finding_donors.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
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<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
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.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

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<style type="text/css">
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<h2 id="Supervised-Learning">Supervised Learning<a class="anchor-link" href="#Supervised-Learning">&#182;</a></h2><h2 id="Project:-Finding-Donors-for-CharityML">Project: Finding Donors for <em>CharityML</em><a class="anchor-link" href="#Project:-Finding-Donors-for-CharityML">&#182;</a></h2>
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<p>In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
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<blockquote><p><strong>Note:</strong> Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
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<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.</p>
<p>The dataset for this project originates from the <a href="https://archive.ics.uci.edu/ml/datasets/Census+Income">UCI Machine Learning Repository</a>. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article <em>"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"</em>. You can find the article by Ron Kohavi <a href="https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf">online</a>. The data we investigate here consists of small changes to the original dataset, such as removing the <code>'fnlwgt'</code> feature and records with missing or ill-formatted entries.</p>

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<h2 id="Exploring-the-Data">Exploring the Data<a class="anchor-link" href="#Exploring-the-Data">&#182;</a></h2><p>Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, <code>'income'</code>, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database.</p>

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<div class=" highlight hl-ipython3"><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="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>

<span class="c1"># Import supplementary visualization code visuals.py</span>
<span class="kn">import</span> <span class="nn">visuals</span> <span class="k">as</span> <span class="nn">vs</span>

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

<span class="c1"># Load the Census 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="s2">&quot;census.csv&quot;</span><span class="p">)</span>

<span class="c1"># Success - Display the first record</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="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
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      <th></th>
      <th>age</th>
      <th>workclass</th>
      <th>education_level</th>
      <th>education-num</th>
      <th>marital-status</th>
      <th>occupation</th>
      <th>relationship</th>
      <th>race</th>
      <th>sex</th>
      <th>capital-gain</th>
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      <th>income</th>
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      <th>0</th>
      <td>39</td>
      <td>State-gov</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Never-married</td>
      <td>Adm-clerical</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>2174.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>1</th>
      <td>50</td>
      <td>Self-emp-not-inc</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>13.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>2</th>
      <td>38</td>
      <td>Private</td>
      <td>HS-grad</td>
      <td>9.0</td>
      <td>Divorced</td>
      <td>Handlers-cleaners</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>3</th>
      <td>53</td>
      <td>Private</td>
      <td>11th</td>
      <td>7.0</td>
      <td>Married-civ-spouse</td>
      <td>Handlers-cleaners</td>
      <td>Husband</td>
      <td>Black</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>4</th>
      <td>28</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Prof-specialty</td>
      <td>Wife</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>Cuba</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>5</th>
      <td>37</td>
      <td>Private</td>
      <td>Masters</td>
      <td>14.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Wife</td>
      <td>White</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>6</th>
      <td>49</td>
      <td>Private</td>
      <td>9th</td>
      <td>5.0</td>
      <td>Married-spouse-absent</td>
      <td>Other-service</td>
      <td>Not-in-family</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>16.0</td>
      <td>Jamaica</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>7</th>
      <td>52</td>
      <td>Self-emp-not-inc</td>
      <td>HS-grad</td>
      <td>9.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>45.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
    <tr>
      <th>8</th>
      <td>31</td>
      <td>Private</td>
      <td>Masters</td>
      <td>14.0</td>
      <td>Never-married</td>
      <td>Prof-specialty</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Female</td>
      <td>14084.0</td>
      <td>0.0</td>
      <td>50.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
    <tr>
      <th>9</th>
      <td>42</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>5178.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
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<h3 id="Implementation:-Data-Exploration">Implementation: Data Exploration<a class="anchor-link" href="#Implementation:-Data-Exploration">&#182;</a></h3><p>A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:</p>
<ul>
<li>The total number of records, <code>'n_records'</code></li>
<li>The number of individuals making more than \$50,000 annually, <code>'n_greater_50k'</code>.</li>
<li>The number of individuals making at most \$50,000 annually, <code>'n_at_most_50k'</code>.</li>
<li>The percentage of individuals making more than \$50,000 annually, <code>'greater_percent'</code>.</li>
</ul>
<p><strong> HINT: </strong> You may need to look at the table above to understand how the <code>'income'</code> entries are formatted.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Total number of records</span>
<span class="n">n_records</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="c1"># TODO: Number of records where individual&#39;s income is more than $50,000</span>
<span class="n">n_greater_50k</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">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;&gt;50K&#39;</span><span class="p">)</span>

<span class="c1"># TODO: Number of records where individual&#39;s income is at most $50,000</span>
<span class="n">n_at_most_50k</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">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;&lt;=50K&#39;</span><span class="p">)</span>

<span class="c1"># TODO: Percentage of individuals whose income is more than $50,000</span>
<span class="n">greater_percent</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">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;&gt;50K&#39;</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="o">/</span><span class="n">n_records</span>

<span class="c1"># Print the results</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total number of records: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_records</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Individuals making more than $50,000: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_greater_50k</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Individuals making at most $50,000: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_at_most_50k</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Percentage of individuals making more than $50,000: </span><span class="si">{}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">greater_percent</span><span class="p">))</span>
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<pre>Total number of records: 45222
Individuals making more than $50,000: 11208
Individuals making at most $50,000: 34014
Percentage of individuals making more than $50,000: 24.78439697492371%
</pre>
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<p><strong> Featureset Exploration </strong></p>
<ul>
<li><strong>age</strong>: continuous. </li>
<li><strong>workclass</strong>: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. </li>
<li><strong>education</strong>: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. </li>
<li><strong>education-num</strong>: continuous. </li>
<li><strong>marital-status</strong>: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. </li>
<li><strong>occupation</strong>: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. </li>
<li><strong>relationship</strong>: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. </li>
<li><strong>race</strong>: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. </li>
<li><strong>sex</strong>: Female, Male. </li>
<li><strong>capital-gain</strong>: continuous. </li>
<li><strong>capital-loss</strong>: continuous. </li>
<li><strong>hours-per-week</strong>: continuous. </li>
<li><strong>native-country</strong>: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&amp;Tobago, Peru, Hong, Holand-Netherlands.</li>
</ul>

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<h2 id="Preparing-the-Data">Preparing the Data<a class="anchor-link" href="#Preparing-the-Data">&#182;</a></h2><p>Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as <strong>preprocessing</strong>. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms.</p>

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<h3 id="Transforming-Skewed-Continuous-Features">Transforming Skewed Continuous Features<a class="anchor-link" href="#Transforming-Skewed-Continuous-Features">&#182;</a></h3><p>A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '<code>capital-gain'</code> and <code>'capital-loss'</code>.</p>
<p>Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Split the data into features and target label</span>
<span class="n">income_raw</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span>
<span class="n">features_raw</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;income&#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"># Visualize skewed continuous features of original data</span>
<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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FBKOTbJZ6lXkventlzcRO2WsCvwmrJilKfvUUdBeRQrms2hBhYvBX7brvb01tl12YdQRyD5cpLXUbs4vahtV1fr9AUgt2bFg+QeSr0ZcconGSfZj9oC8F3qVavNqaMAnVrq8xSg3vj84iT/Qr16fWUp5cxZlK/fbYHDknyEOvrKO6h90D/Zl+dzwBuSvB44ntqK84why+pUrlLKz9ux2LddYf4Rdd+8EfhsKeW02WxAx302zBupfduPSB0i9DbU4PMKZm5BmMqzqFfCT0zy36x4kNy9qaMdrU0drWxO90HzC+DxSb5JbWG5YCDoXhUzHvtSyo1J3kS9+v5paleSzaitMWdTu7p0Xl6SJ1BHuDmMek5Zn3o8r2TlwHYlpZTLWzenw4FT2v7vPUhuO+r/8aHU4/MZ6o/MY1KfD/EzauvAVtSHQP5TKeWarjspyUf7yncJdXCJZ7HiHqveth+U5H3U0XgewMAP7tbt7gBqy8U51B/pe1FHUOryILfN2rlnDWrXse2pA0MEeGIp5S8t3y+pP5zfluRG6g/wqR6wN/R7leR44JVJLqQGbs9hbrs+vol6nv5ekg9QW4U2pP7YvkcppfdU6W9SRwI8nbrPnsLwH/9TnZ++Qf2f/98k+1CDo1dTu7vOWVnn4NhKc6+MwZ3cvibzRa2UD6dWRtdSn0T9U+oJeJ2+fHvRRmHqS9uYeq/EWcBmLW0N6lN/f9aWd0V7/25q60H/un9C30hLLa03QtPBQ8raadnUeziOBK6hjqxxALWl4+aRhKbZHwezYkSRm6g/Kn5JHaVj+yH59+pfLvVq7FHUq4XXUft1f5yVR8a5cyvfle2zx061jwfKdV7f9JYt74uB/dt2XkP9QX33gc/equ2DC9s6P0/9QXbzyDkdy7VlX961qX2vz6f+eDm/Ta89pIzPGyjPji19x677bJrjtQv1R99f2vfhq8C9BvJ0GoWpL/9tqMNI/pT6/3Ad9Sr9AdQfE3O+D1raw6itKtey8sg+Ux37LsvsdOxb3j2o/0/XUbt0fArYdLbfJWrA/Xlq8HAt9bt5JPCQjvv/btRRkXo3915Fvcl5b+B2A2XZF/hVy3d5y7cvbWSuvn3y6Bn+b5dRR/m5pC3rXGpLY//61qD+0Dyf+r92FDVg6T9WG1MvYpzV8lxObcF5bIft7h/N6K/UH/U/oI7stdGQ/A9s86+hDpywH/A8bvm/OtX3akvqj+8r23Z/gPq/uNJ3aIqy7tvyrTVDvs2pLUu/pwbiF1JHNtqjL8+dqMHZH9vrM9T7MTqdn9q8h7djf03b93vQ8f+ma1lX59j68jWqV28YOUmSJEmakfdASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQkMlOTjJEXOwnH2TnDEXZZphPVsmKUm2HfW6Jl2SvZJcNaJlH5vkA33T5yV51YjWNbLtkCbBfNYTc7Uujc4o6/vBuqDV908b0brm5XfLYmcAsQi0E+e+87zalwF79JVhpR92Y+h3wKbAqV0/kGTHJOfNkOe8dqLqf/1pNcs6uI4F37dtX/S276Ykf05yWpIDktx9IPvngXt0XO5sA7unAK+dTdk7lmNYZdN5O6RxZz0xd9rFhWNnyDNYL5QkneufjuUY2QWUWZRhr77tuzHJn5KclORtSTYeyP5fwCM6LrdX59ypY1EeDHxwNmXvUIap6qfO2zHJ1lroAmg8lVKuWOgyzEYp5UbgohEtfj/gQ33TN41oPastydqllL+uxiL+DrgcuA3wAODlwOlJHl9KOQ6glPIX4C+rXdg+SdYppVxfSrl8Lpc7nVFshzRJFls9MQLPB/pbRVbn3DsySdYA0urJVXENsBUQ4HbUH/OvAZ6f5BGllF8ClFKuAua0Vbevbrh0Lpc7nVFsx1JkC8QilGSdJG9Pcn6S65L8Jsl/tHlrJvl4knOT/CXJ2Ule3U4gvc8fnOSIJG9IcnGSq5J8Isl6g3l676nR+Ev6rkRs2WVdHbdn/SSfbOW4OMlrW/kO7suzR5ITk1yZ5JIkX0yyWd/8la4k9F3deFSSnyS5pl012XoVdvmVpZSL+l6X9K13gyQfbWW6Mslx/VczktwxyWeTLG/76OdJnt03f6p9e4urM9Ns465JTkhyPfDYNu+JSU5Ocm07Pm9Lsk6Hbb2kbeM5pZQvATsCPwUOSrJmW/ZKXX+SbJHkq0kub/v5V0l2b7PPbX9PbGU9trfd7Ri/JslyYHlLH3YF8zZJPt2+Hxdl4IpchrQupO/KXVa0Mn2x5T1v2Ha0tBcmOSfJ9e3v84es6wXt+3d1+9/bA2nMZInVE0O2b90k729luzbJ8Uke3jd/7SQHJrmgbf/vkryzb/5TUltZ/9LOXccl2WSWxfjTQN1wWd/yN0vyuSR/bK+vJ7ln3/yt2nnzonYuOSXJE/rmHwvcDXhPb3+29GHnrZXqi16eVjecAVwP3KfNe3aSX7R9dlaS/+xwLErbvgtLKWeWUj4NPBT4E/DhvnKs1PUnyd8nOSa1RfvKJD9LslOSLYHvtmyXtrIf3NvuJB9K8l9JLgV+2NKHtcbcue3Xa9r3vL81bGjrQlauL6aqnwa3Y40kb2zfoeuSnJ5ktyHremqSo1t5fpHkMTPs10XNAGJxOgTYE3gF9aTwXOo/MtRj+nvgn9u81wOvA549sIxHUK8wPwp4KrAz8K4p1vcy4MfAJ6jdhDaldhnquq6ZvLeV58nAI1u5/nEgzzrAPm3eE4A7AZ/tsOx3AHsDWwOXAZ9JklmWb6i2nK8Dm7UyPQj4HvCdJJu2bLcCTmnz/w44APhIkke1+VPt29l4F/AG4N7AT5I8FvgM8IG2zucATwPePtttbFes3kft6vOgKbJ9ELg1sFNb38tZ8X3crv3dhbptT+n73COA+7d5j2JqrwB+ST2G+wBvT/KUafIPenD7+/xWhgcPy5TkydR99n7gftRj9cEkTxzI+ibgq9Tv4uepwdXdZlEeaT4stXpi0LuBf6Ge3x4EnA58s+/c+x/UOmV34J4t75kASe4MfI66j+4D7AB8ajXLc7Mkt6b+QL6Wug8fClwIfLvNg9rK+w3gMdR9/CXgy0nu3eY/hXphZT9W7M/ZuBW1XnghcF/g/NQLIm+nnsPuA7yS2pLw4tluY7tK/2FghyQbTZHt/6jbvR31GO1L3Se/o36foNYZm1K/Pz17UFs7/pH6HZ7Km4HDgQcCHwU+ORgwzGC6+qnfy4D/R91Xfw98hXqsHjiQ723AgdTjeSLwuSS3mUV5FpdSiq9F9KKeCAuwyyw+807g233TB1Mrktv0pe0BXAes35fniL75xwIfWIV17QucMU3+21Cvjuzel7Y+8Efg4Gk+d++2HzZv01u26W3b9I5t+rF9n3lY/2c67rvz2n65qu/1ujbvkW16vYHPnAq8epplfg742HT7tq/8d+pLm2obnzrw2e8BbxxI+6dW1kxRplusb8i+/uc2vRdwVd/804B9pljuSmUe+A5eCqw7kL7Svmj7/+iBPB8DftA3XYCnDTlur5ohz+B2/BA4aEg5B9f1jr7ptajN+3t0/U758jXqF0usnhhcF7WOuB7Ys2/+msCvgbe26QOBY4ad86gXIwpwt9XYx4XaBbK/bnhmm/cc4Oz+dbfyXdY7j06xzOOBN/RNr3Qea2krnbda2o70nb9bngJsM5Dvt8CzBtJeDvximjLdYn1983Zp69lu2HEE/gwsm+KzK5V54Dt02pD8K+2L9tn/HcjzbeDT7f2WDK97bq4LpskzuB2/B940pJyD63ph3/zNWtrDV/U7Nu4v74FYfB5E7YP/3akyJHkR8Dxq8+d6wNrA+QPZTiv1CkLPj6lX+bei/iDspOO6enn/kXrFpeeFwBntMyf0EkspV2dgBITUrkf7UK803IF6dQLgrrTuL1Po35YL2t+NZ/jMoP2Bj/dN9/rpb0O98n7pQKPGraj7kdRuP3tTr35tBqxL3c/HzmL9MzlpYHobYLskr+lLW4N6fO5MvSI0G72NK1PMPwD4cJJdqBX2V0opJ3dY7hmllOs65PvxkOnZtEB0dR/goIG0HwBPGki7+TtVSrmhNbMP3kwoLaQlVU+UUj4zkG2rtowf9hJKKTcm+TH1ajvUgONo4Kwk3wKOBL5RSrkJ+Bn1x+YZbd63gUPL7PvZ/z/gm33TF7e/2wB3B64cqBtuzYq6YX1qnfYE6tXvtal1R+f9OoMb6BtUpLUSbEFtAe+/p28tVpzjZ2umumF/4GNJllHrhi+VUn7VYbld6g8YXjc8vuNnO0lyO+Au9H3Xmh8Auw6kTfV7Y0kygFh8pv1HT/Iv1C4YrwJ+RL0C8BJqU+7cFmT26zqJGgD0XEw7mTL1Cah3oj2KepJ/FnAJtQvT96mV2XT6b2rrrWO2XfcuK6WcMyR9Deo2DHa3grovoO6bV1KbQE+nXqV6OzOfVHo3avcf77WnyHv1kHK9GfjikLyrciNar0L+zbCZpZSPJzmKejJ9NPCjJO8opew7w3IHy72qCrf8v5hqX3VZ1kxpgzdKFuwOqvGy1OqJWyy2/Z3y/7WUckrra78LtbX4EOBnSR7Tgo2dge2p3bKeC7wj9Ybgn3XfOi6apm44ldp9alDvAtR/tbK9itpacQ3wSWau026i2/nuurLyTdO9c9SLqMdhLtyXur/PGzazlLJvks8Aj6Pen7dPkheVUgYv1Ayai7rhFnVoklWtF2CWdUMppbTgccnWDQYQi88p1C/kTqx85aPn4cBPSin9Y+lvNSTf3ydZv5TS+0fdntok/Osp1ns9tQl2VdYF3DzqzUon2yTnUP/ptqPd0NT6iN6vryz3pgYMryul9PKM4gr0bJ0CbALcVEoZ+uOauo++Vkr5FNx838TfsqIvMgzft70f+pv2vR/sbzldue49RcU2K60F5eXUYzHlEIWllOXUPqgfbS0fL6M2A1/fsgxu32xsP2T6l33Tl9LXPzj1RsjB/sJ/7VCGX1KPV3/l9nDgF7MprDQGllQ9McQ5bV0Pp13YaOeqh1L73feWdSX1QsoX2026xwN/A5xVaj+THwM/TrIf8HNqS/FsAoipnAI8A/hDKWWqYb8fDnyy1MEqSNJruT6rL89UdcOtk9yulNK7UDVj3VBKuTjJ74GtSimf7L4pw7W+/S8Cjpuu5aaUcjY1QDqwtXw8j3qOnau64aCB6V7d0F+H9gzupxnLUEr5c5ILqMfrO32zJr5uMIBYZEopZyf5ArVZ8GXUE9XmwJbtR+pZwF5JHkc9ye5OvYnrjwOLWot68+d+1Oa5d1L7E04V+Z9H7RazJfUq+uWzWNd023NVkoOAdyX5A7V7zRuolV8vuv8ttd/tS5P8D7WryVu6rmOEvk1t1vxqklcDv6J2EdqF2r/3+9R99C+po4P8Afh3atP2T/uWcx633LfnUG802zfJ3tQ+lm/oWK79gCOSnA98gdqUfT9qP9VXz/DZjZOsRb035f7Af1K7Q+xaphgCMMkB1C4HZ1GH+NuFFSfWS6j9hB+bOvrRtWX2Qz9un+S1wKHUfrN7As/sm/8d6sgvPwJupLbwXDuwjPOARyU5jnplbth39D3UHxonA99q2/FMRtNdShqZpVZPDNm+q9uP0Xe2euNc6rlqE9qzApK8glqfnEq9gPCv1NaP5Um2p7aWHkVt4XgQtXvPXP0g/Ay1ZeGrSd5ErcO2AHYDPtx+VJ8FPDnJV1v59qF2Yep3HvCPST5NPW/9AfgJ9Qr9O5K8j3rDbteboPcF/jv1WUZHUlsutgY2K6W8Y5rPpd14DrABK4Zx3YBbdvHsfWA9aivLF9t2bEILJluW86l1/OOTfA34y0B3uS6ekuREapfgp1Fv9n8I1EA0yfHAa5L8upV1cBu71k/vAfZLcja1e9Ue1J4H28yyvEvKkm1aWeL2pF5lOZD6o/Vg6j8HwEeoPxr/jzoKwJbUUY4GHUe94vJd6ogC3wGm+3H5X9Ro/RfUyP6us1jXTF5F7Y50eCvPadRm7GsB2tWNZdQbgX9BPdG+YhXWM6faFaxdqfvuf6kjfHwBuBcr+j++lXp/xzeoNzdfTa1c+t1i35b6LIfdqaMf/YzaJel1Hct1FLVRkIAnAAAgAElEQVQf6E5t3SdQ78P4bYeP/5xa6f6UGoj8FLh/KeV703xmDeC/W/mPplbIy1pZbqCOhvI86j75apdtGLA/NZj5KXV/vqmUcmjf/FdSr0IeSw0yPkatGBjIsxM1KPspQ5RSDqMGeP/ZtuVlwItLKV9bhTJLC22p1RODXtOW+wlqkHB/6k3jvXu8rqTeo3ACNYB6IPC4Uso1wBXUQTWOoF4dfy/wllKHJ11tbR07UM9LX6Tu/0OADVkROL2Cep76PrV+OL697/cmauDxa9oV9VKflfNM6uhNpwMvAN7YsVwfo97g/SxqvfL99vlzZ/joran1wgXU/fkK4GvA/Up7BsQQN1K39xBq3fgVaovPK1pZfk+ty99GrTNW5QGE+1JHczoN+Dfg2aWUE/vmP6f9PZH6PVzpItws6qcDqUHEu6n3bT6ZOnjJnD44cLFJ/Q2kSdKacu9USnnCTHkXQpJ1qVcn3lNKmYuKRpI0C+NeT0haWHZh0oJL8iBqt6QTgNtSryzdljrGviRJksbIgnVhSvKZJGcmOSPJQb2741MdmPoU2NPS9+TgJMtSn2J5dhsWrJe+TeqTAc9pn52TB4VpXr2C2rXkO9S+kju0G3MlTRjrB0kabyPrwpRkwyluVOzN35UVYz3/H/C9UsqHWvq/U/uWPwQ4oJTykCR3oPaL35Z6483J1Iek/DHJCdT+ysdTbww6sJTyDSRJY8f6QZIWt1G2QJyU5P+SPHLYFZ9SypGloXZd2bzN2o06tFkppRwP3D710fSPpT6R9vJW8RwN7NLm3a6U8uO2rE9Sb7aVJI0n6wdJWsRGeQ/E31IfHvJS4H+SfAo4uJRyQX+m1jT9LOoVIqhP6/1dX5blLW269OVD0m8hyQuoIw6w/vrrb3Pve9971ht18mWXzSr/Nne846zXIUmjdPLJJ/+hlLLRAhZhrOqHuagbwPpB0uLXtX4YWQDRxow/gjoe/UbU8Xd/m+QfSikn9GX9ILV5ujd82bD+qcOeNDtT+rAyfZT6sCu23XbbctJJJ3Xaln455JBZ5T9p2bKZM0nSPGrPCFkw41Y/zEXdANYPkha/rvXDSG+iTrJBu7JzOPWK03Op4/X25u8DbMTKY/ovp4573LM5dXze6dI3H5IuSRpT1g+StHiNLIBoT048hfogrD1LKTuUUg4ppVzb5j+P2m/1GaWUm/o+ejiwZxttY3vgivZgmKOAnZNsmGRDYGfgqDbvyiTbt760e7JqD6uSJM0D6wdJWtxGeQ/EF4C92pP+hvkw9WFhP2730H25lLIfdZSMXamPvL8GeDbUpy8meQv1iYIA+7UnMkJ9AuHBwHrUkTscYUOSxpf1gyQtYqO8B+LwGeYPXXcbKeMlU8w7CDhoSPpJwP1WoZiSpHlm/SBJi9uCPUhOkiRJ0uJjACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLU2YIFEEkOSnJJkjP60vZN8vskp7bXrn3zXpvknCRnJnlsX/ouLe2cJHvP93ZIkuaW9YMkjbeFbIE4GNhlSPr7SikPbK8jAZLcF9gd+Lv2mQ8mWTPJmsD/AI8D7gs8o+WVJC1eB2P9IElja62FWnEp5XtJtuyYfTfgc6WU64Bzk5wDbNfmnVNK+Q1Aks+1vL+Y4+JKkuaJ9YMkjbcFCyCm8dIkewInAa8spfwR2Aw4vi/P8pYG8LuB9IfMSyk7yiGHdM5bli0bYUkkadFbUvWDJC1W43YT9YeArYAHAhcC723pGZK3TJM+VJIXJDkpyUmXXnrp6pZVkjR/RlY/WDdI0uyMVQBRSrm4lHJjKeUm4H9Z0Qy9HNiiL+vmwAXTpE+1/I+WUrYtpWy70UYbzW3hJUkjM8r6wbpBkmZnrAKIJJv2TT4Z6I3AcTiwe5J1k9wduCdwAnAicM8kd0+yDvVGusPns8ySpNGzfpCk8bFg90Ak+SywI3CnJMuBfYAdkzyQ2sx8HvBCgFLKz5N8gXrz2w3AS0opN7blvBQ4ClgTOKiU8vN53hRJ0hyyfpCk8baQozA9Y0jyx6fJ/zbgbUPSjwSOnMOiSZIWkPWDJI23serCJEmSJGm8GUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLU2YwBRJKHJVm/vd8jyf5J7jb6okmSxpn1gyRNpi4tEB8CrknyAODVwPnAJ0daKknSYmD9IEkTqEsAcUMppQC7AQeUUg4AbjvaYkmSFgHrB0maQGt1yHNlktcCewA7JFkTWHu0xZIkLQLWD5I0gbq0QPwLcB3w3FLKRcBmwHtGWipJ0mJg/SBJE2jGFohWKezfN/1b7OMqSRPP+kGSJtOUAUSSK4Ey1fxSyu1GUiJJ0lizfpCkyTZlAFFKuS1Akv2Ai4BPAQGeiTfJSdLEsn6QpMnW5R6Ix5ZSPlhKubKU8udSyoeAp466YJKksWf9IEkTqEsAcWOSZyZZM8kaSZ4J3DjqgkmSxp71gyRNoC4BxL8C/wxc3F5Pb2mSpMlm/SBJE2jaUZjamN5PLqXsNk/lkSQtAtYPkjS5pm2BKKXcSH3CqCRJN7N+kKTJ1eVJ1D9M8gHg88DVvcRSyikjK5UkaTGwfpCkCdQlgPiH9ne/vrQCPHLuiyNJWkSsHyRpAnV5EvVO81EQSdLiYv0gSZNpxlGYkmyQZP8kJ7XXe5NsMB+FkySNL+sHSZpMXYZxPQi4kjpU3z8DfwY+McpCSZIWBesHSZpAXe6B2KqU0v9k0TcnOXVUBZIkLRrWD5I0gbq0QPwlycN7E0keBvxldEWSJC0S1g+SNIG6tED8G3BIX7/WPwJ7jaxEkqTFwvpBkiZQl1GYTgUekOR2bfrPIy+VJGnsWT9I0mTqMgrT25PcvpTy51LKn5NsmOSt81E4SdL4sn6QpMnU5R6Ix5VS/tSbKKX8Edh1dEWSJC0S1g+SNIG6BBBrJlm3N5FkPWDdafJLkiaD9YMkTaAuN1F/GjgmySeAAjwHOGSkpZIkLQbWD5I0gbrcRP3uJKcBjwYCvKWUctTISyZJGmvWD5I0mbq0QAD8ErihlPLtJLdOcttSypWjLJgkaVGwfpCkCdNlFKbnA4cCH2lJmwGHjbJQkqTxZ/0gSZOpy03ULwEeBvwZoJRyNrDxKAslSVoUrB8kaQJ1CSCuK6Vc35tIshb1ZjlJ0mSzfpCkCdQlgDguyeuA9ZI8Bvgi8LXRFkuStAhYP0jSBOoSQOwNXAqcDrwQOBJ4wygLJUlaFKwfJGkCdRnG9Sbgf9sLgCQPA344wnJJksac9YMkTaYpA4gkawL/TB1V45ullDOSPAF4HbAe8KD5KaIkaZxYP0jSZJuuBeLjwBbACcCBSc4HHgrsXUpxmD5JmlzWD5I0waYLILYF7l9KuSnJrYA/AH9TSrlofoomSRpT1g+SNMGmu4n6+ta/lVLKtcBZVg6SJKwfJGmiTdcCce8kp7X3AbZq0wFKKeX+Iy+dJGkcWT9I0gSbLoC4z7yVQpK0mFg/SNIEmzKAKKWcP58FkSQtDtYPkjTZujxITpIkSZIAAwhJkiRJszBlAJHkmPb3XaNaeZKDklyS5Iy+tDskOTrJ2e3vhi09SQ5Mck6S05Js3feZZS3/2UmWjaq8kqTR1w/WDZI03qZrgdg0ySOAJyV5UJKt+19ztP6DgV0G0vYGjiml3BM4pk0DPA64Z3u9APgQ1EoF2Ad4CLAdsE+vYpEkjcSo64eDsW6QpLE13ShMb6KeoDcH9h+YV4BHru7KSynfS7LlQPJuwI7t/SHAscBrWvonSykFOD7J7ZNs2vIeXUq5HCDJ0dSK57OrWz5J0lAjrR+sGyRpvE03CtOhwKFJ3lhKecs8lmmTUsqFrQwXJtm4pW8G/K4v3/KWNlW6JGkEFqh+sG6QpDExXQsEAKWUtyR5ErBDSzq2lHLEaIs1VIaklWnSb7mA5AXUJm7uete7zl3JJGkCjUn9YN0gSfNsxlGYkrwDeBnwi/Z6WUsblYtb8zPt7yUtfTmwRV++zYELpkm/hVLKR0sp25ZStt1oo43mvOCSNEnmuX6wbpCkMdFlGNfHA48ppRxUSjmI2of08SMs0+FAb7SMZcBX+9L3bCNubA9c0ZqzjwJ2TrJhu0Fu55YmSRqt+awfrBskaUzM2IWpuT1weXu/wVytPMlnqTe63SnJcuqIGe8EvpDkucBvgae37EcCuwLnANcAzwYopVye5C3AiS3ffr2b5iRJIzfn9YN1gySNty4BxDuAnyb5LrVP6Q7Aa+di5aWUZ0wx61FD8hbgJVMs5yDgoLkokySps5HUD9YNkjTeutxE/dkkxwIPplYQrymlXDTqgkmSxpv1gyRNpk5dmFp/0sNHXBZJ0iJj/SBJk6fLTdSSJEmSBBhASJIkSZqFaQOIJGskOWO+CiNJWhysHyRpck0bQJRSbgJ+lsRHc0qSbmb9IEmTq8tN1JsCP09yAnB1L7GU8qSRlUqStBhYP0jSBOoSQLx55KWQJC1G1g+SNIG6PAfiuCR3A+5ZSvl2klsDa46+aJKkcWb9IEmTacZRmJI8HzgU+EhL2gw4bJSFkiSNP+sHSZpMXYZxfQnwMODPAKWUs4GNR1koSdKiYP0gSROoSwBxXSnl+t5EkrWAMroiSZIWCesHSZpAXQKI45K8DlgvyWOALwJfG22xJEmLgPWDJE2gLgHE3sClwOnAC4EjgTeMslCSpEXB+kGSJlCXUZhuSnII8BNq0/SZpRSbqCVpwlk/SNJkmjGASPJ44MPAr4EAd0/ywlLKN0ZdOEnS+LJ+kKTJ1OVBcu8FdiqlnAOQZCvg64AVhCRNNusHSZpAXe6BuKRXOTS/AS4ZUXkkSYuH9YMkTaApWyCSPKW9/XmSI4EvUPu4Ph04cR7KJkkaQ9YPkjTZpuvC9MS+9xcDj2jvLwU2HFmJJEnjzvpBkibYlAFEKeXZ81kQSdLiYP0gSZOtyyhMdwf+HdiyP38p5UmjK5YkadxZP0jSZOoyCtNhwMepTxe9abTFkSQtItYPkjSBugQQ15ZSDhx5SSRJi431gyRNoC4BxAFJ9gG+BVzXSyylnDKyUkmSFgPrB0maQF0CiL8HngU8khVN1KVNS5Iml/WDJE2gLgHEk4F7lFKuH3VhJEmLivWDJE2gLk+i/hlw+1EXRJK06Fg/SNIE6tICsQnwqyQnsnIfV4fpk6TJZv0gSROoSwCxz8hLIUlajKwfJGkCzRhAlFKOm4+CSJIWF+sHSZpMXZ5EfSV1VA2AdYC1gatLKbcbZcEkSePN+kGSJlOXFojb9k8n+Sdgu5GVSJK0KFg/SNJk6jIK00pKKYfhGN+SpAHWD5I0Gbp0YXpK3+QawLasaLKWJE0o6wdJmkxdRmF6Yt/7G4DzgN1GUhpJ0mJi/SBJE6jLPRDPno+CSJIWF+sHSZpMUwYQSd40zedKKeUtIyiPJGnMWT9I0mSbrgXi6iFp6wPPBe4IWEFI0mSyfpCkCTZlAFFKeW/vfZLbAi8Dng18DnjvVJ+TJC1t1g+SNNmmvQciyR2AVwDPBA4Bti6l/HE+CiZJGl/WD5I0uaa7B+I9wFOAjwJ/X0q5at5KJUkaW9YPkjTZpmuBeCVwHfAG4PVJeumh3iR3uxGXTZI0nqwfNBFyyCGd85Zly0ZYEmm8THcPxKyfUi1JWvqsHyRpslkJSJIkSerMAEKSJElSZwYQkiRJkjqbdhhXjbfZ3NwF3uAlSZKk1WcLhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktTZ2AYQSc5LcnqSU5Oc1NLukOToJGe3vxu29CQ5MMk5SU5LsvXCll6SNArWDZK08MY2gGh2KqU8sJSybZveGzimlHJP4Jg2DfA44J7t9QLgQ/NeUknSfLFukKQFNO4BxKDdgEPa+0OAf+pL/2Spjgdun2TThSigJGneWTdI0jwa5wCiAN9KcnKSF7S0TUopFwK0vxu39M2A3/V9dnlLW0mSFyQ5KclJl1566QiLLkkaEesGSVpgay10AabxsFLKBUk2Bo5O8qtp8mZIWrlFQikfBT4KsO22295iviRp7Fk3SNICG9sWiFLKBe3vJcBXgO2Ai3vNz+3vJS37cmCLvo9vDlwwf6WVJM0H6wZJWnhjGUAkWT/JbXvvgZ2BM4DDgWUt2zLgq+394cCebcSN7YEres3ZkqSlwbpBksbDuHZh2gT4ShKoZfy/Uso3k5wIfCHJc4HfAk9v+Y8EdgXOAa4Bnj3/RZYkjZh1gySNgbEMIEopvwEeMCT9MuBRQ9IL8JJ5KJokaYFYN0jSeBjLLkySJEmSxpMBhCRJkqTOxrILkyRJ0lzLIYfMnEnSjGyBkCRJktSZLRCSJEmrabatG2XZspkzSWPKFghJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLU2VoLXQBJUpVDDplV/rJs2YhKIknS1GyBkCRJktSZAYQkSZKkzgwgJEmSJHXmPRBjZLb9nyVJkqT5ZguEJEmSpM4MICRJkiR1ZgAhSZIkqTPvgZAkSYuS9w5KC8MWCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZozBJkiSNudmMOFWWLRthSSRbICRJkiTNgi0QkiRJ88xnWGgxswVCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdeYoTJoTsx1NwjGqJUmSFidbICRJkiR1ZgAhSZIkqTO7MEmSJC0hdivWqBlAaCifkClJkqRh7MIkSZIkqTMDCEmSJEmdLZkuTEl2AQ4A1gQ+Vkp55wIXSZI0BqwfFhe70Erjb0kEEEnWBP4HeAywHDgxyeGllF8sbMkkSQtpUusHb6KVNEpLIoAAtgPOKaX8BiDJ54DdgCVdQUiSZjS29cNsfuQv5h/4tigsPZPy3dXUlkoAsRnwu77p5cBDFqgsmmOjvpLmiVBa0qwf5pgBgWZjMbeGLeayj1pKKQtdhtWW5OnAY0spz2vTzwK2K6X8+0C+FwAvaJP3As5chdXdCfjDahR3sZiU7YTJ2Va3c+lZ1W29Wyllo7kuzDjqUj/MUd0Ak/Xdm4n7YgX3xQruixXGdV90qh+WSgvEcmCLvunNgQsGM5VSPgp8dHVWlOSkUsq2q7OMxWBSthMmZ1vdzqVnkrZ1NcxYP8xF3QAej37uixXcFyu4L1ZY7PtiqQzjeiJwzyR3T7IOsDtw+AKXSZK08KwfJGmOLYkWiFLKDUleChxFHabvoFLKzxe4WJKkBWb9IElzb0kEEACllCOBI+dhVavdzL1ITMp2wuRsq9u59EzStq4y64cF4b5YwX2xgvtihUW9L5bETdSSJEmS5sdSuQdCkiRJ0jwwgJiFJLskOTPJOUn2XujydJFkiyTfTfLLJD9P8rKWfockRyc5u/3dsKUnyYFtG09LsnXfspa1/GcnWdaXvk2S09tnDkyS+d/Sm8uyZpKfJjmiTd89yU9amT/fbqIkybpt+pw2f8u+Zby2pZ+Z5LF96WNx/JPcPsmhSX7VjutDl+LxTPKf7Tt7RpLPJrnVUjmeSQ5KckmSM/rSRn4Mp1qHVt+4nB9GadTf28Ui81CvLhbtvHxCkp+1ffHmlj5n5+rFJiP8HTJWSim+OryoN9/9GrgHsA7wM+C+C12uDuXeFNi6vb8tcBZwX+DdwN4tfW/gXe39rsA3gADbAz9p6XcAftP+btjeb9jmnQA8tH3mG8DjFnB7XwH8H3BEm/4CsHt7/2Hg39r7FwMfbu93Bz7f3t+3Hdt1gbu3Y77mOB1/4BDgee39OsDtl9rxpD7861xgvb7juNdSOZ7ADsDWwBl9aSM/hlOtw9dqH8+xOT+MeDtH+r1dLC/moV5dLK+2Tbdp79cGftK2cU7O1Qu9fau4T0byO2Sht+sW27nQBVgsr1YZH9U3/VrgtQtdrlXYjq8Cj6E+KGnTlrYpcGZ7/xHgGX35z2zznwF8pC/9Iy1tU+BXfekr5ZvnbdscOAZ4JHBEO7H9AVhr8BhSR2R5aHu/VsuXwePayzcuxx+4HfWHdQbSl9TxZMXTg+/Qjs8RwGOX0vEEtmTlH2IjP4ZTrcPXah/LBf8+zeO2juR7u9DbtZr7ZE7r1YXentXYD7cGTqE+6X1OztULvU2rsA9G9jtkobdt8GUXpu56P2h6lre0RaM1jz2IeoVgk1LKhQDt78Yt21TbOV368iHpC+H9wKuBm9r0HYE/lVJuaNP9Zbt5e9r8K1r+2W7/fLsHcCnwidZE+rEk67PEjmcp5ffAfwG/BS6kHp+TWXrHs998HMOp1qHVM47fp/kyV9/bRWlE9eqi0rrsnApcAhxNvWI+V+fqxWaUv0PGigFEd8P6gS+aIayS3Ab4EvDyUsqfp8s6JK2sQvq8SvIE4JJSysn9yUOylhnmjfV2Uq9SbA18qJTyIOBqalP5VBbldra+w7tRm2/vAqwPPG5I1sV+PLtYytu2VLjPb2nJfz9HWK8uKqWUG0spD6Refd8OuM+wbO3vkt0X8/A7ZKwYQHS3HNiib3pz4IIFKsusJFmbepL7TCnlyy354iSbtvmbUq8cwNTbOV365kPS59vDgCclOQ/4HLX58P3A7ZP0nnfSX7abt6fN3wC4nNlv/3xbDiwvpfykTR9KDSiW2vF8NHBuKeXSUspfgS8D/8DSO5795uMYTrUOrZ5x/D7Nl7n63i4qI65XF6VSyp+AY6n3QMzVuXoxGfXvkLFiANHdicA9293061BveDl8gcs0oyQBPg78spSyf9+sw4Fl7f0yah/OXvqebdSI7YErWlPsUcDOSTZsV4d3pvbjuxC4Msn2bV179i1r3pRSXltK2byUsiX12HynlPJM4LvA01q2we3sbf/TWv7S0ndvoyPcHbgn9YbUsTj+pZSLgN8luVdLehTwC5bY8aR2Xdo+ya1bOXrbuaSO54D5OIZTrUOrZxy/T/NlTr63813o1THqenVeNmKOJNkoye3b+/WoF39+ydydqxeNefgdMl4W+iaMxfSijqRwFrV/3+sXujwdy/xwatPXacCp7bUrtZ/dMcDZ7e8dWv4A/9O28XRg275lPQc4p72e3Ze+LXBG+8wHGLjBdwG2eUdWjH5wD+o/3jnAF4F1W/qt2vQ5bf49+j7/+rYtZ9I3AtG4HH/ggcBJ7ZgeRh29Y8kdT+DNwK9aWT5FHZFiSRxP4LPUezv+Sr3a9Nz5OIZTrcPXnBzTsTg/jHgbR/q9XSwv5qFeXSwv4P7AT9u+OAN4U0ufs3P1Ynwxot8h4/TySdSSJEmSOrMLkyRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEJaDUmOTfLYgbSXJ/ngNJ+5avQlkyQtJOsHLWUGENLq+Sz1gTH9dm/pkqTJZf2gJcsAQlo9hwJPSLIuQJItgbsApyY5JskpSU5PstvgB5PsmOSIvukPJNmrvd8myXFJTk5yVJJN52NjJElzxvpBS5YBhLQaSimXUZ8guUtL2h34PPAX4MmllK2BnYD3JkmXZSZZG/hv4GmllG2Ag4C3zXXZJUmjY/2gpWythS6AtAT0mqm/2v4+Bwjw9iQ7ADcBmwGbABd1WN69gPsBR7c6ZU3gwrkvtiRpxKwftCQZQEir7zBg/yRbA+uVUk5pTc0bAduUUv6a5DzgVgOfu4GVWwF78wP8vJTy0NEWW5I0YtYPWpLswiStplLKVcCx1Kbk3s1xGwCXtMphJ+BuQz56PnDfJOsm2QB4VEs/E9goyUOhNlkn+btRboMk6f+3c8coCMRAGEb/AY/owcQ7iGBh4zUERRAES29hExtBsJpiRZT3ykBgtxo+EjI984F/5QQCprFOss3rxY1Vkl1V7SUm4XQAAABkSURBVJMck1zeN4wxblW1SXJKck1yeK7fq2qeZPkcHLMkiyTnj/8FAFMzH/g7Ncb49jcAAAA/whUmAACgTUAAAABtAgIAAGgTEAAAQJuAAAAA2gQEAADQJiAAAIA2AQEAALQ9AGaz6XodUMKrAAAAAElFTkSuQmCC
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<p>For highly-skewed feature distributions such as <code>'capital-gain'</code> and <code>'capital-loss'</code>, it is common practice to apply a <a href="https://en.wikipedia.org/wiki/Data_transformation_(statistics)">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of <code>0</code> is undefined, so we must translate the values by a small amount above <code>0</code> to apply the the logarithm successfully.</p>
<p>Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Log-transform the skewed features</span>
<span class="n">skewed</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">]</span>
<span class="n">features_log_transformed</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">features_raw</span><span class="p">)</span>
<span class="n">features_log_transformed</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span> <span class="o">=</span> <span class="n">features_raw</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</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">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>

<span class="c1"># Visualize the new log distributions</span>
<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">features_log_transformed</span><span class="p">,</span> <span class="n">transformed</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
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<h3 id="Normalizing-Numerical-Features">Normalizing Numerical Features<a class="anchor-link" href="#Normalizing-Numerical-Features">&#182;</a></h3><p>In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as <code>'capital-gain'</code> or <code>'capital-loss'</code> above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.</p>
<p>Run the code cell below to normalize each numerical feature. We will use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html"><code>sklearn.preprocessing.MinMaxScaler</code></a> for this.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import sklearn.preprocessing.StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">MinMaxScaler</span>

<span class="c1"># Initialize a scaler, then apply it to the features</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span> <span class="c1"># default=(0, 1)</span>
<span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;education-num&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">,</span> <span class="s1">&#39;hours-per-week&#39;</span><span class="p">]</span>

<span class="n">features_log_minmax_transform</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">features_log_transformed</span><span class="p">)</span>
<span class="n">features_log_minmax_transform</span><span class="p">[</span><span class="n">numerical</span><span class="p">]</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">features_log_transformed</span><span class="p">[</span><span class="n">numerical</span><span class="p">])</span>

<span class="c1"># Show an example of a record with scaling applied</span>
<span class="n">display</span><span class="p">(</span><span class="n">features_log_minmax_transform</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">5</span><span class="p">))</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>age</th>
      <th>workclass</th>
      <th>education_level</th>
      <th>education-num</th>
      <th>marital-status</th>
      <th>occupation</th>
      <th>relationship</th>
      <th>race</th>
      <th>sex</th>
      <th>capital-gain</th>
      <th>capital-loss</th>
      <th>hours-per-week</th>
      <th>native-country</th>
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  <tbody>
    <tr>
      <th>0</th>
      <td>0.301370</td>
      <td>State-gov</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Never-married</td>
      <td>Adm-clerical</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.667492</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.452055</td>
      <td>Self-emp-not-inc</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.122449</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.287671</td>
      <td>Private</td>
      <td>HS-grad</td>
      <td>0.533333</td>
      <td>Divorced</td>
      <td>Handlers-cleaners</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.493151</td>
      <td>Private</td>
      <td>11th</td>
      <td>0.400000</td>
      <td>Married-civ-spouse</td>
      <td>Handlers-cleaners</td>
      <td>Husband</td>
      <td>Black</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.150685</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Married-civ-spouse</td>
      <td>Prof-specialty</td>
      <td>Wife</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>Cuba</td>
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<h3 id="Implementation:-Data-Preprocessing">Implementation: Data Preprocessing<a class="anchor-link" href="#Implementation:-Data-Preprocessing">&#182;</a></h3><p>From the table in <strong>Exploring the Data</strong> above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called <em>categorical variables</em>) be converted. One popular way to convert categorical variables is by using the <strong>one-hot encoding</strong> scheme. One-hot encoding creates a <em>"dummy"</em> variable for each possible category of each non-numeric feature. For example, assume <code>someFeature</code> has three possible entries: <code>A</code>, <code>B</code>, or <code>C</code>. We then encode this feature into <code>someFeature_A</code>, <code>someFeature_B</code> and <code>someFeature_C</code>.</p>
<table>
<thead><tr>
<th style="text-align:center"></th>
<th style="text-align:center">someFeature</th>
<th></th>
<th style="text-align:center">someFeature_A</th>
<th style="text-align:center">someFeature_B</th>
<th style="text-align:center">someFeature_C</th>
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<td style="text-align:center">0</td>
<td style="text-align:center">B</td>
<td></td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
</tr>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">C</td>
<td>----&gt; one-hot encode ----&gt;</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
</tr>
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<td style="text-align:center">2</td>
<td style="text-align:center">A</td>
<td></td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
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<p>Additionally, as with the non-numeric features, we need to convert the non-numeric target label, <code>'income'</code> to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("&lt;=50K" and "&gt;50K"), we can avoid using one-hot encoding and simply encode these two categories as <code>0</code> and <code>1</code>, respectively. In code cell below, you will need to implement the following:</p>
<ul>
<li>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies"><code>pandas.get_dummies()</code></a> to perform one-hot encoding on the <code>'features_log_minmax_transform'</code> data.</li>
<li>Convert the target label <code>'income_raw'</code> to numerical entries.<ul>
<li>Set records with "&lt;=50K" to <code>0</code> and records with "&gt;50K" to <code>1</code>.</li>
</ul>
</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: One-hot encode the &#39;features_log_minmax_transform&#39; data using pandas.get_dummies()</span>
<span class="n">features_final</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">features_log_minmax_transform</span><span class="p">)</span>

<span class="c1"># TODO: Encode the &#39;income_raw&#39; data to numerical values</span>
<span class="n">income</span> <span class="o">=</span> <span class="n">income_raw</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;&lt;=50K&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;&gt;50K&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">})</span>

<span class="c1"># Print the number of features after one-hot encoding</span>
<span class="n">encoded</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">features_final</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> total features after one-hot encoding.&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">encoded</span><span class="p">)))</span>

<span class="c1"># Uncomment the following line to see the encoded feature names</span>
<span class="nb">print</span><span class="p">(</span><span class="n">encoded</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">features_final</span><span class="o">.</span><span class="n">head</span><span class="p">(),</span><span class="n">income</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
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<pre>103 total features after one-hot encoding.
[&#39;age&#39;, &#39;education-num&#39;, &#39;capital-gain&#39;, &#39;capital-loss&#39;, &#39;hours-per-week&#39;, &#39;workclass_ Federal-gov&#39;, &#39;workclass_ Local-gov&#39;, &#39;workclass_ Private&#39;, &#39;workclass_ Self-emp-inc&#39;, &#39;workclass_ Self-emp-not-inc&#39;, &#39;workclass_ State-gov&#39;, &#39;workclass_ Without-pay&#39;, &#39;education_level_ 10th&#39;, &#39;education_level_ 11th&#39;, &#39;education_level_ 12th&#39;, &#39;education_level_ 1st-4th&#39;, &#39;education_level_ 5th-6th&#39;, &#39;education_level_ 7th-8th&#39;, &#39;education_level_ 9th&#39;, &#39;education_level_ Assoc-acdm&#39;, &#39;education_level_ Assoc-voc&#39;, &#39;education_level_ Bachelors&#39;, &#39;education_level_ Doctorate&#39;, &#39;education_level_ HS-grad&#39;, &#39;education_level_ Masters&#39;, &#39;education_level_ Preschool&#39;, &#39;education_level_ Prof-school&#39;, &#39;education_level_ Some-college&#39;, &#39;marital-status_ Divorced&#39;, &#39;marital-status_ Married-AF-spouse&#39;, &#39;marital-status_ Married-civ-spouse&#39;, &#39;marital-status_ Married-spouse-absent&#39;, &#39;marital-status_ Never-married&#39;, &#39;marital-status_ Separated&#39;, &#39;marital-status_ Widowed&#39;, &#39;occupation_ Adm-clerical&#39;, &#39;occupation_ Armed-Forces&#39;, &#39;occupation_ Craft-repair&#39;, &#39;occupation_ Exec-managerial&#39;, &#39;occupation_ Farming-fishing&#39;, &#39;occupation_ Handlers-cleaners&#39;, &#39;occupation_ Machine-op-inspct&#39;, &#39;occupation_ Other-service&#39;, &#39;occupation_ Priv-house-serv&#39;, &#39;occupation_ Prof-specialty&#39;, &#39;occupation_ Protective-serv&#39;, &#39;occupation_ Sales&#39;, &#39;occupation_ Tech-support&#39;, &#39;occupation_ Transport-moving&#39;, &#39;relationship_ Husband&#39;, &#39;relationship_ Not-in-family&#39;, &#39;relationship_ Other-relative&#39;, &#39;relationship_ Own-child&#39;, &#39;relationship_ Unmarried&#39;, &#39;relationship_ Wife&#39;, &#39;race_ Amer-Indian-Eskimo&#39;, &#39;race_ Asian-Pac-Islander&#39;, &#39;race_ Black&#39;, &#39;race_ Other&#39;, &#39;race_ White&#39;, &#39;sex_ Female&#39;, &#39;sex_ Male&#39;, &#39;native-country_ Cambodia&#39;, &#39;native-country_ Canada&#39;, &#39;native-country_ China&#39;, &#39;native-country_ Columbia&#39;, &#39;native-country_ Cuba&#39;, &#39;native-country_ Dominican-Republic&#39;, &#39;native-country_ Ecuador&#39;, &#39;native-country_ El-Salvador&#39;, &#39;native-country_ England&#39;, &#39;native-country_ France&#39;, &#39;native-country_ Germany&#39;, &#39;native-country_ Greece&#39;, &#39;native-country_ Guatemala&#39;, &#39;native-country_ Haiti&#39;, &#39;native-country_ Holand-Netherlands&#39;, &#39;native-country_ Honduras&#39;, &#39;native-country_ Hong&#39;, &#39;native-country_ Hungary&#39;, &#39;native-country_ India&#39;, &#39;native-country_ Iran&#39;, &#39;native-country_ Ireland&#39;, &#39;native-country_ Italy&#39;, &#39;native-country_ Jamaica&#39;, &#39;native-country_ Japan&#39;, &#39;native-country_ Laos&#39;, &#39;native-country_ Mexico&#39;, &#39;native-country_ Nicaragua&#39;, &#39;native-country_ Outlying-US(Guam-USVI-etc)&#39;, &#39;native-country_ Peru&#39;, &#39;native-country_ Philippines&#39;, &#39;native-country_ Poland&#39;, &#39;native-country_ Portugal&#39;, &#39;native-country_ Puerto-Rico&#39;, &#39;native-country_ Scotland&#39;, &#39;native-country_ South&#39;, &#39;native-country_ Taiwan&#39;, &#39;native-country_ Thailand&#39;, &#39;native-country_ Trinadad&amp;Tobago&#39;, &#39;native-country_ United-States&#39;, &#39;native-country_ Vietnam&#39;, &#39;native-country_ Yugoslavia&#39;]
        age  education-num  capital-gain  capital-loss  hours-per-week  \
0  0.301370       0.800000      0.667492           0.0        0.397959   
1  0.452055       0.800000      0.000000           0.0        0.122449   
2  0.287671       0.533333      0.000000           0.0        0.397959   
3  0.493151       0.400000      0.000000           0.0        0.397959   
4  0.150685       0.800000      0.000000           0.0        0.397959   

   workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \
0                       0                     0                   0   
1                       0                     0                   0   
2                       0                     0                   1   
3                       0                     0                   1   
4                       0                     0                   1   

   workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  \
0                        0                            0   
1                        0                            1   
2                        0                            0   
3                        0                            0   
4                        0                            0   

              ...              native-country_ Portugal  \
0             ...                                     0   
1             ...                                     0   
2             ...                                     0   
3             ...                                     0   
4             ...                                     0   

   native-country_ Puerto-Rico  native-country_ Scotland  \
0                            0                         0   
1                            0                         0   
2                            0                         0   
3                            0                         0   
4                            0                         0   

   native-country_ South  native-country_ Taiwan  native-country_ Thailand  \
0                      0                       0                         0   
1                      0                       0                         0   
2                      0                       0                         0   
3                      0                       0                         0   
4                      0                       0                         0   

   native-country_ Trinadad&amp;Tobago  native-country_ United-States  \
0                                0                              1   
1                                0                              1   
2                                0                              1   
3                                0                              1   
4                                0                              0   

   native-country_ Vietnam  native-country_ Yugoslavia  
0                        0                           0  
1                        0                           0  
2                        0                           0  
3                        0                           0  
4                        0                           0  

[5 rows x 103 columns] 0    0
1    0
2    0
3    0
4    0
Name: income, dtype: int64
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<h3 id="Shuffle-and-Split-Data">Shuffle and Split Data<a class="anchor-link" href="#Shuffle-and-Split-Data">&#182;</a></h3><p>Now all <em>categorical variables</em> have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.</p>
<p>Run the code cell below to perform this split.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="c1"># Split the &#39;features&#39; and &#39;income&#39; data into training and testing sets</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">features_final</span><span class="p">,</span> 
                                                    <span class="n">income</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"># Show the results of the split</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training set has </span><span class="si">{}</span><span class="s2"> samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing set has </span><span class="si">{}</span><span class="s2"> samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<pre>Training set has 36177 samples.
Testing set has 9045 samples.
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<hr>
<h2 id="Evaluating-Model-Performance">Evaluating Model Performance<a class="anchor-link" href="#Evaluating-Model-Performance">&#182;</a></h2><p>In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a <em>naive predictor</em>.</p>

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<h3 id="Metrics-and-the-Naive-Predictor">Metrics and the Naive Predictor<a class="anchor-link" href="#Metrics-and-the-Naive-Predictor">&#182;</a></h3><p><em>CharityML</em>, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using <strong>accuracy</strong> as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that <em>does not</em> make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is <em>more important</em> than the model's ability to <strong>recall</strong> those individuals. We can use <strong>F-beta score</strong> as a metric that considers both precision and recall:</p>
$$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$<p>In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the <strong>F$_{0.5}$ score</strong> (or F-score for simplicity).</p>
<p>Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect <strong>accuracy</strong>, since we could simply say <em>"this person does not make more than \$50,000"</em> and generally be right, without ever looking at the data! Making such a statement would be called <strong>naive</strong>, since we have not considered any information to substantiate the claim. It is always important to consider the <em>naive prediction</em> for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, <em>CharityML</em> would identify no one as donors.</p>
<h4 id="Note:-Recap-of-accuracy,-precision,-recall">Note: Recap of accuracy, precision, recall<a class="anchor-link" href="#Note:-Recap-of-accuracy,-precision,-recall">&#182;</a></h4><p><strong> Accuracy </strong> measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).</p>
<p><strong> Precision </strong> tells us what proportion of messages we classified as spam, actually were spam.
It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of</p>
<p><code>[True Positives/(True Positives + False Positives)]</code></p>
<p><strong> Recall(sensitivity)</strong> tells us what proportion of messages that actually were spam were classified by us as spam.
It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of</p>
<p><code>[True Positives/(True Positives + False Negatives)]</code></p>
<p>For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios).</p>

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<h3 id="Question-1---Naive-Predictor-Performace">Question 1 - Naive Predictor Performace<a class="anchor-link" href="#Question-1---Naive-Predictor-Performace">&#182;</a></h3><ul>
<li>If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to <code>'accuracy'</code> and <code>'fscore'</code> to be used later.</li>
</ul>
<p><strong> Please note </strong> that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.</p>
<p><strong> HINT: </strong></p>
<ul>
<li>When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. </li>
<li>Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd">TP = np.sum(income) # Counting the ones as this is the naive case. Note that &#39;income&#39; is the &#39;income_raw&#39; data </span>
<span class="sd">encoded to numerical values done in the data preprocessing step.</span>
<span class="sd">FP = income.count() - TP # Specific to the naive case</span>

<span class="sd">TN = 0 # No predicted negatives in the naive case</span>
<span class="sd">FN = 0 # No predicted negatives in the naive case</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="c1"># TODO: Calculate accuracy, precision and recall</span>
<span class="n">TP</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">income</span><span class="p">)</span>
<span class="n">FP</span> <span class="o">=</span> <span class="n">income</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">TP</span>
<span class="n">TN</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">FN</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="c1">#print(TP)</span>

<span class="n">accuracy</span> <span class="o">=</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span><span class="o">/</span><span class="n">income</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">recall</span> <span class="o">=</span> <span class="n">TP</span><span class="o">/</span><span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FN</span><span class="p">)</span>
<span class="n">precision</span> <span class="o">=</span> <span class="n">TP</span><span class="o">/</span><span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>

<span class="c1"># TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.</span>
<span class="n">fscore</span> <span class="o">=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">beta</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="n">precision</span><span class="o">*</span><span class="n">recall</span><span class="p">)</span><span class="o">/</span><span class="p">(((</span><span class="n">beta</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">precision</span><span class="p">)</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>

<span class="c1"># Print the results </span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Naive Predictor: [Accuracy score: </span><span class="si">{:.4f}</span><span class="s2">, F-score: </span><span class="si">{:.4f}</span><span class="s2">]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">))</span>
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<pre>Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]
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<h3 id="Supervised-Learning-Models">Supervised Learning Models<a class="anchor-link" href="#Supervised-Learning-Models">&#182;</a></h3><p><strong>The following are some of the supervised learning models that are currently available in</strong> <a href="http://scikit-learn.org/stable/supervised_learning.html"><code>scikit-learn</code></a> <strong>that you may choose from:</strong></p>
<ul>
<li>Gaussian Naive Bayes (GaussianNB)</li>
<li>Decision Trees</li>
<li>Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)</li>
<li>K-Nearest Neighbors (KNeighbors)</li>
<li>Stochastic Gradient Descent Classifier (SGDC)</li>
<li>Support Vector Machines (SVM)</li>
<li>Logistic Regression</li>
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<h3 id="Question-2---Model-Application">Question 2 - Model Application<a class="anchor-link" href="#Question-2---Model-Application">&#182;</a></h3><p>List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen</p>
<ul>
<li>Describe one real-world application in industry where the model can be applied. </li>
<li>What are the strengths of the model; when does it perform well?</li>
<li>What are the weaknesses of the model; when does it perform poorly?</li>
<li>What makes this model a good candidate for the problem, given what you know about the data?</li>
</ul>
<p><strong> HINT: </strong></p>
<p>Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer.</p>

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<p><strong>Answer: </strong></p>
<ol>
<li><p>Logistic Regression:</p>
<ol>
<li>Calculate the probability that someone will vote for one of two parties.</li>
<li>It is a widely used technique because it is very efficient, does not require too many computational resources, it’s highly interpretable, it doesn’t require input features to be scaled, it doesn’t require any tuning, it’s easy to regularize, and it outputs well-calibrated predicted probabilities. Another advantage of Logistic Regression is that it is incredibly easy to implement and very efficient to train.
Source(<a href="https://machinelearning-blog.com/2018/04/23/logistic-regression-101/">https://machinelearning-blog.com/2018/04/23/logistic-regression-101/</a>)</li>
<li>A disadvantage of it is that we can’t solve non-linear problems with logistic regression since it’s decision surface is linear.Logistic Regression is also not one of the most powerful algorithms out there and can be easily outperformed by more complex ones. Another disadvantage is its high reliance on a proper presentation of your data. This means that logistic regression is not a useful tool unless you have already identified all the important independent variables. It is also an Algorithm that is known for its vulnerability to overfitting.
Source(<a href="https://machinelearning-blog.com/2018/04/23/logistic-regression-101/">https://machinelearning-blog.com/2018/04/23/logistic-regression-101/</a>)</li>
<li>Logistic Regression is the most basic classification algorithms. It can serve as a good starting point or benchmark for the outperformance of more complex models.</li>
</ol>
</li>
</ol>
<ol>
<li><p>AdaBoost:</p>
<ol>
<li>One of the biggest applications of AdaBoost is the Viola-Jones face detector, which seems to be the standard algorithm for detecting faces in an image.</li>
<li>AdaBoost is one of the best out of the box classification algorithms. The combination of weak learners can also help identify non-linear relationships. It usually has high precision.</li>
<li>The most obvious drawback of AdaBoost is the computational complexity.Another major criticism for Adaboost comes from its sensitivity to noisy data and hence its tendency to overfit.
Source(<a href="https://codesachin.wordpress.com/tag/adaboost/">https://codesachin.wordpress.com/tag/adaboost/</a>)</li>
<li>AdaBoost was chosen for this problem in order for the 'weak models' to look at patterns between individual features and the target feature as there could be correlations in individual features.</li>
</ol>
</li>
</ol>
<ol>
<li><p>SVM:</p>
<ol>
<li>Text and hypertext categorization</li>
<li>SVM works relatively well when there is clear margin of separation between classes. SVM is more effective in high dimensional spaces. SVM is effective in cases where number of dimensions is greater than the number of samples. SVM is relatively memory efficient
Source(<a href="https://medium.com/@dhiraj8899/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107">https://medium.com/@dhiraj8899/top-4-advantages-and-disadvantages-of-support-vector-machine-or-svm-a3c06a2b107</a>)</li>
<li>SVM algorithm is not suitable for large data sets. SVM does not perform very well, when the data set has more noise i.e. target classes are overlapping. In cases where number of features for each data point exceeds the number of training data sample, the SVM will under perform.</li>
<li>SVM is being used as the dataset is high-dimensional. SVM also is more memory efficient.</li>
</ol>
</li>
</ol>

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<h3 id="Implementation---Creating-a-Training-and-Predicting-Pipeline">Implementation - Creating a Training and Predicting Pipeline<a class="anchor-link" href="#Implementation---Creating-a-Training-and-Predicting-Pipeline">&#182;</a></h3><p>To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.
In the code block below, you will need to implement the following:</p>
<ul>
<li>Import <code>fbeta_score</code> and <code>accuracy_score</code> from <a href="http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics"><code>sklearn.metrics</code></a>.</li>
<li>Fit the learner to the sampled training data and record the training time.</li>
<li>Perform predictions on the test data <code>X_test</code>, and also on the first 300 training points <code>X_train[:300]</code>.<ul>
<li>Record the total prediction time.</li>
</ul>
</li>
<li>Calculate the accuracy score for both the training subset and testing set.</li>
<li>Calculate the F-score for both the training subset and testing set.<ul>
<li>Make sure that you set the <code>beta</code> parameter!</li>
</ul>
</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Import two metrics from sklearn - fbeta_score and accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">fbeta_score</span>

<span class="k">def</span> <span class="nf">train_predict</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">):</span> 
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    inputs:</span>
<span class="sd">       - learner: the learning algorithm to be trained and predicted on</span>
<span class="sd">       - sample_size: the size of samples (number) to be drawn from training set</span>
<span class="sd">       - X_train: features training set</span>
<span class="sd">       - y_train: income training set</span>
<span class="sd">       - X_test: features testing set</span>
<span class="sd">       - y_test: income testing set</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
    
    <span class="c1"># TODO: Fit the learner to the training data using slicing with &#39;sample_size&#39; using .fit(training_features[:], training_labels[:])</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
    <span class="n">learner</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">],</span><span class="n">y_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">])</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
    
    <span class="c1"># TODO: Calculate the training time</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;train_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
        
    <span class="c1"># TODO: Get the predictions on the test set(X_test),</span>
    <span class="c1">#       then get predictions on the first 300 training samples(X_train) using .predict()</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
    <span class="n">predictions_test</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="n">predictions_train</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">])</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
    
    <span class="c1"># TODO: Calculate the total prediction time</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;pred_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
            
    <span class="c1"># TODO: Compute accuracy on the first 300 training samples which is y_train[:300]</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_train&#39;</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">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">]</span> <span class="o">==</span> <span class="n">predictions_train</span><span class="p">)</span><span class="o">/</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        
    <span class="c1"># TODO: Compute accuracy on test set using accuracy_score()</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions_test</span><span class="p">)</span>
    
    <span class="c1"># TODO: Compute F-score on the the first 300 training samples using fbeta_score()</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_train&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">],</span> <span class="n">predictions_train</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span>
        
    <span class="c1"># TODO: Compute F-score on the test set which is y_test</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions_test</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)</span>
       
    <span class="c1"># Success</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> trained on </span><span class="si">{}</span><span class="s2"> samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">))</span>   
    <span class="c1"># Return the results</span>
    <span class="k">return</span> <span class="n">results</span>
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<h3 id="Implementation:-Initial-Model-Evaluation">Implementation: Initial Model Evaluation<a class="anchor-link" href="#Implementation:-Initial-Model-Evaluation">&#182;</a></h3><p>In the code cell, you will need to implement the following:</p>
<ul>
<li>Import the three supervised learning models you've discussed in the previous section.</li>
<li>Initialize the three models and store them in <code>'clf_A'</code>, <code>'clf_B'</code>, and <code>'clf_C'</code>.<ul>
<li>Use a <code>'random_state'</code> for each model you use, if provided.</li>
<li><strong>Note:</strong> Use the default settings for each model — you will tune one specific model in a later section.</li>
</ul>
</li>
<li>Calculate the number of records equal to 1%, 10%, and 100% of the training data.<ul>
<li>Store those values in <code>'samples_1'</code>, <code>'samples_10'</code>, and <code>'samples_100'</code> respectively.</li>
</ul>
</li>
</ul>
<p><strong>Note:</strong> Depending on which algorithms you chose, the following implementation may take some time to run!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Import the three supervised learning models from sklearn</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">AdaBoostClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="k">import</span> <span class="n">SVC</span>

<span class="c1"># TODO: Initialize the three models</span>
<span class="n">clf_A</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">clf_B</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">()</span>
<span class="n">clf_C</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">()</span>

<span class="c1"># TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data</span>
<span class="c1"># HINT: samples_100 is the entire training set i.e. len(y_train)</span>
<span class="c1"># HINT: samples_10 is 10% of samples_100 (ensure to set the count of the values to be `int` and not `float`)</span>
<span class="c1"># HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)</span>
<span class="n">samples_100</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">samples_10</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">10</span>
<span class="n">samples_1</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">100</span>

<span class="c1"># Collect results on the learners</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="p">[</span><span class="n">clf_A</span><span class="p">,</span> <span class="n">clf_B</span><span class="p">,</span> <span class="n">clf_C</span><span class="p">]:</span>
    <span class="n">clf_name</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
    <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">samples_1</span><span class="p">,</span> <span class="n">samples_10</span><span class="p">,</span> <span class="n">samples_100</span><span class="p">]):</span>
        <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> \
        <span class="n">train_predict</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
        

<span class="c1"># Run metrics visualization for the three supervised learning models chosen</span>
<span class="n">vs</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">)</span>
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<pre>LogisticRegression trained on 361 samples.
Training Time: 0.0046465396881103516 Prediction Time: 0.004492521286010742
LogisticRegression trained on 3617 samples.
Training Time: 0.02630448341369629 Prediction Time: 0.0038907527923583984
LogisticRegression trained on 36177 samples.
Training Time: 0.3460066318511963 Prediction Time: 0.003945827484130859
AdaBoostClassifier trained on 361 samples.
Training Time: 0.09316587448120117 Prediction Time: 0.0940854549407959
AdaBoostClassifier trained on 3617 samples.
Training Time: 0.23736238479614258 Prediction Time: 0.09285211563110352
AdaBoostClassifier trained on 36177 samples.
Training Time: 2.0266432762145996 Prediction Time: 0.09396004676818848
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<pre>/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
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<pre>SVC trained on 361 samples.
Training Time: 0.012404680252075195 Prediction Time: 0.2547414302825928
SVC trained on 3617 samples.
Training Time: 0.9971408843994141 Prediction Time: 2.17413592338562
SVC trained on 36177 samples.
Training Time: 112.88227224349976 Prediction Time: 18.46828818321228
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>
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<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;LogReg&#39;</span><span class="p">:</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;LogisticRegression&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span><span class="s1">&#39;AdaBoost&#39;</span><span class="p">:</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;AdaBoostClassifier&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;SVC&#39;</span><span class="p">:</span> <span class="n">results</span><span class="p">[</span><span class="s1">&#39;SVC&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">]})</span>
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    <div class="prompt output_prompt">Out[22]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
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    .dataframe thead th {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>LogReg</th>
      <th>AdaBoost</th>
      <th>SVC</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>acc_test</th>
      <td>0.841902</td>
      <td>0.857601</td>
      <td>0.837148</td>
    </tr>
    <tr>
      <th>acc_train</th>
      <td>0.846667</td>
      <td>0.850000</td>
      <td>0.853333</td>
    </tr>
    <tr>
      <th>f_test</th>
      <td>0.683165</td>
      <td>0.724551</td>
      <td>0.674477</td>
    </tr>
    <tr>
      <th>f_train</th>
      <td>0.698529</td>
      <td>0.711538</td>
      <td>0.719697</td>
    </tr>
    <tr>
      <th>pred_time</th>
      <td>0.003946</td>
      <td>0.093960</td>
      <td>18.468288</td>
    </tr>
    <tr>
      <th>train_time</th>
      <td>0.346007</td>
      <td>2.026643</td>
      <td>112.882272</td>
    </tr>
  </tbody>
</table>
</div>
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<h2 id="Improving-Results">Improving Results<a class="anchor-link" href="#Improving-Results">&#182;</a></h2><p>In this final section, you will choose from the three supervised learning models the <em>best</em> model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (<code>X_train</code> and <code>y_train</code>) by tuning at least one parameter to improve upon the untuned model's F-score.</p>

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<h3 id="Question-3---Choosing-the-Best-Model">Question 3 - Choosing the Best Model<a class="anchor-link" href="#Question-3---Choosing-the-Best-Model">&#182;</a></h3><ul>
<li>Based on the evaluation you performed earlier, in one to two paragraphs, explain to <em>CharityML</em> which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000. </li>
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<p><strong> HINT: </strong> 
Look at the graph at the bottom left from the cell above(the visualization created by <code>vs.evaluate(results, accuracy, fscore)</code>) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:</p>
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<li>metrics - F score on the testing when 100% of the training data is used, </li>
<li>prediction/training time</li>
<li>the algorithm's suitability for the data.</li>
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<p><strong>Answer: </strong></p>
<p>AdaBoost received the highest F-score and accuracy for our testing set after sampling 100% of our training dataset. It achieved an accuracy of 85.76% and an F-Score of 72.45%. Compared to our baseline accuracy of 24.78%, F-score of 29.17% from our naive predictor AdaBoost does significantly better prior to tuning.</p>
<p>AdaBoost was also relatively time efficient, going through the entire training dataset in 2.03 seconds, performing much better than the SVM model at 112.88 seconds but slightly worse compared logistic model at 0.35 seconds. The simpler logistic model did remarkably well and can be favoured when computing time is an important factor.</p>
<p>Having said that, considering the performance and time efficiency, AdaBoost is the best model for us to identify potential donors.</p>
<p>I think AdaBoost is a good model for our data as our data doesn't have too many outliers and the features seem to have low variance, which are the strengths of AdaBoost. If we choose the correct weak learners for this Ensemble, our model could benefit from the best of both worlds (as an ensemble) and have low variance (low overfitting) while also preventing over-generalization (lower bias). Lastly, the relative higher-dimensionality of our data indicates that AdaBoost would be a good model since it deals with that issue well.</p>

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<h3 id="Question-4---Describing-the-Model-in-Layman's-Terms">Question 4 - Describing the Model in Layman's Terms<a class="anchor-link" href="#Question-4---Describing-the-Model-in-Layman's-Terms">&#182;</a></h3><ul>
<li>In one to two paragraphs, explain to <em>CharityML</em>, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.</li>
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<p><strong> HINT: </strong></p>
<p>When explaining your model, if using external resources please include all citations.</p>

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<p><strong>Answer: </strong></p>
<p>AdaBoost is a machine learning model that combines weak learners (simple algorithms) to form a strong rule to classify our dataset.</p>
<p>Here the simple algorithms or weak learners are decision trees, which are tree-like models that classify the data based on those features that best split the data. In our dataset this could be any feature of the person such as age, sex or race.</p>
<p>The AdaBoost model combines the predictive power of these weak learners. Consider the simple example below, where our weak learners are D1, D2 and D3.
<img src="data:image/png;base64, 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" alt="image.png">
Source (<a href="https://towardsdatascience.com/understanding-adaboost-for-decision-tree-ff8f07d2851">https://towardsdatascience.com/understanding-adaboost-for-decision-tree-ff8f07d2851</a>)</p>
<p>1: The first learner (D1) takes all the data points and assigns equal weight or attention to each observation. It then uses the features provided to predict the outcome of the data points. For example D1 could try to predict whether the person's income is &gt;50k based on their age and sex.</p>
<p>2: If there are any data points that the first learner L1 predicted incorrectly, then we pay higher attention to these observations by penalizing the next learner L2 for inaccurately classifying them again. Then, we apply D2. For example it could decide whether the person's income is &gt;50k based on their marital status and education level.</p>
<p>3: We keep repeating the above process until we have reached a specific number of weak learners L(n).</p>
<p>Finally, we combines the outputs from all the weak learners by asking the question 'Which class do you think this data point belongs?' for all the data points. In our case all the weak learners vote on whether they think the data points have an income &gt;50k or &lt;=50k. The aggregate decision is the decision of our AdaBoost model. This process creates a strong learner which eventually has good predictive power as shown by the evalutaion metrics above.</p>
<p>Using this single strong prediction rule, the model can now look at new data and provide a good prediction of whether the person has an income greater or less than $50,000.</p>

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<h3 id="Implementation:-Model-Tuning">Implementation: Model Tuning<a class="anchor-link" href="#Implementation:-Model-Tuning">&#182;</a></h3><p>Fine tune the chosen model. Use grid search (<code>GridSearchCV</code>) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:</p>
<ul>
<li>Import <a href="http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html"><code>sklearn.grid_search.GridSearchCV</code></a> and <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"><code>sklearn.metrics.make_scorer</code></a>.</li>
<li>Initialize the classifier you've chosen and store it in <code>clf</code>.<ul>
<li>Set a <code>random_state</code> if one is available to the same state you set before.</li>
</ul>
</li>
<li>Create a dictionary of parameters you wish to tune for the chosen model.<ul>
<li>Example: <code>parameters = {'parameter' : [list of values]}</code>.</li>
<li><strong>Note:</strong> Avoid tuning the <code>max_features</code> parameter of your learner if that parameter is available!</li>
</ul>
</li>
<li>Use <code>make_scorer</code> to create an <code>fbeta_score</code> scoring object (with $\beta = 0.5$).</li>
<li>Perform grid search on the classifier <code>clf</code> using the <code>'scorer'</code>, and store it in <code>grid_obj</code>.</li>
<li>Fit the grid search object to the training data (<code>X_train</code>, <code>y_train</code>), and store it in <code>grid_fit</code>.</li>
</ul>
<p><strong>Note:</strong> Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Import &#39;GridSearchCV&#39;, &#39;make_scorer&#39;, and any other necessary libraries</span>
<span class="kn">from</span>  <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">make_scorer</span>

<span class="c1"># TODO: Initialize the classifier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># TODO: Create the parameters list you wish to tune, using a dictionary if needed.</span>
<span class="c1"># HINT: parameters = {&#39;parameter_1&#39;: [value1, value2], &#39;parameter_2&#39;: [value1, value2]}</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;n_estimators&#39;</span><span class="p">:[</span><span class="mi">10</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">300</span><span class="p">],</span>
              <span class="s1">&#39;learning_rate&#39;</span><span class="p">:[</span><span class="mf">0.1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">]}</span>
              
<span class="c1"># TODO: Make an fbeta_score scoring object using make_scorer()</span>
<span class="n">scorer</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>

<span class="c1"># TODO: Perform grid search on the classifier using &#39;scorer&#39; as the scoring method using GridSearchCV()</span>
<span class="n">grid_obj</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">)</span>

<span class="c1"># TODO: Fit the grid search object to the training data and find the optimal parameters using fit()</span>
<span class="n">grid_fit</span> <span class="o">=</span> <span class="n">grid_obj</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># Get the estimator</span>
<span class="n">best_clf</span> <span class="o">=</span> <span class="n">grid_fit</span><span class="o">.</span><span class="n">best_estimator_</span>

<span class="c1"># Make predictions using the unoptimized and model</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">best_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># Report the before-and-afterscores</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Unoptimized model</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Optimized Model</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final accuracy score on the testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final F-score on the testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)))</span>
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<pre>Unoptimized model
------
Accuracy score on testing data: 0.8576
F-score on testing data: 0.7246

Optimized Model
------
Final accuracy score on the testing data: 0.8663
Final F-score on the testing data: 0.7425
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<h3 id="Question-5---Final-Model-Evaluation">Question 5 - Final Model Evaluation<a class="anchor-link" href="#Question-5---Final-Model-Evaluation">&#182;</a></h3><ul>
<li>What is your optimized model's accuracy and F-score on the testing data? </li>
<li>Are these scores better or worse than the unoptimized model? </li>
<li>How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in <strong>Question 1</strong>?_  </li>
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<p><strong>Note:</strong> Fill in the table below with your results, and then provide discussion in the <strong>Answer</strong> box.</p>

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<h4 id="Results:">Results:<a class="anchor-link" href="#Results:">&#182;</a></h4><table>
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<th style="text-align:center">Metric</th>
<th style="text-align:center">Unoptimized Model</th>
<th style="text-align:center">Optimized Model</th>
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<td style="text-align:center">Accuracy Score</td>
<td style="text-align:center">0.8576</td>
<td style="text-align:center">0.8663</td>
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<td style="text-align:center">F-score</td>
<td style="text-align:center">0.7246</td>
<td style="text-align:center">0.7425</td>
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<p><strong>Answer: </strong></p>
<p>The accuracy score and F-score for the optimized model are better than the unoptimized model. The optimized model's accuracy score is 86.63% whereas the f-score is 74.25%.</p>
<p>In comparison to the naive predictor benchmark, that had an accuracy score and f-score of 24.78% an 29.17% respectively, the final model is significantly better.</p>

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<hr>
<h2 id="Feature-Importance">Feature Importance<a class="anchor-link" href="#Feature-Importance">&#182;</a></h2><p>An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.</p>
<p>Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a <code>feature_importance_</code> attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset.</p>

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<h3 id="Question-6---Feature-Relevance-Observation">Question 6 - Feature Relevance Observation<a class="anchor-link" href="#Question-6---Feature-Relevance-Observation">&#182;</a></h3><p>When <strong>Exploring the Data</strong>, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?</p>

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<p><strong>Answer:</strong></p>
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<li>race: Race has been a major cause of income disparity in the US and is correlated with other factors which predict income.</li>
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<li>education/education_num: Highly qualified workers tend to have a higher income.</li>
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<li>workclass: The salaries of those who are out of work or never worked is likley to be very low and should have good predictive power.</li>
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<li>age: Older people in the workforce tend to be more experienced and earn more.</li>
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<li>native-country: Immigrants and converted citizens tend to be selected by the authorities to have a minimum criteria of being able to support themselves. They may have a higher income compared to native citizens.</li>
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<h3 id="Implementation---Extracting-Feature-Importance">Implementation - Extracting Feature Importance<a class="anchor-link" href="#Implementation---Extracting-Feature-Importance">&#182;</a></h3><p>Choose a <code>scikit-learn</code> supervised learning algorithm that has a <code>feature_importance_</code> attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.</p>
<p>In the code cell below, you will need to implement the following:</p>
<ul>
<li>Import a supervised learning model from sklearn if it is different from the three used earlier.</li>
<li>Train the supervised model on the entire training set.</li>
<li>Extract the feature importances using <code>'.feature_importances_'</code>.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Import a supervised learning model that has &#39;feature_importances_&#39;</span>


<span class="c1"># TODO: Train the supervised model on the training set using .fit(X_train, y_train)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">clf_B</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># TODO: Extract the feature importances using .feature_importances_ </span>
<span class="n">importances</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_importances_</span>

<span class="c1"># Plot</span>
<span class="n">vs</span><span class="o">.</span><span class="n">feature_plot</span><span class="p">(</span><span class="n">importances</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
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<h3 id="Question-7---Extracting-Feature-Importance">Question 7 - Extracting Feature Importance<a class="anchor-link" href="#Question-7---Extracting-Feature-Importance">&#182;</a></h3><p>Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.</p>
<ul>
<li>How do these five features compare to the five features you discussed in <strong>Question 6</strong>?</li>
<li>If you were close to the same answer, how does this visualization confirm your thoughts? </li>
<li>If you were not close, why do you think these features are more relevant?</li>
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<p><strong>Answer:</strong></p>
<ol>
<li><p>Two of my hypothesis were correct while three were wrong.</p>
</li>
<li><p>1,3 - Capital loss and capital gain are strong factors. This may indicate that only those who have a high income are even capable of making investments and reporting capital gains and losses.</p>
<p>2 - Age was expected but the weight is stronger than expected.</p>
<p>4 - Hours-per-week is slightly unexpected. I had initially hypothesized that workclass would be a better predictor of low income levels. But it seems that hours-per-week may be better.</p>
<p>5 - Education was predicted but the weight is weaker than expected.</p>
</li>
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<h3 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h3><p>How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of <strong>all</strong> features present in the data. This hints that we can attempt to <em>reduce the feature space</em> and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set <em>with only the top five important features</em>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import functionality for cloning a model</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="k">import</span> <span class="n">clone</span>

<span class="c1"># Reduce the feature space</span>
<span class="n">X_train_reduced</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>
<span class="n">X_test_reduced</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>

<span class="c1"># Train on the &quot;best&quot; model found from grid search earlier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="p">(</span><span class="n">clone</span><span class="p">(</span><span class="n">best_clf</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_reduced</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># Make new predictions</span>
<span class="n">reduced_predictions</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_reduced</span><span class="p">)</span>

<span class="c1"># Report scores from the final model using both versions of data</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final Model trained on full data</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Final Model trained on reduced data</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">)))</span>
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<pre>Final Model trained on full data
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Accuracy on testing data: 0.8663
F-score on testing data: 0.7425

Final Model trained on reduced data
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Accuracy on testing data: 0.8404
F-score on testing data: 0.6973
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<h3 id="Question-8---Effects-of-Feature-Selection">Question 8 - Effects of Feature Selection<a class="anchor-link" href="#Question-8---Effects-of-Feature-Selection">&#182;</a></h3><ul>
<li>How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?</li>
<li>If training time was a factor, would you consider using the reduced data as your training set?</li>
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<p><strong>Answer:</strong></p>
<p>The final model's F-score on the selected features was 0.6973 in comparison to 0.7425 using the complete data. The final model's accuracy score on the reduced data was 0.8663 compared to 0.8404 using the full data. The F-score has reduced significantly (~5%) while the fall in accuracy is relatively small (~2.5%).</p>
<p>Training time is not much of a factor because AdaBoost is one of the faster ensemble methods. However, if I was training a SVM or using the same model that had much higher number of features or/and data points, which showed very high training and testing times, I would consider using the most important features.</p>

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<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<h2 id="Before-You-Submit">Before You Submit<a class="anchor-link" href="#Before-You-Submit">&#182;</a></h2><p>You will also need run the following in order to convert the Jupyter notebook into HTML, so that your submission will include both files.</p>

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<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!!</span>jupyter nbconvert *.ipynb
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