supplementary_notebook-full.html

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

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

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

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

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


<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 -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // 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 }
        }
    });
    </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">
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<h1 id="A-neural-network-framework-for-modelling-auditory-sensory-cells-and-synapses">A neural-network framework for modelling auditory sensory cells and synapses<a class="anchor-link" href="#A-neural-network-framework-for-modelling-auditory-sensory-cells-and-synapses">&#182;</a></h1><p>Python notebook for comparing the evaluation results of the two additional CNN models to the referebce Dierich et al. IHC model and the Zilany et al. ANF model. The Dierich2020 CNN IHC model is evaluated using the BM outputs of the reference Verhulst2018 cochlear model (generated here).</p>

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<h2 id="Prerequisites">Prerequisites<a class="anchor-link" href="#Prerequisites">&#182;</a></h2><ul>
<li><p>To run the full notebook for the first time, you'll have to compile the cochlea_utils.c file that is used for solving the TL model of the cochlea. This requires some C++ compiler which should be installed beforehand (more information can be found here). In a terminal, run from the Verhulstetal2018 folder:<br>
For Mac/Linux: <code>gcc -shared -fpic -O3 -ffast-math -o tridiag.so cochlea_utils.c</code><br>
For Windows: <code>gcc -shared -fpic -O3 -ffast-math -o tridiag.dll cochlea_utils.c</code></p>
</li>
<li><p>If running on google colab: add the following as a code block and run it to compile cochlea_utils.c in the runtime machine:<br>
<code>!gcc -shared -fpic -O3 -ffast-math -o tridiag.so cochlea_utils.c</code></p>
</li>
<li><p>The Matlab engine for python needs to be installed (see the instructions at the bottom of the <code>README</code> file).</p>
</li>
<li><p>The Zilany2014 model needs to be compiled in Matlab by running the function mexANmodel.m, found under <code>Zilany2014/reference/</code> (see also the instructions on the model's readme file).</p>
</li>
</ul>

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<h2 id="Import-required-python-packages-and-functions">Import required python packages and functions<a class="anchor-link" href="#Import-required-python-packages-and-functions">&#182;</a></h2><p>Import required python packages and define the necessary parameters.
<strong>Notice that for all the simulations, the reference models operate at 100kHz and the CNN models operate at 20kHz.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">scipy.signal</span> <span class="k">as</span> <span class="nn">sp_sig</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">keras</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">Verhulstetal2018.get_tl_vbm</span> <span class="kn">import</span> <span class="n">tl_vbm</span>
<span class="kn">from</span> <span class="nn">extra_functions</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
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<pre>Using TensorFlow backend.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define model specific variables</span>
<span class="n">ds_rate</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># downsampling rate of the frequency channels of the reference model</span>
<span class="n">fs_connear</span> <span class="o">=</span> <span class="mf">20e3</span>
<span class="n">fs_tl</span> <span class="o">=</span> <span class="mf">100e3</span>
<span class="n">ds_factor</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs_tl</span> <span class="o">/</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">p0</span> <span class="o">=</span> <span class="mf">2e-5</span> <span class="c1"># calibrate to 2e-5 Pascal</span>
<span class="c1"># load CFs </span>
<span class="n">CF_connear</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="s1">&#39;connear/cf.txt&#39;</span><span class="p">)</span><span class="o">*</span><span class="mf">1e3</span>
<span class="c1"># scaling values for the CoNNear models</span>
<span class="n">cochlea_scaling</span> <span class="o">=</span> <span class="mf">1e6</span>
<span class="n">ihc_scaling</span> <span class="o">=</span> <span class="mf">1e1</span>
<span class="n">an_scaling</span> <span class="o">=</span> <span class="mf">1e-2</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># To run this notebook and the reference models, the Matlab engine needs to be succesfully installed in python</span>
<span class="kn">import</span> <span class="nn">matlab.engine</span>
<span class="kn">import</span> <span class="nn">os</span>
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<h2 id="IHC-stage">IHC stage<a class="anchor-link" href="#IHC-stage">&#182;</a></h2><p>The input dimensions for the CNN models are (b x L x Ncf), where b is the batch-size (for loading multiple stimuli simultaneously), L is the input length (including the context) and Ncf are the frequency channels.</p>
<p>The ihc.json file can be loaded for the full-channel model (201 channels) or the ihc_1cf.json file for the 1-channel model. At the first block of each individual section, the necessary model is loaded and the rest of the parameters are defined.</p>
<p>The BM outputs extracted from the TL cochlear model of Verhulst et al. were given as inputs to the Dierich2020 model and its CNN counterpart.</p>

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<h3 id="IHC-excitation-patterns">IHC excitation patterns<a class="anchor-link" href="#IHC-excitation-patterns">&#182;</a></h3><p>Compare the simulated average IHC receptor potentials across CF for tone stimuli presented at levels between 0 and 90 dB SPL.
<strong>You can change the <code>f_tone</code> variable to have tone stimuli of different frequencies, say 500Hz, 1kHz, 2kHz, etc..</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Dierich2020/&#39;</span> <span class="c1"># Dierich et al. CNN model directory</span>
<span class="c1"># Define the IHC model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 201-channel IHC model to simulate for all CFs</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">201</span>
<span class="n">ihc</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/ihc.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/ihc.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;ihc_model&quot;</span><span class="p">)</span>
<span class="n">ihc</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>WARNING:tensorflow:From C:\Users\Me\.conda\envs\evaluation_env\lib\site-packages\tensorflow_core\python\ops\resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Model: &#34;ihc_model&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bmm_input (InputLayer)       (None, 2560, 201)         0         
_________________________________________________________________
model_2 (Model)              (None, 2048, 201)         1317505   
=================================================================
Total params: 1,317,505
Trainable params: 1,317,505
Non-trainable params: 0
_________________________________________________________________
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimulus</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="mf">1e3</span> <span class="c1"># frequency of the pure tone</span>
<span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">91.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">)</span> <span class="c1"># levels from 0 to 90dB SPL</span>
<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">102.4e-3</span> <span class="c1"># duration of the stimulus - 102.4 ms correspond to 2048 samples (fs = 20 kHz)</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence before the onset of the stimulus - 12.8 ms correspond to 256 samples (context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">5.0e-3</span> <span class="c1"># 5ms long hanning window for gradual onset</span>

<span class="c1"># make stimulus</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the pure tone</span>
<span class="c1"># apply hanning window</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">hann</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided hanning window</span>
<span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</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">L</span><span class="p">)):</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Verhulstetal ############</span>
<span class="c1"># get the cochlear outputs of the reference model</span>
<span class="n">output</span> <span class="o">=</span> <span class="n">tl_vbm</span><span class="p">(</span><span class="n">stim</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span>

<span class="n">CF_tl</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;cf&#39;</span><span class="p">][::</span><span class="n">ds_rate</span><span class="p">]</span> <span class="c1"># center frequencies</span>
<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">tl_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span> <span class="o">*</span> <span class="n">ds_factor</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span> <span class="c1"># pre-allocate</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span> <span class="c1"># pre-allocate</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">L</span><span class="p">)):</span>
    <span class="n">tl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;v&#39;</span><span class="p">][:,::</span><span class="n">ds_rate</span><span class="p">]</span> <span class="c1"># cochlear output</span>
<span class="k">del</span> <span class="n">output</span> <span class="c1"># remove the output variable to free-up some memory</span>

<span class="c1">############ Dierichetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span> <span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">magic_constant</span> <span class="o">=</span> <span class="mf">0.118</span>
<span class="n">tl_in</span> <span class="o">=</span> <span class="n">magic_constant</span><span class="o">*</span><span class="n">tl_target</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">L</span><span class="p">)):</span>
    <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_cf</span><span class="p">):</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">tl_in</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;v&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;IHC_model(v,Fs);&#39;</span><span class="p">,</span> <span class="n">nargout</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output of the reference model</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        
<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
    
<span class="c1"># remove context from the output</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,:]</span>
<span class="c1"># downsample to 20 kHz</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># apply proper scaling for feeding to the CoNNear</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">tl_target</span> <span class="o">*</span> <span class="n">cochlea_scaling</span> <span class="c1"># scaling for the cochlear input</span>

<span class="c1"># compute the mean across CF for each level</span>
<span class="n">ihc_target_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1">############ CNN model #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">tl_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 8</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">tl_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">tl_target</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="n">tl_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tl_target</span><span class="p">)</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc_connear</span> <span class="o">/</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the IHC output</span>

<span class="c1"># compute the mean across CF for each level</span>
<span class="n">ihc_connear_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ihc_connear</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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<pre>running human auditory model 2018: Verhulst, Altoe, Vasilkov
cochlear simulation: done
WARNING:tensorflow:From C:\Users\Me\.conda\envs\evaluation_env\lib\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="c1"># Plot the mean Vihc patterns for the reference model</span>
<span class="n">CF_rep</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">CF_tl</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span><span class="mi">1</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">CF_rep</span><span class="o">.</span><span class="n">T</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">1e3</span><span class="o">*</span><span class="n">ihc_target_mean</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span><span class="mf">8.</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">)</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">))</span>
<span class="c1">#plt.ylim(-59, -49.5)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;CF (kHz)&#39;</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;Mean of V_m (mV)&#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;Dierich2020 IHC model&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">L</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">frameon</span><span class="o">=</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>

<span class="c1"># Plot the mean Vihc patterns for the CoNNear model</span>
<span class="n">CF_rep</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span><span class="mi">1</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">CF_rep</span><span class="o">.</span><span class="n">T</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">1e3</span><span class="o">*</span><span class="n">ihc_connear_mean</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span><span class="mf">8.</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">)</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">,</span> <span class="mf">8.</span><span class="p">))</span>
<span class="c1">#plt.ylim(-59, -49.5)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;CF (kHz)&#39;</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;Mean of V_m (mV)&#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;Dierich2020 IHC model - CNN&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">L</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">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">ihc</span> <span class="c1"># remove the connear model variable to free-up some memory</span>
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KUkSafhp1nJ3JQS1yf3ncLQR1EgA8CFF17IhRdeGG4xhhVf1DTyq0PFTMfJB6dPRq24Z7pEr1JxZVIMVybFtO1zeDxsqGvmifxyfnmwmP+U1fDsuIyA5rQoDA8U+1RhyLG3ycrte/IZbTLwQ5oV5REgOpWKM2MjeH/qKP4+LpN8q51zvjnAd3cf442yauweT7hFVBgkKApEYUhxqNnG1duPYFKrWDoxG6OiO/qMEILLk2JYNyuPq5Nj2Vzfwk/3F3HuNwf5pr6ZUpuDg802qh2uLqMNFYY+igtLYchQ5XBx9fYjqAS8OWUkGUY9R8Mt1BAgWa/lj2NGIHMlq6obuPdgMZdsPdShTIRaxYOj0rghJVYJUx9GKApEYcjwYnElxx1O/jcjl5GKvz7oCCE4Nz6KOdEW3jpei0aARa2m2unik8p67j5QxJe1jTyZl6FEbw0TFAWiMCRocrn5V0kVFyZEMXEIhucOJiI0ar7TKZrr1rR4ni2s4NGjZTg8khcnZA2JsacGlxuB9zMrnIyiQBSGBK+WVVPvcvODEYnhFmVYohKCH2cmYVKr+NWhEu4/WMxjuekD7s6SUrKloYVGlxshoNbppsbpQgJaIYjTahhp8m+F7H1NVhbvOIJOJfhg6mhSDTo8UvLfynpeLqlkQWwkP8xIHNYTMBUFEmJuvfVWPvroIxITE9m9ezcANTU1LF68mPz8fKKioli5ciUxMTG9tKTQHQ6Ph+eLKpkbbWZalDnc4gxrvpueQJndyV8LK5gcaeL6lLiQ9ymlpNTuZFtDC38pPM6ORmuvdW5Dx/x275vdbj6qqOet4zUct7uYHW3mo4o6dCpBvdPN4h1HuD7Fm6X5qNVOrFbN13XNbK5v5vsZieQY9STqtd11N2RRFEiIueWWW/jhD3/It7/97bZ9jz32GAsXLuS+++5jyZIlPPbYYzz++ONhlPLU5s3yWkrtTv4wZkTvhRVCzgM5KWxvaOFXh0qYE2Uhx88n/kA50mJjWWk1bx+vpcLhXWRrhEHHE2NGkGc24JGSKK2GWK0aFQKnlFQ7XTx6tIyXqyXTj9cyyqTns5pG/l5USbXTRaZBR45Jz9vHa0nRaXltcg6ldifX7TjCw0dKmRlp5t6cZC5OiOZfJVU8eLiE/1V7k7SONukZLQ2sPVSCzeNhdrSFM2IsxGs1QzawQFEgIebMM88kPz+/w77333+fdevWAbBo0SLuv/9+RYH0kWaXmz8cK2NGpImFg2CBJbe7hSNHn8RhryAv7xE0muFnEamE4JmxGSz85gA/2FvAB9NGow3iTHaPlPytqJLHjpYhkZwbF8VZsRHkmgzMiDKhU3U/gJ+s1/L8+EwWfb6N7+8taNs/PyaCH2Umclq0BSEETo9EI7yBA5lGPSum5wIwtl3G5dvSE7goIZp9TVYONNtYVd3Apy169GXVqIB/l1YDYFQJRpoM3J2VzKL4yCGlTIaPAllxH5Tv6rXYlLo6OBbtX5vJE+GCxwIW5fjx46SkpAAQFxdHRcUpu9hi2PlbUSXHHS7+MSE77D/M6pov2b//V9hsRYAKq62EKZNfQquNCqtc4SDNoOOPY0Zw+558niwo557slKC1fceeAj6srOOihCgeHZ0esOvIrFbzS5ppHjuRCLWKHJOBMeaOUXudFV57xdGeZL2WZL2WBXGR3JmRyJq16zj7zPm4pWRHYwub6popdzhZXd3ALbuPMTfazF2ZyZwRYwn79RoMho8CURhyHLc7ea6ogosTopgZxLEPu72ChoYd2GylJCdfilbbcXyq/PiH6PXJxETPBKCp+RCHDz9GdfU6jMZMpk19Haerlt27f8rWbTcwZcq/0Oviu+pqSHNJYjRXV8fwVP5xzo6NZEYQvqOtDc18WFnHTzOTuDc7uc83YbOQXJQc2295OtOqd9SdsiQ/kJPKK6VVPF1wnGt2HGF2lJk/54045cPNh48C8dNS2L5uHfPnzw+pKElJSZSVlZGSkkJ1dTWJiUrkUF/4R3Eldo+HB3KClx6/snIVu3Z/HyndAByv+JhpU19BpdIBUFj4EocOPwJAWtr1AJSW/ge12sSoUfeRnvZt1Gqvz3/y5BfZufNOtm69lqlTlmIwdJTT7qiisuITkpMvQ6MJv/stFPx+dDpf1zXxw30FrJ4xBnM/w2H/XlRJpEbFDzMST6kneK1KcGt6AtenxPGf8hoePVrGOd8c4FcjU/lOWvwpG8mlzPYJA5deein//ve/Afj000+57LLLwizRqYeU8FFlHWdER5AdpEFau72Cffvvw2LOY8b0Nxk39g/U12/mwIEHsVpLKCh8kUOHHyEx4QIyRtxGSckblJYuJy3tBubOWUNmxu1tygMgLnYeU6f8C7u9km82X0lx8at4PHYAbLYytmxZzIGDD7JhwyKOV6wYkulAIjVqnh2bSYHVwa8Pl/hVx+7xUO90nbS/wGrno4o6bkqNx3KKzsswqFXcnBbPull5zIm28MChEq7dcYQSmyPcovWJ4WOBhInrrruOdevWUVVVRXp6Og8//DD33Xcf11xzDS+99BIRERGsWrUq3GKechSi4pjVwQ8y+r7KX3uk9LB33z243VbGj38Ss3kkUVHTaGk5Rn7B3ygtWw5AXNxZjB//Z1QqHSkp30KlMmAyZXbbbnT0DKZPe4MDB3/NgYO/5uixp4iKnEJT80Gczjry8h6huHgZu3f/kKioGYzM+Tlm8yiEENTXb6ep6QAxMbOIjJyK01lNY+NeoqKmnlIWy5xoCz/MSOQvhRWcFx/Fovjux4Q21zdzx558jjucXJoYw48zE8kze8cfni+qRCXgu+mnvjswWa/ltUk5vFJazUNHSrlgy0H+M3lkt2MtgxVFgYSY119/vcv9q1evBrwrIMbGBt8XO9TZiA4VcH4PN6NAKC5eSk3NF4zJ/Q1m88i2/Tk5P8NgSEfiwWLOJSpqKkJ4n34tljF+tR0RMZbp05ZTU7ue8vJ3aWzcg0DF1KlLiYqcTErytygtW86xY0+zddt1Xbah1cbidNYAoFZbSE29muysH6PVRvbzkw8Mv8hOZl1NIz/cW8A1ybFcluhdk6V9xNRrZdXcc6CIVL2Om1LjebO8hv9V1fP21FGU2538s6SKG1LiSNHrwvhJgocQgm+nxTMr2sy1249yxbbDvDwhO9xiBYSiQBROSTahZU60JSjLzTY1HeDwkceJjzu7bVyjFSFUpKVd2+8+hBDExc4jLnbeScdUKg3padeTnHQZVVWrcTpr8UgHERETsJhHU139GdU1X2Ixj8ZiyaP8+Ic+hfclUya/fNLYymBEp1LxjwlZ/OZIKcvKqnmppAq9SjAvOoI/jklnn1Tz+wNFnB4dwQvjM4nSavhRRiKXbjvEdTuOYPNIJkUY+c3otHB/lKCTZzbywbRRXLvjKFduP8w5GJnqdBGlHfy358EvoYJCJw422yhGzfcS+m99eDx29uz9GWp1BGPHPhrWgVmNxkxy8qUn7U9J+RYpKd9qex8ffzY1qdewc+edbN58FdOmvYbJlDWAkvaNTKOelyZkU+908UVtE980NLOstJpzNx/EiZkso54XJ2S1raSYatDx5uRRXLrtENEaFUsn5mAaokkaM4x6Vs7I5Q/HynmhuIJ5m/bz8Kg0rkiMHtTBAkPz21AY0vy3sg6AC/uhQFyuZvILnufrDefR1LSfcWMfR3cKhdrGxsxlxvTluD029u77RVvU2KlAlFbDxYnRPDwqjU+m5xKn1eBE8M8J2W3Ko5Vsk551M/NYNXMMSUM8VYhZo+bh0Wk8QhNpeh3f31vANTuOcLjFFm7RukVRIAqnHP+trCcXV7984YcPP8qRI3/AYEhj0qQXiI9fEEQJBwaLZQy5o39Fff1WiotfCbc4fWK02cDKmbk8TQOjzV3PiYjTaYg9Bdw5wSJbuPnv9NE8mpvOjsYWzt50gPsPFlM6CCO1FAWicEpRYLWzu8nKbJx9bsPjcXK8YgVJSZcyfdprJMQvDKKEA0ty8hXExZ3F4SNP0Nx8JNzi9Am9SkWkGHohzP1BLQTfSYvny1ljuTo5hqWlVczZsI//lNWEW7QOhF2BCCHuFkJIIUR8p/0ZQogmIcTd4ZJNYfDxUWU9ADP7oUBq6zbictWRlHhBsMQKG0II8sb8DrXayJat19LQuDvcIikEkUS9lj/lZfDV7LHMjjbzk/2F/OlY+aCZMxRWBSKEGAGcCxR2cfhJYMXAShR8ioqKWLBgAWPHjmX8+PE8/fTTgDel+7nnnsuNN97IueeeS21tbZglPTX4qKKOSRFGEoWnz21UVHyMWm0mNvbMIEoWPgyGVKZP+w9qlYGtW2+gvn5buEVSCDIZRj2vTsrh6uQY/phfzg07j1Lp6PtDVLAItwXyJHAP0EGdCiEuB44Ce8IgU1DRaDT86U9/Yt++fWzYsIG//vWv7N27ty2l+7Jly1i4cCGPPRZ4UsbhRonNwbbGFi5OiO5zGx6Pi8rKlcTHLUCtPrXzELXHbM5h+ow30eli2bHzDmy20nCLpBBkdCoVz+Rl8MjoNL6qa+KsTfv5d0kVLk/4rBERLlNICHEpsFBK+RMhRD4wQ0pZJYQwA6vwWiZ3A01Syie6aWMJsAQgISFh+vLly/stV1NTExaLpd/tdMcDDzzAFVdcwTPPPMOTTz6JXq/Hbrdz1113sXTp0qD0EazP0J92+lK3tzofSx1LMfFnGohsbvCr/c5tSrkPj3wClfgeQswISL5TASlL8chHgHiEOB1kHYgMBBNpbnZhNhsQYvBNxAv1726g+gtGu/60USxVvISJfWhIx82NWJkiXH3qf8GCBVuklH36MYRUgQghVgHJXRx6ALgfOE9KWd9JgTwBbJJSLhdCPEQPCqQ9Y8aMkQcOHOj2+OObHmd/zf5eZa6rqyM6OrrXcgB5sXncO+tev8oC5Ofnc+aZZ7J7924yMjKoq6tjnS95Y0xMTNDcWOuClBCyP+30pW5PdaSUXLDlEHaPh7Wz8vxuv3O5vXt/wfGKFZx5xjeo1adW2gh/qapex86ddyClCyHUvhBfgdfQF2Rl3klOzs8H1fyCYF2z4e4vGO3624aUkk+q6vnNkVKOWR0sio9kcVUhFy4IrH8hRJ8VSEhj46SU53S1XwgxEcgGdvgu4nRgqxBiFjAbuEoI8QcgGvAIIWxSymdDKWuoaWpq4lvf+hZPPfUUkZGnRvqJwcT7FXVsb2zhT/1YdbCyajVl5e8wYsStQ1Z5AMTHzWfe6V8jhECjiaShYQfVNevJzz9KQoKL/IK/4fE4GDXql4NKiSgEhhCCCxKiWRgXyYvFVTx6tJS9RDDJ5iDdMDBWZliCq6WUu4C2HObtLRDgjHb7H8JrgfRbefhrKYTiycTpdPKtb32LG264gSuvvBI4kdIdoKysTEnp3gMtbg+/PVLKBIuRa1P6ljfMZi9n3757sVjGMTJn6Af26XQnzlNU1DSioqZRWLCOCePP4qAujsKil2ixFpA35rfo9cq1dyqjU6n4QUYiEy1Gbt5xmPM2H+DZsZmcHRf6B9VwD6IPeaSU3HbbbYwdO5af/exnbfvbp3T/97//raR074G/FVZQYnfy29FpqPvwxOzx2Nm9+yd4PHYmjH+6Q8r14YYQgtzRDzJ61P3U1HzBho2LKCp+BY8n/BE9Cv3jzNgIfkcjSTot1+88OiDhvoNieqeUMqub/Q8NrCTBZ/369bzyyitMnDiRKVOmAPDII4+0pXR/9tlnGTNmDG+++WZ4BR2k2Nwe/upbdXBudOCDk1JK9u67j/r6zYwf/xRmc04IpDy1EEKQkXEb8fFns3//rzh48CGKi5cyccKzfmcYVhicpAkPH0/P5Z6DRfwxvxwPkl8EcTnhzgwKBTKUmTdvXrdPAatXrx7wwcNTjW/qm2lxe7g2Ja5P9aV8n+PHP2Rkzt0kJ10SZOlObUymbKZOXUZ19Vr27X+A7TtuZeaMdxWX1imOUa3i6bwMNELwp/zjaITgrqyuYpn6j+LCUhjUfF7biEbA3D6sp+1w1CD5mOSky8jMvDME0p36CCGIjz+bKZP/gcvVwI6dt9PcfHjQzHRW6BsqIXhizAiuTo7h8WPl/KXgeGj6CUmrCgpB4vPaRqZHmvu0lnZF5SeAm4zMJUq0US9ERIxn/PinaGrax4aNi/hy/VyKil9Byr7P+FcIL2oheCovgyuTYvj90TKeCMGYiKJAFAYttU4XOxutnBHTt+Vbj5d/AKRiMSt+fX9IiF/I3Dmryct7BLN5FAcPPsTWrddTVbWmbS13hVMLtRA8k5fBNckxPJFfzo/2FWL3BO+hQFEgCoOWr+qakMCZMYEPnttspdTVf4MQcxTrIwCMxhGkpS5m6pRXGJv3OE3Nh9ix83a++HIOZWVvh1s8hT6gUQmezsvgvuxk3jpey+LtR6h2uILStqJAFAYtn9c0YlarmBoZ+PjH8eMfAiCYFWyxhgVCCFJTr+KMeV8xedI/sFjGsnffPew/8GvcbsUaOdUQQvDTrGT+Pi6TbY0tXLT1IAXW/n+PigJRGLR8UdvEadEWtKrALAgpJeXHPyAqcipCJIRIuuGBSqUnPn4BU6csJSPjdkpKXmXTNxdTW/dNuEVT6AOXJ8Xw9pRR1DndXLP9CMft/Zv/oyiQEGOz2Zg1axaTJ09m/PjxPPjgg4CSzr03imwOjlrtnBGg+8rjsbN33900Ne0nJeWqEEk3/FCpNIwedR9TJv8Lj8fB1q3XUln5v3CLpdAHZkSZeXVSDpVOF9fu6N8iZIoCCTF6vZ41a9awY8cOtm/fzieffMKGDRuUdO69sLLKu3DUwgDSMbjddrZt/w7l5e+Rk/MzUlMXh0q8YUtc3BnMnvUxEZbx7Nv/AA5HVbhFUugD06PM/GtCNsf66cZSFEiIEUK0pVd2Op04nU6EELz//vvcfPPNANx888289957YZRy8PG/qgZGGvWMNPm/Zkd19Vrq6jaSN+b3ZGf9QBk8DxEajZlx457A7W5i3/4HlDkjpyhnxkbw5pRR/Wpj2MxEL3/kEez7ek/nHlNXR8FLL/vVpn5sHsn3399rObfbzfTp0zl8+DA/+MEPmD17NsePHyclJYUDBw6QkpJCRUWFX30OBxpdbtbXNfHd9PjeC7ejqmoNGk1Uv1xXNpuNTZs24XK5MBqNTJkyBaPRm7m3paWFLVu2sGXLFiZOnMjChafuWur9xWLJJSfn5xw+/Cj7DzzAmNyHUKkG3zojCj0zsw8TdNszbBRIOFGr1Wzfvp26ujquuOIKdu9W1q3uiXU1jTilZFF8lN91pPRQVb2WuLizUKn6dll7PB7efvttDh061LZvx44d3HTTTZSWlvL2229js9mIiIhg/fr1TJ48mfj4wJTcUCJjxK04nXUUFPyNluajTJjwjJIGZZgxbBSIP5YCwLF165gcotxU0dHRzJ8/n08++URJ594D/6uuJ0ajZkYA4bsNDTtxOmuIj1vQ537XrFnDoUOHuOiii5gxYwaHDx/mP//5D88//zwNDQ0kJSVxyy23YLFYeOaZZ1i1ahXXXnstLpeL2tpa6urqiIqKGjbfpRAqRo28G4tlDPv23cfGTRcxbuzjxMefHW7RFAYIZQwkxFRWVlJXVweA1Wpl1apV5OXlKencu8HlkayubmBhXCSaAMJ3q6rXACri4s4MuE+73c6nn37Kl19+yfTp05kxYwZCCEaPHs3111+PzWZj4sSJ3HbbbSQnJ2OxWJg3bx779+/n3Xff5U9/+hN//etfefXVV3n55Zdpbm4OWIZTmeSkS5g18wP0+mR27LydkpI3wi2SwgAxbCyQcFFWVsbNN9+M2+3G4/FwzTXXcPHFFzN37lwlnXsXbGlopsbp5rwA3FcAVVVriY6ajlYbHVC9srIyXnvtNRobG5k+fToXXHBBh8H3nJwc7rnnHjSajj+VOXPmsGXLFnbt2kVeXh5jxoxBq9Xy1ltvsWrVqmH3QGA2j2TmjLfYuet77D/wK9Qas5L9eBigKJAQM2nSJLZt23bS/ri4OCWdexfsarICMDfaf/eVzVZGU9NeRo28J6C+pJSsWLGibdGvESO6Xi63s/IA0Ol0LFmyBACz+YSsc+fOZf369UydOpWMjIyA5DnVUan0TJzwV7bvuJW9e39Obc1XZGf/CIMhNdyiKYQIxYWlMKg40mInUqMiXuv/s01N7XoA4uLmB9TXsWPHKCws5Mwzz+xWefSE2WzuoDwAzjzzTCIjI/n444+HZXirWm1k8qQXSUu7gbLy9/h6wzmUl38QbrEUQoTfCkQIESOEGC+EyBFCKIpHISQcbbGTYzQENIejoWEnarUFs3m033WklKxbt46IiAimTp3aF1G7RK/Xc/bZZ1NeXt4hmms4odFYGJP7IHPnrCIyYhJ79t7FkSNPKKnhhyA9KgIhRJQQ4n4hxC5gA/A8sBwoEEK8KYToe8iLgkIXHLXaGWkKbM3yhoadREZMIJDnmvz8fAoLC5k3bx5arTZQMXtk4sSJREVF8cUXXwxLK6QVozGNqVOXkpq6mPyCv7Fz1/dwuZrCLZZCEOntF/cWUAScIaUcI6WcJ6WcIaUcATwGXCaEuC3kUioMCxwSim0Oso3+KxCPx05T034iIycF1NeGDRswm81MmzYtUDF7Ra1Wc9ppp1FUVERBQUHQ2z+VUKl05I35Pbmjf0119Vq2bLkGh6M63GIpBIkeFYiU8lwp5StSyroujm2RUv5USvlSyKRTGFYcR4WEgCyQpqYDSOkkInJiAHWaOHjwIFOmTAm69dHKtGnTMJvNrFu3DpcrOGsvnKoIIRgx4mYmT36ZFmsB27d/B5erMdxiKQSB3lxYe4UQDwghRg6UQArDlzK8y9bmBKBAGhp2ARAZ4b8FsmvXLqSUTJ48OTABA0Cr1TJ//nzy8/NZunQpjY3KDTMudh6TJj5HU/NBtm2/habm/o8RSSmxWgtpbNpPU9MB7PbjSOkOgrQK/tBbqMt1wLXA/4QQVcDrwHIpZWnIJRtiuN1uZsyYQVpaGh999BE1NTUsXryYffv2MXbsWJYvX05MTEy4xQwrZb7nmZwAXFgNjTvRamMxGNL8Ki+lZNu2baSlpYV8xvjMmTMxGAy8//77PP/881x44YWMHTt2WCd5jIs7iwnjn2bf/nvZtOkiYmPPREoXHo8Hh2MSOl1sr204nfVUVq6ksvJT6uo343I1dDguhIbkpEvJyblLCSEOMb25sHZIKX8ppRwJ/ATIBDYIIdYIIW4fEAmHCE8//TRjx45te6+kcz+ZclQk6jREaNR+12lo2Elk5ES/b8rl5eVUVFQwZcqUPkoZGBMnTuT222/HYrGwfPlyli9fjts9vJ+QExMXMXfOGtLSbsBqLcTlakCykU2bLvatv979Ikc1Nev5esNC9u2/l6bmgyQmXkDemN8xccJzTJjwLGNyf0Nq6mKOV3zE1xsWcujwoziddQP34YYZfgfbSyk34FUe7wNPAs8CL4ZKsKFEcXEx//3vf3nggQf485//DMD777/PunXrOHDgADfffDPz58/n8ccfD7Ok4aUMdUDWh9vdQnPzYRITFvldZ+vWrajVaiZMmNAXEftEUlISt99+O+vXr2fNmjV88cUXw37yqE4Xy5jcB9ver127FLVmKTt23o5GE0GEZTwqtRGtNhqTMQuVSofVWkhJ6X8wm0cyedI/iIyc3O2DQ1bmnRw9+hSFhS9RWrqcrMw7SU+/GbXa/+UBFHrHLwUihJiJ1531LSAfeAE4pXJvfLH8IFVFvYcQ1tV5qN2y1a8240dYOOOa3F7L/fSnP+UPf/hDBz+4ks79ZMpQMS2A8Y/Gxr2Ax+8IrNraWrZu3crEiRPbUrQPFGq1mjPPPJPKyko+//xzcnNzSU1V3CutCJHBrJkfUV39GVXVa2lpOYLL0UhT0z7Ky98FQKUykpR0MXljfotG0/NKlQZDKuPG/YGMjNs4fOSPHD7yB4qKlzJu7B+IjT19ID7SsKBHBSKEeARYDNQCbwCnSymLB0KwocJHH31EYmIi06dPZ926deEWZ9DS4HJTjyogC6S+YTsAERH+RWCtWbMGIQQLFoRv+tIFF1zAsWPHePfdd7njjju6TJMyXFGrDSQmLiIxsaNF6Xa3IKUbtdoS8PiRxTKGKZP/QW3tRvYf+DXbtt9MVtYPkDJ0ARTDid6uXjtwgZTy4EAIE0r8sRQAX26q4M0NWL9+PR988AEff/wxNpuNhoYGbrzxRiWdeyeOtniX1gwkhLey4hMs5jHo9Qm9li0tLWXXrl3MmzePqKjAEjUGE5PJxKWXXsprr73GunXrOOecc8Imy6mCWm3qdxsxMbOZNfNdDhx4iPz8ZxGcgZTzA5p8qnAyvQ2iPyylPCiEUAshLhVC/FgI8bPWLRgCCCHuFkJIIUR8u32ThBBfCyH2CCF2CSFOWcflo48+SnFxMfn5+bzxxhucffbZLFu2TEnn3onWtZmz/VQgUlZS37CNpKRL/Sq/du1aTCYT8+bN67OMwSI3N5epU6eyfv16ioqKwi3OsEGtNjF27ONkZ/0YyRfs3XePEvLbT/xVvx8CtwBxQES7rV8IIUYA5wKF7fZpgGXAnVLK8cB8oPuwjFOU++67j5UrV3LjjTeycuVK7rvvvnCLFFaOtNgRSLIMfioQNgGQlHRxr2VtNhtHjhxh6tSpGAyD41lk0aJFREZG8t577+FwOMItzrBBCEFOzk8Q4nLKy99lz9678XiG90TP/uCvAzZdShlYrgj/eBK4B3i/3b7zgJ1Syh0AUsohk/dg/vz5bdE3Sjr3juRb7cQhMaj9e6aRciNRUdMxGtN7LXvs2DE8Hg+jR/ufbDHUGAwGLrvsMpYuXcrq1au54IILwi3SsEIlLiErezRHjv4RKV2MH/cEKlVgOdgUQPiT7E0I8TiwWkr5v6B1LMSlwEIp5U+EEPnADClllRDip8B0IBFIAN6QUv6hmzaWAEsAEhISpi9fvrzfcjU1NWGx9BzhEUxC0V+w2uxPO4HWfVBawO3iYY2t17JSFuORDyLEDahEz8unNjU1UVJSQkVFBaeffjoq1eDyeR86dIiSkhImT54c8omkA31tB0K4fnce+SlSLgdGoRI/QIjIoLQbrjb6UnfBggVbpJQz+tShlLLXDbgCaAasQAPQCDT4UW8VsLuL7TJgIxDlK5cPxPte3w0cA+IBE/A1XkXTY1+5ubkyGKxduzYo7YSzv2C12Z92Aq076ctdcvGaDX6VPXz4j3LV6lHSbq/qteyaNWvkn/70J/nGG28EJM9AYbfb5dNPPy2ffPJJabVaQ9rXQF/bgRDO3115+Udyzdqx8sv1Z8iGht1BazccbfSlLrBZ+qEHutr8fRz7EzAXMEkpI6WUEVLKXlW1lPIcKeWEzhtwFMgGdvisj3RgqxAiGSgGPpNSVkkpW4CPgeCnTFUYNLS4PRx3uEjCv/Ui6uo2A9nodHG9lm1ubqahoYFRo0b1U8rQoNPpuPzyy6mvr+e9994b1unfw0VS0kVMn/YGUrrZvOVqysreDbdIpwz+KpBDwG4ZpKtbSrlLSpkopcySUmbhVRrTpJTlwKfAJCGEyTegfhawNxj9KgxOimzeQeREPxSIlJKm5v0I/FtBsKamBmDQKhCAjIwMzjvvPPbv38+XX34ZbnGGJZGRk5g1830iI6ewd9/dHDj4cI8pVRS8+DuIXgasE0KswDs3BAAp5Z+DLZCUslYI8WfgG0ACH0sp/xvsfhQGDwW+EF5/FIjNVoLL1Yg3gK93ampqSExMDOvcD3+YM2cOJSUlrFmzhqysrD4tsavQP3S6eKZOWcrhI49TVPQyTY37mDDhL37NMxqu+GuBHANWAzqCGMbbis8SqWr3fpmUcrzP5XVPsPpRGJwU+iwQf1xYTU37APyyQKxWK/X19YMq+qo7hBBccsklWCwWPv74YzweZfnXcKBSacgd/QDjxz1JQ+MuvvnmMurrt4dbrEGLXxaIlPLhUAsylMnKyiIiIgK1Wo1Go2Hz5s1KOvd2FFjtmNUqIty9e0gbm/YDAug9ffuhQ4eQUpKXl9d/IQcAvV7PokWLeOutt9iyZQszZ84Mt0jDluTkSzFbctm183ts3XYjkye9QGzsaeEWa9DR24JSLwghukw0JIQwCyFuFULcEBrRhhZr165l+/btbN68GVDSubenwOog06DDnzRHTU37MBoz8Cc5wf79+9HpdKSl+bdWyGBg/PjxZGdns3r1apqbm8MtzrAmwpLH9BlvYjSOYMfO71Jd/UW4RRp09ObCeg74PyHEPiHEm0KI54QQLwshvgC+wuvGeivkUg5B3n//fW6++WYAbr75Zt57773wChRGCmwOMv1MotjUtA+LZWyv5VwuF4cPHyYuLm7Qzf3oCSEEF1xwAXa7nfXr14dbnGGPXhfPtKmvYjLlsGv392ls3BdukQYVPbqwpJTbgWuEEBZgBpCCdy7IPinlgdCLFzzW/usFKgqO9lqurq6O45994lebiZk5LLhlSa/lhBCcd955CCG44447WLJkiZLO3YeUkkKrnfmxvQ+puVxNWK2FpCRfSU0v+QmOHTuGw+EgPj6+54Lt8Djc2PbX4ChsJPLsEahMoVkvvTcSExOZOHEimzZtYu7cuUREBG24UaEP6HSxTJn8Et9svpIdO29n5ox3lYF1H/6OgTQB60IrytBl/fr1pKamUlFRwbnnnnvK+OQHggqHC6tHkmnQ9Vq2udmbFNofC6TVfeXPuJLH4abxs2KavihBOk4k14u+OKfXuqHirLPOYteuXXz55ZdKmpNBgF6fxKRJz7Nly7Xs3v0jpk5dhkqlpOIfNmfAH0sBCEluqtaFgxITE7niiivYtGmTks7dR2sIrz8uLO8AeqsCOdRtObfbzYEDBxg9enSv7iv7sXpq/nMAd50d48R4zLNTaNl6nKavS7GcnoomJjzJF+Pi4pgyZQqbN29mzpw5wzbAYjARGTGBvLzfsXfvzzmW/wwjc4KSkPyU5tRxDp+iNDc3t61E2NzczP/+9z8mTJigpHP3UeAL4c009m6BNDXtQ6OJxGDoeSW/TZs20dTUxKRJPef/tO6tpvKl3QiNioQlE4m7YSyGUdFEnpcFAhpWFvj9OULBWWedhVqt5q233sLlUjLGDgZSki8nJeUq8vOfo6p6XbjFCTuKAgkxx48fZ968eUyePJlZs2Zx0UUXcf755yvp3H0UWB0IIF3vjwLZj8U8psdV6RoaGli7di2jRo0iN7f7RcSse6upXrYXbbKJhO9NRp8T3XZME63HMjeVlm0VOMvDFwkVHR3N5ZdfTklJCZ9++mnY5FDoyJjcB7FYxrJr1/epqRnegQ7+rok+A3gAyPTVEYCUoUnxPqTIyclhx44dJ+1X0rl7KbDZSdFre03jLqWkufkISUkX9Vjuk08+wePxcOGFF/aoaBo/K0YTayTh9omo9Cf/DCLmj6Bly3Fq3z1Mwh2TEKrAllINFuPGjWPu3Ll8/fXX5ObmnhKTIoc6arWJqVP+xdZtN7Jj5xKmTvk30dF9S2Z7quOvBfIq8E/gW8AlwMW+vwoK/aLI6iDDjwF0p7MGl6sek6n7ge3jx4+zd+9e5s2bR2xsbLflXHV2HAUNmKYldqk8ANRmLVEX5eAoaKD5m/LeP0gIOeecc4iOjmbdunVKssVBgk4Xx7Spr2AwpLJz1/ewWofnypL+KpBKKeUHUspjUsqC1i2kkikMC4psDtL9UCAtLccAMJmyuy1TXFwMwMSJXc59bcO6s9Lb1qSeQzFN0xLRj4yifsUxXPX2HsuGErVazbx58ygpKeHo0d5D0RUGBp0unsmTXkBKNzt2LsHlagq3SAOOvwrkQSHEP4QQ1wkhrmzdQiqZwpDH6ZGU2Z2M8EuBeG+cJmP3CqSsrAy9Xt9rxFLLzkq0aRY08cYeywkhiL5iNLglFX/ZhnVv+BbHnDJlCpGRkXz22WeKFTKIMJmymTjhL7S0HGHP3p8h5fDKYeavAvkOMAU4H6/rqtWNpaDQZ0rtDjzglwJpbjmKELoel7AtKysjOTm5x9BdV7UVZ3ETpkn+TTDUxhtJ/MEU1BE6qpfupWlDmV/1go1Go+H000+nsLCQvXuV1Q0GE7GxpzN69P9RVbUaKd8JtzgDir8KZLKUcoaU8mYp5Xd8260hlUxhyFPsC+H1zwI5hsmUiRDqLo+73e622f09trPLm/TZONH/mcTaZDOJP5iCLjuShlUFSGd4njKnTZtGWloa7777LoWFhWGRQaFr0tNuJC3teiQrhtWCVP4qkA1CiHEhlURh2NG6kJR/YyBHexz/qKqqwuVy9apArHuq0aZb0MQGNkFQaFREnpOJp8lJ8+bwDKprtVquv/56IiMjee2116iqquq9ksKAIIQgd/SvgTz27b+f+vqt4RZpQPBXgcwDtgshDgghdgohdgkhdoZSsKFEXV0dV111FXl5eYwdO5avv/6ampoazj33XG688UbOPfdcamtrwy3mgFNscyKAVEPPOac8HhdWa2GPEVjl5d6bek8KxN3kwFnciHFs70vhdoU+JwpdRgSNnxUj3eGxQsxmMzfddBPgTciprBsyeFCptKjE9zAYktmx805aWvLDLVLI8VeBnA+MBs6jizBeIYSSZ6EHfvKTn3D++eezf/9+duzYwdixY5V07nhdWEk6Lfpe0o3YbEVI6erRAikrK0Oj0fSYPNF2oBYkGPK6D/HtCSEEEWdn4K6z07K9sk9tBIOYmBguuOACioqK2LRpU9jkUDgZISxMnvQiINm67Ya26MGhil8KpH3objdhvKtDJN8pT0NDA59//jm33XYbADqdjujoaCWdO60hvL1nvPUnhNefAXTb/hpUETq0qebAhfVhGBODJt5Iy/bwZk+eNGkSo0ePZtWqVYora5BhNo9i2tRleDwOtm69Abs9fA8boSZYyRTDM003AOo+PIKjtPe0FGl1KioO+Oed06Waib5kZI9ljh49SkJCAt/5znfYsWMH06dP5+mnn1bSueO1QKZFmnot1+wL4TV348LyeDyUlZUxefLkbtuQbg+2g7UYJ8b3OEO9N4QQGMbE0LSxHOn0ILThyQYkhODiiy/m+eefZ9myZdx2221K2vdBhMUyhqlTX2Hz5ivZv/9+Jk16oV/X3WAlWFe/EpjeDS6Xi61bt/K9732Pbdu2YTabh6W7qjNuKSmx+zuJ8ChabQxabdee0traWhwOR4/jH46CBqTdjbGP7qv26HNjwOXBnl/f77b6Q1RUFDfccAMtLS288sorWK3WsMqj0JEISx4jR/6Cquo1lJUNzXX3hk06994shVb2rlvH6PnBS/GVnp5Oeno6s2fPBuCqq67iscceG/bp3I/bnbhkICG83buvWgfQk5OTuy1j3V8LaoF+dHTAsnZGnx0FaoHtUB2G0eEd/ktLS2Px4sW8+uqrvPvuu1x77bWn1AqMQ50R6TdTWbmSg4d+g1pjJinxwnCLFFSCdaUNPdssSCQnJzNixAgOHPAu4Lh69WrGjRs37NO5Bx7C230EVnW1d4Z4zwPoNeizIrvNfRUIKp0afWYk9kODI3Ju5MiRLFq0iIMHDyrL4A4yhFAxYfxTmM1j2L37Rxw89PshlUmgx1+TEOK/wGvAe1LKngYQFgZVqiHGX/7yF2644QYcDgc5OTn885//xOPxcM011/Dss88yZswY3nzzzXCLOaD4O4nQ7bbjcFRhNIzotkxNTQ0WiwWdruu23M1OXMdbME3J7LvAndDnxtDwST7uRgfqiN6VYKiZNWsWRUVFrFmzhujo6F7zgSkMHHp9ItOnvcahQ49QVPQyOl08WZl3hFusoNDb49gLwLXAk0KItcDrwMdSSkf7QlLKmhDJNyRoXVmuM8M5nXuxzQlAWi8KxOHwRrDo9d27+GpqanrMvus45h2raL/mR38xjIqmAbAdrsM8NfzuRyEEl1xyCfX19bz99tsUFxdz3nnnoVZ3PXNfYWBRqXTk5j6Iw1nDkSN/xGLOJT5+QbjF6jc9urCklO9LKa/Duw7IO8DNQKEQ4mUhxLkDIaDC0KTI5iBOq8HUyzogrQpEp+s+9UhvCsR+tB6hVaFLs/RN2C7QplpQmTXYDw4ONxaAXq/nlltuYfbs2WzcuJG1a9eGWySFdgghGDf2cSIs49i95yc0NOwKt0j9xt95IFYp5X+klFfgnUw4FfgkpJIpDGmKbQ6/BtDtDm94s07ftQJxOBw0NTX1okDq0GVFIjTBG1wWKoEhNxbbgRqke/D4tNVqNRdccAFTpkzhq6++agswUBgcqNVGJk1+Aa02mu07bj3lJxr69YsSQiQJIX4khFgPvAf8D5geSsEUhjb+TiJ02L2T5PTdWCCtKWC6UyAqBzjLW7yRU0HGMC4WT4sLR0F4w3m74rzzzsNgMPDhhx8q6U4GGQZ9MlOneANotmy9lprar8MsUd/pUYEIIW4XQqwBtgK5wD1Syhwp5b1Syu3BEEAIcbcQQgoh4n3vtUKIf/vybe0TQvwyGP0oDB6kbw6I/xaIQKvtOn9VTY13+K07BWL0eZj0OSFQILkxoBZY9w6+IUCTycT5559PSUkJK1euHFKRP0MBkymbadNeQ6OJYtu2b1NU9K9wi9QnehtEPw14DFglQ7BSihBiBHAu0D439dWAXko5UQhhAvYKIV6XUuYHu3+F8FDpcGHzSL9CeB2OSrTaWFSqri/VVgXS3SJSxhrhHf9ID/4sbZVeg2FUNNa91URdlD3oZhpPnDiR4uJivv76a6SU6PX6cIuk0A6LeTQzZ7zL3r0/5+Ch32IwpJGQcGoNLfc2iP4dKeX/QqE8fDwJ3EPHmewSMAshNIARcAANIepfIQwEsg6Iw17ZawSWyWTCaOx6dUFjjUCXGdzxj/YYxsXhrrHhOt4Skvb7gxCCCy64gNmzZ7NhwwYOHz6sWCKDDI3GzPjxTxEZMYk9e39GY+O+cIsUECJcF5QQ4lJgoZTyJ0KIfGCGlLJKCKEFXsE7t8QE3CWlfKGbNpYASwASEhKmL1++vN9yNTU1YbEEL1qnsLCQ3/zmN23vy8rK+M53vsN5553Hb37zG0pLS0lNTeXBBx8MWi6jYH2G/rTTU92vpZanMfMHGsgQnh7ruD2/BSyoVXd12f727dtxu91Mn37ykJzaDtlr1VSP9lA7MjTXudoG2etC20d/kVJy5MgRiouLSU1NZfTo0YPOWgr27y5c/fW1XSlr8cjfAXbs9hswGecOWP8LFizYIqWc0acOpZTdbkB2T8d724BVwO4utsuAjUCUr1w+EO97fTrwKqAFEoEDQE5vfeXm5spgsHbt2qC00xUul0smJSXJ/Px8+Ytf/EI++uijcu3atfLRRx+V99xzT9D6CdZn6E87PdX9S365TFqzTTY4Xb3W+eLL0+SePb/otv0///nP8q233uqyn+Ztx2XRvZ9Le2GD33L3hfK/bJXlz24LaR/9xePxyH/84x/ywQcflP/6179kVVVVuEXqQCh/dwPZX3/atVpL5MZNl8lVq3NkQeHLA9Y/sFn28R7fm13/FoAQok/p2qWU50gpJ3TegKNANrDDZ32kA1uFEMnA9cAnUkqnlLICWA/0TTsOMlavXs3IkSPJzMwc1unci+1OojVqIjQ9T3KT0oPDUdVtCK/L5aK+vr7bAXTboTrcWok2iPM/usI4Lg5nUSPuBntI++kPQghycnK4+OKLKS0t5bnnnmPXrlN/HsJQwmBIZfq0/wDTOHTodxyvWBFukXqlt0F0lRDiQSBXCPGzzgellH/uS6dSyl14rQsAOrmwCoGzhRDL8Lqw5gBP9aWf9qxYscKvmPi6ujqOHfMvNjs5OZkLLrjAbxneeOMNrrvuOoBhnc69yOpfFl6nsxYpXX0K4ZVSYj9cizXWO2cjlBjHxdHwvwKs+2qwzO55Sd1wIoRgxowZbalz3nnnHdRqNePGKatVDxbUaj0qcTsRES+wd+/PMRhSiYrsfpmCcNObBXItYMOraCK62ELBXwELXlfXN8A/pZSn/PK5DoeDDz74gKuvvjrcooSdYj9DeB0O7xyQ7mah9xTC66qy4q530BIX+nEJTZIJdawB297qkPcVDCIiIrjhhhtIS0vjrbfe4uDBg+EWSaEdQuiYNOl5dNo49uy5C5er93WMwkWPFoiU8gDwuBBip5QyZPaUlDKr3esmvKG8QcVfSyFUualWrFjBtGnTSEpKAhi26dyllBTZHJwZ07tbyW5vnYXe9bnpyQKxH6oDoCU+9ApECIFxXBxNX5fisbuCkvE31Oj1em688UaWLl3Kf/7zH66//npGjvRvyQOF0KPTxTFu3BNs3XYDhw8/Sl7e78ItUpf4G9v4lRDiz0KIzb7tT0KI4M/MGsK8/vrrbe4rYNimc691uWlxe/y0QHyJFHVdp2mvra1Fp9NhMp28qqHtUC3qWAOubhY8bKiqYN+X62hpCM4scuO4WHBLbAfrgtLeQGAwGLjxxhuJj4/n9ddfZ8+ePeEWSaEdMTGzycj4LiWlr1Ne/kG4xekSfxXIy0AjcI1vawD+GSqhhhotLS2sXLmSK6+8sm3ffffdx8qVK7nxxhtZuXIl9913XxglHDiKA1gHxN6WSLFrC6Suro6oqKiTQlKl24P9aD2GUdHdtv3Z0pf4+C9P8PclN/H2ow9ibWr08xN0jS4zCpVJc8q4sVoxmUx8+9vfJjExkTfffJO33nqLlpbBN6dluDIy5y6io2ayd98vqK7+LNzinIS/CmSklPJBKeVR3/Yw0P0KPwodMJlMVFdXExV1wmiLi4tj9erVLFu2jNWrV/eYDHAoEchCUg5HJWq1GY3G3OXx+vp6oqOjT9pvz/cuX2vI7Xp2utNu4+j2zYyedRqzr7yGot07eOeRX2Pvx41TqAWGMYMvuaI/mM1mbrvtNhYsWMDevXt57rnnOHToULjFUgBUKj2TJ7+IxTyGnbu+T33DjnCL1AF/FYhVCDGv9Y0Q4nRAWYBZIWACmYVut1eg68Z9BV4F0l4pt2LbWw0a4V27vAvyd27DZbcz+dwLOf2aG7nkZ7+kIv8o7z7+MG6Xy89PcjKGcXF4WlxhXyu9L6jVas466yy++93vYjQaefXVV/nggw+w2wdvaPJwQaOJYMqUl9HpEti5805s9sGTYdlfBXIn8FchRL4v5PZZYGgsqaUwoBTZHJjVKqJ7mQMC3igsfTfuK5fLhdVqPckCkVJi3VeDYVQMKl3XfRza+BUGSwTp4yYAMHL6bM65/QeU7N/DsW0nL/zlL4bcGNCIU86N1Z7U1FSWLFnCaaedxtatW/nb3/5GSUlJuMUa9uh08Uye9DxudzM7d96J220Lt0iA/+uB7JBSTgYmAZOklFOHQmitwsDTug6IP6k0HI6KbicRtj4Zd7ZAXBUtuGtsGMZ27RJ0u5wc3bKJkdNno9aciJYad8bZGCMi2be+735mlV6NYWQ01n01p3TOKa1Wy3nnncett96KlJJ//vOfygD7IMBiGcP4cX+isXE3+/bfNyiusYAyzEkpG6SUSmJDhT7jXQfEvzXE7fbKbueA2GzeJ7DOFkhranVjNwqkcPdO7C3NjJ59Wof9ao2G3DnzOLplEw5b372zgzm5YqBkZGRw++23k5yczJtvvslnn302KG5aw5mEhHMZmfNzjh//kPyC58ItTmAKREGhvxTbnH6Nf7jdVtzupm5nobcqkM4WiG1fNdp0C+rIrlOXH9q4Hq3BSObEKScdy5t3Fi6HnSPfbOhVvu5oVVzWU9iN1R6LxcLNN9/MpEmTWLt2LW+//TZOpzPcYg1rMjPvJCnpUo4e/TMVlZ+GVRZFgSgMGA0uN/Uut98RWND9UrY2mw21Wt0h86i70YGjqBHj2K4Xn3I5nRzcuJ5RM2aj0Z0sQ1ruWCLiEvrlxlJH6tGOiBgyCgS8Lq0rrriChQsXsnv3bt58801c/Qg2UOgfQgjG5j1KZORk9uz5OY2Ne8Mmi98KRAhxmhDieiHEt1u3UAo2lHjyyScZP348EyZM4LrrrsNms1FTU8O5557LjTfeyLnnnts2q3ooc2IOSO9L2bbOQu/OArHb7URGRqJSnbiEneXNIEGXFdllnfztW7A3NzN23vwujwuVirzTz6Rg57Z+TTA0jovFWdw0qJMrBooQgjPOOIOLLrqIgwcP8vbbb+N2u8Mt1rBFrTYwaeLf0Wqj2LHzdpqbD4dFDn/XRH8FeAKYB8z0bUMiQ26oKSkp4ZlnnmHz5s3s3r0bt9vNG2+8wWOPPcbChQtZtmwZCxcu5LHHHgu3qCGn0OpVIBmG3lfGO5EHq+soLJvNdtL4h7vWe8PWxBq6rLPvy3UYI6PI6MJ91Uru7NPxuN0U7treq4zdYRzntYAG41K3/WXmzJmcf/757Nu3jxUrVihjImFEr09k8qR/IKWLzVuuoa6u7xGEfcVfC2QGcLqU8vtSyh/5th+HUrChRGvIqcvloqWlhdTU1GGZzr0okDkgjtY8WN27sE6KwKq1gYouxz/sLS0c3bKJMXPndYi+6kxi9ki0BiMlB/ruFtAkmlDHGYaUG6s9c+bM4fTTT2fz5s1s3Lgx3OIMayIixjJj+lvodLFs33EbUg7sNedv1rfdQDJQFkJZQsrBg7+lsan35SLdnjq2bO1yAcSTiLCMJTf3/3osk5aWxt13301GRgZGo5HzzjuP8847b1imcy+yOTCpVcRp/ZgDYq9ECDU67cmTAV0uFw6H4yQF4q6zo47UI9Qnhwgf2vQVLqejW/dVKyq1mtTcPEr2912BCCEwjj21kisGysKFC6murubTTz/FYrEwYcKEcIs0bDEaRzBl8sts3HQRHvkvpLxywFac9NcCiQf2CiE+FUJ80LqFUrChQm1tLe+//z7Hjh2jtLSU5uZmli1bFm6xwkKhze73HBC7oxKtNg4hTlY2DQ3eSPLOLixXrQ11TNfusQNffU5UUjIpo/N67TttzDgqC/Oxt/Q9jfaJ5IpDc2xLpVJx5ZVXMmLECN5++21lnkiYMRozGDXyPmAvxSUDd3/x99HooVAKMRD0Zim0sm7dOqZPmx+0fletWkV2djYJCV5XzJVXXslXX301LNO5F9kcZPg5B8ThqETfjfuqrq4OODmE111rRz/y5NQmHo+bkv17GXfWQr+UV1reOJCS0oP7yZ5y8lrr/nAiuWINpoldf45THZ1Oxw033MCrr77Km2++yYoVK0hMTOTCCy8kPr77FDQKoSEt7XoOHPwPBw8+hN1eTk72XahUobV+/Z2J/llXW0glGyJkZGSwYcMGWlpakFKyevVqxo4dO+zSuUspKbT6t5AUeF1Y3U0irK/3Rki1t0Ck24O7wY46+mQLpKakGKfdRsqoXL/6Thk1BqFS9c+NpRYY8mKx7j/1kisGgl6v54YbbuCcc85h1KhRlJeX89JLL1FQUBBu0YYdQghU4oekplxDQcHf2bXre3g8oQ239jcKa44Q4hshRJMQwiGEcAshlBnpfjB79myuuuoqpk2bxsSJE/F4PCxZsmTYpXOvd7lp9HMdEPC6sLrLg9VqgURGngjXddc7QIIm5uQIrPLD3hX3kv1UIFqDgaTskZQc6J9bxpAXi7S6cBT3L1X8YEev1zNv3jwuv/xyvvvd72IymVi6dClVVVXhFm3YIYSOsWMfJTf3Iaqq13Dg4IMhjZTzdwzkWeA64BBgBL7r26fgBw8//DD79+9n9+7dvPLKK+j1+mGXzr3QF4GVYexdgUjpxuGo6jYTb319PTqdDk27aCpXrXdmeldjIOVHDqIzmohNSfNb3rS8cZQfOojb1fdZ14ZR0SDAdmDohfN2R2xsLLfccgtCCL788stwizNsGZF+E1mZ36O09A0KCv4esn78nkgopTwMqKWUbinlP4H5IZNKYcgRSAivw1kLeLpdyra5uRldp5nkbXNAok+2QMoOHyR55GiEyv/EC2ljxuNyOjh+tO8TtFQmLboREUN2IL07IiIimDFjBjt27BgWE2QHKzk5Pyc56TKOHH2C8vL3Q9KHv7+oFiGEDtguhPiDEOIuoOtVfhQUuqCobRKhHwqkl1noLS0taLUdZ7O762wgOGkMxONyUVWY77f7qpWU0WOAE+6vvmIYE4uzpAl3k6Nf7ZxqnHbaaahUKsUKCSNCCMaOfYyY6Dns3XcvNbVfB70PfxXITb6yPwSagRHAt4IujcKQpdDmIEKtIsqvdUB6zoPV3Nx8kgJx1dpRRegQmo6XdEtVBR63O2AFYo6JxRQVTUX+sYDqdcaQGwMS7Ifq+tXOqUZkZCRTp05l27ZtbdGGCgOPSqVj4sS/YTJlsWvX92hq6t8D0Unt+1NISlkACCBFSvmwlPJnPpeWgoJfFNkcZBj9nANi9yqQniyQk11Yti4H0FsqvKu3pYwMTIEIIUjMyqGi4GhA9TqjTbOgMmuGnRsL4KyzzsJisfDqq6+2BT4oDDxabSRTJr+MSmVk+45bsdmCp9D9jcK6BNgOfOJ7P0WZSKgQCIW2AEJ4Wy2QLqKwWmehn2SB1HUdwttcUYYlNg5LbNcZensiISuH6qLCfg2kC5XAMDoG28FapGfohvN2RUREBDfeeCNOp5NXX321LQW/wsBjMKQyZfJLuFyNbNl6LS0t/bOsW/HXhfUQMAuoA5BSbgeygiKBwpBHSklRAArE7qhAo4lAre7ComjxLtTUXoFIj8RdZ+/SAmmuKCd55Og+yZ2YmY3H7aK6uKhP9VsxjIvD0+zEfuzUWyu9vyQmJrJ48WKqqqr48MMPleSLYSQiYhzTpr6C293C5i2LaWo60O82/VUgLinl8Lv6g8TTTz/NhAkTGD9+PE899RTAsErnXu100+L2+JWFF/CF8HbvvoKOCsTd4ACPPCmE12FtwV5fS1L2qD7JnZg9EoCK/P65sQx5sQidCuuOyn61c6qSk5PD2WefzZ49e/jmm2/63I7NZuP48eMUFRUp1kwfiYycxIzpy1EJDdu234LV2r+HI7+TKQohrgfUQojRwI+Br/rV8zBh9+7dvPjii2zatAmdTsf555/PRRddxIsvvsjChQt54IEH2LBhA4899hiPP/54uMUNCYGE8IJ3LZCAFEid92bS2QKpKPCa6a2KIFCik1PQ6PVU5B8BzulTGwAqnRrD2Disu6uIvmwkQj381nE7/fTTKSws5NNPPyUtLY20NP/n5FRWVvL111+zc+fOtoWszGYzl156KWPGjAmVyEMWkymbKVP+xZat17Jte/+WdfL3Sv4RMB6wA68DDcBP+9XzMGHfvn3MmTMHk8mERqPhrLPO4t133x1W6dwLbd45GiP8mEQIvjxYASkQb/vqqI7tV/osh4Ss7MAE9qFSqUnIzKayn5FYAKZJCXhaXNgP1/W7rVMRlUrFFVdcgcVi4c0338Rq9W/d+a1bt/L888+zc+dOJk2axNVXX80111yDxWLh9ddf57PPlIxKfcFiyWXK5JdwOvvn+fDLApFStgAP+LZTkv87VMzupt4v2jpp4elth/xqc4LFyG9Hp/dcZsIEHnjgAaqrqzEajXz88cfMmDFjWKVzb11IKjOAQfTuJhF2qUCavYPcKkvH9ivyj6ExGLHEBD6A3kpiZg77vlyH9HgCmojYGcOYGIRBTcvOKgxjhnbWge4wmUxcffXVvPzyy7z33ntce+21PUblrV27ls8++4zs7Gy+9a1vdVi+ODc3lw8++IC1a9eSkJDAuHHjBuIjDCmioqYyY/rbQN9cvNCLBdI+dXtXW5979bb9kBCiRAix3bdd2O7YL4UQh4UQB4QQi/rTT7gZO3Ys9957L+eeey7nn38+kydP7pCCYzhwzGonQafB4sccEJerCbe7BX03aUxaFUj7c+hpdoIAlbHjea3IP4IxPrFfayMkZuXgsLZQX9k/BS80KozjvG4s6Ry+S8Gmp6dz3nnnceDAAb766oQXvPPgemNjI19++SXjx4/npptu6qA8wPv9X3rppaSlpfHee+8N6QewUGI2982920pvd7K5QBFet9VGvHNBgsmTUson2u8QQowDrsXrMksFVgkhcqWU/frV9WYptLJu3TrmTw3uar233XYbt912GwD3338/6enpwyqd+zGrnSy/B9C7D+EFrwIxGAwd1kL3NDtRmbQI1YnL0+1yUV1UQPyEqf2Q3KtAwOsOi05K7ldbpulJtGytoGV7JeaZ/WvrVGb27NkUFBSwatUqNBoNO3bsoKamhrlz52IweMexNmzYgMfj4eyzz+7wXbdHo9GwePFinn/+ed544w1uv/12jEbjQH6UYU9vNnkycD8wAXgaOBeoCnE698uAN6SUdinlMeAw3hDiU5bWp6PCwkLeeecdrrvuumGVzr3A6iDL5O8Aes+z0FtaWjCZTB32eZqdqMwdn4VqSotxu1yY4vunmOMyMhFC5RtI7x/6nCi0ySaaviod1uGsQgguu+wyYmJiWLFiBY2NjdhsNlatWgV4o602b97MuHHjiIvr2f0YGRnJNddcQ11dHe+88w4ej2cgPoKCD+HvhSyE0OPNyPtH4DdSyr/0q2MhHgJuwTsgvxn4uZSyVgjxLLBBSrnMV+4lYIWU8q0u2lgCLAFISEiYvnz58v6IBEBTU9NJ5nJ/+fGPf0xDQwNqtZrvf//7TJ8+nfr6eh5++GHKy8tJTk7moYce6pCevD8E6zP0p53Wug4J3yaaq7HyLWHvtY7JvA8p/45KPIwQJ1uN27dvx+PxkJub2yZb2kbvc1DJ7BM3j+oDe8hfs4KsSxcTlzaiT5+hlT1v/BN9ZBSjLryyX+0ARBYJEveoKJ7lxjbAQyGhuLb7Q0tLC3V1dSQlJXHw4EGOHz9OXl4eNTU1VFRUMH36dCIiIvxqq6SkhEOHDpGZmUl2du9BE6E6F8FoNxi/u0BYsGDBFilln9wuvTrjfYrjIrzKIwt4BnjHn8aFEKvwWjGdeQD4G/BbQPr+/gm4la7dZF1qOSnlC8ALAGPGjJHz58/3R6weWbduHcFopz07d+7scv9ll10Wkv6C1WZ/2mmtu7/ZCpsOsGBcHvOTTl7fvHOdnJHxHDoEp59+ITrdyXfYffv2ERUVhcViaZOtfOsWtAlGRs8/MZC6rvAQxTo9sSlp/T4XLbs2U7Rvd1DOqcfhpuzRTYy2xhM3f2y/2wuEUFxrwUJKic1mY//+/QghmDlzJhdddFFA9d9//322b9/OnDlzGDu253MbqnMRjHaD8bsbKHpUIEKIf+N1X60AHpZS7g6kcSmlX8HzQogXgY98b4vxJmtsJR0oDaRfhcFDgS8CK8vvEN4qhNCi1UZ3ebylpYWUlJQO+zwtTlTmjtZbRf4x4jMy+xU51UpCljcSq6WhHlPkyUvmBoJKp8Y8K5mmL4px19tRR/k3NjTU0Wg0XHfddRQXF5OXlxfwU7QQgosuuoiKigreffdd4uPj25aRVggdvf26bgJygZ8AXwkhGnxbY39XJBRCtL8LXAG0KqcPgGuFEHohRDYwGtjUn74UwsexFq/bKtvo5yC6vQKdLg4hTr40pZS0tLRgNp9YSUB6pE+BaDuUq8w/2jYA3l8SM1sH0oOTP8gyMxk80LxViRxqT3JyMjNmzOiz+0ar1bJ48WK0Wi1vvPGGMlt9AOhRgUgpVVLKCN8W2W6LkFL212H/ByHELiHETmABcJevzz3AcmAv3uSNP+hvBJZC+DhmtROtUROj9S90uaelbB0OB263u8MgurS5wEMHBdJUU42tuYmEjL5NIOxM60TE/mbmbUUTb0SXHUXL5vJhPZgeCqKiorj66qupra1VBtUHgLDlVJBS3iSlnCilnCSlvFRKWdbu2O+llCOllGOklCvCJaNC/ymwOsj0030FrZMIe56F3l6BtE4iVLdTINXFhQDEjcgIWN6uMEVGYYmLp+JY/yOxWjHPSMJVbcOR3y9DXqELsrKyWLRoEQcPHlRmqoeY4ZeUR2FAOWa1++2+Am8Yb295sNorEE/rLPQOCsSbIC4uPTgKBLyZeSsLguPCAjBOjEfo1TRvPh60NhVOMGvWLKZMmcJnn33G/v37wy3OkEVRIAohw+mRFNsdfisQKd04nTW95sHqqEC8yfVUphMusuqSQowRkf0e8G5PYvZIakqKcdqD41dX6dSYJidg3VmJx+YKSpsKJ2gdVE9NTeWdd95RZqqHCEWBKISMYpsDt4Qsvy2QBkD2mgerSwvEcsICqSkpIraHuR9ul4fPXjvAR8/u8FMu70C6lB6qigr8rtMb5tkpSKeHpg3Kkq+hoHVQXafTsWzZMurrlRUpgo2iQBRCxjGrNwLL3xBe8P7Ae8uD1WEMpMWnQExeBSKlpLqokLj0rhWItdHB+09tY/fnJRTsrqaxxj+LIiEruJFYALo0C/rcGJq+KMHjUOJEQkFUVBQ33HADNpuNV199te0aUggOigJRCBmtCsT/MRCvAukpD5YQoi1fEoCnyYnQqlDpvIkaW+rrsDU3dTn+4XZ6+PAvO6goaGTaokwAyo/691QalZiE3mym/MhBPz+Lf0QuGIGn2UnzN+VBbVfhBCkpKVx77bVUV1fzyiuv+J1KXqF3FAWiEDLyrXZMahUJOv9CeGWrBdKNC6u5uRmTydQhu27nOSBtA+hpJyuQr949TGVhI+fdNp5Zl2aj0ar8ViBCCFJGjaHscHAViD47Cl1WJE2fFyNdSshpqMjJyWHx4sUcP36cV155Bbu957Q6Cv6hKBCFkHGo2c4okz6AdOqtFkjXCfS6T6TYToGU+EJ4O7mwju2oZOeaYiYtSCdnSgJqtYrErEjKj/jvF08ZPYaqogIc1uC6QSLPzsBd71CskBCTm5vL4sWLqaysZPPmzRw8GNyHgeGIokAUQsb+Zht5ZkPvBVuR9Wg00ahUXbu8ulIg7k4KpKakCJ3RhDmmYx6tjR8cJS7NzGlXnlg8J3lkFFVFTTj9HH9IHZ0HUlJ+xL8Fx/xFPzoaXXYkDasLlbGQEDNmzBiWLFmCTqfjtdde4+uvvw63SKc0igJRCAlNUlDucDLG7P/6DJJ69N1MIgROSmMC4GlxdZpEWERc+ogOVo/T7qamtNlreWhPXPLJOVF4PJLKAv8m8yWP8q6/XXowuPMKhBBEnZ+Np8lJ03ol7VuoSUhIYNq0aYwdO5ZPP/2UlStXKhkB+oiiQBRCQpHv0grIAqG+20mE4E1VfZICaXJ2nANSXHjSAHp1SRNSQkJGx9TgyTnebDzlR/1TIAaLhdjUdMoOBX9imj4zEkNeLI2fFbXNrlcIHWq1mquvvprp06ezfv163n//fdxuxfoLFEWBKISEIrxRUYEqkO4mEbpcLmw2W4dEe9LpQTrcbS4sa2MDLfV1J63/UVnYCJysQIwWHdFJJsoCGgfJo+zQgZA8sUadn4V0uGn4JD/obSucjEql4uKLL+ass85i+/btSgLGPqAoEIWQUIyaCLWKVL2298K0rold320erObmZoAOCqRtDohPgdQd907Ii0lN61C3srARY4QWc/TJYyvJOZGUH633WyGk5uZhbWyg/njwB7y1yWYs89Jo/qYce74y6W0gEEKwYMECLrroIo4cOcILL7xAebkSzOAvigJRCAlFqMkzG/2OwHK7mwBnty6spqYmgA4uLE+nRIrNdXXeMtEdB9ArixpJGBHRpSxJ2VHYmpw0VPn35Jky2jsOEgo3FkDkOZmoo/XUvnNYCesdQGbOnMnNN9+Mw+HgH//4Bzt2+J+lYDijKBCFoCOlpBgVeRb/3Vd2uzdXUXep3FsVSHsLpHMixZb6OgBMUSdyYHnckpqSZuIzul4aNTHTu7/VzdUbcSMy0BqMlBzY51f5QFHp1ERfPgpXRQsNq4KXNkWhdzIzM7njjjtIS0vj3XffZcWKFUo6+F5QFIhC0Kl0uGhExZgAxj8cjkoAdN2kMenKhdWtAomMbitjrwePR5IwomsFEpdqQaUWVBb6N5CuUqlJzxtH4e7QPaEa82Ixz0ym8bNibEfqQtaPwslERETw7W9/m9mzZ7Nx40bee+89ZXC9BxQFohB09jd73UGBDKC3WSDdzELvyoXl7qxAGurQm8xodCdyb1lrvH9bLY3OqLUq4tIsVBT4Z4EAZE6aRm1ZCQ1VocvwGnVJDpo4I7XLDyhRWQOMWq3m/PPP5+yzz2bnzp0sW7ZMyebbDYoCUQg6+5u9uYYCs0CqAHocA9Hr9Wi1JwblPc1OEKAyesN4W+rqOrivAGy1Er1JQ0Rc97IkZERQWdjo90B65qQpABTs3O5X+b6g0qmJvXYM7iYnNW/sR7qVeQoDiRCCM888k0svvZTS0lL+9re/sXLlSsUa6YSiQBSCzv5mG5F4SND5F4EFYLeXA1o0mq5XSm5ubj5prWx3gwOVRYtQeQfHWxrqMUVFdyhjq4X4bgbQW0nIiMDe4vJ7ID0uPQNLTCz5O7f5Vb6v6NIjiLl8FPZDddR/ErwswAr+M23aNH784x8zZcoU1q9fz9KlS2ls9N9aHeooCkQh6OxrsjGCwJ7UWqwFQEK3N/ruJhGqLSfcVS31dR3GP9xuD7a6k+d/dCbQgXQhBJmTplK4azseT2ifSM0zkzHPTaHpixKaNinrhoQDs9nMZZddxhVXXEFJSQl//etf2bFjhzJ7HUWBKAQZu8fD3mYrWYEqkJZ8IKnb411aII0O1JGdFEg7C6S2rAXpgYSMjvU6E+hAOkDmxCnYmhqpOHbU7zp9JfriHAxjYqh79zAtu6pC3p9C10yePJk777yThIQE3n33XV577bVhv0iVokAUgsrORit2j2RMAApESjdWayGiBwXS1NTUpQJRRXgViMftxtrU2GEMpFUhdBeB1UpfBtIzJk4BoCDEbiwAoVYRe8NYdBmR1LyxH9vh2pD3qdA18fHxfOc73+H888/n2LFjPPfcc2zZsmXYWiOKAlEIKt/Ue8Ntc/F/nW+brRQpHSC6ViCtaUw6uLAkeJocqH0KxNrYAFJiioppK1JZ2IRKA9GJps5NnkSgA+nm6BgSMrPJ37nVr/L9RaVTE3/zOLQJRqqX7sNRpPjhw4VKpWLOnDl8//vfJyUlhQ8//JBXXnmF2trhp9gVBaIQVL6pbybLqCNa+P9E5nVf0a0F0tUcELUD8NCmQJrrvD/ejhZII4Zo2gbZeyLQgXSA7KkzKD2wD3tLs991+oPKpCX+1gmoLFqq/rkbZ/nA9KvQNbGxsXz729/moosuori4mOeee46SkpJhNflQUSAKQUNKyab6ZmZGmXsv3I4Wa2uEUdcKpKs5IGrfgnKqiM6z0KMB7+TBquJGDB2zmnRLoAPpANlTpuNxuynYtd3vOv1FHakn4dYJoFFR8fxO7AGM2ygEH5VKxcyZM/n+979PRkYGhw4d4uWXXx42+bQUBaIQNI5ZHVQ7XcwKVIG05KNWm4GoLo93lcZE41MgrRZIS4N3MLM1CqvueAsuhwdjjH+5uPoykJ6aOxa9ycyxbZv9rhMMNPFGEu+cjNqkoerFXdgODj/XyWAjOjqaG2+8kby8PGpqanj++ef55JNPhvzSuYoCUQgareMfMyIDUyDWlmOYjFndhvB26cKye8u2KRCfC8scHQ2csCQMJ4ZEeqQvA+kqtZrMydM4tm3zgA+iamINJNw5GU28kap/76FlZ+WA9q9wMkIIkpOT+dGPfsS0adPYsGEDzz77LHv37h2yg+yKAlEIGt/UNxOlUQc0Ax2gxZqP0ZTZ7fEuXVgO719VOwtErdGgM3oHzCuLGlFrVei7npfYJYEOpAPkTJ1Bc10tFfmhD+ftjDpCR8KSSehGRFDz+n4aPysasjeqUwmj0cgll1zCbbfdhslkYvny5bz66qvU1NSEW7SgEzYFIoR4SAhRIoTY7tsu9O0/VwixRQixy/f37HDJqBAYm+qbmR5pQuVnCncAj8eB1VqMyZTdbZmu0pho7CD0alQ678JV3jkgMW1WTGVBI/HpFr8G0Fvpy0B61uRpAORv3+J3nWCiMmqIv3UCxgnx1K/Ip3rZPjw2/yPgFELHiBEjWLJkCYsWLaKwsJDnnnuOzz//HJdr6Hw/4bZAnpRSTvFtH/v2VQGXSCknAjcDr4RPPAV/qXa4ONhiC3gA3WotBjyYjFndlulqEqHafsJ9Ba0KxDuGIj2SKt8aIIHQl4F0c3QMSTmjOfzN1wH1FUxUOjWx1+cRdVE2tn3VVDy7XYnQGiSo1Wrmzp3LD37wA0aPHs2aNWv4+9//ztGjA2+xhoJwK5CTkFJuk1KW+t7uAQxCiJOXklMYVPyv2juIfXZcAD4jTkRg9WaBdE5jorGLNvcVQHO7WehNdXYcNjdx6T3PQO9MXwbSAfJOP5PyI4eoLikKqF4wEUIQcUY6CbdPwmN3UfHX7TR+WaIkYRwkREVFsXjxYq6//npcLhdLly5l2bJllJWd2ulpRLh8pkKIh4BbgAZgM/BzKWVtpzJXAXdKKc/ppo0lwBKAhISE6cuXL++3XF3NeA4loegvWG0G0s4fpZkC1PyFBoTwv65HfoqUy1GJp2lupss6mzZtwmw2M378+LZ96evAGS04PsV7/e5c+jyR6RlknX0Bzccl+WslmfMFWE62XnriyKce1DrIWuD/s5WzpZmdS/9O8pSZpM050+96oUJth8RdKsxVAnuEpHKcB1vMwF/bgTAUfnf+tut2uykpKaGwsBCXy0ViYiJZWVmYTKZ+y9aXugsWLNgipZzRpw6llCHbgFXA7i62y/AG/avxWkG/B17uVHc8cAQY6U9fubm5MhisXbs2KO2Es79gtelvO41Ol8xYt13+38HigOvu2/eAXPfZtB7rPProo/K///1vh30F938maz84LKWU0uPxyCevv0x+9uo/pZRS7vmyRD57x2pZV9ES8LlY88o++eJdn0mPxxNQvXcee0j+/Y6bpNvtCqheqPB4PLJ5Z6UsfWSDLLr3c1n9n/3y8xVrwy1WtwyF312g7ba0tMiVK1fK3/72t/Khhx6S77zzjqyqquqXbH2pC2yWfbzHa/qkdfxXTl1aDp0RQrwIfNTufTrwLvBtKeWREImnECRW1zRg90guSOh6HkdPNDUf6tF91dzcjM1mI9oXngvgsbtRuUVbIkV7SzNulwtTpLf/+korKpUgIjZwz2dCRgR7vyylocpGVILR73rjz1rI0a3fULhrR9vAejgRQmCaGI9hTAyNawpp/KKELI+KWushIs4agSY2sEg5heBjNBo555xzmD17NuvXr2fz5s3s3LmTxMREJk6cSFxcXLhF7JVwRmGltHt7BV7LBCFENPBf4JdSyvVhEE0hQD6urCdOqwl4AqHbbaWhYSfR0dO7LdPqI05JOXG5eBq9MbwqXyr31lnoZt8YSEOlFUucAZU68Ms7OcerhEoCnJyXM302BrOF3etWBdxnKFHp1ESdn03yz2fQkC5p3nyc8ic2U/PmQRzFgYUsK4SGiIgIzj//fH7yk58we/ZsKisrefbZZ3nnnXcG/UqIIbVAeuEPQogpgATygTt8+38IjAL+Twjxf75950kpB/eZHKbY3B5WVTdwRWIM6gDCdwHq67cipYOYmLndlmlVIMnJyW373D4F0mqBtCoQY6sCqbIGZD20Jy7NjCVGT8Guasadnup3PY1Wy/j5C9m64kNqF99ITLL/dQcCTayByvGSMTfOpOnzYpo2ltOy5TiaJJPXUhkbhzbV3OPCWwqhpVWRqFQqpJR888037Ny5k1GjRjF37lxycnIG3fcTNgUipbypm/2/A343wOIo9JF1NY00uz1c2Af3VU3t1wihITqq+/G7srIyYmJiMBpPKIQ2BdIpkWKrBVJfaWV0ZmDRYK0IIcicGM+BjeW4nR7UWv+tmJmXXsWOlZ/w9Vuvc+EPf96n/kONJkpP9CUjiVyYQcvOKlq2VdCwupCGVYWoLFr0I6MxjIxGPyoadYx+0N2whgM6nY758+czb948Nm/ezKZNm3jllVdISkpi5syZTJgwAYNhcLggw2mBKAwBni+uIFWv5YyYwOZcANTWfk1kxEQ0mu6jRsrKyjq4r+CEAmkN460qKkQIFdHJKdiandhbXET20QIByJoYx57PSyg9VMeIcX5mY8Q7J2Tq+RfzzYfvMOuyq4gf0f3s+nCjMmmxzEnBMicFd5MD2/5a7IdrsR2pw7qj0ldGgzbZjDbFfOJvkgmhVYdZ+uGB2WzmrLPO4rTTTmP37t1s2LCBjz76iE8//ZSJEycybdo00tLSwqrkFQWi0Ge21jfzdV0zD49KRRvAjG8Al6uRxsZdZGbc0W0Zq9VKbW0t06Z1HJT2NDqRQqIyei/fivwjxKSmodUbqC33zuHo7MKqfull3I0NxN9+Oypzz2M16WNi0GhV5O+qCkiBAMy89FvsWPkx6//zCpfd/auA6oYLtUWHeUYS5hlJSClxVbRgP1qPs6wZZ1kzzZvKkU5finLhTeaoTWmvWCyoo3SKtRIitFotU6dOZcqUKZSUlLBlyxZ27drF1q1biYuLY9KkSUyaNImYGD8TvwURRYEo9Jm/FlUQpVFzQ0rg0SJ1dZuR0t3j+EdrSuyTLJAGOy79iXU+KvOPkZY3DvC6r6CjArEfO0bFn/4EHg/1739Aym9+g+WMed32q9GpSc+LIX9XFfOuGR3QjdEYEcmsy67myzeWcuDrLxgz9wy/6w4GhBBok8xok04oWemRuGpsXoVS7lUqjuImrDtPLK8rDBq0KSZ0KZY2a0WTZGpLNaPQf4QQpKenk56ezqJFi9izZw87d+5k7dq1rF27lvT0dHQ6HTU1NcTGBvbg01cUBaLQJ4622Pm4sp4fZSRi0QR+k6it/RqVSkdUVPchr10NoAM4Chpw+Dxm1sYGGqsrSczKAU4okIi4Ez7i6udfQOh0pP3pCSr+/CQld93FqLVrUEd073bLnBhP/q5qastaiE0NLLpsxiVXcnjzBla9+FdSx4wlIjY+oPqDDaESaOONaOONMPHEZ/HYXF6F4lMqzrJmmjcfRzp8yxkL0MQZO7rAks3K2EoQMBgMTJ8+nenTp1NXV8euXbvYs2cPxcXFPPPMMyQlJTF27Fjy8vJISkoK2flWFIhCn/hTfjlaIfhuekKf6tfUfk1U5DTU6u4HA8vKyoiMjOwws9ZVY8NVbaMlzxt+2poFNzFrJOCNwDJG6tAZvJe2urKS+g8/JPbGG4lYuBBNcjL537qK2jfeIP7227vtO3tSPJ+/cZA9X5ZwxjW5AX02tUbDhT/8OUvv/TErnv0zV/7yYTTtEkEOFVQGDfqsKPRZJwIopEfiru1krZQ2Yd3VzlrRqzsqlRQzYujkFxxwoqOjOeOMMzjjjDNYsWIF0dHR7Nu3j3Xr1rFu3ToiIyPJyckhJyeH7OxsInp4cAoURYEoBMyKyjrePl7LXZlJJOoDvzHW12+jqWkvo0c90GO5rgbQbYe9EVct8V4FUulTIAlZ3smIDZVWouJPuK/MKz5BaDTE3nYrAMbx4zGffjo1/15K7E03oeommsUcrWfMnGT2fFHKtEWZmKMCm5QYk5LGObd9n0+ee5KPnnqMS+66D7Vm6CmRzgiVQBNnRBNnxDihnbVid+M8fsJScZY307KtArnBa63koKJ82zftlIoFbYpirQSK0Whk7ty5zJ07l6amJg4cOMCRI0c4cOAA27dvByAxMZHs7GwyMzNJT0/vV3+KAlEIiEqHk7sPFDPRYuSurK6XoO2NY/nPotXGkJq6uNsyDoeDqqoqJkyY0GG//VAd6kgdTrPXVVWRfxRLXHyHWehpud7BRFdtLYaNG4levBhtYmJbG3FLllB4883Uv/suMddd160M08/P5MDXZWxfWcjpV40O+HOOP2shTpuN1S//jQ+ffIwLf3Q3OkPfo8NOZVR6NfqMSPQZJ8KrpZS4a+04y5s5+NVu0g1mnGXNWPdWe2eH4bNW2lkq2mQz2mQTKr1y6+oNi8XS5ubyeDyUl5dz9OhRjh49ypYtW9i4cWO/+1C+BQW/cXkkP9tfRKPLzTNTRqJTBT7Tu6FhJ9XV6xiZczcaTfdjC7t27QIgIyOjbZ/0SGyH6zCOiwNxQoEkZnqtD7fTQ1OdvS2Et/GTTxBuN9FXfatD26ZZMzFOnkzViy8SddllqHxJ7DoTnWhi9Kwkdn9ewrRFmRjbZf/1lymLLgJgzT+f57UHfs4lP/slcWkjAm5nKCKEQBNrQBNroLZCMnm+NxCiN2sFQB2jR5toQpNkRptoQptkQpNoQqVXBu27QqVSkZqaSmpqKvPmzcPlclFeXk5RUREPP/xwn9tVFIiCXzg9ku/vLWBldQO/H53GWEvfnqSP5f8VjSaK9PQbuy3jcrn4/PPPSUtLIzv7RJ4sZ0kT0urCMDoa6kpxOuzUlBYzepY3kquuogXkiQis+vc/wJWagn7s2A7tCyFIvPvnFNz0bSqeeork++/vVpYZF2RxaNNxPnvtAItunxDQAlWtTFl0ETEpafz3mT/w6i/vYu7V1zPtgktRa5SfX1d0a63U2U8olYoWXMdbsB2ug3Yp69XRejQ+heJVMN6/KoNyrtuj0WjaIrr61U6Q5FEYwjS53PxoXyErqup5aGQqt/Vx4Lys/D2qqlaRnf1TNJruB/K2b99OfX09F198cQf/d+v4h35UNGyG6sICpMfTNoC+c10xKo0gLTcGR0EB1u3bsV5xeZc+dNPMmcTceCO1rywj8rzzMM3oejZ8TLKZuVeO4qu3D/PNx/nMurj7xI89kTlpCjc9/gyr/vFXPl/2MnvWrWL6xZeTd/pZaHXKcje9IYRAE2NAE2PwWqA+pFviqrHiOt6Cs6KlTbE0Ha0D1wnFoorQesdm4r2buRwcpU1oYg2KcukHyplT6JHV1Q3cc6CIUruT341O63PUVXn5++zd+wtiYuaSmdF99FN762PUqFFt+z0ON9YdVd6BVV8SxdJD+wFIyMqhscbG/q/KGDcvFUuMnspXPwIhsM2c2W1fiT+7i6bPPqP0vl+S8a9/oUtP67LclHNGUFPaxDcfHUNv0jBpfnqfLJGIuHguv+fXHN68gfVvvML//v4Mn7/yMqPnnM6YOWeQljcOjS5wN9lwRqgF2gQT2gQT7W1i6ZG4a2w4fYrFVWXFVW3Ftr8GT5OTFNRUbN/mbcOgRhNtQB2j91owMYYOf1UWrTKQ3w2KAlE4CZvbw8dV9fxFWti38yijTXo+mDY64OVqAez24xQUvkhR0b+Jjp7J5Ekvdhu6a7VaWbZsGQ0NDVx66aVtP1qPw031v/bgPN5M7HV5ADQU5bPj0w9IzB5JVGISn79+EIBpizJxNzVT//77mGbNwtPDhCqVyUTaH/9A4ZI7yL/qKtKe/DPmuSdPbBRCMP/6PFoanHy5/BBHt1Vy2pWjSMyKCPjGIoRg9My5jJoxh+K9u9i5+lP2f/kZu1Z/ilqrJXlkLvEZWcSlpRObOoKY1DTM0TGKuytAhEq0WRvG8R0nunpsLjZ++iWTs8fjrrXjqrPhrrXjrrVhP1qPtLs7NqYWqMxa1GYtKkvrX92J10YNwqBGZdCgbQZ3kwOVXoMIII/aqUrYViQMNqmZafJ793+/3+243W7U6o4Dca23CCGl77Xsdp9KSkAgkCBBLT0IKVFLDyo8qKUHtQSVx/taOp3o1CrUUnpz65/0dXS6QcnW3roo49vpdLnRdr7hSIFbCpxCg12lw67S4lBpcag0tGj01OrNVBssFETGUBARg0utJra5iTNKijiz8BhaKRFSdOxRgEAihQehdoHKhUpvRaVvwWOowBBXiza2BIQHR/komvfPAY8WD+AREjfgBhxC0iIkdSo3AkGGW0eSW4caDSYZQRxJWIhkH1spdefjslupryhFazASnzsZjyea+pIxmBPKSI3aSvLb69HUNlL53Ys5HGckMS2ZZo8Vp+x6soGhsoGRL63BWF5HS3osdeNGYEuKxhFjxm3U4dGqvR9Wgv14Co3HRiLdWlR6K7rIOjSmFtR6GyqNE6Fyg/AghAQhvX9VsuN564TH5cJe04CtrgFHfSPOZivS3fEmptKoUem0qLRaVGo1Qq1CqNQIlcq7qVUnlJkQvn4ECOH7ngQtNismkwnh3dH6Bfrq+Wq0b+Mk5XjifesnEn25fciTz0JTc/NJyxaLLs9WV+35d7B9a03NzVi6SWmjkVoM0ogBEwZpRCf16NCjlTp06NFJHVr0aHp5/vbgxoULF07cuHELNx7cePDgpovXwoMHD3aHHa1Oi8SDbPvX/nX7vb79wvvOarViMBqQnU9KN/d5ieSqR3/d5xUJh4wC0Y4ZJ+P+/lq4xegXQnpQ4UaDGxVu1HgQeBDIDpuq3b72rwE8bTVVeFC3teRGg130nMFTLZ1kcYwx7GMy2xnHLlS+dqUUyFblJQVSqhBColK5vTfJLmhpiaSuLpmS4nHYbD1PXjJKHameGGY5R2HmhJwe6abBWc2e2q8objlw4lypU9FZLkOovI4Lva2GaduexGivoSQW/naRmoPp/lsHBrvk3G2SmYc85Bb3vFCOU2OkKm4SlQlTaIjIwKGP9rsff5BSgmzG465BemrB04KULUiPFaQVKZ0gnYALKV1tr8ETVDkUekYttOjVRnQqA1qVHq3Qef92uelQCw1qofX99W2qE69VIjwRZCMeP1NRIGkZafLH9/+w3+04nS602hNPFhJAeG/P3jPlu1UL2t22RZu+l+LEa4Tvhi4EHiFwC1Xb5n0vcHhAaDS+/aLdMVWbyvC09u97UvS09S2Qra99ZdTSA243GhWopNfKEUjU0o1eOtF7nOg9Du9r6UAvHRg8NmJdDUR5GlHhAeG9Ebk9TtQaNUL4rAzwHmunMDxuDdKjRno0SLcGj9OA22HGWm9CLSJaT0OHp0QVAjUCFaCWAh0qdKhosVkxGPV45ImnMZvKhhSytRVUGg0anYGGxgaiIyJR4UanaUItrUjpxiOgdmwaLp3AJd2UlJWQmZqJRWVEpzp5Il+XV7/NjramAU1tEyqbA+F0IaRsuxZo91cCbo8Gl0uP26NDejTe70sKkL7vUKp88vfwWztJ17V7cu5WD544L217pO9KFdL30Cnbnj4bG5uIsJi8LfvKtZVB+tSPt7xsV88/Q6CLz9ZlPdnl7g4WwUmnyvcQ46e78KRSXVRramrGYjnZApG9feZeZOi8Jrn/d1cJUqBC0NJsxWK2IIQK0dU/KfCW7PxPhc1qx2Q88QAm2v3f/kO1vpISFj3yk8G5JvpAbsqa6MFvc6DXZg6kjr9lB/r7HMwM5nMxFH53wWr3VFoTfeiP8igoKCgohARFgSgoKCgo9AlFgSgoKCgo9AlFgSgoKCgo9AlFgSgoKCgo9AlFgSgoKCgo9AlFgSgoKCgo9AlFgSgoKCgo9IkhMxNdCNEIHOi1YO9EAfVBaCec/cUDVb2W6p3+yNaXuoHU8bdssM7FUGAwn4uB/t2F6lwE43MM9O9ujJSybwul93UG4mDb6Mdsyk7tvDDAcge9v8FwLvpSN5A6/pYN1rkYCttgPhdh+N2F5FwE43OE4XenzEQPIh8O8f4CoT+y9aVuIHUG83lTCJyh8n0G43MM9O+uzwwlF9Zm2deEYEMM5VycQDkXJ1DOxQmUc3GC/pyLoWSBvBBuAQYRyrk4gXIuTqCcixMo5+IEfT4XQ8YCUVBQUFAYWIaSBaKgoKCgMIAoCkRBQUFBoU+cEgpECHG+EOKAEOKwEOK+Lo7fIITY6du+EkJMbncsXwixSwixXQixeWAlDy1+nJf5Qoh632ffLoT4dTjkDDVCiJeFEBVCiN3dHBdCiGd852mnEGLaQMs4UAghRggh1goh9gkh9gghftJFmWFzPgCEEGohxDYhxEddHBs250IIcZfvmtgthHhdiI5rXPfpXAxk7HUf45rVwBEgB9ABO4BxncqcBsT4Xl8AbGx3LB+ID/fnCNN5mQ98FG5ZB+BcnAlMA3Z3c/xCYAXelTzntL8+htoGpADTfK8jgINdXBfD5nz4Pu/PgNe6+i0Ml3MBpAHHAKPv/XLglv6ei1PBApkFHJZSHpVSOoA3gMvaF5BSfiWlrPW93QCkD7CM4aDX8zJckFJ+DtT0UOQyYKn0sgGIFkKkDIx0A4uUskxKudX3uhHYh/fm0Z5hcz6EEOnARcA/uikybM4FoAGMQggNYAJKOx0P+FycCgokDShq976Yk38Q7bkNrxZtRQL/E0JsEUIsCYF84cLf8zJXCLFDCLFCCDF+YEQbdAR6DQ0JhBBZwFRgY6dDw+l8PAXcA3i6OT4szoWUsgR4AigEyoB6KeX/OhUL+FycCgpEdLGvy9hjIcQCvArk3na7T5dSTsPr2vqBEOLM4IsYFvw5L1uBTCnlZOAvwHuhFmqQ4vc1NFQQQliAt4GfSikbOh/uosqQOx9CiIuBCinllp6KdbFvKJ6LGLwWRjaQCpiFEDd2LtZF1R7PxamgQIqBEe3ep3Oy6YUQYhJeM/UyKWV1634pZanvbwXwLl7Xz1Cg1/MipWyQUjb5Xn8MaIUQ8QMn4qDBr2toqCCE0OJVHq9KKd/poshwOR+nA5cKIfLxunjPFkIs61RmuJyLc4BjUspKKaUTeAfv2HF7Aj4Xp4IC+QYYLYTIFkLogGuBD9oXEEJk4D0hN0kpD7bbbxZCRLS+Bs4DuozUOQXx57wkCyGE7/UsvN939UktDX0+AL7tizKZg9d8Lwu3UKHA932/BOyTUv65m2LD4nxIKX8ppUyXUmbh/X2skVJ2fuoeFucCr+tqjhDC5LtGFuIdH2tPwOdCExpZg4eU0iWE+CHwKd7Io5ellHuEEHf6jv8d+DUQBzznu1+6pDe3SxLwrm+fBnhNSvlJGD5G0PHzvFwFfE8I4QKswLXSF24xlBBCvI434ixeCFEMPAhooe08fIw3wuQw0AJ8JzySDginAzcBu4QQ23377gcyYFiej5Po9BsZFudCSrlRCPEWXre2C9gGvNDfc6GkMlFQUFBQ6BOnggtLQUFBQWEQoigQBQUFBYU+oSgQBQUFBYU+oSgQBQUFBYU+oSgQBQUFBYU+oSgQBYUu8M2heUMIcUQIsVcI8bEQIlcIkSWEsIoTGY63++bhdK4/VQjxD9/rh4QQd3dR5l9CiKs67WvqQSadEOJzXy4jBYWwo1yICgqd8E20ehf4t5TyWt++KXjnFRUBR6SUU3pp5n7gd8GUS0rpEEKsBhYDrwazbQWFvqBYIAoKJ7MAcPomVwEgpdwupfzCn8q+7AeTpJQ7ujh2uy+xpbGXNn7TzsIpEUL803foPeAGvz+JgkIIURSIgsLJTAB6SsA3st3N/a9dHJ9BFylzfJkDLgEul1Jafbv/2N4d1lpWSvlrn5VzFt70M8/6Du0GZgb6gRQUQoHiwlJQCJzeXFgpQGWnfTfhTVZ3uS+ZXSu/kFK+1fqm/RiIz5X2KvBka0ZZKaVbCOEQQkT41vtQUAgbigWioHAye4Dp/ahvBQyd9u0GsghssbOHgGIp5T877dcDtr4Kp6AQLBQFoqBwMmsAvRDi9tYdQoiZQoiz/Ky/DxjVad824A7gAyFEam8N+NayOBf4caf9cUBlJytGQSEsKApEQaETvozFVwDn+sJ49+C1BvxaJ0JKuR+Ial1KoN3+L4G7gf/6sS7Lz/Eu/LPJNz7yG9/+BXizpioohB0lG6+CQggQQtwFNEopu1uLu6/tvgP8Ukp5IJjtKij0BcUCUVAIDX8D7MFs0Ddh8T1FeSgMFhQLREFBQUGhTygWiIKCgoJCn1AUiIKCgoJCn1AUiIKCgoJCn1AUiIKCgoJCn1AUiIKCgoJCn/h/5SRkj+Z3S/kAAAAASUVORK5CYII=
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<h3 id="IHC-AC-DC-ratio">IHC AC-DC ratio<a class="anchor-link" href="#IHC-AC-DC-ratio">&#182;</a></h3><p>Compare the ratio of the AC and DC components of the IHC responses across CF.</p>

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<div class="prompt input_prompt">In&nbsp;[7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Dierich2020/&#39;</span> <span class="c1"># Dierich et al. CNN model directory</span>
<span class="c1"># Define the IHC model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 1-channel IHC model for this section - the results are computed for individual CFs</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">ihc</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/ihc_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/ihc.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;ihc_model&quot;</span><span class="p">)</span>
<span class="c1">#ihc.summary()</span>
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<div class="prompt input_prompt">In&nbsp;[8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimuli</span>
<span class="n">N</span> <span class="o">=</span> <span class="mi">10</span> <span class="c1"># number of frequencies to simulate between 0.15 and 8 kHz</span>
<span class="n">f_tones</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">150</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">8000</span><span class="p">),</span><span class="n">num</span><span class="o">=</span><span class="n">N</span><span class="p">)</span> <span class="c1"># pick N frequencies in logarithmic spacing</span>
<span class="c1"># match the tone frequencies to the corresponding CFs</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">f_tone</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">f_tones</span><span class="p">):</span>
    <span class="n">fno</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span> <span class="n">x</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
    <span class="n">f_tones</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">CF_connear</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">fno</span><span class="p">)]</span>
    
<span class="n">L</span> <span class="o">=</span> <span class="p">[</span><span class="mf">80.</span><span class="p">]</span> <span class="c1"># 80 dB SPL</span>
<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">80e-3</span> <span class="c1"># duration of the stimulus</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">35.2e-3</span> <span class="c1"># silence before the onset of the stimulus - 12.8 ms correspond to 256 samples (context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">5.0e-3</span> <span class="c1"># 5ms long ramp window for gradual onset</span>

<span class="c1"># indicate the time points of the response for computing the AC and DC components</span>
<span class="n">t_ac_start</span> <span class="o">=</span> <span class="n">silence_left</span> <span class="o">+</span> <span class="mf">50e-3</span> <span class="c1"># 50 ms after the stimulus onset</span>
<span class="n">t_ac_dur</span> <span class="o">=</span> <span class="mf">20e-3</span> <span class="c1"># 20 ms after t_ac_start</span>
<span class="n">t_dc_start</span> <span class="o">=</span> <span class="mf">7.4e-3</span> <span class="c1"># 7.4 ms after the left context (15 ms before the stimulus onset)</span>
<span class="n">t_dc_dur</span> <span class="o">=</span> <span class="mf">10e-3</span> <span class="c1"># 10 ms after t_dc_start</span>

<span class="c1"># region of the response for computing the AC component (50 - 70 ms after the stimulus onset)</span>
<span class="n">ac_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_ac_start</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="o">-</span> <span class="n">context_left</span>
<span class="n">ac_end</span> <span class="o">=</span> <span class="n">ac_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_ac_dur</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">ac_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ac_start</span><span class="p">,</span><span class="n">ac_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># region of the response for computing the DC component (5 - 15 ms before the stimulus onset)</span>
<span class="n">dc_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_dc_start</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">dc_end</span> <span class="o">=</span> <span class="n">dc_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_dc_dur</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">dc_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">dc_start</span><span class="p">,</span><span class="n">dc_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># make stimuli</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bartlett</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided ramp</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> 
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">f_tones</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">f_tone</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">f_tones</span><span class="p">):</span>
    <span class="n">stim_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the pure tone</span>
    <span class="c1"># apply ramp window</span>
    <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
    <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
    <span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">))</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Verhulstetal ############</span>
<span class="c1"># get the outputs of the reference model </span>
<span class="n">output</span> <span class="o">=</span> <span class="n">tl_vbm</span><span class="p">(</span><span class="n">stim</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span>

<span class="n">CF_tl</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;cf&#39;</span><span class="p">][::</span><span class="n">ds_rate</span><span class="p">]</span> <span class="c1"># center frequencies</span>
<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">tl_target</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="nb">len</span><span class="p">(</span><span class="n">f_tones</span><span class="p">),</span> <span class="n">window_length</span> <span class="o">*</span> <span class="n">ds_factor</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span> <span class="c1"># pre-allocate</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">f_tones</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">f_tone</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">f_tones</span><span class="p">):</span>
    <span class="c1"># find the CF closest to the stimulus frequency</span>
    <span class="n">No</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_tl</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
    <span class="n">No</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">No</span><span class="p">)</span>
    <span class="c1"># extract the outputs only for the specific CF</span>
    <span class="n">tl_target</span><span class="p">[</span><span class="n">j</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="s1">&#39;v&#39;</span><span class="p">][:,::</span><span class="n">ds_rate</span><span class="p">][:,[</span><span class="n">No</span><span class="p">]]</span> <span class="c1"># cochlear output</span>
<span class="k">del</span> <span class="n">output</span> <span class="c1"># remove the output variable to free-up some memory</span>

<span class="c1">############ Dierichetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span> <span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">magic_constant</span> <span class="o">=</span> <span class="mf">0.118</span>
<span class="n">tl_in</span> <span class="o">=</span> <span class="n">magic_constant</span><span class="o">*</span><span class="n">tl_target</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">L</span><span class="p">)):</span>
    <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_cf</span><span class="p">):</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">tl_in</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;v&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;IHC_model(v,Fs);&#39;</span><span class="p">,</span> <span class="n">nargout</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output of the reference model</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        
<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
    
<span class="c1"># remove context from the output</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,:]</span>
<span class="c1"># downsample to 20 kHz</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># apply proper scaling for feeding to the CoNNear</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">tl_target</span> <span class="o">*</span> <span class="n">cochlea_scaling</span> <span class="c1"># scaling for the cochlear input</span>

<span class="c1"># compute the AC component for each frequency</span>
<span class="n">acm_target</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="mi">2</span>
<span class="n">ac_target</span> <span class="o">=</span> <span class="n">acm_target</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.</span><span class="p">)</span>
<span class="c1"># compute the DC component for each frequency</span>
<span class="n">dcm_target</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="mi">2</span>
<span class="n">dc_target</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="n">acm_target</span><span class="p">)</span><span class="o">-</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="n">dcm_target</span><span class="p">)</span>

<span class="c1">############ CoNNear #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">tl_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 8</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">tl_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">tl_target</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="n">tl_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc_connear</span> <span class="o">/</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the IHC output</span>

<span class="c1"># compute the AC component for each frequency</span>
<span class="n">acm_connear</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="mi">2</span>
<span class="n">ac_connear</span> <span class="o">=</span> <span class="n">acm_connear</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.</span><span class="p">)</span>
<span class="c1"># compute the DC component for each frequency</span>
<span class="n">dcm_connear</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="mi">2</span>
<span class="n">dc_connear</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">ac_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="n">acm_connear</span><span class="p">)</span><span class="o">-</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="n">dcm_connear</span><span class="p">)</span>
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<pre>running human auditory model 2018: Verhulst, Altoe, Vasilkov
cochlear simulation: done
10/10 [==============================] - 0s 22ms/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="c1"># Plot the logarithmic decrease of the AC/DC ratio across frequency</span>
<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">(</span><span class="n">f_tones</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="n">ac_target</span><span class="o">/</span><span class="n">dc_target</span><span class="p">,</span><span class="s1">&#39;o-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">loglog</span><span class="p">(</span><span class="n">f_tones</span><span class="o">/</span><span class="mf">1e3</span><span class="p">,</span> <span class="n">ac_connear</span><span class="o">/</span><span class="n">dc_connear</span><span class="p">,</span><span class="s1">&#39;o-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">10.</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">)</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;CF (kHz)&#39;</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;AC component / DC component&#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;AC-DC ratio&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Dierich2020 IHC model&#39;</span><span class="p">,</span><span class="s1">&#39;Dierich2020 IHC model - CNN&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">ihc</span> <span class="c1"># remove the connear model variable to free-up some memory</span>
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<h3 id="IHC-level-growth">IHC level growth<a class="anchor-link" href="#IHC-level-growth">&#182;</a></h3><p>Compare the growth of the half-wave rectified IHC receptor potential as a function of sound level.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Dierich2020/&#39;</span> <span class="c1"># Dierich et al. CNN model directory</span>
<span class="c1"># Define the IHC model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 1-channel IHC model</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">ihc</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/ihc_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/ihc.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;ihc_model&quot;</span><span class="p">)</span>
<span class="c1">#ihc.summary()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimuli</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="mf">4e3</span> <span class="c1"># frequency of the pure tone</span>
<span class="c1"># match the tone frequency to the corresponding CF</span>
<span class="n">fno</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span> <span class="n">x</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="n">CF_connear</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">fno</span><span class="p">)]</span>

<span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span><span class="mf">101.</span><span class="p">,</span><span class="mf">10.</span><span class="p">)</span> <span class="c1"># levels from 0 to 100dB SPL</span>
<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">80e-3</span> <span class="c1"># duration of the stimulus</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">35.2e-3</span> <span class="c1"># silence before the onset of the stimulus - 12.8 ms correspond to 256 samples (context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">5.0e-3</span> <span class="c1"># 5ms long ramp window for gradual onset</span>

<span class="c1"># indicate the time points of the response for computing the AC and DC components</span>
<span class="n">t_ac_start</span> <span class="o">=</span> <span class="n">silence_left</span> <span class="o">+</span> <span class="mf">50e-3</span> <span class="c1"># 50 ms after the stimulus onset</span>
<span class="n">t_ac_dur</span> <span class="o">=</span> <span class="mf">20e-3</span> <span class="c1"># 20 ms after t_ac_start</span>
<span class="n">t_dc_start</span> <span class="o">=</span> <span class="mf">7.4e-3</span> <span class="c1"># 7.4 ms after the left context (15 ms before the stimulus onset)</span>
<span class="n">t_dc_dur</span> <span class="o">=</span> <span class="mf">10e-3</span> <span class="c1"># 10 ms after t_dc_start</span>

<span class="c1"># region of the response for computing the AC component (50 - 70 ms after the stimulus onset)</span>
<span class="n">ac_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_ac_start</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="o">-</span> <span class="n">context_left</span>
<span class="n">ac_end</span> <span class="o">=</span> <span class="n">ac_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_ac_dur</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">ac_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ac_start</span><span class="p">,</span><span class="n">ac_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># region of the response for computing the DC component (5 - 15 ms before the stimulus onset)</span>
<span class="n">dc_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_dc_start</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">dc_end</span> <span class="o">=</span> <span class="n">dc_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">t_dc_dur</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span>
<span class="n">dc_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">dc_start</span><span class="p">,</span><span class="n">dc_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># make stimulus</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the pure tone</span>
<span class="c1"># apply hanning window</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bartlett</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided ramp</span>
<span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</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">L</span><span class="p">)):</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Verhulstetal ############</span>
<span class="c1"># get the outputs of the reference model </span>
<span class="n">output</span> <span class="o">=</span> <span class="n">tl_vbm</span><span class="p">(</span><span class="n">stim</span><span class="p">,</span> <span class="n">L</span><span class="p">)</span>

<span class="n">CF_tl</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;cf&#39;</span><span class="p">][::</span><span class="n">ds_rate</span><span class="p">]</span> <span class="c1"># center frequencies</span>
<span class="c1"># find the CF closest to the stimulus frequency</span>
<span class="n">No</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_tl</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
<span class="n">No</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">No</span><span class="p">)</span>

<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">tl_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span> <span class="o">*</span> <span class="n">ds_factor</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span> <span class="c1"># pre-allocate</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</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">L</span><span class="p">)):</span>
    <span class="c1"># extract the outputs only for the specific CF</span>
    <span class="n">tl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;v&#39;</span><span class="p">][:,::</span><span class="n">ds_rate</span><span class="p">][:,[</span><span class="n">No</span><span class="p">]]</span> <span class="c1"># cochlear output</span>
<span class="k">del</span> <span class="n">output</span> <span class="c1"># remove the output variable to free-up some memory</span>
    
<span class="c1">############ Dierichetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span> <span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">magic_constant</span> <span class="o">=</span> <span class="mf">0.118</span>
<span class="n">tl_in</span> <span class="o">=</span> <span class="n">magic_constant</span><span class="o">*</span><span class="n">tl_target</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">L</span><span class="p">)):</span>
    <span class="k">for</span> <span class="n">fi</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N_cf</span><span class="p">):</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">tl_in</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;v&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;IHC_model(v,Fs);&#39;</span><span class="p">,</span> <span class="n">nargout</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output of the reference model</span>
        <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="n">fi</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        
<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
    
<span class="c1"># remove context from the output</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,:]</span>
<span class="c1"># downsample to 20 kHz</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># apply proper scaling for feeding to the CoNNear</span>
<span class="n">tl_target</span> <span class="o">=</span> <span class="n">tl_target</span> <span class="o">*</span> <span class="n">cochlea_scaling</span> <span class="c1"># scaling for the cochlear input</span>

<span class="n">dcm_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># compute the DC component </span>
<span class="n">vihc_target</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="n">ihc_target</span><span class="o">.</span><span class="n">shape</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">L</span><span class="p">)):</span>
    <span class="n">vihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span> <span class="o">-</span> <span class="n">dcm_target</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="c1">#half-wave rectify the response by substracting the DC component</span>
<span class="n">vihc_target_rms</span> <span class="o">=</span> <span class="n">rms</span><span class="p">(</span><span class="n">vihc_target</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1">############ CoNNear #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">tl_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 8</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">tl_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">tl_target</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="n">tl_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tl_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">ihc_connear</span> <span class="o">=</span> <span class="n">ihc_connear</span> <span class="o">/</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the IHC output</span>

<span class="n">dcm_connear</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ihc_connear</span><span class="p">[:,</span><span class="n">dc_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># compute the DC component </span>
<span class="n">vihc_connear</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="n">ihc_connear</span><span class="o">.</span><span class="n">shape</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">L</span><span class="p">)):</span>
    <span class="n">vihc_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">ihc_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span> <span class="o">-</span> <span class="n">dcm_connear</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="c1">#half-wave rectify the response by substracting the DC component</span>
<span class="n">vihc_connear_rms</span> <span class="o">=</span> <span class="n">rms</span><span class="p">(</span><span class="n">vihc_connear</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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<pre>running human auditory model 2018: Verhulst, Altoe, Vasilkov
cochlear simulation: done
11/11 [==============================] - 0s 18ms/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="c1"># Plot the RMS of the half-wave rectified response across level</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="mf">1e3</span><span class="o">*</span><span class="n">vihc_target_rms</span><span class="p">,</span><span class="s1">&#39;o-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">L</span><span class="p">,</span> <span class="mf">1e3</span><span class="o">*</span><span class="n">vihc_connear_rms</span><span class="p">,</span><span class="s1">&#39;o-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="c1">#plt.yticks(ticks=np.arange(0.,10.,1.))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Stimulus level (dB-SPL)&#39;</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;Half-wave rectified $\mathregular{V_</span><span class="si">{IHC}</span><span class="s1">}$ (mV)&#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;RMS of half-wave rectified $\mathregular{V_</span><span class="si">{IHC}</span><span class="s1">}$&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Dierich2020 IHC model&#39;</span><span class="p">,</span><span class="s1">&#39;Dierich2020 IHC model - CNN&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">ihc</span> <span class="c1"># remove the connear model variable to free-up some memory</span>
</pre></div>

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<h2 id="ANF-stage">ANF stage<a class="anchor-link" href="#ANF-stage">&#182;</a></h2><p>The input dimensions for the CNN models are (b x L x Ncf), where b is the batch-size (for loading multiple stimuli simultaneously), L is the input length (including the context) and Ncf are the frequency channels.</p>
<p>The anfX.json file can be loaded for the full-channel model (201 channels) or the anfX_1cf.json file for the 1-channel model, where X corresponds to the AN fiber type (h for HSR, m for MSR or l for LSR ANF model). At the first block of each individual section, the necessary model is loaded and the rest of the parameters are defined.</p>
<p>Auditory stimuli are generated here and given as inputs to the reference Zilany2014 cochlea-IHC model, to simulate the IHC outputs. These responses are then given as inputs to the Zilany2014 ANF model and its CNN counterpart, to simulate the mean firing rates or the PSTH responses.</p>

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<h3 id="ANF-firing-rates">ANF firing rates<a class="anchor-link" href="#ANF-firing-rates">&#182;</a></h3><p>Compare the simulated ANF firing rates across time for tone stimuli presented at 70 dB SPL.
<strong>You can change the <code>f_tone</code> variable to have tone stimuli of different frequencies, the <code>f_m</code> variable for generating amplitude-modulated tones or the <code>L</code> variable for having different levels.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Zilany2014/&#39;</span>
<span class="c1"># Define the ANF model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">7936</span> <span class="c1"># longer left-sided context for the ANF models</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">14</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 1-channel ANF models</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">anfh</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfh_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfh.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfh_model&quot;</span><span class="p">)</span>
<span class="n">anfh</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">anfm</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfm_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfm.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfm_model&quot;</span><span class="p">)</span>
<span class="n">anfm</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">anfl</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfl_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfl.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfl_model&quot;</span><span class="p">)</span>
<span class="n">anfl</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
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<pre>Model: &#34;anfh_model&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bmm_input (InputLayer)       (None, 16384, 1)          0         
_________________________________________________________________
model_3 (Model)              (None, 8192, 1)           1250177   
=================================================================
Total params: 1,250,177
Trainable params: 1,250,177
Non-trainable params: 0
_________________________________________________________________
Model: &#34;anfm_model&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bmm_input (InputLayer)       (None, 16384, 1)          0         
_________________________________________________________________
model_3 (Model)              (None, 8192, 1)           1250177   
=================================================================
Total params: 1,250,177
Trainable params: 1,250,177
Non-trainable params: 0
_________________________________________________________________
Model: &#34;anfl_model&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
bmm_input (InputLayer)       (None, 16384, 1)          0         
_________________________________________________________________
model_3 (Model)              (None, 8192, 1)           1250177   
=================================================================
Total params: 1,250,177
Trainable params: 1,250,177
Non-trainable params: 0
_________________________________________________________________
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimulus</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="mf">1e3</span> <span class="c1"># frequency of the pure tone</span>
<span class="n">L</span> <span class="o">=</span> <span class="p">[</span><span class="mf">70.</span><span class="p">]</span> <span class="c1"># stimulus level</span>
<span class="n">m</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># modulation percentage </span>
<span class="n">f_m</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># modulation frequency - leave empty for pure-tone stimulus (no modulation applied)</span>
<span class="c1">#f_m = [] # uncomment for pure-tone stimulus</span>

<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">400e-3</span> <span class="c1"># duration of the stimulus - 409.6 ms correspond to 8192 samples (fs = 20 kHz)</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">396.8e-3</span> <span class="o">+</span> <span class="mf">5e-3</span> <span class="c1"># silence before the onset of the stimulus - 396.8 ms correspond to 7936 samples (left context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples (right context)</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">7.8e-3</span> <span class="c1"># 5ms long ramp window for gradual onset</span>

<span class="c1"># match the tone frequency to the corresponding CF</span>
<span class="n">fno</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span> <span class="n">x</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="n">CF_connear</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">fno</span><span class="p">)]</span>

<span class="c1"># make stimulus</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="k">if</span> <span class="n">f_m</span><span class="p">:</span> <span class="c1"># if f_m is defined make a SAM tone</span>
    <span class="n">stim_sin</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">m</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_m</span> <span class="o">*</span> <span class="n">t</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the SAM tone</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">stim_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the pure tone</span>
<span class="c1"># apply ramp</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bartlett</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided ramp window</span>
<span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</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">L</span><span class="p">)):</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Zilanyetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;nrep&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;noiseType&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;implnt&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cohc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;species&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="c1"># human</span>

<span class="n">sponts</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span><span class="mf">2.</span><span class="p">,</span><span class="mf">3.</span><span class="p">]</span> <span class="c1"># simulate LSR, MSR and HSR ANF fibers</span>
<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_length</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">fs_tl</span> <span class="c1"># make simulation duration 1 sample longer to avoid errors</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;T&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">T</span>

<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span><span class="n">window_length</span><span class="p">,</span><span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfh_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfm_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfl_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</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">L</span><span class="p">)):</span>
    <span class="n">stim_in</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;stim&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stim_in</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;f&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">f_tone</span><span class="p">)</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_IHC(stim,f,nrep,1/Fs,T,cohc,cihc,species);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;ihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ihc_out</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)[:</span><span class="n">window_length</span><span class="p">]</span> <span class="c1"># downsample</span>

    <span class="k">for</span> <span class="n">sponti</span><span class="p">,</span> <span class="n">spont</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sponts</span><span class="p">):</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;spont&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spont</span><span class="p">)</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_Synapse(ihc,f,nrep,1/Fs,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the mean ANF firing rate</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anf_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])[:</span><span class="n">total_length</span><span class="p">]</span>

        <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
                    
<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
    
<span class="c1"># remove context from the outputs (and the last dimension)</span>
<span class="n">anfh_target</span> <span class="o">=</span> <span class="n">anfh_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_target</span> <span class="o">=</span> <span class="n">anfm_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_target</span> <span class="o">=</span> <span class="n">anfl_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># apply proper scaling for feeding to the CNN model</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span> <span class="o">*</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the ihc input</span>

<span class="c1">############ CNN model #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">ihc_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 16384</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">ihc_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">ihc_target</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="n">ihc_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># remove last dimension </span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># scale back to the original ANF values</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>

<span class="c1"># crop the connear time dimensions to match the size of the reference responses</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,:</span><span class="n">anfh_target</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="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,:</span><span class="n">anfm_target</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="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,:</span><span class="n">anfl_target</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
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<pre>1/1 [==============================] - 0s 387ms/step
1/1 [==============================] - 0s 407ms/step
1/1 [==============================] - 0s 436ms/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="n">t_ds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">anfh_target</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="n">fs_connear</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_connear</span><span class="p">)</span> <span class="c1"># time vector of the (downsampled) responses</span>

<span class="c1"># Plot the firing rate patterns for the reference model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfh_target</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfm_target</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfl_target</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">80.</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</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;Time (ms)&#39;</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;Firing rate (spikes/s)&#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;Zilany2014 ANF model&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="c1"># Plot the firing rate patterns for the CoNNear model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfh_connear</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfm_connear</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">1e3</span><span class="o">*</span><span class="n">t_ds</span><span class="p">,</span><span class="n">anfl_connear</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mf">80.</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</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;Time (ms)&#39;</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;Firing rate (spikes/s)&#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;Zilany2014 ANF model - CNN&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">anfh</span><span class="p">,</span> <span class="n">anfm</span><span class="p">,</span> <span class="n">anfl</span> <span class="c1"># remove the connear models variables to free-up some memory</span>
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<h3 id="ANF-rate-level-curves">ANF rate-level curves<a class="anchor-link" href="#ANF-rate-level-curves">&#182;</a></h3><p>Compare the simulated ANF rate-level curves for the three fiber models.
<strong>You can change the <code>f_tone</code> variable to get the curves of 1 kHz or 4 kHz.</strong></p>
<p>To simulate the ANF rate-level curves reported in the paper, the mean firing rate outputs need to be fed to the Zilany2014 ANF spike generator to generate the PSTH outputs. This can be done by setting the <code>simulate_psth</code> variable to 1, but requires many stimulus repetitions and a lot of time to compute (or a powerful computer). Alternatively, the spike generator can be omited to speed up the execution, by setting the <code>simulate_psth</code> variable to 1, and plotting the same results for the mean firing rate outputs instead.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">simulate_psth</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># set to 0 to use the mean firing rates or to 1 to use the PSTH outputs (rather slow)</span>

<span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Zilany2014/&#39;</span>
<span class="c1"># Define the ANF model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">7936</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">14</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 1-channel ANF models</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span>
    <span class="n">crop</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># get uncropped outputs to feed to the spike generator model</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">crop</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">anfh</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfh_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfh.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfh_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfh.summary()</span>
<span class="n">anfm</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfm_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfm.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfm_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfm.summary()</span>
<span class="n">anfl</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfl_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfl.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfl_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfl.summary()</span>
</pre></div>

    </div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimulus</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="mf">4e3</span> <span class="c1"># frequency of the pure tone</span>
<span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span><span class="mf">101.</span><span class="p">,</span><span class="mf">10.</span><span class="p">)</span> <span class="c1"># levels from 0 to 100 dB SPL</span>

<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">50e-3</span> <span class="c1"># duration of the stimulus - 409.6 ms correspond to 8192 samples (fs = 20 kHz)</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">396.8e-3</span> <span class="o">+</span> <span class="mf">5e-3</span> <span class="c1"># silence before the onset of the stimulus - 396.8 ms correspond to 7936 samples (left context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples (right context)</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">2.5e-3</span> <span class="c1"># 2.5ms long ramp window for gradual onset</span>

<span class="c1"># Zilany2014 model parameters</span>
<span class="n">nrep_spike</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># number of repetitions for the spike generator part of the ANF model - decrease for a faster but noisier result</span>
<span class="n">psthbinwidth</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">fs_connear</span>
<span class="n">psthbins</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">psthbinwidth</span><span class="o">*</span><span class="n">fs_connear</span><span class="p">)</span> <span class="c1"># number of psth bins per psth bin</span>

<span class="c1"># match the tone frequency to the corresponding CF</span>
<span class="n">fno</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span> <span class="n">x</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="n">CF_connear</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">fno</span><span class="p">)]</span>

<span class="c1"># indicate the region of the response for computing the mean - 10-40 ms after the stimulus onset</span>
<span class="n">index_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">silence_left</span> <span class="o">+</span> <span class="mf">10e-3</span><span class="p">)</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="o">-</span> <span class="n">context_left</span> <span class="c1"># omit silence + 10 ms (15ms = 300 samples)</span>
<span class="n">index_end</span> <span class="o">=</span> <span class="n">index_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="mf">30e-3</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="c1"># keep 30 ms of response after (30ms = 600 samples)</span>
<span class="n">stim_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">index_start</span><span class="p">,</span><span class="n">index_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># stimulus region</span>
    
<span class="c1"># make stimulus</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim_sin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the pure tone</span>
<span class="c1"># apply ramp</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bartlett</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided ramp window</span>
<span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</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">L</span><span class="p">)):</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Zilanyetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;nrep&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;nrep_spike&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">nrep_spike</span><span class="p">)</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs_psth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_connear</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;noiseType&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;implnt&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cohc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;species&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="c1"># human</span>

<span class="n">sponts</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span><span class="mf">2.</span><span class="p">,</span><span class="mf">3.</span><span class="p">]</span> <span class="c1"># simulate LSR, MSR and HSR ANF fibers</span>
<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_length</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">fs_tl</span> <span class="c1"># make simulation duration 1 sample longer to avoid errors</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;T&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">T</span>

<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span><span class="n">window_length</span><span class="p">,</span><span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfh_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfm_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfl_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</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">L</span><span class="p">)):</span>
    <span class="n">stim_in</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;stim&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stim_in</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;f&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">f_tone</span><span class="p">)</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_IHC(stim,f,nrep,1/Fs,T,cohc,cihc,species);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;ihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ihc_out</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)[:</span><span class="n">window_length</span><span class="p">]</span> <span class="c1"># downsample</span>

    <span class="k">for</span> <span class="n">sponti</span><span class="p">,</span> <span class="n">spont</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sponts</span><span class="p">):</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;spont&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spont</span><span class="p">)</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_Synapse(ihc,f,nrep,1/Fs,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the mean ANF firing rate</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anf_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])[:</span><span class="n">total_length</span><span class="p">]</span>
        
        <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        
        <span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span> <span class="c1"># simulate psth</span>
            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span><span class="n">nrep_spike</span><span class="p">)</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">anf_out</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;rate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">anf_out</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_SpikeGen(rate,f,nrep_spike,1/Fs_psth,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psTH</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

            <span class="n">pr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">psTH</span><span class="p">,(</span><span class="n">psthbins</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">psTH</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">psthbins</span><span class="p">)),</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</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="o">/</span><span class="n">nrep_spike</span> <span class="c1"># pr of spike in each bin</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">pr</span><span class="o">/</span><span class="n">psthbinwidth</span> <span class="c1"># psth in units of /s</span>

            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>

<span class="c1"># remove context from the outputs (and last dimension)</span>
<span class="n">anfh_target</span> <span class="o">=</span> <span class="n">anfh_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_target</span> <span class="o">=</span> <span class="n">anfm_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_target</span> <span class="o">=</span> <span class="n">anfl_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># apply proper scaling for feeding to the CoNNear</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span> <span class="o">*</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the cochlear input</span>

<span class="c1"># compute the mean firing rate over the stimulus region</span>
<span class="n">anfh_target_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfh_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfm_target_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfm_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfl_target_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfl_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1">############ CNN model #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">ihc_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 16384</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">ihc_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">ihc_target</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="n">ihc_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># scale back to the original ANF values</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>

<span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span> <span class="c1"># simulate psth</span>
    <span class="c1"># omit the negative values</span>
    <span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</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">L</span><span class="p">)):</span>
        <span class="k">for</span> <span class="n">sponti</span><span class="p">,</span> <span class="n">spont</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sponts</span><span class="p">):</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;spont&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spont</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span><span class="n">nrep_spike</span><span class="p">)</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">anf_out</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;rate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">anf_out</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_SpikeGen(rate,f,nrep_spike,1/Fs_psth,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psTH</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

            <span class="n">pr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">psTH</span><span class="p">,(</span><span class="n">psthbins</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">psTH</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">psthbins</span><span class="p">)),</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</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="o">/</span><span class="n">nrep_spike</span> <span class="c1"># pr of spike in each bin</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">pr</span><span class="o">/</span><span class="n">psthbinwidth</span> <span class="c1"># psth in units of /s</span>

            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anfl_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anfm_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anfh_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
                
    <span class="c1"># remove context from the outputs</span>
    <span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>
    <span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>
    <span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>

<span class="c1"># remove last dimension </span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>

<span class="c1"># compute the mean firing rate over the stimulus region</span>
<span class="n">anfh_connear_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfh_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfm_connear_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfm_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfl_connear_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">anfl_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
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<pre>WARNING:tensorflow:From C:\Users\Me\.conda\envs\evaluation_env\lib\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

11/11 [==============================] - 1s 90ms/step
11/11 [==============================] - 1s 85ms/step
11/11 [==============================] - 1s 84ms/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="c1"># Plot the rate-level curves for the reference model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfh_target_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;ro-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfm_target_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;bo-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfl_target_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;co-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">L</span><span class="o">/</span><span class="mi">10</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">L</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Stimulus level (dB SPL)&#39;</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;Firing rate (spikes/s)&#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;Zilany2014 ANF model&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="c1"># Plot the rate-level curves for the CoNNear model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfh_connear_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;ro-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfm_connear_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;bo-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfl_connear_mean</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;co-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">L</span><span class="o">/</span><span class="mi">10</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">L</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Stimulus level (dB SPL)&#39;</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;Firing rate (spikes/s)&#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;Zilany2014 ANF model - CNN&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">anfh</span><span class="p">,</span> <span class="n">anfm</span><span class="p">,</span> <span class="n">anfl</span> <span class="c1"># remove the connear models variables to free-up some memory</span>
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<h3 id="ANF-synchrony-level-functions">ANF synchrony-level functions<a class="anchor-link" href="#ANF-synchrony-level-functions">&#182;</a></h3><p>Compare the simulated ANF synchrony-level functions for the three fiber models.
<strong>You can change the <code>f_tone</code> variable to get the curves of 1 kHz or 4 kHz.</strong></p>
<p>To simulate the ANF synchrony-level curves reported in the paper, the mean firing rate outputs need to be fed to the Zilany2014 ANF spike generator to generate the PSTH outputs. This can be done by setting the <code>simulate_psth</code> variable to 1, but requires many stimulus repetitions and a lot of time to compute (or a powerful computer). Alternatively, the spike generator can be omited to speed up the execution, by setting the <code>simulate_psth</code> variable to 1, and plotting the same results for the mean firing rate outputs instead.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">simulate_psth</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># set to 0 to use the mean firing rates or to 1 to use the PSTH outputs (rather slow)</span>

<span class="n">modeldir</span> <span class="o">=</span> <span class="s1">&#39;Zilany2014/&#39;</span>
<span class="c1"># Define the ANF model hyperparameters</span>
<span class="n">context_left</span> <span class="o">=</span> <span class="mi">7936</span>
<span class="n">context_right</span> <span class="o">=</span> <span class="mi">256</span>
<span class="n">Nenc</span> <span class="o">=</span> <span class="mi">14</span> <span class="c1"># number of layers in the encoder - check for the input size</span>
<span class="c1"># Load the 1-channel ANF models</span>
<span class="n">N_cf</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span>
    <span class="n">crop</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># get uncropped outputs to feed to the spike generator model</span>
<span class="k">else</span><span class="p">:</span>
    <span class="n">crop</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">anfh</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfh_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfh.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfh_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfh.summary()</span>
<span class="n">anfm</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfm_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfm.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfm_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfm.summary()</span>
<span class="n">anfl</span> <span class="o">=</span> <span class="n">load_connear_model</span><span class="p">(</span><span class="n">modeldir</span><span class="p">,</span><span class="n">json_name</span><span class="o">=</span><span class="s2">&quot;/anfl_1cf.json&quot;</span><span class="p">,</span><span class="n">weights_name</span><span class="o">=</span><span class="s2">&quot;/anfl.h5&quot;</span><span class="p">,</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;anfl_model&quot;</span><span class="p">,</span><span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
<span class="c1">#anfl.summary()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Define the pure tone stimulus</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="mf">4e3</span> <span class="c1"># frequency of the pure tone</span>
<span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span><span class="mf">101.</span><span class="p">,</span><span class="mf">10.</span><span class="p">)</span> <span class="c1"># levels from 0 to 100 dB SPL with a step of 10 - in the paper the step is 5</span>
<span class="n">f_m</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># modulation frequency</span>
<span class="n">m</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># modulation percentage </span>

<span class="n">stim_dur</span> <span class="o">=</span> <span class="mf">400e-3</span> <span class="c1"># duration of the stimulus - 409.6 ms correspond to 8192 samples (fs = 20 kHz)</span>
<span class="n">silence_left</span> <span class="o">=</span> <span class="mf">396.8e-3</span> <span class="o">+</span> <span class="mf">5e-3</span> <span class="c1"># silence before the onset of the stimulus - 396.8 ms correspond to 7936 samples (left context)</span>
<span class="n">silence_right</span> <span class="o">=</span> <span class="mf">12.8e-3</span> <span class="c1"># silence after the stimulus - 256 samples (right context)</span>
<span class="n">win_dur</span> <span class="o">=</span> <span class="mf">7.8e-3</span> <span class="c1"># 7.8ms long ramp window for gradual onset</span>

<span class="c1"># Zilany2014 model parameters</span>
<span class="n">nrep_spike</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># number of repetitions for the spike generator part of the ANF model - decrease for a faster but noisier result</span>
<span class="n">psthbinwidth</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">fs_connear</span>
<span class="n">psthbins</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">psthbinwidth</span><span class="o">*</span><span class="n">fs_connear</span><span class="p">)</span> <span class="c1"># number of psth bins per psth bin</span>

<span class="c1"># match the tone frequency to the corresponding CF</span>
<span class="n">fno</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">CF_connear</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">abs</span><span class="p">(</span> <span class="n">x</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span> <span class="n">f_tone</span><span class="p">))</span>
<span class="n">f_tone</span> <span class="o">=</span> <span class="n">CF_connear</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">fno</span><span class="p">)]</span>

<span class="c1"># indicate the region of interest for computing the synchrony</span>
<span class="n">index_start</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="o">-</span> <span class="n">context_left</span> <span class="c1"># omit silence (5ms = 100 samples)</span>
<span class="n">index_end</span> <span class="o">=</span> <span class="n">index_start</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">stim_dur</span> <span class="o">*</span> <span class="n">fs_connear</span><span class="p">)</span> <span class="c1"># keep for the stimulus duration (400ms = 8000 samples)</span>
<span class="n">stim_reg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">index_start</span><span class="p">,</span><span class="n">index_end</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># stimulus region</span>

<span class="c1"># make stimulus</span>
<span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stim_dur</span><span class="p">,</span> <span class="mf">1.</span><span class="o">/</span><span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim_sin</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">m</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_m</span> <span class="o">*</span> <span class="n">t</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">f_tone</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span> <span class="c1"># generate the SAM tone</span>
<span class="c1"># apply ramp</span>
<span class="n">winlength</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">win_dur</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">win</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">bartlett</span><span class="p">(</span><span class="n">winlength</span><span class="p">)</span> <span class="c1"># double-sided ramp window</span>
<span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[:</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">)]</span>
<span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">=</span> <span class="n">stim_sin</span><span class="p">[</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span> <span class="o">*</span> <span class="n">win</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">winlength</span><span class="o">/</span><span class="mi">2</span><span class="p">):]</span>
<span class="n">total_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_right</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span>
<span class="n">stim</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">total_length</span><span class="p">))</span>
<span class="n">stimrange</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">silence_left</span> <span class="o">*</span> <span class="n">fs_tl</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">stim_sin</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">L</span><span class="p">)):</span>
    <span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">stimrange</span><span class="p">]</span> <span class="o">=</span> <span class="n">p0</span> <span class="o">*</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">L</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span> <span class="o">*</span> <span class="n">stim_sin</span> <span class="o">/</span> <span class="n">rms</span><span class="p">(</span><span class="n">stim_sin</span><span class="p">)</span> <span class="c1"># calibrate </span>

<span class="c1">############ Zilanyetal ############</span>
<span class="n">parent_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">()</span> <span class="c1"># get current directory </span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;./&quot;</span> <span class="o">+</span> <span class="n">modeldir</span> <span class="o">+</span> <span class="s2">&quot;reference/&quot;</span><span class="p">)</span> <span class="c1"># navigate to the model&#39;s folder</span>
<span class="n">eng</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">start_matlab</span><span class="p">()</span> <span class="c1"># start the matlab engine</span>

<span class="c1"># pass the variables to the matlab workspace</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;nrep&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;nrep_spike&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">nrep_spike</span><span class="p">)</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_tl</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;Fs_psth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fs_connear</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;noiseType&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;implnt&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cohc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;cihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;species&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="c1"># human</span>

<span class="n">sponts</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span><span class="mf">2.</span><span class="p">,</span><span class="mf">3.</span><span class="p">]</span> <span class="c1"># simulate LSR, MSR and HSR ANF fibers</span>
<span class="n">T</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_length</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">fs_tl</span> <span class="c1"># make simulation duration 1 sample longer to avoid errors</span>
<span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;T&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">T</span>

<span class="n">window_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">total_length</span> <span class="o">/</span> <span class="n">ds_factor</span><span class="p">)</span> <span class="c1"># window length of the downsampled outputs</span>
<span class="n">ihc_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span><span class="n">window_length</span><span class="p">,</span><span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfh_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfm_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</span><span class="p">))</span>
<span class="n">anfl_target</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="nb">len</span><span class="p">(</span><span class="n">L</span><span class="p">),</span> <span class="n">window_length</span><span class="p">,</span> <span class="n">N_cf</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">L</span><span class="p">)):</span>
    <span class="n">stim_in</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">stim</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;stim&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stim_in</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;f&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">f_tone</span><span class="p">)</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_IHC(stim,f,nrep,1/Fs,T,cohc,cihc,species);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the IHC output</span>
    <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;ihc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ihc_out</span>
    <span class="n">ihc_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">ihc_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">ihc_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)[:</span><span class="n">window_length</span><span class="p">]</span> <span class="c1"># downsample</span>

    <span class="k">for</span> <span class="n">sponti</span><span class="p">,</span> <span class="n">spont</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sponts</span><span class="p">):</span>
        <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;spont&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spont</span><span class="p">)</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_Synapse(ihc,f,nrep,1/Fs,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># simulate the mean ANF firing rate</span>
        <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">anf_out</span><span class="p">[</span><span class="mi">0</span><span class="p">])[:</span><span class="n">total_length</span><span class="p">]</span>
        
        <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp_sig</span><span class="o">.</span><span class="n">resample_poly</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span> <span class="n">fs_connear</span><span class="p">,</span> <span class="n">fs_tl</span><span class="p">)</span>
        
        <span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span> <span class="c1"># simulate psth</span>
            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span><span class="n">nrep_spike</span><span class="p">)</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">anf_out</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;rate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">anf_out</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_SpikeGen(rate,f,nrep_spike,1/Fs_psth,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psTH</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

            <span class="n">pr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">psTH</span><span class="p">,(</span><span class="n">psthbins</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">psTH</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">psthbins</span><span class="p">)),</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</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="o">/</span><span class="n">nrep_spike</span> <span class="c1"># pr of spike in each bin</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">pr</span><span class="o">/</span><span class="n">psthbinwidth</span> <span class="c1"># psth in units of /s</span>

            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anfl_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anfm_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anfh_target</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>

<span class="c1"># remove context from the outputs (and last dimension)</span>
<span class="n">anfh_target</span> <span class="o">=</span> <span class="n">anfh_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_target</span> <span class="o">=</span> <span class="n">anfm_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_target</span> <span class="o">=</span> <span class="n">anfl_target</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># apply proper scaling for feeding to the CoNNear</span>
<span class="n">ihc_target</span> <span class="o">=</span> <span class="n">ihc_target</span> <span class="o">*</span> <span class="n">ihc_scaling</span> <span class="c1"># scaling for the ihc input</span>

<span class="c1"># compute the fft of the response</span>
<span class="n">N</span> <span class="o">=</span> <span class="n">stim_reg</span><span class="o">.</span><span class="n">size</span>
<span class="n">anfh_target_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfh_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="n">anfm_target_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfm_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="n">anfl_target_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfl_target</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="c1"># divide the modulation frequency component of the Fourier transform by the DC component (1st bin) to get the vector strengh</span>
<span class="n">anfh_target_vs</span> <span class="o">=</span> <span class="n">anfh_target_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfh_target_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_target_vs</span> <span class="o">=</span> <span class="n">anfm_target_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfm_target_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_target_vs</span> <span class="o">=</span> <span class="n">anfl_target_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfl_target_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>

<span class="c1">############ CNN model #################</span>
<span class="c1"># check the time dimension size</span>
<span class="k">if</span> <span class="n">ihc_target</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">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">:</span> <span class="c1"># input size needs to be a multiple of 16384</span>
    <span class="n">Npad</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">ihc_target</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="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)))</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">**</span><span class="n">Nenc</span><span class="p">)</span><span class="o">-</span><span class="n">ihc_target</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="n">ihc_target</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="n">Npad</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span> <span class="c1"># zero-pad</span>
<span class="c1"># simulate</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ihc_target</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># scale back to the original ANF values</span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span> <span class="o">/</span> <span class="n">an_scaling</span>

<span class="k">if</span> <span class="n">simulate_psth</span><span class="p">:</span> <span class="c1"># simulate psth</span>
    <span class="c1"># omit the negative values</span>
    <span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="mi">0</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">L</span><span class="p">)):</span>
        <span class="k">for</span> <span class="n">sponti</span><span class="p">,</span> <span class="n">spont</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sponts</span><span class="p">):</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;spont&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spont</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anf_out</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">anf_out</span><span class="p">,</span><span class="n">nrep_spike</span><span class="p">)</span>
            <span class="n">anf_out</span> <span class="o">=</span> <span class="n">matlab</span><span class="o">.</span><span class="n">double</span><span class="p">(</span><span class="n">anf_out</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
            <span class="n">eng</span><span class="o">.</span><span class="n">workspace</span><span class="p">[</span><span class="s1">&#39;rate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">anf_out</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">eng</span><span class="o">.</span><span class="n">eval</span><span class="p">(</span><span class="s1">&#39;model_SpikeGen(rate,f,nrep_spike,1/Fs,spont,noiseType,implnt);&#39;</span><span class="p">,</span> <span class="n">nargout</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psTH</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

            <span class="n">pr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">psTH</span><span class="p">,(</span><span class="n">psthbins</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">psTH</span><span class="o">.</span><span class="n">size</span><span class="o">/</span><span class="n">psthbins</span><span class="p">)),</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</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="o">/</span><span class="n">nrep_spike</span> <span class="c1"># pr of spike in each bin</span>
            <span class="n">psTH</span> <span class="o">=</span> <span class="n">pr</span><span class="o">/</span><span class="n">psthbinwidth</span> <span class="c1"># psth in units of /s</span>

            <span class="k">if</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="n">anfl_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">elif</span> <span class="n">sponti</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">anfm_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">anfh_connear</span><span class="p">[</span><span class="n">i</span><span class="p">,:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">psTH</span>
                
    <span class="c1"># remove context from the outputs</span>
    <span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>
    <span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>
    <span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,</span> <span class="n">context_left</span><span class="p">:</span><span class="o">-</span><span class="n">context_right</span><span class="p">,</span> <span class="p">:]</span>

<span class="c1"># remove last dimension </span>
<span class="n">anfh_connear</span> <span class="o">=</span> <span class="n">anfh_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_connear</span> <span class="o">=</span> <span class="n">anfm_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_connear</span> <span class="o">=</span> <span class="n">anfl_connear</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>

<span class="c1"># compute the fft of the response</span>
<span class="n">N</span> <span class="o">=</span> <span class="n">stim_reg</span><span class="o">.</span><span class="n">size</span>
<span class="n">anfh_connear_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfh_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="n">anfm_connear_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfm_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="n">anfl_connear_fft</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">anfl_connear</span><span class="p">[:,</span><span class="n">stim_reg</span><span class="p">])))</span>
<span class="c1"># divide the modulation frequency component of the Fourier transform by the DC component (1st bin) to get the vector strengh</span>
<span class="n">anfh_connear_vs</span> <span class="o">=</span> <span class="n">anfh_connear_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfh_connear_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
<span class="n">anfm_connear_vs</span> <span class="o">=</span> <span class="n">anfm_connear_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfm_connear_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
<span class="n">anfl_connear_vs</span> <span class="o">=</span> <span class="n">anfl_connear_fft</span><span class="p">[:,</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">f_m</span><span class="o">/</span><span class="n">fs_connear</span><span class="o">*</span><span class="n">N</span><span class="p">))]</span> <span class="o">/</span> <span class="n">anfl_connear_fft</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>

<span class="n">eng</span><span class="o">.</span><span class="n">quit</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="n">parent_dir</span><span class="p">)</span> <span class="c1"># go back to the original folder</span>
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<pre>11/11 [==============================] - 1s 97ms/step
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">############ Plots ###################</span>
<span class="c1"># Plot the synchrony-level functions for the reference model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfh_target_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;ro-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfm_target_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;bo-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfl_target_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;co-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">L</span><span class="o">/</span><span class="mi">10</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">L</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Stimulus level (dB SPL)&#39;</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;Synchrony to fm&#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;Zilany2014 ANF model&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="c1"># Plot the synchrony-level functions for the CoNNear model</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfh_connear_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;ro-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfm_connear_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;bo-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">anfl_connear_vs</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="s1">&#39;co-&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">which</span><span class="o">=</span><span class="s1">&#39;both&#39;</span><span class="p">),</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">ticks</span><span class="o">=</span><span class="n">L</span><span class="o">/</span><span class="mi">10</span> <span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">L</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Stimulus level (dB SPL)&#39;</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;Synchrony to fm&#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;Zilany2014 ANF model - CNN&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;HSR&#39;</span><span class="p">,</span><span class="s1">&#39;MSR&#39;</span><span class="p">,</span><span class="s1">&#39;LSR&#39;</span><span class="p">],</span> <span class="n">frameon</span><span class="o">=</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>

<span class="k">del</span> <span class="n">anfh</span><span class="p">,</span> <span class="n">anfm</span><span class="p">,</span> <span class="n">anfl</span> <span class="c1"># remove the connear models variables to free-up some memory</span>
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