Hospitalization-Elapse-Time.html

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<title>Hospitalization-Elapse-Time</title>

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: thin dotted;
  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: thin dotted;
  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: thin dotted;
  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;
  }
}
#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
 */
ul#tabs {
  margin-bottom: 4px;
}
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;
}
.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;
}
#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;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.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 {
  /* 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;
}
@-moz-document url-prefix() {
  div.inner_cell {
    overflow-x: hidden;
  }
}
/* 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: 20ex;
  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;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
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<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Captone-Project">Captone Project<a class="anchor-link" href="#Captone-Project">&#182;</a></h2><h2 id="Predicting-Hospitalization-Time-Using-The-Body-Mass-Index">Predicting Hospitalization Time Using The Body Mass Index<a class="anchor-link" href="#Predicting-Hospitalization-Time-Using-The-Body-Mass-Index">&#182;</a></h2><p>Doctors in the health industry consider obesity a risk factor for multiple types of illness.
Conditions like diabetes type 2, hypertension and cardiovascular diseases and various types of tumors, in overweight patients [1] are associated with higher rates of mortality. It seems clear that obesity has a negative impact on humans.</p>
<p>In the medical field, there is a hypothesis know as The obesity paradox [2]. The obesity
paradox hypothesis state that overweight elderly patients have a better chance to survive
illness like the ones describes in the previous paragraph than underweight patients.
Multiples studies try to proof or invalidate such hypothesis [3, 4, 8]. Some of the studies have found evidence that there is indeed some time of protection granted by a high BMI (body mass index) and others argue that confounding variables and lack of follow up invalidate such proofs [5]. One thing is sure, that despite which study is the correct all of them prove one way or another that there is a correlation between the: BMI, the age, the medical condition and the smoking habits; and that this association influence negative or positive the resilient capacity of a human being.</p>
<p>My proposal is based on the background knowledge of the correlation of the features
described in the previous paragraph; it should be possible to train a learner to predict how much time a patient will stay hospitalized (including ICU time) due to surgery, based in the afford mentioned features.</p>
<p>The main reason why I decided to work on this problem is that I am a firm believer that the medical field can benefit so much using technology that the sky is the limit. Patient diagnostic tools have improved way faster than the diagnostic method itself, so much is still to do in this field.</p>

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<h2 id="Proposed-Solution">Proposed Solution<a class="anchor-link" href="#Proposed-Solution">&#182;</a></h2><p>As was described at the beginning of this notebook, obesity is considered a health factor risk in some cases and a protection feature for some others. Previous medical studies are proof one versus the other with no clear conclusion. My study proposes not agreed with any of the two positions but to use the existing information to measure how resilient the body is to surgery using the features of those previous studies. The metric is the time a patient spent hospitalized. 
My solution to the described problem, consists in determine the relation of the medical conditions, age, hospitalization time and the "Body Mass Index." If such correlation is strong enough, it may be possible to train a computer model to predict the time a patient will spend hospitalized based on these features. Note that I do not intend to perform another study to proof or disproof the paradox, but to create a tool that can be used in future experiments to predict the hospitalization days and compare the prediction with the reality. 
My model has to predict a value that of the type continue, so the choice of algorithm will be a regression.</p>

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<h2 id="Strategy">Strategy<a class="anchor-link" href="#Strategy">&#182;</a></h2><p>The proposed strategy to accomplish the solution is to get from the raw dataset, a set of features related to the one discuss in the studies, documented in this project proposal. Once this subset is selected, we need to compare and understand the relation between such features and the hospitalization time. Some of the features are continues which facility the identification of such connection by using statistical methods (ex. correlation). Other features like the medical condition and the gender are categorical data, in this case, the complexity of understanding their relation to the hospitalization time increases, this is where machine learning algorithms like "Decision Tree" can be used to find connections I can not discover using other methods.
The use of the above strategy and some data preparation techniques like encoding,  outliers identification, balancing and data cluster identification will be utilized during this project to prepare the data as much as possible for the prediction model ingestion.</p>

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<h2 id="Dataset">Dataset<a class="anchor-link" href="#Dataset">&#182;</a></h2><p>To train and test our predictor we will use some of the features of the MIMIC-III (MIT Lab for Computational Physiology) dataset. The MIMIC-III dataset contains more than 40,000 critical patients records. Information and details related to it can be found on the website <a href="https://mimic.physionet.org">https://mimic.physionet.org</a>; this is not an open source dataset so access to it must be requested. I selected the sample of the dataset by answering the following questions:</p>

<pre><code>Did the patient survive, yes or no?
Does the patient has pre-existing conditions, other than the one selected in the obesity paradox studies?
Does the patient has both metrics recorded, height and weight.
The MIMICIII database contains information on each visit of the patient to the hospital, for the purpose of this project we only selected the first visit information.

</code></pre>
<p>The resultant dataset has 6862 patients. The data set was exported to a CSV file so it can be loaded into a pandas data frame.</p>
<p>!!!! NOTE: Once the data set was in the CSV format I noticed that there were patients with age set to 300. Further reading of the MIMICIII database description shown that patients that were 89 years old or greater have their age set to 300. While the dataset was in the CSV format, we replaced all the Ages with 300 in its value by the number 89.</p>
<h3 id="FEATURES-SELECTED">FEATURES SELECTED<a class="anchor-link" href="#FEATURES-SELECTED">&#182;</a></h3><p>Body Mass Index: CHARTEVENTS table Sub-Set of Medical Conditions: DIAGNOSESICD table Age: PATIENT table Sex: PATIENT table</p>
<h3 id="LABEL">LABEL<a class="anchor-link" href="#LABEL">&#182;</a></h3><p>Admission Time Discharge Time (MIMIC admissions table): ADMISSION table 2 NOTE: The ”Body Mass Index” needs to be calculated from the height and weight values of the patients' data. The formula we will use is:</p>
<p>BMI = WeightKg/(Heightmtrs) 2</p>
<p>The features were selected based on the medical studies related to the Obesity Paradox.’ The selected label is "continue" as most of the features values. The only feature that requires encoding is the Sex and Icd9 codes.</p>
<p>I will perform the following steps to prepare the data set for exploration:</p>
<ol>
<li>load the dataset into a panda data frame.</li>
<li>Encode the SEX and PAtient_Diagnoctic_Icd9 features. The icd9 codes look like continues data, but in reality, they are classes of the medical conditions observed in the obesity paradox studies.</li>
<li>We will drop the Hospital_Stay_Days from our dataset and stored in a separate variable. Hospital_Stay_Days is the variables we want to predict.</li>
</ol>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span>

<span class="c1"># Load the Obesity Paradox Dataset</span>
<span class="n">data</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;obesity-paradox-dataset.csv&#39;</span><span class="p">)</span>

<span class="c1"># Labels outcome separation</span>
<span class="n">day_in_hospital</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Hospital_Stay_Days&#39;</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="c1"># Drop the patient ID</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Patient_ID&#39;</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">features</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>

<span class="c1"># Data set first 5 rows preview before encoding</span>
<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>

<span class="c1"># Patient_Gender encoder</span>
<span class="n">patient_gender_encoded</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Patient_Gender&#39;</span><span class="p">])</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data</span><span class="p">,</span><span class="n">patient_gender_encoded</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Patient_Gender&#39;</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="c1"># Patient medical coditions encoding.</span>
<span class="n">patient_diagnostic_code</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Patient_Diagnostic_Icd9&#39;</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Patient_Diagnostic_Icd9&#39;</span><span class="p">:</span><span class="s1">&#39;Icd9_Code&#39;</span><span class="p">})</span>

<span class="c1"># Icd9 to lables</span>
<span class="c1"># icd9Codes module contains a list of the deseases that will study and</span>
<span class="c1"># a function that converts the icd9 codes to its names.</span>
<span class="kn">import</span> <span class="nn">icd9Codes</span>
<span class="n">patient_diagnostic_code</span>
<span class="c1"># Merge patient_diagnostic_code back and drop patient_diagnostic_icd9 column</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data</span><span class="p">,</span><span class="n">patient_diagnostic_code</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">icd9Codes</span><span class="o">.</span><span class="n">assign_label</span><span class="p">)],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Icd9_Code&#39;</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="c1"># I have to get rid of the NaN ICD9 codes which does not belogns to the list</span>
<span class="c1"># of medical conditions we are studing. Before doing that I have to merge the label back to data</span>
<span class="c1"># to keep records consistency.</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data</span><span class="p">,</span><span class="n">day_in_hospital</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">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>


<span class="n">day_in_hospital</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Hospital_Stay_Days&#39;</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="c1"># Finally we encode the Patient_Diagnosctic_Icd9 column</span>
<span class="n">patient_diagnostic_encoded</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Patient_Diagnostic_Icd9&#39;</span><span class="p">])</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data</span><span class="p">,</span><span class="n">patient_diagnostic_encoded</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Patient_Diagnostic_Icd9&#39;</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">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
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      <th></th>
      <th>Patient_Gender</th>
      <th>Patient_Diagnostic_Icd9</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
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    <tr>
      <th>0</th>
      <td>M</td>
      <td>4019</td>
      <td>71</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>M</td>
      <td>27401</td>
      <td>71</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>M</td>
      <td>42731</td>
      <td>71</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>M</td>
      <td>4019</td>
      <td>68</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>M</td>
      <td>2724</td>
      <td>68</td>
      <td>30.0</td>
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      <th></th>
      <th>Patient_Age</th>
      <th>BIM</th>
      <th>F</th>
      <th>M</th>
      <th>cardiovascular_disorders</th>
      <th>diabetes</th>
      <th>hypertension</th>
      <th>lipid_disorders</th>
      <th>neoplasia</th>
    </tr>
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    <tr>
      <th>0</th>
      <td>71</td>
      <td>29.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
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    <tr>
      <th>1</th>
      <td>71</td>
      <td>29.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>68</td>
      <td>30.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>68</td>
      <td>30.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>68</td>
      <td>30.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
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<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>After applying most of the transformation, I will explore the data by calculating its minimum, maximum, mean, median, and standard deviation. I will also explore the correlation between the BMI and the hospitalization time.</p>
<p>I will perform the following steps.</p>
<ul>
<li>Calculate statistics metrics</li>
<li>Plot the correlation between the continues features and the label.</li>
<li>Present the conclusion of my observations.</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Minimum hospitalization time</span>
<span class="n">minimum_days</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">amin</span><span class="p">(</span><span class="n">day_in_hospital</span><span class="p">)</span>

<span class="c1"># Maximum hospitalization_time</span>
<span class="n">maximum_days</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">amax</span><span class="p">(</span><span class="n">day_in_hospital</span><span class="p">)</span>

<span class="c1"># Mean hospitalization time</span>
<span class="n">mean_days</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">day_in_hospital</span><span class="p">)</span>

<span class="c1"># Median hospitalization time</span>
<span class="n">median_days</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">day_in_hospital</span><span class="p">)</span>

<span class="c1"># Standard deviation of hospitalization time</span>
<span class="n">std_days</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">day_in_hospital</span><span class="p">)</span>

<span class="c1"># Show the calculated statistics</span>
<span class="k">print</span> <span class="s2">&quot;Statistics for Obesity Paradox dataset:</span><span class="se">\n</span><span class="s2">&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Minimum time in days: {:.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">minimum_days</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Maximum time in days: {:.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">maximum_days</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Mean time: {:.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_days</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Median time {:.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">median_days</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Standard deviation of time: {:.2f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">std_days</span><span class="p">)</span>

<span class="c1"># Plot the correlations</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="o">%</span><span class="k">matplotlib</span> inline


<span class="c1"># Drop categorical features</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;neoplasia&#39;</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="c1"># See the correlation between the remaining features</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">features</span><span class="p">:</span>
    <span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
    <span class="n">fit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">day_in_hospital</span><span class="p">,</span> <span class="n">deg</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">day_in_hospital</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">features</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">day_in_hospital</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">features</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">fit</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">features</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">+</span> <span class="n">fit</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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;DAYS vs &#39;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">col</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="n">col</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;DAYS&#39;</span><span class="p">)</span>
    
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<pre>Statistics for Obesity Paradox dataset:

Minimum time in days: 0.00
Maximum time in days: 88.00
Mean time: 8.16
Median time 6.00
Standard deviation of time: 6.91
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<h3 id="Feature-Observation-conclusions">Feature Observation conclusions<a class="anchor-link" href="#Feature-Observation-conclusions">&#182;</a></h3><p>As a reminder, we are using four features: <code>'Patient_Age'</code>,<code>'Patient_Gender'</code>, <code>'BMI'</code> and <code>'Medical_Condition'</code>. For each data point (hospital first visit):</p>
<ul>
<li><code>'Patient_Age'</code> Is the patient age in years at the moment of its first admission.</li>
<li><code>'Patient_Gender'</code> Is the patient gender.</li>
<li><code>'BMI'</code> BMI is an estimate of body fat and a good measure of your patients' risk for diseases that can occur with overweight and obesity. For adults, a healthy weight is defined as the appropriate body weight in relation to height. This ratio of weight to height is known as the body mass index (BMI). People who are overweight (BMI of 25–29.9) have too much body weight for their height. People who are obese (BMI of 30 or above) almost always have a large amount of body fat in relation to their height (<a href="https://www.nhlbi.nih.gov/health/educational/lose_wt/bmitools.htm">https://www.nhlbi.nih.gov/health/educational/lose_wt/bmitools.htm</a>).</li>
<li><code>'Medical Condition'</code> The medical conditions diagnosed at the moment of the first admission.</li>
</ul>
<p>From the data exploration exercise below, I can determine the following.</p>
<ul>
<li>The dataset label, "days_in_hospital" this means that most of its values are very close to each other. Most of the stays days are contained in one standard deviation (2 to 14 days). There are some data points further from the cluster, but they are not too many; this could be an indication of BIAS produce by the fact that I selected only the first visit to the hospital. In average, a person stays eight days in the hospital the first visit no matter the condition or the BMI. The predictor, if performs well should produce values between two and fourteen days (one standard deviation).</li>
<li>A negative correlation exists between the age and the hospital staying; this makes sense because the older a person is, the slower is the recovery.</li>
<li>It doesn't seem to be a high degree correlation between the BMI and the hospital stay; that said you can see that BMIs between 0 and 50 stays between 0 a 20 days. As per the BMI index description, most of the data points accounts for people that are overweight or obese. </li>
<li>I also see outliers; these will be removed further than this analysis.</li>
</ul>

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<h2 id="Performance-Metric-Function">Performance Metric Function<a class="anchor-link" href="#Performance-Metric-Function">&#182;</a></h2><p>To measure our model’s accuracy, we will use the R2 or the coefficient of determination. This metric tells us how much our prediction is explained by the vector features. The highest good value for R2 is 1.0, and the worst can be negative. A model that predicts correctly not matter what features are input will always give an R2 of 0; this is a clear indication of an overfitted model. The Mathematically operation to calculate R2 is as follow: the square difference between the real label value and the predicted value,’ divided by the square variance of the original label’s distribution minus 1. I am dividing the actual error of my prediction between the spread of the values of the fitted data distribution, in and an ideal scenario where the prediction is close to the real value the different between one and the division is close to 1. R2 metric help to tune the model parameters so this error gets lower, and the R2 gets higher. The mathematical representation of the R2 is:</p>
<p>R (y, yˆ) = 1 − Σ(yi, yi) /Σ(yi, y ̄)</p>
<p>I will perform the following steps:</p>
<ul>
<li>Create a fuction that will be used as score function.</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;r2_score&#39;</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>

<span class="k">def</span> <span class="nf">performance_metric</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Calculates and returns the performance score between </span>
<span class="sd">        true and predicted values based on the metric chosen. &quot;&quot;&quot;</span>
    
    <span class="c1"># TODO: Calculate the performance score between &#39;y_true&#39; and &#39;y_predict&#39;</span>
    <span class="n">score</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span><span class="n">y_predict</span><span class="p">)</span>
    
    <span class="c1"># Return the score</span>
    <span class="k">return</span> <span class="n">score</span>
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<h2 id="Cleaning-The-Data">Cleaning The Data<a class="anchor-link" href="#Cleaning-The-Data">&#182;</a></h2><p>After applying the changes to explore our data we still have approximately 5000 records.  My next step is to get rid of the outliers. During the data exploration, we plotted the data points. Is clear that are outliers that need to be removed. I will use the quartile strategy to get rid of the outliers.</p>
<p>I will perform the following steps.</p>
<ul>
<li>Detect outliers and get read of them using the quartiles of the data set and getting rid of the 1st and 3rd quartile.</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># We first join the label back to features and reset the index.</span>
<span class="n">data_with_outliers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">day_in_hospital</span><span class="p">,</span><span class="n">data</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">data_with_outliers</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>

<span class="c1"># We drop the discrete features.</span>
<span class="n">data_with_outliers</span> <span class="o">=</span> <span class="n">data_with_outliers</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">10</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="c1"># Let us detect the outliers</span>
<span class="n">index_list</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data_with_outliers</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="c1"># Calculate 1st Quartile</span>
    <span class="n">Q1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">],</span><span class="mi">25</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;The {} Q1 is: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">col</span><span class="p">,</span><span class="n">Q1</span><span class="p">)</span>
    <span class="c1"># Calculate 3rd Quartile</span>
    <span class="n">Q3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">],</span><span class="mi">75</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;The {} Q3 is: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">col</span><span class="p">,</span><span class="n">Q3</span><span class="p">)</span>
    <span class="c1"># Any index that is greater than or lesser than 1.5 times the difference between the value of the Q3 minus</span>
    <span class="c1"># Q1 range is considered an outlier.</span>
    <span class="n">step</span> <span class="o">=</span> <span class="mf">1.5</span><span class="o">*</span><span class="p">(</span><span class="n">Q3</span> <span class="o">-</span> <span class="n">Q1</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;Data points considered outliers for the feature &#39;{}&#39;:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
    <span class="n">display</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">))])</span>
    <span class="c1"># Capture outliers index in a list</span>
    <span class="n">index_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">data_with_outliers</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">))]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>

<span class="c1"># Let us see which records contains more than one outlier.</span>
<span class="n">outliers</span>  <span class="o">=</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">index_list</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
<span class="n">repeated_outliers</span><span class="o">=</span><span class="p">[]</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
<span class="n">cnt</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">outliers</span><span class="p">)</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">cnt</span><span class="o">.</span><span class="n">elements</span><span class="p">():</span>
    <span class="k">if</span> <span class="n">cnt</span><span class="p">[</span><span class="n">el</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
        <span class="n">repeated_outliers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">el</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Following records are outliers for more than one feature:&quot;</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">repeated_outliers</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">repeated_outliers</span><span class="p">])</span>


<span class="c1"># Let us remove the outliers</span>
<span class="c1"># First let us add the categorical columns</span>
<span class="n">temp_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</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">data_with_outliers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">data_with_outliers</span><span class="p">,</span><span class="n">temp_data</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">good_data</span> <span class="o">=</span> <span class="n">data_with_outliers</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">data_with_outliers</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">repeated_outliers</span><span class="p">])</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>


<span class="c1"># RESULT GOOD_DATA</span>
<span class="k">print</span> <span class="n">good_data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>The Hospital_Stay_Days Q1 is: 5.0
The Hospital_Stay_Days Q3 is: 9.0
Data points considered outliers for the feature &#39;Hospital_Stay_Days&#39;:
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>21</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>22</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>23</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>24</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>25</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>26</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>27</th>
      <td>18</td>
      <td>78</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>28</th>
      <td>17</td>
      <td>40</td>
      <td>34.0</td>
    </tr>
    <tr>
      <th>81</th>
      <td>16</td>
      <td>49</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>82</th>
      <td>16</td>
      <td>49</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>83</th>
      <td>16</td>
      <td>49</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>85</th>
      <td>16</td>
      <td>65</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>86</th>
      <td>16</td>
      <td>65</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>87</th>
      <td>16</td>
      <td>65</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>88</th>
      <td>16</td>
      <td>65</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>217</th>
      <td>21</td>
      <td>58</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>218</th>
      <td>21</td>
      <td>58</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>313</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>314</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>315</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>316</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>317</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>318</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>319</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>320</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>321</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>322</th>
      <td>36</td>
      <td>74</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>367</th>
      <td>25</td>
      <td>38</td>
      <td>34.0</td>
    </tr>
    <tr>
      <th>368</th>
      <td>25</td>
      <td>38</td>
      <td>34.0</td>
    </tr>
    <tr>
      <th>369</th>
      <td>25</td>
      <td>38</td>
      <td>34.0</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>4642</th>
      <td>31</td>
      <td>65</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>4643</th>
      <td>31</td>
      <td>65</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>4644</th>
      <td>31</td>
      <td>65</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>4764</th>
      <td>36</td>
      <td>73</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4765</th>
      <td>36</td>
      <td>73</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4766</th>
      <td>36</td>
      <td>73</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4767</th>
      <td>36</td>
      <td>73</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4873</th>
      <td>18</td>
      <td>79</td>
      <td>19.0</td>
    </tr>
    <tr>
      <th>4874</th>
      <td>18</td>
      <td>79</td>
      <td>19.0</td>
    </tr>
    <tr>
      <th>4875</th>
      <td>18</td>
      <td>79</td>
      <td>19.0</td>
    </tr>
    <tr>
      <th>4876</th>
      <td>18</td>
      <td>79</td>
      <td>19.0</td>
    </tr>
    <tr>
      <th>4930</th>
      <td>16</td>
      <td>59</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>4931</th>
      <td>16</td>
      <td>59</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>5012</th>
      <td>21</td>
      <td>66</td>
      <td>17.0</td>
    </tr>
    <tr>
      <th>5041</th>
      <td>19</td>
      <td>48</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>5094</th>
      <td>38</td>
      <td>65</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>5095</th>
      <td>38</td>
      <td>65</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>5096</th>
      <td>38</td>
      <td>65</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>5145</th>
      <td>26</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>5146</th>
      <td>26</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>5147</th>
      <td>26</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>5148</th>
      <td>26</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>5149</th>
      <td>26</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>5236</th>
      <td>29</td>
      <td>61</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>5237</th>
      <td>29</td>
      <td>61</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>5281</th>
      <td>21</td>
      <td>74</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>5282</th>
      <td>21</td>
      <td>74</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>5283</th>
      <td>21</td>
      <td>74</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>5284</th>
      <td>21</td>
      <td>74</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>5327</th>
      <td>17</td>
      <td>53</td>
      <td>31.0</td>
    </tr>
  </tbody>
</table>
<p>445 rows × 3 columns</p>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>The Patient_Age Q1 is: 60.0
The Patient_Age Q3 is: 76.0
Data points considered outliers for the feature &#39;Patient_Age&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>31</th>
      <td>7</td>
      <td>33</td>
      <td>35.0</td>
    </tr>
    <tr>
      <th>66</th>
      <td>7</td>
      <td>35</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>67</th>
      <td>7</td>
      <td>35</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>68</th>
      <td>7</td>
      <td>35</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>866</th>
      <td>4</td>
      <td>23</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>867</th>
      <td>4</td>
      <td>23</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>868</th>
      <td>4</td>
      <td>23</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>1242</th>
      <td>7</td>
      <td>306</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>1277</th>
      <td>2</td>
      <td>24</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>1376</th>
      <td>4</td>
      <td>25</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>1377</th>
      <td>4</td>
      <td>25</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>1378</th>
      <td>4</td>
      <td>25</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>1420</th>
      <td>7</td>
      <td>35</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>1421</th>
      <td>7</td>
      <td>35</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>1489</th>
      <td>6</td>
      <td>31</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>1490</th>
      <td>6</td>
      <td>31</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>1561</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>1562</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>1563</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>1575</th>
      <td>4</td>
      <td>35</td>
      <td>50.0</td>
    </tr>
    <tr>
      <th>1786</th>
      <td>4</td>
      <td>35</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>1992</th>
      <td>5</td>
      <td>20</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>1993</th>
      <td>5</td>
      <td>20</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>1994</th>
      <td>5</td>
      <td>20</td>
      <td>29.0</td>
    </tr>
    <tr>
      <th>2001</th>
      <td>8</td>
      <td>32</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>2002</th>
      <td>8</td>
      <td>32</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>2003</th>
      <td>8</td>
      <td>32</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>2004</th>
      <td>8</td>
      <td>32</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>2038</th>
      <td>7</td>
      <td>28</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>2039</th>
      <td>7</td>
      <td>28</td>
      <td>24.0</td>
    </tr>
    <tr>
      <th>3056</th>
      <td>6</td>
      <td>309</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>3057</th>
      <td>6</td>
      <td>309</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>3162</th>
      <td>4</td>
      <td>32</td>
      <td>42.0</td>
    </tr>
    <tr>
      <th>3229</th>
      <td>7</td>
      <td>29</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>3680</th>
      <td>12</td>
      <td>34</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>3681</th>
      <td>12</td>
      <td>34</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>3682</th>
      <td>12</td>
      <td>34</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>3683</th>
      <td>12</td>
      <td>34</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>3784</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3785</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3786</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3875</th>
      <td>3</td>
      <td>30</td>
      <td>30.0</td>
    </tr>
    <tr>
      <th>4084</th>
      <td>5</td>
      <td>23</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>4085</th>
      <td>4</td>
      <td>34</td>
      <td>40.0</td>
    </tr>
    <tr>
      <th>4267</th>
      <td>3</td>
      <td>34</td>
      <td>25.0</td>
    </tr>
    <tr>
      <th>4474</th>
      <td>24</td>
      <td>32</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>4545</th>
      <td>7</td>
      <td>21</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>4546</th>
      <td>7</td>
      <td>21</td>
      <td>26.0</td>
    </tr>
    <tr>
      <th>4609</th>
      <td>4</td>
      <td>34</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>4900</th>
      <td>5</td>
      <td>22</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>4914</th>
      <td>8</td>
      <td>34</td>
      <td>41.0</td>
    </tr>
    <tr>
      <th>5331</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>5332</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>5333</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>5334</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>5335</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
    <tr>
      <th>5336</th>
      <td>15</td>
      <td>34</td>
      <td>20.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>The BIM Q1 is: 25.0
The BIM Q3 is: 32.0
Data points considered outliers for the feature &#39;BIM&#39;:
</pre>
</div>
</div>

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

<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>7</th>
      <td>3</td>
      <td>49</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>8</th>
      <td>7</td>
      <td>65</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>9</th>
      <td>7</td>
      <td>65</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>10</th>
      <td>7</td>
      <td>65</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>7</td>
      <td>65</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>117</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>118</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>119</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>120</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>121</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>122</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>123</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>124</th>
      <td>7</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>140</th>
      <td>6</td>
      <td>85</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>141</th>
      <td>6</td>
      <td>85</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>277</th>
      <td>11</td>
      <td>42</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>278</th>
      <td>11</td>
      <td>42</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>484</th>
      <td>5</td>
      <td>57</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>485</th>
      <td>5</td>
      <td>57</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>486</th>
      <td>5</td>
      <td>57</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>487</th>
      <td>5</td>
      <td>57</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>610</th>
      <td>7</td>
      <td>40</td>
      <td>46.0</td>
    </tr>
    <tr>
      <th>611</th>
      <td>7</td>
      <td>40</td>
      <td>46.0</td>
    </tr>
    <tr>
      <th>810</th>
      <td>1</td>
      <td>48</td>
      <td>207.0</td>
    </tr>
    <tr>
      <th>915</th>
      <td>7</td>
      <td>71</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>916</th>
      <td>7</td>
      <td>71</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>917</th>
      <td>7</td>
      <td>71</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>918</th>
      <td>7</td>
      <td>71</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>941</th>
      <td>7</td>
      <td>66</td>
      <td>45.0</td>
    </tr>
    <tr>
      <th>942</th>
      <td>7</td>
      <td>66</td>
      <td>45.0</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>4564</th>
      <td>14</td>
      <td>74</td>
      <td>64.0</td>
    </tr>
    <tr>
      <th>4565</th>
      <td>14</td>
      <td>74</td>
      <td>64.0</td>
    </tr>
    <tr>
      <th>4768</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4769</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4770</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4771</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4772</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4773</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4774</th>
      <td>4</td>
      <td>68</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>4830</th>
      <td>9</td>
      <td>57</td>
      <td>46.0</td>
    </tr>
    <tr>
      <th>5016</th>
      <td>7</td>
      <td>61</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5017</th>
      <td>7</td>
      <td>61</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5018</th>
      <td>7</td>
      <td>61</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5056</th>
      <td>12</td>
      <td>58</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>5057</th>
      <td>12</td>
      <td>58</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>5058</th>
      <td>12</td>
      <td>58</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>5059</th>
      <td>12</td>
      <td>58</td>
      <td>49.0</td>
    </tr>
    <tr>
      <th>5129</th>
      <td>4</td>
      <td>61</td>
      <td>57.0</td>
    </tr>
    <tr>
      <th>5130</th>
      <td>4</td>
      <td>61</td>
      <td>57.0</td>
    </tr>
    <tr>
      <th>5131</th>
      <td>4</td>
      <td>61</td>
      <td>57.0</td>
    </tr>
    <tr>
      <th>5174</th>
      <td>5</td>
      <td>59</td>
      <td>64.0</td>
    </tr>
    <tr>
      <th>5175</th>
      <td>5</td>
      <td>59</td>
      <td>64.0</td>
    </tr>
    <tr>
      <th>5181</th>
      <td>10</td>
      <td>48</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5182</th>
      <td>10</td>
      <td>48</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5183</th>
      <td>10</td>
      <td>48</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5285</th>
      <td>9</td>
      <td>43</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5286</th>
      <td>9</td>
      <td>43</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5287</th>
      <td>9</td>
      <td>43</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5288</th>
      <td>9</td>
      <td>43</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>5289</th>
      <td>9</td>
      <td>43</td>
      <td>43.0</td>
    </tr>
  </tbody>
</table>
<p>227 rows × 3 columns</p>
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<pre>Following records are outliers for more than one feature: [2816, 2817, 2818, 2062, 1575, 1203, 4288, 4289, 4290, 3784, 3785, 3786, 987, 988, 4459, 4461, 4463, 4465, 4467, 4469, 4474, 2815]
</pre>
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<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2062</th>
      <td>30</td>
      <td>51</td>
      <td>54.0</td>
    </tr>
    <tr>
      <th>2062</th>
      <td>30</td>
      <td>51</td>
      <td>54.0</td>
    </tr>
    <tr>
      <th>4463</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4463</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4465</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4465</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4288</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4288</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4289</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4289</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4290</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4290</th>
      <td>18</td>
      <td>71</td>
      <td>43.0</td>
    </tr>
    <tr>
      <th>4474</th>
      <td>24</td>
      <td>32</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>4474</th>
      <td>24</td>
      <td>32</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>4459</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4459</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4461</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4461</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4467</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4467</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4469</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>4469</th>
      <td>28</td>
      <td>54</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>2815</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2815</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2816</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2816</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2817</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2817</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2818</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>2818</th>
      <td>30</td>
      <td>57</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>987</th>
      <td>34</td>
      <td>59</td>
      <td>51.0</td>
    </tr>
    <tr>
      <th>987</th>
      <td>34</td>
      <td>59</td>
      <td>51.0</td>
    </tr>
    <tr>
      <th>988</th>
      <td>34</td>
      <td>59</td>
      <td>51.0</td>
    </tr>
    <tr>
      <th>988</th>
      <td>34</td>
      <td>59</td>
      <td>51.0</td>
    </tr>
    <tr>
      <th>1203</th>
      <td>25</td>
      <td>50</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>1203</th>
      <td>25</td>
      <td>50</td>
      <td>44.0</td>
    </tr>
    <tr>
      <th>1575</th>
      <td>4</td>
      <td>35</td>
      <td>50.0</td>
    </tr>
    <tr>
      <th>1575</th>
      <td>4</td>
      <td>35</td>
      <td>50.0</td>
    </tr>
    <tr>
      <th>3784</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3784</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3785</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3785</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3786</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
    <tr>
      <th>3786</th>
      <td>13</td>
      <td>24</td>
      <td>63.0</td>
    </tr>
  </tbody>
</table>
</div>
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<pre>   Hospital_Stay_Days  Patient_Age   BIM    F    M  cardiovascular_disorders  \
0                   6           71  29.0  0.0  1.0                       0.0   
1                   6           71  29.0  0.0  1.0                       1.0   
2                   4           68  30.0  0.0  1.0                       0.0   
3                   4           68  30.0  0.0  1.0                       0.0   
4                   4           68  30.0  0.0  1.0                       0.0   

   diabetes  hypertension  lipid_disorders  neoplasia  
0       0.0           1.0              0.0        0.0  
1       0.0           0.0              0.0        0.0  
2       0.0           1.0              0.0        0.0  
3       0.0           0.0              1.0        0.0  
4       1.0           0.0              0.0        0.0  
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<h2 id="Selecting-the-Training-Sample">Selecting the Training Sample<a class="anchor-link" href="#Selecting-the-Training-Sample">&#182;</a></h2><p>Now that my dataset is clean, it is time to sample the data that I will use to train the model. I can just select an even number of records making sure that the dataset contains:</p>
<ul>
<li>An even number of Male and Females.</li>
<li>An even number of each type of medical conditions.</li>
</ul>
<p>Sample my data this way won't be enough, so I will go even further to use a soft clustering model to identify groups using the hospitalization time, age and BMI.
I will use a Gaussian Mixture soft cluster instead of a hard cluster, the data variance is small, so my data points are too close to each other to create clear boundaries.
I will use the cluster's silhouette as a metric to determine the number of clusters.</p>
<p>I will perform the following steps:</p>
<ul>
<li>Sample some data of the data set.</li>
<li>Make sure the samples don't be similar but ranking them using the dataset percentile.</li>
<li>Scale and review the data correlation and distribution.</li>
<li>Reduce the continues data to two dimensions to see if our samples below to multiple classes.</li>
<li>Conclusions.</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Get data and drop the discret outputs to calculate the percentil ranks. This wiil allow me to</span>
<span class="c1"># see the similarity between the sample data points I selected in the previous section.</span>
<span class="c1"># I will choose five samples around the middle of the dataset</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1650</span><span class="p">,</span><span class="mi">166</span><span class="p">,</span><span class="mi">3134</span><span class="p">,</span><span class="mi">2489</span><span class="p">,</span><span class="mi">2650</span> <span class="p">]</span>
<span class="c1"># I will create a dataframe with these samples.</span>
<span class="n">samples</span> <span class="o">=</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">good_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">good_data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="bp">True</span><span class="p">))</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;neoplasia&#39;</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="c1"># I will create the ranking of the sample in relations to the dataset</span>
<span class="k">print</span> <span class="s2">&quot;Selected Samples&quot;</span>
<span class="n">display</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="n">pcts</span> <span class="o">=</span> <span class="mf">100.</span> <span class="o">*</span> <span class="p">(</span><span class="n">good_data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;neoplasia&#39;</span><span class="p">],</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">rank</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">pct</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">decimals</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Sample percentile ranking&quot;</span>
<span class="n">display</span><span class="p">(</span><span class="n">pcts</span><span class="p">)</span>                                                                               
                                                                                
<span class="c1"># Let us visualize the result to see how different each on is respect to the other.</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pcts</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=.</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">99</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;.1f&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;YlOrRd&#39;</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">cbar</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([</span><span class="mf">4.5</span><span class="p">,</span><span class="mf">3.5</span><span class="p">,</span><span class="mf">2.5</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span><span class="o">.</span><span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;Index &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#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;Percentile ranks of</span><span class="se">\n</span><span class="s1">samples</span><span class="se">\&#39;</span><span class="s1"> category spending&#39;</span><span class="p">);</span>


<span class="c1"># Let us review the correlation and data distrbution of the points now that they are scale.</span>
<span class="n">temp_data_ii</span> <span class="o">=</span> <span class="n">good_data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;neoplasia&#39;</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">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">temp_data_ii</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span>
<span class="c1"># Scale data</span>
<span class="n">temp_data_ii_scaled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">temp_data_ii</span><span class="p">)</span>
<span class="c1"># Drop index with infinite value</span>
<span class="n">temp_data_ii_scaled</span> <span class="o">=</span> <span class="n">temp_data_ii_scaled</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">good_data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">2240</span><span class="p">])</span>
<span class="c1"># Plot correlation and data distribution data scaled</span>
<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">temp_data_ii_scaled</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span>


<span class="c1"># Let apply a cluster algorithm to see how the continus data can be grouped. But first we will reduce the data</span>
<span class="c1"># to 2 dimensions to help us vizualize the resulting cluster and tune it</span>

<span class="c1"># Data reduction to two dimensions</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">temp_data_ii_scaled</span><span class="p">)</span>
<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">temp_data_ii_scaled</span><span class="p">)</span>
<span class="n">reduced_data</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">reduced_data</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#39;</span><span class="p">])</span>
<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>


<span class="c1"># Apply the clustering algorithm</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">mixture</span>
<span class="n">clusterer</span> <span class="o">=</span> <span class="n">mixture</span><span class="o">.</span><span class="n">GMM</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">clusterer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

<span class="c1"># Predict the cluster for each data point</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>
<span class="n">centers</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">means_</span>
<span class="c1"># Predic the sample points</span>
<span class="n">sample_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Number of element cluster 0: {} Number of Elements Cluster 1: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">reduced_data</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">preds</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">preds</span><span class="p">))</span>
<span class="k">print</span> <span class="n">centers</span>
</pre></div>

</div>
</div>
</div>

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


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<div class="output_subarea output_stream output_stdout output_text">
<pre>Selected Samples
</pre>
</div>
</div>

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<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>5</td>
      <td>78</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>10</td>
      <td>63</td>
      <td>31.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>5</td>
      <td>48</td>
      <td>27.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>7</td>
      <td>74</td>
      <td>33.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>56</td>
      <td>27.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Sample percentile ranking
</pre>
</div>
</div>

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<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1650</th>
      <td>32.5</td>
      <td>79.0</td>
      <td>51.2</td>
    </tr>
    <tr>
      <th>166</th>
      <td>79.8</td>
      <td>34.1</td>
      <td>71.8</td>
    </tr>
    <tr>
      <th>3134</th>
      <td>32.5</td>
      <td>5.3</td>
      <td>42.9</td>
    </tr>
    <tr>
      <th>2489</th>
      <td>59.5</td>
      <td>68.9</td>
      <td>79.8</td>
    </tr>
    <tr>
      <th>2650</th>
      <td>32.5</td>
      <td>16.4</td>
      <td>42.9</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of element cluster 0: 4007 Number of Elements Cluster 1: 1318
[[-0.14512061  0.01090803]
 [ 0.26058315 -0.0195868 ]]
</pre>
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<h3 id="Conclusion-of-applying-the-clustering">Conclusion of applying the clustering<a class="anchor-link" href="#Conclusion-of-applying-the-clustering">&#182;</a></h3><p>I sampled the original dataset to use the samples later to describe the clusters. I compared the samples with each other using ranking to make sure my sample weren't similar to each other; its similarity can be seen using the heatmap above. Follow the result:</p>
<ul>
<li>Patients (2489, 166) are within the highest BMI in the dataset </li>
<li>Patients (2489, 1650) are the oldest patients in the dataset</li>
<li>Patients (2489, 166) are within the longest stay in the dataset</li>
</ul>
<p>When I see the features data distribution, I can tell that the variance of the age and BMI are small which can cause a high BIAS error in the training models.</p>
<p>Next, I will see in which cluster the sample points belongs too.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># I used the cluster silohouette score to determine the number of cluster.</span>
<span class="c1"># I tried sevelar cluster numbers and the best score result was 2 clusters</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">&#39;euclidean&#39;</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Best Silhouette Score: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>

<span class="c1"># Visualize the cluster.</span>
<span class="n">vs</span><span class="o">.</span><span class="n">cluster_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">centers</span><span class="p">,</span> <span class="n">pca_samples</span><span class="p">)</span>

<span class="c1"># Let us recover the centers to see how they compare to the sample data.</span>
<span class="n">log_centers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">centers</span><span class="p">)</span>
<span class="n">true_centers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_centers</span><span class="p">)</span>
<span class="c1"># Display the true centers</span>
<span class="n">patients</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Patient {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">centers</span><span class="p">))]</span>
<span class="n">true_centers</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">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">true_centers</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">temp_data_ii</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">true_centers</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">patients</span>
<span class="n">display</span><span class="p">(</span><span class="n">true_centers</span><span class="p">)</span>

<span class="c1"># Display the samples</span>
<span class="c1"># full_sample_data = pd.DataFrame(temp_data.loc[indices],columns = temp_data.keys()).reset_index(drop = True)</span>
<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">good_data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">good_data</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
<span class="c1"># Display the predictions</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_preds</span><span class="p">):</span>
    <span class="k">print</span> <span class="s2">&quot;Sample point&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;predicted to be in Cluster&quot;</span><span class="p">,</span> <span class="n">pred</span>
    
<span class="c1"># Display members of the cluster</span>
<span class="k">print</span> <span class="s2">&quot;Number of element cluster 0: {} Number of Elements Cluster 1: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">reduced_data</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">preds</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">preds</span><span class="p">))</span>
</pre></div>

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<pre>Best Silhouette Score: 0.463118163922
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Patient 0</th>
      <td>6.0</td>
      <td>67.0</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>Patient 1</th>
      <td>9.0</td>
      <td>66.0</td>
      <td>29.0</td>
    </tr>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Hospital_Stay_Days</th>
      <th>Patient_Age</th>
      <th>BIM</th>
      <th>F</th>
      <th>M</th>
      <th>cardiovascular_disorders</th>
      <th>diabetes</th>
      <th>hypertension</th>
      <th>lipid_disorders</th>
      <th>neoplasia</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1650</th>
      <td>5</td>
      <td>78</td>
      <td>28.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>166</th>
      <td>10</td>
      <td>63</td>
      <td>31.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3134</th>
      <td>5</td>
      <td>48</td>
      <td>27.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2489</th>
      <td>7</td>
      <td>74</td>
      <td>33.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2650</th>
      <td>5</td>
      <td>56</td>
      <td>27.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area"><div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>Sample point 0 predicted to be in Cluster 0
Sample point 1 predicted to be in Cluster 0
Sample point 2 predicted to be in Cluster 0
Sample point 3 predicted to be in Cluster 0
Sample point 4 predicted to be in Cluster 0
Number of element cluster 0: 4007 Number of Elements Cluster 1: 1318
</pre>
</div>
</div>

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


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<h3 id="Conclusion-of-applying-the-clustering-II">Conclusion of applying the clustering II<a class="anchor-link" href="#Conclusion-of-applying-the-clustering-II">&#182;</a></h3><p>All the selected samples belong to cluster zero.
I got the number of cluster members. 
I can see that is unbalanced. 
Cluster "0" contains more data points that cluster "1". The variance that dominates the data set is the hospital staying days.
Because the medical condition (discrete data) is not taking in consideration in this exercise, it is possible that there some medical conditions that account for this classification. 
I could count the numbers of medical conditions on each cluster to see which medical conditions are more frequent on each cluster, but that wouldn't change the split. I will select an even number of each cluster to balance the dataset.</p>

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<h2 id="Balancing-the--Dataset">Balancing the  Dataset<a class="anchor-link" href="#Balancing-the--Dataset">&#182;</a></h2><p>In this section, I will balance the dataset by getting even samples of each type of clusters and features.</p>
<p>I will perform the following steps:</p>
<ul>
<li><p>Create a new dataset with the following characteristics:</p>
<ul>
<li>Even number of cluster datapoints</li>
<li>Even number of males and females</li>
<li>Even number of medical conditions</li>
</ul>
</li>
<li><p>Create the training data set.</p>
</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Convert the prediction to a Series to join it the the good_data dataframe later.</span>
<span class="n">cluster_membership</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">preds</span><span class="p">,</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;cluster_membership&#39;</span><span class="p">)</span>
<span class="n">temp_data_iii</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">good_data</span><span class="p">,</span><span class="n">cluster_membership</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="c1"># I will  create two even cluster datasets</span>
<span class="n">cluster_0</span> <span class="o">=</span> <span class="n">temp_data_iii</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;cluster_membership&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">1318</span><span class="p">,</span> <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">cluster_1</span> <span class="o">=</span> <span class="n">temp_data_iii</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;cluster_membership&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">1318</span><span class="p">,</span> <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>

<span class="c1"># Join the clusters to get an even sample</span>
<span class="n">clusters_dataset</span> <span class="o">=</span> <span class="n">cluster_0</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cluster_1</span><span class="p">,</span><span class="n">ignore_index</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="c1">## Let us calulate the remaining number of categories.</span>
<span class="n">display</span><span class="p">(</span><span class="s2">&quot;Unbalanced Dataset&quot;</span><span class="p">)</span>
<span class="n">cols_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">,</span>
             <span class="s1">&#39;neoplasia&#39;</span><span class="p">,</span><span class="s1">&#39;cluster_membership&#39;</span><span class="p">]</span>
<span class="k">for</span> <span class="n">cols</span> <span class="ow">in</span> <span class="n">cols_list</span><span class="p">:</span>
    <span class="k">print</span> <span class="s2">&quot;Number of {} {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cols</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">clusters_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">cols</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
    
<span class="c1">## Dataset is unbalanced, diabetes and neoplasia classes are low and the cardiovasculat is too big.</span>
<span class="c1">## I will sample an even number of all the categories</span>
<span class="n">genders</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">genders</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">clusters_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;M&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">1116</span><span class="p">,</span> <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>
<span class="n">genders</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">clusters_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;F&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">1116</span><span class="p">,</span> <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>

<span class="c1"># Now I append the re-sample categories to balance the data set from the genders point of view</span>
<span class="n">genders_dataset</span> <span class="o">=</span> <span class="n">genders</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">genders</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> 

<span class="c1">## Now let use select at least 120 samples of each category</span>
<span class="n">categories</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">genders_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">182</span><span class="p">,</span>
                                                                                         <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
                                                                                         <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>
<span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">genders_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;hypertension&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">182</span><span class="p">,</span>
                                                                                         <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
                                                                                         <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>
<span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">genders_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;lipid_disorders&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">182</span><span class="p">,</span>
                                                                                         <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
                                                                                         <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>
<span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">genders_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;diabetes&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">182</span><span class="p">,</span>
                                                                                         <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
                                                                                         <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>
<span class="n">categories</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">genders_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;neoplasia&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">182</span><span class="p">,</span>
                                                                                         <span class="n">replace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
                                                                                         <span class="n">random_state</span><span class="o">=</span><span class="mi">200</span> <span class="p">))</span>


<span class="c1"># Finall training dataset</span>
<span class="n">training_dataset</span> <span class="o">=</span> <span class="n">categories</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">categories</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">training_dataset</span> <span class="o">=</span> <span class="n">training_dataset</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">categories</span><span class="p">,</span> <span class="n">ignore_index</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>


<span class="n">cols_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;F&#39;</span><span class="p">,</span><span class="s1">&#39;M&#39;</span><span class="p">,</span><span class="s1">&#39;cardiovascular_disorders&#39;</span><span class="p">,</span><span class="s1">&#39;diabetes&#39;</span><span class="p">,</span><span class="s1">&#39;hypertension&#39;</span><span class="p">,</span><span class="s1">&#39;lipid_disorders&#39;</span>
             <span class="p">,</span><span class="s1">&#39;neoplasia&#39;</span><span class="p">,</span><span class="s1">&#39;cluster_membership&#39;</span><span class="p">]</span>
<span class="n">display</span><span class="p">(</span><span class="s2">&quot;Training balanced dataset&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">cols</span> <span class="ow">in</span> <span class="n">cols_list</span><span class="p">:</span>
    <span class="k">print</span> <span class="s2">&quot;Number of {} {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cols</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">training_dataset</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="n">cols</span><span class="p">])</span><span class="o">.</span><span class="n">get_group</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
    

<span class="c1"># We will put the label in a separate series and the features in other</span>
<span class="n">training_dataset</span> <span class="o">=</span> <span class="n">training_dataset</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">training_labels</span> <span class="o">=</span> <span class="n">training_dataset</span><span class="p">[</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">]</span>
<span class="n">training_features</span> <span class="o">=</span> <span class="n">training_dataset</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">,</span><span class="s1">&#39;cluster_membership&#39;</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="c1"># Let visualize the distribution of ages and BMI</span>
<span class="k">print</span> <span class="s2">&quot;&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Distribution of patient ages and BMI&quot;</span>
<span class="k">print</span> <span class="p">(</span><span class="n">training_features</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">training_features</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</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">7</span><span class="p">)))</span>
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<pre>&#39;Unbalanced Dataset&#39;</pre>
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<pre>Number of F 974
Number of M 1662
Number of cardiovascular_disorders 1380
Number of diabetes 182
Number of hypertension 473
Number of lipid_disorders 381
Number of neoplasia 220
Number of cluster_membership 1318
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<pre>&#39;Training balanced dataset&#39;</pre>
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<pre>Number of F 441
Number of M 469
Number of cardiovascular_disorders 182
Number of diabetes 182
Number of hypertension 182
Number of lipid_disorders 182
Number of neoplasia 182
Number of cluster_membership 495

Distribution of patient ages and BMI
[[&lt;matplotlib.axes._subplots.AxesSubplot object at 0x121967d10&gt;
  &lt;matplotlib.axes._subplots.AxesSubplot object at 0x121c195d0&gt;]]
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<h2 id="Algorithms-and-Techniques">Algorithms and Techniques<a class="anchor-link" href="#Algorithms-and-Techniques">&#182;</a></h2><p>The type of algorithm I selected for this project are of three type, instance-based learning algorithms, regression algorithms, decision tree algorithms and clustering algorithms.
To predict a continue's label, it is necessary to select a continues prediction algorithm I choose three of these type:</p>
<ul>
<li>Linear Regression (chosen for its simplicity)</li>
<li>Decision Tree Regression (chosen after explorer the data and understand its correlation)</li>
<li><p>Nearest Neighborly (chosen to benchmark the LR and DT)</p>
</li>
<li><p>A Linear Regressor based model is the simplest regression algorithm available; its sole parameter is the 
Normalization parameter. The normalization parameter is not useful as we will proof in the learning curve because we normalized the data way before passing it to the regressor.</p>
</li>
<li><p>Decision Tree Regressor (DTR), we noticed early in the project that the features will be a mix of continue and categorical data, linear regressors performance when some of the features are discrete is not acceptable, a perfect replacement is the DTR. DTR's models are capable of managing numerical and categorical data at the same time. The sklearn Decision Tree Regressor uses the CART algorithm, which is based on the C4.5 algorithm that in turns is an improvement of the ID3 algorithm. The differents between the ID3 and the C4.5 algorithm is that the C4.5 is capable of use numerical features, it does that by creating a discrete range of numerical features, example patients between 30 and 50 is a range, and a value between then will test true for that range. Finally, the differences between the C4.5 and the CART is that CART does the ranges also for the target which in our case is the days in the hospital (a numeric value). Is because of the previous explanation that this algorithm was selected.
For the DT model, the parameter selected for tuning was the tree max_depth. Another possible setting could be minimum samples per leaf, but because of the numerical nature of the target and the fact that it will become a discrete value this parameter when set beyond to 1 the model performs poorly ( it can't be that many ranges).</p>
</li>
<li><p>Nearest Neighborly, this is an instance base learner because it works by using the existing data a not by calculating its relation like the previous two algorithms.  It is also suitable for a mix of numerical and categorical features and target. It works by querying the existing training data using the features of the target that need to predict; then it measure the distance between the closest features, and finally, it averages the target of those features to calculate the prediction. We selected this algorithm to use it as a benchmark of the DTR and LN. The metric selected was the number of neighbors. Because of the small variance of the data we expect the algorithm to perform better than the other selected algorithms.</p>
</li>
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<h2 id="Determine-model-learning-curve">Determine model learning curve<a class="anchor-link" href="#Determine-model-learning-curve">&#182;</a></h2><p>Now that the dataset is well balanced, I will proceed to determine the model learning curve. As I stay at the beginning of this exercise, I intended to use a linear regressor, after the initial data exploration I don't expect that this linear regressor performs well. The reason is that the data variance is too low producing a high bias in the model. I will try other two type of regressors decisionTrees and nearestNeigborh to see if I can get better results.</p>
<p>I will perform the following steps:</p>
<ul>
<li>I will create two training test split. One with the balanced data and what with all the data points.</li>
<li>I will create a linearRegressor and plot its learning curve.</li>
<li>I will present the analysis of the training curve.</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cross_validation</span>
<span class="kn">import</span> <span class="nn">sklearn.learning_curve</span> <span class="kn">as</span> <span class="nn">curves</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>

<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">ShuffleSplit</span><span class="p">,</span> <span class="n">train_test_split</span>

<span class="c1"># Created two sets balanced and unbalanced</span>
<span class="n">X_train_b</span><span class="p">,</span> <span class="n">X_test_b</span><span class="p">,</span> <span class="n">y_train_b</span><span class="p">,</span> <span class="n">y_test_b</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">training_features</span><span class="p">,</span><span class="n">training_labels</span><span class="p">,</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">X_train_u</span><span class="p">,</span> <span class="n">X_test_u</span><span class="p">,</span> <span class="n">y_train_u</span><span class="p">,</span> <span class="n">y_test_u</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">train_test_split</span><span class="p">(</span><span class="n">good_data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Hospital_Stay_Days&#39;</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">good_data</span><span class="p">[</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">],</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Training and testing split was successful.&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Length of the balanced features training split {}, length of the unbalanced features training split {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train_u</span><span class="p">))</span>


<span class="c1"># Pretty display for notebooks</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># Produce learning curves for varying training sets using a Linear regressor</span>

<span class="k">def</span> <span class="nf">ModelLearning</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Calculates the performance of several models with varying sizes of training data.</span>
<span class="sd">        The learning and testing scores for each model are then plotted. &quot;&quot;&quot;</span>
    
    <span class="c1"># Create 10 cross-validation sets for training and testing</span>
    <span class="n">cv</span> <span class="o">=</span> <span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">X</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">n_iter</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>

    <span class="c1"># Generate the training set sizes increasing by 50</span>
    <span class="n">train_sizes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">rint</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">X</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="mf">0.8</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>

    <span class="c1"># Create the figure window</span>
    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>

    <span class="c1"># Create three different models based on normalization</span>
    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">norm</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="bp">True</span><span class="p">,</span><span class="bp">False</span><span class="p">]):</span>
        
        <span class="c1"># Create a linear regressor at normaliztion = norm</span>
        <span class="n">regressor</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">LinearRegression</span><span class="p">(</span><span class="n">normalize</span> <span class="o">=</span> <span class="n">norm</span><span class="p">)</span>

        <span class="c1"># Calculate the training and testing scores</span>
        <span class="n">sizes</span><span class="p">,</span> <span class="n">train_scores</span><span class="p">,</span> <span class="n">test_scores</span> <span class="o">=</span> <span class="n">curves</span><span class="o">.</span><span class="n">learning_curve</span><span class="p">(</span><span class="n">regressor</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> \
            <span class="n">cv</span> <span class="o">=</span> <span class="n">cv</span><span class="p">,</span> <span class="n">train_sizes</span> <span class="o">=</span> <span class="n">train_sizes</span><span class="p">,</span> <span class="n">scoring</span> <span class="o">=</span> <span class="s1">&#39;r2&#39;</span><span class="p">)</span>
        
        <span class="c1"># Find the mean and standard deviation for smoothing</span>
        <span class="n">train_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">train_scores</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">train_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">train_scores</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">test_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">test_scores</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">test_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">test_scores</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="c1"># Subplot the learning curve </span>
        <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">train_mean</span><span class="p">,</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Training Score&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">test_mean</span><span class="p">,</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Testing Score&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">train_mean</span> <span class="o">-</span> <span class="n">train_std</span><span class="p">,</span> \
            <span class="n">train_mean</span> <span class="o">+</span> <span class="n">train_std</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.15</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">sizes</span><span class="p">,</span> <span class="n">test_mean</span> <span class="o">-</span> <span class="n">test_std</span><span class="p">,</span> \
            <span class="n">test_mean</span> <span class="o">+</span> <span class="n">test_std</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.15</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;g&#39;</span><span class="p">)</span>
        
        <span class="c1"># Labels</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;norm = </span><span class="si">%s</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">norm</span><span class="p">))</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Training Points&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Score&#39;</span><span class="p">)</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">X</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="mf">0.8</span><span class="p">])</span>
        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">1.05</span><span class="p">])</span>
    
    <span class="c1"># Visual aesthetics</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">1.05</span><span class="p">,</span> <span class="mf">2.05</span><span class="p">),</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower left&#39;</span><span class="p">,</span> <span class="n">borderaxespad</span> <span class="o">=</span> <span class="mf">0.</span><span class="p">)</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">&#39;Linear Regressor Learning Performances&#39;</span><span class="p">,</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="mf">1.03</span><span class="p">)</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    

<span class="n">ModelLearning</span><span class="p">(</span><span class="n">training_features</span><span class="p">,</span> <span class="n">training_labels</span><span class="p">)</span>
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<pre>Training and testing split was successful.
Length of the balanced features training split 773, length of the unbalanced features training split 4527
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<h3 id="Analysis-of-the-learning-curve">Analysis of the learning curve<a class="anchor-link" href="#Analysis-of-the-learning-curve">&#182;</a></h3><p>The linear regressor performed very poorly. No matter the type of hyperparameter (normalization) the model after 300 samples stops learning. To fix this BIAS problem, I need to add new features. Also, the used of the normalization parameter as I stated before is not useful because the data is normalized already.</p>

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<h2 id="Benchmark-Model">Benchmark Model<a class="anchor-link" href="#Benchmark-Model">&#182;</a></h2><p>We will use a supervise learning method to predict the time a patient spend in the hospital. Because our vector label continues we will use a regression predictor, and we will compare its prediction with an information based learner (K-nearest neighbor), this will allows to benchmark one model versus the other. We will use cross-validation to make sure with dont overfit the model and the R2 Coefficient of Determination to measure the accuracy of our model or what is the same how well it explains the label based on the features. Our learner contains some bias introduced during the features selection; this is because we are selecting the medical conditions used in previous obesity paradox studies, selecting only patients that survive a surgery and finally only taking in consideration the first visit to a hospital. The added bias is based on the observable features related to the paradox, and the sampling process. Introducing new features will be only a guess that can create model complexity due to the additional dimensions.</p>

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<h2 id="Construction-of-the-Nearest-Neighbor-and-decisionTreeRegressor">Construction of the Nearest Neighbor and decisionTreeRegressor<a class="anchor-link" href="#Construction-of-the-Nearest-Neighbor-and-decisionTreeRegressor">&#182;</a></h2><p>The poor performance of the previous regressor is due to the BIAS.
I will build two more models, and I will test them using both the balanced and the unbalanced splits.</p>
<p>I will perform the following steps:</p>
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<li>Build a decisionTree and a NearestNeighborg regressor.</li>
<li>Use grid searches to look for both models best hyperparameters.</li>
<li>Conclusions</li>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>


<span class="c1"># Nearest Neighborg using balanced data</span>
<span class="n">neigh_b</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">leaf_size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">neigh_b</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">,</span><span class="n">y_train_b</span><span class="p">)</span>
<span class="n">y_predict_NN_b</span> <span class="o">=</span> <span class="n">neigh_b</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_b</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;NN Score using balanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_b</span><span class="p">,</span><span class="n">y_predict_NN_b</span><span class="p">))</span>

<span class="c1"># Nearest Neighborg using unbalanced data</span>
<span class="n">neigh_u</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">leaf_size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">neigh_u</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_u</span><span class="p">,</span><span class="n">y_train_u</span><span class="p">)</span>
<span class="n">y_predict_NN_u</span> <span class="o">=</span> <span class="n">neigh_u</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_u</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;NN Score using ubalanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_u</span><span class="p">,</span><span class="n">y_predict_NN_u</span><span class="p">))</span>

<span class="k">print</span> <span class="s2">&quot;&quot;</span>

<span class="c1"># Decision Tree using balanced data</span>
<span class="n">tree_b</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_depth</span> <span class="o">=</span> <span class="mi">14</span> <span class="p">)</span>
<span class="n">tree_b</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">,</span> <span class="n">y_train_b</span> <span class="p">)</span>
<span class="n">y_predict_tree_b</span> <span class="o">=</span> <span class="n">tree_b</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_b</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;DT Score using balanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_b</span><span class="p">,</span><span class="n">y_predict_tree_b</span><span class="p">))</span>

<span class="c1"># Decision Tree using unbalanced data</span>
<span class="n">tree_u</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_depth</span> <span class="o">=</span> <span class="mi">14</span><span class="p">,</span> <span class="n">min_samples_split</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">tree_u</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_u</span><span class="p">,</span> <span class="n">y_train_u</span> <span class="p">)</span>
<span class="n">y_predict_tree_u</span> <span class="o">=</span> <span class="n">tree_u</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_u</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;DT Score using ubalanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_u</span><span class="p">,</span><span class="n">y_predict_tree_u</span><span class="p">))</span>
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<pre>NN Score using balanced data: 0.234907650606
NN Score using ubalanced data: 0.524965821676

DT Score using balanced data: 0.0140990266578
DT Score using ubalanced data: 0.422960446681
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import &#39;make_scorer&#39;, &#39;DecisionTreeRegressor&#39;, &#39;KNeighborsRegressor&#39; and &#39;GridSearchCV&#39;</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">grid_search</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cross_validation</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsRegressor</span>

<span class="c1"># Improving Decission Tree</span>
<span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Performs grid search over the &#39;max_depth&#39; parameter for a </span>
<span class="sd">        decision tree regressor trained on the input data [X, y]. &quot;&quot;&quot;</span>
    
    <span class="c1"># Create cross-validation sets from the training data</span>
    <span class="n">cv_sets</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">X</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">n_iter</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.10</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>

    <span class="c1"># TODO: Create a decision tree regressor object</span>
    <span class="n">regressor</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">()</span>

    <span class="c1"># TODO: Create a dictionary for the parameter &#39;max_depth&#39; with a range from 1 to 10</span>
    <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;max_depth&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">)}</span>

    <span class="c1"># TODO: Transform &#39;performance_metric&#39; into a scoring function using &#39;make_scorer&#39; </span>
    <span class="n">scoring_fnc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">)</span>

    <span class="c1"># TODO: Create the grid search object</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">regressor</span><span class="p">,</span><span class="n">params</span><span class="p">,</span><span class="n">scoring</span><span class="o">=</span><span class="n">scoring_fnc</span><span class="p">,</span><span class="n">cv</span><span class="o">=</span><span class="n">cv_sets</span><span class="p">)</span>

    <span class="c1"># Fit the grid search object to the data to compute the optimal model</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="c1"># Return the optimal model after fitting the data</span>
    <span class="k">return</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_estimator_</span>

<span class="n">reg_tree_b</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_train_b</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int16&#39;</span><span class="p">))</span>
<span class="c1"># Produce grid parameters</span>
<span class="k">print</span> <span class="s2">&quot;Decision Tree parameters: {} &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">reg_tree_b</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>

<span class="k">print</span> <span class="s2">&quot;&quot;</span>

<span class="c1"># Improving nearestNeighborgh</span>
<span class="c1">#</span>
<span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Performs grid search over the &#39;max_depth&#39; parameter for a </span>
<span class="sd">        decision tree regressor trained on the input data [X, y]. &quot;&quot;&quot;</span>
    
    <span class="c1"># Create cross-validation sets from the training data</span>
    <span class="n">cv_sets</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">X</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">n_iter</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.10</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>

    <span class="c1"># TODO: Create a decision tree regressor object</span>
    <span class="n">regressor</span> <span class="o">=</span> <span class="n">KNeighborsRegressor</span><span class="p">()</span>

    <span class="c1"># TODO: Create a dictionary for the parameter &#39;max_depth&#39; with a range from 1 to 10</span>
    <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;n_neighbors&#39;</span><span class="p">:</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">10</span><span class="p">)}</span>

    <span class="c1"># TODO: Transform &#39;performance_metric&#39; into a scoring function using &#39;make_scorer&#39; </span>
    <span class="n">scoring_fnc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">performance_metric</span><span class="p">)</span>

    <span class="c1"># TODO: Create the grid search object</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">GridSearchCV</span><span class="p">(</span><span class="n">regressor</span><span class="p">,</span><span class="n">params</span><span class="p">,</span><span class="n">scoring</span><span class="o">=</span><span class="n">scoring_fnc</span><span class="p">,</span><span class="n">cv</span><span class="o">=</span><span class="n">cv_sets</span><span class="p">)</span>

    <span class="c1"># Fit the grid search object to the data to compute the optimal model</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="c1"># Return the optimal model after fitting the data</span>
    <span class="k">return</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_estimator_</span>

<span class="n">reg_nn_b</span> <span class="o">=</span> <span class="n">fit_model</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y_train_b</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int16&#39;</span><span class="p">))</span>
<span class="c1"># Produce the grid parameters</span>
<span class="k">print</span> <span class="s2">&quot;NN parameters: {} &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">reg_nn_b</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>
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<pre>Decision Tree parameters: {&#39;presort&#39;: False, &#39;splitter&#39;: &#39;best&#39;, &#39;max_leaf_nodes&#39;: None, &#39;min_samples_leaf&#39;: 1, &#39;min_samples_split&#39;: 2, &#39;min_weight_fraction_leaf&#39;: 0.0, &#39;criterion&#39;: &#39;gini&#39;, &#39;random_state&#39;: None, &#39;max_features&#39;: None, &#39;max_depth&#39;: 12, &#39;class_weight&#39;: None} 

NN parameters: {&#39;n_neighbors&#39;: 2, &#39;n_jobs&#39;: 1, &#39;algorithm&#39;: &#39;auto&#39;, &#39;metric&#39;: &#39;minkowski&#39;, &#39;metric_params&#39;: None, &#39;p&#39;: 2, &#39;weights&#39;: &#39;uniform&#39;, &#39;leaf_size&#39;: 30} 
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<h3 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h3><p>After training the two additional regressors with datasets balanced and unbalanced, we determined that the best regressors are the NN and DT with no balanced data.</p>
<p>Is clear that the BIAS is affecting the models, but the only way to deal with it is finding other features that can be added to the dataset. Adding other features will invalidate the experiment so we will create a final ensemble model with the best regressors to see if we can improve the accuracy of the model. 
Another potential solution is finding patient samples with a more spread variance, but we only have access to one dataset, and we are using all the patients in it.</p>

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<h2 id="Ensemble-model-of-the-best-regressors">Ensemble model of the best regressors<a class="anchor-link" href="#Ensemble-model-of-the-best-regressors">&#182;</a></h2><p>In this section, I will create an ensemble model using the NN and DT models that score higher than 0.40 of accuracy.
An ensemble model is just a group of models working in series where the first model make a prediction and those targets where it did not predict correctly are passed to the next model and so on as many time as estimators were selected; this type of ensemble used a method called Boosting. They are more suitable for a group of weak learners than the other method called bagging.</p>
<p>In the previous sections of this project I used three different models, only two of them got accuracies bigger than 40%, and only one passed the 50% of precision; this by itself justifies the use of an Ensemble. The sklearn ensemble model we used in based on the Adaboost algorithm, its steps are:</p>
<ul>
<li>Fit a sequence of weak learners repeatedly modified versions of the data. </li>
<li>The predictions from all of them are then combined through a weighted majority vote (or sum) to produce the final prediction.</li>
</ul>
<p>I will perform the following steps</p>
<ul>
<li>Create a boost ensemble model using the best regressors from the previous section.</li>
<li>Conclusions</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">AdaBoostRegressor</span>

<span class="c1"># Create ensemble of decision trees</span>
<span class="n">tree_b_ensemble</span> <span class="o">=</span> <span class="n">AdaBoostRegressor</span><span class="p">(</span><span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
                          <span class="n">n_estimators</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">tree_b_ensemble</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_b</span><span class="p">,</span> <span class="n">y_train_b</span> <span class="p">)</span>
<span class="n">y_predict_tree_b_ensemble</span> <span class="o">=</span> <span class="n">tree_b_ensemble</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_b</span><span class="p">)</span>

<span class="k">print</span> <span class="s2">&quot;DT Ensemble Score using 30 estimators balanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
    <span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_b</span><span class="p">,</span><span class="n">y_predict_tree_b_ensemble</span><span class="p">))</span>

<span class="k">print</span> <span class="s2">&quot;&quot;</span>

<span class="c1"># Create ensemble of Nearest Neighborg</span>
<span class="n">nn_u_ensemble</span> <span class="o">=</span> <span class="n">AdaBoostRegressor</span><span class="p">(</span><span class="n">KNeighborsRegressor</span><span class="p">(</span><span class="n">n_neighbors</span> <span class="o">=</span> <span class="mi">4</span><span class="p">),</span>
                          <span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">nn_u_ensemble</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_u</span><span class="p">,</span> <span class="n">y_train_u</span><span class="p">)</span>
<span class="n">y_predict_nn_u_ensemble</span> <span class="o">=</span> <span class="n">nn_u_ensemble</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_u</span><span class="p">)</span>

<span class="k">print</span> <span class="s2">&quot;NN Ensemble Score using 100 estimators unbalanced data: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
    <span class="n">performance_metric</span><span class="p">(</span><span class="n">y_test_u</span><span class="p">,</span><span class="n">y_predict_nn_u_ensemble</span><span class="p">))</span>
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<pre>DT Ensemble Score using 30 estimators balanced data: 0.257476797806

NN Ensemble Score using 100 estimators unbalanced data: 0.57569355522
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<h2 id="Results">Results<a class="anchor-link" href="#Results">&#182;</a></h2><p>Even after using a boosted ensemble which helps with the BIAS still the best model NN produces only a 0.57 score of accuracy.
I believe the model is robust enough, but still can benefit from adding some extract features to help boost the precision.
In a later exercise at the end of the project,  I tested the model with three fake patients and plotted the result over imposing to the Patients Day standard distribution curve.  The predicted values are within the norm deviations what proof the model is predicting a value that belongs to the data distribution.
The final model is reasonable but not aligned with what I was expecting. When I started to balance the data, I used a clustering algorithm to determine if BMI variance explained the hospitalization days, but the result was negative. The purpose of my project was to proof the hypothesis of the BMI be responsible for the patient recovery speed, and the conclusions is that is not on its own.</p>

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<h2 id="Using-the-prediction-model.">Using the prediction model.<a class="anchor-link" href="#Using-the-prediction-model.">&#182;</a></h2><p>In the next section I will used my best model to predict the stay of three patients.</p>
<table>
<thead><tr>
<th style="text-align:center">Features</th>
<th style="text-align:center">Patient 1</th>
<th style="text-align:center">Patient 2</th>
<th style="text-align:center">Patiente 3</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Patient_Age</td>
<td style="text-align:center">25</td>
<td style="text-align:center">50</td>
<td style="text-align:center">80</td>
</tr>
<tr>
<td style="text-align:center">BIM</td>
<td style="text-align:center">20</td>
<td style="text-align:center">45</td>
<td style="text-align:center">35</td>
</tr>
<tr>
<td style="text-align:center">Gender</td>
<td style="text-align:center">M</td>
<td style="text-align:center">M</td>
<td style="text-align:center">F</td>
</tr>
<tr>
<td style="text-align:center">Medical Condition</td>
<td style="text-align:center">diabities</td>
<td style="text-align:center">cardiovascular disorders</td>
<td style="text-align:center">high collesterol</td>
</tr>
</tbody>
</table>
<p>I will perform the following steps</p>
<ul>
<li>Using the best model predict the above patients hospital stay</li>
</ul>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Produce a matrix for patient data</span>
<span class="c1"># Patient_Age,BIM,F,M,cardiovascular_disorders,diabetes,hypertension,lipid_disorders,neoplasia</span>

<span class="n">patient_data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">25</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="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="c1"># Patient 1</span>
               <span class="p">[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">45</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="c1"># Patient 2</span>
               <span class="p">[</span><span class="mi">80</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>  <span class="c1"># Client 3</span>

<span class="c1"># Show predictions</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">days</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">nn_u_ensemble</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">patient_data</span><span class="p">)):</span>
    <span class="k">print</span> <span class="s2">&quot;Predicted days in Hospital patient {} {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">days</span><span class="p">)</span>

<span class="c1"># Plot the patients stayind data histogram and over impose the prediction</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="c1">#plt.hist(good_data[&#39;Hospital_Stay_Days&#39;], bins = 30)</span>
<span class="k">for</span> <span class="n">days</span> <span class="ow">in</span> <span class="n">nn_u_ensemble</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">patient_data</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">days</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">color_codes</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">good_data</span><span class="p">[</span><span class="s1">&#39;Hospital_Stay_Days&#39;</span><span class="p">])</span>
</pre></div>

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<pre>Predicted days in Hospital patient 1 5.75
Predicted days in Hospital patient 2 3.75
Predicted days in Hospital patient 3 8.0
</pre>
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<div class="output_area"><div class="prompt output_prompt">Out[62]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x116d2c850&gt;</pre>
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<h2 id="Free-Form-Visualization">Free-Form Visualization<a class="anchor-link" href="#Free-Form-Visualization">&#182;</a></h2><p>I used several forms of visualization during the project.
At the beginnings of this study, I plotted the data correlation; it helps me to understand in advanced how well the linear regressor will perform.
When I used the clustering model to get information to balance the data. I plotted the cluster using the first two principal components to label the clusters based on the samples I took. The plot helps me to understand that the hospitalization value explains better the data than the BMI or the Age.
I plotted the heatmap of the selected samples to help me compared the selected samples with each other and with the dataset.
I plotted the distribution of the continues features to understand the relation with each other.
I Final use an over imposed plotted to see if my prediction were within the normal distributions of the dataset target.</p>

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<h2 id="Improvements">Improvements<a class="anchor-link" href="#Improvements">&#182;</a></h2><p>I am not happy with 0.57 of precision. It is evident to me that the dataset requires another feature that helps to explain better the hospitalization time. One thing I would do different is work in tandem with a physician to help me understand base on his/her experience what that missing feature might be. Another thing I will be different is to select the days in "intensive care" instead of the hospitalization days of the patient first visits. It is strange that when I plotted the variance of the features versus the hospitalization time the patients within 0 and maybe 15 days where all group by age or BMI. After 15 days in the hospital, it seems like the age and BMI start to spread as more time the patient spent in the hospital; this could be because I selected the specific medical conditions instead of picking all type of medical conditions. Another hypothesis is that because the records come from the same hospital, such hospital has standard time for the first visit based on the patient condition. A way to overcome these problems is to collect data from different hospitals and get all medical conditions.
Also, most of the patients in my sample were overweight or obese; very few were within the normal weight range. So more variance in the samples is also required.</p>

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<h2 id="Capstone-Project-Conclusions">Capstone Project Conclusions<a class="anchor-link" href="#Capstone-Project-Conclusions">&#182;</a></h2><p>The initial goal of the project was to create a model capable of predicting how long a patient will stay in a hospital, based on the features describe in the Obesity Paradox studies.
The main features are the BMI, Age, Gender and four specific medical conditions.
The exploration of the data resulted in the following findings:</p>
<ol>
<li>The correlation between the BMI and Age with the hospital time was little and did not explain the hospitalization time too well. </li>
<li>The staying in the hospital accounted for most of the variances in the whole dataset.</li>
<li>Data was unbalanced, having more patients with shorter stays than longer ones.
The experiment indicates that the BMI, Age and medical conditions explain the staying in the hospital to a certain degree, but the lack of other features creates BIAS in the model, which doesn't allow it to get a better accuracy.
Using other features will invalid the initial goal, so we didn't add any.
The experiment can benefit from more medical domain knowledge, by adding the missing features that will improve the model accuracy.</li>
</ol>
<p>It was exciting and challenging to me work with numerical and categorical features at the same time the target is numerical. Linear regression doesn't like discrete values, so it forces me to use others models capable of predicting numerical values using a mix of numerical and categorical features, this was quite a challenge.</p>

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