650</td>
      <td>9.728181</td>
      <td>9.519735</td>
      <td>11.016479</td>
      <td>7.148346</td>
      <td>8.632128</td>
    </tr>
    <tr>
      <th>420</th>
      <td>8.402007</td>
      <td>8.569026</td>
      <td>9.490015</td>
      <td>3.218876</td>
      <td>8.827321</td>
      <td>7.239215</td>
    </tr>
    <tr>
      <th>429</th>
      <td>9.060331</td>
      <td>7.467371</td>
      <td>8.183118</td>
      <td>3.850148</td>
      <td>4.430817</td>
      <td>7.824446</td>
    </tr>
    <tr>
      <th>439</th>
      <td>7.932721</td>
      <td>7.437206</td>
      <td>7.828038</td>
      <td>4.174387</td>
      <td>6.167516</td>
      <td>3.951244</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Data points considered outliers for the feature &#39;Detergents_Paper&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>75</th>
      <td>9.923192</td>
      <td>7.036148</td>
      <td>1.098612</td>
      <td>8.390949</td>
      <td>1.098612</td>
      <td>6.882437</td>
    </tr>
    <tr>
      <th>161</th>
      <td>9.428190</td>
      <td>6.291569</td>
      <td>5.645447</td>
      <td>6.995766</td>
      <td>1.098612</td>
      <td>7.711101</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Data points considered outliers for the feature &#39;Delicatessen&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>66</th>
      <td>2.197225</td>
      <td>7.335634</td>
      <td>8.911530</td>
      <td>5.164786</td>
      <td>8.151333</td>
      <td>3.295837</td>
    </tr>
    <tr>
      <th>109</th>
      <td>7.248504</td>
      <td>9.724899</td>
      <td>10.274568</td>
      <td>6.511745</td>
      <td>6.728629</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>128</th>
      <td>4.941642</td>
      <td>9.087834</td>
      <td>8.248791</td>
      <td>4.955827</td>
      <td>6.967909</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>137</th>
      <td>8.034955</td>
      <td>8.997147</td>
      <td>9.021840</td>
      <td>6.493754</td>
      <td>6.580639</td>
      <td>3.583519</td>
    </tr>
    <tr>
      <th>142</th>
      <td>10.519646</td>
      <td>8.875147</td>
      <td>9.018332</td>
      <td>8.004700</td>
      <td>2.995732</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
    <tr>
      <th>183</th>
      <td>10.514529</td>
      <td>10.690808</td>
      <td>9.911952</td>
      <td>10.505999</td>
      <td>5.476464</td>
      <td>10.777768</td>
    </tr>
    <tr>
      <th>184</th>
      <td>5.789960</td>
      <td>6.822197</td>
      <td>8.457443</td>
      <td>4.304065</td>
      <td>5.811141</td>
      <td>2.397895</td>
    </tr>
    <tr>
      <th>187</th>
      <td>7.798933</td>
      <td>8.987447</td>
      <td>9.192075</td>
      <td>8.743372</td>
      <td>8.148735</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>203</th>
      <td>6.368187</td>
      <td>6.529419</td>
      <td>7.703459</td>
      <td>6.150603</td>
      <td>6.860664</td>
      <td>2.890372</td>
    </tr>
    <tr>
      <th>233</th>
      <td>6.871091</td>
      <td>8.513988</td>
      <td>8.106515</td>
      <td>6.842683</td>
      <td>6.013715</td>
      <td>1.945910</td>
    </tr>
    <tr>
      <th>285</th>
      <td>10.602965</td>
      <td>6.461468</td>
      <td>8.188689</td>
      <td>6.948897</td>
      <td>6.077642</td>
      <td>2.890372</td>
    </tr>
    <tr>
      <th>289</th>
      <td>10.663966</td>
      <td>5.655992</td>
      <td>6.154858</td>
      <td>7.235619</td>
      <td>3.465736</td>
      <td>3.091042</td>
    </tr>
    <tr>
      <th>343</th>
      <td>7.431892</td>
      <td>8.848509</td>
      <td>10.177932</td>
      <td>7.283448</td>
      <td>9.646593</td>
      <td>3.610918</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Sorted outliers indicies: [38, 57, 65, 65, 66, 66, 75, 75, 81, 86, 95, 96, 98, 109, 128, 128, 137, 142, 145, 154, 154, 154, 161, 171, 175, 183, 184, 187, 193, 203, 218, 233, 264, 285, 289, 304, 305, 325, 338, 343, 353, 355, 356, 357, 412, 420, 429, 439]
</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="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><p><em>Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the <code>outliers</code> list to be removed, explain why.</em></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>Answer:</strong></p>
<p>As you can see above there are several data points considered outliers for more than one feature. Those points are indicies number 65, 66, 75, 128, 154. As far as I have learned previously, if amount of outliers is not very high, they should be removed, because they skew the data. In our case amount of outliers is 42, which is about 10%. Based on that I decided to remove outliers.</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="Feature-Transformation">Feature Transformation<a class="anchor-link" href="#Feature-Transformation">&#182;</a></h2><p>In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers.</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="Implementation:-PCA">Implementation: PCA<a class="anchor-link" href="#Implementation:-PCA">&#182;</a></h3><p>Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the <code>good_data</code> to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the <em>explained variance ratio</em> of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new "feature" of the space, however it is a composition of the original features present in the data.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Import <code>sklearn.decomposition.PCA</code> and assign the results of fitting PCA in six dimensions with <code>good_data</code> to <code>pca</code>.</li>
<li>Apply a PCA transformation of the sample log-data <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Apply PCA by fitting the good data with the same number of dimensions as features</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>

<span class="c1"># TODO: Transform the sample log-data using the PCA fit above</span>
<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>

<span class="c1"># Generate PCA results plot</span>
<span class="n">pca_results</span> <span class="o">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">pca_results</span><span class="p">(</span><span class="n">good_data</span><span class="p">,</span> <span class="n">pca</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,iVBORw0KGgoAAAANSUhEUgAAA0YAAAH6CAYAAAAjs1w/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><p><em>How much variance in the data is explained</em> <strong><em>in total</em></strong> <em>by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.</em><br>
<strong>Hint:</strong> A positive increase in a specific dimension corresponds with an <em>increase</em> of the <em>positive-weighted</em> features and a <em>decrease</em> of the <em>negative-weighted</em> features. The rate of increase or decrease is based on the indivdual feature weights.</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>Answer:</strong></p>
<p>So amount of explained variance of first two features is equal to <strong>0.7252</strong>. And amount of explained variance of first four features is equal to <strong>0.9279</strong>.</p>
<p>So let's speak about <strong>first</strong> PC. As we can see, feature with the highest importance here is Detergents_Paper. A bit less important features are Milk and Grocery. If we take a look on the scatter matrix above, we see that exactly pairs of these features(Detergents_Paper/Grocery, Detergents_Paper/Milk, Grocery/Milk) have the strongest linear correlation on a graph. So basically it means that since clients buy detergents paper together with grocery we can just "compress" these features into one single feature.</p>
<p>The <strong>second</strong> principal component has twice lower explained variance, but altogether with the first PC they explain about 70% of variance. So this PC very good represents customer's spending on Delicatessen, Fresh and Frozen products.</p>
<p>The <strong>third</strong> PC represents customer spending on milk and detergents paper. Also, this is the first deature which has features with significant negative weight. Those features are Delicatessen, Frozen and Milk. This means that during calculation of this principal component it will have negative value if values of Delicatessen, Frozen and Milk will be high while values of Grocery, Detergents_Paper and especially Fresh will be relatevely small.</p>
<p>So and the <strong>fourth</strong> principal component represents customer spending on Frozen and Detergents_Paper. However, all other features have negative weight. It means that a principal component of data point with low values of Frozen and Detergents_Paper and high values of other features may have negative value.</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="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after having a PCA transformation applied</span>
<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">pca_results</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</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_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Dimension 1</th>
      <th>Dimension 2</th>
      <th>Dimension 3</th>
      <th>Dimension 4</th>
      <th>Dimension 5</th>
      <th>Dimension 6</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-2.7931</td>
      <td>2.5401</td>
      <td>0.2880</td>
      <td>0.0064</td>
      <td>-1.4536</td>
      <td>0.1394</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-3.1652</td>
      <td>-2.5448</td>
      <td>-1.6195</td>
      <td>0.2488</td>
      <td>-0.1416</td>
      <td>1.5379</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.5971</td>
      <td>-1.2540</td>
      <td>1.4870</td>
      <td>-1.0766</td>
      <td>-0.9400</td>
      <td>-1.1157</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="Implementation:-Dimensionality-Reduction">Implementation: Dimensionality Reduction<a class="anchor-link" href="#Implementation:-Dimensionality-Reduction">&#182;</a></h3><p>When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the <em>cumulative explained variance ratio</em> is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign the results of fitting PCA in two dimensions with <code>good_data</code> to <code>pca</code>.</li>
<li>Apply a PCA transformation of <code>good_data</code> using <code>pca.transform</code>, and assign the reuslts to <code>reduced_data</code>.</li>
<li>Apply a PCA transformation of the sample log-data <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
</ul>

</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-ipython2"><pre><span></span><span class="c1"># TODO: Apply PCA by fitting the good data with only two dimensions</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>

<span class="c1"># TODO: Transform the good data using the PCA fit above</span>
<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>

<span class="c1"># TODO: Transform the sample log-data using the PCA fit above</span>
<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>

<span class="c1"># Create a DataFrame for the reduced data</span>
<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#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="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after applying PCA transformation in two dimensions</span>
<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#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_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Dimension 1</th>
      <th>Dimension 2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-2.7931</td>
      <td>2.5401</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-3.1652</td>
      <td>-2.5448</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.5971</td>
      <td>-1.2540</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="Clustering">Clustering<a class="anchor-link" href="#Clustering">&#182;</a></h2><p>In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale.</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="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><p><em>What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?</em></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>Answer:</strong></p>
<p><strong>K-Means</strong> - Is most likely the simplest clustering algorithm. It tries to place centroids that are most equidistant to other points in the same cluster. It's fast and easy to understand. It works faster than other clustering algorithms on large datasets and finally it performs good clustering on globular or well separated from each other clusters.</p>
<p><strong>GMM</strong> - Gaussian Mixture Model which performs soft clustering. It means that every sample belongs to every cluster with some non zero probability. Another advantage of mixture models is that they are very flexible in choosing the component distribution.</p>
<p>So far, I would say that GMM algorithm is more preferable. For me it seems clear that some customers may be applied to several customer groups, and soft clustering allows that, while K-Means hard assignment not. Moreover the data doesn't seem globular, which is also one more reason to choose <strong>GMM</strong> model instead of <strong>K-Means</strong> And the amount of training data is not very big.</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="Implementation:-Creating-Clusters">Implementation: Creating Clusters<a class="anchor-link" href="#Implementation:-Creating-Clusters">&#182;</a></h3><p>Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known <em>a priori</em>, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the "goodness" of a clustering by calculating each data point's <em>silhouette coefficient</em>. The <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html">silhouette coefficient</a> for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the <em>mean</em> silhouette coefficient provides for a simple scoring method of a given clustering.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Fit a clustering algorithm to the <code>reduced_data</code> and assign it to <code>clusterer</code>.</li>
<li>Predict the cluster for each data point in <code>reduced_data</code> using <code>clusterer.predict</code> and assign them to <code>preds</code>.</li>
<li>Find the cluster centers using the algorithm's respective attribute and assign them to <code>centers</code>.</li>
<li>Predict the cluster for each sample data point in <code>pca_samples</code> and assign them <code>sample_preds</code>.</li>
<li>Import sklearn.metrics.silhouette_score and calculate the silhouette score of <code>reduced_data</code> against <code>preds</code>.<ul>
<li>Assign the silhouette score to <code>score</code> and print the result.</li>
</ul>
</li>
</ul>

</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-ipython2"><pre><span></span><span class="c1"># TODO: Apply your clustering algorithm of choice to the reduced data </span>
<span class="kn">from</span> <span class="nn">sklearn.mixture</span> <span class="kn">import</span> <span class="n">GMM</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">11</span><span class="p">):</span>
    <span class="n">clusterer</span> <span class="o">=</span> <span class="n">GMM</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">k</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

    <span class="c1"># TODO: Predict the cluster for each data point</span>
    <span class="n">preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

    <span class="c1"># TODO: Find the cluster centers</span>
    <span class="n">centers</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">means_</span>

    <span class="c1"># TODO: Predict the cluster for each transformed sample data point</span>
    <span class="n">sample_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">)</span>

    <span class="c1"># TODO: Calculate the mean silhouette coefficient for the number of clusters chosen</span>
    <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">silhouette_score</span>
    <span class="n">score</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;Number of clusters:&quot;</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="s2">&quot;; Score:&quot;</span><span class="p">,</span> <span class="n">score</span>
</pre></div>

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

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


<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of clusters: 2 ; Score: 0.443601474015
Number of clusters: 3 ; Score: 0.357294514249
Number of clusters: 4 ; Score: 0.292327102837
Number of clusters: 5 ; Score: 0.264212620761
Number of clusters: 6 ; Score: 0.307122866447
Number of clusters: 7 ; Score: 0.300240575761
Number of clusters: 8 ; Score: 0.289770854961
Number of clusters: 9 ; Score: 0.263124488536
Number of clusters: 10 ; Score: 0.260962813124
</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="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#182;</a></h3><p><em>Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?</em></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>Answer:</strong>
It seems like the highest Silhouette Coefficient has GMM model with 2 clusters.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># set up model with best number of clusters</span>
<span class="n">clusterer</span> <span class="o">=</span> <span class="n">GMM</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>
<span class="n">centers</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">means_</span>
<span class="n">sample_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_samples</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="Cluster-Visualization">Cluster Visualization<a class="anchor-link" href="#Cluster-Visualization">&#182;</a></h3><p>Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters.</p>

</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-ipython2"><pre><span></span><span class="c1"># Display the results of the clustering from implementation</span>
<span class="n">rs</span><span class="o">.</span><span class="n">cluster_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">centers</span><span class="p">,</span> <span class="n">pca_samples</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,iVBORw0KGgoAAAANSUhEUgAAA0AAAAIHCAYAAAC2STwiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Data-Recovery">Implementation: Data Recovery<a class="anchor-link" href="#Implementation:-Data-Recovery">&#182;</a></h3><p>Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the <em>averages</em> of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to <em>the average customer of that segment</em>. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Apply the inverse transform to <code>centers</code> using <code>pca.inverse_transform</code> and assign the new centers to <code>log_centers</code>.</li>
<li>Apply the inverse function of <code>np.log</code> to <code>log_centers</code> using <code>np.exp</code> and assign the true centers to <code>true_centers</code>.</li>
</ul>

</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-ipython2"><pre><span></span><span class="c1"># TODO: Inverse transform the centers</span>
<span class="n">log_centers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">centers</span><span class="p">)</span>

<span class="c1"># TODO: Exponentiate the centers</span>
<span class="n">true_centers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_centers</span><span class="p">)</span>

<span class="c1"># Display the true centers</span>
<span class="n">segments</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Segment {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">centers</span><span class="p">))]</span>
<span class="n">true_centers</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">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">true_centers</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">true_centers</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">segments</span>
<span class="n">display</span><span class="p">(</span><span class="n">true_centers</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_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Segment 0</th>
      <td>8967.0</td>
      <td>1920.0</td>
      <td>2437.0</td>
      <td>2081.0</td>
      <td>309.0</td>
      <td>741.0</td>
    </tr>
    <tr>
      <th>Segment 1</th>
      <td>6079.0</td>
      <td>7042.0</td>
      <td>10241.0</td>
      <td>1275.0</td>
      <td>3546.0</td>
      <td>1159.0</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;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Comparing each segment to a median data.</span>
<span class="n">true_centers</span> <span class="o">=</span> <span class="n">true_centers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="s1">&#39;50%&#39;</span><span class="p">])</span>
<span class="n">true_centers</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span> <span class="o">=</span> <span class="s1">&#39;bar&#39;</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">4</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[31]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x10beb6550&gt;</pre>
</div>

</div>

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA68AAAEsCAYAAAAsI4W2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#182;</a></h3><p>Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. <em>What set of establishments could each of the customer segments represent?</em><br>
<strong>Hint:</strong> A customer who is assigned to <code>'Cluster X'</code> should best identify with the establishments represented by the feature set of <code>'Segment X'</code>.</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>Answer:</strong></p>
<p>The cluster visualisation above represents data by first two most valuable principal components(those, which have the highest explained variance rate). The first principal component(first dimension) looks like the main divider between classes. The majority of the data with negative first dimension belongs to the <code>Cluster 0</code> while data with positive value of first dimension belongs to the <code>Cluster 1</code>. Unfortunately the second dimention is not as representative as the first one.</p>
<p>Let's look closer on <strong>Segment 0</strong> centroid and compare it to dataset median values which is labeled as <code>50%</code> on a bar above. Here we see that segment's money spending on Fresh food looks quite close to a dataset median values. However segment's money spending on other categories is way smaller and normally below the dataset's median. Now, let's take a look on dimensions representation from the <em>Implementation:PCA</em> section. It seems like the first dimension will have negative value if <code>Fresh</code> and <code>Frozen</code> features will have higher values than other features, so their sum will overweight the weighted sum of other features. If we look on table above from <em>Implementation: Data Recovery</em> section it seems like <strong>Segment 1</strong> satisfies this requirements. So I would say that a customer who is assigned to <strong>Cluster 0</strong> should best identify with the establishments represented by the feature set of <strong>Segment 1</strong>. From my perspective this cluster may represent some restaurant or hotel. It should be an organization which works with fresh food and most likely buys it for cooking.</p>
<p>Let's look closer on <strong>Cluster 1</strong> centroid and compare it with dataset's median vallues. Here it looks like overall amount of money spending for products is higher. Although money spending for <code>Fresh</code> products is a bit lower than dataset's median, it seems like spending for all other categories is higher or in few cases equal to median. Anyway, <strong>Segment 1</strong> looks more diverse than <strong>Segment 0</strong>. Also, we see that <strong>Cluster 1</strong> are represented by data points with positive <strong>Dimension 1</strong>. This dimension seems to be positive with high values of <code>Grocery</code> or <code>Detergents Paper</code>. And again <strong>Segment 0</strong> seems satisfying these requirements. So here I say that a customer who is assigned to <strong>Cluster 1</strong> should best identify with the establishments represented by the feature set of <strong>Segment 0</strong>. From my point of view such diverse set of producrs may buy some market which provides wide set a products for buyers.</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="Question-9">Question 9<a class="anchor-link" href="#Question-9">&#182;</a></h3><p><em>For each sample point, which customer segment from</em> <strong><em>Question 8</em></strong> <em>best represents it? Are the predictions for each sample point consistent with this?</em></p>
<p>Run the code block below to find which cluster each sample point is predicted to be.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the predictions</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_preds</span><span class="p">):</span>
    <span class="k">print</span> <span class="s2">&quot;Sample point&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;predicted to be in Cluster&quot;</span><span class="p">,</span> <span class="n">pred</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>Sample point 0 predicted to be in Cluster 1
Sample point 1 predicted to be in Cluster 1
Sample point 2 predicted to be in Cluster 0
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<p>I would say that prediction is quite accurate.</p>
<p>The <strong>first</strong> sample point as predicted buys quite a log fresh and frozen food and less grocery and detergents paper. So I would say that this prediction is accurate.</p>
<p>The <strong>second</strong> sample looks a bit more interesting. After quick look I thought that prediction was incorrect. Big value of <code>Milk</code> feature confused me for a second. But then I looked on target features, which are <code>Fresh</code>, <code>Frozen</code>, <code>Grocery</code> and <code>Detergents Paper</code>. These values quite small but anyway, <code>Fresh</code> and <code>Frozen</code> are higher than <code>Grocery</code> and <code>Detergents Paper</code>. So this prediction is also correct.</p>
<p>The <strong>third</strong> sample is from <code>Cluster 1</code>. It seems correct for me, because the <code>Grocery</code> and <code>Detergents Paper</code> feature values are high. With such a big feature values these sample will locate in the right part of the data spread on the graph from cluster visualisation section. This part of the graph contains data from <code>Cluster 1</code>.</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="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#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 this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the <strong><em>customer segments</em></strong>, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which <em>segment</em> that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the <strong><em>customer segments</em></strong> to a hidden variable present in the data, to see whether the clustering identified certain relationships.</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="Question-10">Question 10<a class="anchor-link" href="#Question-10">&#182;</a></h3><p>Companies will often run <a href="https://en.wikipedia.org/wiki/A/B_testing">A/B tests</a> when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. <em>How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively to the change in delivery service?</em><br>
<strong>Hint:</strong> Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?</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>Answer:</strong></p>
<p>Well, I would say that about 1/4 of customers will react negatively. You can find these customers on graph above from <strong>Cluster Visualization</strong> section. This is will be customers who has negative values for first and second dimensions. The fact that they have negative values means that they buy a lot of Fresh, Frozen and Detergents Paper. So if they buy so much these products five times a week, they will need to order even more products in these categories if they receive it 3 times a week.</p>
<p>On the other hand without making any predictions we can just apply A/B testing, which may help to receive customer's feedback and identify thier needs. In our case we can apply A/B testing for both clusters. And we should do it separately and collect feedback separately as well. Both these tests should be completely independent. For each cluster we can randomly assign a delivery plan for each customer so, that both these subcategories will be completely balanced. It means that in each cluster 50% of customers will have 3 days delivery and 50% of customers will have 5 days delivery. And than we can receive a feedback from both customer subgroups from each cluster and make any decision about new delivery plan.</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="Question-11">Question 11<a class="anchor-link" href="#Question-11">&#182;</a></h3><p>Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a <strong><em>customer segment</em></strong> it best identifies with (depending on the clustering algorithm applied), we can consider <em>'customer segment'</em> as an <strong>engineered feature</strong> for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a <strong><em>customer segment</em></strong> to determine the most appropriate delivery service.<br>
<em>How can the wholesale distributor label the new customers using only their estimated product spending and the</em> <strong><em>customer segment</em></strong> <em>data?</em><br>
<strong>Hint:</strong> A supervised learner could be used to train on the original customers. What would be the target variable?</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>Answer:</strong></p>
<p>I think I didn't understand a question. At least the hint has confused me.</p>
<p>Anyway, here is what I see. Since we have a data for customer segment and amount of money spending on each category, by comparing this data with new customer's estimations we can  assign new customer to such cluster which looks closer to the estimated data.</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="Visualizing-Underlying-Distributions">Visualizing Underlying Distributions<a class="anchor-link" href="#Visualizing-Underlying-Distributions">&#182;</a></h3><p>At the beginning of this project, it was discussed that the <code>'Channel'</code> and <code>'Region'</code> features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the <code>'Channel'</code> feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.</p>
<p>Run the code block below to see how each data point is labeled either <code>'HoReCa'</code> (Hotel/Restaurant/Cafe) or <code>'Retail'</code> the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the clustering results based on &#39;Channel&#39; data</span>
<span class="n">rs</span><span class="o">.</span><span class="n">channel_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">outliers</span><span class="p">,</span> <span class="n">pca_samples</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,iVBORw0KGgoAAAANSUhEUgAAA0AAAAIHCAYAAAC2STwiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-12">Question 12<a class="anchor-link" href="#Question-12">&#182;</a></h3><p><em>How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?</em></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>Answer:</strong></p>
<p>The prediction model performed good clusterization and each prediction on a sample data was correct. I would say that data with negative <code>Dimenstion 1</code> could be most likely classified as pure HoReCa. This cluster has several outliers from another cluster, but it doesn't play big role, I guess. However, I cannot say same about the Retailers. Retailers data is much more mixed with HoReCa. I would say that my predictions were not accurate. Most likely it might be so, because I chosen outliers as samples. For instance the second customer has small values for almost all features except a milk products. So I didn't even expected that it might be a HoReCa, however it did.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>

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