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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 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
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<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Model-Evaluation-&amp;-Validation">Model Evaluation &amp; Validation<a class="anchor-link" href="#Model-Evaluation-&amp;-Validation">&#182;</a></h2><h2 id="Project:-Predicting-Boston-Housing-Prices">Project: Predicting Boston Housing Prices<a class="anchor-link" href="#Project:-Predicting-Boston-Housing-Prices">&#182;</a></h2><p>Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. 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 'TODO' statement. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</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 evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a <em>good fit</em> could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.</p>
<p>The dataset for this project originates from the <a href="https://archive.ics.uci.edu/ml/datasets/Housing">UCI Machine Learning Repository</a>. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:</p>
<ul>
<li>16 data points have an <code>'MEDV'</code> value of 50.0. These data points likely contain <strong>missing or censored values</strong> and have been removed.</li>
<li>1 data point has an <code>'RM'</code> value of 8.78. This data point can be considered an <strong>outlier</strong> and has been removed.</li>
<li>The features <code>'RM'</code>, <code>'LSTAT'</code>, <code>'PTRATIO'</code>, and <code>'MEDV'</code> are essential. The remaining <strong>non-relevant features</strong> have been excluded.</li>
<li>The feature <code>'MEDV'</code> has been <strong>multiplicatively scaled</strong> to account for 35 years of market inflation.</li>
</ul>
<p>Run the code cell below to load the Boston housing dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.</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">sklearn.cross_validation</span> <span class="k">import</span> <span class="n">ShuffleSplit</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">pearsonr</span>
<span class="c1"># Import supplementary visualizations 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="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="c1"># Pretty display for notebooks</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># Load the Boston housing dataset</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;housing.csv&#39;</span><span class="p">)</span>
<span class="n">prices</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;MEDV&#39;</span><span class="p">]</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;MEDV&#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="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">IPython.core.display</span> <span class="k">import</span> <span class="n">HTML</span> 
<span class="c1"># Success</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Boston housing dataset has </span><span class="si">{}</span><span class="s2"> data points with </span><span class="si">{}</span><span class="s2"> variables each.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
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<pre>C:\Users\sid321axn\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  &#34;This module will be removed in 0.20.&#34;, DeprecationWarning)
C:\Users\sid321axn\Anaconda3\lib\site-packages\sklearn\learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20
  DeprecationWarning)
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<pre>Boston housing dataset has 489 data points with 4 variables each.
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<h1 id="Preprocessing-Step">Preprocessing Step<a class="anchor-link" href="#Preprocessing-Step">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prices</span> <span class="o">=</span> <span class="n">prices</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;MEDV&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mf">50.0</span><span class="p">]</span>

<span class="n">features</span> <span class="o">=</span><span class="n">features</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;RM&#39;</span><span class="p">]</span><span class="o">!=</span><span class="mf">8.78</span><span class="p">]</span>
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<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>In this first section of this project, you will make a cursory investigation about the Boston housing data and provide your observations. Familiarizing yourself with the data through an explorative process is a fundamental practice to help you better understand and justify your results.</p>
<p>Since the main goal of this project is to construct a working model which has the capability of predicting the value of houses, we will need to separate the dataset into <strong>features</strong> and the <strong>target variable</strong>. The <strong>features</strong>, <code>'RM'</code>, <code>'LSTAT'</code>, and <code>'PTRATIO'</code>, give us quantitative information about each data point. The <strong>target variable</strong>, <code>'MEDV'</code>, will be the variable we seek to predict. These are stored in <code>features</code> and <code>prices</code>, respectively.</p>

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<h3 id="Implementation:-Calculate-Statistics">Implementation: Calculate Statistics<a class="anchor-link" href="#Implementation:-Calculate-Statistics">&#182;</a></h3><p>For your very first coding implementation, you will calculate descriptive statistics about the Boston housing prices. Since <code>numpy</code> has already been imported for you, use this library to perform the necessary calculations. These statistics will be extremely important later on to analyze various prediction results from the constructed model.</p>
<p>In the code cell below, you will need to implement the following:</p>
<ul>
<li>Calculate the minimum, maximum, mean, median, and standard deviation of <code>'MEDV'</code>, which is stored in <code>prices</code>.<ul>
<li>Store each calculation in their respective variable.</li>
</ul>
</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Minimum price of the data</span>
<span class="n">minimum_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>

<span class="c1"># TODO: Maximum price of the data</span>
<span class="n">maximum_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>

<span class="c1"># TODO: Mean price of the data</span>
<span class="n">mean_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>

<span class="c1"># TODO: Median price of the data</span>
<span class="n">median_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>

<span class="c1"># TODO: Standard deviation of prices of the data</span>
<span class="n">std_price</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>

<span class="c1"># Show the calculated statistics</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Statistics for Boston housing dataset:</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;Minimum price: $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">minimum_price</span><span class="p">))</span> 
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Maximum price: $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">maximum_price</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean price: $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_price</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Median price $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">median_price</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Standard deviation of prices: $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">std_price</span><span class="p">))</span>
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<pre>Statistics for Boston housing dataset:

Minimum price: $105,000.00
Maximum price: $1,024,800.00
Mean price: $454,342.94
Median price $438,900.00
Standard deviation of prices: $165,171.13
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<h3 id="Question-1---Feature-Observation">Question 1 - Feature Observation<a class="anchor-link" href="#Question-1---Feature-Observation">&#182;</a></h3><p>As a reminder, we are using three features from the Boston housing dataset: <code>'RM'</code>, <code>'LSTAT'</code>, and <code>'PTRATIO'</code>. For each data point (neighborhood):</p>
<ul>
<li><code>'RM'</code> is the average number of rooms among homes in the neighborhood.</li>
<li><code>'LSTAT'</code> is the percentage of homeowners in the neighborhood considered "lower class" (working poor).</li>
<li><code>'PTRATIO'</code> is the ratio of students to teachers in primary and secondary schools in the neighborhood.</li>
</ul>
<p><strong> Using your intuition, for each of the three features above, do you think that an increase in the value of that feature would lead to an </strong>increase<strong> in the value of <code>'MEDV'</code> or a </strong>decrease<strong> in the value of <code>'MEDV'</code>? Justify your answer for each.</strong></p>
<p><strong>Hint:</strong> This problem can phrased using examples like below.</p>
<ul>
<li>Would you expect a home that has an <code>'RM'</code> value(number of rooms) of 6 be worth more or less than a home that has an <code>'RM'</code> value of 7?</li>
<li>Would you expect a neighborhood that has an <code>'LSTAT'</code> value(percent of lower class workers) of 15 have home prices be worth more or less than a neighborhood that has an <code>'LSTAT'</code> value of 20?</li>
<li>Would you expect a neighborhood that has an <code>'PTRATIO'</code> value(ratio of students to teachers) of 10 have home prices be worth more or less than a neighborhood that has an <code>'PTRATIO'</code> value of 15?</li>
</ul>

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<p><strong>Answer: </strong></p>

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<p>If we find the Pearson Correlation of Features 'RM','LSTAT','PTRATIO' against Target Variable prices i.e., MEDV we can easily interpret that increase or decrease in the value of feature affects the prices of house.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">str_list</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># empty list to contain columns with strings (words)</span>
<span class="k">for</span> <span class="n">colname</span><span class="p">,</span> <span class="n">colvalue</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iteritems</span><span class="p">():</span>
    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">colvalue</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
         <span class="n">str_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colname</span><span class="p">)</span>
<span class="c1"># Get to the numeric columns by inversion            </span>
<span class="n">num_list</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">str_list</span><span class="p">)</span> 
<span class="c1"># Create Dataframe containing only numerical features</span>
<span class="n">df_num</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">num_list</span><span class="p">]</span>
<span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Pearson Correlation of features&#39;</span><span class="p">)</span>
<span class="c1"># Draw the heatmap using seaborn</span>
<span class="c1">#sns.heatmap(house_num.astype(float).corr(),linewidths=0.25,vmax=1.0, square=True, cmap=&quot;PuBuGn&quot;, linecolor=&#39;k&#39;, annot=True)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">df_num</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">corr</span><span class="p">(),</span><span class="n">linewidths</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span><span class="n">vmax</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;cubehelix&quot;</span><span class="p">,</span> <span class="n">linecolor</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x1c4cb187f98&gt;</pre>
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RftcVCqKhtuuH6SZMatt+cPfv+0/N473zLWocO4We6fdqrqL8cqEIa3xx57ZP311x/vMFgFHH744fnc5z6XJLn00kuzwQYbZOrUqX3Hb7/99tl0001zySWXJEmOP/74nHHGGZk7d26S5K677hr9oFnprLX5hnlkzgN59N4H0xa23Hv9zKw7fcnvw4WPPLZouVaflPTaFNbZdpPMn3VfHp51X5JkwfxHF+2DJ+ugQ16WL5/99STJlVdck/XWXy+bbrbsH1COOuaI/P3Hz0yStNZyzz1DPwtn3Hpbfn7dL7LQjOCVTms17q+JaqTa8UFjEgUw7qZNm5Zbb7110fqMGTMybdq0vuOPPvronHPOOYvWd9hhh+ywww75/ve/nx/96Ed5+cu1AjC41dddM4/Oe2jR+qPz5mfyumsuM26j3bbJ9Lftl6kv3TF3/Pe1SZI1Nlo7SbL1a/fMdsfukyl7PnNsgmalNnXzzTLzttsXrd8+845svvlmS4xZb711kySn/PEf5KKL/yNn/r+/y8abTBnTOGEiGSnBmFRVG1bVRsO9+h1UVSdU1RVVdUXH8QKjpGrZv4S05cxgO+qoo3LWWWctWp88eXKmT5+efffdN0cffXQ+85nPqL4xau658pbc+M/fyZ0X/zybvHB6kqRWqzx9y40y4/yr8qsv/CDr7TA1a2+98ThHyopumB+Ny/xsnDx5UqZN2zyXX3plDtz31fnJ5Vfnz/78vWMUIUw8IyUYv5XkJ31efZOH1tqZrbXdW2u7dxUo0L13vOMdiyZsz5w5M1tttdWifVtuuWVmzpw57HE777xzJk+enCuvvHLRthkzZuTrX/96Hnvssdxyyy254YYbMn369FG/BlYuj86bn9XXXWvR+urrrpnH5s3vO/7e629b1EL16Lz5eeDW2Vnw0CNpjy3I/b+clTU3k+QyuGN/9/X51ve+lm9972u58/ZZ2WLa5ov2bb7F1Nxxx6wlxt9zz9w8+MCDueAb30qSnP/1/+o7GZyVR2vj/5qoRkowrm+tbdda23aY13ZjEiEwaj75yU9m1113za677ppzzz03b3rTm5Ike+65Z+69997ccccdwx539NFHL1G9SJJzzz03L33pS5MkU6ZMyQ477JCbb755dC+Alc5Dt8/NGhutndXXXyu1WmX9HbfIvJuW/D5cY8O1Fy2vs/1meWTOA0mS+2++K2tusl5q8qSkKk9/xpQ8PHvemMbPyuHfPvOlHLDPq3LAPq/Kf17w7Rx51OFJkt12f27m3Tcvs+5cdo7ZRRf+T174oucnSV60zwvyixt+OaYxw0TiLlIT3EknnZTLLrssc+bMyT777JMTTzwxRx555HiHxUroggsuyCGHHJKbbropDz74YI477rhF+6666qol7h712te+NocccsgSx1944YU58MADc91112XBggU5+eSTc88994xZ/KwkWsvtF12brV+3V6oqc356ax6++/5s8uJnZf7tczPvpjuz0fO2ydpbb5K2cGEWzn80t33zqiTJwocfzezLf5nt3vziJC33/3JW7v/lrOWfD0bw7Yu+m5cdsE9+dOVFeeih+XnP7//xon3f+t7XcsA+r0qSfOgDH8s//NNHc/qH/ziz774n73nn0Ljn7rpT/vXz/5gNNlgvBxz00px86juz7wsPHZdroVvNTer6quX1WFfVsa21fxtm+5pJDm2tfXnEE1S15Z0DxspwcwxgvFz74fPGOwTITqcdlqkbPGu8w4DcMfeGtIl8W6RhXDbz3nH/Bff5W6w/IT+z5bZILZ5cVNWkqjq4qj6X5NdJlr0JNAAAsEobsUWqqvZJ8vokr0hyWZK9k2zbWntwlGMDAIAJSYNOf8tNMKpqRpLfJPlUkpNba/Oq6leSCwAAYDgjVTD+I8krM9QOtaCqvh7PRQUAYBW3gk0ZGVMjzcH4gyTbJPnbJC9N8oskm1TVa6tqndEPDwAAWJGM9ByMtCHfaa0dn6Fk4/UZqmrcMrqhAQAAK5qR5mD8W2vt2MfXW2uPJjk/yflVtVbfAwEAYCXmORj9jVTB2LnfjtbaQx3HAgAArOBGmuT99KraNcmws1haa1d2HxIAAExwbnvU10gJxrQkH8vwCUZLsl/nEQEAACuskRKMm1prkggAAOAJGfFJ3gAAwJI8B6O/kSZ5n7L4SlWtXlW7VtWmoxgTAACwghqpgnFEVd3WWruuqtZP8qMkC5JsVFV/1Fo7a/RDBACAicVtavsbqYLx4tbadb3l45L8orX2nCTPS/LeUY0MAABY4YyUYDyy2PIBSc5NktbaHaMWEQAAsMIaqUVqblX9TpLbkuyd5K1JUlWTk3iSNwAAqybPwehrpATjbUn+PsnUJO9erHLxsiTfHM3AAACAFc9yE4zW2i+SHDTM9gur6tmjFhUAAExgJnn3N9IcjOU5qbMoAACAlcJTSTA8XQQAAFjCU3mSt6ktAACsmjzJu6/lJhhVNS/DJxIVd5ECAACWMtIk73XHKhAAAGDF91RapAAAYJXkLlL9PZVJ3gAAAEtQwQAAgEG53VFfKhgAAEBnJBgAAEBntEgBAMCgTPLuSwUDAADojAoGAAAMSgWjLxUMAACgMxIMAACgM1qkAABgQM1zMPpSwQAAADqjggEAAIMyybsvFQwAAKAzEgwAAKAzWqQAAGBQWqT6UsEAAAA6o4IBAACDcpvavlQwAACAzkgwAACAzmiRAgCAQZnk3ZcKBgAA0BkVDAAAGFCpYPSlggEAAHRGggEAAHRGixQAAAxKi1RfKhgAAEBnJBgAAEBntEgBAMCg2ngHMHGpYAAAAJ1RwQAAgEEtVMLoRwUDAADojAQDAADojBYpAAAYUHkORl8qGAAAsBKqqoOq6oaquqmqTu0z5rVVdX1VXVdVX+rivCoYAAAwqAlewaiqSUnOSHJAkhlJLq+q81pr1y82ZnqS05Ls3VqbU1WbdnFuFQwAAFj5PD/JTa21m1trjyQ5O8nhS405PskZrbU5SdJam9XFiSUYAACw8pmW5NbF1mf0ti1uhyQ7VNUPqurHVXVQFyfWIgUAAAOqCfAcjKo6IckJi206s7V25uO7hzlk6aAnJ5meZN8kWya5pKp2aq3NfSpxSTAAAGAF1Esmzuyze0aSrRZb3zLJzGHG/Li19miSX1XVDRlKOC5/KnFpkQIAgEG1CfBavsuTTK+qbatqjSRHJTlvqTHnJnlpklTVxhlqmbp5sA9iWRIMAABYybTWHkvyziQXJvl5kn9vrV1XVadX1WG9YRcmmV1V1yf5nyQnt9ZmP9Vza5ECAICVUGvtgiQXLLXt/YsttyQn9V6dkWAAAMCAJsIk74lKixQAANAZFQwAABiUCkZfKhgAAEBnJBgAAEBntEgBAMCATPLuTwUDAADojAQDAADojBYpAAAY1MLxDmDiqqEH+I3iCao0qAEAsFyttRrvGAbxo2/OGfffcV/wig0n5GemgsEqZbQTangiqipbbLfveIcBmXnzxfne9fPGOwzIPjuuO94hDMwk7/7MwQAAADojwQAAADqjRQoAAAZU2q77UsEAAAA6o4IBAACDWug+tf2oYAAAAJ2RYAAAAJ3RIgUAAAPyHIz+VDAAAIDOqGAAAMCAVDD6U8EAAAA6I8EAAAA6o0UKAAAGpUWqLxUMAACgMyoYAAAwoGqe5N2PCgYAANAZCQYAANAZLVIAADAgz8HoTwUDAADojAoGAAAMaqFJ3v2oYAAAAJ2RYAAAAJ3RIgUAAAMqLVJ9qWAAAACdkWAAAACd0SIFAAADquY5GP2oYAAAAJ1RwQAAgAGZ5N2fCgYAANAZCQYAANAZLVIAADAoLVJ9qWAAAACdUcEAAIABVVPB6EcFAwAA6IwEAwAA6IwWKQAAGJRJ3n2pYAAAAJ1RwQAAgAHVwgXjHcKEpYIBAAB0RoIBAAB0RosUAAAMyHMw+lPBAAAAOqOCAQAAg3Kb2r5UMAAAgM5IMAAAgM5okQIAgEGZ5N2XCgYAANAZFQwAABiQJ3n3p4IBAAB0RoIBAAB0RosUAAAMyiTvvlQwAACAzkgwAACAzmiRAgCAAVVzF6l+VDAAAIDOqGAAAMCgPAejLxUMAACgMxIMAACgM1qkAABgUJ6D0ZcKBgAA0BkVDAAAGJTb1PalggEAAHRGggEAAHRGixQAAAzKczD6UsEAAAA6o4IBAAADKpO8+1LBAAAAOiPBAAAAOqNFCgAABqVFqi8VDAAAoDMqGAAAMKCmgtGXCgYAANAZCQYAANAZLVIAADAoLVJ9qWAAAACdkWAAAACd0SI1AXziE5/IIYcckgcffDDHHntsrrrqqiX2r7POOrnkkksWrW+55Zb5whe+kPe85z1JkiOPPDIf+MAH0lrLNddckze84Q1jGj+rhtNOOy0XX3xxpkyZkm984xvjHQ4rudPff2L223fPPPTQ/LznvR/NtdfduMyYL3/x49ls040yf/4jSZKjjz05s2fPzQlvOTJHv/aQPLZgQe65596cdMpf5baZd471JbASuvbKH+asz/xNFi5ckBcf8Moc8urjlth/9r98LDf87IokySOPzM99c+/JP3zpu+MRKmNBi1RfEoxxdvDBB2f69OmZPn169txzz3zqU5/KXnvttcSY+++/P7vuuuui9SuuuCJf/epXkyTbb799TjvttOy9996ZO3duNtlkkzGNn1XHEUcckWOOOSannHLKeIfCSm6/fffMtttMy4v2Oya77fLsfPj09+TQV79j2LHvPOlD+enPfrHEtmuvvzEHv/LtmT//4bzp9Yflfae+Lb/3rtPHInRWYgsXLMgX//kjOemDn8yGUzbLX5z8xuzy/Jdki622WzTmqLf+4aLlb3/j7PzmVzeMR6gw7pbbIlVVu49VIKuqww8/PJ/73OeSJJdeemk22GCDTJ06te/47bffPptuuumiisbxxx+fM844I3Pnzk2S3HXXXaMfNKukPfbYI+uvv/54h8Eq4OX7752vfO2iJMmVV/8866+3djbdZKMnfPwPf3x15s9/OEnyk6uvz+ZT/eGFp+5XN16XTTffKptM3TKTV189z3/Rgbn60ov7jr/skgvz/Be/fOwCZOy1BeP/mqBGmoPx6aq6sapOr6odxySiVcy0adNy6623LlqfMWNGpk2b1nf80UcfnXPOOWfR+g477JAddtgh3//+9/OjH/0oL3+5H2bAim3qZhtn5sxZi9Zvv+PuTJ268bBj//ajp+Si8z+dd7/zjcPuP/rIQ/I/3710VOJk1TLnnlnZcOPNFq1vOGWzzLln+D/qzZ51e+6edVue/Zw9xio8mFCW2yLVWtu1qp6V5KgkX6mqR5KcleTs1tqv+x1XVSckOaHTSFdSVbXMttZa3/FHHXVU3vjG//uHdPLkyZk+fXr23XffbLnllrnkkkuy00475d577x2VeAFG2/A/F5cdd+JJH8odd96dtddeK58+44N5zasOXFT5SJIjDt8/z33Os/Lq1797NMNlVTHMN2Fl2e/VJLns+xfmeS/YP6tNmjTaUcGENOJdpFprN7TWPtha2zHJm5NskOQ7VfWD5RxzZmtt99aaFqthvOMd78hVV12Vq666KjNnzsxWW221aN+WW26ZmTNnDnvczjvvnMmTJ+fKK69ctG3GjBn5+te/nsceeyy33HJLbrjhhkyfPn3UrwGgS28+5pW56PxP56LzP507Zt2dLbbYdNG+zadunDvvvHuZY+7obXvggYdy7vnfzi47/9aifS9+4W551zuOybFv+5M88sijo38BrPQ2nLJZ5tz9fzcLmDP7zmyw0fCVtcsuuSjP30dHwcqutQXj/pqonvBtaqtqtSSbJtksydpJNPs/SZ/85Cez6667Ztddd825556bN73pTUmSPffcM/fee2/uuOOOYY87+uijc9ZZZy2x7dxzz81LX/rSJMmUKVOyww475Oabbx7dCwDo2Ge/cG4OPPT4HHjo8bnwoh/kNa86MEmy2y7Pzn3zHsisu+5ZYvykSatlww3XS5JMnjwp+7/0BbnhF79Kkvz2jtvnI39xUo57259k9uy5Y3shrLS2mb5j7rz91tx152157NFHc9n3L8pzn/+SZcbdcdstefD++/LMZ+08DlHCxDDiXaSq6sVJjk7yyiTXJjk7yXtaa3pwOnA0dRcOAAAgAElEQVTBBRfkkEMOyU033ZQHH3wwxx33f7e8u+qqq5a4e9RrX/vaHHLIIUscf+GFF+bAAw/MddddlwULFuTkk0/OPfcs+Q8xdOGkk07KZZddljlz5mSfffbJiSeemCOPPHK8w2Il9O2Lf5z99t0zP/jOF/LQ/Idz0ikfXbTvovM/nQMPPT5rrLFGvvRvf53Jkydl0mqTcskPf5IvnvPNJMmfnvr2rL32Wvnnf/hAkuS2mXfmuLe9bzwuhZXIpEmT8/rj35u/++A7s3DBguy9/+GZ9oxn5twvfSrbbL9jduklG5d+78Ls8eIDh231YyUzgSsI462W1+9fVbcm+U2Gkop/b60NfCPxqup/Ahhjy/t+h7FSVdliu33HOwzIzJsvzveunzfeYUD22XHdtNZWqKzs2j//wrj/UrHTnx4zIT+zkSoYL1reZG4AAIDFLXcORmvt11X15qq6sqoe6L2uqKo3jVWAAAAw4Yz3MzAmcIvWcisYvUTi3UlOSnJlkkqyW5K/rqq01j43+iECAAAripFapN6R5FWttVsW2/adqnp1huZlSDAAAFjltPbYeIcwYY10m9r1lkoukiS9beuNRkAAAMCKa6QE46EnuQ8AAFgFjdQi9eyq+ukw2yvJdqMQDwAATHgtE3eS9XgbMcEYkygAAIBOVdVBST6RZFKSz7TWPrLU/rcn+f0kC5Lcn+SE1tr1T/W8IyUYa7XW/rcXwNNaaw8vFtBeSTwjAwCAVc8Evk1sklTVpCRnJDkgyYwkl1fVeUslEF9qrf1Tb/xhSf42yUFP9dwjzcH40mLLP1pq3yef6skBAIBR8fwkN7XWbm6tPZKhO8AevviA1tp9i62unaSTp5OPVMGoPsvDrQMAAGOkqk5IcsJim85srZ3ZW56W5NbF9s1Isucw7/H7GXrm3RpJ9usirpESjNZnebh1AABYJbQJ0CLVSybO7LN7uGLAMr+/t9bOSHJGVb0+yfuSvPmpxjVSgrFlVf19L8DHl9Nbn/ZUTw4AAIyKGUm2Wmx9yyQzlzP+7CSf6uLEIyUYJy+2fMVS+5ZeBwCAVULLhH+S9+VJplfVtkluS3JUktcvPqCqprfWbuytviLJjenAchOM1tpnuzgJAAAwdlprj1XVO5NcmKHb1P5ra+26qjo9yRWttfOSvLOq9k/yaJI56aA9Khkhwaiq80YI/LAuggAAALrVWrsgyQVLbXv/Yst/MBrnHalF6gUZmn1+VpJL485RAAAwISZ5T1QjJRhTM/RwjqMz1LP1zSRntdauG+3AAACAFc9yH7TXWlvQWvuv1tqbk+yV5KYkF1fViWMSHQAAsEIZqYKRqnpahmaVH51kmyR/n+SroxsWAABMXC1apPoZaZL3Z5PslOQ/k3ywtXbtmEQFAACskEaqYLwxyQNJdkjyrqpFc7wrSWutrTeKsQEAwIRkknd/Iz0HY7lzNAAAABYngQAAADoz4iRvAABgSQtN8u5LBQMAAOiMCgYAAAyotcfGO4QJSwUDAADojAQDAADojBYpAAAYkCd596eCAQAAdEYFAwAABrTQk7z7UsEAAAA6I8EAAAA6o0UKAAAGZJJ3fyoYAABAZ1QwAABgQAtVMPpSwQAAADojwQAAADqjRQoAAAakRao/FQwAAKAzKhgAADAgT/LuTwUDAADojAQDAADojBYpAAAYkCd596eCAQAAdEaCAQAAdEaLFAAADMhzMPpTwQAAADqjggEAAANaoILRlwoGAADQGQkGAADQGS1SAAAwIJO8+1PBAAAAOqOCAQAAA1LB6E8FAwAA6IwEAwAA6IwWKQAAGJAWqf5UMAAAgM6oYAAAwIAWZuF4hzBhqWAAAACdkWAAAACd0SIFAAADWmCSd18qGAAAQGdUMAAAYEAmefenggEAAHRGggEAAHRGixQAAAzIJO/+VDAAAIDOqGAAAMCATPLuTwUDAADojAQDAADojBYpAAAYkEne/algAAAAnZFgAAAAndEiBQAAA1rgLlJ9STBYpVTVeIcASZKZN1883iFAkmSfHdcd7xCAlcyYJBjXfvi8sTgNLNdOpx2WLbbbd7zDgMy8+eK01sY7DEhVZdPXvWK8w4DMOueb4x3CwDwHoz9zMAAAgM5IMAAAgM6YgwEAAAMyybs/FQwAAKAzKhgAADAgFYz+VDAAAIDOSDAAAIDOaJECAIABLSgtUv2oYAAAAJ1RwQAAgAGZ5N2fCgYAANAZCQYAANAZLVIAADAgLVL9qWAAAACdUcEAAIABLUgb7xAmLBUMAACgMxIMAACgM1qkAABgQCZ596eCAQAAdEaCAQAAdEaLFAAADMhdpPpTwQAAADqjggEAAAMyybs/FQwAAKAzEgwAAKAzWqQAAGBAJnn3p4IBAAB0RgUDAAAGpILRnwoGAADQGQkGAADQGS1SAAAwIC1S/algAAAAnVHBAACAAS0oFYx+VDAAAIDOSDAAAIDOaJECAIABmeTdnwoGAADQGRUMAAAYkApGfyoYAABAZyQYAABAZyQYAAAwoAVp4/4aSVUdVFU3VNVNVXXqMPufVlXn9PZfWlXbdPHZSDAAAGAlU1WTkpyR5OAkOyY5uqp2XGrYW5PMaa1tn+TjST7axbklGAAAMKAFE+A1gucnuam1dnNr7ZEkZyc5fKkxhyf5bG/5K0leVlU1wMcwLAkGAACsgKrqhKq6YrHXCYvtnpbk1sXWZ/S2ZbgxrbXHktybZMpTjcttagEAYAXUWjszyZl9dg9XiVh64sYTGTMwCQYAAAxoBXgOxowkWy22vmWSmX3GzKiqyUnWT3LPUz2xFikAAFj5XJ5kelVtW1VrJDkqyXlLjTkvyZt7y69J8p3WmgoGAACwpNbaY1X1ziQXJpmU5F9ba9dV1elJrmitnZfkX5J8vqpuylDl4qguzi3BAACAAS2Y8B1SSWvtgiQXLLXt/Ystz09yZNfn1SIFAAB0RgUDAAAGtAJM8h43KhgAAEBnJBgAAEBntEgBAMCAFox3ABOYCgYAANAZFQwAABiQCkZ/KhgAAEBnJBgAAEBntEgBAMCAtEj1p4IBAAB0RgUDAAAGpILRnwoGAADQGQkGAADQGS1SAAAwoAVtvCOYuFQwAACAzqhgAADAgEzy7k8FAwAA6IwEAwAA6IwWKQAAGJAWqf5UMAAAgM6oYAAAwIAWjncAE5gKBgAA0BkJBgAA0BktUgAAMKAFrcY7hAlLBQMAAOiMBAMAAOiMFqlxts62m2Tq/jslq1XmXvOb3P3jm5bYv+EuW2ej3bZJay0LH1mQ2//rmjw8+/4kydM2WTdbHLRzVltj9aS13PzZS9IWuKcBT97p7z8x++27Zx56aH7e896P5trrblxmzJe/+PFstulGmT//kSTJ0ceenNmz5+aEtxyZo197SB5bsCD33HNvTjrlr3LbzDvH+hJYBZx22mm5+OKLM2XKlHzjG98Y73BYxXzozSfkZbvunocefjjv+tTf5We3/HKZMWed+sFstuFGmbTaarn0f6/Pqf/6qSxs/n1e2XgORn8SjPFUyeYHPie3nP3jPDbvoWx37Isz78Y7FiUQSXLv9bdlztW/TpKsu/1m2exlv53f/PulSVW2PHS3zPjGVXl41n2ZtObqaQv98OLJ22/fPbPtNtPyov2OyW67PDsfPv09OfTV7xh27DtP+lB++rNfLLHt2utvzMGvfHvmz384b3r9YXnfqW/L773r9LEInVXMEUcckWOOOSannHLKeIfCKuZlu+yebTffInu9+4Q8b/tn5a9+9x05+H1/uMy44z/xkdz/0ENJkn95z2k5bK8X5dwffW+sw4Vxo0VqHK21+YZ5ZM4DefTeB9MWttx7/cysO33qEmMWPvLYouVafVLShpbX2XaTzJ91Xx6edV+SZMH8Rxftgyfj5fvvna987aIkyZVX/zzrr7d2Nt1koyd8/A9/fHXmz384SfKTq6/P5lM3GZU4YY899sj6668/3mGwCjpo9z3z5e99J0nyk5tuyHpPXzubbrDhMuMeTy4mT5qUNSavnuYf6JXSwlbj/pqoRqxgVNVOSd6bZMcM/Qp7fZKPtdZ+OsqxrfRWX3fNPDrvoUXrj86bn7W22GCZcRvttk2m7LFdatJqueWsHyVJ1tho7STJ1q/dM5Oe/rTc+/PbMvvSZcu08ERN3WzjzJw5a9H67XfcnalTN86su+5ZZuzffvSULFywMBdc+L383T9+fpn9Rx95SP7nu5eOarwAY23zjabkttl3L1q//Z7Z2XyjKZk1d84yY88+7fTs+swd8p1rrsj5P/7BWIYJ4265FYyqOjzJ15JcnOQtSX43yXeT/EdvX7/jTqiqK6rqig5jXWXdc+UtufGfv5M7L/55Nnnh9CRJrVZ5+pYbZcb5V+VXX/hB1tthatbeeuNxjpQVWdWyfwlpw/zR7cSTPpT9D3lrXnXUu/L83Z+T17zqwCX2H3H4/nnuc56VT336nNEKFWCcDPdzcvjqxFEffn92/r03Zo3Jq+dFO+082oHBhDJSBeP0JAe01m5ZbNs1VfWdJF/vvZbRWjszyZlJUlXqgn08Om9+Vl93rUXrq6+7Zh6bN7/v+Huvvy2bH/icRcc+cOvsLHhoaKLt/b+clTU3Wz8P/PruvsfD0t58zCvzhte9Ikly9c/+N1tssWnyk6F9m0/dOHfeuez30x29bQ888FDOPf/b2WXn31rUWvXiF+6Wd73jmLz69e/OI488OjYXATCKjjvwFTlmv5cnSa7+5Y2ZNuX//pi3+UZTcsecZau8j3v40Udz4U8uzUG775Xv/ezqUY+VsWWSd38jzcFYfankIknS27b6aAS0Knno9rlZY6O1s/r6a6VWq6y/4xaZd9MdS4xZY8O1Fy2vs/1meWTOA0mS+2++K2tusl5q8qSkKk9/xpQ8PHvemMbPiu+zXzg3Bx56fA489PhceNEPFlUjdtvl2blv3gPLtEdNmrRaNtxwvSTJ5MmTsv9LX5AbfvGrJMlv77h9PvIXJ+W4t/1JZs+eO7YXAjBK/t9F38zLTn1XXnbqu/KfV/woR+6zX5Lkeds/K/MefHCZ9qinP23NRfMyJq22WvbfdffcNHPGmMcN42mkCsajVfWM1tpvFt9YVVsneazPMTxRreX2i67N1q/bK1WVOT+9NQ/ffX82efGzMv/2uZl3053Z6HnbZO2tN0lbuDAL5z+a2755VZJk4cOPZvblv8x2b35xkpb7fzkr9/9y1vLPB8vx7Yt/nP323TM/+M4X8tD8h3PSKR9dtO+i8z+dAw89PmussUa+9G9/ncmTJ2XSapNyyQ9/ki+e880kyZ+e+vasvfZa+ed/+ECS5LaZd+a4t71vPC6FldxJJ52Uyy67LHPmzMk+++yTE088MUceeeR4h8Uq4L+vuiIv22X3XPqJT+ehhx/OH/zT3y3a9+2P/H1eduq7svaaa+ZzJ/9pnjZ59ay22mr5wXU/zWe/dcE4Rs1omciTrMdb9esdTJKqemWSv0rylxlqnGhJ9khyapJTWmvnjniCqnbth8/rJlp4CnY67bBssd2+4x0GZObNF/ft24axVFXZtNcmCeNp1jnfTGsr1m/s+57yvHH/QX7xR38yIT+z5VYwWmvnVtWvkvxhkhMzNLvpuiSvba1dMwbxAQAAK5ARb1PbSyTeNAaxAADACsEk7/6Wm2BU1flZzuPbWmuHdR4RAACwwhqpgvE3YxIFAACsQEzy7m+kBOO41tqxYxEIAACw4hvpORgePQkAADxhI1Uwnl5Vu2bo7lHLaK1d2X1IAAAwsWmR6m+kBGNako9l+ASjJdmv84gAAIAV1kgJxk2tNUkEAAAsZsHwDT5k5DkYAAAAT9hICcYpw22sqq2q6uRRiAcAAFiBLbdFqrV20ePLVbVxkiOTHJ2huRlfG93QAABgYlrY91HUjPQk73WTvCrJ65PskKGkYrvW2pZjEBsAALCCGWmS96wklyV5X5Lvt9ZaVb1q9MMCAABWRCMlGH+c5Kgkn0rypao6Z/RDAgCAic1zMPpb7iTv1trHW2t7JjksQ8/CODfJFlV1SlXtMBYBAgAAK44ndJva1trNrbUPtdaek2SPJBsk+c9RjQwAACaoha3G/TVRjdQitYzW2s+q6k+TXDcK8QAAACuw5VYwqmq9qjqtqv6xqg6sIScmuSlDt6wFAABYZKQKxueTzEnyoyS/m+TkJGskeWVr7epRjg0AACakidyiNN5GSjC26827SFV9JsndSZ7RWps36pEBAAArnJESjEcfX2itLaiqX0kuAABY1alg9DdSgvHcqrqvt1xJ1uqtV5LWWltvVKMDAABWKCMlGGu21h4dYQwAAECSkROMS5PsNhaBAADAiqJpkeprpAft+eQAAIAnbKQKxiZVdVK/na21v+04HgAAmPBM8u5vpARjUpJ1opIBAAA8ASMlGLe31k4fk0gAAIAV3kgJhsoFAAAsRYtUfyMlGK+oqncn2T7Jz5L8S2vtsdEPCwAAWBGNlGB8PENP874kycFJdkzyB6MdFAAATGRuU9vfSAnGjq215yRJVf1LkstGPyQAAGBFNdJzMBY9xVtrFAAAMJKRKhjPrar7esuVZK3eeiVprbX1RjU6AACYgEzy7m+5CUZrbdJYBQIAAKz4RqpgAAAASzHJu7+R5mAAAAA8YRIMAACgM1qkAABgQCZ596eCAQAAdEaCAQAAdEaLFAAADMhdpPpTwQAAADqjggEAAANqC1Uw+lHBAAAAOiPBAAAAOqNFCgAABmSSd38qGAAAQGdUMAAAYEAqGP2pYAAAAJ2RYAAAAJ3RIgUAAAPyHIz+VDAAAIDOqGAAAMCATPLuTwUDAADojAQDAADojBYpAAAYkEne/algAAAAnVHBAACAQZnk3ZcKBgAA0BkJBgAA0BktUgAAMKC2cLwjmLhUMAAAgM6oYAAAwIA8ybs/FQwAAKAzEgwAAFjFVNVGVfWtqrqx998NhxmzdVX9pKqurqrrqurtT+S9JRgAADCohTX+r6fm1CTfbq1NT/Lt3vrSbk/ywtbaLkn2THJqVW0x0htLMAAAYNVzeJLP9pY/m+SVSw9orT3SWnu4t/q0PMHcQYIBAAAroKo6oaquWOx1wgCHb9Zauz1Jev/dtM85tqqqnya5NclHW2szR3pjd5ECAIABTYS7SLXWzkxyZr/9VfXfSaYOs+tPBjjHrUl27rVGnVtVX2mt3bm8YyQYAACwEmqt7d9vX1XdWVWbt9Zur6rNk8wa4b1mVtV1SV6c5CvLG6tFCgAABrVwAryemvOSvLm3/OYkX196QFVtWVVr9ZY3TLJ3khtGemMJBgAArHo+kuSAqroxyQG99VTV7lX1md6YZye5tKquSfLdJH/TWvvZSG+sRQoAAFYxrbXZSV42zPYrkvxub/lbSXYe9L0lGAAAMKin/hyKlVa11kb3BFWjewIAAFZ4bSLclmkAm73m0HH/HffOr5w/IT+zMalgTN3gWWNxGliuO+bekO9dP2+8w4Dss+O62fR1rxjvMCCzzvlmRvsPjfBEVE3I35OXy/86/ZnkDQAAdEaCAQAAdMYkbwAAGJRJ3n2pYAAAAJ1RwQAAgEE99Sdpr7RUMAAAgM5IMAAAgM5okQIAgEGtWM8FHFMqGAAAQGdUMAAAYFAmefelggEAAHRGggEAAHRGixQAAAxKi1RfKhgAAEBnJBgAAEBntEgBAMCgtEj1pYIBAAB0RgUDAAAG1cY7gIlLBQMAAOiMBAMAAOiMFikAABjUwhrvCCYsFQwAAKAzKhgAADCo/9/evYdbVZcJHP++XExtQi4FHrHCS5FmAgqMOkgqoqiVlx4GKBttMrK85RWwnFLLqHTy0jM25qXLU2I+lXcDoiGtMRAUxUtOpUyCIHIbNY+KnN/8cdbBzTlncVi5zt7n8v08z3nYa63fXufd5/m53O9+39/aDa7yzmMFQ5IkSVJpTDAkSZIklcYWKUmSJKmg8Ju8c1nBkCRJklQaKxiSJElSUa7xzmUFQ5IkSVJpTDAkSZIklcYWKUmSJKkoF3nnsoIhSZIkqTRWMCRJkqSirGDksoIhSZIkqTQmGJIkSZJKY4uUJEmSVFSDX4SRxwqGJEmSpNJYwZAkSZIKChd557KCIUmSJKk0JhiSJEmSSmOLlCRJklRUcpF3HisYkiRJkkpjgiFJkiSpNLZISZIkSUV5F6lcVjAkSZIklcYKhiRJklRQ+E3euaxgSJIkSSqNCYYkSZKk0tgiJUmSJBXl92DksoIhSZIkqTRWMCRJkqSiXOSdywqGJEmSpNKYYEiSJEkqjS1SkiRJUkF+D0Y+KxiSJEmSSmMFQ5IkSSrK29TmsoIhSZIkqTQmGJIkSZJKY4uUJEmSVJSLvHNZwZAkSZJUGisYkiRJUlFWMHJZwZAkSZJUGhMMSZIkSaWxRUqSJEkqKPwejFxWMCRJkiSVxgqGJEmSVJSLvHNZwZAkSZJUGhMMSZIkSaWxRUqSJEkqKjXUOoIOywqGJEmSpNKYYEiSJEkqjS1SkiRJUlHeRSqXFQxJkiRJpbGCIUmSJBXlIu9cVjAkSZIklcYEQ5IkSVJpbJHqAC6d+SXGjR9Lff2rfPELM1j66BMtxvTu3ZvLvnURB44ZTWpoYObXruTuO+dwwEEjueSyGez1waGc+plzufuO2TV4BeqKHnvov7n5+stpaNjEweOP4+iPf3qL47NuuIKnli4C4PXXX+XFDeu45qe/rUWo6ga+ftJUxo0YSf1rr3HmtVeydNlfWoy5efrFDOrXn549erDgj08w/cZrabCFQe1oxowZzJ8/nwEDBnDXXXfVOhxVWbjIO5cJRo0dNn4su+/xXg7a/0j2GzmMmVd8hWPGT2ox7qxzT2XNmrWMGTWBiKBfv50AWP7sSs46bQafP/1fqx26urCGTZv4yX/O5JyL/4N+AwbxtfM/xfDRH2aXd+++eczkz5y7+fG8u2bx12eeqkWo6gbGDR/JbnW7cMAXp7L/nkP51ilf4Kgvn9ti3GevmsnL9fUA3HD2DD52wBhue+C+aoerbuSEE07gxBNPZNq0abUORepQTDBqbMLR47h11u0APLToEfrs1IeBg97F6udf2GLc5BNP4ODRRwOQUmLdug0ALH92BQANZtEq0TN/epyBde/mXTvvCsDoMUewZMH8LRKMSgvvn83HpnyumiGqG5kw8h+59b7fALD4z0/RZ8e3M7BvP1ZvWL/FuKbkolfPnmzXqzcJr4tqX6NGjWL58uW1DkO1YoU011YTjIi4emvHU0pnlhtO97Nz3SCeW7Fy8/bK51ZRVzdoiwSjT593ADDtwrM4aMwolj3zLBdecClrXlhb9XjVPaxft5p+7xy0ebvfgEE8/afHWh27dvVK1qxewV4fGlWt8NTN1PUfwIq1azZvr1y3lrr+A1okGACzZlzCiD3ez28eWcSdf/h9NcOUJGXaWuR9KjAGeA5YBCxu9tOqiJgaEYsiYlFZgXZVES33pbTlp269evVk8OA6HlzwEEcc8nEWP7iEr1x6QZUiVLeUWn7yG7QyWYGFv5vN/gceTo+ePds7KnVbLede8+tkk8nf+Df2/fyn2K5Xb8bss297ByZJakVbLVJ1wERgEvAGcAvw85RSy4+NKqSUrgOuA4gIa9TNnHzKJ/jkv0wE4JGHlrLL4LrNx+p22ZlVq1ZvMX7dug288rdXuOeuuQDcefuvmHLix6sXsLqdfgMGsX7N85u31699nr7939nq2IX3z+GTn7P/WOX69BHHcOJhRwKw5C9/YvCAN+dfXf8BrFq/Lve5r23cyOzFC5gw8gDuW7qk3WOV1E3Znp5rqxWMlNLalNL3UkqHAicDfYHHI+JT1Qiuq/rB9T9l/NjjGT/2eO69Zx4TJx8LwH4jh/HSiy+1WH8BMGf2f3HQmNEAjBl7IP/zVMs7qEhlGfK+vXl+5bO88PwK3ti4kYW/m8Ow0R9uMW7VimW88vKL7DHUT4pVrpvm3M246WcybvqZ3LvoASaOPQyA/fccykuvvNKiPWrHt23PwL79AOjZoweHjxjJn5+zN16SamGbFnlHxH7AFGA8cC9baY9SMfPm/JZx48fywENzqK9/lbNPu3Dzsbn3/ZLxY48H4OtfvYJrvvdNLvnGhaxds46zT28cN2zEPtz44+/St28fxk84lPOnn84hB320Jq9FXUfPnr34xGcv4MqLT6dh0yb+6fBjGfyePbjtp9cyZM+9GZ4lGwvum82og48gWuv1k0ry64cXMW74SBZc9X3qX3uNs7535eZj82ZezbjpZ/L27bfnR+dfxNt69aZHjx78/vFH+eHce2oYtbqDc845h4ULF7J+/XrGjh3LGWecwcSJE2sdlqrFRd65Iq+PFSAiLgY+AjwJzAJ+lVJ6o9AviEg79x36loKUyrBqw1Pc98RLtQ5DYuze72DgpGNqHYbE6lvuzl3PIlVTRJBS6lSfVu2664E1/49n+fIHOuTfrK0KxkXA08Cw7Oey7JPKAFJKyb4ISZIkSZu1lWDsVpUoJEmSpM7EFqlcW00wUkr/29r+iOgJTAZaPS5JkiSpe2rri/b6AKcBg4E7gLnA6cB5wBLgJ+0doCRJktTRJG9Tm6utFqkfA+uBB4BTgPOB7YBjU0reXFySJEnSFtpKMHZPKX0IICKuB9YA70kpeSseSZIkSS20lWBsbHqQUtoUEc+YXEiSJKnbc5F3rrYSjGER8WL2OIAdsu2m29T2adfoJEmSJHUqbd1Fqme1ApEkSZI6DSsYuXrUOgBJkiRJXYcJhiRJkqTStLUGQ5IkSVJzDbZI5bGCIUmSJKk0JhiSJEmSSmOLlCRJklRQ6uR3kYqI/sAtwBBgGfDPKaX1rYx7D3A98G4gAUenlJZt7dxWMCRJkqTuZzowL6X0PmBett2aHwHfTintBYwGVrd1YisYkiRJUlGdvIIBHAsckj3+IST2WJQAAAg/SURBVDAfmFY5ICL2BnqllOYCpJRe3pYTW8GQJEmSOqGImBoRiyp+phZ4+qCU0kqA7N+BrYx5P7AhIn4REQ9HxLcjos0v4raCIUmSJHVCKaXrgOvyjkfEr4GdWzn0pW38Fb2Ag4ERwF9pXLNxMnBDW0+SJEmSVEQnaJFKKR2edywino+IupTSyoioo/W1FcuBh1NKT2fPuQ04gDYSDFukJEmSpO7nDuCk7PFJwO2tjHkQ6BcR78q2DwOeaOvEVjAkSZKkgjr7bWqBmcDPIuIzNLY/TQSIiJHAqSmlU1JKmyLiPGBeRASwGPh+Wyc2wZAkSZK6mZTSWmBcK/sXAadUbM8F9i1yblukJEmSJJXGCoYkSZJUVOdvkWo3VjAkSZIklcYKhiRJklRQF1jk3W6sYEiSJEkqjQmGJEmSpNLYIiVJkiQVZItUPisYkiRJkkpjBUOSJEkqygpGLisYkiRJkkpjgiFJkiSpNLZISZIkSQW5yDufFQxJkiRJpTHBkCRJklQaW6QkSZKkgmyRymcFQ5IkSVJprGBIkiRJBVnByGcFQ5IkSVJpTDAkSZIklcYWKUmSJKkgW6TyWcGQJEmSVBorGJIkSVJBCSsYeaxgSJIkSSqNCYYkSZKk0tgiJUmSJBXkIu98VjAkSZIklcYKhiRJklSQFYx8VjAkSZIklcYEQ5IkSVJpbJGSJEmSCrJFKp8VDEmSJEmlsYIhSZIkFdRAqnUIHZYVDEmSJEmlMcGQJEmSVBpbpCRJkqSCXOSdzwqGJEmSpNJYwZAkSZIKsoKRzwqGJEmSpNKYYEiSJEkqjS1SkiRJUkEN2CKVxwqGJEmSpNKYYEiSJEkqjS1SkiRJUkHeRSqfFQxJkiRJpbGCIUmSJBXUQKp1CB2WFQxJkiRJpTHBkCRJklQaW6QkSZKkghpc5J3LCoYkSZKk0ljBkCRJkgpKLvLOZQVDkiRJUmlMMCRJkiSVxhYpSZIkqSAXeeeLlNq3fywibFCTJEnSVqWUotYxFNER3uN21L9ZuycYKkdETE0pXVfrOCTnojoK56I6CueitCXXYHQeU2sdgJRxLqqjcC6qo3AuShVMMCRJkiSVxgRDkiRJUmlMMDoPezvVUTgX1VE4F9VROBelCi7yliRJklQaKxiSJEmSSmOCIUmSJKk0JhhVFBEvt7JvaETMj4glEfFkRFwXEUdm20si4uWIeCp7/KOK510VESsioke2/emK57weEUuzxzOr+RrVeUREiogfV2z3iogXIuKubPvkbHtJxc/eETEkIuoj4uFszi6MiJOy5wyJiOVN87Li3EsiYnR1X6E6mojYlM2FxyLi1ogYXDG3VmXXtKbt7ZqNvzMi+jY739kR8WpE7JRt5147I+KQprmdjT0uIh6NiD9m18vjqv33UOeSNx+z616KiEsrxr4zIjZGxHdrF7FUO71qHYC4GvhOSul2gIj4UEppKTA7254PnJdSWtT0hOzN2/HAs8BYYH5K6Sbgpuz4MuDQlNKaKr4OdT5/A/aJiB1SSvXAeGBFszG3pJROr9wREUOAv6SURmTbuwO/iIgeKaWbIuJZ4GDgt9nxDwDvSCktbNdXo86gPqU0HCAifgJMqtj+KvBySunypsERUTn+h8BpwNcrzjcFeJDG6+EPUkqzybl2RsQhFecdBlwOjE8pPRMRuwFzI+LplNKj7fHC1SVsbT4+DXwEuCjbngg8XvUIpQ7CCkbt1QHLmzay5KIthwKPAdfS+D9Y6e91L3BM9ngKcHPRE6SUngbOAc7Mdt0MTK4YMvnvOa+6vPuBPQuMfwAY3LQREXsA/wB8meLXwfOAy1JKzwBk/34DOL/gedR9bTEfgXrgyYgYmW1PAn5W9aikDsIEo/a+A/wmIu7Nyv1923zGm28Efwl8JCJ6t2uE6spmAZMjYntgX2BBs+OTmrVI7ZBznoeAD2SPfwYcFxFNFdJJ2e+RgMZ2POAoYFs+UCEiegLjgDsqdjddB+8HhkbEwAIhfBBY3Gzfomy/tFU58xHevJ7uCmwCnqt2bFJHYYJRY1lr017ArcAhwB8i4m154yNiO+Bo4LaU0os0viE8ogqhqgvK2kGG0Phm7Z5WhtySUhpe8VOfc6qoOOcqGlsDxkXEcGBjSumxkkNX57RDRCyh8c38X4EbtnH8WqA/MLfi2GRgVkqpAfgFjS0p2yqA5vdob22fVGlr8xHgVzS2mk4BbqlybFKHYoLRAaSUnksp3ZhSOhZ4A9hnK8MnADsBS7O1FmOwTUpvzR009qO/lTamEcCTFdtNbVK2R6lSfUWyekZK6fVtGQ+8F9iOxp53ImJf4H00rptYRuM8K3IdfBwY2WzffsATBc6h7qfV+dgkm8+LgXOBn1c/PKnjMMGosYiY0NTiFBE7AwNoudC20hTglJTSkJTSEGA34IiI2LHdg1VXdSNwyTau/2khW/R9OXBNxe6f01hpsz1Kb1lK6f9oXONzXna9nAJ8tek6mFLaBRgcEe/dxlNeDszI5m7THL4QuKLk0NUFtTIfK10BTEspra1+ZFLH4V2kqmvHiFhesf3vwK7AVRHxarbv/KzFpIUsiTgS+FzTvpTS3yLid8BHsSSrv0NKaTlwVc7hSRExpmL7CzT2Fe8REQ8D2wMvAddk7X5N59wQEX8ABjUtpJXeipTSwxHxCG9Wxo5qNuSX2f5vbsO5lkTENODO7A3iRuCClNKSksNWF9VsPt5fsf9xvHuURKRky6kkSZKkctgiJUmSJKk0JhiSJEmSSmOCIUmSJKk0JhiSJEmSSmOCIUmSJKk0JhiSJEmSSmOCIUmSJKk0/w/PXGhmzixh4gAAAABJRU5ErkJggg==
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<p>As we can see from figure above,RM is positively correlated with MEDV while PTRATIO and LSTAT are negatively correlated with MEDV</p>

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<p>Further according to my intuition, 
'RM' Positively correlated : As RM is the average number of rooms,increase in value of 'RM' means more space so it would be more expensive.
'LSTAT' negatively correlated : As LSTAT indicates people from lower class, which means when 'LSTAT' is high,the housing price is more likely to be cheaper, as they can't afford high housing prices.
'PTRATIO' negatively correlated : Lower value of PTRATIO indicates high number of teachers, lower number of students which means teacher could pay more attention to each of students which indicates a better education system in the society, so that would increase the housing prices in the society, so it would be more expensive.</p>

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<p>Here I am using Linear Regression to visualize gradient is positive for positive relationship and negative for negative relationship</p>

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<div class="prompt input_prompt">In&nbsp;[6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LinearRegression</span>

<span class="k">def</span> <span class="nf">visualize_intuition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">feature_name</span><span class="p">):</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>

    <span class="n">features</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">feature_name</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">features</span><span class="p">,</span>  <span class="n">lr</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">features</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">prices</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">feature_name</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;PRICE&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># showing relationship between price and each feature one by one</span>
<span class="n">visualize_intuition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;RM&#39;</span><span class="p">)</span>
<span class="n">visualize_intuition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;LSTAT&#39;</span><span class="p">)</span>
<span class="n">visualize_intuition</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;PTRATIO&#39;</span><span class="p">)</span>
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<h2 id="Developing-a-Model">Developing a Model<a class="anchor-link" href="#Developing-a-Model">&#182;</a></h2><p>In this second section of the project, you will develop the tools and techniques necessary for a model to make a prediction. Being able to make accurate evaluations of each model's performance through the use of these tools and techniques helps to greatly reinforce the confidence in your predictions.</p>

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<h3 id="Implementation:-Define-a-Performance-Metric">Implementation: Define a Performance Metric<a class="anchor-link" href="#Implementation:-Define-a-Performance-Metric">&#182;</a></h3><p>It is difficult to measure the quality of a given model without quantifying its performance over training and testing. This is typically done using some type of performance metric, whether it is through calculating some type of error, the goodness of fit, or some other useful measurement. For this project, you will be calculating the <a href="http://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination"><em>coefficient of determination</em></a>, R<sup>2</sup>, to quantify your model's performance. The coefficient of determination for a model is a useful statistic in regression analysis, as it often describes how "good" that model is at making predictions.</p>
<p>The values for R<sup>2</sup> range from 0 to 1, which captures the percentage of squared correlation between the predicted and actual values of the <strong>target variable</strong>. A model with an R<sup>2</sup> of 0 is no better than a model that always predicts the <em>mean</em> of the target variable, whereas a model with an R<sup>2</sup> of 1 perfectly predicts the target variable. Any value between 0 and 1 indicates what percentage of the target variable, using this model, can be explained by the <strong>features</strong>. <em>A model can be given a negative R<sup>2</sup> as well, which indicates that the model is <strong>arbitrarily worse</strong> than one that always predicts the mean of the target variable.</em></p>
<p>For the <code>performance_metric</code> function in the code cell below, you will need to implement the following:</p>
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<li>Use <code>r2_score</code> from <code>sklearn.metrics</code> to perform a performance calculation between <code>y_true</code> and <code>y_predict</code>.</li>
<li>Assign the performance score to the <code>score</code> variable.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">r2_score</span>


<span class="k">def</span> <span class="nf">performance_metric</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_predict</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Calculates and returns the performance score between </span>
<span class="sd">        true and predicted values based on the metric chosen. &quot;&quot;&quot;</span>
    
    <span class="c1"># TODO: Calculate the performance score between &#39;y_true&#39; and &#39;y_predict&#39;</span>
    <span class="n">score</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span><span class="n">y_predict</span><span class="p">)</span>
    
    <span class="c1"># Return the score</span>
    <span class="k">return</span> <span class="n">score</span>
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<h3 id="Question-2---Goodness-of-Fit">Question 2 - Goodness of Fit<a class="anchor-link" href="#Question-2---Goodness-of-Fit">&#182;</a></h3><p>Assume that a dataset contains five data points and a model made the following predictions for the target variable:</p>
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<th style="text-align:center">True Value</th>
<th style="text-align:center">Prediction</th>
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<td style="text-align:center">3.0</td>
<td style="text-align:center">2.5</td>
</tr>
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<td style="text-align:center">-0.5</td>
<td style="text-align:center">0.0</td>
</tr>
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<td style="text-align:center">2.0</td>
<td style="text-align:center">2.1</td>
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<td style="text-align:center">7.0</td>
<td style="text-align:center">7.8</td>
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<td style="text-align:center">4.2</td>
<td style="text-align:center">5.3</td>
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<p>Run the code cell below to use the <code>performance_metric</code> function and calculate this model's coefficient of determination.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Calculate the performance of this model</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">performance_metric</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mf">4.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">,</span> <span class="mf">7.8</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model has a coefficient of determination, R^2, of </span><span class="si">{:.3f}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">score</span><span class="p">))</span>
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<pre>Model has a coefficient of determination, R^2, of 0.923.
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<li>Would you consider this model to have successfully captured the variation of the target variable? </li>
<li>Why or why not?</li>
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<p><strong> Hint: </strong>  The R2 score is the proportion of the variance in the dependent variable that is predictable from the independent variable. In other words:</p>
<ul>
<li>R2 score of 0 means that the dependent variable cannot be predicted from the independent variable.</li>
<li>R2 score of 1 means the dependent variable can be predicted from the independent variable.</li>
<li>R2 score between 0 and 1 indicates the extent to which the dependent variable is predictable. An </li>
<li>R2 score of 0.40 means that 40 percent of the variance in Y is predictable from X.</li>
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<p><strong>Answer:</strong></p>

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<p>R-squared is a statistical measure of how close the data are to the fitted regression line.</p>
<p>It is the percentage of the response variable variation that is explained by a linear model. Or:</p>
<p>R-squared = Explained variation / Total variation</p>
<p>R-squared is always between 0 and 100%:</p>
<p>0% indicates that the model explains none of the variability of the response data around its mean.</p>
<p>100% indicates that the model explains all the variability of the response data around its mean.</p>
<p>In general, the higher the R-squared, the better the model fits the data.</p>

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<h1 id="Graphical-Representation-of-R-squared">Graphical Representation of R-squared<a class="anchor-link" href="#Graphical-Representation-of-R-squared">&#182;</a></h1>
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<p><img src="https://image.ibb.co/jd7DE9/r2.png" alt="claass" border="0"></p>

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<p>The regression model on the left accounts for 38.0% of the variance while the one on the right accounts for 87.4%. The more variance that is accounted for by the regression model the closer the data points will fall to the fitted regression line.
If a model could explain 100% of the variance, the fitted values would always equal the observed values and, therefore, all the data points would fall on the fitted regression line.</p>

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<p>In the model above we have find the R2 score of 0.923 i.e., 92.30% which is very near to 100%.
So it would be concluded that, this model should be considered as it successfully captured the variation of the target variable.</p>

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<h1 id="Visual-Representation-of-R2-Score">Visual Representation of R2 Score<a class="anchor-link" href="#Visual-Representation-of-R2-Score">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plotting true values</span>
<span class="n">true</span><span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mf">4.2</span><span class="p">]</span>
<span class="n">true_plot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">true</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;True&#39;</span><span class="p">)</span>

<span class="c1"># Plotting predicted values</span>
<span class="n">pred</span> <span class="o">=</span> <span class="p">[</span><span class="mf">2.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">2.1</span><span class="p">,</span> <span class="mf">7.8</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">]</span>
<span class="n">pred_plot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>

<span class="c1"># Reference line</span>
<span class="n">fit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">poly1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">true</span><span class="p">,</span> <span class="n">true</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">lims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">true</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">true</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lims</span><span class="p">,</span> <span class="n">fit</span><span class="p">(</span><span class="n">lims</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>

<span class="c1"># Legend and show</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles</span><span class="o">=</span><span class="p">[</span><span class="n">true_plot</span><span class="p">,</span> <span class="n">pred_plot</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower right&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h3 id="Implementation:-Shuffle-and-Split-Data">Implementation: Shuffle and Split Data<a class="anchor-link" href="#Implementation:-Shuffle-and-Split-Data">&#182;</a></h3><p>Your next implementation requires that you take the Boston housing dataset and split the data into training and testing subsets. Typically, the data is also shuffled into a random order when creating the training and testing subsets to remove any bias in the ordering of the dataset.</p>
<p>For the code cell below, you will need to implement the following:</p>
<ul>
<li>Use <code>train_test_split</code> from <code>sklearn.cross_validation</code> to shuffle and split the <code>features</code> and <code>prices</code> data into training and testing sets.<ul>
<li>Split the data into 80% training and 20% testing.</li>
<li>Set the <code>random_state</code> for <code>train_test_split</code> to a value of your choice. This ensures results are consistent.</li>
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<li>Assign the train and testing splits to <code>X_train</code>, <code>X_test</code>, <code>y_train</code>, and <code>y_test</code>.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>

<span class="c1"># TODO: Shuffle and split the data into training and testing subsets</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</span><span class="p">,</span> <span class="n">prices</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.20</span><span class="p">)</span>

<span class="c1"># Success</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training and testing split was successful.&quot;</span><span class="p">)</span>
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<pre>Training and testing split was successful.
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<h3 id="Question-3---Training-and-Testing">Question 3 - Training and Testing<a class="anchor-link" href="#Question-3---Training-and-Testing">&#182;</a></h3><ul>
<li>What is the benefit to splitting a dataset into some ratio of training and testing subsets for a learning algorithm?</li>
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<p><strong>Hint:</strong> Think about how overfitting or underfitting is contingent upon how splits on data is done.</p>

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<p><strong>Answer: </strong></p>

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<p>The training dataset and test dataset must be similar, usually have the same predictors or variables. They differ on the observations and specific values in the variables. If you fit the model on the training dataset, then you implicitly minimize error or find correct responses. The fitted model provides a good prediction on the training dataset. Then you test the model on the test dataset. If the model predicts good also on the test dataset, you have more confidence. You have more confidence since the test dataset is similar to the training dataset, but not the same nor seen by the model. It means the model transfers prediction or learning in real sense.</p>
<p>So,by splitting dataset into training and testing subset, we can efficiently measure our trained model since it never sees testing data before.Thus it's possible to prevent overfitting.</p>

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<h2 id="Analyzing-Model-Performance">Analyzing Model Performance<a class="anchor-link" href="#Analyzing-Model-Performance">&#182;</a></h2><p>In this third section of the project, you'll take a look at several models' learning and testing performances on various subsets of training data. Additionally, you'll investigate one particular algorithm with an increasing <code>'max_depth'</code> parameter on the full training set to observe how model complexity affects performance. Graphing your model's performance based on varying criteria can be beneficial in the analysis process, such as visualizing behavior that may not have been apparent from the results alone.</p>

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<h3 id="Learning-Curves">Learning Curves<a class="anchor-link" href="#Learning-Curves">&#182;</a></h3><p>The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.</p>
<p>Run the code cell below and use these graphs to answer the following question.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Produce learning curves for varying training set sizes and maximum depths</span>
<span class="n">vs</span><span class="o">.</span><span class="n">ModelLearning</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">)</span>
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<pre>C:\Users\sid321axn\Anaconda3\lib\site-packages\matplotlib\figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
  &#34;matplotlib is currently using a non-GUI backend, &#34;
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<h3 id="Question-4---Learning-the-Data">Question 4 - Learning the Data<a class="anchor-link" href="#Question-4---Learning-the-Data">&#182;</a></h3><ul>
<li>Choose one of the graphs above and state the maximum depth for the model. </li>
<li>What happens to the score of the training curve as more training points are added? What about the testing curve? </li>
<li>Would having more training points benefit the model? </li>
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<p><strong>Hint:</strong> Are the learning curves converging to particular scores? Generally speaking, the more data you have, the better. But if your training and testing curves are converging with a score above your benchmark threshold, would this be necessary?
Think about the pros and cons of adding more training points based on if the training and testing curves are converging.</p>

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<p><strong>Answer: </strong></p>

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<p>I' ll choose model with max_depth=3 as in this model, both training and testing curves converges to higher score of about 0.75 to 0.8.
As we increase training points , training errors will keep increasing and testing errors will keep decreasing.So, having more training points will benefit the model by making curve converge. Once converged, adding more training points seems to have no significant help.
So,there's a trade-off here that acquiring or training more data might cost you a lot. A good balance should be maintained.</p>

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<h3 id="Complexity-Curves">Complexity Curves<a class="anchor-link" href="#Complexity-Curves">&#182;</a></h3><p>The following code cell produces a graph for a decision tree model that has been trained and validated on the training data using different maximum depths. The graph produces two complexity curves — one for training and one for validation. Similar to the <strong>learning curves</strong>, the shaded regions of both the complexity curves denote the uncertainty in those curves, and the model is scored on both the training and validation sets using the <code>performance_metric</code> function.</p>
<p><strong> Run the code cell below and use this graph to answer the following two questions Q5 and Q6. </strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">ModelComplexity</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-5---Bias-Variance-Tradeoff">Question 5 - Bias-Variance Tradeoff<a class="anchor-link" href="#Question-5---Bias-Variance-Tradeoff">&#182;</a></h3><ul>
<li>When the model is trained with a maximum depth of 1, does the model suffer from high bias or from high variance? </li>
<li>How about when the model is trained with a maximum depth of 10? What visual cues in the graph justify your conclusions?</li>
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<p><strong>Hint:</strong> High bias is a sign of underfitting(model is not complex enough to pick up the nuances in the data) and high variance is a sign of overfitting(model is by-hearting the data and cannot generalize well). Think about which model(depth 1 or 10) aligns with which part of the tradeoff.</p>

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<p><strong>Answer: </strong></p>

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<p>At a maximum depth of 1, model suffers from high bias. Two scores are quite close,but both the scores are too far from acceptable level so I think it's a high bias problem.In other words, the model is underfitting. The data points obviously follow some sort of curve, but our predictor isn’t complex enough to capture that information. Our model is biased in that it assumes that the data will behave in a certain fashion even though that assumption may not be true. A key point is that there’s nothing wrong with our training—this is the best possible fit that a linear model can achieve. There is, however, something wrong with the model itself in that it’s not complex enough to model our data.</p>
<p>At a maximun depth of 10, model suffers from high variance since training score is 1.0 but validation score is about 0.7.In other words, a model is overfitting. Again, the data points suggest a sort of graceful curve. However, our model uses a very complex curve to get as close to every data point as possible. Consequently, a model with high variance has very low bias because it makes little to no assumption about the data. In fact, it adapts too much to the data.</p>

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<h3 id="Question-6---Best-Guess-Optimal-Model">Question 6 - Best-Guess Optimal Model<a class="anchor-link" href="#Question-6---Best-Guess-Optimal-Model">&#182;</a></h3><ul>
<li>Which maximum depth do you think results in a model that best generalizes to unseen data? </li>
<li>What intuition lead you to this answer?</li>
</ul>
<p><strong> Hint: </strong> Look at the graph above Question 5 and see where the validation scores lie for the various depths that have been assigned to the model. Does it get better with increased depth? At what point do we get our best validation score without overcomplicating our model? And remember, Occams Razor states "Among competing hypotheses, the one with the fewest assumptions should be selected."</p>

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<p><strong>Answer: </strong></p>

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<p>From my point of view, max depth of 4 best generalizes the unseen data. As max depth increases, bias becomes lower and variance becomes higher. We should keep the balance between the two. 
Just after depth 4, training score increase upwards and validation score starts to goes down, so I it begins to suffer from overfitting. So that's why  4 should be a good choice.</p>

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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 final section of the project, you will construct a model and make a prediction on the client's feature set using an optimized model from <code>fit_model</code>.</p>

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<h3 id="Question-7---Grid-Search">Question 7 - Grid Search<a class="anchor-link" href="#Question-7---Grid-Search">&#182;</a></h3><ul>
<li>What is the grid search technique?</li>
<li>How it can be applied to optimize a learning algorithm?</li>
</ul>
<p><strong> Hint: </strong> When explaining the Grid Search technique, be sure to touch upon why it is used,  what the 'grid' entails and what the end goal of this method is. To solidify your answer, you can also give an example of a parameter in a model that can be optimized using this approach.</p>

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<p><strong>Answer: </strong></p>

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<p>Grid Search is an algorithm with the help of which we can tune hyper-parameters of a model. We pass the hyper-parameters to tune, the possible values for each hyper-parameter and a performance metric as input to the grid search algorithm. The algorithm will then place all the possible hyper-parameter combination in a grid and then find the performance of the model for each combination against some cross-validation set. Then it outputs the hyper-parameter combination that gives the best result.</p>

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<p>GridSearch is generally used when you are not sure of good values for a parameter. We might have a range of parameter values that you think would work out for the model and you want to test which one of them to use. In that situation it becomes tedious for us to train the model again and again with different parameters and in that situation we use gridSearch.</p>

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<h1 id="Working-of-Grid-Search">Working of Grid Search<a class="anchor-link" href="#Working-of-Grid-Search">&#182;</a></h1>
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<p>Let us suppose that :
To solve the optimization problem for a fixed set of values of hyperparameters α and β gives us a value of w. 
Since the optimal value of w (call it w∗) is a function of α and β, we can write it as follows:</p>
<p>w∗(α,β)=argminwP(w,α,β,Strain)</p>
<p>Now we use this w∗ to predict on the validation sample to get validation error. We can view this scenario in terms of a "validation error function": the function takes as inputs the hyperparameters α and β, and returns the validation error corresponding to w∗(α,β).</p>
<p>So the goal of hyperparameter optimization is to find the set of values of α and β, that minimize this validation error function.</p>
<p>So, in Grid Search technique, it picks a bunch of values of α -- (α1,α2,…) and  a bunch of values of β -- (β1,β2,…) and for each pair of values, evaluates the validation error function. Then pick the pair that gives the minimum value of the validation error function.</p>
<p>The pairs (α1,β1),(α1,β2),…,(α2,β1),(α2,β2),… when plotted in space look like a grid, hence the name.</p>

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<p><img src="https://image.ibb.co/gJFfSU/grid123.png" alt="claass" border="0"></p>

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<h3 id="Question-8---Cross-Validation">Question 8 - Cross-Validation<a class="anchor-link" href="#Question-8---Cross-Validation">&#182;</a></h3><ul>
<li><p>What is the k-fold cross-validation training technique?</p>
</li>
<li><p>What benefit does this technique provide for grid search when optimizing a model?</p>
</li>
</ul>
<p><strong>Hint:</strong> When explaining the k-fold cross validation technique, be sure to touch upon what 'k' is, how the dataset is split into different parts for training and testing and the number of times it is run based on the 'k' value.</p>
<p>When thinking about how k-fold cross validation helps grid search, think about the main drawbacks of grid search which are hinged upon <strong>using a particular subset of data for training or testing</strong> and how k-fold cv could help alleviate that. You can refer to the <a href="http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation">docs</a> for your answer.</p>

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<p><strong>Answer: </strong></p>

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<p>Cross Validation is a useful technique for assessing the performance of machine learning models. It helps in knowing how the machine learning model would generalize to an independent data set. We generally use this technique to estimate how accurate the predictions our model will give in practice.</p>
<p>In K-fold Cross Validation, we first split our data into train and test and then apply k-fold on the train dataset.Now after this step we train our model on (k-1) folds and use 1 fold for cross-validation. This process is repeated k times, each time with a different cross-validation set. The average cross-validation score is used to check the performance of the model. Once you have selected the best parameters using gridSearch along with k-fold cross-validation, we will use the test set to check the final performance of the model.</p>
<p>K-fold cross validation is performed as per the following steps:</p>
<p>1.Partition the original training data set into k equal subsets.Each subset is called a fold. Let the folds be named as f1, f2, …, fk .</p>
<p>2.For i = 1 to i = k</p>
<p>a.Keep the fold fi as Validation set and keep all the remaining k-1 folds in the Cross validation training set.</p>
<p>b.Train your machine learning model using the cross validation training set and calculate the accuracy of your model by validating the predicted results against the validation set.</p>
<p>3.Estimate the accuracy of your machine learning model by averaging the accuracies derived in all the k cases of cross validation.</p>
<p>In the k-fold cross validation method, all the entries in the original training data set are used for both training as well as validation. Also, each entry is used for validation just once.</p>
<p>Generally, the value of k is taken to be 10, but it is not a strict rule, and k can take any value.</p>

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<p><img src="https://image.ibb.co/m5MZMp/kfold.png" alt="claass" border="0"></p>

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<h3 id="Implementation:-Fitting-a-Model">Implementation: Fitting a Model<a class="anchor-link" href="#Implementation:-Fitting-a-Model">&#182;</a></h3><p>Your final implementation requires that you bring everything together and train a model using the <strong>decision tree algorithm</strong>. To ensure that you are producing an optimized model, you will train the model using the grid search technique to optimize the <code>'max_depth'</code> parameter for the decision tree. The <code>'max_depth'</code> parameter can be thought of as how many questions the decision tree algorithm is allowed to ask about the data before making a prediction. Decision trees are part of a class of algorithms called <em>supervised learning algorithms</em>.</p>
<p>In addition, you will find your implementation is using <code>ShuffleSplit()</code> for an alternative form of cross-validation (see the <code>'cv_sets'</code> variable). While it is not the K-Fold cross-validation technique you describe in <strong>Question 8</strong>, this type of cross-validation technique is just as useful!. The <code>ShuffleSplit()</code> implementation below will create 10 (<code>'n_splits'</code>) shuffled sets, and for each shuffle, 20% (<code>'test_size'</code>) of the data will be used as the <em>validation set</em>. While you're working on your implementation, think about the contrasts and similarities it has to the K-fold cross-validation technique.</p>
<p>Please note that ShuffleSplit has different parameters in scikit-learn versions 0.17 and 0.18.
For the <code>fit_model</code> function in the code cell below, you will need to implement the following:</p>
<ul>
<li>Use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html"><code>DecisionTreeRegressor</code></a> from <code>sklearn.tree</code> to create a decision tree regressor object.<ul>
<li>Assign this object to the <code>'regressor'</code> variable.</li>
</ul>
</li>
<li>Create a dictionary for <code>'max_depth'</code> with the values from 1 to 10, and assign this to the <code>'params'</code> variable.</li>
<li>Use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"><code>make_scorer</code></a> from <code>sklearn.metrics</code> to create a scoring function object.<ul>
<li>Pass the <code>performance_metric</code> function as a parameter to the object.</li>
<li>Assign this scoring function to the <code>'scoring_fnc'</code> variable.</li>
</ul>
</li>
<li>Use <a href="http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html"><code>GridSearchCV</code></a> from <code>sklearn.grid_search</code> to create a grid search object.<ul>
<li>Pass the variables <code>'regressor'</code>, <code>'params'</code>, <code>'scoring_fnc'</code>, and <code>'cv_sets'</code> as parameters to the object. </li>
<li>Assign the <code>GridSearchCV</code> object to the <code>'grid'</code> variable.</li>
</ul>
</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></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="kn">from</span> <span class="nn">sklearn.tree</span> <span class="k">import</span> <span class="n">DecisionTreeRegressor</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="k">import</span> <span class="n">GridSearchCV</span>


<span class="k">def</span> <span class="nf">fit_model</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; Performs grid search over the &#39;max_depth&#39; parameter for a </span>
<span class="sd">        decision tree regressor trained on the input data [X, y]. &quot;&quot;&quot;</span>
    
    <span class="c1"># Create cross-validation sets from the training data</span>
    <span class="c1"># sklearn version 0.18: ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)</span>
    <span class="c1"># sklearn versiin 0.17: ShuffleSplit(n, n_iter=10, test_size=0.1, train_size=None, random_state=None)</span>
    <span class="n">cv_sets</span> <span class="o">=</span> <span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">n_iter</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>

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

    <span class="c1"># TODO: Create a dictionary for the parameter &#39;max_depth&#39; with a range from 1 to 10</span>
    <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;max_depth&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">10</span><span class="p">]}</span>

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

    <span class="c1"># TODO: Create the grid search cv object --&gt; GridSearchCV()</span>
    <span class="c1"># Make sure to include the right parameters in the object:</span>
    <span class="c1"># (estimator, param_grid, scoring, cv) which have values &#39;regressor&#39;, &#39;params&#39;, &#39;scoring_fnc&#39;, and &#39;cv_sets&#39; respectively.</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">regressor</span><span class="p">,</span><span class="n">param_grid</span><span class="o">=</span><span class="n">params</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring_fnc</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">cv_sets</span><span class="p">)</span>

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

    <span class="c1"># Return the optimal model after fitting the data</span>
    <span class="k">return</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_estimator_</span>
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<h3 id="Making-Predictions">Making Predictions<a class="anchor-link" href="#Making-Predictions">&#182;</a></h3><p>Once a model has been trained on a given set of data, it can now be used to make predictions on new sets of input data. In the case of a <em>decision tree regressor</em>, the model has learned <em>what the best questions to ask about the input data are</em>, and can respond with a prediction for the <strong>target variable</strong>. You can use these predictions to gain information about data where the value of the target variable is unknown — such as data the model was not trained on.</p>

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<h3 id="Question-9---Optimal-Model">Question 9 - Optimal Model<a class="anchor-link" href="#Question-9---Optimal-Model">&#182;</a></h3><ul>
<li>What maximum depth does the optimal model have? How does this result compare to your guess in <strong>Question 6</strong>?  </li>
</ul>
<p>Run the code block below to fit the decision tree regressor to the training data and produce an optimal model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Fit the training data to the model using grid search</span>
<span class="n">reg</span> <span class="o">=</span> <span class="n">fit_model</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"># Produce the value for &#39;max_depth&#39;</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Parameter &#39;max_depth&#39; is </span><span class="si">{:,.3f}</span><span class="s2"> for the optimal model.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">&#39;max_depth&#39;</span><span class="p">]))</span>
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<pre>Parameter &#39;max_depth&#39; is 4.000 for the optimal model.
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<p><strong> Hint: </strong> The answer comes from the output of the code snipped above.</p>
<p><strong>Answer: </strong></p>

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<p>The optimal model have maximum depth of 4 and it matches with my guess of 4 in question 6</p>

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<h3 id="Question-10---Predicting-Selling-Prices">Question 10 - Predicting Selling Prices<a class="anchor-link" href="#Question-10---Predicting-Selling-Prices">&#182;</a></h3><p>Imagine that you were a real estate agent in the Boston area looking to use this model to help price homes owned by your clients that they wish to sell. You have collected the following information from three of your clients:</p>
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<thead><tr>
<th style="text-align:center">Feature</th>
<th style="text-align:center">Client 1</th>
<th style="text-align:center">Client 2</th>
<th style="text-align:center">Client 3</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Total number of rooms in home</td>
<td style="text-align:center">5 rooms</td>
<td style="text-align:center">4 rooms</td>
<td style="text-align:center">8 rooms</td>
</tr>
<tr>
<td style="text-align:center">Neighborhood poverty level (as %)</td>
<td style="text-align:center">17%</td>
<td style="text-align:center">32%</td>
<td style="text-align:center">3%</td>
</tr>
<tr>
<td style="text-align:center">Student-teacher ratio of nearby schools</td>
<td style="text-align:center">15-to-1</td>
<td style="text-align:center">22-to-1</td>
<td style="text-align:center">12-to-1</td>
</tr>
</tbody>
</table>
<ul>
<li>What price would you recommend each client sell his/her home at? </li>
<li>Do these prices seem reasonable given the values for the respective features? </li>
</ul>
<p><strong>Hint:</strong> Use the statistics you calculated in the <strong>Data Exploration</strong> section to help justify your response.  Of the three clients, client 3 has has the biggest house, in the best public school neighborhood with the lowest poverty level; while client 2 has the smallest house, in a neighborhood with a relatively high poverty rate and not the best public schools.</p>
<p>Run the code block below to have your optimized model make predictions for each client's home.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Produce a matrix for client data</span>
<span class="n">client_data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">17</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span> <span class="c1"># Client 1</span>
               <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">22</span><span class="p">],</span> <span class="c1"># Client 2</span>
               <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">12</span><span class="p">]]</span>  <span class="c1"># Client 3</span>

<span class="c1"># Show predictions</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">price</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">client_data</span><span class="p">)):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicted selling price for Client </span><span class="si">{}</span><span class="s2">&#39;s home: $</span><span class="si">{:,.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">price</span><span class="p">))</span>
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<pre>Predicted selling price for Client 1&#39;s home: $403,025.00
Predicted selling price for Client 2&#39;s home: $237,478.72
Predicted selling price for Client 3&#39;s home: $931,636.36
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<p><strong>Answer: </strong></p>

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<p>From my point of view, the result above is reasonable and it also matches with my intuition which I answered in question 1.
As value of RM increased for client 3 and value of LSTAT and PTRATIO decreased proportionately so that prices increased manifold in comparison to client 1 and client 2.
In between client 1 and client 2, value of RM is greater which is 5 for client 1 and value of LSTAT and PTRATIO is significantly less which is negatively related to price, according to my intuition and that's why prices of client 1 is greater than client 2.</p>

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<h3 id="Sensitivity">Sensitivity<a class="anchor-link" href="#Sensitivity">&#182;</a></h3><p>An optimal model is not necessarily a robust model. Sometimes, a model is either too complex or too simple to sufficiently generalize to new data. Sometimes, a model could use a learning algorithm that is not appropriate for the structure of the data given. Other times, the data itself could be too noisy or contain too few samples to allow a model to adequately capture the target variable — i.e., the model is underfitted.</p>
<p><strong>Run the code cell below to run the <code>fit_model</code> function ten times with different training and testing sets to see how the prediction for a specific client changes with respect to the data it's trained on.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vs</span><span class="o">.</span><span class="n">PredictTrials</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">prices</span><span class="p">,</span> <span class="n">fit_model</span><span class="p">,</span> <span class="n">client_data</span><span class="p">)</span>
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<pre>Trial 1: $411,000.00
Trial 2: $411,417.39
Trial 3: $415,800.00
Trial 4: $420,622.22
Trial 5: $418,377.27
Trial 6: $422,223.53
Trial 7: $417,000.00
Trial 8: $414,430.43
Trial 9: $402,531.82
Trial 10: $413,700.00

Range in prices: $19,691.71
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<h3 id="Question-11---Applicability">Question 11 - Applicability<a class="anchor-link" href="#Question-11---Applicability">&#182;</a></h3><ul>
<li>In a few sentences, discuss whether the constructed model should or should not be used in a real-world setting.  </li>
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<p><strong>Hint:</strong> Take a look at the range in prices as calculated in the code snippet above. Some questions to answering:</p>
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<li>How relevant today is data that was collected from 1978? How important is inflation?</li>
<li>Are the features present in the data sufficient to describe a home? Do you think factors like quality of apppliances in the home, square feet of the plot area, presence of pool or not etc should factor in?</li>
<li>Is the model robust enough to make consistent predictions?</li>
<li>Would data collected in an urban city like Boston be applicable in a rural city?</li>
<li>Is it fair to judge the price of an individual home based on the characteristics of the entire neighborhood?</li>
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<p>I think this model is not reliable as the prices in 1978 will not be same as of today's scenario many things will change like inflation rate will differ,many features such as plot location, importance of number of rooms in the flat may differ and there are many features which need to be captured if we want to fit this model in today's scenario such as carpet area, full area in sqft,parking facility,security guards, availability of swimming pool, playground for children, presence of supermarket in the society premises and many more factors will play a decisive role in predicting actual prices, which our model did'nt capture them.
I think it is a robust model and it can make consistent predictions.
I think data collected in an urban city will not be applicable in rural city and the main factor is purchasing power differs in urban city in comparison to rural city as well as many facors which are important which may include luxury living will also be not applicable in case of rural city.
From my point of view, prices of individual home also depends on the characterstics of the entire neighbourhood becuase it decides what type of area it is , Is it industrial area or Housing colony or posh colony or government sector housing etc.</p>

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<p><strong>Answer: </strong></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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