Recommendations_with_IBM.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<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="#Content-Recs">Content Based Recommendations (EXTRA - NOT REQUIRED)</a><br>
V. <a href="#Matrix-Fact">Matrix Factorization</a><br>
VI. <a href="#conclusions">Extras &amp; Concluding</a></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">import</span> <span class="nn">nltk</span>
<span class="kn">from</span> <span class="nn">nltk.tokenize</span> <span class="k">import</span> <span class="n">word_tokenize</span>
<span class="kn">from</span> <span class="nn">nltk.stem</span> <span class="k">import</span> <span class="n">WordNetLemmatizer</span>
<span class="kn">from</span> <span class="nn">nltk.corpus</span> <span class="k">import</span> <span class="n">stopwords</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;wordnet&#39;</span><span class="p">)</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;stopwords&#39;</span><span class="p">)</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;abc&#39;</span><span class="p">)</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s1">&#39;punkt&#39;</span><span class="p">)</span>

<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">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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<pre>[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\ahmed\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\ahmed\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package abc to
[nltk_data]     C:\Users\ahmed\AppData\Roaming\nltk_data...
[nltk_data]   Package abc is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\ahmed\AppData\Roaming\nltk_data...
[nltk_data]   Unzipping tokenizers\punkt.zip.
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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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<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>
<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="n">df_n</span> <span class="o">=</span> <span class="n">df</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">size</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;article_counts&#39;</span><span class="p">)</span>
<span class="n">df_n</span> <span class="o">=</span> <span class="n">df_n</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="p">[</span><span class="s1">&#39;article_counts&#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="n">df_n</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div class="prompt output_prompt">Out[73]:</div>


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      <th></th>
      <th>email</th>
      <th>article_counts</th>
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    <tr>
      <th>910</th>
      <td>2b6c0f514c2f2b04ad3c4583407dccd0810469ee</td>
      <td>364</td>
    </tr>
    <tr>
      <th>2426</th>
      <td>77959baaa9895a7e2bdc9297f8b27c1b6f2cb52a</td>
      <td>363</td>
    </tr>
    <tr>
      <th>985</th>
      <td>2f5c7feae533ce046f2cb16fb3a29fe00528ed66</td>
      <td>170</td>
    </tr>
    <tr>
      <th>3312</th>
      <td>a37adec71b667b297ed2440a9ff7dad427c7ac85</td>
      <td>169</td>
    </tr>
    <tr>
      <th>2680</th>
      <td>8510a5010a5d4c89f5b07baac6de80cd12cfaf93</td>
      <td>160</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">df_n</span><span class="p">[</span><span class="s1">&#39;article_counts&#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">x</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</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 Interactions&#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;Article Counts&#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;User-Article Interactions&#39;</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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T1s6xLgkhpyNTOzOqulcDwvaSrwAeAd+RmKgfVNy8zMWlUtp6reT3qY7+yI+B3pVtiv1jUrMzNrWbUccXwiIi7smomIJyQdUseczMyshdVyxPEXFWIn9nUiZmbWP/R4xCHpo8DHgP3znVFd9gD+o96JmZlZa6p2quom4KfAl8i3xmbPR8TaumZlZmYtq8fCERHrSf1FnZHvpNont99d0u4R8USDcjQzsxbS68VxSecB04CngJdzOIC31C8tMzNrVbXcVXUB8MaIeKbeyZiZWeur5a6qFaRTVmZmZjUdcTwG3CvpJ6QHAQGIiK/VLSszM2tZtRSOJ/Jrl/wyM7MdWK+FIyIuakQiZmbWP9RyV9UcKo+4d2xdMjIzs5ZWy6mqTxWmBwF/BWzqoa2Zmb3K9XpXVUQsLLz+PSL+Fjiyt/UkjZQ0R9IySUslnZ/j0yQ9KWlRfp1UWGeqpA5Jj0g6oRAfn2MdkqZU+jwzM2uMWk5VDS3M7gS8DfhfNWx7E/DJiHhA0h7AQkmz87LLI+Ifu33OwcDpwCHAvsDPJb0hL/4mqbPFTmC+pFkR8XANOZiZWR+r5VTVQtI1DpGKwePA2b2tFBGrgFV5+nlJy0hjefRkAjAzIjYCj+chZLtGCuzIIwciaWZu68JhZtYEtdxVNWZ7P0TSaNIwsnOBo4HzJE0EFpCOSp4lFZX7C6t18sdCs6Jb/BWnyiRNBiYDjBo1antTNjOzHvR6jUPSQEkfl3Rrfp0nqeahYyXtDvwAuCAingOuAg4AxpGOSC7ralph9agS3zoQMT0i2iOiva2trdb0zMyspFpOVV1FGmP8yjz/wRz7m95WzAXmB8CNEfFDgIh4qrD8GuD2PNsJjCysPgJYmad7ipuZWYPVUjgOj4hDC/P3SHqwt5UkCbgWWFbsnkTSsHz9A+C9wJI8PQu4SdLXSBfHxwLzSEccYyWNAZ4kXUD/6xryNjOzOqilcGyWdEBE/BeApP2BzTWsdzTp6OQhSYty7LOk8T3GkU43LQc+AhARSyXdQrrovQk4NyI25888D7gTGADMiIilNX4/MzPrY7UUjk8DcyQ9RvrX/37AWb2tFBG/pPL1iTuqrHMJcEmF+B3V1jMzs8ap5a6quyWNBd5IKgS/zrfMmpnZDqjHwiHpA4Ai4ru5UCzO8Q9LeiEibmpUkmZm1jqq3Y77SeDHFeI352VmZrYDqlY4BkTE892D+VmMmp/jMDOzV5dqhWOgpN26B3O/Ux7QycxsB1WtcFwL3Jq7CwG2dB0yMy8zM7MdUI8XxyPiHyVtAO7L3YYE8AJwaURc1agEzcystVS9HTcirgauzoVDla55mJnZjqWWBwCJiA31TsTMzPqHXnvHNTMzK3LhMDOzUmoZj+O1kv4hd4GOpLGSTq5/amZm1opqOeL4DrAR+NM83wl8sW4ZmZlZS6ulcBwQEV8B/gAQEf9N5V5vzcxsB1BL4fi9pNeQh2uVdADpCMTMzHZAtRSOzwM/A0ZKuhG4G/hMbytJGilpjqRlkpZKOj/Hh0qaLenR/D4kxyXpCkkdkhZLOqywrUm5/aOSJm3TNzUzsz5Ry3gcsyU9ABxFOkV1fkQ8XcO2NwGfjIgHcv9WCyXNBj4E3B0Rl0qaAkwBLgROJA0XOxY4kjSu+ZGShpKKVzvpqGehpFkR8WzJ72pmZn2g2ngch3ULdY0TPkrSqIh4oNqG87jiq/L085KWAcOBCcAxudn1wL2kwjEBuCEiArhf0mBJw3Lb2RGxNuc1GxgPfL/G72hmZn2o2hHHZVWWBXBsrR+SO0d8KzAX2CcXFSJilaS9c7PhwIrCap051lO8+2dMBiYDjBo1qtbUzMyspGqdHL6zLz4g93P1A+CCiHhO6vGGrEoLokp860DEdGA6QHt7+yuWm5lZ36jlAcBzJQ0uzA+R9LFaNi5pIKlo3BgRP8zhp/IpKPL76hzvBEYWVh8BrKwSNzOzJqjlrqoPR8S6rpl8UfrDva2kdGhxLbAsIr5WWDQL6LozahJwWyE+Md9ddRSwPp/SuhM4PhesIcDxOWZmZk1QS++4O0lSvmiNpAHUNgLg0cAHgYckLcqxzwKXArdIOht4AjgtL7sDOAnoAF4EzgKIiLWSvgDMz+0u7rpQbmZmjVdL4biT9EN/Nenawjmk5zqqiohf0vMT5sdVaB/AuT1sawYwo4ZczcyszmopHBcCHwE+SioEdwHfrmdSZmbWump5APBl0sN4Hi7WzMyqPgB4S0S8T9JDVL799S11zczMzFpStSOO8/O7x94wM7Mterwdt+vpbuBjEfHb4guo6TkOMzN79anlOY6/qBA7sa8TMTOz/qHaNY6Pko4sDpC0uLBoD+Df652YmZm1pmrXOG4Cfgp8idT1eZfn/QCemdmOq1onh+slPQ/8Sb6uYWZmVv0aR36G40FJ7qfczMyA2p4cHwYslTQPeKErGBF/WbeszMysZdVSOC6qexZmZtZv1NLlyH3FeUlHA38N3Fd5DTMzezWr5YgDSeNIxeJ9wOOkwZnMzGwHVO05jjcApwNnAM8ANwPqqyFlzcysf6p2V9WvSeNmvCci3h4R3wA217phSTMkrZa0pBCbJulJSYvy66TCsqmSOiQ9IumEQnx8jnVImtL9c8zMrLGqFY6/An4HzJF0jaTj6HlgpkquA8ZXiF8eEePy6w4ASQeTjm4OyetcKWlAHm3wm6QuTg4GzshtzcysSap1cvijiHg/cBBwL/AJYB9JV0k6vrcNR8QvgFqfMJ8AzIyIjRHxOGn42CPyqyMiHouI3wMzc1szM2uSXjs5jIgXIuLGiDgZGAEsYusuSMo6T9LifCprSI4NB1YU2nTmWE/xV5A0WdICSQvWrFmzHemZmVk1tfSOu0VErI2Ib0XEsdv4eVcBBwDjgFXAZTle6RRYVIlXym16RLRHRHtbW9s2pmdmZr2p6XbcvhIRT3VNS7oGuD3PdgIjC01HACvzdE9xMzNrglJHHNtL0rDC7HuBrjuuZgGnS9pV0hhgLDAPmA+MlTRG0i6kC+izGpmzmZltrW5HHJK+DxwD7CWpE/g8cEx+mDCA5cBHACJiqaRbgIeBTcC5EbE5b+c84E5gADAjIpbWK2czM+td3QpHRJxRIXxtlfaXAJdUiN8B3NGHqZmZ2XZo6KkqMzPr/1w4zMysFBcOMzMrxYXDzMxKceEwM7NSXDjMzKwUFw4zMyvFhcPMzEpx4TAzs1JcOMzMrBQXDjMzK8WFw8zMSnHhMDOzUlw4zMysFBcOMzMrpW6FQ9IMSaslLSnEhkqaLenR/D4kxyXpCkkdkhZLOqywzqTc/lFJk+qVr5mZ1aaeRxzXAeO7xaYAd0fEWODuPA9wImm42LHAZOAqSIWGNHLgkcARwOe7io2ZmTVH3QpHRPwCWNstPAG4Pk9fD5xSiN8Qyf3A4Dw++QnA7IhYGxHPArN5ZTEyM7MGavQ1jn0iYhVAft87x4cDKwrtOnOsp/grSJosaYGkBWvWrOnzxM3MLGmVi+OqEIsq8VcGI6ZHRHtEtLe1tfVpcmZm9keNLhxP5VNQ5PfVOd4JjCy0GwGsrBI3M7MmaXThmAV03Rk1CbitEJ+Y7646ClifT2XdCRwvaUi+KH58jpmZWZPsXK8NS/o+cAywl6RO0t1RlwK3SDobeAI4LTe/AzgJ6ABeBM4CiIi1kr4AzM/tLo6I7hfczcysgepWOCLijB4WHVehbQDn9rCdGcCMPkzNzMy2Q6tcHDczs37ChcPMzEpx4TAzs1JcOMzMrBQXDjMzK8WFw8zMSnHhMDOzUlw4zMysFBcOMzMrxYXDzMxKceEwM7NSXDjMzKwUFw4zMyvFhcPMzEpx4TAzs1KaUjgkLZf0kKRFkhbk2FBJsyU9mt+H5LgkXSGpQ9JiSYc1I2czM0uaecTxzogYFxHteX4KcHdEjAXuzvMAJwJj82sycFXDMzUzsy1a6VTVBOD6PH09cEohfkMk9wODJQ1rRoJmZta8whHAXZIWSpqcY/tExCqA/L53jg8HVhTW7cwxMzNrgrqNOd6LoyNipaS9gdmSfl2lrSrE4hWNUgGaDDBq1Ki+ydLMzF6hKUccEbEyv68GfgQcATzVdQoqv6/OzTuBkYXVRwArK2xzekS0R0R7W1tbPdM3M9uhNbxwSNpN0h5d08DxwBJgFjApN5sE3JanZwET891VRwHru05pmZlZ4zXjVNU+wI8kdX3+TRHxM0nzgVsknQ08AZyW298BnAR0AC8CZzU+5cpGT/lJTe2WX/ruOmdiZtY4DS8cEfEYcGiF+DPAcRXiAZzbgNTMzKwGrXQ7rpmZ9QMuHGZmVooLh5mZleLCYWZmpbhwmJlZKS4cZmZWiguHmZmV4sJhZmaluHCYmVkpzeodd4firknM7NXERxxmZlaKC4eZmZXiwmFmZqW4cJiZWSm+ON5CfBHdzPoDH3GYmVkp/eaIQ9J44J+AAcC3I+LSJqfUND4yMbNm6heFQ9IA4JvAXwCdwHxJsyLi4eZm9urhYmRmteoXhQM4AujIw84iaSYwAXDhqKLWYtDMbfZ1IXIBNKu//lI4hgMrCvOdwJHFBpImA5Pz7AZJj2zjZ+0FPL2N6zbSqyJPfbmBmVT/3FfF/mwR/SFHcJ6V7FdLo/5SOFQhFlvNREwHpm/3B0kLIqJ9e7dTb86zbznPvtMfcgTnuT36y11VncDIwvwIYGWTcjEz26H1l8IxHxgraYykXYDTgVlNzsnMbIfUL05VRcQmSecBd5Jux50REUvr9HHbfbqrQZxn33Kefac/5AjOc5spInpvZWZmlvWXU1VmZtYiXDjMzKwUF45M0nhJj0jqkDSl2fkUSVou6SFJiyQtyLGhkmZLejS/D2lCXjMkrZa0pBCrmJeSK/L+XSzpsCbnOU3Sk3mfLpJ0UmHZ1JznI5JOaGCeIyXNkbRM0lJJ5+d4S+3TKnm21D6VNEjSPEkP5jwvyvExkubm/XlzvuEGSbvm+Y68fHST87xO0uOF/Tkux5v2t7RFROzwL9IF9/8C9gd2AR4EDm52XoX8lgN7dYt9BZiSp6cAX25CXu8ADgOW9JYXcBLwU9IzOUcBc5uc5zTgUxXaHpz/++8KjMn/XwxoUJ7DgMPy9B7Ab3I+LbVPq+TZUvs075fd8/RAYG7eT7cAp+f41cBH8/THgKvz9OnAzQ3anz3leR1waoX2Tftb6nr5iCPZ0qVJRPwe6OrSpJVNAK7P09cDpzQ6gYj4BbC2W7invCYAN0RyPzBY0rAm5tmTCcDMiNgYEY8DHaT/P+ouIlZFxAN5+nlgGanXhJbap1Xy7ElT9mneLxvy7MD8CuBY4NYc774/u/bzrcBxkio9fNyoPHvStL+lLi4cSaUuTar9ITRaAHdJWpi7VgHYJyJWQfpDBvZuWnZb6ymvVtzH5+VD/RmFU30tkWc+TfJW0r8+W3afdssTWmyfShogaRGwGphNOtpZFxGbKuSyJc+8fD3w+mbkGRFd+/OSvD8vl7Rr9zyzhv93d+FIeu3SpMmOjojDgBOBcyW9o9kJbYNW28dXAQcA44BVwGU53vQ8Je0O/AC4ICKeq9a0QqxhuVbIs+X2aURsjohxpN4mjgDeVCWXlslT0puBqcBBwOHAUODCZufZxYUjaekuTSJiZX5fDfyI9AfwVNfhaX5f3bwMt9JTXi21jyPiqfzH+jJwDX88ddLUPCUNJP0Y3xgRP8zhltunlfJs1X2ac1sH3Eu6JjBYUtfDz8VctuSZl+9J7ac4+zrP8fmUYETERuA7tND+dOFIWrZLE0m7Sdqjaxo4HlhCym9SbjYJuK05Gb5CT3nNAibmO0KOAtZ3nX5phm7nhN9L2qeQ8jw932EzBhgLzGtQTgKuBZZFxNcKi1pqn/aUZ6vtU0ltkgbn6dcA7yJdj5kDnJqbdd+fXfv5VOCeyFejm5Dnrwv/WBDpOkxxfzb3b6nRV+Nb9UW6U+E3pHOgf9fsfAp57U+6I+VBYGlXbqRzr3cDj+b3oU3I7fukUxJ/IP0r6Oye8iIdXn8z79+HgPYm5/ndnMdi0h/isEL7v8t5PgKc2MA830465bAYWJRfJ7XaPq2SZ0vtU+AtwK9yPkuAz+X4/qTC1QH8C7Brjg/K8x15+f5NzvOevD+XAN/jj3c58Wl2AAAEnklEQVReNe1vqevlLkfMzKwUn6oyM7NSXDjMzKwUFw4zMyvFhcPMzEpx4TAzs1JcOKylSApJlxXmPyVpWh9t+zpJp/becrs/5zSlnmPndIuPVqGH3irrf7Z+2W2Vy18X5tslXVHvz7VXBxcOazUbgf8jaa9mJ1IkaUCJ5mcDH4uId27jx5UuHCXzAxgNbCkcEbEgIj5e9nNtx+TCYa1mE2mM5U90X9D9iEHShvx+jKT7JN0i6TeSLpV0Zh7j4CFJBxQ28y5J/5bbnZzXHyDpq5Lm5w7lPlLY7hxJN5EetOqezxl5+0skfTnHPkd6QO5qSV/t6UtK+pCkH0r6mdK4EF/J8UuB1yiNv3Bjjn0gf5dFkr7VVSQkbZB0saS5wJ9K+lz+DkskTc9PHCPpQEk/Vxrv4YG8Py4F/ixv8xP5u96e2w+V9OO8L+6X9JYcn6bUeeG9kh6T9PEc303ST/L2l0h6f+//ma1fa/QTh375Ve0FbABeRxqDZE/gU8C0vOw6CuMTABvy+zHAOtI4EbsCTwIX5WXnA18vrP8z0j+YxpKeIh8ETAb+PrfZFVhAGjfiGOAFYEyFPPcFngDagJ1JT/mekpfdS4WneUn/yl+Spz8EPJa/4yDgt8DI4vfK028C/hUYmOevBCbm6QDeV2g7tDD9XeA9eXou8N48PQh4bf5utxfab5kHvgF8Pk8fCyzK09OA/8j7aC/gGVIX4H8FXFPY1p7N/v/Ir/q+fMRhLSdST6s3AGVOncyP1CncRlJXDHfl+EOkH+wut0TEyxHxKOmH+yBS/18Tlbq1nkvq4mNsbj8v0hgS3R0O3BsRayJ1wX0jacCoMu6OiPUR8RLwMLBfhTbHAW8D5uf8jiN1mQGwmdTRYJd3Ko1c9xDpB/8QpX7OhkfEjwAi4qWIeLGXvN5OKjxExD3A6yXtmZf9JNK4Gk+TOlvch7SP3yXpy5L+LCLWl9oL1u/s3HsTs6b4OvAAqVfQLpvIp1fzaZhdCss2FqZfLsy/zNb/n3fvYydIff/8v4i4s7hA0jGkI45K+mKAn2LOm6n89yjg+oiYWmHZSxGxGdLwo6SjkfaIWJFvKBi0jXlW67b7FTlHxG8kvY3UX9WXJN0VERdvw+daP+EjDmtJEbGWNMTn2YXwctK/viGNgjZwGzZ9mqSd8nn+/Umd7t0JfFSpq3AkvUGpJ+Jq5gJ/LmmvfM3hDOC+bcinkj905ULq1PBUSXvn3IZKqnRkMii/P600TsapsOXorVPSKXn9XSW9FnieNOxrJb8AzsztjwGejirjgkjaF3gxIr4H/CNpmF57FfMRh7Wyy4DzCvPXALdJmkf6Qe3paKCaR0g/8PsA50TES5K+TTqd9UA+kllDL0PxRsQqSVNJXXQLuCMi+qpr++nAYkkPRMSZkv6eNALkTqQefs8lXRMp5rNO0jWk00bLSUMFdPkg8C1JF+f1TyP1xLpJ0oOkaz+/KrSfBnxH0mLgRf7Y1XhP/gT4qqSX8/Y/WvobW7/i3nHNzKwUn6oyM7NSXDjMzKwUFw4zMyvFhcPMzEpx4TAzs1JcOMzMrBQXDjMzK+V/ACQnNlw0DcumAAAAAElFTkSuQmCC
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
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<pre>article_counts    3.0
dtype: float64</pre>
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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="mi">3</span>  <span class="c1"># 50% of individuals interact with ____ number of articles or fewer.</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="n">max_views_by_user</span> <span class="o">=</span> <span class="n">df_n</span><span class="p">[</span><span class="s1">&#39;article_counts&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>  <span class="c1"># The maximum number of user-article interactions by any 1 user is ______.</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 number of articles or fewer.
The maximum number of user-article interactions by any 1 user is 364.
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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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<span class="n">df_content</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 1056 entries, 0 to 1055
Data columns (total 5 columns):
doc_body           1042 non-null object
doc_description    1053 non-null object
doc_full_name      1056 non-null object
doc_status         1056 non-null object
article_id         1056 non-null int64
dtypes: int64(1), object(4)
memory usage: 41.3+ KB
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      <th>doc_status</th>
      <th>article_id</th>
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    </tr>
  </tbody>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Find and explore duplicate articles</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><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="c1"># Checking if there are any article ids which are duplicates</span>
<span class="n">df_cont_dupl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</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> <span class="o">==</span> <span class="kc">True</span><span class="p">)</span>
<span class="n">df_cont_dupl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">df_cont_dupl</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">df_content</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="nb">id</span><span class="p">])</span>
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<pre>                                              doc_body  \
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366  While AWS&#39;s Lambda service treats each functio...   

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366  Go Serverless with Apex and Compose&#39;s MongoDB       Live         365  
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694  Skip to content * Unix\r\n * R\r\n\r\n15 PAGE ...   

                                       doc_description  \
694  For Beginners in R, here is a 15 page example ...   

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694  15 Page Tutorial for R       Live         692  
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764  Elvis Dohmatob Home Blog Publications Photos C...   

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764  In this post, I’ll demo variational auto-encod...   

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764  Variational auto-encoder for &#34;Frey faces&#34; usin...       Live         761  
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975  Compose The Compose logo Articles Sign in Free...   

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975  In this third article in our the series, we&#39;ll...   

                           doc_full_name doc_status  article_id  
975  Launching RESTHeart into Production       Live         970  
                                              doc_body  \
976  Cloudant allows custom Javascript to be run se...   

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976  Cloudant allows custom Javascript to be run se...   

                               doc_full_name doc_status  article_id  
976  Defensive coding in Map/Index functions       Live         971  
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove any rows that have the same article_id and keeyp keep the first only</span>
<span class="n">df_cont_dropDupl</span> <span class="o">=</span> <span class="n">df_content</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">(</span><span class="n">subset</span><span class="o">=</span><span class="s1">&#39;article_id&#39;</span><span class="p">,</span> <span class="n">keep</span><span class="o">=</span><span class="s1">&#39;first&#39;</span><span class="p">)</span>
<span class="n">df_cont_dropDupl</span><span class="o">.</span><span class="n">shape</span>
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<pre>(1051, 5)</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">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
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      <th></th>
      <th>article_id</th>
      <th>title</th>
      <th>email</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>1430.0</td>
      <td>using pixiedust for fast, flexible, and easier...</td>
      <td>ef5f11f77ba020cd36e1105a00ab868bbdbf7fe7</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1314.0</td>
      <td>healthcare python streaming application demo</td>
      <td>083cbdfa93c8444beaa4c5f5e0f5f9198e4f9e0b</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1429.0</td>
      <td>use deep learning for image classification</td>
      <td>b96a4f2e92d8572034b1e9b28f9ac673765cd074</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1338.0</td>
      <td>ml optimization using cognitive assistant</td>
      <td>06485706b34a5c9bf2a0ecdac41daf7e7654ceb7</td>
    </tr>
    <tr>
      <th>4</th>
      <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="n">df</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
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<pre>article_id     714
title          714
email         5148
dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="p">(</span><span class="n">df</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="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;title&#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">head</span><span class="p">()</span>
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    .dataframe tbody tr th {
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    }

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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>title</th>
      <th>email</th>
    </tr>
    <tr>
      <th>article_id</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1429.0</th>
      <td>937</td>
      <td>937</td>
    </tr>
    <tr>
      <th>1330.0</th>
      <td>927</td>
      <td>927</td>
    </tr>
    <tr>
      <th>1431.0</th>
      <td>671</td>
      <td>671</td>
    </tr>
    <tr>
      <th>1427.0</th>
      <td>643</td>
      <td>643</td>
    </tr>
    <tr>
      <th>1364.0</th>
      <td>627</td>
      <td>627</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</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><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>article_id</th>
      <th>title</th>
    </tr>
    <tr>
      <th>email</th>
      <th></th>
      <th></th>
    </tr>
  </thead>
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    <tr>
      <th>0000b6387a0366322d7fbfc6434af145adf7fed1</th>
      <td>13</td>
      <td>13</td>
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      <th>001055fc0bb67f71e8fa17002342b256a30254cd</th>
      <td>4</td>
      <td>4</td>
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      <th>00148e4911c7e04eeff8def7bbbdaf1c59c2c621</th>
      <td>3</td>
      <td>3</td>
    </tr>
    <tr>
      <th>001a852ecbd6cc12ab77a785efa137b2646505fe</th>
      <td>6</td>
      <td>6</td>
    </tr>
    <tr>
      <th>001fc95b90da5c3cb12c501d201a915e4f093290</th>
      <td>2</td>
      <td>2</td>
    </tr>
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<pre>(45993, 3)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_cont_dropDupl</span><span class="o">.</span><span class="n">shape</span>
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<pre>(1051, 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">nunique</span><span class="p">()[</span><span class="s1">&#39;article_id&#39;</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_cont_dropDupl</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 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">nunique</span><span class="p">()[</span><span class="s1">&#39;email&#39;</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>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The number of unique articles that have at least one interaction 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;The number of unique articles on the IBM platform 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;The number of unique users 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;The number of user-article interactions 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>The number of unique articles that have at least one interaction is 714.
The number of unique articles on the IBM platform is 1051.
The number of unique users is 5148.
The number of user-article interactions is 45993.
</pre>
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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">df_view_most</span> <span class="o">=</span> <span class="n">df</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">df_view_most</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_view_most</span><span class="p">[</span><span class="s1">&#39;email&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">df_view_most</span><span class="p">[</span><span class="s1">&#39;email&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()]</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>title</th>
      <th>email</th>
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      <th>article_id</th>
      <th></th>
      <th></th>
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      <th>1429.0</th>
      <td>937</td>
      <td>937</td>
    </tr>
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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></th>
      <th>article_id</th>
      <th>title</th>
      <th>user_id</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1430.0</td>
      <td>using pixiedust for fast, flexible, and easier...</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1314.0</td>
      <td>healthcare python streaming application demo</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1429.0</td>
      <td>use deep learning for image classification</td>
      <td>3</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1338.0</td>
      <td>ml optimization using cognitive assistant</td>
      <td>4</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1276.0</td>
      <td>deploy your python model as a restful api</td>
      <td>5</td>
    </tr>
  </tbody>
</table>
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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="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>user_id</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1430.0</td>
      <td>using pixiedust for fast, flexible, and easier...</td>
      <td>1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1314.0</td>
      <td>healthcare python streaming application demo</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1429.0</td>
      <td>use deep learning for image classification</td>
      <td>3</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1338.0</td>
      <td>ml optimization using cognitive assistant</td>
      <td>4</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1276.0</td>
      <td>deploy your python model as a restful api</td>
      <td>5</td>
    </tr>
  </tbody>
</table>
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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="c1"># Your code here</span>
    <span class="n">top_articles</span> <span class="o">=</span> <span class="n">df</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="s1">&#39;title&#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">reset_index</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;counts&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;counts&#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="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</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="c1"># Your code here</span>
    <span class="n">top_articles</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s2">&quot;article_id&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="n">n</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;str&#39;</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;]
Index([&#39;1429.0&#39;, &#39;1330.0&#39;, &#39;1431.0&#39;, &#39;1427.0&#39;, &#39;1364.0&#39;, &#39;1314.0&#39;, &#39;1293.0&#39;,
       &#39;1170.0&#39;, &#39;1162.0&#39;, &#39;1304.0&#39;],
      dtype=&#39;object&#39;, name=&#39;article_id&#39;)
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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="c1"># Fill in the function here</span>
    <span class="c1"># unstack the user-item interaction dataframe</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="c1"># fill missing values with 0</span>
    <span class="n">user_item</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    
    <span class="c1"># convert int</span>
    <span class="n">user_item</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int&#39;</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">user_similr</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">loc</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">user_item</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>

    <span class="c1"># Sort by similarity</span>
    <span class="n">user_similr</span>  <span class="o">=</span> <span class="n">user_similr</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>

    <span class="c1"># Create list of just the ids</span>
    <span class="c1"># Remove the own user&#39;s id</span>
    <span class="n">most_similar_users</span> <span class="o">=</span> <span class="n">user_similr</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="p">(</span><span class="n">user_similr</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">index</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</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, 131, 3870, 46, 4201, 5041]
The 5 most similar users to user 3933 are: [1, 23, 3782, 4459, 203]
The 3 most similar users to user 46 are: [4201, 23, 3782]
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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="c1"># Your code here</span>
    <span class="n">article_names</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="c1"># Select articles with the same article_id and drop duplicates </span>
    <span class="n">article_names</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">article_ids</span><span class="p">)][</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</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="c1"># Your code here</span>
    <span class="c1"># Get all articles for this user id</span>
    <span class="n">user_idx</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">user_id</span><span class="p">,:]</span>
    <span class="c1"># Get articles user interacted with</span>
    <span class="n">article_ids</span> <span class="o">=</span> <span class="n">user_idx</span><span class="p">[</span><span class="n">user_idx</span> <span class="o">==</span> <span class="mi">1</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="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;str&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    <span class="c1"># Get article names </span>
    <span class="n">article_names</span> <span class="o">=</span> <span class="n">get_article_names</span><span class="p">(</span><span class="n">article_ids</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="c1"># Your code here</span>
    <span class="c1"># Get most similar users </span>
    <span class="n">most_similar_users</span> <span class="o">=</span> <span class="n">find_similar_users</span><span class="p">(</span><span class="n">user_id</span><span class="p">)</span>
    <span class="c1"># Get article ids </span>
    <span class="n">user_article_ids</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="n">recs</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="c1"># Create recommendations for this user</span>
    <span class="k">for</span> <span class="n">user_neighb</span> <span class="ow">in</span> <span class="n">most_similar_users</span><span class="p">:</span>
        <span class="n">neighb_article_ids</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_neighb</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="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">neighb_article_ids</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">user_article_ids</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;visualize data with the matplotlib library&#39;,
 &#39;predicting churn with the spss random tree algorithm&#39;,
 &#39;apache spark lab, part 3: machine learning&#39;,
 &#39;using github for project control in dsx&#39;,
 &#39;analyzing data by using the sparkling.data library features&#39;,
 &#39;502    forgetting the past to learn the future: long ...\nName: title, dtype: object&#39;,
 &#39;spark 2.1 and job monitoring available in dsx&#39;,
 &#39;higher-order logistic regression for large datasets&#39;,
 &#39;recommender systems: approaches &amp; algorithms&#39;,
 &#39;using bigdl in dsx for deep learning on spark&#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="c1"># Your code here</span>
    <span class="c1"># Column names</span>
    <span class="n">colName</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="c1"># Create dataframe to hold top users</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="n">columns</span><span class="o">=</span> <span class="n">colName</span><span class="p">)</span> 
    
    <span class="c1"># Populate the dataframe</span>
    <span class="k">for</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">user_item</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">if</span> <span class="nb">id</span> <span class="o">!=</span> <span class="n">user_id</span><span class="p">:</span>
            <span class="n">neighbor_id</span> <span class="o">=</span> <span class="nb">id</span>
            
            <span class="c1"># Get user to user similarity</span>
            <span class="n">similarity</span> <span class="o">=</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="nb">id</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="c1"># Get number of interactions for user ot article</span>
            <span class="n">num_interactions</span> <span class="o">=</span> <span class="n">user_item</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
            <span class="n">neighbors_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">neighbor_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">neighbor_id</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">neighbors_df</span><span class="p">[</span><span class="s1">&#39;similarity&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors_df</span><span class="p">[</span><span class="s1">&#39;similarity&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int&#39;</span><span class="p">)</span>
    <span class="n">neighbors_df</span><span class="p">[</span><span class="s1">&#39;neighbor_id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors_df</span><span class="p">[</span><span class="s1">&#39;neighbor_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int&#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="n">by</span> <span class="o">=</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="c1"># Your code here</span>
    <span class="c1"># Get similar users</span>
    <span class="n">neighbours</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">top_similar_users</span> <span class="o">=</span> <span class="n">neighbours</span><span class="p">[</span><span class="s1">&#39;neighbor_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    <span class="n">recs</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># recommended article Id&#39;s</span>
    
    <span class="c1"># Get articles read by the user</span>
    <span class="n">user_article_ids</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">user_id</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
    
    <span class="k">for</span> <span class="n">neighbour_id</span> <span class="ow">in</span> <span class="n">top_similar_users</span><span class="p">:</span>
        <span class="n">recs</span> <span class="o">+=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;user_id&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">neighbour_id</span><span class="p">][</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</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">recs</span><span class="p">))</span>
    
    <span class="c1"># Selecting articles not seen by User_id</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</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">user_article_ids</span><span class="p">]</span>
    
    <span class="n">recs_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">recs</span><span class="p">)][[</span><span class="s1">&#39;article_id&#39;</span><span class="p">,</span> <span class="s1">&#39;title&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> 
    <span class="n">recs</span> <span class="o">=</span> <span class="n">recs_df</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="c1"># Get ids</span>
    <span class="n">rec_names</span> <span class="o">=</span> <span class="n">recs_df</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> <span class="c1"># Get title</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"># 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:
[1430.0, 1314.0, 1429.0, 1338.0, 1276.0, 1432.0, 593.0, 1185.0, 993.0, 14.0]

The top 10 recommendations for user 20 are the following article names:
[&#39;using pixiedust for fast, flexible, and easier data analysis and experimentation&#39;, &#39;healthcare python streaming application demo&#39;, &#39;use deep learning for image classification&#39;, &#39;ml optimization using cognitive assistant&#39;, &#39;deploy your python model as a restful api&#39;, &#39;visualize data with the matplotlib library&#39;, &#39;upload files to ibm data science experience using the command line&#39;, &#39;classify tumors with machine learning&#39;, &#39;configuring the apache spark sql context&#39;, &#39;got zip code data? prep it for analytics. – ibm watson data lab – medium&#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="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>I will go for  user_user_recs_part2. It's a good start recommending articles from the most active users and make sure that these articles are the most interacted articles as well. For new users, we can ask them about their preferences, then recommend top articles that are matching their preferences. Once we have more data on them, we can move to matrix factorization.</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="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</span> <span class="o">=</span> <span class="n">new_user_recs</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>
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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-IV:-Content-Based-Recommendations-(EXTRA---NOT-REQUIRED)"><a class="anchor" id="Content-Recs">Part IV: Content Based Recommendations (EXTRA - NOT REQUIRED)</a><a class="anchor-link" href="#Part-IV:-Content-Based-Recommendations-(EXTRA---NOT-REQUIRED)">&#182;</a></h3><p>Another method we might use to make recommendations is to perform a ranking of the highest ranked articles associated with some term.  You might consider content to be the <strong>doc_body</strong>, <strong>doc_description</strong>, or <strong>doc_full_name</strong>.  There isn't one way to create a content based recommendation, especially considering that each of these columns hold content related information.</p>
<p><code>1.</code> Use the function body below to create a content based recommender.  Since there isn't one right answer for this recommendation tactic, no test functions are provided.  Feel free to change the function inputs if you decide you want to try a method that requires more input values.  The input values are currently set with one idea in mind that you may use to make content based recommendations.  One additional idea is that you might want to choose the most popular recommendations that meet your 'content criteria', but again, there is a lot of flexibility in how you might make these recommendations.</p>
<h3 id="This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills.<a class="anchor-link" href="#This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Tokenize a string into words. </span>
<span class="sd">    </span>
<span class="sd">    Args:</span>
<span class="sd">        x(string): string to tokezine.</span>
<span class="sd">            </span>
<span class="sd">    Returns:</span>
<span class="sd">        (list): list of lemmatized words</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># get stop words</span>
    <span class="n">stop_words</span> <span class="o">=</span> <span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">stopwords</span><span class="o">.</span><span class="n">words</span><span class="p">(</span><span class="s1">&#39;english&#39;</span><span class="p">))</span> <span class="o">|</span> <span class="nb">set</span><span class="p">(</span><span class="n">nltk</span><span class="o">.</span><span class="n">corpus</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">words</span><span class="p">()))</span>
    <span class="c1"># split each article title into individual words</span>
    <span class="n">tokens</span> <span class="o">=</span> <span class="n">word_tokenize</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> 
    <span class="n">lemmatizer</span> <span class="o">=</span> <span class="n">WordNetLemmatizer</span><span class="p">()</span>
    <span class="n">clean_tokens</span><span class="o">=</span><span class="p">[]</span>
    <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">:</span>
        <span class="c1">#clean each token from whitespace and punctuation, and conver to root word</span>
        <span class="n">clean_token</span> <span class="o">=</span> <span class="n">lemmatizer</span><span class="o">.</span><span class="n">lemmatize</span><span class="p">(</span><span class="n">token</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
        <span class="n">clean_tokens</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">clean_token</span><span class="p">)</span>
        
    <span class="n">filtered</span> <span class="o">=</span> <span class="p">[</span><span class="n">word</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">clean_tokens</span> <span class="k">if</span> <span class="n">word</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">stop_words</span> <span class="ow">and</span> <span class="n">word</span><span class="o">.</span><span class="n">isalpha</span><span class="p">()]</span>
    <span class="k">return</span> <span class="n">filtered</span>

<span class="k">def</span> <span class="nf">make_content_recs</span><span class="p">(</span><span class="n">data_id</span><span class="p">,</span> <span class="n">user_id</span><span class="o">=</span><span class="kc">True</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="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">    This recommender goes through each article title and nltk library to finds the most common words</span>
<span class="sd">    (related to content) throughout all the articles.</span>
<span class="sd">    </span>
<span class="sd">    The recommender will look at the sums of words in the title of each article </span>
<span class="sd">    and based on the number of matches and popularity of an article.</span>
<span class="sd">       </span>
<span class="sd">    Args:</span>
<span class="sd">        data_id (str) - id of either user or article</span>
<span class="sd">        user_id (bool) - if true, make recs based on user</span>
<span class="sd">        m (int) - number of recommendations to give based on term</span>
<span class="sd">    Returns:</span>
<span class="sd">        recs (list) - list of article ids that are recommended</span>
<span class="sd">        rec_names (list) - list of article names that are recommended   </span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="k">if</span><span class="p">(</span><span class="n">user_id</span><span class="p">):</span>
        <span class="n">user_id</span> <span class="o">=</span> <span class="n">data_id</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="c1"># get past articles read by the user</span>
            <span class="n">article_ids</span><span class="p">,</span> <span class="n">_</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="k">except</span> <span class="ne">KeyError</span><span class="p">:</span> <span class="c1"># user does not exist</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;User Doesn</span><span class="se">\&#39;</span><span class="s1">t Exist, Recommending Top Articles&#39;</span><span class="p">)</span>
            <span class="n">recs</span> <span class="o">=</span> <span class="n">get_top_article_ids</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="p">,</span> <span class="n">get_article_names</span><span class="p">(</span><span class="n">recs</span><span class="p">)</span>
    
    <span class="k">else</span><span class="p">:</span>
        <span class="n">article_ids</span> <span class="o">=</span> <span class="n">data_id</span>
        
    <span class="n">title_data</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="n">subset</span><span class="o">=</span><span class="s1">&#39;article_id&#39;</span><span class="p">)</span> <span class="c1">#drop duplicates </span>
    <span class="n">titles</span> <span class="o">=</span> <span class="n">title_data</span><span class="p">[</span><span class="n">title_data</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">article_ids</span><span class="p">)))]</span><span class="o">.</span><span class="n">title</span> <span class="c1"># get article titles</span>
    
    <span class="c1"># Tokenize the words in each article title</span>
    <span class="n">title_words</span><span class="o">=</span><span class="p">[]</span>
    <span class="n">tokenized</span> <span class="o">=</span> <span class="n">tokenize</span><span class="p">(</span><span class="n">titles</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">sep</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
    <span class="n">title_words</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tokenized</span><span class="p">)</span>
    
    <span class="c1">#Find the highest occuring words</span>
    <span class="n">common_words</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">title_words</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>

    <span class="n">top_matches</span><span class="o">=</span><span class="p">{}</span>
    <span class="c1"># measure of similarity: count number of occurences of each common word in other article titles</span>
    <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">common_words</span><span class="p">:</span>
        <span class="n">word_count</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">title_data</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">word</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span> <span class="c1">#gets occurences of each word in title</span>
        <span class="n">top_matches</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">word_count</span>
    
    <span class="c1"># Most common words</span>
    <span class="n">top_matches</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">top_matches</span><span class="p">)</span> 
    <span class="n">top_matches</span><span class="p">[</span><span class="s1">&#39;top_matches&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">top_matches</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="n">top_matches</span><span class="p">[</span><span class="s1">&#39;article_id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">title_data</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
    
    <span class="c1"># Get most interacted with articles</span>
    <span class="n">article_occurences</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;occurences&#39;</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">value_counts</span><span class="p">()})</span>

    <span class="c1"># Sort matches by most popular articles</span>
    <span class="n">top_matches</span> <span class="o">=</span> <span class="n">top_matches</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">article_occurences</span><span class="p">,</span> <span class="n">left_on</span><span class="o">=</span><span class="s1">&#39;article_id&#39;</span><span class="p">,</span> <span class="n">right_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">top_matches</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;top_matches&#39;</span><span class="p">,</span> <span class="s1">&#39;occurences&#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="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>    
    
    <span class="c1"># Drop already read articles</span>
    <span class="n">recs_df</span> <span class="o">=</span> <span class="n">top_matches</span><span class="p">[</span><span class="o">~</span><span class="n">top_matches</span><span class="o">.</span><span class="n">article_id</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">article_ids</span><span class="p">)))]</span>
    
    <span class="c1"># Get rec id and names</span>
    <span class="n">recs</span> <span class="o">=</span> <span class="n">recs_df</span><span class="o">.</span><span class="n">article_id</span><span class="p">[:</span><span class="n">m</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</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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<p><code>2.</code> Now that you have put together your content-based recommendation system, use the cell below to write a summary explaining how your content based recommender works.  Do you see any possible improvements that could be made to your function?  Is there anything novel about your content based recommender?</p>
<h3 id="This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills.<a class="anchor-link" href="#This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">&#182;</a></h3>
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<p>This content based recommender scans through previously interacted articles. The nltk library finds the most common words in the title of each article. Based on these most common words, the recommender looks at the sums of words relevant words in the title of each article, and based on the number of matches in the titles as well as the general popularity of the article it gives back the best recommendations.</p>
<p>If the user has not read any articles yet, then we can't really give any content based recommendations, and just return back some of the most popular articles.</p>
<p>There is a lot of potential improvement and optimization for this recommender. For example one could construct a custom NLTK corpus which would filter out article words. Currently I used a combination of a couple standard NLTK corpora. Furthermore, If df_content had information for all articles we could expand this recommender to look through not only the title but also the body of the articles.</p>

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<p><code>3.</code> Use your content-recommendation system to make recommendations for the below scenarios based on the comments.  Again no tests are provided here, because there isn't one right answer that could be used to find these content based recommendations.</p>
<h3 id="This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">This part is NOT REQUIRED to pass this project.  However, you may choose to take this on as an extra way to show off your skills.<a class="anchor-link" href="#This-part-is-NOT-REQUIRED-to-pass-this-project.--However,-you-may-choose-to-take-this-on-as-an-extra-way-to-show-off-your-skills.">&#182;</a></h3><p>I used the NLTK library to search for articles with similar keywords. If the user has no history yet, then no content-based recommendation is given, and we will return some of the most popular articles.</p>
<p>We can improve this further by looking at similar keywords but semantically not just the exact keyword. Also, if the user doesn't like a specific article because it has deep learning contents, it doesn't mean that he or she will disklike every article with deep learning content. It will be interesting to augment content-based recommendation with some ML algorithm that can handle such situations.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># make recommendations for a brand new user</span>
<span class="n">make_content_recs</span><span class="p">(</span><span class="s1">&#39;0.0&#39;</span><span class="p">,</span> <span class="n">user_id</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>User Doesn&#39;t Exist, Recommending Top Articles
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<pre>(Index([&#39;1429.0&#39;, &#39;1330.0&#39;, &#39;1431.0&#39;, &#39;1427.0&#39;, &#39;1364.0&#39;, &#39;1314.0&#39;, &#39;1293.0&#39;,
        &#39;1170.0&#39;, &#39;1162.0&#39;, &#39;1304.0&#39;],
       dtype=&#39;object&#39;, name=&#39;article_id&#39;),
 [&#39;healthcare python streaming application demo&#39;,
  &#39;use deep learning for image classification&#39;,
  &#39;apache spark lab, part 1: basic concepts&#39;,
  &#39;predicting churn with the spss random tree algorithm&#39;,
  &#39;analyze energy consumption in buildings&#39;,
  &#39;visualize car data with brunel&#39;,
  &#39;use xgboost, scikit-learn &amp; ibm watson machine learning apis&#39;,
  &#39;gosales transactions for logistic regression model&#39;,
  &#39;insights from new york car accident reports&#39;,
  &#39;finding optimal locations of new store using decision optimization&#39;])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># make a recommendations for a user who only has interacted with article id &#39;1427.0&#39;</span>
<span class="n">make_content_recs</span><span class="p">([</span><span class="s1">&#39;1427.0&#39;</span><span class="p">],</span> <span class="n">user_id</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>(array([&#39;1159.0&#39;, &#39;390.0&#39;, &#39;1332.0&#39;, &#39;1324.0&#39;, &#39;241.0&#39;, &#39;1422.0&#39;, &#39;969.0&#39;,
        &#39;729.0&#39;, &#39;14.0&#39;, &#39;254.0&#39;], dtype=&#39;&lt;U32&#39;),
 [&#39;got zip code data? prep it for analytics. – ibm watson data lab – medium&#39;,
  &#39;ibm watson facebook posts for 2015&#39;,
  &#39;intents &amp; examples for ibm watson conversation&#39;,
  &#39;use r dataframes &amp; ibm watson natural language understanding&#39;,
  &#39;pixiedust 1.0 is here! – ibm watson data lab&#39;,
  &#39;i am not a data scientist – ibm watson data lab&#39;,
  &#39;flightpredict ii: the sequel  – ibm watson data lab&#39;,
  &#39;introducing ibm watson studio &#39;,
  &#39;apple, ibm add machine learning to partnership with watson-core ml coupling&#39;,
  &#39;analyze facebook data using ibm watson and watson studio&#39;])</pre>
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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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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the matrix here</span>
<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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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># quick look at the matrix</span>
<span class="n">user_item_matrix</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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    }

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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>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
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    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <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>
      <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>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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    <tr>
      <th>2</th>
      <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>
      <td>0.0</td>
      <td>0.0</td>
      <td>...</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>
      <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>
      <td>0.0</td>
      <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>
      <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>...</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>
      <td>0.0</td>
    </tr>
    <tr>
      <th>5</th>
      <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>
      <td>0.0</td>
      <td>...</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>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
<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="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Nans in the users to item interactions matrix 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">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">user_item_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Nans in the users to latent features matrix 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">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Nans in the segma matrix 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">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of Nans in the items to latent features matrix 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">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">vt</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
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<pre>Number of Nans in the users to item interactions matrix is: 0
Number of Nans in the users to latent features matrix is: 0
Number of Nans in the segma matrix is: 0
Number of Nans in the items to latent features matrix is: 0
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<p>We can use Singular Value Decomposition because <strong>there are no missing values (NANs) in our data</strong>.</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">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</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="prompt input_prompt">In&nbsp;[115]:</div>
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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="c1"># Your code here</span>
    <span class="c1"># create user item matrix for the train dataset</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="c1"># create the test dataset</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="c1"># get the ids of the train dataset and test dataset</span>
    <span class="n">train_idx</span> <span class="o">=</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="n">test_idx</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">user_item_test</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
    
    <span class="c1"># get shared rows</span>
    <span class="n">shared_rows</span> <span class="o">=</span> <span class="n">train_idx</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">test_idx</span><span class="p">)</span>
    
    <span class="c1"># get columns in train and test datasets</span>
    <span class="n">train_arts</span> <span class="o">=</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="n">test_arts</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">user_item_test</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
    
    <span class="c1"># get shared columns</span>
    <span class="n">shared_cols</span> <span class="o">=</span> <span class="n">train_arts</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">test_arts</span><span class="p">)</span>
    
    <span class="c1"># Creating new user-item matrix for tets with common values</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">ix</span><span class="p">[</span><span class="n">shared_rows</span><span class="p">,</span> <span class="n">shared_cols</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>
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<pre>C:\Users\ahmed\Anaconda3\lib\site-packages\ipykernel_launcher.py:41: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
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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="c1"># This should be article not movies. it was bugging me and wasted some time on it</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"># 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>
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<div class=" highlight hl-ipython3"><pre><span></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="k">def</span> <span class="nf">svd_algorithm</span><span class="p">(</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="p">):</span>
    <span class="sd">&quot;&quot;&quot; Return the results of the svd algorithm.</span>
<span class="sd">    </span>
<span class="sd">        Args:</span>
<span class="sd">            u_train (np.array): user item interaction matrix  </span>
<span class="sd">            s_train (np.array): sigma matrix</span>
<span class="sd">            vt_train (np.array): v transpose matrix</span>
<span class="sd">            </span>
<span class="sd">        Returns:</span>
<span class="sd">            Dataframe: dataframe of user article interaction counts sorted in descending order</span>
<span class="sd">        </span>
<span class="sd">        &quot;&quot;&quot;</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_train</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">sum_errs_test</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">all_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="n">row_idxs</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">index</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">col_idxs</span> <span class="o">=</span> <span class="n">user_item_train</span><span class="o">.</span><span class="n">columns</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">row_idxs</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">col_idxs</span><span class="p">]</span>
        
        <span class="c1"># Split data </span>
        <span class="n">s_train_lat</span><span class="p">,</span> <span class="n">u_train_lat</span><span class="p">,</span> <span class="n">vt_train_lat</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_test_lat</span><span class="p">,</span> <span class="n">vt_test_lat</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_train_preds</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_train_lat</span><span class="p">,</span> <span class="n">s_train_lat</span><span class="p">),</span> <span class="n">vt_train_lat</span><span class="p">))</span>
        <span class="n">user_item_test_preds</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_test_lat</span><span class="p">,</span> <span class="n">s_train_lat</span><span class="p">),</span> <span class="n">vt_test_lat</span><span class="p">))</span>
        <span class="n">all_errs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</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">user_item_test_preds</span><span class="p">)</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">user_item_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="c1"># Calculate the error of each prediction</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_train_preds</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_test_preds</span><span class="p">)</span>

        <span class="c1"># Get 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="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"># Plot accuracy for train and test vs number of latent features</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>

    <span class="c1"># Latent features and training</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">color</span><span class="o">=</span><span class="s1">&#39;darkred&#39;</span><span class="p">)</span>

    <span class="c1"># Latent features and testing</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">color</span><span class="o">=</span><span class="s1">&#39;darkblue&#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="n">all_errs</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Total Error&#39;</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s2">&quot;orange&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="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">legend</span><span class="p">();</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Call the svd algorithm</span>
<span class="n">svd_algorithm</span><span class="p">(</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="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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<li>The Test accuracy decreases as the number of latent features increases for the testing dataset.</li>
<li>In this project, only 20 users had records of old interactions.</li>
<li>To solve the cold-start problem, we can deploy a rank based recommendation method or content based recommendation.</li>
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<p><a id='conclusions'></a></p>
<h3 id="Extras">Extras<a class="anchor-link" href="#Extras">&#182;</a></h3><p>Using your workbook, you could now save your recommendations for each user, develop a class to make new predictions and update your results, and make a flask app to deploy your results.  These tasks are beyond what is required for this project.  However, from what you learned in the lessons, you certainly capable of taking these tasks on to improve upon your work here!</p>
<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2><blockquote><p>Congratulations!  You have reached the end of the Recommendations with IBM project!</p>
<p><strong>Tip</strong>: Once you are satisfied with your work here, check over your report to make sure that it is satisfies all the areas of the <a href="https://review.udacity.com/#!/rubrics/2322/view">rubric</a>. You should also probably remove all of the "Tips" like this one so that the presentation is as polished as possible.</p>
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<h2 id="Directions-to-Submit">Directions to Submit<a class="anchor-link" href="#Directions-to-Submit">&#182;</a></h2><blockquote><p>Before you submit your project, you need to create a .html or .pdf version of this notebook in the workspace here. To do that, run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the orange Jupyter icon in the upper left).</p>
<p>Alternatively, you can download this report as .html via the <strong>File</strong> &gt; <strong>Download as</strong> submenu, and then manually upload it into the workspace directory by clicking on the orange Jupyter icon in the upper left, then using the Upload button.</p>
<p>Once you've done this, you can submit your project by clicking on the "Submit Project" button in the lower right here. This will create and submit a zip file with this .ipynb doc and the .html or .pdf version you created. Congratulations!</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="k">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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