report.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>vui_notebook</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<style type="text/css">
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
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aside,
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figcaption,
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menu,
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summary {
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a:active,
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dfn {
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h1 {
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small {
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sup {
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sub {
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img {
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svg:not(:root) {
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figure {
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input[type="search"]::-webkit-search-cancel-button,
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fieldset {
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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 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;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#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
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
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;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.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;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#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;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.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;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.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 {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  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: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  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;
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<h1 id="voiceAI">voiceAI<a class="anchor-link" href="#voiceAI">&#182;</a></h1><p>Another AIND project, this time centered around <strong>Voice User Interfaces</strong> and <strong>Speech Recognition with Neural Networks</strong>!</p>

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<h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">&#182;</a></h2><p>In this notebook, we will build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline!</p>
<p><img src="images/pipeline.png"></p>
<p>Your completed pipeline will accept raw audio as input and return a predicted transcription of the spoken language.  The full pipeline is summarized in the figure below.</p>
<ul>
<li><strong>STEP 1: PRE-PROCESSING:</strong> Converts raw audio to one of two feature representations that are commonly used for ASR. </li>
<li><strong>STEP 2: ACOUSTIC MODEL:</strong> Accept transformed audio features as input and return a probability distribution over all potential transcriptions, picking it's best guess (using a variety of models!)</li>
<li><strong>STEP 3: PREDICTION:</strong> Lastly, the pipeline takes the output from the acoustic model and returns a predicted transcription for validation.</li>
</ul>
<p>Feel free to use the links below to navigate the notebook:</p>
<ul>
<li><a href="#thedata">The Data</a></li>
<li><a href="#step1"><strong>STEP 1</strong></a>: Acoustic Features for Speech Recognition</li>
<li><a href="#step2"><strong>STEP 2</strong></a>: Deep Neural Networks for Acoustic Modeling<ul>
<li><a href="#model0">Model 0</a>: RNN</li>
<li><a href="#model1">Model 1</a>: RNN + TimeDistributed Dense</li>
<li><a href="#model2">Model 2</a>: CNN + RNN + TimeDistributed Dense</li>
<li><a href="#model3">Model 3</a>: Deeper RNN + TimeDistributed Dense</li>
<li><a href="#model4">Model 4</a>: Bidirectional RNN + TimeDistributed Dense</li>
<li><a href="#model5">Models 5+</a></li>
<li><a href="#compare">Compare the Models</a></li>
<li><a href="#final">Final Model</a></li>
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<li><a href="#step3"><strong>STEP 3</strong></a>: Obtain Predictions</li>
</ul>

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<p><a id='thedata'></a></p>
<h2 id="The-Data">The Data<a class="anchor-link" href="#The-Data">&#182;</a></h2><p>We begin by investigating the dataset that will be used to train and evaluate your pipeline.</p>
<p><a href="http://www.danielpovey.com/files/2015_icassp_librispeech.pdf">LibriSpeech</a> is a large corpus of English-read speech, designed for training and evaluating models for ASR.  The dataset contains <strong>1000 hours of speech derived from audiobooks</strong>.</p>
<p>We will work with a small subset in this project, since larger-scale data would take a long while to train.  However, after completing this project, if you are interested in exploring further, you are encouraged to work with more of the data that is provided <a href="http://www.openslr.org/12/">online</a>.</p>
<p>In the code cells below, you will use the <code>vis_train_features</code> module to visualize a training example.  The supplied argument <code>index=0</code> tells the module to extract the first example in the training set.  (You are welcome to change <code>index=0</code> to point to a different training example, if you like, but please <strong>DO NOT</strong> amend any other code in the cell.)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">data_generator</span> <span class="k">import</span> <span class="n">vis_train_features</span>

<span class="c1"># extract label and audio features for a single training example</span>
<span class="n">vis_text</span><span class="p">,</span> <span class="n">vis_raw_audio</span><span class="p">,</span> <span class="n">vis_mfcc_feature</span><span class="p">,</span> <span class="n">vis_spectrogram_feature</span><span class="p">,</span> <span class="n">vis_audio_path</span> <span class="o">=</span> <span class="n">vis_train_features</span><span class="p">()</span>
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<pre>There are 2136 total training examples.
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<p>The returned variables are:</p>
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<li><code>vis_text</code> - transcribed text (label) for the training example.</li>
<li><code>vis_raw_audio</code> - raw audio waveform for the training example.</li>
<li><code>vis_mfcc_feature</code> - mel-frequency cepstral coefficients (MFCCs) for the training example.</li>
<li><code>vis_spectrogram_feature</code> - spectrogram for the training example. </li>
<li><code>vis_audio_path</code> - the file path to the training example.</li>
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<p>The following code cell visualizes the audio waveform for your chosen example, along with the corresponding transcript.  You also have the option to play the audio in the notebook!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">display</span>
<span class="kn">from</span> <span class="nn">data_generator</span> <span class="k">import</span> <span class="n">vis_train_features</span><span class="p">,</span> <span class="n">plot_raw_audio</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">Audio</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># plot audio signal</span>
<span class="n">plot_raw_audio</span><span class="p">(</span><span class="n">vis_raw_audio</span><span class="p">)</span>
<span class="c1"># print length of audio signal</span>
<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;**Shape of Audio Signal** : &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">vis_raw_audio</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
<span class="c1"># print transcript corresponding to audio clip</span>
<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;**Transcript** : &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">vis_text</span><span class="p">)))</span>
<span class="c1"># play the audio file</span>
<span class="n">Audio</span><span class="p">(</span><span class="n">vis_audio_path</span><span class="p">)</span>
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"
>
</div>

</div>

<div class="output_area">

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


<div class="output_markdown rendered_html output_subarea ">
<p><strong>Shape of Audio Signal</strong> : (103966,)</p>

</div>

</div>

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


<div class="output_markdown rendered_html output_subarea ">
<p><strong>Transcript</strong> : the last two days of the voyage bartley found almost intolerable</p>

</div>

</div>

<div class="output_area">

<div class="prompt output_prompt">Out[2]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">

                <audio controls="controls" >
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<p><a id='step1'></a></p>
<h2 id="STEP-1:-Acoustic-Features-for-Speech-Recognition">STEP 1: Acoustic Features for Speech Recognition<a class="anchor-link" href="#STEP-1:-Acoustic-Features-for-Speech-Recognition">&#182;</a></h2><p>For this project, <em>won't</em> use the raw audio waveform as input to your model.</p>
<p>Instead, we will use the provided code to <strong>first perform a pre-processing step that converts the raw audio to a feature representation that has historically proven successful for ASR models</strong>. Your acoustic model will accept <em>this feature representation</em> as input.</p>
<p>In this project, we will explore two possible feature representations.  <em>After completing the project</em>, if you'd like to read more about deep learning architectures that can accept raw audio input, you are encouraged to explore this <a href="https://pdfs.semanticscholar.org/a566/cd4a8623d661a4931814d9dffc72ecbf63c4.pdf">research paper</a>.</p>
<h3 id="Spectrograms">Spectrograms<a class="anchor-link" href="#Spectrograms">&#182;</a></h3><p>The first option for an audio feature representation is the <a href="https://www.youtube.com/watch?v=_FatxGN3vAM">spectrogram</a>.</p>
<p>In order to complete our project, you will <strong>not</strong> need to dig deeply into the details of how a spectrogram is calculated; but, if you are curious, the code for calculating the spectrogram was borrowed from <a href="https://github.com/baidu-research/ba-dls-deepspeech">this repository</a>.</p>
<p>The implementation appears in the <code>utils.py</code> file in our repository.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">data_generator</span> <span class="k">import</span> <span class="n">plot_spectrogram_feature</span>

<span class="c1"># plot normalized spectrogram</span>
<span class="n">plot_spectrogram_feature</span><span class="p">(</span><span class="n">vis_spectrogram_feature</span><span class="p">)</span>
<span class="c1"># print shape of spectrogram</span>
<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;**Shape of Spectrogram** : &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">vis_spectrogram_feature</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
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<p><strong>Shape of Spectrogram</strong> : (470, 161)</p>

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<p>The code that we give you returns the spectrogram as a 2D tensor, where:</p>
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<li>The first (<em>vertical</em>) dimension indexes time</li>
<li>THe second (<em>horizontal</em>) dimension indexes frequency.  </li>
</ul>
<p>To speed the convergence of your algorithm, we have also normalized the spectrogram.  (You can see this quickly in the visualization below by noting that the mean value hovers around zero, and most entries in the tensor assume values close to zero (hello sparse matrices!)</p>

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<h3 id="Mel-Frequency-Cepstral-Coefficients-(MFCCs)">Mel-Frequency Cepstral Coefficients (MFCCs)<a class="anchor-link" href="#Mel-Frequency-Cepstral-Coefficients-(MFCCs)">&#182;</a></h3><p>The second option for an audio feature representation is <a href="https://en.wikipedia.org/wiki/Mel-frequency_cepstrum">MFCCs</a>.</p>
<p>We are <strong>not</strong> going to dig deeply into the details of how MFCCs are calculated, but if you would like more information, you are welcome to peruse the <a href="https://github.com/jameslyons/python_speech_features">documentation</a> of the <code>python_speech_features</code> Python package.  Just as with the spectrogram features, the MFCCs are normalized in the supplied code.</p>
<p>The main idea behind MFCC features is the same as spectrogram features, the difference is in fidelity: for each time window, the MFCC feature yields a single feature vector that characterizes the sound within the window.</p>
<p>Note that the MFCC feature is much lower-dimensional than the spectrogram feature, which could help an acoustic model to avoid overfitting to the training dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">data_generator</span> <span class="k">import</span> <span class="n">plot_mfcc_feature</span>

<span class="c1"># plot normalized MFCC</span>
<span class="n">plot_mfcc_feature</span><span class="p">(</span><span class="n">vis_mfcc_feature</span><span class="p">)</span>
<span class="c1"># print shape of MFCC</span>
<span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;**Shape of MFCC** : &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">vis_mfcc_feature</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span>
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<p><strong>Shape of MFCC</strong> : (470, 13)</p>

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<p>Comparing these side by side, it is clear that this different treatment of <strong>time</strong> can have different pros and cons, especially with regards to understanding sequences vs. understanding a window.</p>

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<p>When we construct our pipeline, we will be able to choose to use either spectrogram or MFCC features.  If you would like to see different implementations that make use of MFCCs and/or spectrograms, please check out the links below:</p>
<ul>
<li>This <a href="https://github.com/baidu-research/ba-dls-deepspeech">repository</a> uses spectrograms.</li>
<li>This <a href="https://github.com/mozilla/DeepSpeech">repository</a> uses MFCCs.</li>
<li>This <a href="https://github.com/buriburisuri/speech-to-text-wavenet">repository</a> also uses MFCCs.</li>
<li>This <a href="https://github.com/pannous/tensorflow-speech-recognition/blob/master/speech_data.py">repository</a> experiments with raw audio, spectrograms, and MFCCs as features.</li>
</ul>

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<p><a id='step2'></a></p>
<h2 id="STEP-2:-Deep-Neural-Networks-for-Acoustic-Modeling">STEP 2: Deep Neural Networks for Acoustic Modeling<a class="anchor-link" href="#STEP-2:-Deep-Neural-Networks-for-Acoustic-Modeling">&#182;</a></h2><p>In this section, we will experiment with various neural network architectures for acoustic modeling!</p>
<p>All models will be specified in the <code>sample_models.py</code> file.  After importing the <code>sample_models</code> module, we will just train and visualize our architectures in the notebook.</p>
<p>After experimenting with the five simple architectures, we will then compare their performance, from which we will construct an even <strong>deeper architecture that is designed to outperform all of the shallow models</strong>!)</p>
<p>For your convenience, we have designed the notebook so that each model can be specified and trained on separate occasions.  That is, say you decide to take a break from the notebook after training <strong>Model 1</strong>.  Then, you need not re-execute all prior code cells in the notebook before training <strong>Model 2</strong>.  You need only re-execute the code cell below, that is marked with <strong><code>RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK</code></strong>, before transitioning to the code cells corresponding to <strong>Model 2</strong>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#####################################################################</span>
<span class="c1"># RUN THIS CODE CELL IF YOU ARE RESUMING THE NOTEBOOK AFTER A BREAK #</span>
<span class="c1">#####################################################################</span>

<span class="c1"># allocate 50% of GPU memory (if you like, feel free to change this)</span>
<span class="kn">from</span> <span class="nn">keras.backend.tensorflow_backend</span> <span class="k">import</span> <span class="n">set_session</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span> 
<span class="n">config</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ConfigProto</span><span class="p">()</span>
<span class="n">config</span><span class="o">.</span><span class="n">gpu_options</span><span class="o">.</span><span class="n">per_process_gpu_memory_fraction</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">set_session</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">))</span>

<span class="c1"># watch for any changes in the sample_models module, and reload it automatically</span>
<span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="c1"># import NN architectures for speech recognition</span>
<span class="kn">from</span> <span class="nn">sample_models</span> <span class="k">import</span> <span class="o">*</span>
<span class="c1"># import function for training acoustic model</span>
<span class="kn">from</span> <span class="nn">train_utils</span> <span class="k">import</span> <span class="n">train_model</span>
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<pre>Using TensorFlow backend.
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<p><a id='model0'></a></p>
<h3 id="Model-0:-RNN">Model 0: RNN<a class="anchor-link" href="#Model-0:-RNN">&#182;</a></h3><p>Given their effectiveness in modeling sequential data, the first acoustic model you will use is an RNN.  As shown in the figure below, the RNN we supply to you will take the time sequence of audio features as input.</p>
<p><img src="images/simple_rnn.png" width="50%"></p>
<p>At each time step, the speaker pronounces one of 28 possible characters, including each of the 26 letters in the English alphabet, along with a space character (" "), and an apostrophe (').</p>
<p>The output of the RNN at each time step is a vector of probabilities with 29 entries, where the $i$-th entry encodes the probability that the $i$-th character is spoken in the time sequence.  (That extra 29th character is an empty "character" used to pad training examples within batches containing uneven lengths; we need matching sequences for our RNNs!)  If you would like to peek under the hood at how characters are mapped to indices in the probability vector, look at the <code>char_map.py</code> file in the repository.</p>
<p>The figure below shows an equivalent, rolled depiction of the RNN that shows the output layer in greater detail.</p>
<p><img src="images/simple_rnn_unrolled.png" width="60%"></p>
<p>In a general sense, each RNN unit casts a guess against the predicted output vector using the input vector, optimizing using backpropogation to improve guess over time.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_0</span> <span class="o">=</span> <span class="n">simple_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">)</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
rnn (GRU)                    (None, None, 29)          16617     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 16,617
Trainable params: 16,617
Non-trainable params: 0
_________________________________________________________________
None
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<h3 id="Criteria">Criteria<a class="anchor-link" href="#Criteria">&#182;</a></h3><p>We will train the acoustic model with the <a href="http://www.cs.toronto.edu/~graves/icml_2006.pdf">CTC loss</a> criterion; which excels at handling sequences.</p>
<p>Custom loss functions take a bit of hacking in Keras, so let's use the pre-implemented the CTC loss function, so that we can focus on trying out as many deep learning architectures as possible :D. Again, if you'd like to peek at the implementation details, look at the <code>add_ctc_loss</code> function within the <code>train_utils.py</code> file in the repository.</p>

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<h3 id="Training">Training<a class="anchor-link" href="#Training">&#182;</a></h3><p>To train your architecture, you will use the <code>train_model</code> function within the <code>train_utils</code> module; it has already been imported in one of the above code cells.  The <code>train_model</code> function takes three <strong>required</strong> arguments:</p>
<ul>
<li><code>input_to_softmax</code> - a Keras model instance.</li>
<li><code>pickle_path</code> - the name of the pickle file where the loss history will be saved.</li>
<li><code>save_model_path</code> - the name of the HDF5 file where the model will be saved.</li>
</ul>
<p>If we have already supplied values for <code>input_to_softmax</code>, <code>pickle_path</code>, and <code>save_model_path</code>, please <strong>DO NOT</strong> modify these values.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_0</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_0.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_0.h5&#39;</span><span class="p">,</span>
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 224s - loss: 841.9181 - val_loss: 736.3170
Epoch 2/20
106/106 [==============================] - 221s - loss: 758.2415 - val_loss: 730.2314
Epoch 3/20
106/106 [==============================] - 220s - loss: 752.7252 - val_loss: 721.6480
Epoch 4/20
106/106 [==============================] - 217s - loss: 752.0862 - val_loss: 732.1707
Epoch 5/20
106/106 [==============================] - 220s - loss: 751.9443 - val_loss: 724.9035
Epoch 6/20
106/106 [==============================] - 221s - loss: 753.4452 - val_loss: 725.2089
Epoch 7/20
106/106 [==============================] - 221s - loss: 751.5216 - val_loss: 730.7225
Epoch 8/20
106/106 [==============================] - 219s - loss: 752.3161 - val_loss: 719.4578
Epoch 9/20
106/106 [==============================] - 220s - loss: 751.3191 - val_loss: 732.4377
Epoch 10/20
106/106 [==============================] - 220s - loss: 751.8280 - val_loss: 727.7332
Epoch 11/20
106/106 [==============================] - 219s - loss: 750.9857 - val_loss: 727.2653
Epoch 12/20
106/106 [==============================] - 220s - loss: 751.6806 - val_loss: 722.2152
Epoch 13/20
106/106 [==============================] - 218s - loss: 752.6698 - val_loss: 735.1222
Epoch 14/20
106/106 [==============================] - 219s - loss: 751.6500 - val_loss: 721.5078
Epoch 15/20
106/106 [==============================] - 218s - loss: 751.2457 - val_loss: 722.8050
Epoch 16/20
106/106 [==============================] - 221s - loss: 752.1653 - val_loss: 734.7422
Epoch 17/20
106/106 [==============================] - 222s - loss: 751.9123 - val_loss: 731.1000
Epoch 18/20
106/106 [==============================] - 217s - loss: 751.9818 - val_loss: 719.7570
Epoch 19/20
106/106 [==============================] - 218s - loss: 751.9087 - val_loss: 736.3279
Epoch 20/20
106/106 [==============================] - 218s - loss: 752.6974 - val_loss: 727.2684
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<p>There are several <strong>optional</strong> arguments that allow you to have more control over the training process.  You are welcome to supply your own values for these arguments!</p>
<ul>
<li><code>minibatch_size</code> - the size of the minibatches that are generated while training the model (default: <code>20</code>).</li>
<li><code>spectrogram</code> - Boolean value dictating whether spectrogram (<code>True</code>) or MFCC (<code>False</code>) features are used for training (default: <code>True</code>).</li>
<li><code>mfcc_dim</code> - the size of the feature dimension to use when generating MFCC features (default: <code>13</code>).</li>
<li><code>optimizer</code> - the Keras optimizer used to train the model (default: <code>SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)</code>).  </li>
<li><code>epochs</code> - the number of epochs to use to train the model (default: <code>20</code>).  If you choose to modify this parameter, make sure that it is <em>at least</em> 20.</li>
<li><code>verbose</code> - controls the verbosity of the training output in the <code>model.fit_generator</code> method (default: <code>1</code>).</li>
<li><code>sort_by_duration</code> - Boolean value dictating whether the training and validation sets are sorted by (increasing) duration before the start of the first epoch (default: <code>False</code>).</li>
</ul>
<p>The <code>train_model</code> function defaults to using spectrogram features; if you choose to use these features, note that the acoustic model in <code>simple_rnn_model</code> should have <code>input_dim=161</code>.  Otherwise, if you choose to use MFCC features, the acoustic model should have <code>input_dim=13</code>.</p>
<p><strong>IMPORTANT NOTE:</strong> If you notice that your gradient has exploded in any of the models below, feel free to explore more with gradient clipping (the <code>clipnorm</code> argument in your optimizer) or swap out any <code>SimpleRNN</code> cells for <code>LSTM</code> or <code>GRU</code> cells.  You can also try restarting the kernel to restart the training process.</p>

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<p>We have also chosen to use <code>GRU</code> units in the supplied RNN.  If you would like to experiment with <code>LSTM</code> or <code>SimpleRNN</code> cells, feel free to do so here.  If you change the <code>GRU</code> units to <code>SimpleRNN</code> cells in <code>simple_rnn_model</code>, you may notice that the loss quickly becomes undefined (<code>nan</code>) - you are strongly encouraged to check this for yourself!  This is due to the <a href="http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/">exploding gradients problem</a>.  We have already implemented <a href="https://arxiv.org/pdf/1211.5063.pdf">gradient clipping</a> in your optimizer to help you avoid this issue.</p>

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<p><a id='model1'></a></p>
<h3 id="Model-1:-RNN-+-TimeDistributed-Dense">Model 1: RNN + TimeDistributed Dense<a class="anchor-link" href="#Model-1:-RNN-+-TimeDistributed-Dense">&#182;</a></h3><p><img src="images/rnn_model.png" width="60%"></p>
<p>For our next next architecture, let's use the <a href="https://keras.io/layers/wrappers/">TimeDistributed</a> wrapper and the <a href="https://keras.io/layers/normalization/">BatchNormalization</a> layer from keras:</p>
<ul>
<li>Time Distribution via the <code>TimeDistributed</code> layer will allow us to find more complex patterns in the dataset; consider the unrolled snapshot above.</li>
<li><a href="https://arxiv.org/pdf/1510.01378.pdf">Batch normalization</a> in our recurrent layer helps reduce training times.<br>
If we zoom on just the ouput; we can see the (<code>TimeDistrbuted</code>) dense and output layers in greater detail.  </li>
</ul>
<p><img src="images/rnn_model_unrolled.png" width="60%"></p>
<p>Let's build a model in <code>sample_model.py</code> that impements this!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_1</span> <span class="o">=</span> <span class="n">rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                    <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                    <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
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the_input (InputLayer)       (None, None, 161)         0         
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rnn (GRU)                    (None, None, 200)         217200    
_________________________________________________________________
batch_normalization_1 (Batch (None, None, 200)         800       
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 29)          5829      
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 223,829
Trainable params: 223,429
Non-trainable params: 400
_________________________________________________________________
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<li><p>The first layer of the neural network uses an RNN (<code>SimpleRNN</code>, <code>LSTM</code>, or <code>GRU</code>) that takes the time sequence of audio features as input.</p>
</li>
<li><p>The hidden layer in <code>simple_rnn_model</code> treats the RNN output as the input to the final layer of the model, and we use <code>TimeDistributed</code> in this hidden step to apply a <code>Dense</code> connection layer to each of the time steps in the RNN output.</p>
</li>
<li><p>The output layer ensures that each <code>Dense</code> layer has <code>output_dim</code> units, but is essentially plugged straight into the RNN's probability predictions.</p>
</li>
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<p>Let's also execute the code cell below to train the neural network you specified in <code>input_to_softmax</code>.</p>
<p>After the model has finished training, the model is <a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model">saved</a> in the HDF5 file <code>model_1.h5</code>.  The loss history is <a href="https://wiki.python.org/moin/UsingPickle">saved too</a>, using <code>model_1.pickle</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_1</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_1.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_1.h5&#39;</span><span class="p">,</span>
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 225s - loss: 367.9386 - val_loss: 337.5201
Epoch 2/20
106/106 [==============================] - 229s - loss: 227.6331 - val_loss: 265.0209
Epoch 3/20
106/106 [==============================] - 228s - loss: 198.8542 - val_loss: 186.8425
Epoch 4/20
106/106 [==============================] - 227s - loss: 181.7907 - val_loss: 171.4935
Epoch 5/20
106/106 [==============================] - 228s - loss: 168.1042 - val_loss: 168.4838
Epoch 6/20
106/106 [==============================] - 230s - loss: 157.4330 - val_loss: 156.9629
Epoch 7/20
106/106 [==============================] - 227s - loss: 150.3916 - val_loss: 155.1179
Epoch 8/20
106/106 [==============================] - 228s - loss: 145.8944 - val_loss: 157.8674
Epoch 9/20
106/106 [==============================] - 227s - loss: 142.7567 - val_loss: 148.8004
Epoch 10/20
106/106 [==============================] - 227s - loss: 140.0274 - val_loss: 147.1977
Epoch 11/20
106/106 [==============================] - 226s - loss: 137.8551 - val_loss: 147.9762
Epoch 12/20
106/106 [==============================] - 225s - loss: 136.7871 - val_loss: 149.9069
Epoch 13/20
106/106 [==============================] - 227s - loss: 136.5525 - val_loss: 148.2636
Epoch 14/20
106/106 [==============================] - 225s - loss: 134.7058 - val_loss: 144.3187
Epoch 15/20
106/106 [==============================] - 226s - loss: 133.3694 - val_loss: 145.0704
Epoch 16/20
106/106 [==============================] - 227s - loss: 133.3552 - val_loss: 140.7199
Epoch 17/20
106/106 [==============================] - 229s - loss: 137.3734 - val_loss: 146.2730
Epoch 18/20
106/106 [==============================] - 228s - loss: 132.8446 - val_loss: 140.5503
Epoch 19/20
106/106 [==============================] - 226s - loss: 133.1923 - val_loss: 147.7639
Epoch 20/20
106/106 [==============================] - 226s - loss: 137.8584 - val_loss: 142.0406
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<p><a id='model2'></a></p>
<h3 id="Model-2:-CNN-+-RNN-+-TimeDistributed-Dense">Model 2: CNN + RNN + TimeDistributed Dense<a class="anchor-link" href="#Model-2:-CNN-+-RNN-+-TimeDistributed-Dense">&#182;</a></h3><p>The architecture in <code>cnn_rnn_model</code> adds an additional level of complexity, by introducing a <a href="https://keras.io/layers/convolutional/#conv1d">1D convolution layer</a>.</p>
<p><img src="images/cnn_rnn_model.png" width="100%"></p>
<p>This layer incorporates many arguments that can be (optionally) tuned when calling the <code>cnn_rnn_model</code> module.  We provide sample starting parameters, which you might find useful if you choose to use spectrogram audio features.</p>
<p>If you instead want to use MFCC features, these arguments will have to be tuned.  Note that the current architecture only supports values of <code>'same'</code> or <code>'valid'</code> for the <code>conv_border_mode</code> argument.</p>
<p>When tuning the parameters, be careful not to choose settings that make the convolutional layer overly small.  If the temporal length of the CNN layer is shorter than the length of the transcribed text label, your code will throw an error.</p>
<p>Before running the code cell below, you must modify the <code>cnn_rnn_model</code> function in <code>sample_models.py</code>.  Please add batch normalization to the recurrent layer, and provide the same <code>TimeDistributed</code> layer as before.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_2</span> <span class="o">=</span> <span class="n">cnn_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                        <span class="n">filters</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                        <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> 
                        <span class="n">conv_stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                        <span class="n">conv_border_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">,</span>
                        <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
conv1d (Conv1D)              (None, None, 200)         354400    
_________________________________________________________________
bn_conv_1d (BatchNormalizati (None, None, 200)         800       
_________________________________________________________________
rnn (SimpleRNN)              (None, None, 200)         80200     
_________________________________________________________________
batch_normalization_2 (Batch (None, None, 200)         800       
_________________________________________________________________
time_distributed_2 (TimeDist (None, None, 29)          5829      
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 442,029
Trainable params: 441,229
Non-trainable params: 800
_________________________________________________________________
None
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<p>Please execute the code cell below to train the neural network you specified in <code>input_to_softmax</code>.  After the model has finished training, the model is <a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model">saved</a> in the HDF5 file <code>model_2.h5</code>.  The loss history is <a href="https://wiki.python.org/moin/UsingPickle">saved</a> in <code>model_2.pickle</code>.  You are welcome to tweak any of the optional parameters while calling the <code>train_model</code> function, but this is not required.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_2</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_2.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_2.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 61s - loss: 229.0710 - val_loss: 185.6440
Epoch 2/20
106/106 [==============================] - 59s - loss: 166.7276 - val_loss: 154.8832
Epoch 3/20
106/106 [==============================] - 58s - loss: 146.7739 - val_loss: 144.8785
Epoch 4/20
106/106 [==============================] - 57s - loss: 135.9777 - val_loss: 140.5358
Epoch 5/20
106/106 [==============================] - 57s - loss: 128.2075 - val_loss: 135.6018
Epoch 6/20
106/106 [==============================] - 57s - loss: 122.8382 - val_loss: 135.9319
Epoch 7/20
106/106 [==============================] - 58s - loss: 117.2671 - val_loss: 132.8129
Epoch 8/20
106/106 [==============================] - 58s - loss: 113.1700 - val_loss: 133.8851
Epoch 9/20
106/106 [==============================] - 58s - loss: 109.2029 - val_loss: 130.3848
Epoch 10/20
106/106 [==============================] - 57s - loss: 105.9890 - val_loss: 134.8307
Epoch 11/20
106/106 [==============================] - 56s - loss: 102.6300 - val_loss: 131.7514
Epoch 12/20
106/106 [==============================] - 56s - loss: 99.5150 - val_loss: 131.6749
Epoch 13/20
106/106 [==============================] - 58s - loss: 96.6035 - val_loss: 130.2747
Epoch 14/20
106/106 [==============================] - 58s - loss: 93.8159 - val_loss: 132.7941
Epoch 15/20
106/106 [==============================] - 57s - loss: 91.3080 - val_loss: 132.7317
Epoch 16/20
106/106 [==============================] - 57s - loss: 88.7120 - val_loss: 132.5699
Epoch 17/20
106/106 [==============================] - 57s - loss: 86.4573 - val_loss: 135.4340
Epoch 18/20
106/106 [==============================] - 57s - loss: 84.6415 - val_loss: 136.5684
Epoch 19/20
106/106 [==============================] - 58s - loss: 81.9793 - val_loss: 137.6616
Epoch 20/20
106/106 [==============================] - 57s - loss: 80.1717 - val_loss: 141.2940
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<p><a id='model3'></a></p>
<h3 id="Model-3:-Deeper-RNN-+-TimeDistributed-Dense">Model 3: Deeper RNN + TimeDistributed Dense<a class="anchor-link" href="#Model-3:-Deeper-RNN-+-TimeDistributed-Dense">&#182;</a></h3><p>Review the code in <code>rnn_model</code>, which makes use of a single recurrent layer.  Now, specify an architecture in <code>deep_rnn_model</code> that utilizes a variable number <code>recur_layers</code> of recurrent layers.  The figure below shows the architecture that should be returned if <code>recur_layers=2</code>.  In the figure, the output sequence of the first recurrent layer is used as input for the next recurrent layer.</p>
<p><img src="images/deep_rnn_model.png" width="80%"></p>
<p>Feel free to change the supplied values of <code>units</code> to whatever you think performs best.  You can change the value of <code>recur_layers</code>, as long as your final value is greater than 1. (As a quick check that you have implemented the additional functionality in <code>deep_rnn_model</code> correctly, make sure that the architecture that you specify here is identical to <code>rnn_model</code> if <code>recur_layers=1</code>.)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_3</span> <span class="o">=</span> <span class="n">deep_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                         <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                         <span class="n">recur_layers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> 
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
rnn_1 (GRU)                  (None, None, 200)         217200    
_________________________________________________________________
batch_normalization_3 (Batch (None, None, 200)         800       
_________________________________________________________________
rnn_2 (GRU)                  (None, None, 200)         240600    
_________________________________________________________________
batch_normalization_4 (Batch (None, None, 200)         800       
_________________________________________________________________
time_distributed_3 (TimeDist (None, None, 29)          5829      
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 465,229
Trainable params: 464,429
Non-trainable params: 800
_________________________________________________________________
None
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<p>Please execute the code cell below to train the neural network you specified in <code>input_to_softmax</code>.  After the model has finished training, the model is <a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model">saved</a> in the HDF5 file <code>model_3.h5</code>.  The loss history is <a href="https://wiki.python.org/moin/UsingPickle">saved</a> in <code>model_3.pickle</code>.  You are welcome to tweak any of the optional parameters while calling the <code>train_model</code> function, but this is not required.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_3</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_3.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_3.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 385s - loss: 256.8632 - val_loss: 218.4778
Epoch 2/20
106/106 [==============================] - 389s - loss: 220.9016 - val_loss: 214.5276
Epoch 3/20
106/106 [==============================] - 389s - loss: 212.0365 - val_loss: 201.1642
Epoch 4/20
106/106 [==============================] - 391s - loss: 193.2432 - val_loss: 190.5782
Epoch 5/20
106/106 [==============================] - 387s - loss: 162.4471 - val_loss: 153.9289
Epoch 6/20
106/106 [==============================] - 390s - loss: 144.7503 - val_loss: 145.6886
Epoch 7/20
106/106 [==============================] - 389s - loss: 135.3470 - val_loss: 138.2848
Epoch 8/20
106/106 [==============================] - 392s - loss: 128.9875 - val_loss: 140.4947
Epoch 9/20
106/106 [==============================] - 389s - loss: 123.8634 - val_loss: 130.7851
Epoch 10/20
106/106 [==============================] - 386s - loss: 119.6977 - val_loss: 130.6172
Epoch 11/20
106/106 [==============================] - 392s - loss: 116.4428 - val_loss: 130.5726
Epoch 12/20
106/106 [==============================] - 393s - loss: 112.9637 - val_loss: 129.1481
Epoch 13/20
106/106 [==============================] - 390s - loss: 110.7214 - val_loss: 122.8933
Epoch 14/20
106/106 [==============================] - 390s - loss: 107.7262 - val_loss: 123.2316
Epoch 15/20
106/106 [==============================] - 389s - loss: 105.6695 - val_loss: 127.3088
Epoch 16/20
106/106 [==============================] - 387s - loss: 103.5727 - val_loss: 126.1109
Epoch 17/20
106/106 [==============================] - 385s - loss: 101.4479 - val_loss: 120.3132
Epoch 18/20
106/106 [==============================] - 385s - loss: 100.7053 - val_loss: 125.8227
Epoch 19/20
106/106 [==============================] - 385s - loss: 99.8055 - val_loss: 122.9432
Epoch 20/20
106/106 [==============================] - 391s - loss: 97.7769 - val_loss: 120.1020
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<p><a id='model4'></a></p>
<h3 id="Model-4:-Bidirectional-RNN-+-TimeDistributed-Dense">Model 4: Bidirectional RNN + TimeDistributed Dense<a class="anchor-link" href="#Model-4:-Bidirectional-RNN-+-TimeDistributed-Dense">&#182;</a></h3><p>Read about the <a href="https://keras.io/layers/wrappers/">Bidirectional</a> wrapper in the Keras documentation.  For your next architecture, you will specify an architecture that uses a single bidirectional RNN layer, before a (<code>TimeDistributed</code>) dense layer.  The added value of a bidirectional RNN is described well in <a href="http://www.cs.toronto.edu/~hinton/absps/DRNN_speech.pdf">this paper</a>.</p>
<blockquote><p>One shortcoming of conventional RNNs is that they are only able to make use of previous context. In speech recognition, where whole utterances are transcribed at once, there is no reason not to exploit future context as well.  Bidirectional RNNs (BRNNs) do this by processing the data in both directions with two separate hidden layers which are then fed forwards to the same output layer.</p>
</blockquote>
<p><img src="images/bidirectional_rnn_model.png" width="80%"></p>
<p>Before running the code cell below, you must complete the <code>bidirectional_rnn_model</code> function in <code>sample_models.py</code>.  Feel free to use <code>SimpleRNN</code>, <code>LSTM</code>, or <code>GRU</code> units.  When specifying the <code>Bidirectional</code> wrapper, use <code>merge_mode='concat'</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_4</span> <span class="o">=</span> <span class="n">bidirectional_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                                  <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 400)         434400    
_________________________________________________________________
batch_normalization_5 (Batch (None, None, 400)         1600      
_________________________________________________________________
time_distributed_4 (TimeDist (None, None, 29)          11629     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 447,629
Trainable params: 446,829
Non-trainable params: 800
_________________________________________________________________
None
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<p>Please execute the code cell below to train the neural network you specified in <code>input_to_softmax</code>.  After the model has finished training, the model is <a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model">saved</a> in the HDF5 file <code>model_4.h5</code>.  The loss history is <a href="https://wiki.python.org/moin/UsingPickle">saved</a> in <code>model_4.pickle</code>.  You are welcome to tweak any of the optional parameters while calling the <code>train_model</code> function, but this is not required.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_4</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_4.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_4.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 377s - loss: 309.1368 - val_loss: 274.6563
Epoch 2/20
106/106 [==============================] - 385s - loss: 213.9504 - val_loss: 189.8949
Epoch 3/20
106/106 [==============================] - 385s - loss: 184.9473 - val_loss: 175.1875
Epoch 4/20
106/106 [==============================] - 378s - loss: 168.9086 - val_loss: 167.5569
Epoch 5/20
106/106 [==============================] - 384s - loss: 157.2887 - val_loss: 153.2339
Epoch 6/20
106/106 [==============================] - 386s - loss: 148.0640 - val_loss: 149.4466
Epoch 7/20
106/106 [==============================] - 384s - loss: 140.8739 - val_loss: 143.0215
Epoch 8/20
106/106 [==============================] - 379s - loss: 135.7555 - val_loss: 141.9769
Epoch 9/20
106/106 [==============================] - 382s - loss: 130.8454 - val_loss: 139.7064
Epoch 10/20
106/106 [==============================] - 385s - loss: 126.9327 - val_loss: 141.0884
Epoch 11/20
106/106 [==============================] - 384s - loss: 122.9366 - val_loss: 139.9223
Epoch 12/20
106/106 [==============================] - 386s - loss: 119.7802 - val_loss: 138.2814
Epoch 13/20
106/106 [==============================] - 383s - loss: 116.4796 - val_loss: 134.3777
Epoch 14/20
106/106 [==============================] - 383s - loss: 113.4044 - val_loss: 132.4903
Epoch 15/20
106/106 [==============================] - 388s - loss: 110.5372 - val_loss: 132.6841
Epoch 16/20
106/106 [==============================] - 387s - loss: 108.3411 - val_loss: 133.8261
Epoch 17/20
106/106 [==============================] - 383s - loss: 105.4478 - val_loss: 131.3938
Epoch 18/20
106/106 [==============================] - 387s - loss: 103.3623 - val_loss: 132.4524
Epoch 19/20
106/106 [==============================] - 384s - loss: 100.7206 - val_loss: 131.9914
Epoch 20/20
106/106 [==============================] - 379s - loss: 98.5222 - val_loss: 134.2463
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<p><a id='model5'></a></p>
<h3 id="Model-5:-Deep-Bidirectional-RNN-+-TimeDistributed">Model 5: Deep Bidirectional RNN + TimeDistributed<a class="anchor-link" href="#Model-5:-Deep-Bidirectional-RNN-+-TimeDistributed">&#182;</a></h3><p>Bonus architectures, from the kind folks at Udacity!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_5</span> <span class="o">=</span> <span class="n">deep_bidirectional_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                         <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                         <span class="n">recur_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                         <span class="n">RNN_type</span><span class="o">=</span><span class="n">GRU</span><span class="p">)</span>

<span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_5</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_5.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_5.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 400)         434400    
_________________________________________________________________
batch_normalization_6 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_3 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_7 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_4 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_8 (Batch (None, None, 400)         1600      
_________________________________________________________________
time_distributed_5 (TimeDist (None, None, 29)          11629     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 1,893,229
Trainable params: 1,890,829
Non-trainable params: 2,400
_________________________________________________________________
None
Epoch 1/20
106/106 [==============================] - 1061s - loss: 273.2050 - val_loss: 257.1091
Epoch 2/20
106/106 [==============================] - 1064s - loss: 207.0537 - val_loss: 186.3370
Epoch 3/20
106/106 [==============================] - 1069s - loss: 165.2725 - val_loss: 160.0458
Epoch 4/20
106/106 [==============================] - 1081s - loss: 142.9217 - val_loss: 142.1774
Epoch 5/20
106/106 [==============================] - 1083s - loss: 129.2055 - val_loss: 147.5106
Epoch 6/20
106/106 [==============================] - 1083s - loss: 118.4962 - val_loss: 131.3726
Epoch 7/20
106/106 [==============================] - 1082s - loss: 109.6594 - val_loss: 124.8024
Epoch 8/20
106/106 [==============================] - 1074s - loss: 102.1803 - val_loss: 123.3295
Epoch 9/20
106/106 [==============================] - 1065s - loss: 95.2571 - val_loss: 121.9507
Epoch 10/20
106/106 [==============================] - 1080s - loss: 89.0488 - val_loss: 118.4347
Epoch 11/20
106/106 [==============================] - 1079s - loss: 83.0019 - val_loss: 118.7419
Epoch 12/20
 75/106 [====================&gt;.........] - ETA: 252s - loss: 76.6052</pre>
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<h3 id="Model-6:-Deep-Bidirectional-RNN-+-TimeDistributed-with-Dropout">Model 6: Deep Bidirectional RNN + TimeDistributed with Dropout<a class="anchor-link" href="#Model-6:-Deep-Bidirectional-RNN-+-TimeDistributed-with-Dropout">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_6</span> <span class="o">=</span> <span class="n">deep_bidirectional_rnn_model_with_dropout</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                         <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                         <span class="n">recur_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
                         <span class="n">RNN_type</span><span class="o">=</span><span class="n">GRU</span><span class="p">)</span>

<span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_6</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_6.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_6.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="n">epochs</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre> 54/106 [==============&gt;...............] - ETA: 434s - loss: 139.8122</pre>
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<h3 id="Model-7:-CNN-+-RNN-+-TimeDistributed-with-Dropout">Model 7: CNN + RNN + TimeDistributed with Dropout<a class="anchor-link" href="#Model-7:-CNN-+-RNN-+-TimeDistributed-with-Dropout">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_7</span> <span class="o">=</span> <span class="n">cnn_rnn_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> <span class="c1"># change to 13 if you would like to use MFCC features</span>
                        <span class="n">filters</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
                        <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> 
                        <span class="n">conv_stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                        <span class="n">conv_border_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">,</span>
                        <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_7</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_7.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_7.h5&#39;</span><span class="p">,</span>
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<p><a id='compare'></a></p>
<h3 id="Compare-the-Models">Compare the Models<a class="anchor-link" href="#Compare-the-Models">&#182;</a></h3><p>Execute the code cell below to evaluate the performance of the drafted deep learning models.  The training and validation loss are plotted for each model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">_pickle</span> <span class="k">as</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">)</span>

<span class="c1"># obtain the paths for the saved model history</span>
<span class="n">all_pickles</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;results/*.pickle&quot;</span><span class="p">))</span>
<span class="c1"># extract the name of each model</span>
<span class="n">model_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">8</span><span class="p">:</span><span class="o">-</span><span class="mi">7</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">all_pickles</span><span class="p">]</span>
<span class="c1"># extract the loss history for each model</span>
<span class="n">valid_loss</span> <span class="o">=</span> <span class="p">[</span><span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span> <span class="nb">open</span><span class="p">(</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span> <span class="p">)</span> <span class="p">)[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">all_pickles</span><span class="p">]</span>
<span class="n">train_loss</span> <span class="o">=</span> <span class="p">[</span><span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span> <span class="nb">open</span><span class="p">(</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span> <span class="p">)</span> <span class="p">)[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">all_pickles</span><span class="p">]</span>
<span class="c1"># save the number of epochs used to train each model</span>
<span class="n">num_epochs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">valid_loss</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="nb">len</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">))]</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</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">5</span><span class="p">))</span>

<span class="c1"># plot the training loss vs. epoch for each model</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">121</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="nb">len</span><span class="p">(</span><span class="n">all_pickles</span><span class="p">)):</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_epochs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num_epochs</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> 
            <span class="n">train_loss</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="c1"># clean up the plot</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>  
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">)])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epoch&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>

<span class="c1"># plot the validation loss vs. epoch for each model</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">122</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="nb">len</span><span class="p">(</span><span class="n">all_pickles</span><span class="p">)):</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_epochs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num_epochs</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> 
            <span class="n">valid_loss</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="c1"># clean up the plot</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>  
<span class="n">ax2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">)])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Epoch&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p><strong>Question 1:</strong> Use the plot above to analyze the performance of each of the attempted architectures.  Which performs best?  Provide an explanation regarding why you think some models perform better than others.</p>
<p><strong>Answer:</strong></p>
<table>
<thead><tr>
<th>Model</th>
<th>Description</th>
<th>Lowest Validation Loss</th>
</tr>
</thead>
<tbody>
<tr>
<td>Model 0</td>
<td>RNN</td>
<td>752.6974</td>
<td>727.2684</td>
</tr>
<tr>
<td>Model 1</td>
<td>RNN + TimeDistributed</td>
<td>137.8584</td>
<td>142.0406</td>
</tr>
<tr>
<td>Model 2</td>
<td>CNN + RNN + TimeDistributed</td>
<td>80.1717</td>
<td>141.2940</td>
</tr>
<tr>
<td>Model 3</td>
<td>Deep RNN + TimeDistributed</td>
<td>97.7769</td>
<td>120.1020</td>
</tr>
<tr>
<td>Model 4</td>
<td>Bidirectional RNN + TimeDistributed</td>
<td>98.5222</td>
<td>134.2463</td>
</tr>
<tr>
<td>Model 5</td>
<td>Deep Bidirectional RNN + TimeDistributed</td>
<td>N/A</td>
<td>N/A</td>
</tr>
<tr>
<td>Model 6</td>
<td>Deep Bidirectional RNN + TimeDistributed with Dropout</td>
<td>N/A</td>
<td>N/A</td>
</tr>
<tr>
<td>Model 7</td>
<td>CNN + RNN + TimeDistributed with Dropout</td>
<td>N/A</td>
<td>N/A</td>
</tr>
</tbody>
</table>
<p>By looking at the Training Loss on the right, we can see that all models except model 0 behaving in the similar way; converging to some sort of optima, switching dramatically around the 3rd epoch. The best model appears to be model_2 over model_4 by a hair, but the unfinished Model 5 seems potentially superior.</p>
<p><strong>Model 0:</strong> A basic RNN (model_0) is the worst of them all, and may as well be gibberish.</p>
<p><strong>Model 1:</strong> A remarkable improvement in performance, through the simple addition of a <code>TimeDistributed(Dense())</code> layer which, when tied to every unit in the RNN sequence and allowed to softmax (and therefore be backpropogated), which can be seen in the doubling of trainable parameters (464K vs 233K), and therefore trainable space of the model. Problem is, it converges quickly onto a pretty bad loss. We need more noise or feature space to get deeper curves.</p>
<p><strong>Model 2:</strong> This time, we slap on a CNN layer at the beginning (before RNN layer), and given what each CNN recognizes in its Spectogram (!), we wrap it into an RNN sequence, densely connect all that to activations, and see what we get. It's importnat to note that this is not a pre-trained CNN looking for image vectors, but rather a simple direct convolution; a <strong>zooming in</strong> on a tiny piece of the spectrogram, filtering and convolving all of its neighbours for features that match over time, and then relating those sequences with the expected sequences from our data. This makes it the most performant model with regard to training and validation loss.</p>
<p><strong>Model 3</strong>: If we stack a series of RNN's together, we get a structure reminiscent of <a href="http://github.com/vvkmnn/touristAI">our last project</a>. Surprisingly, the replacement of CNN feature "zooming" by stacks of bidirectional RNNs sequences should be way way off, but it turns out it actually sort of works. My hypothesis is this has to do with elongating time space for sequences to interact with each other, as opposed to increasing resolution space and zooming on sequences; essentially higher resolution for positive signals using either medium.</p>
<p><strong>Model 4</strong>: Reversing some of these sequences appers to have little effects; actually performing worse in the while. Would've been nice to compare to 5 - 8.</p>
<p>The absence of models 5, 7, and 7 are evidence of Amazon's EC2 pricing, and how annoying it is to keep a Jupyter ssh tunnel open while Tensorflow does its thing. I concede.</p>

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<p><a id='final'></a></p>
<h3 id="Final-Model">Final Model<a class="anchor-link" href="#Final-Model">&#182;</a></h3><p>Now that you've tried out many sample models, use what you've learned to draft your own architecture!  While your final acoustic model should not be identical to any of the architectures explored above, you are welcome to merely combine the explored layers above into a deeper architecture.  It is <strong>NOT</strong> necessary to include new layer types that were not explored in the notebook.</p>
<p>However, if you would like some ideas for even more layer types, check out these ideas for some additional, optional extensions to your model:</p>
<ul>
<li>If you notice your model is overfitting to the training dataset, consider adding <strong>dropout</strong>!  To add dropout to <a href="https://faroit.github.io/keras-docs/1.0.2/layers/recurrent/">recurrent layers</a>, pay special attention to the <code>dropout_W</code> and <code>dropout_U</code> arguments.  This <a href="http://arxiv.org/abs/1512.05287">paper</a> may also provide some interesting theoretical background.</li>
<li>If you choose to include a convolutional layer in your model, you may get better results by working with <strong>dilated convolutions</strong>.  If you choose to use dilated convolutions, make sure that you are able to accurately calculate the length of the acoustic model's output in the <code>model.output_length</code> lambda function.  You can read more about dilated convolutions in Google's <a href="https://arxiv.org/abs/1609.03499">WaveNet paper</a>.  For an example of a speech-to-text system that makes use of dilated convolutions, check out this GitHub <a href="https://github.com/buriburisuri/speech-to-text-wavenet">repository</a>.  You can work with dilated convolutions <a href="https://keras.io/layers/convolutional/">in Keras</a> by paying special attention to the <code>padding</code> argument when you specify a convolutional layer.</li>
<li>If your model makes use of convolutional layers, why not also experiment with adding <strong>max pooling</strong>?  Check out <a href="https://arxiv.org/pdf/1701.02720.pdf">this paper</a> for example architecture that makes use of max pooling in an acoustic model.</li>
<li>So far, you have experimented with a single bidirectional RNN layer.  Consider stacking the bidirectional layers, to produce a <a href="https://www.cs.toronto.edu/~graves/asru_2013.pdf">deep bidirectional RNN</a>!</li>
</ul>
<p>All models that you specify in this repository should have <code>output_length</code> defined as an attribute.  This attribute is a lambda function that maps the (temporal) length of the input acoustic features to the (temporal) length of the output softmax layer.  This function is used in the computation of CTC loss; to see this, look at the <code>add_ctc_loss</code> function in <code>train_utils.py</code>.  To see where the <code>output_length</code> attribute is defined for the models in the code, take a look at the <code>sample_models.py</code> file.  You will notice this line of code within most models:</p>

<pre><code>model.output_length = lambda x: x</code></pre>
<p>The acoustic model that incorporates a convolutional layer (<code>cnn_rnn_model</code>) has a line that is a bit different:</p>

<pre><code>model.output_length = lambda x: cnn_output_length(
        x, kernel_size, conv_border_mode, conv_stride)</code></pre>
<p>In the case of models that use purely recurrent layers, the lambda function is the identity function, as the recurrent layers do not modify the (temporal) length of their input tensors.  However, convolutional layers are more complicated and require a specialized function (<code>cnn_output_length</code> in <code>sample_models.py</code>) to determine the temporal length of their output.</p>
<p>You will have to add the <code>output_length</code> attribute to your final model before running the code cell below.  Feel free to use the <code>cnn_output_length</code> function, if it suits your model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># specify the model</span>
<span class="n">model_end</span> <span class="o">=</span> <span class="n">final_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> 
                        <span class="n">filters</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> 
                        <span class="n">conv_stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                        <span class="n">conv_border_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">,</span> 
                        <span class="n">recur_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> 
                        <span class="n">RNN_type</span><span class="o">=</span><span class="n">GRU</span><span class="p">,</span> 
                        <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> 
                        <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">output_dim</span><span class="o">=</span><span class="mi">29</span><span class="p">)</span> <span class="c1"># 200 Filters of 11x11, </span>
                                       <span class="c1"># with a stride of 2, with &quot;valid&quot; / no padding</span>
                                       <span class="c1"># Using 3 RNN Bidirecional Chains with GRU Units</span>
                                       <span class="c1"># Over 200 Units, with 20% Dropout, in Sequences of 29</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
conv1d (Conv1D)              (None, None, 200)         354400    
_________________________________________________________________
bn_conv_1d (BatchNormalizati (None, None, 200)         800       
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 400)         481200    
_________________________________________________________________
batch_normalization_1 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_2 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_3 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_3 (Batch (None, None, 400)         1600      
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 29)          11629     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 2,295,229
Trainable params: 2,292,429
Non-trainable params: 2,800
_________________________________________________________________
None
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<p>Please execute the code cell below to train the neural network you specified in <code>input_to_softmax</code>.  After the model has finished training, the model is <a href="https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model">saved</a> in the HDF5 file <code>model_end.h5</code>.  The loss history is <a href="https://wiki.python.org/moin/UsingPickle">saved</a> in <code>model_end.pickle</code>.  You are welcome to tweak any of the optional parameters while calling the <code>train_model</code> function, but this is not required.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_model</span><span class="p">(</span><span class="n">input_to_softmax</span><span class="o">=</span><span class="n">model_end</span><span class="p">,</span> 
            <span class="n">pickle_path</span><span class="o">=</span><span class="s1">&#39;model_end.pickle&#39;</span><span class="p">,</span> 
            <span class="n">save_model_path</span><span class="o">=</span><span class="s1">&#39;model_end.h5&#39;</span><span class="p">,</span> 
            <span class="n">spectrogram</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># change to False if you would like to use MFCC features</span>
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<pre>Epoch 1/20
106/106 [==============================] - 515s - loss: 251.6511 - val_loss: 231.2747
Epoch 2/20
106/106 [==============================] - 517s - loss: 224.0173 - val_loss: 205.9121
Epoch 3/20
106/106 [==============================] - 517s - loss: 208.3266 - val_loss: 179.7048
Epoch 4/20
106/106 [==============================] - 517s - loss: 178.5236 - val_loss: 155.6232
Epoch 5/20
106/106 [==============================] - 510s - loss: 157.4867 - val_loss: 138.5522
Epoch 6/20
106/106 [==============================] - 515s - loss: 142.6672 - val_loss: 131.4548
Epoch 7/20
106/106 [==============================] - 518s - loss: 132.5957 - val_loss: 125.0108
Epoch 8/20
106/106 [==============================] - 512s - loss: 124.9007 - val_loss: 117.8693
Epoch 9/20
106/106 [==============================] - 507s - loss: 118.7908 - val_loss: 114.7536
Epoch 10/20
106/106 [==============================] - 509s - loss: 112.8262 - val_loss: 111.7047
Epoch 11/20
106/106 [==============================] - 506s - loss: 108.1205 - val_loss: 108.5606
Epoch 12/20
106/106 [==============================] - 512s - loss: 104.0067 - val_loss: 106.1705
Epoch 13/20
106/106 [==============================] - 502s - loss: 100.3817 - val_loss: 104.7133
Epoch 14/20
106/106 [==============================] - 513s - loss: 96.9186 - val_loss: 103.7046
Epoch 15/20
106/106 [==============================] - 503s - loss: 93.7232 - val_loss: 98.3857
Epoch 16/20
106/106 [==============================] - 511s - loss: 90.6794 - val_loss: 100.1045
Epoch 17/20
106/106 [==============================] - 510s - loss: 88.1375 - val_loss: 99.0563
Epoch 18/20
106/106 [==============================] - 512s - loss: 85.2925 - val_loss: 95.9856
Epoch 19/20
106/106 [==============================] - 509s - loss: 83.0669 - val_loss: 97.0832
Epoch 20/20
106/106 [==============================] - 517s - loss: 80.7469 - val_loss: 97.0541
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<p><strong>Question 2:</strong> Describe your final model architecture and your reasoning at each step.</p>
<p><strong>Answer:</strong></p>
<p>The model consists of 9 layers, depending on how many recursive RNN chains are used (3 here). It borrows heavily from the highly successful <a href="#model2">Model 2</a>.</p>
<p>1) <strong>Input</strong>: An input vector of <em>BatchID, 161</em> (Spectograms)</p>
<p><code>Input(shape=(None, input_dim))</code></p>
<p>2) <strong>Convolution</strong>: N filters, using specified kernel size, stride, and padding, mapping over the image and activating via ReLU</p>
<p><code>Conv1D(filters, kernel_size, 
                     strides=conv_stride, 
                     padding=conv_border_mode,
                     activation='relu',
                     name='conv1d')(input)</code></p>
<p>3) <strong>Batch Normalization</strong>: Noise reduction layer, normalizing outliers</p>
<p><code>BatchNormalization(prev)</code></p>
<p>4-6) <strong>RNN Layers</strong> - A chain of bidirectional RNNs to improve temporal comprehension, returning in sequences, with dropout.</p>
<p><code>for i in range(recur_layers): 
rnn = Bidirectional(RNN_type(units, 
activation='relu',
    return_sequences=True, 
    implementation=2, 
    recurrent_dropout=dropout, 
    dropout=dropout, 
    name='bidir_rnn_{}'.format(i + 1)))(prev)</code></p>
<p>7) <strong>Batch Normalization</strong>: Another Noise reduction layer, normalizing outliers</p>
<p><code>BatchNormalization(prev)</code></p>
<p>8) <strong>Dense Activation</strong>: Relate every sequence in every RNN densely with the next layer.</p>
<p><code>TimeDistributed(Dense(prev))</code></p>
<p>9) <strong>Final Activation</strong>: Pass each densely connected unit to Softmax activation for probability prediction.</p>
<p><code>Activation('softmax')(prev)</code></p>
<p>Then we specify inputs and outputs, pad the uneven sequences, use our pickled hyperparameters, and find predictions!</p>

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<p><a id='step3'></a></p>
<h2 id="STEP-3:-Obtain-Predictions">STEP 3: Obtain Predictions<a class="anchor-link" href="#STEP-3:-Obtain-Predictions">&#182;</a></h2><p>We have written a function for you to decode the predictions of your acoustic model.  To use the function, please execute the code cell below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">data_generator</span> <span class="k">import</span> <span class="n">AudioGenerator</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">backend</span> <span class="k">as</span> <span class="n">K</span>
<span class="kn">from</span> <span class="nn">utils</span> <span class="k">import</span> <span class="n">int_sequence_to_text</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">Audio</span>

<span class="k">def</span> <span class="nf">get_predictions</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">input_to_softmax</span><span class="p">,</span> <span class="n">model_path</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Print a model&#39;s decoded predictions</span>
<span class="sd">    Params:</span>
<span class="sd">        index (int): The example you would like to visualize</span>
<span class="sd">        partition (str): One of &#39;train&#39; or &#39;validation&#39;</span>
<span class="sd">        input_to_softmax (Model): The acoustic model</span>
<span class="sd">        model_path (str): Path to saved acoustic model&#39;s weights</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># load the train and test data</span>
    <span class="n">data_gen</span> <span class="o">=</span> <span class="n">AudioGenerator</span><span class="p">()</span>
    <span class="n">data_gen</span><span class="o">.</span><span class="n">load_train_data</span><span class="p">()</span>
    <span class="n">data_gen</span><span class="o">.</span><span class="n">load_validation_data</span><span class="p">()</span>
    
    <span class="c1"># obtain the true transcription and the audio features </span>
    <span class="k">if</span> <span class="n">partition</span> <span class="o">==</span> <span class="s1">&#39;validation&#39;</span><span class="p">:</span>
        <span class="n">transcr</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">valid_texts</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">audio_path</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">valid_audio_paths</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">data_point</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">data_gen</span><span class="o">.</span><span class="n">featurize</span><span class="p">(</span><span class="n">audio_path</span><span class="p">))</span>
    <span class="k">elif</span> <span class="n">partition</span> <span class="o">==</span> <span class="s1">&#39;train&#39;</span><span class="p">:</span>
        <span class="n">transcr</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">train_texts</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">audio_path</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">train_audio_paths</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">data_point</span> <span class="o">=</span> <span class="n">data_gen</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">data_gen</span><span class="o">.</span><span class="n">featurize</span><span class="p">(</span><span class="n">audio_path</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Invalid partition!  Must be &quot;train&quot; or &quot;validation&quot;&#39;</span><span class="p">)</span>
        
    <span class="c1"># obtain and decode the acoustic model&#39;s predictions</span>
    <span class="n">input_to_softmax</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">model_path</span><span class="p">)</span>
    <span class="n">prediction</span> <span class="o">=</span> <span class="n">input_to_softmax</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
    <span class="n">output_length</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_to_softmax</span><span class="o">.</span><span class="n">output_length</span><span class="p">(</span><span class="n">data_point</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span> 
    <span class="n">pred_ints</span> <span class="o">=</span> <span class="p">(</span><span class="n">K</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="n">K</span><span class="o">.</span><span class="n">ctc_decode</span><span class="p">(</span>
                <span class="n">prediction</span><span class="p">,</span> <span class="n">output_length</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    
    <span class="c1"># play the audio file, and display the true and predicted transcriptions</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
    <span class="n">Audio</span><span class="p">(</span><span class="n">audio_path</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;True transcription:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="n">transcr</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Predicted transcription:</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">int_sequence_to_text</span><span class="p">(</span><span class="n">pred_ints</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
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<p>Use the code cell below to obtain the transcription predicted by your final model for the first example in the training dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">get_predictions</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">partition</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">,</span>
                <span class="n">input_to_softmax</span><span class="o">=</span><span class="n">final_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> 
                        <span class="n">filters</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> 
                        <span class="n">conv_stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                        <span class="n">conv_border_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">,</span> 
                        <span class="n">recur_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> 
                        <span class="n">RNN_type</span><span class="o">=</span><span class="n">GRU</span><span class="p">,</span> 
                        <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> 
                        <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">output_dim</span><span class="o">=</span><span class="mi">29</span><span class="p">),</span> 
                <span class="n">model_path</span><span class="o">=</span><span class="s1">&#39;results/model_end.h5&#39;</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
conv1d (Conv1D)              (None, None, 200)         354400    
_________________________________________________________________
bn_conv_1d (BatchNormalizati (None, None, 200)         800       
_________________________________________________________________
bidirectional_7 (Bidirection (None, None, 400)         481200    
_________________________________________________________________
batch_normalization_7 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_8 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_8 (Batch (None, None, 400)         1600      
_________________________________________________________________
bidirectional_9 (Bidirection (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_9 (Batch (None, None, 400)         1600      
_________________________________________________________________
time_distributed_3 (TimeDist (None, None, 29)          11629     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 2,295,229
Trainable params: 2,292,429
Non-trainable params: 2,800
_________________________________________________________________
None
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True transcription:

the last two days of the voyage bartley found almost intolerable
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Predicted transcription:

the las to das of the voage barttly foned omost in telerble
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<p>Use the next code cell to visualize the model's prediction for the first example in the validation dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">get_predictions</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">partition</span><span class="o">=</span><span class="s1">&#39;validation&#39;</span><span class="p">,</span>
            <span class="n">input_to_softmax</span><span class="o">=</span><span class="n">final_model</span><span class="p">(</span><span class="n">input_dim</span><span class="o">=</span><span class="mi">161</span><span class="p">,</span> 
                        <span class="n">filters</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> 
                        <span class="n">conv_stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                        <span class="n">conv_border_mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">,</span> 
                        <span class="n">recur_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> 
                        <span class="n">RNN_type</span><span class="o">=</span><span class="n">GRU</span><span class="p">,</span> 
                        <span class="n">dropout</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> 
                        <span class="n">units</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> 
                        <span class="n">output_dim</span><span class="o">=</span><span class="mi">29</span><span class="p">),</span> 
                <span class="n">model_path</span><span class="o">=</span><span class="s1">&#39;results/model_end.h5&#39;</span><span class="p">)</span>
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<pre>_________________________________________________________________
Layer (type)                 Output Shape              Param #   
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the_input (InputLayer)       (None, None, 161)         0         
_________________________________________________________________
conv1d (Conv1D)              (None, None, 200)         354400    
_________________________________________________________________
bn_conv_1d (BatchNormalizati (None, None, 200)         800       
_________________________________________________________________
bidirectional_10 (Bidirectio (None, None, 400)         481200    
_________________________________________________________________
batch_normalization_10 (Batc (None, None, 400)         1600      
_________________________________________________________________
bidirectional_11 (Bidirectio (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_11 (Batc (None, None, 400)         1600      
_________________________________________________________________
bidirectional_12 (Bidirectio (None, None, 400)         721200    
_________________________________________________________________
batch_normalization_12 (Batc (None, None, 400)         1600      
_________________________________________________________________
time_distributed_4 (TimeDist (None, None, 29)          11629     
_________________________________________________________________
softmax (Activation)         (None, None, 29)          0         
=================================================================
Total params: 2,295,229
Trainable params: 2,292,429
Non-trainable params: 2,800
_________________________________________________________________
None
--------------------------------------------------------------------------------
True transcription:

out in the woods stood a nice little fir tree
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Predicted transcription:

ondein the wod stood an nice  the hol firtr
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<p>One standard way to improve the results of the decoder is to incorporate a language model.  We won't pursue this in the notebook, but you are welcome to do so as an <em>optional extension</em>.</p>
<p>If you are interested in creating models that provide improved transcriptions, you are encouraged to download <a href="http://www.openslr.org/12/">more data</a> and train bigger, deeper models.  But beware - the model will likely take a long while to train.  For instance, training this <a href="https://arxiv.org/pdf/1512.02595v1.pdf">state-of-the-art</a> model would take 3-6 weeks on a single GPU!</p>

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