Assignment4-TransferLearning/Assignment4.html

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<head><meta charset="utf-8" />

<title>Assignment4</title>

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

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

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

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

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

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

    </style>


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

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
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    page-break-inside: avoid;
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<h1 id="Advanced-Machine-Learning:-Assignment-4">Advanced Machine Learning: Assignment 4<a class="anchor-link" href="#Advanced-Machine-Learning:-Assignment-4">&#182;</a></h1><p>Nome e cognome: Confalonieri Riccardo <br>
Matricola: 830404<br>
E-mail: r.confalonieri5@campus.unimib.it</p>

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<h2 id="Transfer-Learning">Transfer Learning<a class="anchor-link" href="#Transfer-Learning">&#182;</a></h2><p><p align = 'justify'>
Implement the transfer learning approach using a CNN pretrained on ImageNet, the suggested architecture is the VGG16. The CNN should be used as fixed feature extractor on a new task of your choice containing a number of classes in the range from $2$ to $10$, in particular it is required to consider threed different cutting point and discuss the results for each choice.  <br></p>
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Il dataset utilizzato per svolgere questo assignment è stato preso da <a href='https://www.kaggle.com/puneet6060/intel-image-classification'>Kaggle</a> e contiene $6$ differenti classi, alcune delle quali presenti anche nel task originale di imagenet (e.g. sea, mountain):
<ol>
<li>buildings </li>
<li> forest</li>
<li>glacier</li>
<li>mountain</li>
<li>sea</li>
<li>street</li>
</ol>

In particolare il dataset è suddiviso in tre cartelle: training, test e prediction le prime due sono composte a loro volta da sottocartelle diverse il cui nome corrisponde alla label dell'immagine. Si utilizzerà quindi un opportuno data loader per caricare in memoria queste immagini, si segnala che la cartella prediction non verrà utilizzata in quanto contiene immagini senza la corrispettiva label e quindi non è utile ai fini dell'esercizio in quanto non si possono valutare le predizioni. <br>
Si segnala inoltre che il dataset contiene $14k+$ per il traning e $3k+$ per il test, ognuna di dimension $150x150$. Per semplificare il task e velocizzare la classificazione <i>soltanto una piccola parte di queste immagini verrà effettivamente caricata</i>, verranno utilizzate circa $500$ immagini per classe nel traninig set e $300$ per classe nel test set. Questo accorgimento permette inoltre di gestire meglio la memoria e velocizzare anche la predizione sfruttando i layer del modello VGG16.

Per quanto riguarda la fase di predizione si è optato per utilizzare un classificatore semplice, una SVM con kernel rbf. In questo modo si è evitato di trainare nuove reti neurali ad hoc che potrebbero essere fin troppo performanti rispetto al task che vogliamo risolvere e quindi non evidenziare differenze rispetto ai punti di cut scelti.
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<h3 id="Import-packages">Import packages<a class="anchor-link" href="#Import-packages">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># for get data folder</span>
<span class="kn">from</span> <span class="nn">google.colab</span> <span class="k">import</span> <span class="n">drive</span>
<span class="c1"># keras</span>
<span class="kn">from</span> <span class="nn">tensorflow</span> <span class="k">import</span> <span class="n">keras</span>

<span class="c1"># pre trained model</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.applications.vgg16</span> <span class="k">import</span> <span class="n">VGG16</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.applications.vgg16</span> <span class="k">import</span> <span class="n">preprocess_input</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.models</span> <span class="k">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span>

<span class="c1"># Classification tools</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span>
<span class="c1"># for dimensionality reduction</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>

<span class="c1"># for loading and unzip dataset</span>
<span class="kn">import</span> <span class="nn">zipfile</span>
<span class="kn">from</span> <span class="nn">shutil</span> <span class="k">import</span> <span class="n">copyfile</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="c1"># for visualization</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span><span class="p">;</span> <span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1"># for evaluation</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="kn">import</span> <span class="nn">time</span>
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<h3 id="Loading-data">Loading data<a class="anchor-link" href="#Loading-data">&#182;</a></h3><p align='justify'>
<b> Disclaimer: </b> Il modo in cui ho importato il dataset non è sicuramente il migliore in quanto carica subito tutte le immagini in memoria, sarebbe stato meglio utilizzare metodi alternativi che caricando i dati in modo più opportuno. Si è scelta questa opzione per semplicità e velocità nell'eseguire le manipolazioni successive sui dati. <br>

Carico il dataset da google drive utilizzando delle librerie apposite per copiare e unzippare il file zip.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Mount drive and set folder path</span>
<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">&#39;/content/drive&#39;</span><span class="p">)</span>

<span class="n">drive_path</span> <span class="o">=</span> <span class="s1">&#39;/content/drive/MyDrive/Colab Notebooks/AML/Assignment/Assignment4/&#39;</span>
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<pre>Mounted at /content/drive
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># loading dataset and unzipping into the root</span>
<span class="n">copyfile</span><span class="p">(</span><span class="n">drive_path</span> <span class="o">+</span> <span class="s1">&#39;archive.zip&#39;</span><span class="p">,</span> <span class="s1">&#39;archive.zip&#39;</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="s2">&quot;archive.zip&quot;</span><span class="p">,</span><span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zip_ref</span><span class="p">:</span>
    <span class="n">zip_ref</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="s2">&quot;archive&quot;</span><span class="p">)</span>
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<p align='justify'>
Per caricare le immagini definisco una funzione apposita che ritorna la lista di immagini e la corrispondente lista di label. Si poteva utilizzare anche la funzione preposta di Keras per caricare questo tipo di struttura, tuttavia in questo caso le immagini sarebbero state di tipo proprietario e quindi sarebbe stato necessario applicare ulteriori passaggi per convertirle a lista (codice di importazione comunque disponibile commentato nelle celle sottostanti). <br>
Dato che per semplicità, nei passi successivi, si è deciso di applicare un semplice classificatore SVM si è preferito questo tipo di caricamento handwritten che richiede qualche secondo in più piuttosto che dover gestire un tipo proprietario. Al contrario se si risolvesse il nuovo task di classificazione con una neural network sarebbe più semplice e veloce utilizzare la funzione preposta di keras. 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># function for loading images from the structured folder</span>
<span class="c1"># basedir = external directory</span>
<span class="c1"># lst_subdir = list of subdirectory names</span>
<span class="c1"># img_size = size of images for reshaping</span>
<span class="c1"># n_examples = n. of example to load for each class</span>
<span class="c1"># Shuffle = true if shuffling data is needed</span>
<span class="k">def</span> <span class="nf">load_imgs</span><span class="p">(</span><span class="n">base_dir</span><span class="p">,</span> <span class="n">lst_subdir</span><span class="p">,</span> <span class="n">img_size</span><span class="p">,</span> <span class="n">n_examples</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">X</span> <span class="o">=</span> <span class="p">[]</span>
  <span class="n">Y</span> <span class="o">=</span> <span class="p">[]</span>
  <span class="c1"># iterate over each subdirectory</span>
  <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">lst_subdir</span><span class="p">:</span>
    <span class="c1"># get all image in each directory</span>
    <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">base_dir</span> <span class="o">+</span> <span class="n">class_name</span><span class="p">):</span>
      <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">base_dir</span> <span class="o">+</span> <span class="n">class_name</span><span class="p">,</span> <span class="n">img</span><span class="p">)</span>
      <span class="c1"># get class label as number</span>
      <span class="n">Y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lst_subdir</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">class_name</span><span class="p">))</span>
      <span class="c1"># load image and resize to match vgg16 dimension</span>
      <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="n">img_size</span><span class="p">)))</span>

      <span class="c1"># load only 800 example per class</span>
      <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span><span class="o">%</span><span class="k">n_examples</span> == 0):
        <span class="k">break</span>

  <span class="c1"># convert to numpy array</span>
  <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
  <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>

  <span class="c1"># shuffling data</span>
  <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
  <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">Y</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
  <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lst_subdir</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;buildings&#39;</span><span class="p">,</span> <span class="s1">&#39;forest&#39;</span><span class="p">,</span> <span class="s1">&#39;glacier&#39;</span><span class="p">,</span> <span class="s1">&#39;mountain&#39;</span><span class="p">,</span> <span class="s1">&#39;sea&#39;</span><span class="p">,</span> <span class="s1">&#39;street&#39;</span><span class="p">]</span>
<span class="c1"># loading training data </span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">load_imgs</span><span class="p">(</span><span class="s1">&#39;archive/seg_train/seg_train/&#39;</span><span class="p">,</span> <span class="n">lst_subdir</span><span class="p">,</span> 
                             <span class="p">(</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">),</span> <span class="mi">500</span><span class="p">)</span>
<span class="c1"># loading test data</span>
<span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_imgs</span><span class="p">(</span><span class="s1">&#39;archive/seg_test/seg_test/&#39;</span><span class="p">,</span> <span class="n">lst_subdir</span><span class="p">,</span> <span class="p">(</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">),</span> <span class="mi">300</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># informazioni di base sul dataset</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tipo delle immagini:&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">x_train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Numero di esempi di training:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">x_train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Numero di label per il training:&#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dimensione dell&#39;immagine: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Range di valori dell&#39;immagine [&quot;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">())</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span> 
      <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="o">+</span> <span class="s2">&quot;]&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Tipo dei valori dell&#39;immagine: &quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]))</span>
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<pre>Tipo delle immagini: &lt;class &#39;numpy.ndarray&#39;&gt;
Numero di esempi di training: 3000
Numero di label per il training: 3000
Dimensione dell&#39;immagine: (224, 224, 3)
Range di valori dell&#39;immagine [0.0,255.0]
Tipo dei valori dell&#39;immagine:  &lt;class &#39;numpy.float32&#39;&gt;
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<p>Visualizzo tutte le possibili classi di appartenenza presenti nel dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Base idea = https://stackoverflow.com/questions/53837545/plotting-mnist-samples</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="c1"># visualizzo tutte e 10 le classi presenti nel dataset</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">6</span><span class="p">):</span>
  <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">i</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="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
  <span class="c1"># colormap = gray for better visualization</span>
  <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">/</span><span class="mf">255.</span><span class="p">)</span>
  <span class="c1"># add label title</span>
  <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;(Label: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">lst_subdir</span><span class="p">[</span><span class="n">y_train</span><span class="p">[</span><span class="n">idx</span><span class="p">]])</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span><span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
  <span class="c1"># remove axis from visualization</span>
  <span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
  <span class="n">ax</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
</pre></div>

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Il dataset contiene anche una cartella con delle immagini senza la label definita per effettuare le predizioni, in questo caso non carico queste immagini perchè non conoscendo la label non posso effettuare alcuna verifica sull'accuratezza del modello creato.
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<h3 id="Loading-VGG16">Loading VGG16<a class="anchor-link" href="#Loading-VGG16">&#182;</a></h3><p>Carico il modello pre-trainato VGG16 a partire da questo modello nelle sezioni successive definirò dei sottomodelli con punti di cutting diversi. In questo modo analizzerò il comportamento della classificazione sfruttando la rappresentazione delle features generata dal modello fino a quel layer.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get VGG16 base model</span>
<span class="n">base_model</span> <span class="o">=</span> <span class="n">VGG16</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">)</span>
<span class="c1"># print summary for check layer names</span>
<span class="n">base_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5
553467904/553467096 [==============================] - 5s 0us/step
553476096/553467096 [==============================] - 5s 0us/step
Model: &#34;vgg16&#34;
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 fc1 (Dense)                 (None, 4096)              102764544 
                                                                 
 fc2 (Dense)                 (None, 4096)              16781312  
                                                                 
 predictions (Dense)         (None, 1000)              4097000   
                                                                 
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
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Applico quindi lo stesso preprocessing effettuato da VGG16 alle immagini caricate in precedenza. Lo applico già sia al training che al test set.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># preprocess image as in VGG16</span>
<span class="n">x_train</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
<span class="n">x_test</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>
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<h3 id="Feature-extractor:-fc1">Feature extractor: fc1<a class="anchor-link" href="#Feature-extractor:-fc1">&#182;</a></h3><p align='justify'>
Il primo cutting point che analizzerò è stato effettuato dopo il primo layer fully connected, quindi con una rappresentazione molto elevata delle features, q
questo è ideale quanto il nuovo dataset è piccolo ma molto simile ad ImageNet. Nel nostro caso stiamo classificando dei paesaggi, molti dei quali sono già compresi nel primo task di imagenet (e.g. spiagge, montagne) quindi mi aspetto che tagliando la rete in un punto molto alto riesca ad ottenere delle buone performance.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># I start with cutting at a very high point, after the first fully connected layer</span>
<span class="n">model_cut1</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">base_model</span><span class="o">.</span><span class="n">get_layer</span><span class="p">(</span><span class="s1">&#39;fc1&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
<span class="n">model_cut1</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Model: &#34;model&#34;
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
 block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         
                                                                 
 flatten (Flatten)           (None, 25088)             0         
                                                                 
 fc1 (Dense)                 (None, 4096)              102764544 
                                                                 
=================================================================
Total params: 117,479,232
Trainable params: 117,479,232
Non-trainable params: 0
_________________________________________________________________
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Questo modello è caratterizzato da un <b>numero molto alto di parametri trainabili</b> mentre le features di rappresenzazione dell'immagine, essendo estratte dal layer fully connected, sono $4096$ e quindi sono un numero che può essere gestito anche senza l'applicazione della PCA.
Utilizzo quindi un semplice classificatore a partire da queste features per risolvere il nuovo task.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="c1"># extract feature for training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">model_cut1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
<span class="c1"># saving time for prediction</span>
<span class="n">time_pred</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># extract feature for test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">model_cut1</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>

<span class="c1"># check features shape (we should have 4096 features for each image)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(3000, 4096)
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Addestro il classificatore SVM sfruttando la grid search per la ricerca dei migliori iperparametri. In particolare permetto a gamma di variare tra le due scelte di default: gamma e scale, senza quindi settare un valore decimale, mentre permetto al parametro di regolarizzazione C di variare tra $1,5$ o $10$. Tali valori resteranno gli stessi, per semplicità, anche per i classificatori SVM che verranno implementati negli step successivi. Si segnala inoltre che questi valori sono stati scelti dopo alcuni tentativi su questa primo cutting point e potrebbero quindi non essere i migliori per le successive scelte. <br>
Inoltre per come è implementata la grid search, all'interno della libreria sklearn, viene in automatico applicata una cross validation con 5 fold.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># possibile parameters for grid search</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">]}</span>

<span class="c1"># define a SVM with gridsearch</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>

<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># fitting with training features</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># saving time for fitting</span>
<span class="n">fit_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># check score for training set</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Parametri trovati con GridSearchCV:&#39;</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
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<pre>Parametri trovati con GridSearchCV: {&#39;C&#39;: 5, &#39;gamma&#39;: &#39;scale&#39;}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># function to make and evaluate prediction given a classifier, a set of features and real labels</span>
<span class="k">def</span> <span class="nf">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">):</span>
  <span class="c1"># predicting label for test set, using features extracted before</span>
  <span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
  <span class="c1"># print report</span>
  <span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">))</span>

  <span class="c1"># get confusion matrix</span>
  <span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">)</span>

  <span class="c1"># plot beautiful confusion matrix</span>
  <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">()</span>
  <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;BuPu&quot;</span><span class="p">);</span>  

  <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;True labels&#39;</span><span class="p">);</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Predicted labels&#39;</span><span class="p">);</span> 
  <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Confusion Matrix&#39;</span><span class="p">);</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># evaluate performance on training set</span>
<span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       1.00      1.00      1.00       499
           1       1.00      1.00      1.00       500
           2       0.99      1.00      0.99       497
           3       1.00      0.99      1.00       502
           4       1.00      1.00      1.00       501
           5       1.00      1.00      1.00       501

    accuracy                           1.00      3000
   macro avg       1.00      1.00      1.00      3000
weighted avg       1.00      1.00      1.00      3000

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># evaluate prediction on test set</span>
<span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       0.92      0.89      0.91       311
           1       0.99      0.99      0.99       302
           2       0.86      0.82      0.84       314
           3       0.81      0.91      0.85       267
           4       0.95      0.93      0.94       306
           5       0.92      0.92      0.92       300

    accuracy                           0.91      1800
   macro avg       0.91      0.91      0.91      1800
weighted avg       0.91      0.91      0.91      1800

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<div class="prompt input_prompt">In&nbsp;[14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per la creazione delle features:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">time_pred</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per il fitting del SVM:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">fit_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
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<pre>Tempo necessario per la creazione delle features: 84.89 secondi
Tempo necessario per il fitting del SVM: 479.13 secondi
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Possiamo notare come il tempo per la creazione delle features e per il fitting sia molto alto. Per il primo non possiamo agire direttamente mentre possiamo provare ad ridurre la dimensionalità delle features per semplificare il classificatore e quindi ridurre i tempi. <br>
Andando oltre questa problematica possiamo notare come la classificazione sia buona e raggiunga score molto elevati, con errori soprattutto nella classificazione delle classi $2$ e $3$ (montagne e ghiacciai) che vengono erroneamente scambiate tra loro. Tuttavia come si può verificare dal plotting delle immagini proposto nelle prime sezioni queste classi contengono immagini molto simili che può essere difficile distinguere. <br>
Confrontando i risultati ottenuti tra training e test set gli score sono paragonabili anche se probabilmente il training è soggetto ad overfitting.
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<h4 id="PCA">PCA<a class="anchor-link" href="#PCA">&#182;</a></h4><p align='justify'>
Come si è visto gestire $4096$ features è possibile e dà buoni risultati, tuttavia richiede un tempo considerevole sia per definire le features sul training set sia per fittare il modello SVM più appropiato. Per questo motivo provo ad applicare già in questo caso una dimensionality reduction per cercare di diminuire questi tempi.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># idea from:</span>
<span class="c1"># https://medium.com/predict/dimensionality-reduction-using-python-principal-component-analysis-fb365f39ae5c</span>
<span class="c1"># https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html</span>


<span class="c1"># function to check pca cumulative explained variance in order to decide how much</span>
<span class="c1"># features to keep</span>
<span class="k">def</span> <span class="nf">check_pca</span><span class="p">(</span><span class="n">features</span><span class="p">):</span>
  <span class="c1"># copy=False is needed for saving memory with the others cutting point</span>
  <span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">features</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">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
  <span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>

  <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
  <span class="n">cum_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</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">cum_sum</span><span class="p">)</span>
  <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;number of components&#39;</span><span class="p">)</span>
  <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;cumulative explained variance&#39;</span><span class="p">)</span>
  <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

  <span class="c1"># for saving memory space</span>
  <span class="k">del</span> <span class="n">pca</span>

  <span class="k">return</span> <span class="n">cum_sum</span>
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<div class=" highlight hl-ipython3"><pre><span></span>   <span class="c1"># plotting pca cumulative explained variance</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">check_pca</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
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Dall'andamento del grafico si nota che con $500$ si riesce a spiegare oltre l'$80\%$ della varianza dei dati, mentre gia con $1500$ su $3000$ si raggiunge il $100\%$. Provo allora ad effettuare effettivamente la PCA mantenendo solo $500$ elementi e verificare nuovamente il risultato della classificazione. La PCA verrà trainata e fittata inizialmente sul training set, successivamente la stessa PCA verrà applicata al test set. <br>
Lo stesso procedimento può essere effettuato in maniera più semplice ed immediata sfruttando opportunamente i parametri della PCA(), è infatti possibile inserire un valore compreso tra $[0,1]$ ad indicare la percentuale di varianza che vogliamo spiegare, in questo modo il numero ottimale di componenti sarà dedotto in automatico.</p>

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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># define pca to keep only 500 components</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>  <span class="c1"># alternative ---&gt; pca = PCA(0.8)</span>
<span class="c1"># fit and apply pca to training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="c1"># saving time needed for pca</span>
<span class="n">pca_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># apply pca to test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;new features shape:&#39;</span><span class="p">,</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>new features shape: (3000, 500)
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Possiamo notare che effettivamente il numero di features è stato ridotto a $500$ come ci aspettavamo. Procedo quindi fittando un nuovo classificatore SVM.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># possibile parameters for grid search</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">]}</span>

<span class="c1"># define a SVM with gridsearch</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>

<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># fitting with training features</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># save fitting time</span>
<span class="n">fit_time_pca</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># check score for training set</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Parametri trovati con GridSearchCV:&#39;</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
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<pre>Parametri trovati con GridSearchCV: {&#39;C&#39;: 5, &#39;gamma&#39;: &#39;scale&#39;}
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<p align='justify'>
L'SVM utilizza parametri differenti dal primo classificatore che usava tutte le features. Valuto quindi la predizione con del classificatore sul test set, a cui è stata applicata la stessa PCA del training set.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       1.00      1.00      1.00       499
           1       1.00      1.00      1.00       500
           2       0.99      1.00      0.99       494
           3       1.00      0.99      0.99       504
           4       1.00      1.00      1.00       502
           5       1.00      1.00      1.00       501

    accuracy                           1.00      3000
   macro avg       1.00      1.00      1.00      3000
weighted avg       1.00      1.00      1.00      3000

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       0.86      0.91      0.88       282
           1       0.97      0.99      0.98       295
           2       0.86      0.75      0.80       341
           3       0.83      0.77      0.80       322
           4       0.79      0.99      0.88       240
           5       0.94      0.88      0.91       320

    accuracy                           0.88      1800
   macro avg       0.88      0.88      0.88      1800
weighted avg       0.88      0.88      0.87      1800

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<div class="prompt input_prompt">In&nbsp;[25]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo per PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">pca_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per il fitting del SVM dopo PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">fit_time_pca</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
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<pre>Tempo per PCA: 3.95 secondi
Tempo necessario per il fitting del SVM dopo PCA: 59.77 secondi
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Si può notare come il <i>tempo richiesto</i> per fittare il classificatore SVM si sia <i>notevolmente ridotto</i> dopo aver effettuato la PCA sulle features. 
Mentre dal punto di vista delle perfomance di classificazione la perdita risulta essere minima e ristretta soprattutto alle classi $0$ e $2$, nel complesso però se si analizza la metrica della <i> macro_avg </i> la differenza risulta essere minima rispetto al classificatore che utilizza tutti i dati tuttavia in questo caso si può notare come il training sia soggetto ad overfitting.
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<h3 id="Feature-extractor:-block4_pool">Feature extractor: block4_pool<a class="anchor-link" href="#Feature-extractor:-block4_pool">&#182;</a></h3><p align='justify'>
Provo ad analizzare ora cosa succede applicando il taglio in un punto intermedio dell'architettura, in particolare il punto scelto è il quarto blocco convoluzionale.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># cut in the middle of the model</span>
<span class="n">model_cut2</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">base_model</span><span class="o">.</span><span class="n">get_layer</span><span class="p">(</span><span class="s1">&#39;block4_pool&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
<span class="n">model_cut2</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Model: &#34;model_1&#34;
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         
                                                                 
=================================================================
Total params: 7,635,264
Trainable params: 7,635,264
Non-trainable params: 0
_________________________________________________________________
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Rispetto al punto di cutting precedente si nota come il numero di parametri trainabili è <b>diminuito sensibilmente</b> tuttavia la rappresentazione delle features avrà una <b>dimensione molto più elevata</b> pari a $14x14x512 = 100352$, quindi in questo caso sarà fondamentale l'applicazione della PCA. 
Prima di procedere con l'estrazione delle features elimino dalla memoria le vecchie variabili.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">del</span> <span class="n">model_cut1</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">clf</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="c1"># extract feature for training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">model_cut2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
<span class="c1"># saving time for prediction</span>
<span class="n">time_pred</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># extract feature for test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">model_cut2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>

<span class="c1"># reshaping features dimension</span>
<span class="n">final_dim</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">features</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">final_dim</span><span class="p">))</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">features_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">features_test</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">final_dim</span><span class="p">))</span>

<span class="c1"># check features shape (should have 100352 features for each image)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(3000, 100352)
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Una volta estratte le features, sia per training che per test set per semplicità dei passi successivi, procedo all'applicazione della PCA.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># plotting pca cumulative explained variance</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">check_pca</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
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Dal grafico possiamo notare che con $1500$ features riusciamo ancora a spiegare un'ottima percentuale di varianza dei nostri dati $&gt;80\%$, applico quindi la PCA mantenendo soltando questa dimensionalità per le features. Come nel caso precedente potevo evitare di plottare il grafo cumulato e applicare la PCA definendo la percentuale di varianza da spiegare desiderata.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># define pca to keep only 1500 components</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">1500</span><span class="p">,</span> <span class="n">copy</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>  <span class="c1"># alternative ---&gt; pca = PCA(0.8)</span>
<span class="c1"># fit and apply pca to training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="c1"># saving time needed for pca</span>
<span class="n">pca_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># apply pca to test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;new features shape:&#39;</span><span class="p">,</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>new features shape: (3000, 1500)
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In questo caso la riduzione del numero di features era essenziale per poter trainare il classificatore.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># possibile parameters for grid search</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">]}</span>

<span class="c1"># define a SVM with gridsearch</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>

<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># fitting with training features</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># saving time for fitting</span>
<span class="n">fit_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># check score for training set</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Parametri trovati con GridSearchCV:&#39;</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
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<pre>Parametri trovati con GridSearchCV: {&#39;C&#39;: 5, &#39;gamma&#39;: &#39;scale&#39;}
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<p>I parametri trovati dalla gridsearch sono diversi rispetto alla SVM precedente in cui era stato applicato PCA.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       1.00      1.00      1.00       499
           1       1.00      1.00      1.00       500
           2       0.99      1.00      0.99       494
           3       1.00      0.99      0.99       503
           4       1.00      0.99      1.00       503
           5       1.00      1.00      1.00       501

    accuracy                           1.00      3000
   macro avg       1.00      1.00      1.00      3000
weighted avg       1.00      1.00      1.00      3000

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       0.83      0.93      0.88       269
           1       0.99      0.92      0.95       325
           2       0.81      0.77      0.79       315
           3       0.81      0.78      0.80       313
           4       0.86      0.92      0.89       282
           5       0.87      0.88      0.88       296

    accuracy                           0.86      1800
   macro avg       0.86      0.87      0.86      1800
weighted avg       0.86      0.86      0.86      1800

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per la creazione delle features:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">time_pred</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo per PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">pca_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per il fitting del SVM dopo PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">fit_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
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<pre>Tempo necessario per la creazione delle features: 85.1 secondi
Tempo per PCA: 216.48 secondi
Tempo necessario per il fitting del SVM dopo PCA: 174.14 secondi
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Possiamo notare come i risultati ottenuti con questo secondo cutting point risultino essere lievemente peggiori rispetto al caso precedente, i risultati hanno comunque score buoni anche se nuovamente soggetti ad overfitting. Questo perchè come detto il task è abbastanza simile a quello originale inoltre abbiamo un buon numero di immagini di training per il classificatore SVM. <br>
Per quanto riguarda i tempi richiesti si nota una riduzione per il tempo iniziale di creazione delle features, dovuto al minor numero di parametri presenti nel modello, mentre il tempo per la PCA e per SVM aumenta se confrontato con la PCA effettuata sul primo cutting point in quanto i parametri da considerare sono maggiori ($500$ vs $1500$).
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<h3 id="Feature-extractor:-block3_pool">Feature extractor: block3_pool<a class="anchor-link" href="#Feature-extractor:-block3_pool">&#182;</a></h3><p align='justify'>
Provo ad analizzare ora cosa succede se il taglio viene effettuato ancora in un layer superiore. Inizialmente si era provato ad applicare il taglio a layer  <i> block2_pool </i> che però dava problemi di out of memory (OOM) durante la generazione delle features per il training set in quanto ogni immagine era rappresentata da $401408$ features. Per questo motivo ho optato per tagliare al layer <i> block3_pool </i>.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># cut in the middle of the model</span>
<span class="n">model_cut3</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">base_model</span><span class="o">.</span><span class="n">get_layer</span><span class="p">(</span><span class="s1">&#39;block3_pool&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">output</span><span class="p">)</span>
<span class="n">model_cut3</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Model: &#34;model_2&#34;
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         
                                                                 
 block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         
                                                                 
=================================================================
Total params: 1,735,488
Trainable params: 1,735,488
Non-trainable params: 0
_________________________________________________________________
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Rispetto al punto di cutting precedente si nota come il numero di parametri trainabili è ancora <b>diminuito</b> tuttavia la rappresentazione delle features avrà una <b>dimensione molto elevata</b> pari a $28x28x256 = 200704$. Questo richiede di gestire in modo più accurato la memoria se si vogliono usare tutte le immagini di training utilizzate per le altre casistiche. La soluzione più semplice e far ripartire la sessione colab, inoltre definisco le features soltanto per il traning set mentre il test set verrà gestito successivamente una volta trainato il classificatore SVM in modo da occupare il minor spazio possibile. 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="c1"># extract feature for training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">model_cut3</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_train</span><span class="p">)</span>
<span class="c1"># saving time for prediction</span>
<span class="n">time_pred</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># prevent out of mem</span>
<span class="k">del</span> <span class="n">x_train</span>

<span class="c1"># reshaping features dimension</span>
<span class="n">final_dim</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">features</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">final_dim</span><span class="p">))</span>

<span class="c1"># check features shape (should have 200704 features for each image)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(3000, 200704)
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Una volta estratte le features procedo all'applicazione della PCA, in questo caso dato i problemi di memoria procedo in maniera automatica e quindi attraverso i parametri della libreria richiedo che venga preservato l'$80\%$ di varianza.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># define pca to keep 80% of explainable variance</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">copy</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> 
<span class="c1"># fit and apply pca to training set</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="c1"># saving time needed for pca</span>
<span class="n">pca_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"># apply pca to test set</span>
<span class="sd">features_test = pca.transform(features_test)</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;new features shape:&#39;</span><span class="p">,</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>new features shape: (3000, 1346)
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In automatico le features sono state ridotte a $1369$ , anche in questo caso come in precedenza la riduzione del numero di features è essenziale per poter trainare il classificatore.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># possibile parameters for grid search</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;scale&#39;</span><span class="p">]}</span>

<span class="c1"># define a SVM with gridsearch</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>

<span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="c1"># fitting with training features</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># saving time for fitting</span>
<span class="n">fit_time</span> <span class="o">=</span> <span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span>

<span class="c1"># check score for training set</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Parametri trovati con GridSearchCV:&#39;</span><span class="p">,</span> <span class="n">clf</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
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<pre>Parametri trovati con GridSearchCV: {&#39;C&#39;: 10, &#39;gamma&#39;: &#39;scale&#39;}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       1.00      1.00      1.00       499
           1       1.00      1.00      1.00       500
           2       1.00      1.00      1.00       498
           3       1.00      1.00      1.00       501
           4       1.00      1.00      1.00       501
           5       1.00      1.00      1.00       501

    accuracy                           1.00      3000
   macro avg       1.00      1.00      1.00      3000
weighted avg       1.00      1.00      1.00      3000

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Anche in questo caso i parametri della SVM sono ancora differenti dal caso precedente con block4_pool. Elimino quindi dalla memoria le features riguardanti il tranining set e carico il test set su cui applico la stessa pca definita per il tranining.
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<div class="prompt input_prompt">In&nbsp;[41]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># removing useless variable</span>
<span class="k">del</span> <span class="n">features</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># extract feature for test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">model_cut3</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x_test</span><span class="p">)</span>

<span class="c1"># prevent out of mem</span>
<span class="k">del</span> <span class="n">x_test</span>
<span class="c1"># reshaping features</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">features_test</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">features_test</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">final_dim</span><span class="p">))</span>

<span class="c1"># apply pca to test set</span>
<span class="n">features_test</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">features_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluate_prediction</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">features_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
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<pre>              precision    recall  f1-score   support

           0       0.76      0.92      0.83       247
           1       0.99      0.88      0.93       336
           2       0.81      0.71      0.76       345
           3       0.72      0.74      0.73       294
           4       0.80      0.84      0.82       285
           5       0.81      0.83      0.82       293

    accuracy                           0.82      1800
   macro avg       0.82      0.82      0.82      1800
weighted avg       0.82      0.82      0.82      1800

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per la creazione delle features:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">time_pred</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo per PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">pca_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Tempo necessario per il fitting del SVM dopo PCA:&#39;</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">fit_time</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="s1">&#39;secondi&#39;</span><span class="p">)</span>
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<pre>Tempo necessario per la creazione delle features: 22.87 secondi
Tempo per PCA: 524.47 secondi
Tempo necessario per il fitting del SVM dopo PCA: 169.11 secondi
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In questo caso i risultati peggiorano ulteriormente soprattuto nella classe $2$ anche se in generale tutte le classi segnalano dei peggioramenti rispetto alla feature extraction a <i>block4_pool</i>. Inoltre questa casistica ha richiesto un notevole tempo computazionale per il fitting della PCA sulle features del traning set e anche una particolare gestione della memoria per evitare problematica di OOM. <br>
In generale possiamo notare come avvicinandoci ai primi layer per il cutting gli score peggiorino, questo probabilmente perchè le features estratte sono troppo generiche rispetto al nuovo task. Inoltre anche in questo caso si nota la presenta di overfitting durante il training.
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<h3 id="Final-considerations">Final considerations<a class="anchor-link" href="#Final-considerations">&#182;</a></h3><p><p align='justify'>
In generale si è visto che i livelli più alti richiedono molto tempo per la creazione delle features però permetto una classificazione molto accurata, con il $92\%$ di macro_avg. Risalendo invece il modello e quindi applicando il cutting sui primi layer si è visto come le features aumentano di numero richiendo quindi un'applicazione della PCA per poter essere utilizzate nel classificatore SVM, inoltre le performance sembrano calare ad ogni step. Infine si rimarca la presenza di ove</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># download notebook in .html extension</span>
<span class="o">%%</span><span class="k">shell</span>
jupyter nbconvert --to html &#39;/content/drive/MyDrive/Colab Notebooks/AML/Assignment/Assignment4/Assignment4.ipynb&#39;
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<pre>[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/AML/Assignment/Assignment4/Assignment4.ipynb to html
[NbConvertApp] Writing 692357 bytes to /content/drive/MyDrive/Colab Notebooks/AML/Assignment/Assignment4/Assignment4.html
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