Traffic_Sign_Classifier.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Traffic_Sign_Classifier</title>

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html {
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body {
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article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
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audio,
canvas,
progress,
video {
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audio:not([controls]) {
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[hidden],
template {
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a {
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a:hover {
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b,
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dfn {
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h1 {
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small {
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sup {
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sup {
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sub {
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svg:not(:root) {
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figure {
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hr {
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input,
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input[type="submit"] {
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td,
th {
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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

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

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

    </style>


<style type="text/css">
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<h1 id="Self-Driving-Car-Engineer-Nanodegree">Self-Driving Car Engineer Nanodegree<a class="anchor-link" href="#Self-Driving-Car-Engineer-Nanodegree">&#182;</a></h1><h2 id="Deep-Learning">Deep Learning<a class="anchor-link" href="#Deep-Learning">&#182;</a></h2><h2 id="Project:-Build-a-Traffic-Sign-Recognition-Classifier">Project: Build a Traffic Sign Recognition Classifier<a class="anchor-link" href="#Project:-Build-a-Traffic-Sign-Recognition-Classifier">&#182;</a></h2><p>In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<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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<p>In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a <a href="https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md">write up template</a> that can be used to guide the writing process. Completing the code template and writeup template will cover all of the <a href="https://review.udacity.com/#!/rubrics/481/view">rubric points</a> for this project.</p>
<p>The <a href="https://review.udacity.com/#!/rubrics/481/view">rubric</a> contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.</p>
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<h2 id="Step-0:-Load-The-Data">Step 0: Load The Data<a class="anchor-link" href="#Step-0:-Load-The-Data">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load pickled data</span>
<span class="kn">import</span> <span class="nn">pickle</span>

<span class="c1"># TODO: Fill this in based on where you saved the training and testing data</span>

<span class="n">training_file</span> <span class="o">=</span> <span class="s2">&quot;traffic-signs-data/train.p&quot;</span>
<span class="n">validation_file</span><span class="o">=</span><span class="s2">&quot;traffic-signs-data/valid.p&quot;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s2">&quot;traffic-signs-data/test.p&quot;</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">training_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">validation_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">valid</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">testing_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</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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<h2 id="Step-1:-Dataset-Summary-&amp;-Exploration">Step 1: Dataset Summary &amp; Exploration<a class="anchor-link" href="#Step-1:-Dataset-Summary-&amp;-Exploration">&#182;</a></h2><p>The pickled data is a dictionary with 4 key/value pairs:</p>
<ul>
<li><code>'features'</code> is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).</li>
<li><code>'labels'</code> is a 1D array containing the label/class id of the traffic sign. The file <code>signnames.csv</code> contains id -&gt; name mappings for each id.</li>
<li><code>'sizes'</code> is a list containing tuples, (width, height) representing the original width and height the image.</li>
<li><code>'coords'</code> is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. <strong>THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES</strong></li>
</ul>
<p>Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html">pandas shape method</a> might be useful for calculating some of the summary results.</p>

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<h3 id="Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas<a class="anchor-link" href="#Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Replace each question mark with the appropriate value. </span>
<span class="c1">### Use python, pandas or numpy methods rather than hard coding the results</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1"># TODO: Number of training examples</span>
<span class="n">n_train</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>

<span class="c1"># TODO: Number of validation examples</span>
<span class="n">n_validation</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">)</span>

<span class="c1"># TODO: Number of testing examples.</span>
<span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># TODO: What&#39;s the shape of an traffic sign image?</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>

<span class="c1"># TODO: How many unique classes/labels there are in the dataset.</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of training examples =&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of testing examples =&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of validation examples =&quot;</span><span class="p">,</span> <span class="n">n_validation</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Image data shape =&quot;</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of classes =&quot;</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
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<pre>Number of training examples = 34799
Number of testing examples = 12630
Number of validation examples = 4410
Image data shape = (32, 32, 3)
Number of classes = 43
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<h3 id="Include-an-exploratory-visualization-of-the-dataset">Include an exploratory visualization of the dataset<a class="anchor-link" href="#Include-an-exploratory-visualization-of-the-dataset">&#182;</a></h3>
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<p>Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.</p>
<p>The <a href="http://matplotlib.org/">Matplotlib</a> <a href="http://matplotlib.org/examples/index.html">examples</a> and <a href="http://matplotlib.org/gallery.html">gallery</a> pages are a great resource for doing visualizations in Python.</p>
<p><strong>NOTE:</strong> It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Data exploration visualization code goes here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="c1"># Visualizations will be shown in the notebook.</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="c1"># show image of 10 random data points in X_train dataset</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">001</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
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<div class="prompt input_prompt">In&nbsp;[5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># show image of 10 random data points in X_valid dataset</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">001</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">))</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_valid</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># show image of 10 random data points in X_test dataset</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">001</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
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38SH/j177wEtjuv/nNTbq9iG/zeZ78IttOZ1VitUMnVvRNjjwocv+c3cU9Z6Cyb8uvX7605/8vKA5ba1gi28Ze/+OumfPEVXDufWX0Ur+SEpmxlYYl+PXR1J8PMJwOedN+J/H5I9vGIvOvDtUq6Y90T/edKCCGEEEIIIWpAH1dCCCGEEEIIUQP6uBJCCCGEEEKIGtDHlRBCCCGEEELUwENOIkyCHTgBOROPR0TBmqRWrJYQ0XJKomOk7ndRgcEGEpJY2OdubJPgFVNtIjp3P/T3DyGEKfezfISJExPyLIkTczPdfWCJ2nyyWJLAtySBMOLYtlVZoIiXyRQhSABRMjIBZO77hgQbqI0xEdWSpLMIk2XaxK0soe2F6yhCv3vTqrlXSAVu3biBd3PJo+eiOazlHkvGa9v8AwsY/MAHakgG2E+NAfaLn3sJiy5DxKSlU7Tvb18Hnz5JdtyLnbi6QxLAHgFTmG7Zek77yRgC72IfUMdHLKiZlFw/y1xdST3vdjGR6K997Y9NeW2IwQYaJCBAcOsiybccCrIINSIvSCaQ4CbgwsTOTlw9QVeFEELwK9eQZaF/gMsNe5YxqUJz2gbQOHEUk+NuD3FubQ3sEw4mSFR/v7Q7OH52RnaslBHuFUyE3mzZeTsg63IxJO8DtplCN78GPiOS9HoUbBLfcedj4LO0gPWcdgFJDgZcbzYKaytJAA8SQygsPP99pvzx598HPh/7K5tgu7Ftbdv5Gvj8+p+8CLYXv/WGKf/YX8U6hcCSOVubfycLIYQd8orwtW//ninPLOyik6gZXCPSnl2E1r79LfBZeh7H/rCwQXXmA26sbDn1CyoLTFHlOCFczDsaqC4i77mVX/lZsCUap8UFYCN1mgT950oIIYQQQgghakAfV0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtTAQw1oUZFMx4UXJFf4vRdFRIzvVG4RUbllGQosKyeczkjghohoq1tOwNoi155qoGC12bSi8yRGUV3adDmumciOJap3arycqPNGJJhC6do8S9vgw0SDSWrrXpC+6g3xfl7kXpEAJSVReBcugEVBgo/UxfjWbbBlx5cm+CXrLCvCzsfb4HHn9nfAtnf7m6Y8XMcxNtrBa2Wu7UpaJxx33quaYOBF5DpUuxr5300G1IH8MCbjZ88FuWgcOw0+Mz+A/Tl1cN7enwYxYJWwtvgBB7RokoA9TRcsJw84PxopinFbB2zAldwFEQkhhP4ABfq7m3bszR9YBp/l44+BLQuu7iUGKSgLFMwXpa17Tny82Lgi692IBHOo3LXHOSrvC3K/siidD9ks2DrlRdF0XcafJZEdn8OwAD63dzC4wcAFKWJ7Q13EJNhSq2XrnfexD1KvVA8hNN3fe/sDFoiDCNNduf0IjsNRhoE3msGuCePmKfC52sU59Pa2DUh0kwRbykd9U65ynGejEe6h0x37NIcrHCvPHTgMthOnjpnyydAFn+wX/zuw3XjnFtgQtvfavklTXAMPtLHNP/GUrfvzTz8+wf3FX4gcx9nwih2fYYeMz42rYHv/s3bdv7yPwbnIay68cDCXtEkCSrlgVez9kQW0i9xaEtGgRSxKkrXFE7+9WPSfKyGEEEIIIYSoAX1cCSGEEEIIIUQN6ONKCCGEEEIIIWrgoWquWEJbn0jMJxUOgZ9rT70fOTtZkbPvmdMNJSSRKTs77PPjVeR+oyGeWY1ye26/mZBkZ+7seSPB+zNNmT/rztL8BdJ2w5G1dft4pnxQom3szn6XFaknq4PPWUyOuebEWLpv/zHRKdTG/v4Du3RM2nJ2sAG2pdJqJ6J4HnxWn3kBbLmbVxXR//THJJsjnEEmuqzSl/FZ6PFqP+4K/B3TYHptSEwyq/buYiLlfdeeJMd3WDmA7TndtnrHNCbCl4o8YeQzf9PZVxsJWTdityaws+ER0Ws0XOLk5hTOvfYc6kd7e7ZtNjdR6zNzCPt5+thxU67ISsUSysKVyJqbuvWbXYdJCWOnIeF6Q4KbEOx+DVLPfPumKV985RXwudnDtltYsQnB7+zfBZ89okvKXR2KB7h2dnt4/5t37LPMNlETsUuS0A/cRpu1cC1LGmjzI+rH/to/AJ8DM9i+aThj60Sm8VcvoqbktXVbvjpi7x9ubDDd2wT66hWyJF1d6IPt6bNWJznbxETgx576MbA998FJ/sZOs62aUkzWzpkI23w+2TPlpw6QhOXiL4Rfgta/+Q743HnV6r7LHiZzvvY27rXZEatnTpYPgE8SoVYr8e/6ZEhFRL+ZJHaP5u+YOM78eCRbKNWGQbV4RuR7ov9cCSGEEEIIIUQN6ONKCCGEEEIIIWpAH1dCCCGEEEIIUQP6uBJCCCGEEEKIGnioAS0ioh6LXIJJliCMipQdLAmtT/gYAgmcEOPvIqJ8y52MrmDi/5gEeHCJdosBEd65Sw1J0IDRCJW2aWpF5yUR3o998soQQuXas2pg4rYRGRr+mVmC4tEY7+eT/7LgFWOWJNkJ9lm+zrrISLCVyWBj0/ZLo4WBFB45i0kuL165Zsp5gUlan3/vp8B27DGbhLFP+rNPBJ9j1y9VhUEM0theK8IwA3TORm4+Jj4yRgghlCThdGHrcOv8Gvi89ae/B7bB2kum/MKTZ8Hn0BI+Xx8CUbBxgCLbfN8Kyjf2dsBnlVzpfklJQJvKiX/Z/K/I7/LU9un8IgqSxz281mjH9lf/LgZX2LpDglwcPGHKSRvXFr8mhRBC6qXFZI33P2NLBAs+FLmgQTET7LPfueasyPxnm+rGrhXx7w0wIEEzbYItjG173t7EQDjdAQmm4OvOs2PXwtoa1mnbJw3OMFH91pgkk2/Yei6RwCohYYEprN9zZz8IPpOs8PvE6RqZV3eGtp6DHPuuKpquTPqpJHPWuV0nXXfrFgZgennP2g7MYPt+ZBHXsk+fdol+MU91+HPCFjkXvHaDbOz7F6+Y8u2p6+DzyF9jdRCTUrh97Y+/+RXweeW2CzoxwqTTe7s4IRYW7LU7JFhWHOHenrjAbSSWHAS9CCGEWRd0KiNDMSbfFj5YHVvj2b7jVxcyRSdC/7kSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBvRxJYQQQgghhBA18FADWiQpU4Z5G/owsRro0IhgtyiJYNZJnqOE3I8I33xQjYwJsMekni54QzUiQTZcpIZyiMEr8pz8zokGS9J2JQkkULleT6fAJcy2UYCcu0/xFsZNCN0YhdqFa4Mhe5bBCGxj70eExXWx10Ux58x9X83WMyYNPHv0e8C2dGzLlF9/7XXw+dqLX8S7zc+Z8vIz7wWfMrn3mGbLQeQU/AmZixWZL2VhZaEVCV4RSGCTamzHT3MXxc6jeBtsM1NWhT0X4+CcbWIdIhdcYdAbYD1He2Ba27Ei/nd3tsCnzoAWSYJi8cjZCtLEOKtCaLp5lGUt8JmdmwXb1vyurdMOBrTo3r0Nts21G6a8cvwoVooEERq5NT1jfwqE9Y2sywlZqNzeQINekPtF7vopWV+jEY6h3XUbmKIocK7NL+KI2e/Zsdft4vqa+wU94PM8uHAWIZy/cgVsUWXHVDbGxhwQpXi7YdvzfR84Re7YA0sebFAGNu5JaAz46/IciSny/jMdsFWF3aPfuoV37JW2DYZkbJIYFwD2eAhljHXa27ULwO0e1qk1xLHSSWx7PjuD+9Vcyiaft+E6Mo4xmFMyYwPcrHUxCI74i1EWdt889/X/BX1KFwDl+GHwubN2C2yjgX0vaQzOgE9Cviz8UGfvDQkZZ5mbuCy+XatBZnflNkQSQSMigcxi//52n6un/nMlhBBCCCGEEDWgjyshhBBCCCGEqAF9XAkhhBBCCCFEDTxUzVUco26gdGffaTIwch4f/FgS4QmSTrIDnBHR9qRep0D0DemYaDpcosSIJAMunS0imqSM1ClyZ0Gp5orpYXKfYBI1AlmFbT5uuoSyGdGBtPHc9dBpygqir+K6OqcbeICJMItd1NXcL6O+fd43v4R6nHNffRdsu5vurG+FY2WvfxNs3cIm8ZspyKHkBk71MvgzyRMk8CaXhmSvAc8yV2ypIfrDpG81O1fOfxZ8bt99A2xnHvuIKa88/hT4ZC0cr2HD3m+WJGCOhqgJaKV2DK8szYFPnVTkbLh/mgbpvzAiCR7b/pf4u/Y0dvScs+3PoCpxewfn0fa6TVa5cuQI1pNoXxM39nKioU1cu6RkifBJzEMIsO7T9YesN/48fuLnUAhhe+Ma2HY27RiKm6hpTUgy52tXrb5wWDAh2L0TIHutWJ3sjnE9z2J7/3TEktLjfHzumeOm/AOfPE3uiPrYXrBt98YNTOr95BGco96CadtD+J5ZrOfik9aWkneb165YjWKfqL7KCkVeMBZJ15VkHFTub+V9sg+83MV69pwebooI1l5IiW4Rngev3W+jfuvxj33KlNeufYlc+y8rdk/u7aLGdDDE+R/FVs/UaZ8EnybK6CarERHlfeUf/7Yp33hlHXzac3ZuLZ56DnyGxTtgu37tnCkvk4TBnTbqt+B/OeTdm726JJmdI2mC75gtohv2enE2PmkUCKej9Wv+pOg/V0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtSAPq6EEEIIIYQQogYeakALlrDLJ3NEEVoIEcsa5kRmFRX1oil2dUhY0klSBx/gISPa8WTMEkrm/6/lEEKoxtaW0OAViNMME1l6CAVpF5/UtyhJkI0CEzVWHSe+baNAN2sR4XLsxeNYz5QkmE7dA7Hnq4ucJBGeDJIIN9jkfNuDL4PPG1c/D7ZH5m0i3KVVFDtfWtsA27k3rOi0c/ID4NMifRXFLpEq+1uLD0xRkfFLAg34ecwCq2TEtnn7jilffhsTlAYirl45ddCUiw4+7x7Gdgh3btrAInG0Bj5z7afBtnTqrC0/4KXUr1vfxbZfTGYIS3IbXPJWlsyxIOL7Kpk25Zkl9Nnpo5i7e9eO2c3bGOBl7ugi2OLIC4vvnZmyICtl5BfKEEIc+YAPCN2LXHtWQ1w3rl7GgCvD3K6xsyv4vDu7GOQm79mAFj6ARwg0F3cIbs1lwTnqojvEgEgHF+wcHQ/3wSdJUYR+9a0/MeVRD9ebEH4ELK+5OD+feQcTXI8KDGjxMRs/gyYaZrYTLk7Dp5/ENakV2QgF37iEbdAtSeJdN6jZe1ODDFhsKazTLm714dVLdj6+ZwoDq7zwJFt/fAQGEpSGJIX9gZ/9OVN+6UskcftfCrBeww2bnPfbf/Q58NnYwvkfT9n94vTKD4HP0x8mCbOnSVZrx8Xv4Lrxhc99yZTHGYZqOXrmeVNuLp8An/l9jG6yvnbZlLu3cK7NHjkJtoYLKFGx93piit37cIskhZ8mSYSbqd0vKvJeTQPa+UBx97l26j9XQgghhBBCCFED+rgSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBh5qQIuKiasBFJhRPZnPQD+ZNg4eOCFi7gb5ZcOJ4bKCiKRJQIvKB7DIMZt14oJlsEAcEakT6rRJcAXSeF7AzgTRZUWCXLhGjohYtcIk2PAJnyb4u1aD/dC2Z0HavC629jCAB8p6GVin0cgGZXjt7d8Hn3dvvwK2weioKT9y+Cj4TO+iyPbuhTdN+ca7KKg9vfAs2DLX5qx1vRA+irGfKiqyd+MVh30IYzTevWKV6XtbmH5+amkJbLOLbVNuJBi94p2rV8G2XViR+ckVFJhXGVGBx1boywJ93F9e9z8HEpTBB2pg86NIyHrqAkWw9ZWt1aXr+s40CosX56fBtnnHBmVYu/YW+EwvvwC2tOXE3BHZPyZo5Kpke4qzkeAVocLxmbo2372DAWb2tlAE3mjZYDUL8ygw39vGYCBp0wq1o4IIsBPcxhsuQFCHrLl1cfjYPNjala3TvhsDIYTQDLiWDO5YcfzV89gm7/8AjruvXrxhytcWVsHni+dw/p/KV0z5+Ol7BxAIIYQFV55ro0/zKVvPKpkFnxff2gTbnttnkxj7/JHTvgYhnHvTtl0BASdCiHNc38q+HRuX3lgHn3AIx2tY8GMK52cnwf3q6je+YMo7o5vg85eBEQkw8/qXfs+UN69jkJKcBE7Ju7afb+xiIIz59k87nnmYAAAgAElEQVSC7cj32iAXUYz7zJXXfgNsm5uvm/LS4cfAZ27F7qPjBK+9eBTfglY2bfCoO5dvgE8zx32gGT1lyjH5307Cgg+5RT6NcV1uZ7gGZi7w1b3D4v3Z9d38y+4zlJr+cyWEEEIIIYQQNaCPKyGEEEIIIYSoAX1cCSGEEEIIIUQNPFzNFUlW67/vKnLOHfQbIYTI2WJyLJJ9OXoNScr0VeQgZub8EiJUigqse1W4RJjkWbwGiiVNZudTvRtTLVXkvKjXYdF2Iu0ycnqxaIxtULI8n+78P0uI2mzgOXrfDuOCJZSsh1u7qO151EsnWDZJolSKI1vPmQzHxWiEc+H82o69zsxh8FldRC3B9k2baPfWpa+Az7HH8VpNp0uoyNn+yo2Dgh4/JmenvYaFJBq+dgn1FG++YvViRY4DavUYJjvszNjRn+SY2HCujbq6xYPHTHmZiCcai6hv8Ke1aYL0GqkSMrudjqYc4/zISYdVbtmPJtQyRXA/HMOL86jz6G1bXdLuNp7R37x+EGyHTz1q70eaoHD7RYP0wwT5l6lm1ydbDiGE3r7VWNy8jEmuiZQwNDo26+zldRyfmyQZb9K0GpkZsmUvLKAGcX7OLlaotKmP/R72Z15a3clghOvrkDxLMbSddfrsh8kdcYy9sGrXsrMHsNNvjo6A7bcv2KThT5e4Tj535t41wBqFcMjJt374KRzAh2JcW966fMmUT53C5LJPPI73+4JTgv3Jm6jnqgIRh8VWH5PF2C9dIsOagqqj1nAx7IHtO7/+a6Z8+gewzR82ZY77+N0vvQq2W1fsWlaQJNBVQZI+u3eXXsBE6pcu/gHYDs1/yt6vgfW89dk/BlscW33fzPxx8EkadoAOiTC6S95vVs+8z5S37mIi9cHWbbA1KvvMGXlpH5PFM/U6b7JYE/l/yJyR7gNk6yuGtq9Ik0+E/nMlhBBCCCGEEDWgjyshhBBCCCGEqAF9XAkhhBBCCCFEDejjSgghhBBCCCFq4KEGtJhqY4K+3AWGKEigCJbk1ptYIkyeWNg6NlNsgnaCwRUyFy6iyFE8XhDFXNRwURCIsN/XnSb1ZQEtsBXw2iTMBdFpEx8SRCSz7TLyyY9DCD5ncgghjF2gBKL3DEWJQkafJLXBRP01cZcMoKJrhZoJU9Qn2J+NS1Yk/diBs+Az03gZbFe6NkDA9S4GYDh78jTY9u9a8ei7b34bfI4/9gGwPfK8DeZQkoSzPnltQZJgFyTQiO+rYrQLPht3UDC8u2nF3PNzmMQwaWGCwqiySYNnSKLBA6sYDCTt2PkZt7CPI5KgEISw2YP9O1VEktz6QD9xhPWsSH+VbhzHNJoDCdTiqoDy9RDiBiZKbU3bBKTdTVTH762hwHu4YuvenMV1uYKASFgnlr86hiBC+MOE2Hb37Nzudm+BT2cOI9805mwbb3ex9aYyHNdznRlTXp5Gn0aG/QcBZWgf10O3j8/bHdjgHI0IfQZD7Jmf/pt/x5Sfee8vkDtiktbvf8TNWzI4f2sO5/b/5XT3b76CY3NrjAl0P/qELbOAFj618hxZRs48Teb1U3aNpwGuWNCt99hrvXgR96bdAQZNydq2YrtbO+BTdkkgDAiTgs+yTN4RPvX8R035Vo4Jph82b38dA9NcdMFOQgghL207sCBbJVuEHNUIB2i/+w7YLrxlo4Z8848xeMxXL2Mi83TO7nXxAgYM2ncBdDbv4vNe3MZ9++kTdvDPHcBAMZuXzoHt/KtfN+XWwZPgkzfu/Y1A/ydE3g1jF4ApDthX411MAr3mAtMdPIrvDZOg/1wJIYQQQgghRA3o40oIIYQQQgghakAfV0IIIYQQQghRAw9Vc5WRg8JJas9KjongiOmwqsqeJ/bJgUPgX46ZS0jWTPGMZzMmmqvI/q49h6esk5Kcn85tPROmZfAJikkSP6a58M/skxF/93fkvLZPBkquTXLqhVFq26Vb4hnWnRzPdA9Kq4cZkmTSFUvK7JMkP8BErbcuXQbbL//Sf2PK6/t4tvl9J/E87gunbIbHixdRO7W6eBRsd8c3TXnrDuo5usuYMHT1kNUlvfkunsu+8J3XwHbg2HtMubOKepnIaQRZAmim5mi55K5x7w747F77Gti2+zax8NlTnwCfBpmzWWXPsM9kOIfjgsy9xCfUJZpPNja9LrOJySTrVLlk5Ex56ibIiMz/nNQiivy1WBJh1s/WNibizZIkgZ9ZsDqhvT2cR73dm2Db2LBal4Odk1gn9ygFkw2iiSTHxjbIxriWbV239RyOcK3uTKO+odOySYRXE6zVTBvHUCtzY53sc2ycZU4/GTORTk2UQ6y3H5tRhPPxAIzDEN74nW+Y8meWngafn/j3ngIbPB4uEaFFZEOjse2H7Rz1VV94FXVY3aFdcz/xHLavk9kRpRiXwnEN5L3ZcHmpM5I5upOzvrJjf2YeExvvDlAXNTOB5uoK0VxdPPKkKX/mG58Fn78Llrqx+8WV658Dj+4I96zEvT8WFa4bZDUNsCyS9XVjHW3nvvmvTfntd94Fn90M14SVFft+evkWarwHI/se1uuT9855ojdy91s6ikmud2/jer5+086jhQjHWeMQ3i8vbD3Zu2kgcRISt2d2GvjeubmOOrPlg/bdrKjQZxL0nyshhBBCCCGEqAF9XAkhhBBCCCFEDejjSgghhBBCCCFqQB9XQgghhBBCCFEDDzWgRUkSxcZOQN5OUeSaE4XgmCmXHUSbHiInYCc65pCk+M051bYCwfmpGfBpZ6iYjd33K9MVY3AOdPLJMr/7u3snq2MBLSIXlKAiQQpKIkDuu+5r5X3wKXqYgHCvawNf5EMiAqVjw1vu/bz3SzzG4BxZagW7d7bPg8/F66+D7ad++NOmfPbTPw0+07/yf4Bt60ufN+VBgQn89vcugW2ms2LKnQQTdu7eugC22zdskIsj88+AT5q5JLssKA0R56Zj21e330Ih7vU3L4OtM2uDahw6jklT4xQTMLadgL9N6pnRRL8uKAwRYEcsZIBbI6IOBgOpE5aYEmpFFrycLHB+pPvgAyGEkBAbrDck0MeIJL6em7bBHGZmcO28exfH+t3bV0156dBJ8En9Wh3h89KASD6IUECxc797G2w7W5v2OikGCDh28gzYRtWeKadN3OdaJIFmw43PhAVuYv3n9tHoAQa0IHnUQUzO9qqlBG0nStvnP/az2JYhYMLpcWHF8SRWRlg5TOrpglftkKTbuyWup3/wqh0bgxLn/w8/b8f93AP+U/ayy7f++FkMrPLqd7pgq9waMcrxtbA5w9Ike0hAixI74pc+9yVTvnULE/jWGdBi/5tfB9s371w35auXcX+aK7DuY7c/+ETuIYRAYlwE3zYFeX/t49IZLt+wQa3WdzfBZ+kk7ts3K/cuuotBWS5etUmLO4fOgs9HjmLgrbYPHnEAx9nSIzhvb7xh+yEd4Jo/18JJWhZ2vy+qIfhs7d4F21TDjuOD81jPVgvDzJRuD9nYuL8k1/rPlRBCCCGEEELUgD6uhBBCCCGEEKIG9HElhBBCCCGEEDWgjyshhBBCCCGEqIGHGtCCBVfwmeQTortNiBg3ciJ6lik7IiLayom+KxZIgYgUE6+QJcJ3+q3qAmgULIKGryOpd0Fs974SzwDvde8sLoYPsvHd+9mLDYcY0CIfD/B3ro1ZGxQF2ny3pyQLd20UKFq+O7Ji9anTj4JPPIvZu/NlK5zcvo3ttBnh1Dt2zIk5h+izuoABHtqRFWUeXMUOvX4LA2Gcf/2rprxAsqPPLFlbSSZoHGMwkP2uFd5eu34ZfXo49448agW001N4v7zE+8ESQYK00Ag3zo3EPggkdERIfPCBxoMNaMHiEfggBTGpZ0TXG/uQMWnPQRfFxqORHccbGyiSPkHG0KoLUtImbdztYpCSwbYVEu/evgM+iycOmXKcsIAkSFTZsTca4xy9+PZrYOuP9k25s7wCPoMIgwbc3LDrxEYP1evvfexxsLXctpOR/apgwUdi98MJ9p37BfbGQET9ZO4NY6zTemEF/Je//Svgc/qDfw9sL12x62Kxgm3ypxiPKITIrvv+vSKEEOIY19wDsQ3K0rq1gb87ZQNahAVwqZUjrvzXMT5BiK5g0JQ31u1aPTW1CD7tmUleFbHttndwLrx13gYtKQsMJlMn/+s/+ydgGzdsvy/NYWCDEXmeOLbjuCBBtvKCvKsVdt6OhziPN9dwDG1ctfMhbeE+s7xwHGw7N+1aubZPgpa17PiMcuyHdhvHiw+u5oPChBDCymEMhLF3c96Ut7cxYNBigc/S69n7tWbxfldc8KMQQhh37dhrZRhIaWuI76s7O3ZfGw7ub+3Uf66EEEIIIYQQogb0cSWEEEIIIYQQNaCPKyGEEEIIIYSogYequQr+HHhAHVZCtBIVEUIk7gx3TM5KR+R3THbhyYn+p9+1icuqLuoU+tE+2PzzMS1TcOfomQs5xhvKSZLqMtGVTyJM2q4iv8udrTfG89SjBM8SB3+mmmnh2DO75xuPybVrIosOgO3jH/0ZUz70QazkYBMTIE4tW21W0d0Dn8MdPDecHrSH8vv72Aezs3jmeqZpz4tPBbz2zt2bYLv1zsumfPv0c+AzPWP1Y0mLzLMCx8H6rVdM+dLF74BPkyTdPnXstL3/FCZp3evi/SDJLssiStU3E/x9ic0h0P/hee46qYjCMnfziq1boxGeo4+dXznGpIzdTdRTTQ+sLumTT50Cn7lp7FNf9WEP23xmFn83XLcJyW9efgN8pubt+GgT7QTr9sStLeUWJorsb+M4yxo2merSKmquQoLtOR5ZW0mS1aYsQbfXkBEfpsfzWx9osGqE6vrcHsP0sgNS743Cajxe+jZqKU5/EBPaXtqyiYU//xbuzztdHGNF5LRaLOE0SVo67ZJAv/8xTFB64MEuCXi/e5RDCGFhjOM8DV5fifNzapFd7d5UPWy7M8tWj3PpOuod66TIUE897bTDMXnBGjBVu1s7uyPUiu72UMezt2/XkukEX71vX0VddOz2teVVr6wLYXEZNXLHKqvpXBujvilL7ftGmuBe6/VVIaDG2sdNCCGEqIPr8OySTXZ8e/dl8Hn39YtgWz37lCmPBtjm166jXi1ystb+GN/DbvRJUu2R7T+W3H0S9J8rIYQQQgghhKgBfVwJIYQQQgghRA3o40oIIYQQQgghakAfV0IIIYQQQghRAw81oAULkjB2gr2IqI8LJix0+sOMJI+MWfLh0trY/Vgi0WHfCjOjEdZpTETKlRNA0sTGLqEljVPBEv3e24WrnZ24uWQBLchn99DZiozcsU2ClvikpfirEBNr4QJ9PMiAFhF54E//hBXsZ6ijDvHpR8i1bLm5igLM90+hwPvcnE3qdyHH3yUJinObmW3zDumD5TaOg+6WTWy6dw2Dc+SPPm/KGWmEoo9C0XfP2YAWd26hMP3sySfBdmx52ZSTDIXpXTLXcy/gB4/As2675I5xSoT/LHl15GzVg11KSV7zUFa2bfb3dsCnmWD7TS9aEX9K1uXOQRSwTzftMzdIW6VkLfEJ0BcWsD2Xepiotbtvx9VOF5MI79y1QuZsBoXULMl1OXKCbzL2xyS36czcMVvuYNLkTob9sOnW3D0SYKIKZO10flnM9g/yfG6fY8lO66Ik4yfx+w6JJNUn++Vubte3iy9fxxuuY4LrJ4/bdfhfruFY6ZdTYPPjNSIBtViC+7suueq7JEHweM3+7rklsvNhDIEHyuNncF6/8Zptq6eeJsErJgnO0cPAFB9J8QHPrNqAFn9AkknXyZ1dDFpwdNauE9u7ONnv7GGAsg1nW9vBvW9vhAEtglsrH5vB9W6HBNXpzNhgFQdPPQE+/Sa+E6y5l9ge2UCmDyyZclTiutzt4fO1Zmwy4P3+GviwF8hDp21giv3Ny+DT28JrbV+z2b+zDq6T+RCf78aOfWfvjnBdHpGk7DMNO2YPLdxf9m/950oIIYQQQgghakAfV0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtTAQw1owSid8G4UEXEjiTDh5WtE5xuyFB8vcYLVhAaKYN+cLvAGyUof50RYPLa2iD2LD+pB6hQR0XBwtoqIlisiMPdBLiqSYbskgTdy58YCYbBgIGhi7YvtyYKNPCi2xxg84qWvW7HjmY/h7w6QceDHXdzE503mUejbXXN1SPF3wwLH2Ki0YtzWDF778OMYeOP2t94y5UvnXgOfpVM2q/qZpx7D+xMh7uatW6bcL1AwPDWPAvOZthXVjiN83jhF4W3lBLQlEfCWRJgefP/R3+EITku3trCgFzWSkGAue/s2KENJfJoucEMIIUwnti8apOpFhlnpIz/f2fwnAXT8UtJKcHwutpbBtt6y9dzt3gafzRvrpjy9dAx8ZmawnoNdOz73tm+ATxmhcHp6yYrHZxo4rtsJ7jvTTnRednG92e/itRbd2pGRpbMoSRAWFySALOf14QMyhRAKF6yiIusWC9KyM7Jt9+55FKG/+bufBdvJ/+AXTfkDzyyBz8uvY4CCrT0X4CqeBZ8o4Fy4vGef73fPYcc874LltBawnRpHcPKNSzs2pjoYsGCOBJgorX4/zGC1w1FslvDMql2HHzmKPhOxi8EPjpN18fG+XZPOlRhwok7euHITbJdTO652uzg+90doG7ouzEnghkYD5//RORsIqntrHXzKId5v9sRpU+7MYedc38Lne/tdu549evwM+OxuvWvKJ46fAJ/x/l2wXdy09by29g3w6Q1w33n/mU+a8vEzeL+Lr2BgoVZs61A2DoHPG9dx/e7n914DG0kbbIeXVkx5oXV/AVf0nyshhBBCCCGEqAF9XAkhhBBCCCFEDejjSgghhBBCCCFq4KFqrhKmG3K6mopoWFj+QzjaTw5wJ0Sz03TJ3FIiEipZImOnS8pJssFAbLFLAhlX+HyVPwxKfOipT9+epH3ZOdPKaUhYgsmSCL/yxD5LP8Gz/l2SlG3gaj8mGhaWqDGGBI8PToO1l+PZ/t/8zD8z5d7XMDlgce7fgO0f/5f/tSnPPPY4/m5hHmw7uT00zzQJI6Lr645te26S5KdFhonwDq4eN+XLVy6Cz4XXv2jKqwdwydi69A7Y1q/a5J8HVzAx5coh1BJ0GlYXkRM5STok2kI3Z5lGMU5JgzpdG5tnCdEkBqeLCDlp9BpX181rr4Jt0QlN2wcOgs/ZY9juDfc71lZZRvSqzq9k6xRZv71lYQavnZJr7QysNmtnD+uZd63eoLuFopKF5hGwbd20yYdHPkN6CGFqFttzfskm9fQJvEMIIRRoi13y74yIp3b6m2DbHdn+azSJkIaNWq+HI+1bF0xtCGs30/CSOg0Ke7U3drCdfvvyBbD9nWATC//8MdRzHkwwwfS/Omd1QqMtXOPbJLlz5XRuN67july49ebSbdShTN3ENhgVdg08uIi6kGOreL8ppxt6bIFofbs4935o2Sa0JdKwydayVZwv7xJNUuneN66PUAtXJzfIhlhGtt9L8oDsPdCvleyVpBHjHJ0Z2UYdbW+hTwd1p4tHT9k6Zfgs63dx3z5z0vbFYwfx2q/feNuUpyscZ73hEGwXrnzNlK9toOarKLGe8y2rzXr2KOq327OnwHbntn2XaBJtcTaF7zd517ZxRPp4fhaTOc+7JMXNEufaJOg/V0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtSAPq6EEEIIIYQQogYeakALnprXCcqJIJqHMXDCQuLViFGImjhhLYtLAQEmAibajVvYdDTegguYQeTPkDWY5XssSUWrCQJasHbxgRIiEpiiapDEmw3rNyCZm3skWezAJZkcsYSSBREpumADPlFsnaT5Ltjmtq3g8/KLL4HP1DSO169ftILrTz31NPhUThgfAvZxIyYidBIMZDS2bb7tsz2HEEoyF9JZK/rOMkzmuHP9ZVO+8Rpmr1y/dBVsjb7tz0dPY+K/1Tb2eSu24uZRguLglGUMB3E8S7p978g4bP2JiaA18snOH/Cfqc4cw+S4qRMNNzPsm5jmNnZtwyKnkDWwJMliEWwIyD1MEsW3SKLtmbZdY5fmMcHrnQ07b9euYLCDWbJ2bt6xSTwL0lCryytg29u2QvjlaRSK5zkJ1OCTyZOtd6eLv7ue2gAL2QFMwDxFdvHENToNylITWYpzFPZCtpSRYFI+ME0vR587e5gw9Orrv2XKTz79o+DzocMY3GU3sXPm/BsYdGLzTh9sY5dseESeb31k674VkcTcW/jDJLJ9fHsf5935a7hfPbtoB0IDXcJJEhzjkUNu3WAxU+6TqyRQ1bbbr24MMaF2neQVBk7y48y/h4YQ6PuUD5KWkSBJcx1swKRngyvkZH86cBIDX00t2cAU2xWOxYJMrpNub2+TF89xbMfLfoZry9uXMKnv7U2bzD0as/dOvOHFdfu7mQOPgs+hZdznqnUb0KLaxOBjx0lgitGiXb/3STCwZhufOfLvCfcZSE3/uRJCCCGEEEKIGtDHlRBCCCGEEELUgD6uhBBCCCGEEKIG9HElhBBCCCGEEDXwUANaFFQk7YSFVKuOgj2vwWZ63RER2qWZFdrF5IcxqUTiAgJEDWy6KEXhpP9+zfuY8Tr2An0ipGQCfdAMk98VxFa6ABYVeZaC2MaZexYScCEnmbkLF+QiIiE74pQE7HBlFmygLpICM6afPTFvyhffwUzy+0MUbr5502Ys/xQJJlHNzIFt5HWUJPBHysKdjKzAMyFZ1dsZjs3lQ9ZWdVEUeuWyzf7+8pcx6EU5wGAAi3OHTXll4TipEwq1Exc0ISVjLGE238QkeAULmhLlbmwm+DsQuIYQQuX7hoWhqY+pFgrRY688p4Ep8Jm9mDsmawSJOYHrEmmXiKyd/loRmesZCWhxdMHOkWEf59FO14rhhz1U8V96+zWwDQs7R9pLOB+XMP5B2Ltrx39ULoHP/hgF+vvuft0uriXNJs6//T0bQOPdCq/96CoGx5maINhRfWC/jH2AGRZcigVgcmNqlQjOR6+/AbaXPmOvlQ8wuMvhMx8C28+5derSMx3w+dI5HNM39+3zbYwG4FOMbJ+zuRGToB6pa4MZIqh/9AAGd/nQE9a2kuD+MTdFIhtMP7ixcXOA7XKpZ4OGbAxwv6qVCJ85dgFfmg3cw6ZaU2DrdOx4nCOBvxaiPbDt33LBHFo411cOHAZbxwV0KnNsq2ePYyAMH8QnIcHA3vesDbS1M8a+Or6A8+hI5z2mnPqIRSGEkuyHPmDXNAkidOwErmXVjrWtXb8LPrMNDCwUZfb9rUhJhBfyLuH3w5ztqxOg/1wJIYQQQgghRA3o40oIIYQQQgghakAfV0IIIYQQQghRAw9Vc1WSb7nSnc1mspqKJKIrQXNFrk30E/57MiXnPtMYmyWNrK0kZ1hLcpw5cwkBA0mAWjiNTJqwzMZMc+XOhjLtRIrPkjTtM+dEXzUgoovcaSVGJGFwEbGEq/aZI5aUjTyfb6kHqRpokP4cuLPa6WHUDQ33MKFda8HqMMbkeUuizxvmVh+TjTFhYN5Hrcas0zOcJXNhuYP3S+btmfJyF5OmbrvzzXduXwefpL0AtieOP2HKS4fnwSdubKPN9TrTUsZsbI7tHKoCaieGQ9QfNVt2LsQZTmKmESydrobp4+r90xW5mDvDzhKGMxGr11yxmVWSNi6cG3s81l8+cXvpF++A63kIIUxP27UzbqAusjVj+29M5kcxwH5PM6thW5jHs/4LU2Tf2bS2iCQM3t1BzcVe187l/T7O7bSLtsV5O44rMs5ispYUXrvIdIM1kZO1E3Kdk70ppUnvLT5pawghVGPUG734jU1Tfun8/w4+H/4AJoE/e9olG158Cnx+8Blc385dtf2QLuAeOhrYsVGQJKYNstc33aXmydw4Po/r2yNO/tcMeL+rb38TbH/0xzZR/PPv/QT4vOe9j2ElJiAlSXan5q2+sT9AvWOdLC6ieHJ6ymqeFmdxf5proy6q6TpnpsCk0zdf+ROwpU5/l0Q4Xq6fx74ZX7Z+VYmaS69TDCGEm07LV5AX68Lp41lsg5TMv8zN0ojM43u/eYcQdjbA550NvNZw22rBRmTN3evju0TLjbOK6OOp3th9E7C2mwT950oIIYQQQgghakAfV0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtTAww1oQYT2Rbi38I7hY1UUJBnfGFS1IYxyF1yBJNlk4t8iccEGSGI6EncjjIML+pCQxKlt2w1JjALliHwHV84Wk0SNLKBFlVkR3yjCOpGmC2OfOJUEryCxMUDLXHhlfAg00Z9XRT7IJMLTCQqEP/jCJ0353/8H/xB8SpYY22W0pX/BoIn3fLAV7JfD802wHZmyws2pBMdP5lXSIYTdxAo3l1ZOgc/ikg0QsLF5Hnw683jt6QUXpCXFYARpTIKfJPZaMWk8Nj/bLkhLTITiLLFhgOTK2OYlST5clc5G5lmdsISrlQ8UQZ6PxTFInB/NPUyCsPjAHhGZjzEb7TDW2bPgtRpuMXnmsTN47WD778I+BtnZ2UfR+fy8FbkfOrgKPlMNTJi95dp8g1x7t4fjZewyhPvh8916YiCMzrSd762SBCgii3XTNXH1AJMIs70C+niiwCohVLHtv5F/kBBCdvQ02C6v2cA7exvXwOfW9ctge+Sw9UuXMHDDf/s//BLYjrux2ZrGtawsbX+WBSYCTxJMVJvEVpyfBlw7k4DJZBvBJnzdPfc18Hnpj/412G5cs0GZPvVXMKBFCBhsJQR8Hs+xGfQ50zlhytcPYSClOnnycRwvnczNKxJgYpYkbk9dsuEtktB2MMI1KHHvN6MRBqbYuHUJbH5d9Gv+n1nB4l+HSbilUDknFripIBvw2G0YjSYGhYtJUm2ycmGlJgiIxILQjbdxjgzcnhyToCXsndk/XyaNGr4AACAASURBVBzh/SZB/7kSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBvRxJYQQQgghhBA18FADWkQpCsMguzMTvpJIEV7YxwJasEzOUWXFhnmMPhkR1Y1j74NCOJZN3qt9KxJAw3/hNnwQjD+7OvzOBSTIMvRJUyLsd4ES+kRM3ifC/twFtCC3C1mGQ6pR2jrkpF9YMBDfp/mD02SHhdYM2J579kPWgBrNEGYweASCbRnFeLHpjm3fg1Modj5O+vNQc9Ya2kvgk86yYBVWSLzUuw0+/d2bprzbvw4+rWWs58yirWcjRQF2MyVzyAVSqEjgj4TY+j0bWKBcmAYfooMNYWznWpSQoDAkKkReOUFyjs8X7k8HS4lJgImWaz/SLGE4xomVl37tJOOTRLmInLiZBb2oyO9YKB4PW3PPtK3o/OJNFC0vueAtazNz4LMxwL6ZXrIBbI5M47rVIc+XQXQesl/laBvnto1LsleMx1jPQW7H57jC9aYk/Q79wKNO1ELB9jQ3GFnAEtK8IUnsPH7y7AHwee4xDFAwd936nb91By++uw+mtcsvmvL3P4l7bzvcQtuoZQ3rb+H9ltdsOT4GLuMRBlI5f/537WWWMWjC8sGP4P2Gx03xT7/xOXC5ePdtsJ39wFOmfOj4Ol6bBq84TmyWOMVOfu5932fKjetr4FMnzRzbr+3WjSbZo+MBCVo2sutEk+xrR09h4J3m2O7tJZkPVcX2Ov9+TCLhBLxW6fyGJPjQcGj3sC6bHzsY1Kc1d9CU55cwyFZaYACUGff+P53Ngk+DfCOUlW3jvMQ5WpB95+bYtkGX9LF/3wgB39b6QwxaNAn6z5UQQgghhBBC1IA+roQQQgghhBCiBvRxJYQQQgghhBA18FA1V60Wns0sCnuutSA6jDLHhGvB6XZYskyfZDeEEPLCJ8LE9Go8qaa9Vsny4NIfes0VERc5zcOgwDqxZGdpbK/dILqslNyvKOyZ1QE5xztkv3OnUWOfyTmEECfYBmnik+OCS8iJ6Ar6ih03roksIVMhdraIaeEmENaQtuwEHNPPHbZ6kTmSiI+dSd4q7Xn4u0Psl5NHHwfb3JlHTbm98zL4HLxkz0U/nZ8An2R+AWw+/2Ic4fNGAdcD0OOQiU2GWGg07bVikjS1JHMd9T+TJbgtnRaGCkhqZKGNWpvZKWvrNHFi3VnfBdtdJxMoKlw3WLJK/4SsjYkMM8Q+aTHxCTFab7tEqYdXMdF3NbQ6gYsLOD9Wp0+CbeWwHddTDZzbKRkuDSfcy9nfJ8l6WsCaTrRaZJzlbpyxxM0l0RtHbh9tPMg/ozL9iBsbFUtwTbQiBw8smvL3vfAc+CyTvfD4R6xuaPwtkvR2HW1zI9swP/Fj78XfXf4W2vZc4tv+d9Bn2euUjoDLcIjPksVW//vuhVfx0gfJPtC38+WrV6+CT58IpVeXl025S9bAWaYnn4B90sf7HWs7uUzeiWrk2BSuG22XDHiqheur9wkhhNglH47TFvgw8eskutOC6CL9SsK2mfEQdV+jgX132B/i2B/0bZLrN964AD79Ia6LlXvk3gC1WlWMa+CKewd43/veAz7NBrZnr2frubO/DT5DsocN9m0bdO+gDrMieuO+02HfubsJPpOg/1wJIYQQQgghRA3o40oIIYQQQgghakAfV0IIIYQQQghRA/q4EkIIIYQQQogaeKgBLTKixkuDDxRBxNUk+2eZuIAWJMKEv3YIIcROqM0E2CyRceSCMjBVNtEVU2E41Mm1CwuMwQJF+N+xRKMs8MbYBY/wyYFD4AJkH1ygJOJqJhBMXd0bLOgFqXvuErrGJPlwXeRjFEm+de5Lptw+iMl5T7zvhXtfnDQle96Dczbx7fSAJL1bxqSa67kVQK+9fRN8ljuYqHFuzv6uWWDQicoFSVgj4s5qzOaLbasDGQv8waLC2GIcYZ/7QC4hhBBcYsGqIEEFWLCKyC6BJEZDiMg6UhQ+ueODDWhxcmUebAdmrbh6roNt3OxiUJSuE0CXRIBdECG6hyUMZr8DN5IssyQL1d2+7cPDhzAJa2vdCbcHKFo+c/pRsB2bswL2RkmCJpF+T31yXDJgWFLmAvaBewcMCSGE0q15LIG2T9gbAu4FrE4PFPe8TIi/RIzfe9T2cb6JAVnGy7hrL8zZ+f/kafQZHQRTKAY2sMmtq2+Az/YQ67AyZ8X4v/Mv/gn4/NwHftFZMBFvVC2D7eQjHzXlI/kK+ISAD7PftWP4yhruabMJrt/n/tVnTfkXPv5T5H4sifC9WW5i0KKkd82Up9Mb93XtSVldxvbz71MsoNVUkwQRatqAC602BstIGySwmI/ixYIkkSXXJ3wfjjB4xXCEQSd8IIrG/h743Ly+YcoLM/gsI7Iq7ec2qe6IVJwFijt2ys7t1gyuW/u7mPC56/b2ZBrrtJzgONvo2gTIGQukRuq517PP12je39jXf66EEEIIIYQQogb0cSWEEEIIIYQQNaCPKyGEEEIIIYSoAX1cCSGEEEIIIUQNPNSAFvkQRcOFE9FXTNXHxL9OSJymKCJkgROyygnYSROwoAyQTZ5k0/YBH75rc/Uiwt7Y1SEhGejjmIiWnYgvijG7NQtoUbj25NJ1bIPSiTALJmQkV0uhr7DNI/J8CWvjB8Ro1AXb25fOmfLc0gfB5zC5FoQVIMLtqoltsOcCi+zvYebzLEGRbTJrhahxikEMymoDbCGyQSfiDNu7zKyY8+oGttO4h883t7hgr4N350FTnM0HQwkhhIwEuBnnLtBAhe2bj7FdIODMhEOu8AEzCvKENa6uj5w4ArYpG/8kpFMo6j10AwXsl3esQLjP1lzyZ7eIRexxsAAPpbNV5OIlCfwTu7Vzb4j13I9tI6StGfBZmsE6zcSu/1hQD/Isfu3K2Z8n/bVDCIVru4is8Wz7GDuteu4DqQQe4AVsLKJEbeC1/bSNycMdzvB3Z4sdU/7+D70XfHamZsG2N7BC/488/wz4/J+fuwC2eMkGhvjC114Hn//kR58G29Zt6/fjP/4z4BOCq0OOAZESMsayYAX1WacHPntX8Fn6g6OmPChxPWhfw8BGf/8X/iNbp4BBk0LAYAfA9atgOtLCcf7Eot1Tog994t7X/gswO4119+8ybHZMtTGQwVzbvmO1W/jOlTZxf0rchIhIoCYWc6bM7bzpp7j/99I+/tCNqyJMgcvelO3n/gwGxhhm5F3UxdTY2sH3lKWZRbC1G3OuivgsIxK0zK+dMVtvyPo2Gtlvi94Ag4GwgBaZfzersJ6ToP9cCSGEEEIIIUQN6ONKCCGEEEIIIWpAH1dCCCGEEEIIUQMPVXNFj+y7o5JeDxQC6qtCIElYyTn3skLbyJ2fLEZEZ5Lj2cymSwJH9QdE4BS7s/VJTPRGLlluSPHMbkSSK4fYPsuYdGdOtAy5S5xakIR2TO7kE2FiYswQsowMKXf+vyJ6LpYI019rgnzM980+0ePMx/YM8qtf/TL4fOyD3zPB1VnFSZs7/eH2JtZpiugUmplNELh6kCS9K1Ar5XV1YzJ+h7G9VrdCTUt/gGNz7Lo4JnOYJvAO/nw1tl0zwXPug7GdC2WF5+yrCNszFO7MdcXmGdY98rbGg11K5w5hstEw6xp5Cttlah7P2pfXt0yZad+83ikE1AkwjRBLmu5tVcBz7mz++zbeG6D2ZNdp3QqWhB6rGfzG4zUYIYRQER1ocGt1NaGWKXKNFdE2x9/FkWsrNjzZnuIuzxK+1wXtO6+dJE25PIdjMx3ZJNA72+fA5/An/gbYtq64hKgnToLPXyXt9J3XXjXl6Qj1XOnZE2A78KQdi1GDJZN3Wq2UrK85rklf+epvm/Lzz6Km5fwFrOfygeOmfMLpEUMIIV5A7ebiz/5tZ2GaqwnG+RFM1pt3SCLuj37IGl77fbzWc5+89/0mZKqFmpnKvRsSaW+YIdqpdtvq2KamcK9l+v84se2Q+ne+EEJVYEyCcc/qqbo9XANHI7xWWdi9ICUa5JUFO67zLmqurpI1d27pkCnPTOMY7hBN6VzTzfcB3i/PsSMa7h0ki7Cdbt64BbZrt2+b8toOatMGA3wvqtycHOf8TeVe6D9XQgghhBBCCFED+rgSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBiImQBZCCCGEEEII8W+H/nMlhBBCCCGEEDWgjyshhBBCCCGEqAF9XAkhhBBCCCFEDejjSgghhBBCCCFqQB9XQgghhBBCCFED+rgSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBvRxJYQQQgghhBA1oI8rIYQQQgghhKgBfVwJIYQQQgghRA3o40oIIYQQQgghakAfV0IIIYQQQghRA/q4EkIIIYQQQoga0MeVEEIIIYQQQtSAPq6EEEIIIYQQogb0cSWEEEIIIYQQNaCPKyGEEEIIIYSoAX1cCSGEEEIIIUQN6ONKCCGEEEIIIWpAH1dCCCGEEEIIUQP6uBJCCCGEEEKIGtDHlRBCCCGEEELUgD6uhBBCCCGEEKIG9HElhBBCCCGEEDWgjyshhBBCCCGEqAF9XAkhhBBCCCFEDejjSgghhBBCCCFqQB9XQgghhBBCCFED6cO82fNPPVZ5W1HachyDS0jIJ2ASRdYwzsFn6C8eQshLe/0q4P2qEn+XuNvFcQQ+AX8WiqKwZXLtyj1LHOMD++uEEELpftdsNNBnPMJreR/wCCH27RtCaPhnJu07LrA9c/fM7H5VhfeLXcc3swx83rp4hXTEffDu17DiJ5+w5aQNLjd7OO6+8tY7pvzKiy+Bz86l82ArdtdMuVGOwaeZNsE2dOXeEPt8SGxjbyN9N9W092t3OuAzMz0FtuXZaVM+fRjb7tAxtD369CdNeTD7LPj8ziuXwfb5L3zBlIt3XwOfA2kfbKNoYMqbO7vg0xj5Fg7h4JRth/YUPss/+tXfqmdshhAOLB+Czqkqa4oiXDeSOAFbVbEZeG/8kuDvH0IIFamDn9usUaqSXCv45yN1cj60TsQWuVpUEfpwfCWwUvR+7vrRhPX0e0FM+rMkdYgi68f6fP3OtVrG5//8L17Esenrw+o9QZOzvT+Qvsrd47ERHpH1tOXal7UlHdNwP/xd4UyTjN8Q8C/eMbl2HGF7hsi+zo3I3p/5F5mAYzOQd5QR6azE7Q2sQ2P693vrx975/tZPPlfb2vnjP/1DcIPuvt0LqhLrOZ2R/bBjx1DWWgCfrb19sFVuMGQp9l9CurTp2irPB+CTRvhuOBq5vh/he0Me7LtL6idRCKHLxktlK9ov8f5svfFrbuTrGEJIUnzHG7hLJQn2y3CA14rdu1Krg3v0dAv7PWu3TJm9s//W//2H9xyf+s+VEEIIIYQQQtSAPq6EEEIIIYQQogb0cSWEEEIIIYQQNfBQNVeb29tgyxr2fGMaE70TOZdcuvO9oxGep85Ldn7a6ZvIyclWE5slTqytGOP9SqJBGjktGDlWH4I79x2RM7QM/yy9HmpKmJQAjheT8/ApaZeRO1sbkTPWBbM5LU9JzuMyzVWR2/uNh9jmdTEif2bwp3+jgO0UkzPCUWr1OEXA885VhdeK/N862Fl/cnDf9wPoEUMIKREvVE7DlhdEl+XG77jbA5+42QLbjOvinf0u+ByK8XdJZ8aUN9ax7cbEtujaeJ8M/DTC8TN2toQN/DGuB6Vbk0agZKwXdsA78v3MZKDkPPwkVwcdRsA1l0PULjC3yWRjgpR7KnnYtchZ/wnUGzGZawy/ftP1nOL0BkRXy7S+4EM1XszRXmuSNrhfvLYohBDiSbRwZDx53SDbF0KFbectERsHMZnHTu/D5gvTN/lqseaNI78uT9bnib8anRpkn3V6HFpv0p5eH0O1m0R748dYSdqcvQ943VdZ4r5TJx23p4QQQur0OFNE99Uo8X2ql1u/cRf1VU3SVEns9ePYxr0C98htrw3LUVcfZ9juZWWfr5lhpbx0qdXGdsrH2Abj3L4TNFKm9cX75U4X1ZzGfTymWlQ7J6cz8u40Pw+2Hacpb0Soubq1uQW26badR50OasonQf+5EkIIIYQQQoga0MeVEEIIIYQQQtSAPq6EEEIIIYQQogYequZqTOLoBxdrPyd6o5Lom/xBZJZngpn82VemRfHx+EMIoXB6KqY/KGkOK1fGKpHz8eRMOdFFxS4xQlFizqUyRxvk6KB5WVgeDZefgmk1iJbAa+YipoWbQLzAtFp1kU3PgS2K/Plm8mzkvLE/x19UmCcpVDimIWcPqSeXvTgdIfNgZ+0n0HiM3PgZEN3bxt41sF27fMWUL7fxXq+/jTV98qLLz9PEfGA3b6yDLV9/25RbAc/CxwHr7qUEKdPQkfP4vpXHD/jvVGx6oD6F/ZD8zmkjKqKV4NPR56ti+aPu9SuuF2EVhYxSTG/or8LGObkbiFEnXFsgPxbx4Xm1JoDVga3D+MN72ibXhv3bw/R5rK88bP/wY7EiewXLmeVnH88niXXweRlZnVi/+CdmubAw1xheOrC9EMpEhxLjvh4qa2O6lz8ny9w9fVjey9JpYVi/VGRd9Lq2B/0X/rSHWv8ytnoflitqnOO+XVT2lXm/jz5TDWyrXt9qlfMhtlW3IHEDXLVGRAPVIFrw6Sm7j8UpvuoXucuhR3RZc9OrYOvt+/bEevdJ27WdNrsg7xKDBOda6t5zfZuEEMI80TzPrFod1s1NbLtji9NgW9vfM+VG//40gfrPlRBCCCGEEELUgD6uhBBCCCGEEKIG9HElhBBCCCGEEDWgjyshhBBCCCGEqIGHGtAiJopSsLCkt0z46nRvTLvJ8oHGXuhKAlqwYBWgV2XiVCa4ju8tOoc0mDTQAGkDHzyCiYhJw0SuEmwQsJyBk0jFIREuqwIR/7IEhF48PolI+n6J5haJ0Se5Y0mp8WeFE8eWRPAZxyRZpWv0hPRMkpBEmGN3LVIpJotveqFoin0wHlmbTyAYQgj7QxSv9lxS7/42CeCxhm1w/urvm3KVfhl8BgXWIY6s6HSuje3USDBpoQ8YkKWYpDEnfZW7cZ6PH1ywle9y7+SxLMHsJOuUT9L4XZhA3yc3nUQcz2z3nut/ZnSXuXdUBrYPlJMkKKaPQgIJ+J+xak8SPOI+I0ywYBnsmdHvwa2dbF/3ezbL0ewD/7BrsaSiLNGvH1EsWE9MGgp+h9WkgaIKH1CLTKGitOtiRUT+WYIBdFJnY3sMq6h/lYmIyJ8nEXYXIwO4qLANotQF7GDvTewdwd3PB+aqmwYZC71dGxRpbwoT2nf3d8GWZTZIwuIUJqYtChYIw5Y3uxhcISZ7e6dhx0K7hXtYK8PfLR48ZMrL88vgc2jugCkPyNjIfcVDCBvrV0351voV8NkfYZvPtW0bd3cxafJwgMEjEre35k1SJxLfZXbP9t+xNs61a7uYRHhlwQY363bJxSdA/7kSQgghhBBCiBrQx5UQQgghhBBC1IA+roQQQgghhBCiBvRxJYQQQgghhBA18FADWtwvVMTrxH+tBorVYiLMhGTrNLM6CQLh/KpoMlF24oJOTBLUo6pQEJmTQAK+5iRBekhYNnsvKMWfhYqIzknqeIAFpvD9x6XcTBkeO4/7E4FPRIbjB9XqRBDNqu3GYkSyo6ekX1LnF5FAAzEdmz6yAY6fqERbK+lYQzIFPsHpdTtkTmVdFKGOnS0jwlhGUXkR+D74pIHNBdt2TGCeE8F17sTOxIX2sdfrjjFeR61UsHDhOsXE6mxNKv212O8mCB7D1uWJwiaQa1dU+O4D2pBLuTtOukLA/SaJwxHIXGMBfOiCatucxxlhwQbs73jgj3vvV+V9BtCYhCrg2lmW9v5JTCYIWd+qyq2BrDFJgAIIpsCCkZA55INcsDEdSGCIENln7g7QZ8MJ/dcvvww+M+kc2M488awpzx1YAJ+SBD8IldtTWPAKGnfHB6ohHmTd925lwd6JWNv5Oj24YCshhNAb98AWt21bjXbugk+WYXCjxAVT6Ba493XIfIirPeszhYEpUtKnz5w6Y8rPP/EC+ByfI+PDBXPYu/EO+Axuv2vKvQH2X2sa59/K4eOmfPjn/xb49JqzYHvxO79myr/xuT8Bn3iE7en3tYKMs5L8rlva95uZOWzfM4tLYFvv2UAb8wvT4DMJ+s+VEEIIIYQQQtSAPq6EEEIIIYQQogb0cSWEEEIIIYQQNfBQNVeTnClnZ++9FiWEEJLUnWslyYD52X6r14gmTNhbuLPY/kx7CCGkJPOuP8Kd53i/3CcpJAejU5Zoz90uZfox/BX0AzvHX7Bz7WBh+iq0+bZjUI2H07UlDzLZIMtyOQHsV9Mtmyyv3cDz1SNynrtRWL+qIMmHyXnjjkvw2Gqgz/KBebCtzD9iygudw+DT6jitYRiAz3CEZ8z7A2sbEsnFtS1M0nhh/bwpb4+u47VJQsv+wCW0JBovlhy8cuIslqA0IclO/bnvB625YrMPcn8ynwlSf7PpyZZTTDZKfNDEdUIT+EySM3wSJdEkSX2py0RJyyfUMoE+bsLf+SZnOmKaZXYCrVZNsBSbfl7FFdmbiC3P7USKyDpJE2oX9n5cD4jvEeOx9RsR7cbu3g2wbWzdNuXbdzAZ6dWrVueyf+E18Hnh1KNgOzDjEimHQ+ATOpj0PortGg/vSN/1Ir/z/cAmNsvObdsumSRZdyDr8ETz7P6ZmkPNTDG22t67faKLJgmeW4kdZyQvdKjIHE1ze/3nH30CfJ4++yTYjmX2nWB44S3wuXv1ItiKgd2nywLnmtfeTk+oKd96y86H61/9Gvg0MnwHWXzsg6b8P/2n/z34/OqX/whsX/7Tz5ly3MdrN1hy5YZ9JyhybIMheV+dblqt1vYQ14RJ0H+uhBBCCCGEEKIG9HElhBBCCCGEEDWgjyshhBBCCCGEqAF9XAkhhBBCCCFEDTzUgBY0uaIPisCEk+x3TjCbEyE6S2rnkwZmRLkNSTZJJTKS8K3RwIALU20b3CBNUKCbuYYZjvrgwwNhlM4H26AoUG6cO/Evy+/K4nx4jXDMxLEkyEUSez+WnRNNXujKgmzUx/2JatkESl0/tFiwDBKcA4YiEXzPxJjo92jLJqI8OY0q25VpvF9xd9sarm2Cz3xsxZwFSeqbRCgY3gtWFFrNYqLD5U4HbKOWm5//T3tnFmNbdpf3taczVZ2a7jx0355ut02722PbYGMGB2MERJABJSFKhCKFICBCfiESEslLBiUKkSKhRBEhgBwggEQAi2AHg41tPJt2t7vdo7tv3+57b9Wt+VSdcU956CjS//99po7Kuy4P+X5va2mdvdde495V6/v+HTTi2CMBn2d7LvjwwRDKRDURprohFfPFBnOc2UibiJ9PHmeyQ0swkwvL3HpyH2uUBa9lwYAh0O8xA9qSn811rWOa1RwXblYxRyB1GgTa/W6OMq9f/wQDrsPNWIBrV+8K15/aB70NIUTOPIab0DCTFnutvMS9cDRGM57bu/smfe2pp6DMxrUvQN7Whg3AOhzuQxlvmtAhffLEAQavffblJ0363F1oaPHQW94LeZcuPGJ/d/o8lCnaXcibwXjF9o0jXN+wj0nAYGaS4Pv0hKfn4Rb2Te7WrlaKhgjUWAxMPPBnURff8d776PtN+u2XLkGZ7PqLkHf7+edsmSmO4RZZEwofRDxjZiN2zsTMxGyOtXOBjOssJ2Y1T33RpJ9/9stQ5oc+8D2Q96Yf//cm/Su/9i+wDmRup2nfpKfEeqeaEeOr2l6rf0wjNf3nSgghhBBCCCEaQB9XQgghhBBCCNEA+rgSQgghhBBCiAbQx5UQQgghhBBCNMAdNrQ4+luOCXFpdHkn4gPPBCwSQgghda4BTMydEgHbQnfJpNsJCiB77Rzy6srmTYcjKDNx4tuKCEMTYqCRxq6eJCJ7RALcD6fWlGCc4/1YlPHKNxb1pSBiYyeYjUhnxaTNvbyS+ow0xvEE4MwLYDKyfeyFzSGEkARs835s89aI+cm55DTkXYxs210MN6FMevsVyLvxpI20Xm2i4UPiQtBXJSkT47gburxyCY04wrk1yDof2XEeL5+FMu1FbIMqsiYwhwXOxapAQas3ZUhjnGcFGRtFae/Xbd1BA4H/hzdJYGYHiDeiYfORm1XMAfkdtAx3czjy0uz50JyDPcvRt6Pr1jx1msO84vVyUCsow+tw5IXmciTh9WyGmKxlvh8qJpanz2KTzMSIeDSF2czu69s7u1DmtZvPQd7jX/mcSe9fwzLREK9V53aNT2psg7T28xOf95B47AydOH93Fw0Zbt9GI4yr9z1h0g9dfTeUOffA2yAv61tDpDJme//R4y6ixib4MxjmJzg2QwhhPCF7Vsuu870M131vfhZCCLUzQOgvolHT2x95B+S9wZWbfRVNUg7Wcd9u+7FO3qGHxEypfeaiSZ+77yqUWbl8t0l3e7hHb27ehrwXP/9Jk57tYZm1GTED8XMbSoSw8b8/Bnmrb/yqSf/8T/4ClPnX/+XnIK87tnv0LOA7Afv3Usd9IxST47146j9XQgghhBBCCNEA+rgSQgghhBBCiAbQx5UQQgghhBBCNMCd1VyRs7U+EG5MgvpSPZUPREeCnaVEp5Q4bU/axvOqZ1f6kNcp7PXHexhMdbpPgtVN7bnPssA6+cfjQSGRyGmuopQEKF7EoIFLLVsujfD065jkTd1h94IF9Zwjjx3fZlGL/Zn8kgRJbo7jBRFmp3Enrp1mOZ71bZOGOt+yOr43kOl5jlwrHg1Muth/GcqM15+BvOzWgU1XOFZSd44+IvWOiQ5r0bXndAs1AuXhq5B3t9NYxeSY9DjtQN5qz87ZeBmDDw8GRHNV2bqnLOglWUeK1M69aU2EICfO8f42huvN0WUoZCxQ3RcEH56nVoEIqo7+FdNcMaDqc+o+mI73yGsHXMviucQoeDF6f3JDbIeTE6ymTHPlHVD15QAAIABJREFUnqUgwYBZjfz4KckDz0jU+809GwD9mRe/BmWefxz1HMNXrcaqng6gDLxrhBBip82MiO4F+oD0U0WCHdd+L5xima1Xr0PeZLBu0sMh/u4R0uj3PvRWk44XlqBMwfTy7l0qJm8pbIWCsX+yYupQET13XNmaTUtSJsF9pp3Z3z10N2qZrnbx/XH6tNVYlevrUKZF9L7Tjq3DmUcfgzKnvu19kLf80BtNOl1EPZUPHu21uCGEcJmsN/f/g39k0skY++/VT/4u5D39G79j0u3JDpRpkTrkX7OarsP616HMT/39n4G8X/2tXzTprBxCmf0D1MPXLVuHpINzex70nyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEaQB9XQgghhBBCCNEAd9TQoiKB9nwcXOJnEZjQt3JyWCpk9hcPIWROIHhqBcWbWYGBfgcbWyadT5mAnQiLs55JtxcXoUzPmU60UrxOPkbhXT6xwQZnUzQWmAz2IC/tWuOELEMjA9YR3mhjTIKyslbBYHxECE9Ew3j/k4QNvKPvyCw2vICWXblfY+6lzIpOLxfYnwsHGAz48LYTMm+gUUTYRzHnamWFmhXpA98CVKBMus6Lm9vkly0SnK8MVlA+mKI5R68i4+7Sg64QzutxOYG8YmzvV87ow2A9Iy/YxzJ3GrYGcp8GF9R7TpMEmLdzx/7092POFKSR/boxh/CdmgHNEey8mrcNfBPM2QbzGm1gFbwRxrzX9gFsTxDSdbnbG8oKC1Xsh77LozYU2R+imdTLrzxr0s89/gkos3P9achrT+y14gTbMmlhHdKW3TPj9GhDi3nnWTmz636R47qVk3VqsG/nx0svPA5leimunYvOwGLt3m+BMlEbzbJKN2li0p/+PS0EDKYczeMS800QETOOdtc+D4YC5uZNa+ds4N2Hz1+AMvEraB41W9+w9w/k3fTsJci79L7vN+mL70Hzihl5h/UGYUUgpgy1e78gXwN5jfUsc9uerQzH1PkP/AjkXXnXD5j0l/7zv4QyO098HvJabu0onnkKyvTXzkLeY9/5gyb9iY/8BpSJyPicuccZjfF7YB70nyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEaQB9XQgghhBBCCNEAd9TQImKiZSw017VqX479jIhTFxasoUVWoFHE/vptyEunVvgWJyjwPHUJBYn9C+dMenFtDeuZemEoCgTrGYnkfmjFuPtONBlCCKMdjIK9O9i1tyeN1yECXa8LLUmbl8QUofLCXiYwJ/3uBaUJixLfGMcT1VJDi5YdG21S77tSFEnf07Z5vXwXykz2SB+vf92ko8EYymQVRpv33RCRjvGtQluJGgYcnZMRUW88sSLb1TKHMvdkODb3+ssmfe0MzsXNBTSTKZzwvi6I6UWN83Gc23ql0V+Bo0U0h1HEHFYG3LsCn+e4pgzehIEsyyEhTg21+9sfrPkhhAjqSQwtyNgr3f0q0ggV6dOq8nmknch894Y9XtT/ja5V+zzWBhHOI/843sCjSSY5roI+pyauN970JoQQSvfDosQ22SB72hNPfM6kD199Fsq0p2jqk6a27dJ2D8owQ4so9a9OpM+PSH8jvAFUkhKjCDLG8old90cDFOJfe/F5yDtz7ssm3TvThzIL7fshD/cPKEJNkrx/RXzSVlX9M5CXte1A2x7gXrvSwnZ42333mfTi8ADK7F6/DnmJe8boHBph3P39aAJx2hlYVD1870ypmZt9vrLG91wYn2QdabFx7dbAirzX+zIhhDBYtIZdj/30P4cyn/+lf4O/+8JnTTol7lGDL3wS8r717/6kSX/p/BugzHDzJcgbje08imP2lnc0+s+VEEIIIYQQQjSAPq6EEEIIIYQQogH0cSWEEEIIIYQQDXBHNVdJgrfz52/ZWXR2pDRxB/cxUG0Iq+6MZwghLLsqTG5vQpmK6Jvy1GpW7noQA+2tXToPeVHPndcmLV473QA7g5ykeNa2ats69dsYDDhawDPk03V7/clgH8rE5Ix8y9WhRc5Tl+Rg+cydv2X6CtrJJxxcsAmY0qbds23e76De6a6FFcg76wJm5geoLTjYxDPe1YHVCS2SQcb1jvMEbp4jADQZK/P0HNP6ZW4cLJPz1dEhno+PD+zZ6VtncL7EbTznDnrAGINuhwh1X1nL6b5KPNPeJFSTGB2tG6LaKfc3NTr1mGrOFWTTk9UzdvqQFqlmUhKdqb8WCbztK0GbgEkS3Dim+lGmN3A6N97kbFVw9aQljg7cXLOHIZXA+X5yYYQjsujDOCB6ThawN89t2x0cYsDgV7+GeqqD68/Z201xHidk+LQ6dq2OW7hfxilqUbBf5tA2zpkXuyD0VGsckzq5scGWpO3dAeQ99+ynTfrSFaIdX7ob8qK23WdmOa6T/t0mhBASp1cr5wgO/s3QWyA699iuN91F3C/uv3QF8tbatt33n8GxmDDdecu+A5x55/uhzLl3fzfkTbp2n/HrTwio5wohwKIelTheErgU0w1in0Zu3pbk3SJlgZtdNaeLqGV809/7Gcj781deMOlkYwvrNMMxNP3yx0z6ne96L5T5kw9fw3q6ucU0pfOg/1wJIYQQQgghRAPo40oIIYQQQgghGkAfV0IIIYQQQgjRAPq4EkIIIYQQQogGuKOGFhURLsZemDmHMDSEEGInoktJ0NsFYgKRD6ygM5qgkLqdYbDRlbvvMunu2hKUGcwwAOl0ZgP51SQwZZbZblhskXpPMADi7oE1N8hzYoQRYbt0V52wn6i5R0RI3HLXahODkipgAFsaaRd+hxw3aOnxmEduTNqX/GrZGVjkyzhWlqZ4v+lNK8IeXLuFNdrDwJCZM8KoyLMwkxTImaO56fRkUaHnaDt6fS/EZdeuZ5A1Gq6b9HR8Csq0T1+FvFlmTW9KIspOI5wLPbfeJN2TXUqpkQEYUxCzEdpfx5tX3qeB3Y8JmVMXnLL2kWJDCHWEeZkL3pqkuC4nmTXxYe1U5Kjsn+Z2HpWkDNPZ+zWJrVHzmBvwoL7HM51gRiZgjnGCpgFRjntT0nLzijxvRQIEF5UdB8+/9AyU2XjlKchLR3ZfZ32QZiig98YtFTFWKUge2PywAOzOmCKOmdEQaRfII2s32XvTlp0LeYFrWU7m3ta6NbTavIZBcM9eRtOrKrNzvarmM6ryfTPnK9+x6Xdxl56Vrv2IucLFuzFwcndiTaZG+2gQkpK3gtUHHzXptXe9G8qMM3zvK50LS0bGYk7WvLS2fU98ocK0sNdq4aUD83Ip3Hqelbgfz0ifpol9vjrFSqXn0HzkgR/8MZN+/tf+I5RpkfXt4IUXTfqet74H77eA72YT985+3LdQ/edKCCGEEEIIIRpAH1dCCCGEEEII0QD6uBJCCCGEEEKIBtDHlRBCCCGEEEI0wB01tGDKMC9ujmMmvMe80qkgWRR1Ejg6zKZetYeFVs+eh7z+mbMm/eprr0KZ8RRF0TMn9os72ORrly7ajG4Hyuyub0De1qaNVB0l+LvO8irer2OF4dEiivoS8izF1D5LmuGztImg3Wsbfd+FEEJNopp7MSyL9t4cx7s2M7RYyGxutdjD321hf+7vv2TSxQzLLCxgH+e5vV9B5kvKzDhggmDfQbcwMwQmqHcCb29AEwKf117Mzf76c2oZxcfbLWukMp1iO0UBDRGylm27crILZRIiRO92rWC/io8XxX1emJ8FdAXth+PJcandAjUusbANJXYibGY20Oph33T7fXvtDpYJztAiRFiDOiemRWMrWp6MUJh+MMS8onLXYoYEbH2bI4cCa+B8v0NDi/ludxzqIRq+1G5lrIm5FBvTXhq/OcE+2NpGw4U6t/M/IvtQnOK6kTtRfzlCwyC2NUDdyUbQXrDjNWmRscm2vXn6KsIbppmtVE5ML+oS22U2tg+4t34byhTjbaxD1+5rSYRrLnuYyO3jbG9okttkrx25ap3rLkOZXo3rxmjdGielZBBHbdzvlx582KT75/Ads2qRtWRmZ8SMDLQ6wnr+0m//D5P+xB9/GMqE3LZ77Ne2QAznAr4TkFf2kGY4ForcPksd4/1+9Hu+D/L+5vf+oEnfPnMZr73+MuSV7u0h27wJZfqX7oO8w11rJDY75vjUf66EEEIIIYQQogH0cSWEEEIIIYQQDaCPKyGEEEIIIYRogDuqucpSEnQWgtqx7z1yrtWd5c1SEqyuwKC+xczrhvAcdm8Fz9/GLRs0dDrDwGmryyuQt7jidAM9bIPYBbAbba1DmekYNVAXL9kgdwU537y5h2fWy44tt7yKmqt4iNqTsXvmjJzp9udcQyABF0nwyJoFvo18mRP8WwCNGHr0z8gp83DaBY9sd1BvkEXYn9lle0b/YOUNUGZnQrQEsdWdJAmek0Z9VQixD+5KA9W6oKnkDHbCAsc6DaRP/9+LkfvZvIT0eavEQNX3L9p6DhI89/65W0SvltjfTTDuZpjM8Gz4/swGTs0LotVoEqbjqe3aOW9A29j3Fw00fHRWQsokTDPrdC0J2QfaLuhsCCEkwc6biGgCosrOo5hcO+6Qa8d2LEQ10aLMSED0mW3zimhfWADN40ZKxb6aT1fndREnGY79MCdjbGR1WHGGweyjFs7RfGafd2sTA6mPhluQ13VBU2OiB2Zjo5zZ8cOCiDNdLcwrpr1xE4vNRSY19oGNWf+yuef1TUmCbZ7X+DS5izC7tYvtOxzhO0lr5Yy9fYzvUnHMHnCed77miCrUw0au4XttrEOXLIyjHfteFPvI6iGEVhfXm97dNoD9pI1982ef/Bjkrb92zaQ3NlH79s57r0LeaGqf+cZLN7Ce7p2Hr1BHB6ZnPglMh1XXdv5FRNO2cRPne7Zk36GXHsDn3V2/BnmJF0tuo5bw6uV7sQ5Pfc6kux22AhyN/nMlhBBCCCGEEA2gjyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEa4I4aWlQFCtjy2oswSXC8hAjmgxMplkSJzkwKSnu/qIMizBYJ+pq6AG9pgoYWVYEmBaOJFWXHIOYMIXXCuzonZSrSLi74b69LhIUkcGG+b8X3eYTP0u92IS8/sM9XFET828b7eUFuSQwtKiKcTH2wQTI2GmNMxo9vAiLSxNETQtcFy2NeDmv9U5C39M7vMulLDz+KP5xLmT6vfP0kZe4nyMsYwNsPn8EhzqFnP/ok5OVja/hSEFXvkMzHg0NrljM6OCAVbQ4mavei4YqIq1kf165cRIxFmPjeV8EbooQQQkLyai9cJvP/YA+F2pF/wJiJx+1a3V/F4JxFhv3nHy8iQaBTEjy6Tux6XpL2LSrcB0oQb89pcOGE9/RXLEC376xjGmrMw9d3UYS+4ExnTq+chjLt5QuQV7r9shwOsQwxGqncHsoCf7M55I0NuICfzAUI0kzmkLt2RdwrZmQulM6YImJBt9l+CVWYz6Qpd/UqW7j3D8j+uOYuz0w2ImL04duzLk9ubIYQwoSMlyiyhhIdEmC6TQwXBofWqKVDmri3iIZondPW7Oz2zj6UObt6BfIefeAxk/69j/w+lPnMZz4DefV5azbCxrDf69g44zHo7UBj+w4zKKtciHD28REFHGeHte2r+PxdWKMIDUJq900QH2Kw8zLD9aVKrdkRex+fB/3nSgghhBBCCCEaQB9XQgghhBBCCNEA+rgSQgghhBBCiAa4o5qrAgIGBzgvXrKAbwHPvrba9mx/0sIzl6NDPGvrA6VmbQwsmhCdUuKO5CZdPFP66i0M1FbesM/XXkA91+p5ex69l5Cz2ST4X+XOKqfkd8N91DIsZDYo22IXzxtPR3g+FQ7g0nP8eP42dYEw4bx64OfhvVipOkHdwGiEurOuC7bMzx8jpQu2XBV47dDDwM1h6IINb5PfnWZhi/8/457LkDV18QHzlzAQZtpZg7yi3jPpmuhXyoroako/H5n6rjliGiCYaayOhqhK5sjBM/k0aDGtkm2rmIgQixwDvvsAryku8SFzGqjZBLVvswkJBuxIiTakleFcq6b2AfOC6LlYIPV5Fw//O7d2FkS3nNCxARU41v3nYeOFxyHv9JoNpJpl2Odri6jDKgvXn1PUYNCgsLXXETIBENFFHZEOIYR6juDc7K/UXnOVz8izsIDvXpdNKlWQNoDgrqTNiVQcYvru7AygzMY6aoRO32snJAvEzXRmUex+d5Ja6hBCSHAel14vTrSiLMIzPA8bZixYdduO6/XN61BmOkL9z0HH1nMyxfeynOgLW25EsmDOiXt/rFgAeKbZhX0AivBXQ5dXMR0xGQstl9XOWlAmZ++Ubk2vyFq91sP38SSya8nBZARl5kH/uRJCCCGEEEKIBtDHlRBCCCGEEEI0gD6uhBBCCCGEEKIB9HElhBBCCCGEEA1wRw0tuKDUis6I7o4GmfMGBBERH2fk0xGqEM0XOC1yosHuEorjr67dA3m9yP5uc3sDygyHLuhktgBlZkQpXvoghSWK85b65yDvYGvXpEeBBCgm4kYMDMc6CxsdRdg02iD+rrS/g6CiDdIlph7HFaFnToFZk+Cn4WAd8y67AKhrTBXKxPlzGH2Mye9uOReI+x4gP/R1n0OpSvMa7LuvPgVZrR0rBq5rXA+2B69A3nBqf8cMdXJixDNxJgmHByj4bhIeINji19IQ5mt1FmCS5fmLxfR++Ds/bTMiSC6JoYQP9JmQQL/joTOPGe9CmTzg/epgr+1Nd0IIoc0E165dOkxcTfar0rkGVCSYPFtv5jHxYWPDB4Y+yTCtd6+u4v0XrGlSQQwDWBD6qrb9mSTMHASvFfvxQ8wImNsKtDkLyByTwNi+zVlw3qnd18tovrXFr7lsDrO9sJXZd42EjOmiwDbI3EvX6gq+f1y8QPrYPU9EzKyYoUXlRmNM+rhJhkPc+xIX8ZiuW8TjwgdKJk0cSmI6Uxe24JefwT3sd3/5l8jv7A13yVz/4cfeBXkXVm25i1ewons3jjYSqSv8XewMH+Y1GvMjtg5ozsHelfy7aJGTdYPd0AWBjjN8x5v18Z05y2zeCvEemwf950oIIYQQQgghGkAfV0IIIYQQQgjRAPq4EkIIIYQQQogG0MeVEEIIIYQQQjTAHTW0YH4EqVMNMmEhM0mofPRlIiJO0wzyymDFcBERx5UkknoSrMjtrstXoExUo2AucUq7okBx5fTmti1DGqrKsKuKfGLTExTxz6bECKP0eUz4im3gI86zSOQFE7DWvgwRvhJDi9iNhYSIjZsi6nZJ5tF2AChdDSHPnKFFB4Wjw/FtyNv8w/9m0jd+A00S9gczyAvOkKQk8s4ZeZZpZefHOEVx57iy91sqplBmpcSxEnvBPpvWmAWR3XFEh7A0xnYZdewPnzp3L5TZCQ9C3mBox/Ckwh6dkPVgMrVzr8xZvzTHPJrhef1XfLk59cihdmuuN9QJIYSSrV3OSCAjKvClNTTeSZxZRJQQEwgnxie+GKEg21xV2LEfFXjt6ZCYY7hxPcvZWoZ52MakoqQfYtdZ9TFNdqImDWUcneXTkNdecIZPxLSAGaIE13YxW5djZlDiDS2IxJ0Zt/jxmhCzjATfIyJ3rbrGdSOOj54vNTHZgDJkYJAhhs9HxjQbZC03hzrE1KffQZMLP4apCQ65n19H5l6AjkmWYf9lqd1Z9qbkPTDDsVe5d8poivvhZIDrxmhny6S7Ke5sb/6Wt0Be3rbj6uzi/VDmU0/+AeRdHdj97/zyG6HM2YWj15Yswb45/9DD9ncpzu3xIc6HvmvOmy98Dco88MDDkFe4d/vp9iaUYWZAsd9nFnEM39rHd4nSvQMws7N50H+uhBBCCCGEEKIB9HElhBBCCCGEEA2gjyshhBBCCCGEaIA7qrlqkai+PihboMEGyVl7d+a4IsHx6hY+nj9XOhvjmdnxAeal7gj5wf4OlJmN8X6L3UV77TGe7R3nNpDpxYt4hn1ngAHXyrGtw95kCGV29g8g72zf1ikjB7hHTlMSAp6pTojmigVhLVygRBYA0WsLQsCAnUxX1xgsevUcMKXN1AW0npR45rsbr0BetP6ySa88i0Fvz0+wP+vKjqmihXPo8Nx5yLvy/h+2v3vgESiz+6cfNenXPv5RKHPfjARpdDosr1H4RoA+jwlRyKp12Lc6jL0adRn7iyTPVb0k9xtNMS/P7Zg+ac0VC9AJsLi/ZF7Npf/hlTBUpE5UE+TmVlkRTWtKApn37Bn5isxR3y4+eG4IIWQRDpjY6Q1nB3tQZjrF9bSKXJBboudg+i3Qo9AmZ/oUuDr74dHMoe05LlGyBnndlg0622qTgKWsX1x/nlrFvXC71Ye8emK1EyyQa1linh8uaRvrlBLNldctM71RkrZcGRybaYRrUhzbOrDf0Wep/JqEezhbRzLXN2unzkKZdgc1kUVl97mYvLuVRI9bu3nMAg03CQtQfrpvx2dFgjmPKrJvu3enKsd3xXyIa0m49ZJJfvdj74Aif/H0k5D3+Mc/YtLf+SZsz5/90Z+EvOt7I5NOyBiKnf8A0/XPiKZsa2DH0J988o+gTG//FuT9k3/4Uyb98Le/H8pcue8hyBvdumnS4+tfhzIJ0UqP3eROV05BmR2nhQshhBXX7QVZS+ZB/7kSQgghhBBCiAbQx5UQQgghhBBCNIA+roQQQgghhBCiAfRxJYQQQgghhBANcEcNLaghQWSFhAk4XIQQsQCETqBbE0FiXqMgMTgThpoEyx3tYxC47GDJ1qlAIere1gbk3TywQsa98QjKdE9bQfDqyjKW8UFZQwg72zb48OYeBqaN+3it82fs/fJNFB8WYxToe8FqRcTVJaln5YXacwpYfdDF8gRF2YEaBvh64vMyG4ORCz64N8NxeCpFgfDSkg30F6XEvGLyHOQVlRUu1zUKixfWUHR+/gesoUXdRvH42WVrvHGZGJ3kf4ImF1FuRaA1CeoJgT8JRBoctsjzbbn5OJnh/eJiHfI6bVuH0RSNRqqSGOM4A50uMc9plqPnDDOvoFdy4nv+u6ODf/rgwK+XwSv5Jb0ggdunE1wXI2cIkHVJSOnIzS32LBUJoD2yZhXj4QB/VuLsjmL7zEWJgu95BPokZjoPFnvklb5RDFbXx3Nc57jsbeH+kbh9oN/HAJ41MYpI3CtJRsZYr4eGFtNDtzeRvitJAPTUGZIkKTEoIcYbCQQRJoHUY1unJOC6xbou8sYQzNACbxfKmV1zK2YcQ0xhOgt2ni1fQPOjpIPvETNXr4oMxNkM29yvN0nreEFa5yWO8J3k0AUIL53xWAghrB+ioc23XL5k0qOdbSjD2v3Glz5j0m957DugzD/98Q9C3u8sWXORb33wHijz3d/1AcjLXTDslOxPEbxD41yLMhyz+cy254U3vRnKzF74LOS99Vvfa393HwZELmfYVzc+ag0zDm5ex3pCTgjlin1n38pw3Ri8/HnImzljuOlUhhZCCCGEEEII8VeGPq6EEEIIIYQQogH0cSWEEEIIIYQQDaCPKyGEEEIIIYRogDtqaFEQoa8Xos0KJiLGb0B/KS+2DiGEnBhotFwU6qocQ5nDAUZt7uxaQ4DWAgrfz19EIehgYAWyF7v3QpnVc9bcoJOiiHBlCe935sJFk34DkcdmKTHeeM1GvB7som3AmIj46sSKkgvS5iWT6DoBa03GQUlE514fWxFBcHMcT/KNct0QBu5S29NDKHNpimLZs858JD6Dphc7By9D3t7Q9hXrlz4xbgmvvGKS0SM4ftMtK9id7u1Ambgkgs85PEy47N/WcxhhvXcW0HijvnDVpHuLKMAOUzR8qYNruwr7JYnQbKHfsfM6q1EQ3SgR+TuYN6YgP6MmCVCQdQ7Jc2YObM0tiCja/w0vIkXGw328VmHNU7JJD8qAGJ4YWpRT3FO8oUWeY5mKGKc4H5NQEPE6E/b73mH9wvClvBkJLxVCxMbLCXH79tchr5hag5CdDs6PdAEF5rnrvy4xilgmJk2bO3Y+VlM0AyqIWL7TsvtqHOF+GRGTLexj0t7uWegOw/rJjbGKzakK19wyd+sUMZfK2jimT1+w6+npK3fhtcl7hG+BnJgWTYjR2GRo+yYlpglNUpA6JJk1x1la6EKZ9UM0uXnH1TeY9PDZZ6FMNsMxO3jhKZPe/goaPtz3134A8v7ZT/+EvXYL+2E0JsYlqR1XRYHjpeXfM4lxXEHeyzLXXz/2ve/G373vPZDnxywZLmH40ouQt/GFPzXpaIx7dE2+ERYeeNCkvzLC30128H2mdIt8p5ahhRBCCCGEEEL8laGPKyGEEEIIIYRoAH1cCSGEEEIIIUQD3FHNFT1b67UZRJzBgzLas6AZORedk6CBsQtcmKYYmHI8Qo3Fzs1rJr168TKU6Z9Zhbwr99pzn1GMbRB7nQA5+1qRqJNp154TjkjAvoNbr0HeYP2GSY8HqHeoyVnwuGPrXpAz3QXpqtKfGSe6CBY8Gvt9Pp3C8WB/Zzhah4UqkBBWfGjhFezzaYpjbJDYdmqv4tW7a0uQl1VWF1USfVV58wbkvfyh/27S0ZXHoUy4Zs+U5y/iGfNlEtGydsIBCCQdQkC1Ywgjp7naJ9Kp8ZULkHd4ygYkHOLx6lBOcJxHma1nRDSYiwvk+SKr9cmHJ6sbmC9A8PHmx3whhFGGVZM1KSdXq30wVRIUNRDNU+kCwU5JAPY4cUHhiQ6sJvqUyml5KlKnosKAsqVbJ0r2vA0GO/fdHtH7sYDPLm/OANPHYXsbg3OXudXLrazhfhmTPeZwZOffaBeDtMYx7nMtF2B6VqA2pSR67unQ6mGzDgk+nmGQ28iPO/IsMF9IP9G55zRCxQzXpHKKeZV7vnaGz3Lm7FnIu/eBbzPpuId9lZP90WugRzNs35191LRUru410cY3yUoftX39RTte4hTrvkiCzucLNmDu4tWHoMzgq09Cnh9B1z78m3i/c6ghcQFDAAAWHklEQVR16z7yiL1/TrRTHVynYrc2VzkJYO3WypKMTyL/h/e5iIwNv/+/fj+bnt7E4OPXPvYHkLf//FdNukf0Vd1lfPfeP323Sb/0zFNQpqpIYHH3TVD6oN5zov9cCSGEEEIIIUQD6ONKCCGEEEIIIRpAH1dCCCGEEEII0QD6uBJCCCGEEEKIBrijhhY+OFcIIcSxD7RHJJ4kq+1E0gstfJQpCfDoLRgKIo6LU7zW2AkzoxIFkPWQBDc9ZfN6/QUo4+uQddBkY5qj8K52eQebm1BmuIGiwdGBFfbXFbZB1sV6jiv7zEMiQp8RcWrpjCmYOB9MPUIIkRNF9ohwsynGT6DYsb1ig+rGC9gmSz0MPljetIHwJrdvQpmbw13I80H1Li2gkLK9hoF+V52QebKL5hXlbcybbFphcfylp6FMFFlhentGzE+IgN8H2WQC10NiiDBctcG6Z/dchDIbp65A3gsTe63BCOdLJ8F55Y0NAgk+GioUj88K2zfT2Qn/nYoYNaDtRHN14EFoXZ+SOjErBy+UjlMy12MSvDUcvTfQmLqeGNfzyInxqSkEsfUoK7uD0IDBpKvAX4JUk8cH9vOItAENAg1OGCfG4QEGSfdVKitcy/rTCeRV3igmJgZXMRopdRft2lyVOP+nQxJYOLfzv66wTJKRIK0uCC0zqgKTC9LB7J2ocPt6WeD6w0xaEleF/jK6AZ29gKYJ2YINVl8kaAAxnOBe33LbcZJjPVPy3jKb2rzxGMs0yWSGbdWfWnMc9mqxTQLv/vEXP2rSf/ud74cyhy9jUO3gjFqKnS0o8vh//XeQ9/Yf+6BJ997yVigzy3E+xIkztCiJKYMrk8TMFI4EYHYBinNibBYRZ7Pixqsm/cr/+i0os/OFT0Ne282bggRSjh5+M+R9bWL7eHvzJSgDZmshhKkb653u8RZP/edKCCGEEEIIIRpAH1dCCCGEEEII0QD6uBJCCCGEEEKIBtDHlRBCCCGEEEI0wB01tChKIrwr7fddRKIhx16pGUJIE/c7IqVOyKdjHaw4tSSGFlmGgjl/v9l4CGW2b6Iwc3fTChc7xBQhdQK9tI33n8zw2qUTBE8PUIxbT1A0HGVWvdlZXIQy4wr7auQEniUTbkfYDxGYliBxwgwtbJt785Mm+b1f+RDkpWunTLpexL5LlrDtrjnziBkxr+iTsVm2rZi7R8bK8nmMCN9JrWlKp4vzZXLwMuTt3bYmLe1Aor87sWpBxKtD0qO5E3MXMY6LwTKagVQPPmyvfeYBKLMxwmttj6zRRquFgtpeQubVga17NURRdlzhtRYWVkw6H2LbNQkf+cebD2AowzwZ5jFlYCY05HdpZreZFjHLybw6PoQQu72AmWz42zFjipIYrnhjipT8LibmPPXEjo9yivtATUTSYDpBGorVHTqHdgz5mS/GftcQ/RU0nRkdunbK0VCnnqI5TtSx5gqzHM0kQo3joN22a0m8vIrXJu8W40Nn7kSMqqoS99B86soxAxg/zZhpCllbvEFQxPZU4k/Q69s2OHMB184Vsp62Fu26WCa4lu3tDSAvqWzbtcmaG5V4rcIZYeXE2KRJspQYQ3Vs54yIqUYE9mchTIM1SXjamTSEEMLbPvDXIe+JD/+2SXdLfPWOd/A94TO/+PMmfc93vA/KPPojH4S8w55dT9n7VYi8QRFZt0oc1/nMtkt3ioY2r33xk5B36+PWDGT86gtQJiXTPXcGSEtvegTKPN1B85bPfP7DJr2/sQFlsox9b9jnK/PjfSbpP1dCCCGEEEII0QD6uBJCCCGEEEKIBtDHlRBCCCGEEEI0wB3VXPEz8z7YITtATgLtuYirUxK0LCLX8kfPc3ZGn9Sg7QILtzNsunqKB0Yrd554Qs4Xx65dKlJv1iqxa4OIPEtKAhK3lqzm4YAEoRuQAHozuDye401JAObS9U3to+V+ozynsSISiMb49c99BvJyN1hyMg47GbbBKafNOnNqDcoUq6jVShbtOelqEdvywioGET7TXbL3v0zGQY51uPWUPS8+28ffLblhMCNBL1tEI1h0rH7s9ggDXI+vnIG8jTM278YY7zc7QF3UJXu7UJOokAfkaL/XfLZIFNwu0QglrbMmXQ1PMEprCN9AXuXXU7Le0ZXjONfG9ZStrzEJppq17BrUJ3qYTq+H14IgrHhtH8S3YroBohPEVR6vPZ0yPYzVKfogtCFwzRXuYXPoq74JfN8kTIDcEOMC9Sr5zM7ROqAu44AEwo2spCWwrksLIsxw/dntYyDc7iKugdt72yY93l3HS+eow6phf5xDc0k1ikykaPsqydpQpLeA+8ep01Z7u3zqApSJe9guIzde6xGurxvrI8jrJXYutDIcB0WJ7x8HY9tWBywIdYPURDMXgt2zJkRrl4+w7mnH7skfefKzeL/3/C3Ie/sP/R2Tfvp//iaU6ZKFeMFV6+bHPg5lXvnTj+HvLr/RpC8++hYo07tgtZIxeXebHKAucu/rNkjy+AbquQckr53bcdYmQ3+U4hq4+CYbOPnZ5UtQ5iNf+RTkFQNb95LsaV0yt8LMjpfj7uz6z5UQQgghhBBCNIA+roQQQgghhBCiAfRxJYQQQgghhBANoI8rIYQQQgghhGiAO2powYIBe8E1ixObsQCzLrAeMzuI6qMjYZYksDETp+Yu6FvVxgCo7QUU9ofCXr8iQfWCC2hZEmFzRWR1rbYV2reJOK+MUPy7N7XBhgcjLFOQ7+7aBVxOSH9WJPgwlCF9VZFMr/VNT/BvAZ3L5yAvdYMqq1D0ukBE2aedocXSIor1Q4pjZTu39zs8xKDQO+2bkHfVmZakfQyoF7dRAL2yeJ/N2MLnW3HjfjDC541b+CwzN1Y2rj8BZaYrOIe+snvLVmkPgzTe1cFnOdO3YvVBioYWexM0GoidsHkhxTplXey/hZV7TDqZ4e+aJM7webwZEDNzSEiQdAxmOp9kF2IPk/ulZE3IXN3bHVynEhbk0q3N7Nq5M6IochJwlaxJcWTzEtq+uD2OXbkkYQY+rM1dHeb0rsCems8Iw48NNg6aIgpogFDX1gAhz3HdqAr8XeGMBQpmaEGepdu2BVcW2RqIZkDdFWsCsdfDeXy4uwV53rCjKnDt9IZTbN9LSBDTzK2n3S6ud90FNIVJUhvYfECMf8YFrqfxln3mywXerxrh/Lyxdd2kV9eIKUyOvyvc/JhMTjYA+2SKg+iVLRsU+dwaPnNG1sXErXnnlnBv+IsnP42VePM7TPKt//hnocjzH/pPkBeP3TsAGUMtEuh3+uqLJv0iC9jrHs+P1xD4mA2uDSryXpaQ96Kp20AyYjpz+u3fDnkvr1jzqI98+g+hTL5xHfI6bTsex8TUJ5rh2PBren1MMyD950oIIYQQQgghGkAfV0IIIYQQQgjRAPq4EkIIIYQQQogG0MeVEEIIIYQQQjTAHTW0YOK4qJ4jkjz5nQsqHmpSKI6Z4YKvE4oWKxB8h1DX1vRhBBHaQ4gj/F2W2SZe6mDU7zC110o7KKqd+AcOIRy6SPXllERIr9CsonJ5dUXagLRn7RqvJB2aEdF57QXlLCo9cTKJoP+OGyv7aN725jdCXp7behc5ipZbNY6DXmTrXZFxOKNj095vQkTSm6NNvNbUtsv1EfZLh5gILPSsULR/9RSUqXtW8H04xSVjcx+juN+4aY03bnRXoEyxvwt5ezsbJp2SdkqX74K8PLPPnJO/G1UJmhbUbglsZ2jOsbKC4vhH3/5tJv3cV3GeNUnawv7zy1TEDC0iYiIExhRzuivA/Yl5BJnbfk33ZkSv5xHDA7fmFmS9KZ2hRUkMgxJvJhFCqJw5TczWH8jBNT5iRhFsfYOrzdvm/nd47Zrl+bFB69QM5Qz7M7h2Ksk+WxLzEU9NhPEpMavx5iflBM0c0g7uj6OJNTaY1jiP2ysovE9yu05MRyMoU419HXCsEC+CELl9djjGaw8nuDdE0Y69PzFyYcOg1bbtOSNGSmsrl/F3zrRkfIhjuk3W/U5mK9FfxPZtkhZ5J/Gvb6Mx9nu/j+9hi2572J+S9SaZQN7vf+oTJv3E6hqU+YkP/lvI2/nCH5n0lz7xEShzlrwb+hfkiJm01XbwxWRwlMTkouXGbEFcZ8ouvucuXX3UpPtveBuU+fT2Dcj74p//tklXm3tQpu3dOUII5czOkSzGdaPbwrHR7dp+r+MBlJkH/edKCCGEEEIIIRpAH1dCCCGEEEII0QD6uBJCCCGEEEKIBrijmis4CB7gaHYoiJYppFhNfxLUB00MgQem9d+TUYTflzFcnQTQpUfYSRA2lzdl13aahHqGZ8NnOTnX6s+ZEw1ETc7RxpFtTyYbiEl7lq7/atK+OTkj79uKne2NST/4sVHmJ6drWVtagLyicBqzHPulLrBOrdi2b06m2ZScU55O7FntLMIzwjnRIK27Nn9lMIQy1e4h5MXuLHGdYoDJVu+CSSf9S1Dm2uYtyBtsW83VjOgG0l2iU9i39VxcJDoFoqEpEtt/eY3n5asYtQTBBdlOE7x2muA4Hx5um/Sp0xegTJNEVBPkgggT7SRV9sA6PKf+x81bGl+SxW33aRK4vWbaE7cwsTbwa0tGgrISKSwIgKlKiq2n/mmOK2Vi+uM5CjLtFJPMwVp9gnrVmumPXXpWYiUrMg68jrckmtZZTvRUbqzsj9ehTHFjA/LGfv0m9/NBU1/PO3rOeM33vLo3v2ezvovYoHZrGdOvMy117oI533j1eShzsLcNeV6T1K5RZxPVuO8stG3gXR4YuzlmZJwlsZsfJWpaD0e433fdUBi3UPvWi/F+a07zvDfG9vyFX/45yLv/0r0m/X0/86+gzHT3aci79pWvmvRrr+DYz90YvjDFd5kDMoaWz1od9pn7HoAyw7XTkPf0un0neO7zfwBl9g9Rv127+Z62sa/SCNt8MrHPs9zD97BZihrrsdO6j8fHWzv1nyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEaQB9XQgghhBBCCNEAd9TQImaGFk68GZGglzSorxd5zhW4EUXg88ZW9MGAWQBNLyJ+Pc+lAxFcO8MOZtxQk8CbJQhfmSocs+DatH2JyNSVY6YXLBgnZrEyRwfxPDrk5PFpt3EqJE6YmpO+q4nZQStzQehIpMioIuYYTsydxFinDgkwPZpaAWZBrs3GQe1aNItJkGQnki47aLJxfYhB/W7ffNnW8dXreG0yzpdTK17NFvB5oxa2S9xeNOm6QHF1HhGjj9RPUDKHmHtNbOv+2sZLWKZBqBjeVb0iAR+5eYwTczPjBqYx9+sNNXzA9itcYN8pCXZeVygsxvuTYMBwf/K8NO8vv04IIUxnGAy0cAYIzEiJGxf4/YoUIfh+YCZCrI/BCOMkDS1KErx+5gOwkzWJ7DF+/59NcU1i/Vl7IxMyF5ghSif1phO4xjNHFGxzssbP1efUFoYVdNci69Q8PjXM/MR5eMzI/Mw7GEh1OrNrbEEm0XSMgeIjFzM4CYtQpklIfNlw9Zy954xUfkBMxPZyW/l7Vk9BmW0yXsZjt/csYxDh1sEO5D23Y9vvhQ/9ByiTEFOG1ukrJn3+O/4GlLmnb5+lKNDwKcvRdOqVrU2TfnoDzWNm6xgMuN+zRib33/cQ3o/s7f3OqknnETERI/PBvbKHJ3ZxLD71qU9BXuQCi6fH/BeU/nMlhBBCCCGEEA2gjyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEa4I4aWvBI3N5g4mhjgxDQKKJimnNm1OCEriUT1ZK8wkWhZ5HO2adq4swqImJSkLhnrrzCNHCBudeTM2OKiLWBaysun8XcJLFiXyL9pXXAPp7vd76NCyJSbopWikYNSWTz0gSfOC9QXBk7gWmM3RniGQq1PXXFIstjuaxlMyNSqKqx7nFi61nOsKLtnv3duIXX2Y1wTF/ftkLYlZqIbonAvLtkDSxaPYzG3ur2Ia+zsGDS+R6279Y+Gm/MDqyBxpXTd0OZSY51f+KJL5r0wWQOEfo3ARPxe6Mftm6x9RRMIEjVj2t/wIxwcrd2HhygcHoeQ5v59PnEWIDtDeRanhKnXyj92kzE69w8xtdpPiMMv37zvmKGRJaKrCVNMRqj6D2fuVFG7s/mf5bZuTYh125584qAa3PNDB9IX83TL9R4A4ocb8ZQMym/sdM6EcMOVytm7sKI3GyoyftHXhDTktqZHZEtjZkPlNW+vf/45MxWQgihg95GYZDb9iO+VOHuy/dC3nBsx+eLO7iWlWQslKV9l0jIGjgI+A6SxW6Nj9HgqUrwd+kr1mRi/bUtKPMV955QV9iB/t00hBAyd7+0hfdvJfhOMJ7YNo+JU0SHfJFcd14gS0tYaHEB15fbu9a05ObWU1DmsTc9BnnPPP1Zk25lx3vv1H+uhBBCCCGEEKIB9HElhBBCCCGEEA2gjyshhBBCCCGEaIA7qrniahx3pnyeAHqBnCcmP6PBhyEII9MpkBuW/hw0FmGBGovKnl9OEqKj6dhzvBELWkgCdsJZexYwlAVchDTRV9Hgo9Ffmg6B66JKl5fE+E1fBxIg1PUViZ/ZGAtOsxNCCMOhP2dOxWKQlTmdXUXOxycs2LLLi9m5+hrPsPvz9zEJehvXONW7LXsYnciyUCNIxmFC1HdZanVR7NxyL8ZnSZ1ALc16UKbbX4W8tG21WVV+CGXiKQaFXcpsG5zuX4YyB0Ps46VVqwVpzW5BmUZhGhLX7/MGio1doNSajEWunYRKzfU7r3PNmf6HSVhdvSqiZZjmXh9CNAIpCToN8V3n0L4EonRhOtc5AguzNZ5q5txaWfp96PWLQZZv85NUtUyGONd8GyREB+o1nyGEUFS+ndheQYJX+/ZkWjjWL77N5xr3OFxBixdQp+01y69fh4wx984Qx/g7pgLx+rxWi4iN2N/T/dhkQ4xpvl3Q2YTUM00wQPBsYnWu5YwFmG6O1T7qdqel3e/PnT8DZcoM8z7wzreZ9P2PvBPKZCmub7ELVt0ma1Ig7ZfEbo6QMVTVqJVK204X5U0KQgiFe6Gasn4nI63t1uGCvheRizlhW0n29s4E9+gxvOdi27WJaG4wtXl/9kl8b/j6kxhEOGu7urOGmQP950oIIYQQQgghGkAfV0IIIYQQQgjRAPq4EkIIIYQQQogG0MeVEEIIIYQQQjRANJ9wWQghhBBCCCHEX4b+cyWEEEIIIYQQDaCPKyGEEEIIIYRoAH1cCSGEEEIIIUQD6ONKCCGEEEIIIRpAH1dCCCGEEEII0QD6uBJCCCGEEEKIBtDHlRBCCCGEEEI0gD6uhBBCCCGEEKIB9HElhBBCCCGEEA2gjyshhBBCCCGEaAB9XAkhhBBCCCFEA+jjSgghhBBCCCEaQB9XQgghhBBCCNEA+rgSQgghhBBCiAbQx5UQQgghhBBCNIA+roQQQgghhBCiAfRxJYQQQgghhBANoI8rIYQQQgghhGgAfVwJIYQQQgghRAPo40oIIYQQQgghGkAfV0IIIYQQQgjRAPq4EkIIIYQQQogG0MeVEEIIIYQQQjTA/wFxVb9U73XufQAAAABJRU5ErkJggg==
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<div class="prompt input_prompt">In&nbsp;[7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#plotting the count of each sign</span>
<span class="c1">#distribution of classes in training,validation and test sets</span>
<span class="n">hist</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.6</span> <span class="o">*</span> <span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">hist</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.6</span> <span class="o">*</span> <span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;g&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">hist</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.6</span> <span class="o">*</span> <span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h2 id="Step-2:-Design-and-Test-a-Model-Architecture">Step 2: Design and Test a Model Architecture<a class="anchor-link" href="#Step-2:-Design-and-Test-a-Model-Architecture">&#182;</a></h2><p>Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the <a href="http://benchmark.ini.rub.de/?section=gtsrb&amp;subsection=dataset">German Traffic Sign Dataset</a>.</p>
<p>The LeNet-5 implementation shown in the <a href="https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81">classroom</a> at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!</p>
<p>With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.</p>
<p>There are various aspects to consider when thinking about this problem:</p>
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<li>Neural network architecture (is the network over or underfitting?)</li>
<li>Play around preprocessing techniques (normalization, rgb to grayscale, etc)</li>
<li>Number of examples per label (some have more than others).</li>
<li>Generate fake data.</li>
</ul>
<p>Here is an example of a <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">published baseline model on this problem</a>. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.</p>

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<h3 id="Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">Pre-process the Data Set (normalization, grayscale, etc.)<a class="anchor-link" href="#Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">&#182;</a></h3>
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<p>Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, <code>(pixel - 128)/ 128</code> is a quick way to approximately normalize the data and can be used in this project.</p>
<p>Other pre-processing steps are optional. You can try different techniques to see if it improves performance.</p>
<p>Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># rotate image     </span>
<span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="k">import</span> <span class="n">rotate</span>
<span class="k">def</span> <span class="nf">rotate_image</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">rotate</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mf">30.0</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">img</span>  

<span class="n">test_img</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">test_dst</span> <span class="o">=</span> <span class="n">rotate_image</span><span class="p">(</span><span class="n">test_img</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_img</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;original&#39;</span><span class="p">)</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_dst</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;rotated&#39;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;shape in/out:&#39;</span><span class="p">,</span> <span class="n">test_img</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_dst</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>shape in/out: (32, 32, 3) (32, 32, 3)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># function to increase brightness of images</span>
<span class="c1"># will choose images randomly for this function </span>
<span class="kn">import</span> <span class="nn">cv2</span> 

<span class="k">def</span> <span class="nf">increase_brightness</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">value</span><span class="o">=</span><span class="mi">20</span><span class="p">):</span>
    <span class="n">hsv</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2HSV</span><span class="p">)</span><span class="c1">#convert it to hsv</span>
    <span class="n">h</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">hsv</span><span class="p">)</span>
    <span class="n">v</span> <span class="o">+=</span> <span class="mi">255</span>
    <span class="n">final_hsv</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">merge</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">final_hsv</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_HSV2BGR</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">img</span>

    
<span class="n">test_img</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">test_dst</span> <span class="o">=</span> <span class="n">increase_brightness</span><span class="p">(</span><span class="n">test_img</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_img</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;original&#39;</span><span class="p">)</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_dst</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;brightened&#39;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;shape in/out:&#39;</span><span class="p">,</span> <span class="n">test_img</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_dst</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>shape in/out: (32, 32, 3) (32, 32, 3)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># function to blur image</span>
<span class="c1"># will choose images randomly to blur</span>

<span class="k">def</span> <span class="nf">blur_image</span><span class="p">(</span><span class="n">img</span><span class="p">):</span>
    
    <span class="n">hsv</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2HSV</span><span class="p">)</span>
    <span class="n">lower_red</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">30</span><span class="p">,</span><span class="mi">150</span><span class="p">,</span><span class="mi">50</span><span class="p">])</span>
    <span class="n">upper_red</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">255</span><span class="p">,</span><span class="mi">255</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
    <span class="n">mask</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">inRange</span><span class="p">(</span><span class="n">hsv</span><span class="p">,</span> <span class="n">lower_red</span><span class="p">,</span> <span class="n">upper_red</span><span class="p">)</span>
    <span class="n">res</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">bitwise_and</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">img</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span> <span class="n">mask</span><span class="p">)</span>
    <span class="n">median</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">medianBlur</span><span class="p">(</span><span class="n">res</span><span class="p">,</span><span class="mi">15</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">img</span>

    
<span class="n">test_img</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">test_dst</span> <span class="o">=</span> <span class="n">blur_image</span><span class="p">(</span><span class="n">test_img</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_img</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;original&#39;</span><span class="p">)</span>

<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">test_dst</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;blurred&#39;</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;shape in/out:&#39;</span><span class="p">,</span> <span class="n">test_img</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">test_dst</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>shape in/out: (32, 32, 3) (32, 32, 3)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Augment images randomly with above methods as new added images</span>
<span class="c1"># which will be concatanate with existing X_train to form new training dataset</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X_train, y_train shapes before adding new images:&#39;</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>

<span class="k">for</span> <span class="n">class_n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
    <span class="n">X_train_new</span><span class="o">=</span> <span class="p">[]</span>
    <span class="n">y_train_new</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">class_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">class_n</span><span class="p">)</span>
    <span class="n">n_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_indices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.3</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_samples</span><span class="p">):</span>
        <span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">class_indices</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
        <span class="n">new_img</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">new_img_label</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
        <span class="n">new_img</span> <span class="o">=</span>  <span class="n">rotate_image</span><span class="p">(</span><span class="n">increase_brightness</span><span class="p">(</span><span class="n">blur_image</span><span class="p">(</span><span class="n">image</span><span class="p">),</span><span class="n">value</span><span class="o">=</span><span class="mi">20</span><span class="p">))</span>
        <span class="n">X_train_new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_img</span><span class="p">)</span>
        <span class="n">y_train_new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_img_label</span><span class="p">)</span>
    <span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_train_new</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_new</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;X_train, y_train shapes after adding new augmentated images:&#39;</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">y_train</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>        
<span class="c1"># #         new_X.append(class_indices[0][i%n_samples])</span>
<span class="c1"># #         new_y.append(X_train.shape[0])</span>
<span class="c1">#             new_img = X_train[class_indices[0][i % n_samples]]</span>
<span class="c1">#             new_img =  rotate_image(increase_brightness(blur_image(image),value=20))</span>
<span class="c1">#             X_train_new = np.concatenate((X_train, [new_img]), axis=0)</span>
<span class="c1">#             y_train = np.concatenate((y_train, [class_n]), axis=0)</span>
<span class="c1">#     print(class_indices)</span>
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<pre>X_train, y_train shapes before adding new images: (34799, 32, 32, 3) (34799,)
X_train, y_train shapes after adding new augmentated images: (46892, 32, 32, 3) (46892,)
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<div class="prompt input_prompt">In&nbsp;[12]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># visualize new X_train labels</span>
<span class="n">hist</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.6</span> <span class="o">*</span> <span class="p">(</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">center</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#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"># Implement Min-Max scaling for grayscale image data</span>
<span class="c1"># tested it is better performance than pixel-128/128</span>
<span class="k">def</span> <span class="nf">normalize_grayscale</span><span class="p">(</span><span class="n">image_data</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Normalize the image data with Min-Max scaling to a range of [0.0, 1.0]</span>
<span class="sd">    :param image_data: The image data to be normalized</span>
<span class="sd">    :return: Normalized image data</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">a</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">b</span> <span class="o">=</span> <span class="mf">1.0</span>
    <span class="n">grayscale_min</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">grayscale_max</span> <span class="o">=</span> <span class="mi">255</span>
    <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="p">(</span> <span class="p">(</span> <span class="p">(</span><span class="n">image_data</span> <span class="o">-</span> <span class="n">grayscale_min</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</span> <span class="p">)</span><span class="o">/</span><span class="p">(</span> <span class="n">grayscale_max</span> <span class="o">-</span> <span class="n">grayscale_min</span> <span class="p">)</span> <span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Preprocess the data here. It is required to normalize the data. </span>

<span class="c1"># grayscale and normalize the data</span>
<span class="c1"># ensoure data is 3D (32,32,1)</span>
<span class="n">X_train2</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">gray</span><span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
<span class="c1">#     gray = (gray - 128)/128</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">normalize_grayscale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">X_train2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    
<span class="n">X_valid2</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">)):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">X_valid</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">gray</span><span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
<span class="c1">#     gray = (gray - 128)/128</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">normalize_grayscale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">X_valid2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>

<span class="n">X_test2</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)):</span>
    <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">X_test</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">gray</span><span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
<span class="c1">#     gray =(gray - 128)/128</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">normalize_grayscale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">X_test2</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train2</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">X_valid2</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test2</span><span class="p">))</span>
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<pre>46892 4410 12630
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">X_train2</span><span class="p">[</span><span class="mi">900</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_valid2</span><span class="p">[</span><span class="mi">300</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">X_test2</span><span class="p">[</span><span class="mi">700</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(32, 32) (32, 32) (32, 32)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#plotting traffic sign images</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train2</span><span class="p">))</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">X_train2</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
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<pre>&lt;matplotlib.image.AxesImage at 0x7f966e27d780&gt;</pre>
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<img src="data:image/png;base64,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
"
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># mean before normalization</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_valid</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="c1"># mean after normalization</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_train2</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_valid2</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_test2</span><span class="p">))</span>
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<pre>61.4677550589
83.5564273756
82.1484603612
0.238398287211
0.324523083539
0.318735230199
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#turn the input data from (32,32) to 3D (32,32,1)</span>
<span class="n">X_train</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train2</span><span class="p">)):</span>
    <span class="n">img_expanded_train</span> <span class="o">=</span> <span class="n">X_train2</span><span class="p">[</span><span class="n">i</span><span class="p">][:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
    <span class="n">X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_expanded_train</span><span class="p">)</span>

<span class="n">X_valid</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_valid2</span><span class="p">)):</span>
    <span class="n">img_expanded_valid</span> <span class="o">=</span> <span class="n">X_valid2</span><span class="p">[</span><span class="n">i</span><span class="p">][:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
    <span class="n">X_valid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_expanded_valid</span><span class="p">)</span>    

<span class="n">X_test</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test2</span><span class="p">)):</span>
    <span class="n">img_expanded_test</span> <span class="o">=</span> <span class="n">X_test2</span><span class="p">[</span><span class="n">i</span><span class="p">][:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
    <span class="n">X_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_expanded_test</span><span class="p">)</span>
    
<span class="nb">print</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="mi">5290</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">X_valid</span><span class="p">[</span><span class="mi">200</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">X_test</span><span class="p">[</span><span class="mi">2890</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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<pre>(32, 32, 1) (32, 32, 1) (32, 32, 1)
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<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3><p>Input
The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.</p>
<p>Architecture
Layer 1: Convolutional. The output shape should be 28x28x6.</p>
<p>Activation. Your choice of activation function.</p>
<p>Pooling. The output shape should be 14x14x6.</p>
<p>Layer 2: Convolutional. The output shape should be 10x10x16.</p>
<p>Activation. Your choice of activation function.</p>
<p>Pooling. The output shape should be 5x5x16.</p>
<p>Flatten. Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using tf.contrib.layers.flatten, which is already imported for you.</p>
<p>Layer 3: Fully Connected. This should have 120 outputs.</p>
<p>Activation. Your choice of activation function.</p>
<p>Layer 4: Fully Connected. This should have 84 outputs.</p>
<p>Activation. Your choice of activation function.</p>
<p>Layer 5: Fully Connected (Logits). This should have 10 outputs.</p>
<p>Output
Return the result of the 2nd fully connected layer.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Define your architecture here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">from</span> <span class="nn">tensorflow.contrib.layers</span> <span class="k">import</span> <span class="n">flatten</span>

<span class="k">def</span> <span class="nf">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>    
    <span class="c1"># Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer</span>
    <span class="n">mu</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.1</span>
    
    <span class="c1"># SOLUTION: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.</span>
    <span class="n">conv1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
    <span class="n">conv1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">conv1_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv1_b</span>

    <span class="c1"># SOLUTION: Activation.</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv1</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Pooling. Input = 28x28x6. Output = 14x14x6.</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Layer 2: Convolutional. Output = 10x10x16.</span>
    <span class="n">conv2_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</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">16</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv2_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
    <span class="n">conv2</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">conv2_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv2_b</span>
    
    <span class="c1"># SOLUTION: Activation.</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Pooling. Input = 10x10x16. Output = 5x5x16.</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</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">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Flatten. Input = 5x5x16. Output = 400.</span>
    <span class="n">fc0</span>   <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>
    
    <span class="c1"># SOLUTION: Layer 3: Fully Connected. Input = 400. Output = 120.</span>
    <span class="n">fc1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">120</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">120</span><span class="p">))</span>
    <span class="n">fc1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc0</span><span class="p">,</span> <span class="n">fc1_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc1_b</span>
    
    <span class="c1"># SOLUTION: Activation.</span>
    <span class="n">fc1</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">fc1</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Layer 4: Fully Connected. Input = 120. Output = 84.</span>
    <span class="n">fc2_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc2_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">84</span><span class="p">))</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc1</span><span class="p">,</span> <span class="n">fc2_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc2_b</span>
    
    <span class="c1"># SOLUTION: Activation.</span>
    <span class="n">fc2</span>    <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">fc2</span><span class="p">)</span>
       
    <span class="c1"># Add dropout before feed to last fully connected layer to prevent overfitting</span>
    <span class="n">fc2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>

    <span class="c1"># SOLUTION: Layer 5: Fully Connected. Input = 84. Output = 43 (n_classes).</span>
    <span class="n">fc3_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc3_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_classes</span><span class="p">))</span>
    <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc2</span><span class="p">,</span> <span class="n">fc3_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc3_b</span>
    
    <span class="k">return</span> <span class="n">logits</span>
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<h3 id="Train,-Validate-and-Test-the-Model">Train, Validate and Test the Model<a class="anchor-link" href="#Train,-Validate-and-Test-the-Model">&#182;</a></h3>
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<p>A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation
sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.</p>

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<h5 id="Features-and-Labels">Features and Labels<a class="anchor-link" href="#Features-and-Labels">&#182;</a></h5>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="n">tf</span><span class="o">.</span><span class="n">reset_default_graph</span><span class="p">()</span> 

<span class="c1"># x is a placeholder for a batch of input images. </span>
<span class="c1"># y is a placeholder for a batch of output labels.</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">one_hot_y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
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<h5 id="Training-Pipeline">Training Pipeline<a class="anchor-link" href="#Training-Pipeline">&#182;</a></h5>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rate</span> <span class="o">=</span> <span class="mf">0.0005</span>

<span class="n">logits</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">)</span>
<span class="n">loss_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">cross_entropy</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">rate</span><span class="p">)</span>
<span class="n">training_operation</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">loss_operation</span><span class="p">)</span>
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<h5 id="Model-Evaluation-Function">Model Evaluation Function<a class="anchor-link" href="#Model-Evaluation-Function">&#182;</a></h5>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">correct_prediction</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">accuracy_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_prediction</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">X_data</span><span class="p">,</span> <span class="n">y_data</span><span class="p">):</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_data</span><span class="p">)</span>
    <span class="n">total_accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">get_default_session</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
        <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">],</span> <span class="n">y_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">]</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
        <span class="n">total_accuracy</span> <span class="o">+=</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_x</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">total_accuracy</span> <span class="o">/</span> <span class="n">num_examples</span>
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<h5 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">&#182;</a></h5><p>Calculate and report the accuracy on the training and validation set.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">200</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</span>

<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training...&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">EPOCHS</span><span class="p">):</span>
        <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</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="k">for</span> <span class="n">offset</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
            <span class="n">end</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
            <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
            <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">training_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">})</span>
            
        <span class="n">training_accuracy</span> <span class="o">=</span> <span class="n">evaluate</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="nb">print</span><span class="p">(</span><span class="s2">&quot;EPOCH </span><span class="si">{}</span><span class="s2"> ...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training Accuracy = </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">training_accuracy</span><span class="p">))</span>
        <span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Validation Accuracy = </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">validation_accuracy</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">()</span>
        
    <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="s1">&#39;./lenet&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model saved&quot;</span><span class="p">)</span>
</pre></div>

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<pre>Training...

EPOCH 1 ...
Training Accuracy = 0.400
Validation Accuracy = 0.443

EPOCH 2 ...
Training Accuracy = 0.572
Validation Accuracy = 0.695

EPOCH 3 ...
Training Accuracy = 0.626
Validation Accuracy = 0.762

EPOCH 4 ...
Training Accuracy = 0.668
Validation Accuracy = 0.825

EPOCH 5 ...
Training Accuracy = 0.689
Validation Accuracy = 0.843

EPOCH 6 ...
Training Accuracy = 0.695
Validation Accuracy = 0.866

EPOCH 7 ...
Training Accuracy = 0.710
Validation Accuracy = 0.880

EPOCH 8 ...
Training Accuracy = 0.714
Validation Accuracy = 0.880

EPOCH 9 ...
Training Accuracy = 0.725
Validation Accuracy = 0.891

EPOCH 10 ...
Training Accuracy = 0.724
Validation Accuracy = 0.892

EPOCH 11 ...
Training Accuracy = 0.729
Validation Accuracy = 0.907

EPOCH 12 ...
Training Accuracy = 0.733
Validation Accuracy = 0.909

EPOCH 13 ...
Training Accuracy = 0.735
Validation Accuracy = 0.912

EPOCH 14 ...
Training Accuracy = 0.738
Validation Accuracy = 0.916

EPOCH 15 ...
Training Accuracy = 0.741
Validation Accuracy = 0.926

EPOCH 16 ...
Training Accuracy = 0.740
Validation Accuracy = 0.921

EPOCH 17 ...
Training Accuracy = 0.743
Validation Accuracy = 0.928

EPOCH 18 ...
Training Accuracy = 0.744
Validation Accuracy = 0.929

EPOCH 19 ...
Training Accuracy = 0.744
Validation Accuracy = 0.927

EPOCH 20 ...
Training Accuracy = 0.746
Validation Accuracy = 0.928

EPOCH 21 ...
Training Accuracy = 0.748
Validation Accuracy = 0.933

EPOCH 22 ...
Training Accuracy = 0.748
Validation Accuracy = 0.940

EPOCH 23 ...
Training Accuracy = 0.748
Validation Accuracy = 0.934

EPOCH 24 ...
Training Accuracy = 0.750
Validation Accuracy = 0.934

EPOCH 25 ...
Training Accuracy = 0.750
Validation Accuracy = 0.940

EPOCH 26 ...
Training Accuracy = 0.750
Validation Accuracy = 0.940

EPOCH 27 ...
Training Accuracy = 0.751
Validation Accuracy = 0.941

EPOCH 28 ...
Training Accuracy = 0.752
Validation Accuracy = 0.939

EPOCH 29 ...
Training Accuracy = 0.752
Validation Accuracy = 0.939

EPOCH 30 ...
Training Accuracy = 0.752
Validation Accuracy = 0.939

EPOCH 31 ...
Training Accuracy = 0.753
Validation Accuracy = 0.941

EPOCH 32 ...
Training Accuracy = 0.754
Validation Accuracy = 0.944

EPOCH 33 ...
Training Accuracy = 0.752
Validation Accuracy = 0.938

EPOCH 34 ...
Training Accuracy = 0.753
Validation Accuracy = 0.942

EPOCH 35 ...
Training Accuracy = 0.753
Validation Accuracy = 0.941

EPOCH 36 ...
Training Accuracy = 0.754
Validation Accuracy = 0.948

EPOCH 37 ...
Training Accuracy = 0.754
Validation Accuracy = 0.943

EPOCH 38 ...
Training Accuracy = 0.754
Validation Accuracy = 0.946

EPOCH 39 ...
Training Accuracy = 0.754
Validation Accuracy = 0.948

EPOCH 40 ...
Training Accuracy = 0.754
Validation Accuracy = 0.950

EPOCH 41 ...
Training Accuracy = 0.755
Validation Accuracy = 0.942

EPOCH 42 ...
Training Accuracy = 0.755
Validation Accuracy = 0.951

EPOCH 43 ...
Training Accuracy = 0.756
Validation Accuracy = 0.952

EPOCH 44 ...
Training Accuracy = 0.755
Validation Accuracy = 0.944

EPOCH 45 ...
Training Accuracy = 0.755
Validation Accuracy = 0.947

EPOCH 46 ...
Training Accuracy = 0.755
Validation Accuracy = 0.945

EPOCH 47 ...
Training Accuracy = 0.755
Validation Accuracy = 0.943

EPOCH 48 ...
Training Accuracy = 0.755
Validation Accuracy = 0.946

EPOCH 49 ...
Training Accuracy = 0.756
Validation Accuracy = 0.951

EPOCH 50 ...
Training Accuracy = 0.755
Validation Accuracy = 0.947

EPOCH 51 ...
Training Accuracy = 0.755
Validation Accuracy = 0.946

EPOCH 52 ...
Training Accuracy = 0.754
Validation Accuracy = 0.943

EPOCH 53 ...
Training Accuracy = 0.756
Validation Accuracy = 0.954

EPOCH 54 ...
Training Accuracy = 0.756
Validation Accuracy = 0.951

EPOCH 55 ...
Training Accuracy = 0.756
Validation Accuracy = 0.946

EPOCH 56 ...
Training Accuracy = 0.756
Validation Accuracy = 0.952

EPOCH 57 ...
Training Accuracy = 0.756
Validation Accuracy = 0.952

EPOCH 58 ...
Training Accuracy = 0.756
Validation Accuracy = 0.949

EPOCH 59 ...
Training Accuracy = 0.756
Validation Accuracy = 0.955

EPOCH 60 ...
Training Accuracy = 0.757
Validation Accuracy = 0.950

EPOCH 61 ...
Training Accuracy = 0.756
Validation Accuracy = 0.954

EPOCH 62 ...
Training Accuracy = 0.756
Validation Accuracy = 0.951

EPOCH 63 ...
Training Accuracy = 0.757
Validation Accuracy = 0.949

EPOCH 64 ...
Training Accuracy = 0.756
Validation Accuracy = 0.944

EPOCH 65 ...
Training Accuracy = 0.756
Validation Accuracy = 0.954

EPOCH 66 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 67 ...
Training Accuracy = 0.756
Validation Accuracy = 0.958

EPOCH 68 ...
Training Accuracy = 0.756
Validation Accuracy = 0.946

EPOCH 69 ...
Training Accuracy = 0.757
Validation Accuracy = 0.948

EPOCH 70 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 71 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 72 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 73 ...
Training Accuracy = 0.756
Validation Accuracy = 0.950

EPOCH 74 ...
Training Accuracy = 0.756
Validation Accuracy = 0.959

EPOCH 75 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 76 ...
Training Accuracy = 0.756
Validation Accuracy = 0.952

EPOCH 77 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 78 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 79 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 80 ...
Training Accuracy = 0.756
Validation Accuracy = 0.957

EPOCH 81 ...
Training Accuracy = 0.757
Validation Accuracy = 0.961

EPOCH 82 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 83 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 84 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 85 ...
Training Accuracy = 0.756
Validation Accuracy = 0.951

EPOCH 86 ...
Training Accuracy = 0.756
Validation Accuracy = 0.949

EPOCH 87 ...
Training Accuracy = 0.756
Validation Accuracy = 0.954

EPOCH 88 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 89 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 90 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 91 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 92 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 93 ...
Training Accuracy = 0.756
Validation Accuracy = 0.952

EPOCH 94 ...
Training Accuracy = 0.757
Validation Accuracy = 0.950

EPOCH 95 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 96 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 97 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 98 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 99 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 100 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 101 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 102 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 103 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 104 ...
Training Accuracy = 0.757
Validation Accuracy = 0.950

EPOCH 105 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 106 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 107 ...
Training Accuracy = 0.757
Validation Accuracy = 0.950

EPOCH 108 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 109 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 110 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 111 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 112 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 113 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 114 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 115 ...
Training Accuracy = 0.756
Validation Accuracy = 0.958

EPOCH 116 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 117 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 118 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 119 ...
Training Accuracy = 0.757
Validation Accuracy = 0.950

EPOCH 120 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 121 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 122 ...
Training Accuracy = 0.757
Validation Accuracy = 0.948

EPOCH 123 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 124 ...
Training Accuracy = 0.757
Validation Accuracy = 0.948

EPOCH 125 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 126 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 127 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 128 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 129 ...
Training Accuracy = 0.756
Validation Accuracy = 0.957

EPOCH 130 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 131 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 132 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 133 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 134 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 135 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 136 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 137 ...
Training Accuracy = 0.757
Validation Accuracy = 0.953

EPOCH 138 ...
Training Accuracy = 0.757
Validation Accuracy = 0.948

EPOCH 139 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 140 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 141 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 142 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 143 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 144 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 145 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 146 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 147 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 148 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 149 ...
Training Accuracy = 0.756
Validation Accuracy = 0.959

EPOCH 150 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 151 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 152 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 153 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 154 ...
Training Accuracy = 0.757
Validation Accuracy = 0.961

EPOCH 155 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 156 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 157 ...
Training Accuracy = 0.756
Validation Accuracy = 0.945

EPOCH 158 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 159 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 160 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 161 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 162 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 163 ...
Training Accuracy = 0.757
Validation Accuracy = 0.963

EPOCH 164 ...
Training Accuracy = 0.756
Validation Accuracy = 0.956

EPOCH 165 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 166 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 167 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 168 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 169 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 170 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 171 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 172 ...
Training Accuracy = 0.757
Validation Accuracy = 0.958

EPOCH 173 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 174 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 175 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 176 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 177 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 178 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 179 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 180 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 181 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 182 ...
Training Accuracy = 0.757
Validation Accuracy = 0.949

EPOCH 183 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 184 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 185 ...
Training Accuracy = 0.757
Validation Accuracy = 0.961

EPOCH 186 ...
Training Accuracy = 0.757
Validation Accuracy = 0.954

EPOCH 187 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 188 ...
Training Accuracy = 0.757
Validation Accuracy = 0.956

EPOCH 189 ...
Training Accuracy = 0.757
Validation Accuracy = 0.952

EPOCH 190 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 191 ...
Training Accuracy = 0.757
Validation Accuracy = 0.957

EPOCH 192 ...
Training Accuracy = 0.757
Validation Accuracy = 0.959

EPOCH 193 ...
Training Accuracy = 0.757
Validation Accuracy = 0.961

EPOCH 194 ...
Training Accuracy = 0.757
Validation Accuracy = 0.955

EPOCH 195 ...
Training Accuracy = 0.757
Validation Accuracy = 0.961

EPOCH 196 ...
Training Accuracy = 0.757
Validation Accuracy = 0.949

EPOCH 197 ...
Training Accuracy = 0.757
Validation Accuracy = 0.945

EPOCH 198 ...
Training Accuracy = 0.757
Validation Accuracy = 0.951

EPOCH 199 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

EPOCH 200 ...
Training Accuracy = 0.757
Validation Accuracy = 0.960

Model saved
</pre>
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<div class="prompt input_prompt">In&nbsp;[25]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Once a final model architecture is selected, </span>
<span class="c1"># the accuracy on the test set should be calculated and reported as well.</span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">))</span>
    <span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Accuracy = </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">test_accuracy</span><span class="p">))</span>
</pre></div>

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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.933
</pre>
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<hr>
<h2 id="Step-3:-Test-a-Model-on-New-Images">Step 3: Test a Model on New Images<a class="anchor-link" href="#Step-3:-Test-a-Model-on-New-Images">&#182;</a></h2><p>To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.</p>
<p>You may find <code>signnames.csv</code> useful as it contains mappings from the class id (integer) to the actual sign name.</p>

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<h3 id="Load-and-Output-the-Images">Load and Output the Images<a class="anchor-link" href="#Load-and-Output-the-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the images found from web and plot them here</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="n">web_images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">fig</span><span class="p">,</span><span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=.</span><span class="mi">2</span><span class="p">,</span><span class="n">wspace</span><span class="o">=.</span><span class="mi">002</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s1">&#39;examples/*.png&#39;</span><span class="p">)):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">web_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># y label for web images</span>
<span class="n">y_web</span> <span class="o">=</span> <span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">38</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">34</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># grayscale and normalize the web images first</span>
<span class="n">X_web</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">web_images</span><span class="p">)):</span>
    <span class="n">gray</span><span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">web_images</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">normalize_grayscale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">img_expanded</span> <span class="o">=</span> <span class="n">gray</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
    <span class="n">X_web</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_expanded</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># show images</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">001</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_web</span><span class="p">)):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_web</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
<span class="c1">#     plt.imshow(image,cmap=&#39;gray&#39;)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_web</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_web</span><span class="p">))</span>
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<pre>8 8
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<h3 id="Predict-the-Sign-Type-for-Each-Image">Predict the Sign Type for Each Image<a class="anchor-link" href="#Predict-the-Sign-Type-for-Each-Image">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Once a final model architecture is selected, </span>
<span class="c1"># the accuracy on the test set should be calculated and reported as well.</span>
<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">import_meta_graph</span><span class="p">(</span><span class="s1">&#39;./lenet.meta&#39;</span><span class="p">)</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="s2">&quot;./lenet&quot;</span><span class="p">)</span>
    <span class="n">web_image_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_web</span><span class="p">,</span> <span class="n">y_web</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Web Image Accuracy = </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">web_image_accuracy</span><span class="p">))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Web Image Accuracy = 0.750
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<h3 id="Output-Top-5-Softmax-Probabilities-For-Each-Image-Found-on-the-Web">Output Top 5 Softmax Probabilities For Each Image Found on the Web<a class="anchor-link" href="#Output-Top-5-Softmax-Probabilities-For-Each-Image-Found-on-the-Web">&#182;</a></h3>
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<p>For each of the new images, print out the model's softmax probabilities to show the <strong>certainty</strong> of the model's predictions (limit the output to the top 5 probabilities for each image). <a href="https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k"><code>tf.nn.top_k</code></a> could prove helpful here.</p>
<p>The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.</p>
<p><code>tf.nn.top_k</code> will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.</p>
<p>Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. <code>tf.nn.top_k</code> is used to choose the three classes with the highest probability:</p>

<pre><code># (5, 6) array
a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,
         0.12789202],
       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,
         0.15899337],
       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,
         0.23892179],
       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,
         0.16505091],
       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,
         0.09155967]])</code></pre>
<p>Running it through <code>sess.run(tf.nn.top_k(tf.constant(a), k=3))</code> produces:</p>

<pre><code>TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],
       [ 0.28086119,  0.27569815,  0.18063401],
       [ 0.26076848,  0.23892179,  0.23664738],
       [ 0.29198961,  0.26234032,  0.16505091],
       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],
       [0, 1, 4],
       [0, 5, 1],
       [1, 3, 5],
       [1, 4, 3]], dtype=int32))</code></pre>
<p>Looking just at the first row we get <code>[ 0.34763842,  0.24879643,  0.12789202]</code>, you can confirm these are the 3 largest probabilities in <code>a</code>. You'll also notice <code>[3, 0, 5]</code> are the corresponding indices.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Print out the top five softmax probabilities for the predictions </span>
<span class="c1"># on the German traffic sign images found on the web. </span>

<span class="n">softmax_logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
<span class="n">top_k</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">softmax_logits</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">))</span>
    <span class="n">my_softmax_logits</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">softmax_logits</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_web</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
    <span class="n">values</span><span class="p">,</span><span class="n">indices</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">top_k</span><span class="p">,</span><span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">X_web</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">indices</span><span class="p">)</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
[[  1.00000000e+00   1.34324388e-12   6.91444218e-16   4.15413867e-18
    2.07773729e-26]
 [  1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00]
 [  9.99971390e-01   2.85842380e-05   1.20358412e-09   2.07426472e-11
    1.62081145e-12]
 [  1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00
    0.00000000e+00]
 [  1.00000000e+00   1.30338902e-32   0.00000000e+00   0.00000000e+00
    0.00000000e+00]
 [  6.39133573e-01   3.60866398e-01   7.63609947e-32   5.15263255e-32
    4.48792206e-38]
 [  1.00000000e+00   1.55883969e-25   5.20872653e-26   2.49145769e-31
    6.21531015e-36]
 [  6.17030442e-01   3.35868508e-01   2.55320650e-02   2.01739427e-02
    1.39464159e-03]] [[11 30 27 21 40]
 [12  0  1  2  3]
 [ 3  1  2 28  5]
 [38  0  1  2  3]
 [18 27  0  1  2]
 [ 2  1  5  3 38]
 [34  3 38 35 36]
 [29 39 22 25 24]]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-capture the top_k and Top 5 Softmax Probabilities For Each Image Found on the Web</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">values</span> <span class="o">=</span> <span class="p">[[</span>  <span class="mf">1.00000000e+00</span><span class="p">,</span>   <span class="mf">1.34324388e-12</span><span class="p">,</span>   <span class="mf">6.91444218e-16</span><span class="p">,</span>   <span class="mf">4.15413867e-18</span><span class="p">,</span>  <span class="mf">2.07773729e-26</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">1.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>  <span class="mf">0.00000000e+00</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">9.99971390e-01</span><span class="p">,</span>   <span class="mf">2.85842380e-05</span><span class="p">,</span>   <span class="mf">1.20358412e-09</span><span class="p">,</span>   <span class="mf">2.07426472e-11</span><span class="p">,</span>  <span class="mf">1.62081145e-12</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">1.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>  <span class="mf">0.00000000e+00</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">1.00000000e+00</span><span class="p">,</span>   <span class="mf">1.30338902e-32</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>   <span class="mf">0.00000000e+00</span><span class="p">,</span>  <span class="mf">0.00000000e+00</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">6.39133573e-01</span><span class="p">,</span>   <span class="mf">3.60866398e-01</span><span class="p">,</span>   <span class="mf">7.63609947e-32</span><span class="p">,</span>   <span class="mf">5.15263255e-32</span><span class="p">,</span>  <span class="mf">4.48792206e-38</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">1.00000000e+00</span><span class="p">,</span>   <span class="mf">1.55883969e-25</span><span class="p">,</span>   <span class="mf">5.20872653e-26</span><span class="p">,</span>   <span class="mf">2.49145769e-31</span><span class="p">,</span>  <span class="mf">6.21531015e-36</span><span class="p">],</span>
     <span class="p">[</span>  <span class="mf">6.17030442e-01</span><span class="p">,</span>   <span class="mf">3.35868508e-01</span><span class="p">,</span>   <span class="mf">2.55320650e-02</span><span class="p">,</span>   <span class="mf">2.01739427e-02</span><span class="p">,</span>  <span class="mf">1.39464159e-03</span><span class="p">]]</span>
    
<span class="n">indices</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">21</span><span class="p">,</span> <span class="mi">40</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">12</span><span class="p">,</span>  <span class="mi">0</span><span class="p">,</span>  <span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">,</span>  <span class="mi">3</span><span class="p">],</span>
     <span class="p">[</span> <span class="mi">3</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">28</span><span class="p">,</span>  <span class="mi">5</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">38</span><span class="p">,</span>  <span class="mi">0</span><span class="p">,</span>  <span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">,</span>  <span class="mi">3</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span>  <span class="mi">0</span><span class="p">,</span>  <span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">],</span>
     <span class="p">[</span> <span class="mi">2</span><span class="p">,</span>  <span class="mi">1</span><span class="p">,</span>  <span class="mi">5</span><span class="p">,</span>  <span class="mi">3</span><span class="p">,</span> <span class="mi">38</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">34</span><span class="p">,</span>  <span class="mi">3</span><span class="p">,</span> <span class="mi">38</span><span class="p">,</span> <span class="mi">35</span><span class="p">,</span> <span class="mi">36</span><span class="p">],</span>
     <span class="p">[</span><span class="mi">29</span><span class="p">,</span> <span class="mi">39</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">24</span><span class="p">]]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># The same grayscale and normalization function with all the other datasets</span>
<span class="c1"># Implement Min-Max scaling for grayscale image data</span>
<span class="k">def</span> <span class="nf">normalize_grayscale</span><span class="p">(</span><span class="n">image_data</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Normalize the image data with Min-Max scaling to a range of [0.0, 1.0]</span>
<span class="sd">    :param image_data: The image data to be normalized</span>
<span class="sd">    :return: Normalized image data</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">a</span> <span class="o">=</span> <span class="mf">0.0</span>
    <span class="n">b</span> <span class="o">=</span> <span class="mf">1.0</span>
    <span class="n">grayscale_min</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">grayscale_max</span> <span class="o">=</span> <span class="mi">255</span>
    <span class="k">return</span> <span class="n">a</span> <span class="o">+</span> <span class="p">(</span> <span class="p">(</span> <span class="p">(</span><span class="n">image_data</span> <span class="o">-</span> <span class="n">grayscale_min</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</span> <span class="p">)</span><span class="o">/</span><span class="p">(</span> <span class="n">grayscale_max</span> <span class="o">-</span> <span class="n">grayscale_min</span> <span class="p">)</span> <span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load the images found from web and plot them here</span>
<span class="kn">import</span> <span class="nn">glob</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="n">web_images</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">fig</span><span class="p">,</span><span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=.</span><span class="mi">2</span><span class="p">,</span><span class="n">wspace</span><span class="o">=.</span><span class="mi">002</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s1">&#39;examples/*.png&#39;</span><span class="p">)):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">web_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># y label for web images</span>
<span class="n">y_web</span> <span class="o">=</span> <span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">38</span><span class="p">,</span> <span class="mi">18</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">34</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># grayscale and normalize the web images first</span>
<span class="n">X_web</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">web_images</span><span class="p">)):</span>
    <span class="n">gray</span><span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">web_images</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_RGB2GRAY</span><span class="p">)</span>
    <span class="n">gray</span> <span class="o">=</span> <span class="n">normalize_grayscale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span>
    <span class="n">img_expanded</span> <span class="o">=</span> <span class="n">gray</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
    <span class="n">X_web</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img_expanded</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># show images</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axs</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="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">001</span><span class="p">)</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X_web</span><span class="p">)):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">X_web</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">web_images</span><span class="p">),</span><span class="mi">3</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">20</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">005</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.4</span> <span class="c1"># width of the bars</span>
<span class="n">x</span> <span class="o">=</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="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">8</span><span class="p">):</span>
    <span class="n">val</span> <span class="o">=</span> <span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">top_guesses</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">web_images</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;input is </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_web</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> 
    
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_web</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">),</span><span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;gray&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;normalized is </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">y_web</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> 

    <span class="n">rects</span> <span class="o">=</span> <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">1.4</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Top 5 Guesses&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,(</span><span class="n">width</span><span class="o">/</span><span class="mi">2</span><span class="p">)))</span> <span class="c1"># set the position of the x ticks</span>
    <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">((</span><span class="s1">&#39;1st&#39;</span><span class="p">,</span> <span class="s1">&#39;2nd&#39;</span><span class="p">,</span> <span class="s1">&#39;3rd&#39;</span><span class="p">,</span> <span class="s1">&#39;4th&#39;</span><span class="p">,</span> <span class="s1">&#39;5th&#39;</span><span class="p">))</span>
    <span class="c1"># attach top 5 possibility labels on the top of each bar</span>
    <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">rect</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rects</span><span class="p">):</span>
        <span class="n">height</span> <span class="o">=</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_height</span><span class="p">()</span>
        <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">rect</span><span class="o">.</span><span class="n">get_x</span><span class="p">()</span> <span class="o">+</span> <span class="n">rect</span><span class="o">.</span><span class="n">get_width</span><span class="p">()</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.2</span><span class="o">*</span><span class="n">height</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">top_guesses</span><span class="p">[</span><span class="n">j</span><span class="p">]),</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#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="Analyze-Performance">Analyze Performance<a class="anchor-link" href="#Analyze-Performance">&#182;</a></h3>
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<p>Two incorrect predictions over 6 correct predictions to the 8 new images found on the internet, so the model shows 75% accuracy on these new images predictions. The two wrongly predicted images are:</p>
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<li>"Speed limit of 30km/h" was wrongly predicted as "Speed limit 50km/h", the model was close to predict correctly as its second highest probability is correct as itself.</li>
<li>"Roadwork" was wrongly predicted as "Bicyble crossing" as first guess, "Keep left" as second guess. The correct label was in its 4th guess.</li>
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<p>The images among the correct label and wrongly predicted labels contain pariticial simalities. Such as, the model could not see the difference between 30 and 50 to some images; pedestrian are shown on both roadwork sign and bicycles corssing signs, which confused the model.</p>
<p>Further work can be focusing on improving the model on these relatively difficult classes.</p>

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