cropland-mapping.html

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<title>cropland-mapping-random-forest-neural-network</title>

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

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

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

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

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

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
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    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
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            processEscapes: true,
            processEnvironments: true
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        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
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    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Cropland-Mapping">Cropland Mapping<a class="anchor-link" href="#Cropland-Mapping">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="1.-Introduction">1. Introduction<a class="anchor-link" href="#1.-Introduction">&#182;</a></h2>
</div>
</div>
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<h3 id="1.1.-Cropland-Mapping">1.1. Cropland Mapping<a class="anchor-link" href="#1.1.-Cropland-Mapping">&#182;</a></h3>
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<p style="text-align: justify">In a global scenario still affected by severe income distribution imbalances and chronic famine in several communities, the accurate classification of cropland pieces is of utmost strategic and economic importance. As described in [1] reference below, crop maps play "<em>an important role in various applications such as crop inventories, crop insurance, yield estimation and the enforcement of quota limits</em>".</p>
<p style="text-align: justify">Remote sensing and geographic information systems (GIS) have been reliable tools in remote crop mapping. Remote sensors like radiometers and radars positioned in unmanned aerial vehicles and satellites enable a continuous, high quality, relatively reduced cost monitoring of the Earth surface use by agriculture, addressing specific issues inherent to the problem, like for example the dynamic changes of crop characteristics and growing stages over time. As the evaluation of such technologies is not a purpose of this work, readers are encouraged to consult the brilliant references cited for a high quality education on the subject.</p>
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<h3 id="1.2.-Combining-and-processing-optical-and-radar-based-data-for-cropland-classification">1.2. Combining and processing optical and radar-based data for cropland classification<a class="anchor-link" href="#1.2.-Combining-and-processing-optical-and-radar-based-data-for-cropland-classification">&#182;</a></h3>
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<p style="text-align: justify">Among remote sensing solutions, <b>optical</b> and <b>radar-based</b> sensing techniques demand special consideration when it comes to cropland classification. Furthermore, a combination of both has consistently led to high accuracy identification of crops of varied nature and in different development stages.</p>
<p style="text-align: justify">The dataset serving as the basis for this machine learning study, described in detail in Section 3, contains a large set of numerical features derived from remote sensing information assembled by two primary sources:</p>
<ul>
    <li style="text-align: justify">Optical information (actual images) was collected by RapidEye satellites;</li>
    <li style="text-align: justify">Radar-based information was collected by an Unihnabitated Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system.</li>
</ul>
<p style="text-align: justify">As the amount and complexity of data gathered from such different sources over several time spans is inherently huge, <b>machine learning</b> plays a central role when it comes to the final classification purpose. The literature is vast and describes inumerous approaches and techniques to manipulate such information. The researchers cited in foreword have themselves assessed several solutions, stacked random forests in particular, with overall accuracies ranging from 90 to 96% for different stacking strategies. It has been noted though that some of the references are outdated, which suggests that the use of state-of-the-art classification engines - random forest and neural network classifiers - may lead to further metric improvements.</p>
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<p><img src="https://i.imgur.com/4oTGmTA.png" width="500" height="500"></p>

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<h2 id="2.-Objectives-of-this-machine-learning-exercise">2. Objectives of this machine learning exercise<a class="anchor-link" href="#2.-Objectives-of-this-machine-learning-exercise">&#182;</a></h2>
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<p style="text-align: justify">Two major objectives are proposed for this study:</p>
<ol>
    <li style="text-align: justify">Assess the performance of scikit-learn's Random Forest Classifier;</li>
    <li style="text-align: justify">Investigate potential performance improvements that may be offered by an Artificial Neural Network (ANN).</li>
</ol>
<p style="text-align: justify">A third objective would enhance even more this exercise: the processing of actual RapidEye satellite images with computer vision learning engines - a convolutional neural network (CNN), for example - and the corresponding performance assessment. Future revisions of this work may incorporate such analysis, if and when such images are made available by the respective sources.</p>
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<h2 id="3.-The-dataset">3. The dataset<a class="anchor-link" href="#3.-The-dataset">&#182;</a></h2>
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<p><p style="text-align: justify">The dataset chosen for this machine learning exercise contains fused, bi-temporal optical-radar data for cropland classification, in tabular form, derived from images collected by RapidEye satellites (optical) and polarimetric radar information collected by Unmanned Aerial Vehicle Synthetic Aperture Radars (UAVSAR) over an agricultural region near <b>Winnipeg, Canada</b> on <b>July 5th and July 14th, 2012</b>. In this area, seven different crop cultures existed at the time of collection:</p></p>
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    <li style="text-align: justify">Corn</li>
    <li style="text-align: justify">Pea</li>
    <li style="text-align: justify">Canola</li>
    <li style="text-align: justify">Soy</li>
    <li style="text-align: justify">Oat</li>
    <li style="text-align: justify">Wheat</li>
    <li style="text-align: justify">Broadleaf</li>
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<p><img src="https://i.imgur.com/KAaR1oa.png" width="800" height="800"></p>

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<p style="text-align: justify">The dataset has 325,834 observations, each containing 175 attributes structured the following way:</p>
<p><b>Dependent variable</b>:</p>
<ul>
    <li style="text-align: justify">Crop class, as described above ('label', integer value ranging from 1 to 7);</li>
</ul>
<p><b>Predictive features</b>:</p>
<ul>
    <li style="text-align: justify">Polarimetric radar features (49) collected on July 5th, 2012 ('f1' to 'f49', real values);</li>
    <li style="text-align: justify">Polarimetric radar features (49) collected on July 14th, 2012 ('f50' to 'f98', real values);</li>
    <li style="text-align: justify">Optical features (38) collected on July 5th, 2012 ('f99' to 'f136', real values);</li>
    <li style="text-align: justify">Optical features (38) collected on July 14th, 2012 ('f137' to 'f174', real values).</li>
</ul>
<p style="text-align: justify">There are no missing values.</p>
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<h2 id="4.-Initial-setup">4. Initial setup<a class="anchor-link" href="#4.-Initial-setup">&#182;</a></h2>
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<h3 id="4.1.-Importing-required-libraries">4.1. Importing required libraries<a class="anchor-link" href="#4.1.-Importing-required-libraries">&#182;</a></h3>
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<p style="text-align: justify">Along with traditional libraries imported for tensor manipulation, mathematical operations and graphics development, specific scikit-learn modules (<a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> for dataset scaling, <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html">accuracy_score</a>, <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html">precision_score</a>, <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html">recall_score</a> and <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html">precision_recall_fscore_support</a> as performance metric estimators) were selected. The random forest is implemented with and instantiation of scikit-learn's <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html">RandomForestClassifier</a> class. The artificial neural network (ANN) is implemented instantiating a Keras <a href="https://keras.io/api/models/sequential/">Sequential</a> model including <a href="https://keras.io/api/layers/core_layers/dense/">Dense</a> and <a href="https://keras.io/api/layers/regularization_layers/dropout/">Dropout</a> layers. <a href="https://keras.io/api/optimizers/adam/">Adam</a> has been elected as the optimization function of choice for this multiclass classification exercise.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">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">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>

<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">precision_recall_fscore_support</span>

<span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="kn">import</span> <span class="n">Adam</span>

<span class="kn">import</span> <span class="nn">warnings</span>
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<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
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<h3 id="4.2.-Customized-functions">4.2. Customized functions<a class="anchor-link" href="#4.2.-Customized-functions">&#182;</a></h3>
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<p style="text-align: justify">Custom functions were developed to assist with graphical analysis of specific dataset elements (features or observations) and mapping correlation. Please refer to the respective docstrings for further insight. All function variable names, by coding principle, start with the "f_" string, allowing for containerized processing within the function execution environment, not affecting or interacting with global variables.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">assessment</span><span class="p">(</span><span class="n">f_data</span><span class="p">,</span> <span class="n">f_y_feature</span><span class="p">,</span> <span class="n">f_x_feature</span><span class="p">,</span> <span class="n">f_index</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Develops and displays a histogram and a scatter plot for a dependent / independent variable pair from</span>
<span class="sd">    a dataframe and, optionally, highlights a specific observation on the plot in a different color (red).</span>
<span class="sd">    </span>
<span class="sd">    Also optionally, if an independent feature is not informed, the scatterplot is not displayed.</span>
<span class="sd">    </span>
<span class="sd">    Keyword arguments:</span>
<span class="sd">    </span>
<span class="sd">    f_data      Tensor containing the dependent / independent variable pair.</span>
<span class="sd">                Pandas dataframe</span>
<span class="sd">    f_y_feature Dependent variable designation.</span>
<span class="sd">                String</span>
<span class="sd">    f_x_feature Independent variable designation.</span>
<span class="sd">                String</span>
<span class="sd">    f_index     If greater or equal to zero, the observation denoted by f_index will be plotted in red.</span>
<span class="sd">                Integer</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">for</span> <span class="n">f_row</span> <span class="ow">in</span> <span class="n">f_data</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">f_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">f_color</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">f_data</span><span class="p">[</span><span class="n">f_row</span><span class="p">]</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">f_index</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
            <span class="n">f_hue</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">f_color</span> <span class="o">=</span> <span class="s1">&#39;b&#39;</span>
            <span class="n">f_hue</span> <span class="o">=</span> <span class="kc">None</span>
    
    <span class="n">f_fig</span><span class="p">,</span> <span class="n">f_a</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">16</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
    
    <span class="n">f_chart1</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">f_data</span><span class="p">[</span><span class="n">f_x_feature</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">f_a</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">kde</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;orangered&#39;</span><span class="p">)</span>
    <span class="n">f_chart1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">f_x_feature</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">f_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">f_chart2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">f_data</span><span class="p">[</span><span class="n">f_x_feature</span><span class="p">],</span> <span class="n">f_data</span><span class="p">[</span><span class="n">f_y_feature</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">f_color</span><span class="p">,</span> <span class="n">edgecolors</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
        <span class="n">f_chart2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">f_x_feature</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
        <span class="n">f_chart2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">f_y_feature</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">f_chart2</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">f_x_feature</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">f_y_feature</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">f_data</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="n">f_hue</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">f_chart2</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">f_x_feature</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
        <span class="n">f_chart2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">f_y_feature</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</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="k">def</span> <span class="nf">correlation_map</span><span class="p">(</span><span class="n">f_data</span><span class="p">,</span> <span class="n">f_feature</span><span class="p">,</span> <span class="n">f_number</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Develops and displays a heatmap plot referenced to a primary feature of a dataframe, highlighting</span>
<span class="sd">    the correlation among the &#39;n&#39; mostly correlated features of the dataframe.</span>
<span class="sd">    </span>
<span class="sd">    Keyword arguments:</span>
<span class="sd">    </span>
<span class="sd">    f_data      Tensor containing all relevant features, including the primary.</span>
<span class="sd">                Pandas dataframe</span>
<span class="sd">    f_feature   The primary feature.</span>
<span class="sd">                String</span>
<span class="sd">    f_number    The number of features most correlated to the primary feature.</span>
<span class="sd">                Integer</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">f_most_correlated</span> <span class="o">=</span> <span class="n">f_data</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="n">f_number</span><span class="p">,</span><span class="n">f_feature</span><span class="p">)[</span><span class="n">f_feature</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
    <span class="n">f_correlation</span> <span class="o">=</span> <span class="n">f_data</span><span class="p">[</span><span class="n">f_most_correlated</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
    
    <span class="n">f_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">f_correlation</span><span class="p">)</span>
    <span class="n">f_mask</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">triu_indices_from</span><span class="p">(</span><span class="n">f_mask</span><span class="p">)]</span> <span class="o">=</span> <span class="kc">True</span>
    
    <span class="k">with</span> <span class="n">sns</span><span class="o">.</span><span class="n">axes_style</span><span class="p">(</span><span class="s2">&quot;white&quot;</span><span class="p">):</span>
        <span class="n">f_fig</span><span class="p">,</span> <span class="n">f_ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
        <span class="n">f_map</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">f_correlation</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">f_mask</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                            <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;RdYlBu&quot;</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="4.3.-Importing-the-dataset">4.3. Importing the dataset<a class="anchor-link" href="#4.3.-Importing-the-dataset">&#182;</a></h3>
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<p style="text-align: justify">The dataset containing 325,834 observations is imported and shuffled.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Importing the dataset</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;../input/cropland-mapping/WinnipegDataset.txt&#39;</span><span class="p">)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">frac</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<h2 id="5.-Exploratory-data-analysis-and-preprocessing">5. Exploratory data analysis and preprocessing<a class="anchor-link" href="#5.-Exploratory-data-analysis-and-preprocessing">&#182;</a></h2>
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<p style="text-align: justify">A glimpse at the dataset structure confirms observation shuffling.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th>f4</th>
      <th>f5</th>
      <th>f6</th>
      <th>f7</th>
      <th>f8</th>
      <th>f9</th>
      <th>...</th>
      <th>f165</th>
      <th>f166</th>
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      <th>f170</th>
      <th>f171</th>
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      <th>f174</th>
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      <th>21597</th>
      <td>1</td>
      <td>-11.760</td>
      <td>-19.615</td>
      <td>-8.8321</td>
      <td>-12.217</td>
      <td>-10.186</td>
      <td>-12.786</td>
      <td>-2.92800</td>
      <td>-7.8547</td>
      <td>-10.7830</td>
      <td>...</td>
      <td>0.50617</td>
      <td>-0.18898</td>
      <td>0.0000</td>
      <td>0.00000</td>
      <td>1.00000</td>
      <td>0.00000</td>
      <td>0.00000</td>
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      <td>1.00000</td>
      <td>1.00000</td>
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      <th>226300</th>
      <td>5</td>
      <td>-15.544</td>
      <td>-24.281</td>
      <td>-16.0640</td>
      <td>-19.786</td>
      <td>-14.564</td>
      <td>-19.618</td>
      <td>0.52015</td>
      <td>-8.7377</td>
      <td>-8.2176</td>
      <td>...</td>
      <td>0.48148</td>
      <td>0.00000</td>
      <td>1.7778</td>
      <td>0.17284</td>
      <td>0.83333</td>
      <td>0.33333</td>
      <td>0.33333</td>
      <td>0.84869</td>
      <td>0.50617</td>
      <td>-0.18898</td>
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      <td>-18.634</td>
      <td>-13.6870</td>
      <td>-14.516</td>
      <td>-11.739</td>
      <td>-15.307</td>
      <td>2.45720</td>
      <td>-7.4046</td>
      <td>-4.9474</td>
      <td>...</td>
      <td>1.00000</td>
      <td>1.00000</td>
      <td>3.0000</td>
      <td>0.00000</td>
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      <td>-17.454</td>
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      <td>-17.003</td>
      <td>-23.096</td>
      <td>2.60370</td>
      <td>-11.2710</td>
      <td>-8.6675</td>
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      <td>0.40741</td>
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      <td>0.66667</td>
      <td>0.66667</td>
      <td>1.06090</td>
      <td>0.35802</td>
      <td>-0.47809</td>
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      <th>61544</th>
      <td>3</td>
      <td>-11.304</td>
      <td>-19.543</td>
      <td>-14.0880</td>
      <td>-15.490</td>
      <td>-11.646</td>
      <td>-15.610</td>
      <td>2.78370</td>
      <td>-8.2384</td>
      <td>-5.4547</td>
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      <td>0.22222</td>
      <td>0.22222</td>
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<p>5 rows × 175 columns</p>
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<p style="text-align: justify">In addition, the analysis of unique labels shows a somehow balanced distribution (class share percentages mapped and charted below), with the exception of '<b>broadleaf</b>' and '<b>pea</b>'. Watchful eyes will be on these two classes along the exercise.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">class_share</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">dataset</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Class share</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">7</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Class </span><span class="si">{</span><span class="n">class_share</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">class_share</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
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<pre>Class share

Class 1: 12.02 %
Class 2: 1.10 %
Class 3: 23.22 %
Class 4: 22.73 %
Class 5: 14.46 %
Class 6: 26.11 %
Class 7: 0.35 %
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">class_share</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">class_share</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="s1">&#39;Greens_d&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Cropland Class Share&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Cropland Class&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Share (%)&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
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<pre>Text(0, 0.5, &#39;Share (%)&#39;)</pre>
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<h3 id="5.1.-Correlation">5.1. Correlation<a class="anchor-link" href="#5.1.-Correlation">&#182;</a></h3>
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<p style="text-align: justify">The correlation between each feature and the dependent variable, as well as the correlation among features (intercorrelation), provide elements to tackle potential undesired colinearity.</p>
<p style="text-align: justify">The code below assists in identifying and sorting high feature intercorrelation cases based on the corresponding correlation coefficients (in descending order). A threshold of 0.95 has been arbitrarily defined for filtering highly intercorrelated features.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">highly_correlated_feature_pairs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">highly_correlated_features_to_drop</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">correlation_matrix</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span><span class="o">.</span><span class="n">abs</span><span class="p">()</span>
<span class="n">upper_matrix</span> <span class="o">=</span> <span class="n">correlation_matrix</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">triu</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">correlation_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">bool</span><span class="p">))</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">upper_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
    <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">upper_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
        <span class="k">if</span> <span class="n">upper_matrix</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">column</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.95</span><span class="p">:</span>
            <span class="n">highly_correlated_feature_pairs</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">row</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="n">upper_matrix</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">column</span><span class="p">]])</span>
            
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Number of highly intercorrelated feature pairs: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">highly_correlated_feature_pairs</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre>Number of highly intercorrelated feature pairs: 117
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<p style="text-align: justify">As the articles cited in foreword anticipate in deep detail, there is indeed a set of features strongly intercorrelated.</p>
<p style="text-align: justify">For information only, the 10 most intercorrelated feature pairs are identified below along with their corresponding correlation coefficients. Readers must note that <b>six pairs of features</b> (!) are perfectly correlated (coefficient equals 1). This problem must always be addressed in data preprocessing.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">highly_correlated_feature_pairs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">highly_correlated_feature_pairs</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">highly_correlated_feature_pairs</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;feature_1&#39;</span><span class="p">,</span> <span class="s1">&#39;feature_2&#39;</span><span class="p">,</span> <span class="s1">&#39;correl&#39;</span><span class="p">]</span>
<span class="n">highly_correlated_feature_pairs</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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      <th></th>
      <th>feature_1</th>
      <th>feature_2</th>
      <th>correl</th>
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      <th>4</th>
      <td>2</td>
      <td>39</td>
      <td>1.000000</td>
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      <th>38</th>
      <td>51</td>
      <td>88</td>
      <td>1.000000</td>
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    <tr>
      <th>109</th>
      <td>151</td>
      <td>153</td>
      <td>1.000000</td>
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    <tr>
      <th>45</th>
      <td>54</td>
      <td>86</td>
      <td>1.000000</td>
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      <th>8</th>
      <td>5</td>
      <td>37</td>
      <td>1.000000</td>
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      <th>102</th>
      <td>142</td>
      <td>147</td>
      <td>1.000000</td>
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      <th>113</th>
      <td>156</td>
      <td>157</td>
      <td>0.998716</td>
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      <th>13</th>
      <td>7</td>
      <td>15</td>
      <td>0.995524</td>
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      <th>10</th>
      <td>6</td>
      <td>38</td>
      <td>0.995455</td>
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      <th>60</th>
      <td>62</td>
      <td>79</td>
      <td>0.995254</td>
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<p style="text-align: justify">Features to be dropped are pinned on a correlation map and removed from the dataset.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">highly_correlated_features_to_drop</span> <span class="o">=</span> <span class="p">[</span><span class="n">column</span> <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">upper_matrix</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">upper_matrix</span><span class="p">[</span><span class="n">column</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.95</span><span class="p">)]</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="n">highly_correlated_features_to_drop</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nr_features</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Optimized number of features: </span><span class="si">{</span><span class="n">nr_features</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre>Optimized number of features: 102
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<p style="text-align: justify">After dropping, the number of features was substantially reduced from 174 down to 102 (a 41.4 % decrease). Such dimensionality optimization not only assists in mitigating colinearity but also allows for the creation of streamlined machines and faster learning with lower processing and memory requirements.</p>
<p style="text-align: justify">A final correlation map including the dependent variable ('label') and the remaining feature set is charted below.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">correlation_map</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">nr_features</span><span class="p">)</span>
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<h3 id="5.2.-Feature-assessment">5.2. Feature assessment<a class="anchor-link" href="#5.2.-Feature-assessment">&#182;</a></h3>
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<p style="text-align: justify">Invoking the 'assessment' custom function allows for plotting feature distribution patterns and their relationship with the 'label' dependent variable. This is done for the 10 features most correlated with the dependent variable 'label', just to provide a first glimpse of data behavior:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">most_correlated_features</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span><span class="o">.</span><span class="n">nlargest</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="s1">&#39;label&#39;</span><span class="p">)[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>

<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">most_correlated_features</span><span class="p">:</span>
    <span class="n">assessment</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
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PzM7dD48pe/jGuvvRbnnHPOYZc9kp/Nfaclu3MydKcleyfzOu7pxkp4pyV7m+D805LdA00jpyV7JxH3nZbsPpbSfeFs4KclR08mRuj0Zf/0Ym3ccTHuQc5uf0XotGRbu/047NOS3djyTku23ZNk+05L7jt52AAw2vSdluwdch2cluxdj56W7LVT6LRkb0yipyUb77Rk9/Rl5Z1o3dPrhE5LBmLeHPjxJWISWUdDOwjeApx/WrJBItbXjpJikE9Ldueu/NOS/TqOxmnJ7um9ybiCk3GG+LTkvvEqfFqyu9HKOy3ZuGvbfZHbDOtpybnfp8o7Ldm4px2XdVqym953WnLfeqy005L5mVsiIiI6ZmzYsGFY2nUc7W4iASD0F4jK/WNEfnkLAByTV85/wqa9BOlVroG+chrI2k5fHKHneI5fRgN2Th7HdoL6guuhtnozoTbsvjYRatcJpWe8+v1O+O2MCT1pjJSHu5EK15e1nb7HTl96OO6Mlyccm2P3petQfX5M4T74/P768drhvACMA/hvkA7aKRKbX5+T0UF+u0dH2rZtx30SntFBmXCbY+ui4+T3QQPwX0/yr4fbgQPYObH4/fLnQ8NdB9KLJWMjsu6yoYXn5zO2u9l18ziRPI6jgcjaRZBeaD3769yXu9aQk69Q+XB/R9WMCMYqmwnNWyjO/u6tYnmc0L3mj1c2lB9OKM3pu9cjcaBvLLJO/lgfblzRMg60U7p8Of0slAdeHuMtOH+8w/OUN48OkMlZC3Y5QR2BcvpzLJLDHQARERERERHRkeLmloiIiIiIiCoeN7dERERERERU8bi5JSIiIiIioorHzS0RERERERFVPJ6WTMcVS2hUd+4qmq6Tteix+Gc/iIiIiIgqDTe3dFwRvV3QL/2haLpMfQWo4eaWiIiIiKjS8G3JREREREREVPG4uSUiIiIiIqKKx80tERERERERVTxubomIiIiIiKji8UApqjhJuxOyp6NouqWzyBzFeIiIiIiIaPhxc0sVR/Z0QLc9VjRdTDv7KEZDRERERETHAr4tmYiIiIiIiCoeX7klCrGERnXnroJpOlmLHot/A5eIiIiI6FjEzS1RiOjtgn7pDwXTZOorQA03t0RERERExyK+LZmIiIiIiIgqHje3REREREREVPG4uSUiIiIiIqKKx80tERERERERVTxubomIiIiIiKji9XtacjqdxtVXX423334b8XgcEydOxA033ID6+nrMnj0b8XgciUQCALB48WLMmjULALBjxw60tLTgwIEDGD16NFpbWzFp0qQjSiMiIiIiIiIqpN9XboUQWLhwITZs2IB169ZhwoQJuPXWW4P022+/HWvXrsXatWuDjS0ALFu2DPPnz8eGDRswf/58XHfddUecRjSc/L+BW+j/pN053OERERERER3X+t3cjh49GjNnzgweT5s2De+//37JMvv27cPWrVsxd+5cAMDcuXOxdetW7N+/f8BpRMNN9HZBtz1W8H/Z0zHc4RERERERHdf6fVtymNYa9913H2bPnh1cW7x4MYwxmD59Oq666iqMHDkSu3btwrhx46CUAgAopdDQ0IBdu3bBGDOgtPr6+sHqMxERER2m3t5e3HTTTXj++eeRSCQwbdo03HjjjcMdFhERUeCwNrc33ngjqqurcemllwIA7r33XowfPx6ZTAbLly/HDTfcEHnL8nAZM6ZmQOXGjq0d5Eg+uoZ1rPR+oCZZPD1mIV4svVTakZStTmBEgTHhmiofx6o8HKfycawG109+8hMkEgls2LABQgjs3bt3uEMaUrG4goaA7RgoJeBoA8fRUFIiFpPQxsCSAr1ZHVyPxyUy3uN4TEFJ4T7WGpaSUFKg19aQQiAWk3BsDSkFAMB2NLQGLEtACreckm6+rK3heHHELAnb1rAdAykFLOWW//BQD1TCgm0bONrNq7x02zFeGwZaG1hKIB6X6M1oAAYwwu2PEhBCIOvFpZSAYxv3fX4G0Nq4dVoSmazjxqsE4jEZGYdEQiKT0VBe247jlovHJbLZaOzaGEjhj68bt5Ru7MYYGK9dR7v54P7npgPBOCklIISb1wCQQgT9jVluvzJZjX0fdgOWgu24cxKLueNgjBujkEA265cBHK+Pbj/65jHjzSMEYEk3fr9fiZhExtbBeEkpkMk6UEoiZkkYYwADZB0D4U4fHG0QtyQEAA1AO26fkwnlzb8OymdtB5Z063GMNzcm3FZ0TPw5BQwAEcSlLHc+/LVsAEgpYLy+7N7fBRW3ABgoKYPrjtaIWW4svRl3fffF5vZbCrd9v03H64/lrWEdGgPt3R/xmIQxQNbWMKF1GfTL1khY0hvrvjZ7M457HwlACsBowPZiiMUkoI3Xjulbrxkd3CeWdCdaKgEHIrgX3XsIEN7cA4BSEjbgLkIjoLVGuqMHSskgz1DzY3D8+xHIi69Q2mC2M9C62tNdgJJHVFd/yt7ctra2YufOnbjzzjshpftu5vHjxwMA4vE45s+fj8svvzy4vnv3bjiOA6UUHMdBe3s7xo8fD2PMgNIOx759ndDaHFaZsWNrsWcP31pajuEeq+quXujOnqLpiayN3iLppdKOpKzs6kVXzpgM9zhVEo5VeThO5RussZJSDPgXph8lhw4dwkMPPYTf//73EN6z8RNOOGGYoxo6sbhCR4+NdEcvXtz6AWY1nYSbV29Ce7obDXVVaFmQwgmjE9hzoAcrVrWhPd2Ni75wMs45YwJWrGpDXW0S/9cln0U2qyPlFs1rwt2PvIp0Rw9aFszAqzv24bOnjMWHnRmsXLMlyLf40un4z7WvYHRtHJd86VNBHTOnjsO8804N2vTrHFkThxIC6Y7eSD1XzZ+O+lEJdPfa6OzKRtKWNKfwzB/fwfTGE3H7/VvyYvTbvv+J13HBrMmRPC0LZuD+J17HC6/sLhjTkuYU3m/vwPixtbh5dd/4fOGMCbgpJ/baEXF0ddv46W82R/o/qiaOzu4sHMfg1nv60q68pAnrnt2Oi2dPgVIC//FfLyPd0YNrFpwJJSXu2/Aa/v7cU9CbcSL9vfpbZ+Kx59/C3505Ibg+c+q4yPjmztGS5hS2vrkXjZ8YUzTP1ZedCa2jMYbHp1CdI2ti+GBvFx5+ZnswtnW1SXz3G58GAPT0urF/5pMn4PzPfyLS9pIFKcRjAj1ZB//vE3/Nm5twW4vmNWF0bQLGAL9+dGskb6G+/+9/mI54TOWtr2RCoaYqhvTBTDBP4fWeu6Ye/P2bQdkTRicj5RrqqnD9d86CrQ06DkXX/dLmFOIxidWPbM3r11Xzp+PJtp2R+fPH+tHnduDPb+wtOBfXLZyJzq7o+lranMJ9j78WzM9V86ejfnQCdo9T8B4aOSKGETH3XaXprizue/y1vPiWNqdQVx0b8g2uUhLprmzkPvLb9uMrlHa4cZVqZzjrKkdZfwrotttuw8svv4yf//zniMfjAICuri50dLhPHIwxePTRR9HY2AgAGDNmDBobG7F+/XoAwPr169HY2Ij6+voBpxEREdHweOeddzB69Gj87Gc/wze+8Q1861vfwosvvjjcYQ2ZjAPs3t+FlWu24NzUpGADAADt6W7cvLoN2awJntgDwLmpScHji2dPgRIyr9zKNVtw8ewpXh2bkJo6Hnu86+F8t96zGRfPnoI5MyZG6pgzY2KkTb9OJWQQbzjtp7/ZDNs22P9hT17ailVtODc1KXiCnhuj3/acGRPz8vjXi8W0YlUbTp00JtjY+uNzU4HYLSmDjUe4/44DHOzMBBsVP+32+7dgzoyJuPWezTjYmQnG82BnBjf9qg1zZkyMbJr8crf8+kVc9MUpkeu545s7RytWtSE1dXzJPB2H8mMMj0+hOrUDrFyzJTK2F8+egoOdGRwM/aLjoi9OyWt7xeo2CCFxsDNTcG7Cba1cswXt+7uwJ92Vl7dQ3//vezfjQEdPXn0HOzNwHETmKbzec9dUuGxuufZ0N3bv78K+A/nr/qZVbdi9Pz9Wfy3nzp8/1hd9sfhc7PHK5rYTnp+f/mYz7Kwpeg/t3t8FG4ANBGVz47tpVZv7iu4Q82Mo1HaptMFsZzjrKke/r9xu27YNd955JyZNmoR58+YBAE466SS0tLTg+9//PhzHgdYakydPxrJly4Jy119/PVpaWnDHHXdg5MiRaG1tPeI0IiIiOvps28Y777yD0047Dddccw3+9Kc/4Z//+Z/xxBNPoKamvFe2K+kjQ7v2HkIybqE93Q0lETwp87Wnu+FoHbkezldbHYMUhcvVeq+wtKe7YYwJ2imVz1dbHSuYVwoUradUWrG+hdsu1qafp1h6qfHJje9w4/bbTMYtJN3XXIK8hcatWH/765s/R6Xy9Dd/peoMt5+bv78x8/veX/zJuOXFWV7f/fy513LnqVhsSkYfF5pfv41i7RfrV39tFpqLcuen1HpLxi347x8vdU9AiCH/ftWe7iradvD1IMRVqp3hrKsc/W5up0yZgtdff71g2kMPPVS03OTJk/Hb3/52UNOIiIjo6PvYxz4Gy7KCv2bw2c9+FnV1ddixYwdOP/30suqopI8MKaXQk7HRUFcFRwMNdVWRJ2cNdVVQUkauh/N1dGUxqiZRsFxHVzb4WggRtFMqn5/W0ZUtmFcbFK2nVFqxvoXbLtamn6dYeqnxyY3vcOP22+zJ2Mja7pry83Z0ZRGzRFn97a9v/hyVytPf/JWqM9y+H3d4zkuNmd/3/uLvybivj+XmLdZ3P3/uNW2i67lYbOF3mRabX7+NYu0X61d/bRaai3Lnp9R668nYgDGRsoXywZih/36lZNG2/a8HJa4S7QxrXZ5SHxkq623JREREdPyqr6/HzJkz8dxzzwEAduzYgX379mHixInDHNnQiCtgXH01Fs1rwpNtb6FlwQz3yRgQfOY2FhNY0pwKrj/Z9lbw+IGN2+AYnVdu0bwmPLBxW/BZwbZXdmGsdz2cb/Gl0/HAxm14atPOSB1PbdoZadOv0zE6iDecdtX86bAsgfpRyby0Jc0pPNn2Fq68pKlgjH7bT23amZfHv14spiXNKbz21j60LIiOz9ICsdta46r50/P6rxQwsiaOxZdG0668pAlPbdqJxZdOx8iaeDCeI2viWPrtFJ7atBO1I+J5/b36W2fiwae3Ra7njm/uHC1pTqHtlV0l89SOyI8xPD6F6pQKWDSvKTK2D2zchpE1cYys6Yv9wae35bW9ZEEKxmiMrIkXnJtwW4vmNaGhvhpj66rz8hbq+//+h+kYXZvMq29kTRxKITJP4fWeu6bCZXPLNdRVYVx9NcaMzl/3S5tTGFefH6u/lnPnzx/rB58uPhdjvbK57YTn56r502HFRNF7aFx9NSy4rwj6ZXPjW9qcOrxTegfIj6FQ26XSBrOd4ayrHMIYc3i/Rq0AlfTb4Uo03GNV3bkLuu2xoumJaWej96U/HHbakZSVqa+gqyZ68Nlwj1Ml4ViVh+NUPh4oNfjeeecdLF26FAcOHIBlWfiXf/kXfOELXyi7fKX9bI6clmx5p716p/7GYjI4Xbc36526KkTotGSDeEyGTkt2T2n1T9kV4dOSlQCMd5Kxd2KxFG4+JUKnJXsnlganJXunwfqnJVtejHmnJVsCtl3qtGQEp+2WdVqyErCU20//lN2+05LdGPNOSw6NT8nTkkOnCxc9LVkCwoROS/bGqehpycYg5vUrY2vELQntnfprKVHgtGS3/34Zf+78U5/D8+jHY4lyTkvWwWnXkdOSvbEteFqyMUjGVd5p2Vlbu6cbGwMHboHcU4XDY9J3WrIr97Rkv++R05K9OZPe+lKhU5Qd7wTqvtOSo7FFT0t2OV5/LCUQU7mnJfedYtx3WjLyToHOevPnj7Xfpn9ast9X460X/z4relqyV3f+ackmWOv9n5ZskEgomKzD05LLqAtCAMYc8WnJpX42H41fMhAREVGFmzBhAn79618PdxhHTTbjAHCfw2rH/dcCAAewHTfNQeg6gGy3EzzWGQ0dTrfd/MrL63h1aO9doNL7H9rd3Ph12k5fndCAbbvl/HTtPsSoUe4vAsLtaQCZTF+fgjZsIGO79foU3D+hYvwYtRub8Dsaum5nnUi8fl3++GS6vPGx88cHoXx+7JFx8mLIPWzGHzc/Fu+fvHHw6VB//X5ZACWQnmoAACAASURBVMaMGoE9ezqCMcraTt/bGEP5/DIitx858wgnJxYHyHpzFoxjqKw/f/DHz+krqzN9T/b99uweHWnb9sbaCeULxidn7YTHJHeuoQEnp+/+H+7x4wn/cskO1WMBMFkgk3UKxuaPTW77/vxms9ExCK/J3HjD/VIAHG+Mwm36eRCKP/c+K7T2/XicIG9+TLnbL8fReWNZVzviqP4Szo/BXW8GTplpg9nOQOvy19SR1NUfvi2ZiIiIiIiIKh43t0RERERERFTxuLklIiIiIiKiisfNLREREREREVU8bm6JiIiIiIio4nFzS0RERERERBWPm1siIiIiIiKqeNzcEhERERERUcXj5paIiIiIiIgqHje3REREREREVPG4uSUiIiIiIqKKx80tERERERERVTxruAMg+iiwhEZ1567oRb0f1V297pfJWvRYNcMQGRERERHR8YGbW6JBIHq7oF/6Q/RiTRK6swcAIFNfAWq4uSUiIiIiGip8WzIRERERERFVPG5uiYiIiIiIqOJxc0tEREREREQVj5tbIiIiIiIiqnjc3BIREREREVHF4+aWiIiIiIiIKh43t0RERERERFTxuLklIiIiIiKiisfNLREREREREVU8bm6JiIiIiIio4ln9ZUin07j66qvx9ttvIx6PY+LEibjhhhtQX1+PHTt2oKWlBQcOHMDo0aPR2tqKSZMmAcCQpBEREdHwmD17NuLxOBKJBABg8eLFmDVr1jBHRURE1KffV26FEFi4cCE2bNiAdevWYcKECbj11lsBAMuWLcP8+fOxYcMGzJ8/H9ddd11QbijSiIiIaPjcfvvtWLt2LdauXXvMbWxjcQWjFGwhIOMWYLlfW8kYjPe1sRRU0oItJGApyLjy8kloJWElYxAxN6/72IKIKThSwlgKxlKhuv06LDhSBtetZAxWMgYVt6CVWwZeWaPcumApxKtikbZl3IKVjAX1JKvduK2qWNAvYylUVbuPjeXG5EgJoxTa011B+8ar14Tii3nX/XJ2KOZCffHjD6ch5vZDJay+MVXRvoTbdOOx+trN6X+4fmMpaK8uv26/P+F8VtJty5+TWJU7ZiLuljdejOF0f+6MpfDBvkNBjLaUEDHva298c+elb03ISDm/D7lzoZWbx1gKwltf/hz5+eLV+XPvz7cfRywZc9tUKlgXwTjEovMcnsdw+1rJoP9+vTJuwSgFxNx5DI+lH5+Mu/n2pLuCcQ/m3F8fCTceR8q8eyxRZI36c2OFY1J995Q7D24Z5bURXjNBO944F5obPx53vGTkXvfrMkoBlnTvf//eUhJK9W2LlJIwShZMo2Nbv6/cjh49GjNnzgweT5s2Dffddx/27duHrVu34le/+hUAYO7cubjxxhuxf/9+GGMGPa2+vn7QO09ERESVLxZX2NuRwYpVbairTeKyrzZi5Zot+MwnT8D5n/8Ebl69Ce3pbjTUVaFlwQw8+twO/PmNvVjSnMKax1/DC6/sDtLuf+J1vPDKbsycOg7zvnQqVqxuC8pefdmZsG2Dn/5mc3Bt0bwm/O7Fd3BuaiK2v5vGjKknwrY10h29ePiZ7fj6Fz6Jh37/Bi6YNRm3378F7elut+7zTsWKVW2RekbVJBCzBB7777dwzhkT8H57Bz7WUBvJt6Q5ha1v7sXE8aOC+hrqqnDlJU3Y/OoHmNU0ATevjta79c19mN44Dvc/8XoQR3iccvvy1bNPRldPNpK2+NLpkFLguZfei7RRrC93P/IqTvtEHc45YwLWPP5apP8NdVVY2pzCfaGx/9dvnoE33zuA004+IVJXeE78/vtzNnPqOFz21dPQk7HR0+vg4We257WzpDmFZ/74DqY3nhi57seY7ugJvh5dG4/0ZebUcbjkS5+KrJ9y+rZoXhNqR8TR1W1H1sqVlzRh3bPbMe+8UyPr7oZ//hzSHU5evx99bgc6ujL41vmN+LAzE5kPfxwOdGQKzmMirvD6W/sLjuezW97F56d9PG8t+/F96/xGdPXYuPWezaFyKdz/hNved7/xafT0OkXvsVJrdOeuD4vGNPPTH8Ov1r2CdEcPljSnACAyF7nrLHdt+Gv9c58ZX3S8wustHpdoXf1iZE3WVccAAOmuLG4KteWnOY4ezm91VAZhjDHlZtZa4x//8R8xe/ZsnHHGGbjmmmvwyCOPBOnnn38+fvKTn8AYM+hpU6dOPdK+0kfF7p3AixuKpzeeBbz6P4efdiRl+6v3zC8D4yYWTyciOsbNnj0bNTU1MMZg+vTpuOqqqzBy5Miyy+/b1wmty37KAQAYO7YWe/Z09JvPKIUld/wB7eluLG1O4a61f0F7uhs//7fZ+NFdz6M93R3kbairwrKFn8P3frIRDXVVWHjh6bhpVVuQ5j8O1+NbtnAmfvHAnwvW96O7nsfyyz+PrK2xe/8h/OKBP2PhhafjrrV/Cf71yxWqu6GuCpdf/BmMqx8BY4Af3fU8VlxxdtCvcL7ll38e1/7iuaJxFMsfjqNYDMsWfg7t6UMF+3n5xZ9BQ92ISBvF6ll44en4+Nga/Oiu5/P6nzvW/fWr1BzFLAEAkfEud1z8evyvAZQ1T+X07frvfA7X/7Jwm/6a8Pv0y6XnFp3P9/Z0ImaJgvNRKObwXJ3UUFu03mJzvPDC0/ttL5xe7B4rNpf9rV1/XHLvtVJzkbuG3m3vKBp/OO/lF38GP7rrhUieFVd8HgCw5I78GFdc8XmIApvbcr9P0eCNlZQCY8bUFEzr95XbsBtvvBHV1dW49NJLsXXr1iMObKgM5Q9QGv6xqu7qhe7sKZqeyNroLZJeKu1IyhZKq61JosO7Jrt60cX1VdRwr6lKwXEq39H4AXq8uffeezF+/HhkMhksX74cN9xwQ/AxpXIMdBzHjq3tN8+uvYeCJ6K11bHgayUReYIKuI/9dxi2p7tR671Sk/s4XI8vGbeK1tee7oYxBlL05fPryK2rUN3t6W4k4xakACDcx47WBfMZY0rGUSx/uN1iMShZvJ/JuJXXRrF6aqtjQd5SecrpV6k5yr1+OOPil8/9ejD6JkXxNnP7VGo+C8VVKmb/cTJulay32ByX0144vb81V+718HgC+fEdzhrqr2/hMcrNAyGK9h9CFP1+VM73KXIN9ViVvbltbW3Fzp07ceedd0JKifHjx2P37t1wHAdKKTiOg/b2dowfPx7GmEFPIyIiouHj/yyOx+OYP38+Lr/88sMqP5S/eFZKoaGuCu3pbnR0ZYOvHY3ga19DXRX8F18a6qrQ0ZWNpPmPw/X4ejJ20foa6qoghIA2Jsjn15FbV6G6G+qq0JOxoU0CxriPlZQF8wkhSsZRLH+43WIxOLp4P3syNhydKKsvHV1ZjByRKNj/YmNfrF+l5sh/5bZUO8XGxa8n9+vB6Js2xdvM7VOp+fT72F/8heaqVL3F5ric9sLp/a25w127fhu58R3OGirVt9wxCmuoqwK8N7QWKg9jCn4/4i+ey3c0fvFc1qejb7vtNrz88sv4+c9/jng8DgAYM2YMGhsbsX79egDA+vXr0djYiPr6+iFJIyIiouHR1dWFjg73CYkxBo8++igaGxuHOao+cQUsaU6hoa4KD2zchkXzmtBQV4UHn96GlgUz3CemcJ+gtiyYgQef3hZ8Du+pTTsjaf7jpzbtxJIFqUjZ2hFxXDV/euTaonlNePDpbbhq/nS0vbILliUwrr4ai+Y14alNO/Gv3zwDT23aiSsvaQrKPbVpZxBvuJ6xddVQCniy7S0saU7htbf25eVb0pxC2yu7IvU11LmfZ3yy7S20LMiv9+nN7wR988uFxym3L6Nrk3lpiy+djtoR8bw2ivXlgY3bgn7k9r+hzv0MY3js//WbZ6DtlV15dYXnJHfOntq0Ew311RhZEw/GO7edJc0pPNn2Vt51P8bw17l9eWrTzrz1U07fFs1rgq113lq58pKmoI1wnxyjC/b7wafdmMZ6deb266lNO4vOY+2IeNHxfLLtrYJr2Y9vrDff0XJ97fnjXeweK7VGS8V01fzpwZyMrk3mzUV/a8Nf66XGK7zeRtbE89akBfeVv6U5bflpdOzr9zO327Ztw9y5czFp0iQkk0kAwEknnYSf//zn2L59O1paWnDw4EGMHDkSra2tOPnkkwFgSNLKxbclD63hHqvqzl3QbY8VTU9MOxu9L/3hsNOOpGyhtMjbklNfQVcN34FQzHCvqUrBcSof35Y8uN555x18//vfh+M40Fpj8uTJ+MEPfoCGhoay6xjqn82xuELGARytEY8paG1ga4NkXCFraziOgVICliXQ26thKQEpAa0BW7tvJ47HFBxHI2sbSAnEYxKOY2A7BlK6rxAqKYK6LSkgpUDW1ohZElobWJb7uoHRBlnHQAhAADACgAa0MVBSIB6TyGR1ULcbj1u31gbJuER3RiMWk8hmNBztxl8dl+jq1W6lXjvCa0QAsCwZ6a/y4kvGFTK2V84AjjaIezEX6osAkHXcWILYjIExgKUEbMe4bUiBRNzri3Yf+20qKYJ43GCj/e/JaGjjtm3gvmiWjEv0Zt34LeWWz2T78lmWhG274yG9ehzHwABwHHfejAF0KD2T7eu3f11JgYytEVMCQghkshpKeX3JaG9N+PW7a8Ifz4ytYXl192b65kJ7c2EpAce45Y02bqz+QgD62gjNfdwS6M70zVvckujOOFBCIBbz5skx7jgoAUv1zXM81jePMtS+0QjmJlgPSsCxDYR017LjmGAsfcr7Orw2lD/n3roRAsjaBsYYJHLusWRcoqc3Oi5SCPclNQN3TfsxSQFlCWhvLPx5sKQ7XrZtgjVjeeX8cQ7mxomuu0RcRcfLW9uO91gKASHg3v8OYGtvrQLBgVFKSdhw75PctCP5PnW8Oxo/mw/rQKlKwc3t0BruseLm9qNnuNdUpeA4lY+b22MPfzYPLY5VeThO5eNYlYfjVL5j7kApoqMlaXdC9hRe/JbOInOU4yEiIiIiomMbN7d0TJI9HUVfnRXTzj7K0RARERER0bGurAOliIiIiIiIiI5l3NwSERERERFRxePmloiIiIiIiCoeN7dERERERERU8bi5JSIiIiIioorHzS0RERERERFVPG5uiYiIiIiIqOJxc0tEREREREQVj5tbIiIiIiIiqnjc3BIREREREVHF4+aWiIiIiIiIKh43t0RERERERFTxuLklIiIiIiKiimcNdwBExwNLaFR37iqYppO16LFqjnJEREREREQfLdzcEh0ForcL+qU/FEyTqa8ANdzcEhEREREdCb4tmYiIiIiIiCoeX7klIiI6Djz++OMl088777yjFAkREdHQ4OaWiIjoOPC73/2uZDo3t0REVOm4uSUiIjoOrFixYrhDICIiGlL8zC0REdFxZO/evVi6dCkWLlwIAHjjjTfw29/+dpijIiIiOnLc3BIRER1HWlpacPbZZ6O9vR0AMGnSJNx9993DHBUREdGR4+aWiIjoOJJOp3H++edDSvcpgGVZwddERESVjD/NiIiIjiPV1dVIp9MQQgAAXnrpJdTW1pZd/mc/+xk+9alP4a9//etQhUhERDQgPFCKiIjoONLS0oLLL78cb7/9NubNm4d0Oo2VK1eWVfaVV17BSy+9hI997GNDHOXgUUrCBuBoAyUF4kog4xg42sCSElK5aTACWmtIKWABcBwd1BFPWrA1YDsaWgMxS0AqATtroI1br7IkpACyWQ3b0VBSQikBR2sAAtVxiV7bBHVYSsCyJASATFZDGwMpBeIxiUxWw3E04jEFASDraBgDxCwJrQ1sx81rKQFHGxhjkO7ogYgpaOPGbCkB2zZwtIZSEkoKaO2WEwBsxwDu7zcgBGAM3McaQZ/89rKOgdYaiZiCo90+xGMKxhhoAygp4HhjqpSAJQUsS6A3o+FoAykEYjEJ29ZB7DHlNt6b1VBSwIoJQLtx+fXELYmso2Hb7mMlBbK2RtySMMaNSwoB5dXlaAPHcWO3LDd/JqthAMC4/WpPdwGWG7ulJKQUyHrjr6SAlH3t2N6aCdaSd11K4VYIgaq4RHevDsY55q2DXm8O/TaC+LSBEgKQgDBAPCaDcbK8PvZm3TZiloAxCNaQ1gbC66/0IshkHcRjClobOI5234UhjLtelISBQcYfY68f4fshd60rJeEIAdsx2L3/EKy4BSkB4/WnnPvNKLe81sZdM46BrXVee7n3Zm4sRAPR7+a2tbUVGzZswHvvvYd169bhlFNOAQDMnj0b8XgciUQCALB48WLMmjULALBjxw60tLTgwIEDGD16NFpbWzFp0qQjSiMiIqIjN3XqVNxzzz3YsWMHjDH4xCc+gVgs1m+5TCaDG264AbfeeisWLFhwFCI9ckpJpLuyuGlVG9rT3Zg5dRzmnXcqVniPG+qqcNX86bAsgVvufjG4trQ5hbrqmLvBTFr4sCuLAx29WLlmS5CnZcEM3P/E63jhld1oqKvCiu99Hh2H+tpqqKvConlNSMQVXn9rP6Z9qiGvjqXNKcRiEj/65f8E15Y0p7Dm8ddwoCOD737j0+jpdbByzRbU1SbxTxdOxa33bM6r/7mX3sOspgm4/4nXcMGsydj86geY1XQSbl69KRRvCq/u2ItppzTgQGcvHn5mOy6YNRnrno3+e/v9WyKxaKPRuvpF1NUmcdlXG4NYLvtqIx5+Zjv+/txT0JtxIv269h9TMBolx33RvCYkEwr/8V8vY3RtHN86vxEfdmYi9fhj4Y/xonlN+N2L7+DvzpyQlw9AXv2jaxN48oWdmN54YqRfV17ShHXPbsfXzpkc5Hnw928GsWsdrSuc/+5HXkW6owdXXtKEnbs+xGknnxDJu2RBCiOqLfzgF/8dXPvhwpmwswYrVufX+c3zTsV9OX3021g0rwkja+LoPJTFbff9MW/sAOD/e+6tvPEIx+uPcbqjB9d66+360HoLr3WlJA50Z7H8V9FxHFUTRzymkFCy5OZTKYlDWQfp/b2RdZK75uuq3e834XszNxaiger3bclz5szBvffei49//ON5abfffjvWrl2LtWvXBhtbAFi2bBnmz5+PDRs2YP78+bjuuuuOOI2IiIiOXG9vL379619j5cqV+Pd//3fce++96O3t7bfcypUr8bWvfQ0TJkw4ClEODht9GywAmDNjYrARAYD2dDd++pvN6DiUiVy7aVUbbK+O3qxB+/6u4Am6n+fm1ZswZ8bE4LHW0bba091YuWYLOg5lkJo6vmAdN61qQ/v+rsi1FavaMGfGRFw8ewoOhjZ7F8+eEmxsc+s/NzUJN692y91+/xbv8aaceNvcONJuHH7e3H9zYznYmQnaD8fi19FxKJPXr/0f9vQ77ivXbMHBzgwunj0Fc2ZMxB7vWqGxCJe56ItTCuY70NGTV3/7/i6cm5qU1y+/v+E84dhzYw3nv3j2lOBaaur4vLwrVrfBtk3kmhIy2Njm1nlTgT76baxcswVKyGBjmzt2BzszBccjHK8/xu3pbixf1YbdOestvNZtINjYhtvak+6GbZsgXzE2gN2hdR5eM7nt5d6bubEQDVS/r9yeeeaZh1Xhvn37sHXrVvzqV78CAMydOxc33ngj9u/fD2PMgNLq6+sPt19ERERUwNVXX40RI0bg0ksvBQA88sgj+Ld/+zfcfvvtRcts2bIFf/nLX7B48eIBtztmTM2Ayo0dW/7ngXO1p/ueyANAbXUs8tjN041k3Mq7BiEwdmwtdu3tRDJuFSxXW933ircxpmjdxpiidRRqO1yvX6ZU7Er2lWtPdwePc/OG4/Dz5v5bLL5werhMOEZfbl9L1Z2MR6/1NxbF+lZoHMNjU6jecJ5isefmD/e52JyH3skMAJCidAy5fQy3Uays399y+uePcX9rPfd+CZeRAkG+YtrTXZHxKzbv8D7vXyztSO754VKJMQ+XoR6rI/rM7eLFi2GMwfTp03HVVVdh5MiR2LVrF8aNGwel3LdLKKXQ0NCAXbt2wRgzoDRubomIiAbHjh078PDDDwePzzrrLHzta18rWWbTpk148803MWfOHADABx98gH/6p3/CihUrcPbZZ5fV7r59ndD+B0LLNHZsLfbs6TisMhFKoqGuKngS3dGVjTwGgIa6KvRkoq8XNdRVAcZgz54OKKXQk7ELluvoygaPhRBF6xZCFK2jUNt+vTGrr85SsTs6EZRrqKuCo1EwbzgOP2/uv8XiC6eHy4Rj9OX2tVTdWdvk9bXYGJfqW6FxDI9NoXrDeYrFnpvfj8cfz0J5c5e5NoVjzq0zt89+XaX6W07//DHub63n3i/hMtok+vIVo2Rk/IrNu/sB78L96reNY9ARf586jgzWWEkpiv7CdMCb23vvvRfjx49HJpPB8uXLg8/hHAuG47fDx5shHyu9H6hJFk6LWYgXS+svfajKFkmr9a+VKludwAiuPd5/ZeI4lY9jVdhpp52Gl156CdOmTQMA/OlPf8IZZ5xRssx3v/tdfPe73w0ez549G3feeWdwDsexygKwtDkVvP3xqU07saQ5VfAzt/4Tbf+zfxYAB0AiJtBQX41F85oKfuYWcJ+USxltK/yZ2LZXdmHapxry6vA/cxtuO/czt36ZBzZuw+JLpxf8zO2TbW+hZUEK9z/xGq68pMl7PCPvM7dtr+zCtFPcOB5+Znvw2czwv4U+c9tQV4UHNm6LxOLX8ffnnpLXr/pRyX7HvdBnbnPr8cfCH+NF85rw4NPbCubz84Trdz9P+1Zev/z+hvP45etHJfNiDee/+5FXg2ttr+zKy7tkQSpvPTlGY8mCVMHP3C5tTuG+nD76bSya1wTHaPzrN88o+pnbQuMRjtcf44a6quAzt8XWugXg2m+nCn7m1rJEkK/U/TYudK+E10xue0D+/RKOhWighDGmrF+jlvpB9vrrr+Pyyy/Hxo0bsW/fPnz5y1/GCy+8AKUUHMfBzJkz8fjjj8MYM6C0w33ldlh+O3wcORpjVd25C7rtsYJpiWlno/elPxQtWyp9qMoWSqutSaKjs6ffsjL1FXTVjC8a0/GA9195OE7lOxq/Ha40F1xwAQDAtm3s2LEjOPH4/fffxyc/+UmsX7++7LoGsrkdrp/Nh3dasoGUKH1asgFiSkAqCTt0yq6yJJRwTz62vRN7lXeaMYDQacnuKbL9n5ZsEI9J77Rk90Tk4LRk7wRiSwk4xsBo99Rdx9HQAGDCpyV7Jw0rAe0YSCUgTOHTkoUATNHTkg0SMemdluzGZoyBBqDEEZyWbGsoIWDFJODVfdinJVsCMOWdliylALx+WkqUPC3Z0X35YXJPS3b1nZbsxhg9LdkEbQDR05KFd9xxwdOSbe2OWXBasruGCp6W7I2H9voe9M+YIz4t2T+Re+CnJcNdMx/x05L5s7l8x+wrt11dXXAcB7W1tTDG4NFHH0VjYyMAYMyYMWhsbMT69etx4YUXYv369WhsbAw2qANNIyIiooG78847B62ujRs3DlpdQ81xNAS8JzyOQdZB6LED7QR7PCgAcPJfOcr0uG/llN7/RgNO1oHwy2jAsZ2gnOXVE66723YidcAGbO9auJ6s7QTx6YwOyiD02H/y5tcvANTVV2PPno6gPccO9dMGtPduVP/fnI+FBo9zYwnH7ITa92Px/npQpC0HgJMJjSkA23EiY+M9DPriOH1j4dfjtx/0wavPj8OvWzuIlvXq9+cjPL9jx9QG46Sdvtj9PmuvPwhd8+kC13pC85U7p+G4I9e8GAEgk1PeQXhM8uMHABPqW3gu/PXrl/E3iv7aD49H7rUgrFCZsfXuWB3OdtNxNOAUXjO57eXem3zFlgZDv5vbH//4x3j88cexd+9efPvb38bo0aNx55134vvf/z4cx4HWGpMnT8ayZcuCMtdffz1aWlpwxx13YOTIkWhtbT3iNCIiIhq43L96sG/fvrJOSSYiIqoU/W5uf/CDH+AHP/hB3vWHHnqoaJnJkyfjt7/97aCmERER0ZF76qmn0Nraivb2dtTX1+P999/H5MmT8cgjjwx3aEREREek379zS0RERB8dK1euxP33349JkyZh48aNWLVqVb8HShEREVUCbm6JiIiOI5Zloa6uDlpraK1x1lln4dVXXx3usIiIiI7YEf2dWyIaXkm7E7Kn8KlzOlmLHuujccorEQ2ekSNH4tChQ5gxYwYWL16M+vp6WBafDhARUeXjTzOiCiZ7Oor+ySSZ+gpQw80tEUXdcccdSCQSWLJkCdatW4eOjg5873vfG+6wiIiIjhg3t0TDzBIa1Z27Cqbx1VciGmzV1dXB1xdddNEwRkJERDS4uLklGmaitwv6pT8UTOOrr0Q0WJqamiBE7l83BYwxEELgj3/84zBERURENHi4uSUiIjoObNmyZbhDICIiGlI8LZmIiIiIiIgqHl+5JTqGlfo8LgBYOovMUYyHiIiIiOhYxc0t0TGs1OdxAUBMO/soRkNEREREdOzi5pboI6q/V315EjMRERERfZRwc0v0EdXfq748iZmIiIiIPkp4oBQRERERERFVPG5uiYiIiIiIqOJxc0tEREREREQVj5tbIiIiIiIiqng8UIqGTdLuhOzpKJjGv99KRERERESHg5tbGjaypwO67bGCafz7rUREREREdDj4tmQiIiIiIiKqeNzcEhERERERUcXj5paIiIiIiIgqHje3REREREREVPF4oBQRERH164orrsC7774LKSWqq6vxwx/+EI2NjcMdFhERUYCbWyIiIupXa2sramtrAQBPPvkkli5digcffHCYoxpcsbiCkRLZrIajNZSSSMQkso6GdgBHGyglICWgpIRtayglYDsGjqMRjykIATiOge0YxGICRgMQgBRuPq0NLCVgWRJxCfTY7jXbMZBSuPUDEFJE4lASEBBwtIGtDWJKIB6T6MloOI5G+/4uJKpisB2DTNYJYu/NajiOgZIClhIwALK2hpQCybhE1jYwMJH+Kem245dLKLIJDAAAIABJREFUxCWytoYBoLx+ONpgRJUK2ldSojoh0ZXRbj1SADCAcctr47brpwkBGA0oBTga0NpACgEIA0CgKi7RnXFjl1IgpgQ0gGzWQVXCQsb2+qX66orF3Dlxx17CaDdOKfv69MG+Q5CWcmODd90xiMckHG0gvLCzobmKxSS6exxICVhKwlICWVsja5vItd6sAwEBWxso4Y63rQ0AIBGX6PXGylISSrr5lZKIWRK2o2E0oI0bryUFIAW0Nw9+LPD6CmEgICClQCbrIBFT0MYga4fr14jHpLe+3DmKKyCbcYbj9jqmKSVhA8H6tAA4jh7usGgAuLklIiKifvkbWwDo7OyEEGIYoxl8sbhCt63R2dWLm1dvQnu6GzOnjsNlXz0NBzp6sXLNFrSnu9FQV4Wr5k9HTbWFP/11Dxo/MQY3r96EutokvvuNT8NxDG69ZzPqapO47KuNePiZ7fj7c09Bb8aJ1LH02ylUJy0c6OjFrfdsDq4vmteEZEKhKm5h9SNb8cIru9FQV4XrFs5EV48d5P3OhVNx2sknYMWqtqDskuYUqpIKt927BaNr4/jmeafiplC6X/d//NfLSHf0YGlzCsoSONiZicTWsiCF+594LWh7SXMKz/zxHZzTdBK6em2sXLMFl5w7BSefVBe0P3PqOMw779RIPFde0oR1z27HN887FfGYxPW//J9I2uZXP8CsppOC8Q6XmXfeqVjzeF8Mi+Y1oToZw4tbP8D0xnF5Zdy6JuDm1W3B2Pt9mjl1HC750qcKtnPJlz6FZ7e8i1lNJ2FkTRy9GSdvvpc0p/DIH97En9/Yi0XzmjC6NoEnX9iJB3//ZhBbXW0CtmPw4//nhch4VycsPLPlXZxzxoTI2Cya14S7H3kV6Y4eLGlOAcZgRSi+Jc0pxGMyL5Z//eYZeOj3b+CCWZOx7tnt+No5k/G7F9/B3505IZJv0bymgteXNKdwQm2cG9wQpSTSXdnIvbK0OYW66hg3uBWIn7klIiKislx77bX44he/iNtuuw2tra3DHc6gyjiAbZtgAwQAc2ZMRPv+rmBzAADt6W789DebsSfdjdTU8UH+i2dPwcHOTLD5vHj2FKxcswVzZkxEx6FMXh03/aoNtm2C/P71lWu24GBnBrv3d2HOjInB9T3p7kje1NTxwWbJz7NiVRuUkLh49hTMmTExeLKeW/fFs6e4MXj5c2O7eXVbpO0Vq9pwbmoSDnT2bbSmnTIu0v6cGRPz4rn9/i1BHLv3d+WlnZuaFBnvcJkVq6IxrFyzBQc6evDF6RMKlnHraouMfTi2Yu3cvHpTEIcwouB8r1jVhou+OCWIo31/F85NTYrEtnt/F/Z92J033gc6e3FualLe2KxcsyWYhxWr2nCgszeSfqCjp2Ast933R8yZMTGIf+WaLbjoi1Py8hW7vmJVG7ivjbKBvHvlplVtsIc3LBogvnJLREREZVm+fDkA4KGHHsItt9yCX/7yl2WXHTOmZkBtjh1b23+mQbBr7yFIgeAJLgDUVscARK/5j5NxC8aYIC03b211DO3p7pJ15LYXrhsAkvG+68m4Fckbbjtc1hjTb9x+vaVi8OvwHysZjcHROm+sitUT7lNunaXK5MaQO+bF6sqNpb/Y/LKO1nnjHK4/HIeS0fTc/uXmLTXGhcr7j/sbn1LjWOy6YwzGl3lfHa37bzi1p7sKjhOEKLv/x8M4DZahHqt+N7etra3YsGED3nvvPaxbtw6nnHIKAGDHjh1oaWnBgQMHMHr0aLS2tmLSpElDlkZERETHhq9//eu47rrrkE6nUVdXV1aZffs6ob3PH5Zr7Nha7NnTMZAQD5tSCrYDNNRVBU90O7qyiFkicg1w8/RkbAjRl5abt6Mr+/+3d+/BVdR3/8Dfu3vOycUEkiDBWC0UK5rSCzFyaKfylBJUHgX5iVZovBCsdkZnCiNVTJCCN9Q8rVrp6Dh25ify+6m01IpjcJQHKD/lmT4kIh2nIj6IgLeYQBJqwklyzu5+f3/s5eyeWzYhOZfk/ZpxzO5397uf72f3sOd7Lp+D8tKClH3oAkn7BoCIGs1XX1h1bes8tnNfSZLQHYrYy4n6tvpNFYPVh7Ws6e4YFFmOy1Wyfpxjiu0z1T6xMcTmPFlfsbEMFJu1ryLLcXl29u+MQ9PzEp6z2DFa26bKcaL9reWB8pMqj8nWK5Lk6XGVzsdfRilywjxBCOZpmA1XrmRZSvqC6YAfS66pqcGLL76Ib3zjG67169evR21tLd566y3U1tZi3bp1I9pGRMPLJ+ko7GlN+F++2pPp8Igoi5w+fRqtra328u7duzF+/HiUlJRkMKrhFVAAn09C/bKZxhNbALtajqO8rBArl1bZ66zv3E4sLUDzB6329q/sPoxxRQHcfVO1vbxyaRV2tRxH8VmBuD7WLA/C55Ps7a31K5dWYVxRAJPKCrGr5bi9fmJpgWvb5g9a0VAXdO3bUBeEJnS8svswdrUcx5qYdqvvV3Yftr9XqAk9Lrb6ZUHXsRvqgtjZfAwlRXn2tv/4nzbX8Xe1HI+LZ8WSKjuOSWWFcW07m4+58u3cp6HOHYPxXdd87Nn/WcJ9jL6Crtw7Y0t2nPplM+04hCQSnu+GuiBe3XPYjqO8rBA7m4+5YptUVogJ4wvi8l1SlIedzcficrNyaZV9HhrqgigpynO1lxTnJ4zlrp9fgl0tx+34Vy6twqt7Dsdtl2x9Q10QAWUID5BRzAfEPVbW1AX58dYcJQkhPL2MOnfuXDz77LOYNm0aOjo6cOWVV2Lfvn1QFAWapmHWrFnYsWMHhBDD3lZWVjaoQWX7q8O5brhyVdjTCr35zYRteTMuQ/8/9g66LVP7JmorLspHd0/fsPebjn3l4HyEiiqS7jvc+PjzhnnyLh2vDo8lJ0+exJ133one3l7Isozx48fj3nvvxfTp0z33kQv3Zne1ZKMSr1EtWUDXBDRhVFK1qu+6qyUbFXftasm6gN9nVOyVJOOd1pTVks1qwfHVkqMVjCUgSbXkaFVjo1qybsfurVoyXONTFKOCsGZW/R24WrKxX1y1ZAmADle1ZN2sXhytlmxUMTbyZO4DpKiWrKMgTzGqJcdUXvb7jarDqhbNvV0t2RyTdXyLokjQVA/Vkvs1yGYFZLtasiZc6/ojun2OrPWqEIBwVks2+rSqGSuKlLJasjAraetCGOusasmyUVjaqJasI88vm9WS3f3b1ZLNczmYaslj6Z5zJtWSx1KezlQ67s1DelGitbUVkyZNgqIYL/0oioLy8nK0trZCCDHsbYOd3Gb793pGg2HJld4JFOUnbvP7EBhKW6b2TdJWbK0b5n5HfN/CPJyV5scDH3/eME/eMVfD5+yzz8af//znTIcx4own/RokmE+QVCCsGhMBe51u/IiM9SFSTY226eHok2EfAGEuC/M/2fwPKqCqmqtgjfWETGiANfVwxqHHbCt0mD89YyxPLIs+aXTGbvehAZrZsWKOo1+NTnKc49MdYwKAcG90O82xbX9Id/XfG9Jc/SBmf8lxbGtZ14z/x9be7ksSuw9ApE+PO44EQDU3cubeOp41pnNinlxb6yOae8LnPFcRVbNzoWtAGNFYnOusMVhvjGpadJ3rXKhGHq2/VTU+P85o7FgcY7U20M1+NHO8sf3rzvUAIiwmlZCmOa9lAaYpd43Kd9xz4dXhXDZs79yG+qGb72zGyouo6B9CW6b2TdTmeud2GPtNx75yqB+hND4e+Pjzhnnyju/cEhERjT1DmtxWVFSgra0NmqbZHyFub29HRUUFhBDD3kZERERERESUypB+53bChAmorKxEU1MTAKCpqQmVlZUoKysbkTYiIiIiIiKiVAZ85/bhhx/Gjh07cPLkSSxfvhwlJSXYvn077r//ftTX1+OZZ57BuHHjXD/mPhJtRERERERERMkMOLldu3Yt1q5dG7f+ggsuwNatWxPuMxJtRERERERERMkM6WPJRERERERERNmEk1siIiIiIiLKeZzcEhERERERUc7j5JaIiIiIiIhyHie3RERERERElPM4uSUiIiIiIqKcx8ktERERERER5TxObomIiIiIiCjn+TIdAI1e+WoP5L7upO0+PYJwGuMhIiIiIqLRi5NbGjFyXzf05jeTtkszLktjNERERERENJrxY8lERERERESU8zi5JSIiIiIiopzHyS0RERERERHlPE5uiYiIiIiIKOdxcktEREREREQ5j9WSiSiOT9JR2NOasE3PL0afryjNERERERERpcbJLRHFkfpD0P+xN2GbHJwPFHFyS0RERETZhR9LJiIiIiIiopzHyS0RERERERHlPH4smYiIiFLq6urC6tWr8emnnyIQCGDy5Ml48MEHUVZWlunQ0kZRZGiQoPgkaJqApgvIsgSfIkHVBHRdwO+T4VMk9Ed0aJoORZaRF5ChmttrmoAiSwj4ZURUHcLsW9cFZEkCJECWAL9PRl9YgyxLEMJoV2QJkgxEIgJ5fhmquc7oV0d7Vwj+fD/6+jV7W1mSIEmAah7XiFuHohhx9Yd1+/iSJCHPL0PXBSKaDl0H8gIyNE1A1Yx9CgMyesM6VM0Yu1+RoCgS+sM6dGGsAwQgjDEKAJGIDk0XUBQJimxsH47o8MkSwhENfr8MXQNUXYciGzH0R8w4ZRkBBYiENfscqIDRnyzBB0DT9AxcDUSUrTi5JSIiopQkScJtt92GWbNmAQAaGxvxu9/9Do888kiGI0sPRZFxKhTBnvc+w79dcj4e3dSM9q5ezJo+CUsuvwiPvdCC0uJ83HVjFXq7Nbu9vLQAa+qCEIBrXf2ymXjnwOeorjwHG/90wF6/YkkVXn/nCGqvvBj/PHISkyvGu9pXLq3C3979DD+99Hy8e/ArzK46D4+90OLq943/Oor3Pz6Ju35+CYrO8uN0SMW+f34Zt21DXRB5ARn3P/ff9rp1t81CT0jFEy/tR2lxPm65uhJPbTng2mfLjkPY90EbyksLcO+yS6FIMh5xjM0aw5LLL8ZZBQp+8+zf7bZVtdUoPsuPf3zUjinnjrfHkuoYDXVBnF0cgK4JdIUirmOtqQuitNDPCS4R2fixZCIiIkqppKTEntgCwIwZM/Dll19mMKL0UgFs2NSMecEp9iQVAGpmTrYnjNfNvRCKJLva27t68cimZpzq7nOte+yFFswLTrEnrtb6jX86gJqZk7Hh+WYEp1fEtT+15QCunXMhntpyAPOCU+xjO/u9ds6FaO/qxZMvvwdFkvHES/sTbvvopmZASK51J7p68cRL++3xWJNO5z41Myfby1/3hO3JZuwYHnuhGZoGV9sTL+1He2cIwekVrrGkOsajm5oR1oxzEHusRzY1Qx2B801EuYvv3NIZyVd7IPd1J2zz6RGE0xwPERGNLF3X8fLLL2Pu3LmD2m/ChKFVWZ84sXhI+w2n9q4Q2rt6ocjRyRoAFBf67eXiQj+EEK52Y99e5Ad8ceti+7LWW30m68vaL9n+ihz9W5ZSbytL7nHmB3yu8SSLL9H2icYQ27+VC2tsqXLgXNaE8THkRNtCkgZ9jWTDNZUrmCtvmCfvRjpXnNzSGZH7uqE3v5mwTZpxWZqjISKikfbQQw+hsLAQN91006D26+joga6LgTd0mDixGCdOJH4BNa0UGeWlBdB0oLy0wJ5kdYci9nJ3KIKzSwpc7YCxfV/Y/f5ior6s9VafkiQlbLf2S7a/9Qnd8tIC6CL1trGnoy+susaTLL5E2ycaQ2z/Vi6ssaXKgXNZkSRAiITbQohBXSNZc03lAObKG+bJu+HKlSxLSV8w5ceSiYiIyJPGxkYcP34cv//97yHLY+cphA/AfXVB7Gw+hoa6oDGpArCr5Tjql81EeWkBXtl9GJrQXe3W90JLivNd6+qXzcTO5mNYsaTKtX7FkirsajmO+5YH0fxBa1z7yqVVeHXPYaxcWoWdzcfsYzv7fXXPYZSXFuCun18CTehYVVudcNuGuiAgCde6ieb3Yq3xrFxaFbfPrpbj9vK4ogDWxIzXGkP9siAUBa62VbXVKC8rRPMHra6xpDpGQ10QAcU4B7HHWlMX5Ls0ROQiCSEG9zJqDsjpV4dzgDNXhT2tSd+5zZtxGfr/sTdpP6nah9qWqX0TtRUX5aO7p2/Y+830vnJwPkJFFUn7HQo+/rxhnrxLx6vDY82TTz6J9957D8899xwKCgoGvX+u35sHXy3Z+Citq1qyLqBIjmrJZnFhu1qybLzrYFRL1iHLiKmWLCGi6sjzmdWSFTMWzfjbqrKsSAmqJSsSNFXYlYtTV0sW0IVRldmolmzsY1dLNuNNXC3ZGJO3ask6/H4prdWSs+maynbMlTfMk3fpuDef8Qtec+fORSAQQF5eHgDg7rvvxuzZs3H06FHU19fj1KlTKCkpQWNjI6ZMmQIAQ24jIiKi9Dt8+DCeffZZTJkyBUuXLgUAnHfeeXj66aczHFn6WJMozZhnQQEAHdBUQDKX9bCOMIxlHwBoQLjX2MFeByCiRdfZfZnbCwDhiGZ8tE6L9g3daPMB0MI6JAC6Gu13YqnxpNHn6Mua9sVuCxUIq1rc8a24ZPM/rd/owdqnV9Wiy2YuNLhjRExfzmPq5iaSEZ6ZM0T71ATCmu7In4aIFu1Tc7UJOJqIiAAM03duN27ciGnTprnWrV+/HrW1tVi0aBFee+01rFu3Dps3bz6jNiIiIkq/Cy+8EB999FGmwyAiIkppRL4w09HRgYMHD2LBggUAgAULFuDgwYPo7OwcchsRZQefpKOwpzXpf/lqT6ZDJCIiIqIxaFjeub377rshhEB1dTVWrVqF1tZWTJo0CYpifNBFURSUl5ejtbUVQoghtZWVlQ1HqER0hqT+EPQU3+WVg/OBIn5HkYiIiIjS64wnty+++CIqKioQDoexYcMGPPjgg6irqxuG0IYul39LL1fYudI7gaL8xBv5fQgkaxuofahtmdo3SVuxtW4MjNVWmIezhvBY4uPPG+bJO+aKiIhobDnjyW1FhVE1NRAIoLa2FnfccQcaGhrQ1tYGTdOgKAo0TUN7ezsqKioghBhS22DkekXGbOeqlhzqh25WBI6VF1HRn6RtoPahtmVq30RtrmrJo3ysTnKoH6FBPpb4+POGefKO1ZKJiIjGnjP6zm0oFEJ3t/HkQQiBN954A5WVlZgwYQIqKyvR1NQEAGhqakJlZSXKysqG3EaZk6/2uL5Tibbj9t8+PTJwB0RERERERCPsjN657ejowK9+9StomgZd13HBBRdg/fr1AID7778f9fX1eOaZZzBu3Dg0Njba+w21jTJD7ut2/5ZtUb79bq0047IMRUXZyio4lYieX4w+H98FIyIiIqLhd0aT2/PPPx/btm1L2HbBBRdg69atw9pGRNkvVcEpFpsiIiIiopEyIj8FRERERERERJROnNwSERERERFRzuPkloiIiIiIiHIeJ7dERERERESU8874d26JiLxKWklZ7zR+M5nVlImIiIhoiDi5JaK0SVpJ2fx5KVZTJiIiIqKh4seSiYiIiIiIKOdxcktEREREREQ5j5NbIiIiIiIiynn8zu0oka/2QO7rTtrOQj1ERERERDSacXI7Ssh93dCb30zenqJQz0ATY58eQfiMIyQaWNJqyuALNERERESUGie3NODEWJpxWRqjobEsaTVlpH6BhoiIiIiI37klIiIiIiKinMfJLREREREREeU8Tm6JiIiIiIgo53FyS0RERERERDmPBaXGiFRVaFkNmYiIiIiIch0ntzkk1U/2DDRBTVWFltWQKRfwZ4KIMqexsRFvvfUWvvjiC7z++uuYNm1apkNKO0WRoQLQdQG/X4EuBFRVQJaBgF+BqupQdYE8nwxNF1A1HX6fDEmSEI5oUGQZgYAMTRMQQkAXAASgCwGfIkGSJACApglourFOkSWourC3k2UJiiJFt5ElyLKxX3tXCJokwafI8Ptl9Id16LoOnyLD55ehm/voutFPnl9GOKJDQABCsvu3x2seKxLRoWrRfYQQCKsCkmzEr+kCiiyhMCAjFNbt5YBfhhCAqul2nnyKDEmW0N+vwacYsYdVHQGfbOdTMccdMdcDgKoLaGYM1n6qpkPoRl78Zs41zcibzyejP6zB75Ohm+dCUWQUBmQ7T4oiQ5IAIYyxWlTNyJEdX0SHokjwyRIkWTL7E8ZxzfOm6TokSHaeFMXoz69I6I/o0MzjW+c5okbX+c1YZTNn4bAOTTfaAuY50nUBWZLg80kQwn2NyGaujHMnoMgyJHMczm1kM3ZhXmPWGAUACYCiGPFbdF2grTMEJeCDDEAyjwMIx1iNcxrwSegN61BkQNNgx+/zSQiHo7EBEvIDMvr6o2MsCMjo7dcByd1vnl9BRDWvTxixS5JxPAUCkbBmPyYhAX6fgkjE7FeWEVAAXRNQYVyjeX7FflwqsgxFMWINKEAkrNnj9gcUhK0xyDLy/OY5NK9rHwBN04ft3xUafpzc5pBUP9nDCSqNdvyZIKLMqampwS233IIbb7wx06FkhKLI6ApF8MimZpQW5+MXi6bjd/93P9q7ejFr+iQsufwiPPZCC0qL83HL1ZV4assBtHf1ory0ACuXVmHz9g/R1d2H9bf/EH39Gv688yMsnH0BNv4pul1DXRC60NH4wruudX39Kp58+T17Xf2ymfjTf36EfR+0oby0AL+5bRYiEYHHXmiOi8faZ01dEJIMbPjf0W2WXnExtuw4FBfHiiVVeP2dI1h+zXT0dWt4ZFNzXIx//s//SRj/lh2H7Ljuv/2HCKs6Hnm+2ZWL8UUBbNtzBO9/fBKraquxs/k4fnrp+XE5+9u7n+HffzwFkYjAEy/td7WdXZKPU91hPP7i/oQ5b6gL4t2DX+E7UyfY66/9yVT82yXn41HHeFYsqcL+D79CTfCb6I9o6OvXXP2sqq3G869/gK7uPqyqrUbxWX70hdW4cxSOaHj8RXeMJcV5iER0V/7W1AXh98t44I//7Tqfb/zXUbz/8cm4HDqXZ02fhJuvqsS/esJJY1x9y6VQZBmhvohrm9U3X4pxRQF0h8JxY/z1jdX4698OY8nlF+OdA5+huvIc13l1juXlHYfwv37ybdf1aLUf++JfqJhY5LrunGOzrqulV1yMt9/7DK/+v0/sc7IlQb/1y2binQOfJ41nfKEfJ//Vj5d3HMKN/14JVQ27jr3+9h9CVXVseL454TVi9f9vl5yPs4sDiIQ1+AMKTnaH7WvEepw8GnMOSwv9nOBmMX7nloiIiFK69NJLUVFRkekwMkYF7EnKdXMvtCe2AFAzc7L9pPq6uRfaT6ABoL2rF09tOYDr5l6I9q5etHeG0Li5BTUzJ9tP2K3tHt3UjK97wq51p7r77Cf81rrHXjD2t5YVSbYntrHxWNs8sqkZnf/qc23z6KbmhHFs/NMB1MycDEWS7THHxpgsfmdcbZ0he2LrzMWJrl5cO8fIxxMv7ce1cxLn7No5F+LrnrA9sXW2aRrsyWSinD+6qRlzqs93rZ8XnGJPUpxjnRecghNdvfjaMWm02p94ab997p54aT/aO0MJz5EVizNGCVJc/h7Z1Iz2zlDc+bTyEZtD53LNzMk4YfadLMbu02Gc6u6L2+Y//s+7UFWRcIyPv7jfvGaaMS84Je68OsdSM3Ny3PX41JYDaO8M4eIpE+KuO+fYrOvq0U3GcZznJFG/j73QkjSe9s4Q+iPCjskny3HHbu8MYcPz0cds7Lit/h/d1AzrjduwBtc1YsUbew5VUDbjO7dEREQ04iZMGNqnKyZOLB7mSAavvSs6ISku9Nt/xy7Hthn79qK40A8AyA/47OVE2+UH3E/LrO2T9QcAsoSk8STr29omVbxCiKT95AcwYFzJYs8P+KDI0WVFTtyXIifvwznmZGOIjX+g41jLycaU6PykGmfseUnWhxVD7PFil51xJIsx1ThkKfX1lOpcWGNJdW1pup40v4mOA0TPSbJ+k8VjHE/Y+ybKtXOsA/WvCYGKicVoPXna02MJkhT371I2/DuVK0Y6V5zcEhER0Yjr6OiBrouBN3SYOLEYJ04krjWRVoqM8tIC492xUMT+G4BrObYNAMpLC9AdigAA+sKqvZxou76w+z0ha/tk/QGALpA0nmR9W9ukivfskoKk/URUMWBcyWLvC6vQ9Dx7WdORcDtNT96Hc8zJxiBJkmv9QMexlpONKdH5STXO2POSrA8rhkQ5dC53hyLw+6SUMaYahy5SX0+pzoU1llTXliLLSfOb6DhA9Jwk6zdZPMbxJHvf8UV5cds5xzpQ/4ok4cSJbiiK4umxBCFc/y5lzb9TOWC4ciXLUtIXTPmxZCIiIqIUfADW1AVRXlqAV3Yfxt03VRtPcgHsajmO+mUz7baVS6vsNus7gq/sPozy0gKUlxXi3ltmYlfLcaxY4t6uoS6IcUUB17qS4nzc9fNLXOvqlxn7W8ua0FG/LJgwHmubNXVBlI3Pd23TUBdMGMeKJVXY1XIcmtDtMcfGmCx+Z1yTygqxZnkwLhcTSwvw6p7D9vdFX92TOGev7jmMcUUBrKqtjmtTFODXN1YnzXlDXRB79n/mWr+z+RgaYsazYkkVdjYfw8TSAowrCsT1s6q22j53q2qrUV5WmPAcWbE4YxQQcflbUxdEeVlh3Pm08hGbQ+fyrpbjmGj2nSzG4rMCKCnOj9tm9c2XwueTEo7x1zdWm9dMEDubj8WdV+dYdrUcj7seVy6tQnlZIQ4d64i77pxjs66rhjrjOM5zkqjf+mUzk8ZTXlaIPL9kx6Tqetyxy8sKcd/y6GM2dtxW/w11QQQU43EeUOC6Rqx4Y88h3xnMbpIQYnAvo+aAnH51OIXCntakBaXyZlyG/iTFdgZqH+y+xUX56O7pG/Z+RyreTMU0HHnKlbGe6b5WroZ6XDk4H6Gi0f99wFz4dypbpOPV4bFo7ty5ePbZZ4dULTnX783RasmA32+M02nNAAAL7UlEQVRW99WMKrYBv5ygWrKA3yeZ1ZJ1o4Kws1oyAOiOasmyZFQf1gQ0YVRCVmQJqhD2dkmrJUvmfma14Gi1ZLN6cKJqyQGjMq8AXNWYYRa2HahasiwblYY9VUs282SNsz+s2bG7qiVrxr4JqyXr0T4GXy3ZqMIcG6NdLVkxcg/AroTsjE+R46slC0eVa00ISMKI04pRwFkt2Ti+u1qysS5xtWSjLWW1ZEeM0WrJxnmzqyU7tomrlmy2CQmQhKNasnn+jerExjUgA5DMawEwNnGONVotOXptKuZ5CJtjs0SrJRvbRKslu/vN88tmtWSDFY9PkQaolmxef0mrJZvX2DBWS86mf6eyXTruzXzxgYhyXqqfCQL4U0FEZ+rhhx/Gjh07cPLkSSxfvhwlJSXYvn17psNKK03TjZ9MAaCHjSe31pMoVdPsZc3RJiLGnMkHABoQ6Y0+ibae7isAhG7PrSBZ2+uAFrMddEBX3dtYT7PLrSeNKhBRNcgwP56nAqqq2X1b/YQd65z9W3THoh2/OU7ZXHbG0atqruWIGh2rlSddcyybsfti82muVxy5lGL6sOJyng97G3O8cedJNWK086RH+9AdnxS28+aIzzoXzm2A6Hmzchg7zrAzdhXQ1OgxY2O1cuZss5bNNxZhpj/u/DvPnTM3zm2c0zHnGK3YNTX6N8w+rYmINXZnu3OsfWEzj3Hj1eKuq/6YMfaZy7H9Wtea6/qE+/xbj0kAUFXHNaABkZhcaeZHtq123bx+I84TC2OiG+1HQ9h5nWsCMZtTFuLklohyXqqfCQL4U0FEZ2rt2rVYu3ZtpsMgIiJKid+5JSIiIiIiopyXle/cHj16FPX19Th16hRKSkrQ2NiIKVOmZDosIspRqT62zI8sExEREY0OWTm5Xb9+PWpra7Fo0SK89tprWLduHTZv3pzpsIgoR6X62DI/skxEREQ0OmTd5LajowMHDx7E888/DwBYsGABHnroIXR2dqKsrMxTH86qbIMx1P2GU54Wgtzfk7BNkYFIYZIfPvYFICVrG6h9sPsW5EHS/cPf70jFm6mYhiNPuTLWM93XylUGYvL5FZzVdyJhm55XhH6lMHm/GZAN/07liuHIFfM9fHL53pwrmCtvmCfvmCtvmCfvRvrenHU/BfTPf/4T9957r6sK41VXXYXf/va3mD59egYjIyIiIiIiomzFglJERERERESU87JucltRUYG2tjZo5u9baZqG9vZ2VFRUZDgyIiIiIiIiylZZN7mdMGECKisr0dTUBABoampCZWWl5+/bEhERERER0diTdd+5BYAjR46gvr4eX3/9NcaNG4fGxkZMnTo102ERERERERFRlsrKyS0RERERERHRYGTdx5KJiIiIiIiIBouTWyIiIiIiIsp5nNwSERERERFRzuPkloiIiIiIiHLemJvcHj16FEuWLMGVV16JJUuW4NixY3HbaJqGBx54APPmzcPll1+OrVu3pj/QDPOSp71792Lx4sX47ne/i8bGxvQHmQW85Onpp5/G1VdfjWuuuQaLFy/GO++8k/5As4CXXL3yyitYuHAhFi1ahIULF2Lz5s3pDzTDvOTJ8sknn+AHP/gBH38pcvWHP/wBP/rRj7Bo0SIsWrQIDzzwQPoDpQHx3uwN783e8N7sHe/N3vDe7F3G781ijLn55pvFtm3bhBBCbNu2Tdx8881x27z66qvi1ltvFZqmiY6ODjF79mzx2WefpTvUjPKSp2PHjokPPvhAPPHEE+Kxxx5Ld4hZwUue3n77bREKhYQQQnz44Yeiurpa9Pb2pjXObOAlV93d3ULXdfvvOXPmiA8//DCtcWaalzwJIYSqquKmm24Sq1at4uNPJM/Vxo0bx2x+cgnvzd7w3uwN783e8d7sDe/N3mX63jym3rnt6OjAwYMHsWDBAgDAggULcPDgQXR2drq2e+ONN/Czn/0MsiyjrKwM8+bNw5tvvpmJkDPCa54mT56M73znO/D5fJkIM+O85mn27NkoKCgAAFx00UUQQuDUqVNpjzeTvOaqqKgIkiQBAPr6+hCJROzlscBrngDgueeew5w5czBlypQ0R5kdBpMrym68N3vDe7M3vDd7x3uzN7w3e5cN9+YxNbltbW3FpEmToCgKAEBRFJSXl6O1tTVuu3PPPdderqiowFdffZXWWDPJa57GuqHkadu2bfjmN7+Jc845J11hZoXB5GrXrl24+uqr8dOf/hS33XYbLrroonSHmzFe83To0CHs3bsXdXV1GYgyOwzmmtq+fTsWLlyIW2+9FQcOHEh3qDQA3pu94b3ZG96bveO92Rvem73LhnvzmJrcEmVSc3MznnrqKTz++OOZDiWr1dTUYPv27Xjrrbfw2muv4ZNPPsl0SFklEongN7/5DR544AH75kHJLV26FLt27cLrr7+OX/ziF7jzzjvR1dWV6bCIKEvw3uwN782p8d48OCN5bx5Tn1mpqKhAW1sbNE2DoijQNA3t7e2oqKiI2+7LL7/E97//fQDxrxaPdl7zNNYNJk8HDhzAPffcg2eeeQZTp07NQLSZNZRr6txzz8X3vvc97NmzZ8zkzEueTpw4gU8//RS//OUvAQBff/01hBDo6enBQw89lKnQ087rNTVx4kT77x//+MeoqKjA4cOHEQwG0x0yJcF7sze8N3vDe7N3vDd7w3uzd9lwbx5T79xOmDABlZWVaGpqAgA0NTWhsrISZWVlru3mz5+PrVu3Qtd1dHZ2YufOnbjyyiszEXJGeM3TWOc1T++//z7uuusubNy4EdOnT89EqBnnNVdHjhyx/+7s7MS+ffswbdq0tMaaSV7ydO6552Lfvn3YvXs3du/ejWXLluGGG24YUzdPwPs11dbWZv/94Ycf4osvvsC3vvWttMZKqfHe7A3vzd7w3uwd783e8N7sXVbcm0ekTFUW+/jjj8X1118vrrjiCnH99deLI0eOCCGEuO2228T7778vhDAqna1bt07U1NSImpoasWXLlkyGnBFe8tTS0iJmz54tqqqqxIwZM8Ts2bPF22+/ncmw085LnhYvXixmzZolrrnmGvu/Q4cOZTLsjPCSqw0bNoirrrpKXHPNNWLhwoVi8+bNmQw5I7zkyWksVwP2kqvVq1eLq6++WixcuFAsXrxY7NmzJ5MhUxK8N3vDe7M3vDd7x3uzN7w3e5fpe7MkhBDDM00mIiIiIiIiyowx9bFkIiIiIiIiGp04uSUiIiIiIqKcx8ktERERERER5TxObomIiIiIiCjncXJLREREREREOc+X6QCIaORs3rwZL7/8MqZOnQpN0/Dll19C0zTceuutuO6669Da2orVq1fj5MmTkGUZN9xwA5YtW5bpsImIiEYt3puJRg5/CohoFJs/fz7++Mc/Yvv27eju7sY999yDzs5OzJ8/H3v37sWpU6dw4sQJTJ8+HT09Pbjuuuvw9NNP49vf/namQyciIhqVeG8mGjn8WDLRKLVu3Tp8/vnnuPPOOyFJEk6fPg0hBE6fPo3x48fD5/OhvLwc06dPBwAUFRVh6tSpaGtry3DkREREoxPvzUQji+/cEo1ic+fOxV/+8hcEAgHccccdOHr0KE6fPo0nn3wSc+bMcW37+eef46abbkJTUxOKiooyEzAREdEox3sz0cjhO7dEY8DevXtRWVmJd955B9u2bcODDz6Inp4eu/306dNYsWIF1qxZw5snERFRGvDeTDT8OLklGgP++te/4oorroAkSZg8eTLOO+88fPLJJwCASCSCFStWYOHChbjiiisyHCkREdHYwHsz0fDj5JZoDKioqMDf//53AMDJkydx9OhRnHfeeRBC4L777sPUqVOxfPnyDEdJREQ0dvDeTDT8+J1bolHM+l5PJBJBQ0MDTpw4ASEEbr/9dixatAjvvvsubrzxRkybNg2ybLzWtWrVKvzkJz/JcORERESjE+/NRCOHk1siIiIiIiLKefxYMhEREREREeU8Tm6JiIiIiIgo53FyS0RERERERDmPk1siIiIiIiLKeZzcEhERERERUc7j5JaIiIiIiIhyHie3RERERERElPM4uSUiIiIiIqKc9/8BVKbkEsfWGoMAAAAASUVORK5CYII=
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Qb+/d//HdFoFACQTqfR1+c8WdFa47nnnkNraysAYPz48WhtbcUzzzwDAHjmmWfQ2tqKpqamIy4jotogsmmorudD/8lMbS5wiIiIiOj4N+grt9u3b8fq1asxefJkzJ07FwBw6qmnYtGiRfjmN78J27ahlMKUKVOwdOlS/7zbb78dixYtwve//32MHTsWK1asOOoyIiIiGh3ZbBbLli3Dr3/9a8RiMZxzzjm48847RzssIiIi36CL26lTp+Ltt98OLXvyyScrnjdlyhQ89thjw1pGREREo+Oee+5BLBbDCy+8ACEE9u7de8z6NgwJC4BS2vlolNCAdp7E2LY6ZnEcLW8cttIwpYQ0gFxeIdWXgWE4b6bzyg0pamZ8SmloQwICMKSEbSsoDURMCVtp2LaGaQiYZqHMlAKWWyalQCQiYVsKEIBWgNJODqQUyOUVDEPAkAIxUyBraWgAtq2hlEY0IqG1045ladhKwTAkYhGJbF7BNAQsW8O2FaIRA0pp2MqpL6VAz95DiEQMSCmQzSnUxQ3k8gq27bYTlVDuNOQtBUMCtoJfHjElhACUrZG3NYRw6goBaO1dtwKmFMhaChFTQisNy3bGaBhOHHmr0Gc8KpG3nDpKOfmLmBJSAtmcgmUrSCmx/+AAZNRELm87Y9MaluX057VrWwqWrRGJOP3aSkMKp9xWCqaUUFpDaQDayX1w7rzcB8dk2RranSMhBfJ5hYgpIIUzrwJOHSkFTEP414rfpjvn+z4agDYNaK0RjxrIunmvj5v+14aUiEQl8nlnEgSASEQiZykYQvg5MrwcCSdH3jVkmhJKFecyGpHI5GzEooG5lhJ1MQkBIJ0rHIvHJDI5VTTfecuJ5U/7DkGahpM4CMSjEgNZ5fdjmk7ctvvYm2cvL7bS0N4nKbUzBuFeq3RkDmtDKSKqHXGrv+rbgE2VR+4YxkNEtevQoUN48skn8fOf/xzCfeZ+8sknH5O+DUMilc5j2Zou9KYG0Jyow01z2vD0K+/imkvOQqI+UhNPBMPGMX9uGx589k2k+jK4/WvnI59XuCtQvqQzedyPzzAkdv3pIB5+/k389ewzkM3ZWLluCxKNcVz/xVasXLelaDxb39uLsz81Htm8wn2PbEZvagAzpk3EnM+fifUvvY0rLpyCVeu3hOZoQcd0jB0TgQZwoC/r9/N3V07Dr3//IS5sOxV3r+32z13cmcTB/gwax8Rx99qu0JhK2//v3oM4/dQElgfmYXFnEuNPimHvgSxe2fJ+WT9LOpOorzOxJzWAn/7iXVxx4RQ8/cq7oWP52avvY3Zykj92r30BFF0bS792PvoO5YvqLelMIhqRuP2HvylqMx4z8P/9amdZu/PntuGkhhh+8tw2HOjLhY69cUwU6QELT7y83Y83LE8LOqYjEhFoqIsgdTBX1o+Xw8WdSeTyNv7Xw5vLYoxFDPzkuTfx26270Zyow6J57YhEJLI5G2/v3I+zTz8Zy9d04fPJP8N5Z3+ibA62vbcXp39yHJ78+R9wzSVnIRqVSB3Mll1jpTm67YYZ6E9bZbn8794+nNLcWNbP2IYIFv/7r4qO/eK19/HEz9/z2zs0YBWN8aY5bdj85p9w0bmnFbc3L4l1L73lj3lBx3Q88PRW/3qrj5t4+Pk3/WvmSxdNQaIxhjER47i+749nQ/rMLRHVHpnpq/jZV9X1PISVPeYxmUKhvr+n4r+41X/MYyKiwb3//vsYN24cvve97+Gv/uqv8JWvfAWvvvrqMenbQuFJPwD0pgawav0WzGqfhGVrumAdkyiOXtg4Vq7bgqtnTkVvagC796f9ha1XXgvjswD82//+LWa1T0LfoZy/0Lh65lT/a6AwnuS0Fhzoz/oLDQCY1T4Jd6/txqz2Sf5i0DsnmKP7HtmM3fvTEBBF/dz70GbMTk72F5zeucvXdOHU5rG4e21XxZhK2z/njIn+4iTYTj6vcffartB+lq3pgmVprFy3xR9DpbFc9bmpRWPvTQ3gQF+m7Nro3Z8uq7dsTRd270+XtXmwPxfa7sp1W7Anlcas9kkVx25Kifse2VwUb1jd+x7ZjIP9Odg2QvvxcnigL+Mv+kpj3JMawKz2Sf7xu9d2wxASfYdySE5r8fP+uemnhc5BcloL7n/0Nf/ehxah11hpjva48ZfWO2vy+NB+lI2yY7OTk4vaKx3jqvVbMDs5uby9tV1FY77vkc1F19u+jwaKrpmV67Zg9/70cX/fH8/4yi0RHTMim4Z6/ZcVy2XyC0ADd6YlOt5YloX3338fZ599Nm6++Wb87ne/wz/8wz/gpZdeQsMQ79kj3XW6N1V4olo4NoDG+ohzXAj/T00dz39yqto4ACAeNUPLg+M7Hnnj8sbhjcGfn6K6A+7bT4vH6tWtdE6w7XjUhBTl/RgSoefaSg0aU7D9YP2wdir148U02FjCzg+b+0rXQzxqhh6rFFc8aiIeLTweLO5qeSrNfbBssOvYi9uLJdh/PGpCa+2fF/w6WNc77sVXKZbSHFWKqdJca63LjhmBlwMrtVdpDrzclD4Ozk9wXPGoeczu++P5e8uR4uKWiIiIqjrllFNgmiYuv/xyAMCf//mfI5FIYMeOHfjMZz4zpDaO+M/0GRLNibqiJ43NiTr0pfNoTtQBWmPPnr7j/09OVRkHAGRyVmi5N77jljuuvnQeEVP4Y/Dmp3Q8QoiysXp1K53j5ag5UYdMzoLSsbJ+bIXQcw0pB40p2H6wflg7lfpRuvi6rNRX2Plhc1/pesjkil/T847ZKlaxft7S/uPB4q6Wp9Lch+VwsLi9WIL9Z3IWhChcO8Gvg3W94158XuyD5ahSTJXm2vvoRfBY8B3CldqrdG14uSl9HJyf4LgyOeuY3PfH/ffMKqr9mT6+LZmIiIiqampqwowZM/CrX/0KALBjxw7s27cPkyZNGvG+TQBLOpPOQg/wP9+2sXsXlnQma+a39GHjmD+3DY9v2o7mRB0mNtXjlpLyWhifCeDWv52Bjd270Dgmivlz29CcqMPjm7b7XwOF8XRt7cG4hhgWdEz3yzZ278Kiee3Y2L0LN81pq5ijBR3TMbGpHhq6qJ+F103Hhq6dWDSvvejcxZ1JfNB7EIvmJSvGVNr+6+/sxuKSeVjcmUQkIrBoXjK0nyWdSZimwPy5bf4YKo3liZe3F429OVGHcY3xsmujuam+rN6SziQmNtWXtTm2IRra7vy5bZiQqMfG7l0Vx24phQUd04viDau7oGM6xjZEYRgI7cfL4bjGOP752vLysQ1RTEjUYWP3Lv/4onntsLVC45gourb2+Hl/efP7oXPQtbUH37rmXP/eh9Ch11hpjia48ZfWe2vnvtB+pIGyYxu6dha1VzrGm+a0YUPXzvL25iWLxrygY3rR9Tb+pLqia2b+3DZMbKo/7u/745nQpa+9fwwc8W+Hh6iWf9NRy7EDjP9w1Pf3QHU9X7E8ds4FyFZ4i3BYWWNDHH39marnHUm7QTL5BaQbWiqWHw1eO6OnlmOv9tvhE83777+PJUuW4MCBAzBNE//0T/+Eiy++eMjnH83P5sJuyc6chO2WXAvXWaXdkuMxEzpvA6jN3ZLHj2/A3gPpwG7JumzHXdMQMN0dkRUA091V19u5t+puyZZydhUO2y1Za0TN0t2SnV1ny3dLdnZWLt0tOW8pRNydbIt3S3bbKdstWRTtJHw4uyXnLAXT2y1ZaRjursXScHYc9tr0dku2be3vtFu8W7K7y7QpoLVzHUUjzq7Hll3YDdnfLVlpf5fm4t2SNUwpnN2SAUCV7JbsXouD7pbs5TC4W7Lbj79bsoA/HsOdc6U0bK2hFRCPSneHZI36uOF/bUiBaFQiV7Jbct5SkEIU2hzKbsnaGa+zW7JCzG3X66d4t2TnWGG35EIfeVs5O0u7cwsBQKOwW7J2r3nD3S3Z7bf6bsmAYeCY7ZZcC98zK6n2s5m/GCAiIqJBnXbaafjJT34yKn3btoIAYACAHTg+KtEcOW8cpvMAyna+TjTG/SeZhXJdM+OTUkC4T8YVCnOlcoHxWoBlOSMScObOLwNg24XR+nOt4CyEA18PBLb5l+4/lXP6LmrTAnKW7fRlFY57db32NYAW90m+7dbJD6iydoKxqZJ+rEB56Vsig2Ox3a+1G4M3dmWXt5kN5Mo7lg/0Y7oDbmpyYg+OzWtX24V7xAz0awT69ebC68srL5o7N/dB/jjdHJpwfilhB9oJji+YD++4Zdv+AksAyA3YfnkuOAc2kHfLPF4uKs1FMO/eMe96gXLmVKLQrtdPJl2SdxvIpu2yPrxYPlGyQMy67Xr9WHm7LI/+9WAV5700V3Rk+LZkIiIiIiIiqnlc3BIREREREVHN4+KWiIiIiIiIah4/c0tExw1TKNT394SWqXgjMiY39iEiIiKicFzcEtFxQ2TTUBV2U5bJLwANXNwSERERUTi+LZmIiIiIiIhqHhe3REREREREVPO4uCUiIiIiIqKax8UtERERERER1TwubomIiIiIiKjmcXFLRERERERENY+LWyIiIiIiIqp5XNwSERERERFRzePiloiIiIiIiGoeF7dERERERERU87i4JSIiIiIioprHxS0RERERERHVPC5uiYiIiIiIqOZxcUtEREREREQ1j4tbIiIiIiIiqnmDLm5TqRS+9rWv4dJLL8UVV1yBf/zHf8T+/fsBADt27MCcOXNw6aWXYs6cOdi5c6d/3kiUEREREREREYUZdHErhMANN9yAF154AU8//TROO+003HvvvQCApUuXoqOjAy+88AI6Ojpw2223+eeNRBkRERERERFRmEEXt+PGjcOMGTP8x+eccw4+/PBD7Nu3D9u2bcPll18OALj88suxbds27N+/f0TKiKhc3OpHfX9P6D9T5Uc7PCIiIiKiY8Y8nMpKKTz66KOYOXMmenp6MHHiRBiGAQAwDAPNzc3o6emB1nrYy5qamoZz3EQfCzLTB9X1fGiZOOeCYxwNEREREdHoOazF7Z133on6+npcd9112LZt20jFdNTGj28Y8T4mTGgc8T5GSi3HDjD+Imo/0BAPL4uYiFYqG6y8QlljQ3xE2h1SeX0MY44yd7x2Rk8tx06OmTNnIhqNIhaLAQAWLlyICy+8cJSjIiIiKhjy4nbFihXYtWsXVq9eDSklWlpasHv3bti2DcMwYNs2ent70dLSAq31sJcdjn37+qGUPuxkDNWECY3Ys6dvxNofSbUcO8D4S9Wns1D9mdCyWN5CtkLZYOVhZY0NcfT1Z4a93aGWy3QW6aPIHa+d0VPLsUspjskvTGvFqlWrcMYZZ4x2GPQxYBgS2hCwbA2lNExDwpACllKImgZyeQVba5hSoC4qkc4p2LaCISViUYls3kbENJC33OOGhCEBKaV7TMOQAkICUggopWHZGlIC8aiBbL5w3sFDGWjTgBCAVoCtnOPRiEQ+ryClQC6vYBgChnT+AUAurwChATjtR0wJASBvawinCpTSMAwBKQRyeRuGIRGLSNgasKzCmCIR55OC3nhMQyJiSr8fL6ZYRCKbVwCAWERi74E0ZNSAgEDezWXEFBBef27bUnjtOHmJuu0E+xduvJbSfv4iESef3vi8PBpSwDCc/NqWhjQEbEvDVoXYbbeuM79unzk3BjeXfW7uldKIBs6RUiBiCmjtzIeXYykFAA0BASkFbPf5vq00DCEQiQhYVqEN0xBQ2qkjhUDeUqiLFeY/GjGgtNO3ZWtYtoJSgGkKv74hhX8NmIZANCrdPhR69h5CJGLAMJzYpRSwSubLtrU7N05unNzbEBCAgDtGL/7ifNvuXHhzqrR26rvzVheTGMgF7wHnnopFJHLufWAawrluBk6Mj6sNaXF7//3344033sB//Md/IBqNAgDGjx+P1tZWPPPMM7jyyivxzDPPoLW11X/78EiUEREREVFtMwyJQ3kbqf1ZrFy3Bb2pATQn6nDzvPNQH4vgo/4c7l7bjd7UAK66+HRcdO5pWL6my6+3uDOJkxqj2Hsgg7vXFo7fdsP5sCyFZYG637rmXESjEivWvhra3oxpEzH3krOw7sW3cMWFU7Bq/Zaifra9txeTTzkJDz77JlJ9GSzomI4JiTg+6s9j/UuFcxKNcfz9X/0PZLI2fvqLd8vamj+3zW/j9q+dj2xelY0pFtSWxdQAACAASURBVJG4/Ye/8Y/92z/8P+gfsIrGuLgziTFxAz/+6VZcc8lZ2PreXpxzZjMO9BXnMthfaduVcjqmzsT+j7K475HNfm7mfP5M3L22G4nGOL56xTS/zOtjXEMMr7/Ti9ZPjffnbMa0ibj+i2cXxeTlOdjnwuumY0xdBN/54W+QaIzj+i+2lo0hMTaGj/pyuP/R1/zjN81pw9OvvIu/+fwZyOVUUdniziTWvfgWfrt1t99GLGrgsQ3v4KrPTcV/9x7E6acmsHxNlz+mfQfSmPzJk8pyuKBjOh54eitSfRk/n2d/KoHZMyaV1V3SmUQ8biA9YPl5aE7UYenXzkffoXxR3pZ0JiGlwMPPvxl6zf3itfexbUeqLB83zzsvdLzb3tuLHz61Fc2JOiya145XtnyAC9tOxfqX3vbzsKQziQnj4ifEAnfQDaW2b9+O1atXo7e3F3PnzsWVV16Jb3zjGwCA22+/HQ899BAuvfRSPPTQQ/jOd77jnzcSZURERDR6Fi5ciCuuuAK33347Dh48ONrhUI2yAOzen/afuANAb2oAB/tz0Br+4gAAZicn+wsir97yNV3QNvxFn3d8TyrtL2y9Y/c/+hoO9ucqtjerfRKWr+nCrPZJ/iIj2E9yWgtWrtuCq2dORW9qAPc9shlKOX0Hz7l65lQc7M9h5botoW0F29i9Px06pt3700XHLFuXjXH5mi4IITGrfRKWufH1huQy2F9p25VyalnaX4R5ufHm4uqZU4vKvD56U2kkp7UUzdms9kllMXl5Dp5/70Ob0evGdfXMqaFjgBb+Ys47vmq9k+OD/bmyMm8ug230HcphVvsk3PfIZpxzxkQ/Dm9MZ00eH5rD+x7Z7OfQy+fs5OTQusvWdMEQsigPvakB9O5Pl+Vt2Zou7PtooOI1Nzs5OTQflcabnNbiP757bTdmJyfj7rXdRXlYtqYLmZyqfmN+TAz6yu3UqVPx9ttvh5ZNmTIFjz322DErIyIiotHx8MMPo6WlBblcDnfddRfuuOMO/08DDgX3w6iulmMHDi/+3lQa8ajpP0n3xKMmpEDRcUOirF5vagC2UqHnh9WNRwtPd0vba6yPoDc14P9feq7W2i8PO+adEyyv1JZXZyhxAijLRbB/rw+tdcX2gjFVy4FXp7S/0vFVitnLR/A8rzysrbAxVyqvlIOwPkrLgn3Eo+XXjdenrdSQcthYH4EhK89faR6A6nPtxVRaZsjwfFTrt/T8sDzYSqOl5D6t9e87YQ5rQykiIiI6MXn7X0SjUXR0dODGG288rPO5H0ZltRw7cATxGxKZnIXmRF3Rk/VMzoLSsaLjtkJZveZEHQwpQ88Pq5vJWf7j0vb60nk0J+r8/0vPFUL45WHHvHP60nlEzPLjwba8NoYSJwAoHT52IYTfhxCiYnvBmKvlwKtT2l/p+CrF7OUjeJ6Xi7C2wsZcqbxSDsL6KB13sI+8pcuuG69PQ4Zfj6U57EvnMXZMrGLd0jwMNtdeTKVltgrPR7V+S88Py4MhRdF9Wsvfd6rthzHo25KJiIjoxJZOp9HX5zwJ0lrjueeeQ2tr6yhHRbXKBDCxqR7z57ahOVEHwHnyPbYhCiGARfPa/eMbunZicWeyqN7iziSEASyaV3x8QqIeS0rqfuuaczG2IVqxvY3du7C4M4mN3btw05y2sn66tvZg/tw2PL5pu/85TCmdvoPnPL5pO8Y2RDF/bltoW8E2JjbVh45pYlN90THTEGVjXNyZhNYKG7t3YYkbX3NILoP9lbZdKaemKbCgY3pRbry5eHzT9qIyr4/mRD26tvYUzdnG7l1lMXl5Dp6/8LrpaHbjenzT9tAxQGh865pzi47fNMfJ8diGaFmZN5fBNhrHRLGxexcWdEzH6+/s9uPwxvTWzn2hOVzQMd3PoZfPDV07Q+su6UzC1qooD82JOjQ31ZflbUlnEuNPqqt4zW3o2hmaj0rj7dra4z9eNK8dG7p2YtG89qI8LOlMIh49MZZ9Qgdfy/6Y4G+HK6vl2AHGX6q+v6fi37mNnXMBsq//suK51crDyvzdkoe53aGWy+QXkG44vJ3Tg3jtjJ5ajp27JTvef/99fPOb34Rt21BKYcqUKbj11lvR3Nw85Db4s7myWo4dOLL4i3dLBkx391zL3TU3fLdkZ5feWFQiaylEjMDOyO75Ujo73Hq7zxbtlqw0pBCIR72dgp3z4lGJTE4VdkvWhR2FB98t2RnP0HZLdtoo3i25sCsxBJDPF3a4Ld4tWfvnZi0FoeHEZykIIfx+i3dLVn7blXdLLvRftFtyhd17y3dLFrAtZ1yW5ZznxV5xt2Q3v4YUiJoCAzk1yG7J2s+ldHMv4Hx/trUGSnYbtizlz3X13ZI1ohFZsluyhtJOvGW7JVsKpgzullzIt7dbsiGFM4eB+SrslhzIQ145l07pbslR55obdLdkt35ht2T3HnDnwd8tWTn3UNhuybX8fafaz2a+LZmIaoIpFOr7e0LLVLwRGZMLEKKRctppp+HJJ58c7TDoY8S2FWA7byGUAGABNpxFS96yIeA+SVXAQPCxDeQGnMdWPnDcApRTvXDMre9to+Md8873zhubqEc27TzJD/abt2y4X5b14dX1GACUu2GPLDkOVdxGzm03OCbLLjlmFfoXJed6/eYsu2iB4uVS5wEd0nYwnlxJToN1gvmzbKdecHzeOapwCmwrPPbg/OZK5lUBaDypEZl0HwwAdkn7XkjeeL3YPSpQFow3+DgYo3LbyA0oPw5vTLl8SbyqUL9o/lRh/iSAiW7+Lfd8C+XzVSkPnqJ8DxTn2yvz5rS0fiZdcg9YTj95uzjXWSuQiI85Lm6JqCaIbBqqyqu6aODiloiIiOhEdmK8+ZqIiIiIiIg+1ri4JSIiIiIioprHxS0RERERERHVPC5uiYiIiIiIqOZxcUtEREREREQ1j4tbIiIiIiIiqnlc3BIREREREVHN4+KWiIiIiIiIap452gEQUWVxqx8y01ex3FR55I5hPERERERExysubomOYzLTB9X1fMVycc4FxzAaIiIiIqLjF9+WTERERERERDWPi1siIiIiIiKqeVzcEhERERERUc3j4paIiIiIiIhqHhe3REREREREVPO4uCUiIiIiIqKax8UtERERERER1Tz+nVsiIqITwIsvvli1/JJLLjlGkRAREY0MLm6JiIhOAD/72c+qlnNxS0REtY6LWyIiohPA8uXLRzsEIiKiEcXP3BIREZ1A9u7diyVLluCGG24AAPzhD3/AY489NspRERERHT0ubomIiE4gixYtwgUXXIDe3l4AwOTJk/Hggw+OclRERERHj4tbIiKiE0gqlcJll10GKZ2nAKZp+l8TERHVskF/mq1YsQIzZ87EmWeeiXfeecc/PnPmTHzhC1/AlVdeiSuvvBKvvPKKX7Zjxw7MmTMHl156KebMmYOdO3cedRkREREdvfr6eqRSKQghAACvv/46Ghsbh3z+9773vbLnBERERMeDQTeUmjVrFq6//npce+21ZWWrVq3CGWecUXZ86dKl6OjowJVXXomnnnoKt912m/+WpyMtIyIioqO3aNEi3HjjjfjjH/+IuXPnIpVKYeXKlUM6d+vWrXj99ddxyimnjHCUdLQMQ0JGDOTyChAaAgLSELAt7by0oQGlNKQUiEUksnkF21YwDYlIRCKbU1Baw5ACQjj1DVPCyivYSiMakVBKw1Yavak0YEoYsnDMNAQMQ8JWCrYNKK1hSgENp61YTMKyNCxbQ7n1I6aEpTRs75gpIIWA0hpSCNh2oG0pkM0rGNIZV9QUyOScMcRjJvKW83U0YkApDQhACgHLVlAKMA0BCEAroDd1CEbUhNYaeUtDSsA0JIQUyGZtxKIStq39+rZSMAyJWNQZg21rGIbwxy6FAIQGIGBIwFZANCKRc3MqAzkVEhAQsJV2DmhRVMeQTpnlxhWLGMhbCpbtxLD/4ABk1HTPBwQAyy7MKwDk8sqP2ZAClu3Mn2Upv27EcOZGaQ1owFYaQjh5kFIgm7MRMSVspf3rBu5cmqbw59Ebu3ctWLZzDRmGgK2UmxNnbJat0bP3ECIRAxAClqXcdp3zDUM416/LdmM1Dadv241De3l3y7STiqJrSwPQGogYzvWSzxfGbhpOzp26Ts6U0ohHJTI5J9dSOvPtnOflEuhNpRGJR6DcvHhxCOH0BwFAOfOsvYsfAnVRiYFc4BrVznUkhZMrb3xKaUTMCrnUArGI8O8ZWwGGe2945VEDULaG5bYXc/vKWxq79x+CGTUhJZDJ2jCkgAnAtgs5r0WDLm7PO++8w2pw37592LZtGx544AEAwOWXX44777wT+/fvh9b6iMqampoOd1xEREQUYtq0aXjooYewY8cOaK3xqU99CpFIZNDzcrkc7rjjDtx7772YN2/eMYiUjpRhSGSh0X9gAOtfehtfvvjT6NragwvbTsX6l97GFRdOwar1W9CbGsBVF5+Oi849DcvXdKE3NYAZ0yZizufPxN1ru9GbGkBzog43zWnD06+8izmfPxPrX3obB/pyuP6LrVi5botf51vXnItoVGLF2lf9dr5yWSs+6s+V1eva2oPZMybhQF+2qGxJZxK2Vn4bzYk6fPsr5yEaNdB3qLid+XPb8OCzbyLVl8GCjuloHBPBd374G3z20yfjsr/8FO5e241EYxzXf7EVP/3Fu/jr2Wcgm7PLYnny53/AX88+A9DAd3/yalH7JzVE8evf9+Ds08fjp794tyhvzYk6LO5MwjQFNnX9sSiHxTk7C2/u2IuzTz85tPy6y1qRz2usf+mtsva/ff15sCyN+x7Z7B9bNK8d6196G7/dutt9nEQkIvDQc2/iyxd/Gvc/+pqf/7/90jQcGrCK5nLRvGRoPPPntmFcYwwH+3N+G2F5CObvpjlt2Pzmn3Bh26lFfZReC147saiBxza8g69cdjbSmTzufagwroXXTcePn9qKVF8G37rmXIwfF8ehPgvrXnyraFxeW/GYAQkBBV3UjzcnB0uuu3++djr+z8+2428+fwYMIbGsZOxebF+6aApOaojid+/sKctRee7b8cqWDzB7xiQMZKyi68eb3ysunFL2/66ej/y2vWs0GOuCjuloqDdxx49+G1oejPeaS84qu2cWdEyHaQo8tuEdXP/Fs2FZCnc94PT11SumFV1PzvzG8OTLf8Dv/7AXSzqTSNRHanqBK7T2fr9R3cyZM7F69Wr/ldqZM2eioaEBWmtMnz4dCxYswNixY/HGG2/g5ptvxrPPPuufe9lll+Gee+6B1vqIyqZNmzZc4yWqLbt3Aa++ULm89Xzgzd8cftnRnDtS7R7NueddCkycVLldIvJls1k88sgj2Lx5M4QQmD59Oq655hrEYrGq591zzz045ZRTcO2115Y9JxiKffv6nVfQRsiECY3Ys6dvxNofScMduzacV2Jv/+GvccOVn8GPnvovLL3hL/CdHxUe96YGAAD//i8z8Z0f/dp/vKQzWVQOAM2JOv+8G678DACE1rnx6s/iOz/6rd9OxBT4weO/L6u39Ia/QG/qUGhZsA3v2O1f+wvc/sNfh8a0bE1X0XnB8XhjueHKz1SMpVrZjVd/Fqc2N+KWH/yqLG/B2LRGUQ5Lc3bXjX+JW37wq9DyU5sbiuYpWGfpDTMqxrxsTVdRDB/09hedv6Qz6bdden6leCrlOZiHsLkMG3vYPN549WeRt3TVufDm04uxUt5vvPqz/uPDuV6qzXXe0vjRU/9Vdbylua92LQfvmeD/wfxXut+8/FUrD8Y7lFxXa6s5MQbfuGcTmhN1WP71v4Q4zhe3UgqMH98QWnbEf+f24YcfRktLC3K5HO666y7/t7nHA/4ArayWYwdOvPjr01mo/kzF8ljeQrZCebWyIzm3sSGOvv7MsLc7HOfKdBbpQfJ6ol07x5Najr3aD9Ba9e1vfxtjxozBddddBwB49tln8S//8i9YtWpVxXO2bNmC//qv/8LChQuPuN9jkccJE4b+2eHjzXDG3ptKQwqgNzWAxvoIelMD7lsoC4893nFPabnTXuG8xvqIf6y0TjxqFrVTqZ4hgXjUHLQN75g3lrCYSs8LjmcoMQ82Hq11aN6CsaFKfL2pAb+NsPLSeQqqlCMv5mAMpecH2y49v1I8leoH81BaVnr9BM8JOxaPFh5XGlcwxkp5L21/KOMYbK7j0cHHW5r7atdy8PqrdD0MNr5q5cF4K5UHx1utLffd2E65EDX9vfSIF7ctLS0AgGg0io6ODtx4443+8d27d8O2bRiGAdu20dvbi5aWFmitj6iMiKgaUyjU9/dULFfxRgC1+42aaDjt2LEDP/3pT/3H559/Pr70pS9VPae7uxvvvfceZs2aBQD405/+hL/7u7/D8uXLccEFFwypX/7iubJhj92QUNp5VaYvnUdzog62Kn7sPcn1jnuPS8uB4vP60nn/WGmdTM7yH/el84iYIrSerYBMzhq0De+YN5awmErPC44nGHOlWKqVZXIWhBCheQvGpqvE15yo89sIKz+pIVax/Uo58sYdjKH0/GDbpedXiqdSnoN5CJvLoc5jJmf5ryYONp+D5b20/aGMY7C5zlt60PGW5r7atRy8/ipdD4ONr1p5MN6h5LpaW7aK+Y+h9XH/vbTaL56PaO//dDqNvj5n0FprPPfcc2htbQUAjB8/Hq2trXjmmWcAAM888wxaW1vR1NR0xGVERNWIbBqq6/mK/2Tm+P4mTXQsnX322Xj99df9x7/73e9w7rnnVj3n7//+7/HLX/4SmzZtwqZNm/CJT3wCP/7xj4e8sKVjywRgRgQWzWvHxu5d+NY152JD107/8U1z2pwnsQA2dO3E4s6k/3hj9y4smtfuP/Y+P+gd39i9C49v2o75c9uK6nzrmnMxtiFa1M4E9zN9pfU2dO1Ec1N9WdmSzmRRG80J5zO3llJldefPbcPjm7ajOeF8xrC5qR7NiTo88fJ2P34vzo3du9A4Jhoai1f27a+cV9b+hEQdXt78vt9GMG/NCefznbZWZTkszlkSXVt7KpbbWmHRvGRo+41jos7YAse8OSg8dmLw5jmYf8NA2VxWimf+3DZo6KI2wvJQOgbvuqp2LXjtNI6JutdFPRZeVzyuhddN9+fzW9ecCw2NxZ3JsnF5bY1tiGJcQ6ysH29OSmP952unY2P3LoxtiGJJyNi92LzxhuWoPPft/rVcev148xv2f7DtsHtpQcd0TEjUVSwPxht2zzifQXfKm5vqcctXC32VXk/OeOvxxMvb/XvwiF/5PE4M+pnbf/u3f8OLL76IvXv3IpFIYNy4cVi9ejW++c1vwrZtKKUwZcoU3HrrrWhubgYAvPvuu1i0aBEOHjyIsWPHYsWKFTj99NOPquxw8LfDldVy7MCJF399fw9U1/MVy2PnXIDs67887LIjOdd/W/Iwtzsc5w7Wrkx+AWM+dcYJde0cT2o59o/T25KvuOIKAIBlWdixY4e/4/GHH36IT3/60/4vl4eCn7kdXiMRe/Fuyc6mrYYhYAV2S9ZKQxTtluzuWuzulqxLdvb1d0vWGlFTFu0OCzg7+5bvluzufuztluzuHlu0W7Jb5u3G6x8zQnZLdusaUiBrKRiidLdkjXjMcHdLLuzqXNgtudCft/sx3F2JtdbIuzvWmoZwdkvO2YhFSnZLdneRLtot2RRQdnC3ZPg58XaXDu6WLN3ddIWbX9t27w2Nst2SlfJ2cXbmKu/ucmwYhd2BbXeMQgOWG0PxbslOfe8a8HdLduuG75bstF9xt2R3Lst3Sy7EbSkNQ3g7/Gr32hT+rs620ogYomS3ZPi7AhftlqwKcwM48yDFILslB3bpHny35MIu2soO7pbs5t7fLVkHdiXWiEQklD3E3ZLde6mwW7J7jR71bskozPEQd0v2xl6LuyUf1Wdub731Vtx6661lx5988smK50yZMgWPPfbYsJYRERHRkVu9evWwtbVp06Zha4tGhm07f2ZEBI9Z7prLdh4LAFBAzrIh4D4ptIC8ZRfe2qeC5xfqqZxTYACYMN5ZnHtVDTiLQCvvdCTdf1D+mg+5dHlZ3io/5rXprkGdGJUzBO9JrLaBbK5QbmVUWZxeG8G2vRwEf7ngtancHJkA7GyhjWAMOTdeEahvoJgXd97NneEe9H7N4/4BoKJ58uoA/lQ5fdpA3rYLjy2gKeHEHjzfG4NXNzi3ynLGbwXbAWDbKOKNQ9lOKAacXAbHgMC5AsVj94qD+fRiVO67aAWAlkDug+0qFHJXOi4VuH69PHrn2oF36JZed8KNtTSnXnsy0DYAZCy7qF5+oDj3CkBzyS+mgmMIxg67+HGmwr1UOj4jUB6M17+PAnWDcXnl+ZJy230LswlgQlPhvnXa1ii5DGpSrb/yTEREREPwyU9+sujxvn37kM1mRykaIiKi4cfFLRER0Qlk48aNWLFiBXp7e9HU1IQPP/wQU6ZMKfpTfERERLXoiDaUIiIiotq0cuVKrF+/HpMnT8amTZuwZs2aQTeUIiIiqgV85ZZolMWt/oq7+Zoqj9wxjoeIPt5M00QikYBSCkopnH/++cfN36knIiI6GlzcEo0ymemruCOyOId/ZoOIhtfYsWNx6NAhtLe3Y+HChWhqaoJp8ukAERHVPr4tmYiI6ATy/e9/H/F4HIsXL8aFF16IP/uzP8MPfvCD0Q6LiIjoqPFXtURERCeQ+vp6/+urrrpqFCMhIiIaXlzcEhERnQDa2toghCg7rrWGEAKvvfbaKERFREQ0fLi4JSIiOgFs2bJltEMgIiIaUfzMLREREREREdU8Lm6JiIiIiIio5nFxS0RERERERDWPi1siIiIiIiKqeVzcEhERERERUc3j4paIiIiIiIhqHhe3REREREREVPO4uCUiIiIiIqKax8UtERERERER1TwubomIiIiIiKjmcXFLRERERERENY+LWyIiIiIiIqp5XNwSERERERFRzePiloiIiIiIiGoeF7dERERERERU88zRDoCIiIiOf1//+tfxwQcfQEqJ+vp6/Ou//itaW1tHOywiIiIfF7dEREQ0qBUrVqCxsREAsGHDBixZsgRPPPHEKEc1MgxDwgJgKw1DCpgAbFsdUd3DaWu0lMYYNQRytoatNKIRCaUADQ1DSti2hq01IoaAEAK5vI3eVBp19RHkLA3DEMjlFGylYBjSPV9DCAHLUrBsBdOQkFLAVhrQgK01oqZTz7Kd82IRiZylIIVw+lQapiEQjUhk3fajEQNaa1i2hhQC8ZhEJqcAaAgI57h0zrEsBVs5jw0JAE67SmvsSaUhogbyeY1YVPr9SSEA4bRlSIFs3oZhSETMQnvRiFPfshUMKWEaAhpALq8gpXBiNgVyltOmbTs5NgwBAMhbyrku/PFrqJCxmoZ0Y3DqG6aAsjV69h2CNCRMQyJiCmSyCnCahnLnMxaVyGQLcxKLSKQzNmIRCUs5/XmxCiGQzxfXzeYVbHdeIqZA3tIQAtDKvWYMgbqoRN4do2U7xwwpkLecPERNCctW7mOJupgztp69/ZCGgYgpAAEoBWdelC6LDwKALly3EVMim7OdMgBaa+eaUBoK8OfXlMK53txr13Tznc3ZkMKZC6Wda9Gbm2zgGo5HnXxFTemOz5nrWEQg1ZeBJcSo3dvevevkSALCGcdIxOL15cyDgFLu3Aa+X8Qihp8jKSUihoClnGtW2fC/HslccXFLREREg/IWtgDQ398PIcQoRjNyDEMilc5j2Zou9KYG0Jyow5LOJBL1kbInY4PVPZy2RktYjIs7k1j34ls40JfDV6+Yhide3o6/nn0GsjkbK9dt8evNn9uGB599E2d/KoHZMybBMAQG+mwsL2mrcUwEfYfyRccXdEyHaQp898FXkWiM4/ovtha1vfRr58O2NfrSuaLjwdhKz1ncmcQvXnsfyWktuP/R19CbGsCMaRMx5/Nn4u613X69226Ygf60hfse2Vw0lp+9+j7+53mnFbV505w2PP3Ku/jSRVPw4LNvYlxjFHMvOQvL13Qh0RjHV6+YVtZOPGbgP/7PG0j1ZTB/bhsmJOpwsD+H7/7k1dB64xqj+MplZ+Oj/mxR30s6k3j0xbfw2627i/I9rjGKOZ8/C3ev7Spqb1xjDBt+uwvTWz+BVeuddq66+HRcdO5pZXPy3gcpfOLkBr+/GdMm4m+/NA2HBqyiXHn59mLwchzsw4tVSOCu/91Vdn2k+jJYNC+J9S857cyYNtHPYWn8mZyFFWvDrwlvLq64cAqefuVdzPn8mXjuVzvw+z/sxU1z2rD5zT9hVvLPkM3byGTt0OsmOI5nf/kefv+HvZg/tw2xqIHHNryD6/7fVti2Lrsf9qYOob4uWrXNY31vh927Xo6uueSsYY3F6+vRF9/CFRdOKZr7avfk/LltGNcQRV/J/TaSuRr0M7crVqzAzJkzceaZZ+Kdd97xj+/YsQNz5szBpZdeijlz5mDnzp0jWkZERESj65ZbbsHnPvc53H///VixYsVohzMiLMB/sggAvakBLFvT5bxicZh1D6et0RIW4/I1XZjVPglXz5yK+x7ZjFntk9B3qLDI9OqtXLcFV8+citnJyejdn4YhpL9gCbYltCg7ft8jm9F3KIfe1ACunjm1rO3e/Wns+2ig7HgwtrCy2cnJ/sIWAGa1T/IXa169PW7/pWO56nPlba5avwWz2if5Y53VPskfi5ef0nYO9udw9cyp/mPL0v7CNqzerPZJ2JNKl/W9zB1rab6dMXWVtde7P43Zycn+wgMAZicnh87JOWdMLOpvVvsk2DbKcrW8JAYvx8E+vFj3f5QJvT56UwO4e22hnWAOS+M/2F/5mvDmwvv/7rXduOpzU/2y2cnJ2JMawMH+8ms1bBzeuSvXbUHfoRxmtU/C3gMDoffD6Z9MDNrmsb63w+5dLzfDHYvXl5f/od6TK9dtgRCy7D4ZyVwN+srt8KfOGwAAIABJREFUrFmzcP311+Paa68tOr506VJ0dHTgyiuvxFNPPYXbbrsNDz744IiVERER0ei66667AABPPvkkvvvd7+KHP/zhkM8dP75hpMLyTZjQOHilQfSm0v6TsMKxAUCIsvYHq3s4bQ1H7EeiUoyN9ZHQr8PqGRKIR01orUPr2EqFHo9HnaehjfWRsnKvbLDYSssMWXy8UttDOTfYX1i/YW1744pHC4+lCG83WG+wsQ5l7PGoWTaGSmMqnZPG+kjFOEtjqNSmN2eV4vW+rpa3YDyDzYUXSzCuoVw3wfrBvoNzNli+KrUZdm+PlGr37lBjGWqsXl/V5sX7urSs0nU1UrkadHF73nnnlR3bt28ftm3bhgceeAAAcPnll+POO+/E/v37obUe9rKmpqZhGzAREREdnS9/+cu47bbbkEqlkEgkhnTOvn39UEoPXvEITZjQiD17+o6+IUOiOVFX9GSsOVEHaF3e/mB1h9jWsMV+JCrE2JfOF30dMUXFemPHOG8nFSK8jiHD+8jknNdu+tL5snKvbLDYSstsVXy8UttDOTfYX1i/YW1748pb2n+sdHi7wXrV8jvUsWdyFmwVKyqrNKbSOelL53FSQ2xIMVRq05uzSvF6X1fLm6dSneBceLEE4xrKdROsXzoXleah0jVc2mbo94mRUuXeHUosh/V9x+2r2rx4X5eWVbr+jyZXUoqKvzA9oj8F1NPTg4kTJ8IwDACAYRhobm5GT0/PiJQRERHR6Dl06FDRz+NNmzbhpJNOwrhx40YxqpFhAljSmXSefAH+58PCXg0YrO7htDVawmJc3JnExu5deHzTdizomI6N3bvQOCaK+XPbiurNn9uGxzdtx4aunWhuqoetFRaHtKWFLju+oGM6GsdE0Zyow+Obtpe13dxUj/En1ZUdD8YWVrahaye+dc25/vGN3buwaF57Ub0Jbv+lY3ni5fI2b5rTho3du/yxbuze5Y/Fy09pO2Mbonh803b/sWkKfPsr51Wst7F7FyYk6sv6XuKOtTTfzpiSZe01N9VjQ9dO3DSn0M6Grp2hc/L6O7uL+tvYvQuGgbJcLS6JwctxsA8v1qaT4qHXR3OiDovmFdoJ5rA0/rENla8Jby68/xfNa8cTL2/3yzZ07cSERB3GNpRfq2Hj8M6dP7cNjWOi2Ni9CyePqwu9H97779SgbR7rezvs3vVyM9yxeH15+R/qPTl/bhu0VmX3yUjmSmith/Rr1JkzZ2L16tU444wz8MYbb+Dmm2/Gs88+65dfdtlluOeee6C1HvayadOmDcdYiY5Pu3cBr74QXtZ6PvDmbyqfW618pM6txZjOuxSYOKlyORFVtXfvXnz961/HwMAApJQ46aSTcPPNNx/Wz+eaeeUWx3635FF95RZD3S3Z2UnW34HW3y1Z+bvlFu+W7Hxdvluyc66UArbWgCrdLdk5r2y3ZHeX28IOwk5s4bslOxu6Wu6Ox+W7JTuboXljMaSAkM4uweW7JTtt+TsVGwKRiISVL90t2Z3fwXZLVhqGGOJuyVGJbFb5O0UbUiBrKRhCwDSdvHhjNA1R2C1ZAtBhuyUXcpvO2oiZ1XZLLtR1dkt2HkdMibylCrslu/kb+m7JTl/ebsnejtbObskCyt1RWmldFp+/W7L7f/luyXCuicBuydqNTxoCtlWY79DdkgF/brKBa7jabskKApmsdRzslgw3R0PfLflwv++U75asISUq7JbszusI7ZZc7ZXbI1o0t7S0YPfu3bBtG4ZhwLZt9Pb2oqWlBVrrYS87XLX0A/RYq+XYgY9n/PXpLFR/JrR+LG8hW6FssPLhPrexIY6+/sxxFdNQ240ODCC2excOpbNlZSreiIw58p8FPFq1fO3XcuzVfoCeSE4++WT853/+52iHcczYtoKA+yTJ1rCPou7htDVaSmPM2/Afq5wTsQCg3P8NOAsb7daZkCjc414dEwAsIG8Vj9g77tXzjil3UeqV59zzitpTznFRco7htpNN236bflsALNsu1FNOm96YDAATxjvxmwDsbHGbfo4CsVlWob3/v717D46qvv8//jqX3VxMYhJKNB0o/KRqU62FBkI7X7QIVG0HtLb1C6VYcKxOa0Uqw9Tgl4oFFTK13kYdrOMItt7G6U+swbEgaL8/ezHI1/YnxZ+liJQqEAhhSAzJZs85vz/2kt1kNzdIzp7wfIyM2fO5nPf57J588t49+zmJ+na8UnxXyceuI7VHuvaXiMmN10vElOjTjP9LjEHqWDop7RP7qYz/fnUdqSMSH9NufXd06ycSdWLHGnGTYyBXcqLd4swQQzTaNcapz0t7yvOc+hwn+k68DhLj0tHmpMXvpLxMEmPQPb7uolEnrcxQ13Od6EfxcjeqtL6SbdX1XCSGzuk2Bh3xuk7qa9RxFHHi81t7i2/nduLctRLBJ7YP4b4SEvtM/X3hxC8LTz0fEr87urYP7VgN6rLkUaNGqaqqSvX19ZKk+vp6VVVVqby8fEjKAOBkGB1t0tu/l9vwao9/Znswky4AAACk6/OT27vuukubN2/WkSNHdN1116m0tFSbNm3SnXfeqdraWj366KMqKSlJuyXAUJQBAAAAAJBNn8ntihUrtGLFih7bJ0yYoBdeeCFjm6EoAwAAAAAgm1xasA8YsfKjrbHLX92jKuz2vU/b7VTEp7gAAACAkYLkFhgGZnuL3IZXpaL8HotHGROn+RQVAAAAMHIMakEpAAAAAAByCcktAAAAACDwSG4BAAAAAIFHcgsAAAAACDySWwAAAABA4JHcAgAAAAACj+QWAAAAABB4JLcAAAAAgMAjuQUAAAAABB7JLQAAAAAg8EhuAQAAAACBR3ILAAAAAAg8klsAAAAAQOCR3AIAAAAAAo/kFgAAAAAQeCS3AAAAAIDAI7kFAAAAAAQeyS0AAAAAIPBIbgEAAAAAgWf7HQAwEuRHW2W2t2Qtt91ORYYxHvSfbbgqbD2QsczNL1a7XTTMEQG5p7m5WT/96U/1r3/9S+FwWOPGjdOqVatUXl7ud2hAzrMsU44MRV1X+Xm2OqOuJE/yDNm2IXlS1PHkuJ4sy5BlGsq3DX0SceU4rizLVMg21d7hKBQy5bmxuoYhHf+kXXZ+SK7ryZPkup4cx5NlGrIsQ5LkuJ5c15NpGLIsyXGVfJwXNhV1PEUdV64r2ZYh247to9Px5HqebNOQJ8nzPNmWKdsy1NHpxvowDdlm7Dg6Iq6ijifTlGzLlOTJcaWQbaoj4sg0YomH47j+PRkY8UhugVPAbG+R2/Bq1nJj4rRhjAYDYXS0yf3rmxnLzJorpCKSW8AwDP3gBz/Q1KlTJUl1dXW69957dc899/gcGZDbLMvUsbZO3b2+QRd99lP6xn/8Lz2/5X3NuXiCdrx3ULOmjtOxlg49+Nw7amw+oYqyAq284ctq7nS1Zn1Dclvtwina8d4hff6cUWl1b19Uo8ICWy1tEXV2errvmR3JstsWTpZpmD36eX7L+3rr74eS7R3PVd2Gt9XYfEJTLzhL3//G53WsNT2mW7/7JW38wz/1n187T5Zh6p6UPpctqFZRQUh3Pv6X5LYl8ybpzKI8bW3Yp4snjdErf9yr//vPI7p9UY3KCkMkuBgyXJYMAAB6VVpamkxsJWnixIn6+OOPfYwICIaopLvjieDV08/V2g3bNXPKOD30/DuaVTNejUfbkkmkJDU2n5AhI5mQJrat3bBd06vH9qh7z/oGRaOejrdGkoltoux4ayRjPzOnjEtrf7w1kqwzc8o4NTb3jOn+Z/9HM6eM0/HWSDKxTZTd+5sdOnS0LW3bg8+9o8PNbZpVM15rN2zX1dPP7Yp3GMYdpy8+uQUAAP3muq6effZZzZgxY0DtRo0a+qsgRo8uHvJ9DJUgxy4RfzaNzV1Jn2XGEr/iwpAam0/IMqX8sJ0sTzAN9djW2HxCnudl3G4amfvJtC2x/9TH+eGudCBR1lu7TGWpfaRuSxyzZXZtl2GkjTevHf8EOfZsSG4BAEC/rV69WoWFhVqwYMGA2jU1tcp1vSGKKvZH2uHD2dc+yGVBjl0i/l5ZpirKCtTYfEKOK1WUFailrVMVZQVyXKk9Ek2WJ7ieemyrKCuQYRgZt7te5n4ybUvsP/Vxe6Trs9SWtk6F7Mz76a0stY/UbY6blzzWxHZ5XnK8ee34J8ixm6aR9Q1TLksGAAD9UldXp3379umBBx6QafInBNAXW9J/LapRRVmBXnxjt2oXTtHW7ft0y9xJeq3hQ1WUF2rJvEmxpE+x5M+Tp+XxNolttQun6I0d+3vUvX1RjWzbUElRWEvnV6eVlRSFM/azdfu+tPYlReFkna3b96mirGdMt373S9q6fZ9KisK6vVufyxZU66zywrRtS+ZN0uiyQr3W8KFqF07Ri2/s7op3GMYdp6+Tfn3NmDFD4XBYeXl5kqRly5bp4osv1t69e1VbW6tjx46ptLRUdXV1Gj9+vCQNugwAAPjj/vvv186dO/WrX/1K4XDY73CAQHAcV6WFIa29aZqirqf8PEs3Xn2RJGn2tAmybUNF+SGtuWla2mrJo4oMrfnxtNjKx5ahkG3qq18aq1DI1NofT4uvlmyoIM+U40ph25Inxdq4niwjvlqyEdvmxetblqEbr75IP7gqfbXke26allwZ2bZNFRWEdM9N0+R5sZWXPUO64ZsXxVZTtmKxdV8tee2PY8doGrE6kjT74gkK2abmfu18ffey81ktGUPulLx58tBDD+m8885L27Zy5UrNnz9fV111lV566SXdcccdeuqpp06qDAAADL/du3dr3bp1Gj9+vObNmydJGjNmjB555BGfIwNyXyKZsyVF210ZaWWx/xvxckUlV1JbJH1bNOrIluRFYn1Z8fYlZxSmXVqabCPJTenbSNlmpLSPnIhVMuP/5Mb2ldim+LZEH64jRZTShys5kpz4/Q4z7TsadZL7c3oZJ+BUGJIrA5qamrRr1y49+eSTkqTZs2dr9erVOnr0qDzPG1QZ99IDAMAf5557rt5//32/wwAAoFenJLldtmyZPM9TdXW1li5dqgMHDuiss86SZcXep7EsSxUVFTpw4IA8zxtUGcktAAAAACCbk05un376aVVWVioSiejuu+/WqlWrtGjRolMQ2uBxu4HeBTl2KUfjd49KRfnZy0O2wvHy4u71Usp6azfg8iFoW1yUn3Mx9bdfKcPY99W2ME9n5NDrLSdf+/0U5NgBAEAwnHRyW1lZKUkKh8OaP3++fvSjH2n58uU6dOiQHMeRZVlyHEeNjY2qrKyU53mDKhsIbjeQXZBjl3I3/sK2Drmt7VnL8zqj6mhtV3FRvlq61UuU9daur36Ho20i9lyKaSD9hqUeY99XW7OtQ2058nrL1dd+fwQ59t5uNwAAAHLLSa3j39bWppaW2B8snufplVdeUVVVlUaNGqWqqirV19dLkurr61VVVaXy8vJBlwEAAAAAkM1JfXLb1NSkxYsXy3Ecua6rCRMmaOXKlZKkO++8U7W1tXr00UdVUlKiurq6ZLvBlgEAAAAAkMlJJbdjx47Vxo0bM5ZNmDBBL7zwwiktAwAAAAAgkyG5FRAwEuVHW2W2Z/7eoO12KjLM8QAAAADoQnIL9JPZ3iK34dWMZcbEacMcDQAAAIBUJ7WgFAAAAAAAuYDkFgAAAAAQeFyWDABZ2IarwtYDWcvd/GK129wDFQAAIBeQ3AJAFkZHm9y/vpm13Ky5QioiuQUAAMgFXJYMAAAAAAg8klsAAAAAQOCR3AIAAAAAAo/kFgAAAAAQeCS3AAAAAIDAI7kFAAAAAAQeyS0AAAAAIPC4zy2QIj/aKrO9JWOZ7XYqMszxAAAAAOgfkluMOL0lqFYoLKcze4pqu52KvL01Y5kxcdopiQ8jh224Kmw9kLHMzS9Wu100zBEBAACcvkhuMeKY7S1yG17NWBaaOE2df30za1sSWAyE0dEmN8vryay5QioiuQUAABgufOcWAAAAABB4fHILAEOgt0uWJS5bBgAAONVIbgFgCPR2ybIkhadeNrDvhrtHVdjWQVIMX9TV1en3v/+9PvroI7388ss677zz/A4Jw8yyTDkyFHU9hUOmPM+TJ8nzpINNnygctuW4XqzcNuW6nqKOJ9M0FLIMeZIinY5sy1TINtXR6cowYn27bqyeZRpyPU+K/Sd5kut5si1DpmGoo9NVOGTKkNTpePK8RDvJdaWo48myDIVtU5FOV44be2ybhgzTUKTTlW0Zclwv1rkX219jc5uMkCXPi8XjeVI4ZCrquHIcT5ZpyIy3tyxDIburzDSNZBvLMhQ/JDmeJ8sw5MZjcJye4yFJjuPJ9TyF7NjFlFHHk+vGjjlkmzrREVU4ZMnzPHVGY+1tK7YXx3XVeLRN+YUhdUY9OY6XPOZwyFRnp6uo48qyTOWFTLVHHLmukn13Rt1kTJZlSPJkyJBhSp4r2bahjoibfH5s01BH1JVpdMXgul7yuTLM2Dik1pfjyonHEJVi8ZmGwpahiBMbe1mmbEmO4w7Z6xenD5JbAPBBb8lvxu+GF+XLbW3nu7zwxcyZM/X9739f3/ve9/wOBT6wLFPH2jp19/oGlRXn67o5F+jFN3ZrzsUT9NDz7yS33ffMjrSfG5tPqKKsQEvmTVJ+nqVf/e+dKi0Oa+7XztfzW95Ptk/UWzq/WnkhU89lKFu2oFqb/7JPX/+P8WrvcPTgc7GyqRecpXlf+5zWbGhI1q1dOEXPb3lfb/39kCrKCrR8UY0k6b//Z78unjQm475vmTtJL/+fPZpz8QTteO+gLp40VmtT+lwyb5Ke2vRePP7PpZUl2l55yQTl51kyDVOWZajtRFRv7fxYl3xprNasT++rtDhP7ZGo6ja8rbLifN34rQvTjisR99u7DuoLnx2dcTw9T/rjXz/SrKnjdKylI61t7cIaPb/l/+mtvx+KjdFln0uLYfnCGj0XL0/tU5LyQpZs21R7i6N71vccg+aWdi2ZN0lnFuVpa8M+VVednRy77s9nSVFIxXm2mloiaX0tX1Sj5zZ37f/2RTUqKwyR4OKk8Z1bBE5+tFWFrQey/rPdTr9DBIARZfLkyaqsrPQ7DPgkKunueGLy7Rnn6r5ndmjmlHHJRCaxrfvPktTYfEIPPveOjrdG9O0Z52rmlHFau2F7WvtEvfue2aFjrR0Zy+79zQ5dPf1cHW+NJJM4SZo5ZVwysU3UTfSfeHyspV1r1jdoVs34rPt+6Pl3kttj9Rp6HENX/A0Z2yaO81hLu2zT1H3P7NCsmvHJpDK1r8ajbTreGkmOWffjamw+oTXrGzS9emzW8Wz5JKJZNePVeLStR9u1GxqSYzBzyrgeMaxJKU/t83hrRIebT8gyzGQy2n0MEj8fbm7TrJrxaWPX/fk81NSmiKMefa1Zn77/e9Y3KHpKXq043fHJLQKnt9WQJVY8BoBcNGrU0F9xMHp08ZDvY6jkcuyNzW3JxKS4MKTG5hPJ/6du6/5zV/sTyg/byg93Pe6rXqYyy5Tyw3ZaWbZ+igtDyceJNpbZ+74T2xP1svXZW9v8cOxPa9NQr30l6iWOIVu/nuf12j7TmHSPtz9j1D2mbPtNjTU/bPc5pvnxy9X7s38ZRk6fB9kEMeaEIMeeDcktAAAYck1NrXJdr++KgzR6dLEOH878PfZcl/OxW6YqygrU2HxCLW2dqigrSP4/dVv3nxMqygrUHomqM+olH/dVL1OZ40rtkWhaWbZ+Wtq6ruJKtHHc3ved2J6ol63P3tq2R2KfP7peXq99JeoljiFkGxnrGUbm7Yn2jpvXY0y6x9ufMeoeU7b9po5BeyQqx83r8/m0zN77SjyW5+X2eZBBzp+7vQhy7KZpZH3DlMuSkZOyXXqsQ/u47BintcQqzNn+5Udb/Q4RwAhjS/qvRTWqKCvQb7ft1tL51dq6fZ9umTspbVv3nyUlv6tZUhTWb7ft1tbt+1S7cEpa+0S9pfOrVVqUl7Fs2YJqvfjGbpUUhbVkXlfZ1u37tHxhTVrdRP+Jx6XF+Vq+qEavNXyYdd+3zJ2U3B6rV9PjGLrir8nYNnGcpcX5irquls6v1msNH2r5op59VZQXqqQonByz7seV+F7qGzv2Zx3P4jPCeq3hQ1WUF/ZoW7uwJjkGW7fv6xHD8pTy1D5LisIaXVYgx3N1e4a4f7ttd/Ln0WWFeq3hw7Sx6/58njWqUGFLPfpavih9/7cvquETN5wShud5Q/c2qk94dzi7oMRe2Hog46XHxUX5inx2sjp6WYU2b+K0rOW9lZ1M2/72W1yUr5bWdt/jHUzbROy5FNNA+g3/8+0eY+9XvINp29/xN2uuUFtRbn03Mii/dzLp7d3h09GMGTO0bt26Qa2WzNycXRBi7221ZDe+QrLjxlbrDSVWS3Y9mUbqaslucqXejk5XhqnYisiZVks2JLndVkuOugrbmVZLNpL7s8z+rpas5GrMpmnIMNT3aslRV5aZYbVkI7a6cOpqya4XO/ZBrZbsebLNxGrJTnK8Ox0vbaVix42NVX6e2bVashcfg+RqybH9x1ZLdtNWYu6MurExMwyZ8T4NSYZpyHO9jKslR+KrJVtW7Lhdx0s+V4NZLTkx8EFdLTkI5242QY69t7mZN0kAAECv7rrrLm3evFlHjhzRddddp9LSUm3atMnvsDCMEomHLcmNdCUhhqSzU/5ItlLK7WRbdT2OSp1RJ3bpYHy7JcUS2ZT9JZJES7HE0em27+Slhynt7PjjzqgjI2V/8d3IlOQ6XX0n+h89KhZ/YrshKRoPOjW2RP/RaHpZoo3nSKlv3yTicqIp8Tld45FolzpmZuLY4seResyJ8XSdrrapCUrymONtk22iUiQ+5mb8cVp5PHYpPf5IpCs+xZ8Dq1sMif0qfmzd6yeLHLcrPsdTp5Mef2pd4GSQ3ALACJK4bDkT7pGLwVqxYoVWrFjhdxgAAPSK5Ba+yY+2ymzPfDmE7XYqMszxACNBb/fP5R65AABgJMvJ5Hbv3r2qra3VsWPHVFpaqrq6Oo0fP97vsHCK9XZLH27nA5x6fKoLAABGspxMbleuXKn58+frqquu0ksvvaQ77rhDTz31lN9hAUCg9fapbnjqZVmvpJBIfgEAQO7LueS2qalJu3bt0pNPPilJmj17tlavXq2jR4+qvLy8X32YptF3pZM0HPsYKsMVe57TJrMj+21JLFPqLMxy82g7LCNTWUFe9rK+2vZVdjJt+9tvQZ4MN5S5bDjjHUzbROy5FNMA+lVBUc+x9yvewbQd4vE3nKi8XduzdhuedIms6ImMZW5ekTqswuwxKbi/M4Mady5ibu5dkGOXiN9PQY5dIn4/BTX23uLOuVsB7dy5U7fddlvaKozf+MY39Itf/EIXXHCBj5EBAAAAAHKV2XcVAAAAAAByW84lt5WVlTp06JCc+E3AHMdRY2OjKisrfY4MAAAAAJCrci65HTVqlKqqqlRfXy9Jqq+vV1VVVb+/bwsAAAAAOP3k3HduJWnPnj2qra3V8ePHVVJSorq6Op1zzjl+hwUAAAAAyFE5mdwCAAAAADAQOXdZMgAAAAAAA0VyCwAAAAAIPJJbAAAAAEDgkdwCAAAAAAKP5PYkvfXWW6qqqtJvfvMbv0Ppt5///Oe64oordOWVV2revHl69913/Q6pT3v37tXcuXN1+eWXa+7cufrwww/9DqnfmpubdcMNN+jyyy/XnDlzdPPNN+vo0aN+hzVgDz/8sM4//3z94x//8DuUAeno6NDKlSt12WWXac6cOfrZz37md0gD8vrrr+ub3/ymrrrqKs2ZM0ebN2/2O6Ss6urqNGPGjB6vkyCfvwgm5ubhEeRzm7nZX8zNw+e0m5s9DFpLS4v3ne98x7vxxhu9X//6136H02/btm3zIpFI8ueZM2f6HFHfrr32Wm/jxo2e53nexo0bvWuvvdbniPqvubnZ+8tf/pJ8vHbtWm/58uU+RjRwO3fu9K6//npv+vTp3vvvv+93OAOyevVq7+677/Zc1/U8z/MOHz7sc0T957quN3ny5OSYv/fee97EiRM9x3F8jiyz7du3ex9//LF36aWXpr1Ognz+IniYm4dPkM9t5mZ/MTcPn9NtbuaT25Owdu1aXX/99SorK/M7lAG59NJLFQqFJEkTJ07UwYMH5bquz1Fl19TUpF27dmn27NmSpNmzZ2vXrl2BeYe1tLRUU6dOTT6eOHGiPv74Yx8jGphIJKJVq1Zp5cqVMgzD73AG5JNPPtHGjRu1ZMmSZOyf+tSnfI5qYEzTVEtLiySppaVFFRUVMs3c/NU9efJkVVZWpm0L+vmL4GFuHh5BP7eZm/3D3Dy8Tre52fY7gKD6wx/+oOPHj+uKK67QG2+84Xc4g/b0009r+vTpOXtCStKBAwd01llnybIsSZJlWaqoqNCBAwdUXl7uc3QD47qunn32Wc2YMcPvUPrtwQcf1JVXXqmxY8f6HcqA7d+/X6WlpXr44Yf11ltv6YwzztCSJUs0efJkv0PrF8Mw9MADD+imm25SYWGhPvnkEz322GN+hzUgI+n8Re5jbh4+I+ncZm4eXszN/htJ5293JLdZXH311VnfwXv11Vf1y1/+Uk8++eQwR9U/vcX+pz/9KflC3rRpk15++WU9/fTTwxneaW316tUqLCzUggUL/A6lX9555x29++67WrZsmd+hDEo0GtX+/fv1+c9/Xrfddpv+9re/6Yc//KG2bNmioqIiv8PrUzQa1WOPPaZHH31U1dXV2rFjh2699VZt2rRJZ5xxht/hAcOOuRlDgbl5eDE3YyiR3Gbx4osvZi17++23dfjwYV1zzTWSYosSvP766zp27Jhuvvnm4Qoxq95iT9iyZYvuv/9+rV+/PucvBamsrNShQ4fkOI4sy5LjOGpsbOxxiUWuq6ur0759+7Ru3bqcfjc+1fbt2/XBBx9o5syZkqSDBw/q+uuv15o1azRt2jSYKJnmAAADeElEQVSfo+vbpz/9adm2nbzs5otf/KLKysq0d+9efeELX/A5ur699957amxsVHV1tSSpurpaBQUF2rNnjy666CKfo+ufkXL+IjcwN+eOkXJuMzcPP+Zm/42U8zeTYJzFOWby5Mn685//rG3btmnbtm26/PLLtXjx4pyYPPvj9ddf15o1a/TEE09ozJgxfofTp1GjRqmqqkr19fWSpPr6elVVVQXqson7779fO3fu1COPPKJwOOx3OP1244036s0330y+1s8++2w98cQTgZg8Jam8vFxTp07VH//4R0mxlQGbmpo0btw4nyPrn7PPPlsHDx7UBx98IEnas2ePjhw5os985jM+R9Z/I+H8RTAwNw+vkXBuMzf7g7nZfyPh/M3G8DzP8zuIoKutrdWFF14YmMtZvvzlLysUCqW9gNevX5/Ti2/s2bNHtbW1On78uEpKSlRXV6dzzjnH77D6Zffu3Zo9e7bGjx+v/Px8SdKYMWP0yCOP+BzZwM2YMUPr1q3Teeed53co/bZ//37dfvvtOnbsmGzb1k9+8hN99atf9Tusfvvd736nxx9/PLnoxi233KJZs2b5HFVmd911lzZv3qwjR46orKxMpaWl2rRpU6DPXwQXc/PQC/K5zdzsL+bm4XO6zc0ktwAAAACAwOOyZAAAAABA4JHcAgAAAAACj+QWAAAAABB4JLcAAAAAgMAjuQUAAAAABB7JLTCCPfXUU/r617+uxYsXa+7cubrwwgv1xBNPpNVZvny5vvKVryRvpg4AAIYOczMwdGy/AwAwdJ555hk9/vjjKiws1EcffaStW7f2qPOtb31LCxYs0G233eZDhAAAnF6Ym4Ghwye3wAh1xx136N///rduuukmvfzyy7roootk2z3fz5oyZYrOPPNMHyIEAOD0wtwMDC0+uQVGqFWrVunNN9/Uhg0bVF5e7nc4AACc9pibgaHFJ7cAAAAAgMAjuQUAAAAABB7JLQAAAAAg8PjOLXAaOHz4sL797W+rtbVVpmlqw4YNeuWVV1RUVKSlS5eqoaFBzc3NuuSSS7R48WJdc801focMAMCIxtwMnHqG53me30EAAAAAAHAyuCwZAAAAABB4JLcAAAAAgMAjuQUAAAAABB7JLQAAAAAg8EhuAQAAAACBR3ILAAAAAAg8klsAAAAAQOCR3AIAAAAAAu//AzFEUQDV6UR7AAAAAElFTkSuQmCC
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<p style="text-align: justify">The analysis shows that some variables are indeed skewed. However, no transformation will be performed <em>a priori</em>. This decision may be revisited later in light of the overall performance of trained classifiers.</p>
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<h3 id="5.3.-Segregating-train-and-test-sets">5.3. Segregating train and test sets<a class="anchor-link" href="#5.3.-Segregating-train-and-test-sets">&#182;</a></h3>
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<p style="text-align: justify">As both classical (random forests) and deep (neural networks) machine learning solutions will be compared, two different sets of tensors will be developed, allowing for their use in each learning exercise.</p>
<p style="text-align: justify">After the elimination of highly intercorrelated features, our dataset now contains 325,834 observations with:</p>
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    <li style="text-align: justify">One column for labels (integers ranging from 1 to 7);</li>
    <li style="text-align: justify">102 columns for features.</li> 
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<p style="text-align: justify">The <b>features tensor</b> will be unique, serving both exercises.</p>
<p style="text-align: justify">Two <b>label tensors</b> will be constructed:</p>
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    <li style="text-align: justify">For the <b>random forest</b> classification, original labels (integers ranging from 1 to 7) will be preserved and accommodated into one unidimensional array;</li>
    <li style="text-align: justify">For the <b>neural network</b> classification, the label column will be one-hot enconded with the help of Pandas' get_dummies method. Therefore, labels will now consist of arrays with seven binary elements, each of them referring to a specific crop class, allowing for the final class identification based on the array element with the highest predicted value.</li>
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<p style="text-align: justify">The unique features tensor and the two label tensors are subsequently split into training and testing sets. The training set will host 80% of observations, while the testing set will accommodate the remaining 20%. Such proportions were arbitrarily chosen by the author.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">iloc</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">dataset</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_size</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="mf">0.8</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="n">train_size</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="n">train_size</span><span class="p">]</span>

<span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_size</span><span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_size</span><span class="p">:]</span>
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<p style="text-align: justify">In preparation for testing at the end of this exercise, mapping class counts in the set of testing labels is important:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">class_count</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Class count - Test labels</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">7</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Class </span><span class="si">{</span><span class="n">class_count</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">class_count</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre>Class count - Test labels

Class 1: 7742
Class 2: 728
Class 3: 15011
Class 4: 14825
Class 5: 9591
Class 6: 17034
Class 7: 236
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<p style="text-align: justify">Finally, label tensors (numpy arrays) are created for each separate exercise (random forest and neural network).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_train_rf</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_test_rf</span> <span class="o">=</span> <span class="n">y_test</span><span class="o">.</span><span class="n">label</span><span class="o">.</span><span class="n">values</span>

<span class="n">y_train_ann</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">y_train</span><span class="o">.</span><span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_test_ann</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">y_test</span><span class="o">.</span><span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>
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<h3 id="5.4.-Feature-scaling">5.4. Feature scaling<a class="anchor-link" href="#5.4.-Feature-scaling">&#182;</a></h3>
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<p style="text-align: justify">Scaling is performed on the training feature set only and applied to both training and testing feature sets (with the 'fit_transform' and 'transform' methods, respectively).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Scaling</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<h2 id="6.-Machine-Learning">6. Machine Learning<a class="anchor-link" href="#6.-Machine-Learning">&#182;</a></h2>
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<h3 id="6.1.-Part-I---Random-Forest">6.1. Part I - Random Forest<a class="anchor-link" href="#6.1.-Part-I---Random-Forest">&#182;</a></h3>
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<h4 id="6.1.1.-Model-definition">6.1.1. Model definition<a class="anchor-link" href="#6.1.1.-Model-definition">&#182;</a></h4>
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<p><p style="text-align: justify">In the first part of this exercise a random forest-based classifying machine will be trained and tested in order to validate previous results achieved by researchers cited in foreword.</p></p>
<p><p style="text-align: justify">Such random forest-based engine will be implemented with scikit-learn's RandomForestClassifier. Initially, the default set of parameters will be used, with few exceptions outlined below. No parameter optimization will be performed in advance. This decision may also be reassessed based on performance metrics achieved in testing.</p></p>
<p><p style="text-align: justify">Some relevant parameters and default values for the RandomForestClassifier are listed below:</p></p>
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    <li style="text-align: justify">'n_estimators'- Number of trees in the forest (default 10);</li>
    <li style="text-align: justify">'max_depth' - Maximum tree depth (default None). Per the documentation, if 'None', "<em>nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split</em> (see below)";</li>
    <li style="text-align: justify">'min_samples_split' - Minimum number of samples required to split an internal node, tree specific (default 2);</li>
    <li style="text-align: justify">'min_samples_leaf' - Minimum number of samples in newly created leaves (default 1);</li>
    <li style="text-align: justify">'n_jobs' - Number of jobs to run in parallel for both fitting and predicting (default 1). Per the documentation, "<em>if -1, then the number of jobs is set to the number of cores</em>".</li>
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<h4 id="6.1.2.-Model-fitting">6.1.2. Model fitting<a class="anchor-link" href="#6.1.2.-Model-fitting">&#182;</a></h4>
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<p style="text-align: justify">The classifier will be instantiated with n_estimators = 100 (i.e. the forest will comprise 100 decision trees) and n_jobs = 4 (allowing for the use of 4 processing cores).</p>
<p style="text-align: justify">The author selected a number of trees relatively close to the final number of features (102) for a first shot. However, a more robust optimization of relevant hyperparameters is recommended, e.g. through the use of <b>validation curves</b>. Readers are welcome to experiment.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">random_forest_classifier</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>

<span class="n">random_forest_classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train_rf</span><span class="p">)</span>
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<pre>RandomForestClassifier(n_jobs=4)</pre>
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<h4 id="6.1.3.-Predictions">6.1.3. Predictions<a class="anchor-link" href="#6.1.3.-Predictions">&#182;</a></h4>
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<p style="text-align: justify">After fitting the model to the training set, predictions are generated for the testing set. As a reminder, predictions for each observation will be scalars ranging from 1 to 7, referring to one particular crop class:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_pred_rf</span> <span class="o">=</span> <span class="n">random_forest_classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<p style="text-align: justify">For information only (a first sanity check), the first prediction in the 'y_pred_rf' array matches the actual class for the corresponding test set observation hosted in the 'y_test_rf' array. So far, so good...</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted class: </span><span class="si">{</span><span class="n">y_pred_rf</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Actual class: </span><span class="si">{</span><span class="n">y_test_rf</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre>Predicted class: 6
Actual class: 6
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<h3 id="6.2.-Part-II---Deep-Learning-(Artificial-Neural-Network)">6.2. Part II - Deep Learning (Artificial Neural Network)<a class="anchor-link" href="#6.2.-Part-II---Deep-Learning-(Artificial-Neural-Network)">&#182;</a></h3>
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<h4 id="6.2.1.-Model-definition">6.2.1. Model definition<a class="anchor-link" href="#6.2.1.-Model-definition">&#182;</a></h4>
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<p style="text-align: justify">The artificial neural network (ANN) architecture comprises a sequential structure with:</p>
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    <li>one input layer (102 input nodes);</li>
    <li>three hidden layers (204, 204 and 102 nodes, respectively);</li>
    <li>one seven-node output layer.</li>
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<p style="text-align: justify">As features are standardized real numbers, '<b>relu</b>' is elected as the activation function of choice for hidden layers. In addition, as this is a multiclass classification exercise, where the output is intended to be binary ('1' for the identified class, '0' for all other classes), the choice of '<b>softmax</b>' as the activation function for the output layer seems adequate.</p>
<p style="text-align: justify">'<b>Adam</b>' has been elected as the optimizer, and its constituent parameters expanded to give some flexibility to play, in particular, with the learning rate. As this is a multiclass classification exercise, '<b>categorical_crossentropy</b>' is selected as the loss function. The fitting performance will be assessed using '<b>accuracy</b>' as the metric of choice.</p>
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<p><img src="https://i.imgur.com/XcKIjmy.png" width="600" height="600"></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># ANN</span>
<span class="n">ann_classifier</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

<span class="c1"># Input layer and first dense layer</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">204</span><span class="p">,</span> <span class="n">kernel_initializer</span> <span class="o">=</span> <span class="s1">&#39;uniform&#39;</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">input_dim</span> <span class="o">=</span> <span class="mi">102</span><span class="p">))</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">))</span>

<span class="c1"># Second dense layer</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">204</span><span class="p">,</span> <span class="n">kernel_initializer</span> <span class="o">=</span> <span class="s1">&#39;uniform&#39;</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">))</span>

<span class="c1"># Third dense layer</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">102</span><span class="p">,</span> <span class="n">kernel_initializer</span> <span class="o">=</span> <span class="s1">&#39;uniform&#39;</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">))</span>

<span class="c1"># Output layer</span>
<span class="n">ann_classifier</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">units</span> <span class="o">=</span> <span class="mi">7</span><span class="p">,</span> <span class="n">kernel_initializer</span> <span class="o">=</span> <span class="s1">&#39;uniform&#39;</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;softmax&#39;</span><span class="p">))</span>

<span class="c1"># Model compilation</span>

<span class="n">optim</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.0005</span><span class="p">)</span>

<span class="n">ann_classifier</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<h4 id="6.2.2.-Model-fitting">6.2.2. Model fitting<a class="anchor-link" href="#6.2.2.-Model-fitting">&#182;</a></h4>
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<p style="text-align: justify">As the number of observations is huge, no cross-validation mechanism will be implemented <em>a priori</em>, another decision subject to later revision, if necessary.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ann_train_history</span> <span class="o">=</span> <span class="n">ann_classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train_ann</span><span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<p style="text-align: justify">Training accuracy and loss per training epoch are charted below.</p>
<p style="text-align: justify">As we are dealing with hundreds of thousands of observations, it is not unusual to see a neural network model converging fast. In this case, batches contain 64 observations. In every epoch the model will be exposed to more than 5,000 different batches. Few epochs may suffice to lead to high accuracy, low loss levels already in the beggining of the training session.</p>
<p style="text-align: justify">In this exercise 100 epochs were scheduled, as the author was interested in investigating potential overfitting. In a final implementation, a shorter training might be considered.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_style</span><span class="p">(</span><span class="s2">&quot;darkgrid&quot;</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ann_train_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ann_train_history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epochs&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Accuracy&#39;</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</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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<h4 id="6.2.3.-Predictions">6.2.3. Predictions<a class="anchor-link" href="#6.2.3.-Predictions">&#182;</a></h4>
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<p style="text-align: justify">After fitting the model to the training set, predictions are generated for the testing set. As a reminder, each prediction will correspond to an array with seven elements, each of them referring to one particular crop class. The final array is '<b>binarized</b>': the array element with the highest value will be converted to '1' while all other array elements will be converted to '0'.</p>
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<div class="prompt input_prompt">In&nbsp;[26]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_pred_ann</span> <span class="o">=</span> <span class="n">ann_classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="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">y_pred_ann</span><span class="p">)):</span>
    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">7</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">y_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">==</span> <span class="n">y_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">():</span>
            <span class="n">y_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">y_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">y_pred_ann</span> <span class="o">=</span> <span class="n">y_pred_ann</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
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<p style="text-align: justify">A new sanity check reveals that the first prediction in the 'y_pred_ann' tensor matches the array generated for the first test set label - the class has been correctly predicted for this particular observation with our neural network.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Predicted: </span><span class="si">{</span><span class="n">y_pred_ann</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Actual: </span><span class="si">{</span><span class="n">y_test_ann</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
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<pre>Predicted: [0 0 0 0 0 1 0]
Actual: [0 0 0 0 0 1 0]
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<h2 id="7.-Classification-performance">7. Classification performance<a class="anchor-link" href="#7.-Classification-performance">&#182;</a></h2>
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<p style="text-align: justify">In classification problems the overall model performance can be measured in more than one way. Along with the traditional calculation of statistical performance metrics directly from the 'predicted' and 'actual' testing tensors, <b>confusion matrices</b> provide a visual, quantitative assessment of the model performance on both overall and 'per class' basis.</p>
<p style="text-align: justify">Performance is herein evaluated with both statistical metrics and confusion matrices for the two models introduced in Sections 7.1 (Random Forest) and 7.2 (Neural Network). As a reminder, in confusion matrices:</p>
<ul>
    <li>Columns correspond to <b>predicted</b> classes;</li>
    <li>Rows correspond to <b>actual</b> classes.</li>
</ul>
<p style="text-align: justify">The outstanding results achieved in each case are discussed in detail in Section 8.</p>
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<h3 id="7.1.-Random-Forest">7.1. Random Forest<a class="anchor-link" href="#7.1.-Random-Forest">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rf_cm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rf_pred_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">rf_pred_incorrect</span> <span class="o">=</span> <span class="mi">0</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">y_test</span><span class="p">)):</span>
    <span class="n">rf_index_test</span> <span class="o">=</span> <span class="n">y_test_rf</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
    <span class="n">rf_index_pred</span> <span class="o">=</span> <span class="n">y_pred_rf</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span>
    <span class="n">rf_cm</span><span class="p">[</span><span class="n">rf_index_test</span><span class="p">][</span><span class="n">rf_index_pred</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">rf_index_test</span> <span class="o">==</span> <span class="n">rf_index_pred</span><span class="p">:</span>
        <span class="n">rf_pred_correct</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">rf_pred_incorrect</span> <span class="o">+=</span> <span class="mi">1</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rf_cmatrix</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rf_cm</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
                          <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Corn&#39;</span><span class="p">,</span> <span class="s1">&#39;Pea&#39;</span><span class="p">,</span> <span class="s1">&#39;Canola&#39;</span><span class="p">,</span> <span class="s1">&#39;Soy&#39;</span><span class="p">,</span> <span class="s1">&#39;Oat&#39;</span><span class="p">,</span> <span class="s1">&#39;Wheat&#39;</span><span class="p">,</span> <span class="s1">&#39;Broadleaf&#39;</span><span class="p">],</span>
                          <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Corn&#39;</span><span class="p">,</span> <span class="s1">&#39;Pea&#39;</span><span class="p">,</span> <span class="s1">&#39;Canola&#39;</span><span class="p">,</span> <span class="s1">&#39;Soy&#39;</span><span class="p">,</span> <span class="s1">&#39;Oat&#39;</span><span class="p">,</span> <span class="s1">&#39;Wheat&#39;</span><span class="p">,</span> <span class="s1">&#39;Broadleaf&#39;</span><span class="p">])</span>
<span class="n">rf_cmatrix</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Corn</th>
      <th>Pea</th>
      <th>Canola</th>
      <th>Soy</th>
      <th>Oat</th>
      <th>Wheat</th>
      <th>Broadleaf</th>
    </tr>
  </thead>
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    <tr>
      <th>Corn</th>
      <td>7714</td>
      <td>0</td>
      <td>6</td>
      <td>14</td>
      <td>0</td>
      <td>3</td>
      <td>5</td>
    </tr>
    <tr>
      <th>Pea</th>
      <td>1</td>
      <td>726</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Canola</th>
      <td>1</td>
      <td>2</td>
      <td>14991</td>
      <td>9</td>
      <td>3</td>
      <td>5</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Soy</th>
      <td>5</td>
      <td>0</td>
      <td>9</td>
      <td>14764</td>
      <td>22</td>
      <td>25</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Oat</th>
      <td>3</td>
      <td>0</td>
      <td>5</td>
      <td>24</td>
      <td>9502</td>
      <td>54</td>
      <td>3</td>
    </tr>
    <tr>
      <th>Wheat</th>
      <td>6</td>
      <td>0</td>
      <td>4</td>
      <td>21</td>
      <td>22</td>
      <td>16978</td>
      <td>3</td>
    </tr>
    <tr>
      <th>Broadleaf</th>
      <td>4</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>4</td>
      <td>227</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rf_accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test_rf</span><span class="p">,</span> <span class="n">y_pred_rf</span><span class="p">)</span>
<span class="n">rf_precision</span><span class="p">,</span> <span class="n">rf_recall</span><span class="p">,</span> <span class="n">rf_f_score</span><span class="p">,</span> <span class="n">rf_support</span> <span class="o">=</span> <span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_test_rf</span><span class="p">,</span> <span class="n">y_pred_rf</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Accuracy: </span><span class="si">{</span><span class="n">rf_accuracy</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Precision: </span><span class="si">{</span><span class="n">rf_precision</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Recall: </span><span class="si">{</span><span class="n">rf_recall</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;F-Score: </span><span class="si">{</span><span class="n">rf_f_score</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
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<pre>Accuracy: 99.59 %
Precision: 99.03 %
Recall: 99.11 %
F-Score: 99.07 %
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy per class</span><span class="se">\n</span><span class="s1">&#39;</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">rf_cmatrix</span><span class="p">)):</span>
    <span class="n">class_accuracy</span> <span class="o">=</span> <span class="n">rf_cmatrix</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">rf_cmatrix</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="n">i</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">rf_cmatrix</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">class_accuracy</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
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<pre>Accuracy per class

Corn: 99.74 %
Pea: 99.73 %
Canola: 99.83 %
Soy: 99.53 %
Oat: 99.51 %
Wheat: 99.47 %
Broadleaf: 95.38 %
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<h3 id="7.2.-Deep-Learning-(Artificial-Neural-Network)">7.2. Deep Learning (Artificial Neural Network)<a class="anchor-link" href="#7.2.-Deep-Learning-(Artificial-Neural-Network)">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ann_cm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ann_pred_correct</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">ann_pred_incorrect</span> <span class="o">=</span> <span class="mi">0</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">y_test_ann</span><span class="p">)):</span>
    <span class="k">if</span> <span class="n">y_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">ann_index_test</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_test_ann</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">ann_index_pred</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_pred_ann</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">ann_cm</span><span class="p">[</span><span class="n">ann_index_test</span><span class="p">][</span><span class="n">ann_index_pred</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
        <span class="k">if</span> <span class="n">ann_index_test</span> <span class="o">==</span> <span class="n">ann_index_pred</span><span class="p">:</span>
            <span class="n">ann_pred_correct</span> <span class="o">+=</span> <span class="mi">1</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">ann_pred_incorrect</span> <span class="o">+=</span> <span class="mi">1</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ann_cmatrix</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ann_cm</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span>
                           <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Corn&#39;</span><span class="p">,</span> <span class="s1">&#39;Pea&#39;</span><span class="p">,</span> <span class="s1">&#39;Canola&#39;</span><span class="p">,</span> <span class="s1">&#39;Soy&#39;</span><span class="p">,</span> <span class="s1">&#39;Oat&#39;</span><span class="p">,</span> <span class="s1">&#39;Wheat&#39;</span><span class="p">,</span> <span class="s1">&#39;Broadleaf&#39;</span><span class="p">],</span>
                           <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Corn&#39;</span><span class="p">,</span> <span class="s1">&#39;Pea&#39;</span><span class="p">,</span> <span class="s1">&#39;Canola&#39;</span><span class="p">,</span> <span class="s1">&#39;Soy&#39;</span><span class="p">,</span> <span class="s1">&#39;Oat&#39;</span><span class="p">,</span> <span class="s1">&#39;Wheat&#39;</span><span class="p">,</span> <span class="s1">&#39;Broadleaf&#39;</span><span class="p">])</span>
<span class="n">ann_cmatrix</span>
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    }

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        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Corn</th>
      <th>Pea</th>
      <th>Canola</th>
      <th>Soy</th>
      <th>Oat</th>
      <th>Wheat</th>
      <th>Broadleaf</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>Corn</th>
      <td>7735</td>
      <td>0</td>
      <td>2</td>
      <td>3</td>
      <td>0</td>
      <td>1</td>
      <td>1</td>
    </tr>
    <tr>
      <th>Pea</th>
      <td>0</td>
      <td>728</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Canola</th>
      <td>1</td>
      <td>0</td>
      <td>14992</td>
      <td>7</td>
      <td>6</td>
      <td>5</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Soy</th>
      <td>2</td>
      <td>0</td>
      <td>3</td>
      <td>14806</td>
      <td>4</td>
      <td>10</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Oat</th>
      <td>2</td>
      <td>0</td>
      <td>2</td>
      <td>10</td>
      <td>9540</td>
      <td>37</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Wheat</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>4</td>
      <td>5</td>
      <td>17021</td>
      <td>0</td>
    </tr>
    <tr>
      <th>Broadleaf</th>
      <td>7</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>4</td>
      <td>6</td>
      <td>218</td>
    </tr>
  </tbody>
</table>
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<span class="n">ann_precision</span><span class="p">,</span> <span class="n">ann_recall</span><span class="p">,</span> <span class="n">ann_f_score</span><span class="p">,</span> <span class="n">ann_support</span> <span class="o">=</span> <span class="n">precision_recall_fscore_support</span><span class="p">(</span><span class="n">y_test_ann</span><span class="p">,</span> <span class="n">y_pred_ann</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Accuracy: </span><span class="si">{</span><span class="n">ann_accuracy</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Precision: </span><span class="si">{</span><span class="n">ann_precision</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Recall: </span><span class="si">{</span><span class="n">ann_recall</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;F-Score: </span><span class="si">{</span><span class="n">ann_f_score</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
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<pre>Accuracy: 99.81 %
Precision: 99.80 %
Recall: 98.77 %
F-Score: 99.27 %
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accuracy per class</span><span class="se">\n</span><span class="s1">&#39;</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">ann_cmatrix</span><span class="p">)):</span>
    <span class="n">class_accuracy</span> <span class="o">=</span> <span class="n">ann_cmatrix</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">ann_cmatrix</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="n">i</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">ann_cmatrix</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">class_accuracy</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1"> %&#39;</span><span class="p">)</span>
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<pre>Accuracy per class

Corn: 99.83 %
Pea: 100.00 %
Canola: 99.93 %
Soy: 99.84 %
Oat: 99.80 %
Wheat: 99.65 %
Broadleaf: 99.54 %
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<h2 id="8.-Discussion-and-Final-Remarks">8. Discussion and Final Remarks<a class="anchor-link" href="#8.-Discussion-and-Final-Remarks">&#182;</a></h2>
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<p><p style="text-align: justify">This 'dual' machine learning exercise was of particular interest from a data science educational perspective. Comments:</p></p>
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    <li style="text-align: justify">First and foremost, results confirm that the combination of optical and radar-based information leads to very accurate remote cropland mapping;</li>
    <li style="text-align: justify">Once again the importance of proper data preprocessing was revealed. This particular dataset - which proved at the end to be very robust and representative - was originally affected by <b>severe feature intercorrelation</b>. Indeed, one of the articles co-authored by Dr. Khosravi was entirely dedicated to a method to reduce the dataset dimensionality. Dropping redundant estimators is a healthy measure to mitigate colinearity and optimize machine architectures - and so was done;</li>
    <li style="text-align: justify">Nice results were obtained with one of the most classic random forest classifiers available. It is though important to mention that the selection of a proper number of trees and depth parameters may affect results significantly. The author experimented with both smaller numbers of trees and lower depth architectures, and some deterioration was observed, as expected. If properly manipulated, random forests are great predictive tools and their performance pretty much matches those of more complex, sophisticated algorithms;</li>
    <li style="text-align: justify">The neural network performed slightly better than the random forest. As discussed above, also here the adequate architecture and hyperparameter selection is key;</li>
    <li style="text-align: justify">Some potential improvements were left aside (treatment of skewed features, cross-validation and hyperparameter optimization) to be revisited after the final performance was known. As both machines performed outstandingly, such potential improvements were not pursued. Readers are though invited to experiment;</li>
    <li style="text-align: justify">At last, the inspection of both confusion matrices indicates that:</li>
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        <li style="text-align: justify">More 'confusion' is experienced between '<b>wheat</b>' and '<b>oat</b>' crop cultures, which account for the highest numbers of incorrect predictions;</li>
        <li style="text-align: justify">In general terms, accuracies per crop class did not differ much - all above 99%. The exception was the '<b>broadleaf</b>' class, where accuracies were relatively lower. This should be expected, as this is the most misrepresented class (smallest number of observations);</li>
        <li>Focusing now on the '<b>broadleaf</b>' class, deep learning was evidently superior in predicting such crops, suggesting that it might be a better alternative to tackle misrepresented classes in general.</li>
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<h1 id="References">References<a class="anchor-link" href="#References">&#182;</a></h1>
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<p><p style="text-align: justify">This case study is based on the "<b>Crop mapping using fused optical-radar dataset</b>", created by <a href="http://i-khosravi.ir/">Dr. Iman Khosravi</a> (Department of Remote Sensing & GIS, Faculty of Geography, University of Tehran, Iran) and donated to the <b>University of California, Irvine (UCI) Machine Learning Repository</b> (<a href="https://archive.ics.uci.edu/ml/datasets/Crop+mapping+using+fused+optical-radar+data+set">here</a>), where it is currently hosted.</p></p>
<p><p style="text-align: justify">Dr. Khosravi's co-authored papers mentioned in the dataset repository provide the background information supporting this machine learning exercise:</p></p>
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    <li style="text-align: justify">"<em><b><a href="http://i-khosravi.ir/_sources/tres2019.pdf">A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations</a></b></em>" (Khosravi, I., & Alavipanah, S. K., International Journal of Remote Sensing, 2019, 40(18), 7221-7251), in which the overall strategy to combine both radar and optical-based information is described;</li>
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    <li style="text-align: justify">"<em><b><a href="http://i-khosravi.ir/_sources/tres2018b.pdf">MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data</a></b></em>" (Khosravi, I., et al., International Journal of Remote Sensing, 2018, 39(8), 2159-2176), in which a feature selection procedure aiming at reducing data dimensionality and improving cropland classification accuracy, in light of high feature intercorrelation, is described.</li>
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