AndyLee-NLP.html

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}
.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 {
    display: block;
    page-break-inside: avoid;
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<h1 id="Step-3:-Preload-setup">Step 3: Preload setup<a class="anchor-link" href="#Step-3:-Preload-setup">&#182;</a></h1><p>Basic setup so results can utilize the full width of the screen.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">config</span> IPCompleter.greedy=True
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<h1 id="Step-4:-Import-Dataset">Step 4: Import Dataset<a class="anchor-link" href="#Step-4:-Import-Dataset">&#182;</a></h1><p>The hydrated file is stored in a Google Drive, and using the follow code will mount Google Drive onto Colaboratory.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">google.colab</span> <span class="k">import</span> <span class="n">drive</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>
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<pre>Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(&#34;/content/drive&#34;, force_remount=True).
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<h1 id="Step-5.-Perform-Data-Cleaning">Step 5. Perform Data Cleaning<a class="anchor-link" href="#Step-5.-Perform-Data-Cleaning">&#182;</a></h1>
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<h2 id="Preview-data-after-cleaning-and-manipulation">Preview data after cleaning and manipulation<a class="anchor-link" href="#Preview-data-after-cleaning-and-manipulation">&#182;</a></h2><p>Original Tweet dataframe datatypes</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Reference: https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/overview/user-object</span>
<span class="c1"># Observe the datatypes for each feature.</span>

<span class="n">df</span><span class="o">.</span><span class="n">dtypes</span>
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<pre>coordinates                    object
created_at                     object
hashtags                       object
media                          object
urls                           object
favorite_count                  int64
id                              int64
in_reply_to_screen_name        object
in_reply_to_status_id         float64
in_reply_to_user_id           float64
lang                           object
place                          object
possibly_sensitive             object
retweet_count                   int64
retweet_id                    float64
retweet_screen_name            object
source                         object
text                           object
tweet_url                      object
user_created_at                object
user_screen_name               object
user_default_profile_image       bool
user_description               object
user_favourites_count           int64
user_followers_count            int64
user_friends_count              int64
user_listed_count               int64
user_location                  object
user_name                      object
user_screen_name.1             object
user_statuses_count             int64
user_time_zone                float64
user_urls                      object
user_verified                    bool
dtype: object</pre>
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<h2 id="Readjust-attribute-datatype">Readjust attribute datatype<a class="anchor-link" href="#Readjust-attribute-datatype">&#182;</a></h2><p>For <code>retweet_id</code>, <code>in_reply_to_status_id</code>, <code>in_reply_to_user_id</code></p>
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<li>Convert <code>null</code> values to 0</li>
<li>Convert attribute as int64</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">retweet_id</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;retweet_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">notnull</span><span class="p">()]</span><span class="o">.</span><span class="n">retweet_id</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">retweet_id</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">retweet_id</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">in_reply_to_status_id</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">in_reply_to_status_id</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">in_reply_to_user_id</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">in_reply_to_user_id</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[[</span><span class="s1">&#39;text&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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      <th>510733</th>
      <td>corona is making me go insane. I had a 2 hour long conversation with my bts and exo poster last night</td>
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      <th>198914</th>
      <td>RT @megirish2001: GOI order is binding on all Indians Sir.\n\nFatwa is not.\n\nDid not expect this from you @Javedakhtarjadu https://t.co/V5LXx…</td>
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      <th>375507</th>
      <td>RT @WWUTTcom: In an article yesterday from @DrMichaelLBrown, he said two charismatic prophets see a major "shift" coming with the corona vi…</td>
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      <th>327161</th>
      <td>RT @WaqasGujjar229: Forget about religion cast or creed of human when they need help, people r wanting there are thousands of people out th…</td>
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      <th>278683</th>
      <td>RT @OpIndia_com: Coronavirus: Bollywood Singer Kanika Kapoor tests positive for COVID-19 for 5th time https://t.co/PCGXKU8IwI</td>
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      <th>756744</th>
      <td>RT @thesaralisa: This corona virus is unfortunate but  really reveals a lot of people’s true colours - corporations, your favourite influen…</td>
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      <th>412235</th>
      <td>RT @desimojito: Delhi Police release a video of its warning to senior members of Markaz, Nizamuddin to vacate Markaz &amp;amp; follow lockdown guid…</td>
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      <th>151243</th>
      <td>RT @SilahAkiro: #HeavenIsCalling\nThere are things you can do because of folly and they humiliate you...\n\n~Like rebuking Corona Virus that i…</td>
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      <th>361451</th>
      <td>RT @_sxphiee_: When boris and Prince Charles have somehow been diagnosed with and recovered from corona in the space of about a week</td>
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      <th>792416</th>
      <td>RT @ctraywick: Ppl keep saying we'll see a corona baby boom in 9 months.... more likely we'll see a corona divorce boom\n https://t.co/f4TuS…</td>
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<h2 id="Misc.-cleaning-up-to-reduce-noise-when-conducting-sentimental-analysis">Misc. cleaning up to reduce noise when conducting sentimental analysis<a class="anchor-link" href="#Misc.-cleaning-up-to-reduce-noise-when-conducting-sentimental-analysis">&#182;</a></h2><ol>
<li>remove \n</li>
<li>remove URL</li>
<li>remove user referrals</li>
<li>remove hashtags</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;modified_text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">r&#39;\n&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;modified_text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">modified_text</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">r&#39;(http|ftp|https)://([\w_-]+(?:(?:\.[\w_-]+)+))([\w.,@?^=%&amp;:/~+#-]*[\w@?^=%&amp;/~+#-])?&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;modified_text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">modified_text</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">r&#39;\B@\w+&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;modified_text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">modified_text</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">r&#39;\B#\w+&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
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<h2 id="Splitting-Dataframes-(originals-and-retweets)">Splitting Dataframes (originals and retweets)<a class="anchor-link" href="#Splitting-Dataframes-(originals-and-retweets)">&#182;</a></h2><p>The collection includes both original tweets and retweets. Since retweets mirrors the original tweet by someone else other than the author, we don't need to run sentimental analysis on the retweet as it would have been run on the original tweet, therefore we can split original tweets and retweets into two dataframes to avoid wasting resources.</p>
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<li>Original tweets: <code>dfOriginals</code></li>
<li>Retweets: <code>dfRetweets</code> cons</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">retweet_id</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">dfRetweets</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">retweet_id</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">]</span>
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<p>Two samples of the original tweet dataframe to make sure we are on track.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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      <th></th>
      <th>coordinates</th>
      <th>created_at</th>
      <th>hashtags</th>
      <th>media</th>
      <th>urls</th>
      <th>favorite_count</th>
      <th>id</th>
      <th>in_reply_to_screen_name</th>
      <th>in_reply_to_status_id</th>
      <th>in_reply_to_user_id</th>
      <th>lang</th>
      <th>place</th>
      <th>possibly_sensitive</th>
      <th>retweet_count</th>
      <th>retweet_id</th>
      <th>retweet_screen_name</th>
      <th>source</th>
      <th>text</th>
      <th>tweet_url</th>
      <th>user_created_at</th>
      <th>user_screen_name</th>
      <th>user_default_profile_image</th>
      <th>user_description</th>
      <th>user_favourites_count</th>
      <th>user_followers_count</th>
      <th>user_friends_count</th>
      <th>user_listed_count</th>
      <th>user_location</th>
      <th>user_name</th>
      <th>user_screen_name.1</th>
      <th>user_statuses_count</th>
      <th>user_time_zone</th>
      <th>user_urls</th>
      <th>user_verified</th>
      <th>modified_text</th>
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    <tr>
      <th>371832</th>
      <td>NaN</td>
      <td>Tue Mar 31 15:30:28 +0000 2020</td>
      <td>Repost covid covıd19 corona coronavirusontour Benuemusic World Bonezworld Bonezworldng Bonezworldmedia Bonezmusic</td>
      <td>NaN</td>
      <td>https://www.instagram.com/p/B-ZzBdxpoU2/?igshid=14bsqe0ox6s5b</td>
      <td>0</td>
      <td>1245010582086041600</td>
      <td>NaN</td>
      <td>0</td>
      <td>0</td>
      <td>en</td>
      <td>NaN</td>
      <td>False</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>&lt;a href="http://instagram.com" rel="nofollow"&gt;Instagram&lt;/a&gt;</td>
      <td>#Repost @realmrlyriq\n• • • • • •\n#covid #covıd19 #corona #coronavirusontour Stay Healthy and Eat Right\n@bonezworldng\n#Benuemusic to the #World\n#Bonezworld #Bonezworldng #Bonezworldmedia #Bonezmusic https://t.co/PA4nMN2dXZ</td>
      <td>https://twitter.com/Bonezworldmedia/status/1245010582086041600</td>
      <td>Thu Apr 18 02:12:38 +0000 2019</td>
      <td>Bonezworldmedia</td>
      <td>False</td>
      <td>Welcome to Bonezworld\nWe blog, promote Songs and videos.\nWe hype artist.\nEmail us-Bonezworldng@gmail.com\nVisit our website below 👇</td>
      <td>3040</td>
      <td>635</td>
      <td>743</td>
      <td>0</td>
      <td>Nigeria</td>
      <td>Bonezworldmedia</td>
      <td>Bonezworldmedia</td>
      <td>2985</td>
      <td>NaN</td>
      <td>http://www.bonezworld.com</td>
      <td>False</td>
      <td>• • • • • •    Stay Healthy and Eat Right@bonezworldng#Benuemusic to the #Bonezworld</td>
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      <th>454664</th>
      <td>NaN</td>
      <td>Tue Mar 31 17:24:28 +0000 2020</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>https://youtu.be/CqftFUINpyk</td>
      <td>0</td>
      <td>1245039272010317824</td>
      <td>NaN</td>
      <td>0</td>
      <td>0</td>
      <td>en</td>
      <td>NaN</td>
      <td>False</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>&lt;a href="http://twitter.com" rel="nofollow"&gt;Twitter Web Client&lt;/a&gt;</td>
      <td>A Corona virus patient infected everyone | coronavirus | Korea | south k... https://t.co/MiaZJSztre via @YouTube</td>
      <td>https://twitter.com/SonsShehed/status/1245039272010317824</td>
      <td>Thu Jan 30 10:10:18 +0000 2020</td>
      <td>SonsShehed</td>
      <td>False</td>
      <td>Saeed Sons And Shehed Center\nLandhi no:4,karachi,pakistan\nSaeedsons.shehedcenter@gmail.com</td>
      <td>0</td>
      <td>14</td>
      <td>3982</td>
      <td>0</td>
      <td>NaN</td>
      <td>Saeed Sons And Shehed Center</td>
      <td>SonsShehed</td>
      <td>120</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>False</td>
      <td>A Corona virus patient infected everyone | coronavirus | Korea | south k...  via</td>
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<h2 id="Guessing-language">Guessing language<a class="anchor-link" href="#Guessing-language">&#182;</a></h2><p>Since our sentiment analysis focuses on only English tweets, we will install a <code>langdetect</code> library to help filter out tweets that might be full/partially not in English.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#https://pypi.org/project/langdetect/</span>
<span class="o">!</span>pip install langdetect
<span class="kn">from</span> <span class="nn">langdetect</span> <span class="k">import</span> <span class="n">detect</span>
<span class="kn">from</span> <span class="nn">langdetect</span> <span class="k">import</span> <span class="n">DetectorFactory</span>
<span class="n">DetectorFactory</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="mi">0</span>
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<pre>Requirement already satisfied: langdetect in /usr/local/lib/python3.6/dist-packages (1.0.8)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from langdetect) (1.15.0)
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<p>Function to test whether tweet is English with error handling.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#https://stackoverflow.com/questions/60930935/exclude-non-english-rows-in-pandas</span>

<span class="k">def</span> <span class="nf">is_en</span><span class="p">(</span><span class="n">txt</span><span class="p">):</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">detect</span><span class="p">(</span><span class="n">txt</span><span class="p">)</span><span class="o">==</span><span class="s1">&#39;en&#39;</span>
    <span class="k">except</span><span class="p">:</span>
        <span class="k">return</span> <span class="kc">False</span>
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<p>Passing as the <code>is_en()</code> as first-class function; returning a boolean value as a attribute <code>guessed_language</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals</span><span class="p">[</span><span class="s1">&#39;guessed_language&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfOriginals</span><span class="o">.</span><span class="n">modified_text</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">is_en</span><span class="p">)</span>
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<pre>/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  &#34;&#34;&#34;Entry point for launching an IPython kernel.
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<p>By filtering out non-English tweets.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dfOriginals</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; tweets, and &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">dfOriginals</span><span class="o">.</span><span class="n">guessed_language</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="nb">round</span><span class="p">((</span><span class="nb">sum</span><span class="p">(</span><span class="n">dfOriginals</span><span class="o">.</span><span class="n">guessed_language</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">dfOriginals</span><span class="p">))</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">+</span><span class="s2">&quot;%) tweets detected as English.&quot;</span><span class="p">)</span>
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<pre>There are 245389 tweets, and 232816 (94.88%) tweets detected as English.
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<h1 id="Step-6.-Conduct-Sentiment-Analysis">Step 6. Conduct Sentiment Analysis<a class="anchor-link" href="#Step-6.-Conduct-Sentiment-Analysis">&#182;</a></h1>
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<p>Import NLTK library, load class and function for Sentiment Analysis.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">nltk</span>
<span class="kn">from</span> <span class="nn">nltk.sentiment.util</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">nltk.sentiment</span> <span class="k">import</span> <span class="n">SentimentIntensityAnalyzer</span>
<span class="kn">from</span> <span class="nn">nltk</span> <span class="k">import</span> <span class="n">tokenize</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;punkt&#39;</span><span class="p">)</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;vader_lexicon&#39;</span><span class="p">)</span>
<span class="n">sid</span> <span class="o">=</span> <span class="n">SentimentIntensityAnalyzer</span><span class="p">()</span>
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<pre>[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package vader_lexicon to /root/nltk_data...
[nltk_data]   Package vader_lexicon is already up-to-date!
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<pre>/usr/local/lib/python3.6/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.
  warnings.warn(&#34;The twython library has not been installed. &#34;
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<h2 id="Applying-Sentiment-Analysis-function">Applying Sentiment Analysis function<a class="anchor-link" href="#Applying-Sentiment-Analysis-function">&#182;</a></h2><p>We apply the polarity score function and store results on a new attribute <code>sentimentscore</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals</span><span class="p">[</span><span class="s1">&#39;sentimentscore&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfOriginals</span><span class="o">.</span><span class="n">modified_text</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">sid</span><span class="o">.</span><span class="n">polarity_scores</span><span class="p">)</span>
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<pre>/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
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<p>A quick sample preview of the text and its sentiment score.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="mi">2</span><span class="p">)[[</span><span class="s1">&#39;modified_text&#39;</span><span class="p">,</span><span class="s1">&#39;sentimentscore&#39;</span><span class="p">]]</span>
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      <th>172008</th>
      <td>LolZ 😂 ! Who did enter in Kashmir???? Who did the same in Palestine???? 🧐</td>
      <td>{'neg': 0.0, 'neu': 0.69, 'pos': 0.31, 'compound': 0.7142}</td>
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      <th>89503</th>
      <td>He continued, "You know that's how 93% percent of people have caught Corona?!" Then he went up to buy some lollies – weird – and got pissed off that he had to scan it himself. I'd never been so confused in my life.</td>
      <td>{'neg': 0.195, 'neu': 0.805, 'pos': 0.0, 'compound': -0.8475}</td>
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<p>The <code>SentimentIntensityAnalyzer()</code> returns a dictionary of scores negative, neutral, positive, and compound. Compound is the normalization of negative, neutral, and positive values.</p>
<p>reference: <a href="https://stackoverflow.com/questions/40325980/how-is-the-vader-compound-polarity-score-calculated-in-python-nltk">https://stackoverflow.com/questions/40325980/how-is-the-vader-compound-polarity-score-calculated-in-python-nltk</a></p>
<p>Next, we expand the dictionary into their own feature for easier data manipulation--especially the compound feature which will be used later.</p>

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<span class="n">dfOriginalSScore</span><span class="p">[</span><span class="s1">&#39;original_index&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfOriginals</span><span class="o">.</span><span class="n">index</span>
<span class="n">dfOriginalSScore</span> <span class="o">=</span> <span class="n">dfOriginalSScore</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;original_index&#39;</span><span class="p">)</span>
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<p>Merge the dataframe with sentiment scores and the dataframe with the original tweets together, onto a new dataframe <code>dfOriginals_Analyzed</code>.</p>

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<p>Quick preview of modified text and compound on the merged dataframe.</p>

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      <th>181799</th>
      <td>Wow... The sultan of secularism should tell why there's is need of Fatwa when the State and already issued an order of shutting down of all religious places? Ye jo aapka tweet hai na Javed saab, ye humare desh k secularism ki sachhai hai.</td>
      <td>0.0000</td>
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      <th>478282</th>
      <td>Did a great job for us, thanks for everything and good luck for your next adventures. Maybe we can have our cerverza in Torrevieja after this Corona shit ❤️🇪🇸</td>
      <td>0.8934</td>
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      <th>74820</th>
      <td>Shirley must see this.</td>
      <td>0.0000</td>
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      <th>401586</th>
      <td>Thank you so much Khabazela, one question, i saw in China they had a dedicated hospital for Corona Virus patients, is it like that here in SA. Thanks</td>
      <td>0.8720</td>
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      <th>669494</th>
      <td>You can't even prank someone on  because corona keeps surprising us day in day out.</td>
      <td>0.2732</td>
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      <th>6728</th>
      <td>If people like you can’t not understand it’s for helalth and safety of the country . Then better stay Sushhh... bloody bunch of uneducated people .</td>
      <td>0.6486</td>
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      <th>619038</th>
      <td>Only a Damn Fool would use  and if any company with 10+ employees didn't already have in place a way to talk via videotape long before this Corona Virus showed-up that is actually SECURE those people are Beyond Damn Idiots!  Amazing the Idiocy of Americans sometimes!</td>
      <td>-0.2385</td>
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      <th>540136</th>
      <td>Corona lockdown Day 1.0 income.0 business activity.3x feeding.Expenses for just today ,780.Data sub ,500Total cash spent ,280.And we have 13 more days to go 😭😭😭😭😭😭😭</td>
      <td>0.0000</td>
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      <th>223700</th>
      <td>HIV+ not as hygienically conscious as HIV negatives, but  depends on training you received,   willingness to follow   standards set in your home, and workplace. So corona's told to keep their standards intact and keep hands clean. personal hygiene is important to avoid contagion</td>
      <td>0.4215</td>
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      <th>589038</th>
      <td>I hate being stuck in the house corona please go awayyy</td>
      <td>-0.5267</td>
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<p>We binning based on compound scores</p>
<ul>
<li>-1 to -0.1 = negative </li>
<li>-0.1 to 0.1  = neutral</li>
<li>0.1 to 1 = positive</li>
</ul>
<p>The threshold chosen is based on the paper "Predictive Analysis of Resource Usage Data in Academic Libraries using the VADER Sentiment Algorithm"</p>
<p>Deo, G. S., Mishra, A., Jalaluddin, Z. M., &amp; Mahamuni, C. V. (2020, September). Predictive Analysis of Resource Usage Data in Academic Libraries using the VADER Sentiment Algorithm. In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 221-228). IEEE.</p>
<p><a href="https://www.researchgate.net/profile/Gouri_Deo/publication/342988903_Predictive_Analysis_of_Resource_Usage_Data_in_Academic_Libraries_using_the_VADER_Sentiment_Algorithm/links/5f1085a6a6fdcc3ed70bcf20/Predictive-Analysis-of-Resource-Usage-Data-in-Academic-Libraries-using-the-VADER-Sentiment-Algorithm.pdf">https://www.researchgate.net/profile/Gouri_Deo/publication/342988903_Predictive_Analysis_of_Resource_Usage_Data_in_Academic_Libraries_using_the_VADER_Sentiment_Algorithm/links/5f1085a6a6fdcc3ed70bcf20/Predictive-Analysis-of-Resource-Usage-Data-in-Academic-Libraries-using-the-VADER-Sentiment-Algorithm.pdf</a></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals_Analyzed</span><span class="p">[</span><span class="s1">&#39;sentiment_class_relaxed&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">cut</span><span class="p">(</span><span class="n">dfOriginals_Analyzed</span><span class="p">[</span><span class="s1">&#39;compound&#39;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;neg&#39;</span><span class="p">,</span> <span class="s1">&#39;neu&#39;</span><span class="p">,</span> <span class="s1">&#39;pos&#39;</span><span class="p">])</span>
<span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">sentiment_class_relaxed</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>neg    89051
pos    84039
neu    72299
Name: sentiment_class_relaxed, dtype: int64</pre>
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<h1 id="Step-7.-Conduct-Basic-Analysis">Step 7. Conduct Basic Analysis<a class="anchor-link" href="#Step-7.-Conduct-Basic-Analysis">&#182;</a></h1><p>Observing the correlation between variables.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dfOriginals_Analyzed</span><span class="p">[[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
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      <th></th>
      <th>favorite_count</th>
      <th>retweet_count</th>
      <th>user_followers_count</th>
      <th>compound</th>
      <th>user_statuses_count</th>
      <th>user_favourites_count</th>
      <th>user_friends_count</th>
      <th>user_listed_count</th>
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      <th>favorite_count</th>
      <td>1.000000</td>
      <td>0.908576</td>
      <td>0.165683</td>
      <td>-0.001108</td>
      <td>0.015366</td>
      <td>0.007852</td>
      <td>0.005575</td>
      <td>0.051646</td>
    </tr>
    <tr>
      <th>retweet_count</th>
      <td>0.908576</td>
      <td>1.000000</td>
      <td>0.090025</td>
      <td>-0.003071</td>
      <td>0.015991</td>
      <td>0.007261</td>
      <td>0.007084</td>
      <td>0.039251</td>
    </tr>
    <tr>
      <th>user_followers_count</th>
      <td>0.165683</td>
      <td>0.090025</td>
      <td>1.000000</td>
      <td>0.003693</td>
      <td>0.116452</td>
      <td>0.000535</td>
      <td>0.032721</td>
      <td>0.609057</td>
    </tr>
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      <th>compound</th>
      <td>-0.001108</td>
      <td>-0.003071</td>
      <td>0.003693</td>
      <td>1.000000</td>
      <td>-0.022604</td>
      <td>-0.028941</td>
      <td>-0.006560</td>
      <td>0.000249</td>
    </tr>
    <tr>
      <th>user_statuses_count</th>
      <td>0.015366</td>
      <td>0.015991</td>
      <td>0.116452</td>
      <td>-0.022604</td>
      <td>1.000000</td>
      <td>0.326723</td>
      <td>0.105958</td>
      <td>0.128835</td>
    </tr>
    <tr>
      <th>user_favourites_count</th>
      <td>0.007852</td>
      <td>0.007261</td>
      <td>0.000535</td>
      <td>-0.028941</td>
      <td>0.326723</td>
      <td>1.000000</td>
      <td>0.100920</td>
      <td>0.016049</td>
    </tr>
    <tr>
      <th>user_friends_count</th>
      <td>0.005575</td>
      <td>0.007084</td>
      <td>0.032721</td>
      <td>-0.006560</td>
      <td>0.105958</td>
      <td>0.100920</td>
      <td>1.000000</td>
      <td>0.048344</td>
    </tr>
    <tr>
      <th>user_listed_count</th>
      <td>0.051646</td>
      <td>0.039251</td>
      <td>0.609057</td>
      <td>0.000249</td>
      <td>0.128835</td>
      <td>0.016049</td>
      <td>0.048344</td>
      <td>1.000000</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Basic stats of each feature</span>

<span class="n">dfOriginals_Analyzed</span><span class="p">[[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{0:.5f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">))</span>
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      <th></th>
      <th>favorite_count</th>
      <th>retweet_count</th>
      <th>user_followers_count</th>
      <th>compound</th>
      <th>user_statuses_count</th>
      <th>user_favourites_count</th>
      <th>user_friends_count</th>
      <th>user_listed_count</th>
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      <th>count</th>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
      <td>245389.00000</td>
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      <th>mean</th>
      <td>9.52971</td>
      <td>2.70475</td>
      <td>9251.20519</td>
      <td>-0.01655</td>
      <td>27468.30799</td>
      <td>18028.51423</td>
      <td>1284.43071</td>
      <td>34.64758</td>
    </tr>
    <tr>
      <th>std</th>
      <td>384.21091</td>
      <td>122.27732</td>
      <td>250678.23602</td>
      <td>0.47919</td>
      <td>75560.36124</td>
      <td>39592.12977</td>
      <td>10409.72569</td>
      <td>680.04372</td>
    </tr>
    <tr>
      <th>min</th>
      <td>0.00000</td>
      <td>0.00000</td>
      <td>0.00000</td>
      <td>-0.99520</td>
      <td>1.00000</td>
      <td>0.00000</td>
      <td>0.00000</td>
      <td>0.00000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>0.00000</td>
      <td>0.00000</td>
      <td>64.00000</td>
      <td>-0.39760</td>
      <td>1156.00000</td>
      <td>510.00000</td>
      <td>135.00000</td>
      <td>0.00000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>0.00000</td>
      <td>0.00000</td>
      <td>331.00000</td>
      <td>0.00000</td>
      <td>6344.00000</td>
      <td>4200.00000</td>
      <td>399.00000</td>
      <td>1.00000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>1.00000</td>
      <td>0.00000</td>
      <td>1307.00000</td>
      <td>0.36120</td>
      <td>24616.00000</td>
      <td>17807.00000</td>
      <td>1038.00000</td>
      <td>6.00000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>108137.00000</td>
      <td>35137.00000</td>
      <td>62855265.00000</td>
      <td>0.99190</td>
      <td>4472178.00000</td>
      <td>1254400.00000</td>
      <td>4322723.00000</td>
      <td>202433.00000</td>
    </tr>
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<p>Plot Distribution of sentiment scores</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</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">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span> 
<span class="n">histo_plot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">dfOriginals_Analyzed</span><span class="p">[</span><span class="s1">&#39;compound&#39;</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">40</span><span class="p">)</span>
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8mdYz5JLk1y7nhcneQdyUr4J7kuyUuTnJ/kugMfAEab1y2sd8nTHxoAHB/WDfPufri7/9eY/j9J7ktyRpLLktw0mt2U5PIxfVmSm3vFXUlOrqrTk1ycZFd37+/uR5PsSnLJWPas7r6ruzvJzQvbAgDWcVjfmVfV2UlelOQjSU7r7ofHoi8mOW1Mn5HkwYXV9o7aoep7V6kDAEtYOsyr6ruS/HaSf9rdX11cNo6oe4P7tlofrq6q3VW1e9++fUf65QBgCkuFeVV9e1aC/De7+7+N8pfGKfKM50dG/aEkZy2sfuaoHap+5ir1J+nuG7p7e3dv37Zt2zJdB4Bj3jJXs1eSdyW5r7v//cKinUkOXJG+I8n7FupXjqvaL0jy2Dgdf0eSi6rqlHHh20VJ7hjLvlpVF4zXunJhWwDAOk5cos0PJ/mHSe6pqk+M2r9K8pYkt1bVVUm+kOTVY9ntSV6eZE+SryV5bZJ09/6qelOSu0e7N3b3/jH9+iTvTvLMJB8YDwBgCeuGeXf/zyRr/d/3hau07yTXrLGtG5PcuEp9d5IXrNcXAODJ3AEOACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcuuGeVXdWFWPVNWnFmqnVtWuqrp/PJ8y6lVV11fVnqr6ZFW9eGGdHaP9/VW1Y6H+kqq6Z6xzfVXVRg8SAI5lyxyZvzvJJQfVrk1yZ3efm+TOMZ8klyY5dzyuTvKOZCX8k1yX5KVJzk9y3YEPAKPN6xbWO/i1AIBDWDfMu/v3k+w/qHxZkpvG9E1JLl+o39wr7kpyclWdnuTiJLu6e393P5pkV5JLxrJndfdd3d1Jbl7YFgCwhKf6nflp3f3wmP5iktPG9BlJHlxot3fUDlXfu0p9VVV1dVXtrqrd+/bte4pdB4Bjy9O+AG4cUfcG9GWZ17qhu7d39/Zt27YdjZcEgC3vxKe43peq6vTufnicKn9k1B9KctZCuzNH7aEkP3ZQ/cOjfuYq7QGOWWdf+/6l2n3+La84wj3hWPFUj8x3JjlwRfqOJO9bqF85rmq/IMlj43T8HUkuqqpTxoVvFyW5Yyz7alVdMK5iv3JhWwDAEtY9Mq+q38rKUfVzq2pvVq5Kf0uSW6vqqiRfSPLq0fz2JC9PsifJ15K8Nkm6e39VvSnJ3aPdG7v7wEV1r8/KFfPPTPKB8QAAlrRumHf3a9ZYdOEqbTvJNWts58YkN65S353kBev1AwBYnTvAAcDkhDkATE6YA8DkhDkATE6YA8DkhDkATE6YA8DkhDkATE6YA8DkhDkATE6YA8DkhDkATE6YA8DkhDkATE6YA8DkhDkATO7Eze4AzOzsa9+/VLvPv+UVR7gnwPFMmLNlCUqOd34HWJYwZ10b/Qdl2e0BsBxhDrBBfFBlswjz45g/PMBanOKfizCfxOEE7/H2y+VnA8vxAf7YJczZMP5QAGwO/2cOAJMT5gAwOafZOa64qIdF3g8cKxyZA8DkHJlvMheNHR8cAc7N7+navLe3BmF+DPKHB44vfucR5rAKfxxhY23075Qj/ScS5keIMADgaHEBHABMzpE5ANNx4d0TOTIHgMkJcwCYnDAHgMn5zhy2kGPpe8BjaSzM63h5Hwpz4Jji30I5Hgnzw+QPBQBbje/MAWByjszhGHY4Z5I26ztDZ7vg6RPmMCEBCCxymh0AJufIHIDj3gxfSR2KI3MAmJwwB4DJCXMAmJwwB4DJCXMAmJyr2YEk/ncdZubIHAAmJ8wBYHJOsw9OMQIwK0fmADA5R+YAcBiWPZN7NG/7umWOzKvqkqr6TFXtqaprN7s/ADCLLRHmVXVCkrcnuTTJeUleU1XnbW6vAGAOWyLMk5yfZE93P9DdX09yS5LLNrlPADCFrRLmZyR5cGF+76gBAOuY6gK4qro6ydVj9k+r6jMbtOnnJvmTDdrWZjOWrclYtiZj2ZqOibHUW5Ns7Fi+d60FWyXMH0py1sL8maP2BN19Q5IbNvrFq2p3d2/f6O1uBmPZmoxlazKWrclYDt9WOc1+d5Jzq+qcqjopyRVJdm5ynwBgClviyLy7v1FVP5fkjiQnJLmxu+/d5G4BwBS2RJgnSXffnuT2TXr5DT91v4mMZWsylq3JWLYmYzlM1d1H43UAgCNkq3xnDgA8RcdNmFfVT1bVvVX1F1W15pWFa91Wdlyc95FRf8+4UG9TVNWpVbWrqu4fz6es0ubHq+oTC48/q6rLx7J3V9XnFpa98OiP4lv9XHcso903F/q7c6E+2355YVX9wXgvfrKq/v7Csk3fL+vdVrmqnjF+znvGz/3shWVvGPXPVNXFR7Pfq1liLP+sqj499sOdVfW9C8tWfb9tliXG8tNVtW+hz/94YdmO8Z68v6p2HN2eP9kSY3nbwjj+uKq+srBsy+yXqrqxqh6pqk+tsbyq6voxzk9W1YsXlm38Punu4+KR5K8n+f4kH06yfY02JyT5bJLnJTkpyR8mOW8suzXJFWP615L87CaO5d8luXZMX5vkreu0PzXJ/iR/acy/O8mrNnufHM5YkvzpGvWp9kuSv5bk3DH93UkeTnLyVtgvh3r/L7R5fZJfG9NXJHnPmD5vtH9GknPGdk7Y4mP58YXfiZ89MJZDvd+28Fh+Osl/XGXdU5M8MJ5PGdOnbOWxHNT+57NyQfRW3C8/kuTFST61xvKXJ/lAkkpyQZKPHMl9ctwcmXf3fd293k1mVr2tbFVVkpcluW20uynJ5Ueut+u6bPRh2b68KskHuvtrR7RXT83hjuVbZtwv3f3H3X3/mP7fSR5Jsu2o9fDQlrmt8uIYb0ty4dgPlyW5pbsf7+7PJdkztrdZ1h1Ld39o4Xfirqzc32Irejq3u744ya7u3t/djybZleSSI9TPZRzuWF6T5LeOSs8OU3f/flYOktZyWZKbe8VdSU6uqtNzhPbJcRPmS1rrtrLPSfKV7v7GQfXNclp3Pzymv5jktHXaX5En/0K8eZz6eVtVPWPDe7i8ZcfyHVW1u6ruOvB1QSbfL1V1flaOTj67UN7M/bLMbZW/1Wb83B/Lyn7YardkPtz+XJWVo6gDVnu/bZZlx/L3xnvntqo6cBOuaffL+NrjnCQfXChvpf2ynrXGekT2yZb517SNUFW/l+SvrrLol7r7fUe7P0/HocayONPdXVVr/kvC+CT4N7LyP/wHvCErYXNSVv5t4heTvPHp9vkQfdiIsXxvdz9UVc9L8sGquicrQXJUbfB++Y0kO7r7L0b5qO4XVlTVTyXZnuRHF8pPer9192dX38KW8N+T/FZ3P15V/yQrZ09etsl9erquSHJbd39zoTbbfjlqjqkw7+6feJqbWOu2sl/OyimSE8fRyKq3m91IhxpLVX2pqk7v7odHKDxyiE29Osl7u/vPF7Z94Ojx8ar69ST/YkM6vYaNGEt3PzSeH6iqDyd5UZLfzoT7paqeleT9WfmQedfCto/qflnFMrdVPtBmb1WdmOTZWfn9WOqWzEfRUv2pqp/IygexH+3uxw/U13i/bVZorDuW7v7ywuw7s3L9xoF1f+ygdT+84T1c3uG8T65Ics1iYYvtl/WsNdYjsk+cZn+iVW8r2ytXLXwoK989J8mOJJt5pL9z9GGZvjzpO6cRNAe+c748yapXYx4l646lqk45cMq5qp6b5IeTfHrG/TLeV+/Nyndptx20bLP3yzK3VV4c46uSfHDsh51JrqiVq93PSXJuko8epX6vZt2xVNWLkvynJK/s7kcW6qu+345az59smbGcvjD7yiT3jek7klw0xnRKkovyxLN0R9tSt+6uqudn5eKwP1iobbX9sp6dSa4cV7VfkOSx8YH9yOyTjby6bys/kvzdrHw38XiSLyW5Y9S/O8ntC+1enuSPs/Jp75cW6s/Lyh+nPUn+a5JnbOJYnpPkziT3J/m9JKeO+vYk71xod3ZWPgV+20HrfzDJPVkJi/+c5Lu28liS/M3R3z8cz1fNul+S/FSSP0/yiYXHC7fKflnt/Z+VU/2vHNPfMX7Oe8bP/XkL6/7SWO8zSS7drP1wGGP5vfG34MB+2Lne+20Lj+XfJrl39PlDSZ6/sO4/GvtrT5LXbvWxjPl/neQtB623pfZLVg6SHh6/z3uzct3FzyT5mbG8krx9jPOeLPwX1ZHYJ+4ABwCTc5odACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcsIcACYnzAFgcv8POTQ8ghZBrloAAAAASUVORK5CYII=
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot pie chart of the ratio of the sentiment categories</span>
<span class="c1"># </span>

<span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">sentiment_class_relaxed</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">sentiment_class_relaxed</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">rename_categories</span><span class="p">([</span><span class="s1">&#39;Negative&#39;</span><span class="p">,</span><span class="s1">&#39;Neutral&#39;</span><span class="p">,</span><span class="s1">&#39;Positive&#39;</span><span class="p">]),</span> <span class="n">autopct</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%1.1f%%</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">shadow</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">set_text</span><span class="p">(</span><span class="s2">&quot;Percentage of Sentiment Levels in Tweets&quot;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
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<pre>(-1.1182944691568912,
 1.1129382929025038,
 -1.1162563432183583,
 1.1007741115818266)</pre>
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<h1 id="Step-8.-Build-Prediction-Model-for-&quot;Favourite-Count&quot;-with-Linear-Regression">Step 8. Build Prediction Model for "Favourite Count" with Linear Regression<a class="anchor-link" href="#Step-8.-Build-Prediction-Model-for-&quot;Favourite-Count&quot;-with-Linear-Regression">&#182;</a></h1><h2 id="Split-Train-and-Test-set">Split Train and Test set<a class="anchor-link" href="#Split-Train-and-Test-set">&#182;</a></h2>
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<div class="prompt input_prompt">In&nbsp;[26]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">linear_model</span>


<span class="c1"># Defining</span>
<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;favorite_count&#39;</span>

<span class="c1"># Sample 80/20</span>
<span class="n">trainSet</span><span class="o">=</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
<span class="n">testSet</span><span class="o">=</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">trainSet</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>

<span class="c1"># Split and fit data</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

<span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>

<span class="c1"># print Coefficient</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Coefficient</span><span class="se">\t</span><span class="s1">Label</span><span class="se">\n</span><span class="s1">===========================================&#39;</span><span class="p">)</span>
<span class="n">coefficient_list</span> <span class="o">=</span> <span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">,</span> <span class="n">independent_variables</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">coefficient_list</span><span class="p">:</span>
  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{: 4f}</span><span class="se">\t</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">R^2-score (train set): {reg.score(X_train,Y_train):4f}&quot;</span><span class="p">)</span>
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<pre>Coefficient	Label
===========================================
 2.536713	retweet_count
 0.000211	user_followers_count
 1.016376	compound
-0.000038	user_statuses_count
 0.000057	user_favourites_count
-0.000056	user_friends_count
-0.034559	user_listed_count

R^2-score (train set): 0.799859
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<p>Since this is a regression model with continous values, we use Mean Square Error (MSE), Root MSE, and R-square metrics to measure the performance of the model.</p>
<p>reference: <a href="https://stackoverflow.com/questions/49103139/calculating-accuracy-scores-of-predicted-continuous-values">https://stackoverflow.com/questions/49103139/calculating-accuracy-scores-of-predicted-continuous-values</a></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">r2_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">mean_absolute_error</span>

<span class="kn">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">sqrt</span>

<span class="c1"># Run prediction; Concating results onto a single dataframe; rounding prediction numbers as integer.</span>
<span class="n">plot_1_Y</span> <span class="o">=</span> <span class="n">reg</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"># predicted as Y</span>
<span class="n">plot_1_X</span> <span class="o">=</span> <span class="n">Y_test</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>  <span class="c1"># actual as X</span>
<span class="n">df_plot_1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">plot_1_X</span><span class="p">,</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">plot_1_Y</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">df_plot_1</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;index&#39;</span><span class="p">,</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span> <span class="s1">&#39;predicted&#39;</span><span class="p">]</span>
<span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>

<span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">adj_r2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">r2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">)</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Metrics</span><span class="se">\n</span><span class="s2">============================================&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Mean Square Error (MSE): </span><span class="si">{mse:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Root MSE:</span><span class="se">\t\t</span><span class="s2"> {sqrt(mse):4f}&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Mean Absolute Error:</span><span class="se">\t</span><span class="s2"> </span><span class="si">{mae:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;R^2:</span><span class="se">\t\t\t</span><span class="s2"> </span><span class="si">{r2:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Adjusted R-Square:</span><span class="se">\t</span><span class="s2"> </span><span class="si">{adj_r2:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
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<pre>
Metrics
============================================
Mean Square Error (MSE): 33404.189046
Root MSE:		 182.768129
Mean Absolute Error:	 7.281756
R^2:			 0.873217
Adjusted R-Square:	 0.873199
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<p>Scatterplot to visually observe how well the model's prediction vs actual value.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.polynomial.polynomial</span> <span class="k">import</span> <span class="n">polyfit</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>


<span class="n">plot_1</span> <span class="o">=</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">title</span><span class="o">=</span><span class="s2">&quot;Dependent Variable: &quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;; Predicted versus Actual&quot;</span><span class="p">)</span>
<span class="n">plot_1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
<span class="n">plot_1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Actual values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
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<pre>Text(0.5, 0, &#39;Actual values (favorite_count)&#39;)</pre>
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<p>Since our outliers are stretching the graph, we will limit the observation to 2-sigma (95%).</p>

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<div class="prompt input_prompt">In&nbsp;[29]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_plot_2</span> <span class="o">=</span> <span class="n">df_plot_1</span><span class="p">[</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.025</span><span class="p">),</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.975</span><span class="p">))]</span>
<span class="c1"># df_plot_2 = df_plot_1[df_plot_1.predicted.between(df_plot_1.predicted.quantile(0.0015), df_plot_1.predicted.quantile(0.9985))]</span>
<span class="n">plot_2</span> <span class="o">=</span> <span class="n">df_plot_2</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">title</span><span class="o">=</span><span class="s2">&quot;Dependent Variable: &quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;; Predicted versus Actual (2-sigma)&quot;</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Actual values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>


<span class="c1"># b, m = polyfit(df_plot_2.actual, df_plot_2.predicted, 1)</span>
<span class="c1"># plot_2.plot(df_plot_2.actual, b + m * df_plot_2.actual, &#39;-&#39;)</span>

<span class="n">lims</span> <span class="o">=</span> <span class="p">[</span>
    <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">plot_2</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">(),</span> <span class="n">plot_2</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()]),</span>  <span class="c1"># min of both axes</span>
    <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">plot_2</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">(),</span> <span class="n">plot_2</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()]),</span>  <span class="c1"># max of both axes</span>
<span class="p">]</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lims</span><span class="p">,</span> <span class="n">lims</span><span class="p">,</span> <span class="s1">&#39;r--&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>[&lt;matplotlib.lines.Line2D at 0x7fbef0598da0&gt;]</pre>
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<h1 id="Step-9.-Build-Prediction-Model-for-&quot;Retweet-Count&quot;-with-Linear-Regression">Step 9. Build Prediction Model for "Retweet Count" with Linear Regression<a class="anchor-link" href="#Step-9.-Build-Prediction-Model-for-&quot;Retweet-Count&quot;-with-Linear-Regression">&#182;</a></h1><h2 id="Split-Train-and-Test-set">Split Train and Test set<a class="anchor-link" href="#Split-Train-and-Test-set">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Repeating step 8 but with retweet_count as dependent variable.</span>

<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">linear_model</span>

<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;retweet_count&#39;</span>

<span class="n">trainSet</span><span class="o">=</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">150</span><span class="p">)</span>
<span class="n">testSet</span><span class="o">=</span><span class="n">dfOriginals_Analyzed</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">trainSet</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>

<span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

<span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>

<span class="c1"># print(&quot;Coefficients: &quot; + &#39;, &#39;.join(independent_variables))</span>
<span class="c1"># print(reg.coef_)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Coefficient</span><span class="se">\t</span><span class="s1">Label</span><span class="se">\n</span><span class="s1">===========================================&#39;</span><span class="p">)</span>
<span class="n">coefficient_list</span> <span class="o">=</span> <span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">,</span> <span class="n">independent_variables</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">coefficient_list</span><span class="p">:</span>
  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{: 4f}</span><span class="se">\t</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">R^2-score (train set): {reg.score(X_train,Y_train):4f}&quot;</span><span class="p">)</span>


<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">r2_score</span>

<span class="kn">from</span> <span class="nn">math</span> <span class="k">import</span> <span class="n">sqrt</span>

<span class="c1"># Run prediction; Concating results onto a single dataframe; rounding prediction numbers as integer.</span>
<span class="n">plot_1_Y</span> <span class="o">=</span> <span class="n">reg</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"># predicted as Y</span>
<span class="n">plot_1_X</span> <span class="o">=</span> <span class="n">Y_test</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>  <span class="c1"># actual as X</span>
<span class="n">df_plot_1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">plot_1_X</span><span class="p">,</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">plot_1_Y</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">df_plot_1</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;index&#39;</span><span class="p">,</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span> <span class="s1">&#39;predicted&#39;</span><span class="p">]</span>
<span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>


<span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">,</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="p">)</span>
<span class="n">adj_r2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">r2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">actual</span><span class="p">)</span><span class="o">-</span><span class="nb">len</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Metrics</span><span class="se">\n</span><span class="s2">============================================&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Mean Square Error (MSE): </span><span class="si">{mse:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Root MSE:</span><span class="se">\t\t</span><span class="s2"> {sqrt(mse):4f}&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Mean Absolute Error:</span><span class="se">\t</span><span class="s2"> </span><span class="si">{mae:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;R-Square:</span><span class="se">\t\t</span><span class="s2"> </span><span class="si">{r2:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Adjusted R-Square:</span><span class="se">\t</span><span class="s2"> </span><span class="si">{adj_r2:4f}</span><span class="s2">&quot;</span><span class="p">)</span>
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<pre>Coefficient	Label
===========================================
 0.311235	favorite_count
-0.000055	user_followers_count
-0.449341	compound
 0.000013	user_statuses_count
-0.000014	user_favourites_count
 0.000025	user_friends_count
 0.009861	user_listed_count

R^2-score (train set): 0.791882

Metrics
============================================
Mean Square Error (MSE): 1234.588084
Root MSE:		 35.136706
Mean Absolute Error:	 1.967480
R-Square:		 0.934187
Adjusted R-Square:	 0.934178
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<p>Scatterplot of Predicted value vs Actual Value.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.polynomial.polynomial</span> <span class="k">import</span> <span class="n">polyfit</span>

<span class="n">plot_1</span> <span class="o">=</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">title</span><span class="o">=</span><span class="s2">&quot;Dependent Variable: &quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;; Predicted versus Actual&quot;</span><span class="p">)</span>
<span class="n">plot_1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
<span class="n">plot_1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Actual values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
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<pre>Text(0.5, 0, &#39;Actual values (retweet_count)&#39;)</pre>
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<div class="prompt input_prompt">In&nbsp;[32]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scatterplot covering 2.5-sigma.</span>
<span class="n">df_plot_2</span> <span class="o">=</span> <span class="n">df_plot_1</span><span class="p">[</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">between</span><span class="p">(</span><span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="o">.</span><span class="mi">0031</span><span class="p">),</span> <span class="n">df_plot_1</span><span class="o">.</span><span class="n">predicted</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.9969</span><span class="p">))]</span>
<span class="n">plot_2</span> <span class="o">=</span> <span class="n">df_plot_2</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;actual&#39;</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="s1">&#39;predicted&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">15</span><span class="p">),</span><span class="n">title</span><span class="o">=</span><span class="s2">&quot;Dependent Variable: &quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;; Predicted versus Actual (2.5-sigma)&quot;</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Actual values (&quot;</span> <span class="o">+</span> <span class="n">dependent_variables</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>

<span class="c1"># b, m = polyfit(df_plot_2.actual, df_plot_2.predicted, 1)</span>
<span class="c1"># plot_2.plot(df_plot_2.actual, b + m * df_plot_2.actual, &#39;-&#39;)</span>

<span class="n">lims</span> <span class="o">=</span> <span class="p">[</span>
    <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">plot_2</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">(),</span> <span class="n">plot_2</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()]),</span>  <span class="c1"># min of both axes</span>
    <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">plot_2</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">(),</span> <span class="n">plot_2</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()]),</span>  <span class="c1"># max of both axes</span>
<span class="p">]</span>
<span class="n">plot_2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lims</span><span class="p">,</span> <span class="n">lims</span><span class="p">,</span> <span class="s1">&#39;r--&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>[&lt;matplotlib.lines.Line2D at 0x7fbef04e8f98&gt;]</pre>
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<h1 id="Step-10:-Build-Linear-Regression-with-k-Fold">Step 10: Build Linear Regression with k-Fold<a class="anchor-link" href="#Step-10:-Build-Linear-Regression-with-k-Fold">&#182;</a></h1><p>Since the metrics we got from the previous regression consist of only one sampling, we should implement the same regression model using K-fold cross-validation in order to get more accurate metrics.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># K-fold - #1 favourite count as dependent</span>
<span class="c1">#</span>
<span class="c1"># References:</span>
<span class="c1"># https://stackoverflow.com/questions/45115964/separate-pandas-dataframe-using-sklearns-kfold</span>
<span class="c1"># https://stackoverflow.com/questions/57398411/next-always-gives-the-same-index-with-a-kfold-generator</span>
<span class="c1"># https://machinelearningmastery.com/k-fold-cross-validation/</span>
<span class="c1"># https://machinelearningmastery.com/robust-regression-for-machine-learning-in-python/</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>

<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;favorite_count&#39;</span>


<span class="n">kfold_n</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">kfold_mse_total</span><span class="p">,</span> <span class="n">kfold_rmse_total</span><span class="p">,</span> <span class="n">kfold_rsqr_total</span> <span class="o">=</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="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span> <span class="o">=</span> <span class="n">kfold_n</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">random_state</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

<span class="n">dfKfold</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">kf_result</span> <span class="o">=</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dfKfold</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dependent Variable: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dependent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Independent Variables: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;K-fold: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE</span><span class="se">\t\t\t</span><span class="s2">RMSE</span><span class="se">\t\t\t</span><span class="s2">R-Square</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>

<span class="c1"># run each K-fold</span>
<span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">kf_result</span><span class="p">:</span>
  <span class="n">trainSet</span><span class="o">=</span><span class="n">dfKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
  <span class="n">testSet</span><span class="o">=</span><span class="n">dfKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>

  <span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
  <span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

  <span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

  <span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>

  <span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">reg</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="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
  <span class="n">rsqr</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>

  <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="si">{mse:4f}</span><span class="se">\t\t</span><span class="s2">{sqrt(mse):4f}</span><span class="se">\t\t</span><span class="si">{rsqr:4f}</span><span class="s2">&quot;</span><span class="p">)</span>

  <span class="n">kfold_mse_total</span> <span class="o">+=</span> <span class="n">mse</span>
  <span class="n">kfold_rmse_total</span> <span class="o">+=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
  <span class="n">kfold_rsqr_total</span> <span class="o">+=</span> <span class="n">rsqr</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Average</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;{kfold_mse_total / kfold_n:4f}</span><span class="se">\t\t</span><span class="s2">{kfold_rmse_total / kfold_n:4f}</span><span class="se">\t\t</span><span class="s2">{kfold_rsqr_total / kfold_n:4f}&quot;</span><span class="p">)</span>
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<pre>Dependent Variable: favorite_count
Independent Variables: [&#39;retweet_count&#39;, &#39;user_followers_count&#39;, &#39;compound&#39;, &#39;user_statuses_count&#39;, &#39;user_favourites_count&#39;, &#39;user_friends_count&#39;, &#39;user_listed_count&#39;]
K-fold: 5
MSE			RMSE			R-Square
=============================================================================================
31564.218170		177.663216		0.877825
10521.044828		102.572145		0.704872
130028.329157		360.594411		0.378130
10211.723622		101.053073		0.782832
13119.346895		114.539718		0.930201

Average
=============================================================================================
39088.932535		171.284512		0.734772
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># K-fold #2 Retweet as independent</span>
<span class="c1">#</span>

<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>


<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_favourites_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_friends_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_listed_count&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;retweet_count&#39;</span>

<span class="n">kfold_n</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">kfold_mse_total</span><span class="p">,</span> <span class="n">kfold_rmse_total</span><span class="p">,</span> <span class="n">kfold_rsqr_total</span> <span class="o">=</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="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span> <span class="o">=</span> <span class="n">kfold_n</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">random_state</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

<span class="n">dfKfold</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">kf_result</span> <span class="o">=</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dfKfold</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dependent Variable: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dependent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Independent Variables: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;K-fold: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE</span><span class="se">\t\t\t</span><span class="s2">RMSE</span><span class="se">\t\t\t</span><span class="s2">R-Square</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">kf_result</span><span class="p">:</span>
  <span class="c1"># print(train_idx)</span>
  <span class="n">trainSet</span><span class="o">=</span><span class="n">dfKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
  <span class="n">testSet</span><span class="o">=</span><span class="n">dfKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>

  <span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
  <span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

  <span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

  <span class="n">reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>

  <span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">reg</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="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
  <span class="n">rsqr</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>

  <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="si">{mse:4f}</span><span class="se">\t\t</span><span class="s2">{sqrt(mse):4f}</span><span class="se">\t\t</span><span class="si">{rsqr:4f}</span><span class="s2">&quot;</span><span class="p">)</span>

  <span class="n">kfold_mse_total</span> <span class="o">+=</span> <span class="n">mse</span>
  <span class="n">kfold_rmse_total</span> <span class="o">+=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
  <span class="n">kfold_rsqr_total</span> <span class="o">+=</span> <span class="n">rsqr</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Average</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;{kfold_mse_total / kfold_n:4f}</span><span class="se">\t\t</span><span class="s2">{kfold_rmse_total / kfold_n:4f}</span><span class="se">\t\t</span><span class="s2">{kfold_rsqr_total / kfold_n:4f}&quot;</span><span class="p">)</span>
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<pre>Dependent Variable: retweet_count
Independent Variables: [&#39;favorite_count&#39;, &#39;user_followers_count&#39;, &#39;compound&#39;, &#39;user_statuses_count&#39;, &#39;user_favourites_count&#39;, &#39;user_friends_count&#39;, &#39;user_listed_count&#39;]
K-fold: 5
MSE			RMSE			R-Square
=============================================================================================
1001.568224		31.647563		0.945563
798.111384		28.250865		0.605363
10974.758804		104.760483		0.685319
728.033332		26.982093		0.750776
666.921016		25.824814		0.959674

Average
=============================================================================================
2833.878552		43.493164		0.789339
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<h1 id="Step-11:-Build-Polynomial-Regression">Step 11: Build Polynomial Regression<a class="anchor-link" href="#Step-11:-Build-Polynomial-Regression">&#182;</a></h1><p>Here we attempt to fit our model onto a non-linear regression model--Polynomial regression model to see whether we are able to build a better fit of the model than linear regression.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># POLYNOMIAL REGRESSION #1 favorite_count</span>
<span class="c1">#</span>
<span class="c1"># Reference</span>
<span class="c1"># https://www.kaggle.com/jnikhilsai/cross-validation-with-linear-regression</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="k">import</span> <span class="n">make_pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">PolynomialFeatures</span>

<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_verified&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;favorite_count&#39;</span>

<span class="n">degrees</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>


<span class="n">dfPoly</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">trainSet</span><span class="o">=</span><span class="n">dfPoly</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">testSet</span><span class="o">=</span><span class="n">dfPoly</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">trainSet</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>


<span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">)))</span>

<span class="c1"># calculate values for each degree</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="p">),</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
    
    <span class="c1"># predict on test and train data</span>
    <span class="c1"># store the predictions of each degree in the corresponding column</span>
    <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</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"># plot the graph</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">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Actual&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">1000</span><span class="p">,</span><span class="mi">15000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">12500</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">degree</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">)</span>


<span class="c1"># print metrics</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;R-squared values: </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>
    <span class="n">test_r2</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Polynomial degree </span><span class="si">{0}</span><span class="s2">: test score=</span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">degree</span><span class="p">,</span> <span class="n">test_r2</span><span class="p">))</span>
</pre></div>

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<pre>R-squared values: 

Polynomial degree 1: test score=0.87
Polynomial degree 2: test score=0.72
Polynomial degree 3: test score=-5.57
Polynomial degree 6: test score=0.11
</pre>
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<img 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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># POLYNOMIAL REGRESSION #2 retweet_count</span>
<span class="c1">#</span>


<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_verified&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;retweet_count&#39;</span>
<span class="n">degrees</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>

<span class="n">dfPoly</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">trainSet</span><span class="o">=</span><span class="n">dfPoly</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">testSet</span><span class="o">=</span><span class="n">dfPoly</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">trainSet</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>


<span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">)))</span>

<span class="c1"># calculate values for each degree</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="c1"># make pipeline: create features, then feed them to linear_reg model</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="p">),</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
    
    <span class="c1"># predict on test and train data</span>
    <span class="c1"># store the predictions of each degree in the corresponding column</span>
    <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</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"># plot the graph</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">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Actual&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Predicted&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">1000</span><span class="p">,</span><span class="mi">15000</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">12500</span><span class="p">])</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">degree</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper left&#39;</span><span class="p">)</span>


<span class="c1"># print metrics</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;R-squared values: </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>
    <span class="c1"># train_r2 = round(sklearn.metrics.r2_score(y_train, y_train_pred[:, i]), 2)</span>
    <span class="n">test_r2</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Polynomial degree </span><span class="si">{0}</span><span class="s2">: test score=</span><span class="si">{1}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">degree</span><span class="p">,</span> <span class="n">test_r2</span><span class="p">))</span>
</pre></div>

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<pre>R-squared values: 

Polynomial degree 1: test score=0.94
Polynomial degree 2: test score=0.08
Polynomial degree 3: test score=0.01
Polynomial degree 6: test score=-422.7
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<h1 id="Step-12:-Build-Polynomial-Regression-K-fold">Step 12: Build Polynomial Regression K-fold<a class="anchor-link" href="#Step-12:-Build-Polynomial-Regression-K-fold">&#182;</a></h1>
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<div class="prompt input_prompt">In&nbsp;[37]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>

<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;retweet_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_verified&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;favorite_count&#39;</span>
<span class="c1"># independent_variables = [&#39;favorite_count&#39;,&#39;user_followers_count&#39;,&#39;compound&#39;,&#39;user_statuses_count&#39;,&#39;user_verified&#39;]</span>
<span class="c1"># dependent_variables = &#39;retweet_count&#39;</span>
<span class="c1"># degrees = [1, 2 ]</span>
<span class="n">degrees</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>

<span class="n">kfold_n</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">kfold_mse_total</span><span class="p">,</span> <span class="n">kfold_rmse_total</span><span class="p">,</span> <span class="n">kfold_rsqr_total</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">),</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">),</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">)</span>
<span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span> <span class="o">=</span> <span class="n">kfold_n</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">random_state</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

<span class="c1"># Excluding outliers, only including within 3-sigma</span>
<span class="c1"># dfKfold = dfOriginals_Analyzed[dfOriginals_Analyzed[dependent_variables].between(dfOriginals_Analyzed[dependent_variables].quantile(.0015), dfOriginals_Analyzed[dependent_variables].quantile(0.9985))]</span>
<span class="n">dfPolyKfold</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">kf_result</span> <span class="o">=</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dfPolyKfold</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dependent Variable: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dependent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Independent Variables: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;K-fold: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Polynomial</span><span class="se">\t\t</span><span class="s2">Root MSE</span><span class="se">\t\t\t</span><span class="s2">R-Square</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">kf_result</span><span class="p">:</span>
  <span class="c1"># print(train_idx)</span>
  <span class="n">trainSet</span><span class="o">=</span><span class="n">dfPolyKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
  <span class="n">testSet</span><span class="o">=</span><span class="n">dfPolyKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>

  <span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
  <span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

  <span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

  <span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">)))</span>

  <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="p">),</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
    

    <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</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="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>
    
    <span class="n">rsqr</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
    <span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree </span><span class="si">{0}</span><span class="s2">: </span><span class="se">\t\t</span><span class="si">{1}</span><span class="s2"> </span><span class="se">\t\t</span><span class="si">{2}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">degree</span><span class="p">,</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">),</span><span class="n">rsqr</span><span class="p">))</span>
    <span class="n">row</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">count</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">))</span>
    <span class="n">kfold_mse_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span> <span class="n">row</span><span class="p">,</span><span class="n">degree</span> <span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
    <span class="n">kfold_rmse_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span> <span class="n">row</span><span class="p">,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
    <span class="n">kfold_rsqr_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">rsqr</span>
    <span class="c1"># print(f&quot;count: {count}  row: {row}\n{kfold_rsqr_total.loc[row]}&quot;)</span>
    <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
    

  <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;---------------------------------------------------------------------------------&quot;</span><span class="p">)</span>

<span class="n">polykfold_overall_metrics</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">degrees</span><span class="p">,</span><span class="n">kfold_rmse_total</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span><span class="n">kfold_rsqr_total</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Degree</span><span class="se">\t</span><span class="s1">Average RMSE</span><span class="se">\t</span><span class="s1">Average R^2&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">polykfold_overall_metrics</span><span class="p">:</span>
  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="se">\t</span><span class="si">{: 4f}</span><span class="se">\t</span><span class="si">{: 4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>
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<pre>Dependent Variable: favorite_count
Independent Variables: [&#39;retweet_count&#39;, &#39;user_followers_count&#39;, &#39;compound&#39;, &#39;user_statuses_count&#39;, &#39;user_verified&#39;]
K-fold: 5
Polynomial		Root MSE			R-Square
=============================================================================================
Degree 1: 		176.1003309179579 		0.88
Degree 2: 		283.8671924780902 		0.69
Degree 3: 		906.2368606263769 		-2.18
Degree 6: 		856.3979925552468 		-1.84
---------------------------------------------------------------------------------
Degree 1: 		101.57117876732413 		0.71
Degree 2: 		113.61553501192776 		0.64
Degree 3: 		164.8107158447843 		0.24
Degree 6: 		1067.8173629126154 		-30.98
---------------------------------------------------------------------------------
Degree 1: 		361.7648735168686 		0.37
Degree 2: 		578.1225176563707 		-0.6
Degree 3: 		10089.810890024544 		-485.89
Degree 6: 		38011377.335343964 		-6910168447.01
---------------------------------------------------------------------------------
Degree 1: 		106.07747474685985 		0.76
Degree 2: 		132.28407898975604 		0.63
Degree 3: 		429.9725980224988 		-2.93
Degree 6: 		21654.500707596068 		-9971.23
---------------------------------------------------------------------------------
Degree 1: 		113.14836415856713 		0.93
Degree 2: 		284.245851156108 		0.57
Degree 3: 		1548.2663280761865 		-11.75
Degree 6: 		15713.147572038102 		-1312.61
---------------------------------------------------------------------------------
Degree	Average RMSE	Average R^2
1	 171.732444	 0.730000
2	 278.427035	 0.386000
3	 2627.819479	-100.502000
6	 7610133.839796	-1382035952.734000
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># K-fold Poly #2 Retweet</span>

<span class="kn">import</span> <span class="nn">math</span>

<span class="c1"># independent_variables = [&#39;retweet_count&#39;,&#39;user_followers_count&#39;,&#39;compound&#39;,&#39;user_statuses_count&#39;,&#39;user_verified&#39;]</span>
<span class="c1"># dependent_variables = &#39;favorite_count&#39;</span>
<span class="n">independent_variables</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;favorite_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_followers_count&#39;</span><span class="p">,</span><span class="s1">&#39;compound&#39;</span><span class="p">,</span><span class="s1">&#39;user_statuses_count&#39;</span><span class="p">,</span><span class="s1">&#39;user_verified&#39;</span><span class="p">]</span>
<span class="n">dependent_variables</span> <span class="o">=</span> <span class="s1">&#39;retweet_count&#39;</span>
<span class="c1"># degrees = [1, 2 ]</span>
<span class="n">degrees</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>

<span class="n">kfold_n</span> <span class="o">=</span> <span class="mi">5</span>
<span class="n">kfold_mse_total</span><span class="p">,</span> <span class="n">kfold_rmse_total</span><span class="p">,</span> <span class="n">kfold_rsqr_total</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">),</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">),</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">),</span> <span class="n">columns</span><span class="o">=</span><span class="n">degrees</span><span class="p">)</span>
<span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span> <span class="o">=</span> <span class="n">kfold_n</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">random_state</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>

<span class="c1"># Excluding outliers, only including within 3-sigma</span>
<span class="n">dfPolyKfold</span> <span class="o">=</span> <span class="n">dfOriginals_Analyzed</span>

<span class="n">kf_result</span> <span class="o">=</span> <span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">dfPolyKfold</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Dependent Variable: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dependent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Independent Variables: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">independent_variables</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;K-fold: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">kfold_n</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Polynomial</span><span class="se">\t\t</span><span class="s2">Root MSE</span><span class="se">\t\t\t</span><span class="s2">R-Square</span><span class="se">\n</span><span class="s2">=============================================================================================&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">kf_result</span><span class="p">:</span>
  <span class="n">trainSet</span><span class="o">=</span><span class="n">dfPolyKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_idx</span><span class="p">]</span>
  <span class="n">testSet</span><span class="o">=</span><span class="n">dfPolyKfold</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">test_idx</span><span class="p">]</span>

  <span class="n">X_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_train</span> <span class="o">=</span> <span class="n">trainSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>
  <span class="n">X_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">independent_variables</span><span class="p">]</span>
  <span class="n">Y_test</span> <span class="o">=</span> <span class="n">testSet</span><span class="p">[</span><span class="n">dependent_variables</span><span class="p">]</span>

  <span class="n">reg</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">()</span>

  <span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">)))</span>

  <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>    
    <span class="n">model</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="p">),</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">())</span>
    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train</span><span class="p">)</span>
    

    <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</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="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>
    
    <span class="n">rsqr</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
    <span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">Y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Degree </span><span class="si">{0}</span><span class="s2">: </span><span class="se">\t\t</span><span class="si">{1}</span><span class="s2"> </span><span class="se">\t\t</span><span class="si">{2}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">degree</span><span class="p">,</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">),</span><span class="n">rsqr</span><span class="p">))</span>
    <span class="n">row</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">count</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">degrees</span><span class="p">))</span>
    <span class="n">kfold_mse_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span> <span class="n">row</span><span class="p">,</span><span class="n">degree</span> <span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
    <span class="n">kfold_rmse_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span> <span class="n">row</span><span class="p">,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">mse</span><span class="p">)</span>
    <span class="n">kfold_rsqr_total</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">rsqr</span>
    <span class="c1"># print(f&quot;count: {count}  row: {row}\n{kfold_rsqr_total.loc[row]}&quot;)</span>
    <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
    

  <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;---------------------------------------------------------------------------------&quot;</span><span class="p">)</span>

<span class="n">polykfold_overall_metrics</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">degrees</span><span class="p">,</span><span class="n">kfold_rmse_total</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span><span class="n">kfold_rsqr_total</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Degree</span><span class="se">\t</span><span class="s1">Average RMSE</span><span class="se">\t</span><span class="s1">Average R^2&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">polykfold_overall_metrics</span><span class="p">:</span>
  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="se">\t</span><span class="si">{: 4f}</span><span class="se">\t</span><span class="si">{: 4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">el</span><span class="p">))</span>
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<pre>Dependent Variable: retweet_count
Independent Variables: [&#39;favorite_count&#39;, &#39;user_followers_count&#39;, &#39;compound&#39;, &#39;user_statuses_count&#39;, &#39;user_verified&#39;]
K-fold: 5
Polynomial		Root MSE			R-Square
=============================================================================================
Degree 1: 		30.619157472439177 		0.95
Degree 2: 		143.57465611146827 		-0.12
Degree 3: 		197.92059429558387 		-1.13
Degree 6: 		140.91449714880363 		-0.08
---------------------------------------------------------------------------------
Degree 1: 		27.778555948274388 		0.62
Degree 2: 		33.17703268992806 		0.46
Degree 3: 		44.99600146557491 		-0.0
Degree 6: 		1595.006459641652 		-1256.94
---------------------------------------------------------------------------------
Degree 1: 		105.01778633851833 		0.68
Degree 2: 		109.10569803537199 		0.66
Degree 3: 		1123.4793102354063 		-35.19
Degree 6: 		185441.21308761396 		-986024.03
---------------------------------------------------------------------------------
Degree 1: 		28.269222892920038 		0.73
Degree 2: 		47.405288929894574 		0.23
Degree 3: 		55.005669883911004 		-0.04
Degree 6: 		10069.950456869094 		-34712.13
---------------------------------------------------------------------------------
Degree 1: 		25.580491200820568 		0.96
Degree 2: 		73.46282149176497 		0.67
Degree 3: 		241.73438207411132 		-2.53
Degree 6: 		5081.564815280062 		-1560.37
---------------------------------------------------------------------------------
Degree	Average RMSE	Average R^2
1	 43.453043	 0.788000
2	 81.345099	 0.380000
3	 332.627192	-7.778000
6	 40465.729863	-204710.710000
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%</span><span class="k">shell</span>
jupyter nbconvert --to html &quot;/content/drive/MyDrive/Colab Notebooks/AndyLee-NLP.ipynb&quot;
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<pre>[NbConvertApp] Converting notebook /content/drive/MyDrive/Colab Notebooks/AndyLee-NLP.ipynb to html
[NbConvertApp] Writing 777767 bytes to /content/drive/MyDrive/Colab Notebooks/AndyLee-NLP.html
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