C1M1_Assignment.htm

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}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 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;
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<h1 id="Chest-X-Ray-Medical-Diagnosis-with-Deep-Learning">Chest X-Ray Medical Diagnosis with Deep Learning<a class="anchor-link" href="#Chest-X-Ray-Medical-Diagnosis-with-Deep-Learning">&#182;</a></h1>
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<p><img src="xray-header-image.png" style="padding-top: 50px;width: 87%;left: 0px;margin-left: 0px;margin-right: 0px;"></p>
<p><strong>Welcome to the first assignment of course 1!</strong></p>
<p>In this assignment! You will explore medical image diagnosis by building a state-of-the-art chest X-ray classifier using Keras.</p>
<p>The assignment will walk through some of the steps of building and evaluating this deep learning classifier model. In particular, you will:</p>
<ul>
<li>Pre-process and prepare a real-world X-ray dataset</li>
<li>Use transfer learning to retrain a DenseNet model for X-ray image classification</li>
<li>Learn a technique to handle class imbalance</li>
<li>Measure diagnostic performance by computing the AUC (Area Under the Curve) for the ROC (Receiver Operating Characteristic) curve</li>
<li>Visualize model activity using GradCAMs</li>
</ul>
<p>In completing this assignment you will learn about the following topics:</p>
<ul>
<li>Data preparation<ul>
<li>Visualizing data</li>
<li>Preventing data leakage</li>
</ul>
</li>
<li>Model Development<ul>
<li>Addressing class imbalance</li>
<li>Leveraging pre-trained models using transfer learning</li>
</ul>
</li>
<li>Evaluation<ul>
<li>AUC and ROC curves</li>
</ul>
</li>
</ul>

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<h2 id="Outline">Outline<a class="anchor-link" href="#Outline">&#182;</a></h2><p>Use these links to jump to specific sections of this assignment!</p>
<ul>
<li><a href="#1">1. Import Packages and Function</a></li>
<li><a href="#2">2. Load the Datasets</a><ul>
<li><a href="#2-1">2.1 Preventing Data Leakage</a><ul>
<li><a href="#Ex-1">Exercise 1 - Checking Data Leakage</a></li>
</ul>
</li>
<li><a href="#2-2">2.2 Preparing Images</a></li>
</ul>
</li>
<li><a href="#3">3. Model Development</a><ul>
<li><a href="#3-1">3.1 Addressing Class Imbalance</a><ul>
<li><a href="#Ex-2">Exercise 2 - Computing Class Frequencies</a></li>
<li><a href="#Ex-3">Exercise 3 - Weighted Loss</a></li>
</ul>
</li>
<li><a href="#3-3">3.3 DenseNet121</a></li>
</ul>
</li>
<li><a href="#4">4. Training [optional]</a><ul>
<li><a href="#4-1">4.1 Training on the Larger Dataset</a></li>
</ul>
</li>
<li><a href="#5">5. Prediction and Evaluation</a><ul>
<li><a href="#5-1">5.1 ROC Curve and AUROC</a></li>
<li><a href="#5-2">5.2 Visualizing Learning with GradCAM</a></li>
</ul>
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<p><a name='1'></a></p>
<h2 id="1.-Import-Packages-and-Functions&#182;">1. Import Packages and Functions&#182;<a class="anchor-link" href="#1.-Import-Packages-and-Functions&#182;">&#182;</a></h2><p>We'll make use of the following packages:</p>
<ul>
<li><code>numpy</code> and <code>pandas</code> is what we'll use to manipulate our data</li>
<li><code>matplotlib.pyplot</code> and <code>seaborn</code> will be used to produce plots for visualization</li>
<li><code>util</code> will provide the locally defined utility functions that have been provided for this assignment</li>
</ul>
<p>We will also use several modules from the <code>keras</code> framework for building deep learning models.</p>
<p>Run the next cell to import all the necessary packages.</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">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">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="kn">from</span> <span class="nn">keras.preprocessing.image</span> <span class="k">import</span> <span class="n">ImageDataGenerator</span>
<span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="k">import</span> <span class="n">DenseNet121</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Model</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">keras.models</span> <span class="k">import</span> <span class="n">load_model</span>

<span class="kn">import</span> <span class="nn">util</span>
</pre></div>

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<pre>Using TensorFlow backend.
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<p><a name='2'></a></p>
<h2 id="2-Load-the-Datasets">2 Load the Datasets<a class="anchor-link" href="#2-Load-the-Datasets">&#182;</a></h2><p>For this assignment, we will be using the <a href="https://arxiv.org/abs/1705.02315">ChestX-ray8 dataset</a> which contains 108,948 frontal-view X-ray images of 32,717 unique patients.</p>
<ul>
<li>Each image in the data set contains multiple text-mined labels identifying 14 different pathological conditions. </li>
<li>These in turn can be used by physicians to diagnose 8 different diseases. </li>
<li>We will use this data to develop a single model that will provide binary classification predictions for each of the 14 labeled pathologies. </li>
<li>In other words it will predict 'positive' or 'negative' for each of the pathologies.</li>
</ul>
<p>You can download the entire dataset for free <a href="https://nihcc.app.box.com/v/ChestXray-NIHCC">here</a>.</p>
<ul>
<li>We have provided a ~1000 image subset of the images for you.</li>
<li>These can be accessed in the folder path stored in the <code>IMAGE_DIR</code> variable.</li>
</ul>
<p>The dataset includes a CSV file that provides the labels for each X-ray.</p>
<p>To make your job a bit easier, we have processed the labels for our small sample and generated three new files to get you started. These three files are:</p>
<ol>
<li><code>nih/train-small.csv</code>: 875 images from our dataset to be used for training.</li>
<li><code>nih/valid-small.csv</code>: 109 images from our dataset to be used for validation.</li>
<li><code>nih/test.csv</code>: 420 images from our dataset to be used for testing. </li>
</ol>
<p>This dataset has been annotated by consensus among four different radiologists for 5 of our 14 pathologies:</p>
<ul>
<li><code>Consolidation</code></li>
<li><code>Edema</code></li>
<li><code>Effusion</code></li>
<li><code>Cardiomegaly</code></li>
<li><code>Atelectasis</code></li>
</ul>

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<h4 id="Sidebar-on-meaning-of-'class'">Sidebar on meaning of 'class'<a class="anchor-link" href="#Sidebar-on-meaning-of-'class'">&#182;</a></h4><p>It is worth noting that the word <strong>'class'</strong> is used in multiple ways is these discussions.</p>
<ul>
<li>We sometimes refer to each of the 14 pathological conditions that are labeled in our dataset as a class. </li>
<li>But for each of those pathologies we are attempting to predict whether a certain condition is present (i.e. positive result) or absent (i.e. negative result). <ul>
<li>These two possible labels of 'positive' or 'negative' (or the numerical equivalent of 1 or 0) are also typically referred to as classes. </li>
</ul>
</li>
<li>Moreover, we also use the term in reference to software code 'classes' such as <code>ImageDataGenerator</code>.</li>
</ul>
<p>As long as you are aware of all this though, it should not cause you any confusion as the term 'class' is usually clear from the context in which it is used.</p>

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<h4 id="Read-in-the-data">Read in the data<a class="anchor-link" href="#Read-in-the-data">&#182;</a></h4><p>Let's open these files using the <a href="https://pandas.pydata.org/">pandas</a> library</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;nih/train-small.csv&quot;</span><span class="p">)</span>
<span class="n">valid_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;nih/valid-small.csv&quot;</span><span class="p">)</span>

<span class="n">test_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;nih/test.csv&quot;</span><span class="p">)</span>

<span class="n">train_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>Consolidation</th>
      <th>Edema</th>
      <th>Effusion</th>
      <th>Emphysema</th>
      <th>Fibrosis</th>
      <th>Hernia</th>
      <th>Infiltration</th>
      <th>Mass</th>
      <th>Nodule</th>
      <th>PatientId</th>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Cardiomegaly&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Emphysema&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Effusion&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Hernia&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Infiltration&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Mass&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Nodule&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Atelectasis&#39;</span><span class="p">,</span>
          <span class="s1">&#39;Pneumothorax&#39;</span><span class="p">,</span>
          <span class="s1">&#39;Pleural_Thickening&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Pneumonia&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Fibrosis&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Edema&#39;</span><span class="p">,</span> 
          <span class="s1">&#39;Consolidation&#39;</span><span class="p">]</span>
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<p><a name='2-1'></a></p>
<h3 id="2.1-Preventing-Data-Leakage">2.1 Preventing Data Leakage<a class="anchor-link" href="#2.1-Preventing-Data-Leakage">&#182;</a></h3><p>It is worth noting that our dataset contains multiple images for each patient. This could be the case, for example, when a patient has taken multiple X-ray images at different times during their hospital visits. In our data splitting, we have ensured that the split is done on the patient level so that there is no data "leakage" between the train, validation, and test datasets.</p>

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<p><a name='Ex-1'></a></p>
<h3 id="Exercise-1---Checking-Data-Leakage">Exercise 1 - Checking Data Leakage<a class="anchor-link" href="#Exercise-1---Checking-Data-Leakage">&#182;</a></h3><p>In the cell below, write a function to check whether there is leakage between two datasets. We'll use this to make sure there are no patients in the test set that are also present in either the train or validation sets.</p>

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<p><details></p>
<p><summary>
    <font size="3" color="darkgreen"><b>Hints</b></font>
</summary></p>
<p>
<ul>
    <li> Make use of python's set.intersection() function. </li>
    <li> In order to match the automatic grader's expectations, please start the line of code with <code>df1_patients_unique...[continue your code here]</code> </li>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># UNQ_C1 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)</span>
<span class="k">def</span> <span class="nf">check_for_leakage</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">patient_col</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Return True if there any patients are in both df1 and df2.</span>

<span class="sd">    Args:</span>
<span class="sd">        df1 (dataframe): dataframe describing first dataset</span>
<span class="sd">        df2 (dataframe): dataframe describing second dataset</span>
<span class="sd">        patient_col (str): string name of column with patient IDs</span>
<span class="sd">    </span>
<span class="sd">    Returns:</span>
<span class="sd">        leakage (bool): True if there is leakage, otherwise False</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c1">### START CODE HERE (REPLACE INSTANCES OF &#39;None&#39; with your code) ###</span>
    
    <span class="n">df1_patients_unique</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">df1</span><span class="p">[</span><span class="n">patient_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
    <span class="n">df2_patients_unique</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">df2</span><span class="p">[</span><span class="n">patient_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
    
    <span class="n">patients_in_both_groups</span> <span class="o">=</span> <span class="n">df1_patients_unique</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">df2_patients_unique</span><span class="p">)</span> 


    <span class="c1"># leakage contains true if there is patient overlap, otherwise false.</span>
    <span class="n">leakage</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">patients_in_both_groups</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="c1"># boolean (true if there is at least 1 patient in both groups)</span>
    
    <span class="c1">### END CODE HERE ###</span>
    
    <span class="k">return</span> <span class="n">leakage</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test case 1&quot;</span><span class="p">)</span>
<span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;patient_id&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;patient_id&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]})</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;df1&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;df2&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;leakage output: {check_for_leakage(df1, df2, &#39;patient_id&#39;)}&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-------------------------------------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test case 2&quot;</span><span class="p">)</span>
<span class="n">df1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;patient_id&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]})</span>
<span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;patient_id&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]})</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;df1:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;df2:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df2</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;leakage output: {check_for_leakage(df1, df2, &#39;patient_id&#39;)}&quot;</span><span class="p">)</span>
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<pre>test case 1
df1
   patient_id
0           0
1           1
2           2
df2
   patient_id
0           2
1           3
2           4
leakage output: True
-------------------------------------
test case 2
df1:
   patient_id
0           0
1           1
2           2
df2:
   patient_id
0           3
1           4
2           5
leakage output: False
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<h5 id="Expected-output">Expected output<a class="anchor-link" href="#Expected-output">&#182;</a></h5><div class="highlight"><pre><span></span><span class="n">test</span> <span class="n">case</span> <span class="mi">1</span>
<span class="n">df1</span>
   <span class="n">patient_id</span>
<span class="mi">0</span>           <span class="mi">0</span>
<span class="mi">1</span>           <span class="mi">1</span>
<span class="mi">2</span>           <span class="mi">2</span>
<span class="n">df2</span>
   <span class="n">patient_id</span>
<span class="mi">0</span>           <span class="mi">2</span>
<span class="mi">1</span>           <span class="mi">3</span>
<span class="mi">2</span>           <span class="mi">4</span>
<span class="n">leakage</span> <span class="n">output</span><span class="p">:</span> <span class="bp">True</span>
<span class="o">-------------------------------------</span>
<span class="n">test</span> <span class="n">case</span> <span class="mi">2</span>
<span class="n">df1</span><span class="p">:</span>
   <span class="n">patient_id</span>
<span class="mi">0</span>           <span class="mi">0</span>
<span class="mi">1</span>           <span class="mi">1</span>
<span class="mi">2</span>           <span class="mi">2</span>
<span class="n">df2</span><span class="p">:</span>
   <span class="n">patient_id</span>
<span class="mi">0</span>           <span class="mi">3</span>
<span class="mi">1</span>           <span class="mi">4</span>
<span class="mi">2</span>           <span class="mi">5</span>
<span class="n">leakage</span> <span class="n">output</span><span class="p">:</span> <span class="bp">False</span>
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<p>Run the next cell to check if there are patients in both train and test or in both valid and test.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;leakage between train and valid: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">check_for_leakage</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">valid_df</span><span class="p">,</span> <span class="s1">&#39;PatientId&#39;</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;leakage between train and test: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">check_for_leakage</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">test_df</span><span class="p">,</span> <span class="s1">&#39;PatientId&#39;</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;leakage between valid and test: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">check_for_leakage</span><span class="p">(</span><span class="n">valid_df</span><span class="p">,</span> <span class="n">test_df</span><span class="p">,</span> <span class="s1">&#39;PatientId&#39;</span><span class="p">)))</span>
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<pre>leakage between train and valid: True
leakage between train and test: False
leakage between valid and test: False
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<p>If we get <code>True</code> for train:valid &amp; <code>False</code> for train:test &amp; valid:test, then we're ready to start preparing the datasets for training. Remember to always check for data leakage!</p>

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<p><a name='2-2'></a></p>
<h3 id="2.2-Preparing-Images">2.2 Preparing Images<a class="anchor-link" href="#2.2-Preparing-Images">&#182;</a></h3>
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<p>With our dataset splits ready, we can now proceed with setting up our model to consume them.</p>
<ul>
<li>For this we will use the off-the-shelf <a href="https://keras.io/preprocessing/image/">ImageDataGenerator</a> class from the Keras framework, which allows us to build a "generator" for images specified in a dataframe. </li>
<li>This class also provides support for basic data augmentation such as random horizontal flipping of images.</li>
<li>We also use the generator to transform the values in each batch so that their mean is $0$ and their standard deviation is 1. <ul>
<li>This will facilitate model training by standardizing the input distribution. </li>
</ul>
</li>
<li>The generator also converts our single channel X-ray images (gray-scale) to a three-channel format by repeating the values in the image across all channels.<ul>
<li>We will want this because the pre-trained model that we'll use requires three-channel inputs.</li>
</ul>
</li>
</ul>
<p>Since it is mainly a matter of reading and understanding Keras documentation, we have implemented the generator for you. There are a few things to note:</p>
<ol>
<li>We normalize the mean and standard deviation of the data</li>
<li>We shuffle the input after each epoch.</li>
<li>We set the image size to be 320px by 320px</li>
</ol>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_train_generator</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">image_dir</span><span class="p">,</span> <span class="n">x_col</span><span class="p">,</span> <span class="n">y_cols</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">target_w</span> <span class="o">=</span> <span class="mi">320</span><span class="p">,</span> <span class="n">target_h</span> <span class="o">=</span> <span class="mi">320</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Return generator for training set, normalizing using batch</span>
<span class="sd">    statistics.</span>

<span class="sd">    Args:</span>
<span class="sd">      train_df (dataframe): dataframe specifying training data.</span>
<span class="sd">      image_dir (str): directory where image files are held.</span>
<span class="sd">      x_col (str): name of column in df that holds filenames.</span>
<span class="sd">      y_cols (list): list of strings that hold y labels for images.</span>
<span class="sd">      sample_size (int): size of sample to use for normalization statistics.</span>
<span class="sd">      batch_size (int): images per batch to be fed into model during training.</span>
<span class="sd">      seed (int): random seed.</span>
<span class="sd">      target_w (int): final width of input images.</span>
<span class="sd">      target_h (int): final height of input images.</span>
<span class="sd">    </span>
<span class="sd">    Returns:</span>
<span class="sd">        train_generator (DataFrameIterator): iterator over training set</span>
<span class="sd">    &quot;&quot;&quot;</span>        
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;getting train generator...&quot;</span><span class="p">)</span> 
    <span class="c1"># normalize images</span>
    <span class="n">image_generator</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
        <span class="n">samplewise_center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="n">samplewise_std_normalization</span><span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
    
    <span class="c1"># flow from directory with specified batch size</span>
    <span class="c1"># and target image size</span>
    <span class="n">generator</span> <span class="o">=</span> <span class="n">image_generator</span><span class="o">.</span><span class="n">flow_from_dataframe</span><span class="p">(</span>
            <span class="n">dataframe</span><span class="o">=</span><span class="n">df</span><span class="p">,</span>
            <span class="n">directory</span><span class="o">=</span><span class="n">image_dir</span><span class="p">,</span>
            <span class="n">x_col</span><span class="o">=</span><span class="n">x_col</span><span class="p">,</span>
            <span class="n">y_col</span><span class="o">=</span><span class="n">y_cols</span><span class="p">,</span>
            <span class="n">class_mode</span><span class="o">=</span><span class="s2">&quot;raw&quot;</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="n">shuffle</span><span class="p">,</span>
            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
            <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="n">target_w</span><span class="p">,</span><span class="n">target_h</span><span class="p">))</span>
    
    <span class="k">return</span> <span class="n">generator</span>
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<h4 id="Build-a-separate-generator-for-valid-and-test-sets">Build a separate generator for valid and test sets<a class="anchor-link" href="#Build-a-separate-generator-for-valid-and-test-sets">&#182;</a></h4><p>Now we need to build a new generator for validation and testing data.</p>
<p><strong>Why can't we use the same generator as for the training data?</strong></p>
<p>Look back at the generator we wrote for the training data.</p>
<ul>
<li>It normalizes each image <strong>per batch</strong>, meaning that it uses batch statistics. </li>
<li>We should not do this with the test and validation data, since in a real life scenario we don't process incoming images a batch at a time (we process one image at a time). </li>
<li>Knowing the average per batch of test data would effectively give our model an advantage.  <ul>
<li>The model should not have any information about the test data.</li>
</ul>
</li>
</ul>
<p>What we need to do is normalize incoming test data using the statistics <strong>computed from the training set</strong>.</p>
<ul>
<li>We implement this in the function below. </li>
<li>There is one technical note. Ideally, we would want to compute our sample mean and standard deviation using the entire training set. </li>
<li>However, since this is extremely large, that would be very time consuming. </li>
<li>In the interest of time, we'll take a random sample of the dataset and calcualte the sample mean and sample standard deviation.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_test_and_valid_generator</span><span class="p">(</span><span class="n">valid_df</span><span class="p">,</span> <span class="n">test_df</span><span class="p">,</span> <span class="n">train_df</span><span class="p">,</span> <span class="n">image_dir</span><span class="p">,</span> <span class="n">x_col</span><span class="p">,</span> <span class="n">y_cols</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">target_w</span> <span class="o">=</span> <span class="mi">320</span><span class="p">,</span> <span class="n">target_h</span> <span class="o">=</span> <span class="mi">320</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Return generator for validation set and test test set using </span>
<span class="sd">    normalization statistics from training set.</span>

<span class="sd">    Args:</span>
<span class="sd">      valid_df (dataframe): dataframe specifying validation data.</span>
<span class="sd">      test_df (dataframe): dataframe specifying test data.</span>
<span class="sd">      train_df (dataframe): dataframe specifying training data.</span>
<span class="sd">      image_dir (str): directory where image files are held.</span>
<span class="sd">      x_col (str): name of column in df that holds filenames.</span>
<span class="sd">      y_cols (list): list of strings that hold y labels for images.</span>
<span class="sd">      sample_size (int): size of sample to use for normalization statistics.</span>
<span class="sd">      batch_size (int): images per batch to be fed into model during training.</span>
<span class="sd">      seed (int): random seed.</span>
<span class="sd">      target_w (int): final width of input images.</span>
<span class="sd">      target_h (int): final height of input images.</span>
<span class="sd">    </span>
<span class="sd">    Returns:</span>
<span class="sd">        test_generator (DataFrameIterator) and valid_generator: iterators over test set and validation set respectively</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;getting train and valid generators...&quot;</span><span class="p">)</span>
    <span class="c1"># get generator to sample dataset</span>
    <span class="n">raw_train_generator</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">()</span><span class="o">.</span><span class="n">flow_from_dataframe</span><span class="p">(</span>
        <span class="n">dataframe</span><span class="o">=</span><span class="n">train_df</span><span class="p">,</span> 
        <span class="n">directory</span><span class="o">=</span><span class="n">IMAGE_DIR</span><span class="p">,</span> 
        <span class="n">x_col</span><span class="o">=</span><span class="s2">&quot;Image&quot;</span><span class="p">,</span> 
        <span class="n">y_col</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> 
        <span class="n">class_mode</span><span class="o">=</span><span class="s2">&quot;raw&quot;</span><span class="p">,</span> 
        <span class="n">batch_size</span><span class="o">=</span><span class="n">sample_size</span><span class="p">,</span> 
        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> 
        <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="n">target_w</span><span class="p">,</span> <span class="n">target_h</span><span class="p">))</span>
    
    <span class="c1"># get data sample</span>
    <span class="n">batch</span> <span class="o">=</span> <span class="n">raw_train_generator</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
    <span class="n">data_sample</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

    <span class="c1"># use sample to fit mean and std for test set generator</span>
    <span class="n">image_generator</span> <span class="o">=</span> <span class="n">ImageDataGenerator</span><span class="p">(</span>
        <span class="n">featurewise_center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="n">featurewise_std_normalization</span><span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
    
    <span class="c1"># fit generator to sample from training data</span>
    <span class="n">image_generator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data_sample</span><span class="p">)</span>

    <span class="c1"># get test generator</span>
    <span class="n">valid_generator</span> <span class="o">=</span> <span class="n">image_generator</span><span class="o">.</span><span class="n">flow_from_dataframe</span><span class="p">(</span>
            <span class="n">dataframe</span><span class="o">=</span><span class="n">valid_df</span><span class="p">,</span>
            <span class="n">directory</span><span class="o">=</span><span class="n">image_dir</span><span class="p">,</span>
            <span class="n">x_col</span><span class="o">=</span><span class="n">x_col</span><span class="p">,</span>
            <span class="n">y_col</span><span class="o">=</span><span class="n">y_cols</span><span class="p">,</span>
            <span class="n">class_mode</span><span class="o">=</span><span class="s2">&quot;raw&quot;</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
            <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="n">target_w</span><span class="p">,</span><span class="n">target_h</span><span class="p">))</span>

    <span class="n">test_generator</span> <span class="o">=</span> <span class="n">image_generator</span><span class="o">.</span><span class="n">flow_from_dataframe</span><span class="p">(</span>
            <span class="n">dataframe</span><span class="o">=</span><span class="n">test_df</span><span class="p">,</span>
            <span class="n">directory</span><span class="o">=</span><span class="n">image_dir</span><span class="p">,</span>
            <span class="n">x_col</span><span class="o">=</span><span class="n">x_col</span><span class="p">,</span>
            <span class="n">y_col</span><span class="o">=</span><span class="n">y_cols</span><span class="p">,</span>
            <span class="n">class_mode</span><span class="o">=</span><span class="s2">&quot;raw&quot;</span><span class="p">,</span>
            <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
            <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
            <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span>
            <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="n">target_w</span><span class="p">,</span><span class="n">target_h</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">valid_generator</span><span class="p">,</span> <span class="n">test_generator</span>
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<p>With our generator function ready, let's make one generator for our training data and one each of our test and  validation datasets.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">IMAGE_DIR</span> <span class="o">=</span> <span class="s2">&quot;nih/images-small/&quot;</span>
<span class="n">train_generator</span> <span class="o">=</span> <span class="n">get_train_generator</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="s2">&quot;Image&quot;</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="n">valid_generator</span><span class="p">,</span> <span class="n">test_generator</span><span class="o">=</span> <span class="n">get_test_and_valid_generator</span><span class="p">(</span><span class="n">valid_df</span><span class="p">,</span> <span class="n">test_df</span><span class="p">,</span> <span class="n">train_df</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="s2">&quot;Image&quot;</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
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<pre>getting train generator...
Found 1000 validated image filenames.
getting train and valid generators...
Found 1000 validated image filenames.
Found 200 validated image filenames.
Found 420 validated image filenames.
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<p>Let's peek into what the generator gives our model during training and validation. We can do this by calling the <code>__get_item__(index)</code> function:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">train_generator</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
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<pre>Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
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<h2 id="3-Model-Development">3 Model Development<a class="anchor-link" href="#3-Model-Development">&#182;</a></h2><p>Now we'll move on to model training and development. We have a few practical challenges to deal with before actually training a neural network, though. The first is class imbalance.</p>

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<h3 id="3.1-Addressing-Class-Imbalance">3.1 Addressing Class Imbalance<a class="anchor-link" href="#3.1-Addressing-Class-Imbalance">&#182;</a></h3><p>One of the challenges with working with medical diagnostic datasets is the large class imbalance present in such datasets. Let's plot the frequency of each of the labels in our dataset:</p>

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<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">labels</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">train_generator</span><span class="o">.</span><span class="n">labels</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">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Frequency of Each Class&quot;</span><span class="p">)</span>
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<p>We can see from this plot that the prevalance of positive cases varies significantly across the different pathologies. (These trends mirror the ones in the full dataset as well.)</p>
<ul>
<li>The <code>Hernia</code> pathology has the greatest imbalance with the proportion of positive training cases being about 0.2%. </li>
<li>But even the <code>Infiltration</code> pathology, which has the least amount of imbalance, has only 17.5% of the training cases labelled positive.</li>
</ul>
<p>Ideally, we would train our model using an evenly balanced dataset so that the positive and negative training cases would contribute equally to the loss.</p>
<p>If we use a normal cross-entropy loss function with a highly unbalanced dataset, as we are seeing here, then the algorithm will be incentivized to prioritize the majority class (i.e negative in our case), since it contributes more to the loss.</p>

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<h4 id="Impact-of-class-imbalance-on-loss-function">Impact of class imbalance on loss function<a class="anchor-link" href="#Impact-of-class-imbalance-on-loss-function">&#182;</a></h4><p>Let's take a closer look at this. Assume we would have used a normal cross-entropy loss for each pathology. We recall that the cross-entropy loss contribution from the $i^{th}$ training data case is:</p>
<p>$$\mathcal{L}_{cross-entropy}(x_i) = -(y_i \log(f(x_i)) + (1-y_i) \log(1-f(x_i))),$$</p>
<p>where $x_i$ and $y_i$ are the input features and the label, and $f(x_i)$ is the output of the model, i.e. the probability that it is positive.</p>
<p>Note that for any training case, either $y_i=0$ or else $(1-y_i)=0$, so only one of these terms contributes to the loss (the other term is multiplied by zero, and becomes zero).</p>
<p>We can rewrite the overall average cross-entropy loss over the entire training set $\mathcal{D}$ of size $N$ as follows:</p>
<p>$$\mathcal{L}_{cross-entropy}(\mathcal{D}) = - \frac{1}{N}\big( \sum_{\text{positive examples}} \log (f(x_i)) + \sum_{\text{negative examples}} \log(1-f(x_i)) \big).$$</p>
<p>Using this formulation, we can see that if there is a large imbalance with very few positive training cases, for example, then the loss will be dominated by the negative class. Summing the contribution over all the training cases for each class (i.e. pathological condition), we see that the contribution of each class (i.e. positive or negative) is:</p>
<p>$$freq_{p} = \frac{\text{number of positive examples}}{N} $$</p>
<p>$$\text{and}$$</p>
<p>$$freq_{n} = \frac{\text{number of negative examples}}{N}.$$</p>

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<p><a name='Ex-2'></a></p>
<h3 id="Exercise-2---Computing-Class-Frequencies">Exercise 2 - Computing Class Frequencies<a class="anchor-link" href="#Exercise-2---Computing-Class-Frequencies">&#182;</a></h3><p>Complete the function below to calculate these frequences for each label in our dataset.</p>

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<p><details></p>
<p><summary>
    <font size="3" color="darkgreen"><b>Hints</b></font>
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<p>
<ul>
    <li> Use numpy.sum(a, axis=), and choose the axis (0 or 1) </li>
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<div class="prompt input_prompt">In&nbsp;[13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># UNQ_C2 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)</span>
<span class="k">def</span> <span class="nf">compute_class_freqs</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Compute positive and negative frequences for each class.</span>

<span class="sd">    Args:</span>
<span class="sd">        labels (np.array): matrix of labels, size (num_examples, num_classes)</span>
<span class="sd">    Returns:</span>
<span class="sd">        positive_frequencies (np.array): array of positive frequences for each</span>
<span class="sd">                                         class, size (num_classes)</span>
<span class="sd">        negative_frequencies (np.array): array of negative frequences for each</span>
<span class="sd">                                         class, size (num_classes)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1">### START CODE HERE (REPLACE INSTANCES OF &#39;None&#39; with your code) ###</span>
    
    <span class="c1"># total number of patients (rows)</span>
    <span class="n">N</span> <span class="o">=</span> <span class="n">labels</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">positive_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">/</span> <span class="n">labels</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">negative_frequencies</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">positive_frequencies</span>

    <span class="c1">### END CODE HERE ###</span>
    <span class="k">return</span> <span class="n">positive_frequencies</span><span class="p">,</span> <span class="n">negative_frequencies</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test</span>
<span class="n">labels_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
    <span class="p">[[</span><span class="mi">1</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="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
<span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;labels:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">labels_matrix</span><span class="p">)</span>

<span class="n">test_pos_freqs</span><span class="p">,</span> <span class="n">test_neg_freqs</span> <span class="o">=</span> <span class="n">compute_class_freqs</span><span class="p">(</span><span class="n">labels_matrix</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;pos freqs: </span><span class="si">{test_pos_freqs}</span><span class="s2">&quot;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;neg freqs: </span><span class="si">{test_neg_freqs}</span><span class="s2">&quot;</span><span class="p">)</span>
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<pre>labels:
[[1 0 0]
 [0 1 1]
 [1 0 1]
 [1 1 1]
 [1 0 1]]
pos freqs: [0.8 0.4 0.8]
neg freqs: [0.2 0.6 0.2]
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<h5 id="Expected-output">Expected output<a class="anchor-link" href="#Expected-output">&#182;</a></h5><div class="highlight"><pre><span></span><span class="n">labels</span><span class="p">:</span>
<span class="p">[[</span><span class="mi">1</span> <span class="mi">0</span> <span class="mi">0</span><span class="p">]</span>
 <span class="p">[</span><span class="mi">0</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
 <span class="p">[</span><span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span><span class="p">]</span>
 <span class="p">[</span><span class="mi">1</span> <span class="mi">1</span> <span class="mi">1</span><span class="p">]</span>
 <span class="p">[</span><span class="mi">1</span> <span class="mi">0</span> <span class="mi">1</span><span class="p">]]</span>
<span class="n">pos</span> <span class="n">freqs</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.8</span> <span class="mf">0.4</span> <span class="mf">0.8</span><span class="p">]</span>
<span class="n">neg</span> <span class="n">freqs</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span> <span class="mf">0.6</span> <span class="mf">0.2</span><span class="p">]</span>
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<p>Now we'll compute frequencies for our training data.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">freq_pos</span><span class="p">,</span> <span class="n">freq_neg</span> <span class="o">=</span> <span class="n">compute_class_freqs</span><span class="p">(</span><span class="n">train_generator</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
<span class="n">freq_pos</span>
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<pre>array([0.02 , 0.013, 0.128, 0.002, 0.175, 0.045, 0.054, 0.106, 0.038,
       0.021, 0.01 , 0.014, 0.016, 0.033])</pre>
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<p>Let's visualize these two contribution ratios next to each other for each of the pathologies:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;Class&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">,</span> <span class="s2">&quot;Label&quot;</span><span class="p">:</span> <span class="s2">&quot;Positive&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">freq_pos</span><span class="p">})</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">([{</span><span class="s2">&quot;Class&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">[</span><span class="n">l</span><span class="p">],</span> <span class="s2">&quot;Label&quot;</span><span class="p">:</span> <span class="s2">&quot;Negative&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">v</span><span class="p">}</span> <span class="k">for</span> <span class="n">l</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">freq_neg</span><span class="p">)],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">f</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Class&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;Label&quot;</span> <span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">)</span>
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<p>As we see in the above plot, the contributions of positive cases is significantly lower than that of the negative ones. However, we want the contributions to be equal. One way of doing this is by multiplying each example from each class by a class-specific weight factor, $w_{pos}$ and $w_{neg}$, so that the overall contribution of each class is the same.</p>
<p>To have this, we want</p>
<p>$$w_{pos} \times freq_{p} = w_{neg} \times freq_{n},$$</p>
<p>which we can do simply by taking</p>
<p>$$w_{pos} = freq_{neg}$$
$$w_{neg} = freq_{pos}$$</p>
<p>This way, we will be balancing the contribution of positive and negative labels.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pos_weights</span> <span class="o">=</span> <span class="n">freq_neg</span>
<span class="n">neg_weights</span> <span class="o">=</span> <span class="n">freq_pos</span>
<span class="n">pos_contribution</span> <span class="o">=</span> <span class="n">freq_pos</span> <span class="o">*</span> <span class="n">pos_weights</span> 
<span class="n">neg_contribution</span> <span class="o">=</span> <span class="n">freq_neg</span> <span class="o">*</span> <span class="n">neg_weights</span>
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<p>Let's verify this by graphing the two contributions next to each other again:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;Class&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">,</span> <span class="s2">&quot;Label&quot;</span><span class="p">:</span> <span class="s2">&quot;Positive&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">pos_contribution</span><span class="p">})</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">([{</span><span class="s2">&quot;Class&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">[</span><span class="n">l</span><span class="p">],</span> <span class="s2">&quot;Label&quot;</span><span class="p">:</span> <span class="s2">&quot;Negative&quot;</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">v</span><span class="p">}</span> 
                        <span class="k">for</span> <span class="n">l</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">neg_contribution</span><span class="p">)],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s2">&quot;Class&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;Value&quot;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s2">&quot;Label&quot;</span> <span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">);</span>
</pre></div>

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<p>As the above figure shows, by applying these weightings the positive and negative labels within each class would have the same aggregate contribution to the loss function. Now let's implement such a loss function.</p>
<p>After computing the weights, our final weighted loss for each training case will be</p>
<p>$$\mathcal{L}_{cross-entropy}^{w}(x) = - (w_{p} y \log(f(x)) + w_{n}(1-y) \log( 1 - f(x) ) ).$$</p>

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<p><a name='Ex-3'></a></p>
<h3 id="Exercise-3---Weighted-Loss">Exercise 3 - Weighted Loss<a class="anchor-link" href="#Exercise-3---Weighted-Loss">&#182;</a></h3><p>Fill out the <code>weighted_loss</code> function below to return a loss function that calculates the weighted loss for each batch. Recall that for the multi-class loss, we add up the average loss for each individual class. Note that we also want to add a small value, $\epsilon$, to the predicted values before taking their logs. This is simply to avoid a numerical error that would otherwise occur if the predicted value happens to be zero.</p>
<h5 id="Note">Note<a class="anchor-link" href="#Note">&#182;</a></h5><p>Please use Keras functions to calculate the mean and the log.</p>
<ul>
<li><a href="https://www.tensorflow.org/api_docs/python/tf/keras/backend/mean">Keras.mean</a></li>
<li><a href="https://www.tensorflow.org/api_docs/python/tf/keras/backend/log">Keras.log</a></li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># UNQ_C3 (UNIQUE CELL IDENTIFIER, DO NOT EDIT)</span>
<span class="k">def</span> <span class="nf">get_weighted_loss</span><span class="p">(</span><span class="n">pos_weights</span><span class="p">,</span> <span class="n">neg_weights</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mf">1e-7</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Return weighted loss function given negative weights and positive weights.</span>

<span class="sd">    Args:</span>
<span class="sd">      pos_weights (np.array): array of positive weights for each class, size (num_classes)</span>
<span class="sd">      neg_weights (np.array): array of negative weights for each class, size (num_classes)</span>
<span class="sd">    </span>
<span class="sd">    Returns:</span>
<span class="sd">      weighted_loss (function): weighted loss function</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">weighted_loss</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Return weighted loss value. </span>

<span class="sd">        Args:</span>
<span class="sd">            y_true (Tensor): Tensor of true labels, size is (num_examples, num_classes)</span>
<span class="sd">            y_pred (Tensor): Tensor of predicted labels, size is (num_examples, num_classes)</span>
<span class="sd">        Returns:</span>
<span class="sd">            loss (float): overall scalar loss summed across all classes</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># initialize loss to zero</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        
        <span class="c1">### START CODE HERE (REPLACE INSTANCES OF &#39;None&#39; with your code) ###</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">pos_weights</span><span class="p">)):</span>
            <span class="c1"># for each class, add average weighted loss for that class </span>
            
            <span class="n">loss</span> <span class="o">+=</span> <span class="o">-</span><span class="p">(</span><span class="n">K</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">pos_weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">y_true</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">K</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">y_pred</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span> <span class="o">+</span> \
                                <span class="n">neg_weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">y_true</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">),</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">))</span>
        
        <span class="k">return</span> <span class="n">loss</span>
    
        <span class="c1">### END CODE HERE ###</span>
    <span class="k">return</span> <span class="n">weighted_loss</span>
 
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<p>Now let's test our function with some simple cases.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test</span>
<span class="n">sess</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">get_session</span><span class="p">()</span>
<span class="k">with</span> <span class="n">sess</span><span class="o">.</span><span class="n">as_default</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test example:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="n">y_true</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
        <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
         <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
         <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
         <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
    <span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;y_true:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">y_true</span><span class="o">.</span><span class="n">eval</span><span class="p">())</span>

    <span class="n">w_p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">])</span>
    <span class="n">w_n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">w_p:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">w_p</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">w_n:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">w_n</span><span class="p">)</span>

    <span class="n">y_pred_1</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.7</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">y_true</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">y_pred_1:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">y_pred_1</span><span class="o">.</span><span class="n">eval</span><span class="p">())</span>

    <span class="n">y_pred_2</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">y_true</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">y_pred_2:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">y_pred_2</span><span class="o">.</span><span class="n">eval</span><span class="p">())</span>

    <span class="c1"># test with a large epsilon in order to catch errors</span>
    <span class="n">L</span> <span class="o">=</span> <span class="n">get_weighted_loss</span><span class="p">(</span><span class="n">w_p</span><span class="p">,</span> <span class="n">w_n</span><span class="p">,</span> <span class="n">epsilon</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">If we weighted them correctly, we expect the two losses to be the same.&quot;</span><span class="p">)</span>
    <span class="n">L1</span> <span class="o">=</span> <span class="n">L</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_1</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="n">L2</span> <span class="o">=</span> <span class="n">L</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_2</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">L(y_pred_1)= </span><span class="si">{L1:.4f}</span><span class="s2">, L(y_pred_2)= </span><span class="si">{L2:.4f}</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Difference is L1 - L2 = {L1 - L2:.4f}&quot;</span><span class="p">)</span>
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<pre>Test example:

y_true:

[[1. 1. 1.]
 [1. 1. 0.]
 [0. 1. 0.]
 [1. 0. 1.]]

w_p:

[0.25 0.25 0.5 ]

w_n:

[0.75 0.75 0.5 ]

y_pred_1:

[[0.7 0.7 0.7]
 [0.7 0.7 0.7]
 [0.7 0.7 0.7]
 [0.7 0.7 0.7]]

y_pred_2:

[[0.3 0.3 0.3]
 [0.3 0.3 0.3]
 [0.3 0.3 0.3]
 [0.3 0.3 0.3]]

If we weighted them correctly, we expect the two losses to be the same.

L(y_pred_1)= -0.4956, L(y_pred_2)= -0.4956
Difference is L1 - L2 = 0.0000
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<h4 id="Additional-check">Additional check<a class="anchor-link" href="#Additional-check">&#182;</a></h4><p>If you implemented the function correctly, then if the epsilon for the <code>get_weighted_loss</code> is set to <code>1</code>, the weighted losses will be as follows:</p>
<div class="highlight"><pre><span></span><span class="n">L</span><span class="p">(</span><span class="n">y_pred_1</span><span class="p">)</span><span class="o">=</span> <span class="o">-</span><span class="mf">0.4956</span><span class="p">,</span> <span class="n">L</span><span class="p">(</span><span class="n">y_pred_2</span><span class="p">)</span><span class="o">=</span> <span class="o">-</span><span class="mf">0.4956</span>
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<p>If you are missing something in your implementation, you will see a different set of losses for L1 and L2 (even though L1 and L2 will be the same).</p>

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<p><a name='3-3'></a></p>
<h3 id="3.3-DenseNet121">3.3 DenseNet121<a class="anchor-link" href="#3.3-DenseNet121">&#182;</a></h3><p>Next, we will use a pre-trained <a href="https://www.kaggle.com/pytorch/densenet121">DenseNet121</a> model which we can load directly from Keras and then add two layers on top of it:</p>
<ol>
<li>A <code>GlobalAveragePooling2D</code> layer to get the average of the last convolution layers from DenseNet121.</li>
<li>A <code>Dense</code> layer with <code>sigmoid</code> activation to get the prediction logits for each of our classes.</li>
</ol>
<p>We can set our custom loss function for the model by specifying the <code>loss</code> parameter in the <code>compile()</code> function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the base pre-trained model</span>
<span class="n">base_model</span> <span class="o">=</span> <span class="n">DenseNet121</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;./nih/densenet.hdf5&#39;</span><span class="p">,</span> <span class="n">include_top</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">base_model</span><span class="o">.</span><span class="n">output</span>

<span class="c1"># add a global spatial average pooling layer</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">GlobalAveragePooling2D</span><span class="p">()(</span><span class="n">x</span><span class="p">)</span>

<span class="c1"># and a logistic layer</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;sigmoid&quot;</span><span class="p">)(</span><span class="n">x</span><span class="p">)</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">inputs</span><span class="o">=</span><span class="n">base_model</span><span class="o">.</span><span class="n">input</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">predictions</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">get_weighted_loss</span><span class="p">(</span><span class="n">pos_weights</span><span class="p">,</span> <span class="n">neg_weights</span><span class="p">))</span>
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<pre>WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4074: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.

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<p><a name='4'></a></p>
<h2 id="4-Training-[optional]">4 Training [optional]<a class="anchor-link" href="#4-Training-[optional]">&#182;</a></h2><p>With our model ready for training, we will use the <code>model.fit()</code> function in Keras to train our model.</p>
<ul>
<li>We are training on a small subset of the dataset (~1%).  </li>
<li>So what we care about at this point is to make sure that the loss on the training set is decreasing.</li>
</ul>
<p>Since training can take a considerable time, for pedagogical purposes we have chosen not to train the model here but rather to load a set of pre-trained weights in the next section. However, you can use the code shown below to practice training the model locally on your machine or in Colab.</p>
<p><strong>NOTE:</strong> Do not run the code below on the Coursera platform as it will exceed the platform's memory limitations.</p>
<p>Python Code for training the model:</p>
<div class="highlight"><pre><span></span><span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit_generator</span><span class="p">(</span><span class="n">train_generator</span><span class="p">,</span> 
                              <span class="n">validation_data</span><span class="o">=</span><span class="n">valid_generator</span><span class="p">,</span>
                              <span class="n">steps_per_epoch</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> 
                              <span class="n">validation_steps</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> 
                              <span class="n">epochs</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#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="s2">&quot;loss&quot;</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="s2">&quot;epoch&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Training Loss Curve&quot;</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><a name='4-1'></a></p>
<h3 id="4.1-Training-on-the-Larger-Dataset">4.1 Training on the Larger Dataset<a class="anchor-link" href="#4.1-Training-on-the-Larger-Dataset">&#182;</a></h3><p>Given that the original dataset is 40GB+ in size and the training process on the full dataset takes a few hours, we have trained the model on a GPU-equipped machine for you and provided the weights file from our model (with a batch size of 32 instead) to be used for the rest of this assignment.</p>
<p>The model architecture for our pre-trained model is exactly the same, but we used a few useful Keras "callbacks" for this training. Do spend time to read about these callbacks at your leisure as they will be very useful for managing long-running training sessions:</p>
<ol>
<li>You can use <code>ModelCheckpoint</code> callback to monitor your model's <code>val_loss</code> metric and keep a snapshot of your model at the point. </li>
<li>You can use the <code>TensorBoard</code> to use the Tensorflow Tensorboard utility to monitor your runs in real-time. </li>
<li>You can use the <code>ReduceLROnPlateau</code> to slowly decay the learning rate for your model as it stops getting better on a metric such as <code>val_loss</code> to fine-tune the model in the final steps of training.</li>
<li>You can use the <code>EarlyStopping</code> callback to stop the training job when your model stops getting better in it's validation loss. You can set a <code>patience</code> value which is the number of epochs the model does not improve after which the training is terminated. This callback can also conveniently restore the weights for the best metric at the end of training to your model.</li>
</ol>
<p>You can read about these callbacks and other useful Keras callbacks <a href="https://keras.io/callbacks/">here</a>.</p>
<p>Let's load our pre-trained weights into the model now:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s2">&quot;./nih/pretrained_model.h5&quot;</span><span class="p">)</span>
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<h2 id="5-Prediction-and-Evaluation">5 Prediction and Evaluation<a class="anchor-link" href="#5-Prediction-and-Evaluation">&#182;</a></h2>
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<p>Now that we have a model, let's evaluate it using our test set. We can conveniently use the <code>predict_generator</code> function to generate the predictions for the images in our test set.</p>
<p><strong>Note:</strong> The following cell can take about 4 minutes to run.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predicted_vals</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_generator</span><span class="p">(</span><span class="n">test_generator</span><span class="p">,</span> <span class="n">steps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_generator</span><span class="p">))</span>
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<h3 id="5.1-ROC-Curve-and-AUROC">5.1 ROC Curve and AUROC<a class="anchor-link" href="#5.1-ROC-Curve-and-AUROC">&#182;</a></h3><p>We'll cover topic of model evaluation in much more detail in later weeks, but for now we'll walk through computing a metric called the AUC (Area Under the Curve) from the ROC (<a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic">Receiver Operating Characteristic</a>) curve. This is also referred to as the AUROC value, but you will see all three terms in reference to the technique, and often used almost interchangeably.</p>
<p>For now, what you need to know in order to interpret the plot is that a curve that is more to the left and the top has more "area" under it, and indicates that the model is performing better.</p>
<p>We will use the <code>util.get_roc_curve()</code> function which has been provided for you in <code>util.py</code>. Look through this function and note the use of the <code>sklearn</code> library functions to generate the ROC curves and AUROC values for our model.</p>
<ul>
<li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html">roc_curve</a></li>
<li><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html">roc_auc_score</a></li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">auc_rocs</span> <span class="o">=</span> <span class="n">util</span><span class="o">.</span><span class="n">get_roc_curve</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">predicted_vals</span><span class="p">,</span> <span class="n">test_generator</span><span class="p">)</span>
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<p>You can compare the performance to the AUCs reported in the original ChexNeXt paper in the table below:</p>

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<p>For reference, here's the AUC figure from the ChexNeXt paper which includes AUC values for their model as well as radiologists on this dataset:</p>
<p><img src="https://journals.plos.org/plosmedicine/article/figure/image?size=large&id=10.1371/journal.pmed.1002686.t001" width="80%"></p>
<p>This method does take advantage of a few other tricks such as self-training and ensembling as well, which can give a significant boost to the performance.</p>

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<p>For details about the best performing methods and their performance on this dataset, we encourage you to read the following papers:</p>
<ul>
<li><a href="https://arxiv.org/abs/1711.05225">CheXNet</a></li>
<li><a href="https://arxiv.org/pdf/1901.07031.pdf">CheXpert</a></li>
<li><a href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002686">ChexNeXt</a></li>
</ul>

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<p><a name='5-2'></a></p>
<h3 id="5.2-Visualizing-Learning-with-GradCAM">5.2 Visualizing Learning with GradCAM<a class="anchor-link" href="#5.2-Visualizing-Learning-with-GradCAM">&#182;</a></h3>
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<p>One of the challenges of using deep learning in medicine is that the complex architecture used for neural networks makes them much harder to interpret compared to traditional machine learning models (e.g. linear models).</p>
<p>One of the most common approaches aimed at increasing the interpretability of models for computer vision tasks is to use Class Activation Maps (CAM).</p>
<ul>
<li>Class activation maps are useful for understanding where the model is "looking" when classifying an image. </li>
</ul>
<p>In this section we will use a <a href="https://arxiv.org/abs/1610.02391">GradCAM's</a> technique to produce a heatmap highlighting the important regions in the image for predicting the pathological condition.</p>
<ul>
<li>This is done by extracting the gradients of each predicted class, flowing into our model's final convolutional layer. Look at the <code>util.compute_gradcam</code> which has been provided for you in <code>util.py</code> to see how this is done with the Keras framework. </li>
</ul>
<p>It is worth mentioning that GradCAM does not provide a full explanation of the reasoning for each classification probability.</p>
<ul>
<li>However, it is still a useful tool for "debugging" our model and augmenting our prediction so that an expert could validate that a prediction is indeed due to the model focusing on the right regions of the image.</li>
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<p>First we will load the small training set and setup to look at the 4 classes with the highest performing AUC measures.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;nih/train-small.csv&quot;</span><span class="p">)</span>
<span class="n">IMAGE_DIR</span> <span class="o">=</span> <span class="s2">&quot;nih/images-small/&quot;</span>

<span class="c1"># only show the lables with top 4 AUC</span>
<span class="n">labels_to_show</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">auc_rocs</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">4</span><span class="p">]</span>
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<p>Now let's look at a few specific images.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">util</span><span class="o">.</span><span class="n">compute_gradcam</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;00008270_015.png&#39;</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">labels_to_show</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">util</span><span class="o">.</span><span class="n">compute_gradcam</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;00011355_002.png&#39;</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">labels_to_show</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">util</span><span class="o">.</span><span class="n">compute_gradcam</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;00029855_001.png&#39;</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">labels_to_show</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">util</span><span class="o">.</span><span class="n">compute_gradcam</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;00005410_000.png&#39;</span><span class="p">,</span> <span class="n">IMAGE_DIR</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">labels_to_show</span><span class="p">)</span>
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<p>Congratulations, you've completed the first assignment of course one! You've learned how to preprocess data, check for data leakage, train a pre-trained model, and evaluate using the AUC. Great work!</p>

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