reddit-jupyter.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>Project3</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
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 * Bootstrap v3.3.7 (http://getbootstrap.com)
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
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  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
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}
audio,
canvas,
progress,
video {
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}
audio:not([controls]) {
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[hidden],
template {
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a {
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a:active,
a:hover {
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abbr[title] {
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b,
strong {
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dfn {
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}
h1 {
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  margin: 0.67em 0;
}
mark {
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}
small {
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sub,
sup {
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  position: relative;
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}
sup {
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}
sub {
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}
img {
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}
svg:not(:root) {
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figure {
  margin: 1em 40px;
}
hr {
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pre {
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code,
kbd,
pre,
samp {
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button,
input,
optgroup,
select,
textarea {
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button {
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button,
select {
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}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
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}
button[disabled],
html input[disabled] {
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}
button::-moz-focus-inner,
input::-moz-focus-inner {
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input {
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input[type="checkbox"],
input[type="radio"] {
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input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
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input[type="search"] {
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}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
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fieldset {
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legend {
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textarea {
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optgroup {
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table {
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td,
th {
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/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this
new spec completely include:

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

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<p><img src="img/single-work/img-180530-12.jpg" alt="houses"></p>

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<h1 id="Using-Reddit's-API-for-Predicting-Comments"><span style="color:#37535e">Using Reddit's API for Predicting Comments</span><a class="anchor-link" href="#Using-Reddit's-API-for-Predicting-Comments">&#182;</a></h1><h2 id="What-characteristics-of-a-post-on-Reddit-contribute-most-to-what-subreddit-it-belongs-to?"><span style="color:#3b748a">What characteristics of a post on Reddit contribute most to what subreddit it belongs to?</span><a class="anchor-link" href="#What-characteristics-of-a-post-on-Reddit-contribute-most-to-what-subreddit-it-belongs-to?">&#182;</a></h2><p><span style="color:#4095b5">In this project, we will practice two major skills. </span></p>
<ul>
<li><span style="color:#4095b5">Collecting data via an API request. </span></li>
<li><span style="color:#4095b5">Building a binary predictor. </span></li>
</ul>
<p><span style="color:#4095b5">Your method for acquiring the data will be scraping threads from at least two subreddits.</span></p>
<p><span style="color:#4095b5">Once you've got the data, you will build a classification model that, using Natural Language Processing and any other relevant features, predicts which subreddit a given post belongs to.</span></p>

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<h2 id="Table-of-contents"><span style="color:#3b748a">Table of contents</span><a class="anchor-link" href="#Table-of-contents">&#182;</a></h2><ul>
<li><a href="#scrape_example"><span style='color:#4095b5'>Demonstrate scraping data from Reddit.</span></a></li>
<li><a href="#step1"><span style='color:#4095b5'>Define the problem.</span></a></li>
<li><a href="#step2"><span style='color:#4095b5'>Gather the raw data from Reddit.</span></a></li>
<li><a href="#step3"><span style='color:#4095b5'>Explore the data.</span></a></li>
<li><a href="#step4"><span style='color:#4095b5'>Model the data.</span></a></li>
<li><a href="#step5"><span style='color:#4095b5'>Evaluate the model.</span></a></li>
<li><a href="#step6"><span style='color:#4095b5'>Answer the problem.</span></a></li>
</ul>
<h2 id="Links"><span style="color:#3b748a">Links</span><a class="anchor-link" href="#Links">&#182;</a></h2><ul>
<li><a href="https://docs.google.com/presentation/d/1nz1mkpfFdeWlSgzQtfxWYRmkuJEpjlnBZvFUYD76EyM/edit?usp=sharing"><span style='color:#4095b5'>(WIP) Slide presentation</span></a></li>
<li><a href="https://github.com/gbkgwyneth/GA-DSI-project-03"><span style='color:#4095b5'>On GitHub</span></a><hr></li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let&#39;s get the administrative stuff done first</span>
<span class="c1"># import all the libraries and set up the plotting</span>

<span class="kn">import</span> <span class="nn">requests</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">combinations</span>

<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">BaggingClassifier</span><span class="p">,</span> <span class="n">RandomForestClassifier</span><span class="p">,</span> <span class="n">ExtraTreesClassifier</span><span class="p">,</span> <span class="n">AdaBoostClassifier</span><span class="p">,</span> <span class="n">GradientBoostingClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="k">import</span> <span class="n">stop_words</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">CountVectorizer</span><span class="p">,</span> <span class="n">HashingVectorizer</span><span class="p">,</span> <span class="n">TfidfVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">LogisticRegressionCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">cross_val_score</span><span class="p">,</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="k">import</span> <span class="n">MultinomialNB</span><span class="p">,</span> <span class="n">BernoulliNB</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KNeighborsClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="k">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">PolynomialFeatures</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="k">import</span> <span class="n">DecisionTreeClassifier</span>

<span class="kn">import</span> <span class="nn">matplotlib</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">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">style</span><span class="o">=</span><span class="s2">&quot;white&quot;</span><span class="p">,</span> <span class="n">color_codes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">colors_palette</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">(</span><span class="s2">&quot;GnBu_d&quot;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="n">colors_palette</span><span class="p">)</span>

<span class="c1"># GnBu_d</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;#37535e&#39;</span><span class="p">,</span> <span class="s1">&#39;#3b748a&#39;</span><span class="p">,</span> <span class="s1">&#39;#4095b5&#39;</span><span class="p">,</span> <span class="s1">&#39;#52aec9&#39;</span><span class="p">,</span> <span class="s1">&#39;#72bfc4&#39;</span><span class="p">,</span> <span class="s1">&#39;#93d0bf&#39;</span><span class="p">]</span>
</pre></div>

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<h2 id="Pre-work.-Demonstrate-scraping-Thread-Info-from-Reddit.com"><span style="color:#37535e">Pre-work. Demonstrate scraping Thread Info from Reddit.com</span><a class="anchor-link" href="#Pre-work.-Demonstrate-scraping-Thread-Info-from-Reddit.com">&#182;</a></h2><h3 id="Set-up-a-request-(using-requests)-to-the-URL-below."><span style="color:#3b748a">Set up a request (using requests) to the URL below.</span><a class="anchor-link" href="#Set-up-a-request-(using-requests)-to-the-URL-below.">&#182;</a></h3><p><span style='color:#4095b5'>*NOTE*: Reddit will throw a [429 error](https://httpstatuses.com/429) when using the following code:</span></p>
<div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">URL</span><span class="p">)</span>
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<p><span style='color:#4095b5'>This is because Reddit has throttled python's default user agent. You'll need to set a custom <span style="font-family:monospace">User-agent</span> to get your request to work.</p>
<div class="highlight"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">URL</span><span class="p">,</span> <span class="n">headers</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;User-agent&#39;</span><span class="p">:</span> <span class="s1">&#39;YOUR NAME Bot 0.1&#39;</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># For example:</span>
<span class="n">pre_work</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">pre_work</span><span class="p">:</span>
    <span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;http://www.reddit.com/r/boardgames.json&quot;</span>
    <span class="n">headers</span> <span class="o">=</span> <span class="n">headers</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;User-agent&#39;</span><span class="p">:</span> <span class="s1">&#39;gbkgwyneth Bot 0.1&#39;</span><span class="p">}</span>
    <span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">)</span>
    <span class="n">res</span><span class="o">.</span><span class="n">status_code</span>
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<h3><span style="color:#3b748a">Use <span style="font-family:monospace">res.json()</span> to convert the response into a dictionary format and set this to a variable.</h3>
<div class="highlight"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">if</span> <span class="n">pre_work</span><span class="p">:</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>

    <span class="c1"># Some initial exploring of the data</span>
    <span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="s1">&#39;kind&#39;</span><span class="p">]</span>
    <span class="nb">sorted</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>

    <span class="p">[</span><span class="s1">&#39;after&#39;</span><span class="p">,</span> <span class="s1">&#39;before&#39;</span><span class="p">,</span> <span class="s1">&#39;children&#39;</span><span class="p">,</span> <span class="s1">&#39;dist&#39;</span><span class="p">,</span> <span class="s1">&#39;modhash&#39;</span><span class="p">]</span>
    <span class="nb">sorted</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>

    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;children&#39;</span><span class="p">])</span>
    <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
    <span class="n">df</span><span class="o">.</span><span class="n">shape</span>
    <span class="p">[</span><span class="n">post</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">post</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;children&#39;</span><span class="p">]]</span>
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<h3 id="Getting-more-results"><span style="color:#3b748a">Getting more results</span><a class="anchor-link" href="#Getting-more-results">&#182;</a></h3><p><span style='color:#4095b5'>By default, Reddit will give you the top 25 posts:</span></p>
<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;children&#39;</span><span class="p">]))</span>
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<p><span style='color:#4095b5'>If you want more, you'll need to do two things:</span></p>
<ol>
<li><span style='color:#4095b5'>Get the name of the last post: <span style="font-family:monospace">data['data']['after']</span></li>
<li><span style='color:#4095b5'>Use that name to hit the following url: <span style="font-family:monospace">http://www.reddit.com/r/boardgames.json?after=THE_AFTER_FROM_STEP_1`</span></li>
<li><span style='color:#4095b5'>Create a loop to repeat steps 1 and 2 until you have a sufficient number of posts. </span></li>
</ol>
<p><span style='color:#4095b5'>*NOTE*: Reddit will limit the number of requests per second you're allowed to make. When you create your loop, be sure to add the following after each iteration.</span></p>
<div class="highlight"><pre><span></span><span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="c1"># sleeps 3 seconds before continuing</span>
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<p><span style='color:#4095b5'>This will throttle your loop and keep you within Reddit's guidelines. You'll need to import the <span style="font-family:monospace">time</span> library for this to work!</p>

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<p><hr>
 <a name="step1"></a></p>
<h2 id="I.-Predicting-posts-from-the-Cape-Cod-and-Galveston-sub-Reddits"><span style="color:#37535e">I. Predicting posts from the Cape Cod and Galveston sub-Reddits</span><a class="anchor-link" href="#I.-Predicting-posts-from-the-Cape-Cod-and-Galveston-sub-Reddits">&#182;</a></h2><p><span style='color:#4095b5'>With no experience of Reddit, I had a challenge underatnding how it worked, what people discuss, and what types of "topics" would be interesting to predict. I settled on predicting posts from the <a href="https://www.reddit.com/r/CapeCod/">r/CapeCod</a> versus the <a href="https://www.reddit.com/r/galveston/">r/galveston</a>. If I were to start-over, I would definitely try to find more interesting topics, but I am not sure that I have yet spent enough time with Reddit to be able to pedict what those might be.</span></p>
<table><tr><td><img src="img/single-work/cape_cod.jpg" width=350></td><td><img src="./img/single-work/galveston.jpg" width=350></td></tr></table>
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<p><a name="step2"></a></p>
<h2 id="II.-Gather-the-raw-data-from-Reddit."><span style="color:#37535e">II. Gather the raw data from Reddit.</span><a class="anchor-link" href="#II.-Gather-the-raw-data-from-Reddit.">&#182;</a></h2><p><span style='color:#3b748a'>Save the scraped data as two CSV files or read previously scraped data from CSV files into two DataFrame.</span></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Function to scrape the data </span>

<span class="k">def</span> <span class="nf">scrape_data</span><span class="p">(</span><span class="n">url</span><span class="p">,</span><span class="n">after</span><span class="p">):</span>

    <span class="n">headers</span> <span class="o">=</span> <span class="n">headers</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;User-agent&#39;</span><span class="p">:</span> <span class="s1">&#39;gbkgwyneth Bot 0.1&#39;</span><span class="p">}</span>

    <span class="n">posts</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">40</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">after</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">params</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;after&#39;</span> <span class="p">:</span> <span class="n">after</span><span class="p">}</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="n">params</span><span class="p">,</span> <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">res</span><span class="o">.</span><span class="n">status_code</span> <span class="o">==</span> <span class="mi">200</span><span class="p">:</span>
            <span class="n">data</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
            <span class="n">posts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;children&#39;</span><span class="p">])</span>
            <span class="n">after</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;data&#39;</span><span class="p">][</span><span class="s1">&#39;after&#39;</span><span class="p">]</span>
            <span class="nb">print</span><span class="p">(</span><span class="n">after</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Problem with request!&quot;</span><span class="p">)</span>
            <span class="k">break</span>
        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">posts</span>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Do we want to scrape data or read from a csv file?</span>
<span class="n">scrape</span> <span class="o">=</span> <span class="kc">False</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scrape posts following &#39;after&#39; from the first URL </span>
<span class="c1"># Place them in a dataframe</span>
<span class="c1"># Export to a file</span>

<span class="k">if</span> <span class="n">scrape</span><span class="p">:</span>
    <span class="n">after</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># &quot;t3_4kqn0c&quot;</span>
    <span class="n">url_gv</span> <span class="o">=</span> <span class="s2">&quot;https://www.reddit.com/r/galveston.json&quot;</span>
    <span class="n">scrape_gv</span> <span class="o">=</span> <span class="n">scrape_data</span><span class="p">(</span><span class="n">url_gv</span><span class="p">,</span> <span class="n">after</span><span class="p">)</span>
    <span class="n">posts_gv</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">scrape_gv</span><span class="p">)):</span>
        <span class="n">posts_gv</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scrape_gv</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">])</span>
    <span class="n">df_gv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">posts_gv</span><span class="p">)</span>
    <span class="n">df_gv</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;title&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">df_gv</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;../data/reddit_gv.csv&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scrape posts following &#39;after&#39; from the second URL </span>
<span class="c1"># Place them in a dataframe</span>
<span class="c1"># Export to a file</span>

<span class="k">if</span> <span class="n">scrape</span><span class="p">:</span>
    <span class="n">after</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># &quot;t3_35fh6e&quot;</span>
    <span class="n">url_cape</span> <span class="o">=</span> <span class="s2">&quot;https://www.reddit.com/r/CapeCod.json&quot;</span>
    <span class="n">scrape_cape</span>  <span class="o">=</span> <span class="n">scrape_data</span><span class="p">(</span><span class="n">url_cape</span><span class="p">,</span><span class="n">after</span><span class="p">)</span>

    <span class="n">posts_cape</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">scrape_cape</span><span class="p">)):</span>
        <span class="n">posts_cape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scrape_cape</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;data&#39;</span><span class="p">])</span>
    <span class="n">df_cape</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">posts_cape</span><span class="p">)</span>
    <span class="n">df_cape</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;title&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If not scraping, read from csv</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">scrape</span><span class="p">:</span>
    <span class="n">df_cape</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../data/reddit_cape.csv&quot;</span><span class="p">)</span>
    <span class="n">df_gv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../data/reddit_gv.csv&quot;</span><span class="p">)</span>
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<p><hr>
<a name="step3"></a></p>
<h2 id="III.-Explore-the-data."><span style="color:#37535e">III. Explore the data.</span><a class="anchor-link" href="#III.-Explore-the-data.">&#182;</a></h2><p><span style="color:#3b748a">Use <span style="font-family:monospace">CountVectorizer</span> or <span style="font-family:monospace">TfidfVectorizer</span> from scikit-learn to create features from the thread titles and descriptions (NOTE: Not all threads have a description).</p>
<h3 id="Create-the-DataFrame-and-the-target-vector"><span style="color:#3b748a">Create the DataFrame and the target vector</span><a class="anchor-link" href="#Create-the-DataFrame-and-the-target-vector">&#182;</a></h3><p><span style="color:#4095b5">Where <span style="font-family:monospace">y=1</span>, the post is from r/CapeCod.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create the target vector </span>
<span class="n">df_gv</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">df_cape</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>

<span class="c1"># Merge the dtafrmaes</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df_gv</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_cape</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">,</span> <span class="s1">&#39;title&#39;</span><span class="p">,</span><span class="s1">&#39;id&#39;</span><span class="p">]]</span>
<span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;id&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># Split the target vector from the dataframe</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;is_cape&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</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</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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      <th>9coabb</th>
      <td>Do NOT Joyride in the high water in The Strand...</td>
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      <th>9cn6wj</th>
      <td>Late-morning commute</td>
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      <th>9cp0as</th>
      <td>Any interest in learning Arduino or Python her...</td>
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      <th>9buy3r</th>
      <td>Waves of Galveston- Iron and Wine</td>
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      <th>9aqjjh</th>
      <td>Higher bond rating could help boost Port</td>
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<h3 id="View-the-word-clouds"><span style="color:#3b748a">View the word clouds</span><a class="anchor-link" href="#View-the-word-clouds">&#182;</a></h3><p><span style="color:#4095b5">The <span style="font-family:monospace">WordCloud</span> is from <a href="http://peekaboo-vision.blogspot.com/2012/11/a-wordcloud-in-python.html">Peekaboo</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># From https://stackoverflow.com/questions/16645799/how-to-create-a-word-cloud-from-a-corpus-in-python</span>
<span class="kn">from</span> <span class="nn">wordcloud</span> <span class="k">import</span> <span class="n">WordCloud</span><span class="p">,</span> <span class="n">STOPWORDS</span>
<span class="n">stopwords</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">STOPWORDS</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">show_wordcloud</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">title</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
    <span class="n">wordcloud</span> <span class="o">=</span> <span class="n">WordCloud</span><span class="p">(</span>
        <span class="n">background_color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span>
        <span class="n">stopwords</span><span class="o">=</span><span class="n">stopwords</span><span class="p">,</span>
        <span class="n">max_words</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
        <span class="n">max_font_size</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
        <span class="n">scale</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
        <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span> <span class="c1"># chosen at random by flipping a coin; it was heads</span>
    <span class="p">)</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>

    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">title</span><span class="p">:</span>
        <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
        <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">top</span><span class="o">=</span><span class="mf">2.3</span><span class="p">)</span>

    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">wordcloud</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_wordcloud</span><span class="p">(</span><span class="n">df_gv</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_wordcloud</span><span class="p">(</span><span class="n">df_cape</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>
</pre></div>

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


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<img 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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Why Python and others</span>
<span class="c1"># df_gv[df_gv[&#39;title&#39;].str.contains(&quot;Texas&quot;)]</span>
<span class="c1"># df_cape[df_cape[&#39;title&#39;].str.contains(&quot;Texas&quot;)]</span>
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<h3 id="Train/test-split"><span style="color:#3b748a">Train/test split</span><a class="anchor-link" href="#Train/test-split">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Train/Test split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
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<h3><span style="color:#3b748a">Run a simple <span style="font-family:monospace">CountVectorizer</span> and explore.</h3>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cv_simple</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">&#39;english&#39;</span><span class="p">)</span>
<span class="n">X_train_cv</span> <span class="o">=</span> <span class="n">cv_simple</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>

<span class="n">cv_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train_cv</span><span class="o">.</span><span class="n">todense</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="n">cv_simple</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">())</span>
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<h4 id="Create-a-dataframe-with-counts-of-most-common-words"><span style="color:#4095b5">Create a dataframe with counts of most common words</span><a class="anchor-link" href="#Create-a-dataframe-with-counts-of-most-common-words">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a data frame of the most common words </span>

<span class="n">n_words</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">cv_train</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)[:</span><span class="n">n_words</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="n">cv_train</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">values</span>
<span class="n">cv_train_small</span> <span class="o">=</span> <span class="n">cv_train</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;is_cape&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()[</span><span class="n">words</span><span class="p">]</span>
<span class="n">cv_train_small</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>galveston</th>
      <th>cape</th>
      <th>cod</th>
      <th>beach</th>
      <th>island</th>
      <th>looking</th>
      <th>best</th>
      <th>good</th>
      <th>weekend</th>
      <th>day</th>
      <th>...</th>
      <th>places</th>
      <th>county</th>
      <th>yarmouth</th>
      <th>tonight</th>
      <th>local</th>
      <th>free</th>
      <th>seawall</th>
      <th>town</th>
      <th>help</th>
      <th>ma</th>
    </tr>
    <tr>
      <th>is_cape</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
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  <tbody>
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      <td>294</td>
      <td>0</td>
      <td>0</td>
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      <td>59</td>
      <td>21</td>
      <td>23</td>
      <td>14</td>
      <td>21</td>
      <td>12</td>
      <td>...</td>
      <td>9</td>
      <td>19</td>
      <td>0</td>
      <td>5</td>
      <td>9</td>
      <td>10</td>
      <td>19</td>
      <td>10</td>
      <td>3</td>
      <td>0</td>
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      <td>119</td>
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      <td>7</td>
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      <td>21</td>
      <td>26</td>
      <td>17</td>
      <td>24</td>
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      <td>1</td>
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      <td>14</td>
      <td>10</td>
      <td>9</td>
      <td>0</td>
      <td>8</td>
      <td>15</td>
      <td>18</td>
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  </tbody>
</table>
<p>2 rows × 40 columns</p>
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<h4 id="Plot-the-most-common-words"><span style="color:#4095b5">Plot the most common words</span><a class="anchor-link" href="#Plot-the-most-common-words">&#182;</a></h4>
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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Adapted from https://matplotlib.org/examples/api/barchart_demo.html</span>

<span class="n">words_cape</span> <span class="o">=</span> <span class="n">words</span>
<span class="n">words_count_cape</span> <span class="o">=</span> <span class="n">cv_train_small</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">words_gv</span> <span class="o">=</span> <span class="n">words</span>
<span class="n">words_count_gv</span> <span class="o">=</span> <span class="n">cv_train_small</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>       <span class="c1"># the width of the bars</span>
<span class="n">ind</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">n_words</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">rects1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">words_count_cape</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">rects2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="o">+</span><span class="n">width</span><span class="p">,</span><span class="n">words_count_gv</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>

<span class="c1"># add some text for labels, title and axes ticks</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Counts&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Counts by word and reddit&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind</span> <span class="o">+</span> <span class="n">width</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">words</span><span class="p">,</span><span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">80</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">rects1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects2</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="s1">&#39;Cape Cod&#39;</span><span class="p">,</span> <span class="s1">&#39;Galveston&#39;</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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xuyyy675Ec/+lHe//7354QTTsiee+6Z008//cH7PfrRj04pJZdddlnuu+++fP/738+OO+6YAw44IB/+8Idz8sknZ/vtt8+xxx6bq6++OmuvvXaOOeaYzJ8/P3feeWde9apX5XGPe1yOPPLInHfeebnpppvy1a9+NaeeemrOPPPM1FqTJBtttFGOO+64vOENb8hpp502KfusZw4AAGjehhtumGuuuebB20cffXSS5NWvfnU22WSTfPazn80aa6yRu+66K2uttdZDHvvqV786Z5xxRm699da84AUvyKxZs3LDDTfkwAMPTJIsWrQom2++ebbffvv8/Oc/z957751Zs2Zlr732ekidG264Ic94xjMyY8aMzJ49O1tvvXVuuOGGJMmWW275YDuvuOKKSdlnPXMAAEDzdtppp/znf/5nrrzyygeX/eIXv8jNN9+c973vfXn729+eQw89NL1eL0NDQw957LOf/exce+21+drXvpZdd901SbL55pvn0EMPzcknn5z3vve92WGHHXLJJZdk/fXXz/HHH5+99torRxxxRJJkxowZWbx4cbbYYosHh1guWrQoP/jBD7Lppps+eJ/JpmcOAABo3pprrpmjjz46hx9+eD75yU/m/vvvz6xZs3LQQQflZz/7Wfbee+885jGPyYYbbpjbbrvtIY+dMWNGdt5553zve997MHx95CMfyfvf//488MADSZKPf/zjWXfddfPOd74zJ554YlZbbbXss88+SZJnPOMZectb3pKTTjop3//+97Pbbrtl0aJF2WWXXSbl2LjRzFgyla4qSimbJbnx3HPPzcYbb7yym7PClnUK1tFOpzp3192XuvzihSeOefuTWQsAAFh5brrppuy0005Jsnmt9edLrjfMEgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDZg2qcClljyR79G+ukWSbJPOSfCrJ/UnOqbUeOKjtAwAATGcD65mrtZ5Qa51Xa52X5PIkb0/yuSSvTfK8JM8qpWw3qO0DAABMZwMfZllKeUaSpyb5SpLVa6031FqHkpydZKdBbx8AAGA6mopj5j6Y5MAkj0pyx4jlf0iyzhRsHwAAYNoZaJgrpayb5Cm11vPSBbm1R6xeO8ntg9w+AADAdDXonrntk3wrSWqtdyS5r5SyRSllRpKdk1w04O0DAABMSwM7m2VfSfKzEbffmuSUJDPTnc3ykgFvHwAAYFoaaJirtR62xO2Lk8wd5DYBAAAeDkwaDgAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGzRpk8VLKB5K8LMmcJJ9NckGSE5IMJbkmyT611sWDbAMAAMB0NLCeuVLKvCTPSfLcJDsk2STJEUnm11qfn2RGkpcPavsAAADT2SCHWe6c5IdJzkjy70nOTPL0dL1zSXJWkhcOcPsAAADT1iCHWT42yaZJXppk8yT/lmS1WutQf/0fkqwzwO0DAABMW4MMc79L8pNa631Jainl3nRDLYetneT2AW4fAABg2hrkMMvvJNmllDKjlLJRkjWTnNs/li5JXpzkogFuHwAAYNoaWM9crfXMUsr2Sb6fLjTuk+TGJMeUUuYkuTbJwkFtHwAAYDob6NQEtdb3LWXxDoPcJgAAwMOBScMBAAAaJMwBAAA0aKDDLGFFHHXV+Utdvu/W86a0HQAA0BI9cwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANmjXI4qWUHyT5ff/mjUk+n+RTSe5Pck6t9cBBbh8AAGC6GliYK6WskSS11nkjll2Z5O+S/CzJf5RStqu1XjGoNgAAAExXg+yZ2zrJI0sp5/S385Ekq9dab0iSUsrZSXZKIswBAACM0SDD3N1JPpnk2CRPTnJWkttHrP9DkicOcPsAAADT1iDD3HVJrq+1DiW5rpTy+ySPHrF+7Tw03AEAALCCBnk2yzclOTxJSikbJXlkkrtKKVuUUmYk2TnJRQPcPgAAwLQ1yJ6545KcUEr5TpKhdOFucZJTksxMdzbLSwa4fQAAgGlrYGGu1npfktcuZdXcQW0TAADg4cKk4QAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQINWOMyVUh7fv3x+KWWfUsojBtcsAAAAlmWFwlwp5egkHyul/HmSU5Nsl+SYQTYMAACA0c1awfv9ZZJnJPlwkuNqrR8ppVw6uGaxKpi76+6jrrt44YlT2BIAAGBJKzrMcmb/vi9PclYp5ZFJ1hxYqwAAAFimFQ1zJyX5TZKf11ovSXJZks8PrFUAAAAs04oOszw7yT/VWhf3b2+f5EmDaRIAAADLs8wwV0p5brohlscmeXMpZcaIx30uSW+wzQMAAGBpltcz91dJdkjy+CQfHbH8/hhmCQAAsNIsM8zVWj+SJKWUN9RaT56SFgEAALBcK3rM3IWllMOSPDrJ8FDL1FrfNJBWAQAAsEwrGua+muSi/s/Q4JoDAADAiljRMDe71vqegbYEAACAFbai88x9p5TyN6WUOQNtDQAAACtkRXvmdk2yb5KUUoaXDdVaZy7rQaWU9ZNcnu6smPcnOSHdMM1rkuwzYt46AAAAxmCFwlytdaOxFi6lzE43fcE9/UVHJJlfaz2/lPK5JC9PcsZY6wIAALCCYa6U8qGlLa+1fnRpy/s+mW5i8Q/0bz89yQX962cleVGEOQAAgHFZ0WPmZoz4mZPkZUk2GO3OpZQ9ktxaaz17ZI1a6/CZMP+QZJ0xtxYAAIAkKz7M8sCRt0spByU5ZxkPeVOSoVLKC5Nsk+SkJOuPWL92ktvH1lQAAACGrWjP3JLWSvKE0VbWWrevte5Qa52X5Mokb0xyVillXv8uL043Zx0AAADjsKLHzN2YP00WvlqS9ZJ8YozbeneSY/rTG1ybZOEYHw8AAEDfik5NMG/E9aEkt9da71iRB/Z754btsILbAwAAYBlWdJjlL5O8JMnhSf45yR6llPEO0QQAAGCCVrRn7hNJnpzk+HRntNwzyRZJ9htQuwAAAFiGFQ1zL0qyba11cZKUUv4jyQ8H1ipWeUdddf5Sl++79bwpbQcAADxcrehQyVlJZi9x+4HJbw4AAAArYkV75k5Jcl4p5cv9269JcupgmgQAAMDyLDfMlVLWS3JMkiuS7JTkBUn+qdZ68oDbBgAAwCiWOcyylLJtkh8neXqt9Ru11vcmOTvJglLKVlPRQAAAAP635R0z98kkr6m1fmN4Qa31g0nelOSIQTYMAACA0S0vzK1Xaz1/yYW11rOTPHYgLQIAAGC5lhfmZi9tcvD+sjmDaRIAAADLs7wwd0GSDy9l+fwkl01+cwAAAFgRyzub5QeSfL2UsnuSK5Pcm2S7JLckedmA2wYAAMAolhnmaq1/KKVsn2THJNsmWZzkM7XWi6aicQAAACzdcueZq7UOJfl2/wcAAIBVwPKOmQMAAGAVtNyeOVjVzN1191HXXbzwxClsCQAArDx65gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaNCsQRUupcxMckySkuSBJHsmmZHkhCRDSa5Jsk+tdfGg2gAAADBdDbJn7m+SpNb63CQfSnJE/2d+rfX56YLdywe4fQAAgGlrYGGu1vovSd7Sv7lpkv9O8vQkF/SXnZXkhYPaPgAAwHQ20GPmaq33l1JOTPLpJAuTzKi1DvVX/yHJOoPcPgAAwHQ18BOg1Fp3T9JLd/zcI0asWjvJ7YPePgAAwHQ0sDBXSnlDKeUD/Zt3J1mc5LJSyrz+shcnuWhQ2wcAAJjOBnY2yySnJ/liKeXCJLOTvCPJtUmOKaXM6V9fOMDtAwAATFsDC3O11ruSvHopq3YY1DYBAAAeLkwaDgAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANCgWSu7AUBb5u66+6jrLl544hS2BADg4U3PHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQqQmAae+oq84fdd2+W8+bsnYAAEwmPXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQaYmgEk0d9fdl7r84oUnTnFLAACY7vTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBs1Z2A4DBm7vr7qOuu3jhiVPYEgAAJoueOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgUxPAKI666vxR1+279bwpawcwMaNNzWFaDgBap2cOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANMjUBAA0Y2VPGWKaAwB2nuj0AAAgAElEQVRWJXrmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHCHAAAQINMTcC0MtbTlo92mvEkef0Be05Ciybfyj41OwAAqwY9cwAAAA0S5gAAABo0kGGWpZTZSY5PslmS1ZN8LMmPk5yQZCjJNUn2qbUuHsT2AQAAprtB9cy9Psnvaq3PT/LiJEclOSLJ/P6yGUlePqBtAwAATHuDCnP/L8kBI27fn+TpSS7o3z4ryQsHtG0AAIBpbyDDLGutdyZJKWXtJAuTzE/yyVrrUP8uf0iyziC2DQAA8HAwsKkJSimbJDkjyWdrraeWUj4xYvXaSW4f1LYBmFrLmubj4oUnTmFLaIVpVgbv4fK+HG0/p9M+wmgGMsyylLJBknOSvL/Wenx/8Q9KKfP611+c5KJBbBsAAODhYFA9cx9Msl6SA0opw8fO7Zfkn0spc5Jcm274JQAAAOMwqGPm9ksX3pa0wyC2BwAA8HBj0nAAAIAGCXMAAAANGtjZLAEAYCyc5XTl8vy3R88cAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJCpCQAAgGll7q67j7ru4oUnTmFLBkvPHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQqQlgFTXaKXVff8CeU9ySleOoq84fdd2+W88b+PZHe/6n0+mMHy6nbQaA6UrPHAAAQIOEOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQqQkAponRpnOYiqkcAICpp2cOAACgQcIcAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANMjUBMBKM3fX3Udd9/oD9pzClgAAK9to3wsuXnjiFLekHXrmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHOZglT4Kirzh913b5bz5uydrByODsXrHq8L1maZZ1l2WuDVZGeOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgUxMAwErgFOgATJSeOQAAgAYJcwAAAA0S5gAAABokzAEAADRImAMAAGiQMAcAANAgUxPAw9xRV52/1OX7bj1vStvxcDTac594/lk1rezX7GjTOTxcpnLw9xpYkp45AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBTEwDANOZ09kyG0aaFSB4+U0PAqkjPHAAAQIOEOQAAgAYNdJhlKeVZSQ6ttc4rpTwpyQlJhpJck2SfWuviQW4fAABguhpYz1wp5X1Jjk2yRn/REUnm11qfn2RGkpcPatsAAADT3SCHWd6Q5JUjbj89yQX962cleeEAtw0AADCtDSzM1Vq/lmTRiEUzaq1D/et/SLLOoLYNAAAw3U3l1AQjj49bO8ntU7htgEkx2mnek6k51fuyTg/++gP2HPj2WTV5Xaxc43n+TQ0x/U3VdA4r+3NpNKtqu6abqTyb5Q9KKfP611+c5KIp3DYAAMC0MpU9c+9OckwpZU6Sa5MsnMJtAwAATCsDDXO11p8nmdu/fl2SHQa5PQAAgIcLk4YDAAA0SJgDAABo0FQeMwcArOKcGZOxctbCsRvtORvt+fK+ZDR65gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDTE0ATJqxnmoZWDrvpbGZTqfGdwp6WjPaa9brdWromQMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGCXMAAAANMjUBU8KplqEtTo0PAKs+PXMAAAANEuYAAAAaJMwBAAA0SJgDAABokDAHAADQIGEOAACgQaYmAADoG21ajsTUHMCqR88cAABAg4Q5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJCpCQAAYDlGm7bClBWsTHrmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBhDgAAoEHOZgnAQI12BrjEWeAAYCL0zAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDhDkAAIAGmZoAAABYZZniZnR65gAAABokzAEAADRImAMAAGiQMAcAANAgYQ4AAKBBwhwAAECDTE0AwCpl7q67j7ru9QfsOYUtAYBVm545AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJAwBwAA0CBTEwDwsHTUVeePum7fredNWTumM9NMMJrRXhteFzA2euYAAAAaJMwBAAA0aEqHWZZSVkvy2SRbJ/ljkn+otV4/lW0AAACYDqa6Z+5vk6xRa312kv2THD7F2wcAAJgWpjrMPS/JN5Kk1npxkmdM8fYBAACmhak+m+Wjkvx+xO0HSimzaq33L+W+M5Pk5ptvnpKGTZY7bvntqOtuuummpS5/4L57x3T/ZZmKWsno+znWfZyqWqPVm8xay6q3qtZaVr1Vtday6k1mLb9L78ux1PI3dvr8Lh8u78uxvmZb/F16X/q8HOtjVkUjstDMpa2fMTQ0NGWNKaUckeTiWutX+7dvqrVuPMp9n5fkoilrHAAAwKrp+bXW7yy5cKp75r6b5G+SfLWUMjfJD5dx30uTPD/Jb5I8MAVtAwAAWJXMTPL4dNnof5nqnrnhs1lulWRGkj1rrT+ZsgYAAABME1Ma5gAAAJgcJg0HAABokDAHAADQIGEOAACgQcIcAABAg4S5VVAp5VGllL8opay5stvC9FNKmb2y27AspZTVSikblFJmrOy2tKCU8viV3YalKaXsuLLb8HBVSpm/xO1DVlZbllRKec/KbgOsqFLKxkvcLiurLTAaZ7OcBKWUTZK8Jskaw8tqrR8dZ61dk/xjujkAv5pkqNb6sTHW+NBo68bbrn7dzZLsmuSR461XSjkvyVJfdLXWF4yzXW9cYtGiJL9a2sSKK1DrqFrrviNun1RrXbL+8mpM2j6WUrYfbV2t9cKx1BpR8+ok305ybK31mvHUWErNnZI8McklSa6rtd47zjqvTHJEktuSrJ1kr1rrN8dZ67IkX0pyUq31f8ZTY0StmUn2SPKEJOcluabW+ttx1np0kp2TzE43RctGtdZxf9kupZydZPUk/57k9FrrjROoNX/k35tSyiG11g+Ms9aFtdZRX79jrPX4JOsluT/J+5N8utZ65ThrLfn3cVGSXyU5rda6aJw1V0v3u3xOkktqrfeNo8a7071Wbx1PG/o13pzkH5JsmeTH/cUzk8yutW43gbprp3veH5/kP5JcXWu9fpy1vp3kr2qt454/doCfcZP5Pp/MWk9YYtGiJL8dz+t1kr+vrJnufbkoyVvSvX5/Mc5aE379j6i1TpK/ykO/q5w0xhpPS/JnSQ5N8r7+4plJDqm1bjOONs0Zbd04/14Mt+1xSRame09eMo46v0n3fWXJf54O1Vo3Gmu9fs1t0r0eRr7G3jSeWkvULEl+XGtd1hzVy6szK8kz89DP3y9PpG2rgqmeNHy6+n9JvpXuC8FEvTPJ3CTfSPKxJJf1L8fiv/uXf5vkxnSTtT8z3YfKRHy5366bJ1Djrf3LDyf5l3Rt+8skL51Azb9PsmaS7/VrrZHk/lLKFbXWd65IgVLKPknmJ3l0P1DM6P/8aBztmcx93Kt/uUWSOekmjNw2yZ1J5o2jXpJsk2SXJB8upTwuXeD5Sq31zvEUK6UcnGTjdF8g70vygXRfFsbjgCR/WWu9pZSyQbqAMq4wl+SFSV6b5N9LKb9KF16/Nc5an0/y63RfEC5LclKSl4yz1sIk1yX5iyT3Jrl7nHWSJLXWnUspj0ry4iSnlFIeUWvddiw1RoaAUsrwfs1M94E3rjCXZKiUckaSmmRxv60fHGetk5IcnGSfdM/fkUnG2/O3dZJ7klyU7m/tJkl+ky5gv2GsxUophyb5WZJNk2yX7u/v7uNo111J/qX/5eq4JN+otY71v61fSnJukg8m+Xh/2eIkt4yjPSMdn+SsJDv023Zc//p4PDbJr0spN6b7EjlUa33OGGsM6jNuMt/nk1nrzHR/Y2uSXrrXyqxSyvtqrV8aY63J/L5ySpIvJvm7dP88+EK699F4TMbrf9gZSX6RP31XGU+d9dJ9t9ggf/o8W5xuruTxqBklNKX7R+hYfSHJ4ek+My9McmK6v2dja1StgxjZcUKSozI5r7GUUj6W5AXp/lm8XynljFrrYeMsd3q671J/lu4z7tfpvtu2bWhoyM8Ef3q93jcnsdZF/ctv9y8vnECtsyeznb1e79xJ3M9zl7j97Yk8/71eb7X+9dV6vd43+te/N45aH1xF9/E/er3erP71mcP7OIF6M3q93ot7vd7Xer3eVb1e77u9Xu8t46x1Yf/yvP7lxRNo17eWdXucNbfs9Xqn9Hq9W3q93iW9Xu+vx1FjeN+G35ffnUB7hmsc33+9fmeC+/fyXq93VK/Xu7T/+xzz77HX663e6/U26/V6X+j1epv2fzbp9XqrT6Bduy/5M4Fa5/Vf998a+ZobZ60l35fn9C/H9XsYfi2MeI1M6O9kr9d7aq/XO7XX6/2y1+t9pNfrrTuOGmv2er2Ne73eBr1e74Ber7fpBNv07SUuJ/L8b7rkzwRqTfZn3PDvcDLe55NZ6197vd5j+9fX67/PH93r9S4ZR63J/L5yQf+z5Nz+7fMnoeZkvP7Pm8R93G6yak3mz4jnfPj1Na7nvtfrfbn/fP+vnwm0bULfT5ZS77IR3/Fm9nq970+g1gX9y2N7vd4jJvr5u6r86JmbHNeUUv4+yQ/S/w9QrfW6cda6qJTy5SQbl1I+l64nZrweU0rZotZ6Q3+c96PGU6SU0utf/e9SymuTXJ6J7+dwb8D30w1NmkjvxGPS9SD8sX/56P7y1cdR64ullD/Pn4Zz/XOt9arxNmwS93Hkf89mJVl/Am36RJKXJ7kgyaG11u/3h4ldnu6/fWM1q5SyRrqemJlJxj18Kskd/WGDFyR5RpJH9nv+xtyrU0rZO8kbk9yR5Jh0Q55mJ7k43VCxsZhVSnlsv+7a6fc0jVf/+Voz3ftorYnUSjfU5t4kC9L9N/v2sRaotf4xyc/7z9nu+dPQsHvSva/G45R0z/km/VoTGdI7J93w2wv7x+JN5LNr3VLKY2utvy2lPCbJOv3jSB+5vAeOYmYp5S/TPX9z0g17GrNSyrrpegLemOT2JPul289/zdh7wSazx2S4fU/pX26cib3H788Sw8PS9aKMx6R8xo0wme/zyay1wfAQzVrrbaWUDWqt/1NKGU/Nyfy+MifJu5Nc0f/cHPffssl4/Y8YyvizUsqzk1yRP+3jmIcy9j29lHJykkcML6i1jqcnbbiNL0s3wmB4mN9jaq1bjaPUH0spO6f7+zM33WfAeHxunI9blp+XUvbPQ19j50yg3k3pDrv4fbrn7b+Xffdlur9/uWat9Z5Syni+J65yhLnJsU3/Z9hQui7hMau1frCUsku6P0LX1lrPnEC73pHky6WUjdINNxjzEKK+z4+4/n/6P8kE9jPJ69J9CPxdkmuT7DbOOknymSRXl1J+lOQpST5RSvlguiGhY7XkcK5/yviHcw3v4yuT/CQT28fjkvyolHJNkj9PN4RzvH6a5Okjh1XWWheXUl4xznpHpguCj0s3DOLICbTtX0dc/68J1Em6YRSvWeIYskWllP87jlrz0w3leny6MPiOCbTrM+mGU5+TbhjKmI/tHKnW+pT+8aw7Jzm9lPLIWuuYh9v0fS6TNzRsMmvt0a9zXLp/RLx+nHWS7r1zSSnljnRfPt+W7n163DjrnZTk00nelOQTST41zjqXphsmuVut9cHhSf1jRcZqvST/lmS/Wusb+58pE/H2dOFwy3R/F/eeQK1JGR7WN1mfccOWfJ/vN4Fa/ziJtS7v/5P3P5M8O8mVpZTdMr4vtZP2fSXd++Zv0w3pfV0m9rqYjNf/yKGMI/dpvEMZk+6wiZdkYoeXjPShdH9z3prun1x/Nc46b0nyyXTDlt+TPx2SMSa11guSpR/Lne6fquOxerrj24ZPFjOU7vNuvDZKcl0p5ap033/uK6V8L0nGMUT79FLKAUmuKqX8Z7qA2DxhbhLUWnfs/4d3iyQ/G+9BzklSSlk/3bEvJckGpZTv1lpvG2e7vlNKefFE21Vr3bHftjWSbFlr/UEp5W8z9t6NkTVvLqUclj8dIPuYdGPmx1PruFLKvyR5UpLra62/K6XMHOcB9rPSfcH4x1rrV/o9FePS38fzk9za3azj2r9+rc/0/zv4lEzwNZbk/OT/t3fu8bqPZf5/2/bWLnSSQjopfaiMaVR+FbsklV/JdDD5TU0hYiSJ6fer5Bc51HRQlCbHHCqnVNIYh0KMpDRDhI8QxnFHg4qS9p4/ru/Xevay99qe+76XZ621r/frtV/rYD23ez3reb7f+7quz/W5+EBXjegbgHe0fUPh3k6W9BNgNeAO2zdV7O27RBZ2sHH6pGEW6KqDyxMHz5u7bO0s4HTbr7F90bCb6m54krRqbYO+7VO6fT4JONn2vTXrSfob4rCxGVH9Her5GsdzbW8vaSPbp3XZ1dq1Nm6w1q+JLO+GxAF2Q6JPbWhsf1/S6UTyYX7Xl1OS+OnX+wpjfTQ1Qf7zB3uEJK1u+zbbexas1VdMfl5bMen4FbDzwLW/2IAAmGv7HIXZjiWVVhT6e9xmRL/i9aV9vwP8OLakVYE7KQ8AAO4ZXKui/wvb7+8qOusCx9k+vatEnlaw3GHAaQ2eK2z/WNLjgK2IHtRipQ7wGduH919I2tX2wcO8/m0/p3vsS20/pGqS9OqKfd1ZauqyBO6yfZGknWwfLWnbkkVs36wwAnoeUd2uTX627OW+BjilRrk1jq0arQORQH0dcY28j7FK3bQmg7kGSNqKMCm5CniRpL0LmpJ7Tuz+HQW8EjiOQnMQSX8H7EvIbNar3BdE1uwHxKHq+cDfEQYTJXv7ChG03kYEFAsJKWLJWos4J0mqcU5qJudS2IGvTVRe3iNpnu09Ctdq+TseQxwCNiIqJ1UHve6G8gTbe0g6WdIltv+5cLmziNdrLxVcyPDByXaEAcRqRKZ2OULidEHhnuiqeTsCc9U5U9t+QeFa84jD//LAyZJutF1aFYKocJwCvNl2bZZxMmRmCxus1axpvTv8f4hF30tDVyYkfcv22/VwN7hSF7i9u+TRCoTk8xrghQXrQNuKCYRss8m1n3byMCS9jaimzQZOkjS0+/M4jgfebvs33Xt+D+L3LWG/Lsn7tW7d4uCpe//MJe6XT5H0bg/pzjjAWsDpku4m3lenliaM1cD8StL/Ad4MbKKxcSbLAy8CDh5yrY2Iys3ukg7svj0L2KVbb5i1Dug+XUEh/R+UbJYaOUG8/ucBc7r3QZEBiaRdgLcQbSVHE2eNXSZ6zNKwvZOkowgzrCK37I6bgE8qnFPPJlyWfzHsIt0ZaknO4KV/g88S9/Ki1/xUJYO5NuxOJ1vrLrrnEIFPEbZ7DfNlXUBWyoda7gt4er83259RWPCX8jIic1/Ve9RxNO2ck7YhKhxHEIehGjnXPNuvBJB0ECG1KeVo2v2O99n+lKS1bW8nqTjI6djS9gYAtreSdCHRE1PCPbaLMpU9XXb3cEnb2T6qZq0BPkhUv1rcAPYD5hEB2AGEFKsmmHsPYZ29iaQq23jayklbrvUE26+SdAQhUSp1OIWQAe9G5XvJ9tu7j63c4DYnDsZfIBJKpa55ED0mXyZ6yEqqN+Npee0fLw/baeIfn5DdqXd/HuQHnQLiiURCacPShWxvIWk1Qvp5lqQrbW9fuNypRAKjf83WVPn2B/aX9BJCHnwYkUAoYSPb8ySda/sYSSVSvzOIIHUVxlo6FgDXFax1N3G9eQxjQdICxkYLDIPHfWzFPxLKq/2IZPsSx2wsha2BjYFzbB8kqcZfAWjXy237G5JOIO5zBwAfYUBtMwRXl+5hAn5p+7xJWHekZDDXhgW9ZMH272pkI8DVkt5JaKk3AO5SZ0BSULJuuS8gzFBsXyPpuUT2rJRriTd3lS17x+22j2iwDoR06wGi3+FcwjyjlDmSZnUBa199LKXl77hcd8hYWTEn6MlLe8BSWCBpBdsPdNLNWRVrnSlpJ8ZmZA09T0/S9t1ztfZAdrVfqzSb9wtidmGN8UPPAod5wULbf5T0u8r1jqSRbfygnBT4b9vFEpSW0lRijhWMNa2XHj4BbnL5iIqHMb5qDsUzle6y/SdJK9u+tpOvlXIicb2ZBTyHkEluVLFey2v/G2xvPbDurgxZgRlgQfecLbS9UFKRlH3g9XQUcYjdFHhv4Z4GmUMEFrOok3PNsl2TWHwISV8gAuA7gW9SNkajp9r8qqsKngecp2gz6d9HQ59PHXNTr5B0mO3bhn38uLWOgcXPsVXI0Et7necShjbnSPoV5Une/j7bnytKjap6mvVyK9pe1iR6PPcn/r5DM/A3mE07M61TFb1yVw38f6pm4E0FMphrw3WSPk+UpedRllHqWaf7N5jBO5SyJuXBfW1cuS+IbPZJivlft1CXUX0mcKOkvoJQMmuop6VzUsvZQCcCFyr6yTYETihcB9r+jvsQVcfjiOD1uIp9QRhdXCHpcjoDmoq1NiYOP30wspDh5R599voewo3xbiI7+PmKfZ1DOKRdRxeYl0jzOq7t5COrdH/T2n6MVWwfJeldXQ/L+DlGj5hOMr488Tf4jKTP2v5c4VrNpKnAdzTWtP4ToCYAnq9wCh58L5W4uPYcTZuq+c2StgP+0L0+ip0Zbb+8/1zhEnjoBD/+SKi+9o+T0/XvnVlEj05pMNfK/XlxM8D67xX1zUn6IXFwPxLY1BU904TB14bApdS7Mz6WuC7eRFx7bq3YVzPzK0mHAG/s9lPVegH8WNJg8vReFwz67tiakD1fROEc23EcSySLIXwHjiSSB8PyTeLe+CxFD/B3C9YY5BaP9XPX9nJfTCQynkG8f35FXYWzpZnWrsQZZWjX56lMBnNt2I44tGxGRPv/r3Qhd2YjPZLm2P7zkn5+KRxGHIo3I3TsVdbUti9mwAWrq8KUUjpUenG0dE5qZgBh+/Od1l7EwOqSAeQ91b+jxob0Qtws/0xURt9IyJ2KcBjQfI+4aF/nOnOWlWy/tuLx2D6z+/StwDa2r+ykpEcT8rUSdiT6hFrcAHYmrhn/Tpj+7DDxjy8dtbON/yfiJnkCkXA5i5DEldBSmvovRAC9sJOS3lCxVu9uulr3saZiDu2q5jsSh5+TiSz01hP+9CPnHsIEq5jx1/5CWsrp+n01cX/ujTMas5vtGqOYQV4FbDHwdXGQaXsnCJMQ4lB7MmVjfFqbX20IrNWo9WKd7uNyhMKpxkBjDvAah+Nzb6T1BnVuiiX0Mj/bF3RrlnA28EOiF9AlPWnjeK+kLxNB67cl/aj0b2H7UwCdlPezRNvFYyd80MS0NNO63faJFY+fkmQw14Yv2n6o8VTSscSslKHpstm7M2YP+2fKG7APZOwweyBxmJ1XuNbi9vYg0XRbwhziAjtogzuUZbyk2Z0MrMRqfkk0M4DoDtafIEwMLOlDLneM3LaT2z6XcJIryaauQzzXhwCHOubLvZhCS+MehYnBtnR/S0lr2C5NHFzRZfAHm81LHbEetH1lt8b1KpvJ1HMz8LNGB43v235dg3V6diXkYesSB7Mas4teiv27XvJXsVZLaeq5RHBzG2NZ8mENDda0fTOFxikTUFU1X4yMCyIAewkDcuNh6GREfZD6VAp7DPVwkxcYq0wPZfIyTk63OmPX/mdRWB3qXp8bEf1R10l6Xkm/qKQv295lMQf0he76noddCzhsXHWoxEa9f9z6JY9bHJL2AN5AvI/+lYrrv9qaXzVrvXDMzey5sKt0l9Jyji3A3ZLex1ilr1RlcKTtjRiQC9ZgewcASRsTQf7zKJ+Z+SVCZXMNMeN1y8rttTTTul/SGSx6va4xtJkSZDBXgaT3E03+T5b01u7bs4CaCswOwKu7dU+mzjSg5WG29d6Opd5R8VjCUa2Xw8CYPKPUUnpPomLyDOJiW/M7Hk5UFM4nnrdSOUUT56r+BqcYsvvT7nv/2Vd1KjiYkNe8nQg0a/qZ1gcGB6g+hnB1LeFGRc9cf9OssW5+DCHxu4KxG0Cpm9/dkrYkXrcLurVqLJz/hpgrdjeRIT+F8tf/rwkZywckfYKQy5TSUpq6D+HA1w+Tf3vBGrt3/8ZLDmvmbEF91Xzd7uP/Ig6yPwZeShwch3Is1Fi/6A0D376MeM3tDZxl+xFXFNze5AVJRxKz0lYkAorrKJ8zdxRt+kWX764V4yXPJVXbfq0batcaCDIHg3OgPDAkkrDbdomNWlqaXzVrvdCiLohrUHf4bznHFqLqvifR6nAVodJ4xEh6gsO1+A+K/sfB+0ixXFzSB4HXEgHchdTNsv0BYTqzEtELXHv27M9lq1N/LmthCDXlyGCuAtuHAIdI+pjtA5b6gEfGnbZvUzTBnyfpkxVrtTzMtt5btaPiwGFag/0Dkp5Xsa+ViGDkBkIWUFNVmGv7e93n35VUoq/vaelcdbekfYGfEoeqGyrWArjb9vGSXmd77+7AXcqJPLwyXcq2jA18vYo6l7uazO54VmXRAcI1ASuErHsLGjid2t5G0koOB9yf2S4ZStzTUpr6S2A+IRk/kwJpnu3du4+bLO1nh1y31n31owCSzrD9xv77kkqk4v1rYHEHzTlE78lfLea/LZauH21J1uClyYx1CbXCocQIkW8VrgPt+kV7E4oWzoUt19q3+9hKcgtR5f5Opxy5HXiv7f8oXKul+VXL1otBF8TLqJsj2XKOLY6xF2cTSWwXtCWcRiisfk1I2J9aso/FsDnh4noKcGalbPNe4pp9D/AkSTvYrnEgFhGwzgZWJlqISmXGx1TsY8qSwVwbvqYYzPogcbA62PZlhWvdoxjKurCTNT6lYl8tD7OL21tRCb6jd1RcSfWOit+ky9SrfjbQJ4ANuwvuakRTcWnWeLak9WxfLmm9winLnDMAABJuSURBVDV6WjpXvZPIDm5O3PT2qlgL4vXwQuBxksRYL1IJ2xOZ9b76+8GJf3zJ2P4j8MWKvSDpTV0fjhbzn0uD1pYBK8Sw5NJRBIugzplR4VBXO8+wpTT1AuDDtk+V9E9EguolJQtJ2o9wKnwoSBlWMjhuvY8S1/37KJQgdjxV0hNt362YT7bKsAv0/aJLOrBIGjah99Wl/8jQ/L7rfVzR9p2qcyZt0i/a8oDXeK0+mfIXQv3wAkK6VpMYPAjY3vZl3fv9EMqTSS3NrxbnqlmaMP4GkUzqn6/7h11AMdR+v/EJje6aWJrIaDF/9v4umbs2i0osF1L+fNH1Ac4FNgEOkrRORUV+X2Jsxa2Snk7MM6wJ5nYiziu3V6wxo8lgrg3HEm557yeyjF8k3hAlHELIpj5COKQVz59qcZgdx/ZEz9ZHCKOEml6rfQjZ4NeJDFPpAFRoOBuI6BX6DYDt21Vodd3xAeBISWsQVdH3VazVzLnK4ap2SMVexrM7kWk/mNhnzQGwZfW3BX2S4SsseqOsaeZuFrB23Cfp31jU6a60B+Bo2s0zbClNfU0vC7P9OdXNOXsT8OxxfTU1vANYw3Ztr8/+wCWS7iWcLJvbZdv+tyF//kcAkh5PJH36w/G+Ez1uKVzSBeS3KmZR1ZxDWvaLTmWaSfaJMQeXAdi+VFLN+JGW5ld94LoccQaqqfIdSpwFziautUcwvI9BL8drndConT+7OSEdPZSGr/euVeh/E8/9JZTLZQH+YvtWANu3qH4s1p22a12fZzQZzLVhNnGR3dP2CZJq3mCfI0xLbpP0YeJwVWOp3pL7iGz4M4gLXfGsD8fssN5yvkgmoIazgTQ2j2y2pO8TWbOXUVcB+1fgacBviAvkBZLuAHYeVnJg+8sKq+sXEfKdJk3PNQw8/7/q/kFYSde4A7as/rZgTtev8gfiJgpxyJgDfLRwzdYB6+mVjx+k5TzDltLUJ3QZ8icSWfeaOUP/SRgttArmbqAg8z8e26dI+jbxmr+zUUWzFUcRlehvEIfjo4kxAyW8nkjgHd+t+dOKfbXsF53KjJfs716x1p8lvYmods+j4n2ghuZXthfpZe0SVKWsbbs3e/uuCpwnB9RV/0Fc+0uGXi+Owfmzsxjyftk97mbChbolGxPv6x1s1zr83ivpA4yN67qrZJGBc9kKCmfwQWO0aW9a0pIM5tqwAuEceb6kTah7XlublrSkegabFnVF6ymVJo03Phn83rA3dI/7CHDqkGuM53xgb9tWDNr9BFHd+TpDSg4kfdj2Z4GrOsnmxcRBZpQMPv+ziUPofEISVHqg2p7oTWhR/W3B1wn7548RlRMI7f78ijWbBqyNewBazjO8iejlGzwElUpTDyYOjYczNiS9yIaeCARvk3Q7Y9eemgBgBeDyTmpGt97QFcju3nEk7fpMWrKK7S91n18qqcSABgDbG0halwgG30xUZN468aOWSLN+0SnOeMl+zWH7vUTS+NOEW+r2E//4hDQzv1K4NfesThiilDJX0uNs3yfpsdQNuT+VOPf0r7HaQOcE2s2fbclXiL/ja7re0zVslzqF/5RI+u9HJJ5/U7jO4s5lyWLIYK4N2xABzhGEQ9G7KtZqbVrSkuoZbBUa7MWt9dBsoO7i8xTgtyWNyZPUFLumbXfrXyfpmbavLZS1rCdpJ6IC+W7qBrY3oX/+O3nGgUT/3eOpCMBs/44IJCB6H0dKJ8W7gTqJ7HimWsA6SMuZjacSvRIt5szRvXcWdv2sNUPD3wE8h3ZDY2vkSIO07jNpyWMlrdZJz1ej4nAsaX3iftk7iF49wY8vjWb9olOclpL9zWw/NHdN0q6UD21vaX41WJn7IxWzT4m+wF7i/QJg74q1ZtmuOdMBD3PYvIVIQlxKOwOTWo6h0mFc0nuJ+9u6jKmHXkVUbodmppqVTAYZzLXheuABwj71XMLJp5TWpiUtaTmDbR5hSz0L+BKwl+1vFq71akIGNNUy2rdJ+jRhNf4K4HZJmxGvlWHZhpA4rQq8tGG/Twv2Al5me76kpxE3hNIAYMYz1QLWQVzpzDiO/7K9d6O1fttVMVeUtDV1AeKNwB8avocuJ6SDgzMzSw61rftMWvJxoprwAFF9qRl0fz5xz9zTdq1EuGW/6FSmWrKvmN/5ZmATSX0gPQtYj/Jgrpn5le1NOuOf5xJBenH/ne1vdK+LtYBf2y6S+XX8QtKGLPoaK7mHDyYtzNSzyK92GGdylCzJIyCDuTZUyw97JsG0pCUtZ7B9hnBV7J20TiLMM0rYj6mZ0X43kUHdnJB27Q28mCEsmLXofKE5xBy2cztHrdI5Q625y/Z8CPe1zsAhmYYMyKCXI8xfrre97sSPWiKndcmMhwZf2y41OroceDZxmH0J5bIdiOvXdZKu774unmfV8S3CFGQ9oqJQaoTSpM9kkng8UY37C/HaqDk7rEJk/1+vGGA933apLX3LftGpTAvJ/hnAbcTz31fBFlAw5mOAZuZXkrZiTJb3Ikl72/564VpbEInxQVfeojMZUVnaYuDrojm206DK1DuMr6xCh/FJUrIkj4AM5tpQLT+cJrScwXY/0SvxYCfdeUzFWlMyo90F5uMznhcNuUzL+UKTxb1dc/KPgA2ILO0BMGOz5DOWQRm0pGdRJ0/amjiY9cFgydDkxcl2NqZQttPxjorHLhbbO0k6itjr+Uv7+SXQqs9kMmhZfX8i8HTgWYQ6o9ilbhockFtRLdm3/d/AecB5kjYlApKLgd8Ou5lJMr/aHdjAMeNyZeAcIlgt4XPEaIJqibft9bs2jlWJxGXNuWcq0zuMH0e9w3jyKJPBXBuayQ+nOC1nsN0L/IAYur4zFTd0Hp7RHvrmNFXp7Xi7iuM/EzeUbwG/oO45a8mgUcxU6vFMKrB9o7oZXoX8yXZtP+BkyHbmAFuxqCyytNEfAMV8phWJw+xQvSat+0wmiZbV9zOB7wD72/5lk93NfJpJ9rtE25rE6+0BwpV32MroZJhfLbD9ewgpemVS9pe2z6t4/ENM4TaOptg+X9KlRJJlrf5vkUwPMphrw57AhYQD00+onxs1VamewSZp+876/ErCEnldYsD3NRX7ehfR09FntJvPZ5oCHEaMqNiLCFqPoTyQbsoylB2f8WjRAbmrUzek9UbFQO1BO+mhqjmTJNs5lspG/3EcQgxxPotwvPv3IR8/HfpMmlXfbW8wCfub6VRL9gfYyPY8SefaPkbS0AmXyTC/IqTPnyfubxtTJ/88tWtReGiEj+3Sc8FUbeNoiqS3Eeeo2cBJndnUVPJsSCYgg7k23BNSdvXzgWqta6cUajuDrbf3vZqxhuAq21nb90g6j5AluZOTzDTm2j5H0se7vokpISVNZhynEXO7HiTkiPtP/OMTModI1PSW4zXOmC1p0eg/yA22TwGQdLLtoapW06TPJKvvI6SRZL9ndldJXiip74MspaX89jCiIr0ZEaQWzavr2JXoy2/hWDsl2zgmgd2JBPEZRAB7CVPLgC+ZgAzm2rBf58L0NWIQ6kwrTzebwWb7zO5js2pOZ/m7NhFkvkfSPNtTyiWwAX+S9HpgecWg1pl6Q0lGy7bAAcD7icPVZ4FNShZq7IzZkupG/3F8WNKziQpbaY/PlCar7zOKLwA/J6SRF3dfl9JSfnsgsI3tKyUdSAywnjfxQ5bI7bZPrNjLIFPZmKglC2z/qavILSxRXiWjI4O5Btjeojsc/ANwlqQrbdcM4pxSTIMb+TzbrwSQdBAhdZ1pvI9o6n4KU282WTJzmE0cWva0fULXz1pEY2fMluxDzAM9jrDIP65mMdtbS3oS8PfAyZLmA4e36tlJkpbYPlkxsHo14A7bN1Us19L86kHbV3aPvV5SjffA/ZLOIEbA1I6smMrGRC25QNI3gTUlfRX42ag3lDxyMphrxxxi4O4sQqKUPHrMkTTL9gLi+Z9RMlcA2zdL+v/EsOlfkFKnZHJYgciQny9pEyruEY2dMVtyPGMzu54MvE/Slgwxs2sxPA14JpFsuRLYStK7K/p0kmRS6O4jT7C9h6STJV1iu3TwfUv57Y1dIHgR0cZRs97p1MlHp4sxUUu+DLyV+F23Bd422u0kw5DBXAMk/ZCYZ3IksKntLE8/upxADLT9CbBh9/WMQtIuhG3wkwn5ydrALqPcUzIj2YboWTkS2JIwF6qmgTNmS1rM7HoISRcTs+UOB/bqBwp3FYskmWps2ZvQ2N5K0oWEU/LQNFbtbAvsRMzovYq6fq2tbb+ucj/TwZioJccwJrH/GJHUK5LYJ48+Gcy1YTfbl496E8saXa9cX4W7hRjseSnw1JFtavLYmnD4Osf2QZJSApE0x/bg3KiTatZq7IzZkuqZXeM4AtiNOPDt1w0oXst2jYFDkkwWCyStYPsBSXMINcvI6UxevthoubslvZlwyV7QrT+UY/Y0MSZqSS+x/1itxD559MlgroLO+nZh93n/7eWAhbZfMap9LUNcPfC5CSetmUp/w+0PxyVOoknyaNLSGbMlzWZ2dfTVhKkSrCbJRHwVuELS5cA6hOvjTGNVYlxIz0LgNSPay3Shl9hfUCuxTx598o9Vx9aj3sCyzDQwZmnJCUST+bMlnU4MbE+SqUwzZ8zGtJzZBTGO5sY2W0uSycX2kZK+Rwz3vs72naPeU2tsL3KdkbTCqPYyjdiGSZDYJ48Oyy1cOOO8Ih51JD0P2IpojF0OWMP2jqPdVTKTkPRz4FrgFOCqlPUmUx1J5wKvBc60/VpJ59sutRqfcgzM33w5UdUbHI5e6pyXJJOKpL8mkhlz++/NNKMeSTsSc9P6M9mfbT9/4kclyfQlK3NtOJaQFG0E3AqsNNrtJDMN2xtIWpfoC/ygpDtsv3XU+0qSCWjmjDlFWdz8zSSZ6hxNOBf+14j3MZnsALwa+DhwMtHTmiQzlpl2cx0V99n+lKS1bW8n6YJRbyiZWUhan5BAbNp96+oJfjxJpgLbMINlO8uYzDuZOdxu+4hRb2KSucv2bZJWtn2epE+OekNJMplkMNeG5bqh4StLWpGwj0+SlpxPDDje0/bpo95MkiyNls6YSZI04wZJH2HRgdpnjXZLzblb0t8CCzvJ5aqj3lCSTCYZzLVhH+BvgeOIA/dxo91OMgNZhZDxvl7SHsB826WGDUmSJMmyyWMAdf8gArqZFsytCjybkFnuBvzjSHeTJJNMBnNtOB54GvAboir3PklbAjvbHnoIbZIshicCTweeBTwOSPe8JEmS5BEhabbtB4FlwZxtZ2A7wsvgTMLLIElmLFNiWOQM4HzghbZXJ+a2fJewvd53pLtKZhJnAs8B9rf9ctsfGfWGkiRJkmnDsd1HEz3XVw98PqOwfbXt/0v07D4DuFzS2ZJeMuKtJcmkkJW5Nqxp2wC2r5P0TNvXSnpw1BtLZga2Nxj1HpIkSZLpie2/7z59i+1LR7qZSUbS5oQB0zrA1wmp5RzgdGD90e0sSSaHDObacJukTwM/Bl4B3C5pM2L2UJIkSZIkyVRgX0mrAF8Djrf9+1FvaBJ4F/Avts8b/KakfUaznSSZXHJoeAMkzSWGcK4LXEFYcb8YuN72HaPcW5IkSZIkSU/nvv0PwFuAK21vP+ItJUlSQVbmGmD7j8DB47590Sj2kiRJkiRJMgFzCFfLWUC2gyTJNCeDuSRJkiRJkmUAST8E5hIKok1t/2HEW0qSpJJ0s0ySJEmSJFk22M32Kwnb/vtHvZkkSerJYC5JkiRJkmTZ4CmSrgfOBq7vzNqSJJnGZDCXJEmSJEmybLAvsJHtvwZeCew34v0kSVJJBnNJkiRJkiTLBn+xfSuA7VuAP454P0mSVJIGKEmSJEmSJMsG90r6AHA+MA/47Yj3kyRJJVmZS5IkSZIkWTZ4F/BMYH/gGcB2o91OkiS1ZGUuSZIkSZJk2eArtt856k0kSdKODOaSJEmSJEmWDeZK+ivgGmABgO0HRrulJElqyGAuSZIkSZJk2eD5wPeBVYH5wF+AtUa6oyRJqsieuSRJkiRJkmWDTxAVuauBB4GdRrudJElqyWAuSZIkSZJk2WAv4GW2Xwy8gpwzlyTTngzmkiRJkiRJlg3usj0fwPYdwL0j3k+SJJUst3DhwlHvIUmSJEmSJJlkJH0bWBH4EbABsDpwHoDtj41uZ0mSlJIGKEmSJEmSJMsGpw58fsvIdpEkSTOyMpckSZIkSZIkSTINyZ65JEmSJEmSJEmSaUgGc0mSJEmSJEmSJNOQDOaSJEmSJEmSJEmmIRnMJUmSJEmSJEmSTEMymEuSJEmSJEmSJJmG/A98eCZOdOeU7AAAAABJRU5ErkJggg==
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<p><span style="color:#4095b5">The most common words are not very surprising - place names.</span></p>
<p><span style="color:#4095b5">Since the beach in Galveston faces southeast, 'sunset' is a Cape Cod term. And Galveston has floating casinos, thus 'gaming'.</span></p>

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<h3><span style="color:#3b748a">Run a simple <span style="font-family:monospace">TfidfVectorizer</span> and explore.</h3>

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<div class="prompt input_prompt">In&nbsp;[18]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tv_simple</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">&#39;english&#39;</span><span class="p">)</span>
<span class="n">X_train_tv</span> <span class="o">=</span> <span class="n">tv_simple</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>

<span class="n">tv_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_train_tv</span><span class="o">.</span><span class="n">todense</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="n">tv_simple</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">())</span>
<span class="n">tv_train</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>000</th>
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      <th>06</th>
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      <th>100</th>
      <th>105</th>
      <th>10th</th>
      <th>11</th>
      <th>...</th>
      <th>yes</th>
      <th>yesterday</th>
      <th>yield</th>
      <th>yields</th>
      <th>ying</th>
      <th>young</th>
      <th>youtube</th>
      <th>yugioh</th>
      <th>yummm</th>
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<p>5 rows × 3248 columns</p>
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<h4 id="Create-a-dataframe-with-counts-of-most-frequent-words"><span style="color:#4095b5">Create a dataframe with counts of most frequent words</span><a class="anchor-link" href="#Create-a-dataframe-with-counts-of-most-frequent-words">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a data frame of the most common words </span>

<span class="n">n_words</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tv_train</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)[:</span><span class="n">n_words</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="n">tv_train</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">values</span>
<span class="n">tv_train_small</span> <span class="o">=</span> <span class="n">tv_train</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;is_cape&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()[</span><span class="n">words</span><span class="p">]</span>
<span class="n">tv_train_small</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th></th>
      <th>galveston</th>
      <th>cape</th>
      <th>cod</th>
      <th>beach</th>
      <th>island</th>
      <th>looking</th>
      <th>best</th>
      <th>weekend</th>
      <th>good</th>
      <th>place</th>
      <th>day</th>
      <th>sunset</th>
      <th>time</th>
      <th>area</th>
      <th>summer</th>
      <th>amp</th>
      <th>new</th>
      <th>gaming</th>
      <th>fishing</th>
      <th>places</th>
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      <th>is_cape</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
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      <th>0</th>
      <td>54.909474</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>11.215843</td>
      <td>19.074638</td>
      <td>6.784648</td>
      <td>8.254628</td>
      <td>8.584183</td>
      <td>4.006316</td>
      <td>2.759745</td>
      <td>3.341260</td>
      <td>3.104370</td>
      <td>4.054767</td>
      <td>4.137787</td>
      <td>1.537047</td>
      <td>4.557625</td>
      <td>5.423647</td>
      <td>0.276753</td>
      <td>3.738765</td>
      <td>4.462399</td>
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      <th>1</th>
      <td>0.000000</td>
      <td>43.804898</td>
      <td>30.133008</td>
      <td>12.789435</td>
      <td>1.839713</td>
      <td>11.505212</td>
      <td>6.899604</td>
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      <td>8.410196</td>
      <td>9.470202</td>
      <td>7.765916</td>
      <td>7.707984</td>
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      <td>7.259536</td>
      <td>4.229522</td>
      <td>3.040881</td>
      <td>8.092985</td>
      <td>4.566479</td>
      <td>3.792706</td>
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<h4 id="Plot-the-most-frequent-words"><span style="color:#4095b5">Plot the most frequent words</span><a class="anchor-link" href="#Plot-the-most-frequent-words">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Adapted from https://matplotlib.org/examples/api/barchart_demo.html</span>

<span class="n">words_cape</span> <span class="o">=</span> <span class="n">words</span>
<span class="n">words_count_cape</span> <span class="o">=</span> <span class="n">tv_train_small</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">words_gv</span> <span class="o">=</span> <span class="n">words</span>
<span class="n">words_count_gv</span> <span class="o">=</span> <span class="n">tv_train_small</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.35</span>       <span class="c1"># the width of the bars</span>
<span class="n">ind</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">n_words</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">rects1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">words_count_cape</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">rects2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">ind</span><span class="o">+</span><span class="n">width</span><span class="p">,</span><span class="n">words_count_gv</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>

<span class="c1"># add some text for labels, title and axes ticks</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;TF-IDF value&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;TF-IDF by word and reddit&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">ind</span> <span class="o">+</span> <span class="n">width</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>  <span class="c1">#,rotation=&#39;vertical&#39;)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">rects1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects2</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="s1">&#39;Cape Cod&#39;</span><span class="p">,</span> <span class="s1">&#39;Galveston&#39;</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p><span style="color:#4095b5">From looking at the most frequent words, I think that without place names, it will be difficult to differentiate between the two sets of posts. But we'll give it the good old college try.</span></p>

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<h2 id="-IV.-Natural-Language-Processing-(NLP)"><span style="color:#37535e"> IV. Natural Language Processing (NLP)</span><a class="anchor-link" href="#-IV.-Natural-Language-Processing-(NLP)">&#182;</a></h2><h3><span style="color:#3b748a">Use <span style="font-family:monospace">CountVectorizer</span> or <span style="font-family:monospace">TfidfVectorizer</span> from scikit-learn to create features from the thread titles and descriptions (NOTE: Not all threads have a description).</h3>
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<li><span style="color:#4095b5">Examine using count or binary features in the model</span></li>
<li><span style="color:#4095b5">Re-evaluate your models using these. Does this improve the model performance? </span><ul>
<li><span style="color:#4095b5">What text features are the most valuable? </span></li>
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<h4><span style="color:#3b748a">As a first step to get a baseline, use a simple <span style="font-family:monospace">CountVectorizer</span> and model with with regression.</h4>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Basic CountVectorizer and LogisticRegression to get a simple first model</span>

<span class="n">cv_simple</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span>
<span class="n">X_train_cv</span> <span class="o">=</span> <span class="n">cv_simple</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>
<span class="n">X_test_cv</span> <span class="o">=</span> <span class="n">cv_simple</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are </span><span class="si">{}</span><span class="s2"> features in the model.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cv_simple</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">())))</span>

<span class="n">lr_simple</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">lr_simple</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_cv</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">score_train</span> <span class="o">=</span> <span class="n">lr_simple</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_cv</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">score_test</span> <span class="o">=</span> <span class="n">lr_simple</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_cv</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

<span class="c1"># There are 3440 features in the model.</span>
<span class="c1"># Train score: 0.9695740365111561 Test score 0.8157894736842105</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train score: </span><span class="si">{}</span><span class="s2"> Test score </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">score_train</span><span class="p">,</span> <span class="n">score_test</span><span class="p">))</span>
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<pre>There are 3440 features in the model.
Train score: 0.9695740365111561 Test score 0.8157894736842105
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<p><span style="color:#4095b5">The model seems to be quite overfit. If I could, I would gather more data.</span></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What are the words that best predict the target based on the coefficients</span>

<span class="n">coef_names</span> <span class="o">=</span> <span class="n">cv_simple</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">()</span>

<span class="n">coef_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span> <span class="p">({</span>
    <span class="s1">&#39;coefs&#39;</span> <span class="p">:</span> <span class="n">coef_names</span><span class="p">,</span>
    <span class="s1">&#39;vals&#39;</span> <span class="p">:</span> <span class="n">lr_simple</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="p">})</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;coefs&#39;</span><span class="p">)</span>

<span class="n">coef_df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">coef_df</span><span class="p">[</span><span class="s1">&#39;vals&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">)[:</span><span class="mi">20</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
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      <th>coefs</th>
      <th>galveston</th>
      <th>cape</th>
      <th>island</th>
      <th>yarmouth</th>
      <th>hyannis</th>
      <th>provincetown</th>
      <th>seawall</th>
      <th>wellfleet</th>
      <th>water</th>
      <th>falmouth</th>
      <th>cod</th>
      <th>texas</th>
      <th>brewster</th>
      <th>chatham</th>
      <th>houston</th>
      <th>city</th>
      <th>gaming</th>
      <th>orleans</th>
      <th>eastham</th>
      <th>hole</th>
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      <th>vals</th>
      <td>-3.334658</td>
      <td>2.654186</td>
      <td>-1.250879</td>
      <td>1.091906</td>
      <td>1.072297</td>
      <td>1.058891</td>
      <td>-1.025158</td>
      <td>0.985732</td>
      <td>-0.963286</td>
      <td>0.933963</td>
      <td>0.915743</td>
      <td>-0.904865</td>
      <td>0.893887</td>
      <td>0.858306</td>
      <td>-0.793727</td>
      <td>-0.710236</td>
      <td>0.685653</td>
      <td>0.665388</td>
      <td>0.661821</td>
      <td>0.635328</td>
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<ul>
<li><span style="color:#4095b5">The model appears to be overfit since the "training score" is much highter than the "testing score". Again, the words with the largest coefficients are mostly unsurprising.</span></li>
<li><span style="color:#4095b5">Galveston is an island and has a seawall.</span></li>
<li><span style="color:#4095b5">I do wonder why "water" has such a large coefficient.</span></li>
<li><span style="color:#4095b5">The word "hole" makes me think of "Woods hole", so I'll try bi-grams as well.</span></li>
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<h4><span style="color:#3b748a">Second step: add in bi-grams, use a simple <span style="font-family:monospace">CountVectorizer</span>, and model with with regression.</h4>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cv_gram</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">ngram_range</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="n">X_train_cvg</span> <span class="o">=</span> <span class="n">cv_gram</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>
<span class="n">X_test_cvg</span> <span class="o">=</span> <span class="n">cv_gram</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are </span><span class="si">{}</span><span class="s2"> features in the model.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cv_gram</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">())))</span>

<span class="n">lr_simple</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span><span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">lr_simple</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_cvg</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">score_train</span> <span class="o">=</span> <span class="n">lr_simple</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train_cvg</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">score_test</span> <span class="o">=</span> <span class="n">lr_simple</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test_cvg</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

<span class="c1"># There are 11975 features in the model.</span>
<span class="c1"># Train score: 0.9959432048681541 Test score 0.8218623481781376</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train score: </span><span class="si">{}</span><span class="s2"> Test score </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">score_train</span><span class="p">,</span> <span class="n">score_test</span><span class="p">))</span>
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<pre>There are 11975 features in the model.
Train score: 0.9959432048681541 Test score 0.8218623481781376
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<p><span style="color:#4095b5">The training score is much higher, but the test score is not much higher... And the mode is still overfit.</span></p>

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<h4 id="What-is-the-baseline-accuracy-for-this-model?"><span style="color:#3b748a">What is the baseline accuracy for this model?</span><a class="anchor-link" href="#What-is-the-baseline-accuracy-for-this-model?">&#182;</a></h4>
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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;The baseline accuracy for this model is </span><span class="si">{:.2f}</span><span class="s2">%.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
    <span class="n">cross_val_score</span><span class="p">(</span><span class="n">lr_simple</span><span class="p">,</span> <span class="n">X_train_cvg</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>The baseline accuracy for this model is 78.70%.
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<h4><span style="color:#3b748a">Run a <span style="font-family:monospace">CountVectorizer</span> and regression adding in a pipeline and grid search. Also, eliminate the stop words.</h4>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create the pipeline</span>
<span class="n">pipe_cv</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s1">&#39;cv&#39;</span><span class="p">,</span> <span class="n">CountVectorizer</span><span class="p">()),</span>
    <span class="p">(</span><span class="s1">&#39;lr&#39;</span><span class="p">,</span> <span class="n">LogisticRegression</span><span class="p">()),</span>
<span class="p">])</span>

<span class="n">params_grid_cv</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;cv__stop_words&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;english&#39;</span><span class="p">],</span>
    <span class="s1">&#39;cv__ngram_range&#39;</span> <span class="p">:</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)],</span>
    <span class="s1">&#39;cv__max_df&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">],</span>
    <span class="s1">&#39;cv__min_df&#39;</span> <span class="p">:</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="s1">&#39;cv__max_features&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2250</span><span class="p">,</span> <span class="mi">2500</span><span class="p">],</span>
    <span class="s1">&#39;lr__C&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">],</span>
    <span class="s1">&#39;lr__penalty&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="s1">&#39;l1&#39;</span><span class="p">,</span> <span class="s1">&#39;l2&#39;</span><span class="p">]</span>
<span class="p">}</span>

<span class="c1"># Grid Search!</span>
<span class="n">gs_cv</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipe_cv</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">params_grid_cv</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">gs_cv</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="s1">&#39;title&#39;</span><span class="p">],</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">score_train</span> <span class="o">=</span> <span class="n">gs_cv</span><span class="o">.</span><span class="n">best_score_</span>
<span class="n">score_test</span> <span class="o">=</span> <span class="n">gs_cv</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">],</span> <span class="n">y_test</span><span class="p">)</span>
<span class="n">params_train</span> <span class="o">=</span> <span class="n">gs_cv</span><span class="o">.</span><span class="n">best_params_</span>

<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">params_grid_cv</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: </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">k</span><span class="p">,</span><span class="n">params_train</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train score: </span><span class="si">{}</span><span class="s2"> Test score </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">score_train</span><span class="p">,</span> <span class="n">score_test</span><span class="p">))</span>

<span class="c1"># Fitting 3 folds for each of 192 candidates, totalling 576 fits</span>
<span class="c1"># cv__stop_words: english</span>
<span class="c1"># cv__ngram_range: (1, 2)</span>
<span class="c1"># cv__max_df: 1.0</span>
<span class="c1"># cv__min_df: 1</span>
<span class="c1"># cv__max_features: 2250</span>
<span class="c1"># lr__C: 1</span>
<span class="c1"># lr__penalty: l2</span>
<span class="c1"># Train score: 0.8079783637592968 Test score 0.8178137651821862</span>
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<pre>Fitting 3 folds for each of 192 candidates, totalling 576 fits
cv__stop_words: english
cv__ngram_range: (1, 2)
cv__max_df: 1.0
cv__min_df: 1
cv__max_features: 2250
lr__C: 1
lr__penalty: l2
Train score: 0.8079783637592968 Test score 0.8178137651821862
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<pre>[Parallel(n_jobs=1)]: Done 576 out of 576 | elapsed:   33.9s finished
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<h4 id="Take-a-look-at-the-coefficients-in-the-model-to-see-which-words-best-predict-the-target."><span style="color:#3b748a">Take a look at the coefficients in the model to see which words best predict the target.</span><a class="anchor-link" href="#Take-a-look-at-the-coefficients-in-the-model-to-see-which-words-best-predict-the-target.">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What are the words that best predict the target?</span>

<span class="n">coef_names</span> <span class="o">=</span> <span class="n">gs_cv</span><span class="o">.</span><span class="n">best_estimator_</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;cv&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">()</span>
<span class="n">coef_vals</span> <span class="o">=</span> <span class="n">gs_cv</span><span class="o">.</span><span class="n">best_estimator_</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;lr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="n">coef_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span> <span class="p">({</span>
    <span class="s1">&#39;coefs&#39;</span> <span class="p">:</span> <span class="n">coef_names</span><span class="p">,</span>
    <span class="s1">&#39;vals&#39;</span> <span class="p">:</span> <span class="n">coef_vals</span>
<span class="p">})</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s1">&#39;coefs&#39;</span><span class="p">)</span>

<span class="n">coef_df</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">coef_df</span><span class="p">[</span><span class="s1">&#39;vals&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">)[:</span><span class="mi">20</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
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      <th>coefs</th>
      <th>galveston</th>
      <th>cape</th>
      <th>provincetown</th>
      <th>hyannis</th>
      <th>yarmouth</th>
      <th>island</th>
      <th>wellfleet</th>
      <th>brewster</th>
      <th>seawall</th>
      <th>falmouth</th>
      <th>chatham</th>
      <th>water</th>
      <th>texas</th>
      <th>sandwich</th>
      <th>orleans</th>
      <th>houston</th>
      <th>eastham</th>
      <th>dennis</th>
      <th>city</th>
      <th>nauset</th>
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      <th>vals</th>
      <td>-3.96042</td>
      <td>3.299226</td>
      <td>1.883469</td>
      <td>1.809364</td>
      <td>1.780308</td>
      <td>-1.720992</td>
      <td>1.681471</td>
      <td>1.59938</td>
      <td>-1.596657</td>
      <td>1.549377</td>
      <td>1.512315</td>
      <td>-1.362117</td>
      <td>-1.324841</td>
      <td>1.251788</td>
      <td>1.25174</td>
      <td>-1.237409</td>
      <td>1.186306</td>
      <td>1.122293</td>
      <td>-1.077585</td>
      <td>1.052525</td>
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<h3 id="Create-a-function-to-run-grid-search-on-anything-we-might-want-to-investigate."><span style="color:#3b748a">Create a function to run grid search on anything we might want to investigate.</span><a class="anchor-link" href="#Create-a-function-to-run-grid-search-on-anything-we-might-want-to-investigate.">&#182;</a></h3><p><span style="color:#4095b5">This could be generalized further, I'm sure, but for now it is enough.</span></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_pipline</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="n">use_params</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">):</span>

    <span class="c1"># Add a pipe, add a param !</span>
    <span class="n">pipe_items</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s1">&#39;cv&#39;</span><span class="p">:</span> <span class="n">CountVectorizer</span><span class="p">(),</span>
        <span class="s1">&#39;tv&#39;</span><span class="p">:</span> <span class="n">TfidfVectorizer</span><span class="p">(),</span>
        <span class="s1">&#39;hv&#39;</span><span class="p">:</span> <span class="n">HashingVectorizer</span><span class="p">(),</span>

        <span class="s1">&#39;ss&#39;</span> <span class="p">:</span> <span class="n">StandardScaler</span><span class="p">(),</span>
        <span class="s1">&#39;pf&#39;</span> <span class="p">:</span> <span class="n">PolynomialFeatures</span><span class="p">(),</span>

        <span class="s1">&#39;lr&#39;</span> <span class="p">:</span> <span class="n">LogisticRegression</span><span class="p">(),</span>
        <span class="s1">&#39;bnb&#39;</span> <span class="p">:</span> <span class="n">BernoulliNB</span><span class="p">(),</span>
        <span class="s1">&#39;mnb&#39;</span> <span class="p">:</span> <span class="n">MultinomialNB</span><span class="p">(),</span>
        <span class="s1">&#39;rf&#39;</span> <span class="p">:</span> <span class="n">RandomForestClassifier</span><span class="p">(),</span>
        <span class="s1">&#39;gb&#39;</span> <span class="p">:</span> <span class="n">GradientBoostingClassifier</span><span class="p">(),</span>
        <span class="s1">&#39;ab&#39;</span> <span class="p">:</span> <span class="n">AdaBoostClassifier</span><span class="p">(),</span>
        <span class="s1">&#39;svc&#39;</span> <span class="p">:</span> <span class="n">SVC</span><span class="p">(),</span>
        <span class="s1">&#39;knn&#39;</span> <span class="p">:</span> <span class="n">KNeighborsClassifier</span><span class="p">()</span>
    <span class="p">}</span>

    <span class="c1"># Include at least one param for each pipe item</span>
    <span class="n">param_items</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s1">&#39;cv&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;cv__stop_words&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;english&#39;</span><span class="p">],</span>
            <span class="s1">&#39;cv__ngram_range&#39;</span> <span class="p">:</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)],</span>
            <span class="s1">&#39;cv__max_df&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">],</span>
            <span class="s1">&#39;cv__min_df&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span>
            <span class="s1">&#39;cv__max_features&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2250</span><span class="p">,</span> <span class="mi">2500</span><span class="p">,</span> <span class="mi">2750</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;tv&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;tv__stop_words&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;english&#39;</span><span class="p">],</span>
            <span class="s1">&#39;tv__ngram_range&#39;</span> <span class="p">:</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)],</span>
            <span class="s1">&#39;tv__max_df&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">],</span>
            <span class="s1">&#39;tv__min_df&#39;</span> <span class="p">:</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="s1">&#39;tv__max_features&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">2250</span><span class="p">,</span> <span class="mi">2500</span><span class="p">,</span> <span class="mi">2750</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;hv&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;hv__stop_words&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;english&#39;</span><span class="p">],</span>
            <span class="s1">&#39;hv__ngram_range&#39;</span> <span class="p">:</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)]</span>
        <span class="p">},</span>
        <span class="s1">&#39;ss&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;ss__with_mean&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;pf&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;pf__degree&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;lr&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;lr__C&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">],</span>
            <span class="s1">&#39;lr__penalty&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="s1">&#39;l2&#39;</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;bnb&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;bnb__alpha&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.8</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;mnb&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;mnb__alpha&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;rf&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;rf__n_estimators&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;gb&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;gb__n_estimators&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">120</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;ab&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;ab__n_estimators&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">75</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">125</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;svc&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;svc__kernel&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span><span class="s1">&#39;poly&#39;</span><span class="p">]</span>
        <span class="p">},</span>
        <span class="s1">&#39;knn&#39;</span> <span class="p">:</span> <span class="p">{</span>
            <span class="s1">&#39;knn__n_neighbors&#39;</span> <span class="p">:</span> <span class="p">[</span><span class="mi">25</span><span class="p">,</span><span class="mi">35</span><span class="p">,</span><span class="mi">45</span><span class="p">]</span>
        <span class="p">}</span>
    <span class="p">}</span>

    <span class="c1"># Create the parameters for GridSearch</span>
    <span class="n">params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">use_params</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">items</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">param_items</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
                <span class="n">params</span><span class="p">[</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">param_items</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">p</span><span class="p">]</span>

    <span class="c1"># Create the pipeline</span>
    <span class="n">pipe_list</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">,</span><span class="n">pipe_items</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">items</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Using:&quot;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">pipe_list</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;(&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">pipe</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">pipe_list</span><span class="p">)</span>

    <span class="c1"># Grid search</span>
    <span class="n">gs</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">pipe</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">params</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">gs</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 the results</span>
    <span class="n">train_params</span> <span class="o">=</span> <span class="n">gs</span><span class="o">.</span><span class="n">best_params_</span>
    <span class="n">train_score</span> <span class="o">=</span> <span class="n">gs</span><span class="o">.</span><span class="n">best_score_</span>
    <span class="n">y_test_hat</span> <span class="o">=</span> <span class="n">gs</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">test_score</span> <span class="o">=</span> <span class="n">gs</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">train_params</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: </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">k</span><span class="p">,</span><span class="n">train_params</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train score: </span><span class="si">{}</span><span class="s2"> Test score </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">train_score</span><span class="p">,</span> <span class="n">test_score</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">train_score</span><span class="p">,</span> <span class="n">test_score</span><span class="p">,</span> <span class="n">y_test_hat</span><span class="p">,</span> <span class="n">train_params</span>
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<h4 id="Choose-some-vectorizers-and-models-to-test."><span style="color:#3b748a">Choose some vectorizers and models to test.</span><a class="anchor-link" href="#Choose-some-vectorizers-and-models-to-test.">&#182;</a></h4><ul>
    <li><span style="color:#4095b5">This runs a long time!</span></li>
    <li><span style="color:#4095b5">TI ran this many times, updating the parameters to tune.</span></li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Decide what to put into the pipline, grid searh, and save the &quot;best&quot; for each grid search</span>
<span class="n">use_params</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">vects</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cv&#39;</span><span class="p">,</span><span class="s1">&#39;tv&#39;</span><span class="p">,</span><span class="s1">&#39;hv&#39;</span><span class="p">]</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;lr&#39;</span><span class="p">,</span><span class="s1">&#39;bnb&#39;</span><span class="p">,</span> <span class="s1">&#39;mnb&#39;</span><span class="p">,</span><span class="s1">&#39;rf&#39;</span><span class="p">,</span><span class="s1">&#39;gb&#39;</span><span class="p">,</span><span class="s1">&#39;ab&#39;</span><span class="p">,</span><span class="s1">&#39;svc&#39;</span><span class="p">,</span><span class="s1">&#39;knn&#39;</span><span class="p">]</span>
<span class="n">other</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;pf&#39;</span><span class="p">,</span><span class="s1">&#39;ss&#39;</span><span class="p">]</span>

<span class="c1"># After some initial tests, these seem like the best to pursue further</span>
<span class="n">vects</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cv&#39;</span><span class="p">,</span><span class="s1">&#39;tv&#39;</span><span class="p">]</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;lr&#39;</span><span class="p">,</span><span class="s1">&#39;bnb&#39;</span><span class="p">,</span> <span class="s1">&#39;mnb&#39;</span><span class="p">]</span>
<span class="n">other</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">model_solns</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vects</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">other</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">combinations</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">i</span><span class="p">)):</span>
            <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
                <span class="n">idx</span> <span class="o">+=</span> <span class="mi">1</span>
                <span class="n">pipe_items</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span>
                <span class="n">pipe_items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">o</span><span class="p">))</span>
                <span class="n">pipe_items</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
                <span class="p">[</span><span class="n">train_score</span><span class="p">,</span> <span class="n">test_score</span><span class="p">,</span> <span class="n">y_test_hat</span><span class="p">,</span> <span class="n">best_params</span><span class="p">]</span> <span class="o">=</span> <span class="n">create_pipline</span><span class="p">(</span><span class="n">pipe_items</span><span class="p">,</span> <span class="n">use_params</span><span class="p">,</span>
                                                                        <span class="n">X_train</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">],</span> <span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">],</span>
                                                                        <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
                <span class="n">model_solns</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;vectorizer&#39;</span> <span class="p">:</span> <span class="n">v</span><span class="p">,</span> <span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">m</span><span class="p">,</span> <span class="s1">&#39;features&#39;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">o</span><span class="p">),</span>
                                    <span class="s1">&#39;train_score&#39;</span><span class="p">:</span> <span class="n">train_score</span><span class="p">,</span> <span class="s1">&#39;test_score&#39;</span><span class="p">:</span> <span class="n">test_score</span><span class="p">,</span>
                                    <span class="s1">&#39;best_params&#39;</span><span class="p">:</span> <span class="n">best_params</span><span class="p">,</span> <span class="s1">&#39;y_test_hat&#39;</span> <span class="p">:</span> <span class="n">y_test_hat</span><span class="p">}</span>
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<pre>Using:
	CountVectorizer
	LogisticRegression
Fitting 3 folds for each of 64 candidates, totalling 192 fits
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<pre>[Parallel(n_jobs=1)]: Done 192 out of 192 | elapsed:   10.5s finished
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<pre>cv__max_df: 1.0
cv__max_features: 2250
cv__min_df: 1
cv__ngram_range: (1, 2)
cv__stop_words: english
lr__C: 1
lr__penalty: l2
Train score: 0.8079783637592968 Test score 0.8178137651821862

Using:
	CountVectorizer
	BernoulliNB
Fitting 3 folds for each of 128 candidates, totalling 384 fits
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<pre>[Parallel(n_jobs=1)]: Done 384 out of 384 | elapsed:   22.9s finished
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<pre>bnb__alpha: 1.8
cv__max_df: 1.0
cv__max_features: 2750
cv__min_df: 1
cv__ngram_range: (1, 1)
cv__stop_words: english
Train score: 0.8275862068965517 Test score 0.8299595141700404

Using:
	CountVectorizer
	MultinomialNB
Fitting 3 folds for each of 96 candidates, totalling 288 fits
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<pre>[Parallel(n_jobs=1)]: Done 288 out of 288 | elapsed:   15.8s finished
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<pre>cv__max_df: 1.0
cv__max_features: 2250
cv__min_df: 1
cv__ngram_range: (1, 1)
cv__stop_words: english
mnb__alpha: 1.0
Train score: 0.8194726166328601 Test score 0.8299595141700404

Using:
	TfidfVectorizer
	LogisticRegression
Fitting 3 folds for each of 128 candidates, totalling 384 fits
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<pre>[Parallel(n_jobs=1)]: Done 384 out of 384 | elapsed:   21.2s finished
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<pre>lr__C: 1
lr__penalty: l2
tv__max_df: 1.0
tv__max_features: 2750
tv__min_df: 1
tv__ngram_range: (1, 1)
tv__stop_words: english
Train score: 0.8181203515889114 Test score 0.8157894736842105

Using:
	TfidfVectorizer
	BernoulliNB
Fitting 3 folds for each of 256 candidates, totalling 768 fits
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<pre>[Parallel(n_jobs=1)]: Done 768 out of 768 | elapsed:   43.0s finished
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<pre>bnb__alpha: 1.8
tv__max_df: 1.0
tv__max_features: 2750
tv__min_df: 1
tv__ngram_range: (1, 1)
tv__stop_words: english
Train score: 0.8275862068965517 Test score 0.8299595141700404

Using:
	TfidfVectorizer
	MultinomialNB
Fitting 3 folds for each of 192 candidates, totalling 576 fits
mnb__alpha: 1.0
tv__max_df: 1.0
tv__max_features: 2000
tv__min_df: 1
tv__ngram_range: (1, 1)
tv__stop_words: english
Train score: 0.8187964841108857 Test score 0.8218623481781376

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<pre>[Parallel(n_jobs=1)]: Done 576 out of 576 | elapsed:   30.9s finished
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<p><span style="color:#4095b5">Lots of results to examine. Unfortunately, none are exactly great. This is unsurprising given the two subreddits that I chose.</span></p>

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<p><hr>
<a name="step5"></a></p>
<h2 id="-V.-Evaluate-the-model"><span style="color:#37535e"> V. Evaluate the model</span><a class="anchor-link" href="#-V.-Evaluate-the-model">&#182;</a></h2><h3 id="Examine-the-best-solutions-from-each-call-to-the-grid-search."><span style="color:#3b748a">Examine the best solutions from each call to the grid search.</span><a class="anchor-link" href="#Examine-the-best-solutions-from-each-call-to-the-grid-search.">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_solns</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model_solns</span><span class="p">)</span>
<span class="n">df_solns</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s1">&#39;test_score&#39;</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>2</th>
      <th>3</th>
      <th>5</th>
      <th>6</th>
      <th>1</th>
      <th>4</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>best_params</th>
      <td>{'bnb__alpha': 1.8, 'cv__max_df': 1.0, 'cv__ma...</td>
      <td>{'cv__max_df': 1.0, 'cv__max_features': 2250, ...</td>
      <td>{'bnb__alpha': 1.8, 'tv__max_df': 1.0, 'tv__ma...</td>
      <td>{'mnb__alpha': 1.0, 'tv__max_df': 1.0, 'tv__ma...</td>
      <td>{'cv__max_df': 1.0, 'cv__max_features': 2250, ...</td>
      <td>{'lr__C': 1, 'lr__penalty': 'l2', 'tv__max_df'...</td>
    </tr>
    <tr>
      <th>features</th>
      <td>[]</td>
      <td>[]</td>
      <td>[]</td>
      <td>[]</td>
      <td>[]</td>
      <td>[]</td>
    </tr>
    <tr>
      <th>model</th>
      <td>bnb</td>
      <td>mnb</td>
      <td>bnb</td>
      <td>mnb</td>
      <td>lr</td>
      <td>lr</td>
    </tr>
    <tr>
      <th>test_score</th>
      <td>0.82996</td>
      <td>0.82996</td>
      <td>0.82996</td>
      <td>0.821862</td>
      <td>0.817814</td>
      <td>0.815789</td>
    </tr>
    <tr>
      <th>train_score</th>
      <td>0.827586</td>
      <td>0.819473</td>
      <td>0.827586</td>
      <td>0.818796</td>
      <td>0.807978</td>
      <td>0.81812</td>
    </tr>
    <tr>
      <th>vectorizer</th>
      <td>cv</td>
      <td>cv</td>
      <td>tv</td>
      <td>tv</td>
      <td>cv</td>
      <td>tv</td>
    </tr>
    <tr>
      <th>y_test_hat</th>
      <td>[1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...</td>
      <td>[1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, ...</td>
      <td>[1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...</td>
      <td>[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, ...</td>
      <td>[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, ...</td>
      <td>[1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_solns</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="s1">&#39;vectorizer&#39;</span><span class="p">,</span><span class="s1">&#39;model&#39;</span><span class="p">,</span><span class="s1">&#39;train_score&#39;</span><span class="p">,</span><span class="s1">&#39;test_score&#39;</span><span class="p">,</span><span class="s1">&#39;best_params&#39;</span><span class="p">],:]</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
      <th>6</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>vectorizer</th>
      <td>cv</td>
      <td>cv</td>
      <td>cv</td>
      <td>tv</td>
      <td>tv</td>
      <td>tv</td>
    </tr>
    <tr>
      <th>model</th>
      <td>lr</td>
      <td>bnb</td>
      <td>mnb</td>
      <td>lr</td>
      <td>bnb</td>
      <td>mnb</td>
    </tr>
    <tr>
      <th>train_score</th>
      <td>0.807978</td>
      <td>0.827586</td>
      <td>0.819473</td>
      <td>0.81812</td>
      <td>0.827586</td>
      <td>0.818796</td>
    </tr>
    <tr>
      <th>test_score</th>
      <td>0.817814</td>
      <td>0.82996</td>
      <td>0.82996</td>
      <td>0.815789</td>
      <td>0.82996</td>
      <td>0.821862</td>
    </tr>
    <tr>
      <th>best_params</th>
      <td>{'cv__max_df': 1.0, 'cv__max_features': 2250, ...</td>
      <td>{'bnb__alpha': 1.8, 'cv__max_df': 1.0, 'cv__ma...</td>
      <td>{'cv__max_df': 1.0, 'cv__max_features': 2250, ...</td>
      <td>{'lr__C': 1, 'lr__penalty': 'l2', 'tv__max_df'...</td>
      <td>{'bnb__alpha': 1.8, 'tv__max_df': 1.0, 'tv__ma...</td>
      <td>{'mnb__alpha': 1.0, 'tv__max_df': 1.0, 'tv__ma...</td>
    </tr>
  </tbody>
</table>
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<h4 id="Closely-look-at-the-parameters-and-see-if-we-hit-an-boundary-on-our-parameter-options."><span style="color:#3b748a">Closely look at the parameters and see if we hit an boundary on our parameter options.</span><a class="anchor-link" href="#Closely-look-at-the-parameters-and-see-if-we-hit-an-boundary-on-our-parameter-options.">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_solns</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="mi">2</span><span class="p">]</span>
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<pre>best_params    {&#39;bnb__alpha&#39;: 1.8, &#39;cv__max_df&#39;: 1.0, &#39;cv__ma...
features                                                      []
model                                                        bnb
test_score                                               0.82996
train_score                                             0.827586
vectorizer                                                    cv
y_test_hat     [1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, ...
Name: 2, dtype: object</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let&#39;s look at the parameters of one solution</span>
<span class="n">df_solns</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;best_params&#39;</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span>
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<pre>{&#39;bnb__alpha&#39;: 1.8,
 &#39;cv__max_df&#39;: 1.0,
 &#39;cv__max_features&#39;: 2750,
 &#39;cv__min_df&#39;: 1,
 &#39;cv__ngram_range&#39;: (1, 1),
 &#39;cv__stop_words&#39;: &#39;english&#39;}</pre>
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<h4 id="Can-get-a-better-value-by-averaging-our-top-models?"><span style="color:#3b748a">Can get a better value by averaging our top models?</span><a class="anchor-link" href="#Can-get-a-better-value-by-averaging-our-top-models?">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Average the y_test_hat vectors to see if we can improve the score</span>
<span class="n">y_test_hat_agg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">df_solns</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;y_test_hat&#39;</span><span class="p">,:])</span>
<span class="n">y_test_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">y_test_hat_agg</span><span class="o">/</span><span class="n">df_solns</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The accuracy score of the average test solutions is </span><span class="si">{:.2f}</span><span class="s2">%.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">y_test_hat</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>The accuracy score of the average test solutions is 83.20%.
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<p><span style="color:#4095b5">This aggregated solution is actually a little better!</span></p>

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<h4 id="What-are-some-of-the-&quot;misses&quot;?"><span style="color:#3b748a">What are some of the "misses"?</span><a class="anchor-link" href="#What-are-some-of-the-&quot;misses&quot;?">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Not sure why I get warnings when adding these</span>
<span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">values</span>
<span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;y_hat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_test_hat</span>
<span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;y_hat_agg&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_test_hat_agg</span>
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<pre>/Users/gwyneth/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

/Users/gwyneth/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  This is separate from the ipykernel package so we can avoid doing imports until
/Users/gwyneth/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  after removing the cwd from sys.path.
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<h4 id="Remember,-where-y=1,-the-post-is-from-r/CapeCod."><span style="color:#3b748a">Remember, where y=1, the post is from r/CapeCod.</span><a class="anchor-link" href="#Remember,-where-y=1,-the-post-is-from-r/CapeCod.">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_test_incorrect</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">X_test</span><span class="p">[</span><span class="s2">&quot;y&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="n">X_test</span><span class="p">[</span><span class="s1">&#39;y_hat&#39;</span><span class="p">]]</span>
<span class="n">X_test_incorrect</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
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      <th>9448u8</th>
      <td>Xpost from accidental wes anderson</td>
      <td>0</td>
      <td>1.0</td>
      <td>5</td>
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      <td>Study Spot?</td>
      <td>1</td>
      <td>0.0</td>
      <td>1</td>
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      <th>7zzzhp</th>
      <td>Self Defense Lessons</td>
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      <td>0.0</td>
      <td>0</td>
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      <td>Free Dental Care for Vets 11/10. (article)</td>
      <td>1</td>
      <td>0.0</td>
      <td>0</td>
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      <td>New to town. Looking for good mechanic/ shop a...</td>
      <td>0</td>
      <td>1.0</td>
      <td>6</td>
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      <th>3erx9e</th>
      <td>Mediocre longboarder looking for people to cru...</td>
      <td>1</td>
      <td>0.0</td>
      <td>0</td>
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      <th>8mjvg2</th>
      <td>First Day on the Water 2018</td>
      <td>1</td>
      <td>0.0</td>
      <td>0</td>
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      <th>4v27is</th>
      <td>In case it was unclear what they sold at this ...</td>
      <td>0</td>
      <td>1.0</td>
      <td>6</td>
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      <th>4ssu7q</th>
      <td>Hobby stores where I could find a D&amp;amp;D group?</td>
      <td>1</td>
      <td>0.0</td>
      <td>1</td>
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      <td>Batting cages???</td>
      <td>1</td>
      <td>0.0</td>
      <td>0</td>
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      <th>3f2tqq</th>
      <td>Ship wine?</td>
      <td>1</td>
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      <td>Fireworks Starting at 9:30 this Saturday</td>
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      <td>Finally felt comfortable flying my quad copter...</td>
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      <td>Enter to Win: Tickets to see Dan + Shay at CCM...</td>
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      <td>0.0</td>
      <td>0</td>
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      <td>What's up with all these state troopers?</td>
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      <td>Mill Creak Salt Marsh</td>
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      <td>Dog friendly restaurants?</td>
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      <td>1.0</td>
      <td>5</td>
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      <th>60a4vx</th>
      <td>This safe washed up on the beach</td>
      <td>1</td>
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      <th>8w9z8b</th>
      <td>From Marstons Mills to the Blue Angels for USM...</td>
      <td>1</td>
      <td>0.0</td>
      <td>0</td>
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      <th>5e7lkw</th>
      <td>Did y'all know Fertitta had a reality show?</td>
      <td>0</td>
      <td>1.0</td>
      <td>6</td>
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<p><span style="color:#4095b5">With more data, I think some of these could be mode accurately predicted. For example:</span></p>
<ul>
    <li><span style="color:#4095b5">"Marstons Mills" is a town on Cape Cod.</span></li>
    <li><span style="color:#4095b5">Fertitta is a chef in Galveston.</span></li>
</ul>
<span style="color:#4095b5">But some are generic and likely could never be determined:</span>
<ul>
    <li><span style="color:#4095b5">Dog friendly restaurants?</span></li>
    <li><span style="color:#4095b5">Batting cages???</span></li>
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<h4 id="Examine-unigrams-of-the-titles-of-incorrect-predictions."><span style="color:#3b748a">Examine unigrams of the titles of incorrect predictions.</span><a class="anchor-link" href="#Examine-unigrams-of-the-titles-of-incorrect-predictions.">&#182;</a></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cv_simple</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">&#39;english&#39;</span><span class="p">)</span>
<span class="n">X_test_cv</span> <span class="o">=</span> <span class="n">cv_simple</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_test_incorrect</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">])</span>
<span class="n">cv_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_test_cv</span><span class="o">.</span><span class="n">todense</span><span class="p">(),</span> <span class="n">columns</span><span class="o">=</span><span class="n">cv_simple</span><span class="o">.</span><span class="n">get_feature_names</span><span class="p">())</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a data frame of the most common words </span>
<span class="n">n_words</span> <span class="o">=</span> <span class="mi">40</span>
<span class="n">words</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">cv_test</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)[:</span><span class="n">n_words</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>

<span class="n">cv_test</span><span class="p">[</span><span class="s1">&#39;is_cape_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">X_test_incorrect</span><span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">cv_test_small</span> <span class="o">=</span> <span class="n">cv_test</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;is_cape_test&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()[</span><span class="n">words</span><span class="p">]</span>
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<pre>[&#39;like&#39;, &#39;need&#39;, &#39;looking&#39;, &#39;today&#39;, &#39;place&#39;, &#39;good&#39;, &#39;beach&#39;, &#39;help&#39;, &#39;know&#39;, &#39;near&#39;, &#39;new&#39;, &#39;town&#39;, &#39;bar&#39;, &#39;restaurants&#39;, &#39;national&#39;, &#39;did&#39;, &#39;car&#39;, &#39;guy&#39;, &#39;spring&#39;, &#39;tickets&#39;, &#39;thanks&#39;, &#39;stores&#39;, &#39;sandbars&#39;, &#39;sounds&#39;, &#39;shop&#39;, &#39;dog&#39;, &#39;island&#39;, &#39;people&#39;, &#39;huli&#39;, &#39;reddit&#39;, &#39;day&#39;, &#39;water&#39;, &#39;watch&#39;, &#39;amp&#39;, &#39;giant&#39;, &#39;galvestonfishing&#39;, &#39;game&#39;, &#39;games&#39;, &#39;activities&#39;, &#39;gaping&#39;]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cv_test_small</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cv_train</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
<span class="n">cv_train</span> <span class="o">=</span> <span class="n">cv_train</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span>
<span class="n">cv_train</span><span class="p">[</span><span class="s1">&#39;is_cape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">values</span>
<span class="n">cv_train_small</span> <span class="o">=</span> <span class="n">cv_train</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;is_cape&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>

<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cv_train</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">cv_test_small</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
<span class="n">cv_test_small</span> <span class="o">=</span> <span class="n">cv_test_small</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span>

<span class="n">cv_merge</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">cv_train_small</span><span class="p">,</span> <span class="n">cv_test_small</span><span class="p">],</span><span class="n">keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;incorrect&#39;</span><span class="p">],</span> <span class="n">sort</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">cv_merge</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th></th>
      <th></th>
      <th>activities</th>
      <th>amp</th>
      <th>bar</th>
      <th>beach</th>
      <th>car</th>
      <th>day</th>
      <th>did</th>
      <th>dog</th>
      <th>game</th>
      <th>games</th>
      <th>...</th>
      <th>reddit</th>
      <th>restaurants</th>
      <th>shop</th>
      <th>spring</th>
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      <th>thanks</th>
      <th>today</th>
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      <th>water</th>
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      <th rowspan="2" valign="top">train</th>
      <th>0</th>
      <td>2</td>
      <td>17</td>
      <td>2</td>
      <td>39</td>
      <td>7</td>
      <td>12</td>
      <td>2</td>
      <td>4</td>
      <td>3</td>
      <td>0</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>8</td>
      <td>10</td>
      <td>3</td>
      <td>20</td>
    </tr>
    <tr>
      <th>1</th>
      <td>6</td>
      <td>15</td>
      <td>5</td>
      <td>42</td>
      <td>5</td>
      <td>24</td>
      <td>5</td>
      <td>5</td>
      <td>7</td>
      <td>15</td>
      <td>...</td>
      <td>2</td>
      <td>5</td>
      <td>6</td>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>13</td>
      <td>8</td>
      <td>7</td>
      <td>1</td>
    </tr>
    <tr>
      <th rowspan="2" valign="top">incorrect</th>
      <th>0</th>
      <td>0</td>
      <td>1</td>
      <td>3</td>
      <td>3</td>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>...</td>
      <td>2</td>
      <td>3</td>
      <td>2</td>
      <td>2</td>
      <td>0</td>
      <td>2</td>
      <td>4</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>2</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
<p>4 rows × 34 columns</p>
</div>
</div>

</div>

</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Adapted from https://matplotlib.org/examples/api/barchart_demo.html</span>

<span class="n">words</span> <span class="o">=</span> <span class="n">cv_merge</span><span class="o">.</span><span class="n">columns</span>
<span class="n">n_words</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">words</span><span class="p">)</span>
<span class="n">words_count_cape_train</span> <span class="o">=</span> <span class="n">cv_merge</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">words_count_gv_train</span> <span class="o">=</span> <span class="n">cv_merge</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">words_count_cape_wrong</span> <span class="o">=</span> <span class="n">cv_merge</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;incorrect&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">words_count_gv_wrong</span> <span class="o">=</span> <span class="n">cv_merge</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s2">&quot;incorrect&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

<span class="n">width</span> <span class="o">=</span> <span class="mf">0.25</span>       <span class="c1"># the width of the bars</span>
<span class="n">ind</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">n_words</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">rects0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span>         <span class="n">words_count_cape_train</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">rects1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="o">+</span><span class="n">width</span><span class="p">,</span>   <span class="n">words_count_cape_wrong</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">rects2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">width</span><span class="p">,</span> <span class="n">words_count_gv_train</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
<span class="n">rects3</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="o">+</span><span class="mi">3</span><span class="o">*</span><span class="n">width</span><span class="p">,</span> <span class="n">words_count_gv_wrong</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>

<span class="c1"># add some text for labels, title and axes ticks</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Counts&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Counts by word and reddit&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind</span> <span class="o">+</span> <span class="n">width</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">words</span><span class="p">,</span><span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;vertical&#39;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">80</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">rects0</span><span class="p">,</span> <span class="n">rects1</span><span class="p">,</span> <span class="n">rects2</span><span class="p">,</span> <span class="n">rects3</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;Cape Cod Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Cape Cod Incorrect&#39;</span><span class="p">,</span>
                                                         <span class="s1">&#39;Galveston&#39;</span><span class="p">,</span> <span class="s1">&#39;Gslveston Incorrect&#39;</span><span class="p">));</span>
</pre></div>

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<p><span style="color:#4095b5">This plot did not come out as well as I had hoped, but I think I can improve on it in the future.</span></p>

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<h2 id="-VI.-Executive-summary"><span style="color:#37535e"> VI. Executive summary</span><a class="anchor-link" href="#-VI.-Executive-summary">&#182;</a></h2>
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<h4 id="The-data"><span style="color:#3b748a">The data</span><a class="anchor-link" href="#The-data">&#182;</a></h4><p><span style="color:#4095b5">
For this project, I chose two sub-reddits to compare, r/Cape Cod and r/galveston. As this was my first experience with Reddit, I had some challenege to come up with two forums to compare since most anything I chose did not have enough posts. With more experience with Reddit, I might have been able to chose more interesting topics to compare. Scraping was easy enough, but since the Reddits are not extremely active, I did not end up with as much data as I would have liked
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<h4 id="Exploring-the-data"><span style="color:#3b748a">Exploring the data</span><a class="anchor-link" href="#Exploring-the-data">&#182;</a></h4><p><span style="color:#4095b5">
Once I had scraped the data, I ran a number of initial tests on it to get some insight. I used a word cloud to illustrate the word used most often in each subreddit. I then ran a vectorizer on unigrams and made some bar charts comparing the usage of the most words. The results were not particularly surprising, though there were some words I had not anticipated such as 'seawall' and 'gaming'.</span></p>

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<h4 id="Modeling-the-problem"><span style="color:#3b748a">Modeling the problem</span><a class="anchor-link" href="#Modeling-the-problem">&#182;</a></h4><p><span style="color:#4095b5">
From there, I dove into NLP. I ran a few simple tests, determined a baseline accuracy, and examined the coefficients determied by logistical regression. Again, the words with the most influence were not surprising - mostly place names or things unique to one place or the other. Actually, there was one word with a aurprisingly high coefficient - 'water'.
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<h4 id="Evaluate-the-model"><span style="color:#3b748a">Evaluate the model</span><a class="anchor-link" href="#Evaluate-the-model">&#182;</a></h4><p><span style="color:#4095b5">
I then spent some time trying to generalize pipelines in order to efficiently run lots of different grid searches on many different models and parameters. This generalized function has room for improvement, but was quite helpful for me to stay organized in my tests. I saved the "best" from each test and turned the data into a dataframe in order to explore the incorrect predictions.</span></p>
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Despite tuning the parameters, I could not get better than 83% accuracy, unfortunately.
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