Recommendations_with_IBM.html

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<title>Recommendations_with_IBM</title>

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
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span.autosave_status {
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[dir="rtl"] .btn-group > .btn,
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li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
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/**
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 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
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 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
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 * the left with fix button on the top right of the tooltip
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@-webkit-keyframes fadeIn {
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  -moz-transition-duration: 500ms;
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}
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[dir="rtl"] .command-shortcut.pull-right {
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<h1 id="Recommendations-with-IBM">Recommendations with IBM<a class="anchor-link" href="#Recommendations-with-IBM">&#182;</a></h1><p>In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform.</p>
<p>You may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page.  Either way assure that your code passes the project <a href="https://review.udacity.com/#!/rubrics/2322/view">RUBRIC</a>.  <strong>Please save regularly.</strong></p>
<p>By following the table of contents, you will build out a number of different methods for making recommendations that can be used for different situations.</p>
<h2 id="Table-of-Contents">Table of Contents<a class="anchor-link" href="#Table-of-Contents">&#182;</a></h2><p>I. <a href="#Exploratory-Data-Analysis">Exploratory Data Analysis</a><br>
II. <a href="#Rank">Rank Based Recommendations</a><br>
III. <a href="#User-User">User-User Based Collaborative Filtering</a><br>
IV. <a href="#Matrix-Fact">Matrix Factorization</a><br></p>
<p>At the end of the notebook, you will find directions for how to submit your work.  Let's get started by importing the necessary libraries and reading in the data.</p>

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<div class=" highlight hl-ipython3"><pre><span></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">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">project_tests</span> <span class="k">as</span> <span class="nn">t</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>

<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;data/user-item-interactions.csv&#39;</span><span class="p">)</span>
<span class="n">df_content</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="s1">&#39;data/articles_community.csv&#39;</span><span class="p">)</span>
<span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Unnamed: 0&#39;</span><span class="p">]</span>
<span class="k">del</span> <span class="n">df_content</span><span class="p">[</span><span class="s1">&#39;Unnamed: 0&#39;</span><span class="p">]</span>

<span class="c1"># Show df to get an idea of the data</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <td>1430.0</td>
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      <td>1314.0</td>
      <td>healthcare python streaming application demo</td>
      <td>083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b</td>
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      <th>2</th>
      <td>1429.0</td>
      <td>use deep learning for image classification</td>
      <td>b96a4f2e92d8572034b1e9b28f9ac673765cd074</td>
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      <td>1338.0</td>
      <td>ml optimization using cognitive assistant</td>
      <td>06485706b34a5c9bf2a0ecdac41daf7e7654ceb7</td>
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      <td>1276.0</td>
      <td>deploy your python model as a restful api</td>
      <td>f01220c46fc92c6e6b161b1849de11faacd7ccb2</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Show df_content to get an idea of the data</span>
<span class="n">df_content</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <td>Skip navigation Sign in SearchLoading...\r\n\r...</td>
      <td>Detect bad readings in real time using Python ...</td>
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      <td>No Free Hunch Navigation * kaggle.com\r\n\r\n ...</td>
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      <td>☰ * Login\r\n * Sign Up\r\n\r\n * Learning Pat...</td>
      <td>Here’s this week’s news in Data Science and Bi...</td>
      <td>This Week in Data Science (April 18, 2017)</td>
      <td>Live</td>
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<h3 id="Part-I-:-Exploratory-Data-Analysis"><a class="anchor" id="Exploratory-Data-Analysis">Part I : Exploratory Data Analysis</a><a class="anchor-link" href="#Part-I-:-Exploratory-Data-Analysis">&#182;</a></h3><p>Use the dictionary and cells below to provide some insight into the descriptive statistics of the data.</p>

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<p><code>1.</code> What is the distribution of how many articles a user interacts with in the dataset?  Provide a visual and descriptive statistics to assist with giving a look at the number of times each user interacts with an article.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># To create the user-item matrix, we only need the email=user id and item=article_id</span>
<span class="n">user_items</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;email&#39;</span><span class="p">,</span> <span class="s1">&#39;article_id&#39;</span><span class="p">]]</span>
<span class="n">user_items</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>0</th>
      <td>ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7</td>
      <td>1430.0</td>
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      <th>1</th>
      <td>083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b</td>
      <td>1314.0</td>
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      <th>2</th>
      <td>b96a4f2e92d8572034b1e9b28f9ac673765cd074</td>
      <td>1429.0</td>
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      <th>3</th>
      <td>06485706b34a5c9bf2a0ecdac41daf7e7654ceb7</td>
      <td>1338.0</td>
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      <th>4</th>
      <td>f01220c46fc92c6e6b161b1849de11faacd7ccb2</td>
      <td>1276.0</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># insepct nan values</span>
<span class="n">user_items</span><span class="p">[</span><span class="n">user_items</span><span class="o">.</span><span class="n">email</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span>
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      <th>25131</th>
      <td>NaN</td>
      <td>1016.0</td>
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      <th>29758</th>
      <td>NaN</td>
      <td>1393.0</td>
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      <th>29759</th>
      <td>NaN</td>
      <td>20.0</td>
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      <th>29760</th>
      <td>NaN</td>
      <td>1174.0</td>
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      <th>29761</th>
      <td>NaN</td>
      <td>62.0</td>
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      <th>35264</th>
      <td>NaN</td>
      <td>224.0</td>
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      <th>35276</th>
      <td>NaN</td>
      <td>961.0</td>
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      <th>35277</th>
      <td>NaN</td>
      <td>268.0</td>
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      <th>35278</th>
      <td>NaN</td>
      <td>268.0</td>
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      <th>35279</th>
      <td>NaN</td>
      <td>268.0</td>
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      <th>35280</th>
      <td>NaN</td>
      <td>268.0</td>
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      <th>35281</th>
      <td>NaN</td>
      <td>415.0</td>
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      <th>35282</th>
      <td>NaN</td>
      <td>846.0</td>
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      <th>35283</th>
      <td>NaN</td>
      <td>268.0</td>
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      <th>35284</th>
      <td>NaN</td>
      <td>162.0</td>
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      <th>42749</th>
      <td>NaN</td>
      <td>647.0</td>
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      <th>42750</th>
      <td>NaN</td>
      <td>965.0</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">user_distribution</span> <span class="o">=</span> <span class="n">user_items</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;email&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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      <th>0000b6387a0366322d7fbfc6434af145adf7fed1</th>
      <td>13</td>
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      <th>001055fc0bb67f71e8fa17002342b256a30254cd</th>
      <td>4</td>
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      <th>00148e4911c7e04eeff8def7bbbdaf1c59c2c621</th>
      <td>3</td>
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      <th>001a852ecbd6cc12ab77a785efa137b2646505fe</th>
      <td>6</td>
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      <th>001fc95b90da5c3cb12c501d201a915e4f093290</th>
      <td>2</td>
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      <th>count</th>
      <td>5148.000000</td>
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      <th>mean</th>
      <td>8.930847</td>
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      <th>std</th>
      <td>16.802267</td>
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      <th>min</th>
      <td>1.000000</td>
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      <th>25%</th>
      <td>1.000000</td>
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      <th>50%</th>
      <td>3.000000</td>
    </tr>
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      <th>75%</th>
      <td>9.000000</td>
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      <th>max</th>
      <td>364.000000</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fill in the median and maximum number of user_article interactios below</span>
<span class="n">median_val</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="o">.</span><span class="n">median</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># 50% of individuals interact with ____ number of articles or fewer.</span>
<span class="n">max_views_by_user</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="o">.</span><span class="n">max</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># The maximum number of user-article interactions by any 1 user is ______.</span>
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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;50</span><span class="si">% o</span><span class="s2">f individuals interact with </span><span class="si">{}</span><span class="s2"> number of articles or fewer.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">median_val</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The maximum number of user-article interactions by any 1 user is </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">max_views_by_user</span><span class="p">))</span>
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<pre>50% of individuals interact with 3.0 number of articles or fewer.
The maximum number of user-article interactions by any 1 user is 364.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Frequency graph of user interactions with articles&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Number of Interactions&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of articles&quot;</span><span class="p">);</span>
</pre></div>

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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Frequency graph of user interactions with articles per user&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;User&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of articles&quot;</span><span class="p">);</span>
</pre></div>

    </div>
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<div class="output_wrapper">
<div class="output">


<div class="output_area">

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


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<div class="prompt input_prompt">In&nbsp;[12]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;box&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Box plot of user interactions with articles&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of interactions&quot;</span><span class="p">);</span>
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    <div class="prompt"></div>


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<div class="prompt input_prompt">In&nbsp;[13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">distribution_75</span> <span class="o">=</span> <span class="n">user_distribution</span><span class="p">[</span><span class="n">user_distribution</span><span class="o">.</span><span class="n">article_id</span> <span class="o">&lt;</span> <span class="mi">9</span> <span class="p">]</span> <span class="c1">#75%	9.000000</span>
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<div class="prompt input_prompt">In&nbsp;[14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">distribution_75</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;box&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Box plot of user interactions with articles of 75%&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Number of interactions&quot;</span><span class="p">);</span>
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<p><code>2.</code> Explore and remove duplicate articles from the <strong>df_content</strong> dataframe.</p>

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<div class="prompt input_prompt">In&nbsp;[15]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Find and explore duplicate articles</span>
<span class="nb">sum</span><span class="p">(</span><span class="n">df_content</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">duplicated</span><span class="p">())</span>
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<pre>5</pre>
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<div class="prompt input_prompt">In&nbsp;[16]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># show duplicates</span>
<span class="n">df_content</span><span class="p">[</span><span class="n">df_content</span><span class="o">.</span><span class="n">duplicated</span><span class="p">([</span><span class="s1">&#39;article_id&#39;</span><span class="p">])]</span>
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      <td>Follow Sign in / Sign up Home About Insight Da...</td>
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      <td>This video shows you how to construct queries ...</td>
      <td>This video shows you how to construct queries ...</td>
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      <td>577</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove any rows that have the same article_id - only keep the first</span>
<span class="n">df_content</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">drop_duplicates</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"># verify</span>
<span class="nb">sum</span><span class="p">(</span><span class="n">df_content</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">duplicated</span><span class="p">())</span>
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<pre>0</pre>
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<p><code>3.</code> Use the cells below to find:</p>
<p><strong>a.</strong> The number of unique articles that have an interaction with a user.<br>
<strong>b.</strong> The number of unique articles in the dataset (whether they have any interactions or not).<br>
<strong>c.</strong> The number of unique users in the dataset. (excluding null values) <br>
<strong>d.</strong> The number of user-article interactions in the dataset.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">df_content</span><span class="o">.</span><span class="n">shape</span>
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<pre>((45993, 3), (1056, 5))</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">unique_articles</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="c1"># The number of unique articles that have at least one interaction</span>
<span class="n">total_articles</span> <span class="o">=</span> <span class="n">df_content</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="c1"># The number of unique articles on the IBM platform</span>
<span class="n">unique_users</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">email</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="c1"># The number of unique users</span>
<span class="n">user_article_interactions</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># The number of user-article interactions</span>
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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;a. The number of unique articles that have an interaction with a user is </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">unique_articles</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;b. The number of unique articles in the dataset (whether they have any interactions or not) is </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">total_articles</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;c. The number of unique users in the dataset. (excluding null values) is </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">unique_users</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;d. The number of user-article interactions in the dataset is </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">user_article_interactions</span><span class="p">))</span>
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<pre>a. The number of unique articles that have an interaction with a user is 714.
b. The number of unique articles in the dataset (whether they have any interactions or not) is 1051.
c. The number of unique users in the dataset. (excluding null values) is 5148.
d. The number of user-article interactions in the dataset is 45993.
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<p><code>4.</code> Use the cells below to find the most viewed <strong>article_id</strong>, as well as how often it was viewed.  After talking to the company leaders, the <code>email_mapper</code> function was deemed a reasonable way to map users to ids.  There were a small number of null values, and it was found that all of these null values likely belonged to a single user (which is how they are stored using the function below).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">article_distribution</span> <span class="o">=</span> <span class="n">user_items</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;article_id&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">article_distribution</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>0.0</th>
      <td>14</td>
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      <th>2.0</th>
      <td>58</td>
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      <th>4.0</th>
      <td>13</td>
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      <th>8.0</th>
      <td>85</td>
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      <th>9.0</th>
      <td>10</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">article_distribution</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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      <th>count</th>
      <td>714.000000</td>
    </tr>
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      <th>mean</th>
      <td>64.392157</td>
    </tr>
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      <th>std</th>
      <td>109.161462</td>
    </tr>
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      <th>min</th>
      <td>1.000000</td>
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      <th>25%</th>
      <td>8.000000</td>
    </tr>
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      <th>50%</th>
      <td>25.000000</td>
    </tr>
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      <th>75%</th>
      <td>69.000000</td>
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      <th>max</th>
      <td>937.000000</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">article_distribution</span><span class="p">[</span><span class="n">article_distribution</span><span class="o">.</span><span class="n">email</span> <span class="o">==</span> <span class="n">article_distribution</span><span class="o">.</span><span class="n">max</span><span class="p">()[</span><span class="mi">0</span><span class="p">]]</span>
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      <th>1429.0</th>
      <td>937</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">most_viewed_article_id</span> <span class="o">=</span> <span class="s2">&quot;1429.0&quot;</span> <span class="c1"># The most viewed article in the dataset as a string with one value following the decimal </span>
<span class="n">max_views</span> <span class="o">=</span> <span class="mi">937</span> <span class="c1"># The most viewed article in the dataset was viewed how many times?</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## No need to change the code here - this will be helpful for later parts of the notebook</span>
<span class="c1"># Run this cell to map the user email to a user_id column and remove the email column</span>

<span class="k">def</span> <span class="nf">email_mapper</span><span class="p">():</span>
    <span class="n">coded_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    <span class="n">cter</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="n">email_encoded</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;email&#39;</span><span class="p">]:</span>
        <span class="k">if</span> <span class="n">val</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">coded_dict</span><span class="p">:</span>
            <span class="n">coded_dict</span><span class="p">[</span><span class="n">val</span><span class="p">]</span> <span class="o">=</span> <span class="n">cter</span>
            <span class="n">cter</span><span class="o">+=</span><span class="mi">1</span>
        
        <span class="n">email_encoded</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coded_dict</span><span class="p">[</span><span class="n">val</span><span class="p">])</span>
    <span class="k">return</span> <span class="n">email_encoded</span>

<span class="n">email_encoded</span> <span class="o">=</span> <span class="n">email_mapper</span><span class="p">()</span>
<span class="k">del</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;email&#39;</span><span class="p">]</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;user_id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">email_encoded</span>

<span class="c1"># show header</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>0</th>
      <td>1430.0</td>
      <td>using pixiedust for fast, flexible, and easier...</td>
      <td>1</td>
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      <td>1314.0</td>
      <td>healthcare python streaming application demo</td>
      <td>2</td>
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      <th>2</th>
      <td>1429.0</td>
      <td>use deep learning for image classification</td>
      <td>3</td>
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      <td>1338.0</td>
      <td>ml optimization using cognitive assistant</td>
      <td>4</td>
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      <td>1276.0</td>
      <td>deploy your python model as a restful api</td>
      <td>5</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## If you stored all your results in the variable names above, </span>
<span class="c1">## you shouldn&#39;t need to change anything in this cell</span>

<span class="n">sol_1_dict</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;`50</span><span class="si">% o</span><span class="s1">f individuals have _____ or fewer interactions.`&#39;</span><span class="p">:</span> <span class="n">median_val</span><span class="p">,</span>
    <span class="s1">&#39;`The total number of user-article interactions in the dataset is ______.`&#39;</span><span class="p">:</span> <span class="n">user_article_interactions</span><span class="p">,</span>
    <span class="s1">&#39;`The maximum number of user-article interactions by any 1 user is ______.`&#39;</span><span class="p">:</span> <span class="n">max_views_by_user</span><span class="p">,</span>
    <span class="s1">&#39;`The most viewed article in the dataset was viewed _____ times.`&#39;</span><span class="p">:</span> <span class="n">max_views</span><span class="p">,</span>
    <span class="s1">&#39;`The article_id of the most viewed article is ______.`&#39;</span><span class="p">:</span> <span class="n">most_viewed_article_id</span><span class="p">,</span>
    <span class="s1">&#39;`The number of unique articles that have at least 1 rating ______.`&#39;</span><span class="p">:</span> <span class="n">unique_articles</span><span class="p">,</span>
    <span class="s1">&#39;`The number of unique users in the dataset is ______`&#39;</span><span class="p">:</span> <span class="n">unique_users</span><span class="p">,</span>
    <span class="s1">&#39;`The number of unique articles on the IBM platform`&#39;</span><span class="p">:</span> <span class="n">total_articles</span>
<span class="p">}</span>

<span class="c1"># Test your dictionary against the solution</span>
<span class="n">t</span><span class="o">.</span><span class="n">sol_1_test</span><span class="p">(</span><span class="n">sol_1_dict</span><span class="p">)</span>
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<pre>It looks like you have everything right here! Nice job!
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<h3 id="Part-II:-Rank-Based-Recommendations"><a class="anchor" id="Rank">Part II: Rank-Based Recommendations</a><a class="anchor-link" href="#Part-II:-Rank-Based-Recommendations">&#182;</a></h3><p>Unlike in the earlier lessons, we don't actually have ratings for whether a user liked an article or not.  We only know that a user has interacted with an article.  In these cases, the popularity of an article can really only be based on how often an article was interacted with.</p>
<p><code>1.</code> Fill in the function below to return the <strong>n</strong> top articles ordered with most interactions as the top. Test your function using the tests below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_top_articles</span><span class="p">(</span><span class="n">n</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="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    n - (int) the number of top articles to return</span>
<span class="sd">    df - (pandas dataframe) df as defined at the top of the notebook </span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    top_articles - (list) A list of the top &#39;n&#39; article titles </span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">top_articles</span> <span class="o">=</span> <span class="n">get_top_article_ids</span><span class="p">(</span><span class="n">n</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="n">titles</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">top_articles</span><span class="p">):</span>
        <span class="n">titles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">article_id</span> <span class="o">==</span> <span class="n">top_articles</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    
    <span class="n">top_articles</span> <span class="o">=</span> <span class="n">titles</span>
    <span class="k">return</span> <span class="n">top_articles</span> <span class="c1"># Return the top article titles from df (not df_content)</span>

<span class="k">def</span> <span class="nf">get_top_article_ids</span><span class="p">(</span><span class="n">n</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="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    n - (int) the number of top articles to return</span>
<span class="sd">    df - (pandas dataframe) df as defined at the top of the notebook </span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    top_articles - (list) A list of the top &#39;n&#39; article titles </span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
        
    <span class="n">user_items</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;user_id&#39;</span><span class="p">,</span> <span class="s1">&#39;article_id&#39;</span><span class="p">]]</span>
    <span class="n">article_distribution</span> <span class="o">=</span> <span class="n">user_items</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;article_id&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
    <span class="n">top_articles</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">article_distribution</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;user_id&#39;</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">iloc</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
     
    <span class="k">return</span> <span class="n">top_articles</span> <span class="c1"># Return the top article ids</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">get_top_articles</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">get_top_article_ids</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
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<pre>[&#39;use deep learning for image classification&#39;, &#39;insights from new york car accident reports&#39;, &#39;visualize car data with brunel&#39;, &#39;use xgboost, scikit-learn &amp; ibm watson machine learning apis&#39;, &#39;predicting churn with the spss random tree algorithm&#39;, &#39;healthcare python streaming application demo&#39;, &#39;finding optimal locations of new store using decision optimization&#39;, &#39;apache spark lab, part 1: basic concepts&#39;, &#39;analyze energy consumption in buildings&#39;, &#39;gosales transactions for logistic regression model&#39;]
[1429.0, 1330.0, 1431.0, 1427.0, 1364.0, 1314.0, 1293.0, 1170.0, 1162.0, 1304.0]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test your function by returning the top 5, 10, and 20 articles</span>
<span class="n">top_5</span> <span class="o">=</span> <span class="n">get_top_articles</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="n">top_10</span> <span class="o">=</span> <span class="n">get_top_articles</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">top_20</span> <span class="o">=</span> <span class="n">get_top_articles</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>

<span class="c1"># Test each of your three lists from above</span>
<span class="n">t</span><span class="o">.</span><span class="n">sol_2_test</span><span class="p">(</span><span class="n">get_top_articles</span><span class="p">)</span>
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<pre>Your top_5 looks like the solution list! Nice job.
Your top_10 looks like the solution list! Nice job.
Your top_20 looks like the solution list! Nice job.
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<h3 id="Part-III:-User-User-Based-Collaborative-Filtering"><a class="anchor" id="User-User">Part III: User-User Based Collaborative Filtering</a><a class="anchor-link" href="#Part-III:-User-User-Based-Collaborative-Filtering">&#182;</a></h3><p><code>1.</code> Use the function below to reformat the <strong>df</strong> dataframe to be shaped with users as the rows and articles as the columns.</p>
<ul>
<li>Each <strong>user</strong> should only appear in each <strong>row</strong> once.</li>
</ul>
<ul>
<li>Each <strong>article</strong> should only show up in one <strong>column</strong>.  </li>
</ul>
<ul>
<li><strong>If a user has interacted with an article, then place a 1 where the user-row meets for that article-column</strong>.  It does not matter how many times a user has interacted with the article, all entries where a user has interacted with an article should be a 1.  </li>
</ul>
<ul>
<li><strong>If a user has not interacted with an item, then place a zero where the user-row meets for that article-column</strong>. </li>
</ul>
<p>Use the tests to make sure the basic structure of your matrix matches what is expected by the solution.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create the user-article matrix with 1&#39;s and 0&#39;s</span>

<span class="k">def</span> <span class="nf">create_user_item_matrix</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    df - pandas dataframe with article_id, title, user_id columns</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    user_item - user item matrix </span>
<span class="sd">    </span>
<span class="sd">    Description:</span>
<span class="sd">    Return a matrix with user ids as rows and article ids on the columns with 1 values where a user interacted with </span>
<span class="sd">    an article and a 0 otherwise</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">user_item</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;user_id&#39;</span><span class="p">,</span> <span class="s1">&#39;article_id&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">size</span><span class="p">()</span><span class="o">.</span><span class="n">unstack</span><span class="p">(</span><span class="n">fill_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="n">user_item</span> <span class="c1"># return the user_item matrix </span>

<span class="n">user_item</span> <span class="o">=</span> <span class="n">create_user_item_matrix</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Tests: You should just need to run this cell.  Don&#39;t change the code.</span>
<span class="k">assert</span> <span class="n">user_item</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">5149</span><span class="p">,</span> <span class="s2">&quot;Oops!  The number of users in the user-article matrix doesn&#39;t look right.&quot;</span>
<span class="k">assert</span> <span class="n">user_item</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">714</span><span class="p">,</span> <span class="s2">&quot;Oops!  The number of articles in the user-article matrix doesn&#39;t look right.&quot;</span>
<span class="k">assert</span> <span class="n">user_item</span><span class="o">.</span><span class="n">sum</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="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">36</span><span class="p">,</span> <span class="s2">&quot;Oops!  The number of articles seen by user 1 doesn&#39;t look right.&quot;</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;You have passed our quick tests!  Please proceed!&quot;</span><span class="p">)</span>
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<pre>You have passed our quick tests!  Please proceed!
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<p><code>2.</code> Complete the function below which should take a user_id and provide an ordered list of the most similar users to that user (from most similar to least similar).  The returned result should not contain the provided user_id, as we know that each user is similar to him/herself. Because the results for each user here are binary, it (perhaps) makes sense to compute similarity as the dot product of two users.</p>
<p>Use the tests to test your function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">find_similar_users</span><span class="p">(</span><span class="n">user_id</span><span class="p">,</span> <span class="n">user_item</span><span class="o">=</span><span class="n">user_item</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    user_id - (int) a user_id</span>
<span class="sd">    user_item - (pandas dataframe) matrix of users by articles: </span>
<span class="sd">                1&#39;s when a user has interacted with an article, 0 otherwise</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    similar_users - (list) an ordered list where the closest users (largest dot product users)</span>
<span class="sd">                    are listed first</span>
<span class="sd">    </span>
<span class="sd">    Description:</span>
<span class="sd">    Computes the similarity of every pair of users based on the dot product</span>
<span class="sd">    Returns an ordered</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># compute similarity of each user to the provided user</span>
    <span class="n">dot_prod</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">user_id</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">user_id</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">user_item</span><span class="p">))</span>

    <span class="c1"># sort by similarity</span>
    <span class="n">dot_prod</span> <span class="o">=</span> <span class="n">dot_prod</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="n">user_id</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">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
   
    <span class="c1"># remove the own user&#39;s id</span>
    <span class="n">dot_prod</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="n">user_id</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">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    
    <span class="c1"># create list of just the ids</span>
    <span class="n">most_similar_users</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">dot_prod</span><span class="p">)</span>
       
    <span class="k">return</span> <span class="n">most_similar_users</span> <span class="c1"># return a list of the users in order from most to least similar</span>
        
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Do a spot check of your function</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The 10 most similar users to user 1 are: </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">find_similar_users</span><span class="p">(</span><span class="mi">1</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The 5 most similar users to user 3933 are: </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">find_similar_users</span><span class="p">(</span><span class="mi">3933</span><span class="p">)[:</span><span class="mi">5</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The 3 most similar users to user 46 are: </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">find_similar_users</span><span class="p">(</span><span class="mi">46</span><span class="p">)[:</span><span class="mi">3</span><span class="p">]))</span>
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<pre>The 10 most similar users to user 1 are: [3933, 23, 3782, 203, 4459, 3870, 131, 4201, 46, 5041]
The 5 most similar users to user 3933 are: [1, 23, 3782, 203, 4459]
The 3 most similar users to user 46 are: [4201, 3782, 23]
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<p><code>3.</code> Now that you have a function that provides the most similar users to each user, you will want to use these users to find articles you can recommend.  Complete the functions below to return the articles you would recommend to each user.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_article_names</span><span class="p">(</span><span class="n">article_ids</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="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    article_ids - (list) a list of article ids</span>
<span class="sd">    df - (pandas dataframe) df as defined at the top of the notebook</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    article_names - (list) a list of article names associated with the list of article ids </span>
<span class="sd">                    (this is identified by the title column)</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">article_names</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">article_ids</span><span class="p">:</span>
        <span class="n">article_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">article_id</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="n">item</span><span class="p">)]</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
            
    <span class="k">return</span> <span class="n">article_names</span> <span class="c1"># Return the article names associated with list of article ids</span>


<span class="k">def</span> <span class="nf">get_user_articles</span><span class="p">(</span><span class="n">user_id</span><span class="p">,</span> <span class="n">user_item</span><span class="o">=</span><span class="n">user_item</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    user_id - (int) a user id</span>
<span class="sd">    user_item - (pandas dataframe) matrix of users by articles: </span>
<span class="sd">                1&#39;s when a user has interacted with an article, 0 otherwise</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    article_ids - (list) a list of the article ids seen by the user</span>
<span class="sd">    article_names - (list) a list of article names associated with the list of article ids </span>
<span class="sd">                    (this is identified by the doc_full_name column in df_content)</span>
<span class="sd">    </span>
<span class="sd">    Description:</span>
<span class="sd">    Provides a list of the article_ids and article titles that have been seen by a user</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">user_df</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">user_id</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">user_id</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="n">user_id</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">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>

    <span class="n">seen_items</span> <span class="o">=</span> <span class="n">user_df</span><span class="p">[(</span><span class="n">user_df</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)]</span>
    <span class="n">seen_items</span><span class="o">.</span><span class="n">dropna</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">seen_items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">seen_items</span><span class="p">)</span>
    
    <span class="n">article_ids</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">article_names</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">seen_items</span><span class="p">:</span>
        <span class="n">article_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">))</span>
        <span class="n">article_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">article_id</span> <span class="o">==</span> <span class="n">item</span><span class="p">]</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">article_ids</span><span class="p">,</span> <span class="n">article_names</span> <span class="c1"># return the ids and names</span>


<span class="k">def</span> <span class="nf">user_user_recs</span><span class="p">(</span><span class="n">user_id</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    user_id - (int) a user id</span>
<span class="sd">    m - (int) the number of recommendations you want for the user</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    recs - (list) a list of recommendations for the user</span>
<span class="sd">    </span>
<span class="sd">    Description:</span>
<span class="sd">    Loops through the users based on closeness to the input user_id</span>
<span class="sd">    For each user - finds articles the user hasn&#39;t seen before and provides them as recs</span>
<span class="sd">    Does this until m recommendations are found</span>
<span class="sd">    </span>
<span class="sd">    Notes:</span>
<span class="sd">    Users who are the same closeness are chosen arbitrarily as the &#39;next&#39; user</span>
<span class="sd">    </span>
<span class="sd">    For the user where the number of recommended articles starts below m </span>
<span class="sd">    and ends exceeding m, the last items are chosen arbitrarily</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">recs</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">sim_user</span> <span class="ow">in</span> <span class="n">find_similar_users</span><span class="p">(</span><span class="n">user_id</span><span class="p">):</span>
            <span class="n">recs</span> <span class="o">+=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="n">sim_user</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="n">user_id</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">recs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">m</span><span class="p">:</span>
                <span class="k">break</span>

    <span class="n">recs</span> <span class="o">=</span> <span class="n">recs</span><span class="p">[:</span><span class="n">m</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">recs</span> <span class="c1"># return your recommendations for this user_id    </span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check Results</span>
<span class="n">get_article_names</span><span class="p">(</span><span class="n">user_user_recs</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> <span class="c1"># Return 10 recommendations for user 1</span>
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<pre>[&#39;awesome deep learning papers&#39;,
 &#39;using brunel in ipython/jupyter notebooks&#39;,
 &#39;optimizing a marketing campaign: moving from predictions to actions&#39;,
 &#39;insights from new york car accident reports&#39;,
 &#39;the unit commitment problem&#39;,
 &#39;markdown for jupyter notebooks cheatsheet&#39;,
 &#39;discover hidden facebook usage insights&#39;,
 &#39;twelve\xa0ways to color a map of africa using brunel&#39;,
 &#39;shaping data with ibm data refinery&#39;,
 &#39;maximize oil company profits&#39;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test your functions here - No need to change this code - just run this cell</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_article_names</span><span class="p">([</span><span class="s1">&#39;1024.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1176.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1305.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1314.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1422.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1427.0&#39;</span><span class="p">]))</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;using deep learning to reconstruct high-resolution audio&#39;</span><span class="p">,</span> <span class="s1">&#39;build a python app on the streaming analytics service&#39;</span><span class="p">,</span> <span class="s1">&#39;gosales transactions for naive bayes model&#39;</span><span class="p">,</span> <span class="s1">&#39;healthcare python streaming application demo&#39;</span><span class="p">,</span> <span class="s1">&#39;use r dataframes &amp; ibm watson natural language understanding&#39;</span><span class="p">,</span> <span class="s1">&#39;use xgboost, scikit-learn &amp; ibm watson machine learning apis&#39;</span><span class="p">]),</span> <span class="s2">&quot;Oops! Your the get_article_names function doesn&#39;t work quite how we expect.&quot;</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_article_names</span><span class="p">([</span><span class="s1">&#39;1320.0&#39;</span><span class="p">,</span> <span class="s1">&#39;232.0&#39;</span><span class="p">,</span> <span class="s1">&#39;844.0&#39;</span><span class="p">]))</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;housing (2015): united states demographic measures&#39;</span><span class="p">,</span><span class="s1">&#39;self-service data preparation with ibm data refinery&#39;</span><span class="p">,</span><span class="s1">&#39;use the cloudant-spark connector in python notebook&#39;</span><span class="p">]),</span> <span class="s2">&quot;Oops! Your the get_article_names function doesn&#39;t work quite how we expect.&quot;</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="mi">20</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;1320.0&#39;</span><span class="p">,</span> <span class="s1">&#39;232.0&#39;</span><span class="p">,</span> <span class="s1">&#39;844.0&#39;</span><span class="p">])</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="mi">20</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;housing (2015): united states demographic measures&#39;</span><span class="p">,</span> <span class="s1">&#39;self-service data preparation with ibm data refinery&#39;</span><span class="p">,</span><span class="s1">&#39;use the cloudant-spark connector in python notebook&#39;</span><span class="p">])</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="mi">2</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;1024.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1176.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1305.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1314.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1422.0&#39;</span><span class="p">,</span> <span class="s1">&#39;1427.0&#39;</span><span class="p">])</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">get_user_articles</span><span class="p">(</span><span class="mi">2</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;using deep learning to reconstruct high-resolution audio&#39;</span><span class="p">,</span> <span class="s1">&#39;build a python app on the streaming analytics service&#39;</span><span class="p">,</span> <span class="s1">&#39;gosales transactions for naive bayes model&#39;</span><span class="p">,</span> <span class="s1">&#39;healthcare python streaming application demo&#39;</span><span class="p">,</span> <span class="s1">&#39;use r dataframes &amp; ibm watson natural language understanding&#39;</span><span class="p">,</span> <span class="s1">&#39;use xgboost, scikit-learn &amp; ibm watson machine learning apis&#39;</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;If this is all you see, you passed all of our tests!  Nice job!&quot;</span><span class="p">)</span>
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<pre>If this is all you see, you passed all of our tests!  Nice job!
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<p><code>4.</code> Now we are going to improve the consistency of the <strong>user_user_recs</strong> function from above.</p>
<ul>
<li>Instead of arbitrarily choosing when we obtain users who are all the same closeness to a given user - choose the users that have the most total article interactions before choosing those with fewer article interactions.</li>
</ul>
<ul>
<li>Instead of arbitrarily choosing articles from the user where the number of recommended articles starts below m and ends exceeding m, choose articles with the articles with the most total interactions before choosing those with fewer total interactions. This ranking should be  what would be obtained from the <strong>top_articles</strong> function you wrote earlier.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">get_top_sorted_users</span><span class="p">(</span><span class="n">user_id</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="n">user_item</span><span class="o">=</span><span class="n">user_item</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    user_id - (int)</span>
<span class="sd">    df - (pandas dataframe) df as defined at the top of the notebook </span>
<span class="sd">    user_item - (pandas dataframe) matrix of users by articles: </span>
<span class="sd">            1&#39;s when a user has interacted with an article, 0 otherwise</span>
<span class="sd">    </span>
<span class="sd">            </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    neighbors_df - (pandas dataframe) a dataframe with:</span>
<span class="sd">                    neighbor_id - is a neighbor user_id</span>
<span class="sd">                    similarity - measure of the similarity of each user to the provided user_id</span>
<span class="sd">                    num_interactions - the number of articles viewed by the user - if a u</span>
<span class="sd">                    </span>
<span class="sd">    Other Details - sort the neighbors_df by the similarity and then by number of interactions where </span>
<span class="sd">                    highest of each is higher in the dataframe</span>
<span class="sd">     </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">num_interactions</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">similarity</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">neighbor_ids</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">for</span> <span class="n">sim_user</span> <span class="ow">in</span> <span class="n">find_similar_users</span><span class="p">(</span><span class="n">user_id</span><span class="p">):</span>
        <span class="n">num_interactions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">user_item</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">user_id</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">user_id</span><span class="p">]</span><span class="o">.</span><span class="n">sum</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="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="n">neighbor_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sim_user</span><span class="p">)</span>
        <span class="n">similarity</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">user_item</span><span class="p">[</span><span class="n">user_item</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">user_id</span><span class="p">]</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">user_item</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim_user</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

    <span class="n">neighbors_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="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">neighbor_ids</span><span class="p">,</span> <span class="n">similarity</span><span class="p">,</span> <span class="n">num_interactions</span><span class="p">)),</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;neighbor_id&#39;</span><span class="p">,</span> <span class="s1">&#39;similarity&#39;</span><span class="p">,</span> <span class="s1">&#39;num_interactions&#39;</span><span class="p">])</span>
    <span class="n">neighbors_df</span> <span class="o">=</span> <span class="n">neighbors_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;similarity&#39;</span><span class="p">,</span> <span class="s1">&#39;neighbor_id&#39;</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>

    <span class="k">return</span> <span class="n">neighbors_df</span> <span class="c1"># Return the dataframe specified in the doc_string</span>


<span class="k">def</span> <span class="nf">user_user_recs_part2</span><span class="p">(</span><span class="n">user_id</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    user_id - (int) a user id</span>
<span class="sd">    m - (int) the number of recommendations you want for the user</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    recs - (list) a list of recommendations for the user by article id</span>
<span class="sd">    rec_names - (list) a list of recommendations for the user by article title</span>
<span class="sd">    </span>
<span class="sd">    Description:</span>
<span class="sd">    Loops through the users based on closeness to the input user_id</span>
<span class="sd">    For each user - finds articles the user hasn&#39;t seen before and provides them as recs</span>
<span class="sd">    Does this until m recommendations are found</span>
<span class="sd">    </span>
<span class="sd">    Notes:</span>
<span class="sd">    * Choose the users that have the most total article interactions </span>
<span class="sd">    before choosing those with fewer article interactions.</span>

<span class="sd">    * Choose articles with the articles with the most total interactions </span>
<span class="sd">    before choosing those with fewer total interactions. </span>
<span class="sd">   </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">recs</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="c1"># choose the users that have the most total article interaction</span>
    <span class="n">neighbors_df</span> <span class="o">=</span> <span class="n">get_top_sorted_users</span><span class="p">(</span><span class="n">user_id</span><span class="p">)</span>
    <span class="n">seen</span> <span class="o">=</span> <span class="n">get_user_articles</span><span class="p">(</span><span class="n">user_id</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">neighbors_df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
        <span class="n">sim_user</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;neighbor_id&#39;</span><span class="p">]</span>
        <span class="n">recs_sim_user</span> <span class="o">=</span> <span class="n">get_user_articles</span><span class="p">(</span><span class="n">sim_user</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>    
        <span class="n">recs</span> <span class="o">+=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">recs_sim_user</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">seen</span><span class="p">]</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">recs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">m</span><span class="p">:</span>
            <span class="k">break</span>

    <span class="c1"># choose articles with the articles with the most total interactions</span>
    <span class="n">top_articles</span> <span class="o">=</span> <span class="n">get_top_article_ids</span><span class="p">(</span><span class="mi">714</span><span class="p">)</span> <span class="c1"># means all</span>
    <span class="n">top_articles_dict</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># shows position in top articles</span>
    <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">recs</span><span class="p">[:</span><span class="n">m</span><span class="p">])):</span> <span class="c1"># remove duplicates</span>
        <span class="n">top_articles_dict</span><span class="p">[</span><span class="n">item</span><span class="p">]</span> <span class="o">=</span> <span class="n">top_articles</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">item</span><span class="p">))</span>
    <span class="n">top_articles_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">top_articles_dict</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">item</span><span class="p">:</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">])}</span>
    <span class="n">recs</span> <span class="o">=</span> <span class="n">top_articles_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
    
    <span class="n">rec_names</span> <span class="o">=</span> <span class="n">get_article_names</span><span class="p">(</span><span class="n">recs</span><span class="p">)</span>
        
    <span class="k">return</span> <span class="n">recs</span><span class="p">,</span> <span class="n">rec_names</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># verify the order by top_articles index (the lower the better)</span>
<span class="n">rec_ids</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">user_user_recs_part2</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">top_articles</span> <span class="o">=</span> <span class="n">get_top_article_ids</span><span class="p">(</span><span class="mi">714</span><span class="p">)</span> <span class="c1"># means all</span>

<span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">rec_ids</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">top_articles</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">item</span><span class="p">)))</span>
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<pre>1364.0 4
1351.0 14
1400.0 33
164.0 40
213.0 112
379.0 173
1366.0 177
967.0 253
225.0 322
1397.0 363
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Quick spot check - don&#39;t change this code - just use it to test your functions</span>
<span class="n">rec_ids</span><span class="p">,</span> <span class="n">rec_names</span> <span class="o">=</span> <span class="n">user_user_recs_part2</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The top 10 recommendations for user 20 are the following article ids:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rec_ids</span><span class="p">)</span>
<span class="nb">print</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The top 10 recommendations for user 20 are the following article names:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rec_names</span><span class="p">)</span>
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<pre>The top 10 recommendations for user 20 are the following article ids:
dict_keys([&#39;1364.0&#39;, &#39;1351.0&#39;, &#39;1400.0&#39;, &#39;164.0&#39;, &#39;213.0&#39;, &#39;379.0&#39;, &#39;1366.0&#39;, &#39;967.0&#39;, &#39;225.0&#39;, &#39;1397.0&#39;])

The top 10 recommendations for user 20 are the following article names:
[&#39;predicting churn with the spss random tree algorithm&#39;, &#39;model bike sharing data with spss&#39;, &#39;uci ml repository: chronic kidney disease data set&#39;, &#39;learn tensorflow and deep learning together and now!&#39;, &#39;modeling energy usage in new york city&#39;, &#39;data structures related to machine learning algorithms&#39;, &#39;process events from the watson iot platform in a streams python application&#39;, &#39;ml algorithm != learning machine&#39;, &#39;a visual explanation of the back propagation algorithm for neural networks&#39;, &#39;total employment, by economic activity (thousands)&#39;]
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<p><code>5.</code> Use your functions from above to correctly fill in the solutions to the dictionary below.  Then test your dictionary against the solution.  Provide the code you need to answer each following the comments below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">get_top_sorted_users</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">neighbor_id</span><span class="p">,</span> <span class="n">get_top_sorted_users</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">neighbor_id</span>
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<pre>(3933, 242)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Tests with a dictionary of results</span>
<span class="n">user1_most_sim</span> <span class="o">=</span> <span class="n">get_top_sorted_users</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">neighbor_id</span> <span class="c1"># Find the user that is most similar to user 1 </span>
<span class="n">user131_10th_sim</span> <span class="o">=</span> <span class="n">get_top_sorted_users</span><span class="p">(</span><span class="mi">131</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">neighbor_id</span> <span class="c1"># Find the 10th most similar user to user 131</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Dictionary Test Here</span>
<span class="n">sol_5_dict</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;The user that is most similar to user 1.&#39;</span><span class="p">:</span> <span class="n">user1_most_sim</span><span class="p">,</span> 
    <span class="s1">&#39;The user that is the 10th most similar to user 131&#39;</span><span class="p">:</span> <span class="n">user131_10th_sim</span><span class="p">,</span>
<span class="p">}</span>

<span class="n">t</span><span class="o">.</span><span class="n">sol_5_test</span><span class="p">(</span><span class="n">sol_5_dict</span><span class="p">)</span>
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<pre>This all looks good!  Nice job!
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<p><code>6.</code> If we were given a new user, which of the above functions would you be able to use to make recommendations?  Explain.  Can you think of a better way we might make recommendations?  Use the cell below to explain a better method for new users.</p>

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<p><strong>Provide your response here.</strong></p>

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<p><em>We are only able to make recommendatins using the rank based recommendations. This functions should work fine for new users. The Collaborative Filtering function has no data to provide a recommendation to a new user, this is known as the "Cold Start Problem". 
A better method would be use a mix of Rank based Collaborative Filtering and Content Based Collaborative Filtering for new user because we would have a higher chance to really meet the taste of the new user instead of the general taste.</em></p>

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<p><code>7.</code> Using your existing functions, provide the top 10 recommended articles you would provide for the a new user below.  You can test your function against our thoughts to make sure we are all on the same page with how we might make a recommendation.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">new_user</span> <span class="o">=</span> <span class="s1">&#39;0.0&#39;</span>
<span class="n">new_user_recs</span> <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># What would your recommendations be for this new user &#39;0.0&#39;?  As a new user, they have no observed articles.</span>
<span class="c1"># Provide a list of the top 10 article ids you would give to </span>

<span class="n">new_user_recs_as_float</span> <span class="o">=</span> <span class="n">get_top_article_ids</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="c1"># Your recommendations here</span>
<span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">new_user_recs_as_float</span><span class="p">:</span>
    <span class="n">new_user_recs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">item</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">new_user_recs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="s1">&#39;1314.0&#39;</span><span class="p">,</span><span class="s1">&#39;1429.0&#39;</span><span class="p">,</span><span class="s1">&#39;1293.0&#39;</span><span class="p">,</span><span class="s1">&#39;1427.0&#39;</span><span class="p">,</span><span class="s1">&#39;1162.0&#39;</span><span class="p">,</span><span class="s1">&#39;1364.0&#39;</span><span class="p">,</span><span class="s1">&#39;1304.0&#39;</span><span class="p">,</span><span class="s1">&#39;1170.0&#39;</span><span class="p">,</span><span class="s1">&#39;1431.0&#39;</span><span class="p">,</span><span class="s1">&#39;1330.0&#39;</span><span class="p">]),</span> <span class="s2">&quot;Oops!  It makes sense that in this case we would want to recommend the most popular articles, because we don&#39;t know anything about these users.&quot;</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;That&#39;s right!  Nice job!&quot;</span><span class="p">)</span>
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<pre>That&#39;s right!  Nice job!
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<h3 id="Part-V:-Matrix-Factorization"><a class="anchor" id="Matrix-Fact">Part V: Matrix Factorization</a><a class="anchor-link" href="#Part-V:-Matrix-Factorization">&#182;</a></h3><p>In this part of the notebook, you will build use matrix factorization to make article recommendations to the users on the IBM Watson Studio platform.</p>
<p><code>1.</code> You should have already created a <strong>user_item</strong> matrix above in <strong>question 1</strong> of <strong>Part III</strong> above.  This first question here will just require that you run the cells to get things set up for the rest of <strong>Part V</strong> of the notebook.</p>

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<span class="n">user_item_matrix</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s1">&#39;user_item_matrix.p&#39;</span><span class="p">)</span>
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<span class="n">user_item_matrix</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
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        vertical-align: top;
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    .dataframe thead th {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th>article_id</th>
      <th>0.0</th>
      <th>100.0</th>
      <th>1000.0</th>
      <th>1004.0</th>
      <th>1006.0</th>
      <th>1008.0</th>
      <th>101.0</th>
      <th>1014.0</th>
      <th>1015.0</th>
      <th>1016.0</th>
      <th>...</th>
      <th>977.0</th>
      <th>98.0</th>
      <th>981.0</th>
      <th>984.0</th>
      <th>985.0</th>
      <th>986.0</th>
      <th>990.0</th>
      <th>993.0</th>
      <th>996.0</th>
      <th>997.0</th>
    </tr>
    <tr>
      <th>user_id</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
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      <th></th>
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  </thead>
  <tbody>
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      <th>1</th>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
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      <td>0.0</td>
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    </tr>
    <tr>
      <th>2</th>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>0.0</td>
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      <td>0.0</td>
      <td>0.0</td>
      <td>...</td>
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      <td>0.0</td>
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<p>5 rows × 714 columns</p>
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<p><code>2.</code> In this situation, you can use Singular Value Decomposition from <a href="https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.svd.html">numpy</a> on the user-item matrix.  Use the cell to perform SVD, and explain why this is different than in the lesson.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Perform SVD on the User-Item Matrix Here</span>

<span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">user_item_matrix</span><span class="p">)</span> <span class="c1"># use the built in to get the three matrices</span>
<span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">vt</span><span class="o">.</span><span class="n">shape</span>
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<pre>((714,), (5149, 5149), (714, 714))</pre>
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<p><strong>Provide your response here.</strong></p>

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<p><em>SVD can achieve a very good result on data with no missing values. In contrast to the lesson, we have no missing data. That is the reason why SVD works really well in this case and we don't need to switch to FunkSVD.
(<a href="https://medium.com/datadriveninvestor/how-funk-singular-value-decomposition-algorithm-work-in-recommendation-engines-36f2fbf62cac">https://medium.com/datadriveninvestor/how-funk-singular-value-decomposition-algorithm-work-in-recommendation-engines-36f2fbf62cac</a>)</em></p>

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<p><code>3.</code> Now for the tricky part, how do we choose the number of latent features to use?  Running the below cell, you can see that as the number of latent features increases, we obtain a lower error rate on making predictions for the 1 and 0 values in the user-item matrix.  Run the cell below to get an idea of how the accuracy improves as we increase the number of latent features.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">num_latent_feats</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="mi">10</span><span class="p">,</span><span class="mi">700</span><span class="o">+</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
<span class="n">sum_errs</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">num_latent_feats</span><span class="p">:</span>
    <span class="c1"># restructure with k latent features</span>
    <span class="n">s_new</span><span class="p">,</span> <span class="n">u_new</span><span class="p">,</span> <span class="n">vt_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">[:</span><span class="n">k</span><span class="p">]),</span> <span class="n">u</span><span class="p">[:,</span> <span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">vt</span><span class="p">[:</span><span class="n">k</span><span class="p">,</span> <span class="p">:]</span>
    
    <span class="c1"># take dot product</span>
    <span class="n">user_item_est</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">u_new</span><span class="p">,</span> <span class="n">s_new</span><span class="p">),</span> <span class="n">vt_new</span><span class="p">))</span>
    
    <span class="c1"># compute error for each prediction to actual value</span>
    <span class="n">diffs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">user_item_matrix</span><span class="p">,</span> <span class="n">user_item_est</span><span class="p">)</span>
    
    <span class="c1"># total errors and keep track of them</span>
    <span class="n">err</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">diffs</span><span class="p">)))</span>
    <span class="n">sum_errs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">err</span><span class="p">)</span>
    
    
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sum_errs</span><span class="p">)</span><span class="o">/</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Latent Features&#39;</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Accuracy vs. Number of Latent Features&#39;</span><span class="p">);</span>
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<p><code>4.</code> From the above, we can't really be sure how many features to use, because simply having a better way to predict the 1's and 0's of the matrix doesn't exactly give us an indication of if we are able to make good recommendations.  Instead, we might split our dataset into a training and test set of data, as shown in the cell below.</p>
<p>Use the code from question 3 to understand the impact on accuracy of the training and test sets of data with different numbers of latent features. Using the split below:</p>
<ul>
<li>How many users can we make predictions for in the test set?  </li>
<li>How many users are we not able to make predictions for because of the cold start problem?</li>
<li>How many articles can we make predictions for in the test set?  </li>
<li>How many articles are we not able to make predictions for because of the cold start problem?</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_train</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">40000</span><span class="p">)</span>
<span class="n">df_test</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">5993</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">create_test_and_train_user_item</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    df_train - training dataframe</span>
<span class="sd">    df_test - test dataframe</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    user_item_train - a user-item matrix of the training dataframe </span>
<span class="sd">                      (unique users for each row and unique articles for each column)</span>
<span class="sd">    user_item_test - a user-item matrix of the testing dataframe </span>
<span class="sd">                    (unique users for each row and unique articles for each column)</span>
<span class="sd">    test_idx - all of the test user ids</span>
<span class="sd">    test_arts - all of the test article ids</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">user_item_train</span> <span class="o">=</span> <span class="n">create_user_item_matrix</span><span class="p">(</span><span class="n">df_train</span><span class="p">)</span>
    <span class="n">user_item_test</span> <span class="o">=</span> <span class="n">create_user_item_matrix</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
    
    <span class="n">test_idx</span> <span class="o">=</span> <span class="n">user_item_test</span><span class="o">.</span><span class="n">index</span>
    <span class="n">test_arts</span> <span class="o">=</span> <span class="n">user_item_test</span><span class="o">.</span><span class="n">columns</span>
    
    <span class="n">user_item_test</span> <span class="o">=</span> <span class="n">user_item_test</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_idx</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">user_item_train</span><span class="o">.</span><span class="n">index</span><span class="p">)),</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_arts</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">user_item_train</span><span class="o">.</span><span class="n">columns</span><span class="p">))]</span>
    
    <span class="k">return</span> <span class="n">user_item_train</span><span class="p">,</span> <span class="n">user_item_test</span><span class="p">,</span> <span class="n">test_idx</span><span class="p">,</span> <span class="n">test_arts</span>

<span class="n">user_item_train</span><span class="p">,</span> <span class="n">user_item_test</span><span class="p">,</span> <span class="n">test_idx</span><span class="p">,</span> <span class="n">test_arts</span> <span class="o">=</span> <span class="n">create_test_and_train_user_item</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span>

<span class="c1"># additional defs for training data</span>
<span class="n">train_idx</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">index</span>
<span class="n">train_arts</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">columns</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">df_test</span><span class="o">.</span><span class="n">shape</span>
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<pre>((40000, 3), (5993, 3))</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># how many users/items can we make predictions for?</span>
<span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_idx</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">train_idx</span><span class="p">)),</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_arts</span><span class="p">)</span> <span class="o">&amp;</span> <span class="nb">set</span><span class="p">(</span><span class="n">train_arts</span><span class="p">))</span>
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<pre>(20, 574)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># how many users/items are we not able to we make predictions for because of the cold start problem?</span>
<span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_idx</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">train_idx</span><span class="p">)),</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">test_arts</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">train_arts</span><span class="p">))</span>
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<pre>(662, 0)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Replace the values in the dictionary below</span>
<span class="n">a</span> <span class="o">=</span> <span class="mi">662</span> 
<span class="n">b</span> <span class="o">=</span> <span class="mi">574</span> 
<span class="n">c</span> <span class="o">=</span> <span class="mi">20</span> 
<span class="n">d</span> <span class="o">=</span> <span class="mi">0</span> 


<span class="n">sol_4_dict</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;How many users can we make predictions for in the test set?&#39;</span><span class="p">:</span> <span class="n">c</span><span class="p">,</span> 
    <span class="s1">&#39;How many users in the test set are we not able to make predictions for because of the cold start problem?&#39;</span><span class="p">:</span> <span class="n">a</span><span class="p">,</span> 
    <span class="s1">&#39;How many movies can we make predictions for in the test set?&#39;</span><span class="p">:</span> <span class="n">b</span><span class="p">,</span>
    <span class="s1">&#39;How many movies in the test set are we not able to make predictions for because of the cold start problem?&#39;</span><span class="p">:</span> <span class="n">d</span>
<span class="p">}</span>

<span class="n">t</span><span class="o">.</span><span class="n">sol_4_test</span><span class="p">(</span><span class="n">sol_4_dict</span><span class="p">)</span>
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<pre>Awesome job!  That&#39;s right!  All of the test movies are in the training data, but there are only 20 test users that were also in the training set.  All of the other users that are in the test set we have no data on.  Therefore, we cannot make predictions for these users using SVD.
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<p><code>5.</code> Now use the <strong>user_item_train</strong> dataset from above to find U, S, and V transpose using SVD. Then find the subset of rows in the <strong>user_item_test</strong> dataset that you can predict using this matrix decomposition with different numbers of latent features to see how many features makes sense to keep based on the accuracy on the test data. This will require combining what was done in questions <code>2</code> - <code>4</code>.</p>
<p>Use the cells below to explore how well SVD works towards making predictions for recommendations on the test data.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># for better changing threshold, we define the svd as a function</span>

<span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    df_train - training dataframe</span>
<span class="sd">    df_test - test dataframe</span>
<span class="sd">    </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">    user_item_train - a user-item matrix of the training dataframe </span>
<span class="sd">                      (unique users for each row and unique articles for each column)</span>
<span class="sd">    user_item_test - a user-item matrix of the testing dataframe </span>
<span class="sd">                    (unique users for each row and unique articles for each column)</span>
<span class="sd">    test_idx - all of the test user ids</span>
<span class="sd">    test_arts - all of the test article ids</span>
<span class="sd">    sum_errs_train - errors of train data set</span>
<span class="sd">    sum_errs_test - error of test data set</span>
<span class="sd">    f1_score_train - f1 score of training data set</span>
<span class="sd">    f1_score_test - f1 score of test data set</span>
<span class="sd">    num_latent_feats - number of latent features</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">user_item_train</span><span class="p">,</span> <span class="n">user_item_test</span><span class="p">,</span> <span class="n">test_idx</span><span class="p">,</span> <span class="n">test_arts</span> <span class="o">=</span> <span class="n">create_test_and_train_user_item</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span>
    <span class="c1"># additional defs for training data</span>
    <span class="n">train_idx</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">index</span>
    <span class="n">train_arts</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">columns</span>
    
    <span class="c1"># fit SVD on the user_item_train matrix</span>
    <span class="n">u_train</span><span class="p">,</span> <span class="n">s_train</span><span class="p">,</span> <span class="n">vt_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">user_item_train</span><span class="p">)</span> <span class="c1"># fit svd similar to above then use the cells below</span>
    
    <span class="c1"># test data</span>
    <span class="n">idx</span> <span class="o">=</span> <span class="n">train_idx</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">test_idx</span><span class="p">)</span>
    <span class="n">arts</span> <span class="o">=</span> <span class="n">train_arts</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">test_arts</span><span class="p">)</span>
    <span class="n">u_test</span> <span class="o">=</span> <span class="n">u_train</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</span>
    <span class="n">vt_test</span> <span class="o">=</span> <span class="n">vt_train</span><span class="p">[:,</span> <span class="n">arts</span><span class="p">]</span>

    <span class="c1"># Use these cells to see how well you can use the training </span>
    <span class="c1"># decomposition to predict on test data</span>
    <span class="n">num_latent_feats</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="mi">10</span><span class="p">,</span><span class="mi">700</span><span class="o">+</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
    <span class="n">sum_errs_test</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">sum_errs_train</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">sum_errs_all</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">f1_score_train</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">f1_score_test</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">num_latent_feats</span><span class="p">:</span>
        <span class="c1"># restructure with k latent features</span>

        <span class="n">s_new</span><span class="p">,</span> <span class="n">u_new</span><span class="p">,</span> <span class="n">vt_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s_train</span><span class="p">[:</span><span class="n">k</span><span class="p">]),</span> <span class="n">u_train</span><span class="p">[:,</span> <span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">vt_train</span><span class="p">[:</span><span class="n">k</span><span class="p">,</span> <span class="p">:]</span>
        <span class="n">u_new_test</span><span class="p">,</span> <span class="n">vt_new_test</span> <span class="o">=</span> <span class="n">u_test</span><span class="p">[:,</span> <span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">vt_test</span><span class="p">[:</span><span class="n">k</span><span class="p">,</span> <span class="p">:]</span>

        <span class="c1"># dot product:</span>
        <span class="n">user_item_est_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">u_new</span><span class="p">,</span> <span class="n">s_new</span><span class="p">),</span> <span class="n">vt_new</span><span class="p">))</span>
        <span class="n">user_item_est_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">u_new_test</span><span class="p">,</span> <span class="n">s_new</span><span class="p">),</span> <span class="n">vt_new_test</span><span class="p">))</span>

        <span class="c1"># Calculate the error of each prediction with the true value</span>
        <span class="n">diffs_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">user_item_train</span><span class="p">,</span> <span class="n">user_item_est_train</span><span class="p">)</span>
        <span class="n">diffs_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">user_item_est_test</span><span class="p">)</span>

        <span class="c1"># Total Error</span>
        <span class="n">err_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">diffs_train</span><span class="p">)))</span>
        <span class="n">err_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">diffs_test</span><span class="p">)))</span>

        <span class="c1"># append error list</span>
        <span class="n">sum_errs_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">err_train</span><span class="p">)</span>
        <span class="n">sum_errs_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">err_test</span><span class="p">)</span>
        
        <span class="c1"># https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html</span>
        <span class="n">f1_score_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f1_score</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">user_item_train</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">user_item_est_train</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">],</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">))</span>
        <span class="n">f1_score_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f1_score</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">user_item_test</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">user_item_est_test</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">],</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">))</span>

    <span class="k">return</span> <span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">,</span> <span class="n">f1_score_train</span><span class="p">,</span> <span class="n">f1_score_test</span><span class="p">,</span> <span class="n">num_latent_feats</span>

<span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">,</span> <span class="n">f1_score_train</span><span class="p">,</span> <span class="n">f1_score_test</span><span class="p">,</span> <span class="n">num_latent_feats</span> <span class="o">=</span> <span class="n">svd</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_accuracy</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    num_latent_feats - number of latent features</span>
<span class="sd">    df_test - test dataframe</span>
<span class="sd">    user_item_train - a user-item matrix of the training dataframe </span>
<span class="sd">                      (unique users for each row and unique articles for each column)</span>
<span class="sd">    user_item_test - a user-item matrix of the testing dataframe </span>
<span class="sd">                    (unique users for each row and unique articles for each column)</span>
<span class="sd">    sum_errs_train - errors of train data set</span>
<span class="sd">    sum_errs_test - error of test data set</span>
<span class="sd">    </span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sum_errs_train</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">user_item_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">user_item_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sum_errs_test</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">user_item_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">user_item_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Latent Features&#39;</span><span class="p">);</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">);</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Accuracy vs. Number of Latent Features&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
    
<span class="n">plot_accuracy</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[60]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_f1_score</span><span class="p">(</span><span class="n">f1_score_train</span><span class="p">,</span><span class="n">f1_score_test</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    f1_score_train - f1 score of training data set</span>
<span class="sd">    f1_score_test - f1 score of test data set</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f1_score_train</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;F1 Score Train&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">f1_score_test</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;F1 Score Test&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Latent Features&#39;</span><span class="p">);</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;F1 Score&#39;</span><span class="p">);</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;F1 Score vs. Number of Latent Features&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">();</span>
                  
<span class="n">plot_f1_score</span><span class="p">(</span><span class="n">f1_score_train</span><span class="p">,</span><span class="n">f1_score_test</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[61]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_threshold</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">df_train</span><span class="p">,</span> <span class="n">title</span><span class="p">):</span>
    
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    INPUT:</span>
<span class="sd">    df_train - training dataframe</span>
<span class="sd">    df_test - test dataframe</span>
<span class="sd">    </span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">user_id</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s1">&#39;step&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">df_test</span><span class="o">.</span><span class="n">user_id</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">histtype</span><span class="o">=</span><span class="s1">&#39;step&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;Test&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">();</span>
    <span class="c1"># https://stackoverflow.com/questions/43562301/plotting-multiple-histograms-using-different-line-styles-python</span>

<span class="n">plot_threshold</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">df_train</span><span class="p">,</span> <span class="s1">&#39;Trainset/Testset current threshold (~0.13)&#39;</span><span class="p">)</span>
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<p><em>Lets' try to change the treshold to 0.5</em></p>

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<div class="prompt input_prompt">In&nbsp;[62]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">round</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="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
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<pre>22996.0</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># threshold 0.5</span>
<span class="n">df_train</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">22996</span><span class="p">)</span>
<span class="n">df_test</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">22996</span><span class="p">)</span>
<span class="n">df_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">df_test</span><span class="o">.</span><span class="n">shape</span>
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<pre>((22996, 3), (22996, 3))</pre>
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<div class="prompt input_prompt">In&nbsp;[64]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">,</span> <span class="n">f1_score_train</span><span class="p">,</span> <span class="n">f1_score_test</span><span class="p">,</span> <span class="n">num_latent_feats</span> <span class="o">=</span> <span class="n">svd</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plot_threshold</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">df_train</span><span class="p">,</span> <span class="s1">&#39;Trainset/Testset current threshold (0.5)&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plot_accuracy</span><span class="p">(</span><span class="n">num_latent_feats</span><span class="p">,</span> <span class="n">user_item_train</span> <span class="p">,</span><span class="n">user_item_test</span><span class="p">,</span> <span class="n">sum_errs_train</span><span class="p">,</span> <span class="n">sum_errs_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plot_f1_score</span><span class="p">(</span><span class="n">f1_score_train</span><span class="p">,</span><span class="n">f1_score_test</span><span class="p">)</span>
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<p><code>6.</code> Use the cell below to comment on the results you found in the previous question. Given the circumstances of your results, discuss what you might do to determine if the recommendations you make with any of the above recommendation systems are an improvement to how users currently find articles?</p>

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<p><strong>Comment &amp; Disussion</strong></p>
<p><em>The accuracy of the test data drops on every latent feature while the accuracy of the training data increases.
A reason might be the few interactions which the test and the training data have in common. Te accuracy is not a good metric for measuring the quality of our recommendations as it tends to overfit the data. We tried another metric:
the f1 score which converges at around 35 latent features.</em></p>
<p><em>We showed that the quality of our model can be changed by using different thresholds. We have already seen two different ones: 0.13 and 0.5. We could try some more. We are able to evaluate them by several distinct metrics (accuracy/f1/precision/recall) to find th turning point where the metrics satures and the model does not overfit. Based on the results we have already seen, we assume that the model converges between around 35 features because the f1 score increases and the accuracy (on the test data set) don't drop too fast. The accuracy is more dependent on the the threshold than the f1 score. But the f1 performs poorly on the test data set, an indicator that our model is not working well in practice.</em></p>
<p><strong>Next steps</strong></p>
<p><em>A method by which we could test how well our recommendation engine is working in practice could be according to the data we have</em></p>
<ul>
<li><em>using like/dislike (=heart) to evaluate how much the user likes the recommendation</em></li>
<li><em>using pinned/not pinned (=pin sign) to evaluate how much the user likes the recommendation</em></li>
<li><em>using click/read or not clicked/not read  to evaluate how much the user likes the recommendation</em></li>
<li><em>using a system which combines alle the three steps above</em></li>
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<p><strong>Submit</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">call</span>
<span class="n">call</span><span class="p">([</span><span class="s1">&#39;python&#39;</span><span class="p">,</span> <span class="s1">&#39;-m&#39;</span><span class="p">,</span> <span class="s1">&#39;nbconvert&#39;</span><span class="p">,</span> <span class="s1">&#39;Recommendations_with_IBM.ipynb&#39;</span><span class="p">])</span>
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<pre>0</pre>
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